observational research examples psychology

10 Observational Research Examples

observational research examples and definition, explained below

Observational research involves observing the actions of people or animals, usually in their natural environments.

For example, Jane Goodall famously observed chimpanzees in the wild and reported on their group behaviors. Similarly, many educational researchers will conduct observations in classrooms to gain insights into how children learn.

Examples of Observational Research

1. jane goodall’s research.

Jane Goodall is famous for her discovery that chimpanzees use tools. It is one of the most remarkable findings in psychology and anthropology .

Her primary method of study involved simply entering the natural habitat of her research subjects, sitting down with pencil and paper, and making detailed notes of what she observed.

Those observations were later organized and transformed into research papers that provided the world with amazing insights into animal behavior.

When she first discovered that chimpanzees use twigs to “fish” for termites, it was absolutely stunning. The renowned Louis Leakey proclaimed: “we must now redefine tool, redefine man, or accept chimps as humans.”

2. Linguistic Development of Children

Answering a question like, “how do children learn to speak,” can only be answered by observing young children at home.

By the time kids get to first grade, their language skills have already become well-developed, with a vocabulary of thousands of words and the ability to use relatively complex sentences.

Therefore, a researcher has to conduct their study in the child’s home environment. This typically involves having a trained data collector sit in a corner of a room and take detailed notes about what and how parents speak to their child.

Those observations are later classified in a way that they can be converted into quantifiable measures for statistical analysis.

For example, the data might be coded in terms of how many words the parents spoke, degree of sentence complexity, or emotional dynamic of being encouraging or critical. When the data is analyzed, it might reveal how patterns of parental comments are linked to the child’s level of linguistic development.

Related Article: 15 Action Research Examples

3. Consumer Product Design

Before Apple releases a new product to the market, they conduct extensive analyses of how the product will be perceived and used by consumers.

The company wants to know what kind of experience the consumer will have when using the product. Is the interface user-friendly and smooth? Does it fit comfortably in a person’s hand?

Is the overall experience pleasant?

So, the company will arrange for groups of prospective customers come to the lab and simply use the next iteration of one of their great products. That lab will absolutely contain a two-way mirror and a team of trained observers sitting behind it, taking detailed notes of what the test groups are doing. The groups might even be video recorded so their behavior can be observed again and again.

That will be followed by a focus group discussion , maybe a survey or two, and possibly some one-on-one interviews.

4. Satellite Images of Walmart

Observational research can even make some people millions of dollars. For example, a report by NPR describes how stock market analysts observe Walmart parking lots to predict the company’s earnings.

The analysts purchase satellite images of selected parking lots across the country, maybe even worldwide. That data is combined with what they know about customer purchasing habits, broken down by time of day and geographic region.

Over time, a detailed set of calculations are performed that allows the analysts to predict the company’s earnings with a remarkable degree of accuracy .

This kind of observational research can result in substantial profits.

5. Spying on Farms

Similar to the example above, observational research can also be implemented to study agriculture and farming.

By using infrared imaging software from satellites, some companies can observe crops across the globe. The images provide measures of chlorophyll absorption and moisture content, which can then be used to predict yields. Those images also allow analysts to simply count the number of acres being planted for specific crops across the globe.

In commodities such as wheat and corn, that prediction can lead to huge profits in the futures markets.

It’s an interesting application of observational research with serious monetary implications.

6. Decision-making Group Dynamics

When large corporations make big decisions, it can have serious consequences to the company’s profitability, or even survival.

Therefore, having a deep understanding of decision-making processes is essential. Although most of us think that we are quite rational in how we process information and formulate a solution, as it turns out, that’s not entirely true.

Decades of psychological research has focused on the function of statements that people make to each other during meetings. For example, there are task-masters, harmonizers, jokers, and others that are not involved at all.

A typical study involves having professional, trained observers watch a meeting transpire, either from a two-way mirror, by sitting-in on the meeting at the side, or observing through CCTV.

By tracking who says what to whom, and the type of statements being made, researchers can identify weaknesses and inefficiencies in how a particular group engages the decision-making process.

See More: Decision-Making Examples

7. Case Studies

A case study is an in-depth examination of one particular person. It is a form of observational research that involves the researcher spending a great deal of time with a single individual to gain a very detailed understanding of their behavior.

The researcher may take extensive notes, conduct interviews with the individual, or take video recordings of behavior for further study.

Case studies give a level of detailed information that is not available when studying large groups of people. That level of detail can often provide insights into a phenomenon that could lead to the development of a new theory or help a researcher identify new areas of research.

Researchers sometimes have no choice but to conduct a case study in situations in which the phenomenon under study is “rare and unusual” (Lee & Saunders, 2017). Because the condition is so uncommon, it is impossible to find a large enough sample of cases to study with quantitative methods.

Go Deeper: Pros and Cons of Case Study Research

8. Infant Attachment

One of the first studies on infant attachment utilized an observational research methodology . Mary Ainsworth went to Uganda in 1954 to study maternal practices and mother/infant bonding.

Ainsworth visited the homes of 26 families on a bi-monthly basis for 2 years, taking detailed notes and interviewing the mothers regarding their parenting practices.

Her notes were then turned into academic papers and formed the basis for the Strange Situations test that she developed for the laboratory setting.

The Strange Situations test consists of 8 situations, each one lasting no more than a few minutes. Trained observers are stationed behind a two-way mirror and have been trained to make systematic observations of the baby’s actions in each situation.

9. Ethnographic Research

Ethnography is a type of observational research where the researcher becomes part of a particular group or society.

The researcher’s role as data collector is hidden and they attempt to immerse themselves in the community as a regular member of the group.

By being a part of the group and keeping one’s purpose hidden, the researcher can observe the natural behavior of the members up-close. The group will behave as they would naturally and treat the researcher as if they were just another member. This can lead to insights into the group dynamics , beliefs, customs and rituals that could never be studied otherwise.

10. Time and Motion Studies

Time and motion studies involve observing work processes in the work environment. The goal is to make procedures more efficient, which can involve reducing the number of movements needed to complete a task.

Reducing the movements necessary to complete a task increases efficiency, and therefore improves productivity. A time and motion study can also identify safety issues that may cause harm to workers, and thereby help create a safer work environment.

The two most famous early pioneers of this type of observational research are Frank and Lillian Gilbreth.

Lilian was a psychologist that began to study the bricklayers of her husband Frank’s construction company. Together, they figured out a way to reduce the number of movements needed to lay bricks from 18 to 4 (see original video footage here ).

The couple became quite famous for their work during the industrial revolution and

Lillian became the only psychologist to appear on a postage stamp (in 1884).

Why do Observational Research?

Psychologists and anthropologists employ this methodology because:

Psychologists find that studying people in a laboratory setting is very artificial. People often change their behavior if they know it is going to be analyzed by a psychologist later.
Anthropologists often study unique cultures and indigenous peoples that have little contact with modern society. They often live in remote regions of the world, so, observing their behavior in a natural setting may be the only option.
In animal studies , there are lots of interesting phenomenon that simply cannot be observed in a laboratory, such as foraging behavior or mate selection. Therefore, observational research is the best and only option available.

Read Also: Difference Between Observation and Inference

Observational research is an incredibly useful way to collect data on a phenomenon that simply can’t be observed in a lab setting. This can provide insights into human behavior that could never be revealed in an experiment (see: experimental vs observational research ).

Researchers employ observational research methodologies when they travel to remote regions of the world to study indigenous people, try to understand how parental interactions affect a child’s language development, or how animals survive in their natural habitats.

On the business side, observational research is used to understand how products are perceived by customers, how groups make important decisions that affect profits, or make economic predictions that can lead to huge monetary gains.

Ainsworth, M. D. S. (1967). Infancy in Uganda . Baltimore: Johns Hopkins University Press.

Ainsworth, M. D. S., Blehar, M., Waters, E., & Wall, S. (1978). Patterns of attachment: A

psychological study of the Strange Situation. Hillsdale: Erlbaum.

Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The case study approach. BMC Medical Research Methodology , 11 , 100. https://doi.org/10.1186/1471-2288-11-100

d’Apice, K., Latham, R., & Stumm, S. (2019). A naturalistic home observational approach to children’s language, cognition, and behavior. Developmental Psychology, 55 (7),1414-1427. https://doi.org/10.1037/dev0000733

Lee, B., & Saunders, M. N. K. (2017). Conducting Case Study Research for Business and Management Students. SAGE Publications.

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Dissociation Examples (Psychology)
Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Zone of Proximal Development Examples
Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
Chris Drew (PhD) #molongui-disabled-link 15 Zone of Proximal Development Examples
Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition

6.5 Observational Research

Learning objectives.

List the various types of observational research methods and distinguish between each
Describe the strengths and weakness of each observational research method.

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

Naturalistic Observation

Naturalistic observation is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr. Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation. Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

Participant Observation

Another approach to data collection in observational research is participant observation. In participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation, the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with undisguised participant observation, the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation. First no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation.

Rosenhan’s study (1973) [1] of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly ) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [2] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are apart of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation. Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic and participant observation. Often the setting in which the observations are made is not the natural setting, rather the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation. Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [3] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186). Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds. In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [4] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interested which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A case study is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individuals’ depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

www.youtube.com/watch?v=KkaXNvzE4pk

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [5] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [6] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample to individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation. However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods.

The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with internal and external validity. Case studies lack the proper controls that true experiments contain. As such they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (indeed questioning into the possibility of a separate brain lesion began after HM’s death and dissection of his brain) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So as with all observational methods case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically a very abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity, with case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research is the use of archival research which involves analyzing data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [7] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [8] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s r was +.25.

This method is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

There are several different approaches to observational research including naturalistic observation, participant observation, structured observation, case studies, and archival research.
Naturalistic observation is used to observe people in their natural setting, participant observation involves becoming an active member of the group being observed, structured observation involves coding a small number of behaviors in a quantitative manner, case studies are typically used to collect in-depth information on a single individual, and archival research involves analysing existing data.
Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton. ↵
Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

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Non-Experimental Research

32 Observational Research

Learning objectives.

List the various types of observational research methods and distinguish between each.
Describe the strengths and weakness of each observational research method.

What Is Observational Research?

Naturalistic Observation

Naturalistic observation is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr. Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation . Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation , the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation. First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation.

Rosenhan’s study (1973) [2] of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Structured observation is very similar to naturalistic observation and participant observation in that in all three cases researchers are observing naturally occurring behavior; however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic or participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds. In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

Figure 6.8 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s r was +.25.

Media Attributions

What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
Pappenheim 1882 by unknown is in the Public Domain .
Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton. ↵
Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Methodology

What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on March 31, 2022 by Tegan George . Revised on June 22, 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables and observer bias impacting your analysis.

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs. experiment, other interesting articles, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies .

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyze a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analyzing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for practical or ethical reasons , or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

Are you determining what you want to observe beforehand, or going in open-minded?
Is there another research method that would make sense in tandem with an observational study?
If yes, make sure you conduct a covert observation.
If not, think about whether observing from afar or actively participating in your observation is a better fit.
How can you preempt confounding variables that could impact your analysis?
You could observe the children playing at the playground in a naturalistic observation.
You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organized. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
Get informed consent from your participants (or their parents) if you want to record: Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or “lurking” variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyze your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
If you had specific hypotheses prior to conducting your observations, a deductive approach analyzes whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis .

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

Observational studies can provide information about difficult-to-analyze topics in a low-cost, efficient manner.
They allow you to study subjects that cannot be randomized safely, efficiently, or ethically .
They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilize preexisting data.
They’re often invaluable in informing later, larger-scale clinical trials or experimental designs.

Disadvantages

Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables or omitted variables .
They lack conclusive results, typically are not externally valid or generalizable, and can usually only form a basis for further research.
They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomize your participants safely and your research question is definitely causal in nature, consider using an experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

A testable hypothesis
At least one independent variable that can be precisely manipulated
At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

How you will manipulate the variable(s)
How you will control for any potential confounding variables
How many subjects or samples will be included in the study
How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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6.6: Observational Research

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Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton

Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton
Kwantlen Polytechnic U., Washington State U., & Texas A&M U.—Texarkana

Learning Objectives

List the various types of observational research methods and distinguish between each.
Describe the strengths and weakness of each observational research method.

What Is Observational Research?

Naturalistic Observation

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

In contrast with undisguised participant observation, the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation. First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation.

Structured Observation

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

Archival Research

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s r was +.25.

Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton. ↵
Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

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The Oxford Handbook of Quantitative Methods in Psychology, Vol. 1

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15 Observational Methods

Jamie M. Ostrov, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY

Emily J. Hart, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY

Published: 01 October 2013
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Systematic observational methods require clearly defined codes, structured sampling and recording procedures, and are subject to rigorous psychometric analysis. We review best practices in each of these areas with attention to the application of these methods for addressing empirical questions that quantitative researchers may posit. Special focus is placed on the selection of appropriate observational methods and coding systems as well as on the analysis of reliability and validity. The use of technology to facilitate the collection and analysis of observational data is discussed. Ethical considerations and future directions are raised.

Introduction

Systematic observational methods have been a common technique employed by psychologists studying human and animal behavior since the inception of our field, and yet best practices for the use of observational instruments ( see Table 15.1 ) are often not known or adopted by researchers in our field. As such, the quality of observational research varies widely, and thus, it is our goal in the present chapter to review and explicitly define the standards of practice for this important methodological tool in the psychological sciences. Bakeman and Gottman (1987) have previously defined observational methods to include the a priori use of operationally defined behavioral codes by observers who have achieved interobserver reliability. Importantly, the setting or context is not what defines a method as

being systematic ( Pellegrini, 2004 ). That is, systematic observations may be conducted in the laboratory, schools, workplace, public spaces and coded

live or via recordings/transcripts. Therefore, having clear definitions and sampling/recording rules as well as reliable codes delineates informal, unsystematic observation from systematic observation. We also distinguish between the use of nonsystematic field notes and other data collection techniques that are often used in qualitative studies by ethologists and educational practitioners in naturalistic contexts and only include a review and analysis of systematic observational methods (Pellegrini, Ostrov, Roseth, Solberg, & Dupuis, in press).

Nonsystematic sampling techniques such as Ad libitum (i.e., ad lib) in which there are no a priori systematic sampling or recording rules are often used by researchers as a part of pilot testing and help to inform the development of systematic observational coding systems ( Pellegrini, 2004 ). Thus, ad lib sampling approaches are important to understand the context and nature of the behaviors under study, but they will not be discussed further in this review. Observational methods may be used in a variety of designs from correlational and quasi-experimental to experimental and even randomized trial designs ( Bakeman & Gnisci, 2006 ). However, it is more typical to find systematic observational methods used outside the laboratory to maximize ecological validity and, thus, less likely as part of experimental manipulations ( Bakeman & Gnisci, 2006 ). The current review will be relevant to all research designs with a focus on those methods that are well designed for quantitative data analysis.

History of Observational Methods

The use of systematic observational methods has been used extensively by psychologists throughout the history of our field to examine various empirical questions ( see Langfeld, 1913 ). One of the first documented cases of systematic observational methods in the extant literature was from a study by Goodenough (1930) and was part of an increasing trend in the systematic study of young children as part of the Child Welfare Movement in the United States, which was supported by the National Research Council (for review, see Arrington, 1943 ). In fact, her seminal work was also one of the first studies in psychology to be published using time sampling ( see Sampling section below) observational procedures ( Arrington, 1943 ). In her classic work (appearing in the first issue of Child Development ), Florence L. Goodenough reported on several observational studies conducted in her laboratory at the Institute of Child Welfare (now Institute of Child Development) of the University of Minnesota. This study highlights several best practices that are still endorsed today. For example, careful pilot testing of the observational codes was conducted, and revisions were made to generate mutually exclusive codes ( see Coding section below) and reliable distinctions between the categories. In addition, observations of each child’s physical activity were conducted only once per day and only by one observer at a time so that observations of behavior were conducted independent of one another. Goodenough (1930) carefully defined the a priori categories or observational codes and demonstrated interobserver reliability for each of these codes. Finally, Goodenough (1930) described the justification for her observational procedures and discussed alternative techniques (e.g., the optimum duration for an interval within a time-sampling procedure). There are other well-known examples of systematic observation conducted by contemporaries of Goodenough, including Parten’s (1932) study of young children’s play behavior, which also illustrate best practices (e.g., clearly defined, mutually exclusive observational codes; rules designed to maintain independence of sampling and decrease observer error). Some of the earliest observational studies focused on either children or non-human animals (e.g., Crawford, 1942 ), as other techniques for studying behavior (and often social domains of study) were either not as well suited for the research questions or not available at the time. Today, systematic observational methods are used in research and applied settings ( Pellegrini, 2001 ) and relevant for training in all domains and subdisciplines of the social and behavioral sciences ( Krehbiel & Lewis, 1994 ).

Sampling and Recording Rules

Systematic observational systems follow various sampling and recording rules that are designed for different contexts and research questions. The following section includes a review of the central sampling and recording rules that quantitative scholars would use for conducting systematic observations ( see Table 15.2 for a summary of the strengths and weaknesses of each approach). Recently adopted best practices for direct systematic observation are relevant for each of these types of observational methods, and they are briefly reviewed here. These practices, which were first introduced by Hintze, Volpe, and Shapiro (2002) , include (1) the observational system is designed to measure well-defined behaviors; (2) the behaviors are operationally defined a priori ; (3) observations are recorded using objective, standardized (i.e., manualized training protocols) sampling procedures and recording rules; (4) the context and timing of sampling is explicitly determined; and (5) scoring and coding of data are conducted in a standardized fashion ( see Leff & Lakin, 2005 , p. 476).

Time Sampling

A time-dependent observational procedure in which the researcher a priori divides the behavior stream into discrete intervals and each time interval is scored for the presence or absence of the behavior in question is defined as a time sampling observational approach. That is, the time interval is the unit coded ( Bakeman & Gottman, 1987 ). Time sampling procedures may be conceptualized as either 0/1 (i.e., absent/present or nonoccurrence/occurrence) or continuous in nature. A time sampling procedure is an efficient method of sampling, as multiple data points may be collected from a single participant in a short period of time. Time sampling is well suited for measuring rather discrete behaviors, such as overt behaviors (e.g., on task and off task behavior in classrooms), or with behaviors that are frequently occurring. For example, a recent study of the frequency of various behaviors (e.g., off task behavior, noncompliance) during several naturalistic activities in 30 children with various psychiatric diagnoses used a reliable 0/1 time sampling approach with a 15-second interval ( Quake-Rapp, Miller, Ananthan, & Chiu, 2008 ). Alternatively, time sampling is not well designed for infrequently occurring events or events that are long in duration ( Slee, 1987 ). A clear advantage is that time sampling is relatively inexpensive because it is an efficient use of the research assistant ( Bakeman & Gottman, 1987 ). Further, 0/1 sampling is also easier for the observer than alternatives such as instantaneous sampling, in which the research assistant notes if the behavior is present at a precise moment in time rather than it occurring during a larger interval of time. A major disadvantage of the time sampling approach is that the researcher delineates the particular time interval and therefore arbitrarily categorizes the behavior into discrete artificial units of time that may or may not be meaningful ( Slee, 1987 ). Moreover, some behaviors may exceed the often brief interval of time that is selected for the sampling. Thus, it is crucial to carefully justify the interval that is selected. The intervals are often brief and the behaviors in question should be readily apparent and easily observable by trained research assistants. If frequency estimates are to be obtained, then the interval in question needs to be sufficiently brief so that an accurate assessment can be made. That is, typically with an interval approach, a maximum of one behavior is recorded during an interval even if the behavior independently occurs more frequently during this interval ( Slee, 1987 ). Thus, special attention needs to be given to the pilot testing of the observational scheme and various durations of the interval if frequency assessments are desired.

Time sampling procedures are used in a range of settings and studies to test various empirical questions that often have applied significance. For example, Macintosh and Dissanayake (2006) adopted a 0/1 time sampling technique to assess spontaneous social interactions in school-aged children with high-functioning autism or Asperger’s disorder as well as typically developing children. Observations were conducted in the schoolyard. For each timed interval of 30 seconds, one type of behavior (e.g., parallel play) from a particular behavioral domain (e.g., social participation) was coded. For reliability purposes, a second observer made independent ratings for 20% of the entire sample. Intraclass correlation reliability coefficients were all acceptable for each type of behavior (0.78–0.99) with the exception of nonverbal interaction (i.e., gestures; 0.58), which are often difficult to reliably assess in live settings ( see also Ostrov & Keating, 2004 ). Results meaningfully distinguished between the typically developing children and the clinical groups and revealed few differences between the two clinical groups, supporting the use of time sampling as a means to discriminate between clinical and nonclinical groups ( Macintosh & Dissanayake, 2006 ). Time sampling procedures have several other applications and clinical considerations. For example, time sampling methods may differentially affect how treatment effects are interpreted ( Meany-Daboul, Roscoe, Bourret, & Ahearn, 2007 ) and may be appropriate for classroom-based research that tests adherence to educational policies intended to aid students with special needs ( Jackson & Neel, 2006 ; Soukup, Wehmeyer, Bashinski, & Boyaird, 2007 ).

Event Sampling

Event-based sampling is also known as behavior sampling and permits a researcher to study the frequency, duration, latency, and intensity of the behavior under study ( Pellegrini, 2004 ). Essentially, unlike time sampling, event sampling is a type of observational sampling in which the events are time-independent and the behavior is the unit of analysis ( Bakeman & Gottman, 1987 ). Event sampling allows the behavior to remain as part of the naturally occurring phenomenon and may unfold in a manner generally consistent with the timing of the behavior in the natural setting. This type of sampling also can be efficient in terms of the total amount of time needed for observations. Unlike other sampling techniques (e.g., time sampling), a third advantage is that event sampling may be used when the construct under study is either frequently or infrequently occurring ( Slee, 1987 ). There are some clear disadvantages to event-based sampling procedures, and this may be a reason that it is less commonly seen in the literature. First, it is sometimes challenging to delineate the independence of events—that is, the researcher must specify when one event ends and the next event begins. Second, event sampling does not lend itself well to coding of dyadic interactions such as parent–child or romantic partner relations in which there is a fair amount of interdependence between the participants ( Slee, 1987 ).

Event sampling also has wide applicability and has even been used to understand the propensity to violence at sporting events. For example, Bowker et al. (2009) used an event-sampling approach to examine spectator comments at youth hockey games in a large Canadian city. A group of five observers attended 69 hockey games played by youth in two age groups: 11–12 years and 13–14 years. Verbal comments were coded as positive, negative, corrective, or neutral and rated for intensity. Most of the comments elicited by spectators were positively toned. The valence of spectator comments was influenced by gender (i.e., the gender of the children playing) and the purpose for which the game was being played (i.e., competitive or recreational). These results support the utility of event sampling at social and athletic events, where particular behaviors are likely to occur during a finite period of time. Time sampling may not be appropriate in such circumstances because of the presence of a high concentration of individuals in a single setting and many potential interruptions arising from the nature of the activity.

Participant Observation

Although participant observation has been more frequently used with nonsystematic field observation and in disciplines that focus on qualitative methods, it is possible to conduct systematic participant observation as part of quantitative studies. Systematic participant observation has been the method of choice for behaviors of interest that require “an insider’s perspective” ( Pellegrini, 2004 , p. 288) or for contexts in which the sampling period may be long and informal. Moreover, this method is well suited for the use of more global observational ratings that sample events. This procedure has wide applicability, and participant observation has an extensive history of successful use from studies of children with behavioral problems at summer camps in clinical psychology (e.g., Newcomb, 1931 ; Pelham et al., 2000 ) to worker stress in organizational psychology (e.g., Länsisalmi, Peiró, & Kivimäki, 2000 ). For example, a recent study of children diagnosed with disruptive behavioral disorders and enrolled in a summer treatment program used staff counselors to complete daily participant observations of social behaviors of the children while they engaged in various camp activities ( Lopez-Williams et al., 2005 ). A second study of social competence among reunited adolescents ( M a g e = 1 5 . 5 years) who had attended a research-based summer camp when they were 10 years old revealed the predictive validity of participant observer (i.e., camp counselor) ratings of social skills ( Englund, Levy, Hyson, & Sroufe, 2000 ). The validity of the participant observations of social competence when the participants were 10 years old was determined by revealing significant prospective correlations with a group-problem solving task that was videotaped and coded by two independent raters along several dimensions (e.g., self-confidence, agency, overall social competence) when the participants were 15 years old. The results support the use of participant observations in studying the development and stability of complex, multifaceted constructs like social competence.

Focal Sampling

Focal person sampling involves selecting (typically at random from a roster of participants) one participant and observing the individual for a defined time period. For each sampling interval (ranges vary depending on the question of interest), the observer records all relevant behaviors of the focal person. As we have previously discussed ( see Pellegrini et al., in press), for studies of dyads or small groups, the sampling interval should be as long as the typical interaction or displayed behavior of interest. For example, in our work, we study the display of relational aggression (i.e., the use of the relationship as the means of harm via social exclusion, withdrawing friendship, spreading malicious rumors), and given the nature of these behaviors, we have found that an interval of 10 minutes is a reasonable interval for assessing the intent for harm as well as the subtle nature of these peer interactions ( Ostrov, 2008 ; Ostrov & Keating, 2004 ).

Focal sampling may technically use continuous (e.g., Fagot & Hagan, 1985 ; Laursen & Hartup, 1989 ), 0/1 (e.g., Hall & McGregor, 2000 ; Harrist & Bradley, 2003 ), or instantaneous recording rules ( see Pellegrini, 2004 ). However, focal sampling often uses continuous recording procedures because it permits the simultaneous coding of various behaviors, sequences of behaviors, and interactions with multiple partners in a live setting (e.g., Arsenio & Lover, 1997 ; Keating & Heltman, 1994 ). For example, in our observational studies of relational aggression among young children, we always have used focal sampling with continuous recording given the somewhat covert nature of the behaviors we have targeted for observation, which require a longer period of direct assessment to decipher and appropriately record the behaviors ( Ostrov & Keating, 2004 ). Focal participant sampling is often conducted across multiple days and contexts to better capture the true nature of the behavior rather than any state-dependent artifacts. Given the amount of time and the continuous nature of the recordings, this technique permits the recording of behavior that is a close approximation to real-time recording, and a researcher may recreate the behavior of the focal participants with a high degree of accuracy (Pellegrini et al., in press). For example, we observe children in their naturally occurring play contexts on 8 separate days, and they are only ever observed once per day to maintain independence of the data. Thus, in our work, each participant is observed for 80 minutes (8 sessions at 10 minutes each session). More specifically, a study of 120 children resulted in more than 370 hours of observation across the two time-points of the short-term longitudinal study ( Ostrov, 2008 ). Therefore, time is a major cost of focal sampling because of the large number of independent observations typically conducted with this approach. Focal sampling may also be used with 0/1 or instantaneous sampling as recording procedures, but this is rarely done. As previously mentioned, both of these recording procedures require an a priori specified time interval, which is usually relatively brief (i.e., 1–10 seconds). Instantaneous recording is typically used only with scan sampling procedures ( see Scan Sampling section below). 0/1 time sampling is not usually used with focal sampling because we are often interested in assessing the true frequency of behaviors that may not be obtained with this procedure (i.e., an independent behavior could occur once or more than once during a set interval, but with 0/1 coding only one point is scored).

Despite the emphasis on the use of these methods for studying basic social behavior, focal sampling procedures may be used in a wide range of studies. It is common in the literature to find focal participant sampling studies on a range of social behavior topics: social dominance in children ( Keating & Heltman, 1994 ) and adults ( Ostrov & Collins, 2007 ), play behavior ( Pellegrini, 1989 ), emotion and aggression ( Arsenio & Lover, 1997 ), conflict ( Laursen & Hartup, 1989 ), and peer relations with young children and non-human primates (e.g., Hinde, Easton, & Meller, 1984 ; Silk, Cheney, & Seyfarth, 1996 ). However, there are many practical applications of focal participant sampling ( see Leff & Lakin, 2005 ; Pellegrini, 2001 ). For example, applied studies have been conducted that have used these observational techniques for examining the adjustment of children with special needs in elementary schools ( Hall & McGregor, 2000 ), peer victimization in early adolescence ( Pellegrini & Bartini, 2000 ), and for testing the efficacy of randomized behavioral interventions (e.g., Harrist & Bradley, 2003 ; Ostrov et al., 2009 ).

Scan Sampling

Instantaneous or scan sampling is a more efficient observational procedure than focal sampling. Scan sampling exclusively relies on instantaneous recording rules ( Pellegrini, 2001 ). With this procedure the observer scans the entire observation field for a possible behavior or event for a particular period of time. If an event is noted during that scan, then it is recorded. Typically, a number of discrete scans occur across a number of days to maximize the independence of the data. A participant’s data is usually summed across the scans to yield a behavioral score for the construct of interest. A concern with this approach is that it may not accurately assess the true frequency of behaviors if spacing is not adequate between the scans ( Pellegrini, 2004 ). Moreover, given the typical approach in which scans are conducted on an entire reference group in their natural context, behaviors that are selected for this approach must be readily apparent, discrete, and overt behaviors that require typically only a few seconds to observe. In our own field, McNeilly-Choque, Hart, Robinson, Nelson, and Olsen (1996) conducted a study of young children’s aggressive behavior in which they used a random scan sampling method that yielded 100 five-second scans during a 5- to 7-week period, resulting in 8 minutes of total observation per participant ( McNeilly-Choque, Hart, Robinson, Nelson, & Olsen, 1996 ). Thus, this study demonstrated the feasibility and efficiency of systematic scan sampling observations of aggressive behavior on the playground.

Semi-Structured Observations

Analog tasks or semi-structured observations, involving controlled simulations or analog situations, are observational tasks designed to mimic naturalistic conditions. Semi-structured observational procedures are another observational paradigm well suited for low base rate events. The recording and coding procedures are often identical to the procedures an observer would use in a naturalistic setting; however, the context in which the behaviors emerge is different. Often analog tasks are completed in a laboratory or similarly controlled setting and are videotaped for subsequent coding by unaware observers. Thus, analog observational paradigms permit a great deal of experimental control/standardization of procedures, and with the use of videotapes, observers are able to objectively code the session using the same recording rules as permitted in other contexts. A clear advantage of these procedures is that they are efficient and require less cost and time spent observing participants. If the study is not designed well, then a major disadvantage is a lack of ecological validity (i.e., degree to which the context in which the research is conducted parallels the real-life experience of the participants), and poor generalizability of the findings is possible. Moreover, a relatively small sampling of behavior does not provide for a true frequency of behavior or for a representative sample of behavior with many interaction partners (i.e., the researcher is not able to examine individual–partner interactions). Other researchers have addressed this concern by using a “round robin” approach in which each participant completes an analog session with several (or all) other member of the reference group, which may improve the validity of the approach but, of course, adds a great deal of time and expense ( see Hawley & Little, 1999 ).

In our own research we have used a semi-structured observational paradigm to provide an efficient estimate of young children’s aggressive behavior. To this end, we created a brief (9-minute) analog situation to observe various aggressive and prosocial behaviors (i.e., within dyads or triads) in early childhood ( Ostrov & Keating, 2004 ; Ostrov, Woods, Jansen, Casas, & Crick, 2004 ). The procedures and a review of the psychometric findings are described extensively elsewhere (e.g., Ostrov & Godleski, 2007 ), but essentially, each assessment includes three trials of 3 minutes each. For each trial, the children are given the same developmentally appropriate picture to color (e.g., Winnie the Pooh). For triads, three crayons are placed on the table equidistant from all participants, and only one crayon is the functional instrument (e.g., orange crayon for Winnie the Pooh) and two are functionally useless white crayons. At the end of the trial, a new picture and new crayons are placed on the table. This procedure is designed to produce mild conflict among the children and was developed to permit the children to engage in a variety of behaviors: prosocial behavior (e.g., sharing the one functional crayon or breaking into pieces to share), relational aggression (e.g., telling the child they will not be their friend anymore unless they give them the crayon), and physical aggression (e.g., taking the crayon away from someone else). The analog task was designed to be developmentally appropriate and resemble everyday conflict interactions concerning limited resources that young children experience in their typical preschool classroom. Highly trained research assistants monitored the entire session and intervened if needed to guarantee the safety of all participants and reduce the likelihood of participant distress. Moreover, at the end of the session, the children were each individually given access to a full box of crayons to diminish any distress and they were praised for their performance ( see Ostrov et al., 2004 ). This paradigm is thus designed to elicit the behavioral constructs of interest in a more controlled environment than free play yet ensures the ethical treatment of participants.

One way to demonstrate the ecological validity of semi-structured observations is to correlate behaviors observed in a semi-structured context with behaviors observed in a more naturalistic context. For example, Coie and Kupersmidt (1983) found that social status in experimentally contrived playgroups comprised of unfamiliar peers matched social status in the classroom, supporting the validity of a contrived playgroup paradigm for studying social development ( see also Dodge, 1983 ). Similarly, our own brief semi-structured observational paradigm (i.e., coloring task) has been shown to significantly predict observational scores collected from concurrently assessed naturalistic (i.e., classroom and playground free play) focal child observations with continuous recording ( r = 0 . 4 8 ) and to predict future (i.e., 12 months later) behavior in naturalistic contexts at moderate levels ( see Ostrov et al., 2004 ).

Methods of Recording

Various methods of recording (i.e., checklist, detailed records, or observation forms) vary widely and should be based on the type of recording procedures that a researcher adopts. For example, time sampling (i.e., 0/1) and instantaneous or scan sampling procedures are well suited for checklist forms in which the prescribed intervals simply receive a check or a precise code indicating the occurrence or absence of the behavior in question. However, focal participant sampling often requires observation forms that permit greater detail and several codes that are recorded either simultaneously or in close temporal proximity, and, as such, a form that includes the behaviors or events of interest with space for recording the behavior in detail may be needed (for example forms and templates, see Pellegrini et al., in press). A general concern here is that the more time spent writing details about the behavior/event removes the observer’s attention from the participants and important details may be lost. Some observational procedures like time sampling provide the observer with a set period of time after the interval for recording behavior. In general, the easier the observation form is to complete, the less room there is for error. With that said, checklists often do not permit systematic reviews for accuracy of codes by the master trainer. For example, observers that are observing the same participant as part of a reliability check could both code a behavior as “PA” for physical aggression when in fact one research assistant observed a “hit” and the other observed a “kick,” which, depending on the observational system, may be different and might not warrant a positive match or agreement. Thus, depending on the coding scheme and intentions of the researcher, these may artificially match for reliability purposes when in fact they were closely related but discrete behaviors. Finally, if observers record some written details about the event, they may inform subsequent decision rules concerning whether a recorded behavior from observer 1 matches or does not match observer 2 for reliability assessments.

Coding Considerations

The development of a reliable coding scheme is crucial for appropriately capturing the behaviors in question and testing the experimenter’s a priori hypotheses ( Bakeman & Gottman, 1987 ). There are three types of coding categories that are often included in observational systems: physical description codes, consequence codes, and relational or environmental relations codes ( Pellegrini, 2004 ). Physical description is believed to be the most “objective” type of codes because these describe “muscle contraction” ( Pellegrini, 2004 , p. 108) and might, for example, be involved in recording a participant’s social dominance or submissiveness (e.g., direct eye contact, rigid posture, arms akimbo; see Ostrov & Collins, 2007 ). The second type of codes is for those of consequence in which a constellation of behaviors are part of a single code if they lead to the same outcome ( Pellegrini, 2004 ). For example, if we were interested in studying social dominance, then we might code taking objects away from others that result in a submissive posture on the part of the nonfocal participant to be an indicator of social dominance ( Ostrov & Collins, 2007 ). The third type of codes includes categories in which participants are described in relation to the context in which they are observed ( Pellegrini, 2004 ). An example of a relational observational category would be a coding scheme that accounted for where and with whom an individual was socially dominant. In terms of costs and benefits, it is clear that physical description codes are often easier to train and therefore potentially more reliable. It is possible that consequence codes may be unreliable given a misunderstanding of the sequence of events ( Pellegrini, 2004 ). Relational codes involve the appropriate documentation of multiple factors and therefore create more possibilities of error (for discussion, see Pellegrini, 2004 ; Bakeman & Gottman, 1987 ). Overall, the level of analysis from micro- to macro-coding schemes is important to consider and the most objective and reliable system for addressing a researcher’s particular research question should be adopted.

A second consideration is the determination of whether to use mutually exclusive and exhaustive codes. Mutually exclusive codes are used when a single behavior may be recorded under one and only one code. In our observational studies, our coding scheme includes mutually exclusive codes such that a single behavior may be coded as either physical aggression or relational aggression, but not both. Exhaustive coding schemes are designed such that for any given behavior of a theoretical construct, there is an appropriate code for that behavior. For example, in our work we have codes for physical, relational, verbal, or nonverbal aggression as well as aggression not otherwise specified. Thus, if we determine a behavior is an act of aggression, then it may be coded as one of our behaviors in our scheme. Often schemes include mutually exclusive and exhaustive codes because there are several benefits to this approach ( see Bakeman & Gottman, 1987 ). Having mutually exclusive codes means that researchers are not violating assumptions of independence, which are often needed for parametric statistics. For example, if a single behavior may be coded as both physical and relational aggression, then that may violate our assumption that the data are independent and come from independent behavioral interactions ( Pellegrini, 2004 ). Having exhaustive codes also speaks to the content validity of a coding scheme. That is, if the overall construct appropriately measures all facets of that construct, then the behavior in question should be included in the observational system, and exhaustive schemes guarantee this occurrence. It is important to recall that the larger the coding scheme, the more taxing the observational procedures will be for observers and the greater the possibility of observer error.

Scoring of observational data is similar to the scoring of any quantitative data within the social and behavioral sciences, and it often depends on the convention within a particular field and the type of observational sampling and recording techniques that are adopted. For example, for focal participant sampling with continuous recording, frequency counts are often generated by summing each independently recorded behavior across the various sessions. In our own research, that would mean that an individual participant would get a score for each of the constructs (i.e., physical aggression, relational aggression, verbal aggression, etc.) by summing all the behaviors within a construct (e.g., all physical aggression behaviors) across all eight sessions ( Ostrov & Keating, 2004 ). If the number of sessions is different for each participant because of missing data, then it is often common practice to divide by the number of sessions completed to generate an average rate of behavior per session ( see Crick, Ostrov, Burr et al., 2006 ). Occasionally it is apparent that an error was made in the original coding of behaviors. Best practices have not been established for addressing these concerns, but as long as these errors are not systematic, the adopted solutions are often not a concern. To avoid problems with potential scoring biases, the observers and coders should always be unaware of the participant’s condition and/or past history. In addition, whenever possible, observers and coders should be unaware of the study hypotheses.

Psychometric Properties

Reliability.

Reliability is often conceptualized as consistency within or between individuals (i.e., intra-observer or inter-observer), within measures (internal consistency), or across time (i.e., test–retest). Arguably, for observational methods, the most important measure of consistency is inter-observer reliability, or the degree to which two sets of observations from two independent observers agree ( Stangor, 2011 ). In the present review, we will first address intra-observer reliability and then focus on the assessment of inter-observer reliability.

Intra-observer, or within-observer, reliability is defined as a situation in which two sets of observations by the same research assistant agree or are consistent. Essentially, intra-observer reliability is assessing how consistent a particular observer is when coding specific behaviors either between sessions (i.e., across time) or within a single session. As Pellegrini (2004) has discussed in more detail, we may conceptualize and test (e.g., Pearson’s Product-Moment Correlation Coefficient) intra-observer reliability in ways similar to test–retest reliability, and thus, intra-observer reliability is essentially the temporal stability of the observational measure for a given observer between testing sessions. We might desire to know the degree to which the observational score on a given behavioral construct for the same observer is stable across time to test for observer drift (a threat to the validity of the observational data), or the likelihood that observers are deviating from initial training procedures over time and modifying the definitions of the constructs under study ( Smith, 1986 ). Intra-observer reliability or consistency within an observer may also be conceptualized as the reliability of an observer’s scores within a single session, and in this case the test is analogous to assessments of internal consistency (e.g., Cronbach’s α ). As Pellegrini (2004) has stated, we assume an observer is first reliable or consistent in their scoring/recording by themselves prior to testing if they agree with an independent observer (i.e., inter-observer reliability).

As mentioned, inter-observer reliability or consistency between observers is the gold standard for observational research. Essentially, inter-observer reliability involves comparing the independent codes of the observers with other trained observers. There are several ways to assess this psychometric property ( see Pellegrini, 2004 ), but the key task is comparing agreement across all of the observers. An important best practice for inter-observer reliability procedures is to ensure that observers are sampling/recording the same behaviors independently. Independent coding may be conducted with the use of video and private coding sessions without discussion until all codes have been completed. Inter-observer reliability may be assessed live in the field if the observers take precautions to avoid conveying to their partner how (and, in some cases, when) they are recording the behavior in question. A second best practice is to assess for reliability across the study to help avoid various biases (e.g., observer drift) and coding/recording errors from corrupting the integrity of the data. That is, observers should be checked against a master coder at the start of the study just after training ends, and each observer should pass an a priori reliability threshold (e.g., Cohen’s κ 〉 0 . 7 0 ). Next, their observations should be compared against other independent reliable observers throughout the duration of the study, and the trainer should provide constructive feedback for any deviations from the training protocol. Finally, an important consideration is for what percentage of time inter-observer reliability will be checked. This percentage should be a function of the number of cases or possible events that will be recorded, but typically 15% to 30% of a randomly selected sample of the possible sessions is coded by more than one observer for assessing inter-observer reliability. To avoid potential biases, a best practice is for each observer to conduct reliability observations with all other observers in a round-robin format.

There are several ways to statistically measure inter-observer reliability. In the past, authors relied on zero-order correlations (Pearson’s r ) but that problematic practice is not seen as often in the recent literature. A second statistical method that is still reported in peer-reviewed journals is percent agreement. Percent agreement may be expressed in Equation 1 :

where P o b s is the proportion of agreement observed, N A is the total number of agreements, and N D is the total number of disagreements. Percent agreement is not currently best practice, as it is influenced by the number of cases (i.e., it may be biased by relatively few cases) and because it is not compared against a standard threshold ( Bakeman & Gottman, 1987 ). Finally, one of the central concerns with percent agreement (as well as Pearson’s r ) as a measure of inter-observer reliability is that it does not control for chance agreement ( Bakeman & Gottman, 1987 ).

Cohen’s (1960) κ is a preferred statistic for inter-observer reliability because it does control for chance agreements and is a more “stringent statistic,” allowing greater precision in assessing reliability at a specific moment in time or for particular events rather than overall summaries of association ( Bakeman & Gottman, 1987 , p. 836). Importantly, κ may only be used when coders use a categorical scale ( Bakeman & Gottman, 1987 ) and when a 2 x 2 matrix may be created to depict the proportion of agreements/disagreements for occurrences/nonoccurrences of behavior for any two observers ( Pellegrini, 2004 ). When calculating the rate of agreement, it is important to a priori indicate any time parameters (i.e., within what period of time must both observers note the occurrence of a behavior, also known as the tolerance interval). Some experts caution that extremely short tolerance intervals (e.g., 1 sec) may be overly stringent and artificially reduce the degree of agreement given typical reaction times of observers ( see Bakeman & Gnisci, 2006 ). If time sampling is being used, then observers should be signaled by an external source (e.g., audible tone from an electronic device) to indicate when they should record the behavior ( see Pellegrini, 2004 ). κ may be expressed in Equation 2 :

where P o b s is the proportion of agreement observed, and P e x p is the expected proportion of agreement by chance ( Bakeman & Gnisci, 2006 ). Equation 2 indicates that agreement anticipated as a result of chance is subtracted from both the numerator and denominator, thus κ provides the proportion of agreement corrected for chance agreements ( Bakeman & Gnisci, 2006 ). The range for κ is from - 1 . 0 0 to + 1 . 0 0 , with a value of “0” indicating that obtained agreement is equivalent to agreement anticipated by chance, and greater than chance agreement would yield positive values with +1.00 equal to perfect agreement between the observers ( Cohen, 1960 ). Interestingly, Cohen (1960) revealed that negative values (less than 0) were rare and suggested agreement at less than chance levels. It is possible to test if κ is significantly different from 0, but statistical significance is often not used as a threshold for determining an “adequate” or “good” criterion ( Bakeman & Gottman, 1987 ). Initially, Landis and Koch (1977) provided an index of the strength of agreement or “benchmarks” and reported the following standards: κ of < 0 . 0 0 was “poor,” 0 . 0 0 - 0 . 2 0 was “slight,” 0 . 2 1 - 0 . 4 0 was “fair,” 0 . 4 1 - 0 . 6 0 was “moderate,” 0 . 6 1 - 0 . 8 0 was “substantial,” and 〉 0 . 8 1 was “almost perfect” (p. 165). However, Bakeman and Gottman (1987) reported that a significant κ of less than 0.70 may be a reason for concern. Other scholars have noted that the conservative nature of κ permits one to use a slightly lower threshold for adequate levels of reliability than the typical convention of 0.70 and suggest that a κ coefficient of 0.60 or higher is “acceptable” and 0.80 or above is considered “good” ( Pellegrini, 2001 ).

Under circumstances when a κ coefficient may not be calculated (e.g., when noncategorical data is used or quadrants of the aforementioned occurrence matrix may not be available given the recording rules of the adopted observational procedure), scholars have suggested that an intraclass correlation coefficient (ICC) be computed between independent raters on the continuous data ( Bartko, 1976 ; McGraw & Wong, 1996 ; Shrout & Fleiss, 1979 ). There are several possible ICC formulas that could be depicted that are beyond the scope of the present review, and as such the interested reader is referred to the prior literature on this topic ( Shrout & Fleiss, 1979 ; McGraw & Wong, 1996 ). Intra-class correlation coefficients may be expressed as a function of either the reliability for a single rating (i.e., the reliability of a typical, single observer compared to another observer) or the average rating of the observations across all the raters ( McGraw & Wong, 1996 ). The average rating ICC uses the Spearman-Brown correction to indicate the reliability for all the observers averaged together ( Bartko, 1976 ). The absolute value of an ICC assessing average ratings will be greater or equal to the ICC for a single rater ( Bartko, 1976 ). Intra-class correlation coefficients may also be calculated as an index of “consistency” or as a measure of “absolute agreement.” Essentially, if systematic differences among observers are of interest, then the “absolute agreement” formula accounts for observer variability in the denominator of the ICC estimate, and this is not included for ICCs that measure “consistency” (for further detail, see McGraw & Wong, 1996 ). Intra-class correlation coefficients range from –1.00 to +1.00, where negative values indicate a lack of reliability and +1.00 would indicate perfect agreement ( Bartko, 1976 ). An advantage to ICCs is that confidence intervals may be calculated ( see McGraw & Wong, 1996 ). Typically, acceptable levels of reliability for ICCs are similar to other criteria in the field, and as such, levels greater than or equal to 0.70 are considered “acceptable” (e.g., Ostrov, 2008 ; NICHD Early Child Care Research Network, 2004 ).

In using observational research methods, an assessment of validity is equally as important as an assessment of reliability. Different types of validity should be considered to strengthen the inferences drawn from a particular method, with construct validity being most fundamental to any empirical inquiry. Construct validity is the degree to which the construct being studied actually measures the concept that a researcher intends to study ( Stangor, 2011 ). Construct validity is often established through assessments designed to measure convergent and discriminant validity. Convergent validity rests on the assumption that if a construct is truly being measured, then alternative assessments of the same construct should be correlated with each other ( Stangor, 2011 ). For example, an observational method intended to measure disruptive behaviors in the classroom should be correlated with teacher reports of disruptive behaviors. Alternatively, discriminant validity suggests that the construct being studied should not be correlated with other variables unrelated to the construct ( Stangor, 2011 ). Should the expected convergent and discriminant associations not be observed, then it is unclear what an instrument or observational system is measuring.

Other types of validity that are secondary yet still important to the establishment of a psychometrically sound observational system include content validity and criterion validity. Content validity refers to the extent to which a measure adequately assesses the full breadth of the construct being studied ( Stangor, 2011 ). For example, an observational study of children’s play behavior should code for different types of play, given that it is a diverse construct. To ensure that all facets of a construct are included in an observational system, correspondence with experts and focus groups/review panels may be used. Criterion validity involves an assessment of whether a study variable is associated with a theoretically relevant outcome measure. If observations are associated with an outcome that is measured at the same point in time at which observations are conducted, then concurrent validity is demonstrated. If observations are associated with an outcome that is measured at a future point in time, then predictive validity is demonstrated. For example, concurrent validity would be confirmed by associations between classroom observations of disruptive behavior and teacher report of rejection by peers, and predictive validity would be confirmed by associations between classroom observations of disruptive behavior and future parent -report of academic performance.

threats to validity: sources of bias and error

There are numerous biases for which observational methods are susceptible. A key bias is the aforementioned observer drift, and it is paramount that investigators monitor for this threat to the validity of the data by carefully assessing observational records and calculating reliability coefficients for the duration of the study. Importantly, in addition to the aforementioned discussion about intra-observer reliability, observer drift may also be indicated if there is a drop in inter-observer reliability among the phases of training and data collection ( Smith, 1986 ). A second strategy to mitigate observer drift is to regularly retrain observers. In instances where particular observers demonstrate problematic coding patterns, retraining should be individualized and should target the particular area of concern. In general, retraining is a practice that is beneficial for every observer because it reinforces proper coding procedures and observer behavior, thereby ensuring the integrity of the study.

A second type of distortion that must be considered results from participant reactivity, which is also a threat to the validity of the observational data. Reactivity occurs when the individuals under study alter their behavior because of the presence or influence of an observer. Consequently, the behavior observed does not provide a true representation of the construct being measured. If participants avoid a particular location within a setting or modify their behavior because they know they are being recorded, this is a major concern for the validity of the data ( Stangor, 2011 ). Depending on the nature of the study, reactivity may be more probable. For example, when observers need to remain within earshot of a focal participant to hear and see the behavioral interactions, it is crucial that the observers remain unobtrusive (e.g., Pellegrini, 1989 ). Researchers should explicitly address reactivity by training observers in the field to have a minimally responsive manner ( Pellegrini, 2004 ). Essentially, observers should use neutral facial expressions and control their nonverbal behavior, posture, movement, and reactions to events during live coding. It is also possible that participants may be reactive to cameras and other recording devices, and efforts should be made to habituate participants to this equipment ( see Use of Technology and Software section below) and monitor for this occurrence. Thus, this habituation process should occur prior to the actual collection of data ( Pellegrini, 2004 ). In our studies, we spend a minimum of several days in the observational environment (and will do so for as long as needed) simulating our observations, which provide the participants an opportunity to habituate to our presence and reduce reactivity prior to actual data collection. Therefore, regardless of live or videotaped coding, researchers should observe for participant reactivity and report the degree of reactivity in their studies (e.g., Atlas & Pepler, 1998 ). We define participant reactivity as any direct eye contact between the focal participant and observer, comments from the focal participant to the observer about our presence, or comments about our presence to others in the environment ( Ostrov, 2008 ). Our training procedures and careful monitoring has resulted in relatively low levels of reactivity in several studies (e.g., 1.5–2.5 times per focal participant during 80 min of observation; Crick, Ostrov, Burr et al., 2006 ).

Observer expectancy effects are a third bias ( Hartmann & Pelzel, 2005 ), which is essentially when observers form expectations about the nature of the data based on their knowledge or assumptions about the study goals and hypotheses, which is why best practice is to use unaware observers, when possible, and to use unaware observers for reliability purposes, at a minimum.

A final source of bias that we will discuss is gender bias as this is a well-documented concern with observational methods ( Ostrov, Crick, & Keating, 2005 ). Past research has documented that untrained observers maintain gender biases when observing, for example, physical aggression ( Lyons & Serbin, 1986 ; see also Condry & Ross; 1985 ; Susser & Keating, 1990 ). That is, men tend to rate boys as more physically aggressive than girls, even when boys and girls are displaying comparable levels of aggression ( Lyons & Serbin, 1986 ). Moreover, male and female college students have shown documented gender biases based on knowledge about gender of young children in past experimental studies ( Gurwtiz & Dodge, 1975 ). Finally, in our own research, we have documented that male college students are less likely to correctly identify relational aggression or prosocial behavior than their female peers ( Ostrov et al., 2005 ). Please note that although the examples were related to our field of study (i.e., aggression), gender biases may be present for a variety of topics of study. Importantly, it may be that when individuals are trained to recognize potential biases, they are more likely to be objective in their coding of behavior ( Lyons & Serbin, 1986 ).

Use of Technology and Software

Excellent detailed reviews of computer-assisted recording devices and observational software programs are available ( see Hoch & Symons, 2004 ), and thus, the present goal of this section is to briefly review the current state of technology and software for assisting in systematic observations in the laboratory and field. The following will include a review of the three most common observational software programs as well as the use of handheld devices and remote audiovisual equipment. The commercially available programs vary widely in function and cost, but most permit the observer to define a coding scheme and corresponding letter or number codes that observers can quickly use when making observations live or when coding digital media in the laboratory. Overall, advances in technology have made observational methods more efficient (e.g., flexible data reduction procedures and automatic statistical analyses), accurate (i.e., automatic rewind and playback functions reduce errors in coding), and applicable to a wider range of settings and topics of study ( Bakeman & Gnisci, 2006 , p. 140).

The first software program and associated computer-assisted recording devices that we will discuss is the Observer ® ; system by Noldus Inc. ( Noldus, Trienes, Hendriksen, Jansen, & Jansen, 2000 ). The current version is Observer XT, which permits both time sampling as well as continuous event-based observational systems and has been used in both human and animal research ( see http://www.noldus.com/the-observer-xt/observer-xt-research ). A notable feature is that this software permits an assessment of response latency of the time between the onset of a stimulus and the initiation of the response, which facilitates consequence coding ( see Coding Considerations section above). The software also permits the linking of data from multiple modalities (e.g., observational reports, physiological responses) with a continuous time synch. The software may be used in the field with durable handheld devices or in the laboratory with live streaming video linked directly with the coding program ( Noldus et al., 2000 ). Finally, the new version of the software permits searches of the data for particular comments, events, or behaviors, and data may be exported to various statistical software packages ( Noldus et al., 2000 ). Jonge, Kemner, Naber, and van Engeland (2009) used an earlier version of the Observer software to code data from a study on block design reconstruction in children with autism spectrum disorders and a group of comparison participants. The use of the videotaped sessions and later coding by unaware observers meant that the coders using the software were unaware of the child’s group status. The software permitted the coders to record the amount of time the children took to reconstruct the block design pattern as well as a range of errors ( Jonge et al., 2009 ). The program was used to calculate Cohen’s κ based on two independent coders ( Jonge et al., 2009 ), who could make independent evaluations of the behavior without biasing their coding partner.

The second observational software program that we examine is the Multi-Option Observation System for Experimental Studies (MOOSES; Tapp, Wehby, & Ellis, 1995 ) and the associated Procoder for Digital Video (PCDV; Tapp& Walden, 1993 ), which permits viewing and coding of digital media ( see http://mooses.vueinnovations.com/overview ). The MOOSES and PCDV programs also permit event and time sampling and for the coding of real-time digital media files or verbatim transcripts of observational sessions ( Tapp & Walden, 1993 ; Tapp et al., 1995 ). In fact, data files may be exported to MOOSES for event coding or to another format known as the Systematic Analysis of Language Transcripts (SALT) for transcription data coding. MOOSES automatically timestamps events and may provide frequency and duration codes as well as basic reliability statistics (e.g., Cohen’s κ ), and MOOSES is designed for sequential analysis ( Tapp et al., 1995 ). A handheld version of MOOSES is available. MOOSES/PCDV has been described as a lower cost alternative to The Observer ( Hoch & Symons, 2004 ).

The third system we review is the Behavior Evaluation Strategies and Taxonomies (BEST; Sharpe & Koperwas, 2003 ). This computer system includes both the BEST Collection for capturing digital media files and the BEST Analysis program for both qualitative and quantitative analysis of the observational data ( Sidener, Shabani, & Carr, 2004 ). The BEST program may be used for examining the frequency or duration of events, and sophisticated sequential analysis may be conducted. Much like the more expensive alternatives, this program will calculate reliability statistics (e.g., Cohen’s κ ) and will summarize data in table or various graph formats. A review of this program suggests that BEST does not handle the collection of interval-based data well, but the BEST Analysis program will allow a researcher to analyze this type of observational data ( Sidener et al., 2004 ). A new platform permits video display for captured data from video files, and although the program was initially written for Windows ® ; , there are inexpensive Apple ® ; iPhone ® ; and iPod Touch ® ; applications available for data collection ( see http://www.skware.com ).

Various types of technology (e.g., audio and video recordings) have an extensive history in the field and laboratory to assist researchers in better capturing verbal and nonverbal interactions (e.g., Abramovitch, Corter, Pepler, & Stanhope, 1986 ; Stauffacher & DeHart, 2005 ). Remote audiovisual recordings provided an opportunity to combine the benefits of both audio and video recording while also reducing reactivity to typical recording devices when participants were observed in naturally occurring settings ( Asher & Gabriel, 1993 ; Atlas & Pepler 1998 ; Pellegrini, 2004 ; Pepler & Craig, 1995 ; Pepler, Craig, & Roberts, 1998 ). That is, videotaping with a telephoto zoom lens from an unobtrusive location in the natural setting and recording audio via a system of wireless microphones provides an externally valid way to record behavior and a time-synched verbal record of the interaction ( Pepler & Craig, 1995 ). Thus, remote audiovisual observational recordings provide all the benefits of having a video for subsequent coding by unaware observers (i.e., the ability to pause, rewind, and analyze subtle nonverbal behaviors) as well as a complete verbal transcript, which helps to put the video data in proper context ( Asher & Gabriel, 1993 ; Pepler & Craig, 1995 ). Wireless microphones typically are housed within small vests or waist pouches that participants wear, and often only the focal participant has an active or live microphone, and others in the reference group have “dummy” microphones that resemble the weight and look of the real microphone. Importantly, observational codes made with the remote audiovisual equipment have demonstrated acceptable inter-observer reliability coefficients (e.g., κ = 0 . 7 6 ; Pepler & Craig, 1995 ). Moreover, this procedure as well as sufficient exposure to the equipment by the participants has been found to produce low levels of participant reactivity (e.g., <5%, Atlas & Pepler, 1998 ; see also Asher & Gabriel, 1993 ). The benefits of a rich observational record with low levels of reactivity within settings of high ecological validity seem to outweigh the costs, which include additional training, equipment costs, and some ethical considerations. A central ethical consideration is that individuals without consent may be recorded indirectly. A possible solution is to temporarily store and then, after processing, discard film clips of individuals without consent ( Pepler & Craig, 1995 ), but this solution may violate the rights of nonparticipants. Alternatively, a researcher could restrict access to the observational setting to only those with consent, but this second approach is a threat to the ecological validity of the procedures ( Pepler & Craig, 1995 ). An additional concern is that third parties may wish to use the data as surveillance, which might limit the rights of participants being recorded. As such, policies related to confidentiality and any possible limits of confidentiality should be discussed with the participants and any other possible party that may desire access to the data ( see Pepler & Craig, 1995 ). Importantly, to our knowledge, remote audiovisual observational methodology has only been used with school-aged children in the classroom ( Atlas & Pepler, 1998 ) and typically on the playground (e.g., Asher & Gabriel, 1993 ; Pepler, Craig, & Roberts, 1998 ); thus, it is not clear if older individuals would be more aware and reactive to the procedure and equipment ( Pepler & Craig, 1995 ).

Ethical Considerations

There are several ethical considerations with observational research. With naturally occurring phenomena, there may be a temptation to observe social interactions and behavior without obtaining informed consent. Although this practice may technically be exempt from most Institutional Review Board (IRB) review (i.e., if identifying information is not collected and video or audio recordings of the public behavior are not made), we strongly encourage researchers to obtain informed consent from participants and assent from legal minors to support their right for autonomy but also so that all risks (e.g., breaches of confidentiality) may be appropriately conveyed. To avoid these breaches of confidentiality, researchers conducting live observations typically use identification codes rather than identifying information about the participants on all observation forms and in data files. Access to video or audio recordings of observational sessions is typically restricted to only those individuals (e.g., coders) who must have access as part of the research study. Participants should be fully informed for how long the observational recordings will be maintained and when they will be destroyed. A final ethical consideration concerns intervention efforts or at what point the researcher or observers will intervene (for a discussion of duty to warn with observational methods, see Pepler & Craig, 1995 ) and directly or indirectly act on the behalf of the participants. For example, in our observational studies, we have clearly established procedures for when we will notify a teacher that a child in the observation setting is in danger or in need of help (e.g., leaving the controlled area, serious injury). These procedures are discussed at the start of the study with school officials and are part of our consent process, which we believe are best practices.

An Overview of Procedures for a High-Quality Systematic Observational Study

The researcher begins by a priori selecting and operationally defining behaviors of interest. Next, the researcher adopts a coding scheme by selecting the most appropriate sampling and recording procedures given the nature of the behavior under study and the observational context ( see Table 15.2 ). Ethical considerations should be addressed during this development stage of the observational method and should be evaluated for the duration of the study. If the observational scheme is newly developed for the study, then it is imperative that pilot testing occur within a similar context and with a sample representing the target population. If it is not a new scheme or if pilot testing does not indicate any problems, then the investigator may begin training observers. If there are problems noted, then it is important to rectify these issues as quickly as possible to avoid further errors in the study. It is possible that modifications will be needed regarding the operational definition of the observed constructs or changes may be needed to the procedures and coding scheme given the nature of the context or sample under study. Once these changes are adopted, additional checks should be made to verify the solution has worked to ameliorate the original concerns. Training involves the use of a standardized manual, and initial reliability training assessments are conducted prior to the collection of data. Behavior is sampled in the lab or in the field in accordance with the adopted sampling and recording rules, and inter-observer reliability is collected for the duration of the study. Validity assessments are also conducted using alternative informants and methods. If reliability or validity problems are detected, then this may also yield further modifications to the coding scheme to address the problems. If no psychometric problems are noted, then coding and scoring of the observational data occurs using standardized procedures. Finally, the data are analyzed and reported, which concludes the systematic observational study ( see Fig. 15.1 ).

Systematic observational methods provide an opportunity to record the behavior of humans and animals in a relatively objective manner, without sacrificing ecological validity. In the present chapter, we have attempted to identify best practices as well as benefits and costs of various sampling and recording techniques. Quantitative researchers should be guided by a priori research questions and hypotheses when selecting the most appropriate sampling and recording procedure for the specific research setting. Systematic observations require careful attention to coding and scoring decisions and a focus on achieving acceptable levels of reliability and validity. As a field, we must work to establish more stringent standards of reliability (i.e., inter-observer) and validity (i.e., construct) for observational methods. Moreover, we must continue to address and reduce various sources of bias and error. The use of computer-assisted software and digital analysis technology provide some promising options for increasing the efficiency and appeal of systematic observations in the field. Attention must also be given to key ethical considerations to guide appropriate conduct as an observational researcher. Careful consideration of these issues may inform quality research in a wide variety of basic, clinical, and educational contexts.

Procedures for a high-quality systematic observational study.

Future Directions

Observational methods have been a part of the social and behavioral sciences since the early years of our field, and we anticipate that there is a bright future for observational methods within the quantitative scholar’s toolbox. We have defined seven questions and two remaining issues that we believe the field should work to address. This list is not exhaustive, but we hope these questions will generate future work using systematic observational methods.

1. What is the utility of observational methods above and beyond additional informants? Given the time and cost of observational methods, it is necessary to continue to demonstrate that observational methods have incremental predictive utility or may explain unique amounts of variance in relevant outcomes, above and beyond other informants and measures ( Doctoroff & Arnold, 2004 ; Shaw et al., 1998 ). For example, we have demonstrated that observations of relational and physical aggression account for a significant amount of unique variance above and beyond teacher reports of relational and physical aggression in the prediction of teacher-reported deceptive and lying behaviors ( Ostrov, Ries, Stauffacher, Godleski, & Mullins, 2008 ).

2. How does one best examine the construct validity of observational methods? To date, there is not wide consensus on the best approach for demonstrating the construct validity of observational systems. The typical approach is to compare observational data to other “gold standard” methods. For example, convergent evidence is achieved when high levels of association are found across methods such as between observations of aggression subtypes in classrooms, observations of aggression subtypes via semi-structured observations, and with various informants including teacher reports and parent reports of aggression subtypes (e.g., Crick, Ostrov, Burr, et al., 2006 ; Hinde et al., 1984 ; Ostrov & Bishop, 2008 ; Ostrov & Keating, 2004 ; Pellegrini & Bartini, 2000 ).

3. How do we detect observer biases? We believe the field has only begun to address the important issue of how to assess and identify observer biases. Much further work is needed to examine a host of possible biases from observer drift and observer expectancy effects to gender biases as well as other possible sources of distortion such as halo effects and potential expectancy biases derived from prior knowledge of participants in longitudinal studies ( Hartmann & Pelzel, 2005 ). In addition, more focus should be placed on assessing participant reactivity. Few studies report this source of error and threat to validity, and we encourage observational researchers to quantify the degree to which their participants are reactive to the observational procedures.

4. How do we eliminate observer biases and other sources of error? Once we identify observer biases, we need more evidence-based information on how to appropriately eliminate these biases and sources of error. The literature has indicated few possible solutions (e.g., increased training for individuals with identified biases). In addition, more emphasis should be placed on identifying best practices for reducing reactivity. It is clear that minimally responsive procedures and habituation practices have worked effectively to reduce reactivity to low levels (e.g., <5% of time), but our goal should be to eliminate this source of error from our data.

5. What is the sufficient amount of time for observational sampling? Too often the time interval for time sampling as well as the total duration of observed time for event-based coding systems is decided without sufficient justification, and greater work is needed to establish parameters and strategies for determining the most efficient and useful time intervals for various behaviors and settings.

6. How do we reduce the cost of observational methods? One of the biggest obstacles to greater adoption of systematic observational methods is the cost of observational procedures. Typically, large staffs of highly trained individuals are needed for observational work, and although volunteer research assistants may be used to address this concern, this is still a significant barrier to further work in this area. Moreover, the overall amount of time to conduct an observational study is potentially longer than comparable studies with other methods, and thus we must work to make training procedures, data collection, and coding processes more efficient. The use of computer-assisted software and coding technology will continue to greatly help in this regard.

7. How do we refine and create observational software so that it is compatible with all types of observational systems and more flexible as well as affordable? Although observational software and recording devices have advanced a great deal in recent years ( see Hoch & Symons, 2004 ), the software must become more flexible to accommodate a greater range of observational sampling and recording procedures. Moreover, the financial cost of these programs and licenses are often prohibitive, and efforts must be made to develop high-quality, affordable, and flexible computer-assisted observational software programs.

8. A key remaining issue is that as a field we need to move away from the use of Pearson product moment correlations and percent agreement as a standard measure of assessing inter-observer reliability. Given what we know about the role of chance agreement from classic (e.g., Cohen, 1960 ) and modern sources ( Bakeman & Gottman, 1987 ; Pellegrini, 2004 ), it is not clear why some peer-reviewed manuscripts continue to only present either Pearson product moment correlations or percent agreement as strong evidence of inter-observer reliability.

9. A second remaining concern is that greater discussion of the ethical issues involved in observational methods is needed. For example, as we have discussed, it is not always clear when intervention is needed by observers in the field. Further, greater work needs to be conducted to examine how we may best ensure confidentiality of data with detailed observational records. Finally, we must focus on how we ensure confidentiality with the transfer of electronic observational data via handheld devices and other electronic technology.

Author Note

We wish to thank Jennifer Kane and members of the UB Social Development Laboratory for their assistance with the preparation of this chapter. Thanks to Dr. Leonard J. Simms for comments on an earlier draft. Special thanks to Dr. Anthony D. Pellegrini, who has greatly influenced the way we conceptualize systematic observational methods. The authors are affiliated with the Department of Psychology, University at Buffalo, The State University of New York. Please direct correspondence to the first author at [email protected] or 716-645-3680.

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Home » Observational Research – Methods and Guide

Observational Research – Methods and Guide

Table of Contents

Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

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What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

Prevent plagiarism, run a free check.

There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Case–control studies

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

Are you determining what you want to observe beforehand, or going in open-minded?
Is there another research method that would make sense in tandem with an observational study?
If yes, make sure you conduct a covert observation.
If not, think about whether observing from afar or actively participating in your observation is a better fit.
How can you preempt confounding variables that could impact your analysis?
You could observe the children playing at the playground in a naturalistic observation.
You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
Get informed consent from your participants (or their parents) if you want to record: Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

Your analysis can take an inductive or deductive approach :

If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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6.4 Observational Learning (Modeling)

Learning objectives.

By the end of this section, you will be able to:

Define observational learning
Discuss the steps in the modeling process
Explain the prosocial and antisocial effects of observational learning

Previous sections of this chapter focused on classical and operant conditioning, which are forms of associative learning. In observational learning , we learn by watching others and then imitating, or modeling, what they do or say. For instance, have you ever gone to YouTube to find a video showing you how to do something? The individuals performing the imitated behavior are called models . Research suggests that this imitative learning involves a specific type of neuron, called a mirror neuron (Hickock, 2010; Rizzolatti, Fadiga, Fogassi, & Gallese, 2002; Rizzolatti, Fogassi, & Gallese, 2006).

Humans and other animals are capable of observational learning. For example, in a study of social learning in chimpanzees, researchers gave juice boxes with straws to two groups of captive chimpanzees. The first group dipped the straw into the juice box, and then sucked on the small amount of juice at the end of the straw. The second group sucked through the straw directly, getting much more juice. When the first group, the “dippers,” observed the second group, “the suckers,” what do you think happened? All of the “dippers” in the first group switched to sucking through the straws directly. By simply observing the other chimps and modeling their behavior, they learned that this was a more efficient method of getting juice (Yamamoto, Humle, and Tanaka, 2013).

Imitation is sometimes called the highest form of flattery. But consider Claire’s experience with observational learning. Claire’s nine-year-old son, Jay, was getting into trouble at school and was defiant at home. Claire feared that Jay would end up like her brothers, two of whom were in prison. One day, after yet another bad day at school and another negative note from the teacher, Claire, at her wit’s end, beat her son with a belt to get him to behave. Later that night, as she put her children to bed, Claire witnessed her four-year-old daughter, Anna, take a belt to her teddy bear and whip it. Claire was horrified, realizing that Anna was imitating her mother. It was then that Claire knew she wanted to discipline her children in a different manner.

Link to Learning

Are chimps smarter than children? Watch this video showing chimps and children performing tasks and contemplate who performed the task better. How about quicker?

Like Tolman, whose experiments with rats suggested a cognitive component to learning, psychologist Albert Bandura’s ideas about learning were different from those of strict behaviorists. Bandura and other researchers proposed a brand of behaviorism called social learning theory, which took cognitive processes into account. According to Bandura , pure behaviorism could not explain why learning can take place in the absence of external reinforcement. He felt that internal mental states must also have a role in learning and that observational learning involves much more than imitation. In imitation, a person simply copies what the model does. Observational learning is much more complex. According to Lefrançois (2012) there are several ways that observational learning can occur:

You learn a new response. After watching your coworker get chewed out by your boss for coming in late, you start leaving home 10 minutes earlier so that you won’t be late.
You choose whether or not to imitate the model depending on what you saw happen to the model. Remember Julian and his father? When learning to surf, Julian might watch how his father pops up successfully on his surfboard and then attempt to do the same thing. On the other hand, Julian might learn not to touch a hot stove after watching his father get burned on a stove.
You learn a general rule that you can apply to other situations.

Bandura identified three kinds of models: live, verbal, and symbolic. A live model demonstrates a behavior in person, as when Ben stood up on his surfboard so that Julian could see how he did it. A verbal instructional model does not perform the behavior, but instead explains or describes the behavior, as when a soccer coach tells his young players to kick the ball with the side of the foot, not with the toe. A symbolic model can be fictional characters or real people who demonstrate behaviors in books, movies, television shows, video games, or Internet sources ( Figure 6.17 ).

Latent learning and modeling are used all the time in the world of marketing and advertising. This Ford commercial starring Derek Jeter played for months across the New York, New Jersey, and Connecticut areas. Jeter was an award-winning baseball player for the New York Yankees. The commercial aired in a part of the country where Jeter is an incredibly well-known athlete. He is wealthy, and considered very loyal and good looking. What message are the advertisers sending by having him featured in the ad? How effective do you think it is?

Steps in the Modeling Process

Of course, we don’t learn a behavior simply by observing a model. Bandura described specific steps in the process of modeling that must be followed if learning is to be successful: attention, retention, reproduction, and motivation. First, you must be focused on what the model is doing—you have to pay attention. Next, you must be able to retain, or remember, what you observed; this is retention. Then, you must be able to perform the behavior that you observed and committed to memory; this is reproduction. Finally, you must have motivation. You need to want to copy the behavior, and whether or not you are motivated depends on what happened to the model. If you saw that the model was reinforced for their behavior, you will be more motivated to copy them. This is known as vicarious reinforcement . On the other hand, if you observed the model being punished, you would be less motivated to copy them. This is called vicarious punishment . For example, imagine that four-year-old Allison watched her older sister Kaitlyn playing in their mother’s makeup, and then saw Kaitlyn get a time out when their mother came in. After their mother left the room, Allison was tempted to play in the make-up, but she did not want to get a time-out from her mother. What do you think she did? Once you actually demonstrate the new behavior, the reinforcement you receive plays a part in whether or not you will repeat the behavior.

Bandura researched modeling behavior, particularly children’s modeling of adults’ aggressive and violent behaviors (Bandura, Ross, & Ross, 1961). He conducted an experiment with a five-foot inflatable doll that he called a Bobo doll. In the experiment, children’s aggressive behavior was influenced by whether the teacher was punished for her behavior. In one scenario, a teacher acted aggressively with the doll, hitting, throwing, and even punching the doll, while a child watched. There were two types of responses by the children to the teacher’s behavior. When the teacher was punished for her bad behavior, the children decreased their tendency to act as she had. When the teacher was praised or ignored (and not punished for her behavior), the children imitated what she did, and even what she said. They punched, kicked, and yelled at the doll.

Watch this video clip about the famous Bobo doll experiment to see a portion of the experiment and an interview with Albert Bandura.

What are the implications of this study? Bandura concluded that we watch and learn, and that this learning can have both prosocial and antisocial effects. Prosocial (positive) models can be used to encourage socially acceptable behavior. Parents in particular should take note of this finding. If you want your children to read, then read to them. Let them see you reading. Keep books in your home. Talk about your favorite books. If you want your children to be healthy, then let them see you eat right and exercise, and spend time engaging in physical fitness activities together. The same holds true for qualities like kindness, courtesy, and honesty. The main idea is that children observe and learn from their parents, even their parents’ morals, so be consistent and toss out the old adage “Do as I say, not as I do,” because children tend to copy what you do instead of what you say. Besides parents, many public figures, such as Martin Luther King, Jr. and Mahatma Gandhi, are viewed as prosocial models who are able to inspire global social change. Can you think of someone who has been a prosocial model in your life?

The antisocial effects of observational learning are also worth mentioning. As you saw from the example of Claire at the beginning of this section, her daughter viewed Claire’s aggressive behavior and copied it. Research suggests that this may help to explain why victims of abuse often grow up to be abusers themselves (Murrell, Christoff, & Henning, 2007). In fact, about 30% of child abuse victims become abusive parents (U.S. Department of Health & Human Services, 2013). We tend to do what we know. Children who grow up witnessing their parents deal with anger and frustration through violent and aggressive acts often learn to behave in that manner themselves.

Some studies suggest that violent television shows, movies, and video games may also have antisocial effects ( Figure 6.18 ) although further research needs to be done to understand the correlational and causational aspects of media violence and behavior. Some studies have found a link between viewing violence and aggression seen in children (Anderson & Gentile, 2008; Kirsch, 2010; Miller, Grabell, Thomas, Bermann, & Graham-Bermann, 2012). These findings may not be surprising, given that a child graduating from high school has been exposed to around 200,000 violent acts including murder, robbery, torture, bombings, beatings, and rape through various forms of media (Huston et al., 1992). Not only might viewing media violence affect aggressive behavior by teaching people to act that way in real life situations, but it has also been suggested that repeated exposure to violent acts also desensitizes people to it. Psychologists are working to understand this dynamic.

View this video about the connection between violent video games and violent behavior to learn more.

What Do You Think?

Violent media and aggression.

Does watching violent media or playing violent video games cause aggression? Albert Bandura's early studies suggested television violence increased aggression in children, and more recent studies support these findings. For example, research by Craig Anderson and colleagues (Anderson, Bushman, Donnerstein, Hummer, & Warburton, 2015; Anderson et al., 2010; Bushman et al., 2016) found extensive evidence to suggest a causal link between hours of exposure to violent media and aggressive thoughts and behaviors. However, studies by Christopher Ferguson and others suggests that while there may be a link between violent media exposure and aggression, research to date has not accounted for other risk factors for aggression including mental health and family life (Ferguson, 2011; Gentile, 2016). What do you think?

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Observational Learning In Psychology

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

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BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Key Takeaways

Observational learning involves acquiring skills or new or changed behaviors through watching the behavior of others.
The person or actor performing the action that the observational learner replicates is called a model.
The educational psychologist Albert Bandura was the first to recognize observational learning through his Bobo Doll experiment.
Observational learning consists of attentive, retentive, reproductive, and motivational processes.
Observational learning pervades how children, as well as adults, learn to interact with and behave in the world.

Observational learning, otherwise known as vicarious learning, is the acquisition of information, skills, or behavior through watching others perform, either directly or through another medium, such as video.

Those who do experiments on animals alternatively define observational learning as the conditioning of an animal to perform an act that it observes in a member of the same or a different species.

For example, a mockingbird could learn to imitate the song patterns of other kinds of birds.

The Canadian-American psychologist Albert Bandura was one of the first to recognize the phenomenon of observational learning (Bandura, 1985).

His theory, social learning theory, stresses the importance of observation and modeling of behaviors, attitudes, and the emotional reactions of others.

Stages of Observational Learning

Bandura (1985) found that humans, who are social animals, naturally gravitate toward observational learning. For example, children may watch their family members and mimic their behaviors.

In observational learning, people learn by watching others and then imitating or modeling what they do or say. Thus, the individuals or objects performing the imitated behavior are called models (Bandura, 1985).

Even infants may start imitating the mouth movements and facial expressions of the adults around them.

There are four processes that Bandura’s research identified as influencing observational learning: attention, retention, reproduction, and motivation (Debell, 2021).

In order to learn, observers must pay attention to their environment. The attention levels of a learned person can vary based on the characteristics of the model and the environment where they are learning the behavior.
These variables can include how similar the model is to the observer and the observer’s current mood. Humans, Bandura (1985) proposed, are likely to pay attention to the behaviors of models that are high-status, talented, intelligent, or similar to the learner in some way.
For example, someone seeking to climb the corporate ladder may observe the behavior of their managers and the vice presidents of their company and try to mimic their behavior (Debell, 2021).
Attention in itself, however, is not enough to learn a new behavior. Observers Must also retain or remember the behavior at a later time. In order to increase the chances of retention, the observer can structure the information in a way that is easy to remember.
This could involve using a mnemonic device or a daily learning habit, such as spaced repetition. In the end, however, the behavior must be easily remembered so that the action can later be performed by the learner with little or no effort (Debell, 2021).

Motor Reproduction

After retention comes the ability to actually perform a behavior in real life, often, producing a new behavior can require hours of practice in order to obtain the necessary skills to do so.
Thus, the process of reproduction is one that can potentially take years to craft and perfect (Debell, 2021).
Finally, all learning requires, to some extent, personal motivation. Thus, in observational learning, an observer must be motivated to produce the desired behavior.
This motivation can be either intrinsic or extrinsic to the observer. In the latter case, motivation comes in the form of rewards and punishments.
For example, the extrinsic motivation of someone seeking to climb the corporate ladder could include the incentive of earning a high salary and more autonomy at work (Debell, 2021).

The Bobo Doll Experiment

Bandura’s Bobo Doll experiment is one classic in the field of observational learning. In all, this experiment showed that children could and would mimic violent behaviors simply by observing others.

In these experiments, Bandura (1985) and his researchers showed children a video where a model would act aggressively toward an inflatable doll by hitting, punching, kicking, and verbally assaulting the doll.

The end of the video had three different outcomes. Either the model was punished for their behavior, rewarded for it, or there were no consequences.

After watching this behavior, the researchers gave the children a Bobo doll identical to the one in the video.

The researchers found that children were more likely to mimic violent behaviors when they observed the model receiving a reward or when no consequences occurred.

Alternatively, children who observed the model being punished for their violence showed less violence toward the doll (Debell, 2021).

Observational Learning Examples

There are numerous examples of observational learning in everyday life for people of all ages.

Nonetheless, observational learning is especially prevalent in the socialization of children. For example:

An infant could learn to chew by watching adults chew food.
After witnessing an older sibling being punished for taking a cookie without permission, the young child does not take cookies without permission.
A school child may learn to write cursive letters by observing their teacher write them on the board.
Children may learn to play hide and seek by seeing other children playing the game and being rewarded in the form of entertainment.
Children may also learn to say swear words after watching other children say swear words and gain social status.
A child may learn how to drive a car by making appropriate motions after seeing a parent driving.
A young boy can swing a baseball bat without being explicitly taught how to do it after attending a baseball game. Similarly, a child could learn how to shoot hoops after a basketball game without instruction.
A child may be able to put on roller skates and stand on them without explicit instruction.
A student may learn not to cheat by watching another student be punished for doing so
A child may avoid stepping on ice after seeing another child fall in front of them.

Positive and Negative Outcomes

Bandura concluded that people and animals alike watch and learn and that this learning can have both prosocial and antisocial effects.

Prosocial or positive models can be used to encourage socially acceptable behavior. For example, parents, by reading to their children, can teach their children to read more.

Meanwhile, parents who want their children to eat healthily can in themselves eat healthily and exercise, as well as spend time engaging in physical fitness activities together.

Observational learning argues that children tend to copy what parents do above what they say (Daffin, 2021).

Observational learning has also been used to explain how antisocial behaviors develop. For example, research suggests that observational learning is a reason why many abused children grow up to become abusers themselves (Murrel, Christoff, & Henning, 2007).

Abused children tend to grow up witnessing their parents deal with anger and frustration through violent and aggressive acts, often learning to behave in that manner themselves.

Some studies have also suggested that violent television shows may also have antisocial effects, though this is a controversial claim (Kirsh, 2011).

Observational Learning and Behavioral Modification

Observational learning can be used to change already learned behaviors, both positive and negative.

Bandura asserted that if all behaviors are learned by observing others, and people can model their behavior on that of those around them, then undesirable behaviors can be altered or relearned in the same way.

Banduras suggested showing people a model in a situation that usually causes them some anxiety.

For example, a psychologist may attempt to help someone overcome their fear of getting blood drawn by showing someone using relaxation techniques during a blood draw to stay calm.

By seeing the model interact nicely with the fear-evoking stimulus, the fear should subside. This method of behavioral modification is widely used in clinical, business, and classroom situations (Daffin, 2021).

In the classroom, a teacher may use modeling to demonstrate how to do a math problem for a student. Through a prompt delay, that teacher may then encourage the students to try the problem for themselves.

If the student can solve the problem, no further action is needed; however, if the student struggles, a teacher may use one of four types of prompts — verbal, gestural, modeling, or physical — to assist the student. Similarly, a trainer may show a trainee how to use a computer program to run a register.

As before, the trainer can use prompt delays and prompts to test the level of learning the employee has gained.

Reinforcers can then be delivered through social support after the trainee has successfully completed the task themself (Daffin, 2021).

Observational Learning vs. Operant and Classical Conditioning

Classical conditioning , also known as Pavlovian or respondent conditioning, is a type of learning in which an initially neutral stimulus — the conditioned stimulus — is paired with a stimulus that elicits a reflex response — the unconditioned stimulus.

This results in a learned, or conditioned, response when the conditioned stimulus is present. Perhaps the most famous example of classical conditioning is that of Pavlov’s dogs.

Pavlov conditioned a number of dogs by pairing food with the tone of a bell. After several repetitions, he was able to trigger his dogs to salivate by ringing the bell, even in the absence of food.

Operant conditioning, meanwhile, is a process of learning that takes place by seeing the consequences of behavior. For example, a trainer may teach a dog to do tricks by giving a dog a reward to, say, sit down (Daffin, 2021).

Observational learning extends the effective range of both classical and operant conditioning.

In contrast to classical and operant conditioning, in which learning can only occur through direct experience, observational learning takes place through watching others and then imitating what they do.

While classical and operant conditioning may rely on trial and error alone as a means of changing behavior, observational conditioning creates room for observing a model whose actions someone can replicate.

This can result in a more controlled and ultimately more efficient learning process for all involved (Daffin, 2021).

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Prentice-Hall, Inc.

Bandura, A. (1977). Social learning theory . Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84 (2), 191.

Bandura, A. (1985). Model of causality in social learning theory. I n Cognition and psychotherapy (pp. 81-99). Springer, Boston, MA.

Bandura, A. (1986). Fearful expectations and avoidant actions as coeffects of perceived self-inefficacy.

Bandura, A. (1989). Human agency in social cognitive theory. American psychologist, 44 (9), 1175.

Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology and health, 13 (4), 623-649.

Bandura, A. (2003). Social cognitive theory for personal and social change by enabling media. In Entertainment-education and social change (pp. 97-118). Routledge.

Bandura, A. Ross, D., & Ross, S. A. (1961). Transmission of aggression through the imitation of aggressive models. Journal of Abnormal and Social Psychology , 63, 575-582.

Debell, A. (2021). What is Observational Learning?

Daffin, L. (2021). Principles of Learning and Behavior. Washington State University.

Kirsh, S. J. (2011). Children, adolescents, and media violence: A critical look at the research.

LaMort, W. (2019). The Social Cognitive Theory. Boston University.

Murrell, A. R., Christoff, K. A., & Henning, K. R. (2007). Characteristics of domestic violence offenders: Associations with childhood exposure to violence. Journal of Family violence, 22 (7), 523-532.

Reed, M. S., Evely, A. C., Cundill, G., Fazey, I., Glass, J., Laing, A., … & Stringer, L. C. (2010). What is social learning?. Ecology and society, 15 (4).

Schunk, D. H. (2012). Social cognitive theory .

Skinner, B. F. (1950). Are theories of learning necessary?. Psychological Review, 57 (4), 193.

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Two examples of on-site observational studies with older persons

In some cases, it is necessary to bring your research to your participants. For example, you might want to observe people in a natural setting: at home , in a shop, in the classroom, or in the office.

Another case where on-site research would be beneficial is when your participants are experiencing health issues, preventing them from travelling to your lab. Conducting your research on location enables you to study people that are otherwise difficult to reach.

These factors should be taken into consideration when choosing the location for your research, especially when conducting studies with older age groups.

Practical aspects of on-site observational studies

If you want to conduct your study in another location, there are some practical aspects to think about. For example, you have to set the right lighting conditions and camera position. You also have to make sure that you capture voices and other sounds accurately.

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Observational research examples

The first study we describe in this blog post explores how familiarity influences the use of electronic devices in different age groups. In the second study, researchers compared different methods of observing pain expressions in dementia patients.

Example 1: Familiarity and intuitive use

Improving products for older age groups.

It is generally assumed that older adults have difficulty using modern electronic devices, such as mobile telephones or computers. Because this age group is growing in most countries, changing products and processes to adapt to their needs is increasingly more important.

Improving technological experiences in older age groups supports their social inclusion, productivity, and their independence. To gain more insight in the learning processes involved in using electronic devices, Lawry and his colleagues compared levels of familiarity between age groups.

Comparing familiarity between older and younger adults

Familiarity describes the way an action is recognized or understood, based on prior experience and knowledge. It develops from a general level of knowledge, to knowledge that is based on experience, and finally to effortless and unconscious action.

The researchers identified several behaviors that suggest familiarity with an action. These include anticipation and planning, relative speed, verbalization, and task attention. They recorded these behaviors during a verbal report of how participants thought they would perform a task, as well as during the performance of the task itself.

Specifically, participants were asked to use a product they were familiar with and a product that was new to them. The study included 32 participants with different educational backgrounds, who were divided into different age groups (18-44, 45-59, 60-74, and 75+).

Observing familiarity at home

What better place to study familiarity than in the familiar place of home? By conducting part of their research in the participants’ homes, researcher Lawry and his team created a more realistic learning environment, had easy access to familiar products, and were able to recruit older participants more easily.

They coded verbal and visual data from both studies using The Observer XT , and used this software to calculate inter-rater reliability as well. Results showed significant differences in familiarity between age groups, concerning both known and new products.

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Differences between younger and older adults

When using their own products, the youngest adult group showed more familiar behaviors than the two oldest groups. These younger participants also showed more familiarity when using a new product, compared to all other age groups.

Importantly, these results indicate that intuitive use of new products declines as early as during middle age. Therefore, the researchers advise product designers to incorporate more features that are based on prior knowledge of both middle-aged and older adults.

Example 2: Pain expressions in dementia

Observing pain expressions.

In dementia, severe cognitive impairment can lead to an inability to communicate verbally. When these patients can’t tell their caregivers about the pain they feel, an accurate assessment of pain expressions becomes essential.

Browne and her colleagues examined how the angle of observation influences this assessment, both in trained and untrained observers. It is widely assumed that a front view provides the most information on pain. However, caregivers also often observe patients from the side.

Not only can this information be used to improve human observations, but it can also support the development of computer vision systems, further improving care for people with dementia.

On-site observational study

The researchers included 102 adults over the age of 65 in their study, with and without dementia.

Video recordings were made from both the front and profile of their faces, during a physiotherapy examination and a baseline period. Observers used this video data to assess pain expressions with two different coding systems.

They made their observations in long-term care facilities and an outpatient physiotherapy clinic. This approach provided opportunities to observe participants in their beds, as well as during their treatments.

When analyzing their data, Browne and her colleagues used The Observer XT to code the video data and calculate reliability between raters.

Benefits of a profile view

Both trained and untrained observers were able to discriminate between pain and pain-free situations. When comparing assessments between these observers, results showed that undergraduate students relied less on specific pain cues when making their observations.

During physiotherapy, the students also rated pain intensity higher and more accurately when viewing patients’ profiles. This suggests that the assessments of less experienced observers could improve when including a profile view.

Observer accuracy may also benefit from the use of computerized systems, particularly when viewing patients from the front.

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Lawry, S.; Popovic, V.; Blackler, A.; Thompson, H. (2019). Age, familiarity, and intuitive use: an empirical investigation. Applied Ergonomics , 74 , 74-84.
Browne, E.; Hadjistavropoulos, T.; Prkachin, K.; Ashraf, A.; Taati, B. (2019). Pain expressions in dementia: validity of observers’ pain judgments as a function of angle of observation. Journal of Nonverbal Behavior , https://doi.org/10.1007/s10919-019-00303-4.

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Observational studies and their utility for practice

Julia fm gilmartin-thomas.

2 Research Department of Practice and Policy, University College London, School of Pharmacy, London

1 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne

Ingrid Hopper

Randomised controlled clinical trials are the best source of evidence for assessing the efficacy of drugs. Observational studies provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense.

Observational studies include case reports and case series, ecological studies, cross-sectional studies, case-control studies and cohort studies. New and ongoing developments in data and analytical technology, such as data linkage and propensity score matching, offer a promising future for observational studies. However, no study design or statistical method can account for confounders and bias in the way that randomised controlled trials can.

Clinical registries are gaining importance as a method to monitor and improve the quality of care in Australia. Although registries are a form of cohort study, clinical trials can be incorporated into them to exploit the routine follow-up of patients to capture relevant outcomes.

Introduction

Observational studies involve the study of participants without any forced change to their circumstances, that is, without any intervention. 1 Although the participants’ behaviour may change under observation, the intent of observational studies is to investigate the ‘natural’ state of risk factors, diseases or outcomes. For drug therapy, a group of people taking the drug can be compared to people not taking the drug.

The main types of observational studies used in health research, their purpose and main strengths and limitations are shown in the Table . 2 - 8

Their purpose may be descriptive, analytical or both.

Descriptive studies are primarily designed to describe the characteristics of a studied population.
Analytical studies seek to address cause-and-effect questions.

Case reports and case series

Case reports and case series are strictly speaking not studies. However, they serve a useful role in describing new or notable events in detail. These events often warrant further formal investigation. Examples include reports of unexpected benefits or adverse events, such as a case report describing the use of high-dose quetiapine in treatment-resistant schizophrenia after intolerance to clozapine developed 9 and a case report of a medication error involving lookalike packaging. 10

Ecological studies

Ecological studies are based on analysis of aggregated data at group levels (for example populations), and do not involve data on individuals. These data can be analysed descriptively, but not definitively for causation. Typical examples include studies that examine patterns of drug use over time. One example is the comparison of the use of non-steroidal anti-inflammatory drugs and COX-2 inhibitors in Australia and Canada. 11 Sometimes ecological studies describe associations between drugs and outcomes, such as changes in the rates of upper gastrointestinal haemorrhage after the introduction of COX-2 inhibitors. 12 However, because individual-level data are not presented, causality is at best only implied in ecological studies. The 'ecological fallacy' refers to the error of assuming that associations observed in ecological studies are causal when they are not.

Cross-sectional studies

Cross-sectional studies collect data at a single point in time for each single individual, but the actual data collection may take place over a period of time or on more than one occasion. There is no longitudinal follow-up of individuals. Cross-sectional studies represent the archetypal descriptive study. 1 Typically, they provide a profile of a population of interest, which may be broad, like the Australian Health Survey undertaken intermittently by the Australian Bureau of Statistics, 13 or focused on specific populations, such as older Australians. 14

Case-control studies

Case-control studies focus on determining risk factors for an outcome of interest (such as a disease or a drug’s adverse effect) that has already occurred. 5

those who already have the outcome (cases)
those who do not have the outcome (controls), who are often matched to the cases to make them similar and reduce bias.

Second, data on previous exposure to selected risk factors are collected and compared to see if these risk factors are more (or less) common among cases versus controls. Case-control studies are useful for studying the risk factors of rare outcomes, as there is no need to wait for these to occur. Multiple risk factors can be studied, but each case-control study can involve only one outcome. 5 One example explored the relationship between the use of antiplatelet and anticoagulant drugs (risk factor) and the risk of hospitalisation for bleeding (outcome) in older people with a history of stroke. 15 Another case-control study explored the risk factors for the development of flucloxacillin-associated jaundice (outcome). 16

Cohort studies

Cohort studies compare outcomes between or among subgroups of participants defined on the basis of whether or not they are exposed to a particular risk or protective factor (defined as an exposure). They provide information on how these exposures are associated with changes in the risk of particular downstream outcomes. Compared to case-control studies, cohort studies take individuals with exposures and look for outcomes, rather than taking those with outcomes and looking for exposures. Cohort studies are longitudinal, that is they involve follow-up of a cohort of participants over time. This follow-up can be prospective or retrospective. Retrospective cohort studies are those for which follow-up has already occurred. They are typically used to estimate the incidence of outcomes of interest, including the adverse effects of drugs.

Cohort studies provide a higher level of evidence of causality than case-control studies because temporality (the explicit time relationship between exposures and outcomes) is preserved. They also have the advantage of not being limited to a single outcome of interest. Their main disadvantage, compared to case-control studies, has been that longitudinal data are more expensive and time-consuming to collect. However, with the availability of electronic data, it has become easier to collect longitudinal data.

One prospective cohort study explored the relationship between the continuous use of antipsychotic drugs (exposure) and mortality (outcome) and hospitalisation (outcome) in older people. 17 In another older cohort, a retrospective study was used to explore the relationship between long-term treatment adherence (exposure) and hospital readmission (outcome). 18

Observational studies versus randomised controlled trials

Compared to randomised controlled trials, observational studies are relatively quick, inexpensive and easy to undertake. Observational studies can be much larger than randomised controlled trials so they can explore a rare outcome. They can be undertaken when a randomised controlled trial would be unethical. However, observational studies cannot control for bias and confounding to the extent that clinical trials can. Randomisation in clinical trials remains the best way to control for confounding by ensuring that potential confounders (such as age, sex and comorbidities) are evenly matched between the groups being compared. In observational studies, adjustment for potential confounders can be undertaken, but only for a limited number of confounders, and only those that are known. Randomisation in clinical trials also minimises selection bias, while blinding (masking) controls for information bias. Hence, for questions regarding drug efficacy, randomised controlled trials provide the most robust evidence.

New and upcoming developments

New methods of analysis and advances in technology are changing the way observational studies are performed.

Clinical registries

Clinical registries are essentially cohort studies, and are gaining importance as a method to monitor and improve the quality of care. 19 These registries systematically collect a uniform longitudinal dataset to evaluate specific outcomes for a population that is identified by a specific disease, condition or exposure. This allows for the identification of variations in clinical practice 20 and benchmarking across practitioners or institutions. These data can then be used to develop initiatives to improve evidence-based care and patient outcomes. 21

An example of a clinical registry in Australia is the Australian Rheumatology Association Database, 22 which collects data on the biologic disease-modifying antirheumatic drugs used for inflammatory arthritis. Clinical data from treating specialists are combined with patient-reported quality of life data and linked to national databases such as Medicare and the National Death Index. This registry has provided insight into the safety and efficacy of drugs and their effect on quality of life. It was used by the Pharmaceutical Benefits Advisory Committee to assess cost-effectiveness of these drugs. 23

Another example is the Haemostasis Registry. It was used to determine the thromboembolic adverse effects of off-label use of recombinant factor VII. 24

Clinical registries can also be used to undertake clinical trials which are nested within the registry architecture. Patients within a registry are randomised to interventions and comparators of interest. Their outcome data are then collected as part of the routine operation of the registry. The key advantages are convenience, reduced costs and greater representativeness of registry populations as opposed to those of traditional clinical trials.

One of the first registry-based trials was nested within the SWEDEHEART registry. 25 This prospectively examined manual aspiration of thrombus at the time of percutaneous coronary intervention in over 7000 patients. 26 The primary endpoint of all-cause mortality was ascertained through linkage to another Swedish registry. The cost of the trial was estimated to be US$400 000, which was a fraction of the many millions that a randomised controlled trial would have cost.

Propensity score matching

Even without randomising people within cohorts, methods have emerged in recent years that allow for less biased comparisons of two or more subgroups. Propensity score matching is a way to assemble two or more groups for comparison so that they appear like they had been randomised to an intervention or a comparator. 27 In short, the method involves logistic regression analyses to determine the likelihood (propensity) of each person within a cohort being on the intervention, and then matching people who were on the intervention to those who were not on the basis of propensity scores. Outcomes are then compared between the groups. Propensity score analysis of a large cohort of patients with relapsing remitting multiple sclerosis found that natalizumab was superior to interferon beta and glatiramer acetate in terms of improved outcomes. 28

Data technology

Increasing sophistication in techniques for data collection will lead to ongoing improvements in the capacity to undertake observational studies (and also clinical trials). Data linkage already offers a convenient way to capture outcomes, including retrospectively. However, ethical considerations must be taken into account, such as the possibility that informed consent might be required before linking data. Machine learning will soon allow for easy analyses of unstructured text (such as free text entries in an electronic prescription). 29 Patient-reported outcome measures are important and in future will be greatly facilitated by standardised, secure hardware and software platforms that allow for their capture, processing and analyses.

While clinical trials remain the best source of evidence regarding the efficacy of drugs, observational studies provide critical descriptive data. Observational studies can also provide information on long-term efficacy and safety that is usually lacking in clinical trials. New and ongoing developments in data and analytical technology offer a promising future for observational studies in pharmaceutical research.

Conflict of interest: Julia Gilmartin-Thomas is a Dementia research development fellow with the National Health and Medical Research Council (NHMRC) - Australian Research Council (ARC). Ingrid Hopper is supported by an NHMRC Early Career Fellowship.

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Descriptive Research in Psychology

Sometimes you need to dig deeper than the pure statistics

John Loeppky is a freelance journalist based in Regina, Saskatchewan, Canada, who has written about disability and health for outlets of all kinds.

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Types of Descriptive Research and the Methods Used

Advantages & Limitations of Descriptive Research

Best Practices for Conducting Descriptive Research

Descriptive research is one of the key tools needed in any psychology researcher’s toolbox in order to create and lead a project that is both equitable and effective. Because psychology, as a field, loves definitions, let’s start with one. The University of Minnesota’s Introduction to Psychology defines this type of research as one that is “...designed to provide a snapshot of the current state of affairs.” That's pretty broad, so what does that mean in practice? Dr. Heather Derry-Vick (PhD) , an assistant professor in psychiatry at Hackensack Meridian School of Medicine, helps us put it into perspective. "Descriptive research really focuses on defining, understanding, and measuring a phenomenon or an experience," she says. "Not trying to change a person's experience or outcome, or even really looking at the mechanisms for why that might be happening, but more so describing an experience or a process as it unfolds naturally.”

Within the descriptive research methodology there are multiple types, including the following.

Descriptive Survey Research

This involves going beyond a typical tool like a LIkert Scale —where you typically place your response to a prompt on a one to five scale. We already know that scales like this can be ineffective, particularly when studying pain, for example.

When that's the case, using a descriptive methodology can help dig deeper into how a person is thinking, feeling, and acting rather than simply quantifying it in a way that might be unclear or confusing.

Descriptive Observational Research

Think of observational research like an ethically-focused version of people-watching. One example would be watching the patterns of children on a playground—perhaps when looking at a concept like risky play or seeking to observe social behaviors between children of different ages.

Descriptive Case Study Research

A descriptive approach to a case study is akin to a biography of a person, honing in on the experiences of a small group to extrapolate to larger themes. We most commonly see descriptive case studies when those in the psychology field are using past clients as an example to illustrate a point.

Correlational Descriptive Research

While descriptive research is often about the here and now, this form of the methodology allows researchers to make connections between groups of people. As an example from her research, Derry-Vick says she uses this method to identify how gender might play a role in cancer scan anxiety, aka scanxiety.

Dr. Derry-Vick's research uses surveys and interviews to get a sense of how cancer patients are feeling and what they are experiencing both in the course of their treatment and in the lead-up to their next scan, which can be a significant source of stress.

David Marlon, PsyD, MBA , who works as a clinician and as CEO at Vegas Stronger, and whose research focused on leadership styles at community-based clinics, says that using descriptive research allowed him to get beyond the numbers.

In his case, that includes data points like how many unhoused people found stable housing over a certain period or how many people became drug-free—and identify the reasons for those changes.

Those [data points] are some practical, quantitative tools that are helpful. But when I question them on how safe they feel, when I question them on the depth of the bond or the therapeutic alliance, when I talk to them about their processing of traumas, wellbeing...these are things that don't really fall on to a yes, no, or even on a Likert scale.

For the portion of his thesis that was focused on descriptive research, Marlon used semi-structured interviews to look at the how and the why of transformational leadership and its impact on clinics’ clients and staff.

Advantages & Limitations of Descriptive Research

So, if the advantages of using descriptive research include that it centers the research participants, gives us a clear picture of what is happening to a person in a particular moment, and gives us very nuanced insights into how a particular situation is being perceived by the very person affected, are there drawbacks? Yes, there are. Dr. Derry-Vick says that it’s important to keep in mind that just because descriptive research tells us something is happening doesn’t mean it necessarily leads us to the resolution of a given problem.

I think that, by design, the descriptive research might not tell you why a phenomenon is happening. So it might tell you, very well, how often it's happening, or what the levels are, or help you understand it in depth. But that may or may not always tell you information about the causes or mechanisms for why something is happening.

Another limitation she identifies is that it also can’t tell you, on its own, whether a particular treatment pathway is having the desired effect.

“Descriptive research in and of itself can't really tell you whether a specific approach is going to be helpful until you take in a different approach to actually test it.”

Marlon, who believes in a multi-disciplinary approach, says that his subfield—addictions—is one where descriptive research had its limits, but helps readers go beyond preconceived notions of what addictions treatment looks and feels like when it is effective. “If we talked to and interviewed and got descriptive information from the clinicians and the clients, a much more precise picture would be painted, showing the need for a client's specific multidisciplinary approach augmented with a variety of modalities," he says. "If you tried to look at my discipline in a pure quantitative approach , it wouldn't begin to tell the real story.”

Because you’re controlling far fewer variables than other forms of research, it’s important to identify whether those you are describing, your study participants, should be informed that they are part of a study.

For example, if you’re observing and describing who is buying what in a grocery store to identify patterns, then you might not need to identify yourself.

However, if you’re asking people about their fear of certain treatment, or how their marginalized identities impact their mental health in a particular way, there is far more of a pressure to think deeply about how you, as the researcher, are connected to the people you are researching.

Many descriptive research projects use interviews as a form of research gathering and, as a result, descriptive research that is focused on this type of data gathering also has ethical and practical concerns attached. Thankfully, there are plenty of guides from established researchers about how to best conduct these interviews and/or formulate surveys .

While descriptive research has its limits, it is commonly used by researchers to get a clear vantage point on what is happening in a given situation.

Tools like surveys, interviews, and observation are often employed to dive deeper into a given issue and really highlight the human element in psychological research. At its core, descriptive research is rooted in a collaborative style that allows deeper insights when used effectively.

University of Minnesota. Introduction to Psychology .

By John Loeppky John Loeppky is a freelance journalist based in Regina, Saskatchewan, Canada, who has written about disability and health for outlets of all kinds.

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10 Observational Research Examples (2024)
Examples of Observational Research. 1. Jane Goodall's Research. Jane Goodall is famous for her discovery that chimpanzees use tools. It is one of the most remarkable findings in psychology and anthropology. Her primary method of study involved simply entering the natural habitat of her research subjects, sitting down with pencil and paper ...
6.5 Observational Research
Naturalistic observation is an observational method that involves observing people's behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall's famous research on chimpanzees is a classic example of naturalistic observation.
Observational Research
Naturalistic observation is an observational method that involves observing people's behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall's famous research on chimpanzees is a classic example of naturalistic observation ...
Observation Methods: Naturalistic, Participant and Controlled
The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed. Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with ...
What Is an Observational Study?
Observational study example. Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps. Step 1: Identify your research topic and objectives. The first step is to determine what you're interested in observing and why.
6.6: Observational Research
Naturalistic observation is an observational method that involves observing people's behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall's famous research on chimpanzees is a classic example of naturalistic observation ...
Naturalistic Observation: Definition, Examples, Pros and Cons
Naturalistic observation is a research method that involves observing subjects in their natural environment. This approach is often used by psychologists and other social scientists. It is a form of qualitative research, which focuses on collecting, evaluating, and describing non-numerical data. It can be useful if conducting lab research would ...
Observational Methods
Systematic observational methods require clearly defined codes, structured sampling and recording procedures, and are subject to rigorous psychom ... Research Methods in Psychology. Social Psychology. ... Concepts, and Analysis With Examples and More Advanced Applications Within Psychology Notes. Notes. 17 Program Evaluation: Principles ...
Observational Study Designs: Synopsis for Selecting an Appropriate
The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the ...
Observational methods in psychology
Observational methods in psychological research entail the observation and description of a subject's behavior. Researchers utilizing the observational method can exert varying amounts of control over the environment in which the observation takes place. This makes observational research a sort of middle ground between the highly controlled ...
Observational Research
The following examples of different observational techniques are all illustrated with examples from child psychology. Naturalistic observation is observational research that takes place in a natural or everyday setting such as a school. Usually there is an effort to minimize the observer's impact by carrying out observations secretly or from a ...
Observational Methods
Observation is one of the founding principles of the scientific method, and it can be a very effective tool for studying human-environment interactions. This chapter provides the reader with a practical guide for conducting observational research both in lab and field settings. Using casual, passive, and active observation, a researcher can ...
Cross-Sectional Study: Definition, Designs & Examples
Cross-Sectional vs. Longitudinal. A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time. This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of ...
Observational Research
Psychology: Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people's reactions to stress to better understand how stress affects behavior. ... Observational Research Examples. Here are some real ...
What Is an Observational Study?
Observational study example. Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps. Step 1: Identify your research topic and objectives. The first step is to determine what you're interested in observing and why.
6.4 Observational Learning (Modeling)
Our mission is to improve educational access and learning for everyone. OpenStax is part of Rice University, which is a 501 (c) (3) nonprofit. Give today and help us reach more students. This free textbook is an OpenStax resource written to increase student access to high-quality, peer-reviewed learning materials.
Observational Learning In Psychology
Observational learning, a concept studied in psychology, occurs when an individual watches others perform behaviors and then copies those actions, often acquiring new skills and knowledge through observing models. ... For example, research suggests that observational learning is a reason why many abused children grow up to become abusers ...
Observational Learning: Examples, Stages, History
See observational learning examples and learn the four stages of this type of learning. ... Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book." ... For example, previous research drew a direct connection between playing certain violent video games and an increase ...
Two examples of on-site observational studies
Observational research examples. The first study we describe in this blog post explores how familiarity influences the use of electronic devices in different age groups. In the second study, researchers compared different methods of observing pain expressions in dementia patients. Example 1: Familiarity and intuitive use
Observational studies and their utility for practice
The main types of observational studies used in health research, their purpose and main strengths and limitations are shown in the Table.2-8. Table. Summary of observational studies used in health research ... An example of a clinical registry in Australia is the Australian Rheumatology Association Database,22 which collects data on the ...
The 3 Descriptive Research Methods of Psychology
Types of descriptive research. Observational method. Case studies. Surveys. Recap. Descriptive research methods are used to define the who, what, and where of human behavior and other ...
Observational Research
Naturalistic observation is an observational method that involves observing people's behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall's famous research on chimpanzees is a classic example of naturalistic observation ...
Descriptive Research in Psychology
Descriptive Observational Research . Think of observational research like an ethically-focused version of people-watching. One example would be watching the patterns of children on a playground—perhaps when looking at a concept like risky play or seeking to observe social behaviors between children of different ages.

10 Observational Research Examples

Examples of Observational Research

2. Linguistic Development of Children

3. Consumer Product Design

4. Satellite Images of Walmart

5. Spying on Farms

6. Decision-making Group Dynamics

7. Case Studies

8. Infant Attachment

9. Ethnographic Research

10. Time and Motion Studies

Why do Observational Research?

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6.5 Observational Research

What Is Observational Research?

Naturalistic Observation

Participant Observation

Structured Observation

Case Studies

The Case of “Anna O.”

Archival Research

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32 Observational Research

What Is Observational Research?

Naturalistic Observation

Participant Observation

Structured Observation

Case Studies

The Case of “Anna O.”

Archival Research

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What Is an Observational Study? | Guide & Examples

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Cohort studies

Case–control studies

Cross-sectional studies

Step 1: Identify your research topic and objectives

Step 2: Choose your observation type and technique

Step 3: Set up your observational study

Step 4: Conduct your observation

Step 5: Analyze your data

Step 6: Discuss avenues for future research

Disadvantages

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6.6: Observational Research

Learning Objectives

What Is Observational Research?

Naturalistic Observation

Participant Observation

Structured Observation

Case Studies

The Case of “Anna O.”

Archival Research

Bibliography

15 Observational Methods

Introduction

History of Observational Methods

Sampling and Recording Rules

Time Sampling

Event Sampling

Participant Observation

Focal Sampling

Scan Sampling

Semi-Structured Observations

Methods of Recording

Coding Considerations

Psychometric Properties

threats to validity: sources of bias and error

Use of Technology and Software

Ethical Considerations

An Overview of Procedures for a High-Quality Systematic Observational Study

Future Directions

Author Note

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