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Concept – Definition, Types and Examples

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Definition:

Concept is a mental representation or an abstract idea that we use to understand and organize the world around us. It is a general notion that summarizes and simplifies complex information or experiences, making it easier to communicate and process.

For example, the concept of “love” is an abstract idea that represents a range of emotions and behaviors that people experience in their relationships with others. Similarly, the concept of “justice” represents a set of principles and standards that guide our sense of fairness and equality.

Types of Concept

Types of Concepts are as follows:

Concrete Concepts

These are concepts that refer to tangible objects or physical entities that can be perceived through the senses, such as a table, a car, or a flower.

Abstract Concepts

These are concepts that refer to ideas, qualities, or attributes that cannot be perceived through the senses, such as freedom, justice, or happiness.

Formal Concepts

These are concepts that are defined by specific rules or criteria, such as mathematical concepts like a triangle or a circle.

Natural Concepts

These are concepts that are based on our experience and interactions with the world, such as concepts like water, food, or family.

Social Concepts

These are concepts that are based on cultural or social norms, such as concepts like marriage, friendship, or etiquette.

Prototype Concepts

These are concepts that are based on typical or idealized examples of a category, such as a prototype concept of a bird that includes features like wings, feathers, and the ability to fly.

Exemplar Concepts

These are concepts that are based on specific examples or instances of a category, rather than on an idealized prototype.

Examples of Concept

Here are some examples of concepts:

• Love – a feeling of strong attachment or deep affection towards someone or something.
• Democracy – a system of government in which power is vested in the people and exercised through free and fair elections.
• Justice – the quality of being fair and impartial, particularly in the administration of the law.
• Equality – the state of being equal in status, rights, and opportunities.
• Freedom – the state of being free from coercion, constraint, or oppression.
• Creativity – the ability to produce original and imaginative ideas, works, or solutions.
• Sustainability – the ability to maintain ecological balance and meet the needs of the present generation without compromising the ability of future generations to meet their own needs.
• Globalization – the process of integration and interdependence among people, companies, and governments across the world.
• Diversity – the range of different cultures, ethnicities, genders, and other characteristics that exist within a group or society.
• Leadership – the ability to inspire and guide others towards a common goal or vision.

Applications of Concept

Applications of Concept are as follows:

• Education : Concepts play a crucial role in education, where they are used to help students develop a deeper understanding of various subjects. For example, in mathematics, concepts such as fractions, decimals, and geometric shapes are used to solve problems.
• Science : Concepts are used extensively in scientific research to help scientists understand and explain the natural world. For instance, concepts such as energy, matter, and gravity are used to describe and explain various phenomena.
• Business : Concepts such as marketing, branding, and customer service are essential for businesses to succeed. These concepts help businesses develop effective strategies to reach their target audience and improve customer satisfaction.
• Technology : Concepts are the foundation of many technological innovations. For example, the concept of artificial intelligence is used to develop intelligent machines that can perform tasks that would otherwise require human intervention.
• Philosophy : Concepts are a key aspect of philosophical inquiry, where they are used to analyze and understand complex ideas and arguments. For instance, concepts such as justice, ethics, and morality are used to explore ethical dilemmas and the nature of right and wrong.

Purpose of Concept

The purpose of a concept is to provide a mental framework or idea that helps us understand a particular topic or phenomenon. Concepts can range from simple ideas like “honesty” or “loyalty” to more complex ideas like “democracy” or “social justice.”

Concepts allow us to classify, organize, and analyze information, making it easier to understand and communicate. They also help us identify patterns, similarities, and differences between different ideas or things.

Concepts are essential for learning and intellectual development, as they provide a foundation for more advanced understanding and learning. They also allow us to build upon existing knowledge and make connections between different fields or areas of study.

Characteristics of Concept

There are several characteristics of a concept, including:

• Abstractness: A concept is an abstract idea that represents a class of objects, events, or phenomena. It is a mental construct that does not have a physical existence.
• Generalization : A concept represents a general idea that applies to a broad range of situations, objects, or events. It helps to identify commonalities among various things or phenomena.
• Mental Representation : A concept is a mental representation of an idea that we use to understand the world around us.
• Clarity : A concept should be clearly defined and understandable, so that others can comprehend it.
• Universality : A concept is universal and can be applied across different domains or contexts.
• Coherence : A concept should be logically consistent and coherent, so that it can be used to make sense of information and solve problems.
• Relevance : A concept should be relevant to the context in which it is used, and should have practical applications.
• Flexibility : A concept should be flexible enough to accommodate changes in our understanding of the world, and to adapt to new situations and contexts.
• Abstraction : A concept is an abstraction, meaning that it represents a simplified version of reality that is easier to understand and manipulate.

Advantage of Concept

Here are some advantages of concepts:

• Efficient Communication: Concepts provide a way to communicate efficiently by encapsulating complex ideas into simple, easily understandable units. For example, the concept of “love” represents a broad range of emotional experiences and allows us to communicate about this complex subject more easily.
• Problem-Solving: Concepts help us to solve problems by allowing us to identify patterns and similarities between different situations. This enables us to apply solutions that have worked in similar situations to new problems.
• Learning : Concepts provide a way to organize and structure new information, making it easier to learn and remember. By understanding the concept of “gravity,” for example, we can better understand the behavior of objects in the physical world.
• Decision Making: Concepts enable us to make more informed decisions by providing a framework for evaluating options and considering trade-offs. For example, the concept of “opportunity cost” helps us to weigh the benefits and drawbacks of different choices.

Limitations of Concept

Limitations of the Concept are as follows:

• Subjectivity : Concepts are inherently subjective, as they are based on individual experiences, beliefs, and cultural contexts. The meaning and interpretation of a concept may vary from person to person or culture to culture.
• Incompleteness : Concepts are often incomplete, as they represent a simplified version of reality. They may leave out important details or nuances, leading to misunderstandings or misinterpretations.
• Rigidity : Concepts can be rigid and inflexible, as they may not be able to accommodate new information or perspectives. This can lead to resistance to change or an inability to adapt to new situations.
• Overgeneralization : Concepts can also be overgeneralized, as people may apply a concept to situations where it does not apply or make assumptions based on incomplete or inaccurate information.
• Context dependence: The meaning of a concept can depend on the context in which it is used, making it difficult to apply the concept universally. This can lead to confusion or misinterpretation.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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2.2: Concepts, Constructs, and Variables

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• Page ID 26212

• Anol Bhattacherjee
• University of South Florida via Global Text Project

We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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Chapter 1: Introduction to Research Methods

1.4 Understanding Key Research Concepts and Terms

In this textbook you will be exposed to many terms and concepts associated with research methods, particularly as they relate to the research planning decisions you must make along the way. Figure 1.1 will help you contextualize many of these terms and understand the research process. This general chart begins with two key concepts: ontology and epistemology, advances through other concepts, and concludes with three research methodological approaches: qualitative, quantitative and mixed methods.

Research does not end with making decisions about the type of methods you will use; we could argue that the work is just beginning at this point. Figure 1.3 does not represent an all-encompassing list of concepts and terms related to research methods. Keep in mind that each strategy has its own data collection and analysis approaches associated with the various methodological approaches you choose. Figure 1.1 is intentioned to provide a general overview of the research concept. You may want to keep this figure handy as you read through the various chapters.

Figure 1.3: Shows the research paradigms and research process © JIBC 2019

Ontology & Epistemology

Thinking about what you know and how you know what you know involves questions of ontology and epistemology. Perhaps you have heard these concepts before in a philosophy class? These concepts are relevant to the work of sociologists as well. As sociologists (those who undertake socially-focused research), we want to understand some aspect of our social world. Usually, we are not starting with zero knowledge. In fact, we usually start with some understanding of three concepts: 1) what is; 2) what can be known about what is; and, 3) what the best mechanism happens to be for learning about what is (Saylor Academy, 2012). In the following sections, we will define these concepts and provide an example of the terms, ontology and epistemology.

Ontology is a Greek word that means the study, theory, or science of being. Ontology is concerned with the what is or the nature of reality (Saunders, Lewis, & Thornhill, 2009). It can involve some very large and difficult to answer questions, such as:

• What is the purpose of life?
• What, if anything, exists beyond our universe?
• What categories does it belong to?
• Is there such a thing as objective reality?
• What does the verb “to be” mean?

Ontology is comprised of two aspects: objectivism and subjectivism. Objectivism means that social entities exist externally to the social actors who are concerned with their existence. Subjectivism means that social phenomena are created from the perceptions and actions of the social actors who are concerned with their existence (Saunders, et al., 2009). Figure 1.2 provides an example of a similar research project to be undertaken by two different students. While the projects being proposed by the students are similar, they each have different research questions. Read the scenario and then answer the questions that follow.

Subjectivist and objectivist approaches (adapted from Saunders et al., 2009)

Ana is an Emergency & Security Management Studies (ESMS) student at a local college. She is just beginning her capstone research project and she plans to do research at the City of Vancouver. Her research question is: What is the role of City of Vancouver managers in the Emergency Management Department (EMD) in enabling positive community relationships? She will be collecting data related to the roles and duties of managers in enabling positive community relationships.

Robert is also an ESMS student at the same college. He, too, will be undertaking his research at the City of Vancouver. His research question is: What is the effect of the City of Vancouver’s corporate culture in enabling EMD managers to develop a positive relationship with the local community? He will be collecting data related to perceptions of corporate culture and its effect on enabling positive community-emergency management department relationships.

Before the students begin collecting data, they learn that six months ago, the long-time emergency department manager and assistance manager both retired. They have been replaced by two senior staff managers who have Bachelor’s degrees in Emergency Services Management. These new managers are considered more up-to-date and knowledgeable on emergency services management, given their specialized academic training and practical on-the-job work experience in this department. The new managers have essentially the same job duties and operate under the same procedures as the managers they replaced. When Ana and Robert approach the managers to ask them to participate in their separate studies, the new managers state that they are just new on the job and probably cannot answer the research questions; they decline to participate. Ana and Robert are worried that they will need to start all over again with a new research project. They return to their supervisors to get their opinions on what they should do.

Before reading about their supervisors’ responses, answer the following questions:

• Is Ana’s research question indicative of an objectivist or a subjectivist approach?
• Is Robert’s research question indicative of an objectivist or a subjectivist approach?
• Given your answer in question 1, which managers could Ana interview (new, old, or both) for her research study? Why?
• Given your answer in question 2, which managers could Robert interview (new, old, or both) for his research study? Why?

Ana’s supervisor tells her that her research question is set up for an objectivist approach. Her supervisor tells her that in her study the social entity (the City) exists in reality external to the social actors (the managers), i.e., there is a formal management structure at the City that has largely remained unchanged since the old managers left and the new ones started. The procedures remain the same regardless of whoever occupies those positions. As such, Ana, using an objectivist approach, could state that the new managers have job descriptions which describe their duties and that they are a part of a formal structure with a hierarchy of people reporting to them and to whom they report. She could further state that this hierarchy, which is unique to this organization, also resembles hierarchies found in other similar organizations. As such, she can argue that the new managers will be able to speak about the role they play in enabling positive community relationships. Their answers would likely be no different than those of the old managers, because the management structure and the procedures remain the same. Therefore, she could go back to the new managers and ask them to participate in her research study.

Robert’s supervisor tells him that his research is set up for a subjectivist approach. In his study, the social phenomena (the effect of corporate culture on the relationship with the community) is created from the perceptions and consequent actions of the social actors (the managers); i.e., the corporate culture at the City continually influences the process of social interaction, and these interactions influence perceptions of the relationship with the community. The relationship is in a constant state of revision. As such, Robert, using a subjectivist approach, could state that the new managers may have had few interactions with the community members to date and therefore may not be fully cognizant of how the corporate culture affects the department’s relationship with the community. While it would be important to get the new managers’ perceptions, he would also need to speak with the previous managers to get their perceptions from the time they were employed in their positions. This is because the community-department relationship is in a state of constant revision, which is influenced by the various managers’ perceptions of the corporate culture and its effect on their ability to form positive community relationships. Therefore, he could go back to the current managers and ask them to participate in his study, and also ask that the department please contact the previous managers to see if they would be willing to participate in his study.

As you can see the research question of each study guides the decision as to whether the researcher should take a subjective or an objective ontological approach. This decision, in turn, guides their approach to the research study, including whom they should interview.

Epistemology

Epistemology has to do with knowledge. Rather than dealing with questions about what is, epistemology deals with questions of how we know what is.  In sociology, there are many ways to uncover knowledge. We might interview people to understand public opinion about a topic, or perhaps observe them in their natural environment. We could avoid face-to-face interaction altogether by mailing people surveys to complete on their own or by reading people’s opinions in newspaper editorials. Each method of data collection comes with its own set of epistemological assumptions about how to find things out (Saylor Academy, 2012). There are two main subsections of epistemology: positivist and interpretivist philosophies. We will examine these philosophies or paradigms in the following sections.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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10.2 Conceptual definitions

Learning objectives.

Learners will be able to…

• Define conceptualization
• Identify the role previous research and theory play in defining concepts
• Distinguish between unidimensional and multidimensional concepts
• Critically apply reification to how you conceptualize the key variables in your research project

You can measure phenomena in many different ways, but you must be sure that how you choose to measure gives you information and data that lets you answer your research question. If you’re looking for information about a person’s income, but your main points of measurement have to do with the money they have in the bank, you’re not really going to find the information you’re looking for!  Let’s consider Melissa Milkie and Catharine Warner’s study (2011) [1] of first graders’ mental health. In order to conduct that study, Milkie and Warner needed to have some idea about how they were going to measure mental health. What does mental health mean, exactly? And how do we know when we’re observing someone whose mental health is good and when we see someone whose mental health is compromised?

In quantitative methods, conceptualization involves writing out clear, concise definitions for our key concepts. These are the kind of definitions you are used to, like the ones in a dictionary. A conceptual definition involves defining a concept in terms of other concepts, usually by making reference to how other social scientists and theorists have defined those concepts in the past.

Measurement starts with conceptualization

In order to measure the concepts in your research question, we first have to understand what we think about them. For example, masculinity is a concept. What do you think of when you hear that word? Presumably, you imagine some set of behaviors and perhaps even a particular style of self-presentation. Of course, we can’t necessarily assume that everyone conjures up the same set of ideas or images when they hear the word masculinity . While there are many possible ways to define the term and some may be more common or have more support than others, there is no universal definition of masculinity. What counts as masculine may shift over time, from culture to culture, and even from individual to individual (Kimmel, 2008). This is why defining our concepts is so important.

Not all researchers clearly explain their theoretical framework for their study, but they should! Without understanding how a researcher has defined their key concepts, it would be nearly impossible to understand the meaning of that researcher’s findings and conclusions. Back in Chapter 5, you worked toward developing a theoretical framework for your study based on the theoretical literature in your topic area. If you haven’t done that yet, consider flipping back to that section to familiarize yourself with some of the techniques for finding and using theories relevant to your research question. Continuing with our example on masculinity, we would need to survey the literature on theories of masculinity.  Wong et al. (2010) [2] analyzed eight years of the journal Psychology of Men & Masculinity examining how often different theories of masculinity were used . Reading articles like this can help give you a sense of which theories are more accepted and which are more marginal in your field and give you a range of options from which you can find theories that will inform your project.

Conceptualization is a thorough process

Conceptualization is deceptively challenging—spelling out exactly what the concepts in your research question mean to you. Following along with our example, think about what comes to mind when you read the term masculinity. How do you know masculinity when you see it? Does it have something to do with men or with social norms? If so, perhaps we could define masculinity as the social norms that men are expected to follow. That seems like a reasonable start, and at this early stage of conceptualization, brainstorming about the images conjured up by concepts and playing around with possible definitions is appropriate. However, this is just the first step. At this point, you should be beyond brainstorming for your key variables because you have read a good amount of literature about them.

In addition, we should look at previous research and theory to understand the definitions that other scholars have already given for the concepts we are interested in. This doesn’t mean we must use their definitions, but understanding how concepts have been defined in the past will help us to compare our conceptualizations with how other scholars define and relate concepts. Understanding prior definitions of our key concepts will also help us decide whether we plan to challenge those conceptualizations or rely on them for our own work. Finally, working on conceptualization is likely to help in the process of refining your research question to one that is specific and clear in what it asks. Conceptualization and operationalization (next section) are where “the rubber meets the road,” so to speak and we have to specify what we mean by the question we are asking. As our conceptualization deepens, we will often find that our research question becomes more specific and clear.

If we turn to the literature on masculinity, we will surely come across work by Michael Kimmel , one of the preeminent masculinity scholars in the United States. After consulting Kimmel’s prior work (2000; 2008), [3] we might tweak our initial definition of masculinity. Rather than defining masculinity as “the social norms that men are expected to follow,” perhaps instead we’ll define it as “the social roles, behaviors, and meanings prescribed for men in any given society at any one time” (Kimmel & Aronson, 2004, p. 503). [4] Our revised definition is more precise and complex because it goes beyond addressing one aspect of men’s lives (norms), and addresses three aspects: roles, behaviors, and meanings. It also implies that roles, behaviors, and meanings may vary across societies and over time.

Conceptualization isn’t as simple as applying any random definition that we come up with to a term. Defining terms may involve some brainstorming at the very beginning. But conceptualization must go beyond that, to engage with or critique existing definitions and conceptualizations in the literature. Once we’ve brainstormed about the images associated with a particular word, we should also consult prior work to understand how others define the term in question. After we’ve identified a clear definition, we should make sure that every term used in our definition will make sense to others. Are there terms used within our definition that also need to be defined? If so, our conceptualization is not yet complete. Our definition includes the concept of “social roles,” so we should have a definition for what those mean and become familiar with role theory to help us with our conceptualization. If we don’t know what roles are, how can we study them?

Let’s say we do all of that. We have a clear definition of the term masculinity with reference to previous literature and we also have a good understanding of the terms in our conceptual definition…then we’re done, right? Not so fast. You’ve likely met more than one man in your life, and you’ve probably noticed that they are not the same, even if they live in the same society during the same historical time period. This could mean there are dimensions of masculinity. In terms of social scientific measurement, concepts can be said to have multiple dimensions  when there are multiple elements that make up a single concept. With respect to the term  masculinity , dimensions could be based on gender identity, gender performance, sexual orientation, etc. In any of these cases, the concept of masculinity would be considered to have multiple dimensions.

While you do not need to spell out every possible dimension of the concepts you wish to measure, it is important to identify whether your concepts are unidimensional or multidimensional. In this way, how you conceptualize your variables determines how you will measure them in your study. Unidimensional concepts are those that are expected to have a single underlying dimension and can be measured using a single measure or test. Examples include simple concepts such as a person’s weight, time spent sleeping, and so forth.

One frustrating thing is that there is no clear demarcation between concepts that are inherently unidimensional or multidimensional. Even something as simple as age could be broken down into multiple dimensions including mental age and chronological age, so where does conceptualization stop? How far down the dimensional rabbit hole do we have to go? Researchers should consider two things. First, how important is this variable in your study? If age is not important in your study (maybe it is a control variable), it seems like a waste of time to do a lot of work drawing from developmental theory to conceptualize this variable. On the other hand, if we were measuring the impact of age on masculinity, conceptualizing our independent variable (age) as multidimensional may provide a richer understanding of its impact on masculinity. Finally, as we will discuss in the next section, your conceptualizations lead directly to your operationalization of variables, and once your operationalization is complete, it will inform the measures you chose for your variables.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Write a conceptual definition for your independent and dependent variables.

• Cite and attribute definitions to other scholars, if you use their words.
• Describe how your definitions are informed by your theoretical framework.
• Place your definition in conversation with other theories and conceptual definitions commonly used in the literature.
• Are there multiple dimensions of your variables?
• Are any of these dimensions important for you to measure?

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS): 

You are interested in studying older adults’ social-emotional well-being. Specifically, you would like to research the impact on levels of older adult loneliness of an intervention that pairs older adults living in assisted living communities with university student volunteers for a weekly conversation.

Write a conceptual definition for your dependent variable.

• Describe how your definition is informed by your theoretical framework.
• Are there multiple dimensions of your variable?

Conceptualization looks different in qualitative research

Conceptualization proceeds differently in qualitative research compared to quantitative research. Since qualitative researchers are interested in the understandings and experiences of their participants, they generally do not look for one fixed definition for a concept before starting to interview or interact with participants. A qualitative researcher’s job is to accurately and completely represent how their participants understand a concept, not to test their own definition of that concept.

If you were conducting qualitative research on masculinity, you would likely consult previous literature like Kimmel’s work mentioned above. From your literature review, you may come up with a  working definition  for the terms you plan to use in your study, which can change over the course of the investigation. However, the definition that matters is the definition that your participants share during data collection. A working definition is merely a place to start, and researchers should take care not to think it is the only or best definition out there.

In qualitative inquiry, your participants are the experts on the concepts that arise during the research study. Your job as the researcher is to accurately and reliably collect and interpret their understanding of the concepts they describe while answering your questions. Your understanding of concepts is likely to change over the course of qualitative inquiry, as you learn more information from your participants. Indeed, getting participants to comment on, extend, or challenge the researcher’s interpretations (called member checking, informant feedback, or respondent validation) is a hallmark of qualitative research. This is the opposite of quantitative research, in which definitions are predetermined before the inquiry begins.

The contrast between qualitative and quantitative conceptualization is instructive for understanding how quantitative methods (and positivist research in general) give greater weight to the knowledge of the researcher over the knowledge of study participants and community members. In positivism, the implication is that the researcher is the “expert,” and can define concepts based on their expert knowledge of the scientific literature. This knowledge is in contrast to the lived experience that participants possess from experiencing the topic under examination day-in, day-out. For this reason, it would be wise to remind ourselves not to take our definitions too seriously and be critical about the limitations of our knowledge.

Reification

Reification is the process of considering something abstract to be a concrete object or thing. To some extent, reification occurs whenever we define a contruct in terms of observational terms or indirect observables. However, we should not think that any conceptualization is “real,” or more real than other conceptualizations. It would also be wrong to assume that just because definitions exist for some concept that the concept itself exists beyond some abstract idea in our heads. Although we rely on our conceptualizations of constructs in research and everyday life, it may sometimes lead us to commit the “fallacy of reification.” This happens when we mistakenly consider something abstract to actually be a concrete model that represents it. This Logically Fallacious webpage has some good examples. You can also watch this short 2-minute video for a clear explanation:

Key Takeaways

• Measurement is the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating.
• Kaplan identified three categories of things that social scientists measure including observational terms, indirect observables, and constructs.
• Some concepts have multiple elements or dimensions.
• Researchers often use measures previously developed and studied by other researchers.
• Conceptualization is a process that involves coming up with clear, concise definitions.
• Conceptual definitions are based on the theoretical framework you are using for your study (and the paradigmatic assumptions underlying those theories).
• Whether your conceptual definitions come from your own ideas or the literature, you should be able to situate them in terms of other commonly used conceptual definitions.
• Researchers should acknowledge the limited explanatory power of their definitions for concepts and how oppression can shape what explanations are considered true or scientific.

Think historically about the variables in your research question.

• How has our conceptual definition of your topic changed over time?
• What scholars or social forces were responsible for this change?

Take a critical look at your conceptual definitions.

• How might participants define terms for themselves differently, in terms of their daily experience?
• On what cultural assumptions are your conceptual definitions based?
• Are your conceptual definitions applicable across all cultures that will be represented in your sample?

Take a critical look at your conceptual definition from the previous exercise.

• Milkie, M. A., & Warner, C. H. (2011). Classroom learning environments and the mental health of first grade children. Journal of Health and Social Behavior, 52 , 4–22 ↵
• Wong, Y. J., Steinfeldt, J. A., Speight, Q. L., & Hickman, S. J. (2010). Content analysis of Psychology of men & masculinity (2000–2008). Psychology of Men & Masculinity ,  11 (3), 170. ↵
• Kimmel, M. (2000).  The  gendered society . New York, NY: Oxford University Press; Kimmel, M. (2008). Masculinity. In W. A. Darity Jr. (Ed.),  International  encyclopedia of the social sciences  (2nd ed., Vol. 5, p. 1–5). Detroit, MI: Macmillan Reference USA ↵
• Kimmel, M. & Aronson, A. B. (2004).  Men and masculinities: A-J . Denver, CO: ABL-CLIO. ↵

developing clear, concise definitions for the key concepts in a research question

concepts that are comprised of multiple elements

concepts that are expected to have a single underlying dimension

the process of considering something abstract to be a concrete object or thing; the fallacy of reification is assuming that abstract concepts exist in some concrete, tangible way

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

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• Last Updated: Dec 21, 2023 3:49 PM
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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

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Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

198 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

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how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Table of Contents

Collaboration, information literacy, writing process.

• © 2023 by Joseph M. Moxley - University of South Florida

Research refers to a systematic investigation carried out to discover new knowledge , expand existing knowledge , solve practical problems , and develop new products, apps, and services. This article explores why different research communities have different ideas about what research is and how to conduct it. Learn about the different epistemological assumptions that undergird  informal , qualitative , quantitative , textual , and mixed research methods .

What is Research?

Research may refer to

• For most researchers, the first step in any research project involves strategic searching to learn what the current and best research, theory, and scholarship is on a topic .
• scholars create knowledge by engaging in textual research , interpretation , and hermeneutics .
• Ethnography
• Participant Observation
• Survey Research
• “a systematic application of knowledge toward the production of useful materials, devices, and systems or methods, including design, development, and improvement of prototypes and new processes” (NSF n.d.)
• a process,  a research methodology , that follows  the principles of lean design .

Key Words: Research Community ; Research Methodology ; Research Methods ; Epistemology

Why Does Research Matter?

Overall, research is essential for advancing knowledge, solving problems, informing decision-making, fostering innovation, and promoting critical thinking. It plays a crucial role in shaping the world we live in and the future we create.

• Research allows us to better understand the world around us, from the fundamental workings of the universe to the intricacies of human behavior. By conducting research, scholars can uncover new information, develop new theories and models, and identify gaps in existing knowledge that need to be filled. This knowledge can help students and teachers to better understand the world around them and develop new solutions to the problems facing society.
• Research helps us identify and solve problems. It can help us find ways to improve our health, protect the environment, reduce poverty, and develop new technologies.
• Research provides important information that can inform policy decisions, business strategies, and individual choices. By studying trends, analyzing data, and conducting experiments, researchers can help us make better-informed decisions.
• Research often leads to new technologies, products, and services. By pushing the boundaries of what is currently possible, researchers can inspire and fuel innovation.
• Research teaches us to question assumptions, evaluate evidence, and think critically. These skills are important for students to develop because they enable them to become more informed and engaged citizens, able to make more informed decisions and contribute to society in meaningful ways.
• Research experience can be an asset in many career fields, including academia, business, government, and nonprofit organizations. By conducting research as an undergraduate student, students can develop valuable skills and experience that can help them to succeed in their future careers.

Types of Research

The choice of research methods depends on the epistemological assumptions of the researchers and the practices of a particular methodological community , the research question , the type of data needed, and the resources available.

Epistemology and Research Communities

Investigators across academic disciplines — the humanities, social sciences, sciences, and the arts — share some common methods and values. For instance, in both workplace writing and academic writing , investigators are careful

• to cite sources , particularly sources that have changed the conversation on a topic
• to provide evidence for claims (as opposed to opinion or other forms of anecdotal knowledge .

Yet it is also important to note that different research communities also develop unique approaches to exploring and solving problems in their knowledge domains. Research communities develop different ways of conducting research because they face different problems and because they may have different epistemological assumptions about what knowledge is and how to measure it. For example, if a researcher believes that knowledge can only be gained through observation and empirical evidence , they may choose to use quantitative research methods such as experiments or surveys . Conversely, if a researcher believes that knowledge can also be gained through subjective experience and interpretation , they may choose to use qualitative research methods such as case study , ethnography or participant observation

While there are many nuanced definitions of epistemology , scholars have identified three major epistemological perspectives that inform the works of three research communities

• The Scholars – aka Scholarship
• The Positivists – aka Positivism
• The Postpositivists – aka Postpositivism

Research & Mindset

Researchers are curious about the world. They embrace openness , a growth mindset , and collaboration . They undertake research projects in order to review existing knowledge and generate original knowledge claims about the topic , thesis, research question they are investigating. Research finds evidence.

Research Ethics

Researchers and consumers of research are wise to view research claims and research plans from an ethical perspective. Given human nature — such as the tendency to look for confirming evidence and ignore disconfirming evidence and to allow emotions to cloud reasoning — it’s foolhardy to disregard critical literacy practices when consuming the research of others.

Ethics are important to undergraduate students as researchers because ethics provide a framework for conducting research that is responsible, respectful, and accountable :

• Ethics ensure that participants in research are treated with respect and dignity, and that their rights and well-being are protected. As a student researcher, it is important to obtain informed consent from participants, ensure their confidentiality, and minimize any potential harm or discomfort.
• Ethics ensure that research is conducted with integrity and honesty. This means that data is collected and analyzed accurately, and that findings are reported truthfully and transparently.
• Ethics help to build trust between researchers and the public. When research is conducted ethically, participants and the wider community are more likely to trust the findings and the researchers themselves.
• Adhering to ethical standards in research can help students to develop important professional skills, such as critical thinking, problem-solving, and communication . These skills can be useful in a wide range of career fields, including academia, healthcare, and government.
• Ethical research is a professional obligation. By conducting research ethically, students are fulfilling their obligations to the wider research community.

Research as an Iterative, Recursive, Chaotic Process

Research is commonly depicted on websites and textbooks on research methods as systematic work (see, e.g., Wikipedia’s Research page).

Depicting research as systematic work is certainly valid, especially in natural and social science research. For instance, scientists in the lab working with a virus like COVID-19 or Ebola aren’t going to play around. Their professionalism and safety is tied to rigorously following research protocols.

That said, it’s an oversimplification to suggest research processes are invariably systematic. Discoveries have emerged from basic research that have been wildly popular and useful real-world applications . (See, for example, 24 Unintended Scientific Discoveries — the video below). Scientists may begin researching hypothesis A but rewrite that hypothesis multiple times until they find hypothesis Z — something that explains the data. Then they go back and repackage their investigation, following ethical standards, for a wider audience.

Ultimately, because research is such an iterative process, the thesis or hypothesis a researcher began with may not be the one the researcher ends up with. The takeaway here is that research is a learning process. Research efforts can lead to unpredictable applications and insights. Research finds evidence. Ultimately, research is about curiosity and openness. The question that initiates a research effort may morph into other questions as researchers

• dig deeper into the literature on the topic and become more conversant
• endeavor to make sense of the data/information they have gathered during the conduct of the study.

Related Concepts

Research methods.

Research results— knowledge claims -—are important. But, how researchers claim to know what they know—their research methods and research methodology —are equally important.

During the early stages of a writing project, you can identify research questions worth asking by engaging in Information Literacy practices.

Using Evidence

Learn to summarize,  paraphrase , and  cite sources . Weave others’ ideas and words into your texts in ways that support your  thesis/research question ,  information ,  rhetorical stance .

Hale, J. (2018). Understanding research methodology 5: Applied and basic research, PsychCentral . https://psychcentral.com/blog/understanding-research-methodology-5-applied-and-basic-research/

Related Articles:

Applied Research, Basic Research

Research Methodology

Scholarship

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Understand the difference between Applied Research and Basic Research.

As an investigator be sure to protect your research subjects and follow ethical standards. As a consumer of research, be mindful of when investigators may be exaggerating results, making claims...

Not all research methods are equal or produce the same kind of knowledge. Learn about the philosophies, the epistemologies, that inform qualitative, quantitative, mixed, and textual research methods.

Understand how to identify appropriate research methods for particular methodological communities, rhetorical situations, and research questions.

Scholarship is not just about memorizing facts or regurgitating information. It’s about developing a deep understanding of a subject, making connections across disciplines, and contributing to the ongoing conversation about...

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Home Market Research

Conceptual Research: Definition, Framework, Example and Advantages

Conceptual Research: Definition

Conceptual research is defined as a methodology wherein research is conducted by observing and analyzing already present information on a given topic. Conceptual research doesn’t involve conducting any practical experiments. It is related to abstract concepts or ideas. Philosophers have long used conceptual research to develop new theories or interpret existing theories in a different light.

For example, Copernicus used conceptual research to come up with the concepts of stellar constellations based on his observations of the universe. Down the line, Galileo simplified Copernicus’s research by making his own conceptual observations which gave rise to more experimental research and confirmed the predictions made at that time.

The most famous example of conceptual research is Sir Issac Newton. He observed his surroundings to conceptualize and develop theories about gravitation and motion.

Einstein is widely known and appreciated for his work on conceptual research. Although his theories were based on conceptual observations, Einstein also proposed experiments to come up with theories to test the conceptual research.

Nowadays, conceptual research is used to answer business questions and solve real-world problems. Researchers use analytical research tools called conceptual frameworks to make conceptual distinctions and organize ideas required for research purposes.

Conceptual Research Framework

Conceptual research framework constitutes of a researcher’s combination of previous research and associated work and explains the occurring phenomenon. It systematically explains the actions needed in the course of the research study based on the knowledge obtained from other ongoing research and other researchers’ points of view on the subject matter.

Here is a stepwise guide on how to create the conceptual research framework:

01. Choose the topic for research

Before you start working on collecting any research material, you should have decided on your topic for research. It is important that the topic is selected beforehand and should be within your field of specialization.

02. Collect relevant literature

Once you have narrowed down a topic, it is time to collect relevant information about it. This is an important step, and much of your research is dependent on this particular step, as conceptual research is mostly based on information obtained from previous research. Here collecting relevant literature and information is the key to successfully completing research.

The material that you should preferably use is scientific journals , research papers published by well-known scientists , and similar material. There is a lot of information available on the internet and in public libraries as well. All the information that you find on the internet may not be relevant or true. So before you use the information, make sure you verify it.  

03. Identify specific variables

Identify the specific variables that are related to the research study you want to conduct. These variables can give your research a new scope and can also help you identify how these can be related to your research design . For example, consider hypothetically you want to conduct research about the occurrence of cancer in married women. Here the two variables that you will be concentrating on are married women and cancer.

While collecting relevant literature, you understand that the spread of cancer is more aggressive in married women who are beyond 40 years of age. Here there is a third variable which is age, and this is a relevant variable that can affect the end result of your research.  

04. Generate the framework

In this step, you start building the required framework using the mix of variables from the scientific articles and other relevant materials. The research problem statement in your research becomes the research framework. Your attempt to start answering the question becomes the basis of your research study. The study is carried out to reduce the knowledge gap and make available more relevant and correct information.

Example of Conceptual Research Framework

Thesis statement/ Purpose of research: Chronic exposure to sunlight can lead to precancerous (actinic keratosis), cancerous (basal cell carcinoma, squamous cell carcinoma, and melanoma), and even skin lesions (caused by loss of skin’s immune function) in women over 40 years of age.

The study claims that constant exposure to sunlight can cause the precancerous condition and can eventually lead to cancer and other skin abnormalities. Those affected by these experience symptoms like fatigue, fine or coarse wrinkles, discoloration of the skin, freckles, and a burning sensation in the more exposed areas.

Note that in this study, there are two variables associated- cancer and women over 40 years in the African subcontinent. But one is a dependent variable (women over 40 years, in the African subcontinent), and the other is an independent variable (cancer). Cumulative exposure to the sun till the age of 18 years can lead to symptoms similar to skin cancer. If this is not taken care of, there are chances that cancer can spread entirely.

Assuming that the other factors are constant during the research period, it will be possible to correlate the two variables and thus confirm that, indeed, chronic exposure to sunlight causes cancer in women over the age of 40 in the African subcontinent. Further, correlational research can verify this association further.

Advantages of Conceptual Research

1. Conceptual research mainly focuses on the concept of the research or the theory that explains a phenomenon. What causes the phenomenon, what are its building blocks, and so on? It’s research based on pen and paper.

2. This type of research heavily relies on previously conducted studies; no form of experiment is conducted, which saves time, effort, and resources. More relevant information can be generated by conducting conceptual research.

3. Conceptual research is considered the most convenient form of research. In this type of research, if the conceptual framework is ready, only relevant information and literature need to be sorted.

QuestionPro for Conceptual Research

QuestionPro offers readily available conceptual frameworks. These frameworks can be used to research consumer trust, customer satisfaction (CSAT) , product evaluations, etc. You can select from a wide range of templates question types, and examples curated by expert researchers.

We also help you decide which conceptual framework might be best suited for your specific situation.

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

• Observational
• Experimental
• Descriptive
• Retrospective
• Prospective
• Cross-sectional
• Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

• Observational studies: cohort, case control and cross- sectional research
• Interventional research: quasi-experimental and clinical research
• Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
• Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
• Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

• Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

• Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
• Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

• H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
• H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

• Type-I (α) and type-II (β) error levels
• Difference between groups (d-difference) and effect size (ES)
• Distribution ratio of groups
• Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

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What is Research?

Research is an often-misused term, its usage in everyday language very different from the strict scientific meaning.

This article is a part of the guide:

• Definition of Research
• Research Basics
• Steps of the Scientific Method
• Purpose of Research
• What is the Scientific Method?

Browse Full Outline

• 1 Research Basics
• 2.1 What is Research?
• 2.2 What is the Scientific Method?
• 2.3 Empirical Research
• 3.1 Definition of Research
• 3.2 Definition of the Scientific Method
• 3.3 Definition of Science
• 4 Steps of the Scientific Method
• 5 Scientific Elements
• 6 Aims of Research
• 7 Purpose of Research
• 8 Science Misconceptions

In the field of science, it is important to move away from the looser meaning and use it only in its proper context. Scientific research adheres to a set of strict protocols and long established structures.

Definition of the Scientific Method

Often, we will talk about conducting internet research or say that we are researching in the library. In everyday language, it is perfectly correct grammatically, but in science , it gives a misleading impression. The correct and most common term used in science is that we are conducting a literature review .

The Guidelines

What is research ? For a successful career in science, you must understand the methodology behind any research and be aware of the correct protocols.

Science has developed these guidelines over many years as the benchmark for measuring the validity of the results obtained.

Failure to follow the guidelines will prevent your findings from being accepted and taken seriously. These protocols can vary slightly between scientific disciplines, but all follow the same basic structure.

Aims of Research

The general aims of research are:

Observe and Describe

Determination of the Causes

Purpose of Research - Why do we conduct research? Why is it necessary?

Steps of the Scientific Process

The steps of the scientific process has a structure similar to an hourglass - The structure starts with general questions, narrowing down to focus on one specific aspect , then designing research where we can observe and analyze this aspect. At last, the hourglass widens and the researcher concludes and generalizes the findings to the real world.

• Summary of the Elements in Scientific Research

1) Setting a Goal

Research in all disciplines and subjects, not just science, must begin with a clearly defined goal . This usually, but not always, takes the form of a hypothesis .

For example, an anthropological study may not have a specific hypothesis or principle, but does have a specific goal, in studying the culture of a certain people and trying to understand and interpret their behavior.

The whole study is designed around this clearly defined goal, and it should address a unique issue, building upon previous research and scientifically accepted fundamentals. Whilst nothing in science can be regarded as truth, basic assumptions are made at all stages of the research, building upon widely accepted knowledge.

2) Interpretation of the Results

Research does require some interpretation and extrapolation of results.

In scientific research, there is always some kind of connection between data (information gathered) and why the scientist think that the data looks as it does. Often the researcher looks at the data gathered, and then comes to a conclusion of why the data looks like it does.

A history paper, for example, which just reorganizes facts and makes no commentary on the results, is not research but a review .

If you think of it this way, somebody writing a school textbook is not performing research and is offering no new insights. They are merely documenting pre-existing data into a new format.

If the same writer interjects their personal opinion and tries to prove or disprove a hypothesis , then they are moving into the area of genuine research. Science tends to use experimentation to study and interpret a specific hypothesis or question, allowing a gradual accumulation of knowledge that slowly becomes a basic assumption.

3) Replication and Gradual Accumulation

For any study, there must be a clear procedure so that the experiment can be replicated and the results verified.

Again, there is a bit of a grey area for observation-based research , as is found in anthropology, behavioral biology and social science, but they still fit most of the other criteria.

Planning and designing the experimental method , is an important part of the project and should revolve around answering specific predictions and questions . This will allow an exact duplication and verification by independent researchers, ensuring that the results are accepted as real.

Most scientific research looks at an area and breaks it down into easily tested pieces.

The gradual experimentation upon these individual pieces will allow the larger questions to be approached and answered, breaking down a large and seemingly insurmountable problem, into manageable chunks.

True research never gives a definitive answer but encourages more research in another direction. Even if a hypothesis is disproved, that will give an answer and generate new ideas, as it is refined and developed.

Research is cyclical, with the results generated leading to new areas or a refinement of the original process.

4) Conclusion

The term, research , is much stricter in science than in everyday life.

It revolves around using the scientific method to generate hypotheses and provide analyzable results. All scientific research has a goal and ultimate aim , repeated and refined experimentation gradually reaching an answer.

These results are a way of gradually uncovering truths and finding out about the processes that drive the universe around us. Only by having a rigid structure to experimentation, can results be verified as acceptable contributions to science.

Some other areas, such as history and economics, also perform true research, but tend to have their own structures in place for generating solid results. They also contribute to human knowledge but with different processes and systems.

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Martyn Shuttleworth (Feb 2, 2008). What is Research?. Retrieved Apr 01, 2024 from Explorable.com: https://explorable.com/what-is-research

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Concept and Definition with Examples

Research Concept what does it mean? Simply, research is a scientific, systematic, and creative practical work with a view to producing new knowledge. Research is the most powerful tool for getting accurate and real knowledge on a particular topic. For example, the actual reason for the spread of the Novel Corona V irus if we would like to explore there is no better alternative to Scientific Research.

Some terms are deeply associated with the research concept such as What, Why, and How. If we want to prevent Novel Corona Virus, first of all, we must know what is the Novel Corona Virus? Then Why this has spread across the Chinese and some other countries? Naturally, the next question that comes up is how did it spread?

The Greek philosopher Aristotle’s question was why the smoke of fire rises up in the sky? Why it doesn’t go down or to the side? The Why question of Aristotle was very popular at that time among the philosopher.

Today’s researchers and scientists are more interested in how the event occurs. Researchers believe that a kind of thought and inference is sought after in order to find out why something happens in nature. Often, the right thing is not known for thought and inference.

Therefore, at present, the result is now scientifically proven through research so we accept it as true knowledge.

Now we can define Research as a careful and detailed study of a specific problem, concern, or issue, for example, “Graduate Unemployment” using the scientific method.

Definition of Research  

Let’s see what the scholars say about the research concepts and definitions.

American Sociologist Earl Robert Babbie, “Research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. Research involves inductive and deductive methods.”

Cambridge English Dictionary defined Research as-

“To study a subject in detail, especially in order to discover new information or reach a new understanding.”

Merriam-Webster defines as:

“Investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws”

Katzer, Cook & Crough say the research as follows:

“I define research here as the systematic process of collecting and analyzing information (data) in order to increase our understanding of the phenomenon with which we are concerned or interested.”

Research can start with questions or problems . But there are some important steps to take for research. For example-

• Collection of questions arising from problems.
• Willingness to solve problems.
• Setting goals to solve problems.
• Determining the problem-solving strategy.
• Making decisions based on the information obtained using the strategy.

Research in Nature

• Research is a systematic approach that necessarily studies accurate data. Some rules and procedures are an integral part of the research process that sets the objective of the research.
• The research basically is based on logical reasoning and involves both inductive and deductive methods.
• The information and data derived are in reality from actual observations in natural settings.
• The research follows an in-depth analysis of all data collected so that there is no deviation associated with the research investigation.
• Research creates a way of originating new questions related to practical problems existing in society.
• Research, in fact, is analytical in nature. It makes use of all the available data so that there is no ambiguity in the hypothesis .
• The most important aspect of the research is accurate. The information and data that is to collect should be accurate and real in nature.

7 Basic Steps in Formulating a Research Problem  

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Scientific Research and Methodology

2.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester. A conceptual definition would describe what is meant by ‘stress.’ An operational definition would describe how the ‘stress’ would be measured.

Sometimes the definitions themselves aren’t important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an ‘adult’ in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 2.2 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful… it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

… a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)… [ Note : This table includes symptoms such as ‘convulsion,’ ‘clearly dazed,’ etc.]; An abnormal post game, same day assessment…; An abnormal 36–48 h assessment…; The presence of clinical suspicion by the treating doctor at any time…

Example 2.3 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 2.4 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen et al. 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of ‘snacking’:

…an eating occasion that occurred between meals based on time of day. — Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the ‘pace of life’ and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for ‘pace of life’ (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave ‘change for two $20 bills or [gave] two $20 bills for change.’
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure ‘pace of life,’ of course. Nonetheless, the researchers found that, compared to people on the West Coast,

… people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch… — Levine ( 1990 ) (p. 455)

• What is Conceptual Research? Definition, Framework, Pros & Cons

Introduction

Conducting research is an important part of human life and for centuries, identifying and evaluating a subject or topic to gain knowledge has been a constant practice. There are different classifications of research based on the approach, methodology, and purpose. 

One such classification is conceptual research, which focuses on exploring and developing abstract ideas or concepts. And in today’s article, we will discuss the definition of conceptual research, its purpose, and its significance in various fields.

What is Conceptual Research?

The process defined as conceptual research focuses on the exploration and development of abstract concepts or theories. It involves the study and analysis of existing concepts to refine or develop a conceptual framework. 

Theoretical research is also known as such because a theoretical approach is used to understand the world. Additionally, it is utilized by both academics and non-academics.

The purpose of conceptual research is to understand how people make sense of their environment, how they make decisions, and what influences them.

Conceptual research can take various forms, including descriptive, explanatory, exploratory, or theoretical research. The research can focus on a broad range of topics, such as cultural differences, political ideologies, social norms, philosophical concepts, and ethical principles. 

For example, a conceptual study can explore the concept of social justice, the impact of globalization on culture, or the implications of artificial intelligence on society.

It allows you to determine whether the results of your experiment are what you want them to be, and it helps to understand why things happened as they did. Through conceptual research, researchers can develop a theoretical framework that can guide future research and practice. 

Conceptual research can generate new insights and challenge existing assumptions or theories. It can contribute to the development of new ideas, theories, or concepts, which advance understanding. In philosophy, social sciences, and humanities, theoretical analysis and critical thinking are essential. Therefore, the use of conceptual research is common in these disciplines.

What is the Purpose of Conceptual Research?

The purpose of conceptual research is to provide a deeper understanding of complex concepts. It clarifies ambiguous terms or definitions and generates new insights or perspectives. These can guide future research and practice. Conceptual research serves as a basis for empirical research. It provides a theoretical framework for hypothesis testing and data analysis.

This is because one of the primary purposes of conceptual research is to develop a theoretical framework. In the paragraph below, we will discuss more on the conceptual research framework and how you can understand it. Another importance of conceptual research is that it helps to clarify vague concepts or terms. For example, in psychology, the concept of intelligence has been a topic of debate for decades. Researchers have used several definitions and measures of intelligence, that led to conflicting findings. 

However, through conceptual research, researchers can identify the underlying assumptions and theories that truly shape the concept of intelligence. This will allow them to develop a clearer and more concise definition of intelligence that can guide future research.

Furthermore, conceptual research also aims to generate new ideas or concepts that can contribute to the development of a more comprehensive understanding of social phenomena.

Understanding the Conceptual Research Framework

The framework for conceptual research is a set of steps that you can follow to ensure that your study meets all necessary requirements for scientific rigor. It is a way for researchers to organize their ideas about a topic and how that topic affects other topics. 

For example, studying people’s thoughts about time can involve a conceptual framework with five categories. These include past, present, future, money, and health. You can then use this framework as a guide to examine how people perceive time with respect to income or health.

What are the Methods of Conceptual Research?

Conceptual research relies on literature review, expert opinions, philosophical analysis, or critical thinking. It provides insights into abstract ideas or concepts. Empirical research collects and analyzes quantitative or qualitative data. Here are the four methods of conceptual research:

• Literature Review:  The literature review is a primary conceptual research method. It involves a systematic search and analysis of existing literature. This is done on a particular subject to identify main concepts, theories, and research findings. It also helps in developing a conceptual framework, identifying research gaps, and generating new ideas. Various sources are used for conducting it, like books, academic journals, and online databases.
• Theory Development: Theory development is a method of conceptual research that involves the construction or refinement of theories that explain a particular concept. To use this method, the researcher may use empirical data, expert opinions, or philosophical analysis. The aim of theory development is to provide a framework that can be used to guide future research or to inform practice.
• Critical Analyses: Critical analyses also involve the evaluation of arguments, ideas, or concepts. The aim of critical analyses is to identify logical fallacies, inconsistencies, and biases in the reasoning. Critical analyses can help to generate new ideas or perspectives that challenge existing assumptions or theories.
• Historical Research : Historical research is a method of conceptual research that involves the study of past events or phenomena to gain insights into the present or future. The researcher may use primary sources, such as diaries, letters, or newspapers. Alternatively, they can use secondary sources, such as historical texts or biographies, to study the past. The aim of historical research is to provide a context for understanding current or future events or phenomena.

Advantages of Conceptual Research

The advantages of conceptual research are:

• This type of research allows exploring ideas freely without worrying about one study’s outcome.
• It may bring new insights by asking questions beyond surveys and traditional methods.
• It allows you to use more complex statistical models that are harder to do with simple experiments.
• Another advantage of conceptual research is that it can be done without having to go through all the steps of a traditional survey.
• Conceptual research helps to reach a goal/objective/ better than other types of research methods like survey research etc. It uses surveys or interviews to collect data from respondents. This is preferable to collecting data from objects or entities. Some cases may not allow the latter due to ethical issues.

Limitations of Conceptual Research

The limitations of conceptual research include:

• Theoretical Gaps : Conceptual researchers may not be aware of all the relevant literature on their topic. They may also lack expertise in a particular field, which can lead them to misinterpret or misinterpret data from their own or other researchers’ studies. You won’t always find an answer when you conduct a conceptual investigation; instead, you may find that the answer comes from another source (e.g., a literature review).
• Difficulties with Data Collection: Conceptual researchers often face difficulties collecting data because they do not have access to the same resources as other types of researchers (e.g., financial support).
• Low Levels of Participation : Because conceptual research involves creating new knowledge through thoughtful analysis rather than empiricism, it often requires more time than other kinds of research.

Example of a Conceptual Research Framework

Thesis Statement/ Purpose of Research: 

The conceptual research project aims to develop a framework for understanding employee engagement. It also proposes interventions to improve employee engagement and organizational outcomes.

Based on this thesis statement, a possible conceptual research framework could involve reviewing existing literature on employee engagement.

It involves examining theories and models and analyzing factors that affect employee engagement. Factors include leadership, organizational culture, and job characteristics.

The framework can explore the effectiveness of different interventions. These include training programs, rewards, and employee involvement initiatives. The goal is to enhance organizational performance.

Ultimately, the framework can guide future research and practice. It can provide a theoretical foundation for understanding employee engagement.

In conclusion, conceptual research is a type of research that focuses on exploring and developing abstract ideas or concepts. The purpose of conceptual research is to provide a deeper understanding of complex concepts. It aims to clarify ambiguous terms or definitions. It also seeks to generate new insights or perspectives. These insights can help guide future research and practice.

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• conceptual research
• conceptual research framework
• Olayemi Jemimah Aransiola

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Is globalization in retreat? Here is what a new study shows

Sebastian franco bedoya.

The debate is raging: Is globalization in retreat or not? If yes, to what extent, and what are the implications for global prosperity and poverty reduction? These aren’t easy questions to answer, largely because there are different definitions of globalization, which give rise to different ways of measuring it. Recent research at the World Bank, based on a new definition, suggests that globalization is alive and well.

But first, some context. For more than 50 years, globalization has been a catalyst for economic development, trade integration, and prosperity building. It has helped lift more than 1 billion people out of poverty. Since the 1990s, it has become a pathway for businesses in emerging economies to enter global value chains and nearly double their share of exports. Breathtaking advances in communications, transportation, and information technology have made it easier and cheaper for countries on opposite sides of the world to transact business, tap into each other’s markets, and share resources, knowhow, and technology. On the other hand, some critics in advanced economies blame globalization for the loss of manufacturing jobs, and others point to it as a source of greenhouse gas emissions.

Recently, the COVID-19 pandemic, the war in Ukraine, and China-US tensions have led countries and companies to rethink global strategies. But to what extent is globalization actually retreating?  Some studies find little systematic evidence that it is, while others conclude that “trade openness” has recently fallen in some regions, coinciding with a slower pace of trade reforms and posing a threat to growth. This isn’t just an academic exercise. Accurately measuring globalization is necessary to understand the impact of the current challenges on the world economy. Economic policy cannot risk either overestimating deglobalization or underestimating the costs of such a scenario. For this reason, we need a clear definition with precise empirical applications that can guide economic policy.

The trade-to-GDP ratio — which calculates the relative importance of a country’s imports and exports to its economy — is one way to measure “openness” to trade  . This ratio steadily rose until 2008, then suffered a sudden drop in 2009 following the global financial crisis. By 2011 it had recovered, but it lacked the same vigor as before the crisis, suggesting to some that globalization was waning.

Some economists continue to the use trade-to-GDP ratio as a measure of openness, although many others argue that it is an inadequate yardstick and doesn’t necessarily imply that high trade barriers exist. Instead, it could reflect factors such as the size or structure of the economy or its geographic proximity to trading partners.

So globalization is better understood as an extension beyond national borders of the same market forces operating at all levels of economic activity. Using this definition, we measured the intensity of globalization as the growth of international trade relative to domestic trade. For instance, automakers sell some cars in the domestic market and export the rest. Comparing the evolution of the exports of cars with domestic sales offers a better measure of globalization dynamics than the trade-to-GDP ratio.  The model used to capture the relative dynamics of international and domestic trade is what economists call a structural gravity model. It allows for comparisons across countries and over time, capturing more intuitive globalization dynamics than the trade-to-GDP ratio. Among other factors, the reduction in trade barriers and advances in information technology make international trade grow faster than domestic trade, with the world becoming more globalized and with greater economic connectivity and cooperation among countries.

Based on this research, there is no evidence that the world economy has entered an era of deglobalization.  China’s trade-to-GDP ratio, for example, has trended downward since 2006 and is now below both the world average and the level in 2001, when China entered the World Trade Organization. Yet even considering recent trade tensions with the United States, it would be difficult to argue that the Chinese economy is drastically less “open,” as the trade-to-GDP ratio would suggest. A better explanation is that trade has become less important to China’s GDP as its domestic economy has boomed.

A globalization analysis consistent with economic theory requires the study of sector-specific dynamics. For instance, manufacturing has traditionally been a more trade-intensive sector, but information and communication technology (ICT) advances seem to be making services more tradable, pointing to more globalization opportunities in the future. Figure 1 plots the main results of our research. It shows that globalization dynamics in manufacturing were already strong in 1965, while agriculture and services “took off” in the late 1970s and 1990s, respectively. There is no sign of a deglobalization trend post-2008.

Figure 1. Globalization took off at various times, depending on the sector and country.

Figure 1 (b) investigates manufacturing dynamics across countries. These results uncover differing dynamics, situating China as a globalization leader starting in the 1980s, outperforming the world economy significantly during the entire period. This story is different from the one told by the trade-to-GDP ratio. Other results also offer deep insights by illustrating how countries like India, while lagging in manufacturing globalization, have outperformed the world economy in services.

The debate on globalization uses various terms --  slowbalization, deglobalization, reglobalization. Each tells a very different story about changes in the world economy. Our research contributes to these debates by offering a globalization toolkit to understand where the world economy stands today  and helping us to prepare for future dynamics.

• Jobs & Development
• The World Region

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