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- 10 December, 2021
Beginning the design of a quantitative research project can feel like stepping foot into a maze; there are lots of different potential routes you can take, and it can be hard to know which the right one is. Due to the potential complexity of designing a study like this, knowing which first step to take can be confusing. To help clarify the process and make it easier for you, we’ve split up the decision making into several distinct points that you can address separately as you plan your quantitative research project.
1. Decide what your key question(s) is/are
First, the most important thing to do is to work out why you’re designing and conducting this experiment in the first place; in other words…
What is the key question that you’re trying to answer?
Focus on what interests you and use this to guide some of your reading in the area. Read relevant articles and concentrate on the other experiments that they reference. This will help you work out what gaps in knowledge there are in the field and how your own project can make a novel contribution.
2. Identify the methods you will use
Once you know what the question is that you’re trying to answer, your next step is to work out how you will answer it. In other words…
What will your methodology be?
Reading other papers in the area will be helpful at this stage too. You might find that you can adapt a paradigm from another experiment, or that there are commonly used measures in your area.
3. Narrow in on your variables
A good thing to do after identifying the method that you will use is to decide exactly what the independent and dependent variables will be in your experiment(s).
- Independent variable (IV) is the factor that you will manipulate in your experiment. For example, this might be which stimuli a participant is shown or which treatment they are given.
- Dependent variable (DV) is what you are measuring. This could be reaction time, score on a particular measure or ratings that the participants give.
4. Formulate your hypothesis
Now that you’ve identified your question, methodology and variables, you can begin to formulate the hypothesis for your experiment(s). In other words…
What do you expect to happen?
A hypothesis should be clear and directional, for example:
In this experiment, we expect that participants who see the colourful stimuli will give higher ratings than those who see the black and white stimuli.
Your hypothesis should always be based in evidence, using findings from other previous studies and research to guide what you expect to see. Again, reading relevant papers will help you to arrive at better hypotheses.
Now that you have more clarity on designing your research project, you can proceed to actually put those plans into action. Start preparing for and conducting your experiments to collect the data, then analyse those results to find out if your hypothesis is correct.
Read next (third/ final ) in series: How to design a qualitative research study
Read previous (first) in series: Deciding between a quantitative design and a qualitative design for your study
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How to write the Rationale for your research
The Quantitative Methods Paper is made up of five sections:
Introduction Lit Review Methods Analysis Discussion and Conclusion
These sections are discussed below.
Introduction (Point Value - 10)
Sure the introduction to any paper introduces your paper to the reader, but the introduction section is more important than that to an academic paper (yes, that's what you are writing). There are many papers and journals out there in the world for social scientists to read. Your introduction needs to convince the sociologist that he or she needs to spend precious time reading YOUR paper. If you can't show why studying your dependent variable is important in a couple of paragraphs, then you need to get a new dependent variable. Why are things interesting or important? Perhaps it is because the topic is controversial (Some people believe/feel/act one way, and some another.). In any event that is the point of the intro.
Lit Review (Point Value - 10)
In this section, the main question that needs to be answered is what has been written before on your topic? In particular, you are interested in what has been written concerning any relationship between your dependent variable and your independent variables. In a normal academic paper, you need to demonstrate that you know every detail of the material important to your hypotheses. However, in this class I am only asking you to produce a minimal literature review.
What do I do if nothing has been written before on the topic?
This is an extremely unlikely occurrence. I would begin by looking for articles using alternate terms which have the same meaning as your concept. I would also talk to the professor. He is wise in the ways of science and can probably help.
At the end of the lit review, you state your hypotheses.
Methods (Point Value - 10)
The method section has three parts:
- Describe the data set.
This analysis utilizes interview data collected by the National Opinion Research Center (NORC) in the 1994 General Social Survey (hereafter GS S). The GSS, a nationwide annual survey, offers the advantage of multi-stage probability sampling and can be considered representative of English-speaking, noninstitutionalized adults (18 years of age and older) living in U.S. households. (For more detailed information on the GSS, see Babbie and Halley .) This examination of the relationships between x, y, and z relies on a subset of 958 of the 2992 original respondents. The data extract includes only questions asked on both interview ballots B and C for Version 2 of the 1994 GSS. This provides the researcher with a continuous set of questions with a lower number of missing cases; however, the trade-off is the lower number of total cases. Following is a brief description of the variables considered and of the frequency distributions for these variables.
Describe the variables
- How was the question asked in the survey?
- What were the response categories?
- If you had to recode the response categories, what are the categories that will be used in your analysis?
- What is the distribution of the dependent variable?
- Answer the same four questions with each of your independent and control variables in your analysis.
What type of analysis are you going to do?
In this class we are going to concentrate on making sure you can calculate univariate frequency distributions, crosstabular analysis, including control variables, and regression analysis.
Analysis (Total Point value - 60--itemized below)
The analysis section starts off with you restating your hypotheses. Then you begin your examination of whether those hypotheses were supported by the data.
- 1st Crosstab (Point Value - 15)
Using the output from the 1st crosstab, tell the reader if it was supported. Then you show the reader how you know it was supported (Hint: Talk about the %s in the crosstab table) Then you tell the reader if the results you see are statistically significant. Display the Chi-Square value (for this course use the Pearson's Chi-Square) and the p-value (normally the Asymp Sig; however, if you have 2X2 table use the Exact Sig (1-sided) result). If your table has significant results talk about the strength of the relationship.
- 2nd Crosstab (Point Value - 15)
Do the same thing as crosstab 1.
- Controlled Crosstab (Point Value - 20)
The controlled crosstabular analysis is also referred to by the phrase "the elaboration method". While we will have gone over this in class, you may want to look that phrase up in a couple of methods texts for a more in depth discussion. The first thing you have to do is choose which of the two hypotheses you tested is your primary hypothesis (HINT: it is most likely the hypothesis tested in crosstab 1. You are then going to control the relationship between the variables in your primary hypothesis by looking at the relationship between your independent variable and your dependent variable at every level of your control variable. What this means is that the computer builds a crosstab table to examine the relationship between your IV and DB for each responce category of the control variable. For example, if I were interested in the relationship between political party (PARTYID) and frequency of sexual relations (SEXFREQ) and I controlled that relationship by sex. SPSS would build a table crossing PARTYID and SEXFREQ for males and another table crossing PARTYID and SEXFREQ for females. If I had controlled by AGE instead, SPSS would have built a table crossing PARTYID and SEXFREQ for each age category. Each of these separate tables will have its own chi-square statistics and its own lambda and/or gamma statistics (if you asked SPSS to calculate statistics). Now, for the write up there are just about 5 different variations for the controlled crosstab write-up. You will need to see which one fits your situation. One of the major factors in deciding which variation you use will be the relationship you originally observed between your IV and DV in your earlier crosstabular analysis. Here we go: The first two cases occur when your initial crosstabular analysis weren't significant. If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and they are still not significant , you can then say: " My original relationship was not significant and when controlled by my control variable, Z, the relationship remained non-significant." . If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and one or more of the tables IS SIGNIFICANT, then you can say: " My original relationship was not significant; however, controlling by Z revealed a suppressed relationship between X and Y ". The next three cases occur when your initial crosstabular relationship was significant. If the original crosstabular analysis relating your independent variable to your dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the tables STILL SHOW A SIGNIFICANT RELATIONSHIP, then you can say: " My original relationship was significant and when controlled by Z remains significant. The relationship between X and Y is not caused by the influence of Z ". If the original crosstabular analysis relating your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the crosstab tables ARE NOT SIGNIFICANT, then you can say: " My original relationship was significant, but controlling for Z, the relationship now appears to be spurious. Z appears to be responsible for the observed relationships between X and Y. " Lastly, we have the tricky one--the mixed case. This case is, of course, what most of you are likely to see when you look at your controlled crosstabular analysis. IF the original crosstab comparing your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and see that SOME of the tables ARE SIGNIFICANT and SOME ARE NOT SIGNIFICANT, then you will need to make a judgment call. Here's the judgment: Were there enough respondents in each of the controlled crosstab tables? WHY IS THIS THE IMPORTANT JUDGMENT CALL? We know that as your N in a crosstab table increases that smaller differences are more likely to be considered statistically significant. It is possible that your data still exhibits the same patterns (in the percentages) that you saw in your earlier crosstab , but since your sample is divided across several tables it won't be statistically significant. IF you believe that the table does show the same pattern, but fails to be significant due to a small number of respondents. You may argue that. If you can argue that for all the controlled crosstab tables that aren't significant (if there aren't too many), then you could state that " It appears that the relationship between X and Y persists when one looks at the patterns in the column percentages; however, some of the controlled crosstab tables are not statistically significant. Still, I would argue against calling this a spurious relationship. My reading is that the relationship between X and Y is not truly caused by Z. " OTHERWISE, you will need to argue that the control variable mediates the relationship. That is, the control variable really helps delineate in which situations the relationship holds. For instance, you might find that your relationship between X and Y holds for whites but not for blacks or holds for males but not for females. This can be very important information. In this case you will need to report the significant relationships like you did in Crosstab 1.
- Regression (Point Value - 10)
We didn't get to regression this year, however, I would like to point out a few things that you will have to interpret. We look at the F statistic (and its significance) to determine if the model is significant. We look at the r-square to determine the amount of variation in the dependent variable that can be explained by the variables in the model. We look at the t-statistic (and its significance) for each independent variable. These tell us whether each IV is significantly related to the DV, controlling for the other variables in the model. We look at the b line to figure out the slope of the line. We look at the Betas to determine which variable has the most strongest relationship with the dependent variable.
- Conclusion (Point Value - 10)
As opposed to the rest of the paper which tends to be heavily formatted, the conclusion section is yours to say what you want. HOWEVER, you must say something. Traditionally, the conclusion section begins one more time with a statement of your hypotheses. This is followed by a summary of your findings. Were your hypotheses supported or not? The conclusion is more than just a summary, however, because you also get to speculate on how to do things better. For instance, it could be the case that your hypotheses weren't supported, but you really believe that the relationship exists. You could then bring up issues of validity and reliability. You could state that future research should ask more or different questions. You could state that future research should use more variables or add different variables. You could argue that the sample was poor. It's your opportunity to brainstorm on how future research should be done. Why do this? Well, the idea is that we, as social scientists, stand on the shoulders of the others that have come before. We owe future researchers who are reading your article to glean some knowledge about how to approach your concept at least some guideposts as to what we think worked, what didn't work, why, and what we would do if we were going to continue to do research on your topic.
Sample 1: Paper without tables attached.
A Complete Guide to Quantitative Research Methods
Numbers are everywhere and drive our day-to-day lives. We take decisions based on numbers, both at work and in our personal lives. For example, an organization may rely on sales numbers to see if it’s succeeding or failing, and a group of friends planning a vacation may look at ticket prices to pick a place.
In the social domain, numbers are just as important. They help identify what interventions are needed, whether ongoing projects are effective, and more. But how do organizations in the social domain get the numbers they need?
This is where quantitative research comes in. Quantitative research is the process of collecting numerical data through standardized techniques, then applying statistical methods to derive insights from it.
When is quantitative research useful?
The goal of quantitative research methods is to collect numerical data from a group of people, then generalize those results to a larger group of people to explain a phenomenon. Researchers generally use quantitative research when they want get objective, conclusive answers.
For example, a chocolate brand may run a survey among a sample of their target group (teenagers in the United States) to check whether they like the taste of the chocolate. The result of this survey would reveal how all teenagers in the U.S. feel about the chocolate.
Similarly, an organization running a project to improve a village’s literacy rate may look at how many people came to their program, how many people dropped out, and each person’s literacy score before and after the program. They can use these metrics to evaluate the overall success of their program.
Unlike qualitative research , quantitative research is generally not used in the early stages of research for exploring a question or scoping out a problem. It is generally used to answer clear, pre-defined questions in the advanced stages of a research study.
How can you plan a quantitative research exercise?
- Identify the research problem . An example would be, how well do New Delhi’s government schools ensure that students complete their education?
- Prepare the research questions that need to be answered to address the research problem. For example, what percentage of students drop out of government schools in New Delhi?
- Review existing literature on the research problem and questions to ensure that there is no duplication. If someone has already answered this, you can rely on their results.
- Develop a research plan . This includes identifying the target group, sample , and method of data collection ; conducting data analysis; collating recommendations; and arriving at a conclusion.
What are the advantages of quantitative research methods?
- Quantitative research methods provide an relatively conclusive answer to the research questions.
- When the data is collected and analyzed in accordance with standardized, reputable methodology, the results are usually trustworthy.
- With statistically significant sample sizes, the results can be generalized to an entire target group.
Samples have to be carefully designed and chosen, else their results can’t be generalized. Learn how to choose the right sampling technique for your survey.
What are the limitations of quantitative research methods?
- Does not account for people’s thoughts or perceptions about what you’re evaluating.
- Does not explore the “why” and “how” behind a phenomenon.
What quantitative research methods can you use?
Here are four quantitative research methods that you can use to collect data for a quantitative research study:
This is the most common way to collect quantitative data. A questionnaire (also called a survey) is a series of questions, usually written on paper or a digital form. Researchers give the questionnaire to their sample, and each participant answers the questions. The questions are designed to gather data that will help researchers answer their research questions.
Typically, a questionnaire has closed-ended questions — that is, the participant chooses an answer from the given options. However, a questionnaire may also have quantitative open-ended questions. In the open-ended example above, the participants could write a simple number like “4”, a range like “I usually go one or two times per week” or a more complex response like “Most weeks I go twice, but this week I went 4 times because I kept forgetting my grocery list. During the winter, I only go once a week.”
Understanding closed and open-ended questions is crucial to designing a great survey and collecting high quality data. Learn more with our complete guide about when and how to use closed and open-ended questions.
A good questionnaire should have clear language, correct grammar and spelling, and a clear objective.
- Questionnaires are often less time consuming than interviews or other in-person quantitative research methods.
- They’re a common, fairly simple way to collect data.
- They can be a cost-effective option for gathering data from a large sample.
- Responses may lack depth and provide limited information.
- Respondents may lose interest or quit if the questionnaire is long.
- Respondents may not understand all questions, which would lead to inaccurate responses.
Response bias — a set of factors that lead participants answer a question incorrectly — can be deadly for data quality. Learn how it happens and how to avoid it.
An interview for quantitative research involves verbal communication between the participant and researcher, whose goal is to gather numerical data. The interview can be conducted face-to-face or over the phone, and it can be structured or unstructured.
In a structured interview, the researcher asks a fixed set of questions to every participant. The questions and their order are pre-decided by the researcher. The interview follows a formal pattern. Structured interviews are more cost efficient and can be less time consuming.
In an unstructured interview, the researcher thinks of his/her questions as the interview proceeds. This type of interview is conversational in nature and can last a few hours. This type of interview allows the researcher to be flexible and ask questions depending on the participant’s responses. This quantitative research method can provide more in-depth information, since it allows researchers to delve deeper into a participant’s response.
- Interviews can provide more in-depth information.
- Interviews are more flexible than questionnaires, since interviewers can adapt their questions to each participant or ask follow-up questions.
- Interviewers can clarify participants’ questions, which will help them get clearer, more accurate data.
- Interviewing one person at a time can be time-consuming.
- Travel, interviewer salaries and other expenses can make interviews an expensive data collection tool.
- With unstructured interviews, it can be difficult to quantify some responses.
One way to speed up interviews is to conduct them with multiple people at one time in a focus group discussion. Learn more about how to conduct a great FGD.
Observation is a systematic way to collect data by observing people in natural situations or settings. Though it is mostly used for collecting qualitative data, observation can also be used to collect quantitative data.
Observation can be simple or behavioral. Simple observations are usually numerical, like how many cars pass through a given intersection each hour or how many students are asleep during a class. Behavioral observation, on the other hand, observes and interprets people’s behavior, like how many cars are driving dangerously or how engaging a lecturer is.
Simple observation can be a good way to collect numerical data. This can be done by pre-defining clear numerical variables that can be collected during observation — for example, what time employees leave the office. This data can be collected by observing employees over a period of time and recording when each person leaves.
- Observation is often an inexpensive way to collect data.
- Since researchers are recording the data themselves (rather than participants reporting the data), most of the collected data will generally be usable.
- Data collection can be stopped and started by researchers at any time, making it a flexible data collection tool.
- Researchers need to be extensively trained to undertake observation and record data correctly.
- Sometimes the environment or research may bias the data, like when participants know they’re being observed.
- If the situation to be observed sometimes doesn’t happen, researchers may waste a lot of time during data collection.
Simple vs. behavioral is just one type of observation. Learn more about the 5 different types of observation and when you should use each to collect different types of data.
Since quantitative research depends on numerical data, records (also known as external data) can provide critical information to answer research questions. Records are numbers and statistics that institutions use to track activities, like attendance in a school or the number of patients admitted in a hospital.
For example, the Government of India conducts the Census every 10 years, which is a record of the country’s population. This data can be used by a researcher who is addressing a population-related research problem.
- Records often include comprehensive data captured over a long period of time.
- Data collection time is minimal, since the data has already been collected and recorded by someone else.
- Records often only provide numerical data, not the reason or cause behind the data.
- Cleaning badly structured or formatted records can take a long time.
- If a record is incomplete or inaccurate, there is often no way to fix it.
Summing it up
Quantitative research methods are one of the best tools to identify a problem or phenomenon, how widespread it is, and how it is changing over time. After identifying a problem, quantitative research can also be used to come up with a trustworthy solution, identified using numerical data collected through standardized techniques.
Image credits: Curtis MacNewton , Brijesh Nirmal , Charles Deluvio , and Atlan.
3 Myths About Paper-Based Data Collection
18 Data Validations That Will Help You Collect Accurate Data
Everything You Need to Know About Informed Consent
Very useful for research
Very easy to read and informative book. Well written. Thany thanks for the download.
It is concise and practical as well as easy to understand.
Nice book but I kind find a way to download it. Kindly let me know how to download it. Thanks
Hello Micah Nalianya Greetings! Kindly tell me how to download the book. Simeon
Hi Micah and Simeon! You can download our data collection ebook here: https://socialcops.com/ebooks/data-collection/
I have loved reviewing the brief write up. Good revision for me. Thanks
The text contains concise and important tips on data collection techniques.
Thanks for an explicit and precise outline of data collection methods.
thank you very much, this guide is really useful and easy to understand. Specially for students that just have started research.
Thank you so much for sharing me this very important material.
I am highly impressed with the simply ways you explain methods of collecting data. I am a Monitoring and Evaluation Specialist and I will like to be receiving your regular publications.
i have benefited from the work. well organized .thank you
interview is a qualitative method not quantitative.
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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.
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Statistics & Data Research Guide
Characteristics of Quantitative Research
Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.
Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].
Its main characteristics are :
- The data is usually gathered using structured research instruments.
- The results are based on larger sample sizes that are representative of the population.
- The research study can usually be replicated or repeated, given its high reliability.
- Researcher has a clearly defined research question to which objective answers are sought.
- All aspects of the study are carefully designed before data is collected.
- Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
- Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
- Researcher uses tools, such as questionnaires or computer software, to collect numerical data.
The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.
Things to keep in mind when reporting the results of a study using quantitative methods :
- Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
- Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
- Explain the techniques you used to "clean" your data set.
- Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
- Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
- When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
- Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
- Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
- Always tell the reader what to look for in tables and figures .
NOTE: When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. [email protected] Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.
Basic Research Design for Quantitative Studies
Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:
- Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
- Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
- Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].
Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.
- Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
- Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
- Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.
Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .
- Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.
Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.
- Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
- Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
- Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
- Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.
Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.
- Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
- Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
- Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.
Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.
Strengths of Using Quantitative Methods
Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.
Among the specific strengths of using quantitative methods to study social science research problems:
- Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
- Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
- Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
- You can summarize vast sources of information and make comparisons across categories and over time; and,
- Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.
Limitations of Using Quantitative Methods
Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.
Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:
- Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
- Uses a static and rigid approach and so employs an inflexible process of discovery;
- The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
- Results provide less detail on behavior, attitudes, and motivation;
- Researcher may collect a much narrower and sometimes superficial dataset;
- Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
- The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
- Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.
Finding Examples of How to Apply Different Types of Research Methods
SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.
SAGE Research Methods Online and Cases
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Example of a Quantitative Research Paper
Posted by Rene Tetzner | Sep 4, 2021 | How To Get Published | 0 |
Example of a Quantitative Research Paper for Students & Researchers This example of a quantitative research paper is designed to help students and other researchers who are learning how to write about their work. The reported research observes the behaviour of restaurant customers, and example paragraphs are combined with instructions for logical argumentation. Authors are encouraged to observe a traditional structure for organising quantitative research papers, to formulate research questions, working hypotheses and investigative tools, to report results accurately and thoroughly, and to present thoughtful interpretation and logical discussion of evidence.
The structure of the example and the nature of its contents follow the recommendations of the Publication Manual of the American Psychological Association . This APA style calls for parenthetical author–date citations in the paper’s main text (with page numbers when material is quoted) and a final list of complete references for all sources cited, so I have given a few sample references here. Content has been kept as simple as possible to focus attention on the way in which the paper presents the research process and its results. As is the case in many research projects, the more the author learns and thinks about the topic, the more complex the issues become, and here the researcher discusses a hypothesis that proved incorrect. An APA research paper would normally include additional elements such as an abstract, keywords and perhaps tables, figures and appendices similar to those referred to in the example. These elements have been eliminated for brevity here, so do be sure to check the APA Manual (or any other guidelines you are following) for the necessary instructions.
Surprises at a Local “Family” Restaurant: Example Quantitative Research Paper
A quantitative research paper with that title might start with a paragraph like this:
Quaintville, located just off the main highway only five miles from the university campus, may normally be a sleepy community, but recent plans to close the only fast-food restaurant ever to grace its main street have been met with something of a public outcry. Regular clients argue that Pudgy’s Burgers fills a vital function and will be sorely missed. As the editor of the Quaintville Times would have it, “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p. A3). On the other hand, the most outspoken of Quaintville residents in favour of the planned closure look forward to the eradication of a local eyesore and tend to consider the restaurant more of “a hazard than a benefit to the health of some of our poorest families” (“Local dive,” 2017, p. 1).
Following this opening a brief introduction to published scholarship and other issues associated with the problem would be appropriate, so here the researcher might add a paragraph or two discussing:
• A selection of recently published studies that investigate the effect of inexpensive fast-food restaurants on the health of low-income families, especially their children (Shunts, 2013; Whinner, 2015). • Fast-food restaurants that have responded to criticism about the quality of their food by offering healthy menu items. This could be enhanced with evidence that when such choices are available, they are rare purchases for many families (Parkson, 2016), particularly in small towns and rural areas (Shemble, 2017). • The interesting trend in several independent studies suggesting that families form a much smaller portion of the clientele of fast-food restaurants than anticipated.
Explaining how the current research is related to the published scholarship as well as the specific problem is vital. Here, for instance, the author might be thinking that Pudgy’s, which has healthy menu items as well as the support of so many long-term residents, will prove an exception to the trends revealed by other studies. Research questions and hypotheses should be constructed to articulate and explore that idea. Research questions, for instance, could be developed from that claim in the Quaintville Times as well as from the published scholarship:
• Do families constitute the majority of Pudgy’s regular clientele? • Does the restaurant offer a decent family meal for a fair price? • Do families linger in the restaurant’s comfort and warmth?> • Do children use the indoor play area provided by the restaurant?
Working hypotheses can be constructed by anticipating answers to these questions. The example paper assumes a simple hypothesis something along the lines of “Families do indeed constitute the majority of Pudgy’s clientele.” The exact opposite supposition would work as well – “Families do not constitute the majority of Pudgy’s clientele” – and so would hypotheses exploring and combining other aspects of the situation, such as “Pudgy’s healthy menu options and indoor play area are positive and appealing considerations for families” or “The comfortable atmosphere of Pudgy’s with its play area makes it much more than a restaurant for local families.”
The exact wording of your questions and hypotheses will ultimately depend on your focus and aims, but certain terms, concepts and categories may require definition to ensure precision in communicating your ideas to readers. Here, for instance, exactly what is meant by ‘a family,’ ‘a decent meal,’ ‘a fair price’ and even ‘comfortable’ could be briefly but carefully defined. A general statement about your understanding of how the current research will explore the problem, answer your questions and test your hypotheses is usually required as well, setting the stage for the more detailed Method section that follows. This statement might be something as simple as “I intend to observe the restaurant’s customers over a two-month period with the objective of learning about Pudgy’s clientele and measuring the use and value of the establishment for local families.” On the other hand, outlining your research might require a paragraph or two of introductory discussion.
Method Whether a brief general statement or a longer explanation of how the research will proceed appears among your introductory material, it is in the Method section that you should report exactly what you did to conduct your investigation, explain the conditions and controls you applied to increase the reliability and value of your research, and reveal any difficulties you encountered. For example:
My observations took place at Pudgy’s Burgers in January and February of 2018. Each session was approximately four hours long, and I aimed to obtain an equivalent number of observations for all opening hours of the week (the restaurant’s hours are listed in Table 1), but course requirements made this difficult. Tuesday and Thursday afternoons are therefore underrepresented, and observations from 1:00 pm to 5:00 pm on two consecutive Tuesdays (6 and 13 February) are the work of my classmate, Jake Jenkins. Without his assistance, I could not have met my objective of gathering observations for every opening hour of the week at least twice (Table 2 outlines the overall pattern of observation sessions). Serving staff at the restaurant assure me that I have now “seen ‘em all,” so I believe my observations have resulted in a representative sampling of local customers over two months when that “winter wind” has been especially busy about its work.
To avoid detection by the customers I was observing and the possibility of altering their behaviour, I obtained permission from Pudgy’s manager, Mr Jobson, to sit at the staff table in a dark and quiet corner of the restaurant where clients never go. This table is labelled in the plan of Pudgy’s Burgers and its grounds that I have included as Figure 1. From there I could see the customers both at the service counter and at their tables, but they could not see me, at least not clearly, and if they did, they paid me no more attention than they did the restaurant employees. From the staff table I could also see the row of indoor park-style children’s toys running down the north wall of windows, as well as the take out lane and the people waiting in their cars.
A Method section often features subheadings to separate and present particularly important aspects of the research methodology, such as the Customer Fact Sheet developed and used by the author of this study.
The Customer Fact Sheet Recording thorough and equivalent information about every Pudgy’s customer I observed was crucial for quantifying and analysing the results of my study. I therefore prepared a Customer Fact Sheet (included as Appendix I at the end of this paper) for gathering key pieces of information and recording observations about each individual, couple or group who purchased food or beverages. This sheet ensured that vital details such as date, weather conditions, time of arrival, eat in or take out order, number in party, approximate age of individuals, food purchased, food consumed, healthy choices, amount spent, who paid, dessert or extra beverage, children playing, interaction with other children and families, time of departure and other important details were recorded in every case. The Customer Fact Sheet proved particularly helpful when my classmate performed observations for me and was invaluable for evaluating the data I collected. I initially hoped to complete at least 500 of these Customer Fact Sheets and was pleased to increase that number by 100 for a total of 600 or an average of just over 10 per day over the 59 days of the study.
Notice in the three example paragraphs for the Method section that clear references to Tables 1 & 2, Figure 1 and Appendix I are provided to let readers know when and why these extra elements are relevant and helpful. Be sure also to include in your description of methods any additional approaches or sources of information that should be considered part of your research procedures, such as:
• Receipt information about customer purchases provided by the restaurant manager. • Conversations with restaurant servers who might confirm family relationships and estimated ages or tell you what was eaten and what was not by particular customer groups. • The analysis you performed to make sense of your results, such as counting customers, meals and behaviours and working out percentages and averages overall as well as for certain categories in order to answer the research questions.
Results The Results section is where you report what you discovered during your research, including the findings that do not support your hypothesis (or hypotheses) as well as those that do. Returning to your research questions to indicate exactly how the data you gathered answers them is an excellent way to stay focused and enable the selectivity that may be necessary to meet length requirements or maintain a clear line of argumentation. A Results section for the Pudgy’s research project might start like this:
The results of my investigation were both surprising and more complex than I had anticipated. I asked whether families constituted the majority of Pudgy’s clientele and assumed they did, but my research shows that they do not (see Figure 2 for information on customer categories). Even when the loosest definition of family as explained in my introduction is applied, only slightly over 25% (152) of the 600 Customer Fact Sheets record family visits to the restaurant. Among them fathers alone with their children are the most frequent patrons (68 Customer Fact Sheets or nearly 45% of the family category). The only day of the week on which families approach 50% of the restaurant’s customers is Sunday, particularly in the afternoon, when family groups account for 48% of the total customers averaged over the eight Sundays of observation. On all other days of the week, individual customers are the most frequent patrons, with their numbers hovering around 50% on most days. Single men visit the restaurant more often than any other customers and constitute as much as 61% of the clientele on a few weekday evenings.
The report of results might then continue by providing information about other categories of customer, what different types of customers ate and did, and any additional results that help answer the other research questions posed in the introductory paragraphs. Major trends revealed by the data should be reported, and both content and writing style should be clear and factual. Interpretation and discussion are best saved for the Discussion section except in those rare instances when guidelines indicate that research results and discussion should be combined in a single section. Although you will need to inform readers about any mathematical or statistical analysis of your raw data if you have not already done so in the Method section, the raw data itself is usually not appropriate for a short research paper. Selecting the most convincing and relevant evidence as the focus is, however, and the raw data can usually be made available via a university’s website or a journal’s online archives for expert readers and future researchers.
Discussion The Discussion section of a quantitative paper is where you interpret your research results and discuss their implications. Here the hypotheses as well as the research questions established in the introductory material are important. Were your primary suppositions confirmed by your results or not? Be precise and concise as you discuss your findings, but keep in mind that matters need not be quite as black and white or as strictly factual as they were in the Results section. Your ideas and argument should be soundly based on the data you collected, of course, but the Discussion is the place for describing complexities and expressing uncertainties as well as offering interpretations and explanations. The following opening briefly restates primary findings, picks up other important threads from the Results section and sets the stage for discussing the complexities involved in assessing the true value of Pudgy’s to the Quaintville community:
Although I had anticipated that families constitute the majority of Pudgy’s clientele, the evidence gathered over two months of observation does not support this supposition. In fact, individuals are the most frequent customers, with groups of teenagers running a close second. These teenagers are often in the restaurant when families are and they sometimes sit on the indoor toys instead of at the plastic tables and chairs, which I can confirm as extremely uncomfortable. On a few occasions the presence of teenagers appeared to intimidate the children and prevent them from playing on the facilities intended for them. In accordance with Parkson (2016) and Shemble (2017), my research also showed that most families who eat at Pudgy’s do not choose the healthier low-fat menu items, with the limited number and extremely high prices of these items offering little incentive. The few parents who make healthy choices for themselves and their children often do not insist upon the children eating those items, adding waste (of both food and money) to the problem. Furthermore, although Pudgy’s prices for their more traditional fast-food items are the lowest in town, at least two of the restaurants in Quaintville offer equivalent meals for similar prices and far healthier ones for just a little more.
The claim, then, in the Quaintville Times that “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p.A3) is revealed as more sentiment than fact. It would be equally erroneous, however, to insist that Pudgy’s Burgers has no value for the local community or to call it more of “a hazard…to the health of some of our poorest families” (“Local dive,” 2017, p.1) than any other restaurants serving burgers and chips in Quaintville. Indeed, I suspect those “poorest families” very rarely visit local restaurants at all, but my observations have revealed a great deal about who does eat at Pudgy’s, what they do when they are there and what kind of value the establishment actually has for Quaintville residents.
The discussion could then continue with information about the customers, behaviours and other issues that render the findings more complex and the restaurant more valuable to the community than the primary results noted above may indicate:
• Perhaps the restaurant serves a vital function as a social gathering place for all those single customers. Do they usually remain alone or do they meet up with others to linger and talk over coffee or lunch? • Do the teenagers who gather at Pudgy’s have an alternative place to meet out of the cold? In towns without recreation centres or other facilities for teens, restaurants with informal, open-door policies can be vital. Where might those teenagers go or what might they be doing were Pudgy’s not there? • Even though the evidence showed that families are not the most frequent customers, you may want to consider the value the restaurant has for the families who do use it. Those single fathers are certainly worthy of some attention, for instance, and perhaps family groups occasionally met up with other families, ate together and then lingered for dessert and talk as their children enjoyed the toys. This would be worth discussing too. • Less measurable considerations viewed through a qualitative research lens may be helpful as well, but the data collected through observations should support such discussions. Remember as you analyse your data, reflect on your findings, determine their meaning and develop your argument that it is important to keep the limitations of your methodology and thus of your results and their implications clearly in mind.
Offering recommendations is also standard in the Discussion section of a quantitative research paper, and here recommendations might be particularly useful if the franchise had not yet finalised its decision about closing Pudgy’s and was actively seeking community feedback. The researcher might suggest that Pudgy’s could better serve families by increasing the number of healthy food items on the menu, offering these for more affordable prices and making an effort to keep the teenagers off the children’s toys. Finally, the last part of a Discussion usually provides concluding comments, so summarising your key points and clearly articulating the main messages you want your readers to take away with them are essential. In some organisational templates, Conclusions are offered in a separate final section of the paper instead of at the end of the Discussion, so always check the guidelines.
References These references follow APA style, but since special fonts may not display properly in all online situations, please note that the titles of books and the names and volume numbers of journals are (and should be) in italic font. The list represents a sample only; a paper the length of the one posited in this example would almost certainly mention, discuss and list more than half a dozen studies and sources.
Chapton, D. (2017, September 29). Will Quaintville lose its favourite family restaurant? Quaintville Times , pp. A1, A3. Local dive sees last days. (2017, Autumn). Quaintville Community Newsletter , pp. 1–2. Shemble, M. (2017). Is anyone really eating healthy fast food in rural towns? Country Food & Families , 14 , 12–23. Shunts, P. (2013). The true cost of high-fat fast food for low-income families. Journal of Family Health & Diet , 37 , 3–19. Parkson, L. (2016). Family diets, fast foods and unhealthy choices. In S. Smith & J. Jones (eds.), Modern diets and family health (pp. 277–294). Philadelphia, PA: The Family Press. Whinner, N. (2015). Healthy families take time: The impact of fatty fast foods on child health. Journal of Family Health & Diet , 39 , 31–43.
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How to structure quantitative research questions
There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .
- STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
- STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
- STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
- STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question
STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.
STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.
To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .
Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.
STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:
Structure of descriptive research questions
- Structure of comparative research questions
- Structure of relationship-based research questions
There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:
Choose your starting phrase
Identify and name the dependent variable
Identify the group(s) you are interested in
Decide whether the dependent variable or group(s) should be included first, last or in two parts
Include any words that provide greater context to your question
Write out the descriptive research question
FIRST Choose your starting phrase
You can start descriptive research questions with any of the following phrases:
How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?
Some of these starting phrases are highlighted in blue text in the examples below:
How many calories do American men and women consume per day?
How often do British university students use Facebook each week?
What are the most important factors that influence the career choices of Australian university students?
What proportion of British male and female university students use the top 5 social networks?
What percentage of American men and women exceed their daily calorific allowance?
SECOND Identify and name the dependent variable
All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .
The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.
THIRD Identify the group(s) you are interested in
All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .
What are the most important factors that influence the career choices of Australian university students ?
The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).
FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts
Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :
Group 1st; dependent variable 2nd:
How often do British university students use Facebook each week ?
Dependent variable 1st; group 2nd:
Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :
How many calories do American men and women consume per day ?
Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:
How many calories are consumed per day by American men and women ?
When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?
FIFTH Include any words that provide greater context to your question
Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:
In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.
However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :
In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.
Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.
SIXTH Write out the descriptive research question
Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:
In the section that follows, the structure of comparative research questions is discussed.
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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles
1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.
Glafera Janet Matanguihan
2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.
Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6
It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4
There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.
DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES
A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5
On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4
Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8
Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12
CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES
Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13
There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10
TYPES OF RESEARCH QUESTIONS AND HYPOTHESES
Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .
Research questions in quantitative research
In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .
Hypotheses in quantitative research
In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .
Research questions in qualitative research
Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15
There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .
Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15
Hypotheses in qualitative research
Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1
FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES
Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14
The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14
As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.
a These statements were composed for comparison and illustrative purposes only.
b These statements are direct quotes from Higashihara and Horiuchi. 16
a This statement is a direct quote from Shimoda et al. 17
The other statements were composed for comparison and illustrative purposes only.
CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES
To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .
Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.
Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12
In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.
EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES
- EXAMPLE 1. Descriptive research question (quantitative research)
- - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
- “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
- RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
- EXAMPLE 2. Relationship research question (quantitative research)
- - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
- “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
- Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
- EXAMPLE 3. Comparative research question (quantitative research)
- - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
- “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
- RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
- STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
- EXAMPLE 4. Exploratory research question (qualitative research)
- - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
- “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
- EXAMPLE 5. Relationship research question (quantitative research)
- - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
- “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23
EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES
- EXAMPLE 1. Working hypothesis (quantitative research)
- - A hypothesis that is initially accepted for further research to produce a feasible theory
- “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
- “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
- EXAMPLE 2. Exploratory hypothesis (qualitative research)
- - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
- “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
- Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
- EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
- “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
- Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
- EXAMPLE 4. Statistical hypothesis (quantitative research)
- - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
- “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
- “Statistical Analysis
- ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27
EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS
- EXAMPLE 1. Background, hypotheses, and aims are provided
- “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
- “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
- “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
- EXAMPLE 2. Background, hypotheses, and aims are provided
- “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
- “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
- EXAMPLE 3. Background, aim, and hypothesis are provided
- “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities . BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times . Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
- “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
- “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30
Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.
Disclosure: The authors have no potential conflicts of interest to disclose.
- Conceptualization: Barroga E, Matanguihan GJ.
- Methodology: Barroga E, Matanguihan GJ.
- Writing - original draft: Barroga E, Matanguihan GJ.
- Writing - review & editing: Barroga E, Matanguihan GJ.
How to design a Quantitative research study · 1. Decide what your key question(s) is/are · 2. Identify the methods you will use · 3. Narrow in on your variables · 4
Methods (Point Value - 10) · Describe the variables. Start with your dependent variable. How was the question asked in the survey? What were the response
Keywords: scientific research papers, quantitative research, scientific writing, general paper outline. 1 Introduction. Introduce the topic under study and the
In this video, I shared some practical tips in starting a Quantitative Research Paper. Hope you learn from this video.
How can you plan a quantitative research exercise? · Identify the research problem. · Prepare the research questions that need to be answered to
Characteristics of Quantitative Research · Explain the data collected · Report unanticipated events · Explain the techniques · Choose a minimally
A general statement about your understanding of how the current research will explore the problem, answer your questions and test your
Structure of descriptive research questions · Choose your starting phrase · Identify and name the dependent variable · Identify the group(s) you are interested in.
In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the
Abstract · Use tables, figures, etc. · Compare your results to the state of the art · Prove the validity of results using statistics (e.g., significance tests).