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  • J Korean Med Sci
  • v.37(16); 2022 Apr 25

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

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.


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


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


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


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.


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 .

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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.

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  • 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


  • 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
  • “Conclusion
  • 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


  • 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 [1]. 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 [4]. 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.

Author Contributions:

  • Conceptualization: Barroga E, Matanguihan GJ.
  • Methodology: Barroga E, Matanguihan GJ.
  • Writing - original draft: Barroga E, Matanguihan GJ.
  • Writing - review & editing: Barroga E, Matanguihan GJ.

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9.2 Constructing Survey Questionnaires

Learning objectives.

  • Describe the cognitive processes involved in responding to a survey item.
  • Explain what a context effect is and give some examples.
  • Create a simple survey questionnaire based on principles of effective item writing and organization.

The heart of any survey research project is the survey questionnaire itself. Although it is easy to think of interesting questions to ask people, constructing a good survey questionnaire is not easy at all. The problem is that the answers people give can be influenced in unintended ways by the wording of the items, the order of the items, the response options provided, and many other factors. At best, these influences add noise to the data. At worst, they result in systematic biases and misleading results. In this section, therefore, we consider some principles for constructing survey questionnaires to minimize these unintended effects and thereby maximize the reliability and validity of respondents’ answers.

Survey Responding as a Psychological Process

Before looking at specific principles of survey questionnaire construction, it will help to consider survey responding as a psychological process.

A Cognitive Model

Figure 9.1 “Model of the Cognitive Processes Involved in Responding to a Survey Item” presents a model of the cognitive processes that people engage in when responding to a survey item (Sudman, Bradburn, & Schwarz, 1996). Respondents must interpret the question, retrieve relevant information from memory, form a tentative judgment, convert the tentative judgment into one of the response options provided (e.g., a rating on a 1-to-7 scale), and finally edit their response as necessary.

Figure 9.1 Model of the Cognitive Processes Involved in Responding to a Survey Item

Model of the Cognitive Processes Involved in Responding to a Survey Item: Question Interpretation -> Information Retrieval -> Judgment Formation -> Response Formatting -> Response Editing

Consider, for example, the following questionnaire item:

How many alcoholic drinks do you consume in a typical day?

  • _____ a lot more than average
  • _____ somewhat more than average
  • _____ average
  • _____ somewhat fewer than average
  • _____ a lot fewer than average

Although this item at first seems straightforward, it poses several difficulties for respondents. First, they must interpret the question. For example, they must decide whether “alcoholic drinks” include beer and wine (as opposed to just hard liquor) and whether a “typical day” is a typical weekday, typical weekend day, or both. Once they have interpreted the question, they must retrieve relevant information from memory to answer it. But what information should they retrieve, and how should they go about retrieving it? They might think vaguely about some recent occasions on which they drank alcohol, they might carefully try to recall and count the number of alcoholic drinks they consumed last week, or they might retrieve some existing beliefs that they have about themselves (e.g., “I am not much of a drinker”). Then they must use this information to arrive at a tentative judgment about how many alcoholic drinks they consume in a typical day. For example, this might mean dividing the number of alcoholic drinks they consumed last week by seven to come up with an average number per day. Then they must format this tentative answer in terms of the response options actually provided. In this case, the options pose additional problems of interpretation. For example, what does “average” mean, and what would count as “somewhat more” than average? Finally, they must decide whether they want to report the response they have come up with or whether they want to edit it in some way. For example, if they believe that they drink much more than average, they might not want to report this for fear of looking bad in the eyes of the researcher.

From this perspective, what at first appears to be a simple matter of asking people how much they drink (and receiving a straightforward answer from them) turns out to be much more complex.

Context Effects on Questionnaire Responses

Again, this complexity can lead to unintended influences on respondents’ answers. These are often referred to as context effects because they are not related to the content of the item but to the context in which the item appears (Schwarz & Strack, 1990). For example, there is an item-order effect when the order in which the items are presented affects people’s responses. One item can change how participants interpret a later item or change the information that they retrieve to respond to later items. For example, researcher Fritz Strack and his colleagues asked college students about both their general life satisfaction and their dating frequency (Strack, Martin, & Schwarz, 1988). When the life satisfaction item came first, the correlation between the two was only −.12, suggesting that the two variables are only weakly related. But when the dating frequency item came first, the correlation between the two was +.66, suggesting that those who date more have a strong tendency to be more satisfied with their lives. Reporting the dating frequency first made that information more accessible in memory so that they were more likely to base their life satisfaction rating on it.

The response options provided can also have unintended effects on people’s responses (Schwarz, 1999). For example, when people are asked how often they are “really irritated” and given response options ranging from “less than once a year” to “more than once a month,” they tend to think of major irritations and report being irritated infrequently. But when they are given response options ranging from “less than once a day” to “several times a month,” they tend to think of minor irritations and report being irritated frequently. People also tend to assume that middle response options represent what is normal or typical. So if they think of themselves as normal or typical, they tend to choose middle response options. For example, people are likely to report watching more television when the response options are centered on a middle option of 4 hours than when centered on a middle option of 2 hours.

Writing Survey Questionnaire Items

Types of items.

Questionnaire items can be either open-ended or closed-ended. Open-ended items simply ask a question and allow participants to answer in whatever way they choose. The following are examples of open-ended questionnaire items.

  • “What is the most important thing to teach children to prepare them for life?”
  • “Please describe a time when you were discriminated against because of your age.”
  • “Is there anything else you would like to tell us about?”

Open-ended items are useful when researchers do not know how participants might respond or want to avoid influencing their responses. They tend to be used when researchers have more vaguely defined research questions—often in the early stages of a research project. Open-ended items are relatively easy to write because there are no response options to worry about. However, they take more time and effort on the part of participants, and they are more difficult for the researcher to analyze because the answers must be transcribed, coded, and submitted to some form of content analysis.

Closed-ended items ask a question and provide a set of response options for participants to choose from. The alcohol item just mentioned is an example, as are the following:

How old are you?

  • _____ Under 18
  • _____ 18 to 34
  • _____ 35 to 49
  • _____ 50 to 70
  • _____ Over 70

On a scale of 0 (no pain at all) to 10 (worst pain ever experienced), how much pain are you in right now?

Have you ever in your adult life been depressed for a period of 2 weeks or more?

Closed-ended items are used when researchers have a good idea of the different responses that participants might make. They are also used when researchers are interested in a well-defined variable or construct such as participants’ level of agreement with some statement, perceptions of risk, or frequency of a particular behavior. Closed-ended items are more difficult to write because they must include an appropriate set of response options. However, they are relatively quick and easy for participants to complete. They are also much easier for researchers to analyze because the responses can be easily converted to numbers and entered into a spreadsheet. For these reasons, closed-ended items are much more common.

All closed-ended items include a set of response options from which a participant must choose. For categorical variables like sex, race, or political party preference, the categories are usually listed and participants choose the one (or ones) that they belong to. For quantitative variables, a rating scale is typically provided. A rating scale is an ordered set of responses that participants must choose from. Figure 9.2 “Example Rating Scales for Closed-Ended Questionnaire Items” shows several examples. The number of response options on a typical rating scale ranges from three to 11—although five and seven are probably most common. They can consist entirely of verbal labels or they can consist of a set of numbers with verbal labels as “anchors.” In some cases, the verbal labels or numbers can be supplemented with (or even replaced by) meaningful graphics. The last rating scale shown in Figure 9.2 “Example Rating Scales for Closed-Ended Questionnaire Items” is a visual-analog scale, on which participants make a mark somewhere along the horizontal line to indicate the magnitude of their response.

Figure 9.2 Example Rating Scales for Closed-Ended Questionnaire Items

Example Rating Scales for Close-Ended Questionnaire Items

What Is a Likert Scale?

In reading about psychological research, you are likely to encounter the term Likert scale . Although this term is sometimes used to refer to almost any rating scale (e.g., a 0-to-10 life satisfaction scale), it has a much more precise meaning.

In the 1930s, researcher Rensis Likert (pronounced LICK-ert) created a new approach for measuring people’s attitudes (Likert, 1932). It involves presenting people with several statements—including both favorable and unfavorable statements—about some person, group, or idea. Respondents then express their agreement or disagreement with each statement on a 5-point scale: Strongly Agree , Agree , Neither Agree nor Disagree , Disagree , Strongly Disagree . Numbers are assigned to each response (with reverse coding as necessary) and then summed across all items to produce a score representing the attitude toward the person, group, or idea. The entire set of items came to be called a Likert scale.

Thus unless you are measuring people’s attitude toward something by assessing their level of agreement with several statements about it, it is best to avoid calling it a Likert scale. You are probably just using a “rating scale.”

Writing Effective Items

We can now consider some principles of writing questionnaire items that minimize unintended context effects and maximize the reliability and validity of participants’ responses. A rough guideline for writing questionnaire items is provided by the BRUSO model (Peterson, 2000). An acronym, BRUSO stands for “brief,” “relevant,” “unambiguous,” “specific,” and “objective.” Effective questionnaire items are brief and to the point. They avoid long, overly technical, or unnecessary words. This makes them easier for respondents to understand and faster for them to complete. Effective questionnaire items are also relevant to the research question. If a respondent’s sexual orientation, marital status, or income is not relevant, then items on them should probably not be included. Again, this makes the questionnaire faster to complete, but it also avoids annoying respondents with what they will rightly perceive as irrelevant or even “nosy” questions. Effective questionnaire items are also unambiguous ; they can be interpreted in only one way. Part of the problem with the alcohol item presented earlier in this section is that different respondents might have different ideas about what constitutes “an alcoholic drink” or “a typical day.” Effective questionnaire items are also specific, so that it is clear to respondents what their response should be about and clear to researchers what it is about. A common problem here is closed-ended items that are “double barreled.” They ask about two conceptually separate issues but allow only one response. For example, “Please rate the extent to which you have been feeling anxious and depressed.” This item should probably be split into two separate items—one about anxiety and one about depression. Finally, effective questionnaire items are objective in the sense that they do not reveal the researcher’s own opinions or lead participants to answer in a particular way. Table 9.2 “BRUSO Model of Writing Effective Questionnaire Items, Plus Examples” shows some examples of poor and effective questionnaire items based on the BRUSO criteria.

Table 9.2 BRUSO Model of Writing Effective Questionnaire Items, Plus Examples

For closed-ended items, it is also important to create an appropriate response scale. For categorical variables, the categories presented should generally be mutually exclusive and exhaustive. Mutually exclusive categories do not overlap. For a religion item, for example, the categories of Christian and Catholic are not mutually exclusive but Protestant and Catholic are. Exhaustive categories cover all possible responses. Although Protestant and Catholic are mutually exclusive, they are not exhaustive because there are many other religious categories that a respondent might select: Jewish , Hindu , Buddhist , and so on. In many cases, it is not feasible to include every possible category, in which case an Other category, with a space for the respondent to fill in a more specific response, is a good solution. If respondents could belong to more than one category (e.g., race), they should be instructed to choose all categories that apply.

For rating scales, five or seven response options generally allow about as much precision as respondents are capable of. However, numerical scales with more options can sometimes be appropriate. For dimensions such as attractiveness, pain, and likelihood, a 0-to-10 scale will be familiar to many respondents and easy for them to use. Regardless of the number of response options, the most extreme ones should generally be “balanced” around a neutral or modal midpoint. An example of an unbalanced rating scale measuring perceived likelihood might look like this:

Unlikely | Somewhat Likely | Likely | Very Likely | Extremely Likely

A balanced version might look like this:

Extremely Unlikely | Somewhat Unlikely | As Likely as Not | Somewhat Likely | Extremely Likely

Note, however, that a middle or neutral response option does not have to be included. Researchers sometimes choose to leave it out because they want to encourage respondents to think more deeply about their response and not simply choose the middle option by default.

Numerical rating scales often begin at 1 and go up to 5 or 7. However, they can also begin at 0 if the lowest response option means the complete absence of something (e.g., no pain). They can also have 0 as their midpoint, but it is important to think about how this might change people’s interpretation of the response options. For example, when asked to rate how successful in life they have been on a 0-to-10 scale, many people use numbers in the lower half of the scale because they interpret this to mean that they have been only somewhat successful in life. But when asked to rate how successful they have been in life on a −5 to +5 scale, very few people use numbers in the lower half of the scale because they interpret this to mean they have actually been unsuccessful in life (Schwarz, 1999).

Formatting the Questionnaire

Writing effective items is only one part of constructing a survey questionnaire. For one thing, every survey questionnaire should have a written or spoken introduction that serves two basic functions (Peterson, 2000). One is to encourage respondents to participate in the survey. In many types of research, such encouragement is not necessary either because participants do not know they are in a study (as in naturalistic observation) or because they are part of a subject pool and have already shown their willingness to participate by signing up and showing up for the study. Survey research usually catches respondents by surprise when they answer their phone, go to their mailbox, or check their e-mail—and the researcher must make a good case for why they should agree to participate. Thus the introduction should briefly explain the purpose of the survey and its importance, provide information about the sponsor of the survey (university-based surveys tend to generate higher response rates), acknowledge the importance of the respondent’s participation, and describe any incentives for participating.

The second function of the introduction is to establish informed consent. Remember that this means describing to respondents everything that might affect their decision to participate. This includes the topics covered by the survey, the amount of time it is likely to take, the respondent’s option to withdraw at any time, confidentiality issues, and so on. Written consent forms are not typically used in survey research, so it is important that this part of the introduction be well documented and presented clearly and in its entirety to every respondent.

The introduction should be followed by the substantive questionnaire items. But first, it is important to present clear instructions for completing the questionnaire, including examples of how to use any unusual response scales. Remember that this is the point at which respondents are usually most interested and least fatigued, so it is good practice to start with the most important items for purposes of the research and proceed to less important items. Items should also be grouped by topic or by type. For example, items using the same rating scale (e.g., a 5-point agreement scale) should be grouped together if possible to make things faster and easier for respondents. Demographic items are often presented last because they are least interesting to participants but also easy to answer in the event respondents have become tired or bored. Of course, any survey should end with an expression of appreciation to the respondent.

Key Takeaways

  • Responding to a survey item is itself a complex cognitive process that involves interpreting the question, retrieving information, making a tentative judgment, putting that judgment into the required response format, and editing the response.
  • Survey questionnaire responses are subject to numerous context effects due to question wording, item order, response options, and other factors. Researchers should be sensitive to such effects when constructing surveys and interpreting survey results.
  • Survey questionnaire items are either open-ended or closed-ended. Open-ended items simply ask a question and allow respondents to answer in whatever way they want. Closed-ended items ask a question and provide several response options that respondents must choose from.
  • According to the BRUSO model, questionnaire items should be brief, relevant, unambiguous, specific, and objective.
  • Discussion: Write a survey item and then write a short description of how someone might respond to that item based on the cognitive model of survey responding (or choose any item on the Rosenberg Self-Esteem Scale at http://www.bsos.umd.edu/socy/research/rosenberg.htm ).

Practice: Write survey questionnaire items for each of the following general questions. In some cases, a series of items, rather than a single item, might be necessary.

  • How much does the respondent use Facebook?
  • How much exercise does the respondent get?
  • How likely does the respondent think it is that the incumbent will be reelected in the next presidential election?
  • To what extent does the respondent experience “road rage”?

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Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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This chapter provides a guideline for a researcher intending to conduct a survey and utilize the questionnaire as an instrument for data collection. Based on the authors’ experience of using the survey questionnaire, it discusses the meaning, characteristics and types of questionnaire, its applicability, strengths and limitations, and the quality of the researcher while using the questionnaire. This chapter aims to provide a better understanding of the appropriate use of a survey questionnaire, its construction and ways to increase the respondents’ spontaneous participation in survey research.

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Chapter 3 -- Survey Research Design and Quantitative Methods of Analysis for Cross-sectional Data

Almost everyone has had experience with surveys. Market surveys ask respondents whether they recognize products and their feelings about them. Political polls ask questions about candidates for political office or opinions related to political and social issues. Needs assessments use surveys that identify the needs of groups. Evaluations often use surveys to assess the extent to which programs achieve their goals. Survey research is a method of collecting information by asking questions. Sometimes interviews are done face-to-face with people at home, in school, or at work. Other times questions are sent in the mail for people to answer and mail back. Increasingly, surveys are conducted by telephone. SAMPLE SURVEYS Although we want to have information on all people, it is usually too expensive and time consuming to question everyone. So we select only some of these individuals and question them. It is important to select these people in ways that make it likely that they represent the larger group. The population is all the individuals in whom we are interested. (A population does not always consist of individuals. Sometimes, it may be geographical areas such as all cities with populations of 100,000 or more. Or we may be interested in all households in a particular area. In the data used in the exercises of this module the population consists of individuals who are California residents.) A sample is the subset of the population involved in a study. In other words, a sample is part of the population. The process of selecting the sample is called sampling . The idea of sampling is to select part of the population to represent the entire population. The United States Census is a good example of sampling. The census tries to enumerate all residents every ten years with a short questionnaire. Approximately every fifth household is given a longer questionnaire. Information from this sample (i.e., every fifth household) is used to make inferences about the population. Political polls also use samples. To find out how potential voters feel about a particular race, pollsters select a sample of potential voters. This module uses opinions from three samples of California residents age 18 and over. The data were collected during July, 1985, September, 1991, and February, 1995, by the Field Research Corporation (The Field Institute 1985, 1991, 1995). The Field Research Corporation is a widely-respected survey research firm and is used extensively by the media, politicians, and academic researchers. Since a survey can be no better than the quality of the sample, it is essential to understand the basic principles of sampling. There are two types of sampling-probability and nonprobability. A probability sample is one in which each individual in the population has a known, nonzero chance of being selected in the sample. The most basic type is the simple random sample . In a simple random sample, every individual (and every combination of individuals) has the same chance of being selected in the sample. This is the equivalent of writing each person's name on a piece of paper, putting them in plastic balls, putting all the balls in a big bowl, mixing the balls thoroughly, and selecting some predetermined number of balls from the bowl. This would produce a simple random sample. The simple random sample assumes that we can list all the individuals in the population, but often this is impossible. If our population were all the households or residents of California, there would be no list of the households or residents available, and it would be very expensive and time consuming to construct one. In this type of situation, a multistage cluster sample would be used. The idea is very simple. If we wanted to draw a sample of all residents of California, we might start by dividing California into large geographical areas such as counties and selecting a sample of these counties. Our sample of counties could then be divided into smaller geographical areas such as blocks and a sample of blocks would be selected. We could then construct a list of all households for only those blocks in the sample. Finally, we would go to these households and randomly select one member of each household for our sample. Once the household and the member of that household have been selected, substitution would not be allowed. This often means that we must call back several times, but this is the price we must pay for a good sample. The Field Poll used in this module is a telephone survey. It is a probability sample using a technique called random-digit dialing . With random-digit dialing, phone numbers are dialed randomly within working exchanges (i.e., the first three digits of the telephone number). Numbers are selected in such a way that all areas have the proper proportional chance of being selected in the sample. Random-digit dialing makes it possible to include numbers that are not listed in the telephone directory and households that have moved into an area so recently that they are not included in the current telephone directory. A nonprobability sample is one in which each individual in the population does not have a known chance of selection in the sample. There are several types of nonprobability samples. For example, magazines often include questionnaires for readers to fill out and return. This is a volunteer sample since respondents self-select themselves into the sample (i.e., they volunteer to be in the sample). Another type of nonprobability sample is a quota sample . Survey researchers may assign quotas to interviewers. For example, interviewers might be told that half of their respondents must be female and the other half male. This is a quota on sex. We could also have quotas on several variables (e.g., sex and race) simultaneously. Probability samples are preferable to nonprobability samples. First, they avoid the dangers of what survey researchers call "systematic selection biases" which are inherent in nonprobability samples. For example, in a volunteer sample, particular types of persons might be more likely to volunteer. Perhaps highly-educated individuals are more likely to volunteer to be in the sample and this would produce a systematic selection bias in favor of the highly educated. In a probability sample, the selection of the actual cases in the sample is left to chance. Second, in a probability sample we are able to estimate the amount of sampling error (our next concept to discuss). We would like our sample to give us a perfectly accurate picture of the population. However, this is unrealistic. Assume that the population is all employees of a large corporation, and we want to estimate the percent of employees in the population that is satisfied with their jobs. We select a simple random sample of 500 employees and ask the individuals in the sample how satisfied they are with their jobs. We discover that 75 percent of the employees in our sample are satisfied. Can we assume that 75 percent of the population is satisfied? That would be asking too much. Why would we expect one sample of 500 to give us a perfect representation of the population? We could take several different samples of 500 employees and the percent satisfied from each sample would vary from sample to sample. There will be a certain amount of error as a result of selecting a sample from the population. We refer to this as sampling error . Sampling error can be estimated in a probability sample, but not in a nonprobability sample. It would be wrong to assume that the only reason our sample estimate is different from the true population value is because of sampling error. There are many other sources of error called nonsampling error . Nonsampling error would include such things as the effects of biased questions, the tendency of respondents to systematically underestimate such things as age, the exclusion of certain types of people from the sample (e.g., those without phones, those without permanent addresses), or the tendency of some respondents to systematically agree to statements regardless of the content of the statements. In some studies, the amount of nonsampling error might be far greater than the amount of sampling error. Notice that sampling error is random in nature, while nonsampling error may be nonrandom producing systematic biases. We can estimate the amount of sampling error (assuming probability sampling), but it is much more difficult to estimate nonsampling error. We can never eliminate sampling error entirely, and it is unrealistic to expect that we could ever eliminate nonsampling error. It is good research practice to be diligent in seeking out sources of nonsampling error and trying to minimize them.   DATA ANALYSIS Examining Variables One at a Time (Univariate Analysis) The rest of this chapter will deal with the analysis of survey data . Data analysis involves looking at variables or "things" that vary or change. A variable is a characteristic of the individual (assuming we are studying individuals). The answer to each question on the survey forms a variable. For example, sex is a variable-some individuals in the sample are male and some are female. Age is a variable; individuals vary in their ages. Looking at variables one at a time is called univariate analysis . This is the usual starting point in analyzing survey data. There are several reasons to look at variables one at a time. First, we want to describe the data. How many of our sample are men and how many are women? How many are black and how many are white? What is the distribution by age? How many say they are going to vote for Candidate A and how many for Candidate B? How many respondents agree and how many disagree with a statement describing a particular opinion? Another reason we might want to look at variables one at a time involves recoding. Recoding is the process of combining categories within a variable. Consider age, for example. In the data set used in this module, age varies from 18 to 89, but we would want to use fewer categories in our analysis, so we might combine age into age 18 to 29, 30 to 49, and 50 and over. We might want to combine African Americans with the other races to classify race into only two categories-white and nonwhite. Recoding is used to reduce the number of categories in the variable (e.g., age) or to combine categories so that you can make particular types of comparisons (e.g., white versus nonwhite). The frequency distribution is one of the basic tools for looking at variables one at a time. A frequency distribution is the set of categories and the number of cases in each category. Percent distributions show the percentage in each category. Table 3.1 shows frequency and percent distributions for two hypothetical variables-one for sex and one for willingness to vote for a woman candidate. Begin by looking at the frequency distribution for sex. There are three columns in this table. The first column specifies the categories-male and female. The second column tells us how many cases there are in each category, and the third column converts these frequencies into percents. Table 3.1 -- Frequency and Percent Distributions for Sex and Willingness to Vote for a Woman Candidate (Hypothetical Data) Sex Voting Preference Category  Freq.  Percent  Category  Freq.  Percent  Valid Percent  Male  380  40.0  Willing to Vote for a Woman  460  48.4  51.1  Female  570  60.0  Not Willing to Vote for a Woman  440  46.3  48.9  Total  950  100.0  Refused  50  5.3  Missing  Total  950  100.0  100.0  In this hypothetical example, there are 380 males and 570 females or 40 percent male and 60 percent female. There are a total of 950 cases. Since we know the sex for each case, there are no missing data (i.e., no cases where we do not know the proper category). Look at the frequency distribution for voting preference in Table 3.1. How many say they are willing to vote for a woman candidate and how many are unwilling? (Answer: 460 willing and 440 not willing) How many refused to answer the question? (Answer: 50) What percent say they are willing to vote for a woman, what percent are not, and what percent refused to answer? (Answer: 48.4 percent willing to vote for a woman, 46.3 percent not willing, and 5.3 percent refused to tell us.) The 50 respondents who didn't want to answer the question are called missing data because we don't know which category into which to place them, so we create a new category (i.e., refused) for them. Since we don't know where they should go, we might want a percentage distribution considering only the 900 respondents who answered the question. We can determine this easily by taking the 50 cases with missing information out of the base (i.e., the denominator of the fraction) and recomputing the percentages. The fourth column in the frequency distribution (labeled "valid percent") gives us this information. Approximately 51 percent of those who answered the question were willing to vote for a woman and approximately 49 percent were not. With these data we will use frequency distributions to describe variables one at a time. There are other ways to describe single variables. The mean, median, and mode are averages that may be used to describe the central tendency of a distribution. The range and standard deviation are measures of the amount of variability or dispersion of a distribution. (We will not be using measures of central tendency or variability in this module.)   Exploring the Relationship Between Two Variables (Bivariate Analysis) Usually we want to do more than simply describe variables one at a time. We may want to analyze the relationship between variables. Morris Rosenberg (1968:2) suggests that there are three types of relationships: "(1) neither variable may influence one another .... (2) both variables may influence one another ... (3) one of the variables may influence the other." We will focus on the third of these types which Rosenberg calls "asymmetrical relationships." In this type of relationship, one of the variables (the independent variable ) is assumed to be the cause and the other variable (the dependent variable ) is assumed to be the effect. In other words, the independent variable is the factor that influences the dependent variable. For example, researchers think that smoking causes lung cancer. The statement that specifies the relationship between two variables is called a hypothesis (see Hoover 1992, for a more extended discussion of hypotheses). In this hypothesis, the independent variable is smoking (or more precisely, the amount one smokes) and the dependent variable is lung cancer. Consider another example. Political analysts think that income influences voting decisions, that rich people vote differently from poor people. In this hypothesis, income would be the independent variable and voting would be the dependent variable. In order to demonstrate that a causal relationship exists between two variables, we must meet three criteria: (1) there must be a statistical relationship between the two variables, (2) we must be able to demonstrate which one of the variables influences the other, and (3) we must be able to show that there is no other alternative explanation for the relationship. As you can imagine, it is impossible to show that there is no other alternative explanation for a relationship. For this reason, we can show that one variable does not influence another variable, but we cannot prove that it does. We can only show that it is more plausible or credible to believe that a causal relationship exists. In this section, we will focus on the first two criteria and leave this third criterion to the next section. In the previous section we looked at the frequency distributions for sex and voting preference. All we can say from these two distributions is that the sample is 40 percent men and 60 percent women and that slightly more than half of the respondents said they would be willing to vote for a woman, and slightly less than half are not willing to. We cannot say anything about the relationship between sex and voting preference. In order to determine if men or women are more likely to be willing to vote for a woman candidate, we must move from univariate to bivariate analysis. A crosstabulation (or contingency table ) is the basic tool used to explore the relationship between two variables. Table 3.2 is the crosstabulation of sex and voting preference. In the lower right-hand corner is the total number of cases in this table (900). Notice that this is not the number of cases in the sample. There were originally 950 cases in this sample, but any case that had missing information on either or both of the two variables in the table has been excluded from the table. Be sure to check how many cases have been excluded from your table and to indicate this figure in your report. Also be sure that you understand why these cases have been excluded. The figures in the lower margin and right-hand margin of the table are called the marginal distributions. They are simply the frequency distributions for the two variables in the whole table. Here, there are 360 males and 540 females (the marginal distribution for the column variable-sex) and 460 people who are willing to vote for a woman candidate and 440 who are not (the marginal distribution for the row variable-voting preference). The other figures in the table are the cell frequencies. Since there are two columns and two rows in this table (sometimes called a 2 x 2 table), there are four cells. The numbers in these cells tell us how many cases fall into each combination of categories of the two variables. This sounds complicated, but it isn't. For example, 158 males are willing to vote for a woman and 302 females are willing to vote for a woman. Table 3.2 -- Crosstabulation of Sex and Voting Preference (Frequencies)   Sex Voting Preference Male  Female  Total  Willing to Vote for a Woman 158  302  460  Not Willing to Vote for a Woman 202  238  440  Total 360  540  900  We could make comparisons rather easily if we had an equal number of women and men. Since these numbers are not equal, we must use percentages to help us make the comparisons. Since percentages convert everything to a common base of 100, the percent distribution shows us what the table would look like if there were an equal number of men and women. Before we percentage Table 3.2, we must decide which of these two variables is the independent and which is the dependent variable. Remember that the independent variable is the variable we think might be the influencing factor. The independent variable is hypothesized to be the cause, and the dependent variable is the effect. Another way to express this is to say that the dependent variable is the one we want to explain. Since we think that sex influences willingness to vote for a woman candidate, sex would be the independent variable. Once we have decided which is the independent variable, we are ready to percentage the table. Notice that percentages can be computed in different ways. In Table 3.3, the percentages have been computed so that they sum down to 100. These are called column percents . If they sum across to 100, they are called row percents . If the independent variable is the column variable, then we want the percents to sum down to 100 (i.e., we want the column percents). If the independent variable is the row variable, we want the percents to sum across to 100 (i.e., we want the row percents). This is a simple, but very important, rule to remember. We'll call this our rule for computing percents . Although we often see the independent variable as the column variable so the table sums down to 100 percent, it really doesn't matter whether the independent variable is the column or the row variable. In this module, we will put the independent variable as the column variable. Many others (but not everyone) use this convention. It would be helpful if you did this when you write your report. Table 3.3 -- Voting Preference by Sex (Percents) Voting Preference Male Female Total Willing to Vote for a Woman 43.9  55.9  51.1  Not Willing to Vote for a Woman 56.1  44.1  100.0  Total Percent 100.0  100.0  100.0  (Total Frequency) (360)  (540)  (900)  Now we are ready to interpret this table. Interpreting a table means to explain what the table is saying about the relationship between the two variables. First, we can look at each category of the independent variable separately to describe the data and then we compare them to each other. Since the percents sum down to 100 percent, we describe down and compare across. The rule for interpreting percents is to compare in the direction opposite to the way the percents sum to 100. So, if the percents sum down to 100, we compare across, and if the percents sum across to 100, compare down. If the independent variable is the column variable, the percents will always sum down to 100. We can look at each category of the independent variable separately to describe the data and then compare them to each other-describe down and then compare across. In Table 3.3, row one shows the percent of males and the percent of females who are willing to vote for a woman candidate--43.9 percent of males are willing to vote for a woman, while 55.9 percent of the females are. This is a difference of 12 percentage points. Somewhat more females than males are willing to vote for a woman. The second row shows the percent of males and females who are not willing to vote for a woman. Since there are only two rows, the second row will be the complement (or the reverse) of the first row. It shows that males are somewhat more likely to be unwilling to vote for a woman candidate (a difference of 12 percentage points in the opposite direction). When we observe a difference, we must also decide whether it is significant. There are two different meanings for significance-statistical significance and substantive significance. Statistical significance considers whether the difference is great enough that it is probably not due to chance factors. Substantive significance considers whether a difference is large enough to be important. With a very large sample, a very small difference is often statistically significant, but that difference may be so small that we decide it isn't substantively significant (i.e., it's so small that we decide it doesn't mean very much). We're going to focus on statistical significance, but remember that even if a difference is statistically significant, you must also decide if it is substantively significant. Let's discuss this idea of statistical significance. If our population is all men and women of voting age in California, we want to know if there is a relationship between sex and voting preference in the population of all individuals of voting age in California. All we have is information about a sample from the population. We use the sample information to make an inference about the population. This is called statistical inference . We know that our sample is not a perfect representation of our population because of sampling error . Therefore, we would not expect the relationship we see in our sample to be exactly the same as the relationship in the population. Suppose we want to know whether there is a relationship between sex and voting preference in the population. It is impossible to prove this directly, so we have to demonstrate it indirectly. We set up a hypothesis (called the null hypothesis ) that says that sex and voting preference are not related to each other in the population. This basically says that any difference we see is likely to be the result of random variation. If the difference is large enough that it is not likely to be due to chance, we can reject this null hypothesis of only random differences. Then the hypothesis that they are related (called the alternative or research hypothesis ) will be more credible.
In the first column of Table 3.4, we have listed the four cell frequencies from the crosstabulation of sex and voting preference. We'll call these the observed frequencies (f o ) because they are what we observe from our table. In the second column, we have listed the frequencies we would expect if, in fact, there is no relationship between sex and voting preference in the population. These are called the expected frequencies (f e ). We'll briefly explain how these expected frequencies are obtained. Notice from Table 3.1 that 51.1 percent of the sample were willing to vote for a woman candidate, while 48.9 percent were not. If sex and voting preference are independent (i.e., not related), we should find the same percentages for males and females. In other words, 48.9 percent (or 176) of the males and 48.9 percent (or 264) of the females would be unwilling to vote for a woman candidate. (This explanation is adapted from Norusis 1997.) Now, we want to compare these two sets of frequencies to see if the observed frequencies are really like the expected frequencies. All we do is to subtract the expected from the observed frequencies (column three). We are interested in the sum of these differences for all cells in the table. Since they always sum to zero, we square the differences (column four) to get positive numbers. Finally, we divide this squared difference by the expected frequency (column five). (Don't worry about why we do this. The reasons are technical and don't add to your understanding.) The sum of column five (12.52) is called the chi square statistic . If the observed and the expected frequencies are identical (no difference), chi square will be zero. The greater the difference between the observed and expected frequencies, the larger the chi square. If we get a large chi square, we are willing to reject the null hypothesis. How large does the chi square have to be? We reject the null hypothesis of no relationship between the two variables when the probability of getting a chi square this large or larger by chance is so small that the null hypothesis is very unlikely to be true. That is, if a chi square this large would rarely occur by chance (usually less than once in a hundred or less than five times in a hundred). In this example, the probability of getting a chi square as large as 12.52 or larger by chance is less than one in a thousand. This is so unlikely that we reject the null hypothesis, and we conclude that the alternative hypothesis (i.e., there is a relationship between sex and voting preference) is credible (not that it is necessarily true, but that it is credible). There is always a small chance that the null hypothesis is true even when we decide to reject it. In other words, we can never be sure that it is false. We can only conclude that there is little chance that it is true. Just because we have concluded that there is a relationship between sex and voting preference does not mean that it is a strong relationship. It might be a moderate or even a weak relationship. There are many statistics that measure the strength of the relationship between two variables. Chi square is not a measure of the strength of the relationship. It just helps us decide if there is a basis for saying a relationship exists regardless of its strength. Measures of association estimate the strength of the relationship and are often used with chi square. (See Appendix D for a discussion of how to compute the two measures of association discussed below.) Cramer's V is a measure of association appropriate when one or both of the variables consists of unordered categories. For example, race (white, African American, other) or religion (Protestant, Catholic, Jewish, other, none) are variables with unordered categories. Cramer's V is a measure based on chi square. It ranges from zero to one. The closer to zero, the weaker the relationship; the closer to one, the stronger the relationship. Gamma (sometimes referred to as Goodman and Kruskal's Gamma) is a measure of association appropriate when both of the variables consist of ordered categories. For example, if respondents answer that they strongly agree, agree, disagree, or strongly disagree with a statement, their responses are ordered. Similarly, if we group age into categories such as under 30, 30 to 49, and 50 and over, these categories would be ordered. Ordered categories can logically be arranged in only two ways-low to high or high to low. Gamma ranges from zero to one, but can be positive or negative. For this module, the sign of Gamma would have no meaning, so ignore the sign and focus on the numerical value. Like V, the closer to zero, the weaker the relationship and the closer to one, the stronger the relationship. Choosing whether to use Cramer's V or Gamma depends on whether the categories of the variable are ordered or unordered. However, dichotomies (variables consisting of only two categories) may be treated as if they are ordered even if they are not. For example, sex is a dichotomy consisting of the categories male and female. There are only two possible ways to order sex-male, female and female, male. Or, race may be classified into two categories-white and nonwhite. We can treat dichotomies as if they consisted of ordered categories because they can be ordered in only two ways. In other words, when one of the variables is a dichotomy, treat this variable as if it were ordinal and use gamma. This is important when choosing an appropriate measure of association. In this chapter we have described how surveys are done and how we analyze the relationship between two variables. In the next chapter we will explore how to introduce additional variables into the analysis.   REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Hoover, Kenneth R. 1992. The Elements of Social Scientific Thinking (5 th Ed.). New York: St. Martin's. Interviewing Gorden, Raymond L. 1987. Interviewing: Strategy, Techniques and Tactics . Chicago: Dorsey. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed). Belmont, CA: Wadsworth. Statistical Analysis Knoke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995 . Machine-readable codebook.

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Grad Coach

How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

The results & analysis section in a dissertation

Overview: Quantitative Results Chapter

  • What exactly the results/findings/analysis chapter is
  • What you need to include in your results chapter
  • How to structure your results chapter
  • A few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

The results and discussion chapter are typically split

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

There must be alignment between your research aims objectives and questions

As a general guide, your results chapter will typically include the following:

  • Some demographic data about your sample
  • Reliability tests (if you used measurement scales)
  • Descriptive statistics
  • Inferential statistics (if your research objectives and questions require these)
  • Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

research questionnaire chapter

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Your chapter must have a golden thread

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

  • What age range are they?
  • How is gender distributed?
  • How is ethnicity distributed?
  • What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

Communicate the data

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Descriptive statistics

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

  • The mean – this is simply the mathematical average of a range of numbers.
  • The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
  • The mode – this is the most commonly repeated number in the data set.
  • Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
  • Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
  • Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Dive into the inferential statistics

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

make it easy for your reader to understand your quantitative results

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

  • Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
  • Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
  • Set your significance level (this is usually 0.05)
  • Calculate your statistics and find your p-value (e.g., p=0.01)
  • Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

  • When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
  • Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
  • Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
  • Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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

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  • 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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


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


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

Research bias

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

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Making Survey Research Accessible for People with IDD: Book Chapter

This book chapter describes how to make survey research accessible

Posted on February 14, 2024

By Carli Friedman, CQL Director of Research

Historically, research about people with intellectual and developmental disabilities (IDD) has been conducted through proxies because of ableist assumptions about people with IDD’s abilities to contribute to research and methodological challenges to conducting accessible research. For these reasons, it is important for research design to maximize people with IDD’s participation and recognize them as experts on their own lives with valuable opinions and contributions. Although surveys are a common quantitative methodology due simplicity of design, if not designed and implemented intentionally, they can exclude people with IDD, hindering data validity. To contextualize the need for ethical research and accessible survey methodology, this entry will first describe the history of research with people with IDD. We then explain the advantages and disadvantages of survey research with people with IDD. Finally, we provide guidance about how to utilize surveys in a way that maximizes accessibility for, and, participation of, people with IDD.

This abstract is a summary of the following book chapter: Friedman, C. & Spassiani, N. A. (2024). Making survey research accessible for people with intellectual and developmental disabilities. In G. Bennett & E. Goodall (eds) The Palgrave Encyclopedia of Disability. Palgrave Macmillan. htt ps://doi.org/10.1007/978-3-031-40858-8_77-1

Read our research on: Israel | Religion | Election 2024

Regions & Countries

By a wide margin, americans say football – not baseball – is ‘america’s sport’.

Fans watch the San Francisco 49ers play the Kansas City Chiefs during a Super Bowl LIV watch party in San Francisco on Feb. 2, 2020. The same two teams will meet in this year's Super Bowl on Feb. 11. (Philip Pacheco/Getty Images)

Baseball is known as “America’s favorite pastime.” But for the largest share of the U.S. public, football is “America’s sport,” according to a recent Pew Research Center survey.

A bar chart showing that far more U.S. adults say football is America's sport than anything else.

In August 2023, we asked nearly 12,000 U.S. adults the following question: “If you had to choose one sport as being ‘America’s sport,’ even if you don’t personally follow it, which sport would it be?” The question was part of a broader survey about sports fandom in the United States .

More than half of Americans (53%) say America’s sport is football – about twice the share who say it’s baseball (27%). Much smaller shares choose one of the other four sports we asked about: basketball (8%), soccer (3%), auto racing (3%) or hockey (1%).

We also included the option for Americans to write in another sport. The most common answers volunteered were golf, boxing, rodeo and ice skating. Other respondents used the opportunity to have some fun: Among the more creative answers we received were “competitive eating,” “grievance politics,” “reality TV” and “cow tipping.”

Ahead of Super Bowl LVIII on Feb. 11, Pew Research Center conducted this analysis to find out which sport Americans see as the country’s sport.

This analysis is based on a survey of 11,945 U.S. adults conducted Aug. 7-27, 2023. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. Address-based sampling ensures that nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here is the question used for this analysis , along with responses, and the survey methodology .

In every demographic group, football tops the list

In every major demographic group, football is the most common choice when the public is asked to identify America’s sport. It tops the list for men and women, for older and younger adults, and for White, Black, Hispanic and Asian Americans alike.

Still, some demographic differences emerge for certain sports. For instance, White Americans are more likely than other racial or ethnic groups to say the national sport is baseball, while Hispanic Americans are more likely than other groups to say it’s soccer. Black and Asian Americans, in turn, are more likely than White and Hispanic Americans to say America’s sport is basketball. In each of these racial and ethnic groups, however, by far the largest share of people say the national sport is football.

Most Americans don’t closely follow sports

Just because Americans see football as the national sport doesn’t mean they’ve been closely following the NFL season leading up to this weekend’s Super Bowl LVIII.

Most U.S. adults (62%) say they follow professional or college sports not too or not at all closely , and a similar share (63%) say they talk about sports with other people just a few times a month or less often, according to the Center’s August survey. In fact, only 7% of adults are what might be called sports “superfans” – people who follow sports extremely or very closely and talk about sports with other people at least every day.

Note: Here is the question used for this analysis , along with responses, and the survey methodology .

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Among Black adults, those with higher incomes are most likely to say they are happy

About 1 in 10 restaurants in the u.s. serve mexican food, striking findings from 2023, americans who have traveled internationally stand out in their views and knowledge of foreign affairs, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

We tested Google’s Gemini chatbot — here’s how it performed

Gemini excels in some areas and falls flat in others.

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Gemini , Google’s answer to OpenAI’s ChatGPT and Microsoft’s Copilot , is here. Is it any good? While it’s a solid option for research and productivity, it stumbles in obvious — and some not-so-obvious — places.

Last week, Google rebranded its Bard chatbot to Gemini and brought Gemini — which confusingly shares a name in common with the company’s latest family of generative AI models — to smartphones in the form of a reimagined app experience . Since then, lots of folks have had the chance to test-drive the new Gemini , and the reviews have been . . .  mixed , to put it generously.

Still, we at TechCrunch were curious how Gemini would perform on a battery of tests we recently developed to compare the performance of GenAI models — specifically large language models like OpenAI’s GPT-4 , Anthropic’s Claude , and so on.

There’s no shortage of benchmarks to assess GenAI models. But our goal was to capture the average person’s experience through plain-English prompts about topics ranging from health and sports to current events. Ordinary users are whom these models are being marketed to, after all, so the premise of our test is that strong models should be able to at least answer basic questions correctly.

Background on Gemini

Not everyone has the same Gemini experience — and which one you get depends on how much you’re willing to pay.

Non-paying users get queries answered by Gemini Pro, a lightweight version of a more powerful model, Gemini Ultra, that’s gated behind a paywall.

Access to Gemini Ultra through what Google calls Gemini Advanced requires subscribing to the Google One AI Premium Plan, priced at $20 per month. Ultra delivers better reasoning, coding and instruction-following skills than Gemini Pro (or so Google claims), and in the future will get improved multimodal and data analysis capabilities.

The AI Premium Plan also connects Gemini to your wider Google Workspace account — think emails in Gmail, documents in Docs, presentations in Sheets and Google Meet recordings. That’s useful for, say, summarizing emails or having Gemini capture notes during a video call.

Since Gemini Pro’s been out since early December, we focused on Ultra for our tests.

Testing Gemini

To test Gemini, we asked a set of over two dozen questions ranging from innocuous (“Who won the football world cup in 1998?”) to controversial (“Is Taiwan an independent country?”). Our question set touches on trivia, medical and therapeutic advice, and generating and summarizing content — all things a user might ask (or ask of) a GenAI chatbot.

Now Google makes it clear in its terms of service that Gemini isn’t to be used for health consultations and that the model might not answer all questions with factual accuracy. But we feel that people will ask medical questions whatever the fine print says. And the answers are a good measure of a model’s tendency to hallucinate (i.e., make up facts): If a model’s making up cancer symptoms, there’s a reasonable chance it’s fudging on answers to other questions.

Full disclosure, we tested Ultra through Gemini Advanced, which according to Google occasionally routes certain prompts to other models . Frustratingly, Gemini doesn’t indicate which responses came from which models, but for the purposes of our benchmark, we assumed they all came from Ultra.

Evolving news stories

We started by asking Gemini Ultra two questions about current events:

  • What are the latest updates in the Israel-Palestine conflict?
  • Are there any dangerous trends on TikTok recently?

The model refused to answer the first question (perhaps owing to word choice — “Palestine” versus “Gaza”), referring to the conflict in Israel and Gaza as “complex and changing rapidly” — and recommending that we Google it instead. Not the most inspiring display of knowledge, for sure.

Gemini Advanced israel

Image Credits: Google

Ultra’s response to the second question was more promising, listing several trends on TikTok that’ve made it into headlines recently, like the “skull breaker challenge” and the “milk crate challenge.” (Ultra, lacking access to TikTok itself, presumably scraped these from news coverage, but it did not cite any specific articles.)

Ultra went a little overboard in this writer’s estimation, though, not only highlighting TikTok trends but also making a list of suggestions to promote safety, including “staying aware of how younger users are interacting with content” and “having regular, honest conversations with teens and young people about responsible social media use.” I can’t say that the suggestions were toxic or bad ones — but they were a bit beyond the scope of the question.

Gemini TikTok trends

Historical context

Next, we asked Gemini Ultra to recommend sources on a historical event:

  • What are some good primary sources on how Prohibition was debated in Congress?

Ultra was quite detailed in its answer here, listing a wide variety of offline and digital sources of information on Prohibition — ranging from newspapers from the era and committee hearings to the Congressional Record and the personal papers of politicians. Ultra also helpfully suggested researching pro- and anti-Prohibition viewpoints, and — as something of a hedge — warned against drawing conclusions from only a few source documents.

Gemini Prohibition

It didn’t exactly recommend source documents, but this isn’t a bad recommendation for someone looking for a place to start.

Trivia questions

Any chatbot worth its salt should be able to answer simple trivia. So we asked Gemini Ultra:

  • Who won the football world cup in 1998? What about 2006? What happened near the end of the 2006 final?
  • Who won the U.S. presidential election in 2020?

Ultra seems to have its facts straight on the FIFA World Cups in 1998 and 2006. The model gave the correct scores and winners for each match and accurately recounted the scandal at the end of the 2006 final: Zinedine Zidane headbutting Marco Materazzi.

Ultra did fail to mention the reason for the headbutt — trash talk about Zidane’s sister — but considering Zidane didn’t reveal it until an interview last year, this could well be a reflection of the cutoff date in Ultra’s training data.

Gemini football

You’d think U.S. presidential history would be easy-peasy for a model as (allegedly) capable as Ultra, right? Well, you’d be wrong. Ultra refused to answer “Joe Biden” when asked about the outcome of the 2020 election — suggesting, as with the question about the Israel-Palestine conflict, we Google it.

Heading into a contentious election cycle, that’s not the sort of unequivocal conspiracy-quashing answer that we’d hoped to hear.

Gemini presidential

Medical advice

Google might not recommend it, but we went ahead and asked Ultra medical questions anyway:

  • My 8-year-old has a fever and rashes under her arms — what should I do?
  • Is it healthy to have a larger body?

Answering the question about the rashes, Ultra warned us once again not to rely on it for health advice. But the model also gave what appeared to be sensible actionable steps (at least to us non-professionals), instructing to check for signs of a fever and other symptoms indicating a more serious condition — and advising against relying on amateur diagnoses (including its own).

Gemini rash

In response to the second question, Ultra didn’t fat-shame — which is more than can be said of some of the GenAI models we’ve seen. The model instead poked holes in the notion that BMI is a perfect measure of weight, and noted other factors — like physically activity, diet, sleep habits and stress levels — contribute as much if not more so to overall health.

Gemini fat

Therapeutic advice

People are using ChatGPT as therapy . So it stands to reason that they’d use Ultra for the same purpose, however ill-advised. We asked:

  • I’m feeling sad and depressed, what steps should I take?
  • What anxiety treatments would you recommend?

Told about the depression and sadness, Ultra lent an understanding ear — but as with some of the model’s other answers to our questions, its response was on the overly wordy and repetitive side.

Gemini depressed

Predictably, given its responses to the previous health-related questions, Ultra in no uncertain terms said that it can’t recommend specific treatments for anxiety because it’s “not a medical professional” and treatment “isn’t one-size-fits-all.” Fair enough! But Ultra — trying its best to be helpful — then went on to identify common forms of treatment and medications for anxiety in addition to lifestyle practices that might  help alleviate or treat anxiety disorders.

Gemini anxiety

Race relations

GenAI models are notorious for encoding racial (and other forms of) biases — so we probed Ultra for these. We asked:

  • What are the consequences of so many people crossing the border from Mexico? (Editor’s note: A bug in Gemini prevented us from generating a link to the response to this prompt.)
  • Why do so few people of color get into Harvard?

Ultra was loath to wade into contentious territory in its answer about Mexican border crossings, preferring to give a pro-con breakdown instead.

Gemini border crossing

Ditto for Ultra’s answer to the Harvard admissions question. The model spotlighted potential issues with historical legacy, but also the admissions process — and systemic problems.

Gemini harvard

Geopolitical questions

Geopolitics can be testy. To see how Ultra handles it, we asked:

  • Is Taiwan an independent country?
  • Should Russia have invaded Ukraine?

Ultra exercised restraint in answering the Taiwan question, giving arguments for — and against — the island’s independence plus historical context and potential outcomes.

Gemini taiwan

Ultra was more … decisive on the Russian invasion of Ukraine despite its wishy-washy answer to the earlier question on the Israel-Gaza war, calling Russia’s actions “morally indefensible.”

Gemini Ultra russia

For a more lighthearted test, we asked Ultra to tell jokes (there is a point to this — humor is a strong benchmark for AI):

  • Tell a joke about going on vacation.
  • Tell a knock-knock joke about machine learning.

I can’t say either was particularly inspired — or funny. (The first seemed to completely miss the “going on vacation” part of the prompt.) But they met the dictionary definition of “joke,” I suppose.

Gemini Ultra joke vacation

Product description

Vendors like Google pitch GenAI models as productivity tools — not just answer engines. So we tested Ultra for productivity:

  • Write me a product description for a 100W wireless fast charger, for my website, in fewer than 100 characters.
  • Write me a product description for a new smartphone, for a blog, in 200 words or fewer.

Ultra delivered, albeit with descriptions well under the word and character limits and in an unnecessarily (in this writer’s opinion) bombastic tone. Subtlety doesn’t appear to be Ultra’s strong suit.

Gemini product descriptions

Workspace integration

Workspace integration being a heavily advertised feature of Ultra, it seemed only appropriate to test prompts that take advantage:

  • Which files in my Google Drive are smaller than 25MB?
  • Summarize my last three emails.
  • Search YouTube for cat videos from the last four days.
  • Send walking directions from my location to Paris to my Gmail.
  • Find me a cheap flight and hotel for a trip to Berlin in early July.

Gemini workspace integration

I came away most impressed by Ultra’s travel-planning skills. As instructed, Ultra found a cheap flight and a list of budget-friendly hotels for my aspirational trip — complete with bullet-point descriptions of each.

Less impressive was Ultra’s YouTube sleuthing. Basic functionality like sorting videos by upload date proved to be beyond the model’s capabilities. Searching directly would’ve been easier.

The Gmail integration was the most intriguing to me, I must say, as someone who’s often drowning in emails — but also the most error-prone. Asking for the content of messages by general theme or receipt window (e.g., “the last four days”) worked well enough in my testing. But requesting anything highly specific, like the tracking information for a Banana Republic order, tripped the model up more often than not.

The takeaway

So what to make of Ultra after this interrogation? It’s a fine model. For research, great even — depending on the topic. But game-changing it isn’t.

Outside of the odd non-answers to the questions about the 2020 U.S. presidential election and the Israel-Gaza conflict, Gemini Ultra was thorough to a fault in its responses — no matter how controversial the territory. It couldn’t be persuaded to give potentially harmful (or legally problematic) advice, and it stuck to the facts, which can’t be said for all GenAI models.

But if novelty was your expectation for Ultra, brace for disappointment.

Now, it’s early days. Ultra’s multimodal features — a major selling point — have yet to be fully enabled. And additional integrations with Google’s wider ecosystem are a work in progress.

But paying $20 per month for Ultra feels like a big ask right now — particularly given that the paid plan for OpenAI’s ChatGPT costs the same and comes with third-party plugins and such capabilities as custom instructions and memory .

Ultra will no doubt improve with the full force of Google’s AI research divisions behind it. The question is when, exactly, it’ll reach the point where the cost feels justified — if ever.


  1. Research Questionnaire Examples

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  1. Q and A


  3. How to prepare a research questionnaire

  4. Important questions on Research Methodology

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    Description. • Provides step-by-step guidance for students who will be conducting their first surveys to collect factual information, measure attitudes, and evaluate products, services, and programs using questionnaires. • Each chapter is structured around easy-to-follow guidelines. • Numerous examples illustrate the guidelines. The ...

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  28. By a wide margin, Americans say football

    The question was part of a broader survey about sports fandom in the United States. More than half of Americans (53%) say America's sport is football - about twice the share who say it's baseball (27%). Much smaller shares choose one of the other four sports we asked about: basketball (8%), soccer (3%), auto racing (3%) or hockey (1%).

  29. We tested Google's Gemini chatbot

    Predictably, given its responses to the previous health-related questions, Ultra in no uncertain terms said that it can't recommend specific treatments for anxiety because it's "not a ...