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Organizing Your Social Sciences Research Paper

  • Quantitative Methods
  • Purpose of Guide
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Glossary of Research Terms
  • Reading Research Effectively
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • Research Process Video Series
  • Executive Summary
  • The C.A.R.S. Model
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tiertiary Sources
  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

  • The data is usually gathered using structured research instruments.
  • The results are based on larger sample sizes that are representative of the population.
  • The research study can usually be replicated or repeated, given its high reliability.
  • Researcher has a clearly defined research question to which objective answers are sought.
  • All aspects of the study are carefully designed before data is collected.
  • Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
  • Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
  • Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

  • Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
  • Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
  • Explain the techniques you used to "clean" your data set.
  • Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
  • Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
  • When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
  • Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
  • Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
  • Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

  • Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
  • Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
  • Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

  • Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
  • Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
  • Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

  • Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

  • Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
  • Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
  • Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
  • Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

  • Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
  • Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
  • Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

  • Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
  • Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
  • Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
  • You can summarize vast sources of information and make comparisons across categories and over time; and,
  • Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

  • Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
  • Uses a static and rigid approach and so employs an inflexible process of discovery;
  • The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
  • Results provide less detail on behavior, attitudes, and motivation;
  • Researcher may collect a much narrower and sometimes superficial dataset;
  • Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
  • The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
  • Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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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.

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?

quantitative thesis writing

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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Reliability and validity, approaches to quantitative research.

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Through quantitative research we seek to understand the relationships between variables. A variable will be a characteristic, value, attribute or behaviour that is of interest to the researcher. Some variables can be simple to measure, for example, height and weight. By contrast, others such as self-esteem or socio-economic status are more complex and therefore harder to measure. This is why it is important to operationalise your variables.

This essentially means being very clear about the way in which variables will be defined and measured in your study; this lends credibility to your methodology and helps the replicability of your research. It is important that you are detailed in your operational definition of any given variable because another researcher may define that variable differently from you. To illustrate, if a study examined memory ability, the researcher would specify exactly how this measure was generated: was it the number of words recalled in 60 seconds after reading a passage of text? Was it details about a picture? Defining your variables is an important of the research process as this will affect the reliability and validity of your study.

is the variable changed or manipulated by the researcher. Research generally seeks to establish whether the independent variable has an affect or influences the dependent variable in some way; this may be through a causal or non-causal relationship.

i s the variable that the researcher is trying to predict or explain through understanding its relationship with the independent variable. For example, if a researcher wants to establish if drinking coffee aids sporting performance, your independent variable would be the amount of coffee consumed (no coffee/1 cup/3 cups) and the dependent variable would be some operational definition of sporting performance (amount of weight lifted/vertical jump height/time taken to sprint 100m).

is  a variable that affects the strength of the relationship between the independent and dependent variable. For example, if you looked at the relationship between personality similarity in friendships (independent variable) and perceived friendship satisfaction (dependent variable), it might be that age is a moderating variable – e.g. the older you are, the weaker the relationship between personality similarity in a friendship and associated satisfaction with that friendship. From this you could make the tentative suggestion that similarity in personality becomes less important in a satisfying relationship as we become older. 

i s a variable that helps to explain the relationship between the independent and dependent variables. Consider the example above, we might discover that the number of shared activities also contributes to perceived friendship satisfaction . We could then remove this from our analysis and find that the relationship between personality similarity in friendships and perceived satisfaction in a friendship disappears - this would suggest that the relationship was mediated by the variable shared interests.

is any variable that is not the independent variable but may affect the results of the experiment. Examples can include; aspects of the environment (temperature/noise/lighting); differences between participants (mood/intellect/concentration); and experimenter effects (clues in an experiment which may convey that purpose of the research). It is important to minimise the influence of extraneous variables through the careful use of controls – for example, there are ways of minimising the effect of differences between participants through your experimental design (more on this later!)

  • Relationships are Complicated toolkit This short guide offers more information and examples of the types of relationships between variables.

A hypothesis is a predictive statement that can be tested through the collection of data. The data can be analysed and can either provide support for, or help to reject, a hypothesis; this in turn should allow a researcher to draw some conclusions about what they are investigating.

Null and alternative hypotheses

Hypothesis are classified by the way they describe the expected association/difference between variables. When we test our hypothesis/hypotheses it is important to remember we are testing it against the assumption that there isn’t an association/difference between the independent and dependent variables: we call this the null hypothesis. By testing this assumption, statistical tests can estimate how likely it is that any observed association/difference between variables is due to chance.

In addition to the null hypothesis we also have the alternative hypothesis. This hypothesis states that there is an association/difference between groups; this cannot be tested directly but can be accepted by rejecting the null hypothesis. This is achieved through statistical tests that can help to demonstrate that any observed association/differences are not due to chance. Once this is established, we can accept our alternative hypothesis and start to draw conclusions from our data.

Hypotheses can either be one-tailed or two-tailed:

  • One-tailed hypothesis –specifies the direction of the predicted association between the independent and dependent variable. For example, the higher an individual’s educational level, the more books they will read in a one-year period.
  • Two-tailed hypothesis – does not specify the direction of the predicted association between variables; only that an association exists. For example, there will a be difference in the number of books read in a one-year period, dependent on the level of an individual’s education.

Key things to remember when writing your hypothesis/hypotheses:

  • Your hypothesis should always be written as a statement and before any data are collected .
  • It should be simple and specific ; include the variables, using concise operational definitions, and the predicted relationship between these variables. If you have several predictor (independent) variables it would be better to write several simple hypotheses – think one predictor and one outcome variable.
  • Always keep your language clear and focused .

It is important that you show rigour within your research. This means demonstrating that you have given careful consideration to how you can enhance the quality of your research project. Within quantitative research this is achieved through examining reliability and validity.

  • Reliability – is a measure of how consistent, dependable and repeatable something is.
  • Validity – is the extent to which research measures the concept that it was designed to measure.

For example, if you had some scales that were always weighed an object as 5kg lighter than it actually is, this would be an example of a measure that was very reliable but not valid : the scales will always give you a consistent measure of weight, but this measure is not accurate.

There are several different types of reliability and validity that you should consider when planning, conducting and writing up your research project. For more information on the different types of reliability and validity have a look at the recommendations below:

  • Designing and Doing Survey Research (Andres, 2012) – see Chapter 7 .
  • Quantitative Health Research Issues and Methods (Curtis & Drennan, 2013) – see Chapter 16 .
  • Research Methods in Psychology (Howitt & Cramer, 2017) – see Chapter 16 .
  • Non-experimental
  • 'True' experiments
  • Quasi-experimental
  • Between-subjects
  • Within-subjects

Non-experimental research designs do not seek to establish cause and effect relationships. This is because the researcher does not manipulate the independent variable(s) to measure any effects on the dependent variable(s). Instead, researchers may use this type of design to begin exploring a topic where there is little current understanding, or to investigate the relationship between two (or more) variables.

  • Descriptive : these research designs help to understand the current state of a phenomenon and are often used when not much is known about a topic. Variables are not controlled, and data tends to be collected through observation or surveys. An example of this might be an investigation into the preferred news sources of 13-18-year olds.
  • Correlational :  these designs measure a relationship between two variables that are not controlled. As such, correlational designs cannot establish cause and effect – always remember correlation does not imply causation! This approach can be useful when there is a suspected relationship between variables, but it would be impractical or unethical to manipulate one of those variables. For example, you might hypothesize

True experiments seek to establish cause and effect relationships between a group of variables. Researchers control for all variables except for the variable(s) being manipulated, to establish its effect on the dependent variable.

These are similar to true experiments: the aim is to establish cause and effect relationships. Crucially however, assignment to groups is not random. This type of design is often used when it is not possible for the researcher to randomly assign participants to groups because they are interested in understanding a particular phenomenon in relation to naturally occurring differences between groups – an example of this could be an experiment where a researcher is interested in examining whether the effect of coffee consumption on sleep differs depending on age. In this example, it is impossible for the researcher to manipulate the age of participants, so instead group assignment would be made based on predetermined criteria e.g. under 40, 40+. As this assignment cannot be random this would be a quasi-experiment.

Between-subjects designs involve the assignments of participants to one of two (or more) conditions, with each participant experiencing only that condition. In its simplest form, a between-subjects design requires a control condition and a treatment condition. If the results of an experiment differ greatly between conditions, then it can be assumed that this due to the effect of the intervention or manipulation that has been applied in the treatment condition. To help minimise the affect of extraneous variables that might impact differences between the groups (and increase the likelihood that observed differences are due to the effect of the independent variable), participants in the control and treatment conditions might be matched for relevant characteristics.

For example, in an experiment to assess the effectiveness of two training programmes in improving athletic performance, participants might be matched for some key measures of fitness such as 100m sprint time, maximum squat etc. This would enable researchers to be more confident that any changes to athletic performance in the participants between the two groups were likely due to the training programme they undertook, rather than natural, pre-exiting differences in athletic performance.

Sometimes referred to as repeated measures , this approach involves obtaining more than one measure from each participant in a study. This means that participants take part in both the control and treatment condition(s). The primary advantage of this is that participants act as their own control; you can be more confident that any observed differences result from the treatment condition rather than naturally occurring differences between the groups. One problem, however, is that of order effects (sometimes called practice effects). These effects may occur because conditions are applied one after the other and this can lead to changes in performance that are not the result of the treatment but instead reflect some effect of the previous condition that a participant has experienced. For example, improvements in performance could be due to learning/practise and a decline in performance could be due to fatigue over experiencing two (or more) experimental conditions back-to-back.

One way to account for this problem is to counterbalance the order of your conditions. To do this, a researcher would ensure that each condition in the experiment is experienced 1 st for an equal number of participants:

10 participants experience condition A 1 st and condition B 2 nd 10 participants experience condition B 1 st and condition A 2 nd

Doing this helps to reduce the impact of order effects by ensuring that any effects are distributed evenly across all conditions.

Another option could be to create a long time between testing conditions to reduce any possible effects of learning and/or fatigue. It should be noted however, that creating distance between conditions isn’t always practical and it can be hard to know how long is sufficient to eliminate a potential order effect: this is particularly true for practice effects as it can be hard to accurately determine how long it takes for potential improvements in performance due to learning, to disappear.

The final type of design is used when a research design has one (or more) factors that is between subjects and one (or more) factors that is within subject. This is often used when for research that is looking at the effect on an intervention in relation to another factor that has a fixed effect.

For example, if a researcher was looking at the effectiveness of a drug for treating pain in those under 40 and over 40, age would be a between-subjects factor because a participant can’t be both under and over 40 at the same time. The drug that participants take would be the within-subjects factor. This type of design is particularly useful if you want to examine if the effect on an intervention is different dependent upon another factor. In the example above, it would be possible to establish if the effect of the drug was beneficial for all participants, or whether it was particularly effective/ineffective depending on the age of the participant – whether they were under or over 40. 

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  • Introduction

Quantitative Dissertations

The Quantitative Dissertations part of Lærd Dissertation helps guide you through the process of doing a quantitative dissertation. When we use the word quantitative to describe quantitative dissertations , we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques . Quantitative research takes a particular approach to theory, answering research questions and/or hypotheses , setting up a research strategy , making conclusions from results , and so forth. It is also a type of dissertation that is commonly used by undergraduates, master's and doctoral students across degrees, whether traditional science-based subjects, or in the social sciences, psychology, education and business studies, amongst others.

This introduction to the Quantitative Dissertations part of Lærd Dissertation has two goals: (a) to provide you with a sense of the broad characteristics of quantitative research, if you do not know about these characteristics already; and (b) to introduce you to the three main types (routes) of quantitative dissertation that we help you with in Lærd Dissertation: replication-based dissertations ; data-driven dissertations; and theory-driven dissertations . When you have chosen which route you want to follow, we send you off to the relevant parts of Lærd Dissertation where you can find out more.

Characteristics of quantitative dissertations

  • Types of quantitative dissertation: Replication, Data and Theory

If you have already read our article that briefly compares qualitative , quantitative and mixed methods dissertations [ here ], you may want to skip this section now . If not, we can say that quantitative dissertations have a number of core characteristics:

They typically attempt to build on and/or test theories , whether adopting an original approach or an approach based on some kind of replication or extension .

They answer quantitative research questions and/or research (or null ) hypotheses .

They are mainly underpinned by positivist or post-positivist research paradigms .

They draw on one of four broad quantitative research designs (i.e., descriptive , experimental , quasi-experimental or relationship-based research designs).

They try to use probability sampling techniques , with the goal of making generalisations from the sample being studied to a wider population , although often end up applying non-probability sampling techniques .

They use research methods that generate quantitative data (e.g., data sets , laboratory-based methods , questionnaires/surveys , structured interviews , structured observation , etc.).

They draw heavily on statistical analysis techniques to examine the data collected, whether descriptive or inferential in nature.

They assess the quality of their findings in terms of their reliability , internal and external validity , and construct validity .

They report their findings using statements , data , tables and graphs that address each research question and/or hypothesis.

They make conclusions in line with the findings , research questions and/or hypotheses , and theories discussed in order to test and/or expand on existing theories, or providing insight for future theories.

If you choose to take on a quantitative dissertation , you will learn more about these characteristics, not only in the Fundamentals section of Lærd Dissertation, but throughout the articles we have written to help guide you through the choices you need to make when doing a quantitative dissertation. For now, we recommend that you read the next section, Types of quantitative dissertation , which will help you choose the type of dissertation you may want to follow.

Types of quantitative dissertation

Replication, data or theory.

When taking on a quantitative dissertation, there are many different routes that you can follow. We focus on three major routes that cover a good proportion of the types of quantitative dissertation that are carried out. We call them Route #1: Replication-based dissertations , Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations . Each of these three routes reflects a very different type of quantitative dissertation that you can take on. In the sections that follow, we describe the main characteristics of these three routes. Rather than being exhaustive, the main goal is to highlight what these types of quantitative research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you.

Route #1: Replication-based dissertations

Route #2: data-driven dissertations, route #3: theory-driven dissertations.

Most quantitative dissertations at the undergraduate, master's or doctoral level involve some form of replication , whether they are duplicating existing research, making generalisations from it, or extending the research in some way.

In most cases, replication is associated with duplication . In other words, you take a piece of published research and repeat it, typically in an identical way to see if the results that you obtain are the same as the original authors. In some cases, you don't even redo the previous study, but simply request the original data that was collected, and reanalyse it to check that the original authors were accurate in their analysis techniques. However, duplication is a very narrow view of replication, and is partly what has led some journal editors to shy away from accepting replication studies into their journals. The reality is that most research, whether completed by academics or dissertation students at the undergraduate, master's or doctoral level involves either generalisation or extension . This may simply be replicating a piece of research to determine whether the findings are generalizable within a different population or setting/context , or across treatment conditions ; terms we explain in more detail later in our main article on replication-based dissertations [ here ]. Alternately, replication can involve extending existing research to take into account new research designs , methods and measurement procedures , and analysis techniques . As a result, we call these different types of replication study: Route A: Duplication , Route B: Generalisation and Route C: Extension .

In reality, it doesn't matter what you call them. We simply give them these names because (a) they reflect three different routes that you can follow when doing a replication-based dissertation (i.e., Route A: Duplication , Route B: Generalisation and Route C: Extension ), and (b) the things you need to think about when doing your dissertation differ somewhat depending on which of these routes you choose to follow.

At this point, the Lærd Dissertation site focuses on helping guide you through Route #1: Replication-based dissertations . When taking on a Route #1: Replication-based dissertation , we guide you through these three possible routes: Route A: Duplication ; Route B: Generalisation ; and Route C: Extension . Each of these routes has different goals, requires different steps to be taken, and will be written up in its own way. To learn whether a Route #1: Replication-based dissertation is right for you, and if so, which of these routes you want to follow, start with our introductory guide: Route #1: Getting started .

Sometimes the goal of quantitative research is not to build on or test theory, but to uncover the antecedents (i.e., the drivers or causes ) of what are known as stylized facts (also known referred to as empirical regularities or empirical patterns ). Whilst you may not have heard the term before, a stylized fact is simply a fact that is surprising , undocumented , forms a pattern rather than being one-off, and has an important outcome variable , amongst other characteristics. A classic stylized fact was the discovery of the many maladies (i.e., diseases or aliments) that resulted from smoking (e.g., cancers, cardiovascular diseases, etc.). Such a discovery, made during the 1930s, was surprising when you consider that smoking was being promoted by some doctors as having positive health benefits, as well as the fact that smoking was viewed as being stylish at the time (Hambrick, 2007). The challenge of discovering a potential stylized fact, as well as collecting suitable data to test that such a stylized fact exists, makes data-driven dissertations a worthy type of quantitative dissertation to pursue.

Sometimes, the focus of data-driven dissertations is entirely on discovering whether the stylized fact exists (e.g., Do domestic firms receive smaller fines for wrongdoings compared with foreign firms?), and if so, uncovering the antecedents of the stylized fact (e.g., if it was found that domestic firms did receive smaller fines compared with foreign firms for wrongdoings, what was the relationship between the fines received and other factors you measured; e.g., factors such as industry type, firm size, financial performance, etc.?). These data-driven dissertations tend to be empirically-focused , and are often in fields where there is little theory to help ground or justify the research, but also where uncovering the stylized fact and its antecedents makes a significant contribution all by itself. On other occasions, the focus starts with discovering the stylized fact, as well as uncovering its antecedents (e.g., the reasons why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). However, the goal is to go one step further and theoretically justify your findings. This can often be achieved when the field you are interested in is more theoretically developed (e.g., theories of decision-making, consumer behaviour, brand exposure, and so on, which may help to explain why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). We call these different types of data-driven dissertation: Route A: Empirically-focused and Route B: Theoretically-justified .

In the part of Lærd Dissertation that deals exclusively with Route #2: Data-driven dissertations , which we will be launching shortly, we introduce you to these two routes (i.e., Route A: Empirically-focused and Route B: Theoretically-justified ), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback .

We have all come across theories during our studies. Well-known theories include social capital theory (Social Sciences), motivation theory (Psychology), agency theory (Business Studies), evolutionary theory (Biology), quantum theory (Physics), adaptation theory (Sports Science), and so forth. Irrespective of what we call these theories, and from which subjects they come, all dissertations involves theory to some extent. However, what makes theory-driven dissertations different from other types of quantitative dissertation (i.e., Route #1: Replication-based dissertations and Route #2: Data-driven dissertations ) is that they place most importance on the theoretical contribution that you make.

By theoretical contribution , we mean that theory-driven dissertations aim to add to the literature through their originality and focus on testing , combining or building theory. We emphasize the words testing , combining and building because these reflect three routes that you can adopt when carrying out a theory-driven dissertation: Route A: Testing , Route B: Combining or Route C: Building . In reality, it doesn't matter what we call these three different routes. They are just there to help guide you through the dissertation process. The important point is that we can do different things with theory, which is reflected in the different routes that you can follow.

Sometimes we test theories (i.e., Route A: Testing ). For example, a researcher may have proposed a new theory in a journal article, but not yet tested it in the field by collecting and analysing data to see if the theory makes sense. Sometimes we want to combine two or more well-established theories (i.e., Route B: Combining ). This can provide a new insight into a problem or issue that we think it is important, but remains unexplained by existing theory. In such cases, the use of well-established theories helps when testing these theoretical combinations. On other occasions, we want to go a step further and build new theory from the ground up (i.e., Route C: Building ). Whilst there are many similarities between Route B: Combining and Route C: Building , the building of new theory goes further because even if the theories you are building on are well-established, you are likely to have to create new constructs and measurement procedures in order to test these theories.

In the part of Lærd Dissertation that deals exclusively with Route #3: Theory-driven dissertations , which we will be launching shortly, we introduce you to these three routes (i.e., Route A: Testing , Route B: Combining and Route C: Building ), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback .

Choosing between routes

Which route should i choose.

A majority of students at the undergraduate, master's, and even doctoral level will take on a Route #1: Replication-based dissertation . At this point, it is also the only route that we cover in depth [ NOTE: We will be launching Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations at a later date]. To learn whether a Route #1: Replication-based dissertation is right for you, and if so, how to proceed, start with our introductory guide: Route #1: Getting started . If there is anything you find unclear about what you have just read, please leave feedback .

Hambrick, D. C. (2007). The field of management's devotion to theory: Too much of a good thing? Academy of Management Journal , 50 (6), 1346-1352.

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Writing a Quantitative Research Thesis

Profile image of H Johnson Nenty

Research is an exciting adventure which if properly carried out adds richly to the student’s experience, to the school academic prestige and to the society through the new knowledge it creates which could be applied in solving related problems and in other services. Young researchers always encounter problems designing and carrying out their first study which usually is their project, thesis or dissertation. Some who are not properly guided or supervised get frustrated and drop out of their programmes because of these problems. The ideas in this paper which metamorphosed over 25 years of teaching and supervising research, represents an attempt to contribute to the solution of such problems especially for graduate students. It presents elaborately, in very simple language and in five sections, the practical steps that should guide beginning researchers on how to carry out their study and report it.

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Fred Ntedika Mvumbi

A thesis/Dissertation, as one piece of work, should be a text that addresses the issues of the community; all divisions and sub-divisions ought to be interconnected and interrelated to become a process leading to one goal. Thus, the text has threefold dimension. The first is that few people know the underlying principle of a research; that is the wisdom behind the idea, the efforts and the pains of carrying out a research in a particular field of knowledge. The second is a request to students to fall in love and to have passion for the kind of study they want to undertake; this increases the motivation and disposes them to go extra miles for comprehensive and immense discovery where understanding, application and generation of new knowledge take place. The third reason, which is equally important, maybe the most important in writing this text, concerns the organization of the report; in this case the presentation of a thesis/dissertation. Students should be more and more motivated to carry out research in various fields of knowledge, particularly when they have means; and postgraduate students should be increasingly encouraged to take part in research initiatives, for this helps to find new meanings of life.

quantitative thesis writing

Kezang sherab


Paul Allieu Kamara

This book is based on various experiences in research with student, practitioner and teacher in the Rudolph Kwanue University across the World. The difficulties they faced in understanding research as students, the discoveries about what was applicable and inapplicable in the field as practitioner, and development of the ability to effectively communicate difficult concepts in simple language without avoiding technicality and accuracy have become the basis of this book. Research methodology is taught as a supporting subject in several ways in many academic disciplines such as health, education, psychology, social work, nursing, public health, library studies, Business and marketing research. The core philosophical base for this book comes from the conviction that, although these disciplines vary in content, their broad approach to a research enquiry is similar. This book, therefore, is addressed to these academic disciplines. It is true that some disciplines place greater emphasis on quantitative research and some on qualitative research. This Book’s approach to research is a combination of both. Firstly, it is the objective that should decide whether a study be carried out adopting a qualitative or a quantitative approach. Secondly, in real life most research is a combination of both methods. Though they differ in the philosophy that underpins their mode of enquiry, to a great extent their broad approach to enquiry is similar. The quantitative research process is reasonably well structured whereas the qualitative one is fairly unstructured, and these are their respective strengths as well as weaknesses. This Book strongly believed that both are important to portray a complete picture. In addition, there are aspects of quantitative research that are qualitative in nature. It depends upon how a piece of information has been collected and analyzed. Therefore, the Book is strongly believed that a good researcher needs to have both types of skill the Book follow a qualitative–quantitative–qualitative approach to an enquiry. This book, therefore, has been written to provide theoretical information in an operational manner about methods, procedures and techniques that are used in both approaches. Research as a subject is taught at different levels. The book is designed specifically for students who are newcomers to research and who may have a psychological barrier with regard to the subject. The Book have therefore not assumed any previous knowledge on the part of the reader; the Book have omitted detailed discussion of aspects that may be inappropriate for beginners; the Book have used many flow charts and examples to communicate concepts; and areas covered in the book follow a ‘simple to complex’ approach in terms of their discussion and coverage. The structure of this book, which is based on the model developed during my teaching career, is designed to be practical. The theoretical knowledge that constitutes research methodology is therefore organized around the operational steps that form this research process for both quantitative and qualitative research. All the information needed to take a particular step, during the actual research journey, is provided in one place. The needed information is organized in chapters and each chapter is devoted to a particular aspect of that step (see Figure 2.3). For example, ‘Formulating a research problem’ is the first conceptual operational step in the research process. For formulating a ‘good’ research problem, in my opinion, you need to know how to review the literature, formulate a research problem, deal with variables and their measurement, and construct hypotheses. Hence, under this step, there are four chapters. The information they provide will enable you to formulate a problem that is researchable. These chapters are titled: ‘Reviewing the literature’, ‘formulating a research problem’, ‘Identifying variables’ and ‘Constructing hypotheses’. Similarly, for the operational step, step III, ‘Constructing an instrument for data collection’, the chapters titled ‘Selecting a method of data collection’, ‘Collecting data using attitudinal scales’ and ‘Establishing the validity and reliability of a research instrument’ will provide sufficient information for you to develop an instrument for data collection for your study. For every aspect at each step, a smorgasbord of methods, models, techniques and procedures is provided for both quantitative and qualitative studies in order for you to build your knowledge base in research methodology and also to help you to select the most appropriate ones when undertaking your own research. It is my belief that a sound knowledge of research methodology is essential for undertaking a valid study. To answer your research questions, up to Step V, ‘Writing a research proposal’, knowledge of research methods is crucial as this enables you to develop a conceptual framework which is sound and has merits for undertaking your research endeavor with confidence. Having completed the preparatory work, the steps that follow are more practical in nature, the quality of which entirely depends upon the soundness of the methodology you proposed in your research proposal. Statistics and computers play a significant role in research but their application is mainly after the data has been collected. To me, statistics are useful in confirming or contradicting conclusions drawn from simply looking at analyzed data, in providing an indication of the magnitude of the relationship between two or more variables under study, in helping to establish causality, and in ascertaining the level of confidence that can be placed in your findings. A computer’s application is primarily in data analysis, the calculation of statistics, word processing and the graphic presentation of data. It saves time and makes it easier for you to undertake these activities; however, you need to learn this additional skill. This book does not include statistics or information about computers. The first edition of the book incorporates some of the suggestions made by the reviewers, colleagues and students from all levels. There are some major changes in the third edition: The Book have taken a very bold step in breaking down, where possible, the wall between qualitative and quantitative research by describing both methodologies parallel to one another within a common framework. A lot more information on qualitative research has been added and integrated with the current eight-step research model. Now, almost each chapter has a new section that is specifically devoted to information related to qualitative research pertaining to the main theme of the chapter. For example, Chapter 9, ‘Selecting a method of data collection’, now has a section ‘Methods of data collection in qualitative research’ that specifically discusses the major methods of data collection in qualitative studies. Similarly, Chapter 8, ‘Selecting a study design’, has a section ‘Study designs in qualitative research’ that is devoted to the designs dominantly used in qualitative research. As far as possible each chapter also has information on other aspects of qualitative research along with the existing quantitative body of knowledge. More in-depth field examples, based upon actual experiences, have been incorporated to explain procedures and methods. Exercises, a part of the Appendix, have now been thoroughly revised with the expectation that those who are developing a research project can operationalize the theoretical knowledge in an actual situation to evaluate the application of theory to practice in addition to developing their research project. A glossary of technical terms is a new addition to this edition. This will provide students with readily available definitions and meanings of technical terms in one place. Title pages dividing chapters and operational steps have now been redesigned to provide greater clarity as well as informing students in advance what they are expected to learn in a chapter. Also, each chapter has a list of keywords that students are likely to encounter in the chapter. In places the language has been changed to enhance flow, understanding and ease of reading. I am grateful to a number of people who have helped me in the writing of this book. First of all, to my Chancellor Prof. Rudolph Q. Kwanue Sr., who have taught me how to teach research methods? The basic structure of this book is an outcome of the feedback I have received from him over the years. How, and at what stage of the research process, a concept or a procedure should be taught, I have learnt from my students. I thankfully acknowledge their contribution to this book. I am extremely grateful to a friend and colleague, Dr. Ehi Eric, whose efforts in editing the first edition were of immense help. The book would not have come to its present stage without his unconditional help. I also thank Professor Joe, a friend and colleague, for his continuous encouragement and support.

khadidja Hammoudi

International Journal of Research

Tahani Bsharat

andrea azures

• Identify the interest • Study the interest • Identify possible interesting dimension or problem in the area of interest • Formulate the initial research question in response to the identified dimension or problem in the area of interest • Make an initial research to gain further understanding of the initial research question • Incorporate new findings to the initial research question • Assess whether the research question remains relevant to the area of interest by studying the current state of literature All research inquiry starts from a simple identification of an area on which someone is interested in. A student researcher could be interested in the current state of information and communication technology, educational system, career tracks, use of social media, effects of personal issues on academic performance and impact of social background on education opportunities to name a few. Interests are either developed through life path, innate (such as inclination to music and other performing arts) or ideologically acquired. The identification of interest as the first stage of research inquiry is only reasonable since demands of the process requires undivided commitment and human nature has it that we struggle doing things when it is outside our area of interest.

WORLD EDUCATION CONNECT Multidisciplinary e-Publication


It provides any reader procedures in making research questions that are cursorily discussed in graduate school, especially during thesis writing. The author hopes that this module will facilitate students' fluid making of research questions which form part of the essentials in writing the entire research output.

Munyaradzi Moyo

Essential Tremor

So you’re a student at university looking to do research and write a dissertation (thesis)? This book is for you. It’s an essential guide to the research process covering all stages from planning to doing to writing up and proofing. The book also has a unique section on publishing your dissertation for those who wish to push their academic career along. Unlike other books, it does not assume that you have infinite time and resources to conduct your research. It recognises that at this level you probably have six months or less to finish the dissertation and gives practical advice on which studies are feasible and which are not. The book gets on top of the research terminology by giving concise, working definitions of the key terms, which will appeal to international students. With over 30 years of experience in leading and teaching research in a variety of fields, Dr Michael Cribb has pulled together all his wisdom and knowledge in one book to help guide students through their first big research project.


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Quantitative Thesis Statement

A quantitative thesis statement outlines the main objective and approach of a research study conducted using quantitative research methods.


Table of Contents

A quantitative thesis statement outlines the main objective and approach of a research study or essay conducted using quantitative research methods. It highlights the researcher’s purpose how he is going to collect and analyze data to measure abstract concepts such as relationships, patterns, or trends within a specific population or sample.

A quantitative thesis statement identifies the context, the variables of interest, and the type of statistical analysis. It also determines the objective and empirical nature of the research, focusing on measurable outcomes and the generalizability of findings.

This thesis statement, therefore, serves as a guide for designing the study, selecting appropriate data collection methods, and conducting statistical analyses to answer research questions or test hypotheses based on quantitative data.

Features of a Quantitative Thesis Statement

Types of quantitative thesis statements.

  • “This quantitative study provides a comprehensive description and analysis of the demographic characteristics, socioeconomic factors, and educational attainment of a specific population, offering insights into the factors influencing educational outcomes.”
  • “This comparative quantitative research examines and compares the effectiveness of two different teaching methods in improving elementary school students’ mathematical problem-solving skills, aiming to identify the most effective instructional approach.”
  • “This correlational quantitative study explores the relationship between social media usage and self-esteem levels among adolescents, aiming to determine whether higher levels of social media engagement are associated with lower self-esteem scores.”

Process of Writing Quantitative Thesis Statement

By following these steps, a quantitive thesis statement could be developed to have a strong focus on the topic. It ensures that your research objective, variables of interest, research design, data collection methods, and data analysis techniques are effectively communicated and aligned with the principles of quantitative research.

Examples of Quantitative Thesis Statement

Topic: effects of exercise on blood pressure, topic: customer satisfaction in e-commerce,  topic: impact of financial education on savings behavior.

These examples illustrate how quantitative thesis statements outline the research topic and focus on the sty type. They set the direction for research aim to examine topics, using numerical data in various fields.

Suggested Readings

  • Butler, Linda. Longman Academic Writing Series 1: Sentences to Paragraphs . Pearson, 2013.
  • Hogue, Ann. Longman Academic Writing Series 2: Paragraphs . Pearson, 2013.
  • Meyers, Alan. Longman Academic Writing Series 5: Essays to Research Papers . Pearson, 2014.
  • Nadell, Judith, et al. The Longman Writer: Rhetoric, Reader, Research Guide, and Handbook . Pearson, 2013.
  • Oshima, Alice, and Ann Hogue. Longman Academic Writing Series 3: Paragraphs to Essays . Pearson, 2014.
  • Oshima, Alice, and Ann Hogue. Longman Academic Writing Series 4: Essays . Pearson, 2014.
  • Shields, MunLing. Essay Writing: A Student’s Guide . Pearson, 2016.
  • Acheson, Katherine O. Writing Essays About Literature: A Brief Guide for University and College Students . Cengage, 2010.
  • Griffith, Kelley. Writing Essays About Literature: A Guide and Style Sheet . Cengage, 2018.

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  • Essay Writing, Objectives, and Key Terms in Essay Writing
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The Plagiarism War Has Begun

Claudine Gay was taken down by a politically motivated investigation. Would the same approach work for any academic?

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When the conservative authors Christopher Rufo and Christopher Brunet accused Harvard’s Claudine Gay last month of having committed plagiarism in her dissertation, they were clearly motivated by a culture-war opportunity . Gay, the school’s first Black president—and, for some critics , an avatar of the identity-politics bureaucracy on college campuses—had just flubbed testimony before Congress about anti-Semitism on campus. She was already under pressure to resign. Evidence of scholarly misconduct was just the parsley decorating an anti-wokeness blue-plate special.

But soon enough, the integrity of Gay’s research became the central issue in a scandal that appears to have led to her resignation on Tuesday. It turned out that the New York Post had gone to Harvard in October with separate allegations of plagiarism in her published articles; and then, earlier this week, still more examples were produced. “My critics found instances in my academic writings where some material duplicated other scholars’ language, without proper attribution,” Gay wrote in a New York Times op-ed shortly after she’d stepped down. She acknowledged having made “citation errors,” and has in recent weeks requested a handful of formal corrections to published works. Still, she avowed in her op-ed, “I have never misrepresented my research findings, nor have I ever claimed credit for the research of others.”

I haven’t either—at least as far as I know. For the past couple of decades, I’ve been a professor at elite research universities; I’ve published 150 or so scholarly articles and conference papers, and 10 books. Might any of these contain the sort of improprieties that led to a university president’s downfall? I felt sure the answer was no, but the question lingered in my mind and was echoed in the claims of the other academics who have lately rushed to Gay’s defense. Some people argued that her citation practices were not egregious or even that they represent business as usual . “If that’s going to count as plagiarism,” one professor wrote , “all writers are vulnerable to it, and anyone who writes anything controversial can expect to suffer for it.” If all writers were vulnerable, was I?

A version of this question lies at the core of many disagreements over Gay’s departure. Does her now-acknowledged sloppiness really stand out among her peers? What would happen if the same degree of scrutiny were applied to the work of any other scholar? In short: Is the baseline rate of these transgressions in academia high or low?

I had no idea. So, as a simple experiment, I decided to launch a targeted plagiarism investigation of myself to see if similar scrutiny of my dissertation, performed for no good reason, could deliver similar results. Perhaps I, too, am guilty of some carelessness that might be taken—maybe out of context, perhaps in bad faith—as a sign of scholarly malfeasance. I promised my editor ahead of time that I’d come clean about whatever I found, reporting any misdeeds to my university’s research-integrity office and facing applicable consequences.

I’ve had a comfortable, 20-year career in academia; perhaps this would be the end of it.

How to do it? The instances of copying in Claudine Gay’s dissertation that I’ve seen are not the kind that jump right out at you, but they are near-direct quotations of other scholars’ work, presented in the form of paraphrases. Brunet and Rufo appear to have reviewed her roughly 200-page text systematically, and I wanted to hew as close to their methods as possible. When I reached out to ask how they’d performed their analysis, Brunet said “No comment” and Rufo didn’t answer. (Isabel Vincent, the Post reporter who had received separate plagiarism allegations from an anonymous source in October, also declined to offer any details.)

I suspected that the probe had been carried out using one of the several plagiarism-detection software packages that are now available for private use. Jonathan Bailey, a copyright and plagiarism consultant who also runs the plagiarism-news website Plagiarism Today , told me that the analysis of Gay’s dissertation is likely to have been carried out with iThenticate, an online service run by the same company that operates the popular student-oriented plagiarism detector Turnitin. “When dealing with cases of research integrity, the best tool is iThenticate,” he said. Turnitin has cooperative agreements with academic publishers, which allows the software to check a document for text shared with sources that would otherwise be hidden behind paywalls or in library archives. “It’s a pricey tool, but in this space, it’s easily the best one out there,” Bailey added. (Turnitin didn’t respond when I asked whether iThenticate might have been used to investigate Gay’s work.)

Tyler Austin Harper : The real Harvard scandal

On December 29, I downloaded my thesis from the institutional repository at UCLA, where I had earned my doctorate, signed up for an iThenticate account, and arranged for The Atlantic to pay the standard rate of $300 to analyze my dissertation’s 68,038 words.

Then I started to wonder what the hell I was doing. I had fairly strong confidence in the integrity of my work. My dissertation is about how to do cultural criticism of computational works such as software, simulations, and video games—a topic that was novel enough in 2004, when I filed it, that there wasn’t a ton of material for me to copy even if I’d wanted to. But other factors worked against me. Like Gay, who submitted her dissertation in 1997, I wrote mine during a period when computers were commonplace but the scholarly literature wasn’t yet easily searchable. That made it easier for acts of plagiarism, whether intended or not, to go unnoticed. Was it really worth risking my career to overturn those rocks?

On the principle that only a coward hides from the truth, I pressed the “Upload” button on the iThenticate website, waited for the progress bar to fill, then closed my laptop. When I came back for my report the next day, it felt a little like calling up my doctor’s office for the news, possibly bad, about whatever test they had run on my aging, mortal body. I took a breath and clicked to see my result.

It was 74. Was I a plagiarist? This, apparently, was my answer. Plagiarism isn’t normally summed up as a number, so I didn’t know quite how to respond. It seemed plausible that 74 might be a good score. Turns out it wasn’t: The number describes what percentage of a document’s material is similar to text from its database of reference works. My result—my 74—suggested that three-quarters of my dissertation had been copied from other sources. “What the heck?” I said aloud, except I didn’t say “heck . ”

This seemed wrong to me. I was there when I wrote the thing, and I’d have remembered copying seven out of every 10 words from other sources, even 20 years later. Turns out it was wrong. I wrote the dissertation from 2002 to 2004, and the plagiarism software checks a work against whatever it finds—even if the compared text was published later. As Bailey told me, “iThenticate doesn’t detect plagiarism. It detects copied or similar text.” From there, Bailey said, “You have to do a lot of manual work.”

So I started doing the manual work.

The first, most obvious source of my plagiarism score was the fact that I’d subsequently published a book based on my dissertation (a common practice in academia), which itself appeared in many forms throughout the iThenticate database. In other words, the software suggested that I’d plagiarized my dissertation from a future version of myself. But to confirm each of these false-positives, a plagiarism sleuth like myself has to go through the report and click on each allegedly copied source individually.

Once I’d excluded the literal copies of (and commentaries upon) my own work from the analysis, my similarity index dropped to 26 percent. Phew! But iThenticate still listed 288 possible sources of copying. Exonerating myself was going to take a while.

I noticed that a lot of the matches were citations of other books, articles, or materials. iThenticate has a checkbox to “Exclude bibliography,” so I ticked it. Now my score was down to 23. Other matches were literal quotes, which I had quoted with footnotes to their sources. Ticking another checkbox, “Exclude quotes,” brought my similarity index to 9.

Most of the remaining matches were boilerplate chaff. The institutional-archive copy of my dissertation had added a line to the footer of each page, “Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.” iThenticate had matched a dozen or more other dissertations with the same notice, including “Pathogenesis of Bartonella Henselae in the Domestic Cat” and “Hyperdeprivation and Race-Specific Homicide, 1980–1990.” Laboriously excluding those and similar materials left me with 87 potential instances of plagiarism, and a similarity index of 3.

I carefully reviewed the matches that remained. Some were just citations of my work. Others were appropriately footnoted quotations that I’d used, but that iThenticate hadn’t construed as such because they were indented in the text. I also had to click through titles or other proper names that were showing up as copied phrases. Bibliographic citations that the filter hadn’t caught came up too. So did a lot of textual noise—phrases such as to preserve the , which appeared in similar patterns across unrelated materials.

After a couple of hours of work, I still had 60 individual entries to review, each requiring precision mousing to assess and exclude. Determined to see if I’d copied any original work according to the software, I persisted—after all, some of the instances of plagiarism that had sunk Claudine Gay were measured in the tens of words. But not one single match that iThenticate had found amounted to illegitimate copying. In the end, my dissertation’s fraud factor had dropped from 74 percent to zero.

The story I’ve told above has been fact-checked by The Atlantic , although the checking did not replicate the several hours of manual verification. And I realize that on some level I’m just asking you to trust me when I report that the work I analyzed does not include uncited text from other authors. I can only hope the same is true of all my other published research.

Does this imply that Gay’s record is unusual among professors? Not in and of itself. Her field of quantitative social science may have different standards for textual reference. The sciences are more concerned with the originality of research findings than the descriptions of experiments. But it does at least refute the case that this was nothing more than academic jaywalking, or, in its purest straw-man form, that everybody does it .

But even if there’s substance to this Harvard scandal, I’m more afraid of what it may portend. The result of my experiment brought me no relief, only a new anxiety. The very ease of the self-investigation, conducted at a relatively modest cost with the help of powerful technology, hints at how a full-bore plagiarism war could end up playing out. In her New York Times op-ed, Gay admitted that she’d been wrong to copy text without attribution. She also characterized the campaign against her as part of a coordinated attempt to undermine educational institutions and their leaders. On both counts, she was right.

Similar probes are sure to follow. Business Insider has already published allegations that Neri Oxman, a former professor at MIT and the wife of the Harvard donor and vociferous Gay critic Bill Ackman, plagiarized in her dissertation, too. (In a post on X, former Twitter, Oxman acknowledged some improper citations and wrote, “I regret and apologize for these errors.”) And after Gay resigned, Rufo announced that he would contribute $10,000 to a “‘plagiarism hunting’ fund” meant to “expose rot” and “restore truth.” That’s enough dough to test a few dozen dissertations or a few hundred articles with iThenticate, and their authors wouldn’t be able to dismiss the findings solely as the product of “bad faith.” I suppose that’s good news for companies such as Turnitin. (Academics may be getting their just deserts for subjecting students to constant surveillance with the company’s student-focused plagiarism-detection software.)

Read: The first year of AI college ends in ruin

If a plagiarism war does break out, I suspect that universities and their leaders will end up fighting it defensively, with bureaucratic weapons directed inward. “If I were a school looking to appoint a new president,” Bailey told me, “I’d consider doing this kind of analysis before doing so.” To run standard plagiarism checks on top brass may end up seeming reasonable, but with that policy in place, what’s to stop beleaguered and embattled administrators from insisting on the same—best practices!—before any faculty hire or award of tenure? Academic publishers could demand iThenticate-style checks on all submissions. Legislatures could demand plagiarism-assessment reports from state colleges, with a special focus on fields that are purportedly “woke.”

Plagiarism assessment, with automated accusations and manual rebuttals, could become a way of life, a necessary evil brought about by, yes, the bad actors who seek to undermine educational institutions and their leaders. That isn’t likely to improve academic work, but it would certainly make higher education worse.

This story previously stated that Neri Oxman is a professor at MIT. Business Insider reports that, according to the university, Oxman left in 2021.

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Claudine Gay's resignation highlights the trouble with regulating academic writing

Vanessa Romo

Vanessa Romo

Ayana Archie

quantitative thesis writing

Claudine Gay speaks during commencement ceremonies at Harvard University in May. Gay resigned as Harvard's president Tuesday amid plagiarism accusations. Steven Senne/AP hide caption

Claudine Gay speaks during commencement ceremonies at Harvard University in May. Gay resigned as Harvard's president Tuesday amid plagiarism accusations.

Harvard University President Claudine Gay resigned her post on Tuesday following controversial congressional testimony over campus antisemitism and amid mounting allegations of plagiarism that have plagued the once-rising star of academia in recent weeks.

Gay's resignation underscores the intense scrutiny confronting university presidents who are the public faces of the institutions they lead.

Gay is not the first head of an academic institution unseated by allegations of plagiarism. Marc Tessier-Lavigne resigned last year as Stanford University's president after an investigation opened by the board of trustees found that several academic reports he authored contained manipulated data. However, a report commissioned by the board concluded that Tessier-Lavigne did not have a big role in publishing the facts in question on the reports he co-authored, or had actual knowledge of any manipulation of research data for the reports in which he was the principal author.

In 2021, Robert Caslen resigned as president of the University of South Carolina after plagiarizing part of a speech. Caslen, a retired Army lieutenant general, apologized afterward. "I was searching for words about resilience in adversity and when they were transcribed into the speech, I failed to ensure its attribution. I take full responsibility for this oversight."

Here's the latest fallout at Harvard, MIT and Penn after the antisemitism hearing

Here's the latest fallout at Harvard, MIT and Penn after the antisemitism hearing

Harvard affirms President Claudine Gay will not step down over antisemitism testimony

Harvard affirms President Claudine Gay will not step down over antisemitism testimony

Gay, who took office in July, made the leap from Harvard professor to president in about 16 years, a trajectory that The Harvard Crimson student newspaper described as " meteoric ." But her downfall raises questions about how people in such high-profile positions can find themselves facing such charges in an age when advanced technology so easily enables detection of alleged cases of plagiarism.

Experts additionally say improved technology could bring forth more alleged transgressions yet to be unearthed from past works.

So how does a sought-after academic star end up here?

Dave Tomar, a self-described "professional cheat" who spent about a decade ghostwriting academic papers for undergraduates and postdoctoral students, said it's easy to understand how Gay's writing went undetected for so long.

"I think 20 years ago, the alarm bells weren't really raised as much," Tomar, author of The Complete Guide to Contract Cheating in Higher Education , told NPR. "It's a no-brainer to me that she was just sort of right ahead of the curve of detection at the time."

That was largely due to the absence of plagiarism-detection technology, he said, noting that the 1990s and even early 2000s were the nascent days of the internet. Research was still conducted in physical libraries using card catalogs. It wasn't unusual for papers to be written out by hand and then typed into a computer or word processor. And the few software tools that eventually became available back then were nowhere near as sophisticated as what exists today.

Without the plagiarism-detection software programs that are now in use, professors were encouraged to use their intuition if something felt off with an assignment. They were urged to hold one-on-one meetings to help them assess a student's grasp of the material.

Tomar began his career as a professional cheater during this pre-internet time. "It was really, really easy to get away with Googling and cutting-and-pasting before educators were really hip to it," he recalled.

A college student created an app that can tell whether AI wrote an essay

A college student created an app that can tell whether AI wrote an essay

Still, Sarah Elaine Eaton, author of Plagiarism in Higher Education : Tackling Tough Topics in Academic Integrity , says allegations of plagiarism are still largely handled manually.

"The software is not foolproof — it still requires human intervention," she said.

Additionally, Ph.D. dissertations go through several steps of verification, including being reviewed by a supervisor, an examination committee and peers.

"Supervisors should bear some responsibility for mentoring and shepherding the student to ensure that the quality of the work that they produce is high," Eaton said.

"And the fact that none of this was found until now, the timing is pretty curious," she added.

This bipartisan Senate duo wants to end legacy college admissions

This bipartisan Senate duo wants to end legacy college admissions

The irony, Tomar said, is that Gay's alleged failings are likely only now coming to light because of the endless amounts of data that gets fed into artificial intelligence programs, such as ChatGPT.

He predicts a slew of academic leaders will likely be outed in similar fashion. And while he feels little sympathy for those who are caught having violated an institution's policies, he says that's the wrong thing on which to focus.

"We may be able to retroactively discover what somebody did in the 1990s. But ought we not to be slightly more concerned about what the person who was going to graduate next year is doing?" he asked.

Harvard has not called it plagiarism in Gay's case

It has been a tumultuous episode for Harvard, whose highest governing board, known as the Harvard Corporation, has since noted that Gay has acknowledged " missteps ." In a Dec. 12 statement in which officials addressed the plagiarism charges, the university said an initial review of Gay's published writings "revealed a few instances of inadequate citation."

The corporation added: "While the analysis found no violation of Harvard's standards for research misconduct, President Gay is proactively requesting four corrections in two articles to insert citations and quotation marks that were omitted from the original publications." The articles date back to 2001 and 2017 .

Gay will remain at Harvard as a professor.

In December, right-wing website The Washington Free Beacon reported that it found problems in four of Gay's published papers, including her 1997 dissertation.

American Council on Education president discusses limits on free speech on campuses

Gay, who was the first person of color and the second woman to hold the post at Harvard, has had a spectacular rise throughout her career and in her field of political science. Even in the early days of her career, she was repeatedly courted by the nation's most prestigious institutions.

In her resignation letter, Gay defended her academic record.

"Amidst all of this, it has been distressing to have doubt cast on my commitments to confronting hate and to upholding scholarly rigor — two bedrock values that are fundamental to who I am," she wrote, "and frightening to be subjected to personal attacks and threats fueled by racial animus."

Gay moved to Harvard after being lured away from a tenured position at Stanford University. In her 16-year journey from Harvard professor to president, Gay, who is Black and the daughter of Haitian immigrants, has been praised by colleagues, bosses and students for her originality of thought, rigor and devotion to data.

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Read Claudine Gay’s resignation letter.

Gay resigned as Harvard’s president on Tuesday after a new round of plagiarism accusations.

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The Harvard seal in Harvard Yard.

By The New York Times

  • Jan. 2, 2024

Dear Members of the Harvard Community,

It is with a heavy heart but a deep love for Harvard that I write to share that I will be stepping down as president. This is not a decision I came to easily. Indeed, it has been difficult beyond words because I have looked forward to working with so many of you to advance the commitment to academic excellence that has propelled this great university across centuries. But, after consultation with members of the Corporation, it has become clear that it is in the best interests of Harvard for me to resign so that our community can navigate this moment of extraordinary challenge with a focus on the institution rather than any individual.

It is a singular honor to be a member of this university, which has been my home and my inspiration for most of my professional career. My deep sense of connection to Harvard and its people has made it all the more painful to witness the tensions and divisions that have riven our community in recent months, weakening the bonds of trust and reciprocity that should be our sources of strength and support in times of crisis. Amidst all of this, it has been distressing to have doubt cast on my commitments to confronting hate and to upholding scholarly rigor — two bedrock values that are fundamental to who I am — and frightening to be subjected to personal attacks and threats fueled by racial animus.

I believe in the people of Harvard because I see in you the possibility and the promise of a better future. These last weeks have helped make clear the work we need to do to build that future — to combat bias and hate in all its forms, to create a learning environment in which we respect each other’s dignity and treat one another with compassion, and to affirm our enduring commitment to open inquiry and free expression in the pursuit of truth. I believe we have within us all that we need to heal from this period of tension and division and to emerge stronger. I had hoped with all my heart to lead us on that journey, in partnership with all of you. As I now return to the faculty, and to the scholarship and teaching that are the lifeblood of what we do, I pledge to continue working alongside you to build the community we all deserve.

When I became president, I considered myself particularly blessed by the opportunity to serve people from around the world who saw in my presidency a vision of Harvard that affirmed their sense of belonging — their sense that Harvard welcomes people of talent and promise, from every background imaginable, to learn from and grow with one another. To all of you, please know that those doors remain open, and Harvard will be stronger and better because they do.

As we welcome a new year and a new semester, I hope we can all look forward to brighter days. Sad as I am to be sending this message, my hopes for Harvard remain undimmed. When my brief presidency is remembered, I hope it will be seen as a moment of reawakening to the importance of striving to find our common humanity — and of not allowing rancor and vituperation to undermine the vital process of education. I trust we will all find ways, in this time of intense challenge and controversy, to recommit ourselves to the excellence, the openness, and the independence that are crucial to what our university stands for — and to our capacity to serve the world.

Sincerely, Claudine Gay

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  • How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

  • An independent variable is something the researcher changes or controls.
  • A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

  • The relevant variables
  • The specific group being studied
  • The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

  • H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
  • H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

Prevent plagiarism. Run a free check.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved January 11, 2024, from https://www.scribbr.com/methodology/hypothesis/

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‘brilliant’ italian mobster serving life earns degree in prison — with 170-page thesis confessing to three unsolved murders.

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He graduated magnum cum laude.

An incarcerated Italian mobster is being hailed as a “brilliant” honor student after writing a 170-page thesis paper based on his life of crime — in which he confessed to three unsolved murders.

Catello Romano, 33, earned a sociology degree with the dissertation while serving a life sentence at the Calabrian prison of Catanzaro.

He was convicted in the 2009 murder of a Naples councilman, Luigi Tommasino, and other crimes, according to El País , a Spanish newspaper.

“My name is Catello Romano. I am 33 years old, and I have been in prison for almost half my life, 14 consecutive years,” the thesis began, according to the outlet.

“I have committed horrendous crimes and have been convicted of several Camorra murders. What follows is my criminal history.”

Catello Romano

The gangster-turned-undergraduate said his first murder victims were rising rival mobster Carmine D’Antuono, and Federico Donnarumma — a man who was only rubbed out because he was conversing with D’Antuono at the time of the assassination.

The 2008 double murder was “the most violent, traumatic and irreparable event” of Romano’s life and left a “hole” in his “soul,” the honor student mafioso wrote.

He also copped to the previously unsolved slaying of rival mafioso Nunzio Mascolo the same year.

“Although I cannot prove it, I am sure that he did nothing wrong to deserve death,” the repentant killer lamented.

The thesis recounts Romano’s non-criminal family history as the prisoner reflected on what made him gravitate to “the allure of crime.”


“I have intimately known misery, and the negative influence it can have, since my childhood,” he wrote, arguing that the mafia is an attractive family “institution” for people who grew up on the margins of society.

“With them, I built my new alternative identity as a tough guy, as a mask with which to hide my inability to accept my fragility as a teenager and as a way of surviving in a violent and extreme world,” he wrote.

For Romano, violence became “a language and a way of claiming respect and social recognition” — something, he admitted, he was not proud of.

The paper ultimately sought to understand “the criminal phenomenon” and contribute “to its possible prevention.”

“I am convinced that words are important and this autoethnographic text aims to change the world around us,” he wrote, according to El Pais.

Catello Romano's mugshot.

Romano’s admission to three unprosecuted killings, however, has now drawn the attention of prosecutors — who are weighing reopening the cases and led to him being transferred to a maximum-security prison in Padua, the outlet reported.

Meanwhile, Catanzaro University professor and sociologist Charlie Barnao, who was Romano’s thesis advisor described the mobster as a “brilliant student, who has gotten very good grades throughout his course of study.”

“He has recounted in detail circumstances that will have consequences; he was very determined to expose that in his thesis,” the professor, who has taught Sociology of Survival to the imprisoned for five years, said.

“He has put his life in order once and for all and organized the episodes of his life to analyze them through a sociological research method, which has also had a kind of therapeutic function.”

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    Step 1: Start with a question Step 2: Write your initial answer Step 3: Develop your answer Step 4: Refine your thesis statement Types of thesis statements Other interesting articles Frequently asked questions about thesis statements What is a thesis statement? A thesis statement summarizes the central points of your essay.

  12. Quantitative research

    Moderating Variable(s) is a variable that affects the strength of the relationship between the independent and dependent variable. For example, if you looked at the relationship between personality similarity in friendships (independent variable) and perceived friendship satisfaction (dependent variable), it might be that age is a moderating variable - e.g. the older you are, the weaker the ...

  13. PDF Writing Chapter 3 Chapter 3: Methodology

    Instruments. This section should include the instruments you plan on using to measure the variables in the research questions. (a) the source or developers of the instrument. (b) validity and reliability information. •. (c) information on how it was normed. •. (d) other salient information (e.g., number of. items in each scale, subscales ...

  14. Quantitative Dissertations

    Types of quantitative dissertation Replication, Data or Theory. When taking on a quantitative dissertation, there are many different routes that you can follow. We focus on three major routes that cover a good proportion of the types of quantitative dissertation that are carried out. We call them Route #1: Replication-based dissertations, Route #2: Data-driven dissertations and Route #3 ...

  15. PDF A Sample Quantitative Thesis Proposal

    A Sample Quantitative Thesis Proposal Prepared by Mary Hayes NOTE: This proposal is included in the ancillary materials of Research Design with permission of the author. If you would like to learn more about this research project, you can examine the following thesis that resulted from this work: Hayes, M. M. (2007).

  16. (PDF) Writing A Quantitative Research Proposal / Thesis

    1. Introduce the overall methodological approach. 2. Indicate how the approach fits the overall research design. 3. Describe the specific methods of data collection. 4. Explain how you intend to ...

  17. [PDF] Writing a Quantitative Research Thesis

    The ideas in this paper which metamorphosed over 25 years of teaching and supervising research, represents an attempt to contribute to the solution of such problems especially for graduate students. It presents elaborately, in very simple language and in five sections, the practical steps that should guide beginning researchers on how to carry ...

  18. PDF 3. Writing a Quantitative Research Proposal / Thesis

    Details: Read: Guidelines on writing a first quantitative academic article 22 ... In the Thesis: Writing Conclusion 25 . 26 . KURSUS PERSEDIAAN IJAZAH DOKTOR FALSAFAH

  19. Qualitative vs. Quantitative Research

    Revised on June 22, 2023. When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research Quantitative research is expressed in numbers and graphs.

  20. Writing a Quantitative Research Thesis

    Writing a Quantitative Research Thesis H Johnson Nenty Research is an exciting adventure which if properly carried out adds richly to the student's experience, to the school academic prestige and to the society through the new knowledge it creates which could be applied in solving related problems and in other services.

  21. Quantitative Thesis Statement

    A quantitative thesis statement outlines the main objective and approach of a research study or essay conducted using quantitative research methods. It highlights the researcher's purpose how he is going to collect and analyze data to measure abstract concepts such as relationships, patterns, or trends within a specific population or sample.

  22. The Plagiarism War Has Begun

    On December 29, I downloaded my thesis from the institutional repository at UCLA, ... Her field of quantitative social science may have different standards for textual reference. The sciences are ...

  23. Claudine Gay's resignation highlights the trouble with regulating ...

    Dave Tomar, a self-described "professional cheat" who spent about a decade ghostwriting academic papers for undergraduates and postdoctoral students, said it's easy to understand how Gay's writing ...

  24. What Is a Dissertation?

    A dissertation is a long-form piece of academic writing based on original research conducted by you. It is usually submitted as the final step in order to finish a PhD program. Your dissertation is probably the longest piece of writing you've ever completed.

  25. Claudine Gay: What Just Happened at Harvard Is Bigger Than Me

    Dr. Gay is a former president of Harvard University, where she is a professor of government and of African and African American studies. On Tuesday, I made the wrenching but necessary decision to ...

  26. Read Claudine Gay's Resignation Letter

    Read Claudine Gay's resignation letter. Gay resigned as Harvard's president on Tuesday after a new round of plagiarism accusations. The Harvard campus last month. Adam Glanzman for The New ...

  27. How to Write a Strong Hypothesis

    Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.

  28. Mobster Catello Romano earns degree in prison with thesis confessing to

    Catello Romano, 33, earned a sociology degree after writing a 170-page thesis paper based on his life of crime -- in which he confessed to three unsolved murders.