example of quantitative thesis

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How to Use Quantitative Data Analysis for a Strong Thesis Statement

Professionals who aspire to ascend to the C-suite or other executive positions often decide to return to school to enhance their academic qualifications. Earning a doctoral degree based on quantitative data analysis is an excellent way to accomplish these goals.

While earning a doctoral degree, you will be required to write a dissertation. A dissertation is a book-length manuscript that explains the problem addressed, processes and findings of original research you conduct. One of the steps you’ll take to complete your dissertation is defining a research topic and writing a strong thesis statement to clearly explain the particular focus of your research. This guide discusses the application of quantitative data analysis to your thesis statement.

Writing a Strong Thesis Statement

In a relatively short essay of 10 to 15 pages, the thesis statement is generally found in the introductory paragraph. This kind of thesis statement is also typically rather short and straightforward. For example, if you’re writing a paper on the differences between corporate charitable donation strategies, your thesis statement might read something like this: It is not known what the differences in charitable donation strategies are in four U.S. corporations.

For a lengthy dissertation, however, the thesis statement may be found throughout the entire introduction or first chapter of the dissertation. You’ll also use your thesis statement in your dissertation proposal.

A dissertation proposal is a 70 to 150 page paper that outlines the research you intend to undertake, the methods you’ll employ to conduct it, and the questions you plan to answer or theories you wish to test. The purpose of a dissertation proposal is to convince your dissertation committee and department to approve your chosen dissertation topic. Although you should have a preliminary idea of your thesis statement when you write your proposal, keep in mind that you may refine it over time. In other words, your thesis statement might look quite different when you finish your dissertation than when you first wrote your proposal, and that’s perfectly fine.

Understanding Quantitative Data Analysis

Quantitative data analysis may sound like a mouthful, but it’s actually quite simple. It refers to the statistical analysis of numerical data. Thus, it contrasts with qualitative data analysis, which refers to the analysis of non-numerical data.

Note that it’s possible to conduct a quantitative analysis of qualitative data; however, you must first convert such qualitative data into numerical form without losing their meaning. For instance, if you’re studying the effects of various colors of wall paint on office workers’ productivity, you might code the color orange ‘1’, the color yellow ‘2’ and so on. You would then be able to perform a quantitative analysis.

All doctoral students who are completing a quantitative-based degree program will conduct quantitative research. This type of data analysis is useful for the following types of research: 

• Testing a scientific hypothesis, such as a hypothesis about the incidence of a specific disease in a certain group of people 
• Analyzing the relationships among variables, such as the difference between the availability of free lunch programs and the duration of students’ attention spans in the afternoon 
• Measuring the differences between groups or variables, such as the relationship between popularity of various employee development programs and employee satisfaction

Before you can write a strong thesis statement for your dissertation, you’ll need to know exactly what you plan to study and which questions you hope to answer through your research. Your thesis statement should also acknowledge your use of quantitative research methods.

A Quick Look at Quantitative Research Methods

Although your main thesis statement will likely include just a few sentences, you’ll need to provide supporting details. When writing your dissertation proposal, you’ll also need to offer some basic information about the quantitative research methods you plan to use for your work. Similarly, when writing your introduction, you will need to explain how you conducted your research and how you completed your quantitative data analysis because these crucial details will substantiate your main thesis statement.

Here’s a quick look at the main types of quantitative research methods : 

• Descriptive research: After identifying a variable, this type of research describes its current status. Often, descriptive research requires very large sample sizes and is used to describe a population. 
• Correlational research: This type of quantitative research explores the relationship between two or more variables. 
• Causal-comparative: This type of research seeks to establish the differences in variable(s) between groups. 
• Quasi-experimental research: This type of research seeks to establish a cause-effect relationship between variables. 
• Experimental research: Employing the scientific method, experimental research determines cause–effect relationships between and among variables by strictly controlling for all variables except one independent variable.

After you have conducted your research and analyzed your findings, you can compare them to the original thesis statement you outlined in your dissertation proposal. From there, you can reflect on your quantitative data analysis and compare your findings to those of other researchers.

Applying Quantitative Data Analysis to Your Thesis Statement

It’s difficult—if not impossible—to flesh out a thesis statement before beginning your preliminary research. If you’re at the beginning stages of your dissertation process and are working to develop your dissertation proposal, you will first need to conduct a brief but broad literature review. You’ll conduct a more in-depth literature review after your topic is approved.

Based on your findings from the extant literature, you can begin to formulate your own original ideas regarding your topic. For instance, let’s say your dissertation focuses on the ways in which secondary school athletics affect students. Scholars have already produced much research about the benefits of sports for students, but you might notice research gaps in certain areas of the field. For example, what effects do sports have on students after graduation? Do years in sports relate to amount of soft skills in students?

You can begin to shape your thesis statement based on the questions that arise during your preliminary literature review. For instance, you may find existing research that indicates high school sports teach students to work cooperatively and communicate effectively with their peers.

Of course, because you’re writing a quantitative, data-driven dissertation, you will need to express these ideas numerically. Therefore, your thesis statement might look like this: “High school students who play sports are more likely to develop teamwork skills and develop solid communication abilities than high school students who do not play sports. My dissertation research will examine if these benefits persist long after students graduate.” As the above discussion and examples demonstrate, the key to writing a strong thesis statement is to substantiate your assertions with concrete statistics using your own quantitative data analysis.

Grand Canyon University’s College of Doctoral Studies  is pleased to offer a wide variety of doctorate degrees, including the Doctor of Education in Organizational Leadership: Health Care Administration (Quantitative Research) degree, the Doctor of Business Administration: Data Analytics (Quantitative Research) program and more. Click on Request Info above to begin planning your doctoral education today.

The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.

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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.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “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

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [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.

Best Practice: How to Write a Dissertation or Thesis Quantitative Chapter 4

Statistics Blog

In the first paragraph of your quantitative chapter 4, the results chapter, restate the research questions that will be examined.  This reminds the reader of what you’re going to investigate after having been trough the details of your methodology.  It’s helpful too that the reader knows what the variables are that are going to be analyzed.

Spend a paragraph telling the reader how you’re going to clean the data.  Did you remove univariate or multivariate outlier?  How are you going to treat missing data? What is your final sample size?

The next paragraph should describe the sample using demographics and research variables.  Provide frequencies and percentages for nominal and ordinal level variables and means and standard deviations for the scale level variables.  You can provide this information in figures and tables.

Here’s a sample:

Frequencies and Percentages.  The most frequently observed category of Cardio was Yes ( n  = 41, 72%). The most frequently observed category of Shock was No ( n  = 34, 60%). Frequencies and percentages are presented.

Summary Statistics.  The observations for MiniCog had an average of 25.49 ( SD  = 14.01,  SE M  = 1.87, Min = 2.00, Max = 55.00). The observations for Digital had an average of 29.12 ( SD  = 10.03,  SE M  = 1.33, Min = 15.50, Max = 48.50). Skewness and kurtosis were also calculated. When the skewness is greater than 2 in absolute value, the variable is considered to be asymmetrical about its mean. When the kurtosis is greater than or equal to 3, then the variable’s distribution is markedly different than a normal distribution in its tendency to produce outliers (Westfall & Henning, 2013).

Now that the data is clean and descriptives have been conducted, turn to conducting the statistics and assumptions of those statistics for research question 1.  Provide the assumptions first, then the results of the statistics.  Have a clear accept or reject of the hypothesis statement if you have one.  Here’s an independent samples t-test example:

Introduction.  An two-tailed independent samples  t -test was conducted to examine whether the mean of MiniCog was significantly different between the No and Yes categories of Cardio.

Assumptions.  The assumptions of normality and homogeneity of variance were assessed.

Normality.  A Shapiro-Wilk test was conducted to determine whether MiniCog could have been produced by a normal distribution (Razali & Wah, 2011). The results of the Shapiro-Wilk test were significant,  W  = 0.94,  p  = .007. These results suggest that MiniCog is unlikely to have been produced by a normal distribution; thus normality cannot be assumed. However, the mean of any random variable will be approximately normally distributed as sample size increases according to the Central Limit Theorem (CLT). Therefore, with a sufficiently large sample size ( n  > 50), deviations from normality will have little effect on the results (Stevens, 2009). An alternative way to test the assumption of normality was utilized by plotting the quantiles of the model residuals against the quantiles of a Chi-square distribution, also called a Q-Q scatterplot (DeCarlo, 1997). For the assumption of normality to be met, the quantiles of the residuals must not strongly deviate from the theoretical quantiles. Strong deviations could indicate that the parameter estimates are unreliable. Figure 1 presents a Q-Q scatterplot of MiniCog.

Homogeneity of variance.  Levene’s test for equality of variance was used to assess whether the homogeneity of variance assumption was met (Levene, 1960). The homogeneity of variance assumption requires the variance of the dependent variable be approximately equal in each group. The result of Levene’s test was significant,  F (1, 54) = 18.30,  p  < .001, indicating that the assumption of homogeneity of variance was violated. Consequently, the results may not be reliable or generalizable. Since equal variances cannot be assumed, Welch’s t-test was used instead of the Student’s t-test, which is more reliable when the two samples have unequal variances and unequal sample sizes (Ruxton, 2006).

Results.  The result of the two-tailed independent samples  t -test was significant,  t (46.88) = -4.81,  p  < .001, indicating the null hypothesis can be rejected. This finding suggests the mean of MiniCog was significantly different between the No and Yes categories of Cardio. The mean of MiniCog in the No category of Cardio was significantly lower than the mean of MiniCog in the Yes category. Present the results of the two-tailed independent samples  t -test, and present the means of MiniCog(No) and MiniCog(Yes).

In the next paragraphs, conduct stats and assumptions for your other research questions.  Again, assumptions first, then the results of the statistics with appropriate tables and figures.

Be sure to add all of the in-text citations to your reference section.  Here is a sample of references.

Conover, W. J., & Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics.  The American Statistician, 35 (3), 124-129.

DeCarlo, L. T. (1997). On the meaning and use of kurtosis.  Psychological Methods,  2(3), 292-307.

Levene, H. (1960). Contributions to Probability and Statistics.  Essays in honor of Harold Hotelling,  I. Olkin et al. eds., Stanford University Press, 278-292.

Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests.  Journal of Statistical Modeling and Analytics, 2 (1), 21-33.

Ruxton, G. D. (2006). The unequal variance t-test is an underused alternative to Student’s t-test and the Mann-Whitney U test.  Behavioral Ecology, 17 (4), 688-690.

Intellectus Statistics [Online computer software]. (2019). Retrieved from  https://analyze.intellectusstatistics.com/

Stevens, J. P. (2009).  Applied multivariate statistics for the social sciences  (5th ed.). Mahwah, NJ: Routledge Academic.

Westfall, P. H., & Henning, K. S. S. (2013).  Texts in statistical science: Understanding advanced statistical methods.  Boca Raton, FL: Taylor & Francis.

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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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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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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100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

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Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

• Consider your area of studies
• Look out for relevant subjects in the area
• Expert advice may come in handy
• Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

• The title should seem interesting to readers
• Ensure that the title represents the content of the research paper
• Reflect on the tone of the writing in the title
• The title should contain important keywords in your chosen subject to help readers find your paper
• The title should not be too lengthy
• It should be grammatically correct and creative
• It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

• What is the correlation between inflation rates and unemployment rates?
• Has climate adaptation influenced the mitigation of funds allocation?
• Job satisfaction and employee turnover: What is the link?
• A look at the relationship between poor households and the development of entrepreneurship skills
• Urbanization and economic growth: What is the link between these elements?
• Does education achievement influence people’s economic status?
• What is the impact of solar electricity on the wholesale energy market?
• Debt accumulation and retirement: What is the relationship between these concepts?
• Can people with psychiatric disorders develop independent living skills?
• Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

• The difference between the colorific intake of men and women in your country
• Top strategies used to measure customer satisfaction and how they work
• Black Friday sales: are they profitable?
• The correlation between estimated target market and practical competitive risk assignment
• Are smartphones making us brighter or dumber?
• Nuclear families Vs. Joint families: Is there a difference?
• What will society look like in the absence of organized religion?
• A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
• How does emotional stability influence your overall well-being?
• The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

• How much exercise is good for lasting physical well-being?
• A comparison of the nutritional therapy uses and contemporary medical approaches
• Does sugar intake have a direct impact on diabetes diagnosis?
• Education attainment: Does it influence crime rates in society?
• Is there an actual link between obesity and cancer rates?
• Do kids with siblings have better social skills than those without?
• Computer games and their impact on the young generation
• Has social media marketing taken over conventional marketing strategies?
• The impact of technology development on human relationships and communication
• What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

• Habitation fragmentation and biodiversity loss: What is the link?
• Radiation has affected biodiversity: Assessing its effects
• An assessment of the impact of the CORONA virus on global population growth
• Is the pandemic truly over, or have human bodies built resistance against the virus?
• The ozone hole and its impact on the environment
• The greenhouse gas effect: What is it and how has it impacted the atmosphere
• GMO crops: are they good or bad for your health?
• Is there a direct link between education quality and job attainment?
• How have education systems changed from traditional to modern times?
• The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

• Online classes: are they helpful or not?
• What changes has the global CORONA pandemic had on the population growth curve?
• Daily habits influenced by the global pandemic
• An analysis of the impact of culture on people’s personalities
• How has feminism influenced the education system’s approach to the girl child’s education?
• Academic competition: what are its benefits and downsides for students?
• Is there a link between education and student integrity?
• An analysis of how the education sector can influence a country’s economy
• An overview of the link between crime rates and concern for crime
• Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

• What are the impacts of online games on students?
• Sex education in schools: how important is it?
• Should schools be teaching about safe sex in their sex education classes?
• The correlation between extreme parent interference on student academic performance
• Is there a real link between academic marks and intelligence?
• Teacher feedback: How necessary is it, and how does it help students?
• An analysis of modern education systems and their impact on student performance
• An overview of the link between academic performance/marks and intelligence
• Are grading systems helpful or harmful to students?
• What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

• A look at the pre-corona and post-corona economy
• How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
• An evaluation of mortality rates of heart attacks
• Effective treatments for cardiovascular issues and their prevention
• A comparison of the effectiveness of home care and nursing home care
• Strategies for managing effective dissemination of information to modern students
• How does educational discrimination influence students’ futures?
• The impacts of unfavorable classroom environment and bullying on students and teachers
• An overview of the implementation of STEM education to K-12 students
• How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

• An elaborate study of the influence of telemedicine in healthcare practices
• How has scientific innovation influenced the defense or military system?
• The link between technology and people’s mental health
• Has social media helped create awareness or worsened people’s mental health?
• How do engineers promote green technology?
• How can engineers raise sustainability in building and structural infrastructures?
• An analysis of how decision-making is dependent on someone’s sub-conscious
• A comprehensive study of ADHD and its impact on students’ capabilities
• The impact of racism on people’s mental health and overall wellbeing
• How has the current surge in social activism helped shape people’s relationships?

Are you looking for an example of a quantitative research title? These ten examples below will get you started.

• The prevalence of nonverbal communication in social control and people’s interactions
• The impacts of stress on people’s behavior in society
• A study of the connection between capital structures and corporate strategies
• How do changes in credit ratings impact equality returns?
• A quantitative analysis of the effect of bond rating changes on stock prices
• The impact of semantics on web technology
• An analysis of persuasion, propaganda, and marketing impact on individuals
• The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
• The role of income inequality in economy growth
• An examination of juvenile delinquents’ treatment in your country

Excellent Topics For Quantitative Research

Here are some titles for quantitative research you should consider:

• Does studying mathematics help implement data safety for businesses
• How are art-related subjects interdependent with mathematics?
• How do eco-friendly practices in the hospitality industry influence tourism rates?
• A deep insight into how people view eco-tourisms
• Religion vs. hospitality: Details on their correlation
• Has your country’s tourist sector revived after the pandemic?
• How effective is non-verbal communication in conveying emotions?
• Are there similarities between the English and French vocabulary?
• How do politicians use persuasive language in political speeches?
• The correlation between popular culture and translation

Here are some quantitative research titles examples for your consideration:

• How do world leaders use language to change the emotional climate in their nations?
• Extensive research on how linguistics cultivate political buzzwords
• The impact of globalization on the global tourism sector
• An analysis of the effects of the pandemic on the worldwide hospitality sector
• The influence of social media platforms on people’s choice of tourism destinations
• Educational tourism: What is it and what you should know about it
• Why do college students experience math anxiety?
• Is math anxiety a phenomenon?
• A guide on effective ways to fight cultural bias in modern society
• Creative ways to solve the overpopulation issue

An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones:

• The link between global warming and climate change
• What is the greenhouse gas impact on biodiversity and the atmosphere
• Has the internet successfully influenced literacy rates in society
• The value and downsides of competition for students
• A comparison of the education system in first-world and third-world countries
• The impact of alcohol addiction on the younger generation
• How has social media influenced human relationships?
• Has education helped boost feminism among men and women?
• Are computers in classrooms beneficial or detrimental to students?
• How has social media improved bullying rates among teenagers?

High school students can apply research titles on social issues  or other elements, depending on the subject. Let’s look at some quantitative topics for students:

• What is the right age to introduce sex education for students
• Can extreme punishment help reduce alcohol consumption among teenagers?
• Should the government increase the age of sexual consent?
• The link between globalization and the local economy collapses
• How are global companies influencing local economies?

There are numerous possible quantitative research topics you can write about. Here are some great quantitative research topics examples:

• The correlation between video games and crime rates
• Do college studies impact future job satisfaction?
• What can the education sector do to encourage more college enrollment?
• The impact of education on self-esteem
• The relationship between income and occupation

You can find inspiration for your research topic from trending affairs on social media or in the news. Such topics will make your research enticing. Find a trending topic for quantitative research example from the list below:

• How the country’s economy is fairing after the pandemic
• An analysis of the riots by women in Iran and what the women gain to achieve
• Is the current US government living up to the voter’s expectations?
• How is the war in Ukraine affecting the global economy?
• Can social media riots affect political decisions?

A proposal is a paper you write proposing the subject you would like to cover for your research and the research techniques you will apply. If the proposal is approved, it turns to your research topic. Here are some quantitative titles you should consider for your research proposal:

• Military support and economic development: What is the impact in developing nations?
• How does gun ownership influence crime rates in developed countries?
• How can the US government reduce gun violence without influencing people’s rights?
• What is the link between school prestige and academic standards?
• Is there a scientific link between abortion and the definition of viability?

You can never have too many sample titles. The samples allow you to find a unique title you’re your research or proposal. Find a sample quantitative research title here:

• Does weight loss indicate good or poor health?
• Should schools do away with grading systems?
• The impact of culture on student interactions and personalities
• How can parents successfully protect their kids from the dangers of the internet?
• Is the US education system better or worse than Europe’s?

If you’re a business major, then you must choose a research title quantitative about business. Let’s look at some research title examples quantitative in business:

• Creating shareholder value in business: How important is it?
• The changes in credit ratings and their impact on equity returns
• The importance of data privacy laws in business operations
• How do businesses benefit from e-waste and carbon footprint reduction?
• Organizational culture in business: what is its importance?

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Interesting, creative, unique, and easy quantitative research topics allow you to explain your paper and make research easy. Therefore, you should not take choosing a research paper or proposal topic lightly. With your topic ready, reach out to us today for excellent research paper writing services .

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500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

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Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

• Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
• Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
• Variables 101 : an introductory video covering the different types of variables that exist within research.
• Sampling 101 : an overview of the main sampling methods
• Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
• Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

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Quantitative Research

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In conducting quantitative research, you need to make sure you have the right numbers and the correct values for specific variables. This is because quantitative research focuses more on numeric and logical results. Quantitative studies report and understand numerical data to make further analysis of a given phenomenon. This research organizes and computes statistics from current and prospect clients to make business forecasts for your company. Quantitative analysis  examples also uses methods like polls, surveys, and sampling to gather information that can help complete your investigation.

31+ Quantitative Research Examples

Quantitative research demands focus and precision from the researcher. If you need a guide in doing your research, here are 10+ Quantitative research examples you can use.

1. Free Quantitative Research Flowchart  Example

• Google Docs
• Apple Pages

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2. Free Quantitative Research Analyst Resume  Example

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3. Quantitative Research Review Template

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4. Quantitative Research Plan Template

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5. Quantitative Research Descriptive Analysis Template

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6. Quantitative Research Checklist Template

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7. Quantitative Research Survey Template

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8. Quantitative Research Data Analysis Template

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9. Quantitative Research Guide Template

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10. Quantitative Research Proposal Template

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11. Quantitative Research Question Template

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12. Quantitative Research Literacy Template

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13. Quantitative Research Correlation Template

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14. Quantitative Research Template

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15. Quantitative Research Report Template

16. Simple Quantitative Research Template

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17. Quantitative Research Paper Template

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18. Example of Quantitative Research

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19. Quantitative Research Design Examples

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20. Quantitative Research Examples for Students

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21. Impact of Social Media Reviews on Brands Perception  Example

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In the age where likes, comments, and retweets measure the relevance of an entity online, brands make sure that their followers and customers have a positive perception of them on the web. The internet puts companies and individuals at a spot where the public eye sees reviews and comments about them. But how do these things affect the way people view a company’s branding? This quantitative study of the impact of social media reviews on brands perception answers that. Use this research as a guide in conducting your quantitative research.

22. Teacher Perceptions of Professional Learning Communities  Example

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Educators lead young minds to great success. That is why there are training programs examples  and models where teachers can collaborate and share how they can improve students’ learning. Saying this, some do question the effectiveness of models such as Professional Learning Communities. Research called “A Quantitative Study of Teacher Perceptions of Professional Learning Communities’ Context, Process, and Content,” looks into these queries. If you are conducting your quantitative research, you can use this research as an example for your study. Format your content like this investigation for a foolproof thesis paper.

23. Quantitative Research On The Level of Social Media Addiction  Example

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The worldwide web is a being of wonder and mystery. That’s what makes it fascinating to young audiences. The internet helps them connect and interact with people through various social media platforms. With features and advancements that intrigue even the unexcited, addiction does become inevitable. An investigation in 2015 titled “A Quantitative Research on the Level of Social Media Addiction among Young People in Turkey” looks into the statistics of this problem. For your quantitative research, use this study as a guide in organizing and formatting your quantitative data.

24. Course Grades and Retention Comparing Online and Face-to-face Classes

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Are you taking online classes, or are your classes held in a classroom? Do you believe there is a difference between online and face-to-face courses? There has always been a discussion between which education instructional method is more effective. Although both help students learn, some argue that the way they are taught makes an education gap. This quantitative study of course grades and retention comparing online and face-to-face classes can help answer your questions. It can also serve as a model in making your own quantitative research. Pattern your research design like this one now!

25. Free Nursing Quantitative Research Proposal  Example

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One of a nurse’s primary duties is to assure patients are taken care of and attended to. Their line of work deals with peoples’ lives and health. This also means that they still need to address patients even if they’re close to death. In Ireland, a study called “A Quantitative Study of the Attitude, Knowledge, and Experience of Staff Nurses on Prioritizing Comfort measures in Care of the Dying Patient in an Acute Hospital Setting” was conducted. If you plan on undertaking any medical  SWOT  analysis , using this study as a guide would be beneficial for you. 

26. Quantitative Research Of Consumer’s Attitude Towards Food Products Advertising

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In the corporate world, you can’t just start selling something without proper research. You first have to make sure that your products and services are relevant and marketable. The first step should be conducting marketing research. Marketing research can use either qualitative or quantitative data collection methods. But if you want to figure out how your clients react to your products and marketing strategy, this quantitative research of consumer’s attitude towards food products advertising could be your guide. You can even use this for your undergraduate research.

27. Free Effective Teacher Leadership  Example

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Research projects have to be conducted with precision and accuracy, especially if it’s quantitative research. You need to make sure you get the right numbers to get valid results. In research called “Effective Teacher Leadership: A Quantitative Study of the Relationship Between School Structures and Effective Teacher Leaders,” quantitative data analysis is conducted to look into the school’s management plans. For your research, this would be a useful guide in doing comprehensive qualitative research. You can outline your investigations and even term papers using this as a sample.

28. Quantitative Studies of Water and Sanitation Utilities  Example

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Quantitative research is a method that studies numerical values. It follows a strict process of data collection. This type of research is used by different industries and even as undergraduate research. That is why the research design should reflect the nature of your research. It should look professional and comprehensive. But that doesn’t mean that your research project plan has to look dull. This study called “Quantitative Studies of Water and Sanitation Utilities: A Literature Survey” can be used as a sample. It’s research methodology utilizes surveys as a way to collect data needed for research.

29. Free Perceptions of First Year College Students  Example

Do you want kids to be college-ready? Are you looking for a college planner to prepare high school kids for a higher level of education? The first year of college serves as an adjustment period for students. The way they cope and accustom themselves use different methods. That’s why you need a study to help you. If your research looks into college kids, this qualitative study of the perceptions of first-year college students regarding technology and college readiness could be your guide. Us it as an outline for the quantitative research you are conducting. 

30. Free Qualitative Research Paper  Example

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Like any research, you must follow a particular format. A poorly organized study might give the impression of having unreliable data and results. You need to make sure your research is detailed and understandable. This applies significantly to quantitative project analysis example . This type of investigation urges researchers to be careful and efficient when gathering and analyzing information and statistics. Getting the wrong value can mess up your whole investigation. For your research, you can make use of this qualitative research paper as an outline. It details all the right parts needed in your research.

31. Quantitative Research For Health Programmes  Example

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If you are creating health newspapers and programs, you need to make sure you have the correct data. Your program will tackle a person’s health so you need to have the correct information as not to cause further complications. That’s also why you need to conduct quantitative research to get precise data. For your research, you can make this quantitative research for health programmes your guide. The World Health Organization uses it so you can be sure it is professionally made. Follow the formats on this document to make sure your research is high-quality.

What are the Quantitative research characteristics?

• Objective and Empirical: Quantitative research is based on objective and empirical observations, focusing on measurable, observable phenomena. It aims to collect data that can be analyzed statistically.
• Numerical Data: It primarily relies on numerical data, such as counts, measurements, percentages, and statistics, to draw conclusions and make comparisons.
• Structured and Controlled: Quantitative research is highly structured and controlled, with predefined methods and data collection procedures. Researchers follow standardized processes to ensure reliability and replicability.
• Large Sample Sizes: It often involves larger sample sizes to ensure statistical significance and generalizability. Sampling techniques are used to select representative samples from the population.
• Hypothesis-Driven: Quantitative research typically begins with a clear hypothesis or research question. Researchers aim to test hypotheses and draw conclusions based on data analysis.
• Quantitative Instruments: Researchers use various quantitative instruments, such as surveys, questionnaires, experiments, and structured observations, to collect data.
• Statistical Analysis: Data collected in quantitative research are subject to statistical analysis. Common statistical techniques include descriptive statistics, inferential statistics, regression analysis, and hypothesis testing.
• Objective Measurement: Measurements are typically objective and standardized to minimize bias and subjectivity. Instruments are designed to ensure consistency and reliability.
• Generalization: Quantitative research aims to generalize findings from a sample to a larger population. The results are often used to make broader conclusions and predictions.
• Numerical Results: Research findings are presented using numerical values, charts, graphs, and tables, making the results easily interpretable and comparable.
• Structured Questioning: Surveys and questionnaires used in quantitative research have structured questions with predefined response options to facilitate data collection and analysis.
• Replicability: Quantitative studies are designed to be replicable, allowing other researchers to conduct similar studies and verify or challenge the findings.
• Causality: While correlation can be established, quantitative research is suited for investigating causal relationships between variables by controlling for extraneous factors.
• Reductionist Approach: It often involves a reductionist approach, breaking down complex phenomena into measurable variables for analysis.
• Predefined Research Design: Quantitative research typically follows a predefined research design, including experimental designs, cross-sectional or longitudinal studies, and surveys.
• Validity and Reliability: Researchers pay careful attention to the validity (the accuracy of measurements) and reliability (the consistency of measurements) of data and instruments.
• Data-Based Conclusions: Conclusions in quantitative research are based on data analysis and statistical significance, emphasizing objectivity and evidence-based decision-making.

What are the 4 types of quantitative research?

1. Descriptive Research:

Descriptive research aims to describe and analyze a phenomenon, population, or variable. It provides a detailed account of the characteristics, behaviors, or attributes of a subject without manipulating it. Surveys, observational studies, and content analysis are often used in descriptive research.

2. Correlational Research:

Correlational research examines the relationship between two or more variables. It assesses how changes in one variable are associated with changes in another. The strength and direction of the relationship are measured using correlation coefficients. This type of research doesn’t establish causation but helps identify patterns and associations.

3. Experimental Research:

Experimental research is conducted to establish cause-and-effect relationships between variables. Researchers manipulate one or more independent variables to observe their impact on a dependent variable while controlling extraneous factors. Randomized controlled trials (RCTs) and laboratory experiments are common experimental research designs.

4. Quasi-Experimental Research:

Quasi-experimental research shares similarities with experimental research but lacks the full level of control over variables. In quasi-experiments, researchers often cannot use random assignment due to ethical or practical constraints. However, they still manipulate independent variables and measure their effects on dependent variables.

What is Quantitative Research vs Qualitative Research?

Which example demonstrates quantitative research?

Example 1: A study that surveys 1,000 consumers to determine the percentage who prefer Product A over Product B for a specific feature.

Example 1 demonstrates quantitative research because it involves collecting numerical data (the percentage of consumers) and relies on surveys, which are a common quantitative data collection method. This type of research is suitable for quantifying preferences and making statistical comparisons between products.

What are the advantages of quantitative research?

• Objectivity: Quantitative research is often highly structured and relies on empirical data, reducing the potential for bias and subjectivity. This enhances the objectivity of the research.
• Measurability: It allows for precise measurement of variables, making it easier to quantify and analyze data. This facilitates the comparison of findings across studies.
• Generalizability: Large sample sizes and statistical analysis enable researchers to generalize findings to a larger population, enhancing the external validity of the results.
• Replicability: Quantitative research is designed to be replicable, allowing other researchers to conduct similar studies and validate or challenge the findings.
• Data Analysis: Statistical analysis provides robust tools for testing hypotheses, identifying patterns, and drawing conclusions from data.
• Causality: It is well-suited for investigating causal relationships, as researchers can manipulate variables and control extraneous factors to establish cause-and-effect links.
• Efficiency: Surveys and questionnaires can collect data from a large number of participants efficiently. This is particularly useful for large-scale studies.
• Quantitative Comparison: It allows for direct comparison between groups or variables, facilitating the identification of differences and relationships.
• Data Precision: The use of standardized instruments and measurements results in precise and consistent data, reducing measurement errors.
• Data Visualization: Numerical data can be presented in charts, graphs, and tables, making it visually accessible and aiding in data interpretation.
• Decision Support: Quantitative research provides empirical evidence that can inform data-driven decision-making in various fields, including business, healthcare, and policy.
• Clear Findings: The structured nature of quantitative research often leads to clear and easily interpretable findings, which can be valuable for making informed conclusions.
• Resource Efficiency: While it may require substantial resources for data collection and analysis, quantitative research can be more cost-effective than qualitative research when dealing with large sample sizes.

General FAQ’s

What is quantitative research.

Quantitative research is a systematic approach to gathering and analyzing numerical data to understand and draw conclusions about a specific phenomenon or problem, often using statistical techniques.

What is the greatest strength of quantitative research?

The greatest strength of quantitative research is its ability to provide precise, objective, and statistically reliable data, enabling researchers to identify patterns, relationships, and make generalizable conclusions.

What is a common weakness of quantitative research?

A common weakness of quantitative research is its potential for oversimplification, as it may not capture the full complexity of human behavior or phenomena and may rely on limited predefined variables.

What are the risks of quantitative research?

Risks in quantitative research include the potential for data inaccuracies, oversimplification of complex phenomena, and overlooking unmeasurable factors, which can lead to biased or incomplete conclusions.

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• Dissertation & Thesis Outline | Example & Free Templates

Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

• Working Title
• “Elevator pitch” of your work (often written last).
• Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
• Subdivide as you see fit into main topics and sub-topics.
• Describe your research methods (e.g., your scope , population , and data collection ).
• Present your research findings and share about your data analysis methods.
• Answer the research question in a concise way.
• Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

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• Deep learning
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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, November 21). Dissertation & Thesis Outline | Example & Free Templates. Scribbr. Retrieved August 6, 2024, from https://www.scribbr.com/dissertation/dissertation-thesis-outline/

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