data analysis for quantitative research proposal

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

• Introduction

Literature review

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Proposal Format

The proposal will usually have a  title page  that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

Introduction The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.. Your introduction should:

• Introduce your  topic
• Give necessary background and context
• Outline your  problem statement  and  research questions To guide your  introduction , include information about:  
• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights will your research contribute
• Why you believe this research is worth doing

As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong  literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or  synthesize  prior scholarship

Research design and methods

Following the literature review, restate your main  objectives . This brings the focus back to your project. Next, your  research design  or  methodology  section will describe your overall approach, and the practical steps you will take to answer your research questions. Write up your projected, if not actual, results.

Contribution to knowledge

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Lastly, your research proposal must include correct  citations  for every source you have used, compiled in a  reference list . To create citations quickly and easily, you can use free APA citation generators like BibGuru. Databases have a citation button you can click on to see your citation. Sometimes you have to re-format it as the citations may have mistakes. 

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Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process .

The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.

For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.

This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.

Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.

Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method . You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.

In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.

A set of analytical software can be used to assist with analysis of quantitative data. The following table  illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

Advantages and disadvantages of popular quantitative analytical software

Quantitative data analysis with the application of statistical software consists of the following stages [1] :

• Preparing and checking the data. Input of data into computer.
• Selecting the most appropriate tables and diagrams to use according to your research objectives.
• Selecting the most appropriate statistics to describe your data.
• Selecting the most appropriate statistics to examine relationships and trends in your data.

It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish” [2] .

Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

[2] Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.

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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.

Research Guide: Data analysis and reporting findings

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. 

Data Analysis Checklist

Cleaning  data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

*  Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

• PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

• Qualitative analysis of interview data: A step-by-step guide
• Qualitative Data Analysis - Coding & Developing Themes

Beginner's Guide to SPSS

• SPSS Guideline for Beginners Presented by Hennie Gerber

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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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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Bachelor of Science in Quantitative Economics and Data Analysis

Prepare for the Professional Application of Economic and Econometric Methods

Develop your capabilities in the application of economic and econometric methods with the Bachelor of Science in Quantitative Economics and Data Analysis program at the University at Albany. This program prepares you for careers in the fields of labor, health, banking and finance, international trade and finance, business, public finance, macroeconomic analysis and forecasting, and economic development. The BS in Quantitative Economics and Data Analysis is ideal if you intend to pursue your professional education beyond the undergraduate level.

Additional Program Information

Undergraduate Bulletin  |   Course Catalog  |   Four-Year Plan   |  Department Contact: Ken Bulko

Program of Study

• Honors Program
• Applied Learning
• Combined Master's

Career Outcomes

• Economics Home

You will begin the BS in Quantitative Economics and Data Analysis program by studying the principles of microeconomics and macroeconomics. These courses will provide you with the basic concepts of the discipline, including the operation of markets, consumer behavior, firm behavior, how goods are produced, how governments operate, and how growth, the interest rate, unemployment and inflation are determined in the economy as a whole.

In addition to the intermediate core courses in microeconomics, macroeconomics and economic statistics, the Bachelor of Science in Quantitative Economics and Data Analysis major adds a capstone course in applied econometrics where you will have the opportunity to develop and present an empirical analysis of an economic question. You will also study advanced economic methods and applications including macroeconomic modeling, game theory, international trade, international macroeconomics, financial economics and labor economics.

Required Courses

• Principles of Economics I: Microeconomics
• Principles of Economics II: Macroeconomics
• Intermediate Microeconomics OR Honors Intermediate Microeconomics
• Intermediate Macroeconomics
• Economic Statistics
• Econometrics and Data Analytics
• Calculus I OR Honors Calculus I
• Calculus II OR Honors Calculus II OR Math for Economists

Additional Economics Credits

Five additional courses (15 credits) in economics at the 300-level or above, with at least two courses (6 credits) at the 400-level or Senior Honors Research Seminar

The Honors Program provides capable and motivated students with a greater understanding of economics and better prepares you for graduate and professional schools. Honors students must complete all requirements of the BS program in economics, including the Senior Honors Research Seminar as part of the program and submit a senior honors thesis acceptable to the Economics Honors Committee.

Accelerated Undergraduate/Graduate Options

You can save time and money by beginning your graduate degree coursework while still enrolled as an undergraduate student. Up to 12 academic credits, billed at the undergraduate rate, will count towards both degrees – so you’ll complete your combined program in only 5 years and spend less than you would if you completed each program separately. Choose to combine your Quantitative Economics and Data Analysis undergraduate degree with the following graduate programs:

MA in Economics This master’s program provides advanced training in economic analysis for expertise in your chosen concentration area, including finance, economic forecasting, international economics and health.  

MPA in Public Administration and Policy This top-ranked master’s program prepares you to help solve complex socioeconomic problems with concentration areas including non-profit management and leadership, public policy, healthcare management, information technology management, public economics and finance, and homeland security.  

MS in Information Science   This ALA-accredited master's program covers a broad range of interdisciplinary topics related to library science, information processing, information management and data analysis.

With a Bachelor of Science in Quantitative Economics and Data Analysis degree you will develop the analytical and econometrics skills that are highly valuable for careers in finance and industry; federal, state and local governments; scientific research and development services; and management, scientific, and technical consulting services. You will also be well qualified to pursue an MBA after graduation.

Potential job titles for a quantitative economics and data analysis degree include:

• Financial Analyst, corporate or independent
• Economist at a bank or corporation
• Consultant or Advisor on labor matters
• Economic policy maker in government agencies
• Financial Department Leader at a health care provider
• Monetary Director at a nonprofit or non-governmental organization

International Students

This degree is designated as a STEM program. International students maintaining F-1 status are allowed to apply for up to 12 months of post-completion Optional Practical Training (OPT) following completion/graduation from their degree program. Currently, this degree program is also designated by the Department of Homeland Security (DHS) as an eligible degree for the F-1 STEM OPT work authorization extension; students who secure qualifying employment may be eligible to apply for the STEM OPT extension for a cumulative total of up to 36 months of F-1 OPT work authorization.

Learning objectives that UAlbany students are expected to attain through their course of study within their academic program.

• Understand the economic issues and problems faced by individuals, organizations and society; the economic principles that help explain behavior; the operation of markets; the gains from trade; and the range of institutions that affect the allocation of resources.
• Be able to apply methods of economic analysis (such as supply and demand, equilibrium, constrained optimization and dynamic analysis) to decision-making, behavior and economic outcomes.
• Have the ability independently to apply state-of-the-art tools and advanced methods of statistical and econometric analysis to estimate economic relations and validity of economic hypotheses.
• Be able to communicate economic analysis and empirical conclusions in the discussion of social phenomena and public policy.
• Develop mastery of issues and methods of analysis in the practice of specific fields of economic study such as labor, money and banking, public finance, international trade and finance, development, health and the environment.

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Build competency in a passion or strengthen your resume.

A minor consists of 18–24 graduation credits which must include a minimum of 9 graduation credits of advanced coursework at or above the 300 level. Most undergraduate degrees require completing a minor and it has to have a different title from your major.

• Africana Studies
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Uav quantitative remote sensing of rriparian zone vegetation for river and lake health assessment: a review.

1. Introduction

2. materials and methods, 2.1. documentation, 2.2. characteristics of the issuance, 2.2.1. annual number of publications, 2.2.2. nation, 2.2.3. keyword analysis, 3.1. progress in rlha research, 3.2. indicators for rlha, 4. riparian zone vegetation, 4.1. riparian zone and riparian zone vegetation, 4.2. ecological functions of riparian zone vegetation, 4.2.1. stabilizing riverbanks, 4.2.2. purifying water quality, 4.2.3. regulating water temperature, 4.2.4. providing food, 4.2.5. replenishing recharge, 4.2.6. providing biological habitats, 4.2.7. beautifying human habitats, 4.3. correlation between the ecological function of riparian zone vegetation and indicators for rlha, 5. uav quantitative remote sensing of vegetation in riparian zone, 5.1. uav remote sensing, 5.2. riparian zone vegetation and uav quantitative remote sensing monitoring content, 5.2.1. riparian zone extent delineation, 5.2.2. vegetation type and distribution, 5.2.3. influence of vegetation on changes in river floodplain, 5.2.4. vegetation cover, 5.2.5. plant diversity, 5.2.6. influence of vegetation distribution on the biological habitat, 5.3. indicators for quantitative remote sensing monitoring of vegetation in riparian zone by uav, 6. challenges and prospects, 6.1. existing challenges, 6.1.1. insufficient research on rlh, 6.1.2. limitations of uav platforms and sensors.

• Insufficient stability. When UAVs fly in complex environments, they are susceptible to interference from external factors such as airflow and precipitation, which leads to unstable flight attitudes and affects the quality of remote sensing data collection [ 178 ]. The riparian zone vegetation, located near rivers and lakes, often lies in complex terrains like mountains and hills. In such areas, UAV flights demand that pilots maintain clear visibility of the UAV or equip the UAV with obstacle avoidance sensors. Pilots must navigate carefully, striving to maintain a safe distance from the ground to ensure the safety of the flight.
• Shorter range. The limited battery capacity of a UAV results in a short single flight time, making it difficult to meet the demand for long-duration, wide-area remote sensing monitoring [ 179 ]. The riparian zone generally stretches from the upstream to the downstream of a river, spanning from a few to thousands of kilometers. For effective monitoring of riparian zone vegetation, UAVs must have long endurance. Additionally, in areas of the riparian zone that are inaccessible to personnel, UAVs are also required to have a prolonged flight time to facilitate long-distance monitoring.
• Insufficient load. The limited payloads of UAVs restrict their ability to carry multiple high-precision and high-performance sensors at the same time, which restricts the efficiency of riparian zone vegetation monitoring [ 180 ]. Vegetation monitoring in the riparian zone requires visual assessments of the vegetation using RGB cameras as well as analyses of vegetation growth conditions through multiple sensors, including multispectral, hyperspectral, radar, thermal infrared technologies, and so on.

6.1.3. Complexity of Information in UAV Remote Sensing Data

• Complexity of data processing. Data obtained from UAV remote sensing monitoring need to be processed and analyzed, and for the identification of riparian zone vegetation, it is necessary to accurately identify individual vegetation and distinguish vegetation types. There are high requirements for algorithms and software.
• Data real-time. For the riparian zone, which is an area with more drastic changes and not obvious change characteristics, the lagging interpretation of remote sensing monitoring data will not be able to guide the riparian zone management and other applications in a timely manner, and it is necessary to solve the challenges associated with data transmission and the timely processing of data to realize real-time monitoring of the riparian zone vegetation. The application of real-time data for UAVs is still in its infancy [ 181 , 182 , 183 ] and will be a hotspot for UAV application research.
• Universality of algorithms and models. The spatial resolution of image data acquired by UAV remote sensing is better than 1 centimeter. Compared with satellite-based remote sensing, it can reflect more detailed and complex subsurface types and features. This poses a challenge to the universality of algorithms and models used for extracting vegetation parameter information. Most current algorithms or models are only applicable to specific research and lack stability, universality, and generality, which restricts their application and promotion across a wide range of fields. For riparian zones under different climatic conditions, the vegetation types and parameters vary. It is necessary to select and adjust the model parameters according to local conditions to achieve regional adaptability. Algorithms and models based on UAV data are now hotspots of research [ 101 , 184 , 185 , 186 , 187 ], and future research should continue in this direction.

6.2. Application Prospects

6.2.1. improvements in uav flight platforms, 6.2.2. improvements in uav sensors, 6.2.3. advances in uav information processing technology, 6.2.4. integrated air-to-ground monitoring, 6.2.5. construction of a platform for the quantitative remote sensing of vegetation in riparian zones by uavs, 7. conclusions, 7.1. rlha system, 7.2. ecological functions of riparian zone vegetation, 7.3. indicators for quantitative remote sensing monitoring of vegetation in riparian zones by uavs, 7.4. challenges and perspectives of quantitative remote sensing of vegetation in riparian zones by uavs, author contributions, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Song, F.; Zhang, W.; Yuan, T.; Ji, Z.; Cao, Z.; Xu, B.; Lu, L.; Zou, S. UAV Quantitative Remote Sensing of Rriparian Zone Vegetation for River and Lake Health Assessment: A Review. Remote Sens. 2024 , 16 , 3560. https://doi.org/10.3390/rs16193560

Song F, Zhang W, Yuan T, Ji Z, Cao Z, Xu B, Lu L, Zou S. UAV Quantitative Remote Sensing of Rriparian Zone Vegetation for River and Lake Health Assessment: A Review. Remote Sensing . 2024; 16(19):3560. https://doi.org/10.3390/rs16193560

Song, Fei, Wenyong Zhang, Tenggang Yuan, Zhenqing Ji, Zhiyu Cao, Baorong Xu, Lei Lu, and Songbing Zou. 2024. "UAV Quantitative Remote Sensing of Rriparian Zone Vegetation for River and Lake Health Assessment: A Review" Remote Sensing 16, no. 19: 3560. https://doi.org/10.3390/rs16193560

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