what are variables in a dissertation

Scale: Nominal and ordinal.

The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat.

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.

Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.

Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement.

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Types of variables for Dissertation Data Analysis

While conducting a research, you will need to gather vast amount of data for analysis. Data analysis is the fourth chapter of a dissertation and is the means through which you can prove the theory that you have proposed in the introduction and literature review. A research work can make use of different types of variables for analyzing data. Variables are logical sets of attributes, which assume certain values and help in measurement of the qualities of the group being studied. Here, I have tried to explain the different variables that you can use:

• Controlled Variables: these are the variables, which the researcher keeps in control, so that they do not impact the other variables. Controlling a variable means that you select the respondents in a restrictive manner. For instance, if you wish to set age as the controlled variable, then you will select respondents who are only within a certain age limit. This is done to ensure that variation in one of the factors is limited.
• Dependent Variables: the Dissertation Data Analysis for most experimental or quasi-experimental research works will seek to establish a cause and effect relationship between variables. The variables which are under some control and whose value depends on the changes in other variables are called dependent variables.
• Independent Variables: these are the causal variables, which impact the dependent variables, and have an impact on the outcome of research.
• Extraneous Variables: in a study that seeks to explain the causal relation between variables, the researcher has to pay attention to some external factors that can impact the study. All factors affecting a set of variables cannot be considered by a study, as this will make the research unnecessarily complex. Hence, few factors have to be eliminated.
• Standard Variables: this category is relevant for social science studies, wherein similar variables are used in most cases. Though the exact variables will depend on the requirement of the study, some factors like age, education level, employment, ethnicity and gender will be used in most studies for data analysis.

When deciding the variables to be used, Dissertation Statistics Consultation must be sought from PhD statisticians. The mentors will guide you not just about the type of variables, but also regarding the manner in which the variables must be analyzed.

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RESEARCH VARIABLES: TYPES, USES AND DEFINITION OF TERMS

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Measuring dissertation variables and selecting instruments

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When we collect information about people, objects and events, we must turn that information into numbers so that we can measure it. Measuring dissertation variables and selecting instruments are among the most challenging parts of the doctoral process. The following descriptive information is provided with the intention of helping you to do a good job with these tasks.

Data are derived from characteristics about individuals, objects or events. These characteristics are called variables . You attach numbers to your dissertation variables in an effort to measure them and apply statistics to them when you use your instruments.

Categorical variables have different categories and each category takes on a whole number or integer to represent it. The number that is assigned to the category does not have any meaning. A simple example of a categorical variable is Gender [male=0 and female=1]

Variables that are quantitative are classified as either discrete or continuous . They can take on numbers or integers that represent some degree of the variable. For example, the variable of household size for the families can be 1 (one person), 2 and up to double digits for a big family.

There are four scales of measurement used to assign numbers to your variables. 1. Nominal 2. Ordinal 3. Interval 4. Ratio Finally, you have to choose data collection instruments which will assign numbers to your variables. You will be asked about their validity and reliability . Return from measuring dissertation variables and selecting instruments to the dissertation statistics home page.

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Quantitative Research is a "means for testing objective theories by examining the relationships among variables.  These variables, in turn, can be measured, typically on instruments, so that numbered data can be analyzed using statistical procedures.  The final written report has a set structure consisting of introduction, literature and theory, methods, results and discussion"  ( Creswell, 2007 ) .

Quantitative Research Books

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Subject Guide

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

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What Is a Dissertation? | Guide, Examples, & Template

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

Your dissertation is probably the longest piece of writing you’ve ever completed. It requires solid research, writing, and analysis skills, and it can be intimidating to know where to begin.

Your department likely has guidelines related to how your dissertation should be structured. When in doubt, consult with your supervisor.

You can also download our full dissertation template in the format of your choice below. The template includes a ready-made table of contents with notes on what to include in each chapter, easily adaptable to your department’s requirements.

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• In the US, a dissertation generally refers to the collection of research you conducted to obtain a PhD.
• In other countries (such as the UK), a dissertation often refers to the research you conduct to obtain your bachelor’s or master’s degree.

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Table of contents

Dissertation committee and prospectus process, how to write and structure a dissertation, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your dissertation, free checklist and lecture slides.

When you’ve finished your coursework, as well as any comprehensive exams or other requirements, you advance to “ABD” (All But Dissertation) status. This means you’ve completed everything except your dissertation.

Prior to starting to write, you must form your committee and write your prospectus or proposal . Your committee comprises your adviser and a few other faculty members. They can be from your own department, or, if your work is more interdisciplinary, from other departments. Your committee will guide you through the dissertation process, and ultimately decide whether you pass your dissertation defense and receive your PhD.

Your prospectus is a formal document presented to your committee, usually orally in a defense, outlining your research aims and objectives and showing why your topic is relevant . After passing your prospectus defense, you’re ready to start your research and writing.

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The structure of your dissertation depends on a variety of factors, such as your discipline, topic, and approach. Dissertations in the humanities are often structured more like a long essay , building an overall argument to support a central thesis , with chapters organized around different themes or case studies.

However, hard science and social science dissertations typically include a review of existing works, a methodology section, an analysis of your original research, and a presentation of your results , presented in different chapters.

Dissertation examples

We’ve compiled a list of dissertation examples to help you get started.

• Example dissertation #1: Heat, Wildfire and Energy Demand: An Examination of Residential Buildings and Community Equity (a dissertation by C. A. Antonopoulos about the impact of extreme heat and wildfire on residential buildings and occupant exposure risks).
• Example dissertation #2: Exploring Income Volatility and Financial Health Among Middle-Income Households (a dissertation by M. Addo about income volatility and declining economic security among middle-income households).
• Example dissertation #3: The Use of Mindfulness Meditation to Increase the Efficacy of Mirror Visual Feedback for Reducing Phantom Limb Pain in Amputees (a dissertation by N. S. Mills about the effect of mindfulness-based interventions on the relationship between mirror visual feedback and the pain level in amputees with phantom limb pain).

The very first page of your document contains your dissertation title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo.

Read more about title pages

The acknowledgements section is usually optional and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you. In some cases, your acknowledgements are part of a preface.

Read more about acknowledgements Read more about prefaces

The abstract is a short summary of your dissertation, usually about 150 to 300 words long. Though this may seem very short, it’s one of the most important parts of your dissertation, because it introduces your work to your audience.

Your abstract should:

• State your main topic and the aims of your research
• Describe your methods
• Summarize your main results
• State your conclusions

Read more about abstracts

The table of contents lists all of your chapters, along with corresponding subheadings and page numbers. This gives your reader an overview of your structure and helps them easily navigate your document.

Remember to include all main parts of your dissertation in your table of contents, even the appendices. It’s easy to generate a table automatically in Word if you used heading styles. Generally speaking, you only include level 2 and level 3 headings, not every subheading you included in your finished work.

Read more about tables of contents

While not usually mandatory, it’s nice to include a list of figures and tables to help guide your reader if you have used a lot of these in your dissertation. It’s easy to generate one of these in Word using the Insert Caption feature.

Read more about lists of figures and tables

Similarly, if you have used a lot of abbreviations (especially industry-specific ones) in your dissertation, you can include them in an alphabetized list of abbreviations so that the reader can easily look up their meanings.

Read more about lists of abbreviations

In addition to the list of abbreviations, if you find yourself using a lot of highly specialized terms that you worry will not be familiar to your reader, consider including a glossary. Here, alphabetize the terms and include a brief description or definition.

Read more about glossaries

The introduction serves to set up your dissertation’s topic, purpose, and relevance. It tells the reader what to expect in the rest of your dissertation. The introduction should:

• Establish your research topic , giving the background information needed to contextualize your work
• Narrow down the focus and define the scope of your research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your research questions and objectives
• Outline the flow of the rest of your work

Everything in the introduction should be clear, engaging, and relevant. By the end, the reader should understand the what, why, and how of your research.

Read more about introductions

A formative part of your research is your literature review . This helps you gain a thorough understanding of the academic work that already exists on your topic.

Literature reviews encompass:

• Finding relevant sources (e.g., books and journal articles)
• Assessing the credibility of your sources
• Critically analyzing and evaluating each source
• Drawing connections between them (e.g., themes, patterns, conflicts, or gaps) to strengthen your overall point

A literature review is not merely a summary of existing sources. Your literature review should have a coherent structure and argument that leads to a clear justification for your own research. It may aim to:

• Address a gap in the literature or build on existing knowledge
• Take a new theoretical or methodological approach to your topic
• Propose a solution to an unresolved problem or advance one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework. Here, you define and analyze the key theories, concepts, and models that frame your research.

Read more about theoretical frameworks

Your methodology chapter describes how you conducted your research, allowing your reader to critically assess its credibility. Your methodology section should accurately report what you did, as well as convince your reader that this was the best way to answer your research question.

A methodology section should generally include:

• The overall research approach ( quantitative vs. qualitative ) and research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment )
• Details of where, when, and with whom the research took place
• Any tools and materials you used (e.g., computer programs, lab equipment)
• Your data analysis methods (e.g., statistical analysis , discourse analysis )
• An evaluation or justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. You can structure this section around sub-questions, hypotheses , or themes, but avoid including any subjective or speculative interpretation here.

Your results section should:

• Concisely state each relevant result together with relevant descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Briefly state how the result relates to the question or whether the hypothesis was supported
• Report all results that are relevant to your research questions , including any that did not meet your expectations.

Additional data (including raw numbers, full questionnaires, or interview transcripts) can be included as an appendix. You can include tables and figures, but only if they help the reader better understand your results. Read more about results sections

Your discussion section is your opportunity to explore the meaning and implications of your results in relation to your research question. Here, interpret your results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. Refer back to relevant source material to show how your results fit within existing research in your field.

Some guiding questions include:

• What do your results mean?
• Why do your results matter?
• What limitations do the results have?

If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data.

Read more about discussion sections

Your dissertation’s conclusion should concisely answer your main research question, leaving your reader with a clear understanding of your central argument and emphasizing what your research has contributed to the field.

In some disciplines, the conclusion is just a short section preceding the discussion section, but in other contexts, it is the final chapter of your work. Here, you wrap up your dissertation with a final reflection on what you found, with recommendations for future research and concluding remarks.

It’s important to leave the reader with a clear impression of why your research matters. What have you added to what was already known? Why is your research necessary for the future of your field?

Read more about conclusions

It is crucial to include a reference list or list of works cited with the full details of all the sources that you used, in order to avoid plagiarism. Be sure to choose one citation style and follow it consistently throughout your dissertation. Each style has strict and specific formatting requirements.

Common styles include MLA , Chicago , and APA , but which style you use is often set by your department or your field.

Create APA citations Create MLA citations

Your dissertation should contain only essential information that directly contributes to answering your research question. Documents such as interview transcripts or survey questions can be added as appendices, rather than adding them to the main body.

Read more about appendices

Making sure that all of your sections are in the right place is only the first step to a well-written dissertation. Don’t forget to leave plenty of time for editing and proofreading, as grammar mistakes and sloppy spelling errors can really negatively impact your work.

Dissertations can take up to five years to write, so you will definitely want to make sure that everything is perfect before submitting. You may want to consider using a professional dissertation editing service , AI proofreader or grammar checker to make sure your final project is perfect prior to submitting.

After your written dissertation is approved, your committee will schedule a defense. Similarly to defending your prospectus, dissertation defenses are oral presentations of your work. You’ll present your dissertation, and your committee will ask you questions. Many departments allow family members, friends, and other people who are interested to join as well.

After your defense, your committee will meet, and then inform you whether you have passed. Keep in mind that defenses are usually just a formality; most committees will have resolved any serious issues with your work with you far prior to your defense, giving you ample time to fix any problems.

As you write your dissertation, you can use this simple checklist to make sure you’ve included all the essentials.

Checklist: Dissertation

My title page includes all information required by my university.

I have included acknowledgements thanking those who helped me.

My abstract provides a concise summary of the dissertation, giving the reader a clear idea of my key results or arguments.

I have created a table of contents to help the reader navigate my dissertation. It includes all chapter titles, but excludes the title page, acknowledgements, and abstract.

My introduction leads into my topic in an engaging way and shows the relevance of my research.

My introduction clearly defines the focus of my research, stating my research questions and research objectives .

My introduction includes an overview of the dissertation’s structure (reading guide).

I have conducted a literature review in which I (1) critically engage with sources, evaluating the strengths and weaknesses of existing research, (2) discuss patterns, themes, and debates in the literature, and (3) address a gap or show how my research contributes to existing research.

I have clearly outlined the theoretical framework of my research, explaining the theories and models that support my approach.

I have thoroughly described my methodology , explaining how I collected data and analyzed data.

I have concisely and objectively reported all relevant results .

I have (1) evaluated and interpreted the meaning of the results and (2) acknowledged any important limitations of the results in my discussion .

I have clearly stated the answer to my main research question in the conclusion .

I have clearly explained the implications of my conclusion, emphasizing what new insight my research has contributed.

I have provided relevant recommendations for further research or practice.

If relevant, I have included appendices with supplemental information.

I have included an in-text citation every time I use words, ideas, or information from a source.

I have listed every source in a reference list at the end of my dissertation.

I have consistently followed the rules of my chosen citation style .

I have followed all formatting guidelines provided by my university.

Congratulations!

The end is in sight—your dissertation is nearly ready to submit! Make sure it's perfectly polished with the help of a Scribbr editor.

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5 Steps to Interpreting Statistical Results for Your Dissertation: From Numbers to Insight

Interpreting results from statistical analysis can be daunting, especially if you are unfamiliar with the field of statistics. However, understanding statistical results is crucial when you’re conducting quantitative research for your dissertation. In this blog post, we will outline a step-by-step guide to help you get started with interpreting the results of statistical analysis for your dissertation.

🔍 Step 1: Review your Research Questions and Hypotheses

Before you start interpreting your statistical results, it is important to revisit your research questions and hypotheses. It is easy to be tempted to include as much information as possible, Doing so will ensure that you are interpreting your results in a way that answers your research questions. When initially confronted with the results of your statistical analyses, you may find it difficult to determine where to start. It is common to feel the temptation to include as much data as possible in your results chapter, fearing that excluding any information might compromise the integrity of the study. However, succumbing to this temptation can lead to a loss of direction and clarity in the presentation of results. Reviewing your research questions and hypotheses will help you to focus on the key findings that are relevant to your research objectives.

📊 Step 2: Examine the Descriptive Statistics

After reviewing your research questions and hypotheses (Step 1), the next crucial step in interpreting your statistical results is to examine your descriptive statistics. Descriptive statistics play a fundamental role in summarizing the basic characteristics of your data, providing valuable insights into its distribution, sample characteristics, frequencies, and potential outliers.

One aspect to consider when examining descriptive statistics is sample characteristics. These characteristics provide an overview of the participants or subjects included in your study. For example, in a survey-based study, you may examine demographic variables such as age, gender, educational background, or socioeconomic status. By analyzing these sample characteristics, you can understand the composition of your sample and evaluate its representativeness or any potential biases.

Additionally, descriptive statistics help you analyze the frequencies of categorical variables. Frequencies provide information about the distribution of responses or categories within a particular variable. This is particularly useful when examining survey questions with multiple response options or categorical variables such as occupation or political affiliation. By examining frequencies, you can identify dominant categories or patterns within your data, which may contribute to your overall understanding of the research topic.

Descriptive statistics allow you to explore additional measures beyond central tendency and dispersion. For example, measures such as skewness and kurtosis provide insights into the shape of your data distribution. Skewness indicates whether your data is skewed towards the left or right, while kurtosis measures the peakedness or flatness of the distribution. These measures help you assess the departure of your data from a normal distribution and determine if any transformation or adjustment is required for further analysis.

Analyzing descriptive statistics also involves considering any potential outliers in your data. Outliers are extreme values that significantly deviate from the majority of your data points. These data points can have a substantial impact on the overall analysis and conclusions. By identifying outliers, you can investigate their potential causes, assess their impact on your results, and make informed decisions about their inclusion or exclusion from further analysis.

Examining your descriptive statistics, including sample characteristics, frequencies, measures of distribution shape, and identification of outliers, provides a comprehensive understanding of your data. These insights not only facilitate a thorough description of your dataset but also serve as a foundation for subsequent analysis and interpretation.

✅ Step 3: Understand the Inferential Statistics and Statistical Significance

After reviewing your research questions and hypotheses (Step 1) and examining descriptive statistics (Step 2), you need to understand the inferential statistics and determine their statistical significance.

Inferential statistics are used to draw conclusions and make inferences about a larger population based on the data collected from a sample. These statistical tests help researchers determine if the observed patterns, relationships, or differences in the data are statistically significant or if they occurred by chance. Inferential statistics involve hypothesis testing, which involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha). The null hypothesis represents the absence of an effect or relationship, while the alternative hypothesis suggests the presence of a specific effect or relationship. By conducting hypothesis tests, you can assess the evidence in favor of or against the alternative hypothesis ( if you need a refresher on hypothesis testing – read more about it here ).

Statistical significance refers to the likelihood that the observed results are not due to random chance. It helps you determine if the findings in your study are meaningful and can be generalized to the larger population. Typically, a significance level (alpha) is predetermined (e.g., 0.05), and if the p-value (probability value) associated with the test statistic is less than the significance level, the results are deemed statistically significant.

By comprehending inferential statistics and assessing statistical significance, you can draw meaningful conclusions from your data and make generalizations about the larger population. However, it is crucial to interpret the results in conjunction with practical significance, considering the effect size, context, and relevance to your research questions and hypotheses.

💡 Step 4: Consider Effect Sizes

It is important to note that statistical significance does not imply practical or substantive significance. Effect size or practical significance refers to the meaningfulness or importance of the observed effect or relationship in real-world terms. While a statistically significant result indicates that the observed effect is unlikely due to chance, it is essential to consider the magnitude of the effect and its practical implications when interpreting the results. They help you assess the importance and meaningfulness of the findings beyond mere statistical significance.

There are various effect size measures depending on the type of analysis and research design employed in your study. For example, in experimental or intervention studies, you might consider measures such as Cohen’s d or standardized mean difference to quantify the difference in means between groups. Cohen’s d represents the effect size in terms of standard deviations, providing an estimate of the distance between the group means.

In correlation or regression analyses, you may examine effect size measures such as Pearson’s r or R-squared. Pearson’s r quantifies the strength and direction of the linear relationship between two variables, while R-squared indicates the proportion of variance in the dependent variable explained by the independent variables.

Effect sizes are important because they help you evaluate the practical significance of your findings. A small effect size may indicate that the observed effect, although statistically significant, has limited practical relevance. Conversely, a large effect size suggests a substantial and meaningful impact in the context of your research.

Additionally, considering effect sizes allows for meaningful comparisons across studies. By examining effect sizes, researchers can assess the consistency of findings in the literature and determine the generalizability and importance of their own results within the broader scientific context.

It is worth noting that effect sizes are influenced by various factors, including sample size, measurement scales, and research design. Therefore, it is crucial to interpret effect sizes within the specific context of your study and research questions.

🗣️ Step 5: Interpret your Results in the Context of your Research Questions

After reviewing your research questions and hypotheses (Step 1), examining descriptive statistics (Step 2), understanding inferential statistics and statistical significance (Step 3), and considering effect sizes (Step 4), the final step in interpreting your statistical results is to interpret them in the context of your research questions.

Interpreting your results involves drawing meaningful conclusions and providing explanations that align with your research objectives. Here are some key considerations for interpreting your results effectively:

• Relate the findings to your research questions: Begin by revisiting your research questions and hypotheses. Determine how your results contribute to answering these questions and whether they support or refute your initial expectations. Consider the implications of the findings in light of your research objectives.
• Analyze patterns and relationships: Look for patterns, trends, or relationships within your data. Are there consistent findings across different variables or subgroups? Are there unexpected findings that require further exploration or explanation? Identify any notable variations or discrepancies that might inform your understanding of the research topic.
• Provide context and theoretical explanations: Situate your results within existing theories, concepts, or prior research. Compare your findings with previous studies and discuss similarities, differences, or contradictions. Explain how your results contribute to advancing knowledge in the field and address gaps or limitations identified in previous research.
• Consider alternative explanations: Acknowledge and discuss alternative explanations for your results. Evaluate potential confounding factors or alternative interpretations that could account for the observed patterns or relationships. By addressing these alternative explanations, you strengthen the validity and reliability of your findings.
• Discuss limitations and future directions: Reflect on the limitations of your study and the potential impact on the interpretation of your results. Address any potential sources of bias, methodological constraints, or limitations in the generalizability of your findings. Suggest future research directions that could build upon or address these limitations to further enhance knowledge in the field.

Remember that interpreting your results is not a standalone process. It requires a holistic understanding of your research questions, data analysis techniques, and the broader context of your research field. Your interpretation should be logical, supported by evidence, and provide meaningful insights that contribute to the overall understanding of the research topic.

Tips for Interpreting Statistical Results

Here are some additional tips to help you interpret your statistical results effectively:

• 👀 Visualize your data: Graphs and charts can be a powerful tool for interpreting statistical results. They can help you to identify patterns and trends in your data that may not be immediately apparent from the numbers alone.
• 📋 Consult with a statistician : If you are struggling to interpret your statistical results, it can be helpful to consult with a statistician. They can provide guidance on statistical analysis and help you to interpret your results in a way that is appropriate for your research questions.
• ✍️ Be clear and concise: When interpreting your results, it is important to be clear and concise. Avoid using technical jargon or making assumptions about your readers’ knowledge of statistics.
• 🧐 Be objective: Approach your statistical results with an objective mindset. Avoid letting your personal biases or preconceptions affect the way you interpret your results.

Interpreting the results of statistical analysis is a crucial step in any quantitative research dissertation. By following the steps outlined in this guide, you can ensure that you are interpreting your results in a way that answers your research questions. Remember to be cautious, objective, and clear when interpreting your results, and don’t hesitate to seek guidance from a statistician if you are struggling. With a little bit of practice and patience, you can unlock the insights hidden within your data and make meaningful contributions to your field of study.

Author:  Kirstie Eastwood

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15 Independent and Dependent Variable Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.  

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.  

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.  

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66.  https://doi.org/10.1080/10790195.2012.10850354

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
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• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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