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How to Write Quantitative Research Questions: Types With Examples

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Want to create a quantitative research survey hassle-free? Explore our library of 1,000,000+ readymade questions.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive 

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Research Question Examples – Guide & Tips

Published by Owen Ingram at August 13th, 2021 , Revised On October 3, 2024

One of the most important parts of your research paper , thesis or dissertation is a research question. A strong research question lays the foundation for an in-depth analysis and insightful conclusions. It serves as a guide for your research paper and states what you want and which problem you want to address.

In this blog, we will cover precise and properly structured research question examples, to facilitate your understanding and help you approach your work with confidence.

What Is A Research Question Example?

A research question example is a sample that provides a deeper understanding of how to write a research question. These examples can help first-time authors comprehend the structure and components of the question.

A research question’s length depends on the topic chosen and the specific requirements of the field. However, the length should not be the main focus. The ultimate goal is to convey the main problem statement being addressed.

Importance Of Research Question

A research question is a critical component of research because of the following reasons:

• It is central to research as it guides the research design , data collection , analysis, evaluation and interpretation of the results.
• The paper relies on the research question to properly address the evaluated problem and inform readers about the research topic. Without a question, the readers and researchers may face difficulty in understanding the purpose of your research.
• It helps researchers understand the quantity and type of data needed to answer the question sufficiently.
• Moreover, it provides a framework for drawing conclusions and builds the credibility of the research design.

Research Questions for Dissertation Examples

Below are 10 examples of research questions that will enable you to develop research questions for your research. These examples will help you to check whether your chosen research questions can be addressed or whether they are too broad to find a conclusive answer.

List of Research Question Examples For Students

Effective research questions are clear and focused, and well-written. Many students struggle to craft such questions, which is why we have listed a few examples of different types of research questions. By examining these questions, students can have a clearer understanding of how to develop research questions of all disciplines.

Examples of Qualitative Research Questions

Qualitative research questions focus on specific areas of study or broader themes. They are adaptable and flexible, unlike quantitative research questions. There are certain categories of qualitative research questions such as contextual, descriptive, evaluative, explanatory and exploratory. Let’s discuss a few examples of qualitative research questions:

Example 1: What are the characteristics of ATP synthase?

Example 2: What factors contribute to homelessness in urban areas?

Example 3: What are the challenges faced by immigrants in learning a new language?

Example 4: What is the cause of increased violence among young adults?

Example 5: What are the spiritual experiences of individuals who practice medication?

Example 6: What are the experiences of patients with chronic illness in getting healthcare services?

Example 7: Is it possible that VEGF has an effect on plant photosynthesis?

Examples of Quantitative Research Questions

Quantitative research questions measure and quantify variables to identify relationships and correlations. These questions aim to answer the “how many” or “how much” aspects of a subject, and are widely used in fields that involve statistical analysis and numerical data. Here are seven examples of quantitative research questions:

Example 1: What is the correlation between sleep duration and productivity levels among office workers?

Example 2: What percentage of people in the city support the ban on plastic bags?

Example 3: What is the relationship between TikTok usage and academic performance among college students?

Example 4: What is the effect of a high-protein diet on muscle growth in fitness individuals?

Example 5: What is the relationship between social media usage and depression in young adults?

Example 6: How does the consumption of dietary fibre affect blood sugar levels in people with type 2 diabetes?

Example 7: What effect does internet speed have in increasing work productivity in the IT sector?

Constructivist Research Questions Examples

Constructive research questions are designed to explore an individual’s interaction with the world, and how they create meaning through it. They examine the process that develops an individual’s understanding, perspectives and knowledge. Here are some examples of constructivist research questions:

Example 1: How do employees learn and respond to organisational change initiatives?

Example 2: What effects do teaching methods have on student’s perception of learning?

Example 3: How do individuals create their identities in relation to their cultural backgrounds?

Example 4: What are the variables that affect an individual’s perception of justice?

Example 5: How does media shape people’s perception of social issues?

Example 6: How do students construct their understanding of complex mathematical concepts?

Example 7: What are the challenges faced by marginalised groups in media production?

Discourse Analysis Research Question Examples

Understanding how language is used to construct meaning, power dynamics and social identities in particular contexts is the main purpose of discourse analysis research questions. They are also known as discursive research questions. They aim to investigate the way language shapes ideologies and social structures. Some popular examples of discourse analysis research questions are:

Example 1: How does discourse in health advertisements promote products and services?

Example 2: How is a discourse in criminal justice policy used to shape public attitudes towards justice and punishment?

Example 3: How is national identity constructed by the usage of discourse in flags and national anthems?

Example 4: How is discourse used to confront racial stereotypes?

Example 5: How is classroom discourse used to maintain power relations among professors and students?

Example 6: How does advertising discourse construct gender stereotypes?

Example 7: How is discourse in political campaigns used to obtain support for specific candidates?

Comparative Research Questions Examples

Comparative research questions aim to identify the differences and similarities between two cases, phenomena and groups. These questions compare and contrast different variables to identify trends, practices and relationships. Let’s explore some examples to gain a better understanding:

Example 1: What are the similarities and differences in political systems between democracies and authoritarian regimes?

Example 2: What are the differences between the economic policies of developed and under-developed countries?

Example 3: How do family structures differ in various cultures?

Example 4: What are the similarities and differences in gender roles across various cultures?

Example 5: What are the similarities and differences in the prevalence of chronic diseases across various countries?

Example 6: How do literary works from different time periods compare in terms of theme and prose?

Example 7: What is the biodiversity comparison between ecosystems of various biomes?

Descriptive Research Question Examples

Descriptive research questions are questions used in research to gain a clearer picture of a particular topic or phenomenon. These questions focus on specific characteristics, conditions and attributes of the topic that is being studied. Let’s study a few examples of descriptive research questions examples:

Example 1: How does childhood trauma affect mental health?

Example 2: What is the impact of globalisation on local business?

Example 3: How does artificial intelligence affect job markets?

Example 4: What are the factors that contribute to the drop-outs in schools?

Example 5: How much do brands invest in digital marketing as compared to traditional advertising?

Example 6: What is the effect of climate change on biodiversity?

Example 7: What are the ethical limitations of genetic engineering?

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Frequently Asked Questions

What is a research question example in psychology.

Research question examples in the field of psychology are:

• How does bipolar disorder’s initial age affect its progression and treatment?  
• How does childhood trauma impact the advancement of borderline personality disorder in adulthood? 
• What are the long-term psychological effects of being the victim of a violent crime?

What is a research question example in natural sciences?

Research question examples in natural sciences are:

• What are the effects of ocean acidification on the biodiversity of coral reef ecosystems? 
• How does air pollution impact respiratory well-being in individuals living in polluted areas? 
• What are the effects of organic and inorganic fertilisers on soil and crop health? 

What are the characteristics of a well-written research question?

A good research question is focused, clear, specific and relevant to the topic and subject. It should also be researchable so that enough data can be collected to answer the question.

What are some examples of research questions in the classroom?

• How do interactive whiteboards impact student engagement?
• Does peer tutoring improve maths proficiency?
• How does classroom seating arrangement influence student participation?
• What’s the effect of gamified learning on student motivation?
• Does integrating technology in lessons enhance critical thinking skills?
• How does feedback frequency affect student performance?

What are some examples of research questions in geography?

• How does urbanisation impact local microclimates?
• What factors influence water scarcity in Region X?
• How do migration patterns correlate with economic disparities?
• What’s the relationship between deforestation and soil erosion in Area Y?
• How have coastlines changed over the past decade?
• Why are certain regions’ biodiversity hotspots?

What are three basic research questions?

The three basic types of research questions are:

• Descriptive: Seeks to depict a phenomenon or issue. E.g., “What are the symptoms of depression?”
• Relational: Investigates relationships between variables. E.g., “Is there a correlation between stress and heart disease?”
• Causal: Determines cause and effect. E.g., “Does smoking cause lung cancer?”

What are some examples of a research question?

Examples of research questions:

• How does social media influence self-esteem in adolescents?
• What are the economic impacts of climate change on agriculture?
• What factors contribute to employee job satisfaction in the tech industry?
• How does exercise frequency affect cardiovascular health?
• What is the relationship between sleep duration and academic performance in college students?

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How To Write The Results/Findings Chapter

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

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

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

But how’s that different from the discussion chapter?

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

Let’s look at an example.

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

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

What should you include in the results chapter?

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

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

How do I decide what’s relevant?

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

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

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

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

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

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

Need a helping hand?

How do I write the results chapter?

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

Step 1 – Revisit your research questions

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

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

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

Step 2 – Craft an overview introduction

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

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

Step 3 – Present the sample demographic data

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

For example:

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

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

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

But what if I’m not interested in generalisability?

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

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

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

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

#1: Composite measures

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

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

#2: Data shape

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

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

Step 5 – Present the descriptive statistics

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

For scaled data, this usually includes statistics such as:

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

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

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

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

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

Step 6 – Present the inferential statistics

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

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

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

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

Step 7 – Test your hypotheses

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

The basic process for hypothesis testing is as follows:

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

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

Step 8 – Provide a chapter summary

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

Some final thoughts, tips and tricks

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

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

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

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Thank you. I will try my best to write my results.

Awesome content 👏🏾

this was great explaination

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