example of data analysis in quantitative research proposal

How to Create a Data Analysis Plan: A Detailed Guide

by Barche Blaise | Aug 12, 2020 | Writing

If a good research question equates to a story then, a roadmap will be very vita l for good storytelling. We advise every student/researcher to personally write his/her data analysis plan before seeking any advice. In this blog article, we will explore how to create a data analysis plan: the content and structure.

This data analysis plan serves as a roadmap to how data collected will be organised and analysed. It includes the following aspects:

• Clearly states the research objectives and hypothesis
• Identifies the dataset to be used
• Inclusion and exclusion criteria
• Clearly states the research variables
• States statistical test hypotheses and the software for statistical analysis
• Creating shell tables

1. Stating research question(s), objectives and hypotheses:

All research objectives or goals must be clearly stated. They must be Specific, Measurable, Attainable, Realistic and Time-bound (SMART). Hypotheses are theories obtained from personal experience or previous literature and they lay a foundation for the statistical methods that will be applied to extrapolate results to the entire population.

2. The dataset:

The dataset that will be used for statistical analysis must be described and important aspects of the dataset outlined. These include; owner of the dataset, how to get access to the dataset, how the dataset was checked for quality control and in what program is the dataset stored (Excel, Epi Info, SQL, Microsoft access etc.).

3. The inclusion and exclusion criteria :

They guide the aspects of the dataset that will be used for data analysis. These criteria will also guide the choice of variables included in the main analysis.

4. Variables:

Every variable collected in the study should be clearly stated. They should be presented based on the level of measurement (ordinal/nominal or ratio/interval levels), or the role the variable plays in the study (independent/predictors or dependent/outcome variables). The variable types should also be outlined.  The variable type in conjunction with the research hypothesis forms the basis for selecting the appropriate statistical tests for inferential statistics. A good data analysis plan should summarize the variables as demonstrated in Figure 1 below.

5. Statistical software

There are tons of software packages for data analysis, some common examples are SPSS, Epi Info, SAS, STATA, Microsoft Excel. Include the version number,  year of release and author/manufacturer. Beginners have the tendency to try different software and finally not master any. It is rather good to select one and master it because almost all statistical software have the same performance for basic and the majority of advance analysis needed for a student thesis. This is what we recommend to all our students at CRENC before they begin writing their results section .

6. Selecting the appropriate statistical method to test hypotheses

Depending on the research question, hypothesis and type of variable, several statistical methods can be used to answer the research question appropriately. This aspect of the data analysis plan outlines clearly why each statistical method will be used to test hypotheses. The level of statistical significance (p-value) which is often but not always <0.05 should also be written.  Presented in figures 2a and 2b are decision trees for some common statistical tests based on the variable type and research question

A good analysis plan should clearly describe how missing data will be analysed.

7. Creating shell tables

Data analysis involves three levels of analysis; univariable, bivariable and multivariable analysis with increasing order of complexity. Shell tables should be created in anticipation for the results that will be obtained from these different levels of analysis. Read our blog article on how to present tables and figures for more details. Suppose you carry out a study to investigate the prevalence and associated factors of a certain disease “X” in a population, then the shell tables can be represented as in Tables 1, Table 2 and Table 3 below.

Table 1: Example of a shell table from univariate analysis

Table 2: Example of a shell table from bivariate analysis

Table 3: Example of a shell table from multivariate analysis

aOR = adjusted odds ratio

Now that you have learned how to create a data analysis plan, these are the takeaway points. It should clearly state the:

• Research question, objectives, and hypotheses
• Dataset to be used
• Variable types and their role
• Statistical software and statistical methods
• Shell tables for univariate, bivariate and multivariate analysis

Further readings

Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552232/pdf/cjhp-68-311.pdf

Creating an Analysis Plan: https://www.cdc.gov/globalhealth/healthprotection/fetp/training_modules/9/creating-analysis-plan_pw_final_09242013.pdf

Data Analysis Plan: https://www.statisticssolutions.com/dissertation-consulting-services/data-analysis-plan-2/

Photo created by freepik – www.freepik.com

Dr Barche is a physician and holds a Masters in Public Health. He is a senior fellow at CRENC with interests in Data Science and Data Analysis.

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16 comments.

Thanks. Quite informative.

Educative write-up. Thanks.

Easy to understand. Thanks Dr

Very explicit Dr. Thanks

I will always remember how you help me conceptualize and understand data science in a simple way. I can only hope that someday I’ll be in a position to repay you, my dear friend.

Plan d’analyse

This is interesting, Thanks

Very understandable and informative. Thank you..

love the figures.

Nice, and informative

This is so much educative and good for beginners, I would love to recommend that you create and share a video because some people are able to grasp when there is an instructor. Lots of love

Thank you Doctor very helpful.

Educative and clearly written. Thanks

Well said doctor,thank you.But when do you present in tables ,bars,pie chart etc?

Very informative guide!

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness 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, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

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 ...

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75 Comments

Hi, I have read your article. Such a brilliant post you have created.

Thank you for the feedback. Good luck with your quantitative analysis.

Thank you so much.

Thank you so much. I learnt much well. I love your summaries of the concepts. I had love you to explain how to input data using SPSS

Amazing and simple way of breaking down quantitative methods.

This is beautiful….especially for non-statisticians. I have skimmed through but I wish to read again. and please include me in other articles of the same nature when you do post. I am interested. I am sure, I could easily learn from you and get off the fear that I have had in the past. Thank you sincerely.

Send me every new information you might have.

i need every new information

Thank you for the blog. It is quite informative. Dr Peter Nemaenzhe PhD

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Your article is so good! However, I am still a bit lost. I am doing a secondary research on Gun control in the US and increase in crime rates and I am not sure which analysis method I should use?

Based on the given learning points, this is inferential analysis, thus, use ‘t-tests, ANOVA, correlation and regression analysis’

Well explained notes. Am an MPH student and currently working on my thesis proposal, this has really helped me understand some of the things I didn’t know.

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Do you provide any assistance for other steps of research methodology like making research problem testing hypothesis report and thesis writing?

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I am doing a quality improvement project to determine if the implementation of a protocol will change prescribing habits. Would this be a t-test?

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So I am writing exams and would like to know how do establish which method of data analysis to use from the below research questions: I am a bit lost as to how I determine the data analysis method from the research questions.

Do female employees report higher job satisfaction than male employees with similar job descriptions across the South African telecommunications sector? – I though that maybe Chi Square could be used here. – Is there a gender difference in talented employees’ actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – Is there a gender difference in the cost of actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – What practical recommendations can be made to the management of South African telecommunications companies on leveraging gender to mitigate employee turnover decisions?

Your assistance will be appreciated if I could get a response as early as possible tomorrow

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Many thanks for your useful lecture, I would be really appreciated if you could possibly share with me the PPT of presentation related to Data type?

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I have been struggling to understand a lot of these concepts. Thank you for the informative piece which is written with outstanding clarity.

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Always greet intro and summary. I learn so much from GradCoach

Quite informative. Simple and clear summary.

I thoroughly enjoyed reading your informative and inspiring piece. Your profound insights into this topic truly provide a better understanding of its complexity. I agree with the points you raised, especially when you delved into the specifics of the article. In my opinion, that aspect is often overlooked and deserves further attention.

Absolutely!!! Thank you

Thank you very much for this post. It made me to understand how to do my data analysis.

its nice work and excellent job ,you have made my work easier

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Designing Research Proposal in Quantitative Approach

• First Online: 27 October 2022

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• Md. Rezaul Karim 4  

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This chapter provides a comprehensive guideline for writing a research proposal in quantitative approach. It starts with the definition and purpose of writing a research proposal followed by a description of essential parts of a research proposal and subjects included in each part, organization of a research proposal, and guidelines for writing different parts of a research proposal including practical considerations and aims of a proposal that facilitate the acceptance of the proposal. Finally, an example of a quantitative research proposal has been presented. It is expected that research students and other interested researchers will be able to write their research proposal(s) using the guidelines presented in the chapter.

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Burns, N., Grove, S. K. (2009). The practice of nursing research: Appraisal, synthesis and generation of evidence. Saunders Elsevier.

Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches . SAGE Publications.

Hasnat, M. A. (2017). School enrollment high but dropouts even higher. Dhaka Tribune September 8, 2017. https://www.Dhakatribune.com/Bangladesh/education/2017/09/08/school-enrollment-high-dropouts-even-higher .

Institute of International Studies. (n.d). Dissertation proposal workshop. Institute of International Studies. http://iis.berkeley.edu/node/424 .

Pajares, F. (n.d). The elements of a proposal. Emory University. Retrieved from http://www.uky.edu/~eushe2/Pajares/ElementsOfaProposal.pdf .

Przeworski, A., & Frank, S. (1995). On the art of writing proposals: some candid suggestions for applicants to social science research council competitions. Social Science Research Council. Retrieved from http://iis.berkeley.edu/sites/default/files/pdf/the_art_of_writing_proposals.pdf .

University of Michigan. (n.d). Research and sponsored projects. http://orsp.umich.edu/proposal-writers-guide-research-proposals-title-page .

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Karim, M.R. (2022). Designing Research Proposal in Quantitative Approach. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_10

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Writing a Rsearch Proposal

A  research proposal  describes what you will investigate, why it’s important, and how you will conduct your research.  Your paper should include the topic, research question and hypothesis, methods, predictions, and results (if not actual, then projected).

Research Proposal Aims

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

• Introduction

Literature review

• Research design

Reference list

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

Proposal Format

The proposal will usually have a  title page  that includes:

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

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

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

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

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

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

Research design and methods

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

Contribution to knowledge

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

For example, your results might have implications for:

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

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

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Quantitative Data Analysis: Types, Analysis & Examples

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Analysis of Quantitative data enables you to transform raw data points, typically organised in spreadsheets, into actionable insights. Refer to the article to know more!

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Table of Contents

What is the Quantitative Analysis Method?

Quantitative Analysis refers to a mathematical approach that gathers and evaluates measurable and verifiable data. This method is utilized to assess performance and various aspects of a business or research. It involves the use of mathematical and statistical techniques to analyze data. Quantitative methods emphasize objective measurements, focusing on statistical, analytical, or numerical analysis of data. It collects data and studies it to derive insights or conclusions.

In a business context, it helps in evaluating the performance and efficiency of operations. Quantitative analysis can be applied across various domains, including finance, research, and chemistry, where data can be converted into numbers for analysis.

Also Read: Analysis vs. Analytics: How Are They Different?

What is the Best Analysis for Quantitative Data?

The “best” analysis for quantitative data largely depends on the specific research objectives, the nature of the data collected, the research questions posed, and the context in which the analysis is conducted. Quantitative data analysis encompasses a wide range of techniques, each suited for different purposes. Here are some commonly employed methods, along with scenarios where they might be considered most appropriate:

1) Descriptive Statistics:

• When to Use: To summarize and describe the basic features of the dataset, providing simple summaries about the sample and measures of central tendency and variability.
• Example: Calculating means, medians, standard deviations, and ranges to describe a dataset.

2) Inferential Statistics:

• When to Use: When you want to make predictions or inferences about a population based on a sample, testing hypotheses, or determining relationships between variables.
• Example: Conducting t-tests to compare means between two groups or performing regression analysis to understand the relationship between an independent variable and a dependent variable.

3) Correlation and Regression Analysis:

• When to Use: To examine relationships between variables, determining the strength and direction of associations, or predicting one variable based on another.
• Example: Assessing the correlation between customer satisfaction scores and sales revenue or predicting house prices based on variables like location, size, and amenities.

4) Factor Analysis:

• When to Use: When dealing with a large set of variables and aiming to identify underlying relationships or latent factors that explain patterns of correlations within the data.
• Example: Exploring underlying constructs influencing employee engagement using survey responses across multiple indicators.

5) Time Series Analysis:

• When to Use: When analyzing data points collected or recorded at successive time intervals to identify patterns, trends, seasonality, or forecast future values.
• Example: Analyzing monthly sales data over several years to detect seasonal trends or forecasting stock prices based on historical data patterns.

6) Cluster Analysis:

• When to Use: To segment a dataset into distinct groups or clusters based on similarities, enabling pattern recognition, customer segmentation, or data reduction.
• Example: Segmenting customers into distinct groups based on purchasing behavior, demographic factors, or preferences.

The “best” analysis for quantitative data is not one-size-fits-all but rather depends on the research objectives, hypotheses, data characteristics, and contextual factors. Often, a combination of analytical techniques may be employed to derive comprehensive insights and address multifaceted research questions effectively. Therefore, selecting the appropriate analysis requires careful consideration of the research goals, methodological rigor, and interpretative relevance to ensure valid, reliable, and actionable outcomes.

Analysis of Quantitative Data in Quantitative Research

Analyzing quantitative data in quantitative research involves a systematic process of examining numerical information to uncover patterns, relationships, and insights that address specific research questions or objectives. Here’s a structured overview of the analysis process:

1) Data Preparation:

• Data Cleaning: Identify and address errors, inconsistencies, missing values, and outliers in the dataset to ensure its integrity and reliability.
• Variable Transformation: Convert variables into appropriate formats or scales, if necessary, for analysis (e.g., normalization, standardization).

2) Descriptive Statistics:

• Central Tendency: Calculate measures like mean, median, and mode to describe the central position of the data.
• Variability: Assess the spread or dispersion of data using measures such as range, variance, standard deviation, and interquartile range.
• Frequency Distribution: Create tables, histograms, or bar charts to display the distribution of values for categorical or discrete variables.

3) Exploratory Data Analysis (EDA):

• Data Visualization: Generate graphical representations like scatter plots, box plots, histograms, or heatmaps to visualize relationships, distributions, and patterns in the data.
• Correlation Analysis: Examine the strength and direction of relationships between variables using correlation coefficients.

4) Inferential Statistics:

• Hypothesis Testing: Formulate null and alternative hypotheses based on research questions, selecting appropriate statistical tests (e.g., t-tests, ANOVA, chi-square tests) to assess differences, associations, or effects.
• Confidence Intervals: Estimate population parameters using sample statistics and determine the range within which the true parameter is likely to fall.

5) Regression Analysis:

• Linear Regression: Identify and quantify relationships between an outcome variable and one or more predictor variables, assessing the strength, direction, and significance of associations.
• Multiple Regression: Evaluate the combined effect of multiple independent variables on a dependent variable, controlling for confounding factors.

6) Factor Analysis and Structural Equation Modeling:

• Factor Analysis: Identify underlying dimensions or constructs that explain patterns of correlations among observed variables, reducing data complexity.
• Structural Equation Modeling (SEM): Examine complex relationships between observed and latent variables, assessing direct and indirect effects within a hypothesized model.

7) Time Series Analysis and Forecasting:

• Trend Analysis: Analyze patterns, trends, and seasonality in time-ordered data to understand historical patterns and predict future values.
• Forecasting Models: Develop predictive models (e.g., ARIMA, exponential smoothing) to anticipate future trends, demand, or outcomes based on historical data patterns.

8) Interpretation and Reporting:

• Interpret Results: Translate statistical findings into meaningful insights, discussing implications, limitations, and conclusions in the context of the research objectives.
• Documentation: Document the analysis process, methodologies, assumptions, and findings systematically for transparency, reproducibility, and peer review.

Also Read: Learning Path to Become a Data Analyst in 2024

Analysis of Quantitative Data Examples

Analyzing quantitative data involves various statistical methods and techniques to derive meaningful insights from numerical data. Here are some examples illustrating the analysis of quantitative data across different contexts:

How to Write Data Analysis in Quantitative Research Proposal?

Writing the data analysis section in a quantitative research proposal requires careful planning and organization to convey a clear, concise, and methodologically sound approach to analyzing the collected data. Here’s a step-by-step guide on how to write the data analysis section effectively:

Step 1: Begin with an Introduction

• Contextualize : Briefly reintroduce the research objectives, questions, and the significance of the study.
• Purpose Statement : Clearly state the purpose of the data analysis section, outlining what readers can expect in this part of the proposal.

Step 2: Describe Data Collection Methods

• Detail Collection Techniques : Provide a concise overview of the methods used for data collection (e.g., surveys, experiments, observations).
• Instrumentation : Mention any tools, instruments, or software employed for data gathering and its relevance.

Step 3 : Discuss Data Cleaning Procedures

• Data Cleaning : Describe the procedures for cleaning and pre-processing the data.
• Handling Outliers & Missing Data : Explain how outliers, missing values, and other inconsistencies will be managed to ensure data quality.

Step 4 : Present Analytical Techniques

• Descriptive Statistics : Outline the descriptive statistics that will be calculated to summarize the data (e.g., mean, median, mode, standard deviation).
• Inferential Statistics : Specify the inferential statistical tests or models planned for deeper analysis (e.g., t-tests, ANOVA, regression).

Step 5: State Hypotheses & Testing Procedures

• Hypothesis Formulation : Clearly state the null and alternative hypotheses based on the research questions or objectives.
• Testing Strategy : Detail the procedures for hypothesis testing, including the chosen significance level (e.g., α = 0.05) and statistical criteria.

Step 6 : Provide a Sample Analysis Plan

• Step-by-Step Plan : Offer a sample plan detailing the sequence of steps involved in the data analysis process.
• Software & Tools : Mention any specific statistical software or tools that will be utilized for analysis.

Step 7 : Address Validity & Reliability

• Validity : Discuss how you will ensure the validity of the data analysis methods and results.
• Reliability : Explain measures taken to enhance the reliability and replicability of the study findings.

Step 8 : Discuss Ethical Considerations

• Ethical Compliance : Address ethical considerations related to data privacy, confidentiality, and informed consent.
• Compliance with Guidelines : Ensure that your data analysis methods align with ethical guidelines and institutional policies.

Step 9 : Acknowledge Limitations

• Limitations : Acknowledge potential limitations in the data analysis methods or data set.
• Mitigation Strategies : Offer strategies or alternative approaches to mitigate identified limitations.

Step 10 : Conclude the Section

• Summary : Summarize the key points discussed in the data analysis section.
• Transition : Provide a smooth transition to subsequent sections of the research proposal, such as the conclusion or references.

Step 11 : Proofread & Revise

• Review : Carefully review the data analysis section for clarity, coherence, and consistency.
• Feedback : Seek feedback from peers, advisors, or mentors to refine your approach and ensure methodological rigor.

What are the 4 Types of Quantitative Analysis?

Quantitative analysis encompasses various methods to evaluate and interpret numerical data. While the specific categorization can vary based on context, here are four broad types of quantitative analysis commonly recognized:

• Descriptive Analysis: This involves summarizing and presenting data to describe its main features, such as mean, median, mode, standard deviation, and range. Descriptive statistics provide a straightforward overview of the dataset’s characteristics.
• Inferential Analysis: This type of analysis uses sample data to make predictions or inferences about a larger population. Techniques like hypothesis testing, regression analysis, and confidence intervals fall under this category. The goal is to draw conclusions that extend beyond the immediate data collected.
• Time-Series Analysis: In this method, data points are collected, recorded, and analyzed over successive time intervals. Time-series analysis helps identify patterns, trends, and seasonal variations within the data. It’s particularly useful in forecasting future values based on historical trends.
• Causal or Experimental Research: This involves establishing a cause-and-effect relationship between variables. Through experimental designs, researchers manipulate one variable to observe the effect on another variable while controlling for external factors. Randomized controlled trials are a common method within this type of quantitative analysis.

Each type of quantitative analysis serves specific purposes and is applied based on the nature of the data and the research objectives.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Steps to Effective Quantitative Data Analysis 

Quantitative data analysis need not be daunting; it’s a systematic process that anyone can master. To harness actionable insights from your company’s data, follow these structured steps:

Step 1 : Gather Data Strategically

Initiating the analysis journey requires a foundation of relevant data. Employ quantitative research methods to accumulate numerical insights from diverse channels such as:

• Interviews or Focus Groups: Engage directly with stakeholders or customers to gather specific numerical feedback.
• Digital Analytics: Utilize tools like Google Analytics to extract metrics related to website traffic, user behavior, and conversions.
• Observational Tools: Leverage heatmaps, click-through rates, or session recordings to capture user interactions and preferences.
• Structured Questionnaires: Deploy surveys or feedback mechanisms that employ close-ended questions for precise responses.

Ensure that your data collection methods align with your research objectives, focusing on granularity and accuracy.

Step 2 : Refine and Cleanse Your Data

Raw data often comes with imperfections. Scrutinize your dataset to identify and rectify:

• Errors and Inconsistencies: Address any inaccuracies or discrepancies that could mislead your analysis.
• Duplicates: Eliminate repeated data points that can skew results.
• Outliers: Identify and assess outliers, determining whether they should be adjusted or excluded based on contextual relevance.

Cleaning your dataset ensures that subsequent analyses are based on reliable and consistent information, enhancing the credibility of your findings.

Step 3 : Delve into Analysis with Precision

With a refined dataset at your disposal, transition into the analytical phase. Employ both descriptive and inferential analysis techniques:

• Descriptive Analysis: Summarize key attributes of your dataset, computing metrics like averages, distributions, and frequencies.
• Inferential Analysis: Leverage statistical methodologies to derive insights, explore relationships between variables, or formulate predictions.

The objective is not just number crunching but deriving actionable insights. Interpret your findings to discern underlying patterns, correlations, or trends that inform strategic decision-making. For instance, if data indicates a notable relationship between user engagement metrics and specific website features, consider optimizing those features for enhanced user experience.

Step 4 : Visual Representation and Communication

Transforming your analytical outcomes into comprehensible narratives is crucial for organizational alignment and decision-making. Leverage visualization tools and techniques to:

• Craft Engaging Visuals: Develop charts, graphs, or dashboards that encapsulate key findings and insights.
• Highlight Insights: Use visual elements to emphasize critical data points, trends, or comparative metrics effectively.
• Facilitate Stakeholder Engagement: Share your visual representations with relevant stakeholders, ensuring clarity and fostering informed discussions.

Tools like Tableau, Power BI, or specialized platforms like Hotjar can simplify the visualization process, enabling seamless representation and dissemination of your quantitative insights.

Also Read: Top 10 Must Use AI Tools for Data Analysis [2024 Edition]

Statistical Analysis in Quantitative Research

Statistical analysis is a cornerstone of quantitative research, providing the tools and techniques to interpret numerical data systematically. By applying statistical methods, researchers can identify patterns, relationships, and trends within datasets, enabling evidence-based conclusions and informed decision-making. Here’s an overview of the key aspects and methodologies involved in statistical analysis within quantitative research:

• Mean, Median, Mode: Measures of central tendency that summarize the average, middle, and most frequent values in a dataset, respectively.
• Standard Deviation, Variance: Indicators of data dispersion or variability around the mean.
• Frequency Distributions: Tabular or graphical representations that display the distribution of data values or categories.
• Hypothesis Testing: Formal methodologies to test hypotheses or assumptions about population parameters using sample data. Common tests include t-tests, chi-square tests, ANOVA, and regression analysis.
• Confidence Intervals: Estimation techniques that provide a range of values within which a population parameter is likely to lie, based on sample data.
• Correlation and Regression Analysis: Techniques to explore relationships between variables, determining the strength and direction of associations. Regression analysis further enables prediction and modeling based on observed data patterns.

3) Probability Distributions:

• Normal Distribution: A bell-shaped distribution often observed in naturally occurring phenomena, forming the basis for many statistical tests.
• Binomial, Poisson, and Exponential Distributions: Specific probability distributions applicable to discrete or continuous random variables, depending on the nature of the research data.

4) Multivariate Analysis:

• Factor Analysis: A technique to identify underlying relationships between observed variables, often used in survey research or data reduction scenarios.
• Cluster Analysis: Methodologies that group similar objects or individuals based on predefined criteria, enabling segmentation or pattern recognition within datasets.
• Multivariate Regression: Extending regression analysis to multiple independent variables, assessing their collective impact on a dependent variable.

5) Data Modeling and Forecasting:

• Time Series Analysis: Analyzing data points collected or recorded at specific time intervals to identify patterns, trends, or seasonality.
• Predictive Analytics: Leveraging statistical models and machine learning algorithms to forecast future trends, outcomes, or behaviors based on historical data.

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Analysis of Quantitative Data FAQs

What is quantitative data analysis.

Quantitative data analysis involves the systematic process of collecting, cleaning, interpreting, and presenting numerical data to identify patterns, trends, and relationships through statistical methods and mathematical calculations.

What are the main steps involved in quantitative data analysis?

The primary steps include data collection, data cleaning, statistical analysis (descriptive and inferential), interpretation of results, and visualization of findings using graphs or charts.

What is the difference between descriptive and inferential analysis?

Descriptive analysis summarizes and describes the main aspects of the dataset (e.g., mean, median, mode), while inferential analysis draws conclusions or predictions about a population based on a sample, using statistical tests and models.

How do I handle outliers in my quantitative data?

Outliers can be managed by identifying them through statistical methods, understanding their nature (error or valid data), and deciding whether to remove them, transform them, or conduct separate analyses to understand their impact.

Which statistical tests should I use for my quantitative research?

The choice of statistical tests depends on your research design, data type, and research questions. Common tests include t-tests, ANOVA, regression analysis, chi-square tests, and correlation analysis, among others.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

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

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on 30 October 2022 by Shona McCombes and Tegan George. Revised on 13 June 2023.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

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

Introduction

Literature review.

• Research design

Reference list

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

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, frequently asked questions.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: ‘A Conceptual Framework for Scheduling Constraint Management’
• Example research proposal #2: ‘ Medical Students as Mediators of Change in Tobacco Use’

Like your dissertation or thesis, the proposal will usually have a title page that includes:

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

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

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

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

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

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

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

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

For example, your results might have implications for:

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

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

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McCombes, S. & George, T. (2023, June 13). How to Write a Research Proposal | Examples & Templates. Scribbr. Retrieved 21 May 2024, from https://www.scribbr.co.uk/the-research-process/research-proposal-explained/

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Home » How To Write A Research Proposal – Step-by-Step [Template]

How To Write A Research Proposal – Step-by-Step [Template]

Table of Contents

How To Write a Research Proposal

Writing a Research proposal involves several steps to ensure a well-structured and comprehensive document. Here is an explanation of each step:

1. Title and Abstract

• Choose a concise and descriptive title that reflects the essence of your research.
• Write an abstract summarizing your research question, objectives, methodology, and expected outcomes. It should provide a brief overview of your proposal.

2. Introduction:

• Provide an introduction to your research topic, highlighting its significance and relevance.
• Clearly state the research problem or question you aim to address.
• Discuss the background and context of the study, including previous research in the field.

3. Research Objectives

• Outline the specific objectives or aims of your research. These objectives should be clear, achievable, and aligned with the research problem.

4. Literature Review:

• Conduct a comprehensive review of relevant literature and studies related to your research topic.
• Summarize key findings, identify gaps, and highlight how your research will contribute to the existing knowledge.

5. Methodology:

• Describe the research design and methodology you plan to employ to address your research objectives.
• Explain the data collection methods, instruments, and analysis techniques you will use.
• Justify why the chosen methods are appropriate and suitable for your research.

6. Timeline:

• Create a timeline or schedule that outlines the major milestones and activities of your research project.
• Break down the research process into smaller tasks and estimate the time required for each task.

7. Resources:

• Identify the resources needed for your research, such as access to specific databases, equipment, or funding.
• Explain how you will acquire or utilize these resources to carry out your research effectively.

8. Ethical Considerations:

• Discuss any ethical issues that may arise during your research and explain how you plan to address them.
• If your research involves human subjects, explain how you will ensure their informed consent and privacy.

9. Expected Outcomes and Significance:

• Clearly state the expected outcomes or results of your research.
• Highlight the potential impact and significance of your research in advancing knowledge or addressing practical issues.

10. References:

• Provide a list of all the references cited in your proposal, following a consistent citation style (e.g., APA, MLA).

11. Appendices:

• Include any additional supporting materials, such as survey questionnaires, interview guides, or data analysis plans.

Research Proposal Format

The format of a research proposal may vary depending on the specific requirements of the institution or funding agency. However, the following is a commonly used format for a research proposal:

1. Title Page:

• Include the title of your research proposal, your name, your affiliation or institution, and the date.

2. Abstract:

• Provide a brief summary of your research proposal, highlighting the research problem, objectives, methodology, and expected outcomes.

3. Introduction:

• Introduce the research topic and provide background information.
• State the research problem or question you aim to address.
• Explain the significance and relevance of the research.
• Review relevant literature and studies related to your research topic.
• Summarize key findings and identify gaps in the existing knowledge.
• Explain how your research will contribute to filling those gaps.

5. Research Objectives:

• Clearly state the specific objectives or aims of your research.
• Ensure that the objectives are clear, focused, and aligned with the research problem.

6. Methodology:

• Describe the research design and methodology you plan to use.
• Explain the data collection methods, instruments, and analysis techniques.
• Justify why the chosen methods are appropriate for your research.

7. Timeline:

8. Resources:

• Explain how you will acquire or utilize these resources effectively.

9. Ethical Considerations:

• If applicable, explain how you will ensure informed consent and protect the privacy of research participants.

10. Expected Outcomes and Significance:

11. References:

12. Appendices:

Research Proposal Template

Here’s a template for a research proposal:

1. Introduction:

2. Literature Review:

3. Research Objectives:

4. Methodology:

5. Timeline:

6. Resources:

7. Ethical Considerations:

8. Expected Outcomes and Significance:

9. References:

10. Appendices:

Research Proposal Sample

Title: The Impact of Online Education on Student Learning Outcomes: A Comparative Study

1. Introduction

Online education has gained significant prominence in recent years, especially due to the COVID-19 pandemic. This research proposal aims to investigate the impact of online education on student learning outcomes by comparing them with traditional face-to-face instruction. The study will explore various aspects of online education, such as instructional methods, student engagement, and academic performance, to provide insights into the effectiveness of online learning.

2. Objectives

The main objectives of this research are as follows:

• To compare student learning outcomes between online and traditional face-to-face education.
• To examine the factors influencing student engagement in online learning environments.
• To assess the effectiveness of different instructional methods employed in online education.
• To identify challenges and opportunities associated with online education and suggest recommendations for improvement.

3. Methodology

3.1 Study Design

This research will utilize a mixed-methods approach to gather both quantitative and qualitative data. The study will include the following components:

3.2 Participants

The research will involve undergraduate students from two universities, one offering online education and the other providing face-to-face instruction. A total of 500 students (250 from each university) will be selected randomly to participate in the study.

3.3 Data Collection

The research will employ the following data collection methods:

• Quantitative: Pre- and post-assessments will be conducted to measure students’ learning outcomes. Data on student demographics and academic performance will also be collected from university records.
• Qualitative: Focus group discussions and individual interviews will be conducted with students to gather their perceptions and experiences regarding online education.

3.4 Data Analysis

Quantitative data will be analyzed using statistical software, employing descriptive statistics, t-tests, and regression analysis. Qualitative data will be transcribed, coded, and analyzed thematically to identify recurring patterns and themes.

4. Ethical Considerations

The study will adhere to ethical guidelines, ensuring the privacy and confidentiality of participants. Informed consent will be obtained, and participants will have the right to withdraw from the study at any time.

5. Significance and Expected Outcomes

This research will contribute to the existing literature by providing empirical evidence on the impact of online education on student learning outcomes. The findings will help educational institutions and policymakers make informed decisions about incorporating online learning methods and improving the quality of online education. Moreover, the study will identify potential challenges and opportunities related to online education and offer recommendations for enhancing student engagement and overall learning outcomes.

6. Timeline

The proposed research will be conducted over a period of 12 months, including data collection, analysis, and report writing.

The estimated budget for this research includes expenses related to data collection, software licenses, participant compensation, and research assistance. A detailed budget breakdown will be provided in the final research plan.

8. Conclusion

This research proposal aims to investigate the impact of online education on student learning outcomes through a comparative study with traditional face-to-face instruction. By exploring various dimensions of online education, this research will provide valuable insights into the effectiveness and challenges associated with online learning. The findings will contribute to the ongoing discourse on educational practices and help shape future strategies for maximizing student learning outcomes in online education settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

• 1.1 AP Statistics Topics for Project
• 1.2 Statistics Project Topics for High School Students
• 1.3 Statistical Survey Topics
• 1.4 Statistical Experiment Ideas
• 1.5 Easy Stats Project Ideas
• 1.6 Business Ideas for Statistics Project
• 1.7 Socio-Economic Easy Statistics Project Ideas
• 1.8 Experiment Ideas for Statistics and Analysis
• 2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

• Challenging concepts suitable for advanced placement courses.
• Accessible ideas that are engaging and educational for younger students.
• Ideas for conducting surveys and analyzing the results.
• Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

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AP Statistics Topics for Project

• Analyzing Variance in Climate Data Over Decades.
• The Correlation Between Economic Indicators and Standard of Living.
• Statistical Analysis of Voter Behavior Patterns.
• Probability Models in Sports: Predicting Outcomes.
• The Effectiveness of Different Teaching Methods: A Statistical Study.
• Analysis of Demographic Data in Public Health.
• Time Series Analysis of Stock Market Trends.
• Investigating the Impact of Social Media on Academic Performance.
• Survival Analysis in Clinical Trial Data.
• Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

• The Mathematics of Personal Finance: Budgeting and Spending Habits.
• Analysis of Class Performance: Test Scores and Study Habits.
• A Statistical Comparison of Local Public Transportation Options.
• Survey on Dietary Habits and Physical Health Among Teenagers.
• Analyzing the Popularity of Various Music Genres in School.
• The Impact of Sleep on Academic Performance: A Statistical Approach.
• Statistical Study on the Use of Technology in Education.
• Comparing Athletic Performance Across Different Sports.
• Trends in Social Media Usage Among High School Students.
• The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

• Public Opinion on Environmental Conservation Efforts.
• Consumer Preferences in the Fast Food Industry.
• Attitudes Towards Online Learning vs. Traditional Classroom Learning.
• Survey on Workplace Satisfaction and Productivity.
• Public Health: Attitudes Towards Vaccination.
• Trends in Mobile Phone Usage and Preferences.
• Community Response to Local Government Policies.
• Consumer Behavior in Online vs. Offline Shopping.
• Perceptions of Public Safety and Law Enforcement.
• Social Media Influence on Political Opinions.

Statistical Experiment Ideas

• The Effect of Light on Plant Growth.
• Memory Retention: Visual vs. Auditory Information.
• Caffeine Consumption and Cognitive Performance.
• The Impact of Exercise on Stress Levels.
• Testing the Efficacy of Natural vs. Chemical Fertilizers.
• The Influence of Color on Mood and Perception.
• Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
• The Effectiveness of Different Types of Water Filters.
• Analyzing the Impact of Room Temperature on Concentration.
• Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

• Average Daily Screen Time Among Students.
• Analyzing the Most Common Birth Months.
• Favorite School Subjects Among Peers.
• Average Time Spent on Homework Weekly.
• Frequency of Public Transport Usage.
• Comparison of Pet Ownership in the Community.
• Favorite Types of Movies or TV Shows.
• Daily Water Consumption Habits.
• Common Breakfast Choices and Their Nutritional Value.
• Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

• Analyzing Customer Satisfaction in Retail Stores.
• Market Analysis of a New Product Launch.
• Employee Performance Metrics and Organizational Success.
• Sales Data Analysis for E-commerce Websites.
• Impact of Advertising on Consumer Buying Behavior.
• Analysis of Supply Chain Efficiency.
• Customer Loyalty and Retention Strategies.
• Trend Analysis in Social Media Marketing.
• Financial Risk Assessment in Investment Decisions.
• Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

• Income Inequality and Its Impact on Education.
• The Correlation Between Unemployment Rates and Crime Levels.
• Analyzing the Effects of Minimum Wage Changes.
• The Relationship Between Public Health Expenditure and Population Health.
• Demographic Analysis of Housing Affordability.
• The Impact of Immigration on Local Economies.
• Analysis of Gender Pay Gap in Different Industries.
• Statistical Study of Homelessness Causes and Solutions.
• Education Levels and Their Impact on Job Opportunities.
• Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

• Multivariate Analysis of Global Climate Change Data.
• Time-Series Analysis in Predicting Economic Recessions.
• Logistic Regression in Medical Outcome Prediction.
• Machine Learning Applications in Statistical Modeling.
• Network Analysis in Social Media Data.
• Bayesian Analysis of Scientific Research Data.
• The Use of Factor Analysis in Psychology Studies.
• Spatial Data Analysis in Geographic Information Systems (GIS).
• Predictive Analysis in Customer Relationship Management (CRM).
• Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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