Globalstats Academic

Statistic consultant for academic research.

Data Analysis in Quantitative Research Proposal

Data Analysis in Quantitative Research Proposal

Definition of data analysis.

Data analysis in quantitative research proposal is one part of the chapter that researchers need in the beginning of writing a research proposal. Whereas in the research, it is an activity after the data from all collected. Activities in data analysis are: grouping data based on variables and types of respondents, tabulating data based on variables from all respondents, presenting data for each variable studied, doing calculations to answer the problem formulation, and doing calculations to test the proposed hypothesis.

Quantitative Data Analysis Techniques

In a research proposal, it must be clear what method of analysis is capable of answering the research hypothesis. Hypothesis is a temporary answer to the research problem. Data analysis techniques in quantitative research commonly use statistics. There are two kinds of statistical data analysis in research. These are descriptive statistics and inferential statistics. Inferential statistics include parametric and non-parametric statistics.

Descriptive statistics

In preparing research proposals, researchers need to explain what is descriptive research. Descriptive statistic is a method to analyze data by describing data without intending to make generalizations. Descriptive statistics only describes the sample data and does not make conclusions that apply to the population. While, conclusion that applies to the population, then the data analysis technique is inferential statistics. In addition descriptive statistics also function to present information in such a way that data generated from research can be utilized by others in need.

Inferential Statistics

When researchers want to generalize broader conclusions in the research proposal, it is necessary to write inferential statistics. Inferential statistics (often also commonly inductive statistics or probability statistics) are statistical techniques used to analyze sample data and the results are applied to populations. It requires a random sampling process.

Inferential research involves statistical probability. Using of probability theory is to approach sample to the population. A conclusion applying to the population has a chance of error and truth level. If the chance of error is 5%, then the truth level is 95%. While the chance of error is 1%, then the truth level is 99%. This opportunity for error and truth is the level of significance. Statistical tables are useful for carrying out tests of the significance of this error. For example, t-test will use table-t. in each table provides significance level of what percentage of the results. For example the correlation analysis found a correlation coefficient of 0.54 and for a significance of 5% it means that a variable relationship of 0.54 can apply to 95 out of 100 samples taken from a population. Inferential statistics is a higher level then descriptive statistics. To that in the research proposal, the flow of conclusions becomes clear. Data Analysis is to make general conclusions (conclusions), make a prediction (prediction), or make an estimate (estimation).

Grad Coach

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.

Quantitative data analysis methods and techniques 101

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?

how to write data analysis in quantitative research proposal

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.

Descriptive statistics vs inferential statistics

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.

Descriptive statistics example data

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!

Examples of descriptive statistics

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.

Inferential statistics are used to make predictions about what you’d expect to find in the full population, based on the sample.

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.

Sample correlation

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.

Remember that every statistical method has its own assumptions and limitations,  so you need to be aware of these.

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.

how to write data analysis in quantitative research proposal

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 ...

You Might Also Like:

Narrative analysis explainer

75 Comments

Oddy Labs

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

Derek Jansen

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

Abdullahi Ramat

Thank you so much.

Obi Eric Onyedikachi

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

Lumbuka Kaunda

Amazing and simple way of breaking down quantitative methods.

Charles Lwanga

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.

Essau Sefolo

Send me every new information you might have.

fatime

i need every new information

Dr Peter

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

Mvogo Mvogo Ephrem

It is wonderful. l’ve understood some of the concepts in a more compréhensive manner

Maya

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?

Joy

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

Peter

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.

Jejamaije Mujoro

I like your page..helpful

prashant pandey

wonderful i got my concept crystal clear. thankyou!!

Dailess Banda

This is really helpful , thank you

Lulu

Thank you so much this helped

wossen

Wonderfully explained

Niamatullah zaheer

thank u so much, it was so informative

mona

THANKYOU, this was very informative and very helpful

Thaddeus Ogwoka

This is great GRADACOACH I am not a statistician but I require more of this in my thesis

Include me in your posts.

Alem Teshome

This is so great and fully useful. I would like to thank you again and again.

Mrinal

Glad to read this article. I’ve read lot of articles but this article is clear on all concepts. Thanks for sharing.

Emiola Adesina

Thank you so much. This is a very good foundation and intro into quantitative data analysis. Appreciate!

Josyl Hey Aquilam

You have a very impressive, simple but concise explanation of data analysis for Quantitative Research here. This is a God-send link for me to appreciate research more. Thank you so much!

Lynnet Chikwaikwai

Avery good presentation followed by the write up. yes you simplified statistics to make sense even to a layman like me. Thank so much keep it up. The presenter did ell too. i would like more of this for Qualitative and exhaust more of the test example like the Anova.

Adewole Ikeoluwa

This is a very helpful article, couldn’t have been clearer. Thank you.

Samih Soud ALBusaidi

Awesome and phenomenal information.Well done

Nūr

The video with the accompanying article is super helpful to demystify this topic. Very well done. Thank you so much.

Lalah

thank you so much, your presentation helped me a lot

Anjali

I don’t know how should I express that ur article is saviour for me 🥺😍

Saiqa Aftab Tunio

It is well defined information and thanks for sharing. It helps me a lot in understanding the statistical data.

Funeka Mvandaba

I gain a lot and thanks for sharing brilliant ideas, so wish to be linked on your email update.

Rita Kathomi Gikonyo

Very helpful and clear .Thank you Gradcoach.

Hilaria Barsabal

Thank for sharing this article, well organized and information presented are very clear.

AMON TAYEBWA

VERY INTERESTING AND SUPPORTIVE TO NEW RESEARCHERS LIKE ME. AT LEAST SOME BASICS ABOUT QUANTITATIVE.

Tariq

An outstanding, well explained and helpful article. This will help me so much with my data analysis for my research project. Thank you!

chikumbutso

wow this has just simplified everything i was scared of how i am gonna analyse my data but thanks to you i will be able to do so

Idris Haruna

simple and constant direction to research. thanks

Mbunda Castro

This is helpful

AshikB

Great writing!! Comprehensive and very helpful.

himalaya ravi

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

Sarah chiwamba

Thank you so much for such useful article!

Lopamudra

Amazing article. So nicely explained. Wow

Thisali Liyanage

Very insightfull. Thanks

Melissa

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?

Aliyah

The is a very helpful blog, however, I’m still not sure how to analyze my data collected. I’m doing a research on “Free Education at the University of Guyana”

Belayneh Kassahun

tnx. fruitful blog!

Suzanne

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

Like

This was quite helpful. Thank you so much.

kidane Getachew

wow I got a lot from this article, thank you very much, keep it up

FAROUK AHMAD NKENGA

Thanks for yhe guidance. Can you send me this guidance on my email? To enable offline reading?

Nosi Ruth Xabendlini

Thank you very much, this service is very helpful.

George William Kiyingi

Every novice researcher needs to read this article as it puts things so clear and easy to follow. Its been very helpful.

Adebisi

Wonderful!!!! you explained everything in a way that anyone can learn. Thank you!!

Miss Annah

I really enjoyed reading though this. Very easy to follow. Thank you

Reza Kia

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?

Protasia Tairo

Thank you very much for sharing, I got much from this article

Fatuma Chobo

This is a very informative write-up. Kindly include me in your latest posts.

naphtal

Very interesting mostly for social scientists

Boy M. Bachtiar

Thank you so much, very helpfull

You’re welcome 🙂

Dr Mafaza Mansoor

woow, its great, its very informative and well understood because of your way of writing like teaching in front of me in simple languages.

Opio Len

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

Eric

very informative article. Easy to understand

Leena Fukey

Beautiful read, much needed.

didin

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

Mmusyoka

Quite informative. Simple and clear summary.

Jewel Faver

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.

Shantae

Absolutely!!! Thank you

Thazika Chitimera

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

lule victor

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

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

  • Print Friendly

CRENC Learn

How to Create a Data Analysis Plan: A Detailed Guide

by Barche Blaise | Aug 12, 2020 | Writing

how to create a data analysis plan

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.

Presentation of variables in a data analysis plan

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.

How to choose a statistical method to determine association between variables

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

Example of a shell table from univariate analysis

Table 2: Example of a shell table from bivariate analysis

Example of a shell table from bivariate analysis

Table 3: Example of a shell table from multivariate analysis

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

Barche Blaise

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.

Post Navigation

16 comments.

Ewane Edwin, MD

Thanks. Quite informative.

James Tony

Educative write-up. Thanks.

Mabou Gabriel

Easy to understand. Thanks Dr

Amabo Miranda N.

Very explicit Dr. Thanks

Dongmo Roosvelt, MD

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.

Menda Blondelle

Plan d’analyse

Marc Lionel Ngamani

This is interesting, Thanks

Nkai

Very understandable and informative. Thank you..

Ndzeshang

love the figures.

Selemani C Ngwira

Nice, and informative

MONICA NAYEBARE

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

Kwasseu

Thank you Doctor very helpful.

Mbapah L. Tasha

Educative and clearly written. Thanks

Philomena Balera

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

Rasheda

Very informative guide!

Submit a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Notify me of follow-up comments by email.

Notify me of new posts by email.

Submit Comment

  Receive updates on new courses and blog posts

Never Miss a Thing!

Never Miss a Thing!

Subscribe to our mailing list to receive the latest news and updates on our webinars, articles and courses.

You have Successfully Subscribed!

Designing Research Proposal in Quantitative Approach

  • First Online: 27 October 2022

Cite this chapter

how to write data analysis in quantitative research proposal

  • Md. Rezaul Karim 4  

2505 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

http://libguides.usc.edu/writingguide/researchproposal .

University of Michigan. Research and Sponsored Projects. http://orsp.umich.edu/proposal-writers-guide-research-proposals-title-page .

Pajares, F. (n.d). The Elements of a Proposal. Emory University.

Wong, P.T. P. http://www.meaning.ca/archives/archive/art_how_to_write_P_Wong.htm .

https://www.scribd.com/document/40384531/Research-Proposal-1 .

Institute of International Studies. Dissertation Proposal Workshop, UC Berkeley, http://iis.berkeley.edu/node/424 .

For details of CSC see CARE Malawi. “The Community Score Card (CSC): A generic guide for implementing CARE’s CSC process to improve quality of services.” Cooperative for Assistance and Relief Everywhere, Inc., 2013. http://www.care.org/sites/default/files/documents/FP-2013-CARE_CommunityScoreCardToolkit.pdf

Institute of International Studies . Dissertation Proposal Workshop, UC Berkeley, http://iis.berkeley.edu/node/424 .

Bangladesh Bureau of Educational Information and Statistics

https://www.dhakatribune.com/uncategorized/2015/12/31/psc-pass-rate-98-52-ebtedayee-95-13 .

https://bdnews24.com/bangladesh/2018/12/24/jsc-jdc-pass-rate-85.83-gpa-5.0-rate-drops-sharply .

Arboleda, C. R. (1981). Communication research . Communication Foundation for Asia.

Google Scholar  

Babbie, E. R. (2010). The practice of social research (12th ed.). Wadsworth Cengage.

BANBEIS (Bangladesh Bureau of Educational Information and Statistics). (2017). Bangladesh education statistics 2016. Bangladesh Bureau of Educational Information and Statistics (BANBEIS).

Borbasi, S., & Jackson, D. (2012). Navigating the maze of research . Mosby Elsevier.

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 .

Download references

Author information

Authors and affiliations.

Department of Social Work, Jagannath University, Dhaka, 1100, Bangladesh

Md. Rezaul Karim

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Md. Rezaul Karim .

Editor information

Editors and affiliations.

Centre for Family and Child Studies, Research Institute of Humanities and Social Sciences, University of Sharjah, Sharjah, United Arab Emirates

M. Rezaul Islam

Department of Development Studies, University of Dhaka, Dhaka, Bangladesh

Niaz Ahmed Khan

Department of Social Work, School of Humanities, University of Johannesburg, Johannesburg, South Africa

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

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

Download citation

DOI : https://doi.org/10.1007/978-981-19-5441-2_10

Published : 27 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5219-7

Online ISBN : 978-981-19-5441-2

eBook Packages : Social Sciences

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research
  • Privacy Policy

Research Method

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

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

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How To Write A Proposal

How To Write A Proposal – Step By Step Guide...

Grant Proposal

Grant Proposal – Example, Template and Guide

How To Write A Business Proposal

How To Write A Business Proposal – Step-by-Step...

Business Proposal

Business Proposal – Templates, Examples and Guide

Proposal

Proposal – Types, Examples, and Writing Guide

How to choose an Appropriate Method for Research?

How to choose an Appropriate Method for Research?

  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • QuestionPro

survey software icon

  • Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
  • Resources Blog eBooks Survey Templates Case Studies Training Help center

how to write data analysis in quantitative research proposal

Home Market Research

Data Analysis in Research: Types & Methods

data-analysis-in-research

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. 

Create a Free Account

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.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

MORE LIKE THIS

how to write data analysis in quantitative research proposal

What Are My Employees Really Thinking? The Power of Open-ended Survey Analysis

May 24, 2024

When I think of “disconnected”, it is important that this is not just in relation to people analytics, Employee Experience or Customer Experience - it is also relevant to looking across them.

I Am Disconnected – Tuesday CX Thoughts

May 21, 2024

Customer success tools

20 Best Customer Success Tools of 2024

May 20, 2024

AI-Based Services in Market Research

AI-Based Services Buying Guide for Market Research (based on ESOMAR’s 20 Questions) 

Other categories.

  • Academic Research
  • Artificial Intelligence
  • Assessments
  • Brand Awareness
  • Case Studies
  • Communities
  • Consumer Insights
  • Customer effort score
  • Customer Engagement
  • Customer Experience
  • Customer Loyalty
  • Customer Research
  • Customer Satisfaction
  • Employee Benefits
  • Employee Engagement
  • Employee Retention
  • Friday Five
  • General Data Protection Regulation
  • Insights Hub
  • Life@QuestionPro
  • Market Research
  • Mobile diaries
  • Mobile Surveys
  • New Features
  • Online Communities
  • Question Types
  • Questionnaire
  • QuestionPro Products
  • Release Notes
  • Research Tools and Apps
  • Revenue at Risk
  • Survey Templates
  • Training Tips
  • Uncategorized
  • Video Learning Series
  • What’s Coming Up
  • Workforce Intelligence

Banner

  • RIT Libraries
  • Data Analytics Resources
  • Writing a Research Proposal
  • Electronic Books
  • Print Books
  • Data Science: Journals
  • More Journals, Websites
  • Alerts, IDS Express
  • Readings on Data
  • Sources with Data
  • Google Scholar Library Links
  • Zotero-Citation Management Tool
  • Writing a Literature Review
  • ProQuest Research Companion
  • Thesis Submission Instructions
  • Associations

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. 

  • << Previous: ProQuest Research Companion
  • Next: DIR >>

Edit this Guide

Log into Dashboard

Use of RIT resources is reserved for current RIT students, faculty and staff for academic and teaching purposes only. Please contact your librarian with any questions.

Facebook icon

Help is Available

how to write data analysis in quantitative research proposal

Email a Librarian

A librarian is available by e-mail at [email protected]

Meet with a Librarian

Call reference desk voicemail.

A librarian is available by phone at (585) 475-2563 or on Skype at llll

Or, call (585) 475-2563 to leave a voicemail with the reference desk during normal business hours .

Chat with a Librarian

Data analytics resources infoguide url.

https://infoguides.rit.edu/DA

Use the box below to email yourself a link to this guide

Research-Methodology

Quantitative Data Analysis

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

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

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

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

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

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

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

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

Advantages and disadvantages of popular quantitative analytical software

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

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

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

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

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

Quantitative Data Analysis

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

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

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Research process
  • 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.

Structure of a research proposal

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

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

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

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/

Is this article helpful?

Shona McCombes

Shona McCombes

Other students also liked, what is a research methodology | steps & tips, what is a literature review | guide, template, & examples, how to write a results section | tips & examples.

  • Cookies & Privacy
  • GETTING STARTED
  • Introduction
  • FUNDAMENTALS

how to write data analysis in quantitative research proposal

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

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

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

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

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

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

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

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

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

Final thoughts...

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

Quantitative research: Understanding the approaches and key elements

Quantitative Research Understanding The Approaches And Key Elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

  • Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
  • Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
  • Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

  • Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
  • Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
  • Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
  • Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
  • Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
  • Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

Canvs AI: Unlock critical insights from unstructured feedback Related Categories: Research Industry, Data Analysis, Quantitative Research Research Industry, Data Analysis, Quantitative Research, Artificial Intelligence / AI, Text Analytics

Segmentation in the pharma industry: How to create resilient strategies Related Categories: Research Industry, Sampling, Survey Research Research Industry, Sampling, Survey Research, Market Segmentation Studies, Segmentation Studies, Health Care (Healthcare), Health Care (Healthcare) Research, Patients , Questionnaire Analysis, Social Media Research

Leveraging AI to unlock qualitative research at scale Related Categories: Research Industry, Quantitative Research, Sampling, Hybrid Research (Qual/Quant) Research Industry, Quantitative Research, Sampling, Hybrid Research (Qual/Quant), Artificial Intelligence / AI, Qualitative Research, Attitude/Usage Studies, Consumer Research, Consumers, Data Visualization/Infographics

Situational choice experiments for marketing research Related Categories: Research Industry, Data Analysis, Survey Research Research Industry, Data Analysis, Survey Research, Conjoint Analysis/Trade-Off Analysis, Discrete Choice Modeling, Physicians, Software-Conjoint Analysis, Questionnaire Analysis

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • Indian J Anaesth
  • v.60(9); 2016 Sep

How to write a research proposal?

Department of Anaesthesiology, Bangalore Medical College and Research Institute, Bengaluru, Karnataka, India

Devika Rani Duggappa

Writing the proposal of a research work in the present era is a challenging task due to the constantly evolving trends in the qualitative research design and the need to incorporate medical advances into the methodology. The proposal is a detailed plan or ‘blueprint’ for the intended study, and once it is completed, the research project should flow smoothly. Even today, many of the proposals at post-graduate evaluation committees and application proposals for funding are substandard. A search was conducted with keywords such as research proposal, writing proposal and qualitative using search engines, namely, PubMed and Google Scholar, and an attempt has been made to provide broad guidelines for writing a scientifically appropriate research proposal.

INTRODUCTION

A clean, well-thought-out proposal forms the backbone for the research itself and hence becomes the most important step in the process of conduct of research.[ 1 ] The objective of preparing a research proposal would be to obtain approvals from various committees including ethics committee [details under ‘Research methodology II’ section [ Table 1 ] in this issue of IJA) and to request for grants. However, there are very few universally accepted guidelines for preparation of a good quality research proposal. A search was performed with keywords such as research proposal, funding, qualitative and writing proposals using search engines, namely, PubMed, Google Scholar and Scopus.

Five ‘C’s while writing a literature review

An external file that holds a picture, illustration, etc.
Object name is IJA-60-631-g001.jpg

BASIC REQUIREMENTS OF A RESEARCH PROPOSAL

A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer.[ 2 ] The proposal must be capable of convincing the evaluation committee about the credibility, achievability, practicality and reproducibility (repeatability) of the research design.[ 3 ] Four categories of audience with different expectations may be present in the evaluation committees, namely academic colleagues, policy-makers, practitioners and lay audiences who evaluate the research proposal. Tips for preparation of a good research proposal include; ‘be practical, be persuasive, make broader links, aim for crystal clarity and plan before you write’. A researcher must be balanced, with a realistic understanding of what can be achieved. Being persuasive implies that researcher must be able to convince other researchers, research funding agencies, educational institutions and supervisors that the research is worth getting approval. The aim of the researcher should be clearly stated in simple language that describes the research in a way that non-specialists can comprehend, without use of jargons. The proposal must not only demonstrate that it is based on an intelligent understanding of the existing literature but also show that the writer has thought about the time needed to conduct each stage of the research.[ 4 , 5 ]

CONTENTS OF A RESEARCH PROPOSAL

The contents or formats of a research proposal vary depending on the requirements of evaluation committee and are generally provided by the evaluation committee or the institution.

In general, a cover page should contain the (i) title of the proposal, (ii) name and affiliation of the researcher (principal investigator) and co-investigators, (iii) institutional affiliation (degree of the investigator and the name of institution where the study will be performed), details of contact such as phone numbers, E-mail id's and lines for signatures of investigators.

The main contents of the proposal may be presented under the following headings: (i) introduction, (ii) review of literature, (iii) aims and objectives, (iv) research design and methods, (v) ethical considerations, (vi) budget, (vii) appendices and (viii) citations.[ 4 ]

Introduction

It is also sometimes termed as ‘need for study’ or ‘abstract’. Introduction is an initial pitch of an idea; it sets the scene and puts the research in context.[ 6 ] The introduction should be designed to create interest in the reader about the topic and proposal. It should convey to the reader, what you want to do, what necessitates the study and your passion for the topic.[ 7 ] Some questions that can be used to assess the significance of the study are: (i) Who has an interest in the domain of inquiry? (ii) What do we already know about the topic? (iii) What has not been answered adequately in previous research and practice? (iv) How will this research add to knowledge, practice and policy in this area? Some of the evaluation committees, expect the last two questions, elaborated under a separate heading of ‘background and significance’.[ 8 ] Introduction should also contain the hypothesis behind the research design. If hypothesis cannot be constructed, the line of inquiry to be used in the research must be indicated.

Review of literature

It refers to all sources of scientific evidence pertaining to the topic in interest. In the present era of digitalisation and easy accessibility, there is an enormous amount of relevant data available, making it a challenge for the researcher to include all of it in his/her review.[ 9 ] It is crucial to structure this section intelligently so that the reader can grasp the argument related to your study in relation to that of other researchers, while still demonstrating to your readers that your work is original and innovative. It is preferable to summarise each article in a paragraph, highlighting the details pertinent to the topic of interest. The progression of review can move from the more general to the more focused studies, or a historical progression can be used to develop the story, without making it exhaustive.[ 1 ] Literature should include supporting data, disagreements and controversies. Five ‘C's may be kept in mind while writing a literature review[ 10 ] [ Table 1 ].

Aims and objectives

The research purpose (or goal or aim) gives a broad indication of what the researcher wishes to achieve in the research. The hypothesis to be tested can be the aim of the study. The objectives related to parameters or tools used to achieve the aim are generally categorised as primary and secondary objectives.

Research design and method

The objective here is to convince the reader that the overall research design and methods of analysis will correctly address the research problem and to impress upon the reader that the methodology/sources chosen are appropriate for the specific topic. It should be unmistakably tied to the specific aims of your study.

In this section, the methods and sources used to conduct the research must be discussed, including specific references to sites, databases, key texts or authors that will be indispensable to the project. There should be specific mention about the methodological approaches to be undertaken to gather information, about the techniques to be used to analyse it and about the tests of external validity to which researcher is committed.[ 10 , 11 ]

The components of this section include the following:[ 4 ]

Population and sample

Population refers to all the elements (individuals, objects or substances) that meet certain criteria for inclusion in a given universe,[ 12 ] and sample refers to subset of population which meets the inclusion criteria for enrolment into the study. The inclusion and exclusion criteria should be clearly defined. The details pertaining to sample size are discussed in the article “Sample size calculation: Basic priniciples” published in this issue of IJA.

Data collection

The researcher is expected to give a detailed account of the methodology adopted for collection of data, which include the time frame required for the research. The methodology should be tested for its validity and ensure that, in pursuit of achieving the results, the participant's life is not jeopardised. The author should anticipate and acknowledge any potential barrier and pitfall in carrying out the research design and explain plans to address them, thereby avoiding lacunae due to incomplete data collection. If the researcher is planning to acquire data through interviews or questionnaires, copy of the questions used for the same should be attached as an annexure with the proposal.

Rigor (soundness of the research)

This addresses the strength of the research with respect to its neutrality, consistency and applicability. Rigor must be reflected throughout the proposal.

It refers to the robustness of a research method against bias. The author should convey the measures taken to avoid bias, viz. blinding and randomisation, in an elaborate way, thus ensuring that the result obtained from the adopted method is purely as chance and not influenced by other confounding variables.

Consistency

Consistency considers whether the findings will be consistent if the inquiry was replicated with the same participants and in a similar context. This can be achieved by adopting standard and universally accepted methods and scales.

Applicability

Applicability refers to the degree to which the findings can be applied to different contexts and groups.[ 13 ]

Data analysis

This section deals with the reduction and reconstruction of data and its analysis including sample size calculation. The researcher is expected to explain the steps adopted for coding and sorting the data obtained. Various tests to be used to analyse the data for its robustness, significance should be clearly stated. Author should also mention the names of statistician and suitable software which will be used in due course of data analysis and their contribution to data analysis and sample calculation.[ 9 ]

Ethical considerations

Medical research introduces special moral and ethical problems that are not usually encountered by other researchers during data collection, and hence, the researcher should take special care in ensuring that ethical standards are met. Ethical considerations refer to the protection of the participants' rights (right to self-determination, right to privacy, right to autonomy and confidentiality, right to fair treatment and right to protection from discomfort and harm), obtaining informed consent and the institutional review process (ethical approval). The researcher needs to provide adequate information on each of these aspects.

Informed consent needs to be obtained from the participants (details discussed in further chapters), as well as the research site and the relevant authorities.

When the researcher prepares a research budget, he/she should predict and cost all aspects of the research and then add an additional allowance for unpredictable disasters, delays and rising costs. All items in the budget should be justified.

Appendices are documents that support the proposal and application. The appendices will be specific for each proposal but documents that are usually required include informed consent form, supporting documents, questionnaires, measurement tools and patient information of the study in layman's language.

As with any scholarly research paper, you must cite the sources you used in composing your proposal. Although the words ‘references and bibliography’ are different, they are used interchangeably. It refers to all references cited in the research proposal.

Successful, qualitative research proposals should communicate the researcher's knowledge of the field and method and convey the emergent nature of the qualitative design. The proposal should follow a discernible logic from the introduction to presentation of the appendices.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

how to write data analysis in quantitative research proposal

How To Write A Research Proposal With AI

To write a research paper using A.I., define your research topic and formulate straightforward, specific research questions, use A.I. to streamline the literature reviews, refine methodologies, and create a structured proposal outline, draft your proposal using the outline you created.

Fredrick Eghosa

Fredrick Eghosa

Apr 21, 2024

How To Write A Research Proposal With AI

Table of Contents

Key Takeaways

Understanding the research proposal and its components, introduction:, literature review:, research objectives:, methodology:, significance and contributions:, budget (if applicable):, references:, how to write a research proposal using a.i., define your research topic, formulate research questions, consider the types of research questions you want to use, characteristics of practical research questions you must keep in mind, how to structure your research questions, conduct a comprehensive literature review, develop a research proposal outline, refine your methodology, don't forget to include visual aids., draft and revise your proposal, wrapping up.

Fredrick Eghosa

  • A research proposal provides a detailed plan for a research project. It presents your proposed study's objectives, methodology, and potential contribution.
  • A research proposal usually includes a title, abstract, literature review, research objectives, methodology, significance and contribution, timeline, budget, and references.
  • Writing a research paper using A.I. involves defining your research topic and formulating straightforward, specific research questions. Use CoWriter to streamline literature reviews, refine methodologies, and create a structured proposal outline. Write your proposal and use CoWriter to correct errors and enhance clarity.

notion image

  • Use Interrogative Language. Begin questions with "What," "How," "Why," or "To what extent" to prompt investigation.
  • Avoid yes-or-no questions. Formulate questions that require detailed responses and deeper exploration.

notion image

  • Start by Determining the scope of your review (e.g., timeframe, geographical focus, key concepts) to ensure relevance. Then, Identify relevant keywords, synonyms, and phrases related to your research topic.
  • Explore library catalogs and repositories for books, theses, dissertations, and other publications related to your topic.
  • When reviewing the literature, applying filters (e.g., publication date, study design, language) will help you narrow search results and focus on high-quality sources.
  • Select literature that directly addresses your research questions or contributes significantly to understanding your topic.
  • Identify gaps or limitations in existing literature that highlight the need for your research.

notion image

  • Familiarize yourself with research designs such as experimental, observational, qualitative, quantitative, or mixed-methods.
  • Select the most appropriate research design based on your research questions, objectives, and available resources.
  • Discuss the advantages and limitations of the chosen design and explain why it is suitable for your study.
  • Describe the methods you will use to collect data (e.g., surveys, interviews, observations, and experiments).
  • Explain how you will develop or adapt data collection instruments (e.g., questionnaires, interview guides, and observation protocols).
  • Specify the target population and describe the sampling frame (e.g., random, purposeful, or convenience).
  • Discuss ethical issues regarding participant recruitment, data collection, and privacy protection.
  • Mention any software tools you will use for data analysis and explain why they are appropriate for your study.
  • Ensure that your methodology aligns with the research objectives and aims to answer the research questions effectively.
  • Please provide a clear rationale for each methodological choice and explain how it contributes to achieving the study's goals.

Join other 3200+ writers now!

Ready to take the next big step for your writing?

Lead engineering teams at Figma, Pitch, and Protocol Labs.

Related posts

How To Write A Research Paper With AI

May 21, 2024

How To Write A Research Paper With AI

To write a research paper, ensure you understand the assignment, choose an engaging topic, conduct thorough research, develop a clear thesis statement, and create a structured outline. Write while integrating research findings, then revise for clarity and coherence. Finally, edit for accuracy and adhere to formatting guidelines to present a polished and professional paper. You can use an A.I. writing assistant to optimize and streamline entire writing.

The Best Free AI MLA Citation Generator

Apr 16, 2024

The Best Free AI MLA Citation Generator

The best free AI MLA citation generators are CoWriter, Opendemia, Scribbr MLA Citation Generator, MyBib, Citefast, Cite This For Me's MLA Citation Generator, and QuillBot M.L.A. Citation Generator. They stay updated on the latest MLA guidelines and can handle various source types like books, articles, and websites.

The Best Free Outline Generator for Essays

The Best Free Outline Generator for Essays

The best free outline generators for essay writing are CoWriter, Ahrefs Free AI Outline Generator, ClickUp, Jenni AI Essay Outline Generator, GitMind, Rytr, and Simplified. They streamline the process by generating structured outlines based on prompts and parameters.

The Best Free AI APA Citation Generator

Apr 12, 2024

The Best Free AI APA Citation Generator

The best free AI APA citation generators are CoWriter, Grammarly, SciSpace APA Citation Generator, Scribbr AI Citation Generators, Quillbot AI Citation Generators, Junia.ai, and Simplified. These generators use intelligent algorithms to automatically create citations for different sources, saving time and ensuring formatting and APA citation style accuracy.

How is a narrative essay organized?

How is a narrative essay organized?

A narrative essay tells a story chronologically, beginning with an introduction, moving into a body where events build to a climax, and finishing with a resolution or final reflection.

10 Best AI Co-Writers

Mar 16, 2024

10 Best AI Co-Writers

The best AI co-writers are CoWriter, HIX.AI, Copy.ai, Wordtune, Scalenut, Rytr, HyperWrite, Texta, TextCortex, and Writesonic. They can help generate ideas, overcome writer's block, and improve the flow of your writing.

How to Edit an Essay Written by AI to Perfection

Mar 18, 2024

How to Edit an Essay Written by AI to Perfection

To edit an AI-written essay perfectly, carefully read the text to ensure it makes sense and conveys your ideas. Please review the entire piece to grasp its tone, purpose, and quality, as AI-generated content often lacks logical progression and may have gaps, awkward transitions, or readability issues.

10 Steps to Writing an Informative Essay with an Outline

Mar 24, 2024

10 Steps to Writing an Informative Essay with an Outline

To write an informative essay with an outline, pick a topic, research it, and create an outline. Write an introduction with a hook and thesis, followed by body paragraphs with evidence. Conclude by summarizing the main points and restating the thesis. Finally, edit, cite sources, and submit.

Jenni AI Review and Best Alternatives

Mar 25, 2024

Jenni AI Review and Best Alternatives

Jenni.AI is an AI writer designed to assist with academic research and writing. It offers a great selection of features for academic purposes, including a built-in research engine. The best alternatives for Jenni AI you can try are CoWriter, Jasper AI, Copy AI, Frase.io, and Writesonic.

How to Write a Biography Essay and Biography Essay Examples

Mar 27, 2024

How to Write a Biography Essay and Biography Essay Examples

To write a biography essay, choose an exciting subject, research extensively, and develop a thesis statement. Then, structure your essay chronologically or thematically, starting with a captivating introduction, exploring the subject's life in the body paragraphs, and concluding with its contemporary significance.

The Good AI Review and Best Alternatives

The Good AI Review and Best Alternatives

Good AI is a handy platform that uses smart technology to assist with writing tasks. Its AI essay writer lets users quickly create 1,500-word, well-structured, and informative essays.

How to Write a Biography Essay With Examples

How to Write a Biography Essay With Examples

30 AI Prompts to Humanize Your Essay

30 AI Prompts to Humanize Your Essay

AI prompts to humanize your essay are invaluable tools for students who want to humanize their AI-generated essays. With prompts for different writing styles and objectives, you can add personality, storytelling, humor, and clarity to their content.

The 7 Best AI Bibliography Generator

The 7 Best AI Bibliography Generator

The best AI bibliography generators are CoWriter, Zotero, EndNote, Cite This For Me, Citation Machine, CiteMaker, and KnightCite. These tools streamline the citation process by supporting various citation styles, source types, and formatting options, ensuring accuracy and adherence to academic standards.

10 Literary Analysis Essay Examples

May 20, 2024

10 Literary Analysis Essay Examples

To write a literary analysis essay always understand the assignment thoroughly and identify the key elements e.g. plot, characters, and themes. Select a central theme to focus on and put together evidence to support your analysis.

The 5 Best AI Writer for Students

The 5 Best AI Writer for Students

The best AI writers for students are CoWriter, Jasper, AI-Writer, Rytr, and Article Forge. Choosing the best AI writer for students depends on their needs. Some prioritize grammar and clarity assistance, while others focus on paraphrasing and summarizing.

Best Free AI Paper Writer

Best Free AI Paper Writer

The best free AI paper writers are CoWriter, Rytr, Simplified, CopyAI, Writesonic, Grammarly, and Paperpal. These tools can help you brainstorm or start and polish your paper.

5 Best AI Writer for Research Papers

Mar 11, 2024

5 Best AI Writer for Research Papers

The best AI writers for research papers are CoWriter, SciSpace Literature Review, Wordtune, Trinka, and Paperpal. These AI writers offer different functionalities, from grammar and citation checking to plagiarism detection and content generation.

Can AI Write An Annotated Bibliography

Can AI Write An Annotated Bibliography

A.I. can write an annotated bibliography by curating and organizing relevant sources. It can generate concise summaries that outline each source's content, relevance, and significance to your research. A.I. writing assistant streamlines the process of compiling an annotated bibliography, saving time and effort while ensuring the inclusion of informative annotations for each source.

How To Write A Historical Essay Introduction

How To Write A Historical Essay Introduction

To write a historical essay introduction, begin with an engaging hook, like a thought-provoking quote or a question. Follow this with background details about your topic and conclude with a clear thesis statement. Utilize CoWriter to assist in composing your introduction.

How To Write a 4-Paragraph Essay

How To Write a 4-Paragraph Essay

To write a 4-paragraph essay effectively, start by thoroughly researching your topic. This step is crucial for gathering supporting information. Next, create a structured outline that divides your essay into four clear paragraphs. Draft your thesis statement to convey the topic of your essay. With the outline in place, draft your essay following the outlined structure. Finally, take time to edit and proofread your essay.

How To Write A Curatorial Statement With AI

How To Write A Curatorial Statement With AI

To write a curatorial statement with AI, begin by conducting thorough research into the historical context of your exhibition and the backgrounds of the artworks, including their styles and mediums. Develop a clear and concise outline to structure your statement effectively. Write the content of your statement and edit it for clarity and correctness. Use CoWriter to streamline and enhance the entire writing and editing process.

How To Write A Discussion Essay

How To Write A Discussion Essay

To write a discussion essay, start with a clear introduction introducing the topic and presenting your main argument. Next, develop the body paragraphs, each addressing a specific aspect or viewpoint related to the topic, supported by evidence and facts. Then address counterarguments to strengthen your argument. Conclude your discussion essay by summarizing key points and restating your main argument, leaving readers with a compelling closing thought.

Difference Between Paraphrasing And Summarizing

Difference Between Paraphrasing And Summarizing

Paraphrasing involves restating the content of a passage in your own words, while Summarizing involves concisely stating the main points of a work or passage. Paraphrasing usually Retains more detail from the original text, while summarizing focuses on capturing the main points of the original text. Paraphrasing can be similar to or longer than the original text, while summaries are usually Significantly shorter.

10 Evaluation Essay examples

10 Evaluation Essay examples

An evaluation essay can take different styles; there are argumentative evaluation essays, analytical evaluation essays,  Descriptive evaluation essays, and comparative evaluation essays. To write an evaluation essay, start by choosing a suitable topic, setting evaluation criteria, gathering evidence to back your evaluation, creating an outline to guide your evaluation essay, writing your evaluation, and editing to ensure the final draft is error-free.

10 Expository Essay Examples

10 Expository Essay Examples

There are different types of expository essays, such as descriptive essays, process essays, comparison and contrast essays, cause-and-effect essays, and problem-and-solution essays. To write an expository essay, you must start by thoroughly understanding the purpose of the essay, conducting research, writing your thesis, creating an outline for your essay, drafting your essay, and editing it.

How To Write Essays With CoWriter

How To Write Essays With CoWriter

To write an essay using CoWriter, sign up on CoWriter to access its writing tools. Choose a suitable for your essay and conduct research using CoWriter to gather relevant sources. Develop a thesis statement and create an outline using CoWriter. Write the essay with the help of our AI writing assistant and cite relevant sources. Lastly, edit and proofread the final draft.

Frequent Searches

Academic Programs

Academic Calendar

Search Directory

Find contact details for people, departments and facilities information.

$50.00 USD application fee (grad only)

Official high school or college transcript from all previous schools

Test Scores (may be optional) AP or IB results

3 letters of recommendation (grad only)

Apply by 3/1 for Fall start

Apply by 12/1 for Spring start

10 Tips on Creating a Research Program and Proposal Writing

Office of sponsored programs and research.

Start small and early. As a new faculty member-reach out to mentors (on campus and off) and inquire what might be a great first proposal to write. Given that grant funding today is more difficult to obtain than ever before, starting early in your career and capitalizing on the advantages of your "early-career" status is key. Reach out to the Office of Sponsored Programs and Research for a listening session and find grant programs specifically aimed at new faculty members. These grant programs typically do not require significant preliminary data. Instead, funding decisions rely most heavily on your promise and potential as a candidate — your training to date, your mentors, and your topic’s importance.

three students talking outside

Early on in your career, it’s critical to envision your ultimate large grant. Drawing a Venn diagram of your research program could help communicate your research expertise to collaborators. Typically a major grant would include five aims. Once you’ve envisioned your big grant and its five aims, your next steps become clear: create component projects of that larger project by writing small grants designed to support one or more of your five specific aims. Small grants will show that each of your aims is feasible. This approach is critical as grant-review panels often see a large grant as the culmination of a growing body of work progressing from modest seed grants to larger and larger awards.

A key factor in developing a vision of your ultimate large grant will be the advice of your mentor(s). If you do not have a mentor in your department ask the chair to assign you one. It is also usually considered acceptable to seek out your own mentor. Indeed, many early-career academics assemble a mentorship team, in which each member provides guidance on different career facets (i.e., a teaching mentor, a research mentor, a work-life mentor). Consider approaching people on other campuses as well as at Coppin.

Look at who and what got funded before. Grant agencies typically list previous award recipients online. This list is critical as it shows the agency’s potential interest in supporting your area of research. Some agencies post abstracts online of both active and prior awards. They can give you a critical sense of what has been successful. Looking at the number of specific aims and the range of acceptable sample sizes will provide you with key insights as to what has appealed to your target agency in the past.

Funding agencies may post a list of prior and current grant reviewers and their affiliations online. Review the list and ask yourself if their expertise overlaps with the aims and methodology of your study. Reach out to program directors with a potential idea many months in advance of the deadline—ask for their feedback.

Spend half of your time on the abstract and aims. Writers of successful grant applications typically report that they spent 50 percent of their time on writing and revising their abstract and aims. When you finally start drafting your proposal, the specific aims should be the first thing you write — well before the background or methods sections.

Send a one-page sketch of your project abstract and aims to your mentor and co-investigators early in the grant-writing process with the goal of kicking off an iterative process of review and revision.

Why is this page so critical? Because of the nature of the peer-review process. Typically, only three or four academics are assigned as primary and secondary reviewers of your grant. The majority of review-panel members will only have read your proposal’s abstract. Therefore, it must not only provide a clear snapshot of the entire study, but also convey what is novel about your application.

Demonstrate your strengths and capabilities to do the research. These skills are a critical factor for reviewers. How do you demonstrate you can feasibly conduct the work?

  • First, if possible, collaborate on the grant with senior investigators who have conducted similar projects. A senior scholar’s involvement will be a key factor supporting your potential for success, particularly if you are early in your career.
  • Co-investigators should not appear in name only. Show established working relationships with them either via co-authored publications, co-presentations, and/or via an established mentoring relationship (e.g., as part of a training grant). Of course, much of this information will appear in the bio sketches in your proposal, but you cannot rely upon reviewers to connect the dots. Make it easy for reviewers by clearly noting these prior collaborations in your "preliminary studies" section.
  • Finally, present evidence that you have conducted smaller-scale feasibility studies. That reassures reviewers that you, as a principal investigator, will be able to conduct your proposed aims and, ideally, translate that work into publications.

Take a focused methodological plan directly tied to your specific aims will be the most impressive to reviewers. For example, include methods in the proposal that relate directly to each of your study’s aims and don’t include additional methods that do not correspond to any aims.

You can never have too many figures or tables. They make it easy for a reviewer to quickly grasp your proposal, as compared with dense text. In addition, the act of creating them will help you to cross check your specific aims and study methods. Figures and tables can save space — reducing the amount of text necessary — which is critical to meeting the page limitations of most grant submissions. This tip is relevant for every section of your grant application:  Figures can be used to show how your specific aims interrelate, to depict study designs, and to demonstrate your anticipated results.

Seek external reviews prior to submission. The same person cannot write a grant and review it for clarity. You will miss errors, simply by virtue of your familiarity with the material. So ask colleagues to read the application. Even a generalist can read your grant proposal with the following questions in mind: Are the goals clearly stated? Does the grant extend prior work in the field? What is the impact of your potential findings?

girl in dorm room at laptop

In fact, it may be preferable for some of your proofreaders not to have expertise in your area at all — given that members of the grant-review panel will not have expertise in every aspect of your proposal.

Use the grant-review criteria as subheadings in your proposal, making it easier for the panelists to fill out their review forms. For example, reviewers typically have to complete a section on "Innovation." A clearly labeled subsection on "Innovation" not only saves the reviewer time, but gives you the opportunity to share your vision with the reviewer on innovative aspects they may not have recognized on their own.

Choose a topic that you find interesting—your expertise and passion will come through in your grant proposal. Having several grants in the pipeline and under review at the same time can help stack the deck in your favor.

Related links

Examples

Review of Related Literature (RRL)

Ai generator.

how to write data analysis in quantitative research proposal

The Review of Related Literature (RRL) is a crucial section in research that examines existing studies and publications related to a specific topic. It summarizes and synthesizes previous findings, identifies gaps, and provides context for the current research. RRL ensures the research is grounded in established knowledge, guiding the direction and focus of new studies.

What Is Review of Related Literature (RRL)?

The Review of Related Literature (RRL) is a detailed analysis of existing research relevant to a specific topic. It evaluates, synthesizes, and summarizes previous studies to identify trends, gaps, and conflicts in the literature. RRL provides a foundation for new research, ensuring it builds on established knowledge and addresses existing gaps.

Format of Review of Related Literature (RRL)

The Review of Related Literature (RRL) is a critical part of any research paper or thesis . It provides an overview of existing research on your topic and helps to establish the context for your study. Here is a typical format for an RRL:

1. Introduction

  • Purpose : Explain the purpose of the review and its importance to your research.
  • Scope : Define the scope of the literature reviewed, including the time frame, types of sources, and key themes.

2. Theoretical Framework

  • Concepts and Theories : Present the main theories and concepts that underpin your research.
  • Relevance : Explain how these theories relate to your study.

3. Review of Empirical Studies

  • Sub-theme 1 : Summarize key studies, including methodologies, findings, and conclusions.
  • Sub-theme 2 : Continue summarizing studies, focusing on different aspects or variables.
  • Sub-theme 3 : Include any additional relevant studies.

4. Methodological Review

  • Approaches : Discuss the various methodologies used in the reviewed studies.
  • Strengths and Weaknesses : Highlight the strengths and weaknesses of these methodologies.
  • Gaps : Identify gaps in the existing research that your study aims to address.

5. Synthesis and Critique

  • Integration : Integrate findings from the reviewed studies to show the current state of knowledge.
  • Critique : Critically evaluate the literature, discussing inconsistencies, limitations, and areas for further research.

6. Conclusion

  • Summary : Summarize the main findings from the literature review.
  • Research Gap : Clearly state the research gap your study will address.
  • Contribution : Explain how your study will contribute to the existing body of knowledge.

7. References

  • Citation Style : List all the sources cited in your literature review in the appropriate citation style (e.g., APA, MLA, Chicago).
Review of Related Literature (RRL) 1. Introduction This review examines research on social media’s impact on mental health, focusing on anxiety and depression across various demographics over the past ten years. 2. Theoretical Framework Anchored in Social Comparison Theory and Uses and Gratifications Theory, this review explores how individuals’ social media interactions affect their mental health. 3. Review of Empirical Studies Adolescents’ Mental Health Instagram & Body Image : Smith & Johnson (2017) found Instagram use linked to body image issues and lower self-esteem among 500 high school students. Facebook & Anxiety : Brown & Green (2016) showed Facebook use correlated with higher anxiety and depressive symptoms in a longitudinal study of 300 students. Young Adults’ Mental Health Twitter & Stress : Davis & Lee (2018) reported higher stress levels among heavy Twitter users in a survey of 400 university students. LinkedIn & Self-Esteem : Miller & White (2019) found LinkedIn use positively influenced professional self-esteem in 200 young professionals. Adult Mental Health General Social Media Use : Thompson & Evans (2020) found moderate social media use associated with better mental health outcomes, while excessive use correlated with higher anxiety and depression in 1,000 adults. 4. Methodological Review Studies used cross-sectional surveys, longitudinal designs, and mixed methods. Cross-sectional surveys provided large data sets but couldn’t infer causation. Longitudinal studies offered insights into long-term effects but were resource-intensive. Mixed methods enriched data through qualitative insights but required careful integration. 5. Synthesis and Critique The literature shows a complex relationship between social media and mental health, with platform-specific and demographic-specific effects. However, reliance on self-reported data introduces bias, and many cross-sectional studies limit causal inference. More longitudinal and experimental research is needed. 6. Conclusion Current research offers insights into social media’s mental health impact but leaves gaps, particularly regarding long-term effects and causation. This study aims to address these gaps through comprehensive longitudinal analysis. 7. References Brown, A., & Green, K. (2016). Facebook Use and Anxiety Among High School Students . Psychology in the Schools, 53(3), 257-264. Davis, R., & Lee, S. (2018). Twitter and Psychological Stress: A Study of University Students . Journal of College Student Development, 59(2), 120-135. Miller, P., & White, H. (2019). LinkedIn and Its Effect on Professional Self-Esteem . Journal of Applied Psychology, 104(1), 78-90. Smith, J., & Johnson, L. (2017). The Impact of Instagram on Teen Body Image . Journal of Adolescent Health, 60(5), 555-560. Thompson, M., & Evans, D. (2020). The Relationship Between Social Media Use and Mental Health in Adults . Cyberpsychology, Behavior, and Social Networking, 23(4), 201-208.

Review of Related Literature (RRL) Examples

Review of related literature in research, review of related literature in research paper, review of related literature qualitative research.

Review-of-Related-Literature-RRL-in-Research-Edit-Download-Pdf

Review of Related Literature Quantitative Research

Review-of-Related-Literature-RRL-in-Quantitative-Research-Edit-Download-Pdf

More Review of Related Literature (RRL) Examples

  • Impact of E-learning on Student Performance
  • Effectiveness of Mindfulness in Workplace
  • Green Building and Energy Efficiency
  • Impact of Technology on Healthcare Delivery
  • Effects of Nutrition on Cognitive Development in Children
  • Impact of Employee Training Programs on Productivity
  • Effects of Climate Change on Biodiversity
  • Impact of Parental Involvement on Student Achievement
  • Effects of Mobile Learning on Student Engagement
  • Effects of Urban Green Spaces on Mental Health

Purpose of the Review of Related Literature (RRL)

The Review of Related Literature (RRL) serves several critical purposes in research:

  • Establishing Context : It situates your research within the broader field, showing how your study relates to existing work.
  • Identifying Gaps : It highlights gaps, inconsistencies, and areas needing further exploration in current knowledge, providing a clear rationale for your study.
  • Avoiding Duplication : By reviewing what has already been done, it helps ensure your research is original and not a repetition of existing studies.
  • Building on Existing Knowledge : It allows you to build on the findings of previous research, using established theories and methodologies to inform your work.
  • Theoretical Foundation : It provides a theoretical basis for your research, grounding it in existing concepts and theories.
  • Methodological Insights : It offers insights into the methods and approaches used in similar studies, helping you choose the most appropriate methods for your research.
  • Establishing Credibility : It demonstrates your familiarity with the field, showing that you are well-informed and have a solid foundation for your research.
  • Supporting Arguments : It provides evidence and support for your research questions, hypotheses, and objectives, strengthening the overall argument of your study.

How to Write Review of Related Literature (RRL)

Writing a Review of Related Literature (RRL) involves several key steps. Here’s a step-by-step guide:

1. Define the Scope and Objectives

  • Determine the Scope : Decide on the breadth of the literature you will review, including specific themes, time frame, and types of sources.
  • Set Objectives : Clearly define the purpose of the review. What do you aim to achieve? Identify gaps, establish context, or build on existing knowledge.

2. Search for Relevant Literature

  • Identify Keywords : Use keywords and phrases related to your research topic.
  • Use Databases : Search academic databases like Google Scholar, PubMed, JSTOR, etc., for relevant articles, books, and papers.
  • Select Sources : Choose sources that are credible, recent, and relevant to your research.

3. Evaluate and Select the Literature

  • Read Abstracts and Summaries : Quickly determine the relevance of each source.
  • Assess Quality : Consider the methodology, credibility of the authors, and publication source.
  • Select Key Studies : Choose studies that are most relevant to your research questions and objectives.

4. Organize the Literature

  • Thematic Organization : Group studies by themes or topics.
  • Chronological Organization : Arrange studies in the order they were published to show the development of ideas over time.
  • Methodological Organization : Categorize studies by the methods they used.

5. Write the Review

  • State the purpose and scope of the review.
  • Explain the importance of the topic.
  • Theoretical Framework : Present and discuss the main theories and concepts.
  • Summarize key studies, including their methodologies, findings, and conclusions.
  • Organize by themes or other chosen organizational methods.
  • Methodological Review : Discuss the various methodologies used, highlighting their strengths and weaknesses.
  • Synthesis and Critique : Integrate findings, critically evaluate the literature, and identify gaps or inconsistencies.
  • Summarize the main findings from the literature review.
  • Highlight the research gaps your study will address.
  • State how your research will contribute to the existing knowledge.

6. Cite the Sources

  • Use Appropriate Citation Style : Follow the required citation style (e.g., APA, MLA, Chicago).
  • List References : Provide a complete list of all sources cited in your review.

What is an RRL?

An RRL summarizes and synthesizes existing research on a specific topic to identify gaps and guide future studies.

Why is RRL important?

It provides context, highlights gaps, and ensures new research builds on existing knowledge.

How do you write an RRL?

Organize by themes, summarize studies, evaluate methodologies, identify gaps, and conclude with relevance to current research.

What sources are used in RRL?

Peer-reviewed journals, books, conference papers, and credible online resources.

How long should an RRL be?

Length varies; typically 10-20% of the total research paper.

What are common RRL mistakes?

Lack of organization, insufficient synthesis, over-reliance on outdated sources, and failure to identify gaps.

Can an RRL include non-scholarly sources?

Primarily scholarly, but reputable non-scholarly sources can be included for context.

What is the difference between RRL and bibliography?

RRL synthesizes and analyzes the literature, while a bibliography lists sources.

How often should an RRL be updated?

Regularly, especially when new relevant research is published.

Can an RRL influence research direction?

Yes, it identifies gaps and trends that shape the focus and methodology of new research.

Twitter

Text prompt

  • Instructive
  • Professional

10 Examples of Public speaking

20 Examples of Gas lighting

IMAGES

  1. Tools for data analysis in research example

    how to write data analysis in quantitative research proposal

  2. Data analysis section of dissertation. How to Use Quantitative Data

    how to write data analysis in quantitative research proposal

  3. Quantitative data analysis

    how to write data analysis in quantitative research proposal

  4. Data Analysis Report Sample Template

    how to write data analysis in quantitative research proposal

  5. What Is Data Analysis In Quantitative Research

    how to write data analysis in quantitative research proposal

  6. quantitative research proposal methodology example

    how to write data analysis in quantitative research proposal

VIDEO

  1. Writng a Data Analysis Chapter

  2. Standard Multiple Regression in SPSS

  3. The State of AI in Insights and Research 2024: Results and Findings

  4. Qualitative and Quantitative ​Data Analysis Approaches​

  5. Lecture 2: Descriptive Stats & Probability Distributions

  6. Data Analytics Job Market: Roles, Growth, and Earning Potential

COMMENTS

  1. Data Analysis in Quantitative Research Proposal

    Definition of Data Analysis. Data analysis in quantitative research proposal is one part of the chapter that researchers need in the beginning of writing a research proposal. Whereas in the research, it is an activity after the data from all collected. Activities in data analysis are: grouping data based on variables and types of respondents ...

  2. PDF Developing a Quantitative Data Analysis Plan

    A Data Analysis Plan (DAP) is about putting thoughts into a plan of action. Research questions are often framed broadly and need to be clarified and funnelled down into testable hypotheses and action steps. The DAP provides an opportunity for input from collaborators and provides a platform for training. Having a clear plan of action is also ...

  3. PDF Quantitative Research Proposal Sample

    A Sample Quantitative Research Proposal Written in the APA 6th Style. [Note: This sample proposal is based on a composite of past proposals, simulated information and references, and material I've included for illustration purposes - it is based roughly on a fairly standard research proposal; I say roughly because there is no one set way of ...

  4. Quantitative Data Analysis Methods & Techniques 101

    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.

  5. How to Write a Research Proposal

    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.

  6. PDF DATA ANALYSIS PLAN

    •Data are random numbers. Plan accordingly. • Statistical analysis is the language of scientific inference. Expand your vocabulary. • Statistical analysis is harder than it looks. • Get help now, before you start writing. • Get help while you are writing. • Budget help for later. • When in doubt, call statistician. • When not in doubt, call statistician.

  7. How to Create a Data Analysis Plan: A Detailed Guide

    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.

  8. Designing Research Proposal in Quantitative Approach

    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 ...

  9. The Beginner's Guide to Statistical Analysis

    Step 1: Write your hypotheses and plan your research design. To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design. Writing statistical hypotheses. The goal of research is often to investigate a relationship between variables within a population. You start with a prediction ...

  10. Research Design: Decide on your Data Analysis Strategy

    The last step of designing your research is planning your data analysis strategies. In this video, we'll take a look at some common approaches for both quant...

  11. A Practical Guide to Writing Quantitative and Qualitative Research

    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 ...

  12. How To Write A Research Proposal

    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.

  13. Data Analysis in Research: Types & Methods

    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 ...

  14. Creating a Data Analysis Plan: What to Consider When Choosing

    The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data. Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable.

  15. PDF Research Proposal Format Example

    1. Research Proposal Format Example. Following is a general outline of the material that should be included in your project proposal. I. Title Page II. Introduction and Literature Review (Chapters 2 and 3) A. Identification of specific problem area (e.g., what is it, why it is important). B. Prevalence, scope of problem.

  16. Data Analytics Resources: Writing a Research Proposal

    Literature review. 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 ...

  17. PDF Writing Chapter 3 Chapter 3: Methodology

    Instruments. This section should include the instruments you plan on using to measure the variables in the research questions. (a) the source or developers of the instrument. (b) validity and reliability information. •. (c) information on how it was normed. •. (d) other salient information (e.g., number of. items in each scale, subscales ...

  18. Quantitative Data Analysis

    My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of quantitative data analysis methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection.

  19. How to Write a Research Proposal

    Research proposal examples. 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'.

  20. How to Write an APA Methods Section

    Report all of the procedures applied for administering the study, processing the data, and for planned data analyses. Data collection methods and research design. Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how ...

  21. Step 7: Data analysis techniques for your dissertation

    An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this ...

  22. What Is a Research Methodology?

    Step 1: Explain your methodological approach. Step 2: Describe your data collection methods. Step 3: Describe your analysis method. Step 4: Evaluate and justify the methodological choices you made. Tips for writing a strong methodology chapter. Other interesting articles.

  23. (PDF) Writing A Quantitative Research Proposal / Thesis

    1. Introduce the overall methodological approach. 2. Indicate how the approach fits the overall research design. 3. Describe the specific methods of data collection. 4. Explain how you intend to ...

  24. Quantitative research: Understanding the approaches and key elements

    The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

  25. How to write a research proposal?

    A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer. [ 2] The proposal must be capable of convincing the evaluation committee about ...

  26. Data analysis

    The process of data analysis Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013) Analysis refers to dividing a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test ...

  27. How To Write A Research Proposal With AI

    To write a research paper using A.I., define your research topic and formulate straightforward, specific research questions. Use A.I. to streamline the literature reviews, refine methodologies, and create a structured proposal outline. Draft your proposal using the outline you created. Use an AI writing assistant for revision, correcting errors, and enhancing clarity; incorporate visual aids ...

  28. 10 Tips on Creating a Research Program and Proposal Writing

    Tip #4. Spend half of your time on the abstract and aims. Writers of successful grant applications typically report that they spent 50 percent of their time on writing and revising their abstract and aims. When you finally start drafting your proposal, the specific aims should be the first thing you write — well before the background or ...

  29. PDF PhD Epidemiology 2024-2025

    • Conduct and analyze data from a research study; • Disseminate research findings to scientific and lay audiences. PROGRAM REQUIREMENTS . Students will complete this 48-credit program by taking Foundational Courses, Required Courses, Tailoring Courses, and credit hours focused on Proposal Writing and Dissertation Research.

  30. Review of Related Literature (RRL)

    Themes or Topics: Organize the literature by themes or topics relevant to your research. Sub-theme 1: Summarize key studies, including methodologies, findings, and conclusions. Sub-theme 2: Continue summarizing studies, focusing on different aspects or variables. Sub-theme 3: Include any additional relevant studies.