Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

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A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

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These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

  • Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

  • Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

  • Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

  • Descriptive Statistics
  • Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

  • Mean:   This calculates the numerical average of a set of values.
  • Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
  • Mode: This is used to find the most commonly occurring value in a dataset.
  • Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
  • Frequency: This indicates the number of times a value is found.
  • Range: This shows the highest and lowest values in a dataset.
  • Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
  • Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

  • Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
  • Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
  • Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
  • Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
  • Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
  • Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
  • MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
  • Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
  • Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
  • SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

  • Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
  • Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

  • Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
  • Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

  • Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
  • This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

  • Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

  • Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

  • Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
  • The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

  • Data Analysis and Modeling: 4 Critical Differences
  • Exploratory Data Analysis Simplified 101
  • 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng

Ofem Eteng is a dynamic Machine Learning Engineer at Braln Ltd, where he pioneers the implementation of Deep Learning solutions and explores emerging technologies. His 9 years experience spans across roles such as System Analyst (DevOps) at Dagbs Nigeria Limited, and as a Full Stack Developer at Pedoquasphere International Limited. With a passion for bridging the gap between intricate technical concepts and accessible understanding, Ofem's work resonates with readers seeking insightful perspectives on data science, analytics, and cutting-edge technologies.

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

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

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

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

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?

data analysis quantitative research example

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.

data analysis quantitative research example

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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.


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


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


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


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

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


Thank you so much this helped


Wonderfully explained

Niamatullah zaheer

thank u so much, it was so informative


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.


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

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


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

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

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


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Wow! So explicit. Well done.

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Part II: Data Analysis Methods in Quantitative Research

Data analysis methods in quantitative research.

We started this module with levels of measurement as a way to categorize our data. Data analysis is directed toward answering the original research question and achieving the study purpose (or aim). Now, we are going to delve into two main statistical analyses to describe our data and make inferences about our data:

Descriptive Statistics and Inferential Statistics.

Descriptive Statistics:

Before you panic, we will not be going into statistical analyses very deeply. We want to simply get a good overview of some of the types of general statistical analyses so that it makes some sense to us when we read results in published research articles.

Descriptive statistics   summarize or describe the characteristics of a data set. This is a method of simply organizing and describing our data. Why? Because data that are not organized in some fashion are super difficult to interpret.

Let’s say our sample is golden retrievers (population “canines”). Our descriptive statistics  tell us more about the same.

  • 37% of our sample is male, 43% female
  • The mean age is 4 years
  • Mode is 6 years
  • Median age is 5.5 years

Image of golden retriever in field

Let’s explore some of the types of descriptive statistics.

Frequency Distributions : A frequency distribution describes the number of observations for each possible value of a measured variable. The numbers are arranged from lowest to highest and features a count of how many times each value occurred.

For example, if 18 students have pet dogs, dog ownership has a frequency of 18.

We might see what other types of pets that students have. Maybe cats, fish, and hamsters. We find that 2 students have hamsters, 9 have fish, 1 has a cat.

You can see that it is very difficult to interpret the various pets into any meaningful interpretation, yes?

Now, let’s take those same pets and place them in a frequency distribution table.                          

As we can now see, this is much easier to interpret.

Let’s say that we want to know how many books our sample population of  students have read in the last year. We collect our data and find this:

We can then take that table and plot it out on a frequency distribution graph. This makes it much easier to see how the numbers are disbursed. Easier on the eyes, yes?

Chart, histogram Description automatically generated

Here’s another example of symmetrical, positive skew, and negative skew:

Understanding Descriptive Statistics | by Sarang Narkhede | Towards Data Science

Correlation : Relationships between two research variables are called correlations . Remember, correlation is not cause-and-effect. Correlations  simply measure the extent of relationship between two variables. To measure correlation in descriptive statistics, the statistical analysis called Pearson’s correlation coefficient I is often used.  You do not need to know how to calculate this for this course. But, do remember that analysis test because you will often see this in published research articles. There really are no set guidelines on what measurement constitutes a “strong” or “weak” correlation, as it really depends on the variables being measured.

However, possible values for correlation coefficients range from -1.00 through .00 to +1.00. A value of +1 means that the two variables are positively correlated, as one variable goes up, the other goes up. A value of r = 0 means that the two variables are not linearly related.

Often, the data will be presented on a scatter plot. Here, we can view the data and there appears to be a straight line (linear) trend between height and weight. The association (or correlation) is positive. That means, that there is a weight increase with height. The Pearson correlation coefficient in this case was r = 0.56.

data analysis quantitative research example

A type I error is made by rejecting a null hypothesis that is true. This means that there was no difference but the researcher concluded that the hypothesis was true.

A type II error is made by accepting that the null hypothesis is true when, in fact, it was false. Meaning there was actually a difference but the researcher did not think their hypothesis was supported.

Hypothesis Testing Procedures : In a general sense, the overall testing of a hypothesis has a systematic methodology. Remember, a hypothesis is an educated guess about the outcome. If we guess wrong, we might set up the tests incorrectly and might get results that are invalid. Sometimes, this is super difficult to get right. The main purpose of statistics is to test a hypothesis.

  • Selecting a statistical test. Lots of factors go into this, including levels of measurement of the variables.
  • Specifying the level of significance. Usually 0.05 is chosen.
  • Computing a test statistic. Lots of software programs to help with this.
  • Determining degrees of freedom ( df ). This refers to the number of observations free to vary about a parameter. Computing this is easy (but you don’t need to know how for this course).
  • Comparing the test statistic to a theoretical value. Theoretical values exist for all test statistics, which is compared to the study statistics to help establish significance.

Some of the common inferential statistics you will see include:

Comparison tests: Comparison tests look for differences among group means. They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

  • t -tests (compares differences in two groups) – either paired t-test (example: What is the effect of two different test prep programs on the average exam scores for students from the same class?) or independent t-test (example: What is the difference in average exam scores for students from two different schools?)
  • analysis of variance (ANOVA, which compares differences in three or more groups) (example: What is the difference in average pain levels among post-surgical patients given three different painkillers?) or MANOVA (compares differences in three or more groups, and 2 or more outcomes) (example: What is the effect of flower species on petal length, petal width, and stem length?)

Correlation tests: Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

  • Pearson r (measures the strength and direction of the relationship between two variables) (example: How are latitude and temperature related?)

Nonparametric tests: Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

  • chi-squared ( X 2 ) test (measures differences in proportions). Chi-square tests are often used to test hypotheses. The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship. For example, the results of tossing a fair coin meet these criteria. We can apply a chi-square test to determine which type of candy is most popular and make sure that our shelves are well stocked. Or maybe you’re a scientist studying the offspring of cats to determine the likelihood of certain genetic traits being passed to a litter of kittens.

Inferential Versus Descriptive Statistics Summary Table

Statistical Significance Versus Clinical Significance

Finally, when it comes to statistical significance  in hypothesis testing, the normal probability value in nursing is <0.05. A p=value (probability) is a statistical measurement used to validate a hypothesis against measured data in the study. Meaning, it measures the likelihood that the results were actually observed due to the intervention, or if the results were just due by chance. The p-value, in measuring the probability of obtaining the observed results, assumes the null hypothesis is true.

The lower the p-value, the greater the statistical significance of the observed difference.

In the example earlier about our diabetic patients receiving online diet education, let’s say we had p = 0.05. Would that be a statistically significant result?

If you answered yes, you are correct!

What if our result was p = 0.8?

Not significant. Good job!

That’s pretty straightforward, right? Below 0.05, significant. Over 0.05 not   significant.

Could we have significance clinically even if we do not have statistically significant results? Yes. Let’s explore this a bit.

Statistical hypothesis testing provides little information for interpretation purposes. It’s pretty mathematical and we can still get it wrong. Additionally, attaining statistical significance does not really state whether a finding is clinically meaningful. With a large enough sample, even a small very tiny relationship may be statistically significant. But, clinical significance  is the practical importance of research. Meaning, we need to ask what the palpable effects may be on the lives of patients or healthcare decisions.

Remember, hypothesis testing cannot prove. It also cannot tell us much other than “yeah, it’s probably likely that there would be some change with this intervention”. Hypothesis testing tells us the likelihood that the outcome was due to an intervention or influence and not just by chance. Also, as nurses and clinicians, we are not concerned with a group of people – we are concerned at the individual, holistic level. The goal of evidence-based practice is to use best evidence for decisions about specific individual needs.

data analysis quantitative research example

Additionally, begin your Discussion section. What are the implications to practice? Is there little evidence or a lot? Would you recommend additional studies? If so, what type of study would you recommend, and why?

data analysis quantitative research example

  • Were all the important results discussed?
  • Did the researchers discuss any study limitations and their possible effects on the credibility of the findings? In discussing limitations, were key threats to the study’s validity and possible biases reviewed? Did the interpretations take limitations into account?
  • What types of evidence were offered in support of the interpretation, and was that evidence persuasive? Were results interpreted in light of findings from other studies?
  • Did the researchers make any unjustifiable causal inferences? Were alternative explanations for the findings considered? Were the rationales for rejecting these alternatives convincing?
  • Did the interpretation consider the precision of the results and/or the magnitude of effects?
  • Did the researchers draw any unwarranted conclusions about the generalizability of the results?
  • Did the researchers discuss the study’s implications for clinical practice or future nursing research? Did they make specific recommendations?
  • If yes, are the stated implications appropriate, given the study’s limitations and the magnitude of the effects as well as evidence from other studies? Are there important implications that the report neglected to include?
  • Did the researchers mention or assess clinical significance? Did they make a distinction between statistical and clinical significance?
  • If clinical significance was examined, was it assessed in terms of group-level information (e.g., effect sizes) or individual-level results? How was clinical significance operationalized?

References & Attribution

“ Green check mark ” by rawpixel licensed CC0 .

“ Magnifying glass ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Polit, D. & Beck, C. (2021).  Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research  (10th ed.). Wolters Kluwer Health 

Vaid, N. K. (2019) Statistical performance measures. Medium. https://neeraj-kumar-vaid.medium.com/statistical-performance-measures-12bad66694b7

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Data Analysis in Quantitative Research

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data analysis quantitative research example

  • Yong Moon Jung 2  

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Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. Conducting quantitative data analysis requires a prerequisite understanding of the statistical knowledge and skills. It also requires rigor in the choice of appropriate analysis model and the interpretation of the analysis outcomes. Basically, the choice of appropriate analysis techniques is determined by the type of research question and the nature of the data. In addition, different analysis techniques require different assumptions of data. This chapter provides introductory guides for readers to assist them with their informed decision-making in choosing the correct analysis models. To this end, it begins with discussion of the levels of measure: nominal, ordinal, and scale. Some commonly used analysis techniques in univariate, bivariate, and multivariate data analysis are presented for practical examples. Example analysis outcomes are produced by the use of SPSS (Statistical Package for Social Sciences).

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Jung, Y.M. (2019). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_109

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Quantitative Data Analysis: Types, Analysis & Examples

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analysis of quantitative data

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

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Table of Contents

What is the Quantitative Analysis Method?

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

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

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

What is the Best Analysis for Quantitative Data?

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

1) Descriptive Statistics:

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

2) Inferential Statistics:

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

3) Correlation and Regression Analysis:

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

4) Factor Analysis:

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

5) Time Series Analysis:

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

6) Cluster Analysis:

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

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

Analysis of Quantitative Data in Quantitative Research

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

1) Data Preparation:

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

2) Descriptive Statistics:

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

3) Exploratory Data Analysis (EDA):

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

4) Inferential Statistics:

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

5) Regression Analysis:

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

6) Factor Analysis and Structural Equation Modeling:

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

7) Time Series Analysis and Forecasting:

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

8) Interpretation and Reporting:

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

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

Analysis of Quantitative Data Examples

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

How to Write Data Analysis in Quantitative Research Proposal?

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

Step 1: Begin with an Introduction

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

Step 2: Describe Data Collection Methods

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

Step 3 : Discuss Data Cleaning Procedures

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

Step 4 : Present Analytical Techniques

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

Step 5: State Hypotheses & Testing Procedures

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

Step 6 : Provide a Sample Analysis Plan

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

Step 7 : Address Validity & Reliability

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

Step 8 : Discuss Ethical Considerations

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

Step 9 : Acknowledge Limitations

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

Step 10 : Conclude the Section

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

Step 11 : Proofread & Revise

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

What are the 4 Types of Quantitative Analysis?

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

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

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

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

Steps to Effective Quantitative Data Analysis 

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

Step 1 : Gather Data Strategically

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

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

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

Step 2 : Refine and Cleanse Your Data

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

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

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

Step 3 : Delve into Analysis with Precision

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

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

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

Step 4 : Visual Representation and Communication

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

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

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

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

Statistical Analysis in Quantitative Research

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

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

3) Probability Distributions:

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

4) Multivariate Analysis:

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

5) Data Modeling and Forecasting:

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

If this blog post has piqued your interest in the field of data analytics, then we highly recommend checking out Physics Wallah’s Data Analytics Course . This course covers all the fundamental concepts of quantitative data analysis and provides hands-on training for various tools and software used in the industry.

With a team of experienced instructors from different backgrounds and industries, you will gain a comprehensive understanding of a wide range of topics related to data analytics. And as an added bonus for being one of our dedicated readers, use the coupon code “ READER ” to get an exclusive discount on this course!

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Analysis of Quantitative Data FAQs

What is quantitative data analysis.

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

What are the main steps involved in quantitative data analysis?

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

What is the difference between descriptive and inferential analysis?

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

How do I handle outliers in my quantitative data?

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

Which statistical tests should I use for my quantitative research?

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

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8 quantitative data analysis methods to turn numbers into insights

Setting up a few new customer surveys or creating a fresh Google Analytics dashboard feels exciting…until the numbers start rolling in. You want to turn responses into a plan to present to your team and leaders—but which quantitative data analysis method do you use to make sense of the facts and figures?

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data analysis quantitative research example

This guide lists eight quantitative research data analysis techniques to help you turn numeric feedback into actionable insights to share with your team and make customer-centric decisions. 

To pick the right technique that helps you bridge the gap between data and decision-making, you first need to collect quantitative data from sources like:

Google Analytics  

Survey results

On-page feedback scores

Fuel your quantitative analysis with real-time data

Use Hotjar’s tools to collect quantitative data that helps you stay close to customers.

Then, choose an analysis method based on the type of data and how you want to use it.

Descriptive data analysis summarizes results—like measuring website traffic—that help you learn about a problem or opportunity. The descriptive analysis methods we’ll review are:

Multiple choice response rates

Response volume over time

Net Promoter Score®

Inferential data analyzes the relationship between data—like which customer segment has the highest average order value—to help you make hypotheses about product decisions. Inferential analysis methods include:


Weighted customer feedback

You don’t need to worry too much about these specific terms since each quantitative data analysis method listed below explains when and how to use them. Let’s dive in!

1. Compare multiple-choice response rates 

The simplest way to analyze survey data is by comparing the percentage of your users who chose each response, which summarizes opinions within your audience. 

To do this, divide the number of people who chose a specific response by the total respondents for your multiple-choice survey. Imagine 100 customers respond to a survey about what product category they want to see. If 25 people said ‘snacks’, 25% of your audience favors that category, so you know that adding a snacks category to your list of filters or drop-down menu will make the purchasing process easier for them.

💡Pro tip: ask open-ended survey questions to dig deeper into customer motivations.

A multiple-choice survey measures your audience’s opinions, but numbers don’t tell you why they think the way they do—you need to combine quantitative and qualitative data to learn that. 

One research method to learn about customer motivations is through an open-ended survey question. Giving customers space to express their thoughts in their own words—unrestricted by your pre-written multiple-choice questions—prevents you from making assumptions.

data analysis quantitative research example

Hotjar’s open-ended surveys have a text box for customers to type a response

2. Cross-tabulate to compare responses between groups

To understand how responses and behavior vary within your audience, compare your quantitative data by group. Use raw numbers, like the number of website visitors, or percentages, like questionnaire responses, across categories like traffic sources or customer segments.

#A cross-tabulated content analysis lets teams focus on work with a higher potential of success

Let’s say you ask your audience what their most-used feature is because you want to know what to highlight on your pricing page. Comparing the most common response for free trial users vs. established customers lets you strategically introduce features at the right point in the customer journey . 

💡Pro tip: get some face-to-face time to discover nuances in customer feedback.

Rather than treating your customers as a monolith, use Hotjar to conduct interviews to learn about individuals and subgroups. If you aren’t sure what to ask, start with your quantitative data results. If you notice competing trends between customer segments, have a few conversations with individuals from each group to dig into their unique motivations.

Hotjar Engage lets you identify specific customer segments you want to talk to

Mode is the most common answer in a data set, which means you use it to discover the most popular response for questions with numeric answer options. Mode and median (that's next on the list) are useful to compare to the average in case responses on extreme ends of the scale (outliers) skew the outcome.

Let’s say you want to know how most customers feel about your website, so you use an on-page feedback widget to collect ratings on a scale of one to five.

#Visitors rate their experience on a scale with happy (or angry) faces, which translates to a quantitative scale

If the mode, or most common response, is a three, you can assume most people feel somewhat positive. But suppose the second-most common response is a one (which would bring the average down). In that case, you need to investigate why so many customers are unhappy. 

💡Pro tip: watch recordings to understand how customers interact with your website.

So you used on-page feedback to learn how customers feel about your website, and the mode was two out of five. Ouch. Use Hotjar Recordings to see how customers move around on and interact with your pages to find the source of frustration.

Hotjar Recordings lets you watch individual visitors interact with your site, like how they scroll, hover, and click

Median reveals the middle of the road of your quantitative data by lining up all numeric values in ascending order and then looking at the data point in the middle. Use the median method when you notice a few outliers that bring the average up or down and compare the analysis outcomes.

For example, if your price sensitivity survey has outlandish responses and you want to identify a reasonable middle ground of what customers are willing to pay—calculate the median.

💡Pro-tip: review and clean your data before analysis. 

Take a few minutes to familiarize yourself with quantitative data results before you push them through analysis methods. Inaccurate or missing information can complicate your calculations, and it’s less frustrating to resolve issues at the start instead of problem-solving later. 

Here are a few data-cleaning tips to keep in mind:

Remove or separate irrelevant data, like responses from a customer segment or time frame you aren’t reviewing right now 

Standardize data from multiple sources, like a survey that let customers indicate they use your product ‘daily’ vs. on-page feedback that used the phrasing ‘more than once a week’

Acknowledge missing data, like some customers not answering every question. Just note that your totals between research questions might not match.

Ensure you have enough responses to have a statistically significant result

Decide if you want to keep or remove outlying data. For example, maybe there’s evidence to support a high-price tier, and you shouldn’t dismiss less price-sensitive respondents. Other times, you might want to get rid of obviously trolling responses.

5. Mean (AKA average)

Finding the average of a dataset is an essential quantitative data analysis method and an easy task. First, add all your quantitative data points, like numeric survey responses or daily sales revenue. Then, divide the sum of your data points by the number of responses to get a single number representing the entire dataset. 

Use the average of your quant data when you want a summary, like the average order value of your transactions between different sales pages. Then, use your average to benchmark performance, compare over time, or uncover winners across segments—like which sales page design produces the most value.

💡Pro tip: use heatmaps to find attention-catching details numbers can’t give you.

Calculating the average of your quant data set reveals the outcome of customer interactions. However, you need qualitative data like a heatmap to learn about everything that led to that moment. A heatmap uses colors to illustrate where most customers look and click on a page to reveal what drives (or drops) momentum.

data analysis quantitative research example

Hotjar Heatmaps uses color to visualize what most visitors see, ignore, and click on

6. Measure the volume of responses over time

Some quantitative data analysis methods are an ongoing project, like comparing top website referral sources by month to gauge the effectiveness of new channels. Analyzing the same metric at regular intervals lets you compare trends and changes. 

Look at quantitative survey results, website sessions, sales, cart abandons, or clicks regularly to spot trouble early or monitor the impact of a new initiative.

Here are a few areas you can measure over time (and how to use qualitative research methods listed above to add context to your results):

7. Net Promoter Score®

Net Promoter Score® ( NPS ®) is a popular customer loyalty and satisfaction measurement that also serves as a quantitative data analysis method. 

NPS surveys ask customers to rate how likely they are to recommend you on a scale of zero to ten. Calculate it by subtracting the percentage of customers who answer the NPS question with a six or lower (known as ‘detractors’) from those who respond with a nine or ten (known as ‘promoters’). Your NPS score will fall between -100 and 100, and you want a positive number indicating more promoters than detractors. 

#NPS scores exist on a scale of zero to ten

💡Pro tip : like other quantitative data analysis methods, you can review NPS scores over time as a satisfaction benchmark. You can also use it to understand which customer segment is most satisfied or which customers may be willing to share their stories for promotional materials.

data analysis quantitative research example

Review NPS score trends with Hotjar to spot any sudden spikes and benchmark performance over time

8. Weight customer feedback 

So far, the quantitative data analysis methods on this list have leveraged numeric data only. However, there are ways to turn qualitative data into quantifiable feedback and to mix and match data sources. For example, you might need to analyze user feedback from multiple surveys.

To leverage multiple data points, create a prioritization matrix that assigns ‘weight’ to customer feedback data and company priorities and then multiply them to reveal the highest-scoring option. 

Let’s say you identify the top four responses to your churn survey . Rate the most common issue as a four and work down the list until one—these are your customer priorities. Then, rate the ease of fixing each problem with a maximum score of four for the easy wins down to one for difficult tasks—these are your company priorities. Finally, multiply the score of each customer priority with its coordinating company priority scores and lead with the highest scoring idea. 

💡Pro-tip: use a product prioritization framework to make decisions.

Try a product prioritization framework when the pressure is on to make high-impact decisions with limited time and budget. These repeatable decision-making tools take the guesswork out of balancing goals, customer priorities, and team resources. Four popular frameworks are:

RICE: weighs four factors—reach, impact, confidence, and effort—to weigh initiatives differently

MoSCoW: considers stakeholder opinions on 'must-have', 'should-have', 'could-have', and 'won't-have' criteria

Kano: ranks ideas based on how likely they are to satisfy customer needs

Cost of delay analysis: determines potential revenue loss by not working on a product or initiative

Share what you learn with data visuals

Data visualization through charts and graphs gives you a new perspective on your results. Plus, removing the clutter of the analysis process helps you and stakeholders focus on the insight over the method.

Data visualization helps you:

Get buy-in with impactful charts that summarize your results

Increase customer empathy and awareness across your company with digestible insights

Use these four data visualization types to illustrate what you learned from your quantitative data analysis: 

Bar charts reveal response distribution across multiple options

Line graphs compare data points over time

Scatter plots showcase how two variables interact

Matrices contrast data between categories like customer segments, product types, or traffic source

#Bar charts, like this example, give a sense of how common responses are within an audience and how responses relate to one another

Use a variety of customer feedback types to get the whole picture

Quantitative data analysis pulls the story out of raw numbers—but you shouldn’t take a single result from your data collection and run with it. Instead, combine numbers-based quantitative data with descriptive qualitative research to learn the what, why, and how of customer experiences. 

Looking at an opportunity from multiple angles helps you make more customer-centric decisions with less guesswork.

Stay close to customers with Hotjar

Hotjar’s tools offer quantitative and qualitative insights you can use to make customer-centric decisions, get buy-in, and highlight your team’s impact.

Frequently asked questions about quantitative data analysis

What is quantitative data.

Quantitative data is numeric feedback and information that you can count and measure. For example, you can calculate multiple-choice response rates, but you can’t tally a customer’s open-ended product feedback response. You have to use qualitative data analysis methods for non-numeric feedback.

What are quantitative data analysis methods?

Quantitative data analysis either summarizes or finds connections between numerical data feedback. Here are eight ways to analyze your online business’s quantitative data:

Compare multiple-choice response rates

Cross-tabulate to compare responses between groups

Measure the volume of response over time

Net Promoter Score

Weight customer feedback

How do you visualize quantitative data?

Data visualization makes it easier to spot trends and share your analysis with stakeholders. Bar charts, line graphs, scatter plots, and matrices are ways to visualize quantitative data.

What are the two types of statistical analysis for online businesses?

Quantitative data analysis is broken down into two analysis technique types:

Descriptive statistics summarize your collected data, like the number of website visitors this month

Inferential statistics compare relationships between multiple types of quantitative data, like survey responses between different customer segments

Quantitative data analysis process

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Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study

There are three kinds of lies: lies, damned lies, and statistics. – Mark Twain 1


Statistics represent an essential part of a study because, regardless of the study design, investigators need to summarize the collected information for interpretation and presentation to others. It is therefore important for us to heed Mr Twain’s concern when creating the data analysis plan. In fact, even before data collection begins, we need to have a clear analysis plan that will guide us from the initial stages of summarizing and describing the data through to testing our hypotheses.

The purpose of this article is to help you create a data analysis plan for a quantitative study. For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2 , 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to summarize study data, and a process to help identify relevant statistical tests. My intention here is to introduce the main elements of data analysis and provide a place for you to start when planning this part of your study. Biostatistical experts, textbooks, statistical software packages, and other resources can certainly add more breadth and depth to this topic when you need additional information and advice.


When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is important to begin with an understanding of the source of the numbers. Let us start with the term variable , which defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values , to help describe the characteristic of an individual study participant. For example, the variable “sex” would have values of “male” and “female”.

Although variables can be defined or grouped in various ways, I will focus on 2 methods at this introductory stage. First, variables can be defined according to the level of measurement. The categories in a nominal variable are names, for example, male and female for the variable “sex”; white, Aboriginal, black, Latin American, South Asian, and East Asian for the variable “ethnicity”; and intervention and control for the variable “treatment group”. Nominal variables with only 2 categories are also referred to as dichotomous variables because the study group can be divided into 2 subgroups based on information in the variable. For example, a study sample can be split into 2 groups (patients receiving the intervention and controls) using the dichotomous variable “treatment group”. An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or always). Nominal-level and ordinal-level variables are also referred to as categorical variables, because each category in the variable can be completely separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive categories also having meaning. Age, weight, and blood glucose can be considered as interval variables, but also as ratio variables, because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories.

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. 4 For example, if age is reduced from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years) we lose the ability to make comparisons across the entire age range and introduce error into the data analysis. 4

A second method of defining variables is to consider them as either dependent or independent. As the terms imply, the value of a dependent variable depends on the value of other variables, whereas the value of an independent variable does not rely on other variables. In addition, an investigator can influence the value of an independent variable, such as treatment-group assignment. Independent variables are also referred to as predictors because we can use information from these variables to predict the value of a dependent variable. Building on the group of variables listed in the first paragraph of this section, blood glucose could be considered a dependent variable, because its value may depend on values of the independent variables age, sex, ethnicity, exercise frequency, weight, and treatment group.

Statistics are mathematical formulae that are used to organize and interpret the information that is collected through variables. There are 2 general categories of statistics, descriptive and inferential. Descriptive statistics are used to describe the collected information, such as the range of values, their average, and the most common category. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. Inferential statistics are used to make comparisons and draw conclusions from the study data. Knowledge gained from inferential statistics allows investigators to make inferences and generalize beyond their study sample to other groups.

Before we move on to specific descriptive and inferential statistics, there are 2 more definitions to review. Parametric statistics are generally used when values in an interval-level or ratio-level variable are normally distributed (i.e., the entire group of values has a bell-shaped curve when plotted by frequency). These statistics are used because we can define parameters of the data, such as the centre and width of the normally distributed curve. In contrast, interval-level and ratio-level variables with values that are not normally distributed, as well as nominal-level and ordinal-level variables, are generally analyzed using nonparametric statistics.


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. Data for nominal-level and ordinal-level variables may be interpreted using a pie graph or bar graph . Both options allow us to examine the relative number of participants within each category (by reporting the percentages within each category), whereas a bar graph can also be used to examine absolute numbers. For example, we could create a pie graph to illustrate the proportions of men and women in a study sample and a bar graph to illustrate the number of people who report exercising at each level of frequency (never, sometimes, often, or always).

Interval-level and ratio-level variables may also be interpreted using a pie graph or bar graph; however, these types of variables often have too many categories for such graphs to provide meaningful information. Instead, these variables may be better interpreted using a histogram . Unlike a bar graph, which displays the frequency for each distinct category, a histogram displays the frequency within a range of continuous categories. Information from this type of figure allows us to determine whether the data are normally distributed. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Interested readers can find additional types of figures in the books recommended in the “Further Readings” section.

Figures are also useful for visualizing comparisons between variables or between subgroups within a variable (for example, the distribution of blood glucose according to sex). Box plots are useful for summarizing information for a variable that does not follow a normal distribution. The lower and upper limits of the box identify the interquartile range (or 25th and 75th percentiles), while the midline indicates the median value (or 50th percentile). Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations.

In addition to using figures to present a visual description of the data, investigators can use statistics to provide a numeric description. Regardless of the measurement level, we can find the mode by identifying the most frequent category within a variable. When summarizing nominal-level and ordinal-level variables, the simplest method is to report the proportion of participants within each category.

The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed. If the values are normally distributed, we can summarize the information using the parametric statistics of mean and standard deviation. The mean is the arithmetic average of all values within the variable, and the standard deviation tells us how widely the values are dispersed around the mean. When values of interval-level and ratio-level variables are not normally distributed, or we are summarizing information from an ordinal-level variable, it may be more appropriate to use the nonparametric statistics of median and range. The first step in identifying these descriptive statistics is to arrange study participants according to the variable categories from lowest value to highest value. The range is used to report the lowest and highest values. The median or 50th percentile is located by dividing the number of participants into 2 groups, such that half (50%) of the participants have values above the median and the other half (50%) have values below the median. Similarly, the 25th percentile is the value with 25% of the participants having values below and 75% of the participants having values above, and the 75th percentile is the value with 75% of participants having values below and 25% of participants having values above. Together, the 25th and 75th percentiles define the interquartile range .


One caveat about the information provided in this section: selecting the most appropriate inferential statistic for a specific study should be a combination of following these suggestions, seeking advice from experts, and discussing with your co-investigators. My intention here is to give you a place to start a conversation with your colleagues about the options available as you develop your data analysis plan.

There are 3 key questions to consider when selecting an appropriate inferential statistic for a study: What is the research question? What is the study design? and What is the level of measurement? It is important for investigators to carefully consider these questions when developing the study protocol and creating the analysis plan. The figures that accompany these questions show decision trees that will help you to narrow down the list of inferential statistics that would be relevant to a particular study. Appendix 1 provides brief definitions of the inferential statistics named in these figures. Additional information, such as the formulae for various inferential statistics, can be obtained from textbooks, statistical software packages, and biostatisticians.

What Is the Research Question?

The first step in identifying relevant inferential statistics for a study is to consider the type of research question being asked. You can find more details about the different types of research questions in a previous article in this Research Primer series that covered questions and hypotheses. 5 A relational question seeks information about the relationship among variables; in this situation, investigators will be interested in determining whether there is an association ( Figure 1 ). A causal question seeks information about the effect of an intervention on an outcome; in this situation, the investigator will be interested in determining whether there is a difference ( Figure 2 ).

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Object name is cjhp-68-311f1.jpg

Decision tree to identify inferential statistics for an association.

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Object name is cjhp-68-311f2.jpg

Decision tree to identify inferential statistics for measuring a difference.

What Is the Study Design?

When considering a question of association, investigators will be interested in measuring the relationship between variables ( Figure 1 ). A study designed to determine whether there is consensus among different raters will be measuring agreement. For example, an investigator may be interested in determining whether 2 raters, using the same assessment tool, arrive at the same score. Correlation analyses examine the strength of a relationship or connection between 2 variables, like age and blood glucose. Regression analyses also examine the strength of a relationship or connection; however, in this type of analysis, one variable is considered an outcome (or dependent variable) and the other variable is considered a predictor (or independent variable). Regression analyses often consider the influence of multiple predictors on an outcome at the same time. For example, an investigator may be interested in examining the association between a treatment and blood glucose, while also considering other factors, like age, sex, ethnicity, exercise frequency, and weight.

When considering a question of difference, investigators must first determine how many groups they will be comparing. In some cases, investigators may be interested in comparing the characteristic of one group with that of an external reference group. For example, is the mean age of study participants similar to the mean age of all people in the target group? If more than one group is involved, then investigators must also determine whether there is an underlying connection between the sets of values (or samples ) to be compared. Samples are considered independent or unpaired when the information is taken from different groups. For example, we could use an unpaired t test to compare the mean age between 2 independent samples, such as the intervention and control groups in a study. Samples are considered related or paired if the information is taken from the same group of people, for example, measurement of blood glucose at the beginning and end of a study. Because blood glucose is measured in the same people at both time points, we could use a paired t test to determine whether there has been a significant change in blood glucose.

What Is the Level of Measurement?

As described in the first section of this article, variables can be grouped according to the level of measurement (nominal, ordinal, or interval). In most cases, the independent variable in an inferential statistic will be nominal; therefore, investigators need to know the level of measurement for the dependent variable before they can select the relevant inferential statistic. Two exceptions to this consideration are correlation analyses and regression analyses ( Figure 1 ). Because a correlation analysis measures the strength of association between 2 variables, we need to consider the level of measurement for both variables. Regression analyses can consider multiple independent variables, often with a variety of measurement levels. However, for these analyses, investigators still need to consider the level of measurement for the dependent variable.

Selection of inferential statistics to test interval-level variables must include consideration of how the data are distributed. An underlying assumption for parametric tests is that the data approximate a normal distribution. When the data are not normally distributed, information derived from a parametric test may be wrong. 6 When the assumption of normality is violated (for example, when the data are skewed), then investigators should use a nonparametric test. If the data are normally distributed, then investigators can use a parametric test.


What is the level of significance.

An inferential statistic is used to calculate a p value, the probability of obtaining the observed data by chance. Investigators can then compare this p value against a prespecified level of significance, which is often chosen to be 0.05. This level of significance represents a 1 in 20 chance that the observation is wrong, which is considered an acceptable level of error.

What Are the Most Commonly Used Statistics?

In 1983, Emerson and Colditz 7 reported the first review of statistics used in original research articles published in the New England Journal of Medicine . This review of statistics used in the journal was updated in 1989 and 2005, 8 and this type of analysis has been replicated in many other journals. 9 – 13 Collectively, these reviews have identified 2 important observations. First, the overall sophistication of statistical methodology used and reported in studies has grown over time, with survival analyses and multivariable regression analyses becoming much more common. The second observation is that, despite this trend, 1 in 4 articles describe no statistical methods or report only simple descriptive statistics. When inferential statistics are used, the most common are t tests, contingency table tests (for example, χ 2 test and Fisher exact test), and simple correlation and regression analyses. This information is important for educators, investigators, reviewers, and readers because it suggests that a good foundational knowledge of descriptive statistics and common inferential statistics will enable us to correctly evaluate the majority of research articles. 11 – 13 However, to fully take advantage of all research published in high-impact journals, we need to become acquainted with some of the more complex methods, such as multivariable regression analyses. 8 , 13

What Are Some Additional Resources?

As an investigator and Associate Editor with CJHP , I have often relied on the advice of colleagues to help create my own analysis plans and review the plans of others. Biostatisticians have a wealth of knowledge in the field of statistical analysis and can provide advice on the correct selection, application, and interpretation of these methods. Colleagues who have “been there and done that” with their own data analysis plans are also valuable sources of information. Identify these individuals and consult with them early and often as you develop your analysis plan.

Another important resource to consider when creating your analysis plan is textbooks. Numerous statistical textbooks are available, differing in levels of complexity and scope. The titles listed in the “Further Reading” section are just a few suggestions. I encourage interested readers to look through these and other books to find resources that best fit their needs. However, one crucial book that I highly recommend to anyone wanting to be an investigator or peer reviewer is Lang and Secic’s How to Report Statistics in Medicine (see “Further Reading”). As the title implies, this book covers a wide range of statistics used in medical research and provides numerous examples of how to correctly report the results.


When it comes to creating an analysis plan for your project, I recommend following the sage advice of Douglas Adams in The Hitchhiker’s Guide to the Galaxy : Don’t panic! 14 Begin with simple methods to summarize and visualize your data, then use the key questions and decision trees provided in this article to identify relevant statistical tests. Information in this article will give you and your co-investigators a place to start discussing the elements necessary for developing an analysis plan. But do not stop there! Use advice from biostatisticians and more experienced colleagues, as well as information in textbooks, to help create your analysis plan and choose the most appropriate statistics for your study. Making careful, informed decisions about the statistics to use in your study should reduce the risk of confirming Mr Twain’s concern.

Appendix 1. Glossary of statistical terms * (part 1 of 2)

  • 1-way ANOVA: Uses 1 variable to define the groups for comparing means. This is similar to the Student t test when comparing the means of 2 groups.
  • Kruskall–Wallis 1-way ANOVA: Nonparametric alternative for the 1-way ANOVA. Used to determine the difference in medians between 3 or more groups.
  • n -way ANOVA: Uses 2 or more variables to define groups when comparing means. Also called a “between-subjects factorial ANOVA”.
  • Repeated-measures ANOVA: A method for analyzing whether the means of 3 or more measures from the same group of participants are different.
  • Freidman ANOVA: Nonparametric alternative for the repeated-measures ANOVA. It is often used to compare rankings and preferences that are measured 3 or more times.
  • Fisher exact: Variation of chi-square that accounts for cell counts < 5.
  • McNemar: Variation of chi-square that tests statistical significance of changes in 2 paired measurements of dichotomous variables.
  • Cochran Q: An extension of the McNemar test that provides a method for testing for differences between 3 or more matched sets of frequencies or proportions. Often used as a measure of heterogeneity in meta-analyses.
  • 1-sample: Used to determine whether the mean of a sample is significantly different from a known or hypothesized value.
  • Independent-samples t test (also referred to as the Student t test): Used when the independent variable is a nominal-level variable that identifies 2 groups and the dependent variable is an interval-level variable.
  • Paired: Used to compare 2 pairs of scores between 2 groups (e.g., baseline and follow-up blood pressure in the intervention and control groups).

Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006.

Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003.

Plichta SB, Kelvin E. Munro’s statistical methods for health care research . 6th ed. Philadelphia (PA): Wolters Kluwer Health/ Lippincott, Williams & Wilkins; 2013.

This article is the 12th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

  • Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.
  • Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.
  • Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.
  • Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.
  • Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.
  • Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.
  • Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.
  • Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.
  • Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.
  • Sutton J, Austin Z. Qualitative research: data collection, analysis, and management. Can J Hosp Pharm . 2014;68(3):226–31.
  • Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2014;68(3):232–7.

Competing interests: None declared.

Further Reading

  • Devor J, Peck R. Statistics: the exploration and analysis of data. 7th ed. Boston (MA): Brooks/Cole Cengage Learning; 2012. [ Google Scholar ]
  • Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006. [ Google Scholar ]
  • Mendenhall W, Beaver RJ, Beaver BM. Introduction to probability and statistics. 13th ed. Belmont (CA): Brooks/Cole Cengage Learning; 2009. [ Google Scholar ]
  • Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003. [ Google Scholar ]
  • Plichta SB, Kelvin E. Munro’s statistical methods for health care research. 6th ed. Philadelphia (PA): Wolters Kluwer Health/Lippincott, Williams & Wilkins; 2013. [ Google Scholar ]
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Data Analysis in Research: Types & Methods


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Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

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

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

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

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

Phase I: Data Validation

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

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

Phase II: Data Editing

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

Phase III: Data Coding

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

LEARN ABOUT: Steps in Qualitative Research

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

Descriptive statistics

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

Measures of Frequency

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

Measures of Central Tendency

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

Measures of Dispersion or Variation

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

Measures of Position

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

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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

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

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

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

LEARN ABOUT: Best Data Collection Tools

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

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

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Data Analysis for Quantitative Data: Techniques and Tools for Accurate Insights

Data Analysis for Quantitative Data: Techniques and Tools for Accurate Insights

Data is the lifeblood of your organization, especially in a constantly evolving marketplace. Every day, you make decisions that impact achieving your organizational goals. If your organization isn’t digging into and exploring all the data it collects, it risks becoming less relevant in your customers’ eyes. Everyone in your organization, from top-level executives to entry-level employees, must understand how to interpret data to make impactful decisions.

Without data literacy , your organization is blind to what is happening and will make decisions based on guesswork and intuition, which may or may not yield the best decisions. Whether attempting to streamline your organization’s operations, boost sales growth, or improve customer satisfaction, understanding your data is the cornerstone of success.

To become data literate, you need to understand the two distinct types of data: quantitative and qualitative. After introducing these distinct types of data, we will look specifically at quantitative data and how to analyze it.

On this page :

What is Quantitative Data?

What's the difference between quantitative and qualitative data, 10 steps to analyzing quantitative data, the role of machine learning in quantitative data analysis, real-world applications of quantitative data analysis, future trends in quantitative data analysis.

Quantitative data refers to information that can be measured and expressed numerically. This type of data deals with numbers and things that can be measured objectively, such as height, width, length, temperature, humidity, and prices. Quantitative data can be analyzed using various statistical methods to uncover patterns, relationships, and trends within the data set.

Here are several examples of quantitative data:

  • Height : A person's height of 175 centimeters.
  • Temperature : The temperature is 25 Celsius.
  • Income : A person's monthly salary is $3000.
  • Age : Someone is 35 years old.
  • Test Scores : A student scores 85 out of 100 on a math test.
  • Number of Items Sold : A store sells 100 product units monthly.
  • Distance : A car travels 50 kilometers an hour.
  • Speed : A train might travel 100 kilometers per hour.
  • Population : A city’s population is 1,000,000 people.
  • Time : An event lasts 2 hours and 30 minutes.

As you can see, these examples represent quantities measured and expressed using numerical values.

Quantitative and qualitative data are the two primary types of data used in research and analysis, and they differ in terms of their nature, characteristics, and how they are collected and analyzed. The table below shows the differences between both data types:

Now that you know what each data type represents, the following table illustrates the differences between quantitative and qualitative data. The 'Price' column provides quantitative information, representing the cost of each product in dollars, whereas the 'Customer Review' column contains qualitative data featuring customer feedback regarding their experiences with the products.

While the two data types represent different data, one thing to remember is that quantitative and qualitative data complement each other. Quantitative data provides you with statistical insight, while qualitative data provides you with depth and context.

Types of Data Analysis

Now that we have defined quantitative data let’s look at the steps you would take to perform quantitative data analysis. Analyzing quantitative data will allow you to unlock insights that can help you make informed decisions. From defining your research objectives to interpreting your analysis results, each step will help you unleash the power of quantitative data.

Following the steps above will provide you with the means to perform effective quantitative data analysis. Using the insights you uncover from your data will help guide your decision-making processes, helping your organization to meet or surpass its goals and objectives.

Any organization's picture can be more precise with more quantitative data. However, if you manually sift through gigabytes of data, you may not be able to see the forest for the trees, leading to incorrect assumptions and decisions. Machine Learning (ML), a subset of Artificial Intelligence (AI), comes to the rescue, helping you see the forest.

The power of ML is that it can identify patterns and trends using algorithms, revealing hidden insights that would take a human analyst a much longer time to discover, if at all. Additionally, ML can help predict future trends by looking at historical data. So, how does ML learn to read quantitative data and provide valuable insights? Let's look at how you teach ML.

There are many different learning methods for ML, but we will define three: supervised, unsupervised, and reinforcement learning.

  • Supervised Learning uses labeled training data, which contains input-output pairs. The ML algorithms then analyze a large data set containing these pairs to learn the desired output when asked to make a prediction using new data.
  • Unsupervised Learning uses unlabeled training data. The ML algorithms try to find patterns and structures in the input data without guidance on the outcomes they should predict.
  • Reinforcement Learning is when an algorithm learns decision-making by performing an action and receiving reward or penalty feedback. The algorithm's overall goal is to discover a strategy that maximizes reward feedback.

While machine learning has become an indispensable tool for quantitative data analysis, it's important to recognize that it does not replace the need for human judgment and decision-making. Python, R, and specialized platforms like TensorFlow and Azure ML have made it easier than ever to integrate machine learning into the data analysis workflow, dramatically enhancing the efficiency and depth of insights that can be uncovered.

When leveraging these resources, analysts can uncover hidden patterns and transform raw data into actionable intelligence. However, rather than replacing human expertise, machine learning augments it by delivering more precise outcomes, freeing up analysts to concentrate on high-level strategic planning and choices that require a human touch. The power of machine learning lies in its ability to work in tandem with human judgment, providing the raw insights that inform critical decisions and drive meaningful change.

Example: Predictive Analytics in E-commerce

Imagine you're a data scientist working for an online retail organization that wants to improve customer retention and increase sales by predicting purchasing behavior and preferences. By leveraging predictive analytics, you can analyze historical transaction data to forecast future buying patterns and identify high-value customers.

  • Data Collection and Preparation: You first collect and preprocess quantitative transactional data, including customer demographics, purchase history, purchase frequency, transaction amounts, customer lifetime value, product attributes, and website interactions. This process will also collect qualitative data such as product categories and customer feedback.
  • Feature Engineering: Next, you extract meaningful features from the raw data through feature engineering, such as average purchase value or total number of orders, and you create derived variables such as recency and frequency. This information provides the inputs for predictive modeling.
  • Predictive Modeling: To forecast customer churn, identify cross-selling opportunities, and create personalized product recommendations, you use ML algorithms to build predictive models. These models use historical data to predict the likelihood of customers’ purchasing in a specific timeframe or which products they will likely purchase next.
  • Model Evaluation and Validation: You evaluate the performance of the predictive models using metrics such as accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). You then use cross-validation techniques and holdout validation to assess model generalization and ensure robustness against overfitting.
  • Deployment and Integration: Once validated, the predictive models are deployed into production systems, integrated with the company's e-commerce platform, and used to generate real-time recommendations and personalized marketing campaigns. Customers receive targeted offers, product suggestions, and promotional discounts based on their predicted preferences and behaviors.
  • Monitoring and Iteration: Continuous monitoring of model performance and customer feedback enables iterative refinement and optimization of the predictive analytics pipeline.

Quantitative data analysis is constantly evolving, driven by advancements in technology, new research methodologies, and emerging trends in data science. To remain ahead of the curve, it is crucial that you are aware of future trends that will impact how you perform data analysis. Some of the future trends that will potentially impact quantitative analysis include:

  • Increased Use of Artificial Intelligence and Machine Learning : Integrating AI and ML techniques is expected to become more prevalent, enabling more sophisticated and automated data analysis.
  • Growth of Big Data : As data grows in volume, variety, and velocity, quantitative analysis methods must adapt to handle larger datasets more efficiently.
  • Advancements in Data Privacy Regulations : Increasing concerns and regulations around data privacy will impact how data is collected, stored, and analyzed, prompting the development of new methods that protect individual privacy.
  • Rise of Edge Computing : With the rise of IoT devices, edge computing will become more critical. It will push data analysis closer to where data is generated to improve speed and reduce data transfer costs.
  • Quantum Computing : The potential rise of quantum computing could revolutionize data analysis by providing the power to process enormous and complex datasets much faster than traditional computers.
  • Proliferation of Data-as-a-Service (DaaS) : DaaS will provide increased access to high-quality and specialized data streams, enabling more refined and real-time analyses.
  • Augmented Analytics : Augmented analytics, which incorporates natural language processing and automated algorithms, will make data analysis more accessible to non-experts and enhance decision-making processes.
  • Focus on Predictive and Prescriptive Analytics : There will be a shift towards more predictive and prescriptive analytics, moving beyond descriptive analytics to offer foresight and guidance on future actions.

As quantitative data analysis evolves, it will be defined by a trifecta of innovation, integration, and integrity. Cutting-edge advancements will reshape analytical methods and tools, while the fusion of diverse data sources and techniques will unearth groundbreaking insights.

However, the true measure of success will lie in the ethical application of these capabilities, prioritizing transparency, privacy, and fairness. The future of data analysis will demand continuous learning, collaboration, and unwavering commitment to using data for the greater good, unlocking transformative opportunities for data-driven decision-making that benefits all.

Quantitative data is a powerful tool for uncovering insights, identifying patterns, and informing decision-making. We've explored the fundamental concepts of quantitative data, delved into the differences between quantitative and qualitative data, and navigated through a step-by-step guide to conducting data analysis using quantitative data. From defining research objectives to deploying advanced ML techniques, each step in the quantitative data analysis process helps you extract meaningful insights from numerical data.

The future of quantitative data analysis is bright, with advancements in technology, methodologies, and interdisciplinary collaboration driving innovation and progress. With the right tools, techniques, and mindset, the vast amount of quantitative data available to you becomes an opportunity to unlock hidden truths, drive informed decisions, and shape a better future through data-driven insights. The power of quantitative data analysis will propel you and your organization forward to greater success.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organisations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organise and summarise the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalise your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarise your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, frequently asked questions about statistics.

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, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

  • Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
  • Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
  • Null hypothesis: Parental income and GPA have no relationship with each other in college students.
  • Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

  • In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
  • In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
  • In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

  • In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
  • In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
  • In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
  • Experimental
  • Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalise your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

  • Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
  • Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

Population vs sample

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

  • Probability sampling: every member of the population has a chance of being selected for the study through random selection.
  • Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalisable findings, you should use a probability sampling method. Random selection reduces sampling bias and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to be biased, they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

  • your sample is representative of the population you’re generalising your findings to.
  • your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalise your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialised, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalised in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

  • Will you have resources to advertise your study widely, including outside of your university setting?
  • Will you have the means to recruit a diverse sample that represents a broad population?
  • Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

  • Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
  • Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
  • Expected effect size : a standardised indication of how large the expected result of your study will be, usually based on other similar studies.
  • Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarise them.

Inspect your data

There are various ways to inspect your data, including the following:

  • Organising data from each variable in frequency distribution tables .
  • Displaying data from a key variable in a bar chart to view the distribution of responses.
  • Visualising the relationship between two variables using a scatter plot .

By visualising your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

  • Mode : the most popular response or value in the data set.
  • Median : the value in the exact middle of the data set when ordered from low to high.
  • Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

  • Range : the highest value minus the lowest value of the data set.
  • Interquartile range : the range of the middle half of the data set.
  • Standard deviation : the average distance between each value in your data set and the mean.
  • Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

  • Estimation: calculating population parameters based on sample statistics.
  • Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

  • A point estimate : a value that represents your best guess of the exact parameter.
  • An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

  • A test statistic tells you how much your data differs from the null hypothesis of the test.
  • A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

  • Comparison tests assess group differences in outcomes.
  • Regression tests assess cause-and-effect relationships between variables.
  • Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

  • A simple linear regression includes one predictor variable and one outcome variable.
  • A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

  • A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
  • A z test is for exactly 1 or 2 groups when the sample is large.
  • An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

  • If you have only one sample that you want to compare to a population mean, use a one-sample test .
  • If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
  • If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
  • If you expect a difference between groups in a specific direction, use a one-tailed test .
  • If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

  • a t value (test statistic) of 3.00
  • a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

  • a t value of 3.08
  • a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimise the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasises null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
  • If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

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Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

What is Quantitative Research

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

  • Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
  • Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
  • Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
  • Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
  • Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

  • Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
  • Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
  • Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
  • Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
  • Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
  • Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
  • Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

  • Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
  • Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
  • Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
  • Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
  • Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
  • Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
  • Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

  • Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
  • Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
  • Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
  • Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
  • Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
  • Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

  • To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
  • To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
  • To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
  • To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
  • To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

  • Description : To provide a detailed and accurate description of a particular phenomenon or population.
  • Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
  • Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
  • Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

  • Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
  • Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
  • Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
  • Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
  • Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
  • Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

  • Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
  • Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
  • Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
  • Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
  • Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
  • Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.


Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

  • Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
  • Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

  • Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
  • Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
  • Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

  • Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
  • Regression Analysis : This method examines the relationship between dependent and independent variables.
  • Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

  • Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
  • Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

  • Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
  • Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

  • Experiments : Controlled experiments are designed to test the causality.
  • Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

  • Statistical Analysis : Includes various statistical tests and measures.
  • Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
  • Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

  • Content Analysis : Systematic coding and categorizing of textual information.
  • Thematic Analysis : Identifying themes and patterns within qualitative data.
  • Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

  • Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
  • Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

  • Clustering : Grouping data points with similar characteristics.
  • Association Rule Learning : Identifying interesting relations between variables in large databases.
  • Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

  • Hadoop and Spark : Frameworks for processing and analyzing large datasets.
  • NoSQL Databases : Designed to handle unstructured data.
  • Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

  • Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
  • Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
  • Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
  • investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
  • Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
  • Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
  • Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
  • Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

  • Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
  • Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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CRO Guide   >  Chapter 3.1

Qualitative Research: Definition, Methodology, Limitation & Examples

Qualitative research is a method focused on understanding human behavior and experiences through non-numerical data. Examples of qualitative research include:

  • One-on-one interviews,
  • Focus groups, Ethnographic research,
  • Case studies,
  • Record keeping,
  • Qualitative observations

In this article, we’ll provide tips and tricks on how to use qualitative research to better understand your audience through real world examples and improve your ROI. We’ll also learn the difference between qualitative and quantitative data.

gathering data

Table of Contents

Marketers often seek to understand their customers deeply. Qualitative research methods such as face-to-face interviews, focus groups, and qualitative observations can provide valuable insights into your products, your market, and your customers’ opinions and motivations. Understanding these nuances can significantly enhance marketing strategies and overall customer satisfaction.

What is Qualitative Research

Qualitative research is a market research method that focuses on obtaining data through open-ended and conversational communication. This method focuses on the “why” rather than the “what” people think about you. Thus, qualitative research seeks to uncover the underlying motivations, attitudes, and beliefs that drive people’s actions. 

Let’s say you have an online shop catering to a general audience. You do a demographic analysis and you find out that most of your customers are male. Naturally, you will want to find out why women are not buying from you. And that’s what qualitative research will help you find out.

In the case of your online shop, qualitative research would involve reaching out to female non-customers through methods such as in-depth interviews or focus groups. These interactions provide a platform for women to express their thoughts, feelings, and concerns regarding your products or brand. Through qualitative analysis, you can uncover valuable insights into factors such as product preferences, user experience, brand perception, and barriers to purchase.

Types of Qualitative Research Methods

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience regarding a particular topic.

The most frequently used qualitative analysis methods are one-on-one interviews, focus groups, ethnographic research, case study research, record keeping, and qualitative observation.

1. One-on-one interviews

Conducting one-on-one interviews is one of the most common qualitative research methods. One of the advantages of this method is that it provides a great opportunity to gather precise data about what people think and their motivations.

Spending time talking to customers not only helps marketers understand who their clients are, but also helps with customer care: clients love hearing from brands. This strengthens the relationship between a brand and its clients and paves the way for customer testimonials.

  • A company might conduct interviews to understand why a product failed to meet sales expectations.
  • A researcher might use interviews to gather personal stories about experiences with healthcare.

These interviews can be performed face-to-face or on the phone and usually last between half an hour to over two hours. 

When a one-on-one interview is conducted face-to-face, it also gives the marketer the opportunity to read the body language of the respondent and match the responses.

2. Focus groups

Focus groups gather a small number of people to discuss and provide feedback on a particular subject. The ideal size of a focus group is usually between five and eight participants. The size of focus groups should reflect the participants’ familiarity with the topic. For less important topics or when participants have little experience, a group of 10 can be effective. For more critical topics or when participants are more knowledgeable, a smaller group of five to six is preferable for deeper discussions.

The main goal of a focus group is to find answers to the “why”, “what”, and “how” questions. This method is highly effective in exploring people’s feelings and ideas in a social setting, where group dynamics can bring out insights that might not emerge in one-on-one situations.

  • A focus group could be used to test reactions to a new product concept.
  • Marketers might use focus groups to see how different demographic groups react to an advertising campaign.

One advantage that focus groups have is that the marketer doesn’t necessarily have to interact with the group in person. Nowadays focus groups can be sent as online qualitative surveys on various devices.

Focus groups are an expensive option compared to the other qualitative research methods, which is why they are typically used to explain complex processes.

3. Ethnographic research

Ethnographic research is the most in-depth observational method that studies individuals in their naturally occurring environment.

This method aims at understanding the cultures, challenges, motivations, and settings that occur.

  • A study of workplace culture within a tech startup.
  • Observational research in a remote village to understand local traditions.

Ethnographic research requires the marketer to adapt to the target audiences’ environments (a different organization, a different city, or even a remote location), which is why geographical constraints can be an issue while collecting data.

This type of research can last from a few days to a few years. It’s challenging and time-consuming and solely depends on the expertise of the marketer to be able to analyze, observe, and infer the data.

4. Case study research

The case study method has grown into a valuable qualitative research method. This type of research method is usually used in education or social sciences. It involves a comprehensive examination of a single instance or event, providing detailed insights into complex issues in real-life contexts.  

  • Analyzing a single school’s innovative teaching method.
  • A detailed study of a patient’s medical treatment over several years.

Case study research may seem difficult to operate, but it’s actually one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

Record keeping is similar to going to the library: you go over books or any other reference material to collect relevant data. This method uses already existing reliable documents and similar sources of information as a data source.

  • Historical research using old newspapers and letters.
  • A study on policy changes over the years by examining government records.

This method is useful for constructing a historical context around a research topic or verifying other findings with documented evidence.

6. Qualitative observation

Qualitative observation is a method that uses subjective methodologies to gather systematic information or data. This method deals with the five major sensory organs and their functioning, sight, smell, touch, taste, and hearing.

  • Sight : Observing the way customers visually interact with product displays in a store to understand their browsing behaviors and preferences.
  • Smell : Noting reactions of consumers to different scents in a fragrance shop to study the impact of olfactory elements on product preference.
  • Touch : Watching how individuals interact with different materials in a clothing store to assess the importance of texture in fabric selection.
  • Taste : Evaluating reactions of participants in a taste test to identify flavor profiles that appeal to different demographic groups.
  • Hearing : Documenting responses to changes in background music within a retail environment to determine its effect on shopping behavior and mood.

Below we are also providing real-life examples of qualitative research that demonstrate practical applications across various contexts:

Qualitative Research Real World Examples

Let’s explore some examples of how qualitative research can be applied in different contexts.

1. Online grocery shop with a predominantly male audience

Method used: one-on-one interviews.

Let’s go back to one of the previous examples. You have an online grocery shop. By nature, it addresses a general audience, but after you do a demographic analysis you find out that most of your customers are male.

One good method to determine why women are not buying from you is to hold one-on-one interviews with potential customers in the category.

Interviewing a sample of potential female customers should reveal why they don’t find your store appealing. The reasons could range from not stocking enough products for women to perhaps the store’s emphasis on heavy-duty tools and automotive products, for example. These insights can guide adjustments in inventory and marketing strategies.

2. Software company launching a new product

Method used: focus groups.

Focus groups are great for establishing product-market fit.

Let’s assume you are a software company that wants to launch a new product and you hold a focus group with 12 people. Although getting their feedback regarding users’ experience with the product is a good thing, this sample is too small to define how the entire market will react to your product.

So what you can do instead is holding multiple focus groups in 20 different geographic regions. Each region should be hosting a group of 12 for each market segment; you can even segment your audience based on age. This would be a better way to establish credibility in the feedback you receive.

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

Method used: ethnographic research.

Moving from a fictional example to a real-life one, let’s analyze Alan Peshkin’s 1986 book “God’s Choice: The Total World of a Fundamentalist Christian School”.

Peshkin studied the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community alike, and spending eighteen months observing them to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

The study highlights the school’s unified purpose, rigorous academic environment, and strong community support while also pointing out its lack of cultural diversity and openness to differing viewpoints. These insights are crucial for understanding how such educational settings operate and what they offer to students.

Even after discovering all this, Peshkin still presented the school in a positive light and stated that public schools have much to learn from such schools.

Peshkin’s in-depth research represents a qualitative study that uses observations and unstructured interviews, without any assumptions or hypotheses. He utilizes descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other Christian schools.

4. Understanding buyers’ trends

Method used: record keeping.

Another way marketers can use quality research is to understand buyers’ trends. To do this, marketers need to look at historical data for both their company and their industry and identify where buyers are purchasing items in higher volumes.

For example, electronics distributors know that the holiday season is a peak market for sales while life insurance agents find that spring and summer wedding months are good seasons for targeting new clients.

5. Determining products/services missing from the market

Conducting your own research isn’t always necessary. If there are significant breakthroughs in your industry, you can use industry data and adapt it to your marketing needs.

The influx of hacking and hijacking of cloud-based information has made Internet security a topic of many industry reports lately. A software company could use these reports to better understand the problems its clients are facing.

As a result, the company can provide solutions prospects already know they need.

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Qualitative Research Approaches

Once the marketer has decided that their research questions will provide data that is qualitative in nature, the next step is to choose the appropriate qualitative approach.

The approach chosen will take into account the purpose of the research, the role of the researcher, the data collected, the method of data analysis , and how the results will be presented. The most common approaches include:

  • Narrative : This method focuses on individual life stories to understand personal experiences and journeys. It examines how people structure their stories and the themes within them to explore human existence. For example, a narrative study might look at cancer survivors to understand their resilience and coping strategies.
  • Phenomenology : attempts to understand or explain life experiences or phenomena; It aims to reveal the depth of human consciousness and perception, such as by studying the daily lives of those with chronic illnesses.
  • Grounded theory : investigates the process, action, or interaction with the goal of developing a theory “grounded” in observations and empirical data. 
  • Ethnography : describes and interprets an ethnic, cultural, or social group;
  • Case study : examines episodic events in a definable framework, develops in-depth analyses of single or multiple cases, and generally explains “how”. An example might be studying a community health program to evaluate its success and impact.

How to Analyze Qualitative Data

Analyzing qualitative data involves interpreting non-numerical data to uncover patterns, themes, and deeper insights. This process is typically more subjective and requires a systematic approach to ensure reliability and validity. 

1. Data Collection

Ensure that your data collection methods (e.g., interviews, focus groups, observations) are well-documented and comprehensive. This step is crucial because the quality and depth of the data collected will significantly influence the analysis.

2. Data Preparation

Once collected, the data needs to be organized. Transcribe audio and video recordings, and gather all notes and documents. Ensure that all data is anonymized to protect participant confidentiality where necessary.

3. Familiarization

Immerse yourself in the data by reading through the materials multiple times. This helps you get a general sense of the information and begin identifying patterns or recurring themes.

Develop a coding system to tag data with labels that summarize and account for each piece of information. Codes can be words, phrases, or acronyms that represent how these segments relate to your research questions.

  • Descriptive Coding : Summarize the primary topic of the data.
  • In Vivo Coding : Use language and terms used by the participants themselves.
  • Process Coding : Use gerunds (“-ing” words) to label the processes at play.
  • Emotion Coding : Identify and record the emotions conveyed or experienced.

5. Thematic Development

Group codes into themes that represent larger patterns in the data. These themes should relate directly to the research questions and form a coherent narrative about the findings.

6. Interpreting the Data

Interpret the data by constructing a logical narrative. This involves piecing together the themes to explain larger insights about the data. Link the results back to your research objectives and existing literature to bolster your interpretations.

7. Validation

Check the reliability and validity of your findings by reviewing if the interpretations are supported by the data. This may involve revisiting the data multiple times or discussing the findings with colleagues or participants for validation.

8. Reporting

Finally, present the findings in a clear and organized manner. Use direct quotes and detailed descriptions to illustrate the themes and insights. The report should communicate the narrative you’ve built from your data, clearly linking your findings to your research questions.

Limitations of qualitative research

The disadvantages of qualitative research are quite unique. The techniques of the data collector and their own unique observations can alter the information in subtle ways. That being said, these are the qualitative research’s limitations:

1. It’s a time-consuming process

The main drawback of qualitative study is that the process is time-consuming. Another problem is that the interpretations are limited. Personal experience and knowledge influence observations and conclusions.

Thus, qualitative research might take several weeks or months. Also, since this process delves into personal interaction for data collection, discussions often tend to deviate from the main issue to be studied.

2. You can’t verify the results of qualitative research

Because qualitative research is open-ended, participants have more control over the content of the data collected. So the marketer is not able to verify the results objectively against the scenarios stated by the respondents. For example, in a focus group discussing a new product, participants might express their feelings about the design and functionality. However, these opinions are influenced by individual tastes and experiences, making it difficult to ascertain a universally applicable conclusion from these discussions.

3. It’s a labor-intensive approach

Qualitative research requires a labor-intensive analysis process such as categorization, recording, etc. Similarly, qualitative research requires well-experienced marketers to obtain the needed data from a group of respondents.

4. It’s difficult to investigate causality

Qualitative research requires thoughtful planning to ensure the obtained results are accurate. There is no way to analyze qualitative data mathematically. This type of research is based more on opinion and judgment rather than results. Because all qualitative studies are unique they are difficult to replicate.

5. Qualitative research is not statistically representative

Because qualitative research is a perspective-based method of research, the responses given are not measured.

Comparisons can be made and this can lead toward duplication, but for the most part, quantitative data is required for circumstances that need statistical representation and that is not part of the qualitative research process.

While doing a qualitative study, it’s important to cross-reference the data obtained with the quantitative data. By continuously surveying prospects and customers marketers can build a stronger database of useful information.

Quantitative vs. Qualitative Research

Qualitative and quantitative research side by side in a table

Image source

Quantitative and qualitative research are two distinct methodologies used in the field of market research, each offering unique insights and approaches to understanding consumer behavior and preferences.

As we already defined, qualitative analysis seeks to explore the deeper meanings, perceptions, and motivations behind human behavior through non-numerical data. On the other hand, quantitative research focuses on collecting and analyzing numerical data to identify patterns, trends, and statistical relationships.  

Let’s explore their key differences: 

Nature of Data:

  • Quantitative research : Involves numerical data that can be measured and analyzed statistically.
  • Qualitative research : Focuses on non-numerical data, such as words, images, and observations, to capture subjective experiences and meanings.

Research Questions:

  • Quantitative research : Typically addresses questions related to “how many,” “how much,” or “to what extent,” aiming to quantify relationships and patterns.
  • Qualitative research: Explores questions related to “why” and “how,” aiming to understand the underlying motivations, beliefs, and perceptions of individuals.

Data Collection Methods:

  • Quantitative research : Relies on structured surveys, experiments, or observations with predefined variables and measures.
  • Qualitative research : Utilizes open-ended interviews, focus groups, participant observations, and textual analysis to gather rich, contextually nuanced data.

Analysis Techniques:

  • Quantitative research: Involves statistical analysis to identify correlations, associations, or differences between variables.
  • Qualitative research: Employs thematic analysis, coding, and interpretation to uncover patterns, themes, and insights within qualitative data.

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  • Conversion Rate Optimization , User Research

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6 Qualitative data examples for thorough market researchers

Types of qualitative data in market research, 6 qualitative data examples, get nuanced insights from qualitative market research.

There are plenty of ways to gather consumer insights for fresh campaigns and better products, but qualitative research is up there with the best sources of insight.

This guide is packed with examples of how to turn qualitative data into actionable insights, to spark your creativity and sharpen your research strategy. You’ll see how qualitative data, especially through surveys, opens doors to deeper understanding by inviting consumers to share their experiences and thoughts freely, in their own words — and how qualitative data can transform your brand.

Before we dig into some examples of how qualitative data can empower your teams to make focused, confident and quick decisions on anything from product to marketing, let’s go back to basics. We can categorize qualitative data into roughly three categories: binary, nominal and ordinal data. Here’s how each of them is used in qualitative data analysis.

Binary data

Binary data represents a choice between two distinct options, like ‘yes’ or ‘no’. In market research, this type of qualitative data is useful for filtering responses or making clear distinctions in consumer preferences.

Binary data in qualitative research is great for straightforward insights, but has its limits. Here’s a quick guide on when to use it and when to opt for qualitative data that is more detailed:

Binary data is great for:

  • Quick Yes/No questions : like “Have you used our app? Yes or No.”
  • Initial screening : to quickly sort participants for further studies.
  • Clear-cut answers : absolute factors, such as ownership or usage.

Avoid binary data for:

  • Understanding motivations : it lacks the depth to explore why behind actions.
  • Measuring intensity : can’t show how much someone likes or uses something.
  • Detail needed for product development : misses the nuanced feedback necessary for innovations.

data analysis quantitative research example

Nominal data

Nominal data categorizes responses without implying any order. For example, when survey respondents choose their favorite brand from a list, the data collected is nominal, offering insights into brand preferences among different demographics.

Some other examples of qualitative data that can be qualified as nominal are asking participants to name their primary information source about products in categories like social media, friends, or online reviews. Or in focus groups, discussing brand perceptions could classify brands into categories such as luxury, budget-friendly, or eco-conscious, based on participant descriptions.

Nominal data is great for:

  • Categorizing responses : such as types of consumer complaints (product quality, customer service, delivery issues).
  • Identifying preferences : like favorite product categories (beverages, electronics, apparel).
  • Segmentation : grouping participants based on attributes (first-time buyers, loyal customers).

Nominal data is not for:

  • Measuring quantities : it can’t quantify how much more one category is preferred over another.
  • Ordering or ranking responses : it doesn’t indicate which category is higher or lower in any hierarchy.
  • Detailed behavioral analysis : While it can group behaviors, it doesn’t delve into the frequency or intensity of those behaviors.

data analysis quantitative research example

Ordinal data

Ordinal data introduces a sense of order, ranking preferences or satisfaction levels. In qualitative analysis, it’s particularly useful for understanding how consumers prioritize features or products, giving researchers a clearer picture of market trends.

Other examples of qualitative data analyses that use ordinal data, are for instance a study on consumer preferences for coffee flavors, participants might rank flavors in order of preference, providing insights into flavor trends. You can also get ordinal data from focus groups on things like customer satisfaction surveys or app usability, by asking users to rate their ease of use or happiness on an ordinal scale.

Ordinal data is great for:

  • Ranking preferences : asking participants to rank product features from most to least important.
  • Measuring satisfaction levels : using scales like “very satisfied,” “satisfied,” “neutral,” “dissatisfied,” “very dissatisfied.”
  • Assessing Agreement : with statements on a scale from “strongly agree” to “strongly disagree.”

Ordinal data is not for:

  • Quantifying differences : it doesn’t show how much more one rank is preferred over another, just the order.
  • Precise measurements : can’t specify the exact degree of satisfaction or agreement, only relative positions.

data analysis quantitative research example

This mix of qualitative and quantitative data will give you a well-rounded view of participant attitudes and preferences.

The things you can do with qualitative data are endless. But this article shouldn’t turn into a work of literature, so we’ll highlight six ways to collect qualitative data and give you examples of how to use these qualitative research methods to get actionable results.

data analysis quantitative research example

How to get qual insights with Attest

You can get to the heart of what your target customers think, with reliable qualitative insights from Attest Video Responses

1. Highlighting brand loyalty drivers with open-ended surveys and questionnaires

Open-ended surveys and questionnaires are great at finding out what makes customers choose and stick with a brand. Here’s why this qualitative data analysis tool is so good for gathering qualitative data on things like brand loyalty and customer experience:

Straight from the source

Open-ended survey responses show the actual thoughts and feelings of your target audience in their own words, while still giving you structure in your data analysis.

Understanding ‘why’

Numbers can show us how many customers are loyal; open-ended survey responses explain why they are. You can also easily add thematic analysis to the mix by counting certain keywords or phrases.

Guiding decisions

The insights from these surveys can help a brand decide where to focus its efforts, from making sure their marketing highlights what customers love most to improving parts of their product.

Surveys are one of the most versatile and efficient qualitative data collection methods out there. We want to bring the power of qualitative data analysis to every business and make it easy to gather qualitative data from the people who matter most to your brand. Check out our survey templates to hit the ground running. And you’re not limited to textual data as your only data source — we also enable you to gather video responses to get additional context from non verbal cues and more.

2. Trend identification with observation notes

Observation notes are a powerful qualitative data analysis tool for spotting trends as they naturally unfold in real-world settings. Here’s why they’re particularly valuable insights and effective for identifying new trends:

Real behavior

Observing people directly shows us how they actually interact with products or services, not just how they say they do. This can highlight emerging trends in consumer behavior or preferences before people can even put into words what they are doing and why.

Immediate insights

By watching how people engage with different products, we can quickly spot patterns or changes in behavior. This immediate feedback is invaluable for catching trends as they start.

Context matters

Observations give you context. You can see not just what people do, but where and how they do it. This context can be key to understanding why a trend is taking off.

Unprompted reactions

Since people don’t know they’re being observed for these purposes, their actions are genuine. This leads to authentic insights about what’s really catching on.

3. Understanding consumer sentiments through semi-structured interviews

Semi-structured interviews for qualitative data analysis are an effective method for data analysts to get a deep understanding of consumer sentiments. It provides a structured yet flexible approach to gather in-depth insights. Here’s why they’re particularly useful for this type of research question:

Personal connection

These interviews create a space for a real conversation, allowing consumers to share their feelings, experiences, and opinions about a brand or product in a more personal setting.


The format lets the interviewer explore interesting points that come up during the conversation, diving deeper into unexpected areas of discussion. This flexibility uncovers richer insights than strictly structured interviews.

Depth of understanding

By engaging in detailed discussions, brands can understand not just what consumers think but why they think that way and what stations their train of thought passes by.

Structure and surprise

Semi-structured interviews can be tailored to explore specific areas of interest while still allowing for new insights to emerge.

4. Using focus groups for informing market entry strategies

Using a focus group to inform market entry strategies provides a dynamic way to discover your potential customers’ needs, preferences, and perceptions before launching a product or entering a new market. Here’s how focus groups can be particularly effective for this kind of research goal:

Real conversations

Focus groups allow for real-time, interactive discussions, giving you a front-row seat to hear what your potential customers think and feel about your product or service idea.

Diverse Perspectives

By bringing together people from various backgrounds, a focus group can offer a wide range of views and insights, highlighting different consumer needs and contextual information that you might miss out on in a survey.

Spotting opportunities and challenges

The dynamic nature of focus groups can help uncover unique market opportunities or potential challenges that might not be evident through other research methods, like cultural nuances.

Testing ideas

A focus group is a great way to test and compare reactions to different market entry strategies, from pricing models to distribution channels, providing clear direction on what approach might work best.

5. Case studies to gain a nuanced understanding of consumers on a broad level

Case studies in qualitative research zoom in on specific stories from customers or groups using a product or service, great for gaining a nuanced understanding of consumers at a broad level. Here’s why case studies are a particularly effective qualitative data analysis tool for this type of research goal:

In-depth analysis

Case studies can provide a 360-degree look at the consumer experience, from initial awareness to post-purchase feelings.

This depth of insight reveals not just what consumers do, but why they do it, uncovering motivations, influences, and decision-making processes.

Longitudinal insight

Case studies can track changes in consumer behavior or satisfaction over time, offering a dynamic view of how perceptions evolve.

This longitudinal perspective is crucial for giving context to the lifecycle of consumer engagement with a brand.

Storytelling power

The narrative nature of case studies — when done right — makes them powerful tools for communicating complex consumer insights in an accessible and engaging way, which can be especially useful for internal strategy discussions or external marketing communications.

6. Driving product development with diary studies

Diary studies are a unique qualitative research method that involves participants recording their thoughts, experiences, or behaviors over a period of time, related to using a product or service. This qualitative data analysis method is especially valuable for driving product development for several reasons:

Real-time insights

Diary studies capture real-time user experiences and feedback as they interact with a product in their daily lives.

This ongoing documentation provides a raw, unfiltered view of how a product fits into the user’s routine, highlighting usability issues or unmet needs that might not be captured in a one-time survey or interview.

Realistic user journey mapping

By analyzing diary entries, you can map out the entire user journey, identifying critical touch points where users feel delighted, frustrated, or indifferent.

This then enables you to implement targeted improvements and innovations at the moments that matter most.

Identifying patterns

Over the course of a diary study, patterns in behavior, preferences, and challenges can emerge, which is great for thematic analysis.

It can guide product developers to prioritize features or fixes that will have the most significant impact on user satisfaction, which is especially great if they don’t know what areas to focus on first.

Qualitative research brings your consumers’ voices directly to your strategy table. The examples we’ve explored show how qualitative data analysis methods like surveys, interviews, and case studies illuminate the ‘why’ behind consumer choices, guiding more informed decisions. Using these insights means crafting products and messages that resonate deeply, ensuring your brand not only meets but exceeds consumer expectations.

data analysis quantitative research example

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