how do you analyze quantitative research data

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 is a seasoned technical content writer with over 12 years of experience. He has held pivotal roles such as System Analyst (DevOps) at Dagbs Nigeria Limited and Full-Stack Developer at Pedoquasphere International Limited. He specializes in data science, data analytics and cutting-edge technologies, making him an expert in the data industry.

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

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

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

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

Overview: Quantitative Data Analysis 101

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

The two “branches” of quantitative analysis

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

What is quantitative data analysis?

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

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

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

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

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

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

How does quantitative analysis work?

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

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

Need a helping hand?

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

So, what are descriptive and inferential statistics?

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

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

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

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

So, why is this sample-population thing important?

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

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

Branch 1: Descriptive Statistics

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

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

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

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

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

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

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

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

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

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

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

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

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

But why do all of these numbers matter?

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

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

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

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

Branch 2: Inferential Statistics

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

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

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

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

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

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

What statistics are usually used in this branch?

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

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

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

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

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

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

Stats overload…

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

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

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

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

How to choose the right analysis method

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

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

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

Factor 1 – Data type

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

Why does this matter?

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

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

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

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

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

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

Factor 2: Your research questions

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

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

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

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

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

Time to recap…

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

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

Psst... there’s more!

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77 Comments

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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.

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Quantitative Data Collection: Best 5 methods

In contrast to qualitative data , quantitative data collection is everything about figures and numbers. Researchers often rely on quantitative data when they intend to quantify attributes, attitudes, behaviors, and other defined variables with a motive to either back or oppose the hypothesis of a specific phenomenon by contextualizing the data obtained via surveying or interviewing the study sample.

Content Index

What is Quantitative Data Collection?

Importance of quantitative data collection, probability sampling, surveys/questionnaires, observations, document review in quantitative data collection.

Quantitative data collection refers to the collection of numerical data that can be analyzed using statistical methods. This type of data collection is often used in surveys, experiments, and other research methods. It measure variables and establish relationships between variables. The data collected through quantitative methods is typically in the form of numbers, such as response frequencies, means, and standard deviations, and can be analyzed using statistical software.

LEARN ABOUT: Research Process Steps

As a researcher, you do have the option to opt either for data collection online or use traditional data collection methods via appropriate research. Quantitative data collection is important for several reasons:

• Objectivity: Quantitative data collection provides objective and verifiable information, as the data is collected in a systematic and standardized manner.
• Generalizability: The results from quantitative data collection can be generalized to a larger population, making it an effective way to study large groups of people.
• Precision: Numerical data allows for precise measurement and unit of analysis , providing more accurate results than other data collection forms.
• Hypothesis testing: Quantitative data collection allows for testing hypotheses and theories, leading to a better understanding of the relationships between variables.
• Comparison: Quantitative data collection allows for data comparison and analysis. It can be useful in making decisions and identifying trends or patterns.
• Replicability: The numerical nature of quantitative data makes it easier to replicate research results. It is essential for building knowledge in a particular field.

Quantitative data collection provides valuable information for understanding complex phenomena and making informed decisions based on empirical evidence.

LEARN ABOUT: Best Data Collection Tools

Methods used for Quantitative Data Collection

A data that can be counted or expressed in numerical’s constitute the quantitative data. It is commonly used to study the events or levels of concurrence. And is collected through a Structured Question & structured questionnaire asking questions starting with “how much” or “how many.” As the quantitative data is numerical, it represents both definitive and objective data. Furthermore, quantitative information is much sorted for statistical analysis and mathematical analysis, making it possible to illustrate it in the form of charts and graphs.

Discrete and continuous are the two major categories of quantitative data where discreet data have finite numbers and the constant data values falling on a continuum possessing the possibility to have fractions or decimals. If research is conducted to find out the number of vehicles owned by the American household, then we get a whole number, which is an excellent example of discrete data. When research is limited to the study of physical measurements of the population like height, weight, age, or distance, then the result is an excellent example of continuous data.

Any traditional or online data collection method that helps in gathering numerical data is a proven method of collecting quantitative data.

LEARN ABOUT: Survey Sampling

There are four significant types of probability sampling:

• Simple random sampling : More often, the targeted demographic is chosen for inclusion in the sample. 
• Cluster sampling : Cluster sampling is a technique in which a population is divided into smaller groups or clusters, and a random sample of these clusters is selected. This method is used when it is impractical or expensive to obtain a random sample from the entire population . 
• Systematic sampling : Any of the targeted demographic would be included in the sample, but only the first unit for inclusion in the sample is selected randomly, rest are selected in the ordered fashion as if one out of every ten people on the list .
• Stratified sampling : It allows selecting each unit from a particular group of the targeted audience while creating a sample. It is useful when the researchers are selective about including a specific set of people in the sample, i.e., only males or females, managers or executives, people working within a particular industry.

Interviewing people is a standard method used for data collection . However, the interviews conducted to collect quantitative data are more structured, wherein the researchers ask only a standard set of online questionnaires and nothing more than that.

There are three major types of interviews conducted for data collection 

• Telephone interviews: For years, telephone interviews ruled the charts of data collection methods. Nowadays, there is a significant rise in conducting video interviews using the internet, Skype, or similar online video calling platforms. 
• Face-to-face interviews: It is a proven technique to collect data directly from the participants. It helps in acquiring quality data as it provides a scope to ask detailed questions and probing further to collect rich and informative data. Literacy requirements of the participant are irrelevant as F2F surveys offer ample opportunities to collect non-verbal data through observation or to explore complex and unknown issues. Although it can be an expensive and time-consuming method, the response rates for F2F interviews are often higher. 
• Computer-Assisted Personal Interviewing (CAPI): It is nothing but a similar setup of the face-to-face interview where the interviewer carries a desktop or laptop along with him at the time of interview to upload the data obtained from the interview directly into the database. CAPI saves a lot of time in updating and processing the data and also makes the entire process paperless as the interviewer does not carry a bunch of papers and questionnaires.

There are two significant types of survey questionnaires used to collect online data for quantitative market research.

• Web-based questionnaire : This is one of the ruling and most trusted methods for internet-based research or online research. In a web-based questionnaire, the receive an email containing the survey link, clicking on which takes the respondent to a secure online survey tool from where he/she can take the survey or fill in the survey questionnaire. Being a cost-efficient, quicker, and having a wider reach, web-based surveys are more preferred by the researchers. The primary benefit of a web-based questionnaire is flexibility. Respondents are free to take the survey in their free time using either a desktop, laptop, tablet, or mobile.
• Mail Questionnaire : In a mail questionnaire, the survey is mailed out to a host of the sample population, enabling the researcher to connect with a wide range of audiences. The mail questionnaire typically consists of a packet containing a cover sheet that introduces the audience about the type of research and reason why it is being conducted along with a prepaid return to collect data online. Although the mail questionnaire has a higher churn rate compared to other quantitative data collection methods, adding certain perks such as reminders and incentives to complete the survey help in drastically improving the churn rate. One of the major benefits of the mail questionnaire is all the responses are anonymous, and respondents are allowed to take as much time as they want to complete the survey and be completely honest about the answer without the fear of prejudice.

LEARN ABOUT: Steps in Qualitative Research

As the name suggests, it is a pretty simple and straightforward method of collecting quantitative data. In this method, researchers collect quantitative data through systematic observations by using techniques like counting the number of people present at the specific event at a particular time and a particular venue or number of people attending the event in a designated place. More often, for quantitative data collection, the researchers have a naturalistic observation approach. It needs keen observation skills and senses for getting the numerical data about the “what” and not about “why” and ”how.”

Naturalistic observation is used to collect both types of data; qualitative and quantitative. However, structured observation is more used to collect quantitative rather than qualitative data collection .

• Structured observation: In this type of observation method, the researcher has to make careful observations of one or more specific behaviors in a more comprehensive or structured setting compared to naturalistic or participant observation . In a structured observation, the researchers, rather than observing everything, focus only on very specific behaviors of interest. It allows them to quantify the behaviors they are observing. When the qualitative observations require a judgment on the part of the observers – it is often described as coding, which requires a clearly defining a set of target behaviors.

Document review is a process used to collect data after reviewing the existing documents. It is an efficient and effective way of gathering data as documents are manageable. Those are the practical resource to get qualified data from the past. Apart from strengthening and supporting the research by providing supplementary research data document review has emerged as one of the beneficial methods to gather quantitative research data.

Three primary document types are being analyzed for collecting supporting quantitative research data.

• Public Records: Under this document review, official, ongoing records of an organization are analyzed for further research. For example, annual reports policy manuals, student activities, game activities in the university, etc.
• Personal Documents: In contrast to public documents, this type of document review deals with individual personal accounts of individuals’ actions, behavior, health, physique, etc. For example, the height and weight of the students, distance students are traveling to attend the school, etc.
• Physical Evidence:  Physical evidence or physical documents deal with previous achievements of an individual or of an organization in terms of monetary and scalable growth.

LEARN ABOUT: 12 Best Tools for Researchers

Quantitative data is not about convergent reasoning, but it is about divergent thinking. It deals with the numerical, logic, and an objective stance, by focusing on numeric and unchanging data. More often, data collection methods are used to collect quantitative research data, and the results are dependent on the larger sample sizes that are commonly representing the population researcher intend to study.

Although there are many other methods to collect quantitative data. Those mentioned above probability sampling, interviews, questionnaire observation, and document review are the most common and widely used methods for data collection.

With QuestionPro, you can precise results, and data analysis . QuestionPro provides the opportunity to collect data from a large number of participants. It increases the representativeness of the sample and providing more accurate results.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

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 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.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Quantitative Data Analysis

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

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

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

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

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

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

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

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

Advantages and disadvantages of popular quantitative analytical software

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

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

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

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

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

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

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

What is quantitative data? How to collect, understand, and analyze it

A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.

• Key takeaways
• What is quantitative data?
• Examples of quantitative data
• Difference between quantitative and qualitative data
• Characteristics of quantitative data
• Types of quantitative data
• When should I use quantitative or qualitative research?
• Pros and cons of quantitative data
• Collection methods

Quantitative data analysis tools

• Return to top

Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet to buying a car, to the algorithms behind news feeds we see, and much more. 

One of the most common and well-known categories of data is quantitative data or data that can be expressed in numbers or numerical values. 

This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more. 

Key takeaways: 

Quantitative data is data that can be counted or measured in numerical values.

The two main types of quantitative data are discrete data and continuous data.

Height in feet, age in years, and weight in pounds are examples of quantitative data. 

Qualitative data is descriptive data that is not expressed numerically. 

Both quantitative research and qualitative research are often conducted through surveys and questionnaires. 

What is quantitative data? 

Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.

Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis. 

In product management, UX design, or software engineering, quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. Regarding vehicles, quantitative data might be how much horsepower a car has. 

What are examples of quantitative data? 

Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include: 

Revenue in dollars

Weight in kilograms or pounds

Age in months or years

Distance in miles or kilometers

Time in days or weeks

Experiment results

Website conversion rates

Website page load speed

What is the difference between quantitative and qualitative data? 

There are many differences between qualitative and quantitative data —each represents very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.

As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured.  Qualitative data, on the other hand, is descriptive data that are expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes. 

Examples of qualitative vs. quantitative data

As mentioned above, examples of quantitative data include distance in miles or age in years. 

Qualitative data, however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified. 

Further reading: The differences between categorical and quantitative Data and examples of qualitative data

Characteristics of quantitative data 

Quantitative data is made up of numerical values has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques. 

Specific types of quantitative data

All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.

There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data. 

Discrete data

In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed. 

Examples of discrete data:

The number of players on a team

The number of employees at a company

The number of items eggs broken when you drop the carton

The number of outs a hitter makes in a baseball game

The number of right and wrong questions on a test

A website's bounce rate (percentages can be no less than 0 or greater than 100)

Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, as shown below.

Continuous data 

Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts. 

Examples of continuous data:

Website traffic

Water temperature

The time it takes to complete a task

Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, as shown below.

Continuous data can be further broken down into two categories: interval data and ratio data. 

Interval data

Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers. 

Examples of interval data include temperature since it can move below and above 0.

Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.

Other examples of ratio data are weight, length, height, and concentration. 

Interval data vs. ratio data

Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.

For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.

Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.

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When should I use quantitative or qualitative research? 

Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research. 

When to use quantitative research

Quantitative research is likely most appropriate if the thing you are trying to study or measure can be counted and expressed in numbers. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on. 

When to use qualitative research

Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts. 

Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.

If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.

To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data. 

In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more. 

In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data. Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better. 

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What are the pros and cons of quantitative data? 

Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers. 

Pros of quantitative data: 

Quantitative data is less susceptible to selection bias than qualitative data.

It can be tested and checked, and anyone can replicate both an experiment and its results.

Quantitative data is relatively quick and easy to collect. 

Cons of quantitative data: 

Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.

Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses.

How do you collect quantitative data? 

There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked. 

Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences. 

For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores. 

Questionnaires and surveys 

Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method. 

These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many e-commerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if any areas could be improved. 

Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior . 

Open-source online datasets 

There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include: 

The Bureau of Labor Statistics Data

The Census Bureau Data

World Bank Open Data

The CIA World Factbook  

Experiments

An experiment is another common method that usually involves a  control group  and an  experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

Controlled experiments,  A/B tests , blind experiments, and many others fall under this category.

With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data, which can save time and resources. There are two types of sampling: random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling). 

Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample. 

In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research. 

Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources. 

For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance. 

Google Analytics is just one example of the many quantitative analytics tools available for different research professionals. 

Other quantitative data tools include…

Microsoft Excel

Microsoft Power BI

Apache Spark

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Frequently asked questions about quantitative data

Is quantitative data objective.

Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.

For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others. 

Who uses quantitative data?

Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology. 

Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.

What is quantitative data in statistics?

Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.

Is quantitative data better than qualitative data?

It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.

Further reading: Qualitative vs. quantitative data — what's the difference?

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Quantitative Data Analysis

9 Presenting the Results of Quantitative Analysis

Mikaila Mariel Lemonik Arthur

This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis.

Writing the Quantitative Paper

Standard quantitative social science papers follow a specific format. They begin with a title page that includes a descriptive title, the author(s)’ name(s), and a 100 to 200 word abstract that summarizes the paper. Next is an introduction that makes clear the paper’s research question, details why this question is important, and previews what the paper will do. After that comes a literature review, which ends with a summary of the research question(s) and/or hypotheses. A methods section, which explains the source of data, sample, and variables and quantitative techniques used, follows. Many analysts will include a short discussion of their descriptive statistics in the methods section. A findings section details the findings of the analysis, supported by a variety of tables, and in some cases graphs, all of which are explained in the text. Some quantitative papers, especially those using more complex techniques, will include equations. Many papers follow the findings section with a discussion section, which provides an interpretation of the results in light of both the prior literature and theory presented in the literature review and the research questions/hypotheses. A conclusion ends the body of the paper. This conclusion should summarize the findings, answering the research questions and stating whether any hypotheses were supported, partially supported, or not supported. Limitations of the research are detailed. Papers typically include suggestions for future research, and where relevant, some papers include policy implications. After the body of the paper comes the works cited; some papers also have an Appendix that includes additional tables and figures that did not fit into the body of the paper or additional methodological details. While this basic format is similar for papers regardless of the type of data they utilize, there are specific concerns relating to quantitative research in terms of the methods and findings that will be discussed here.

In the methods section, researchers clearly describe the methods they used to obtain and analyze the data for their research. When relying on data collected specifically for a given paper, researchers will need to discuss the sample and data collection; in most cases, though, quantitative research relies on pre-existing datasets. In these cases, researchers need to provide information about the dataset, including the source of the data, the time it was collected, the population, and the sample size. Regardless of the source of the data, researchers need to be clear about which variables they are using in their research and any transformations or manipulations of those variables. They also need to explain the specific quantitative techniques that they are using in their analysis; if different techniques are used to test different hypotheses, this should be made clear. In some cases, publications will require that papers be submitted along with any code that was used to produce the analysis (in SPSS terms, the syntax files), which more advanced researchers will usually have on hand. In many cases, basic descriptive statistics are presented in tabular form and explained within the methods section.

The findings sections of quantitative papers are organized around explaining the results as shown in tables and figures. Not all results are depicted in tables and figures—some minor or null findings will simply be referenced—but tables and figures should be produced for all findings to be discussed at any length. If there are too many tables and figures, some can be moved to an appendix after the body of the text and referred to in the text (e.g. “See Table 12 in Appendix A”).

Discussions of the findings should not simply restate the contents of the table. Rather, they should explain and interpret it for readers, and they should do so in light of the hypothesis or hypotheses that are being tested. Conclusions—discussions of whether the hypothesis or hypotheses are supported or not supported—should wait for the conclusion of the paper.

Creating Effective Tables

When creating tables to display the results of quantitative analysis, the most important goals are to create tables that are clear and concise but that also meet standard conventions in the field. This means, first of all, paring down the volume of information produced in the statistical output to just include the information most necessary for interpreting the results, but doing so in keeping with standard table conventions. It also means making tables that are well-formatted and designed, so that readers can understand what the tables are saying without struggling to find information. For example, tables (as well as figures such as graphs) need clear captions; they are typically numbered and referred to by number in the text. Columns and rows should have clear headings. Depending on the content of the table, formatting tools may need to be used to set off header rows/columns and/or total rows/columns; cell-merging tools may be necessary; and shading may be important in tables with many rows or columns.

Here, you will find some instructions for creating tables of results from descriptive, crosstabulation, correlation, and regression analysis that are clear, concise, and meet normal standards for data display in social science. In addition, after the instructions for creating tables, you will find an example of how a paper incorporating each table might describe that table in the text.

Descriptive Statistics

When presenting the results of descriptive statistics, we create one table with columns for each type of descriptive statistic and rows for each variable. Note, of course, that depending on level of measurement only certain descriptive statistics are appropriate for a given variable, so there may be many cells in the table marked with an — to show that this statistic is not calculated for this variable. So, consider the set of descriptive statistics below, for occupational prestige, age, highest degree earned, and whether the respondent was born in this country.

Table 1. SPSS Ouput: Selected Descriptive Statistics

Statistics R’s occupational prestige score (2010) Age of respondent N Valid 3873 3699 Missing 159 333 Mean 46.54 52.16 Median 47.00 53.00 Std. Deviation 13.811 17.233 Variance 190.745 296.988 Skewness .141 .018 Std. Error of Skewness .039 .040 Kurtosis -.809 -1.018 Std. Error of Kurtosis .079 .080 Range 64 71 Minimum 16 18 Maximum 80 89 Percentiles 25 35.00 37.00 50 47.00 53.00 75 59.00 66.00 Statistics R’s highest degree N Valid 4009 Missing 23 Median 2.00 Mode 1 Range 4 Minimum 0 Maximum 4 R’s highest degree Frequency Percent Valid Percent Cumulative Percent Valid less than high school 246 6.1 6.1 6.1 high school 1597 39.6 39.8 46.0 associate/junior college 370 9.2 9.2 55.2 bachelor’s 1036 25.7 25.8 81.0 graduate 760 18.8 19.0 100.0 Total 4009 99.4 100.0 Missing System 23 .6 Total 4032 100.0 Statistics Was r born in this country N Valid 3960 Missing 72 Mean 1.11 Mode 1 Was r born in this country Frequency Percent Valid Percent Cumulative Percent Valid yes 3516 87.2 88.8 88.8 no 444 11.0 11.2 100.0 Total 3960 98.2 100.0 Missing System 72 1.8 Total 4032 100.0 To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value. Table 2. Descriptive Statistics 46.54 52.16 — 1.11 47 53 1: Associates (9.2%) 1: Yes (88.8%) — — 2: High School (39.8%) — 13.811 17.233 — — 190.745 296.988 — — 0.141 0.018 — — -0.809 -1.018 — — 64 (16-80) 71 (18-89) Less than High School (0) –  Graduate (4) — 35-59 37-66 — — 3873 3699 4009 3960 If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves): This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States. Crosstabulation When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below. Table 3. R’s highest degree * R’s subjective class identification Crosstabulation R’s subjective class identification Total lower class working class middle class upper class R’s highest degree less than high school Count 65 106 68 7 246 % within R’s subjective class identification 18.8% 7.1% 3.4% 4.2% 6.2% high school Count 217 800 551 23 1591 % within R’s subjective class identification 62.9% 53.7% 27.6% 13.9% 39.8% associate/junior college Count 30 191 144 3 368 % within R’s subjective class identification 8.7% 12.8% 7.2% 1.8% 9.2% bachelor’s Count 27 269 686 49 1031 % within R’s subjective class identification 7.8% 18.1% 34.4% 29.5% 25.8% graduate Count 6 123 546 84 759 % within R’s subjective class identification 1.7% 8.3% 27.4% 50.6% 19.0% Total Count 345 1489 1995 166 3995 % within R’s subjective class identification 100.0% 100.0% 100.0% 100.0% 100.0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 819.579 12 <.001 Likelihood Ratio 839.200 12 <.001 Linear-by-Linear Association 700.351 1 <.001 N of Valid Cases 3995 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 10.22. Symmetric Measures Value Asymptotic Standard Error Approximate T Approximate Significance Interval by Interval Pearson’s R .419 .013 29.139 <.001 Ordinal by Ordinal Spearman Correlation .419 .013 29.158 <.001 N of Valid Cases 3995 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Based on normal approximation. Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.   18.8% 7.1% 3.4% 4.2% 6.2% 62.9% 53.7% 27.6% 13.9% 39.8% 8.7% 12.8% 7.2% 1.8% 9.2% 7.8% 18.1% 34.4% 29.5% 25.8% 1.7% 8.3% 27.4% 50.6% 19.0% N: 3995 Spearman Correlation 0.419*** If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this: A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree. Correlation When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5. Table 5. SPSS Output: Correlations Age of respondent R’s occupational prestige score (2010) Highest year of school R completed R’s family income in 1986 dollars Age of respondent Pearson Correlation 1 .087 .014 .017 Sig. (2-tailed) <.001 .391 .314 N 3699 3571 3683 3336 R’s occupational prestige score (2010) Pearson Correlation .087 1 .504 .316 Sig. (2-tailed) <.001 <.001 <.001 N 3571 3873 3817 3399 Highest year of school R completed Pearson Correlation .014 .504 1 .360 Sig. (2-tailed) .391 <.001 <.001 N 3683 3817 3966 3497 R’s family income in 1986 dollars Pearson Correlation .017 .316 .360 1 Sig. (2-tailed) .314 <.001 <.001 N 3336 3399 3497 3509 **. Correlation is significant at the 0.01 level (2-tailed). Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication. Table 6. Correlation Matrix 1 0.087*** 1 0.014 0.504*** 1 0.017 0.316*** 0.360*** 1 If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this: Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong. To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7. Table 7. SPSS Output: Regression Model R R Square Adjusted R Square Std. Error of the Estimate 1 .395 .156 .155 36729.04841 a. Predictors: (Constant), Highest year of school R completed, Age of respondent, R’s occupational prestige score (2010) ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 805156927306.583 3 268385642435.528 198.948 <.001 Residual 4351948187487.015 3226 1349022996.741 Total 5157105114793.598 3229 a. Dependent Variable: R’s family income in 1986 dollars b. Predictors: (Constant), Highest year of school R completed, Age of respondent, R’s occupational prestige score (2010) Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) -44403.902 4166.576 -10.657 <.001 Age of respondent 9.547 38.733 .004 .246 .805 .993 1.007 R’s occupational prestige score (2010) 522.887 54.327 .181 9.625 <.001 .744 1.345 Highest year of school R completed 3988.545 274.039 .272 14.555 <.001 .747 1.339 a. Dependent Variable: R’s family income in 1986 dollars The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values. Table 8. Regression Results for Dependent Variable Family Income in 1986 Dollars Age 0.004 (38.733) Occupational Prestige Score 0.181*** (54.327) Highest Year of School Completed 0.272*** (274.039) Degrees of Freedom 3229 Constant -44,403.902 If we were to discuss the results of this regression in a quantitative paper, the results might look like this: Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1] Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper. Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper. Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper. Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper. Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis. Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4. Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵ Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted. 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 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.                           Type of Pet Frequency Dog 18 Fish 9 Hamsters 2 Cat 1 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: Number of Books Frequency (How many students read that number of books) 13 1 12 6 11 18 10 58 9 99 8 138 7 99 6 56 5 21 4 8 3 2 2 1 1 0 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? Here’s another example of symmetrical, positive skew, and negative skew: 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. 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 Inferential Statistics Descriptive Statistics Used to make conclusions about the population by using analytical tools on the sample data. Used to qualify the characteristics of the data. Hypothesis testing. Measures of central tendency and measures of dispersion are the important tools used. Is used to make inferences about an unknown population. Used to describe the characteristics of a known sample or population. Measures of inferential statistics include t-tests, ANOVA, chi-squared test, etc. Measures of descriptive statistics are variances, range, mean, median, etc. 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. 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? 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. Share This Book An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Publications Account settings Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now . Advanced Search Journal List Indian J Anaesth v.60(9); 2016 Sep Basic statistical tools in research and data analysis Zulfiqar ali. Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India S Bala Bhaskar 1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis. INTRODUCTION Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ] Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ]. Classification of variables Quantitative variables Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature. A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ]. Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables. Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale. Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale. Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm. STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 . Example of descriptive and inferential statistics Descriptive statistics The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion. Measures of central tendency The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula: where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula: where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula: where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula: where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 . Example of mean, variance, standard deviation Normal distribution or Gaussian distribution Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ]. Normal distribution curve Skewed distribution It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail. Curves showing negatively skewed and positively skewed distribution Inferential statistics In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects. Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty). In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ] Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ] The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ]. P values with interpretation If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ] Illustration for null hypothesis PARAMETRIC AND NON-PARAMETRIC TESTS Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ] Two most basic prerequisites for parametric statistical analysis are: The assumption of normality which specifies that the means of the sample group are normally distributed The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal. However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data. Parametric tests The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA. Student's t -test Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances: where X = sample mean, u = population mean and SE = standard error of mean where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference. To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment. The formula for paired t -test is: where d is the mean difference and SE denotes the standard error of this difference. The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly. Analysis of variance The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups. In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples. However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test. A simplified formula for the F statistic is: where MS b is the mean squares between the groups and MS w is the mean squares within groups. Repeated measures analysis of variance As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time. As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used. Non-parametric tests When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power. As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 . Analogue of parametric and non-parametric tests Median test for one sample: The sign test and Wilcoxon's signed rank test The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value. This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample. If the null hypothesis is true, there will be an equal number of + signs and − signs. The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values. Wilcoxon's signed rank test There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample. Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums. Mann-Whitney test It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other. Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2. Kolmogorov-Smirnov test The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves. Kruskal-Wallis test The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic. Jonckheere test In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ] Friedman test The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ] Tests to analyse the categorical data Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula: A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used. SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft). There are a number of web resources which are related to statistical power analyses. A few are: StatPages.net – provides links to a number of online power calculators G-Power – provides a downloadable power analysis program that runs under DOS Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document. It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines. Financial support and sponsorship Conflicts of interest. There are no conflicts of interest. Data Analysis in Quantitative Research Reference work entry First Online: 13 January 2019 Cite this reference work entry Yong Moon Jung 2   2340 Accesses 2 Citations 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). This is a preview of subscription content, log in via an institution to check access. Access this chapter Subscribe and save. Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Available as PDF Read on any device Instant download Own it forever Available as EPUB and PDF Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Similar content being viewed by others Data Analysis Techniques for Quantitative Study Meta-Analytic Methods for Public Health Research Armstrong JS. Significance tests harm progress in forecasting. Int J Forecast. 2007;23(2):321–7. Article   Google Scholar   Babbie E. The practice of social research. 14th ed. Belmont: Cengage Learning; 2016. Google Scholar   Brockopp DY, Hastings-Tolsma MT. Fundamentals of nursing research. Boston: Jones & Bartlett; 2003. 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Philadelphia: Lippincott Williams & Wilkins; 2004. Trochim WMK, Donnelly JP. Research methods knowledge base. 3rd ed. Mason: Thomson Custom Publishing; 2007. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Boston: Pearson Education. Wells CS, Hin JM. Dealing with assumptions underlying statistical tests. Psychol Sch. 2007;44(5):495–502. Download references Author information Authors and affiliations. Centre for Business and Social Innovation, University of Technology Sydney, Ultimo, NSW, Australia Yong Moon Jung You can also search for this author in PubMed   Google Scholar Corresponding author Correspondence to Yong Moon Jung . Editor information Editors and affiliations. School of Science and Health, Western Sydney University, Penrith, NSW, Australia Pranee Liamputtong Rights and permissions Reprints and permissions Copyright information © 2019 Springer Nature Singapore Pte Ltd. About this entry Cite this entry. 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 Download citation DOI : https://doi.org/10.1007/978-981-10-5251-4_109 Published : 13 January 2019 Publisher Name : Springer, Singapore Print ISBN : 978-981-10-5250-7 Online ISBN : 978-981-10-5251-4 eBook Packages : Social Sciences Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences Share this entry Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Publish with us Policies and ethics Find a journal Track your research A Complete Guide to Quantitative Research Methods Numbers are everywhere and drive our day-to-day lives. We take decisions based on numbers, both at work and in our personal lives. For example, an organization may rely on sales numbers to see if it’s succeeding or failing, and a group of friends planning a vacation may look at ticket prices to pick a place. In the social domain, numbers are just as important. They help identify what interventions are needed, whether ongoing projects are effective, and more. But how do organizations in the social domain get the numbers they need? This is where quantitative research comes in. Quantitative research is the process of collecting numerical data through standardized techniques, then applying statistical methods to derive insights from it. When is quantitative research useful? The goal of quantitative research methods is to collect numerical data from a group of people, then generalize those results to a larger group of people to explain a phenomenon. Researchers generally use quantitative research when they want get objective, conclusive answers. For example, a chocolate brand may run a survey among a sample of their target group (teenagers in the United States) to check whether they like the taste of the chocolate. The result of this survey would reveal how all teenagers in the U.S. feel about the chocolate. Similarly, an organization running a project to improve a village’s literacy rate may look at how many people came to their program, how many people dropped out, and each person’s literacy score before and after the program. They can use these metrics to evaluate the overall success of their program. Unlike  qualitative research , quantitative research is generally not used in the early stages of research for exploring a question or scoping out a problem. It is generally used to answer clear, pre-defined questions in the advanced stages of a research study. How can you plan a quantitative research exercise? Identify the research problem . An example would be, how well do New Delhi’s government schools ensure that students complete their education? Prepare the research questions that need to be answered to address the research problem. For example, what percentage of students drop out of government schools in New Delhi? Review existing literature on the research problem and questions to ensure that there is no duplication. If someone has already answered this, you can rely on their results. Develop a research plan . This includes identifying the target group, sample , and method of data collection ; conducting data analysis; collating recommendations; and arriving at a conclusion. What are the advantages of quantitative research methods? Quantitative research methods provide an relatively conclusive answer to the research questions. When the data is collected and analyzed in accordance with standardized, reputable methodology, the results are usually trustworthy. With statistically significant sample sizes, the results can be generalized to an entire target group. Samples have to be carefully designed and chosen, else their results can’t be generalized. Learn how to choose the right sampling technique for your survey. What are the limitations of quantitative research methods? Does not account for people’s thoughts or perceptions about what you’re evaluating. Does not explore the “why” and “how” behind a phenomenon. What quantitative research methods can you use? Here are four quantitative research methods that you can use to collect data for a quantitative research study: Questionnaires This is the most common way to collect quantitative data. A questionnaire (also called a survey) is a series of questions, usually written on paper or a digital form. Researchers give the questionnaire to their sample, and each participant answers the questions. The questions are designed to gather data that will help researchers answer their research questions. Typically, a questionnaire has closed-ended questions — that is, the participant chooses an answer from the given options. However, a questionnaire may also have quantitative open-ended questions. In the open-ended example above, the participants could write a simple number like “4”, a range like “I usually go one or two times per week” or a more complex response like “Most weeks I go twice, but this week I went 4 times because I kept forgetting my grocery list. During the winter, I only go once a week.” Understanding closed and open-ended questions is crucial to designing a great survey and collecting high quality data. Learn more with our complete guide about when and how to use closed and open-ended questions. A good questionnaire should have clear language, correct grammar and spelling, and a clear objective. Advantages: Questionnaires are often less time consuming than interviews or other in-person quantitative research methods. They’re a common, fairly simple way to collect data. They can be a cost-effective option for gathering data from a large sample. Limitations: Responses may lack depth and provide limited information. Respondents may lose interest or quit if the questionnaire is long. Respondents may not understand all questions, which would lead to inaccurate responses. Response bias — a set of factors that lead participants answer a question incorrectly — can be deadly for data quality. Learn how it happens and how to avoid it. An interview for quantitative research involves verbal communication between the participant and researcher, whose goal is to gather numerical data. The interview can be conducted face-to-face or over the phone, and it can be structured or unstructured. In a structured interview, the researcher asks a fixed set of questions to every participant. The questions and their order are pre-decided by the researcher. The interview follows a formal pattern. Structured interviews are more cost efficient and can be less time consuming. In an unstructured interview, the researcher thinks of his/her questions as the interview proceeds. This type of interview is conversational in nature and can last a few hours. This type of interview allows the researcher to be flexible and ask questions depending on the participant’s responses. This quantitative research method can provide more in-depth information, since it allows researchers to delve deeper into a participant’s response. Interviews can provide more in-depth information. Interviews are more flexible than questionnaires, since interviewers can adapt their questions to each participant or ask follow-up questions. Interviewers can clarify participants’ questions, which will help them get clearer, more accurate data. Interviewing one person at a time can be time-consuming. Travel, interviewer salaries and other expenses can make interviews an expensive data collection tool. With unstructured interviews, it can be difficult to quantify some responses. One way to speed up interviews is to conduct them with multiple people at one time in a focus group discussion. Learn more about how to conduct a great FGD. Observation Observation is a systematic way to collect data by observing people in natural situations or settings. Though it is mostly used for collecting qualitative data, observation can also be used to collect quantitative data. Observation can be simple or behavioral. Simple observations are usually numerical, like how many cars pass through a given intersection each hour or how many students are asleep during a class. Behavioral observation, on the other hand, observes and interprets people’s behavior, like how many cars are driving dangerously or how engaging a lecturer is. Simple observation can be a good way to collect numerical data. This can be done by pre-defining clear numerical variables that can be collected during observation — for example, what time employees leave the office. This data can be collected by observing employees over a period of time and recording when each person leaves. Observation is often an inexpensive way to collect data. Since researchers are recording the data themselves (rather than participants reporting the data), most of the collected data will generally be usable. Data collection can be stopped and started by researchers at any time, making it a flexible data collection tool. Researchers need to be extensively trained to undertake observation and record data correctly. Sometimes the environment or research may bias the data, like when participants know they’re being observed. If the situation to be observed sometimes doesn’t happen, researchers may waste a lot of time during data collection. Simple vs. behavioral is just one type of observation. Learn more about the 5 different types of observation and when you should use each to collect different types of data. Since quantitative research depends on numerical data, records (also known as external data) can provide critical information to answer research questions. Records are numbers and statistics that institutions use to track activities, like attendance in a school or the number of patients admitted in a hospital. For example, the Government of India conducts the Census every 10 years, which is a record of the country’s population. This data can be used by a researcher who is addressing a population-related research problem. Records often include comprehensive data captured over a long period of time. Data collection time is minimal, since the data has already been collected and recorded by someone else. Records often only provide numerical data, not the reason or cause behind the data. Cleaning badly structured or formatted records can take a long time. If a record is incomplete or inaccurate, there is often no way to fix it. Summing it up Quantitative research methods are one of the best tools to identify a problem or phenomenon, how widespread it is, and how it is changing over time. After identifying a problem, quantitative research can also be used to come up with a trustworthy solution, identified using numerical data collected through standardized techniques. Image credits:  Curtis MacNewton ,  Brijesh Nirmal ,  Charles Deluvio , and Atlan. Related Posts 3 Myths About Paper-Based Data Collection 18 Data Validations That Will Help You Collect Accurate Data Everything You Need to Know About Informed Consent 14 comments. Very useful for research Very easy to read and informative book. Well written. Thany thanks for the download. It is concise and practical as well as easy to understand. Nice book but I kind find a way to download it. Kindly let me know how to download it. Thanks Hello Micah Nalianya Greetings! Kindly tell me how to download the book. Simeon Hi Micah and Simeon! You can download our data collection ebook here: https://socialcops.com/ebooks/data-collection/ I have loved reviewing the brief write up. Good revision for me. Thanks The text contains concise and important tips on data collection techniques. Thanks for an explicit and precise outline of data collection methods. thank you very much, this guide is really useful and easy to understand. Specially for students that just have started research. Thank you so much for sharing me this very important material. I am highly impressed with the simply ways you explain methods of collecting data. I am a Monitoring and Evaluation Specialist and I will like to be receiving your regular publications. i have benefited from the work. well organized .thank you interview is a qualitative method not quantitative. Write A Comment Cancel Reply Save my name, email, and website in this browser for the next time I comment. This site uses Akismet to reduce spam. Learn how your comment data is processed . The Future of the Modern Data Stack in 2023 The Third-Generation Data Catalog Primer The Secrets of a Modern Data Leader The Ultimate Guide to Evaluating Data Lineage How Active Metadata Helps Modern Organizations Embrace the DataOps Way Inside Atlan Type above and press Enter to search. Press Esc to cancel. Are you an agency specialized in UX, digital marketing, or growth? Join our Partner Program Learn / Guides / Quantitative data analysis guide Back to guides How to analyze quantitative data in 5 steps for better customer insights Quantitative data analysis is all about making sense of the numbers you get from sources like surveys, A/B tests, and website analytics. But many marketers and ecommerce managers find it challenging to extract meaningful information from all this raw data.  With a little know-how and the right framework in place, you can turn those perplexing numbers into actionable insights that drive better decisions for your business. Last updated Reading time. This chapter walks you through a step-by-step process of analyzing your quantitative data, helping you get a clear view of what's happening with user behavior by spotting patterns, trends, and relationships in your data.  Are you ready to swap data overwhelm for data mastery? Stick around. Collect quantitative data fast with Hotjar Use Hotjar’s tools to understand your customers better with quantitative (and qualitative!) data. A 5-step guide to conducting effective quantitative data analysis Unlocking the power of quantitative data analysis can have a huge impact on your business. It helps you: Make informed decisions: quantitative data analysis provides you with hard numbers for precise measurement and objective analysis, enabling you to make fine-tuned improvements Understand user behavior: pinpoint where users face difficulties and what drives their actions Optimize your website : use insights from A/B testing and conversion funnels to improve your website design and flow, so you elevate the user experience (UX) and increase conversion rates Track progress and performance: consistently collecting and analyzing quantitative data allows you to monitor progress over time, measure the success of changes you've implemented, and set measurable goals for the future Identify trends : spot emerging trends in user behavior and preferences, which is instrumental in creating viable strategies for the future Reaping the benefits of quantitative data analysis isn’t as complicated as it sounds. Here’s how to make sense of your company’s data in just five steps. 1. Choose your objectives Before you jump into the sea of data, you need to know what you're fishing for: Do you want to spot trends in how users interact with your site?  Are you trying to find connections between specific user actions and conversions?  Do you want to predict what changes could boost your site's performance in the future?  Setting clear goals right from the start helps steer your data analysis in the right direction. Remember: objectives can be broad or specific, but they must be measurable and relevant. Use the SMART (Specific, Measurable, Achievable, Relevant, Time-bound) framework to set robust objectives. For example, instead of a vague objective like ‘Improve the user experience (UX)’, consider something more specific and measurable like ‘Reduce the checkout process drop-off rate by 15% in the next quarter’. ❗ Caveat : don’t set too many objectives at once. While it can be tempting to gather as much data as possible, focusing on a few key areas allows for a deeper, more meaningful analysis, helping you stay focused and dig up the insights that truly matter to your site’s or product’s success. 2. Collect and organize your data To get your data, you need to do some quantitative research. There are many different ways to do this (and chances are, you’re using some of these methods already). Popular quantitative data collection methods include: Surveys , which let you collect data by asking close-ended questions to different user segments. Metrics like the Net Promoter Score ® and Customer Effort Score are perfect for capturing quantitative data through surveys.  A/B testing , which helps you compare two versions of a web page or ad to see which performs better. It gives you quantitative data like conversion rates, click-through rates, and time spent on page. Heatmaps , which show you where activity is highest on your website so you can better understand the user experience. They give you data like click frequency for page elements and the percentage of users who scroll to the bottom of the page. Session recordings , which enable you to capture user sessions to see how people interact with your website. They give you data like the session duration, how many pages the user visited, and how many actions (like clicks or text input) they performed. Conversion funnels , which let you track user steps toward a specific goal, providing you with conversion rates at each step of the funnel Website analytics, which give you insights into visitor behavior and the user experience so you can optimize your site. Tools like the Hotjar Dashboard and Google Analytics provide quantitative data such as website traffic, bounce rates, and session duration. Start by using spreadsheets, data visualization software such as Tableau , or tools like Hotjar to get all your data in one place. Remember, the goal is to organize your collected data in such a way that makes analysis smoother and more effective. Then, group similar types of data together to make it easier to analyze. For example, you might group all data related to website traffic—like page views, bounce rates, and session duration. Your specific needs might also dictate using filters, charts, or different types of categorization. This way, when it’s time for analysis, you can focus on meaningful insights rather than get lost in a sea of numbers. 💡 Pro tip : by clicking on a data point in your Trends graph, you can jump straight to the relevant session recording and heatmap to uncover the ‘why’ behind your metrics. 3. Clean your data Ensuring your data is clean is crucial for the accuracy of your analysis.  During this stage, you need to carefully sift through your data to check for any errors such as incorrect entries, duplicates, or inconsistencies. You can use data cleaning tools like OpenRefine , or something more basic like Google Sheets. If you can’t easily fix the error, you should remove it from your data so it doesn’t skew your analysis. You might find some data that seems wildly different from the rest—outliers—or data that's irrelevant to your current objectives. Say you're analyzing session recordings to understand user behavior on your website and you notice that most visitors spend between two to five minutes on your site.  However, you spot a few instances where visitors browse for an unusually long time, say, more than an hour. These instances could be outliers, possibly due to users leaving a browser tab open and stepping away from their device. It's best to remove these to keep your data set clean and focused, so you can set yourself up for reliable results in your analysis. 4. Analyze your data Once you have your clean data, you're all set to start analyzing it. This stage is all about making sense of the numbers. You might be observing trends, investigating correlations, or predicting future outcomes based on the data. There are two main types of quantitative data analysis methods: Descriptive analysis helps you get a bird's-eye view of your data. It includes basic calculations such as the average (mean), most common (mode), or middle (median) values. Inferential analysis lets you go beyond just describing your data, so you can start making educated guesses. You might see how different data points are linked, or even predict future trends with methods like t-tests, cross-tabulation, or factor analysis. (Keep an eye out for our upcoming guide on quantitative data analysis methods to get a closer look at each of these techniques.) But there's something else you need to consider: data triangulation. This is where you pair quantitative and qualitative data , like website analytics and user feedback. Combining these two types of data gives you a complete picture of what's happening on your website. See it in action Suppose your quantitative data shows a high bounce rate on a particular product page. You can see the 'what' here—people are leaving the page quickly. But the 'why' is not clear: Is the product description unclear? Are images not loading quickly enough? Are visitors searching for more information that's missing from the page? This is where qualitative data analysis comes in handy. By looking at session recordings or analyzing feedback from user surveys, you can dive into the reasons behind the observed behavior. For example: Maybe session recordings reveal that users are having trouble with a particular feature of the product page, such as a slider not functioning properly Or perhaps when you analyze qualitative survey data , it indicates that the product descriptions aren't detailed enough By using quantitative data (the high bounce rate) and pairing it with qualitative data (from user surveys and watching session recordings), you've achieved data triangulation. 5. Share your learnings and put them into action The power of quantitative data analysis lies in its ability to guide decisions and actions . By presenting your findings effectively, you ensure that your team not only understands the data but also knows how to use it to improve and grow. Start by creating a clear and concise report of your analysis. Highlight key insights, trends, and patterns you've discovered. Use data visualization tools to make your findings easier to understand. Graphs, charts, or heatmaps help you effectively communicate the story behind the numbers. For example, the Hotjar Dashboard lets you create automatic bar graphs for all your key metrics. With one click, you can go to the Trends page to compare metrics for various segments, all neatly presented in one chart. This type of data visualization helps make your insights clear and comprehensible for everyone involved. When sharing your findings, remember that not everyone will be as data-savvy as you are. Avoid jargon and explain your findings in simple, relatable terms. Your goal is to ensure everyone understands what the data says and why it matters .  Finally, suggest actionable steps based on your findings: If you've found a high bounce rate on a particular page, recommend a review of its content or design If a certain product category is doing well, suggest capitalizing on its success with targeted marketing campaigns Get focused, actionable insights with quantitative data analysis Mastering quantitative data analysis gives you the power to convert raw numbers into meaningful insights and well-informed action plans . From defining your objectives, gathering and organizing data, through to cleaning, analyzing, and finally, presenting your findings, it allows you to unlock valuable trends and patterns.  This knowledge helps you improve UX, get buy-in from your team, and boost your business growth. So, equip yourself with the right tools and start turning your data into actionable insights. Start making sense of your quantitative data today Faqs about quantitative data analysis, what is quantitative data analysis. Quantitative data analysis is the process of making sense of numerical data from surveys, experiments, or observations. The goal of quantitative analysis is to identify patterns, trends, and relationships in your data to help you make better, more objective decisions about your product or site. What are the different methods you can use to conduct quantitative analysis? Quantitative data analysis is split into two general categories: Descriptive statistics give you a snapshot of your data with calculations like the mean, median, and mode Inferential statistics involve making inferences about what the data means. There are dozens of methods to achieve this, but three common techniques are:  Cross tabulation Factor analysis What are some of the challenges of quantitative data analysis? Quantitative data analysis comes with its fair share of hurdles. Here are the most common: Complexity: numbers aren’t easy for everyone to understand, which can make data analysis overwhelming Data cleaning : before analysis, you need to clean the data, removing any irrelevant or inaccurate data points, which can be a time-consuming process Lack of context: quantitative data tells you 'what' is happening, but not 'why'. Without qualitative data to provide context, interpreting quantitative data can be challenging. Data overload: depending on your number of users and the methods you choose, the sheer amount of data available can be overwhelming. It can be hard to determine which data points are most relevant to your analysis. Misinterpretation of data: numbers don't lie, but you can misinterpret them. You need to be careful not to draw incorrect conclusions from your data. Quantitative data analysis examples Previous chapter Quantitative data analysis methods Next chapter Extract insights from customer & stakeholder interviews. At Scale. Research approach for quantitative vs. qualitative research. Home » Research Approach for Quantitative vs. Qualitative Research Research methodologies are crucial in shaping our understanding of phenomena, influencing both academic and practical outcomes. Methodological distinctions between quantitative and qualitative research greatly impact how data is collected, analyzed, and interpreted. Recognizing these differences allows researchers to choose appropriate methods that align with their objectives and target populations. Quantitative research emphasizes numerical data and statistical analysis, seeking to establish patterns and test hypotheses through measurable variables. In contrast, qualitative research focuses on understanding human experiences and social phenomena through detailed observations and interviews. By grasping the methodological distinctions, researchers can enhance the validity and reliability of their studies, ultimately contributing to deeper insights and informed decision-making. Quantitative Research: Methodological Distinctions and Approach Quantitative research is distinguished by its reliance on numerical data and statistical analysis, setting it apart from qualitative methods. Researchers often use structured tools, such as surveys or experiments, to gather quantifiable data. This data can be analyzed using various statistical methods, allowing for the identification of patterns and relationships. Such methodological distinctions are vital in forming clear conclusions based on measurable evidence, contributing to decision-making processes. In contrast, qualitative research emphasizes understanding human experiences and perspectives through open-ended questions and unstructured approaches. While both methodologies have their strengths, it is essential to recognize the unique contributions of quantitative research. Its focus on quantifiable results helps to ensure objectivity and reliability, providing a solid foundation for further analytical endeavors. Understanding these methodological distinctions enables researchers to select the most appropriate approach for their specific research inquiries. Data Collection Techniques Data collection techniques vary significantly between qualitative and quantitative research, reflecting distinct methodological distinctions. In qualitative research, techniques such as interviews, focus groups, and observations enable researchers to gather in-depth insights. These methods allow for open-ended responses, which help in understanding participants' thoughts, behaviors, and experiences. Conversely, quantitative research relies on structured tools like surveys and experiments, which facilitate the collection of numerical data. This approach aims to quantify variables and ultimately identify relationships, enabling hypothesis testing. By employing both qualitative and quantitative methods, researchers can create a more comprehensive understanding of their study subject. The choice of technique profoundly influences the research outcome, highlighting the importance of selecting the appropriate method based on the research goals. Statistical Analysis and Interpretation Statistical analysis and interpretation play pivotal roles in discerning the methodological distinctions between quantitative and qualitative research. Quantitative research relies on statistical methods to process numerical data, enabling researchers to identify patterns and test hypotheses. In contrast, qualitative research emphasizes understanding phenomena through non-numerical data, such as interviews and observations, often requiring thematic or content analysis for interpretation. The methodological distinctions also dictate the tools employed for analysis. For quantitative approaches, researchers often utilize software for statistical computations and visual representations of data. Qualitative analysis, however, focuses on deriving meaning and insights from textual information, often utilizing coding strategies. Each method’s interpretative framework influences not only how data is collected but also the subsequent conclusions derived, shaping the research output's validity and reliability. This understanding enhances the research's overall impact and informs best practices for conducting robust analyses across different research paradigms. Qualitative Research: Methodological Distinctions and Approach Qualitative research focuses on understanding human experiences and the meanings individuals attach to those experiences. Its methodological distinctions set it apart from quantitative approaches, emphasizing depth over breadth. Data collection methods such as interviews, focus groups, and participant observations allow researchers to gather rich narratives that illuminate complex social phenomena. This depth creates a nuanced understanding of participant perspectives, enabling the extraction of themes and patterns inherent in the data. Moreover, qualitative research prioritizes context and rich descriptions, capturing the variability of human behavior. Unlike quantitative research, which seeks to measure and quantify, qualitative methods emphasize subjective meaning. This approach promotes exploration and discovery, allowing researchers to adapt their inquiries based on emerging findings. Through these methodological distinctions, qualitative research offers valuable insights that inform theory and practice, contributing to a holistic understanding of diverse experiences. Thematic Analysis and Interpretation Thematic analysis and interpretation play a crucial role in understanding qualitative data. By identifying patterns and themes, researchers can gain deeper insights into the perspectives and experiences of participants. This process requires careful coding of data, where segments are categorized based on recurring ideas. Methodological distinctions become evident here, as qualitative analysis focuses on context and meaning, contrasting with the more structured approach of quantitative research. In executing thematic analysis, researchers typically follow several stages. First, they familiarize themselves with the data through thorough reading. Next, they generate initial codes that capture significant features. Following coding, themes are constructed, allowing for interpretation of the results in relation to the research questions. Finally, researchers refine these themes, ensuring they accurately represent the data. Each of these steps underscores the relevance of methodological distinctions in effectively analyzing and interpreting qualitative research. Conclusion: Synthesizing Methodological Distinctions and Choosing the Right Approach In conclusion, understanding methodological distinctions between quantitative and qualitative research is essential for effective inquiry. Each approach offers unique insights and caters to different research questions. Quantitative research excels at measuring and analyzing numerical data, establishing patterns and relationships through statistical techniques. Conversely, qualitative research delves into the rich, subjective experiences of individuals, uncovering deeper meanings and nuanced perspectives. Choosing the right approach hinges on your objectives, context, and the nature of the questions posed. A clear understanding of each methodology's strengths enables researchers to select the most suitable framework. Ultimately, synthesizing these distinctions fosters a more comprehensive understanding of research outcomes and supports informed decision-making in diverse fields. Turn interviews into actionable insights On this Page Qualitative Research Use: Applications and Benefits You may also like, risk sensitivity analysis: ai tools for market research 2024. Purpose of Grounded Theory in Market Research 2024 Sensitivity risk analysis: how to use ai tools in 2024. Unlock Insights from Interviews 10x faster Request demo Get started for free 5 Reasons Why You Should Use SPSS For Your PhD Research February 2022 Scholars Professional Editing Group LLC Discover the world's research 25+ million members 160+ million publication pages 2.3+ billion citations Recruit researchers Join for free Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up SUGGESTED TOPICS The Magazine Newsletters Managing Yourself Managing Teams Work-life Balance The Big Idea Data & Visuals Reading Lists Case Selections HBR Learning Topic Feeds Account Settings Email Preferences Where Data-Driven Decision-Making Can Go Wrong Michael Luca Amy C. Edmondson When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions. Five pitfalls to avoid Idea in Brief The problem. When managers are presented with internal data or an external study, all too often they either automatically accept its accuracy and relevance to their business or dismiss it out of hand. Why It Happens Leaders mistakenly conflate causation with correlation, underestimate the importance of sample size, focus on the wrong outcomes, misjudge generalizability, or overweight a specific result. The Right Approach Leaders should ask probing questions about the evidence in a rigorous discussion about its usefulness. They should create a psychologically safe environment so that participants will feel comfortable offering diverse points of view. Let’s say you’re leading a meeting about the hourly pay of your company’s warehouse employees. For several years it has automatically been increased by small amounts to keep up with inflation. Citing a study of a large company that found that higher pay improved productivity so much that it boosted profits, someone on your team advocates for a different approach: a substantial raise of $2 an hour for all workers in the warehouse. What would you do? Michael Luca is a professor of business administration and the director of the Technology and Society Initiative at Johns Hopkins University, Carey Business School. Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School. Her latest book is Right Kind of Wrong: The Science of Failing Well (Atria Books, 2023). Partner Center Integrative analysis of nanopore direct RNA sequencing data reveals a role of PUS7-dependent pseudouridylation in regulation of m 6 A and m 5 C modifications Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: [email protected] [email protected] Info/History Supplementary material Preview PDF Understanding the interactions between different RNA modifications is essential for unraveling their biological functions. Here, we report NanoPsiPy, a computational pipeline that employs nanopore direct RNA sequencing to identify pseudouridine (Ψ) sites and quantify their levels at single-nucleotide resolution. We validated NanoPsiPy by transcriptome-wide profiling of PUS7-dependent Ψ sites in poly-A RNA and rRNA. NanoPsiPy leverages Ψ-induced U-to-C basecalling errors in nanopore sequencing data, allowing detection of both low and high stoichiometric Ψ sites. We identified 8,624 PUS7-dependent Ψ sites in 1,246 mRNAs encoding proteins associated with ribosome biogenesis, translation, and energy metabolism. Importantly, integrative analysis revealed that PUS7 knockdown increases global mRNA N 6 -methyladenosine (m 6 A) and 5-methylcytosine (m 5 C) levels, suggesting an antagonistic relationship between Ψ and these modifications. Our study underscores the potential of nanopore direct RNA sequencing in revealing the co-regulation of RNA modifications and the capacity of NanoPsiPy in analyzing pseudouridylation and its impact on other RNA modifications. Competing Interest Statement The authors have declared no competing interest. We have included supplementary data illustrating the interactions between RNA modifications. View the discussion thread. Supplementary Material Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Understand its steps, benefits and methods, and learn the importance of data analysis in quantitative research. Quantitative Data Analysis 101 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 Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis. What Is Quantitative Research? Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. A Really Simple Guide to Quantitative Data Analysis It is important to know w hat kind of data you are planning to collect or analyse as this w ill. affect your analysis method. A 12 step approach to quantitative data analysis. Step 1: Start with ... Introduction to Research Statistical Analysis: An Overview of the Introduction Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Data Analysis in Research: Types & Methods QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys. Data analysis in research is an illustrative method of applying the right statistical or logical technique so that the raw data makes sense. Quantitative Data Analysis Methods, Types + Techniques 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. A practical guide to data analysis in general literature reviews This article seeks to describe a systematic method of data analysis appropriate for undergraduate research theses, where the data consists of the results from available published research. We present a step-by-step guide with authentic examples and practical tips. Quantitative Data Collection: Best 5 methods What is Quantitative Data Collection? Quantitative data collection refers to the collection of numerical data that can be analyzed using statistical methods. This type of data collection is often used in surveys, experiments, and other research methods. It measure variables and establish relationships between variables. Quantitative Research 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. Quantitative Data Analysis Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process. The same figure within data set can be interpreted in ... What is Quantitative Data? Types, Examples & Analysis Quantitative data is data that can be counted or measured in numerical values. The two main types of quantitative data are discrete data and continuous data. Height in feet, age in years, and weight in pounds are examples of quantitative data. Qualitative data is descriptive data that is not expressed numerically. Analysing and Interpreting Data in Your Dissertation: Making Sense of Master the art of analysing and interpreting data for your dissertation with our comprehensive guide. Learn essential techniques for quantitative and qualitative analysis, data preparation, and effective presentation to enhance the credibility and impact of your research. A Practical Guide to Writing Quantitative and Qualitative Research It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or ... InfoGuides: Quantative Analysis & Statistics: Write a Paper Reporting Quantitative Research in Psychology: How to meet APA Style Journal Article Reporting Standards by Harris Cooper This updated edition offers practical guidance for understanding and implementing APA Style Journal Article Reporting Standards (JARS) and Meta‑Analysis Reporting Standards (MARS) for quantitative research. These standards provide the essential information researchers ... Presenting the Results of Quantitative Analysis Mikaila Mariel Lemonik Arthur. This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis. 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). Basic statistical tools in research and data analysis Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if ... Data Analysis in Quantitative Research 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. Quantitative Data Analysis: A Complete Guide Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. With quantitative data analysis, you turn spreadsheets of individual data points into ... A Complete Guide to Quantitative Research Methods Quantitative research methods provide an relatively conclusive answer to the research questions. When the data is collected and analyzed in accordance with standardized, reputable methodology, the results are usually trustworthy. With statistically significant sample sizes, the results can be generalized to an entire target group. What Is Quantitative Research? An Overview and Guidelines Abstract. In an era of data-driven decision-making, a comprehensive understanding of quantitative research is indispensable. Current guides often provide fragmented insights, failing to offer a holistic view, while more comprehensive sources remain lengthy and less accessible, hindered by physical and proprietary barriers. 5 Steps to Analyze Quantitative Data Effectively How to analyze quantitative data in 5 steps for better customer insights Quantitative data analysis is all about making sense of the numbers you get from sources like surveys, A/B tests, and website analytics. But many marketers and ecommerce managers find it challenging to extract meaningful information from all this raw data. Research Approach for Quantitative vs. Qualitative Research Quantitative Research: Methodological Distinctions and Approach. Quantitative research is distinguished by its reliance on numerical data and statistical analysis, setting it apart from qualitative methods. Researchers often use structured tools, such as surveys or experiments, to gather quantifiable data. Analyzing Quantitative and Qualitative Data Sets: Software and When doing quantitative data review, it can really depend on what you are trying to research, depending on what the individual needs of the researcher may be (Annechino et al., 2010). However, one software which is used is SPSS, which can be helpful for newer researchers because there is a way to "run analysis with a point and click" (Annechino ... What does science tell us about boxing's gender row? We do know that the process of sex determination starts when a foetus is developing. Most females get two X chromosomes (XX), while most males get an X and a Y chromosome (XY). Chromosomes ... 5 Reasons Why You Should Use SPSS For Your PhD Research Research is a core component of the doctoral degree, and statistical analysis plays a central role in many aspects of research. Data collected by researchers has to be analysed and interpreted so ... Where Data-Driven Decision-Making Can Go Wrong When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders ... Integrative analysis of nanopore direct RNA sequencing data ... Pseudouridylation is a prevalent post-transcriptional RNA modification that impacts many aspects of RNA biology and function. The conversion of uridine to pseudouridine (ψ) is catalyzed by the family of pseudouridine synthases (PUSs). Development of robust methods to determine PUS-dependent regulation of ψ location and stoichiometry in low abundant mRNA is essential for biological and ... assignment essay homework paper phd report resume review thesis