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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!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

77 Comments

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The 11 Best Data Analytics Tools for Data Analysts in 2024

As the field of data analytics evolves, the range of available data analysis tools grows with it. If you’re considering a career in the field, you’ll want to know: Which data analysis tools do I need to learn?

In this post, we’ll highlight some of the key data analytics tools you need to know and why. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. What’s even better, a good few of those on this list contain AI data analytics tools , so you’re at the forefront of the field as 2024 comes around.

We’ll start our list with the must-haves, then we’ll move onto some of the more popular tools and platforms used by organizations large and small. Whether you’re preparing for an interview, or are deciding which tool to learn next, by the end of this post you’ll have an idea how to progress.

If you’re only starting out, then CareerFoundry’s free data analytics short course will help you take your first steps.

Here are the data analysis tools we’ll cover:

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Google Cloud AutoML
• Microsoft Power BI

How to choose a data analysis tool

Data analysis tools faq.

So, let’s get into the list then!

1.  Microsoft Excel

Excel at a glance:

• Type of tool: Spreadsheet software.
• Availability : Commercial.
• Mostly used for: Data wrangling and reporting.
• Pros: Widely-used, with lots of useful functions and plug-ins.
• Cons: Cost, calculation errors, poor at handling big data.

Excel: the world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis.

Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools.

It also has a variety of other functions that streamline data manipulation. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data.

It has limitations though. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies. Nevertheless, it’s an important and powerful data analysis tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings. Get started with these ten Excel formulas that all data analysts should know .

Python at a glance:

• Type of tool: Programming language.
• Availability: Open-source, with thousands of free libraries.
• Used for: Everything from data scraping to analysis and reporting.
• Pros: Easy to learn, highly versatile, widely-used.
• Cons: Memory intensive—doesn’t execute as fast as some other languages.

  A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it.

Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation.

Libraries like Beautiful Soup and Scrapy are used to scrape data from the web, while Matplotlib is excellent for data visualization and reporting. Python’s main drawback is its speed—it is memory intensive and slower than many languages. In general though, if you’re building software from scratch, Python’s benefits far outweigh its drawbacks. You can learn more about Python in our full guide .

R at a glance:

• Availability: Open-source.
• Mostly used for: Statistical analysis and data mining.
• Pros: Platform independent, highly compatible, lots of packages.
• Cons: Slower, less secure, and more complex to learn than Python.

R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software.

R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages.

It integrates well with other languages and systems (including big data software) and can call on code from languages like C, C++, and FORTRAN. On the downside, it has poor memory management, and while there is a good community of users to call on for help, R has no dedicated support team. But there is an excellent R-specific integrated development environment (IDE) called RStudio , which is always a bonus!

4.  Jupyter Notebook

Jupyter Notebook at a glance:

• Type of tool: Interactive authoring software.
• Mostly used for: Sharing code, creating tutorials, presenting work.
• Pros: Great for showcasing, language-independent.
• Cons: Not self-contained, nor great for collaboration.

Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text.

Imagine something a bit like a Microsoft word document, only far more interactive, and designed specifically for data analytics! As a data analytics tool, it’s great for showcasing work: Jupyter Notebook runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data analysis tools, like Apache Spark (see below) and offers various outputs from HTML to images, videos, and more.

But as with every tool, it has its limitations. Jupyter Notebook documents have poor version control, and tracking changes is not intuitive. This means it’s not the best place for development and analytics work (you should use a dedicated IDE for these) and it isn’t well suited to collaboration.

Since it isn’t self-contained, this also means you have to provide any extra assets (e.g. libraries or runtime systems) to anybody you’re sharing the document with. But for presentation and tutorial purposes, it remains an invaluable data science and data analytics tool.

5.  Apache Spark

Apache Spark at a glance:

• Type of tool: Data processing framework
• Availability: Open-source
• Mostly used for: Big data processing, machine learning
• Pros: Fast, dynamic, easy to use
• Cons: No file management system, rigid user interface

Apache Spark is a software framework that allows data analysts and data scientists to quickly process vast data sets. It was first developed in 2012, it’s designed to analyze unstructured big data, Spark distributes computationally heavy analytics tasks across many computers.

While other similar frameworks exist (for example, Apache Hadoop ) Spark is exceptionally fast. By using RAM rather than local memory, it is around 100x faster than Hadoop. That’s why it’s often used for the development of data-heavy machine learning models .

It even has a library of machine learning algorithms, MLlib , including classification, regression, and clustering algorithms, to name a few. On the downside, consuming so much memory means Spark is computationally expensive. It also lacks a file management system, so it usually needs integration with other software, i.e. Hadoop.

6. Google Cloud AutoML

Google Cloud AutoML at a glance:

• Type of tool: Machine learning platform
• Availability:  Cloud-based, commercial
• Mostly used for:  Automating machine learning tasks
• Pros: Allows analysts with limited coding experience to build and deploy ML models , skipping lots of steps
• Cons:  Can be pricey for large-scale projects, lacks some flexibility

A serious proposition for data analysts and scientists in 2024 is Google Cloud’s AutoML tool. With the hype around generative AI in 2023 set to roll over into the next year, tools like AutoML but the capability to create machine learning models into your own hands.

Google Cloud AutoML contains a suite of tools across categories from structured data to language translation, image and video classification. As more and more organizations adopt machine learning, there will be a growing demand for data analysts who can use AutoML tools to automate their work easily.

SAS at a glance:

• Type of tool: Statistical software suite
• Availability: Commercial
• Mostly used for: Business intelligence, multivariate, and predictive analysis
• Pros: Easily accessible, business-focused, good user support
• Cons: High cost, poor graphical representation

SAS (which stands for Statistical Analysis System) is a popular commercial suite of business intelligence and data analysis tools. It was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. Created for an enterprise market, the software is generally more robust, versatile, and easier for large organizations to use. This is because they tend to have varying levels of in-house programming expertise.

But as a commercial product, SAS comes with a hefty price tag. Nevertheless, with cost comes benefits; it regularly has new modules added, based on customer demand. Although it has fewer of these than say, Python libraries, they are highly focused. For instance, it offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

8. Microsoft Power BI

Power BI at a glance:

• Type of tool: Business analytics suite.
• Availability: Commercial software (with a free version available).
• Mostly used for: Everything from data visualization to predictive analytics.  
• Pros: Great data connectivity, regular updates, good visualizations.
• Cons: Clunky user interface, rigid formulas, data limits (in the free version).

At less than a decade old, Power BI is a relative newcomer to the market of data analytics tools. It began life as an Excel plug-in but was redeveloped in the early 2010s as a standalone suite of business data analysis tools. Power BI allows users to create interactive visual reports and dashboards , with a minimal learning curve. Its main selling point is its great data connectivity—it operates seamlessly with Excel (as you’d expect, being a Microsoft product) but also text files, SQL server, and cloud sources, like Google and Facebook analytics.

It also offers strong data visualization but has room for improvement in other areas. For example, it has quite a bulky user interface, rigid formulas, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not that user-friendly. It does offer several subscriptions though, including a free one. This is great if you want to get to grips with the tool, although the free version does have drawbacks—the main limitation being the low data limit (around 2GB).

Tableau at a glance:

• Type of tool: Data visualization tool.
• Availability: Commercial.
• Mostly used for: Creating data dashboards and worksheets.
• Pros: Great visualizations, speed, interactivity, mobile support.
• Cons: Poor version control, no data pre-processing.

If you’re looking to create interactive visualizations and dashboards without extensive coding expertise, Tableau is one of the best commercial data analysis tools available. The suite handles large amounts of data better than many other BI tools, and it is very simple to use. It has a visual drag and drop interface (another definite advantage over many other data analysis tools). However, because it has no scripting layer, there’s a limit to what Tableau can do. For instance, it’s not great for pre-processing data or building more complex calculations.

While it does contain functions for manipulating data, these aren’t great. As a rule, you’ll need to carry out scripting functions using Python or R before importing your data into Tableau. But its visualization is pretty top-notch, making it very popular despite its drawbacks. Furthermore, it’s mobile-ready. As a data analyst , mobility might not be your priority, but it’s nice to have if you want to dabble on the move! You can learn more about Tableau in this post .

KNIME at a glance:

• Type of tool: Data integration platform.
• Mostly used for: Data mining and machine learning.
• Pros: Open-source platform that is great for visually-driven programming.
• Cons: Lacks scalability, and technical expertise is needed for some functions.

Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning.

Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without heavy costs. This makes it popular with smaller businesses, who have limited budgets.

Now that we’ve checked out all of the data analysis tools, let’s see how to choose the right one for your business needs.

11. Streamlit

• Type of tool:  Python library for building web applications
• Availability:  Open-source
• Mostly used for:  Creating interactive data visualizations and dashboards
• Pros: Easy to use, can create a wide range of graphs, charts, and maps, can be deployed as web apps
• Cons: Not as powerful as Power BI or Tableau, requires a Python installation

Sure we mentioned Python itself as a tool earlier and introduced a few of its libraries, but Streamlit is definitely one data analytics tool to watch in 2024, and to consider for your own toolkit.

Essentially, Streamlit is an open-source Python library for building interactive and shareable web apps for data science and machine learning projects. It’s a pretty new tool on the block, but is already one which is getting attention from data professionals looking to create visualizations easily!

Alright, so you’ve got your data ready to go, and you’re looking for the perfect tool to analyze it with. How do you find the one that’s right for your organization?

First, consider that there’s no one singular data analytics tool that will address all the data analytics issues you may have. When looking at this list, you may look at one tool for most of your needs, but require the use of a secondary tool for smaller processes.

Second, consider the business needs of your organization and figure out exactly who will need to make use of the data analysis tools. Will they be used primarily by fellow data analysts or scientists, non-technical users who require an interactive and intuitive interface—or both? Many tools on this list will cater to both types of user.

Third, consider the tool’s data modeling capabilities. Does the tool have these capabilities, or will you need to use SQL or another tool to perform data modeling prior to analysis?

Fourth—and finally!—consider the practical aspect of price and licensing. Some of the options are totally free or have some free-to-use features (but will require licensing for the full product). Some data analysis tools will be offered on a subscription or licencing basis. In this case, you may need to consider the number of users required or—if you’re looking on solely a project-to-project basis—the potential length of the subscription.

In this post, we’ve explored some of the most popular data analysis tools currently in use. The key thing to takeaway is that there’s no one tool that does it all. A good data analyst has wide-ranging knowledge of different languages and software.

CareerFoundry’s own data expert, Tom Gadsby, explains which data analytics tools are best for specific processes in the following short video:

If you found a tool on this list that you didn’t know about, why not research more? Play around with the open-source data analysis tools (they’re free, after all!) and read up on the rest.

At the very least, it helps to know which data analytics tools organizations are using. To learn more about the field, start our free 5-day data analytics short course .

For more industry insights, check out the following:

• The 7 most useful data analysis methods and techniques
• How to build a data analytics portfolio
• Get started with SQL: A cheatsheet

What are data analytics tools?

Data analytics tools are software and apps that help data analysts collect, clean, analyze, and visualize data. These tools are used to extract insights from data that can be used to make informed business decisions.

What is the most used tool by data analysts?

Microsoft Excel continues to be the most widely used tool by data analysts for data wrangling and reporting. Big reasons are that it provides a user-friendly interface for data manipulation, calculations, and data viz.

Is SQL a data analysis tool?

Yes. SQL is a specialized programming language for managing and querying data in relational databases. Data analysts use SQL to extract and analyze data from databases, which can then be used to generate insights and reports.

Which tool is best to analyse data?

It depends on what you want to do with the data and the context. Some of the most popular and versatile tools are included in this article, namely Python, SQL, MS Excel, and Tableau.

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8 quantitative data analysis methods to turn numbers into insights

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

Last updated

Reading time.

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

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

Google Analytics  

Survey results

On-page feedback scores

Fuel your quantitative analysis with real-time data

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

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

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

Multiple choice response rates

Response volume over time

Net Promoter Score®

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

Cross-tabulation

Weighted customer feedback

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

1. Compare multiple-choice response rates 

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

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

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

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

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

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

2. Cross-tabulate to compare responses between groups

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ensure you have enough responses to have a statistically significant result

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

5. Mean (AKA average)

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

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

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

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

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

6. Measure the volume of responses over time

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

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

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

7. Net Promoter Score®

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

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

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

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

8. Weight customer feedback 

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

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

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

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

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

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

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

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

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

Share what you learn with data visuals

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

Data visualization helps you:

Get buy-in with impactful charts that summarize your results

Increase customer empathy and awareness across your company with digestible insights

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

Bar charts reveal response distribution across multiple options

Line graphs compare data points over time

Scatter plots showcase how two variables interact

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

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

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

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

Stay close to customers with Hotjar

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

Frequently asked questions about quantitative data analysis

What is quantitative data.

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

What are quantitative data analysis methods?

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

Compare multiple-choice response rates

Cross-tabulate to compare responses between groups

Measure the volume of response over time

Net Promoter Score

Weight customer feedback

How do you visualize quantitative data?

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

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

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

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

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

Quantitative data analysis process

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Quantitative data analysis software

Next chapter

A Review of Software Tools for Quantitative Data Analysis

How to get started with statistical analysis

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
• Recommended Reading
• Archaeology

If you're a  sociology student or budding social scientist and have started to work with quantitative (statistical) data, analytic software will be very useful.

These programs force researchers to organize and clean their data and offer pre-programmed commands that allow everything from very basic to quite advanced forms of statistical analysis .

They even offer useful visualizations that will be useful as you seek to interpret data, and that you may wish to use when presenting it to others.

There are many programs on the market that are quite expensive. The good news for students and faculty is that most universities have licenses for at least one program students and professors can use.

Also, most programs offer a free, pared-down version of the full software package which will often suffice.

Here's a review of the three main programs that quantitative social scientists use.

Statistical Package for Social Science (SPSS)

SPSS is the most popular quantitative analysis software program used by social scientists.

Made and sold by IBM, it is comprehensive, flexible, and can be used with almost any type of data file. However, it is especially useful for analyzing large-scale survey data .

It can be used to generate tabulated reports, charts, and plots of distributions and trends, as well as generate descriptive statistics such as means, medians, modes and frequencies in addition to more complex statistical analyses like regression models.

SPSS provides a user interface that makes it easy and intuitive for all levels of users. With menus and dialogue boxes, you can perform analyses without having to write command syntax, like in other programs.

It is also simple and easy to enter and edit data directly into the program.

There are a few drawbacks, however, which might not make it the best program for some researchers. For example, there is a limit on the number of cases you can analyze. It is also difficult to account for weights, strata and group effects with SPSS.

STATA is an interactive data analysis program that runs on a variety of platforms. It can be used for both simple and complex statistical analyses.

STATA uses a point-and-click interface as well as command syntax, which makes it easy to use. STATA also makes it simple to generate graphs and plots of data and results.

Analysis in STATA is centered around four windows:

• command window
• review window
• result window
• variable window

Analysis commands are entered into the command window and the review window records those commands. The variables window lists the variables that are available in the current data set along with the variable labels, and the results appear in the results window.

SAS, short for Statistical Analysis System, is also used by many businesses.

In addition to statistical analysis, it also allows programmers to perform report writing, graphics, business planning, forecasting, quality improvement, project management and more.

SAS is a great program for the intermediate and advanced user because it is very powerful; it can be used with extremely large datasets and can perform complex and advanced analyses.

SAS is good for analyses that require you to take into account weights, strata, or groups.

Unlike SPSS and STATA, SAS is run largely by programming syntax rather than point-and-click menus, so some knowledge of the programming language is required.

Other Programs

Other programs popular with sociologists include:

• R: Free to download and use. You can add your own programs to it if you are familiar with statistics and programming.
• NVio: "It helps researchers organize and analyze complex non-numerical or unstructured data, both text and multimedia," according to UCLA Library .
• MATLAB: Provides "Simulations, Multidimensional Data, Image and Signal Processing," according to NYU Libraries .
• An Overview of Qualitative Research Methods
• Data Cleaning for Data Analysis in Sociology
• Definition and Overview of Grounded Theory
• How to Conduct a Sociology Research Interview
• The Study of Cultural Artifacts via Content Analysis
• Cluster Analysis and How Its Used in Research
• The Differences Between Indexes and Scales
• Data Sources For Sociological Research
• Pros and Cons of Secondary Data Analysis
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• Principal Components and Factor Analysis
• Constructing a Deductive Theory
• Structural Equation Modeling
• Full-Text Sociology Journals Online
• Units of Analysis as Related to Sociology
• What are Controlled Experiments?

Quantitative Analysis Guide

• Merging Data Sets
• Reshaping Data Sets
• Choosing a Statistical Test
• Which Statistical Software to Use?

• Data Services Home Page

Statistical Software Comparison

• What statistical test to use?
• Data Visualization Resources
• Data Analysis Examples External (UCLA) examples of regression and power analysis
• Supported software
• Request a consultation
• Making your code reproducible

Software Access

• The first version of SPSS was developed by  Norman H. Nie, Dale H. Bent and C.  Hadlai  Hull in and released in 1968 as the Statistical Package for Social Sciences.
• In July 2009, IBM acquired SPSS.
• Social sciences
• Health sciences

Data Format and Compatibility

• .sav file to save data
• Optional syntax files (.sps)
• Easily export .sav file from Qualtrics
• Import Excel files (.xls, .xlsx), Text files (.csv, .txt, .dat), SAS (.sas7bdat), Stata (.dta)
• Export Excel files (.xls, .xlsx), Text files (.csv, .dat), SAS (.sas7bdat), Stata (.dta)
• SPSS Chart Types
• Chart Builder: Drag and drop graphics
• Easy and intuitive user interface; menus and dialog boxes
• Similar feel to Excel
• SEMs through SPSS Amos
• Easily exclude data and handle missing data

Limitations

• Absence of robust methods (e.g...Least Absolute Deviation Regression, Quantile Regression, ...)
• Unable to perform complex many to many merge

Sample Data

• Developed by SAS 
• Created in the 1980s by John Sall to take advantage of the graphical user interface introduced by Macintosh
• Orginally stood for 'John's Macintosh Program'
• Five products: JMP, JMP Pro, JMP Clinical, JMP Genomics, JMP Graph Builder App
• Engineering: Six Sigma, Quality Control, Scientific Research, Design of Experiments
• Healthcare/Pharmaceutical
• .jmp file to save data
• Optional syntax files (.jsl)
• Import Excel files (.xls, .xlsx), Text files (.csv, .txt, .dat), SAS (.sas7bdat), Stata (.dta), SPSS (.sav)
• Export Excel files (.xls, .xlsx), Text files (.csv, .dat), SAS (.sas7bdat)
• Gallery of JMP Graphs
• Drag and Drop Graph Editor will try to guess what chart is correct for your data
• Dynamic interface can be used to zoom and change view
• Ability to lasso outliers on a graph and regraph without the outliers
• Interactive Graphics
• Scripting Language (JSL)
• SAS, R and MATLAB can be executed using JSL
• Interface for using R from within and add-in for Excel
• Great interface for easily managing output
• Graphs and data tables are dynamically linked
• Great set of online resources!
• Absence of some robust methods (regression: 2SLS, LAD, Quantile)

• Stata was first released in January 1985 as a regression and data management package with 44 commands, written by Bill Gould and Sean Becketti. 
• The name Stata is a syllabic abbreviation of the words  statistics and data.
• The graphical user interface (menus and dialog boxes) was released in 2003.
• Political Science
• Public Health
• Data Science
• Who uses Stata?

Data Format and Compatibility

• .dta file to save dataset
• .do syntax file, where commands can be written and saved
• Import Excel files (.xls, .xlsx), Text files (.txt, .csv, .dat), SAS (.XPT), Other (.XML), and various ODBC data sources
• Export  Excel files  (.xls, . xlsx ), Text files (.txt, .csv, .dat), SAS (.XPT),  Other (.XML),  and various ODBC data sources
• Newer versions of  Stata  can read datasets, commands, graphs, etc., from older versions, and in doing so, reproduce results 
• Older versions of Stata cannot read newer versions of Stata datasets,  but newer versions can save in the format of older versions
• Stata Graph Gallery
• UCLA - Stata Graph Gallery
• Syntax mainly used, but menus are an option as well
• Some user written programs are available to install
• Offers matrix programming in Mata
• Works well with panel, survey, and time-series data
• Data management
• Can only hold one dataset in memory at a time
• The specific Stata package ( Stata/IC, Stata/SE, and Stata/MP ) limits the size of usable datasets.  One may have to sacrifice the number of variables for the number of observations, or vice versa, depending on the package.
• Overall, graphs have limited flexibility.   Stata schemes , however, provide some flexibility in changing the style of the graphs.
• Sample Syntax

* First enter the data manually; input str10 sex test1 test2    "Male" 86 83    "Male" 93 79    "Male" 85 81    "Male" 83 80    "Male" 91 76    "Female" 94 79    "Fem ale" 91 94    "Fem ale" 83 84    "Fem ale" 96 81    "Fem ale" 95 75 end

*   Next run a paired t-test; ttest test1 == test2

* Create a scatterplot; twoway ( scatter test2 test1 if sex == "Male" ) ( scatter test2 test1 if sex == "Fem ale" ), legend (lab(1 "Male" ) lab(2 "Fem ale" ))

• The development of SAS (Statistical Analysis System) began in 1966 by Anthony Bar of North Carolina State University and later joined by James Goodnight. 
• The National Institute of Health funded this project with a goal of analyzing agricultural data to improve crop yields.
• The first release of SAS was in 1972. In 2012, SAS held 36.2% of the market making it the largest market-share holder in 'advanced analytics.'
• Financial Services
• Manufacturing
• Health and Life Sciences
• Available for Windows only
• Import Excel files (.xls, .xlsx), Text files (.txt, .dat, .csv), SPSS (.sav), Stata (.dta), JMP (.jmp), Other (.xml)
• Export  Excel files (.xls, . xlsx ), Text files (.txt, .dat, .csv),  SPSS  (.sav),  Stata  (.dta), JMP (.jmp),  Other (.xml)
• SAS Graphics Samples Output Gallery
• Can be cumbersome at times to create perfect graphics with syntax
• ODS Graphics Designer provides a more interactive interface
• BASE SAS contains the data management facility, programming language, data analysis and reporting tools
• SAS Libraries collect the SAS datasets you create
• Multitude of additional  components are available to complement Base SAS which include SAS/GRAPH, SAS/PH (Clinical Trial Analysis), SAS/ETS (Econometrics and Time Series), SAS/Insight (Data Mining) etc...
• SAS Certification exams
• Handles extremely large datasets
• Predominantly used for data management and statistical procedures
• SAS has two main types of code; DATA steps and  PROC  steps
• With one procedure, test results, post estimation and plots can be produced
• Size of datasets analyzed is only limited by the machine

Limitations 

• Graphics can be cumbersome to manipulate
• Since SAS is a proprietary software, there may be an extensive lag time for the implementation of new methods
• Documentation and books tend to be very technical and not necessarily new user friendly

* First enter the data manually; data example;    input  sex $ test1 test2;   datalines ;     M 86 83     M 93 79     M 85 81     M 83 80     M 91 76     F 94 79     F 91 94     F 83 84     F 96 81     F 95 75    ; run ;

*   Next run a paired t-test; proc ttest data = example;   paired test1*test2; run ;

* Create a scatterplot; proc sgplot data = example;   scatter y = test1 x = test2 / group = sex; run ;

• R first appeared in 1993 and was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. 
• R is an implementation of the S programming language which was developed at Bell Labs.
• It is named partly after its first authors and partly as a play on the name of S.
• R is currently developed by the R Development Core Team. 
• RStudio, an integrated development environment (IDE) was first released in 2011.
• Companies Using R
• Finance and Economics
• Bioinformatics
• Import Excel files (.xls, .xlsx), Text files (.txt, .dat, .csv), SPSS (.sav), Stata (.dta), SAS(.sas7bdat), Other (.xml, .json)
• Export Excel files (.xlsx), Text files (.txt, .csv), SPSS (.sav), Stata (.dta), Other (.json)
• ggplot2 package, grammar of graphics
• Graphs available through ggplot2
• The R Graph Gallery
• Network analysis (igraph)
• Flexible esthetics and options
• Interactive graphics with Shiny
• Many available packages to create field specific graphics
• R is a free and open source
• Over 6000 user contributed packages available through  CRAN
• Large online community
• Network Analysis, Text Analysis, Data Mining, Web Scraping 
• Interacts with other software such as, Python, Bioconductor, WinBUGS, JAGS etc...
• Scope of functions, flexible, versatile etc..

Limitations​

• Large online help community but no 'formal' tech support
• Have to have a good understanding of different data types before real ease of use begins
• Many user written packages may be hard to sift through

# Manually enter the data into a dataframe dataset <- data.frame(sex = c("Male", "Male", "Male", "Male", "Male", "Female", "Female", "Female", "Female", "Female"),                        test1 = c( 86 , 93 , 85 , 83 , 91 , 94 , 91 , 83 , 96 , 95 ),                        test2 = c( 83 , 79 , 81 , 80 , 76 , 79 , 94 , 84 , 81 , 75 ))

# Now we will run a paired t-test t.test(dataset$test1, dataset$test2, paired = TRUE )

# Last let's simply plot these two test variables plot(dataset$test1, dataset$test2, col = c("red","blue")[dataset$sex]) legend("topright", fill = c("blue", "red"), c("Male", "Female"))

# Making the same graph using ggplot2 install.packages('ggplot2') library(ggplot2) mygraph <- ggplot(data = dataset, aes(x = test1, y = test2, color = sex)) mygraph + geom_point(size = 5) + ggtitle('Test1 versus Test2 Scores')

• Cleave Moler of the University of New Mexico began development in the late 1970s.
• With the help of Jack Little, they cofounded MathWorks and released MATLAB (matrix laboratory) in 1984. 
• Education (linear algebra and numerical analysis)
• Popular among scientists involved in image processing
• Engineering
• .m Syntax file
• Import Excel files (.xls, .xlsx), Text files (.txt, .dat, .csv), Other (.xml, .json)
• Export Excel files (.xls, .xlsx), Text files (.txt, .dat, .csv), Other (.xml, .json)
• MATLAB Plot Gallery
• Customizable but not point-and-click visualization
• Optimized for data analysis, matrix manipulation in particular
• Basic unit is a matrix
• Vectorized operations are quick
• Diverse set of available toolboxes (apps) [Statistics, Optimization, Image Processing, Signal Processing, Parallel Computing etc..]
• Large online community (MATLAB Exchange)
• Image processing
• Vast number of pre-defined functions and implemented algorithms
• Lacks implementation of some advanced statistical methods
• Integrates easily with some languages such as C, but not others, such as Python
• Limited GIS capabilities

sex = { 'Male' , 'Male' , 'Male' , 'Male' , 'Male' , 'Female' , 'Female' , 'Female' , 'Female' , 'Female' }; t1 = [86,93,85,83,91,94,91,83,96,95]; t2 = [83,79,81,80,76,79,94,84,81,75];

% paired t-test [h,p,ci,stats] = ttest(t1,t2)

% independent samples t-test sex = categorical(sex); [h,p,ci,stats] = ttest2(t1(sex== 'Male' ),t1(sex== 'Female' ))

plot(t1,t2, 'o' ) g = sex== 'Male' ; plot(t1(g),t2(g), 'bx' ); hold on; plot(t1(~g),t2(~g), 'ro' )

Software Features and Capabilities

*The primary interface is bolded in the case of multiple interface types available.

Learning Curve

Further Reading

• The Popularity of Data Analysis Software
• Statistical Software Capability Table
• The SAS versus R Debate in Industry and Academia
• Why R has a Steep Learning Curve
• Comparison of Data Analysis Packages
• Comparison of Statistical Packages
• MATLAB commands in Python and R
• MATLAB and R Side by Side
• Stata and R Side by Side

• << Previous: Choosing a Statistical Test
• Last Updated: Jul 8, 2024 7:18 PM
• URL: https://guides.nyu.edu/quant

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

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

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

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

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

Phase I: Data Validation

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

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

Phase II: Data Editing

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

Phase III: Data Coding

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

LEARN ABOUT: Steps in Qualitative Research

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

Descriptive statistics

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

Measures of Frequency

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

Measures of Central Tendency

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

Measures of Dispersion or Variation

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

Measures of Position

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

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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

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

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

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

LEARN ABOUT: Best Data Collection Tools

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

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

LEARN ABOUT: Average Order Value

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• 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.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
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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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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• 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.

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Tools for Analyzing Quantitative Data

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Data analysis tools for quantitative studies are addressed in the areas of: (a) enhancements for data acquisition, (b) simple to sophisticated analysis techniques, and (c) extended exploration of relationships in data, often with visualization of results. Examples that are interwoven with data and findings from published research studies are used to illustrate the use of the tools in the service of established research goals and objectives. The authors contend that capabilities have greatly expanded in all three areas over the past 30 years, and especially during the past two decades.

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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.

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

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

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

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

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

Table of contents

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

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

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

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

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

Planning your research design

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

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

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

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

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

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

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

Measuring variables

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

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

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

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

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

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

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

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

Sampling for statistical analysis

There are two main approaches to selecting a sample.

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

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

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

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

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

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

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

Create an appropriate sampling procedure

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

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

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

Calculate sufficient sample size

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

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

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

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

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

Inspect your data

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

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

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

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

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

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

Calculate measures of central tendency

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

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

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

Calculate measures of variability

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hypothesis testing

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

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

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

Statistical tests come in three main varieties:

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

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

Parametric tests

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The final step of statistical analysis is interpreting your results.

Statistical significance

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

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

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

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

Effect size

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

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

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

Decision errors

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

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

Frequentist versus Bayesian statistics

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

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

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

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

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

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

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

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Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies

• Mike Perkins , Jasper Roe British University Vietnam , James Cook University Singapore
• Published 13 August 2024
• Computer Science, Education

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Title: generative ai tools in academic research: applications and implications for qualitative and quantitative research methodologies.

Abstract: This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. As GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. However, their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work. Through an examination of current capabilities and potential future applications, this study provides insights into how researchers may utilise GenAI tools responsibly and ethically. We present case studies that demonstrate the application of GenAI in various research methodologies, discuss the challenges of replicability and consistency in AI-assisted research, and consider the ethical implications of increased AI integration in academia. This study explores both qualitative and quantitative applications of GenAI, highlighting tools for transcription, coding, thematic analysis, visual analytics, and statistical analysis. By addressing these issues, we aim to contribute to the ongoing discourse on the role of AI in shaping the future of academic research and provide guidance for researchers exploring the rapidly evolving landscape of AI-assisted research tools and research.

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This paper is in the following e-collection/theme issue:

Published on 14.8.2024 in Vol 26 (2024)

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

Authors of this article:

Original Paper

• Hailey K Ballard 1, 2 , BS   ; 
• Xiaotong Yang 1 , MS   ; 
• Aditya D Mahadevan 3, 4 , BS   ; 
• Dominick J Lemas 2, 3, 5 , PhD   ; 
• Lana X Garmire 1 , PhD  

1 Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States

2 Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States

3 Center for Research in Perinatal Outcomes, University of Florida, Gainesville, FL, United States

4 Department of Physiology and Aging, University of Florida, Gainesville, FL, United States

5 Department of Obstetrics & Gynecology, University of Florida, Gainesville, FL, United States

Corresponding Author:

Lana X Garmire, PhD

Department of Computational Medicine and Bioinformatics

University of Michigan Medical School

Room 3366, Building 520, NCRC

1600 Huron Parkway

Ann Arbor, MI, 48105

United States

Phone: 1 734 615 0514

Email: [email protected]

Background:  Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and inaccurate.

Objective:  This study aimed to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records.

Methods:  We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System as the discovery cohort, and 881 records from the University of Florida Health System as the validation cohort. We constructed 2 Cox-proportional hazards models: 1 baseline model using maternal and pregnancy characteristics, and the other full model with additional laboratory findings, vitals, and medications. We built the models using 80% of the discovery data, tested the remaining 20% of the discovery data, and validated with the University of Florida data. We further stratified the patients into high- and low-risk groups for preeclampsia onset risk assessment.

Results:  The baseline model reached Concordance indices of 0.64 and 0.61 in the 20% testing data and the validation data, respectively, while the full model increased these Concordance indices to 0.69 and 0.61, respectively. For preeclampsia diagnosed at 34 weeks, the baseline and full models had area under the curve (AUC) values of 0.65 and 0.70, and AUC values of 0.69 and 0.70 for preeclampsia diagnosed at 37 weeks, respectively. Both models contain 5 selective features, among which the number of fetuses in the pregnancy, hypertension, and parity are shared between the 2 models with similar hazard ratios and significant  P  values. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature.

Conclusions:  Electronic health records data provide useful information to predict the gestational age of preeclampsia onset. Stratification of the cohorts using 5-predictor Cox-proportional hazards models provides clinicians with convenient tools to assess the onset time of preeclampsia in patients.

Introduction

Preeclampsia is a pregnancy-associated condition characterized by new-onset hypertension and proteinuria, typically diagnosed after 20 weeks of gestation in approximately 3%-5% of all pregnancies [ 1 ]. As one of the leading causes of maternal mortality and morbidity worldwide, it can lead to a more serious condition called eclampsia if left untreated [ 2 ]. Timely identification of preeclampsia is a key factor in pregnancy risk management and subsequent treatment. Current medical practice guideline recommends prevention therapy of low-dose aspirin on women at high risk for preeclampsia before the 13-week gestation period [ 3 ]. However, preeclampsia does not typically manifest itself clinically until after 20 weeks of gestation, through clinical markers such as blood pressure (BP), urinary protein excretion, mean arterial pressure, and placental growth factor levels. Moreover, the gestational age of preeclampsia onset can vary greatly across pregnancies [ 3 ]. Preeclampsia diagnosed before 34 weeks of gestation is called early-onset preeclampsia, and late-onset preeclampsia is diagnosed after 34 weeks [ 4 ]. To allow for maximal efficiency of prevention therapy, tools that accurately predict the onset time of preeclampsia and the patient risk will be extremely beneficial.

Previous studies have identified some qualitative risk factors of preeclampsia, including preeclampsia in a previous pregnancy, a multifetal pregnancy, chronic hypertension, kidney disease, diabetes before pregnancy, autoimmune disorders, as well as demographic factors including obesity, advanced maternal age, and race [ 5 ]. However, the quantitative importance of these risk factors relative to one another has not been adequately investigated. Haile et al [ 6 ] discuss how maternal age, weight, and history of preeclampsia significantly drive preeclampsia onset time, but many additional factors remain undefined. There is an unmet need to provide clinicians with tools to accurately identify which mothers are at risk for preeclampsia, and further identify when they will develop preeclampsia.

Prognosis modeling using population-level health data provides opportunities to systematically address both issues mentioned above [ 7 ]. These new models enable the investigation of risk factors (features) that may affect the gestational age at preeclampsia diagnosis, using the hazard ratio (HR), which indicates the importance of the risk factors. Each model outputs risk factors that influence preeclampsia development and predicts the gestational age at preeclampsia diagnosis for patients using the weighted impact of each feature. In addition, patients can be stratified into low-risk and high-risk preeclampsia groups, accompanied by differences in risk factors (features). These developed and validated prognosis models will allow clinicians to practically identify when an at-risk mother might develop preeclampsia and reveal any features associated with the onset time of preeclampsia that are not included in the current guidelines.

Data Source

The discovery cohort for this project was obtained from the University of Michigan (UM) Medicine Healthcare System. All deidentified pregnancy records between the years 2015 and 2021, with at least one preeclampsia diagnosis, based on the ICD-10-CM ( International Classification of Diseases, Tenth Revision, Clinical Modification ) codes, were extracted (Table S1 in Multimedia Appendix 1 ). Patients who were diagnosed with competing conditions (Table S1 in Multimedia Appendix 1 ) were removed from the cohort. Patients who did not have any electronic medical record (EMR) in the UM system within 20 weeks of the start of their pregnancy were also removed. Since preeclampsia is clinically defined after 20 weeks, all patients with a preeclampsia diagnosis before 20 weeks of gestation were dropped from the discovery cohort. A total of 1178 pregnancies remained in the UM discovery cohort after this data selection.

Following the same inclusion and exclusion criteria, the validation cohort was generated from the University of Florida Health System and contained 881 preeclamptic pregnancies from 2015 to 2021. The Integrated Data Repository managed the deidentification and transfer of patient data to the researchers.

Feature Extraction and Preprocessing

The electronic medical records include medical history, obstetric diagnostic codes entered during each unique pregnancy, demographics, medications, laboratory results, and vital signs (Table S2 in Multimedia Appendix 1 ). The baseline model initially used age at the start of pregnancy, race, pregnancy start date, date of the first preeclampsia diagnosis, gravidity, parity, and previous history of preeclampsia at the trimester it was diagnosed. In addition, medical histories based on ICD-10-CM diagnosis codes were extracted using the Elixhauser Comorbidities definitions [ 8 ]. Current diagnoses entered within 20 weeks of gestation were extracted using the same ICD-10-CM diagnosis codes and definitions.

The full model includes all features in the baseline model. In addition, laboratory results, vital signs, and medications ordered before 20 weeks of gestation were also added. Laboratory tests that included a complete blood count were considered (Table S2 in Multimedia Appendix 1 ). Vital signs included diastolic and systolic BP. Laboratory findings and vitals collected from the start of pregnancy (0 days gestation) to 20 weeks (140 days) gestation were included. The mean, maximum, minimum, and SD for each laboratory value were calculated. Medication records were retrieved based on previous reports that medications prescribed during pregnancy may be related to preeclampsia development [ 9 ]. Patients who did not have any laboratory finding or vital data collected and entered in the EMR system within the first 20 weeks (~15%) were assigned as “missing”. These missing values were imputed using the predictive mean matching algorithm from the R package “mice” [ 10 ], which has been shown to produce the least-biased results for data sets that use feature selection [ 11 - 13 ]. The standards for missing data used for multiple imputations were followed, and imputation was performed on only the variables with no more than 20% missingness [ 14 ]. All numeric variables were log-transformed to adjust for skewness. Each feature in the medical history, clinical diagnosis, and medication categories was computed as a binary category: 1 for presence, and 0 for absence, to reduce feature dimensionality and improve interpretability. All analysis was conducted using R (version 4.2.2; The R Foundation) [ 15 ]. Data cleaning was carried out using the packages “dplyr” [ 16 ] and “gtsummary” [ 17 ].

Feature Selection, Model Construction, and Evaluations

The UM discovery data set was randomly divided into a training set (80%) and a hold-out testing set (20%) after multiple imputations on missing variables. A Cox-proportional hazards model with Least Absolute Shrinkage and Selection Operator (LASSO) regularization was conducted through 5-fold cross-validation, using the “glmnet” [ 18 ] package in R. We used cross-validation to select the optimal LASSO hyperparameter (lambda) that gave the smallest mean squared error and then performed bootstrapping with 1000 replicates to calculate a concordance index (C-index) and 95% CIs for each data set (training, testing, and validation). The baseline model had an optimal lambda of 0.0058 (Figure S1A in Multimedia Appendix 2 ) and the full model had an optimal lambda of 0.0066 (Figure S1B in Multimedia Appendix 2 ). The baseline model had 31 features and the full model had 92 features before selection. Following regularized feature selection using the LASSO method on the training data sets, both final models have 5 selected features. The output of the Cox-PH model is the log hazard ratio, also called the prognosis index (PI), which depicts the relative risk of a patient when compared with the baseline hazard of the population. The full model was constructed in the same way as the baseline model.

External validation on each finalized model (baseline and full models) was done through collaboration with the University of Florida (UF), where the electronic health record (EHR) data and patient characteristics are different. Each feature chosen by the model was able to be identified in the UF validation cohort except for the nonsteroidal anti-inflammatory drug (NSAID) medication prescription, which was not available at the time of collection.

The performance of each model was evaluated using the C-index with bootstrapping of 1000 replicates to calculate 95% CI and P values from log-rank tests. The C-index is a metric to compare the discriminative power of a risk prediction model that describes the frequency of concordant pairs among all pairs of patients included in the model construction [ 19 ]. We used the C-index calculated from the “cindex” [ 20 ] function. Low- and high-risk pregnancies were stratified based on the median PI score of the model, and Kaplan-Meier curves were plotted for each risk group. Their differences were tested with log-rank tests using the training data set, hold-out testing data set, and the validation data set separately to evaluate the discriminative power of the model. The log-rank test is a significance test in survival analysis, with the null hypothesis that 2 groups have identical distributions of survival time. Any log-rank P value below .05 is considered statistically significant in these analyses. Feature importance was evaluated in the Cox-PH model by their HR P values. HR describes the relative contribution of a feature to the patient’s PI. In the context of our model, HRs above 1 shorten the gestational age of preeclampsia diagnosis, while HRs below 1 lengthen it.

We further measured model performance by calculating the sensitivity and specificity for each model, classified by predicting preeclampsia diagnosis by 34 and 37 weeks, respectively. We also plotted the area under the curve (AUC) from each testing data set for both models at both time points, using the “pROC” [ 21 ] package in R.

Ethical Considerations

The institutional review board (IRB) of the UM Medical School (HUM#00168171) and the UF (#201601899) approved the original data collection and the use of the discovery cohort. All authors have permission for the use of this data. IRB approval was not required for the secondary analysis presented here, as it was deemed exempt. [ 22 ].

Study Design and Data Set Overviews

The overall study design is shown in Figure 1 . The discovery cohort was extracted from patient records in the UM Health System from 2015 to 2022 with ICD-10 ( International Statistical Classification of Diseases, Tenth Revision ) code access. All patients with a preeclampsia diagnosis after 20 weeks of gestation were included in the cohort, and other exclusion criteria are detailed in the Methods section. The finalized UM discovery cohort consists of EMRs from 1178 pregnancies. Using the same inclusion and exclusion criteria, 881 pregnancies were identified in the validation data set from UF. The patient characteristics for each cohort are listed in Table 1 . The average maternal age was 30.2 years (SD 5.67) in the discovery cohort and 29.1 years (SD 6.18) in the validation cohort. The mean gestational age of preeclampsia onset was 251 (SD 25.4) days for the discovery cohort and 257 (SD 25.9) days for the validation cohort. We constructed and validated 2 models using this data: (1) a baseline model using only patient medical history, demographics, and diagnoses of any new medical issues within the first 20 weeks of gestation; and (2) a full model including those features from the baseline model, as well as additional information on medication, laboratory findings, and vitals within the first 20 weeks of pregnancy.

a PE: preeclampsia.

Baseline Model

A baseline model was first built using medical history, demographics, and ICD-10-CM diagnosis codes of new medical conditions entered during the first 20 weeks of pregnancy. To build and test the model, we randomly split the data into an 80:20 ratio for training and testing data sets, and the Cox-PH model with LASSO (L1) regularization was built with the UM training data under 5-fold cross-validation. Alternatively, we explored ElasticNet (combined L1 and L2 regularization) as well as L2 penalization. However, the LASSO (L1) model overall performs better with higher C-indices and fewer features over these alternatives. We therefore chose LASSO as the regularization method (Table S3 in Multimedia Appendix 1 ).

We then applied this model to the 20% UM hold-out testing data and external UF validation cohort. The C-indices for the training, hold-out testing, and external validation data of the baseline model are 0.62 (95% CI 0.61-0.63), 0.64 (95% CI 0.60-0.65), and 0.61 (95% CI 0.59-0.63), confirming its validity. Table 2 shows the baseline model’s C-index and corresponding 95% CI values for each data set. To further facilitate interpretation, we classified each preeclampsia diagnosis prediction by the timeline of its occurrence, specifically by gestational weeks 34 and 37, using the UM hold-out testing data set. Such simple binary classification shows a sensitivity of 0.74, specificity of 0.50, and AUC of 0.65 for preeclampsia diagnosed at 34 weeks ( Table 2 ). It has improved performance for preeclampsia diagnosis by 37 weeks, with a sensitivity of 0.82, specificity of 0.50, and AUC of 0.69 ( Table 2 and Multimedia Appendix 3 ).

Five features were selected for the baseline model. Their respective HRs and rankings in the multivariate Cox-PH are depicted in Figure 2 A and Table 3 . By the descending order of HR, these features are the number of fetuses in pregnancy of interest (HR 25.2; P <.001), parity (HR 2.08; P <.001), history of uncomplicated hypertension (HR 2.01; P <.001), history of uncomplicated type II diabetes (HR 1.87; P <.001), and a mood or anxiety disorder (HR 1.24; P =.01). All features increase preeclampsia risk and shorten the gestational age of preeclampsia diagnosis.

To evaluate the discriminative power of this model, patients from the training data set were dichotomized into high- and low-risk groups by stratifying the samples using the median of the predicted PI (PI=1.17) from the model. The 2 risk groups showed significant differences in prognosis ( Figure 2 B and Table S4 in Multimedia Appendix 1 ). The high-risk group was characterized by higher parity and number of fetuses, while the low-risk pregnancies had no prevalence of hypertension ( P <.001) or diabetes ( P <.001). The median PI value above was applied to categorize samples into high versus low-risk groups in the hold-out (PI=1.17) and validation data (PI=2.38), similar to others [ 23 - 25 ]. As shown in Figures 2 C and 2D, the KM curves on these 2 risk groups are also significantly different ( P <.001).

We next evaluated a model with the addition of laboratory findings, vitals, and medications prescribed in the first 20 weeks of gestation to the clinical data used in the baseline model. We constructed the new Cox-PH model, or the “full model,” in the same manner as the baseline model and obtained a 5-feature Cox-PH model ( Figure 3 A). Similar to the baseline model, LASSO regularization shows better overall performance than ElasticNet and L2 regularization and is chosen as the default (Table S3 in Multimedia Appendix 1 ). This new model reaches the C-indices of 0.66 (95% CI 0.64-0.67) and 0.69 (95% CI 0.64-0.70) for the training and hold-out testing data sets, respectively. It also yields a C-index of 0.61 (95% CI 0.60-0.63) on the UF validation cohort, despite missing 1 feature (NSAID medication) in the UF cohort. Table 2 lists the full-model C-indices and 95% CIs for each data set. Similar to the baseline model, to help interpretation, we classified each preeclampsia diagnosis prediction using the timeline of preeclampsia occurrence by gestational weeks 34 and 37, respectively, using the UM hold-out testing data set. It yields a sensitivity of 0.98, specificity of 0.51, and AUC of 0.70 for correctly predicting preeclampsia by week 34 ( Table 2 ). The model has an improved correct diagnosis by week 37, with a sensitivity of 0.86, specificity of 0.50, and AUC of 0.70 ( Table 2 and Multimedia Appendix 3 ).

The full model also yields 5 features, all with positive HRs ( Figure 3 A and Table 4 ). In descending order of HR, these features are maximum diastolic blood pressure (HR 21.7; P <.001), number of fetuses in current pregnancy (HR 21.1; P <.001), parity (HR 1.81; P <.001), history of uncomplicated hypertension (HR 1.79; P <.001), and NSAID medication prescription (HR 1.35; P <.001). Three of these features, namely the number of fetuses, history of uncomplicated hypertension, and parity features were also selected by the baseline model ( Figure 3 B). Table S5 in Multimedia Appendix 1 shows each of the features and their HRs in a univariate analysis. Their HRs across the baseline and full models remain very similar and had P values less than .05, suggesting that they are all significant in predicting preeclampsia onset time regardless of the other additional input information. Maximum diastolic BP and NSAID medication prescription are newly selected features unique to the full model ( Figures 3 A and 3B).

Like the baseline model, we stratified patients into high- versus low-risk groups using the median predicted PI value of 5.15 from the training data set ( Figure 3 C). The high-risk group was characterized by higher parity, a higher number of fetuses, and higher maximum diastolic BP (Table S4 in Multimedia Appendix 1 ). In contrast, the low-risk group had no history of hypertension and rare use of NSAID medication. BP had the most statistically significant difference ( P <.001), as expected. The same median threshold was applied to the 20% hold-out testing data set (PI=5.08) and validation data (PI=5.18) for dichotomization ( Figures 3 D and 3E). KM curves on these 2 risk groups in the testing set have even more significant differences in their gestational age at diagnosis ( P <.001). Both models are to be used by entering patient information in the predictors to predict when the patient may develop preeclampsia.

To determine the potential impact of missing data on modeling results, we explored building a baseline and full model with only cases that had complete BP data—the main selected feature in the full model. Table S6 in Multimedia Appendix 1 shows the selected features of both of these models. The complete cases baseline model had a training C-index of 0.63 and a testing C-index of 0.64. The complete cases full model had a training C-index of 0.67 and a testing C-index of 0.65. Due to similar performance and selected features, it can be safely assumed that imputation had little impact on the finalized models.

a NSAID: nonsteroidal anti-inflammatory drug.

Principal Results

This paper is the first of its kind to implement and externally validate a prognosis-predicting model for preeclampsia onset time using EHR data from the first 20 weeks of pregnancy [ 26 ]. These models confirmed that factors such as BP in the first 20 weeks of pregnancy, the number of fetuses, parity, and previous history of hypertension are associated with earlier preeclampsia onset time. Moreover, comorbidities such as gestational diabetes and anxiety, as well as NSAID medication, shorten preeclampsia onset time. The similar performance across validation and development data sets provides confidence in the accuracy of the predictive outputs.

Comparison With Previous Work

A recent study stratified patients with preeclampsia by gestational age to build classification models, resulting in many models that are difficult for clinicians to select from [ 27 ]. Moreover, these classification models cannot predict the gestational age of onset for an individual patient, thus failing to assist clinicians in making early decisions on delivery plans and proper antenatal care. Unlike most other accurate preeclampsia onset time prediction models, our models only use EMR data from the first 20 weeks of pregnancy and do not require advanced testing inputs, such as biomarkers [ 27 ], enabling earlier use in clinics. In a systematic review of 68 preeclampsia prediction models [ 27 ], only 6% (4/68) of them were externally validated, and those not requiring complex biomarker features had much lower AUCs (0.58-0.61) than the models presented here (AUC 0.65-0.70), highlighting the accuracy of our models once validated against a different patient population.

Clinical and Research Implications

Due to the difficulty in predicting preeclampsia, accurate models that can identify women at high risk for preeclampsia can provide early targeted treatment as well as increased surveillance to reduce adverse outcomes [ 28 ]. The models here not only confirm the importance of some previously known risk factors, such as the number of fetuses, history of hypertension, and parity but also assign quantitative scores (weights) on the importance of these risk factors relative to each other. This is a significant advancement from most of the other studies focusing on a single risk factor. It also provides clinicians as well as pregnant women with quantitative tools to assess the onset time of preeclampsia more accurately, beyond the qualitative assessment of risks. Risk factors with higher weights can take a higher priority for clinicians to identify potential patients with preeclampsia. The fact that maximum diastolic BP had the highest HR in the full model confirms the importance of monitoring BP as early as possible, even before preeclampsia is diagnosed clinically [ 29 ]. More importantly, it identifies additional alarming factors to be considered in predicting preeclampsia diagnosis at gestational age, such as mood and anxiety disorder.

Further risk stratification of the survival models had slightly low specificity values in predicting the dichotomous diagnosis of preeclampsia at 34 and 37 weeks, suggesting that the continuous risk diagnosis has overall better performance compared with the simple binary prediction. However, the stratification may offer an easier way to identify women who may benefit more significantly from prevention therapy and need more medical attention from doctors for the possibility of preeclampsia. EHR-based models can serve as a screening test. For the patients that are potentially false positive for preeclampsia due to the lower specificity of the model, additional confirmative diagnostic tests using very specific biomarkers should be done, as practiced clinically.

Earlier studies using all pregnant women also revealed that mood and anxiety disorders increase the risk of preeclampsia [ 30 ]. We further show that within patients with preeclampsia, mood and anxiety disorders shorten the onset time of preeclampsia. This provides more context for clinicians to identify pregnant patients who present mood and anxiety disorders and provide preventative care to reduce preeclampsia onset risk. The molecular mechanism linking mood and anxiety disorders with preeclampsia is worth further research. We also show that NSAID use is positively associated with earlier onset of preeclampsia. However, aspirin is a common NSAID used by pregnant women at risk for preeclampsia early in pregnancy [ 31 ]. It was suggested that NSAID use may serve as a proxy for the interaction of many unmeasured risk factors [ 32 ]. Thus, the positive association of NSAID to the earlier onset of preeclampsia may indicate that it is a marker of high-risk preeclampsia in the population, rather than the cause of it.

Strengths and Limitations

A particular strength of the models here is their simplicity despite being quantitative. The models can also be generalized to different medical centers and hospitals, given the good accuracy when validated by vastly different institutions with different protocols, data collection, and data storage. There is a growing need for evidence-based and effective tools for clinicians to screen women at high risk of preeclampsia early in pregnancy, in the first and early second trimesters. This model supplies this need for early prediction models that previous models have not been able to fulfill [ 33 ]. Most clinical models recently published include many predictors from biomarkers and ultrasound markers that need special procedures [ 34 ], further suggesting that a simpler model on routinely collected clinical data is valuable to be implemented in a clinical setting. The main strength of this modeling for clinical use proposed here is providing more context in screening patients at risk for preeclampsia.

Our ultimate goal is to implement these models in the health care system, for example, starting from the University of Michigan. Potential challenges for implementing these models in a clinical setting include institutional buy-in, installation of the software in a HIPAA (Health Insurance Portability and Accountability Act)-compliant computing environment, and explaining the meaning of risk factors and model results to patients informatively without overly stressing them. In addition, these models may potentially require more active updating for improving accuracy, by considering additional multicenter data. Also, the current Cox-PH model is not designed to include longitudinal observations, limiting the kind of input variables to be incorporated into the model. Future work may benefit from more sophisticated modeling approaches [ 35 ]. Besides EHR, other omics information such as genetics, genomics, proteomics, and metabolomics using maternal blood samples [ 34 ] may be used, if they are available, to improve the model performance. However, implementing multimodal and complex models like this in the clinical setting is additionally challenging and would require more advanced modeling that can calculate individual risk scores for clinical application. It is also important to note the use of EHR data to extract medication prescriptions does not accurately capture the actual use or adherence of the medication by patients, and future research could be strengthened by combining data sources that provide such information.

Conclusions

In conclusion, this study reports prognosis models to predict the onset gestational age of preeclampsia with EMR data before the first 20 weeks of pregnancy. They identify clinical and physiological factors that clinicians should monitor as indicators of early preeclampsia development.

Acknowledgments

The authors would like to thank UM Precision Health for providing technical support for data extraction in this study, the UF Integrated Data Repository, and the UF Health Office of the Chief Data Office for providing the analytic data set for this project. DJL was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK115632) and the UF Clinical and Translational Science Institute (UL1TR001427). LXG was supported by grants (K01ES025434) awarded through funds provided by the trans-National Institutes of Health Big Data to Knowledge initiative (R01 LM012373 and LM012907 awarded by the National Library of Medicine, and R01 HD084633 awarded by National Institute of Child Health and Human Development). ADM is supported by the National Center for Advancing Translational Science (5TL1TR001428). No funding sources listed were involved in the study design, collection, analysis, and interpretation of data, writing of the report, or decision to submit for publication.

Data Availability

The data sets generated during and/or analyzed during this study are not publicly available due to the presence of patient-protected health information. Data are available upon reasonable request and must be submitted on an individual basis to the home institution. Table S2 in Multimedia Appendix 1 lists all the EHR features extracted from the UM system that were considered in the starting model.

Authors' Contributions

LXG conceived this project and supervised the study. HKB conducted data analysis and wrote the manuscript. XY collaborated on data extraction of the University of Michigan cohort. ADM and DJL collaborated on validation using the University of Florida cohort. ADM provided clinical assessments and assistance. All authors have read, revised, and approved the manuscript.

Conflicts of Interest

None declared.

Supplementary tables.

Lambdas from Least Absolute Shrinkage and Selection Operator (LASSO) regularization from the baseline and full preeclampsia (PE) prediction models. (A) Scatterplot of tested lambda values and associated errors from baseline model LASSO regularization. (B) Scatterplot of tested lambda values and associated errors from full model LASSO regularization.

AUC values of preeclampsia (PE) diagnosed at 34 and 37 weeks for the baseline and full PE prediction models. (A) Plot of the sensitivity, specificity, and area under the curve (AUC) values for the baseline model of predicting PE diagnosed at 34 weeks (red, AUC=0.654) and 37 weeks (green, AUC=0.694) for the testing dataset. (B) Plot of the sensitivity, specificity, and AUC values for the full model of predicting PE diagnosed at 34 weeks (red, AUC=0.697) and 37 weeks (green, AUC=0.700) for the testing data set.

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Abbreviations

Edited by A Mavragani; submitted 15.05.23; peer-reviewed by S Nagavally, D Heider, B Puladi; comments to author 22.11.23; revised version received 17.01.24; accepted 30.05.24; published 14.08.24.

©Hailey K Ballard, Xiaotong Yang, Aditya D Mahadevan, Dominick J Lemas, Lana X Garmire. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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• Volume 11, Issue 1
• Mycophenolate and azathioprine efficacy in interstitial lung disease: a systematic review and meta-analysis
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• http://orcid.org/0000-0003-2254-5119 Francesco Lombardi 1 ,
• http://orcid.org/0000-0002-1340-2688 Iain Stewart 2 ,
• http://orcid.org/0000-0002-8250-6464 Laura Fabbri 2 ,
• Wendy Adams 3 ,
• http://orcid.org/0000-0003-0784-1331 Leticia Kawano-Dourado 4 , 5 ,
• Christopher J Ryerson 6 and
• http://orcid.org/0000-0002-7929-2119 Gisli Jenkins 7
• REMAP-ILD Consortium
• 1 Pulmonary Medicine , Policlinico Universitario Agostino Gemelli , Roma , Italy
• 2 National Heart & Lung Institute , Imperial College London , London , UK
• 3 Action for Pulmonary Fibrosis , London , UK
• 4 HCOR Research Institute , Hospital do Coracao , Sao Paulo , Brazil
• 5 Pulmonary Division , University of Sao Paulo , Sao Paulo , Brazil
• 6 Medicine , The University of British Columbia , Vancouver , British Columbia , Canada
• 7 Imperial College London , London , UK
• Correspondence to Dr Francesco Lombardi; lombardi.f89{at}gmail.com

Objectives Mycophenolate mofetil (MMF) and azathioprine (AZA) are immunomodulatory treatments in interstitial lung disease (ILD). This systematic review aimed to evaluate the efficacy of MMF or AZA on pulmonary function in ILD.

Design Population included any ILD diagnosis, intervention included MMF or AZA treatment, outcome was delta change from baseline in per cent predicted forced vital capacity (%FVC) and gas transfer (diffusion lung capacity of carbon monoxide, %DLco). The primary endpoint compared outcomes relative to placebo comparator, the secondary endpoint assessed outcomes in treated groups only.

Eligibility criteria Randomised controlled trials (RCTs) and prospective observational studies were included. No language restrictions were applied. Retrospective studies and studies with high-dose concomitant steroids were excluded.

Data synthesis The systematic search was performed on 9 May. Meta-analyses according to drug and outcome were specified with random effects, I 2 evaluated heterogeneity and Grading of Recommendations, Assessment, Development and Evaluation evaluated certainty of evidence. Primary endpoint analysis was restricted to RCT design, secondary endpoint included subgroup analysis according to prospective observational or RCT design.

Results A total of 2831 publications were screened, 12 were suitable for quantitative synthesis. Three MMF RCTs were included with no significant effect on the primary endpoints (%FVC 2.94, 95% CI −4.00 to 9.88, I 2 =79.3%; %DLco −2.03, 95% CI −4.38 to 0.32, I 2 =0.0%). An overall 2.03% change from baseline in %FVC (95% CI 0.65 to 3.42, I 2 =0.0%) was observed in MMF, and RCT subgroup summary estimated a 4.42% change from baseline in %DL CO (95% CI 2.05 to 6.79, I 2 =0.0%). AZA studies were limited. All estimates were considered very low certainty evidence.

Conclusions There were limited RCTs of MMF or AZA and their benefit in ILD was of very low certainty. MMF may support preservation of pulmonary function, yet confidence in the effect was weak. To support high certainty evidence, RCTs should be designed to directly assess MMF efficacy in ILD.

PROSPERO registration number CRD42023423223.

• Interstitial Fibrosis
• Respiratory Function Test

Data availability statement

Data are available in a public, open access repository. We cited published study.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjresp-2023-002163

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Mycophenolate mofetil (MMF) and azathioprine (AZA) are two immunomodulatory drugs used in the treatment of connective tissue disease with both drugs having mechanisms that target lymphocytes. While increasingly used in treatment of interstitial lung disease (ILD), there is limited evidence for the efficacy of MMF or AZA in improving outcomes.

WHAT THIS STUDY ADDS

We undertook a systematic review and meta-analysis to assess whether administration MMF or AZA in ILD was associated with changes in pulmonary function and gas transfer. There was an unclear benefit of MMF on ILD. There was no significant difference in outcome when compared with placebo or standard of care. A minor increase in per cent predicted forced vital capacity and diffusion lung capacity of carbon monoxide from baseline was observed in MMF. Studies on AZA were limited.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Findings may provide indication of an attenuation on lung function decline, however, all estimates should be considered weak evidence with a high likelihood that additional trials may change effect estimates in a manner sufficient to influence decision-making. The limited number of controlled studies in MMF and AZA highlight an important need for additional well-designed randomised controlled trials to directly test their efficacy in ILD.

Introduction

Interstitial lung disease (ILD) is a diverse group of conditions that affect the interstitial structure of the lungs. These diseases can be characterised by progressive lung damage, resulting in symptoms such as dyspnoea, decreased exercise tolerance and a diminished quality of life. 1 Forced vital capacity (FVC) and the diffusion lung capacity of carbon monoxide (DL CO ) are widely used to assess the severity of disease and predict prognosis of people with ILD. 2

Mycophenolate mofetil (MMF) and azathioprine (AZA) are two immunomodulatory drugs commonly used in the treatment of connective tissue disease (CTD) and associated ILD (CTD-ILD). MMF works by blocking the de novo synthesis of DNA, thereby inhibiting the proliferation of lymphocytes. AZA is a purine analogue that hinders purine synthesis and becomes incorporated into DNA during the anabolic process. Similar to MMF, this mechanism of action makes both drugs more specific for targeting lymphocytes, as lymphocytes do not have a salvage pathway in DNA synthesis. 3

There is limited evidence for the safety or efficacy of MMF or AZA in improving outcomes for people with ILD. 4 This systematic review and meta-analysis aims to assess whether the administration of MMF or AZA in ILD is associated with changes in pulmonary function and gas transfer, and to synthesise evidence of safety profiles.

Search strategy

The prespecified protocol was submitted to PROSPERO on 3 May 2023 and registered on 16 May 2023 (CRD42023423223). The search strategy was last performed on 9 May 2023.

The population was defined as people with ILD (Idiopathic pulmonary fibrosis (IPF), chronic hypersensitivity pneumonia and all CTD-ILD, including systemic scleroderma) the intervention was MMF or AZA; the comparator was placebo or standard of care; the primary outcomes were per cent predicted FVC (%FVC) and DL CO (%DL CO ). Adverse events, respiratory symptoms, quality of life and mortality were investigated as secondary outcomes. Relevant studies were searched in Medline and Embase using comprehensive search terms ( online supplemental documents 1 and 2 ). Relevant ongoing trials were searched on clinicaltrials.gov ( online supplemental document 3 ).

Supplemental material

Inclusion criteria.

Eligible studies included interventional randomised controlled trials (RCTs) and observational prospective studies of adults (>18 years old) diagnosed with any ILD, where at least one arm was treated with MMF or AZA. Low doses of steroids concomitant with or prior to MMF or AZA treatment were allowed, while we excluded studies with concomitant high-dose therapies (≥20 mg/day of prednisone or equivalent). Finally, we excluded studies that did not report %FVC or %DL CO . No language restrictions were applied.

Study selection and data extraction

Two authors (FL and LF) independently assessed the titles and abstracts of the identified studies according to the eligibility criteria. Subsequently, two authors (FL and LF) evaluated the full text of the selected articles to determine their inclusion. Any disagreements were resolved through discussion and consensus with a third author (IS) resolving any remaining disagreements.

Data were independently extracted using a proforma and confirmed by two authors (FL and LF). Extracted data included study design, authors, year of publication; patient data namely age, reported sex or gender, duration of disease at the time of evaluation, aetiology of the disease and intervention characteristics, including MMF or AZA treatment, dose and duration of treatments. Primary outcomes of interest, %FVC and %DL CO , were extracted, along with any secondary outcomes reported, at baseline and follow-up time point closest to 12 months.

Continuous primary outcomes were collected as mean and SD at baseline and follow-up time points. When studies reported other summary values, these were converted to mean and SD. 5 Secondary outcomes reported as dichotomous and categorical variables were extracted as ratio and/or per cent.

Risk of bias

Two authors (FL and LF) independently used the Cochrane ‘Risk of Bias’ assessment tool 2.0 to evaluate the included RCTs prior to quantitative synthesis. 6 Risk of bias in the observational prospective studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. 7 To assess the risk of bias in single-arm observational cohorts, specifically for evaluation of ‘selection bias’ and ‘comparative bias’ on the Newcastle-Ottawa Quality Assessment Scale, baseline time points were considered as the ‘not exposed cohort’ and the follow-up time point as the ‘exposed cohort’. Studies that were determined to have a high risk of bias were excluded from quantitative synthesis.

Statistical analysis

When two or more studies were available for a specific treatment, a random effects meta-analysis with inverse-variance was performed to evaluate the effect of the treatment on %FVC and %DL CO values. Estimates were expressed as weighted mean difference (WMD) with 95% CI.

Where there were sufficient RCT data, the primary endpoint analysis assessed the delta difference in %FVC and %DL CO at follow-up from baseline in respiratory function for MMF or AZA relative to the comparator. In a secondary endpoint analysis, the difference in %FVC and %DL CO between follow-up and baseline in people receiving of MMF or AZA was compared. Analyses were performed according to drug, prespecified subgroup analyses were performed according to study design (RCT or prospective observation study) and follow-up time (6 months or 12 months and over).

Heterogeneity was evaluated using I 2 statistic to interpret the proportion of the total variability that was due to between-study heterogeneity, as well as inspection of forest plots. All analyses were performed by using Stata SE V.17.0.

Assessment of certainty of evidence

The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of evidence in effect estimates from RCT data exclusively. The level of certainty was evaluated as high, moderate, low or very low, considering factors of risk of bias, inconsistency, indirectness, imprecision and publication bias. 8 Publication bias was inspected with asymmetry in funnel plots and Egger’s test.

Patient and public involvement

Representatives from the Action for Pulmonary Fibrosis charity were involved in the design and dissemination of this systematic review. Members of the REMAP-ILD Consortium include charity representatives.

Search of relevant studies

A total of 2831 publications from Embase and Medline were identified. After removal of duplicates and evaluating the titles and abstracts, 23 studies were assessed for eligibility. Among these, 11 studies were excluded due to retrospective design (n=2), incompleteness (n=2), lack of the outcome of interest (n=2) or the presence of concomitant treatment with high doses of steroids (n=5) ( figure 1 , online supplemental table 1 ). A total of 13 studies were eligible for qualitative synthesis ( table 1 ). 9–21 Separately, four ongoing MMF studies were identified, including one phase II RCT, two open-label trials and one prospective cohort study; two studies address pulmonary involvement of systemic sclerosis, one study recruits participants with fibrotic hypersensitivity pneumonitis and one study focuses on idiopathic inflammatory myopathy ILD ( online supplemental document 3 ).

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Preferred reporting items for systematic review and meta-analysis (PRISMA) flow of study search and inclusion. AZA, azathioprine; MMF, mycophenolate mofetil.

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Reported study characteristics of included cohorts

A moderate risk of bias was observed for the blinding of outcome assessment in all the included RCTs, 12 14 15 19–21 as there were no mentioned strategies to blind the pulmonary function test evaluations ( figure 2A ). Roig et al 21 and Zhang et al 20 were considered at high risk of bias in terms of blinding of participants and personnel, as they compared intravenous and oral (per os) treatments without implementing a double dummy strategy. Due to the high risks of bias across a number of domains and insufficient data reporting, the study by Roig et al 21 was excluded from quantitative synthesis. In the assessment of prospective observational studies, six studies 10 11 13 16–18 had selection bias in the ascertainment of exposure, but all studies were considered adequate ( figure 2B , online supplemental table 2 ).

Qualitative synthesis: risk of bias. (A) Risk of bias in RCTs assessed using Cochrane ROB2.0 tool. (B) Risk of bias assessed using Newcastle-Ottawa Quality assessment scale for cohort studies. Green has been assessed as: three or four stars in selection bias; two stars in comparability, three stars in outcome. Yellow has been assessed as: two stars in selection bias; one star in comparability, two stars in outcome. RCTs, randomised controlled trial; ROB2.0, Risk of Bias 2.0.

MMF and AZA efficacy in primary endpoint relative to comparator

MMF or AZA were tested in a total of four trials, with three trials using MMF 15 19 20 and one trial using AZA. 14 Only MMF trials were included in primary analysis with a total of 249 participants, of which 119 were in the intervention arm and 130 were in the comparator arm ( figure 3A ). In primary analysis, the overall delta change in %FVC values from baseline to follow-up was not significantly different between the intervention and comparator arms (WMD 2.94, 95% CI −4.00 to 9.88, I 2 =79.3%). Significant heterogeneity was observed and the estimate was interpreted to have very low certainty ( table 2 , online supplemental figure 1A ).

Primary endpoint analysis of efficacy on pulmonary function relative to comparator. (A) Forest plot of difference in %FVC in treatment of MMF versus comparators at follow-up. (B) Forest plot of difference in %DLco in treatment of MMF versus comparators at follow-up. Positive values indicate improvement relative to comparator, negative values indicate decline relative to comparator. Presented with cohort size (N) for intervention and comparator, weighted mean difference (WMD) and 95% CI. Follow-up time reported in months. %DLco, per cent predicted diffusion lung capacity of carbon monoxide; %FVC, per cent predicted forced vital capacity; MMF, mycophenolate mofetil.

GRADE approach to rate certainty of effect estimates

The overall delta change in %DL CO from baseline to follow-up was not significantly different in the interventional arm compared with the comparator arm (WMD %DLco −2.03, 95% CI −4.38 to 0.32, I 2 =0.0% ( figure 3B ). Heterogeneity was not observed and the estimate was interpreted to have very low certainty ( table 2 , online supplemental figure 2B ).

MMF or AZA efficacy in secondary endpoints

A total of 6 prospective observational studies 9–11 16–18 and 5 RCTs 12 14 15 19 20 were included in secondary analysis of the difference between follow-up and baseline in %FVC, including a combined sample of 267 evaluated at baseline and 244 at follow-up, representing 7.5% loss to follow up. In prespecified subgroup analysis by drug ( online supplemental figure 3A ), treatment with AZA suggested a decline in %FVC with treatment, although this was not statistically significant (two studies; WMD −6.14, 95% CI −12.88 to 0.61, I 2 =48.3%). Treatment with MMF was observed to have a small and significant increase in %FVC value at follow-up (nine studies; WMD 2.03, 95% CI 0.65 to 3.42, I 2 =0.0%). Additional subgroup analyses performed on MMF treatment observed similar effect sizes according to study design and very low certainty of evidence ( figure 4A , table 2 ), while a greater effect of MMF was observed at follow-up of 12 months or over with no significant heterogeneity between time points ( online supplemental figure 4A ).

Secondary endpoint analysis of efficacy on pulmonary function compared with baseline. Subgroup analysis of MMF overall and summary estimates presented by study design of trial or prospective observational study. 4 (A) Forest plot of change in %FVC at follow-up versus baseline. (B) Forest plot of change in %DLco versus baseline. Positive values indicate improvement relative to baseline, negative values indicate decline relative to baseline. Presented with cohort size (N) for intervention and comparator, weighted mean difference (WMD) and 95% CIs. Follow-up time reported in months. %DLco, per cent predicted diffusion lung capacity of carbon monoxide; %FVC, per cent predicted forced vital capacity; MMF, mycophenolate mofetil.

Data from a total of 7 observational studies 9–11 13 16–18 and 5 RCTs 12 14 15 19 20 were available for analysis of %DL CO , including 262 and 234 patients, respectively, at baseline and follow-up representing a 10.7% loss to follow up. In subgroup analysis by drug ( online supplemental figure 3B ), treatment with AZA suggested a decline (two studies; −5.72, 95% CI −13.79 to 2.34, I 2 =49.8%), while treatment with MMF suggested an increase (10 studies; 1.62, 95% CI −1.70 to 4.94, I 2 =60.5%), although effect estimates did not reach significance and substantial heterogeneity was observed. Additional subgroup analyses performed on MMF treatment observed a significant decline in %DL CO in prospective observation studies (WMD −1.36, 95% CI −2.37 to −0.36, I 2 =0.0%) and a significant improvement in RCTs (WMD 4.42, 95% CI 2.05 to 6.79; I 2 =0.0%), with substantial heterogeneity between subgroups and very low certainty in evidence ( figure 4B , table 2 ). Subgroup analysis on follow-up time did not observe a significant effect in %DL CO with no significant heterogeneity observed between groups ( figure 4B ).

Qualitative synthesis of adverse events

All the studies reported adverse events. The most frequent adverse events in the treated arms were diarrhoea and pneumonia, followed by lympho/leucopenia, anaemia and skin infection ( online supplemental table 3 ).

Four studies reported on respiratory symptoms. 11 12 15 18 In the study by Mankikian et al , no significant difference was observed in the change from baseline in dyspnoea and cough between the treated patients and the placebo group. Naidu et al reported an improvement in respiratory symptoms in both arms of the study, with no significant difference between the treatment and control groups. Liossis et al reported an improvement in respiratory symptoms compared with baseline after administration of MMF. Vaiarello et al evaluated symptoms during a cardiopulmonary exercise test before and after MMF treatment, observing no significant difference in dyspnoea measured by the Borg scale.

Two studies reported change in quality of life. 12 15 Mankikian et al and Naidu et al evaluated the change of quality of life between the interventional and the control arm using respectively the SF-36 V.1.3 questionnaire and the Medical Outcome Survey SF-36 V.2. Both these studies reported no difference in the QoL in MMF arm compared with control. None of the included studies reported on mortality.

This systematic review and meta-analysis suggested an unclear benefit of MMF or AZA on FVC or DL CO in people with ILD. Secondary endpoint analysis of change over time stratified by treatment suggested a minor increase in %FVC or %DL CO  compared with baseline in MMF treated groups. The review highlighted a limited number of trials and prospective observational studies that directly tested the effect of MMF or AZA on lung function in the current literature, particularly precluding interpretations on the efficacy of AZA.

All estimates based on MMF RCT data were of very low GRADE certainty of evidence. Risk of bias was deemed moderate as one trial included unblinded participants, one study was post hoc analysis of trial data, and all trials had potential issues in blinding of outcome assessment. Heterogeneity and differences in the direction of effect across RCTs contributed to inconsistency. Imprecision was considered high due to limited RCTs, small samples and small effect sizes with wide CIs. Indirectness was deemed moderate as studies included different diagnoses. There was no strong evidence of publication bias. While these findings provide some indication of the effect, all estimates should be considered weak evidence with a high likelihood that additional studies may change effect estimates in a manner sufficient to influence decision-making.

Primary endpoint analysis in MMF observed no significant effect of treatment vs comparator groups for %FVC or %DL CO , although a non-significant effect in %DL CO favoured comparator. In contrast, secondary endpoint analysis suggested that MMF treatments could improve on baseline pulmonary function, although this may be insufficient relative to placebo. In further subanalyses restricted to MMF, greater improvement in %FVC was observed at longer follow-up, with no difference according to study design. Conversely, greater improvement in %DL CO was observed in trial designs, with no difference according to follow-up timing. While heterogeneity was minimised in subgroup analyses, effect sizes were small.

In the narrative review of adverse events, we found that both treatments were well tolerated, however, studies on real-world data suggest difficulties in tolerability. 4 The most frequent adverse events observed with MMF and AZA treatment included respiratory infections and haematological disorders. It is noteworthy that these adverse events were often mild and did not typically require specific treatment nor differ to events encountered in standard treatments. MMF or AZA interruption due to adverse events led to treatment discontinuation only in a few cases. Symptoms appeared to slightly improve after treatment commenced, but stricter interventional vs placebo studies are needed to assess the real effect on patient-reported outcomes.

The first meta-analysis examining the safety and efficacy of MMF in ILD associated with systemic sclerosis, conducted by Tzouvelekis et al included both retrospective and one prospective study. The outcomes of their study align with our findings, indicating an acceptable safety profile for MMF without clear evidence regarding its effectiveness on pulmonary function. 22 Similarly, network meta-analysis in systemic sclerosis associated ILD did not identify significant treatment efficacy of MMF, nor AZA in combination with cyclosporin-A. 23 Further studies are necessary across ILD diagnoses to ascertain potential efficacy in disease subtypes.

This study employed a comprehensive search strategy and strict inclusion criteria, which focused on prospective designs and trials. To support quality, estimates were specifically provided for trial designs along with GRADE assessment. We did not include restrictions on study language or cohort size. MMF and AZA were evaluated in prespecified subgroup analysis based on drug. Where study designs included other treatments, data were collected to support interpretation of MMF or AZA with omission of the drug in comparator arms. Effects regarding AZA should be interpreted with great caution due to limited studies and insufficient studies for primary analysis. Those involving AZA included an active intervention of Cyclosporin-A in the comparator, with addition of AZA in the treatment group, precluded specific interpretation of AZA alone. The limited representation of AZA in the recent literature may be partially attributed to the results of the PANTHER trial, where AZA in combination with n-acetylcysteine and prednisone led to worse outcomes in patients with IPF. 24 Mankikian et al designed an RCT randomising rituximab+MMF versus MMF, we extracted data only from the MMF arm for secondary endpoints. 12 Furthermore, studies were not consistent in ILD diagnosis inclusion, with the majority of prospective observational studies including systemic sclerosis-associated ILD; trials included IPF, non-specific interstitial pneumonia and CTD-ILD, which may contribute to heterogeneity in effect estimates. While ongoing studies were identified, MMF studies did not included blinded phase III RCTs and no AZA studies were identified.

In conclusion, the beneficial impact of MMF and AZA on pulmonary function in patients with ILD is uncertain with some weak evidence that suggests a need to further investigate the effect of MMF in preserving function. While MMF and AZA were generally well tolerated in patients with ILD, it is important to note that the certainty of effects on pulmonary function was very low. Further well-designed RCTs across diagnoses of fibrotic and inflammatory ILD are necessary to support high certainty evidence.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

No ethical approval was sought as the study uses summary information from published literature.

Acknowledgments

We express our gratitude to librarian Jacqueline Kemp, Imperial College London, for her valuable assistance in the development of the search strategy. Additionally, we would like to extend our thanks to Dr Liu Bin, Imperial College London, for providing the translation of Chinese manuscripts.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Twitter @istamina, @IPFdoc

FL and IS contributed equally.

Collaborators REMAP-ILD Consortium: Alexandre Biasi Cavalcanti (Hospital of Coracao), Ali Mojibian (Black Tusk Research Group), Amanda Bravery (Imperial College Clinical Trials Unit), Amanda Goodwin (University of Nottingham), Ana Etges (Federal University of Rio Grande do Sul), Ana Sousa Marcelino Boshoff (Imperial College Clinical Trials Unit), Andreas Guenther (Justus-Liebig-University of Giessen), Andrew Briggs (London School of Hygiene and Tropical Medicine), Andrew Palmer (University of Tasmania), Andrew Wilson (University of East Anglia), Anjali Crawshaw (University Hospitals Birmingham), Anna-MariaHoffmann-Vold (Oslo University Hospital), Anne Bergeron (University Hospitals Geneva), Anne Holland (Monash University), Anthony Gordon (Imperial College London), Antje Prasse (Hannover Medical School), Argyrios Tzouvelekis (Yale University), Athina Trachalaki (Imperial College London), Athol Wells (Royal Brompton Hospital), Avinash Anil Nair (Christian Medical College Vellore), Barbara Wendelberger (Berry Consultants), Ben Hope-Gill (Cardiff and Vale University Hospital), Bhavika Kaul (U.S. Department of Veterans Affairs Center for Innovation in Quality, Effectiveness, and Safety; Baylor College of Medicine and University of California San Francisco), Bibek Gooptu (University of Leicester), Bruno Baldi (Pulmonary Division, Heart Institute (InCor), University of Sao Paulo Medical School, Sao Paulo, Brazil), Bruno Crestani (Public Assistance Hospital of Paris), Carisi Anne Polanczyk (Federal University of Rio Grande do Sul), Carlo Vancheri (University of Catania), Carlos Robalo (European Respiratory Society), Charlotte Summers (University of Cambridge), Chris Grainge (University of Newcastle), Chris Ryerson (Department of Medicine and Centre of Heart Lung Innovations, University of British Columbia), Christophe von Garnier (Centre Hospitalier Universitaire Vaudois), Christopher Huntley (University Hospitals Birmingham), Claudia Ravaglia (University of Bologna), Claudia Valenzuela (Hospital Universitario de La Princesa), Conal Hayton (Manchester University Hospital), Cormac McCarthy (University College Dublin), Daniel Chambers (Queensland Health), Dapeng Wang (National Heart and Lung Institute, Imperial College London), Daphne Bablis (Imperial College Clinical Trials Unit), David Thicket (University of Birmingham), David Turner (University of East Anglia), Deepak Talwar (Metro Respiratory Centre Pulmonology & Sleep Medicine), Deji Adegunsoye (University of Chicago), Devaraj Anand (Royal Brompton Hospital), Devesh Dhasmana (University of St. Andrews), Dhruv Parek (Brimingham University), Diane Griffiths (University Hospitals Birmingham), Duncan Richards (Oxford University), Eliana Santucci (Hospital of Coracao), Elisabeth Bendstrup (Aarhus University), Elisabetta Balestro (University of Padua), Eliza Tsitoura (University of Crete), Emanuela Falaschetti (Imperial College London), Emma Karlsen (Black Tusk Research Group), Ena Gupta (University of Vermont Health Network), Erica Farrand (University of California, San Fransisco), Fasihul Khan (University of Nottingham), Felix Chua (Royal Brompton Hospital), Fernando J Martinez (Weill Cornell Medicine), Francesco Bonella (Essen University Hospital), Francesco Lombardi (Division of Pulmonary Medicine, Fondazione Policlinico Universitario Agostino Gemelli IRCCS), Gary M Hunninghake (Brigham and Women's Hospital), Gauri Saini (Nottingham University Hospital), George Chalmers (Glasgow Royal Infirmary), Gisli Jenkins (Imperial College London), Gunnar Gudmundsson (University of Iceland), Harold Collard (University of California, San Francisco), Helen Parfrey (Royal Papworth Hospital NHS Foundation Trust), Helmut Prosch (Medical University of Vienna), Hernan Fainberg (Imperial College London), Huzaifa Adamali (North Bristol NHS Trust), Iain Stewart (National Heart and Lung Institute, Imperial College London), Ian Forrest (Newcastle Hospitals NHS Foundation Trust), Ian Glaspole (Alfred Hospital), Iazsmin Bauer-Ventura (The University of Chicago), Imre Noth (University of Virginia), Ingrid Cox (University of Tasmania), Irina Strambu (University of Medicine and Pharmacy), Jacobo Sellares (Hospital Clínic de Barcelona), James Eaden (Sheffield University Hospitals), Janet Johnston (Manchester Royal Infirmary NHS Foundation Trust), Jeff Swigris (National Jewish Health), John Blaikley (Manchester University), John S Kim (University of Virginia), Jonathan Chung (The University of Chicago), Joseph A Lasky (Tulane & Pulmonary Fibrosis Foundation), Joseph Jacob (University College London), Joyce Lee (University of Colorado), Juergen Behr (Ludwig Maximilian University of Munich), Karin Storrer (Federal University of Sao Paulo), Karina Negrelli (Hospital of Curacao), Katarzyna Lewandowska (Institute of Tuberculosis and Lung Diseases), Kate Johnson (The University of British Colombia), Katerina Antoniou (University of Crete), Katrin Hostettler (University Hospital Basel), Kerri Johannson (University of Calgary), Killian Hurley (Royal College of Surgeons, Ireland), Kirsty Hett (Cardiff and Vale University Health Board), Larissa Schwarzkopf (The Institute for Therapy Research), Laura Fabbri (National Heart and Lung Institute, Imperial College London), Laura Price (Royal Brompton Hospital), Laurence Pearmain (Manchester University), Leticia Kawano-Dourado (Hcor Research Institute, Hospital do Coracao, Sao Paulo, Brazil. 2. Pulmonary Division, University of Sao Paulo, Sao Paulo, Brazil. 3. MAGIC Evidence Ecosystem Foundation, Oslo, Norway), Liam Galvin (European Pulmonary Fibrosis Federation), Lisa G. Spencer (Liverpool University Hospitals NHS Foundation Trust), Lisa Watson (Sheffield University Hospitals), Louise Crowley (Queen Elizabeth Hospital, University Hospitals Birmingham), Luca Richeldi (Agostino Gemelli IRCCS University Hospital Foundation), Lucilla Piccari (Department of Pulmonary Medicine, Hospital del Mar, Barcelona (Spain)), Manuela Funke Chambour (University of Bern), Maria Molina-Molina (IDIBELL Bellvitge Biomedical Research Institute), Mark Jones (Southampton University), Mark Spears (University of Dundee Scotland), Mark Toshner (University of Cambridge), Marlies Wijsenbeek-Lourens (Erasmus University Medical Hospital), Martin Brutsche (Kantonsspital St.Gallen), Martina Vasakova (Faculty Thomayer Hospital), Melanie Quintana (Berry Consultants), Michael Gibbons (University of Exeter), Michael Henry (Cork University Hospital), Michael Keane (University College Dublin), Michael Kreuter (Heidelberg University Hospital), Milena Man Iuliu Hatieganu (University of Medicine and Pharmacy), Mohsen Sadatsafavi (The University of British Colombia), Naftali Kaminski (Yale University), Nazia Chaudhuri (Ulster University), Nick Weatherley (Sheffield University Hospitals), Nik Hirani (The University of Edinburgh), Ovidiu Fira Mladinescu Victor Babes (University of Medicine and Pharmacy), Paolo Spagnolo (University of Padua), Paul Beirne (Leeds Teaching Hospitals NHS Foundation Trust), Peter Bryce (Pulmonary Fibrosis Trust), Peter George (Royal Brompton Hospital), Philip L Molyneaux (Imperial College London), Pilar Rivera Ortega (Interstitial Lung Disease Unit, Department of Respiratory Medicine, Wythenshawe Hospital. Manchester University NHS Foundation Trust. United Kingdom.), Radu Crisan-Dabija (University of Medicine and Pharmacy "Grigore T. Popa" Iasi), Rahul Maida (University of Birmingham), Raphael Borie (Public Assistance Hospital of Paris), Roger Lewis (Berry Consultants), Rui Rolo (Braga Hospital), Sabina Guler (University Hospital of Bern), Sabrina Paganoni (Massachusetts General Hospital), Sally Singh (University of Leicester.), Sara Freitas (University Hospital Coimbra), Sara Piciucchi (Department of Radiology, GB Morgagni Hospital; Azienda USL Romagna), Shama Malik (Action for Pulmonary Fibrosis), Shaney Barratt (North Bristol NHS Trust), Simon Hart (University of Hull), Simone Dal Corso (Monash University), Sophie Fletcher (Southampton University), Stefan Stanel (Manchester University NHS Foundation Trust), Stephen Bianchi (Thornbury Hospital), Steve Jones (Action for Pulmonary Fibrosis), Wendy Adams (Action for Pulmonary Fibrosis).

Contributors FL: protocol development, formal analysis, data curation, writing–original draft. IS: protocol development, formal analysis, methodology, supervision, writing–original draft, guarantor. LF: protocol development, data curation, writing–review and editing. WA: protocol development, writing–review and editing. LK-D: protocol development, writing–review and editing. CJR: protocol development, writing–review and editing. GJ: conceptualisation, protocol development, supervision, writing–review and editing.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests GJ is supported by a National Institute for Health Research (NIHR) Research Professorship (NIHR reference RP-2017-08-ST2-014). GJ is a trustee of Action for Pulmonary Fibrosis and reports personal fees from Astra Zeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Chiesi, Daewoong, Galapagos, Galecto, GlaxoSmithKline, Heptares, NuMedii, PatientMPower, Pliant, Promedior, Redx, Resolution Therapeutics, Roche, Veracyte and Vicore. CJR reports grants from Boehringer Ingelheim, and honoraria or consulting fees from Boehringer Ingelheim, Pliant Therapeutics, Astra Zeneca, Trevi Therapeutics, Veracyte, Hoffmann-La Roche, Cipla. FL, IS, LF, WA and LK-D report no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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