data analysis tools in research

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Data Analysis in Research: Types & Methods

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Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

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

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

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

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

Phase I: Data Validation

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

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

Phase II: Data Editing

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

Phase III: Data Coding

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

LEARN ABOUT: Steps in Qualitative Research

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

Descriptive statistics

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

Measures of Frequency

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

Measures of Central Tendency

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

Measures of Dispersion or Variation

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

Measures of Position

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

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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

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

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

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

LEARN ABOUT: Best Data Collection Tools

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

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

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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.

Data Analysis in Quantitative Research

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• First Online: 13 January 2019
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• Yong Moon Jung 2  

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

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Pranee Liamputtong

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

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Essential Data Analyst Tools Discover a List of The 17 Best Data Analysis Software & Tools On The Market

Top 17 software & tools for data analysts (2023).

Table of Content 1) What are data analyst tools? 2) The best 17 data analyst tools for 2023 3) Key takeaways & guidance

To be able to perform data analysis at the highest level possible, analysts and data professionals will use software that will ensure the best results in several tasks from executing algorithms, preparing data, generating predictions, and automating processes, to standard tasks such as visualizing and reporting on the data. Although there are many of these solutions on the market, data analysts must choose wisely in order to benefit their analytical efforts. That said, in this article, we will cover the best data analyst tools and name the key features of each based on various types of analysis processes. But first, we will start with a basic definition and a brief introduction.

1) What Are Data Analyst Tools?

Data analyst tools is a term used to describe software and applications that data analysts use in order to develop and perform analytical processes that help companies to make better, informed business decisions while decreasing costs and increasing profits.

In order to make the best possible decision on which software you need to choose as an analyst, we have compiled a list of the top data analyst tools that have various focus and features, organized in software categories, and represented with an example of each. These examples have been researched and selected using rankings from two major software review sites: Capterra and G2Crowd . By looking into each of the software categories presented in this article, we selected the most successful solutions with a minimum of 15 reviews between both review websites until November 2022. The order in which these solutions are listed is completely random and does not represent a grading or ranking system.

2) What Tools Do Data Analysts Use?

To make the most out of the infinite number of software that is currently offered on the market, we will focus on the most prominent tools needed to be an expert data analyst. The image above provides a visual summary of all the areas and tools that will be covered in this insightful post. These data analysis tools are mostly focused on making analysts lives easier by providing them with solutions that make complex analytical tasks more efficient. Like this, they get more time to perform the analytical part of their job. Let’s get started with business intelligence tools.

1. Business intelligence tools

BI tools are one of the most represented means of performing data analysis. Specializing in business analytics, these solutions will prove to be beneficial for every data analyst that needs to analyze, monitor, and report on important findings. Features such as self-service, predictive analytics, and advanced SQL modes make these solutions easily adjustable to every level of knowledge, without the need for heavy IT involvement. By providing a set of useful features, analysts can understand trends and make tactical decisions. Our data analytics tools article wouldn’t be complete without business intelligence, and datapine is one example that covers most of the requirements both for beginner and advanced users. This all-in-one tool aims to facilitate the entire analysis process from data integration and discovery to reporting.

KEY FEATURES:

Visual drag-and-drop interface to build SQL queries automatically, with the option to switch to, advanced (manual) SQL mode

Powerful predictive analytics features, interactive charts and dashboards, and automated reporting

AI-powered alarms that are triggered as soon as an anomaly occurs or a goal is met

datapine is a popular business intelligence software with an outstanding rating of 4.8 stars in Capterra and 4.6 stars in G2Crowd. It focuses on delivering simple, yet powerful analysis features into the hands of beginners and advanced users in need of a fast and reliable online data analysis solution for all analysis stages. An intuitive user interface will enable you to simply drag-and-drop your desired values into datapine’s Analyzer and create numerous charts and graphs that can be united into an interactive dashboard. If you’re an experienced analyst, you might want to consider the SQL mode where you can build your own queries or run existing codes or scripts. Another crucial feature is the predictive analytics forecast engine that can analyze data from multiple sources which can be previously integrated with their various data connectors. While there are numerous predictive solutions out there, datapine provides simplicity and speed at its finest. By simply defining the input and output of the forecast based on specified data points and desired model quality, a complete chart will unfold together with predictions.

We should also mention robust artificial intelligence that is becoming an invaluable assistant in today’s analysis processes. Neural networks, pattern recognition, and threshold alerts will alarm you as soon as a business anomaly occurs or a previously set goal is met so you don’t have to manually analyze large volumes of data – the data analytics software does it for you. Access your data from any device with an internet connection, and share your findings easily and securely via dashboards or customized reports for anyone that needs quick answers to any type of business question.

2. Statistical Analysis Tools

Next in our list of data analytics tools comes a more technical area related to statistical analysis. Referring to computation techniques that often contain a variety of statistical techniques to manipulate, explore, and generate insights, there exist multiple programming languages to make (data) scientists’ work easier and more effective. With the expansion of various languages that are today present on the market, science has its own set of rules and scenarios that need special attention when it comes to statistical data analysis and modeling. Here we will present one of the most popular tools for a data analyst – Posit (previously known as RStudio or R programming). Although there are other languages that focus on (scientific) data analysis, R is particularly popular in the community.

POSIT (R-STUDIO)

An ecosystem of more than 10 000 packages and extensions for distinct types of data analysis

Statistical analysis, modeling, and hypothesis testing (e.g. analysis of variance, t test, etc.)

Active and communicative community of researchers, statisticians, and scientists

Posit , formerly known as RStudio, is one of the top data analyst tools for R and Python. Its development dates back to 2009 and it’s one of the most used software for statistical analysis and data science, keeping an open-source policy and running on a variety of platforms, including Windows, macOS and Linux. As a result of the latest rebranding process, some of the famous products on the platform will change their names, while others will stay the same. For example, RStudio Workbench and RStudio Connect will now be known as Posit Workbench and Posit Connect respectively. On the other side, products like RStudio Desktop and RStudio Server will remain the same. As stated on the software’s website, the rebranding happened because the name RStudio no longer reflected the variety of products and languages that the platform currently supports.

Posit is by far the most popular integrated development environment (IDE) out there with 4,7 stars on Capterra and 4,5 stars on G2Crowd. Its capabilities for data cleaning, data reduction, and data analysis report output with R markdown, make this tool an invaluable analytical assistant that covers both general and academic data analysis. It is compiled of an ecosystem of more than 10 000 packages and extensions that you can explore by categories, and perform any kind of statistical analysis such as regression, conjoint, factor cluster analysis, etc. Easy to understand for those that don’t have a high-level of programming skills, Posit can perform complex mathematical operations by using a single command. A number of graphical libraries such as ggplot and plotly make this language different than others in the statistical community since it has efficient capabilities to create quality visualizations.

Posit was mostly used in the academic area in the past, today it has applications across industries and large companies such as Google, Facebook, Twitter, and Airbnb, among others. Due to an enormous number of researchers, scientists, and statisticians using it, the tool has an extensive and active community where innovative technologies and ideas are presented and communicated regularly.

3. QUALITATIVE DATA ANALYSIS TOOLS

Naturally, when we think about data, our mind automatically takes us to numbers. Although much of the extracted data might be in a numeric format, there is also immense value in collecting and analyzing non-numerical information, especially in a business context. This is where qualitative data analysis tools come into the picture. These solutions offer researchers, analysts, and businesses the necessary functionalities to make sense of massive amounts of qualitative data coming from different sources such as interviews, surveys, e-mails, customer feedback, social media comments, and much more depending on the industry. There is a wide range of qualitative analysis software out there, the most innovative ones rely on artificial intelligence and machine learning algorithms to make the analysis process faster and more efficient. Today, we will discuss MAXQDA, one of the most powerful QDA platforms in the market.

The possibility to mark important information using codes, colors, symbols or emojis

AI-powered audio transcription capabilities such as speed and rewind controls, speaker labels, and others

Possibility to work with multiple languages and scripts thanks to Unicode support

Founded in 1989 “by researchers, for researchers”, MAXQDA is a qualitative data analysis software for Windows and Mac that assists users in organizing and interpreting qualitative data from different sources with the help of innovative features. Unlike some other solutions on the same range, MAXQDA supports a wide range of data sources and formats. Users can import traditional text data from interviews, focus groups, web pages, and YouTube or Twitter comments, as well as various types of multimedia data such as videos or audio files. Paired to that, the software also offers a Mixed Methods tool which allows users to use both qualitative and quantitative data for a more complete analytics process. This level of versatility has earned MAXQDA worldwide recognition for many years. The tool has a positive 4.6 stars rating in Capterra and a 4.5 in G2Crowd.

Amongst its most valuable functions, MAXQDA offers users the capability of setting different codes to mark their most important data and organize it in an efficient way. Codes can be easily generated via drag & drop and labeled using colors, symbols, or emojis. Your findings can later be transformed, automatically or manually, into professional visualizations and exported in various readable formats such as PDF, Excel, or Word, among others.

4. General-purpose programming languages

Programming languages are used to solve a variety of data problems. We have explained R and statistical programming, now we will focus on general ones that use letters, numbers, and symbols to create programs and require formal syntax used by programmers. Often, they’re also called text-based programs because you need to write software that will ultimately solve a problem. Examples include C#, Java, PHP, Ruby, Julia, and Python, among many others on the market. Here we will focus on Python and we will present PyCharm as one of the best tools for data analysts that have coding knowledge as well.

Intelligent code inspection and completion with error detection, code fixes, and automated code refractories

Built-in developer tools for smart debugging, testing, profiling, and deployment

Cross-technology development supporting JavaScript, CoffeeScript, HTML/CSS, Node.js, and more

PyCharm is an integrated development environment (IDE) by JetBrains designed for developers that want to write better, more productive Python code from a single platform. The tool, which is successfully rated with 4.7 stars on Capterra and 4.6 in G2Crowd, offers developers a range of essential features including an integrated visual debugger, GUI-based test runner, integration with major VCS and built-in database tools, and much more. Amongst its most praised features, the intelligent code assistance provides developers with smart code inspections highlighting errors and offering quick fixes and code completions.

PyCharm supports the most important Python implementations including Python 2.x and 3.x, Jython, IronPython, PyPy and Cython, and it is available in three different editions. The Community version, which is free and open-sourced, the Professional paid version, including all advanced features, and the Edu version which is also free and open-sourced for educational purposes. Definitely, one of the best Python data analyst tools in the market.

5. SQL consoles

Our data analyst tools list wouldn’t be complete without SQL consoles. Essentially, SQL is a programming language that is used to manage/query data held in relational databases, particularly effective in handling structured data as a database tool for analysts. It’s highly popular in the data science community and one of the analyst tools used in various business cases and data scenarios. The reason is simple: as most of the data is stored in relational databases and you need to access and unlock its value, SQL is a highly critical component of succeeding in business, and by learning it, analysts can offer a competitive advantage to their skillset. There are different relational (SQL-based) database management systems such as MySQL, PostgreSQL, MS SQL, and Oracle, for example, and by learning these data analysts’ tools would prove to be extremely beneficial to any serious analyst. Here we will focus on MySQL Workbench as the most popular one.

MySQL Workbench

A unified visual tool for data modeling, SQL development, administration, backup, etc.

Instant access to database schema and objects via the Object Browser

SQL Editor that offers color syntax highlighting, reuse of SQL snippets, and execution history

MySQL Workbench is used by analysts to visually design, model, and manage databases, optimize SQL queries, administer MySQL environments, and utilize a suite of tools to improve the performance of MySQL applications. It will allow you to perform tasks such as creating and viewing databases and objects (triggers or stored procedures, e.g.), configuring servers, and much more. You can easily perform backup and recovery as well as inspect audit data. MySQL Workbench will also help in database migration and is a complete solution for analysts working in relational database management and companies that need to keep their databases clean and effective. The tool, which is very popular amongst analysts and developers, is rated 4.6 stars in Capterra and 4.5 in G2Crowd.

6. Standalone predictive analytics tools

Predictive analytics is one of the advanced techniques, used by analysts that combine data mining, machine learning, predictive modeling, and artificial intelligence to predict future events, and it deserves a special place in our list of data analysis tools as its popularity has increased in recent years with the introduction of smart solutions that enabled analysts to simplify their predictive analytics processes. You should keep in mind that some BI tools we already discussed in this list offer easy to use, built-in predictive analytics solutions but, in this section, we focus on standalone, advanced predictive analytics that companies use for various reasons, from detecting fraud with the help of pattern detection to optimizing marketing campaigns by analyzing consumers’ behavior and purchases. Here we will list a data analysis software that is helpful for predictive analytics processes and helps analysts to predict future scenarios.

IBM SPSS PREDICTIVE ANALYTICS ENTERPRISE

A visual predictive analytics interface to generate predictions without code

Can be integrated with other IBM SPSS products for a complete analysis scope

Flexible deployment to support multiple business scenarios and system requirements

IBM SPSS Predictive Analytics provides enterprises with the power to make improved operational decisions with the help of various predictive intelligence features such as in-depth statistical analysis, predictive modeling, and decision management. The tool offers a visual interface for predictive analytics that can be easily used by average business users with no previous coding knowledge, while still providing analysts and data scientists with more advanced capabilities. Like this, users can take advantage of predictions to inform important decisions in real time with a high level of certainty.

Additionally, the platform provides flexible deployment options to support multiple scenarios, business sizes and use cases. For example, for supply chain analysis or cybercrime prevention, among many others. Flexible data integration and manipulation is another important feature included in this software. Unstructured and structured data, including text data, from multiple sources, can be analyzed for predictive modeling that will translate into intelligent business outcomes.

As a part of the IBM product suite, users of the tool can take advantage of other solutions and modules such as the IBM SPSS Modeler, IBM SPSS Statistics, and IMB SPSS Analytic Server for a complete analytical scope. Reviewers gave the software a 4.5 star rating on Capterra and 4.2 on G2Crowd.

7. Data modeling tools

Our list of data analysis tools wouldn’t be complete without data modeling. Creating models to structure the database, and design business systems by utilizing diagrams, symbols, and text, ultimately represent how the data flows and is connected in between. Businesses use data modeling tools to determine the exact nature of the information they control and the relationship between datasets, and analysts are critical in this process. If you need to discover, analyze, and specify changes in information that is stored in a software system, database or other application, chances are your skills are critical for the overall business. Here we will show one of the most popular data analyst software used to create models and design your data assets.

erwin data modeler (DM)

Automated data model generation to increase productivity in analytical processes

Single interface no matter the location or the type of the data

5 different versions of the solution you can choose from and adjust based on your business needs

erwin DM works both with structured and unstructured data in a data warehouse and in the cloud. It’s used to “find, visualize, design, deploy and standardize high-quality enterprise data assets,” as stated on their official website. erwin can help you reduce complexities and understand data sources to meet your business goals and needs. They also offer automated processes where you can automatically generate models and designs to reduce errors and increase productivity. This is one of the tools for analysts that focus on the architecture of the data and enable you to create logical, conceptual, and physical data models.

Additional features such as a single interface for any data you might possess, no matter if it’s structured or unstructured, in a data warehouse or the cloud makes this solution highly adjustable for your analytical needs. With 5 versions of the erwin data modeler, their solution is highly adjustable for companies and analysts that need various data modeling features. This versatility is reflected in its positive reviews, gaining the platform an almost perfect 4.8 star rating on Capterra and 4.3 stars in G2Crowd.

8. ETL tools

ETL is a process used by companies, no matter the size, across the world, and if a business grows, chances are you will need to extract, load, and transform data into another database to be able to analyze it and build queries. There are some core types of ETL tools for data analysts such as batch ETL, real-time ETL, and cloud-based ETL, each with its own specifications and features that adjust to different business needs. These are the tools used by analysts that take part in more technical processes of data management within a company, and one of the best examples is Talend.

Collecting and transforming data through data preparation, integration, cloud pipeline designer

Talend Trust Score to ensure data governance and resolve quality issues across the board

Sharing data internally and externally through comprehensive deliveries via APIs

Talend is a data integration platform used by experts across the globe for data management processes, cloud storage, enterprise application integration, and data quality. It’s a Java-based ETL tool that is used by analysts in order to easily process millions of data records and offers comprehensive solutions for any data project you might have. Talend’s features include (big) data integration, data preparation, cloud pipeline designer, and stitch data loader to cover multiple data management requirements of an organization. Users of the tool rated it with 4.2 stars in Capterra and 4.3 in G2Crowd. This is an analyst software extremely important if you need to work on ETL processes in your analytical department.

Apart from collecting and transforming data, Talend also offers a data governance solution to build a data hub and deliver it through self-service access through a unified cloud platform. You can utilize their data catalog, inventory and produce clean data through their data quality feature. Sharing is also part of their data portfolio; Talend’s data fabric solution will enable you to deliver your information to every stakeholder through a comprehensive API delivery platform. If you need a data analyst tool to cover ETL processes, Talend might be worth considering.

9. Automation Tools

As mentioned, the goal of all the solutions present on this list is to make data analysts lives easier and more efficient. Taking that into account, automation tools could not be left out of this list. In simple words, data analytics automation is the practice of using systems and processes to perform analytical tasks with almost no human interaction. In the past years, automation solutions have impacted the way analysts perform their jobs as these tools assist them in a variety of tasks such as data discovery, preparation, data replication, and more simple ones like report automation or writing scripts. That said, automating analytical processes significantly increases productivity, leaving more time to perform more important tasks. We will see this more in detail through Jenkins one of the leaders in open-source automation software.

Popular continuous integration (CI) solution with advanced automation features such as running code in multiple platforms

Job automations to set up customized tasks can be scheduled or based on a specific event

Several job automation plugins for different purposes such as Jenkins Job Builder, Jenkins Job DLS or Jenkins Pipeline DLS

Developed in 2004 under the name Hudson, Jenkins is an open-source CI automation server that can be integrated with several DevOps tools via plugins. By default, Jenkins assists developers to automate parts of their software development process like building, testing, and deploying. However, it is also highly used by data analysts as a solution to automate jobs such as running codes and scripts daily or when a specific event happened. For example, run a specific command when new data is available.

There are several Jenkins plugins to generate jobs automatically. For example, the Jenkins Job Builder plugin takes simple descriptions of jobs in YAML or JSON format and turns them into runnable jobs in Jenkins’s format. On the other side, the Jenkins Job DLS plugin provides users with the capabilities to easily generate jobs from other jobs and edit the XML configuration to supplement or fix any existing elements in the DLS. Lastly, the Pipeline plugin is mostly used to generate complex automated processes.

For Jenkins, automation is not useful if it’s not tight to integration. For this reason, they provide hundreds of plugins and extensions to integrate Jenkins with your existing tools. This way, the entire process of code generation and execution can be automated at every stage and in different platforms - leaving you enough time to perform other relevant tasks. All the plugins and extensions from Jenkins are developed in Java meaning the tool can also be installed in any other operator that runs on Java. Users rated Jenkins with 4.5 stars in Capterra and 4.4 stars in G2Crowd.

10. DOCUMENT SHARING TOOLS

As an analyst working with programming, it is very likely that you have found yourself in the situation of having to share your code or analytical findings with others. Rather you want someone to look into your code for errors or provide any other kind of feedback to your work, a document sharing tool is the way to go. These solutions enable users to share interactive documents which can contain live code and other multimedia elements for a collaborative process. Below, we will present Jupyter Notebook, one of the most popular and efficient platforms for this purpose.

JUPYTER NOTEBOOK

Supports 40 programming languages including Python, R, Julia, C++, and more

Easily share notebooks with others via email, Dropbox, GitHub and Jupyter Notebook Viewer

In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion

Jupyter Notebook is an open source web based interactive development environment used to generate and share documents called notebooks, containing live codes, data visualizations, and text in a simple and streamlined way. Its name is an abbreviation of the core programming languages it supports: Julia, Python, and R and, according to its website, it has a flexible interface that enables users to view, execute and share their code all in the same platform. Notebooks allow analysts, developers, and anyone else to combine code, comments, multimedia, and visualizations in an interactive document that can be easily shared and reworked directly in your web browser.

Even though it works by default on Python, Jupyter Notebook supports over 40 programming languages and it can be used in multiple scenarios. Some of them include sharing notebooks with interactive visualizations, avoiding the static nature of other software, live documentation to explain how specific Python modules or libraries work, or simply sharing code and data files with others. Notebooks can be easily converted into different output formats such as HTML, LaTeX, PDF, and more. This level of versatility has earned the tool 4.7 stars rating on Capterra and 4.5 in G2Crowd.

11. Unified data analytics engines

If you work for a company that produces massive datasets and needs a big data management solution, then unified data analytics engines might be the best resolution for your analytical processes. To be able to make quality decisions in a big data environment, analysts need tools that will enable them to take full control of their company’s robust data environment. That’s where machine learning and AI play a significant role. That said, Apache Spark is one of the data analysis tools on our list that supports big-scale data processing with the help of an extensive ecosystem.

Apache Spark

High performance: Spark owns the record in the large-scale data processing

A large ecosystem of data frames, streaming, machine learning, and graph computation

Perform Exploratory Analysis on petabyte-scale data without the need for downsampling

Apache Spark was originally developed by UC Berkeley in 2009 and since then, it has expanded across industries and companies such as Netflix, Yahoo, and eBay that have deployed Spark, processed petabytes of data and proved that Apache is the go-to solution for big data management, earning it a positive 4.2 star rating in both Capterra and G2Crowd. Their ecosystem consists of Spark SQL, streaming, machine learning, graph computation, and core Java, Scala, and Python APIs to ease the development. Already in 2014, Spark officially set a record in large-scale sorting. Actually, the engine can be 100x faster than Hadoop and this is one of the features that is extremely crucial for massive volumes of data processing.

You can easily run applications in Java, Python, Scala, R, and SQL while more than 80 high-level operators that Spark offers will make your data transformation easy and effective. As a unified engine, Spark comes with support for SQL queries, MLlib for machine learning and GraphX for streaming data that can be combined to create additional, complex analytical workflows. Additionally, it runs on Hadoop, Kubernetes, Apache Mesos, standalone or in the cloud and can access diverse data sources. Spark is truly a powerful engine for analysts that need support in their big data environment.

12. Spreadsheet applications

Spreadsheets are one of the most traditional forms of data analysis. Quite popular in any industry, business or organization, there is a slim chance that you haven’t created at least one spreadsheet to analyze your data. Often used by people that don’t have high technical abilities to code themselves, spreadsheets can be used for fairly easy analysis that doesn’t require considerable training, complex and large volumes of data and databases to manage. To look at spreadsheets in more detail, we have chosen Excel as one of the most popular in business.

Part of the Microsoft Office family, hence, it’s compatible with other Microsoft applications

Pivot tables and building complex equations through designated rows and columns

Perfect for smaller analysis processes through workbooks and quick sharing

With 4.8 stars rating in Capterra and 4.7 in G2Crowd, Excel needs a category on its own since this powerful tool has been in the hands of analysts for a very long time. Often considered a traditional form of analysis, Excel is still widely used across the globe. The reasons are fairly simple: there aren’t many people who have never used it or come across it at least once in their career. It’s a fairly versatile data analyst tool where you simply manipulate rows and columns to create your analysis. Once this part is finished, you can export your data and send it to the desired recipients, hence, you can use Excel as a reporting tool as well. You do need to update the data on your own, Excel doesn’t have an automation feature similar to other tools on our list. Creating pivot tables, managing smaller amounts of data and tinkering with the tabular form of analysis, Excel has developed as an electronic version of the accounting worksheet to one of the most spread tools for data analysts.

A wide range of functionalities accompany Excel, from arranging to manipulating, calculating and evaluating quantitative data to building complex equations and using pivot tables, conditional formatting, adding multiple rows and creating charts and graphs – Excel has definitely earned its place in traditional data management.

13. Industry-specific analytics tools

While there are many data analysis tools on this list that are used in various industries and are applied daily in analysts’ workflow, there are solutions that are specifically developed to accommodate a single industry and cannot be used in another. For that reason, we have decided to include of one these solutions on our list, although there are many others, industry-specific data analysis programs and software. Here we focus on Qualtrics as one of the leading research software that is used by over 11000 world’s brands and has over 2M users across the globe as well as many industry-specific features focused on market research.

5 main experience features: design, customer, brand, employee, and product

Additional research services by their in-house experts

Advanced statistical analysis with their Stats iQ analysis tool

Qualtrics is a software for data analysis that is focused on experience management (XM) and is used for market research by companies across the globe. The tool, which has a positive 4.8 stars rating on Capterra and 4.4 in G2Crowd, offers 5 product pillars for enterprise XM which include design, customer, brand, employee, and product experiences, as well as additional research services performed by their own experts. Their XM platform consists of a directory, automated actions, Qualtrics iQ tool, and platform security features that combine automated and integrated workflows into a single point of access. That way, users can refine each stakeholder’s experience and use their tool as an “ultimate listening system.”

Since automation is becoming increasingly important in our data-driven age, Qualtrics has also developed drag-and-drop integrations into the systems that companies already use such as CRM, ticketing, or messaging, while enabling users to deliver automatic notifications to the right people. This feature works across brand tracking and product feedback as well as customer and employee experience. Other critical features such as the directory where users can connect data from 130 channels (including web, SMS, voice, video, or social), and Qualtrics iQ to analyze unstructured data will enable users to utilize their predictive analytics engine and build detailed customer journeys. If you’re looking for a data analytic software that needs to take care of market research of your company, Qualtrics is worth the try.

14. Data science platforms

Data science can be used for most software solutions on our list, but it does deserve a special category since it has developed into one of the most sought-after skills of the decade. No matter if you need to utilize preparation, integration or data analyst reporting tools, data science platforms will probably be high on your list for simplifying analytical processes and utilizing advanced analytics models to generate in-depth data science insights. To put this into perspective, we will present RapidMiner as one of the top data analyst software that combines deep but simplified analysis.

A comprehensive data science and machine learning platform with 1500+ algorithms and functions

Possible to integrate with Python and R as well as support for database connections (e.g. Oracle)

Advanced analytics features for descriptive and prescriptive analytics

RapidMiner , which was just acquired by Altair in 2022 as a part of their data analytics portfolio, is a tool used by data scientists across the world to prepare data, utilize machine learning, and model operations in more than 40 000 organizations that heavily rely on analytics in their operations. By unifying the entire data science cycle, RapidMiner is built on 5 core platforms and 3 automated data science products that help in the design and deployment of analytics processes. Their data exploration features such as visualizations and descriptive statistics will enable you to get the information you need while predictive analytics will help you in cases such as churn prevention, risk modeling, text mining, and customer segmentation.

With more than 1500 algorithms and data functions, support for 3rd party machine learning libraries, integration with Python or R, and advanced analytics, RapidMiner has developed into a data science platform for deep analytical purposes. Additionally, comprehensive tutorials and full automation, where needed, will ensure simplified processes if your company requires them, so you don’t need to perform manual analysis. All these positive traits have earned the tool a positive 4.4 stars rating on Capterra and 4.6 stars in G2Crowd. If you’re looking for analyst tools and software focused on deep data science management and machine learning, then RapidMiner should be high on your list.

15. DATA CLEANSING PLATFORMS

The amount of data being produced is only getting bigger, hence, the possibility of it involving errors. To help analysts avoid these errors that can damage the entire analysis process is that data cleansing solutions were developed. These tools help in preparing the data by eliminating errors, inconsistencies, and duplications enabling users to extract accurate conclusions from it. Before cleansing platforms were a thing, analysts would manually clean the data, this is also a dangerous practice since the human eye is prompt to error. That said, powerful cleansing solutions have proved to boost efficiency and productivity while providing a competitive advantage as data becomes reliable. The cleansing software we picked for this section is a popular solution named OpenRefine.

Data explorer to clean “messy” data using transformations, facets, and clustering, among others

Transform data to the format you desire, for example, turn a list into a table by importing the file into OpenRefine

Includes a large list of extensions and plugins to link and extend datasets with various web services

Previously known as Google Refine, OpenRefine is a Java-based open-source desktop application for working with large sets of data that needs to be cleaned. The tool, with ratings of 4.0 stars in Capterra and 4.6 in G2Crowd, also enables users to transform their data from one format to another and extend it with web services and external data. OpenRefine has a similar interface to the one of spreadsheet applications and can handle CSV file formats, but all in all, it behaves more as a database. Upload your datasets into the tool and use their multiple cleaning features that will let you spot anything from extra spaces to duplicated fields.

Available in more than 15 languages, one of the main principles of OpenRefine is privacy. The tool works by running a small server on your computer and your data will never leave that server unless you decide to share it with someone else.

16. DATA MINING TOOLS

Next, in our insightful list of data analyst tools we are going to touch on data mining. In short, data mining is an interdisciplinary subfield of computer science that uses a mix of statistics, artificial intelligence and machine learning techniques and platforms to identify hidden trends and patterns in large, complex data sets. To do so, analysts have to perform various tasks including data classification, cluster analysis, association analysis, regression analysis, and predictive analytics using professional data mining software. Businesses rely on these platforms to anticipate future issues and mitigate risks, make informed decisions to plan their future strategies, and identify new opportunities to grow. There are multiple data mining solutions in the market at the moment, most of them relying on automation as a key feature. We will focus on Orange, one of the leading mining software at the moment.

Visual programming interface to easily perform data mining tasks via drag and drop

Multiple widgets offering a set of data analytics and machine learning functionalities

Add-ons for text mining and natural language processing to extract insights from text data

Orange is an open source data mining and machine learning tool that has existed for more than 20 years as a project from the University of Ljubljana. The tool offers a mix of data mining features, which can be used via visual programming or Python Scripting, as well as other data analytics functionalities for simple and complex analytical scenarios. It works under a “canvas interface” in which users place different widgets to create a data analysis workflow. These widgets offer different functionalities such as reading the data, inputting the data, filtering it, and visualizing it, as well as setting machine learning algorithms for classification and regression, among other things.

What makes this software so popular amongst others in the same category is the fact that it provides beginners and expert users with a pleasant usage experience, especially when it comes to generating swift data visualizations in a quick and uncomplicated way. Orange, which has 4.2 stars ratings on both Capterra and G2Crowd, offers users multiple online tutorials to get them acquainted with the platform. Additionally, the software learns from the user’s preferences and reacts accordingly, this is one of their most praised functionalities.

17. Data visualization platforms

Data visualization has become one of the most indispensable elements of data analytics tools. If you’re an analyst, there is probably a strong chance you had to develop a visual representation of your analysis or utilize some form of data visualization at some point. Here we need to make clear that there are differences between professional data visualization tools often integrated through already mentioned BI tools, free available solutions as well as paid charting libraries. They’re simply not the same. Also, if you look at data visualization in a broad sense, Excel and PowerPoint also have it on offer, but they simply cannot meet the advanced requirements of a data analyst who usually chooses professional BI or data viz tools as well as modern charting libraries, as mentioned. We will take a closer look at Highcharts as one of the most popular charting libraries on the market.

Interactive JavaScript library compatible with all major web browsers and mobile systems like Android and iOS

Designed mostly for a technical-based audience (developers)

WebGL-powered boost module to render millions of datapoints directly in the browser

Highcharts is a multi-platform library that is designed for developers looking to add interactive charts to web and mobile projects. With a promising 4.6 stars rating in Capterra and 4.5 in G2Crowd, this charting library works with any back-end database and data can be given in CSV, JSON, or updated live. They also feature intelligent responsiveness that fits the desired chart into the dimensions of the specific container but also places non-graph elements in the optimal location automatically.

Highcharts supports line, spline, area, column, bar, pie, scatter charts and many others that help developers in their online-based projects. Additionally, their WebGL-powered boost module enables you to render millions of datapoints in the browser. As far as the source code is concerned, they allow you to download and make your own edits, no matter if you use their free or commercial license. In essence, Basically, Highcharts is designed mostly for the technical target group so you should familiarize yourself with developers’ workflow and their JavaScript charting engine. If you’re looking for a more easy to use but still powerful solution, you might want to consider an online data visualization tool like datapine.

3) Key Takeaways & Guidance

We have explained what are data analyst tools and given a brief description of each to provide you with the insights needed to choose the one (or several) that would fit your analytical processes the best. We focused on diversity in presenting tools that would fit technically skilled analysts such as R Studio, Python, or MySQL Workbench. On the other hand, data analysis software like datapine cover needs both for data analysts and business users alike so we tried to cover multiple perspectives and skill levels.

We hope that by now you have a clearer perspective on how modern solutions can help analysts perform their jobs more efficiently in a less prompt to error environment. To conclude, if you want to start an exciting analytical journey and test a professional BI analytics software for yourself, you can try datapine for a 14-day trial , completely free of charge and with no hidden costs.

Take advantage of modern BI software features today!

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

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

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

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

Overview: Quantitative Data Analysis 101

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

The two “branches” of quantitative analysis

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

What is quantitative data analysis?

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

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

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

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

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

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

How does quantitative analysis work?

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

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

Need a helping hand?

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

So, what are descriptive and inferential statistics?

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

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

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

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

So, why is this sample-population thing important?

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

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

Branch 1: Descriptive Statistics

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

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

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

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

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

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

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

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

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

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

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

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

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

But why do all of these numbers matter?

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

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

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

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

Branch 2: Inferential Statistics

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

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

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

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

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

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

What statistics are usually used in this branch?

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

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

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

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

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

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

Stats overload…

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

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

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

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

How to choose the right analysis method

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

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

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

Factor 1 – Data type

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

Why does this matter?

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

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

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

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

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

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

Factor 2: Your research questions

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

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

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

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

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

Time to recap…

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

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

Psst... there’s more!

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

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74 Comments

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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

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

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

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

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

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

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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Top 21 must-have digital tools for researchers

Last updated

12 May 2023

Reviewed by

Jean Kaluza

Research drives many decisions across various industries, including:

Uncovering customer motivations and behaviors to design better products

Assessing whether a market exists for your product or service

Running clinical studies to develop a medical breakthrough

Conducting effective and shareable research can be a painstaking process. Manual processes are sluggish and archaic, and they can also be inaccurate. That’s where advanced online tools can help. 

The right tools can enable businesses to lean into research for better forecasting, planning, and more reliable decisions. 

• Why do researchers need research tools?

Research is challenging and time-consuming. Analyzing data , running focus groups , reading research papers , and looking for useful insights take plenty of heavy lifting. 

These days, researchers can’t just rely on manual processes. Instead, they’re using advanced tools that:

Speed up the research process

Enable new ways of reaching customers

Improve organization and accuracy

Allow better monitoring throughout the process

Enhance collaboration across key stakeholders

• The most important digital tools for researchers

Some tools can help at every stage, making researching simpler and faster.

They ensure accurate and efficient information collection, management, referencing, and analysis. 

Some of the most important digital tools for researchers include:

Research management tools

Research management can be a complex and challenging process. Some tools address the various challenges that arise when referencing and managing papers. 

.css-10ptwjf{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;background:transparent;border:0;color:inherit;cursor:pointer;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-text-decoration:underline;text-decoration:underline;}.css-10ptwjf:disabled{opacity:0.6;pointer-events:none;} Zotero

Coined as a personal research assistant, Zotero is a tool that brings efficiency to the research process. Zotero helps researchers collect, organize, annotate, and share research easily. 

Zotero integrates with internet browsers, so researchers can easily save an article, publication, or research study on the platform for later. 

The tool also has an advanced organizing system to allow users to label, tag, and categorize information for faster insights and a seamless analysis process. 

Messy paper stacks––digital or physical––are a thing of the past with Paperpile. This reference management tool integrates with Google Docs, saving users time with citations and paper management. 

Referencing, researching, and gaining insights is much cleaner and more productive, as all papers are in the same place. Plus, it’s easier to find a paper when you need it. 

Acting as a single source of truth (SSOT), Dovetail houses research from the entire organization in a simple-to-use place. Researchers can use the all-in-one platform to collate and store data from interviews , forms, surveys , focus groups, and more. 

Dovetail helps users quickly categorize and analyze data to uncover truly actionable insights . This helps organizations bring customer insights into every decision for better forecasting, planning, and decision-making. 

Dovetail integrates with other helpful tools like ​Slack, Atlassian, Notion, and Zapier for a truly efficient workflow.

Putting together papers and referencing sources can be a huge time consumer. EndNote claims that researchers waste 200,000 hours per year formatting citations. 

To address the issue, the tool formats citations automatically––simultaneously creating a bibliography while the user writes. 

EndNote is also a cloud-based system that allows remote working, multiple-user interaction and collaboration, and seamless working on different devices. 

Information survey tools

Surveys are a common way to gain data from customers. These tools can make the process simpler and more cost-effective. 

With ready-made survey templates––to collect NPS data, customer effort scores , five-star surveys, and more––getting going with Delighted is straightforward. 

Delighted helps teams collect and analyze survey feedback without needing any technical knowledge. The templates are customizable, so you can align the content with your brand. That way, the survey feels like it’s coming from your company, not a third party. 

SurveyMonkey

With millions of customers worldwide, SurveyMonkey is another leader in online surveys. SurveyMonkey offers hundreds of templates that researchers can use to set up and deploy surveys quickly. 

Whether your survey is about team performance, hotel feedback, post-event feedback, or an employee exit, SurveyMonkey has a ready-to-use template. 

Typeform offers free templates you can quickly embed, which comes with a point of difference: It designs forms and surveys with people in mind, focusing on customer enjoyment. 

Typeform employs the ‘one question at a time’ method to keep engagement rates and completions high. It focuses on surveys that feel more like conversations than a list of questions.

Web data analysis tools

Collecting data can take time––especially technical information. Some tools make that process simpler. 

For those conducting clinical research, data collection can be incredibly time-consuming. Teamscope provides an online platform to collect and manage data simply and easily. 

Researchers and medical professionals often collect clinical data through paper forms or digital means. Those are too easy to lose, tricky to manage, and challenging to collaborate on. 

With Teamscope, you can easily collect, store, and electronically analyze data like patient-reported outcomes and surveys. 

Heap is a digital insights platform providing context on the entire customer journey . This helps businesses improve customer feedback , conversion rates, and loyalty. 

Through Heap, you can seamlessly view and analyze the customer journey across all platforms and touchpoints, whether through the app or website. 

Another analytics tool, Smartlook, combines quantitative and qualitative analytics into one platform. This helps organizations understand user behavior and make crucial improvements. 

Smartlook is useful for analyzing web pages, purchasing flows, and optimizing conversion rates. 

Project management tools

Managing multiple research projects across many teams can be complex and challenging. Project management tools can ease the burden on researchers. 

Visual productivity tool Trello helps research teams manage their projects more efficiently. Trello makes product tracking easier with:

A range of workflow options

Unique project board layouts

Advanced descriptions

Integrations

Trello also works as an SSOT to stay on top of projects and collaborate effectively as a team. 

To connect research, workflows, and teams, Airtable provides a clean interactive interface. 

With Airtable, it’s simple to place research projects in a list view, workstream, or road map to synthesize information and quickly collaborate. The Sync feature makes it easy to link all your research data to one place for faster action. 

For product teams, Asana gathers development, copywriting, design, research teams, and product managers in one space. 

As a task management platform, Asana offers all the expected features and more, including time-tracking and Jira integration. The platform offers reporting alongside data collection methods , so it’s a favorite for product teams in the tech space.

Grammar checker tools

Grammar tools ensure your research projects are professional and proofed. 

No one’s perfect, especially when it comes to spelling, punctuation, and grammar. That’s where Grammarly can help. 

Grammarly’s AI-powered platform reviews your content and corrects any mistakes. Through helpful integrations with other platforms––such as Gmail, Google Docs, Twitter, and LinkedIn––it’s simple to spellcheck as you go. 

Another helpful grammar tool is Trinka AI. Trinka is specifically for technical and academic styles of writing. It doesn’t just correct mistakes in spelling, punctuation, and grammar; it also offers explanations and additional information when errors show. 

Researchers can also use Trinka to enhance their writing and:

Align it with technical and academic styles

Improve areas like syntax and word choice

Discover relevant suggestions based on the content topic

Plagiarism checker tools

Avoiding plagiarism is crucial for the integrity of research. Using checker tools can ensure your work is original. 

Plagiarism checker Quetext uses DeepSearch™ technology to quickly sort through online content to search for signs of plagiarism. 

With color coding, annotations, and an overall score, it’s easy to identify conflict areas and fix them accordingly. 

Duplichecker

Another helpful plagiarism tool is Duplichecker, which scans pieces of content for issues. The service is free for content up to 1000 words, with paid options available after that. 

If plagiarism occurs, a percentage identifies how much is duplicate content. However, the interface is relatively basic, offering little additional information.  

Journal finder tools

Finding the right journals for your project can be challenging––especially with the plethora of inaccurate or predatory content online. Journal finder tools can solve this issue. 

Enago Journal Finder

The Enago Open Access Journal Finder sorts through online journals to verify their legitimacy. Through Engao, you can discover pre-vetted, high-quality journals through a validated journal index. 

Enago’s search tool also helps users find relevant journals for their subject matter, speeding up the research process. 

JournalFinder

JournalFinder is another journal tool that’s popular with academics and researchers. It makes the process of discovering relevant journals fast by leaning into a machine-learning algorithm.

This is useful for discovering key information and finding the right journals to publish and share your work in. 

Social networking for researchers

Collaboration between researchers can improve the accuracy and sharing of information. Promoting research findings can also be essential for public health, safety, and more. 

While typical social networks exist, some are specifically designed for academics.

ResearchGate

Networking platform ResearchGate encourages researchers to connect, collaborate, and share within the scientific community. With 20 million researchers on the platform, it's a popular choice. 

ResearchGate is founded on an intention to advance research. The platform provides topic pages for easy connection within a field of expertise and access to millions of publications to help users stay up to date. 

Academia is another commonly used platform that connects 220 million academics and researchers within their specialties. 

The platform aims to accelerate research with discovery tools and grow a researcher’s audience to promote their ideas. 

On Academia, users can access 47 million PDFs for free. They cover topics from mechanical engineering to applied economics and child psychology. 

• Expedited research with the power of tools

For researchers, finding data and information can be time-consuming and complex to manage. That’s where the power of tools comes in. 

Manual processes are slow, outdated, and have a larger potential for inaccuracies. 

Leaning into tools can help researchers speed up their processes, conduct efficient research, boost their accuracy, and share their work effectively. 

With tools available for project and data management, web data collection, and journal finding, researchers have plenty of assistance at their disposal.

When it comes to connecting with customers, advanced tools boost customer connection while continually bringing their needs and wants into products and services.

What are primary research tools?

Primary research is data and information that you collect firsthand through surveys, customer interviews, or focus groups. 

Secondary research is data and information from other sources, such as journals, research bodies, or online content. 

Primary researcher tools use methods like surveys and customer interviews. You can use these tools to collect, store, or manage information effectively and uncover more accurate insights. 

What is the difference between tools and methods in research?

Research methods relate to how researchers gather information and data. 

For example, surveys, focus groups, customer interviews, and A/B testing are research methods that gather information. 

On the other hand, tools assist areas of research. Researchers may use tools to more efficiently gather data, store data securely, or uncover insights. 

Tools can improve research methods, ensuring efficiency and accuracy while reducing complexity.

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Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Remember that impending feeling you get when you are asked to analyze your data! Now that you have all the required raw data, you need to statistically prove your hypothesis. Representing your numerical data as part of statistics in research will also help in breaking the stereotype of being a biology student who can’t do math.

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings. In this article, we will discuss how using statistical methods for biology could help draw meaningful conclusion to analyze biological studies.

Table of Contents

Role of Statistics in Biological Research

Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis. Furthermore, biology study focuses on study of living organisms and their complex living pathways, which are very dynamic and cannot be explained with logical reasoning. However, statistics is more complex a field of study that defines and explains study patterns based on the sample sizes used. To be precise, statistics provides a trend in the conducted study.

Biological researchers often disregard the use of statistics in their research planning, and mainly use statistical tools at the end of their experiment. Therefore, giving rise to a complicated set of results which are not easily analyzed from statistical tools in research. Statistics in research can help a researcher approach the study in a stepwise manner, wherein the statistical analysis in research follows –

1. Establishing a Sample Size

Usually, a biological experiment starts with choosing samples and selecting the right number of repetitive experiments. Statistics in research deals with basics in statistics that provides statistical randomness and law of using large samples. Statistics teaches how choosing a sample size from a random large pool of sample helps extrapolate statistical findings and reduce experimental bias and errors.

2. Testing of Hypothesis

When conducting a statistical study with large sample pool, biological researchers must make sure that a conclusion is statistically significant. To achieve this, a researcher must create a hypothesis before examining the distribution of data. Furthermore, statistics in research helps interpret the data clustered near the mean of distributed data or spread across the distribution. These trends help analyze the sample and signify the hypothesis.

3. Data Interpretation Through Analysis

When dealing with large data, statistics in research assist in data analysis. This helps researchers to draw an effective conclusion from their experiment and observations. Concluding the study manually or from visual observation may give erroneous results; therefore, thorough statistical analysis will take into consideration all the other statistical measures and variance in the sample to provide a detailed interpretation of the data. Therefore, researchers produce a detailed and important data to support the conclusion.

Types of Statistical Research Methods That Aid in Data Analysis

Statistical analysis is the process of analyzing samples of data into patterns or trends that help researchers anticipate situations and make appropriate research conclusions. Based on the type of data, statistical analyses are of the following type:

1. Descriptive Analysis

The descriptive statistical analysis allows organizing and summarizing the large data into graphs and tables . Descriptive analysis involves various processes such as tabulation, measure of central tendency, measure of dispersion or variance, skewness measurements etc.

2. Inferential Analysis

The inferential statistical analysis allows to extrapolate the data acquired from a small sample size to the complete population. This analysis helps draw conclusions and make decisions about the whole population on the basis of sample data. It is a highly recommended statistical method for research projects that work with smaller sample size and meaning to extrapolate conclusion for large population.

3. Predictive Analysis

Predictive analysis is used to make a prediction of future events. This analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations.

4. Prescriptive Analysis

Prescriptive analysis examines data to find out what can be done next. It is widely used in business analysis for finding out the best possible outcome for a situation. It is nearly related to descriptive and predictive analysis. However, prescriptive analysis deals with giving appropriate suggestions among the available preferences.

5. Exploratory Data Analysis

EDA is generally the first step of the data analysis process that is conducted before performing any other statistical analysis technique. It completely focuses on analyzing patterns in the data to recognize potential relationships. EDA is used to discover unknown associations within data, inspect missing data from collected data and obtain maximum insights.

6. Causal Analysis

Causal analysis assists in understanding and determining the reasons behind “why” things happen in a certain way, as they appear. This analysis helps identify root cause of failures or simply find the basic reason why something could happen. For example, causal analysis is used to understand what will happen to the provided variable if another variable changes.

7. Mechanistic Analysis

This is a least common type of statistical analysis. The mechanistic analysis is used in the process of big data analytics and biological science. It uses the concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences.

Important Statistical Tools In Research

Researchers in the biological field find statistical analysis in research as the scariest aspect of completing research. However, statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible.

1. Statistical Package for Social Science (SPSS)

It is a widely used software package for human behavior research. SPSS can compile descriptive statistics, as well as graphical depictions of result. Moreover, it includes the option to create scripts that automate analysis or carry out more advanced statistical processing.

2. R Foundation for Statistical Computing

This software package is used among human behavior research and other fields. R is a powerful tool and has a steep learning curve. However, it requires a certain level of coding. Furthermore, it comes with an active community that is engaged in building and enhancing the software and the associated plugins.

3. MATLAB (The Mathworks)

It is an analytical platform and a programming language. Researchers and engineers use this software and create their own code and help answer their research question. While MatLab can be a difficult tool to use for novices, it offers flexibility in terms of what the researcher needs.

4. Microsoft Excel

Not the best solution for statistical analysis in research, but MS Excel offers wide variety of tools for data visualization and simple statistics. It is easy to generate summary and customizable graphs and figures. MS Excel is the most accessible option for those wanting to start with statistics.

5. Statistical Analysis Software (SAS)

It is a statistical platform used in business, healthcare, and human behavior research alike. It can carry out advanced analyzes and produce publication-worthy figures, tables and charts .

6. GraphPad Prism

It is a premium software that is primarily used among biology researchers. But, it offers a range of variety to be used in various other fields. Similar to SPSS, GraphPad gives scripting option to automate analyses to carry out complex statistical calculations.

This software offers basic as well as advanced statistical tools for data analysis. However, similar to GraphPad and SPSS, minitab needs command over coding and can offer automated analyses.

Use of Statistical Tools In Research and Data Analysis

Statistical tools manage the large data. Many biological studies use large data to analyze the trends and patterns in studies. Therefore, using statistical tools becomes essential, as they manage the large data sets, making data processing more convenient.

Following these steps will help biological researchers to showcase the statistics in research in detail, and develop accurate hypothesis and use correct tools for it.

There are a range of statistical tools in research which can help researchers manage their research data and improve the outcome of their research by better interpretation of data. You could use statistics in research by understanding the research question, knowledge of statistics and your personal experience in coding.

Have you faced challenges while using statistics in research? How did you manage it? Did you use any of the statistical tools to help you with your research data? Do write to us or comment below!

Frequently Asked Questions

Statistics in research can help a researcher approach the study in a stepwise manner: 1. Establishing a sample size 2. Testing of hypothesis 3. Data interpretation through analysis

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings.

Statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible. They can manage large data sets, making data processing more convenient. A great number of tools are available to carry out statistical analysis of data like SPSS, SAS (Statistical Analysis Software), and Minitab.

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10 best qualitative data analysis tools

A lot of teams spend a lot of time collecting qualitative customer experience data—but how do you make sense of it, and how do you turn insights into action?

Qualitative data analysis tools help you make sense of customer feedback so you can focus on improving the user and product experience and creating customer delight.

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This chapter of Hotjar's qualitative data analysis (QDA) guide covers the ten best QDA tools that will help you make sense of your customer insights and better understand your users.

Collect qualitative customer data with Hotjar

Use Hotjar’s Surveys and Feedback widget to collect user insights and better understand your customers.

10 tools for qualitative data analysis 

Qualitative data analysis involves gathering, structuring, and interpreting contextual data to identify key patterns and themes in text, audio, and video.

Qualitative data analysis software automates this process, allowing you to focus on interpreting the results—and make informed decisions about how to improve your product—rather than wading through pages of often subjective, text-based data.

Pro tip: before you can analyze qualitative data, you need to gather it. 

One way to collect qualitative customer insights is to place Hotjar Surveys on key pages of your site . Surveys make it easy to capture voice-of-the-customer (VoC) feedback about product features, updated designs, and customer satisfaction—or to perform user and market research.

Need some ideas for your next qualitative research survey? Check out our Hotjar Survey Templates for inspiration.

Example product discovery questions from Hotjar’s bank of survey templates

1. Cauliflower

Cauliflower is a no-code qualitative data analysis tool that gives researchers, product marketers, and developers access to AI-based analytics without dealing with complex interfaces.

How Cauliflower analyzes qualitative data

Cauliflower’s AI-powered analytics help you understand the differences and similarities between different pieces of customer feedback. Ready-made visualizations help identify themes in customers’ words without reading through every review, and make it easy to:

Analyze customer survey data and answers to open-ended questions

Process and understand customer reviews

Examine your social media channels

Identify and prioritize product testing initiatives

Visualize results and share them with your team

One of Cauliflower’s customers says, “[Cauliflower is] great for visualizing the output, particularly finding relevant patterns in comparing breakouts and focussing our qualitative analysis on the big themes emerging.”

NVivo is one of the most popular qualitative data analysis tools on the market—and probably the most expensive. It’s a more technical solution than Cauliflower, and requires more training. NVivo is best for tech-savvy customer experience and product development teams at mid-sized companies and enterprises.

How NVivo analyzes qualitative data

NVivo’s Transcription tool transcribes and analyzes audio and video files from recorded calls—like sales calls, customer interviews, and product demos—and lets you automatically transfer text files into NVivo for further analysis to:

Find recurring themes in customer feedback

Analyze different types of qualitative data, like text, audio, and video

Code and visualize customer input

Identify market gaps based on qualitative and consumer-focused research

Dylan Hazlett from Adial Pharmaceuticals says, “ We needed a reliable software to perform qualitative text analysis. The complexity and features of [Nvivo] have created great value for our team.”

3. ​​Quirkos

Quirkos is a simple and affordable qualitative data analysis tool. Its text analyzer identifies common keywords within text documents to help businesses quickly and easily interpret customer reviews and interviews.

How Quirkos analyzes qualitative data

Quirkos displays side-by-side comparison views to help you understand the difference between feedback shared by different audience groups (by age group, location, gender, etc.). You can also use it to:

Identify keywords and phrases in survey responses and customer interviews

Visualize customer insights

Collaborate on projects

Color code texts effortlessly

One of Quirkos's users says, “ The interface is intuitive, easy to use, and follows quite an intuitive method of assigning codes to documents.”

4. Qualtrics

Qualtrics is a sophisticated experience management platform. The platform offers a range of tools, but we’ll focus on Qualtrics CoreXM here.  

Qualtrics CoreXM lets you collect and analyze insights to remove uncertainty from product development. It helps validate product ideas, spot gaps in the market, and identify broken product experiences, and the tool uses predictive intelligence and analytics to put your customer opinion at the heart of your decision-making.

How Qualtrics analyzes qualitative data

Qualtrics helps teams streamline multiple processes in one interface. You can gather and analyze qualitative data, then immediately share results and hypotheses with stakeholders. The platform also allows you to:

Collect customer feedback through various channels

Understand emotions and sentiment behind customers’ words

Predict what your customers will do next

Act immediately based on the results provided through various integrations

A user in project management shares, “The most useful part of Qualtrics is the depth of analytics you receive on your surveys, questionnaires, and other tools. In real-time, as you develop your surveys, you are given insights into how your data can be analyzed. It is designed to help you get the data you need without asking unnecessary questions.”

5. Dovetail

Dovetail is a customer research platform for growing businesses. It offers three core tools: Playback, Markup, and Backstage. For qualitative data analysis, you’ll need Markup.

Markup offers tools for transcription and analysis of all kinds of qualitative data, and is a great way to consolidate insights.

How Dovetail analyzes qualitative data

Dovetail’s charts help you easily quantify qualitative data. If you need to present your findings to the team, the platform makes it easy to loop in your teammates, manage access rights, and collaborate through the interface. You can:

Transcribe recordings automatically

Discover meaningful patterns in textual data

Highlight and tag customer interviews

Run sentiment analysis

Collaborate on customer research through one interface

Kathryn Rounding , Senior Product Designer at You Need A Budget, says, “Dovetail is a fantastic tool for conducting and managing qualitative research. It helps bring all your research planning, source data, analysis, and reporting together, so you can not only share the final results but all the supporting work that helped you get there.”

6. Thematic

Thematic's AI-driven text feedback analysis platform helps you understand what your customers are saying—and why they’re saying it.

How Thematic analyzes qualitative data

Thematic helps you connect feedback from different channels, uncover themes in customer experience data, and run sentiment analysis—all to make better product decisions. Thematic is helpful when you need to:

Analyze unstructured feedback data from across channels

Discover relationships and patterns in feedback

Reveal emerging trends in customer feedback

Split insights by customer segment

Use resulting data in predictive analytics

Emma Glazer , Director of Marketing at DoorDash, says, “Thematic empowers us with information to help make the right decisions, and I love seeing themes as they emerge. We get real-time signals on issues our customers are experiencing and early feedback on new features they love. I love looking at the week-over-week breakdowns and comparing segments of our audience (market, tenure, etc.) Thematic helps me understand what’s driving our metrics and what steps we need to take next.” 

Delve is cloud-based qualitative data analysis software perfect for coding large volumes of textual data, and is best for analyzing long-form customer interviews.

How Delve analyzes qualitative data

Delve helps reveal the core themes and narratives behind transcripts from sales calls and customer interviews. It also helps to:

Find, group, and refine themes in customer feedback

Analyze long-form customer interviews

Categorize your data by code, pattern, and demographic information

Perform thematic analysis, narrative analysis, and grounded theory analysis

One Delve user says, “Using Delve, it is easier to focus just on coding to start, without getting sidetracked analyzing what I am reading. Once coding is finished, the selected excerpts are already organized based on my own custom outline and I can begin analyzing right away, rather than spending time organizing my notes before I can begin the analysis and writing process.”

8. ATLAS.ti

ATLAS.ti is a qualitative data analysis tool that brings together customer and product research data. It has a range of helpful features for marketers, product analysts, UX professionals, and product designers.

How ATLAS.ti analyzes qualitative data

ATLAS.ti helps product teams collect, structure, and evaluate user feedback before realizing new product ideas. To enhance your product design process with ATLAS.ti, you can:

Generate qualitative insights from surveys

Apply any method of qualitative research

Analyze open-ended questions and standardized surveys

Perform prototype testing

Visualize research results with charts

Collaborate with your team through a single platform

One of the ATLAS.ti customers shares,“ATLAS.ti is innovating in the handling of qualitative data. It gives the user total freedom and the possibility of connecting with other software, as it has many export options.” 

MAXQDA is a data analysis software that can analyze and organize a wide range of data, from handwritten texts, to video recordings, to Tweets.

How MAXQDA analyzes qualitative data

MAWQDA organizes your customer interviews and turns the data into digestible statistics by enabling you to:

Easily transcribe audio or video interviews

Structure standardized and open-ended survey responses

Categorize survey data

Combine qualitative and quantitative methods to get deeper insights into customer data

Share your work with team members

One enterprise-level customer says MAXQDA has “lots of useful features for analyzing and reporting interview and survey data. I really appreciated how easy it was to integrate SPSS data and conduct mixed-method research. The reporting features are high-quality and I loved using Word Clouds for quick and easy data representation.”

10. MonkeyLearn

MonkeyLearn is no-code analytics software for CX and product teams.

How MonkeyLearn analyzes qualitative data

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Guide index

Using Graduate Student Research as an Effective Recruitment Tool

Students in the Mechanical Engineering and Materials Science Department presented research on a range of topics to show prospective candidates what’s possible at Duke.

Duke’s MEMS department’s recent research symposium served as a crucial platform for graduate students to present their work to an audience of would-be Blue Devils. The event proved instrumental in highlighting the interdisciplinary nature of the department, showcasing a selection of research presentations from current MEMS graduate students. 

The symposium included more space for informal interactions with students and visitors, as posters stood outside the conference room in the Wilkinson Engineering Building with groups gathered around exchanging ideas. Lawrie Virgin, professor in the MEMS department and director of graduate studies, says it was the first time the symposium was utilized as a recruitment event.

The combination of posters and talks showcased the wide range of research being conducted in the department, providing the recruits with some in-depth access to current research projects. Lawrie Virgin Professor in the MEMS Department and Director of Graduate Studies Google Logo

“The combination of posters and talks showcased the wide range of research being conducted in the department, providing the recruits with some in-depth access to current research projects,” Virgin said. “It also allowed our current students to gain some experience in preparing their posters and engaging in talks with prospective students.”

The MEMS graduate students organizing the symposium brought their multidisciplinary research in the hopes of conducting another event in the future. “My research presentation covered the synthesis of biocompatible polymers, which can be used to 3D print medical devices,” said Maddiy Segal, a PhD candidate in mechanical engineering and materials science and member of Matthew Becker’s, Hugo L. Blomquist distinguished professor of chemistry, research group. 

“The research symposium was a valuable tool to practice presenting our findings to a more general audience. While PhD students have many opportunities to discuss their research with other scholars in their field, finding opportunities to showcase research to a broader audience is less frequent but just as important,” she shared.

The graduate student committee of the MEMS department led the charge in bringing the event to a wider audience, with committee members focusing on organizing more ways to engage with other students considering coming to Duke. “I think this first symposium was a huge success,” said Annika Haughey, a PhD candidate in the TAST NRT program . 

“We had students presenting from all corners of the department–from aeroelasticity research to materials, as well as surgical robotics. I think the students gained valuable experience presenting and communicating their work effectively,” she said.   

Other students reveled in the opportunity to engage with collaborators and learn about the work of their peers. Defne Circi, a graduate student in MEMS, says the symposium sparked greater appreciation for her colleagues. “I connected with fellow computer science master’s students from the MEMS department,” she explained. “And the presentation broadened my perspective on the variety of research endeavors within our department. Personally, the experience rekindled my appreciation for the dynamic of live presentations and the irreplaceable aspect of face-to-face communication.”

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• Efficacy of psilocybin...

Efficacy of psilocybin for treating symptoms of depression: systematic review and meta-analysis

Linked editorial.

Psilocybin for depression

• Related content
• Peer review
• Athina-Marina Metaxa , masters graduate researcher 1 ,
• Mike Clarke , professor 2
• 1 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
• 2 Northern Ireland Methodology Hub, Centre for Public Health, ICS-A Royal Hospitals, Belfast, Ireland, UK
• Correspondence to: A-M Metaxa athina.metaxa{at}hmc.ox.ac.uk (or @Athina_Metaxa12 on X)
• Accepted 6 March 2024

Objective To determine the efficacy of psilocybin as an antidepressant compared with placebo or non-psychoactive drugs.

Design Systematic review and meta-analysis.

Data sources Five electronic databases of published literature (Cochrane Central Register of Controlled Trials, Medline, Embase, Science Citation Index and Conference Proceedings Citation Index, and PsycInfo) and four databases of unpublished and international literature (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, ProQuest Dissertations and Theses Global, and PsycEXTRA), and handsearching of reference lists, conference proceedings, and abstracts.

Data synthesis and study quality Information on potential treatment effect moderators was extracted, including depression type (primary or secondary), previous use of psychedelics, psilocybin dosage, type of outcome measure (clinician rated or self-reported), and personal characteristics (eg, age, sex). Data were synthesised using a random effects meta-analysis model, and observed heterogeneity and the effect of covariates were investigated with subgroup analyses and metaregression. Hedges’ g was used as a measure of treatment effect size, to account for small sample effects and substantial differences between the included studies’ sample sizes. Study quality was appraised using Cochrane’s Risk of Bias 2 tool, and the quality of the aggregated evidence was evaluated using GRADE guidelines.

Eligibility criteria Randomised trials in which psilocybin was administered as a standalone treatment for adults with clinically significant symptoms of depression and change in symptoms was measured using a validated clinician rated or self-report scale. Studies with directive psychotherapy were included if the psychotherapeutic component was present in both experimental and control conditions. Participants with depression regardless of comorbidities (eg, cancer) were eligible.

Results Meta-analysis on 436 participants (228 female participants), average age 36-60 years, from seven of the nine included studies showed a significant benefit of psilocybin (Hedges’ g=1.64, 95% confidence interval (CI) 0.55 to 2.73, P<0.001) on change in depression scores compared with comparator treatment. Subgroup analyses and metaregressions indicated that having secondary depression (Hedges’ g=3.25, 95% CI 0.97 to 5.53), being assessed with self-report depression scales such as the Beck depression inventory (3.25, 0.97 to 5.53), and older age and previous use of psychedelics (metaregression coefficient 0.16, 95% CI 0.08 to 0.24 and 4.2, 1.5 to 6.9, respectively) were correlated with greater improvements in symptoms. All studies had a low risk of bias, but the change from baseline metric was associated with high heterogeneity and a statistically significant risk of small study bias, resulting in a low certainty of evidence rating.

Conclusion Treatment effects of psilocybin were significantly larger among patients with secondary depression, when self-report scales were used to measure symptoms of depression, and when participants had previously used psychedelics. Further research is thus required to delineate the influence of expectancy effects, moderating factors, and treatment delivery on the efficacy of psilocybin as an antidepressant.

Systematic review registration PROSPERO CRD42023388065.

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Introduction

Depression affects an estimated 300 million people around the world, an increase of nearly 20% over the past decade. 1 Worldwide, depression is also the leading cause of disability. 2

Drugs for depression are widely available but these seem to have limited efficacy, can have serious adverse effects, and are associated with low patient adherence. 3 4 Importantly, the treatment effects of antidepressant drugs do not appear until 4-7 weeks after the start of treatment, and remission of symptoms can take months. 4 5 Additionally, the likelihood of relapse is high, with 40-60% of people with depression experiencing a further depressive episode, and the chance of relapse increasing with each subsequent episode. 6 7

Since the early 2000s, the naturally occurring serotonergic hallucinogen psilocybin, found in several species of mushrooms, has been widely discussed as a potential treatment for depression. 8 9 Psilocybin’s mechanism of action differs from that of classic selective serotonin reuptake inhibitors (SSRIs) and might improve the treatment response rate, decrease time to improvement of symptoms, and prevent relapse post-remission. Moreover, more recent assessments of harm have consistently reported that psilocybin generally has low addictive potential and toxicity and that it can be administered safely under clinical supervision. 10

The renewed interest in psilocybin’s antidepressive effects led to several clinical trials on treatment resistant depression, 11 12 major depressive disorder, 13 and depression related to physical illness. 14 15 16 17 These trials mostly reported positive efficacy findings, showing reductions in symptoms of depression within a few hours to a few days after one dose or two doses of psilocybin. 11 12 13 16 17 18 These studies reported only minimal adverse effects, however, and drug harm assessments in healthy volunteers indicated that psilocybin does not induce physiological toxicity, is not addictive, and does not lead to withdrawal. 19 20 Nevertheless, these findings should be interpreted with caution owing to the small sample sizes and open label design of some of these studies. 11 21

Several systematic reviews and meta-analyses since the early 2000s have investigated the use of psilocybin to treat symptoms of depression. Most found encouraging results, but as well as people with depression some included healthy volunteers, 22 and most combined data from studies of multiple serotonergic psychedelics, 23 24 25 even though each compound has unique neurobiological effects and mechanisms of action. 26 27 28 Furthermore, many systematic reviews included non-randomised studies and studies in which psilocybin was tested in conjunction with psychotherapeutic interventions, 25 29 30 31 32 which made it difficult to distinguish psilocybin’s treatment effects. Most systematic reviews and meta-analyses did not consider the impact of factors that could act as moderators to psilocybin’s effects, such as type of depression (primary or secondary), previous use of psychedelics, psilocybin dosage, type of outcome measure (clinician rated or self-reported), and personal characteristics (eg, age, sex). 25 26 29 30 31 32 Lastly, systematic reviews did not consider grey literature, 33 34 which might have led to a substantial overestimation of psilocybin’s efficacy as a treatment for depression. In this review we focused on randomised trials that contained an unconfounded evaluation of psilocybin in adults with symptoms of depression, regardless of country and language of publication.

In this systematic review and meta-analysis of indexed and non-indexed randomised trials we investigated the efficacy of psilocybin to treat symptoms of depression compared with placebo or non-psychoactive drugs. The protocol was registered in the International Prospective Register of Systematic Reviews (see supplementary Appendix A). The study overall did not deviate from the pre-registered protocol; one clarification was made to highlight that any non-psychedelic comparator was eligible for inclusion, including placebo, niacin, micro doses of psychedelics, and drugs that are considered the standard of care in depression (eg, SSRIs).

Inclusion and exclusion criteria

Double blind and open label randomised trials with a crossover or parallel design were eligible for inclusion. We considered only studies in humans and with a control condition, which could include any type of non -active comparator, such as placebo, niacin, or micro doses of psychedelics.

Eligible studies were those that included adults (≥18 years) with clinically significant symptoms of depression, evaluated using a clinically validated tool for depression and mood disorder outcomes. Such tools included the Beck depression inventory, Hamilton depression rating scale, Montgomery-Åsberg depression rating scale, profile of mood states, and quick inventory of depressive symptomatology. Studies of participants with symptoms of depression and comorbidities (eg, cancer) were also eligible. We excluded studies of healthy participants (without depressive symptomatology).

Eligible studies investigated the effect of psilocybin as a standalone treatment on symptoms of depression. Studies with an active psilocybin condition that involved micro dosing (ie, psilocybin <100 μg/kg, according to the commonly accepted convention 22 35 ) were excluded. We included studies with directive psychotherapy if the psychotherapeutic component was present in both the experimental and the control conditions, so that the effects of psilocybin could be distinguished from those of psychotherapy. Studies involving group therapy were also excluded. Any non-psychedelic comparator was eligible for inclusion, including placebo, niacin, and micro doses of psychedelics.

Changes in symptoms, measured by validated clinician rated or self-report scales, such as the Beck depression inventory, Hamilton depression rating scale, Montgomery-Åsberg depression rating scale, profile of mood states, and quick inventory of depressive symptomatology were considered. We excluded outcomes that were measured less than three hours after psilocybin had been administered because any reported changes could be attributed to the transient cognitive and affective effects of the substance being administered. Aside from this, outcomes were included irrespective of the time point at which measurements were taken.

Search strategy

We searched major electronic databases and trial registries of psychological and medical research, with no limits on the publication date. Databases were the Cochrane Central Register of Controlled Trials via the Cochrane Library, Embase via Ovid, Medline via Ovid, Science Citation Index and Conference Proceedings Citation Index-Science via Web of Science, and PsycInfo via Ovid. A search through multiple databases was necessary because each database includes unique journals. Supplementary Appendix B shows the search syntax used for the Cochrane Central Register of Controlled Trials, which was slightly modified to comply with the syntactic rules of the other databases.

Unpublished and grey literature were sought through registries of past and ongoing trials, databases of conference proceedings, government reports, theses, dissertations, and grant registries (eg, ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, ProQuest Dissertations and Theses Global, and PsycEXTRA). The references and bibliographies of eligible studies were checked for relevant publications. The original search was done in January 2023 and updated search was performed on 10 August 2023.

Data collection, extraction, and management

The results of the literature search were imported to the Endnote X9 reference management software, and the references were imported to the Covidence platform after removal of duplicates. Two reviewers (AM and DT) independently screened the title and abstract of each reference and then screened the full text of potentially eligible references. Any disagreements about eligibility were resolved through discussion. If information was insufficient to determine eligibility, the study’s authors were contacted. The reviewers were not blinded to the studies’ authors, institutions, or journal of publication.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram shows the study selection process and reasons for excluding studies that were considered eligible for full text screening. 36

Critical appraisal of individual studies and of aggregated evidence

The methodological quality of eligible studies was assessed using the Cochrane Risk of Bias 2 tool (RoB 2) for assessing risk of bias in randomised trials. 37 In addition to the criteria specified by RoB 2, we considered the potential impact of industry funding and conflicts of interest. The overall methodological quality of the aggregated evidence was evaluated using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). 38

If we found evidence of heterogeneity among the trials, then small study biases, such as publication bias, were assessed using a funnel plot and asymmetry tests (eg, Egger’s test). 39

We used a template for data extraction (see supplementary Appendix C) and summarised the extracted data in tabular form, outlining personal characteristics (age, sex, previous use of psychedelics), methodology (study design, dosage), and outcome related characteristics (mean change from baseline score on a depression questionnaire, response rates, and remission rates) of the included studies. Response conventionally refers to a 50% decrease in symptom severity based on scores on a depression rating scale, whereas remission scores are specific to a questionnaire (eg, score of ≤5 on the quick inventory of depressive symptomatology, score of ≤10 on the Montgomery-Åsberg depression rating scale, 50% or greater reduction in symptoms, score of ≤7 on the Hamilton depression rating scale, or score of ≤12 on the Beck depression inventory). Across depression scales, higher scores signify more severe symptoms of depression.

Continuous data synthesis

From each study we extracted the baseline and post-intervention means and standard deviations (SDs) of the scores between comparison groups for the depression questionnaires and calculated the mean differences and SDs of change. If means and SDs were not available for the included studies, we extracted the values from available graphs and charts using the Web Plot Digitizer application ( https://automeris.io/WebPlotDigitizer/ ). If it was not possible to calculate SDs from the graphs or charts, we generated values by converting standard errors (SEs) or confidence intervals (CIs), depending on availability, using formulas in the Cochrane Handbook (section 7.7.3.2). 40

Standardised mean differences were calculated for each study. We chose these rather than weighted mean differences because, although all the studies measured depression as the primary outcome, they did so with different questionnaires that score depression based on slightly different items. 41 If we had used weighted mean differences, any variability among studies would be assumed to reflect actual methodological or population differences and not differences in how the outcome was measured, which could be misleading. 40

The Hedges’ g effect size estimate was used because it tends to produce less biased results for studies with smaller samples (<20 participants) and when sample sizes differ substantially between studies, in contrast with Cohen’s d. 42 According to the Cochrane Handbook, the Hedges’ g effect size measure is synonymous with the standardised mean difference, 40 and the terms may be used interchangeably. Thus, a Hedges’ g of 0.2, 0.5, 0.8, or 1.2 corresponds to a small, medium, large, or very large effect, respectively. 40

Owing to variation in the participants’ personal characteristics, psilocybin dosage, type of depression investigated (primary or secondary), and type of comparators, we used a random effects model with a Hartung-Knapp-Sidik-Jonkman modification. 43 This model also allowed for heterogeneity and within study variability to be incorporated into the weighting of the results of the included studies. 44 Lastly, this model could help to generalise the findings beyond the studies and patient populations included, making the meta-analysis more clinically useful. 45 We chose the Hartung-Knapp-Sidik-Jonkman adjustment in favour of more widely used random effects models (eg, DerSimonian and Laird) because it allows for better control of type 1 errors, especially for studies with smaller samples, and provides a better estimation of between study variance by accounting for small sample sizes. 46 47

For studies in which multiple treatment groups were compared with a single placebo group, we split the placebo group to avoid multiplicity. 48 Similarly, if studies included multiple primary outcomes (eg, change in depression at three weeks and at six weeks), we split the treatment groups to account for overlapping participants. 40

Prediction intervals (PIs) were calculated and reported to show the expected effect range of a similar future study, in a different setting. In a random effects model, within study measures of variability, such as CIs, can only show the range in which the average effect size could lie, but they are not informative about the range of potential treatment effects given the heterogeneity between studies. 49 Thus, we used PIs as an indication of variation between studies.

Heterogeneity and sensitivity analysis

Statistical heterogeneity was tested using the χ 2 test (significance level P<0.1) and I 2 statistic, and heterogeneity among included studies was evaluated visually and displayed graphically using a forest plot. If substantial or considerable heterogeneity was found (I 2 ≥50% or P<0.1), 50 we considered the study design and characteristics of the included studies. Sources of heterogeneity were explored by subgroup analysis, and the potential effects on the results are discussed.

Planned sensitivity analyses to assess the effect of unpublished studies and studies at high risk of bias were not done because all included studies had been published and none were assessed as high risk of bias. Exclusion sensitivity plots were used to display graphically the impact of individual studies and to determine which studies had a particularly large influence on the results of the meta-analysis. All sensitivity analyses were carried out with Stata 16 software.

Subgroup analysis

To reduce the risk of errors caused by multiplicity and to avoid data fishing, we planned subgroup analyses a priori and limited to: (1) patient characteristics, including age and sex; (2) comorbidities, such as a serious physical condition (previous research indicates that the effects of psilocybin may be less strong for such participants, compared with participants with no comorbidities) 33 ; (3) number of doses and amount of psilocybin administered, because some previous meta-analyses found that a higher number of doses and a higher dose of psilocybin both predicted a greater reduction in symptoms of depression, 34 whereas others reported the opposite 33 ; (4) psilocybin administered alongside psychotherapeutic guidance or as a standalone treatment; (5) severity of depressive symptoms (clinical v subclinical symptomatology); (6) clinician versus patient rated scales; and (7) high versus low quality studies, as determined by RoB 2 assessment scores.

Metaregression

Given that enough studies were identified (≥10 distinct observations according to the Cochrane Handbook’s suggestion 40 ), we performed metaregression to investigate whether covariates, or potential effect modifiers, explained any of the statistical heterogeneity. The metaregression analysis was carried out using Stata 16 software.

Random effects metaregression analyses were used to determine whether continuous variables such as participants’ age, percentage of female participants, and percentage of participants who had previously used psychedelics modified the effect estimate, all of which have been implicated in differentially affecting the efficacy of psychedelics in modifying mood. 51 We chose this approach in favour of converting these continuous variables into categorical variables and conducting subgroup analyses for two primary reasons; firstly, the loss of any data and subsequent loss of statistical power would increase the risk of spurious significant associations, 51 and, secondly, no cut-offs have been agreed for these factors in literature on psychedelic interventions for mood disorders, 52 making any such divisions arbitrary and difficult to reconcile with the findings of other studies. The analyses were based on within study averages, in the absence of individual data points for each participant, with the potential for the results to be affected by aggregate bias, compromising their validity and generalisability. 53 Furthermore, a group level analysis may not be able to detect distinct interactions between the effect modifiers and participant subgroups, resulting in ecological bias. 54 As a result, this analysis should be considered exploratory.

Sensitivity analysis

A sensitivity analysis was performed to determine if choice of analysis method affected the primary findings of meta-analysis. Specifically, we reanalysed the data on change in depression score using a random effects Dersimonian and Laird model without the Hartung-Knapp-Sidik-Jonkman modification and compared the results with those of the originally used model. This comparison is particularly important in the presence of substantial heterogeneity and the potential of small study effects to influence the intervention effect estimate. 55

Patient and public involvement

Research on novel depression treatments is of great interest to both patients and the public. Although patients and members of the public were not directly involved in the planning or writing of this manuscript owing to a lack of available funding for recruitment and researcher training, patients and members of the public read the manuscript after submission.

Figure 1 presents the flow of studies through the systematic review and meta-analysis. 56 A total of 4884 titles were retrieved from the five databases of published literature, and a further 368 titles were identified from the databases of unpublished and international literature in February 2023. After the removal of duplicate records, we screened the abstracts and titles of 875 reports. A further 12 studies were added after handsearching of reference lists and conference proceedings and abstracts. Overall, nine studies totalling 436 participants were eligible. The average age of the participants ranged from 36-60 years. During an updated search on 10 August 2023, no further studies were identified.

Flow of studies in systematic review and meta-analysis

After screening of the title and abstract, 61 titles remained for full text review. Native speakers helped to translate papers in languages other than English. The most common reasons for exclusion were the inclusion of healthy volunteers, absence of control groups, and use of a survey based design rather than an experimental design. After full text screening, nine studies were eligible for inclusion, and 15 clinical trials prospectively registered or underway as of August 2023 were noted for potential future inclusion in an update of this review (see supplementary Appendix D).

We sent requests for further information to the authors of studies by Griffiths et al, 57 Barrett, 58 and Benville et al, 59 because these studies appeared to meet the inclusion criteria but were only provided as summary abstracts online. A potentially eligible poster presentation from the 58th annual meeting of the American College of Neuropsychopharmacology was identified but the lead author (Griffiths) clarified that all information from the presentation was included in the studies by Davis et al 13 and Gukasyan et al 60 ; both of which we had already deemed ineligible.

Barrett 58 reported the effects of psilocybin on the cognitive flexibility and verbal reasoning of a subset of patients with major depressive disorder from Griffith et al’s trial, 61 compared with a waitlist group, but when contacted, Barrett explained that the results were published in the study by Doss et al, 62 which we had already screened and judged ineligible (see supplementary Appendix E). Benville et al’s study 59 presented a follow-up of Ross et al’s study 17 on a subset of patients with cancer and high suicidal ideation and desire for hastened death at baseline. Measures of antidepressant effects of psilocybin treatment compared with niacin were taken before and after treatment crossover, but detailed results are not reported. Table 1 describes the characteristics of the included studies and table 2 lists the main findings of the studies.

Characteristics of included studies

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Main findings of included studies

Side effects and adverse events

Side effects reported in the included studies were minor and transient (eg, short term increases in blood pressure, headache, and anxiety), and none were coded as serious. Cahart-Harris et al noted one instance of abnormal dreams and insomnia. 63 This side effect profile is consistent with findings from other meta-analyses. 30 68 Owing to the different scales and methods used to catalogue side effects and adverse events across trials, it was not possible to combine these data quantitatively (see supplementary Appendix F).

Risk of bias

The Cochrane RoB 2 tools were used to evaluate the included studies ( table 3 ). RoB 2 for randomised trials was used for the five reports of parallel randomised trials (Carhart-Harris et al 63 and its secondary analysis Barba et al, 64 Goodwin et al 18 and its secondary analysis Goodwin et al, 65 and von Rotz et al 66 ) and RoB 2 for crossover trials was used for the four reports of crossover randomised trials (Griffiths et al, 14 Grob et al, 15 and Ross et al 17 and its follow-up Ross et al 67 ). Supplementary Appendix G provides a detailed explanation of the assessment of the included studies.

Summary risk of bias assessment of included studies, based on domains in Cochrane Risk of Bias 2 tool

Quality of included studies

Confidence in the quality of the evidence for the meta-analysis was assessed using GRADE, 38 through the GRADEpro GDT software program. Figure 2 shows the results of this assessment, along with our summary of findings.

GRADE assessment outputs for outcomes investigated in meta-analysis (change in depression scores and response and remission rates). The risk in the intervention group (and its 95% CI) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). BDI=Beck depression inventory; CI=confidence interval; GRADE=Grading of Recommendations, Assessment, Development and Evaluation; HADS-D=hospital anxiety and depression scale; HAM-D=Hamilton depression rating scale; MADRS=Montgomery-Åsberg depression rating scale; QIDS=quick inventory of depressive symptomatology; RCT=randomised controlled trial; SD=standard deviation

Meta-analyses

Continuous data, change in depression scores —Using a Hartung-Knapp-Sidik-Jonkman modified random effects meta-analysis, change in depression scores was significantly greater after treatment with psilocybin compared with active placebo. The overall Hedges’ g (1.64, 95% CI 0.55 to 2.73) indicated a large effect size favouring psilocybin ( fig 3 ). PIs were, however, wide and crossed the line of no difference (95% CI −1.72 to 5.03), indicating that there could be settings or populations in which psilocybin intervention would be less efficacious.

Forest plot for overall change in depression scores from before to after treatment. CI=confidence interval; DL=DerSimonian and Laird; HKSJ=Hartung-Knapp-Sidik-Jonkman

Exploring publication bias in continuous data —We used Egger’s test and a funnel plot to examine the possibility of small study biases, such as publication bias. Statistical significance of Egger’s test for small study effects, along with the asymmetry in the funnel plot ( fig 4 ), indicates the presence of bias against smaller studies with non-significant results, suggesting that the pooled intervention effect estimate is likely to be overestimated. 69 An alternative explanation, however, is that smaller studies conducted at the early stages of a new psychotherapeutic intervention tend to include more high risk or responsive participants, and psychotherapeutic interventions tend to be delivered more effectively in smaller trials; both of these factors can exaggerate treatment effects, resulting in funnel plot asymmetry. 70 Also, because of the relatively small number of included studies and the considerable heterogeneity observed, test power may be insufficient to distinguish real asymmetry from chance. 71 Thus, this analysis should be considered exploratory.

Funnel plot assessing publication bias among studies measuring change in depression scores from before to after treatment. CI=confidence interval; θ IV =estimated effect size under inverse variance random effects model

Dichotomous data

We extracted response and remission rates for each group when reported directly, or imputed information when presented graphically. Two studies did not measure response or remission and thus did not contribute data for this part of the analysis. 15 18 The random effects model with a Hartung-Knapp-Sidik-Jonkman modification was used to allow for heterogeneity to be incorporated into the weighting of the included studies’ results, and to provide a better estimation of between study variance accounting for small sample sizes.

Response rate —Overall, the likelihood of psilocybin intervention leading to treatment response was about two times greater (risk ratio 2.02, 95% CI 1.33 to 3.07) than with placebo. Despite the use of different scales to measure response, the heterogeneity between studies was not significant (I 2 =25.7%, P=0.23). PIs were, however, wide and crossed the line of no difference (−0.94 to 3.88), indicating that there could be settings or populations in which psilocybin intervention would be less efficacious.

Remission rate —Overall, the likelihood of psilocybin intervention leading to remission of depression was nearly three times greater than with placebo (risk ratio 2.71, 95% CI 1.75 to 4.20). Despite the use of different scales to measure response, no statistical heterogeneity was found between studies (I 2 =0.0%, P=0.53). PIs were, however, wide and crossed the line of no difference (0.87 to 2.32), indicating that there could be settings or populations in which psilocybin intervention would be less efficacious.

Exploring publication bias in response and remission rates data —We used Egger’s test and a funnel plot to examine whether response and remission estimates were affected by small study biases. The result for Egger’s test was non-significant (P>0.05) for both response and remission estimates, and no substantial asymmetry was observed in the funnel plots, providing no indication for the presence of bias against smaller studies with non-significant results.

Heterogeneity: subgroup analyses and metaregression

Heterogeneity was considerable across studies exploring changes in depression scores (I 2 =89.7%, P<0.005), triggering subgroup analyses to explore contributory factors. Table 4 and table 5 present the results of the heterogeneity analyses (subgroup analyses and metaregression, respectively). Also see supplementary Appendix H for a more detailed description and graphical representation of these results.

Subgroup analyses to explore potential causes of heterogeneity among included studies

Metaregression analyses to explore potential causes of heterogeneity among included studies

Cumulative meta-analyses

We used cumulative meta-analyses to investigate how the overall estimates of the outcomes of interest changed as each study was added in chronological order 72 ; change in depression scores and likelihood of treatment response both increased as the percentage of participants with past use of psychedelics increased across studies, as expected based on the metaregression analysis (see supplementary Appendix I). No other significant time related patterns were found.

We reanalysed the data for change in depression scores using a random effects Dersimonian and Laird model without the Hartung-Knapp-Sidik-Jonkman modification and compared the results with those of the original model. All comparisons found to be significant using the Dersimonian and Laird model with the Hartung-Knapp-Sidik-Jonkman adjustment were also significant without the Hartung-Knapp-Sidik-Jonkman adjustment, and confidence intervals were only slightly narrower. Thus, small study effects do not appear to have played a major role in the treatment effect estimate.

Additionally, to estimate the accuracy and robustness of the estimated treatment effect, we excluded studies from the meta-analysis one by one; no important differences in the treatment effect, significance, and heterogeneity levels were observed after the exclusion of any study (see supplementary Appendix J).

In our meta-analysis we found that psilocybin use showed a significant benefit on change in depression scores compared with placebo. This is consistent with other recent meta-analyses and trials of psilocybin as a standalone treatment for depression 73 74 or in combination with psychological support. 24 25 29 30 31 32 68 75 This review adds to those finding by exploring the considerable heterogeneity across the studies, with subsequent subgroup analyses showing that the type of depression (primary or secondary) and the depression scale used (Montgomery-Åsberg depression rating scale, quick inventory of depressive symptomatology, or Beck depression inventory) had a significant differential effect on the outcome. High between study heterogeneity has been identified by some other meta-analyses of psilocybin (eg, Goldberg et al 29 ), with a higher treatment effect in studies with patients with comorbid life threatening conditions compared with patients with primary depression. 22 Although possible explanations, including personal factors (eg, patients with life threatening conditions being older) or depression related factors (eg, secondary depression being more severe than primary depression) could be considered, these hypotheses are not supported by baseline data (ie, patients with secondary depression do not differ substantially in age or symptom severity from patients with primary depression). The differential effects from assessment scales used have not been examined in other meta-analyses of psilocybin, but this review’s finding that studies using the Beck depression inventory showed a higher treatment effect than those using the Montgomery-Åsberg depression rating scale and quick inventory of depressive symptomatology is consistent with studies in the psychological literature that have shown larger treatment effects when self-report scales are used (eg, Beck depression inventory). 76 77 This finding may be because clinicians tend to overestimate the severity of depression symptoms at baseline assessments, leading to less pronounced differences between before and after treatment identified in clinician assessed scales (eg, Montgomery-Åsberg depression rating scale, quick inventory of depressive symptomatology). 78

Metaregression analyses further showed that a higher average age and a higher percentage of participants with past use of psychedelics both correlated with a greater improvement in depression scores with psilocybin use and explained a substantial amount of between study variability. However, the cumulative meta-analysis showed that the effects of age might be largely an artefact of the inclusion of one specific study, and alternative explanations are worth considering. For instance, Studerus et al 79 identified participants’ age as the only personal variable significantly associated with psilocybin response, with older participants reporting a higher “blissful state” experience. This might be because of older people’s increased experience in managing negative emotions and the decrease in 5-hydroxytryptamine type 2A receptor density associated with older age. 80 Furthermore, Rootman et al 81 reported that the cognitive performance of older participants (>55 years) improved significantly more than that of younger participants after micro dosing with psilocybin. Therefore, the higher decrease in depressive symptoms associated with older age could be attributed to a decrease in cognitive difficulties experienced by older participants.

Interestingly, a clear pattern emerged for past use of psychedelics—the higher the proportion of study participants who had used psychedelics in the past, the higher the post-psilocybin treatment effect observed. Past use of psychedelics has been proposed to create an expectancy bias among participants and amplify the positive effects of psilocybin 82 83 84 ; however, this important finding has not been examined in other meta-analyses and may highlight the role of expectancy in psilocybin research.

Limitations of this study

Generalisability of the findings of this meta-analysis was limited by the lack of racial and ethnic diversity in the included studies—more than 90% of participants were white across all included trials, resulting in a homogeneous sample that is not representative of the general population. Moreover, it was not possible to distinguish between subgroups of participants who had never used psilocybin and those who had taken psilocybin more than a year before the start of the trial, as these data were not provided in the included studies. Such a distinction would be important, as the effects of psilocybin on mood may wane within a year after being administered. 21 85 Also, how psychological support was conceptualised was inconsistent within studies of psilocybin interventions; many studies failed to clearly describe the type of psychological support participants received, and others used methods ranging from directive guidance throughout the treatment session to passive encouragement or reassurance (eg, Griffiths et al, 14 Carhart-Harris et al 63 ). The included studies also did not gather evidence on participants’ previous experiences with treatment approaches, which could influence their response to the trials’ intervention. Thus, differences between participant subgroups related to past use of psilocybin or psychotherapy may be substantial and could help interpret this study’s findings more accurately. Lastly, the use of graphical extraction software to estimate the findings of studies where exact numerical data were not available (eg, Goodwin et al, 18 Grob et al 15 ), may have affected the robustness of the analyses.

A common limitation in studies of psilocybin is the likelihood of expectancy effects augmenting the treatment effect observed. Although some studies used low dose psychedelics as comparators to deal with this problem (eg, Carhart-Harris et al, 63 Goodwin et al, 18 Griffiths et al 14 ) or used a niacin placebo that can induce effects similar to those of psilocybin (eg, Grob et al, 15 Ross et al 17 ), the extent to which these methods were effective in blinding participants is not known. Other studies have, however, reported that participants can accurately identify the study groups to which they had been assigned 70-85% of the time, 84 86 indicating a high likelihood of insufficient blinding. This is especially likely for studies in which a high proportion of participants had previously used psilocybin and other hallucinogens, making the identification of the drug’s acute effects easier (eg, Griffiths et al, 14 Grob et al, 15 Ross et al 17 ). Patients also have expectations related to the outcome of their treatment, expecting psilocybin to improve their symptoms of depression, and these positive expectancies are strong predictors of actual treatment effects. 87 88 Importantly, the effect of outcome expectations on treatment effect is particularly strong when patient reported measures are used as primary outcomes, 89 which was the case in several of the included studies (eg, Griffiths et al, 14 Grob et al, 15 Ross et al 17 ). Unfortunately, none of the included studies recorded expectations before treatment, so it is not possible to determine the extent to which this factor affected the findings.

Implications for clinical practice

Although this review’s findings are encouraging for psilocybin’s potential as an effective antidepressant, a few areas about its applicability in clinical practice remain unexplored. Firstly, it is unclear whether the protocols for psilocybin interventions in clinical trials can be reliably and safely implemented in clinical practice. In clinical trials, patients receive psilocybin in a non-traditional medical setting, such as a specially designed living room, while they may be listening to curated calming music and are isolated from most external stimuli by wearing eyeshades and external noise-cancelling earphones. A trained therapist closely supervises these sessions, and the patient usually receives one or more preparatory sessions before the treatment commences. Standardising an intervention setting with so many variables is unlikely to be achievable in routine practice, and consensus is considerably lacking on the psychotherapeutic training and accreditations needed for a therapist to deliver such treatment. 90 The combination of these elements makes this a relatively complex and expensive intervention, which could make it challenging to gain approval from regulatory agencies and to gain reimbursement from insurance companies and others. Within publicly funded healthcare systems, the high cost of treatment may make psilocybin treatment inaccessible. The high cost associated with the intervention also increases the risk that unregulated clinics may attempt to cut costs by making alterations to the protocol and the therapeutic process, 91 92 which could have detrimental effects for patients. 92 93 94 Thus, avoiding the conflation of medical and commercial interests is a primary concern that needs to be dealt with before psilocybin enters mainstream practice.

Implications for future research

More large scale randomised trials with long follow-up are needed to fully understand psilocybin’s treatment potential, and future studies should aim to recruit a more diverse population. Another factor that would make clinical trials more representative of routine practice would be to recruit patients who are currently using or have used commonly prescribed serotonergic antidepressants. Clinical trials tend to exclude such participants because many antidepressants that act on the serotonin system modulate the 5-hydroxytryptamine type 2A receptor that psilocybin primarily acts upon, with prolonged use of tricyclic antidepressants associated with more intense psychedelic experiences and use of monoamine oxidase inhibitors or SSRIs inducing weaker responses to psychedelics. 95 96 97 Investigating psilocybin in such patients would, however, provide valuable insight on how psilocybin interacts with commonly prescribed drugs for depression and would help inform clinical practice.

Minimising the influence of expectancy effects is another core problem for future studies. One strategy would be to include expectancy measures and explore the level of expectancy as a covariate in statistical analysis. Researchers should also test the effectiveness of condition masking. Another proposed solution would be to adopt a 2×2 balanced placebo design, where both the drug (psilocybin or placebo) and the instructions given to participants (told they have received psilocybin or told they have received placebo) are crossed. 98 Alternatively, clinical trials could adopt a three arm design that includes both an inactive placebo (eg, saline) and active placebo (eg, niacin, lower psylocibin dose), 98 allowing for the effects of psilocybin to be separated from those of the placebo.

Overall, future studies should explore psilocybin’s exact mechanism of treatment effectiveness and outline how its physiological effects, mystical experiences, dosage, treatment setting, psychological support, and relationship with the therapist all interact to produce a synergistic antidepressant effect. Although this may be difficult to achieve using an explanatory randomised trial design, pragmatic clinical trial designs may be better suited to psilocybin research, as their primary objective is to achieve high external validity and generalisability. Such studies may include multiple alternative treatments rather than simply an active and placebo treatment comparison (eg, psilocybin v SSRI v serotonin-noradrenaline reuptake inhibitor), and participants would be recruited from broader clinical populations. 99 100 Although such studies are usually conducted after a drug’s launch, 100 earlier use of such designs could help assess the clinical effectiveness of psilocybin more robustly and broaden patient access to a novel type of antidepressant treatment.

Conclusions

This review’s findings on psilocybin’s efficacy in reducing symptoms of depression are encouraging for its use in clinical practice as a drug intervention for patients with primary or secondary depression, particularly when combined with psychological support and administered in a supervised clinical environment. However, the highly standardised treatment setting, high cost, and lack of regulatory guidelines and legal safeguards associated with psilocybin treatment need to be dealt with before it can be established in clinical practice.

What is already known on this topic

Recent research on treatments for depression has focused on psychedelic agents that could have strong antidepressant effects without the drawbacks of classic antidepressants; psilocybin being one such substance

Over the past decade, several clinical trials, meta-analyses, and systematic reviews have investigated the use of psilocybin for symptoms of depression, and most have found that psilocybin can have antidepressant effects

Studies published to date have not investigated factors that may moderate psilocybin’s effects, including type of depression, past use of psychedelics, dosage, outcome measures, and publication biases

What this study adds

This review showed a significantly greater efficacy of psilocybin among patients with secondary depression, patients with past use of psychedelics, older patients, and studies using self-report measures for symptoms of depression

Efficacy did not appear to be homogeneous across patient types—for example, those with depression and a life threatening illness appeared to benefit more from treatment

Further research is needed to clarify the factors that maximise psilocybin’s treatment potential for symptoms of depression

Ethics statements

Ethical approval.

This study was approved by the ethics committee of the University of Oxford Nuffield Department of Medicine, which waived the need for ethical approval and the need to obtain consent for the collection, analysis, and publication of the retrospectively obtained anonymised data for this non-interventional study.

Data availability statement

The relevant aggregated data and statistical code will be made available on reasonable request to the corresponding author.

Acknowledgments

We thank DT who acted as an independent secondary reviewer during the study selection and data review process.

Contributors: AMM contributed to the design and implementation of the research, analysis of the results, and writing of the manuscript. MC was involved in planning and supervising the work and contributed to the writing of the manuscript. AMM and MC are the guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: None received.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; AMM is employed by IDEA Pharma, which does consultancy work for pharmaceutical companies developing drugs for physical and mental health conditions; MC was the supervisor for AMM’s University of Oxford MSc dissertation, which forms the basis for this paper; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The corresponding author (AMM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as registered have been explained.

Dissemination to participants and related patient and public communities: To disseminate our findings and increase the impact of our research, we plan on writing several social media posts and blog posts outlining the main conclusions of our paper. These will include blog posts on the websites of the University of Oxford’s Department of Primary Care Health Sciences and Department for Continuing Education, as well as print publications, which are likely to reach a wider audience. Furthermore, we plan to present our findings and discuss them with the public in local mental health related events and conferences, which are routinely attended by patient groups and advocacy organisations.

Provenance and peer review: Not commissioned; externally peer reviewed.

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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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