Learn to Analyze Data and Statistics Using jamovi

As a seasoned data scientist and full-stack developer, I‘ve worked with a wide variety of tools for statistical analysis and visualization. While popular commercial programs like SPSS and SAS have long been industry standards, a more recent open-source alternative has been rapidly gaining traction: jamovi.

jamovi is a free, cross-platform application that aims to make data analysis more accessible and intuitive to a wider audience. It provides a user-friendly graphical interface for exploring data and conducting a range of statistical tests, while leveraging the power and flexibility of the R programming language under the hood.

According to jamovi‘s lead developer Jonathon Love, the goal is to create "the spreadsheet of stats packages" that prioritizes immediate results and ease of use. At the same time, the close integration with R provides a pathway for users to grow and expand their analytical capabilities over time.

In this comprehensive guide, we‘ll cover everything you need to know to start using jamovi for your own data analysis needs, from the perspective of a professional coder and full-stack developer. Whether you‘re a student learning statistics for the first time, or an experienced researcher looking to simplify your workflow, jamovi offers a fresh approach that‘s well worth exploring. Let‘s get started!

Getting Started with jamovi

Installing jamovi on your system is quick and painless. Simply head to the official jamovi website, click ‘Download‘ at the top, and select the appropriate installer for your operating system (Windows, macOS, or Linux). Follow the prompts to complete the installation, and you‘ll be ready to start using jamovi in no time.

When you first open jamovi, you‘ll see a startup window that looks like this:

On the left side is the spreadsheet interface where you can enter, view, and edit your data directly within jamovi. Along the top are options for importing data from external files in formats like CSV, Excel, SPSS, SAS, and R.

The right side of the jamovi window is where you‘ll build and view the results of your analyses. jamovi uses a simple drag-and-drop interface for specifying variables and options, and the output appears in real-time as you make changes. This dynamic workflow makes it easy to explore different analyses and visualizations without needing to re-run your code each time.

Before diving into any analyses, though, it‘s crucial to first examine and clean your data. As famed statistician John Tukey said, "It is important to understand what you CAN DO before you learn to measure how WELL you seem to have DONE it." Let‘s look at some of the ways jamovi facilitates data exploration.

Exploring and Visualizing Data

One of the most powerful features of jamovi is its seamless integration of visual and numerical data exploration. With just a few clicks, you can generate interactive graphs and charts that provide a high-level overview of your variables and their relationships.

For example, suppose we‘re analyzing data from a survey of school students. We can quickly visualize the distribution of math scores by creating a histogram under the ‘Exploration‘ menu:

We can see that the scores roughly follow a normal bell curve distribution, with a majority of students scoring between 60-80. But there are a few potential outliers on both the low and high ends that we may want to investigate further.

In addition to histograms, jamovi offers box plots, scatterplots, bar charts, pie charts, and more. And you‘re not limited to just the default options either – each plot has a variety of customization settings for changing the colors, labels, axes, and scales to suit your specific needs.

To look at relationships between multiple variables, jamovi‘s ‘Scatterplot Matrix‘ provides a quick snapshot:

The diagonal shows histograms for each individual subject, while the cells in the upper and lower triangles show the pairwise correlations between subjects. We can see there are positive linear correlations between each pair of exam scores – for example, students who score higher in reading also tend to score higher in writing and math. The ability to visualize multiple variables simultaneously is a key advantage of jamovi over other point-and-click software.

But graphical exploration is just part of the process. To get a more precise numerical summary, we can run descriptive statistics:

This table displays common metrics like the mean, median, standard deviation, and quartiles for each variable. We can confirm that average scores were highest in reading (69.2) and lowest in math (66.4), and that variability was similar across all three exams based on standard deviations of about 15 points.

Together, the graphical and numerical summaries in jamovi allow us to build a comprehensive understanding of our data before we begin testing any hypotheses. And with the ability to easily filter and split data using the ‘Data‘ toolbar, we can quickly repeat these analyses for different subsets and uncover even more granular insights.

Statistical Analysis and Modeling

Once we have a solid grasp of our data, we can start using jamovi to answer research questions and test hypotheses. The core statistical analysis functions are all available under the ‘Analyses‘ menu, divided into categories like T-Tests, ANOVA, Regression, and Factor.

As a case study, let‘s test whether there‘s a significant difference in exam scores between male and female students. We‘ll use an independent samples t-test, which compares means of a continuous variable across two groups. In the jamovi sidebar, we select ‘T-Tests‘ and drag our variables into the appropriate boxes:

For the Dependent Variable, we choose math score, and for the Grouping Variable we select gender. In the main options, we check the box for ‘Descriptives‘ and ‘Welch procedure‘ for a more robust test given the slightly unequal sample sizes.

When we click the ‘Results‘ button, jamovi displays the full output of our t-test:

The "Independent Samples T-Test" table shows the key results – we see that males scored an average of 5.9 points higher than females, a statistically significant difference at p < .001. The Welch version confirms this finding and indicates that we can assume equal variances. We also get descriptive statistics split by gender group, which shows males were not only higher on average, but also more variable in their scores based on the larger standard deviation.

This is just one basic example of the many statistical tests available in jamovi. Other common procedures include:

One-way ANOVA for comparing means across 3+ groups
Correlation for measuring linear relationships between variables
Linear regression for predicting outcomes from one or more variables
Logistic regression for predicting categorical outcomes
Repeated measures ANOVA for analyzing data collected at multiple timepoints
Factor analysis for uncovering latent structures in multivariate data
Chi-square tests for analyzing contingency tables of categorical variables

Each analysis comes with extensive options for descriptive statistics, post-hoc tests, effect sizes, model fit indices, diagnostic plots, and more. jamovi strikes an ideal balance of flexibility and simplicity – providing just enough control to conduct a thorough analysis, while still maintaining an intuitive interface that doesn‘t overwhelm new users.

And if there‘s an analysis you need that‘s not available by default, chances are you can find an R package that‘s been designed to work seamlessly with jamovi. Let‘s look at jamovi‘s integration with R in more detail.

Extending jamovi with R Modules

Perhaps the biggest selling point of jamovi is that it‘s built on top of the open-source R programming language. This means that even though jamovi presents a graphical user interface, you can also leverage the thousands of community-developed packages in R to extend its functionality.

Installing an R module in jamovi is a breeze. Simply go to the ‘Modules‘ menu, click ‘jamovi library,‘ and browse the list of available modules. For example, if we wanted to perform a mixed effects linear regression, we could install the GAMLj module:

Once installed, the module appears in the Analyses menu just like the built-in options. We can specify our model formula, regression coefficients, variance components, and all the other options we‘d have access to if we were running the analysis in R directly.

In addition to expanding the modeling capabilities, R modules can also provide custom data transformations, advanced visualizations, and even interactive web applications. Some popular examples include:

R-Factor: Factor analysis with rotation tools and visual model summaries
MAJOR: Meta-analysis module for jamovi
jpower: Power analysis for various statistical tests
jsq: Simple questionnaire app for creating surveys within jamovi
jAMM: Advanced mediation models

No matter how specialized or cutting-edge an analysis, the jamovi + R ecosystem has you covered. And the modular, open-source nature of the platform means that users can even develop their own custom analyses and contribute them back to the community.

As a professional coder myself, I see tremendous value in the way jamovi facilitates collaboration and reproducibility. All analyses can be saved as ‘jmv‘ files that bundle the data, selected options, and R code together. When sharing an analysis with colleagues or students, they can see exactly how the results were produced, and even edit or add to the analysis themselves. It‘s like a Jupyter Notebook for statistics.

Teaching and Learning with jamovi

So far we‘ve focused on jamovi‘s applications for research and data analysis. But it‘s also an incredibly powerful tool for statistical education, thanks to its visual, interactive approach.

jamovi‘s intuitive interface means that students can jump right into analyzing real data, without getting bogged down in complex syntax or programming concepts. Instructors can easily create interactive tutorials that walk students through key topics and replicate textbook examples.

For instance, if we were teaching a basic stats course, we could use jamovi to demonstrate the central limit theorem:

By adjusting the sample size and number of samples, students can visualize in real-time how the sampling distribution of the mean approaches a normal curve as n increases. No coding required – just simple controls that illustrate the concept perfectly.

jamovi‘s development team also maintains a companion website called Learning Statistics with jamovi that provides a complete set of open educational resources, including:

Textbook covering introductory statistics using jamovi
Data sets for examples and exercises
Lecture slides and videos
Worksheets and activities
Discussion forums for instructors and students

All of these resources are freely available under Creative Commons licenses, making it easy for educators to adopt, customize, and share content. It‘s an invaluable collection for anyone teaching or learning statistics and data analysis.

Conclusion

I hope this deep dive into jamovi has given you a sense of why it‘s quickly becoming the tool of choice for statistical analysis in both research and education. By combining the ease of use of commercial programs with the expandability of open-source platforms, jamovi truly offers the best of both worlds.

Whether you‘re exploring a new data set for the first time, or conducting rigorous hypothesis tests and regressions, jamovi provides an intuitive yet powerful interface. Its dynamic integration of graphics and numerical results facilitates insight and streamlines your analysis workflow. And with built-in tools for sharing and reproducing analyses, jamovi fosters open science and collaboration.

As a full-stack developer, I‘m constantly evaluating tools for their flexibility, scalability, and interoperability with other systems. jamovi checks all of those boxes and more, thanks to its seamless integration with R. No matter how complex or specialized your analysis needs, the ever-growing library of jamovi and R modules has you covered.

Perhaps most importantly, though, jamovi is built on a foundation of community and accessibility. As an open-source project guided by the principles of user empowerment and statistical literacy, jamovi is uniquely positioned to democratize data science. Its approachable interface and rich set of educational resources can help students and practitioners from all backgrounds develop essential analysis skills.

jamovi‘s ultimate goal is to, in the words of creator Jonathon Love, "make the power and flexibility of R, available in a free, open-source tool usable by scientists and statisticians, students and data analysts, who may not be programmers." After exploring jamovi‘s extensive capabilities in this guide, I think it‘s well on its way to achieving that ambitious vision.

If you‘re intrigued by what you‘ve seen of jamovi so far, I encourage you to download it for yourself and work through some of the tutorials and sample data sets. Connect with the community forums to ask questions, make suggestions, and see how others are using jamovi for research and teaching.

No matter your background or statistical experience level, jamovi provides an exciting new way to explore, analyze, and communicate with data. Give it a try, and see how it can streamline and enhance your own workflow. The future of open, accessible data science is here, and it‘s powered by jamovi.