Confident use of Jamovi - You will learn Jamovi's layout, modules, and key settings, so you can start working with data on your own and find the right analytical tools much faster.
Well-prepared datasets - You will practice entering, importing, organizing data, and creating computed variables, so you can prepare a clean dataset for analysis without confusion or avoidable errors.
Clear statistics and charts - You will create descriptive statistics, histograms, density plots, and scatterplots, helping you quickly inspect distributions and interpret patterns in your data more accurately.
Choosing the right tests - You will work with tests for independent samples, paired samples, and one-sample designs, so you can match the method to the research question and avoid weak conclusions.
Relationships and prediction - You will build correlation matrices as well as linear and logistic regression models, allowing you to examine relationships between variables and make stronger predictions.
Comparing groups reliably - You will learn ANOVA, selected covariance analyses, and nonparametric tests, so you can compare groups reliably even when your data do not meet standard assumptions.
Exploring measurement structure - You will learn how to assess scale reliability, run principal component analysis, and perform exploratory factor analysis to better evaluate the quality of measurement tools.
Saving and sharing results - You will learn how to save datasets and outputs, archive your work, and prepare analyses for publication or sharing with others, making future work and teamwork much easier.
Jamovi is first presented as a statistical environment with a specific module-based structure and defined package capabilities. Using sample data, the section clarifies how the software is organized and what its core components support before advanced analyses are introduced.
Working with the package begins from setup and quickly moves into routine analytical operations. Participants install Jamovi, navigate the interface, enter and import data, and compute variables, building the practical foundation needed for later statistical procedures in the program.
Descriptive output in Jamovi forms the basis for initial data inspection and communication of results. Here the focus is on summary statistics together with histograms, density plots, and scatter plots, used to identify distributions, patterns, and relationships in observed variables.
For hypothesis testing in simpler study designs, the course addresses procedures for one sample, independent samples, and paired samples. Attention is placed on selecting the proper test according to the research setup and interpreting results within the context of the data.
Relationships between variables can be explored at several levels, from association to prediction. This section starts with the correlation matrix and then moves to linear, binomial logistic, and multinomial logistic regression, with emphasis on matching the model to the outcome type.
When group differences must be assessed under varying assumptions, Jamovi offers both parametric and nonparametric approaches. The training covers one-way ANOVA, multivariate analysis of covariance, and the Kruskal–Wallis and Friedman tests for appropriately structured comparisons.
Factor-based methods are used here to examine structure and measurement quality in multivariate data. The section combines reliability analysis with principal component analysis and exploratory factor analysis, showing how these procedures support scale evaluation and variable reduction.
Saving work efficiently in Jamovi involves both data handling and environment setup. Participants work through methods for storing datasets and analyses, then adjust personal program settings so recurring tasks can be performed in a more consistent and controlled way.
Long-term work with analytical results depends on orderly storage of files, outputs, and supporting materials. This section addresses how to archive information so datasets and completed analyses remain accessible, traceable, and easier to reference in later stages of a project.
Publishing results requires more than exporting output. This part focuses on preparing one’s own analyses for sharing, with attention to how results are organized, documented, and presented so they can be communicated clearly and reused in further work.
The closing segment consolidates the analytical workflow covered throughout the course, from data preparation to model selection and result reporting. It helps relate individual Jamovi functions to appropriate statistical tasks and reinforces how to choose methods that match the structure of the data.
Basic statistics - You should understand core statistical terms such as variable, mean, median, and correlation, so you can follow the analysis process and interpret results in Jamovi.
Working with data - You should be comfortable using data tables and distinguishing variable types, because during the course you will import data, organize it, and create new variables.
Computer skills - You should be able to use a computer confidently, install software, and save files, so you can complete the Jamovi setup and work through the exercises on your own.
Reading outputs - You should be ready to work with numerical results and charts, because the training includes interpreting outputs from tests, regression models, and group comparisons.
