JASP (Jeffreys’s Amazing Statistics Program^{[2]}) is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form.^{[3]}^{[4]} JASP generally produces APA style results tables and plots to ease publication. It promotes open science via integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by several universities and research funds. As the JASP GUI is developed in C++ using Qt framework, some of the team left to make a notable fork which is Jamovi which has its GUI developed in JavaScript and HTML5.^{[5]}
JASP offers frequentist inference and Bayesian inference on the same statistical models. Frequentist inference uses p-values and confidence intervals to control error rates in the limit of infinite perfect replications. Bayesian inference uses credible intervals and Bayes factors^{[6]}^{[7]} to estimate credible parameter values and model evidence given the available data and prior knowledge.
The following analyses are available in JASP in comparison to SPSS:
JASP 0.18.2 | SPSS 29 | JASP 0.18.2 | SPSS 29 | |
Analysis | Classic | Classic | Bayesian | Bayesian |
Acceptance Sampling | ✓ | X | ||
(repeated) (M)AN(C)OVA and non-parametrics | ✓ | ✓ | (✓) | (✓) |
Audit - Statistical Methods for Auditing | ✓ | X | ✓ | X |
Bain - Bayesian informative hypotheses evaluation | ✓ | X | ||
BSTS - Bayesian structural time series | ✓ | X | ||
Circular / Directional Statistics - analysis of directions, often angles | ✓ | X | X | X |
Cochrane Meta-Analyses | ✓ | X | ✓ | X |
Descriptives | ✓ | ✓ | ||
Distributions | ✓ | X | ✓ | X |
Equivalence T-Tests (TOST): Independent, Paired, One-Sample | ✓ | X | ✓ | X |
Factor Analysis (PCA, EFA, CFA) | ✓ | ✓ / AMOS | X | X |
Frequencies (Binomial, Multinomial, Contingency, Chi², log-linear regression) | ✓ | ✓ | ✓ | (✓) |
JAGS (Bayesian black-box Markov chain Monte Carlo (MCMC) sampler) | ✓ | (AMOS) | ||
Learn Stats (separate Classical & Bayesian module) | ✓ | X | ✓ | X |
Machine Learning (incl Cluster & Discriminant Analyses) | ✓ | ✓ | X | X |
(Cochrane) Meta-Analysis (PET-PEESE, WAAP-WLS for publication bias correction) | ✓ | ✓ | ✓ | X |
(Generalized or Linear) Mixed Models | ✓ | ✓ | ✓ | X |
Network | ✓ | ✓ | ✓ | X |
Power Analysis / Sample Size Planning | (✓) | (✓) | X | X |
Prophet / Time Series Forecasting | X | ✓ | ✓ | X |
Quality Control | ✓ | (✓) | X | X |
Regression / Correlation (r, Rho, Tau, (log)linear, multinomial, ordinal, firth logistic, residual | ✓ | ✓ | (✓) | (✓) |
Reliability | ✓ | ✓ | (✓) | X |
Structural Equation Modeling inkl. (PLS) Partial Least Squares, Latent Growth & MIMIC | SEM lavaan & PROCESS | AMOS & PROCESS | X | X |
Summary Statistics | X | X | ✓ | X |
non-parametric Survival Analyses | ✓ | ✓ | X | X |
T-Tests: Independent, Paired, One-Sample (incl. z, Welch, non-parametrics & robust bayesian) | ✓ | ✓ | ✓ | (✓) |
Visual Modeling: Automated Plotting, (Non-)Linear, Mixed, Generalized Linear | ✓ | ✓ | X | X |
An always up to date version of this table is maintained here https://docs.google.com/spreadsheets/d/1lQ7Pt8vFfSrHxQ9Kh3rjY6Ttx2Yx5b1sVKEGLYU9v4Y/edit#gid=0 | ||||
Sources https://jasp-stats.org/features/ and official IBM SPSS documentation | ||||
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JASP features seven common modules that are enabled by default:
JASP also features multiple additional modules that can be activated via the module menu: