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JASP
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<h2 id="analyses">Analyses</h2> <p>JASP offers frequentist inference and Bayesian inference on the same <a href="/facts/Statistical_models/jKkT5ftm">statistical models</a>. <a href="/facts/Frequentist_inference/UcNgebQc">Frequentist inference</a> uses <a href="/facts/P-values/InnYd1bz">p-values</a> and <a href="/facts/Confidence_intervals/NS8mG5UE">confidence intervals</a> to control error rates in the limit of infinite perfect replications. <a href="/facts/Bayesian_inference/tEpK3zLx">Bayesian inference</a> uses <a href="/facts/Credible_intervals/BeVTjGrm">credible intervals</a> and <a href="/facts/Bayes_factors/Cu4QH8hv">Bayes factors</a><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a><a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a> to estimate credible parameter values and model evidence given the available data and prior knowledge. </p> <p>The following analyses are available in JASP in comparison to SPSS: </p> GUI Features (features available via R or SPSS Syntax not listed)<table><tbody><tr><td></td><td>JASP 0.19.3</td><td>SPSS 30</td><td>JASP 0.19.3</td><td>SPSS 30</td></tr><tr><td>Analysis</td><td>Classic</td><td>Classic</td><td>Bayesian</td><td>Bayesian</td></tr><tr><td>Acceptance Sampling</td><td>✓</td><td>X</td><td></td><td></td></tr><tr><td>(repeated) (M)AN(C)OVA and non-parametrics</td><td>✓</td><td>✓</td><td>(✓)</td><td>(✓)</td></tr><tr><td>Audit - Statistical Methods for Auditing</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>Bain - Bayesian informative hypotheses evaluation</td><td></td><td></td><td>✓</td><td>X</td></tr><tr><td>BSTS - Bayesian structural time series</td><td></td><td></td><td>✓</td><td>X</td></tr><tr><td>Circular / Directional Statistics - analysis of directions, often angles</td><td>✓</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Cochrane Meta-Analyses</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>Descriptives (stats & plots like Rainclouds, Flexplots, Time Series)</td><td>✓</td><td>(✓)</td><td></td><td></td></tr><tr><td>Distributions (continuous & discrete)</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>Equivalence T-Tests (TOST): Independent, Paired, One-Sample</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>Factor Analysis (PCA, EFA, CFA)</td><td>✓</td><td>✓ / AMOS</td><td>X</td><td>X</td></tr><tr><td>Frequencies (Binomial, Multinomial, Contingency, Chi², log-linear regression)</td><td>✓</td><td>✓</td><td>✓</td><td>(✓)</td></tr><tr><td>JAGS (Bayesian black-box Markov chain Monte Carlo (MCMC) sampler)</td><td></td><td></td><td>✓</td><td>(AMOS)</td></tr><tr><td>Learn Stats (separate Classical & Bayesian module)</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>Machine Learning (incl Cluster & Discriminant Analyses)</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>Meta-Analysis, multilevel/multivariate (effect size, funnel plot, prediction model perf., selection models, PET-PEESE, WAAP-WLS for publication bias correction)</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>(Generalized or Linear) Mixed Models</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Network</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Power Analysis / Sample Size Planning</td><td>(✓)</td><td>(✓)</td><td>X</td><td>X</td></tr><tr><td>PROCESS (Hayes models for mediation, moderation etc.)</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Prophet / Time Series Forecasting</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Quality Control</td><td>✓</td><td>(✓)</td><td>X</td><td>X</td></tr><tr><td>Regression / Correlation (r, Rho, Tau, (log)linear, multinomial, ordinal, firth logistic, residual)</td><td>✓</td><td>✓</td><td>(✓)</td><td>(✓)</td></tr><tr><td>Reliability</td><td>✓</td><td>✓</td><td>(✓)</td><td>X</td></tr><tr><td>Structural Equation Modeling inkl. (PLS) Partial Least Squares, Latent Growth & MIMIC</td><td>✓</td><td>AMOS</td><td>X</td><td>X</td></tr><tr><td>Summary Statistics</td><td>X</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>non- & semi-parametric Survival Analyses</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>T-Tests: Independent, Paired, One-Sample (incl. z, Welch, non-parametrics & robust bayesian)</td><td>✓</td><td>✓</td><td>✓</td><td>(✓)</td></tr><tr><td>Visual Modeling: Automated Plotting, (Non-)Linear, Mixed, Generalized Linear</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td colspan="5">An always up to date version of this table is maintained here <a href="https://docs.google.com/spreadsheets/d/1lQ7Pt8vFfSrHxQ9Kh3rjY6Ttx2Yx5b1sVKEGLYU9v4Y/edit#gid=0">https://docs.google.com/spreadsheets/d/1lQ7Pt8vFfSrHxQ9Kh3rjY6Ttx2Yx5b1sVKEGLYU9v4Y/edit#gid=0</a></td></tr><tr><td colspan="5" rowspan="1">Sources https://jasp-stats.org/features/ and official IBM SPSS documentation</td></tr><tr><td colspan="5" rowspan="1">Empty field suggests, that this may not be possible</td></tr></tbody></table> <h2 id="other-features">Other features</h2> <ul><li>R syntax editing and highlighting.</li> <li>Plot and formula (LaTeX) editing.</li> <li>Exports results as PDF or HTML and tables as <a href="/facts/LaTeX/c1lvjF0A">LaTeX</a> format.; plots as PNG, PPTX (Powerpoint) etc.</li> <li>Imports Excel and SPSS files, comma-separated files etc. (.xls, xlsx, .csv, .txt, .tsv, .ods, .dta, .sav, .zsav, .por, .sas7bdat, .sas7bcat, .xpt, .jasp)</li> <li>Connects to SQL data bases and the <a href="/facts/Open_Science_Framework/As68Udes">Open Science Framework</a>.</li> <li>Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest.</li> <li>Full data editing with one-click recoding; full undo / redo functionality,</li> <li>Compute columns via R code (e.g. via row-wise functions like rowMean, rowMeanNaRm, rowSum, rowSD ...) or a drag-and-drop GUI to create new variables or compute them from existing ones.</li> <li>Empty values settings per variable, per data set or globally.</li> <li>Assumption checks via export and then plotting of residuals and/or per analyses via tests and plots (<a href="/facts/Levene%27s_test/0asLmXnT">Levene's</a>, Brown-Forsythe, <a href="/facts/Shapiro%E2%80%93Wilk_test/6yu3fq4K">Shapiro–Wilk</a>, <a href="/facts/Q%E2%80%93Q_plot/h2jKBjYm">Q–Q</a>, Raincloud etc.)</li></ul> <h2 id="modules">Modules</h2> <p>JASP features seven common modules that are enabled by default: </p> <ol><li>Descriptives: Explore the data with tables and plots.</li> <li>T-Tests: Evaluate the difference between two means.</li> <li>ANOVA: Evaluate the difference between multiple means.</li> <li>Mixed Models: Evaluate the difference between multiple means with random effects.</li> <li>Regression: Evaluate the association between variables.</li> <li>Frequencies: Analyses for count data.</li> <li>Factor: Explore hidden structure in the data.</li></ol> <p>JASP also features multiple additional modules that can be activated via the module menu: </p> <ol><li>Acceptance Sampling: Methods for <a href="/facts/Acceptance_sampling/DID8JcyG">acceptance sampling</a> and a <a href="/facts/Quality_control/lnXbvqvp">quality control</a> setting.</li> <li>Audit: Statistical methods for <a href="/facts/Auditing/u8mr4Xsd">auditing</a>. The audit module offers planning, selection and evaluation of <a href="/facts/Sampling_risk/PIBgB1pE">statistical audit samples</a>, methods for <a href="/facts/Data_auditing/F9ICuoAG">data auditing</a> (e.g., <a href="/facts/Benford%E2%80%99s_law/CDfMJgdq">Benford’s law</a>) and algorithm auditing (e.g., <a href="/facts/Fairness_(machine_learning)/7n115zPr"> model fairness</a>).</li> <li>Bain: Bayesian informative hypotheses evaluation<a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> for t-tests, <a href="/facts/Analysis_of_variance/HHR2jkDk">ANOVA</a>, <a href="/facts/Analysis_of_covariance/yJB0SUy0">ANCOVA</a>, <a href="/facts/Linear_regression/5n998IhK">linear regression</a> and <a href="/facts/Structural_equation_modeling/g5nWG6zy">structural equation modeling</a>.</li> <li>BSTS: Bayesian take on linear Gaussian <a href="/facts/State-space_representation/Jf4EanCP"> state space models</a> suitable for time series analysis.</li> <li>Circular Statistics: Basic methods for <a href="/facts/Directional_statistics/ucnUv52T"> directional data</a>.</li> <li>Cochrane meta-analyses: Analyse <a href="/facts/Cochrane_(organisation)/UlmdJ1H8"> Cochrane</a> medical datasets.</li> <li>Distributions: Visualise <a href="/facts/Probability_distribution/EpsKKVRu"> probability distributions</a> and fit them to data.</li> <li>Equivalence T-Tests: Test the difference between two means with an interval-null hypothesis.</li> <li>JAGS: Implement Bayesian models with the <a href="/facts/Just_another_Gibbs_sampler/QqUyBjYB"> JAGS</a> program for <a href="/facts/Markov_chain_Monte_Carlo/eTjk3WQy">Markov chain Monte Carlo</a>.</li> <li>Learn Bayes: Learn <a href="/facts/Bayesian_statistics/9w7b1Bw4">Bayesian statistics</a> with simple examples and supporting text.</li> <li>Learn Stats: Learn <a href="/facts/Frequentist_inference/UcNgebQc"> classical statistics</a> with simple examples and supporting text.</li> <li>Machine Learning: Explore the relation between variables using data-driven methods for <a href="/facts/Supervised_learning/JlNFNPFt">supervised learning</a> and <a href="/facts/Unsupervised_learning/8a6f61MC">unsupervised learning</a>. The module contains 19 analyses for <a href="/facts/Regression_analysis/n6z5Tf7K"> regression</a>, <a href="/facts/Statistical_classification/jXXHRkXR"> classification</a> and <a href="/facts/Cluster_analysis/HUeDnWPt"> clustering</a>: <ul><li>Regression <ol><li>Boosting Regression</li> <li>Decision Tree Regression</li> <li><a href="/facts/K-nearest_neighbors_algorithm/Bs1UaAmE">K-Nearest Neighbors Regression</a></li> <li>Neural Network Regression</li> <li>Random Forest Regression</li> <li>Regularized Linear Regression</li> <li>Support Vector Machine Regression</li></ol></li> <li>Classification <ol><li><a href="/facts/Boosting_(machine_learning)/HgejTPPu">Boosting Classification</a></li> <li>Decision Tree Classification</li> <li>K-Nearest Neighbors Classification</li> <li>Neural Network Classification</li> <li>Linear Discriminant Classification</li> <li><a href="/facts/Random_forest/Ff1ppJEQ">Random Forest Classification</a></li> <li>Support Vector Machine Classification</li></ol></li> <li>Clustering <ol><li><a href="/facts/Density-based_clustering/HUeDnWPt">Density-Based Clustering</a></li> <li><a href="/facts/Fuzzy_clustering/MEmTL2po">Fuzzy C-Means Clustering</a></li> <li><a href="/facts/Hierarchical_clustering/KxXs6GPK">Hierarchical Clustering</a></li> <li><a href="/facts/Model-based_clustering/zkVRu6qS">Model-based clustering</a></li> <li>Neighborhood-based Clustering (i.e., <a href="/facts/K-means_clustering/vxIcqHwN">K-Means Clustering</a>, K-Medians clustering, K-Medoids clustering)</li> <li>Random Forest Clustering</li></ol></li></ul></li> <li>Meta Analysis: Synthesise evidence across multiple studies. Includes techniques for fixed and random effects analysis, fixed and mixed effects meta-regression, forest and funnel plots, tests for funnel plot asymmetry, trim-and-fill and fail-safe N analysis.</li> <li>Network: Explore the connections between variables organised as a network. Network Analysis allows the user to analyze the network structure.</li> <li>Power: Conduct <a href="/facts/Power_analysis/zvldKaKj"> power analyses</a>.</li> <li>Predictive Analytics: This module offers predictive analytics.</li> <li>Process: Implementation of Hayes' popular SPSS PROCESS module for JASP</li> <li>Prophet: A simple model for time series prediction.</li> <li>Quality Control: Investigate if a manufactured product adheres to a defined set of quality criteria.</li> <li>Reliability: Quantify the reliability of test scores.</li> <li>Robust T-Tests: Robustly evaluate the difference between two means.</li> <li>SEM (<a href="/facts/Structural_equation_modeling/g5nWG6zy">Structural equation modeling</a>): Evaluate latent data structures with Yves Rosseel's lavaan program.<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a></li> <li>Summary statistics: Apply common Bayesian tests from frequentist summary statistics for t-test, regression, and binomial tests.</li> <li>Survival Analyses: non- & semi-parametric</li> <li>Time Series: Time series analysis.</li> <li>Visual Modeling: Graphically explore the dependencies between variables.</li> <li>R Console: Execute R code in a console.</li></ol> <h2 id="external-links">External links</h2> <ul><li><a href="https://jasp-stats.org/">Official website</a></li> <li><a href="https://github.com/jasp-stats/jasp-desktop">jasp-desktop</a> on <a href="/facts/GitHub/n1hNCsc3">GitHub</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>"FAQ - JASP". JASP. Retrieved 18 February 2022. <a href="https://jasp-stats.org/faq/#:~:text=JASP%20stands%20for" target="_blank">https://jasp-stats.org/faq/#:~:text=JASP%20stands%20for</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Wagenmakers EJ, Love J, Marsman M, Jamil T, Ly A, Verhagen J, et al. (February 2018). "Bayesian inference for psychology. Part II: Example applications with JASP". Psychonomic Bulletin & Review. 25 (1): 58–76. doi:10.3758/s13423-017-1323-7. PMC 5862926. PMID 28685272. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862926" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862926</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Love J, Selker R, Verhagen J, Marsman M, Gronau QF, Jamil T, Smira M, Epskamp S, Wil A, Ly A, Matzke D, Wagenmakers EJ, Morey MD, Rouder JN (2015). "Software to Sharpen Your Stats". APS Observer. 28 (3). <a href="http://www.psychologicalscience.org/index.php/publications/observer/2015/march-15/bayes-or-bust-with-new-softwares.html" target="_blank">http://www.psychologicalscience.org/index.php/publications/observer/2015/march-15/bayes-or-bust-with-new-softwares.html</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>"Introducing jamovi: Free and Open Statistical Software Combining Ease of Use with the Power of R". 23 March 2017. <a href="https://blog.efpsa.org/2017/03/23/introducing-jamovi-free-and-open-statistical-software-combining-ease-of-use-with-the-power-of-r/" target="_blank">https://blog.efpsa.org/2017/03/23/introducing-jamovi-free-and-open-statistical-software-combining-ease-of-use-with-the-power-of-r/</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Quintana DS, Williams DR (June 2018). "Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP". BMC Psychiatry. 18 (1): 178. doi:10.1186/s12888-018-1761-4. PMC 5991426. PMID 29879931. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991426" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991426</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Brydges CR, Gaeta L (December 2019). "An Introduction to Calculating Bayes Factors in JASP for Speech, Language, and Hearing Research". Journal of Speech, Language, and Hearing Research. 62 (12): 4523–4533. doi:10.1044/2019_JSLHR-H-19-0183. PMID 31830850. S2CID 209342577. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Gu, Xin; Mulder, Joris; Hoijtink, Herbert (2018). "Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses". British Journal of Mathematical and Statistical Psychology. 71 (2): 229–261. doi:10.1111/bmsp.12110. ISSN 2044-8317. PMID 28857129. <a href="https://doi.org/10.1111%2Fbmsp.12110" target="_blank">https://doi.org/10.1111%2Fbmsp.12110</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Kline, Rex B. (2015-11-03). Principles and Practice of Structural Equation Modeling, Fourth Edition. Guilford Publications. ISBN 9781462523351. <a href="9781462523351" target="_blank">9781462523351</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> </ol>