Section author: Sebastian Jentschke

Comparison of Which Analyses Are Available in SPSS and jamovi

SPSS jamovi


Already at first glance, it becomes clear that jamovi currently has fewer features than SPSS. BUT:
(1) There is a (ever increasing) made available via modules (press the “+” sign in the right upper corner of the jamovi window to add them).
(2) The features implemented already cover “standard” needs (90% of the most frequently used analyses in psychology).
Feel free to check out which modules are available: There is also quite a wealth of modules covering functions that are not available in SPSS but very useful (e.g., for meta-analyses, structural equation models, etc.).
If you are willing to use some R code (e.g., in conjunction with the jamovi-module Rj) then you can (most presumably) do every analysis you can imagine.


Reports → Codebook N/A
Reports → OLAP Cubes N/A
Reports → Case summaries Exploration → Descriptives has the same functionality
Reports → Reports Summaries in Rows N/A
Reports → Reports Summaries in Columns N/A
Descriptive Statistics
Descriptive Statistics → Frequencies
Exploration → Descriptives combines all three procedures
tick «Frequency tables» to get an output that is similar to that of «Frequencies» in SPSS
Descriptive Statistics → Descriptives
Descriptive Statistics → Explore
Descriptive Statistics → Crosstabs Frequencies → (Contingency tables) → Independent samples
Descriptive Statistics → Ratio N/A
Bayesian Statistics requires the jamovi-module «jsq»
Bayesian Statistics → One Sample Normal T-Test → Bayesian One Sample T-Test
Bayesian Statistics → One Sample Binomial Frequencies → Bayesian Proportion Test
Bayesian Statistics → One Sample Poisson Frequencies → Bayesian Contingency Tables
Bayesian Statistics → Related Sample Normal T-Test → Bayesian Paired Samples T-Test
Bayesian Statistics → Independent Samples Normal T-Test → Bayesian Independent Samples T-Test
Bayesian Statistics → Pearson Correlation Regression → Bayesian Correlation Matrix / Bayesian Correlation Pairs
Bayesian Statistics → Linear Regression Regression → Bayesian Linear Regression
Bayesian Statistics → One-way ANOVA ANOVA → Bayesian ANOVA (can handle several factors while SPSS is limited to one factor)
Bayesian Statistics → Log-Linear Models Frequencies → Bayesian Log-Linear Regression
Compare Means
Compare Means → Means… Exploration → Descriptives replaces / integrates that functionality, choose the drop-down menu «Statistics» and set ticks at «Mean», «N» and «Std. deviation»
Compare Means → Independent-Samples T Test T-Test → Independent Samples T-Test
Compare Means → Paired-Samples T Test T-Test → Paired Samples T-Test
Compare Means → One-Sample T Test T-Test → One Sample T-Test
Compare Means → One-Way ANOVA ANOVA → One-Way ANOVA
General Linear Model
General Linear Model → Univariate ANOVA → One-Way ANOVA
General Linear Model → Multivariate ANOVA → MANCOVA
General Linear Model → Repeated Measures ANOVA → Repeated Measures ANOVA
General Linear Model → Variance Components N/A
Generalized Linear Models requires the jamovi-module «GAMLj»
Generalized Linear Models → Generalized Linear Models  
Generalized Linear Models → Generalized Estimating Equations  
Mixed Models requires the jamovi-module «GAMLj»
Mixed Models → Linear  
Mixed Models → Generalized Linear  
Correlate → Bivariate Regression → Correlation Matrix
Correlate → Partial Regression → Partial Correlation
Correlate → Distances N/A
Regression → Automatic Linear Models N/A
Regression → Linear Regression → Linear Regression
Regression → Ordinal Regression → (Logistic Regression) → Ordinal Outcomes
Regression → Curve Estimation N/A
Regression → Partial Least Squares N/A
Loglinear → General Frequencies → Log-Linear Regression
Loglinear → Logit N/A
Loglinear → Model Selection N/A
Classify → Nearest Neighbor N/A
Classify → Discriminant N/A, can be calculated using R-code and the R-library «MASS»
Classify → TwoStep Cluster N/A
Classify → Hierarchical Cluster N/A, can be calculated using R-code and the R-library «pvclust»
Classify → K-Means Cluster
Dimension Reduction
Dimension Reduction → Factor
Factor → (Data reduction) → Principal Component Analysis
Factor → (Data reduction) → Exploratory Factor Analysis [1]
Scale → Reliability Analysis Factor → (Scale analysis) → Reliability analysis
Scale → Multidimensional Scaling N/A
Nonparametric Tests
Nonparametric Tests → One Sample N/A, the tests itself are available (see below), but not a common start menu that allows a selection based on your data (e.g., between- or within-subject)
Nonparametric Tests → Independent Samples
Nonparametric Tests → Related Samples
Nonparametric Tests → Legacy Dialogs → Chi-Square Frequencies → (One Sample Proportion Tests) → N Outcomes (x² goodness of fit)
Nonparametric Tests → Legacy Dialogs → Binomial Frequencies → (One Sample Proportion Tests) → 2 Outcomes (Binomial test)
Nonparametric Tests → Legacy Dialogs → Runs N/A
Nonparametric Tests → Legacy Dialogs → 1-Sample K-S Shapiro-Wilks available under Exploration → Descriptives, choose drop-down menu «Statistics» and tick «Shapiro-Wilks» (Kolmogoroff-Smirnov available via the additional module moretests)
Nonparametric Tests → Legacy Dialogs → 2 Independent Samples T-Test → Independent Samples T-Test, set tick-box «Mann-Whitney U»
Nonparametric Tests → Legacy Dialogs → 2 Related Samples T-Test → Paired Samples T-Test, set tick-box «Wilcoxon Rank»
Nonparametric Tests → Legacy Dialogs → K Independent Samples ANOVA → (Non-Parametric) → One-Way ANOVA (Kruskal-Wallis)
Nonparametric Tests → Legacy Dialogs → K Related Samples ANOVA → (Non-Parametric) → Repeated Measures ANOVA (Friedman)
Survival requires the jamovi-module «Death watch»
Survival → Life Tables  
Survival → Kaplan-Meier  
Survival → Cox Regression  
Survival → Cox w/ Time-Dep Cov  
Multiple Response
Multiple Response → Define Variable Sets N/A
Multiple Response → Frequencies  
Multiple Response → Crosstabs  
ROC Curve
ROC Curve N/A, accessible via R packages (e.g., ROCR eller pROC)
Simulation N/A
Spatial and Temporal Modeling
Spatial and Temporal Modeling → Spatial Modeling N/A
[1]Whereas SPSS puts both methods into one procedure (FACTOR) makes jamovi a conceptual difference between Principal Component Analysis aiming at data reduction (i.e., reducing the number of dimension that are required to describe the data) and Exploratory Factor Analysis aiming at extracting underlying latent variables.