Comparison of analyses available in SPSS and jamovi

SPSS (version 27)

jamovi (versjon 2.0)



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) and
(2) The features implemented already cover “standard” needs (90% of the most common analyses used 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; MAJOR).
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 could imagine.



Reports → Codebook


Reports → OLAP Cubes


Reports → Case summaries

Exploration → Descriptives has the same functionality

Reports → Reports Summaries in Rows


Reports → Reports Summaries in Columns


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


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


General Linear Model

General Linear Model → Univariate


General Linear Model → Multivariate


General Linear Model → Repeated Measures

ANOVA → Repeated Measures ANOVA

General Linear Model → Variance Components


Generalized Linear Models

requires the jamovi-module «GAMLj» (General Analyses for the Linear Model in jamovi)

Generalized Linear Models → Generalized Linear Models

Generalized Linear Models → Generalized Estimating Equations

Mixed Models

requires the jamovi-module «GAMLj» (General Analyses for the Linear Model in jamovi)

Mixed Models → Linear

Mixed Models → Generalized Linear


Correlate → Bivariate

Regression → Correlation Matrix

Correlate → Partial

Regression → Partial Correlation

Correlate → Distances



Regression → Automatic Linear Models


Regression → Linear

Regression → Linear Regression

Regression → Ordinal

Regression → (Logistic Regression) → Ordinal Outcomes

Regression → Curve Estimation

Regression → Partial Least Squares


Loglinear → General

Frequencies → Log-Linear Regression

Loglinear → Logit

Loglinear → Model Selection


Classify → Nearest Neighbor


Classify → Discriminant

N/A, can be calculated using R-code and the R-library «MASS»

Classify → TwoStep Cluster


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


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


Nonparametric Tests → Legacy Dialogs → 1-Sample K-S

N/A, Shapiro-Wilks available under Exploration → Descriptives, choose drop-down menu «Statistics» and tick «Shapiro-Wilks»

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)


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


Multiple Response → Frequencies

Multiple Response → Crosstabs

ROC Curve

ROC Curve

N/A, accessible via R packages (e.g., ROCR eller pROC)




Spatial and Temporal Modeling

Spatial and Temporal Modeling → Spatial Modeling



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.