Comparison of analyses 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 metaanalyses, structural equation models, etc.).
If you are willing to use some R code (e.g., in conjunction with the
jamovimodule Rj) then you can (most presumably) do every analysis you
can imagine.


Reports 

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 jamovimodule «jsq» 
Bayesian Statistics → One Sample Normal 
TTest → Bayesian One Sample TTest 
Bayesian Statistics → One Sample Binomial 
Frequencies → Bayesian Proportion Test 
Bayesian Statistics → One Sample Poisson 
Frequencies → Bayesian Contingency Tables 
Bayesian Statistics → Related Sample Normal 
TTest → Bayesian Paired Samples TTest 
Bayesian Statistics → Independent Samples Normal 
TTest → Bayesian Independent Samples TTest 
Bayesian Statistics → Pearson Correlation 
Regression → Bayesian Correlation Matrix / Bayesian Correlation Pairs 
Bayesian Statistics → Linear Regression 
Regression → Bayesian Linear Regression 
Bayesian Statistics → Oneway ANOVA 
ANOVA → Bayesian ANOVA (can handle several factors while SPSS is limited to one factor) 
Bayesian Statistics → LogLinear Models 
Frequencies → Bayesian LogLinear Regression 
Compare Means 

Compare Means → Means... 
Exploration → Descriptives replaces / integrates that functionality, choose the dropdown menu «Statistics» and set ticks at «Mean», «N» and «Std. deviation» 
Compare Means → IndependentSamples T Test 
TTest → Independent Samples TTest 
Compare Means → PairedSamples T Test 
TTest → Paired Samples TTest 
Compare Means → OneSample T Test 
TTest → One Sample TTest 
Compare Means → OneWay ANOVA 
ANOVA → OneWay ANOVA 
General Linear Model 

General Linear Model → Univariate 
ANOVA → OneWay 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 jamovimodule «GAMLj» 
Generalized Linear Models → Generalized Linear Models 

Generalized Linear Models → Generalized Estimating Equations 

Mixed Models 
requires the jamovimodule «GAMLj» 
Mixed Models → Linear 

Mixed Models → Generalized Linear 

Correlate 

Correlate → Bivariate 
Regression → Correlation Matrix 
Correlate → Partial 
Regression → Partial Correlation 
Correlate → Distances 
N/A 
Regression 

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 

Loglinear → General 
Frequencies → LogLinear Regression 
Loglinear → Logit 
N/A 
Loglinear → Model Selection 
N/A 
Classify 

Classify → Nearest Neighbor 
N/A 
Classify → Discriminant 
N/A, can be calculated using Rcode and the Rlibrary «MASS» 
Classify → TwoStep Cluster 
N/A 
Classify → Hierarchical Cluster 
N/A, can be calculated using Rcode and the Rlibrary «pvclust» 
Classify → KMeans Cluster 

Dimension Reduction 

Dimension Reduction → Factor 
Factor → (Data reduction) → Principal Component Analysis
Factor → (Data reduction) → Exploratory Factor Analysis 1

Scale 

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 withinsubject) 
Nonparametric Tests → Independent Samples 

Nonparametric Tests → Related Samples 

Nonparametric Tests → Legacy Dialogs → ChiSquare 
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 → 1Sample KS 
ShapiroWilks available under Exploration → Descriptives, choose dropdown menu «Statistics» and tick «ShapiroWilks» (KolmogoroffSmirnov available via the additional module moretests) 
Nonparametric Tests → Legacy Dialogs → 2 Independent Samples 
TTest → Independent Samples TTest, set tickbox «MannWhitney U» 
Nonparametric Tests → Legacy Dialogs → 2 Related Samples 
TTest → Paired Samples TTest, set tickbox «Wilcoxon Rank» 
Nonparametric Tests → Legacy Dialogs → K Independent Samples 
ANOVA → (NonParametric) → OneWay ANOVA (KruskalWallis) 
Nonparametric Tests → Legacy Dialogs → K Related Samples 
ANOVA → (NonParametric) → Repeated Measures ANOVA (Friedman) 
Survival 
requires the jamovimodule «Death watch» 
Survival → Life Tables 

Survival → KaplanMeier 

Survival → Cox Regression 

Survival → Cox w/ TimeDep 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 

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.