この節の作者: 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 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. 