Autor des Abschnitts: Rebecca Vederhus, Sebastian Jentschke

From SPSS to jamovi: Analysis of frequencies

This comparison shows how a loglinear analysis is conducted in SPSS and jamovi. The SPSS test follows the description in chapter 19.9.2 in Field (2017), especially figure 19.7 and output 19.7 - 19.10. It uses the data set Cats and Dogs.sav which can be downloaded from the web page accompanying the book.

SPSS	jamovi
In SPSS, you can run a loglinear analysis using: Analyze → Loglinear → Model Selection.	In jamovi, this can be done using: Analyses → Frequencies → Log- Linear Regression.
In SPSS, move the variables Animal, Training and Dance to the Factor(s) box. Then, mark all three variables and click Define Range. In this window, set Minimum as 0 and Maximum as 1. Click Continue. In the box called Model Building, click Enter in single step.	In jamovi, move Animal, Training and Dance to Factors. Open the Model Builder window, click + Add New Block and move the three variables to this block.
	Add another block. Mark all three variables and choose All 2 way from the drop-down menu. Then, add a third block and mark all three variables. Open the drop-down menu and click All 3 way.
	Open Model Fit and tick the box for Overall model test. Lastly, tick Likelihood ratio tests in the Model Coefficients window.
Only the results in the output tables K-Way and Higher-Order Effects and Partial Associations in SPSS are replicated in the jamovi analysis.
In the K-Way and Higher-Order Effects table, you can find df-values, likelihood ratio statistics and significance values when K = 1, 2 and 3. The SPSS results also contains Pearson chi-square statistics. The different rows show if any higher-order effects or one-way effects significantly affect the model fit. The Partial Associations table breaks the model into specific parts and tells us which two-way interactions that significantly affect the fit of the model. You can tell this by looking at the significance values for the different interactions and comparing them.	In jamovi, the values that are found in the K-Way and Higher-Order Effects table in SPSS can be found in the Model Fit Measures and Model Comparisons tables. However, jamovi does not provide Pearson chi-square statistics and the number of iterations. The partial associations table in jamovi is called Omnibus Likelihood Ratio Tests and are presented in three separate tables (one for each model).
Output from the SPSS analysis contains a lot of tables that are not included in the jamovi analysis. In addition, the results from the parameter estimates tables differ from each other, and are therefore not included here. The numerical values for the statistics are the same: χ² = 127.90, p < .001; χ² = 200.16, p < .001; χ² = 51.96, p < .001: χ² = 20.30, p < .001; χ² = 65.27, p < .001; χ² = 61.15, p < .001; χ² = 1.48; χ² = 13.76, p < .001; χ² = 13.75, p < .001; χ² = 8.61, p < .01.
Wenn Sie diese Analysen mit Hilfe der Syntax replizieren möchten, können Sie die unten stehenden Befehle verwenden (in jamovi kopieren Sie einfach den unten stehenden Code in Rj). Alternativ können Sie auch die SPSS-Ausgabedateien und die jamovi-Dateien mit den Analysen unterhalb der Syntax herunterladen.
HILOGLINEAR Animal(0 1) Training(0 1) Dance(0 1) /CRITERIA ITERATION(20) DELTA(.0) /PRINT=FREQ RESID ASSOCIATION ESTIM /DESIGN.	jmv::logLinear( data = data, factors = vars(Animal, Training, Dance), blocks = list( list("Animal", "Training", "Dance"), list(c("Animal", "Training"), c("Animal", "Dance"), c("Training", "Dance")), list(c("Animal", "Training", "Dance"))), refLevels = list( list(var = "Animal", ref = "Cat"), list(var = "Training", ref = "Food as Reward"), list(var = "Dance", ref = "No")), modelTest = TRUE, dev = FALSE, aic = FALSE, pseudoR2 = NULL, omni = TRUE)
SPSS output file containing the analyses	jamovi file containing the analyses

Referenzen

Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications. https://edge.sagepub.com/field5e