Section author: Rebecca Vederhus, Sebastian Jentschke

From SPSS to jamovi: Analysis of Covariance (ANCOVA)

This comparison shows how to test the assumption of homogeneity of regression slopes in SPSS and jamovi. The SPSS test follows the description in chapter 13.7 in Field (2017), especially figure 13.9 and output 13.12. It uses the data set Puppy Love Dummy.sav which can be downloaded from the web page accompanying the book.

SPSS

jamovi

In SPSS, you can run this test using: Analyze → General Linear Model → Univariate.

In jamovi you do this using: Analyses → ANOVA → ANCOVA.

SPSS_Menu_ANCOVA5

jamovi_Menu_ANCOVA5

In SPSS, move Happiness to the Dependent Variable box, Dose to the Fixed Factor(s) box, and Puppy_love to the Covariate(s) box.

In jamovi, move the Happiness variable to Dependent Variable, the Dose variable to Fixed Factors, and the Puppy_love variable to Covariates.

SPSS_Input_ANCOVA5_1

jamovi_Input_ANCOVA5_1

Access the Model window, and click Build terms. Then, mark the variables Dose and Puppy_love, open the drop-down menu and choose Interaction.

Next, open the Model window. Mark both Dose and Puppy_love, then click the arrow with the drop-down menu and click Interaction.

SPSS_Input_ANCOVA5_2

jamovi_Input_ANCOVA5_2

Comparisons between the output in SPSS and jamovi shows that the results are the same.

SPSS_Output_ANCOVA5

jamovi_Output_ANCOVA5

In SPSS, the Tests of Between-Subjects Effects table shows the sum of squares for the variables, as well as the interaction term (Dose * Puppy_love). The tables also includes degrees of freedom, mean square, F-values and significance values.

In jamovi, these results are found in a similar ANCOVA table, which also includes sum of squares, degrees of freedom, mean square, F-values and significance values. These values are located in the same order as in the SPSS table.

The main difference between the results in SPSS and jamovi is that SPSS provides values for the following: Corrected Model, Intercept, Total and Corrected Total. SPSS also gives R² and adjusted R² values for the corrected model.

If one compares the numerical values, they are the same: SS*<sub>Dose</sub> = 36.6, *p < .01; SS*<sub>Puppy_love</sub> = 17.2, *p < .05; SS*<sub>Dose * Puppy_love</sub> = 20.4, *p < .05.

If you wish to replicate those analyses using syntax, you can use the commands below (in jamovi, just copy to code below to Rj). Alternatively, you can download the SPSS output files and the jamovi files with the analyses from below the syntax.

 
UNIANOVA Happiness BY Dose WITH Puppy_love
  /METHOD=SSTYPE(3)
  /INTERCEPT=INCLUDE
  /CRITERIA=ALPHA(0.05)
  /DESIGN=Dose\*Puppy_love Dose Puppy_love.

jmv::ancova(
    formula = Happiness ~ Dose + Puppy_love + Dose:Puppy_love,
    data = data)

SPSS output file containing the analyses

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