Section author: Rebecca Vederhus, Sebastian Jentschke

From SPSS to jamovi: Non-parametric analyses

This comparison shows how a Kruskal-Wallis test is conducted in SPSS and jamovi. The SPSS test follows the description in chapter 7.6.4 in Field (2017), especially figure 7.13 and output 7.5 - 7.6. It uses the data set Soya.sav which can be downloaded from the web page accompanying the book.

SPSS

jamovi

In SPSS, a Kruskal-Wallis test can be run using: Analyze → Nonparametric Tests `` → ``Independent Samples.

In jamovi this is done using the following steps: Analyses → ANOVA → One-Way ANOVA Kruskal-Wallis.

SPSS_Menu_nonParametric3

jamovi_Menu_nonParametric3

In SPSS, choose Fields from the menu. Then, click Use custom field assignments and move the Sperm variable to Test Fields, and the variable Soya to Groups.

In jamovi, move Sperm to the Dependent Variables box and Soya to the Grouping Variable box. Then, tick the box for DSCF pairwise comparisons.

SPSS_Input_nonParametric3_1

jamovi_Input_nonParametric3

Open the Settings window, and click Customize tests. Tick the box for Kruskal-Wallis 1-way ANOVA (k samples), and use the drop-down menu next to Multiple comparisons to select All pairwise.

SPSS_Input_nonParametric3_2

When comparing the outputs, the results in SPSS and jamovi are slightly different. In addition, the results from jamovi appear much clearer.

SPSS_Output_nonParametric3_1

SPSS_Output_nonParametric3_2

SPSS_Output_nonParametric3_3

jamovi_Output_nonParametric3

In SPSS, you have to double-click the summary table to get all of the results. The significance value of the test is found both in the summary table and in the Independent Samples Test View. You can also find the Kruskal-Wallis statistic and the degrees of freedom here.

The output in jamovi contains two tables - Kruskal-Wallis and Pairwise comparisons – Sperm. The Kruskal-Wallis table includes all the same values that are included in the equivalent table in SPSS, except for the sample number. The pairwise comparison looks quite different than the one in SPSS, and only includes a W-value and a p-value.

The Pairwise comparisons tables differ in SPSS and jamovi, but this is likely due to jamovi using a different method to calculate these values than SPSS. In addition, the results from the Kruskal-Wallis test are presented horizontally in jamovi and vertically in SPSS.

The numerical values from the Kruskal-Wallis test are the same in SPSS and jamovi: H = 8.66, df = 3, p = .034.

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.

 
NPTESTS
  /INDEPENDENT TEST (Sperm) GROUP (Soya) KRUSKAL\_WALLIS(COMPARE=PAIRWISE)
  /MISSING SCOPE=ANALYSIS USERMISSING=EXCLUDE
  /CRITERIA ALPHA=0.05 CILEVEL=95.

jmv::anovaNP(
    formula = Sperm ~ Soya,
    data = data,
    es = TRUE,
    pairs = TRUE)

SPSS output file containing the analyses

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