この節の作者: Rebecca Vederhus, Sebastian Jentschke

From SPSS to jamovi: Correlation

This comparison shows how a Kendall’s tau correlation is conducted in SPSS and jamovi. The SPSS test follows the description in chapter 8.4.4 in Field (2017), especially output 8.3. It uses the data set The Biggest Liar.sav which can be downloaded from the web page accompanying the book.
SPSS jamovi
In SPSS you can run a correlation using: Analyze → Correlate → Bivariate. In jamovi you do this using: Analyses → Regression → Correlation Matrix.
SPSS_Menu_corr3 jamovi_Menu_corr3
In SPSS, move the variables Creativity and Position to the variable box Variables. Then, tick Kendall’s tau-b under Correlation Coefficients. In jamovi, move the variables Creativity and Position into the box on the right. Tick Kendall’s tau-b under Correlation Coefficients and Report significance, Flag significant correlations and N under Additional Options.
SPSS_Input_corr3 jamovi_Input_corr3
The results in SPSS and jamovi are identical.
SPSS_Output_corr3 jamovi_Output_corr3
In SPSS, you can tell if a correlation is significant by noting the amount of asterisks after the correlation coefficient. The correlation matrix shows the correlation coefficient, the p-value, and the sample size (N). jamovi, in accordance with APA-style, flags significant correlations using one (p < .05), two (p < .01) or three (p < .001) asterisks. In contrast, in SPSS all correlations above p < .01 level are simply marked using two instead of three asterisks.
The correlation coefficient is found both underneath and over the diagonal in SPSS, while in jamovi the coefficient is only shown underneath. The numerical values for the statistics are the same: r = -.30, p < .01.
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. 
NONPAR CORR
  /VARIABLES=Creativity Position
  /PRINT=KENDALL TWOTAIL NOSIG FULL
  /MISSING=PAIRWISE.

jmv::corrMatrix(
    data = data,
    vars = vars(Creativity, Position),
    pearson = FALSE,
    kendall = TRUE,
    flag = TRUE,
    n = TRUE)
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