The statistical functions of mariposa are checked against reference output of IBM SPSS Statistics 29. This article lists, for each function, how many SPSS values its tests compare and which results are not compared with SPSS.
mariposa 0.7.4, status of 2026-10-19.
Summary
- 42 of 53 statistical functions are compared with SPSS output, with 14,622 SPSS values in total.
- 2 documented algorithmic differences (see below).
How results are compared
Each comparison uses the precision with which SPSS prints the value:
-
Exact: counts, degrees of freedom, and p-values
that SPSS prints as
.000(p < .001). - Printed precision: all other values agree with SPSS to within half a unit of the last decimal SPSS prints (one unit for p-values).
- Documented difference: where SPSS and mariposa use different algorithms by design, the values agree within the tolerance listed under “Documented differences”.
The reference values come from SPSS syntax runs on the
survey_data example data. The comparisons run with the
package’s test suite on every change; the SPSS syntax and output are
part of the source repository (tests/spss_reference).
Results by function
The w_*() functions share one set of SPSS references;
each row shows that total. The last column lists results that are not
compared with SPSS output. They are verified by other means: the
weighted results of the rank-based tests reproduce the unweighted
results exactly when all weights equal 1 (the weighted Mann-Whitney test
also agrees with survey::svyranktest()), and the remaining
results are checked against published formulas or independent
implementations.
| Function | SPSS values | Exact | Printed precision | Documented difference | Not compared with SPSS |
|---|---|---|---|---|---|
ancova() |
800 | 147 | 653 | 0 | — |
binomial_test() |
58 | 24 | 34 | 0 | weighted results |
chi_square() |
235 | 53 | 182 | 0 | — |
chisq_gof() |
133 | 51 | 82 | 0 | — |
crosstab() |
3,369 | 1,419 | 1,950 | 0 | adjusted standardized residuals |
describe() |
288 | 18 | 270 | 0 | — |
dunn_test() |
6 | 0 | 6 | 0 | weighted results |
efa() |
578 | 21 | 473 | 84 | — |
factorial_anova() |
609 | 200 | 409 | 0 | — |
fisher_test() |
412 | 100 | 312 | 0 | — |
frequency() |
150 | 24 | 126 | 0 | — |
friedman_test() |
56 | 18 | 38 | 0 | weighted results |
kendall_tau() |
202 | 100 | 102 | 0 | weighted results |
kruskal_wallis() |
97 | 52 | 45 | 0 | weighted results |
levene_test() |
144 | 0 | 144 | 0 | — |
linear_regression() |
602 | 35 | 567 | 0 | — |
logistic_regression() |
29 | 12 | 17 | 0 | all results (checked against the formulas of the SPSS algorithm) |
mann_whitney() |
93 | 19 | 74 | 0 | weighted results |
mcnemar_test() |
143 | 84 | 59 | 0 | — |
multiple_response() |
52 | 19 | 33 | 0 | all results (checked by direct computation) |
normality_test() |
21 | 6 | 15 | 0 | all results (checked against independent implementations) |
oneway_anova() |
1,443 | 234 | 1,209 | 0 | — |
pairwise_wilcoxon() |
242 | 106 | 136 | 0 | weighted results |
partial_cor() |
20 | 4 | 16 | 0 | all results (checked against an equivalent regression computation) |
pearson_cor() |
73 | 18 | 55 | 0 | — |
reliability() |
458 | 45 | 413 | 0 | McDonald’s omega |
scheffe_test() |
1,422 | 0 | 1,422 | 0 | — |
spearman_rho() |
238 | 118 | 120 | 0 | — |
t_test() |
673 | 9 | 664 | 0 | — |
tukey_test() |
1,418 | 0 | 1,418 | 0 | — |
w_iqr() |
444 | 132 | 312 | 0 | — |
w_kurtosis() |
444 | 132 | 312 | 0 | — |
w_mean() |
444 | 132 | 312 | 0 | — |
w_median() |
444 | 132 | 312 | 0 | — |
w_modus() |
444 | 132 | 312 | 0 | — |
w_quantile() |
444 | 132 | 312 | 0 | — |
w_range() |
444 | 132 | 312 | 0 | — |
w_sd() |
444 | 132 | 312 | 0 | — |
w_se() |
444 | 132 | 312 | 0 | — |
w_skew() |
444 | 132 | 312 | 0 | — |
w_var() |
444 | 132 | 312 | 0 | — |
wilcoxon_test() |
114 | 48 | 66 | 0 | weighted results |
Documented differences
| Function | Results | Tolerance | Reason |
|---|---|---|---|
efa() |
ML loadings, sums of squares, % of variance | 0.002 | SPSS stops the ML iteration at its convergence criterion (.001); mariposa iterates to the optimum. |
efa() |
ML communalities / SS where SPSS did not converge | 0.05 | The SPSS reference runs end without convergence (3 factors from 6 items, df = 0, Heywood cases). |
A documented difference describes a known difference between the algorithms, not an error; its tolerance is the agreement the two algorithms reach.
Functions without their own SPSS comparison
- Computed by
chi_square(), whose results are compared with SPSS:cramers_v(),goodman_gamma(),phi() - Data transformations, covered by unit tests:
center(),pomps(),rec(),row_count(),row_means(),row_sums(),std() - Data documentation without an SPSS counterpart:
codebook()