SPSS Compatibility

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.

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()