Add column proportions z-test for post-hoc testing in the Complex survey module

I would like to request for a future SPSS release to include column proportions z-test for post-hoc testing in the Complex survey module.

Surveys are an often used resource to collect informative data from a large number of people. However, analysis of survey data is often complex. Luckily, SPSS has developed a Custom Survey module to help with this type of analyses. However, when running a chi-square test in this module, there is no solution to run post-hoc tests on significant chi-square tests (this applies to analyses that are greater than 2x2). However, this solution is available for non-survey data or non-complex survey data under the Crosstabs feature or in Custom Tables. However, these modules do not take into account the complex survey design and so post-hoc results are not as accurate.

There were several options provided to me from the SPSS statistics team:

1. Use Custom Tables with effective base weighting (using the final sample weight variable used in Complex Samples as the effective base weighting variable on the Options tab in Custom Tables) and specifying column proportions tests.

   1. However, the results from this should more closely track those from Complex Samples than using CROSSTABS, but they will only be approximations to the results from a full Complex Samples treatment

2. Within the formal Complex Samples module, there are some options, but none are exactly what's desired.

   1. One is to specify table percentages in the Complex Samples Crosstabs Statistics dialog and check the box for Adjusted residuals. These adjusted residuals provide approximate Z values for testing a cell vs. the average of the row (or column) rather than against another column in that row.

   2. Another option is to use the Complex Samples Logistic Regression procedure, which will allow you to specify contrasts of columns against each other using the Odds Ratios option, but unless the row variable in the original table has only two rows, a recoded row variable where one value represents the original row and a second value combines all the other original rows would have to be used to match the proportions comparisons from column proportions tests , and this would have to be done for each row (using this recoded binary variable as the dependent variable). Then, within the analysis for each row, the Odds Ratios specifications would let you compare each level of the original column variable against a chosen reference level, so to get all pairwise, multiple runs would be required, using a different reference category for each.

   3. Finally, the Odds Ratios output produces estimates of odds ratios and confidence intervals, not p values.

While I appreciate the thoughtfulness that went into these recommendations, it would be far easier (and helpful!) to be able to do post-hoc testing directly in the Complex Survey module itself, rather than the work-arounds.