Two-sample configural frequency (CFA) is suggested as a useful statistical tool to compare data from pretest-posttest-designs. The investigated data may be difference or improvement scores. The above procedure is recommended because improvement scores from two dependent samples, although metrically scored, are usually non-normally distributed and therefore not suitable for parametric comparisons. The two-sample CFA is compared to log-linear modeling (LLM); the similarities and dissimilarites between the two statistical methods are presented. LLM takes a model fitting approach, that is LLM tests the goodness-of-fit of a null model, which assumes no interactions between the sample or grouping variable and the outcome variables. Instead of a global approach as used by LLM, CFA takes a local or cell level approach, searching for differences between the hypothesized (null) model and the empirical data. The Fisher-Yates test is introduced as a statistic to test for cell patterns or configurations which discriminate between the two samples under investigation. Real data from educational psychological research is used to demonstrate univariate and bivariate two-sample comparisons.