Purpose of this study is to investigate measurement equivalence with latent class analysis in different conditions. Latent class analysis is an alternative method when both observed and latent variables are discrete, or continuous but assumptions like normal distribution, homogeneity, unidimensionality are violated. Within the study 28 different conditions were simulated by changing; sample size, number of items, inequivalence type, number of inequivalent items. BIC, CAIC, AIC and AIC3 are used for model selection. In general, for all information criteria, increasing the sample size induced the true decision rate. Number of items did not cause any alteration in true decision rates. In scale level analysis true decision rates increase parallel with the number of inequivalent items. Also when there is inequivalence both in slope and intercept parameters, more true decisions are taken with respect to the case when there is inequivalence only in intercept parameter. In conclusion BIC and CAIC tend to select false negative, AIC tends to select false positive models whereas AIC3 seems to be more consistent.