Correlation coefficients are used in many scientific fields. The types of variables used can also vary according to the scientific fields. In the current study, it was aimed to examine the effect of the number of categories and skewness of variables in different sample sizes on the correlation coefficients. Monte Carlo simulation study was conducted and polychoric / tetrachoric, Pearson product moments (PPM), Spearman's rank differences (rho), Kendall's Tau, Goodman-Kruskal Gamma and Lambda coefficients were compared. As a result of the study, it was observed that the polychoric / tetrachoric correlation coefficient had more unbiased results than others. With the increase in the number of categories, unbiased estimates were made by PPM in normally distributed data sets. However, Spearman’s rho could not show sufficient performance in the skewed data sets. The polychoric correlation coefficient gave more unbiased and accurate results in both normal and skewed data compared to other methods. According to the research findings, it is recommended to use the polychoric / tetrachoric correlation coefficient in the correlation analysis performed with categorical data. Although it is stated that the variable can be analyzed as continuous when the number of categories increases, PPM and its non-parametric alternatives Spearman’s rho, Kendall’s Tau coefficient gave biased results.