The second version of Kaiser's Measure of Sampling Adequacy (MSA[subscript 2]) has been widely applied to assess the factorability of data in psychological research. The MSA[subscript 2] is developed in the population and little is known about its behavior in finite samples. If estimated MSA[subscript 2]s are biased due to sampling errors, misleading inferences on the factorability of data are likely to occur. This study investigates the effect of sampling error on MSA[subscript 2] estimation by systematically examining the accuracy and fluctuations of MSA[subscript 2] estimates with simulated continuous and ordered categorical data. Features manipulated included the number of factors, number of variables per factor, factor loading, inter-factor correlations, sample size, number of response categories, skewness of variables, and types of correlation analyzed, Pearson and polychoric correlations. Results revealed that the MSA[subscript 2]s were underestimated due to the effect of sampling error. Severely biased MSA[subscript 2] occurred when analyzing a large number of weakly correlated variables with insufficient participants. The underestimation of MSA[subscript 2] became worse with categorized data. Polychoric correlations yielded slightly more accurate but relatively unstable MSA[subscript 2] estimates compared to Pearson correlations. In practice, researchers need to bear in mind the downward bias of MSA2 estimates and interpret the value of sample MSA[subscript 2]s with caution.