Which analysis method is particularly effective for evaluating demographic differences?

Cross-Tabulation is particularly effective for evaluating demographic differences because it allows researchers to compare two or more categorical variables visually and quantitatively. This method organizes data into a matrix format where the frequencies of responses are displayed, making it easy to determine how different demographic groups respond to various survey questions or variables.

By analyzing the intersections of the demographic variables with other responses, researchers can uncover significant patterns or trends. This is especially useful in market research, social science studies, and any context where understanding how different segments of the population differ in their opinions, preferences, or behaviors is essential.

Other analysis methods serve different purposes. Variance analysis focuses on understanding the variance in a dataset rather than comparing categories of data. Factor analysis is used to identify underlying relationships between variables and reduce data complexity rather than directly comparing demographic groups. Predictive analysis, on the other hand, aims to forecast future outcomes based on historical data rather than evaluating specific demographic differences in the data set.