Which statistical approach involves comparing frequencies or means in quantitative research?

Descriptive statistics is indeed the correct choice when it comes to comparing frequencies or means in quantitative research. This approach is fundamental for summarizing and presenting data in a meaningful way. Descriptive statistics provide simple summaries about the sample and the measures, helping researchers understand the central tendency (mean, median, mode) and the distribution of their data (frequency counts or percentages). By using these summaries, researchers can easily compare different groups or categories, which is particularly useful when looking at frequencies and means.

In contrast, factor analysis is used primarily to identify underlying relationships between variables and to reduce data dimensions rather than to compare frequencies or means. Correlation analysis focuses on measuring the strength and direction of the relationship between two quantitative variables but does not specifically address means or frequencies directly. Regression analysis extends correlation by examining how one variable predicts another, which also does not lead to direct comparisons of frequencies or means. Each of these other approaches serves distinct purposes in statistical analysis, making descriptive statistics the most appropriate for the stated goal.