Which PROC step is best suited for checking for missing values in a dataset?

The choice of PROC UNIVARIATE as the best option for checking for missing values in a dataset is based on its comprehensive statistical analysis capabilities. PROC UNIVARIATE provides detailed summaries, including descriptive statistics for each variable, and highlights missing values directly in its output. This PROC step not only identifies missing data but also presents a range of other statistics, such as measures of central tendency and dispersion, which can help in understanding the overall data distribution and quality.

While PROC FREQ can show frequencies and missing values for categorical variables and PROC MEANS can summarize continuous variables including the count of missing values, PROC UNIVARIATE gives a more in-depth analysis. It includes specific statistical tests and provides graphical outputs such as histograms and quantile plots which can aid in visual assessment, making it highly effective for examining the nuances of missing data.

On the other hand, PROC SORT is primarily used for ordering data and does not provide any analysis related to missing values. Hence, it is not relevant for this task of checking for completeness in the dataset.