PROC TTEST: VAR Statement :: SAS/STAT(R) 9.2 User's Guide, Second Edition

The TTEST Procedure

VAR Statement

VAR variables </ options> ;

The VAR statement names the variables to be used in the analyses. One-sample comparisons are conducted when the VAR statement is used without the CROSSOVER option or CLASS statement. Two-independent-sample comparisons are conducted when the VAR statement is used with a CLASS statement.

An AB/BA crossover analysis is conducted when the CROSSOVER option is used in the VAR statement. In this case, you must specify an even number of variables. Each set of two variables represents the responses in the first and second periods of the AB/BA crossover design. For example, if you use the CROSSOVER option and specify VAR x1 x2 x3 x4, then you will get two analyses. One analysis will have x1 as the period 1 response and x2 as the period 2 response. The other analysis will have x3 as the period 1 response and x4 as the period 2 response.

The VAR statement cannot be used with the PAIRED statement. If the VAR statement is omitted, all numeric variables in the input data set (except a numeric variable appearing in the BY, CLASS, FREQ, or WEIGHT statement) are included in the analysis.

You can specify the following options after a slash (/):

CROSSOVER= ( variable1 variable2 )

specifies the variables representing the treatment applied in each of the two periods in an AB/BA crossover design. The treatment variables must have two, and only two, levels. For any given observation, the levels for the two variables must be different, due to the restrictions of the AB/BA crossover design. You can use either numeric or character variables.

Treatment levels are determined from the formatted values of the variables. Thus, you can use formats to define the treatment levels. See the discussions of the FORMAT procedure, the FORMAT statement, formats, and informats in SAS Language Reference: Dictionary.

IGNOREPERIOD

indicates that the period effect shall be ignored—that is, assumed to be equal to 0 (if TEST=DIFF) or 1 (if TEST=RATIO). This assumption increases the degrees of freedom for the test of the treatment difference by one and is usually more powerful, but it risks incorrect results if there is actually a period effect.

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