* - FILE: Humfacs.sas;
 * repeated measures example--human factors research. Subjects 
  were given a  computer aided instructional program at one sitting.
  The program presented text and then asked a series of questions 
  about the text, providing appropriate feedback and review for 
  incorrect responses to the test  questions. There were five 
  such instructional sets. The dependent variables are the number
  of error in each instructional set. They are called INSTR1 through
  INSTR5 according to the order of presentation. From previous
  research, the level of difficulty had been calibrated to be 
  the same for each instructional set. Two groups of subjects 
  were used. The first completed the instructional program on 
  a standard black/white computer display. The second completed 
  the program on a color display with borders, highlighted text,
  etc. The purpose of the study was to determine whether it was 
  worth the time and effort to write programs with all the 
  "bells and whistles.";
* PS. I haven't the vaguest idea if these data are in any way
  realistic. I just made them up.;

OPTIONS NOCENTER NODATE PAGENO=1 PAGESIZE=60 LINESIZE=76;
DATA HUMFACS;
 LENGTH GROUP INSTR1-INSTR5 3 DEFAULT=4;
 INPUT GROUP INSTR1-INSTR5;
 LENGTH TYPE $8;
 TYPE='B & W'; IF GROUP=2 THEN TYPE='COLOR';
DATALINES;
 1 11 18 17 21 18
 1  8 10 16 17 16
 1  8 15 14 11 17
 1 13 15 17 18 18
 1 13 19 18 19 19
 1 17 14 23 19 20
 1 17 17 22 17 15
 1 12 16 15 15 15
 1 13 14 15 14 16
 1 17 19 16 21 18
 2 10 11 13 11 16
 2 21 22 20 22 22
 2  9 11 11 10 13
 2 16 16 15 15 17
 2 11 12 15 15 17
 2 13 13 14 13 16
 2 11  9 11 12 12
 2 11 10 15 14 16
 2 16 12 14 20 16
 2 13 10 16 21 23
RUN;
TITLE 'PSYCHOLOGY 7291: REPEATED MEASURES ANOVA: HUMAN FACTORS EXAMPLE';
PROC PRINT; BY TYPE;
  VAR INSTR1-INSTR5;
RUN;
PROC MEANS; BY TYPE;
  VAR INSTR1-INSTR5;
run;
* this GLM performs a repeated measures ANOVA and a repeated
   measures MANOVA. A HELMERT transformation of the dependent
   variables is requested. This compares the number of errors
   in an instructional set with the average number of errors in
   all the subsequent instructional sets. This is a useful
   transformation when: (1) you know the means should all be
   the same if the instructional sets were presented in
   random order, and (2) you want to examine inprovement over
   time or decreased performance over time.;
PROC GLM;
 CLASS type;
 MODEL INSTR1-INSTR5=type;
 TITLE2 Traditional MANOVA;
 MANOVA H=type;
 RUN;

 * - options PRINTM and PRINTE print, resepctively, the
 		model and the error matrices. SUMMARY requests a
 		univariate ANOVA for each of the transformed 
 		variables;
 TITLE2 Repeated Measures ANOVA;
 REPEATED INSTRSET 5 HELMERT /PRINTM PRINTE SUMMARY;
RUN;