By Norm O'Rourke
One in a chain of books co-published with SAS, this publication offers a uncomplicated advent to either the SAS process and effortless statistical approaches for researchers and scholars within the Social Sciences. This moment variation, up-to-date to hide model nine of the SAS software program, publications readers step-by-step in the course of the simple innovations of study and knowledge research, to facts enter, and directly to ANOVA (analysis of variance) and MANOVA (multivariate research of variance).
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Extra resources for A step-by-step approach to using SAS for univariate & multivariate statistics
The columns run vertically (up and down). For the most part, a given column represents a different variable that you measured or created. ) For example, look at the last column in the matrix: the vertical column on the right side that goes from 1 (at the top) to 10 (at the bottom). This column codes the Participant Number variable. In other words, this variable simply tells us which participant’s data are included on that line. For the top line, the assigned value of Participant Number is 1, so you know that the top line includes data for participant 1.
Then, go on to type the participant’s response to question 7 in column 7. Do not enter a zero if the participant didn't answer a question; leave the space blank. ) instead of a blank space to represent missing data. When using this convention, if a participant has a missing value on a variable, enter a single period in place of that missing value. If this variable happens to be more than one column wide, you should still enter just one period. For example, if the variable occupies columns 12 to 14 (as does IQ in the table), enter just one period in column 14; do not enter three periods in columns 12, 13, and 14.
Specifically, you could perform an experiment that compares the effectiveness of 10 sessions of relaxation training versus 10 sessions of relaxation training plus hypnosis. In this study, the independent variable might be labeled something like Type of Therapy. Notice that you did not randomly select these two treatment conditions from the population of all possible treatment conditions; you knew which treatments you wished to compare and designed the study accordingly. Therefore, your study represents a fixed-effects model.