A New View of Statistics
It's more than five years since I wrote these pages and the SAS programs! My approach has become more sophisticated during that time, but I haven't had a chance to update things here yet. If you are running SAS and would like advice and/or copies of my recent programs, contact me.
The data in each example are for athletes tested on several occasions to determine the distance they can throw an object, such as a javelin. (The figure shows the simplest example.) In each example, the program creates a sample of athletes drawn randomly from a population with a normal distribution of throwing ability. Next, the program generates normally-distributed within-subject random variation, which is simply the variation in performance that each subject experiences between tests. It then adds a change or changes in performance between some tests, for example changes resulting from a training program. Finally it uses Proc Mixed to analyze the data.
If you re-run any of these programs, the random variation between and within subjects will produce a slightly different outcome, so the data may not look exactly like what's in the figure accompanying each example. It's the same as repeating the study with a different sample of subjects. Try it and see, then play with the sample size, the between- and within-subject variation, and the magnitude of the change or changes in performance.
The main aim of the analysis is to calculate the changes in performance between tests, and the confidence limits or p values for the changes. Calculating the changes is usually easy: you just subtract the mean of one or more tests from the mean of one or more other tests. Calculating confidence intervals or p values is the hard part. That's when you need a procedure like Proc Mixed or analysis of variance. The procedure can also output the changes in performance, to save you doing it on a spreadsheet.
Here are the examples:
More to come soon!
To analyze your own data, you will need to get help from someone who knows how to set up a link from the SAS program to the data file on your computer. That means adding a filename statement that links to a data step containing an infile statement. I might provide examples of that soon, too.
The figure shows data for a single group of subjects who were tested four times. A treatment between test2 and test3 (for example, supplementing for a week with a potentially ergogenic nutritional like creatine) seems to have produced an increase in the distance of the throw.
As noted above, the aim of the analysis is to calculate the mean increase in the distance of the throw between the tests, and its confidence limits or p value.
The data for each athlete show the kind of consistency of performance you expect when there are no problems with sphericity, as I discussed on the page devoted to three or more tests and no between-subjects effect. In the accompanying program, the analysis with an unstructured covariance matrix serves as a check for such problems. If there aren't any (and I didn't deliberately generate any), you use a covariance matrix with compound symmetry, as shown in the program. It won't make much sense until I provide a full explanation. Soon.
a Control Group
The data are the same as above, but this time there is a control group who don't get any special treatment between test2 and test3. The treatment could be something like a week of supplementing with creatine (the drug group) or an inactive substance (the control group).
Again, the aim of the analysis is to determine the confidence limits for the increase, but this time it's the increase in the drug group relative to (minus) the increase in the control group. Make sure you understand the concepts on the pages devoted to two trials plus a between-subjects effect and three or more trials plus a between-subjects effect before you try the SAS program.
The figure shows data for two groups of athletes. After a baseline test at time=0, one group did overload training, while the other group continued with normal training.
The aim of the analysis is to compare the enhancement in the overload group with that of the normal group at various times. You can also compare the linear and quadratic components of the trends between the groups. I'll add lots more on this topic soon.
See the earlier section on polynomial contrasts before you try the SAS program.
Individual Differences, and
The aim of the analysis is...
The SAS program generates data for the treatment group, first without individual differences between test2 and test3, then with them. It also generates the data for a control group who have no shifts in the mean and no individual differences between test2 and test3. The analysis includes test1-test2 comparisons too.
This analysis is difficult, so I have included the output of the program and annotated it a little. Here's a summary of the output:
When there are no individual differences in the treatment group, the change in performance between test2 and test3 is 2.7 ± 0.3 units (mean ± SD); the 95% confidence limits for the mean are 1.6 and 3.8; the 95% confidence limits for the SD are -1.1 and 1.2.
With individual differences present, the change in performance between test2 and test3 is 2.5 ± 2.0 units (mean ± SD); the 95% confidence limits for the mean are 0.9 and 4.1; those for the SD are 1.3 and 4.4.
With individual differences present, and with a covariate that explains them included in the analysis, the change in performance between test2 and test3 is 2.7 ± 0.3 units (mean ± SD); the 95% confidence limits for the mean are 1.5 and 3.9; those for the SD are -1.1 and 1.2. The value of the covariate is 1.8 units of throwing performance per unit of covariate; its 95% confidence limits are 0.5 and 3.2.
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Last updated 2 Nov 03