A New View of Statistics

© 1997 Will G Hopkins

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Generalizing to a Population:


Simulation is where you make up data and analyze them. It's valuable at the planning stage of a complex study, if you're not sure how many subjects you need. It's also a great way to work out how to use a stats program.
 Steps in the Process
Simulating to get sample size consists of the following steps:

  1. Generate values for the variable(s) for a sample of subjects, usually by drawing them at random from a normal distribution.
  2. Introduce an effect, such as a difference between means or a correlation. Make the effect big to start with, but eventually you use the smallest worthwhile effect.
  3. Adjust data to take into account likely validity and/or reliability, if necessary.
  4. Calculate the effect statistic and either its confidence interval or its p value.
  5. If a narrow confidence interval is your goal, go back to Steps 1 or 2 and repeat with different effect magnitudes or sample sizes until you get the sample size that will give the acceptable confidence interval for the smallest worthwhile effect. Stop.
  6. If statistical significance is your goal, repeat Steps 1-4 hundreds of times, with new subjects each time, then work out how many times (as a percent) the smallest effect is statistically significant. Voilà, that is the power of the design, for the given sample size.
  7. Repeat Step 6 with a bigger or smaller sample until you find the sample size that gives acceptable power (usually 80%) for the smallest effect. Stop.

Even if you can't make your program do the above steps automatically, it's worth making up some values and entering them by hand into a data set, then analyzing them. Make the effect you're interested in big to start with, so you can see which part of the output of the program corresponds to the thing you're looking for. Then try it with a small effect, and see if you still get significance. Remember that in the traditional approach, the smallest worthwhile effect is supposed to turn out significant 80% of the time.

Regard the rest of this page as an appendix. I describe how I generate subjects and variables in SAS. The SAS language is a kind of BASIC, so you should be able to follow it and adapt it to other programs. I show a simulation for a cross-sectional study (where validity can be an issue), and for a longitudinal study (where reliability is crucial), and only for a numeric dependent variable.
 Cross-Sectional Study

Let's make two groups of 100 subjects differing by an effect size of 0.2 for a variable with validity 0.9.

In SAS the function rannor(0) generates one randomly chosen value for a normally distributed variable with population mean of 0 and SD of 1. (The "0" has nothing to do with a mean of 0, by the way. It is just a starting "seed" number.) Your stats program should have something like rannor(0). Here I have assigned it to a variable called true (standing for a subject's true value)


I usually stick with means of 0 and SDs of 1, but if you wanted to make it, say, 70 ± 6, you'd write true=70+6*rannor(0)

These few lines of code generate 100 subjects:

do subject=1 to 100;

Now let's generate a variable called depvar (standing for dependent variable) with a validity of 0.9 (its correlation with true). I like to do it in such a way that depvar still has a mean of 0 and an SD of 1. I use rannor(0) again to generate a normally distributed source of error, then add a bit of it in with most of true. In the following, sqrt stands for square root:

do subject=1 to 100;

The fact that the population correlation of depvar with true is 0.9 follows from the definition of the correlation coefficient (for the geeks, the correlation coefficient = the covariance of the two variables, divided by their SDs). Here the covariance is 0.9, and the SDs are 1.

It's now dead easy to make another set of 100 subjects with a true effect size of 0.2 relative to the first 100. Study this closely, because it shows how a true effect of 0.2 is degraded to an observed effect of 0.9*0.2 when the validity is 0.9:

do subject=101 to 200;

Now do a t test and see if you get statistical significance. Write a program to do it 1000 times and see what percentage of the tests gives statistical significance, and hey presto, that's your power. It would be lousy with only 100 subjects in each group!

I'll leave it to you to work out how to generate a simulation for a correlation between two variables, each with its own less-than-perfect validity.
 Longitudinal Study

The trick here is to generate two or more correlated repeated measures. We'll do two and call them repvar1 and repvar2. The correlation between the measures is the reliability correlation, of course. Once again you generate true values for your subjects, then add error, this time in a slightly different way . Let's generate repvar1 and repvar2 with a reliability correlation of 0.95 for 20 subjects:

do subject=1 to 20;

There are two ways to add in an effect, let's say 0.2 for repvar2. The normal way is to add it to the true value, just as we did for the cross-sectional design:


But sometimes repvar is the criterion outcome measure, so it may not be appropriate to consider that the effect is degraded by the less-than-perfect reliability. For example, if repvar represents competitive performance, we may be interested in detecting an effect of 0.2 for repvar, not for true. I'm still thinking about this one. In such cases, this is how you add in the effect:


It's possible to add finite validity along with reliability for variables in a longitudinal simulation. If the reliability is r, the validity is v, and the effect size is es, then the following generates two variables (repvar1 and repvar2) that have a correlation of r with each other and that have a correlation of v with the true value:

do subject=1 to 20;

I have used this simulation to check that the formulae for longitudinal designs are correct.

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Last updated 16 June 97