STUDY GUIDE CEP740 - TEST 1
MAKE SURE YOU CAN:
1. convert standard deviations to variances and vice versa. (variance is the square of the
SD)
2. calculate or reason out the TOTAL df, MS between, MS within, F, and p value if I give
you SS between, SS within, and SS total and between df and within df.
3. write alternative and null hypotheses for t-tests or ANOVAs
4. examine an SPSS printout of an independent t-test and (a) determine if there is
homogeneity, (b) determine the proper t-value to report, (c) determine which row to read
on the printout, and (d) determine whether or not the means differ.
5. determine which test is appropriate (t-test for independent data, t-test for dependent
data, or oneway ANOVA) if I give you a hypothetical scenario.
6. identify the nonparametric test to use if you can't use an independent samples t-test
because you don't have homogeneity of variance and no equal n's.
7. identify the nonparametric test to use if you can't use a oneway ANOVA because you
don't have homogeneity of variance and no equal n's.
8. calculate Cohen's d for an independent samples t-test using the pooled SD, the SD of
the experimental group, and the SD of the control group, and identify when it is
appropriate to use each.
9. calculate Cohen's d for an independent samples t-test using the formula I gave you to
use when you know the t-value by looking at an SPSS run.
10. calculate Cohen's d for a paired samples t-test using the formula I gave you to use
when you know the t-value by looking at an SPSS run.
11. examine a standard oneway ANOVA source table and interpret it properly.
12. examine an SPSS printout from a paired sample t-test and determine if the means
differ.
13. Look at Cohen's d and decide if it is small, medium, or large.
14. identify which source of variance in a oneway ANOVA includes variance caused by
(a)independent differences and (b) measurement error.
15. identify which source of variance in a oneway ANOVA includes variance caused by
(a)independent differences, (b) measurement error, and (c) treatment effect.
16. calculate within subjects SS and mean square within if I give you a set of scores.
17. calculate between subjects SS and mean square between if I give you a set of scores.
18. calculate total SS if I give you a set of scores.
19. calculate the F ratio once you have MSbetween and MSwithin.
20. determine from an F table if the F is significant or not
21. figure out the df within for a set of scores.
22. figure out the df between for a set of scores.
23. figure out the df total for a set of scores.
24. look at a sig. level on a printout and tell me whether it is significant or not at the .05
or .01 level.
25. look at an SPSS printout of an independent t-test or a paired samples t-test and
calculate Cohen's d using any of the three variances (pooled, experimental, or control).
26. decide on the proper p level to put in an article if the SPSS printout lists .000 under
sig. (2-tailed).
27. look at the sig. level from Levene's Test on an SPSS printout and tell me whether or
not you have homogeneity of variance.
28. decide what to do if you run an independent samples t-test if you do not have
homogeneity of variance but you do or do not have equal n's.
29. look at a give Cohen's d (say .50) and finish this phrase: "the mean of the treated
group is at the ____ percentile of the untreated group.
30. calculate the pooled standard deviation if I give you a set of scores.
There was some confusion about when to use which SD when calculating Cohen's d. That
is thoroughly explained with an example in the lecture notes from that night. However,
here it is once again:
If there is an experimental group and a control group, the control group is often the best
one to use if you have a very large sample - say 100 or more cases. (That is because the
control group didn't get the treatement, and so it is probably the best estimate of the real
SD in the population.)
However, we often don't have an experimental group and a control group and we often
don't have 100 or more cases - for example, when we test one new method against
another new method there is no control group - there is just mean1 and mean2.
So, with fewer than 100 scores OR when there is no control group, most of the time we
use the POOLED SD in the formula for Cohen's d.
However, if you DO NOT have homogeneity of variance, do NOT use the POOLED SD
in the formula for Cohen's d. Instead, use the SD of either mean1 or mean 2 if you can
reason out which would be most accurate. In the example we talked about in class, we
looked at the two SD and noticed that the very low SD went with the group with a very
high mean. So, we thought that maybe many people in that group had gotten a perfect
score on the test and that artificially restricted the SD. So, we looked at the raw scores
and saw that was indeed the case - there were lots of perfect scores. So, it made sense to
use the SD of the other group in calculating Cohen's d.
Finally, if you just can't figure out which SD would be best in the formula for Cohen's d,
calculate it with the pooled SD, the SD of group 1, and the SD of group 2 and put in your
writeup that you could not decide which was best.
By the way, if you use the formula for Cohen's d that uses the t value from the SPSS
printout, you have used the POOLED SD.
Email or call if you have questions about any of the above.