By Patrick Guziewicz on March 27th, 2020.Report this adTHIS TUTORIAL HAS 19 COMMENTS: The only way to evaluate it, is computing the actual difference scores as new variables in our data. Since we've a small sample of N = 19 students, we do need this assumption. Thus far, we blindly assumed that the normality assumption for our paired samples t-tests holds. And these are -guess what?- Bonferroni corrected t-tests again. If you choose the ANOVA approach, you may want to follow it up with post hoc tests. run a repeated measures ANOVA on all 3 exams simultaneously.apply a Bonferroni correction in order to adjust the significance levels.This increases the risk that at least 1 test is statistically significant just by chance. A shortcoming here is that all 3 tests use the same tiny student sample. Thus far, we compared 3 pairs of exams using 3 t-tests. d = 0.57 (pair 3) - slightly over a medium effect.d = 0.56 (pair 2) - slightly over a medium effect.d = -0.23 (pair 1) - roughly a small effect.However, it's easily computed in Excel as shown below. Sadly, SPSS 27 is the only version that includes it. One way to answer this is computing an effect size measure. Our t-tests show that exam 3 has a lower mean score than the other 2 exams. The same goes for the final test between exams 2 and 3. In a similar vein, the second test (not shown) indicates that the means for exams 1 and 3 do differ statistically significantly, t(18) = 2.46, p = 0.025. The 95% confidence interval includes zero: a zero mean difference is well within the range of likely population outcomes. The mean difference between exams 1 and 2 is not statistically significant at α = 0.05. It should be close to zero if the populations means are equal. The mean is the difference between the sample means. SPSS reports the mean and standard deviation of the difference scores for each pair of variables. The last one - Paired Samples Test- shows the actual test results. SPSS creates 3 output tables when running the test. T-TEST PAIRS=ex1 to ex3 /CRITERIA=CI(.9500) /MISSING=ANALYSIS. *Shorter version below results in exact same output. T-TEST PAIRS=ex1 ex1 ex2 WITH ex2 ex3 ex3 (PAIRED) /CRITERIA=CI(.9500) /MISSING=ANALYSIS. *Syntax pasted from analyze - compare means - paired-samples t-test. We added a shorter alternative to the pasted syntax for which you can bypass the entire dialog. For 3 pairs of variables, you need to do this 3 times.Ĭlicking Paste creates the syntax below. In the dialog below, select each pair of variables and move it to “Paired Variables”. SPSS Paired Samples T-Test DialogsĪnalyze Compare Means Paired Samples T Test If all is good, proceed with the actual tests as shown below. If necessary, set and count missing values for each variable as well. At the very least, run some histograms over the outcome variables and see if these look plausible. We'll do so later on.Īt this point, you should carefully inspect your data. The only way to look into this is actually computing the difference scores between each pair of examns as new variables in our data. Since we've only N = 19 students, we do require the normality assumption. Our exam data probably hold independent observations: each case holds a separate student who didn't interact with the other students while completing the exams. Normality is only needed for small sample sizes, say N < 25 or so.
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