*Switch off SPLIT FILE for succeeding output. T-TEST /TESTVAL=0 /MISSING=ANALYSIS /VARIABLES=iq depr anxi soci wellb /CRITERIA=CI(.95). *Obtain 95% CI's for means of iq to wellb. *SPLIT FILE -succeeding output is created for males and females separately. Now, if we use 0 as the test value, these differences will be exactly equal to our sample means.Ĭlicking Paste results in the syntax below. The final output will include confidence intervals for the differences between our test value and our sample means. If we want to analyze all cases as a single group, our best option is the one sample t-test dialog. For larger samples, the central limit theorem ensures that the sampling distributions for means, sums and proportions approximate normal distributions. Second, the normality assumption is only required for small samples of N < 25 or so. A visual inspection of our data suggests that each case represents a distinct respondent so it seems safe to assume these are independent observations.Ģ. normality: our variables must be normally distributed in the population represented by our sample.ġ.Assumptions for Confidence Intervals for MeansĬomputing confidence intervals for means requires We'll use adolescents_clean.sav -partly shown below- for all examples. This tutorial quickly walks you through the best (and worst) options for obtaining them. Sadly, they're pretty well hidden in SPSS.
Bonferroni Corrected Confidence IntervalsĬonfidence intervals for means are among the most essential statistics for reporting.Assumptions for Confidence Intervals for Means.
Confidence Intervals for Means in SPSS By Ruben Geert van den Berg under T-Tests