Weeks would be the within-subjects factor and supplement would be the between-subjects factor. Perhaps a better approach to these data is using a single mixed ANOVA. One approach to these data is comparing body fat percentages over the 3 groups (placebo, thyroid, cortisol) for each week separately. This results in fatloss-unequal.sav, part of which is shown below. An additional 40 people receive a placebo.Īll 80 participants have body fat measurements at the start of the experiment (week 11) and weeks 14, 17 and 20. They test 2 supplements (a cortisol blocker and a thyroid booster) on 20 people each.
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In this case we'll reject the null hypothesis of equal population variances. However, very different sample variances suggest that the population variances weren't equal after all. If this is true, we'll probably find slightly different variances in samples from these populations. The groups we're comparing all have equal population variances. The null hypothesis for Levene’s test is that And if these don't differ too much, then the population variances being equal seems credible.īut how do we know if our sample variances differ “too much”? Well, Levene’s test tells us precisely that. Now, we usually don't know our population variances but we do know our sample variances. you do need this assumption if your groups have sharply different sample sizes.you don't need to meet the homogeneity assumption if the groups you're comparing have roughly equal sample sizes.However, you don't always need this assumption: īoth tests require the homogeneity (of variances) assumption: the population variances of the dependent variable must be equal within all groups. a one-way ANOVA for comparing 3+ groups.an independent samples t-test for comparing 2 groups or.For finding out if that's the case, we often use
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If we want to compare 2(+) groups on a quantitative variable, we usually want to know if they have equal mean scores. Levene’s test examines if 2+ populations all have
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How to Run Levene’s Test in SPSS? By Ruben Geert van den Berg under ANOVA & Statistics A-Z