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Lecture%204 - SOCIOLOGY 005 Lecture 4 Testing Hypotheses...

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Unformatted text preview: SOCIOLOGY 005 Lecture 4 Testing Hypotheses About Two Means • Up until this point, we have focused on testing the difference between a sample mean and a population mean identiFed in a null hypothesis • We can also test for a difference between two or more sample means • Two techniques are available for this kind of bivariate analysis • Two-Sample t-test • ANOVA Two-Sample t-test • Two-sample t-test • Can only be used with 2 groups • Requires us to know the mean, std. deviation, and N for each t = X 1 ! X 2 s 1 2 N 1 + s 2 2 N 2 Two-Sample t-test In-Class Example Anova • Analysis of Variance (ANOVA) is a statistical technique designed to handle situations in which two or more groups are being used to predict the value of a dependent variable • It utilizes a F-test to determine if two or more groups have different means • The two-sample t-test is a special case of an F-test Anova • With ANOVA, we can also calculate: • Effects • A measure of association Anova • Effects • Within ANOVA an effect is simply the impact of the classi¡cation variable on the dependent variable • In order to calculate the effects, we need to know: • The grand mean • The group mean Anova • Effects • To calculate effects, we use the following equation: ! K = μ K " μ Anova • Measure of Association • In order to calculate Eta-Squared, our measure of association, it is helpful to wait and calculate this until after we calculate the necessary statistics for our f-test Anova • Since ANOVA is related to variability, we’ll be comparing variability between groups and variability within groups • This is called between variance and within variance Anova Let’s imagine an income distribution in which 3 groups (HS, BA, BA+) had identical means and variances Income In such an example, the within variance of a group would be no different that the within variation of other groups or the...
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Lecture%204 - SOCIOLOGY 005 Lecture 4 Testing Hypotheses...

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