PSY 200 Lecture Notes - Lecture 12: Analysis Of Variance, Null Hypothesis, Statistical Hypothesis Testing
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Used to find mean differences between two or more groups. This is the main difference between t-test and anova. Independent variable: must be nominal, with 2+ levels/categories. Dependent variable: must be interval or ratio. Can use both between-subjects and within- subjects designs. This test is known as the one-way anova. Factor - the independent (or quasi-independent) variable that designates the groups being compared. Levels - individual conditions or values that make up a factor. Factorial design - a study that combines two or more factors. Anova assumptions: independence, normality, homogeneity of variance. If norm1lity is (cid:404)iol1ted slightly, it"s not 1 big de1l(cid:806)especi1lly (cid:405)ith l1rge ns(cid:807). If homogeneity of variance is violated, alternate tests can be used. The null hypothesis states the means of the groups do not differ from each other. The alternative hypothesis states that at least one group is different from the others. This can be in any possible combination of differences.
