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PSYC 210 - ANOVA2 Class Notes

PSYC 210 - ANOVA2 Class Notes - ANOVA(Day 2 One-Way or...

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ANOVA (Day 2) One-Way or Single-Factor ANOVA (review) One manipulation or one factor to see if means are different Practice Problem from Day 1 notes. One-Way (or Single-Factor) ANOVA: Does custodial parenting arrangement affect children’s behavior problems? Additional Notes/Questions: Assumptions Power Sampling distribution of F Minimum value of F is 0 o Ratio of variances; variances are never negative F distribution is positively skewed o F =t squared Distribution of F varies by df Just like we have done with z and t tests, we need to compare our obtained value to something else to see the likelihood of obtaining such a result by chance alone. We use the sampling distribution of F to determine the probability of an obtained value of F . The sampling distribution of F shows the frequency of each possible value of F when 1) H 0 is true, and 2) the population is sampled randomly with replacement until all possible samples have been taken. - Lowest possible value of F = 0. F is never negative because variance is a squared value. - If H 0 is true, then the center of the distribution will be over approximately 1.00. - The F distribution is positively skewed. - The exact shape of the F distribution depends on df within and df between Use the table of critical values of F to identify critical value and mark critical region of the F distribution. A one-way ANOVA divides variance into two sources and produces an F Statistic: 1) Between group variance Differences between group means F = pVariance WithinGrou upVariance BetweenGro
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Caused by the independent variable plus some error 2) Within group variance Spread or dispersion of scores within each group Caused by error (individual differences, sampling error, uncontrolled variables, etc.) Assumptions in ANOVA The F and the t test are very closely related. In fact, when there are only 2 groups F = t 2 . As such the assumptions for a one-way ANOVA are the same as those for the t test for independent samples.
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