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Lecture 7

Lecture 7 - Jan 30.doc

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PSYC 305
Heungsun Hwang

PSYC305 Lecture 8 - Feb. 1 One-Way ANOVA: • Purpose: • To test whether the means of k (≧ 2) populations significantly differ • H 0 µ1 = µ2 … = µk • H 1 Not all µ’s are the same (at least one of the means is different) • Example: One-Way ANOVA The statistics classroom is divided into three rows: front, middle, and back • • The instructor noticed that the further the students were from him, the more likely they were to miss class or use an instant messenger during class • He wanted to see if the students further away did worse on the exams • DV = Exam score • Factor (IV) = Class row • Three levels: front, middle, and back • H 0 µ1 = µ2 = µ3 • H 1 Not all µ’s are the same • In this example, the critical value for a = .05 is 3.47 with df(B) = 2 and df(W) = 21 • F (.05, 2, 21) = 3.47 • The null hypothesis may be rejected because you F value (5.9) > 3.47 • We may thus conclude that where students sit in the stats class has a significant effect on their mid- term exam scores (F (2, 21) = 5.9, p < .05). Also this effect is large (r = .54) One-Way ANOVA: • When the null hypothesis is rejected in ANOVA, it suggests that there is at least one pair of group means is different • Then, we may want to know which pairs of group means are significantly different • µ1≠ µ 2 • µ1≠ µ 3 • µ2≠ µ 3 • F=test gives a global effect of the independent variable (factor) on the dependent variable (om- nibus or overall test) Doesn’t tell which pairs of means are different • • We need to perform post hoc comparison tests to determine this • Post hoc (a posterior/unplanned) comparisons • Decided upo
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