{"id":614,"date":"2015-07-20T13:22:17","date_gmt":"2015-07-20T17:22:17","guid":{"rendered":"http:\/\/sites.berry.edu\/vbissonnette\/?page_id=614"},"modified":"2016-06-24T08:07:44","modified_gmt":"2016-06-24T12:07:44","slug":"friedmans-test","status":"publish","type":"page","link":"https:\/\/sites.berry.edu\/vbissonnette\/index\/stats-homework\/documentation\/friedmans-test\/","title":{"rendered":"Friedman’s Test"},"content":{"rendered":"

Your homework problem:<\/h3>\n

You are interested in the effects of arousal on motor performance. A random sample of subjects perform a complex motor task under 3 conditions: no caffeine (low arousal), a small dose of caffeine (moderate arousal), and a large dose of caffeine (high arousal).
\nThe dependent variable represents performance level: higher scores represent better performance.<\/p>\n

This study resulted in the following data:<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Subject<\/td>\nLow
\nDifficulty<\/td>\n		Moderate
\nDifficulty<\/td>\n		High
\nDifficulty<\/td>\n<\/tr>\n
1<\/td>\n15<\/td>\n17<\/td>\n19<\/td>\n<\/tr>\n
2<\/td>\n2<\/td>\n7<\/td>\n4<\/td>\n<\/tr>\n
3<\/td>\n11<\/td>\n12<\/td>\n6<\/td>\n<\/tr>\n
4<\/td>\n13<\/td>\n15<\/td>\n5<\/td>\n<\/tr>\n
5<\/td>\n12<\/td>\n12<\/td>\n7<\/td>\n<\/tr>\n
6<\/td>\n2<\/td>\n18<\/td>\n11<\/td>\n<\/tr>\n
7<\/td>\n8<\/td>\n12<\/td>\n5<\/td>\n<\/tr>\n
8<\/td>\n9<\/td>\n14<\/td>\n2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Did the perceived level of caffeine significantly affect the participants’ performance (alpha = .05)? If your analysis reveals a significant overall effect, then make sure to explore all possible mean differences with a post-hoc analysis (same alpha).<\/p>\n

Note that these are the same data that we worked with when you were working with the one-factor ANOVA for repeated-measures designs<\/a>. This will allow you to compare and contrast the results of the two procedures.<\/p>\n

\n

Enter these data into Stats Homework’s<\/i> data manager and rename the variables. Your screen should look like this:<\/p>\n

\"anova_rm1_13\"<\/a><\/p>\n

Make sure to double-check and save your data. To conduct your analysis, pull down the Analyze<\/b> menu, choose Non-Parametric Tests<\/b>, and then choose Friedman’s Test<\/b>. You will be presented with a dialog that asks you to specify your variables:<\/p>\n

\"friedmans1\"<\/a><\/p>\n

Add your three variables to the window on the right. When you are ready, click the Compute<\/strong>\u00a0button. Stats Homework<\/i> will produce the following output:<\/p>\n

\"friedmans2\"<\/a><\/p>\n

Rank Data<\/b>. This table includes the number of scores for each group, and the sum of the ranks for each group.<\/p>\n

Inferential Statistics<\/b>. This table presents the Chi Square approximation to estimating the significance level of Friedman’s test.<\/p>\n