Session 6 - More Than One Treatment Per Subject. Tests of Repeated Measures.docx

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University of Toronto St. George
Cell and Systems Biology
William Navarre

HMB325H © Lisa| Page 113 S E S S I O N 6 : T E S T S O F R E P E AT E D M E A S U R E S ( C H A P T E R 9 ) LEARNING OBJECTIVES 1. identify independent & dependent groups 2. calculate & interpret a paired t test analysis 3. describe the assumptions for the paired t test 4. calculate & interpret the power of a paired t test 5. know when a repeated measures analysis of variance (ANOVA) should be used 6. calculate & interpret McNemar’s test 1. assumption for independent group doesn’t hold 2. each subject receives 2 or more treatments 3. the subject often serves as their own control 4. when the independent group assumption doesn’t hold, use – paired t test 5. the study power for a paired t test – McNemar’s test 6. outcomes is tested grip strength 7. if you use an unpaired t test, you are ignoring that there are not 14 individuals, but only 7 1. in this study, each person serves as their own control 2. variability is decreased THE PAIRED t TEST 8. should use a paired t test to acknowledge that the data is paired 9. H :0the new training technique is not affective 1. shouldn’t expect difference in grip strength before & after 2. null = 0 difference (same as unpaired t test) 10. average of the differences = overall difference of improvement = 20 11. degrees of freedom = 7 individuals – 1 = 6 12. the differences follow a normal distribution bc (mean = median) therefore, an assumption of the paired t test is the same as the unpaired t test: the data must follow a normal distribution 13. negative numbers are possible (ex. difference is negative) tdf mean−null = mean−null SED SD √n 1. the paired t test addresses circumstances when the data being assess are not independent – the independent groups assumption is not met 2. the most common scenario is a before-after study – the outcome of interest is measured twice in the same indiv
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