# EDRM 711 Midterm

 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } Total Means - SStot The measure of an individual's score compared to the grand mean. sum((x11.-x...)...(xii.-x...) df=n-1 Between Group Means - SSbg The measure of a group's mean compared to the grand mean. sum((x.1.-x..)...(x.i.-x...) df=k-1MSbg=SSbg/dfbg Within Group Means - SSwg The measure of an individual's mean compared to the group's mean. sum((x11-x.1)..(xii-x.i) df= n-k MS=SSwg/dfbg F-statistics F=MSbg/MSwg Cohen's D values 0.2 - small0.5 - medium0.8 - large Cohen's f values 0.1 - small0.25 - medium0.4 - large Interaction Types Disordinal: non-parallel with an intersection Ordinal: non-parallel with no intersection No interaction: parallel lines SSwg for factorial ANOVA SSwg=SSerror= Sum of the individual deviations from the cell means. dfwg=dferror=N-a*b=(n11-1)+(n12-1)+(n21-1)+(n22-1) MSwg=MSerror=SSwg/dfwg SSa*b Sum of cell mean-row margin+column margin+grand mean)2 for all columns and rows. dfa*b=(a-1)(b-1) MSa*b=SSa*b/dfa*b Mathematical Model for factorial ANOVA yijk= μ +α j + β k +φ jk + eijk μ = overall average in the population j   α= effect of being in jth achievement levelj μ +α = population average for the jth achievement levelk β= effect of being in kth CAI methodk μ + β = population mean for kth CAI methodjk φ = effect of having the jkth combination of achievement level and CAI methodj k jk μ +α + β +φ = population mean for the jkth combination ofachievement level and CAI method ijk e = individual error Key to the factorial ANOVA equation yijk= μ +α j + β k +φ jk + eijk α=SSaβ= SSbμ +α + β +φ = SSa*b Full Regression SS Type III or unique SSAdjust each effect for all other effects in the design to obtain its unique contribution (nothing is being counted twice) Experimental SS Type II estimates main effects adjusting for the other main effects, but ignoring the interaction. Estimates the interaction adjusting for main effects. SS for A and B are too big. A and B are not unique, but A*B is. Hierarchical SS Type IUses theory or previous research to establish order for the effects, Adjusts each effect only for those preceding it in order. Repeated Measures ANOVA - SSbg Sum of the mean for time group and the grand mean to the jth group Repeated Measures ANOVA - SSwg Block score minus average score at each time point Repeated Measures ANOVA - SSbl Row block minus grand mean Repeated Measures ANOVA - SSerror SSerror=SSwg-SSbl - how much error gets pulled out from the blocks Repeated Measures ANOVA - MSerror SSerror/dferror = SSerror/(N-1)(k-1) where N= # of blocks and k=# of time points Repeated Measures ANOVA - F-stat F=MSbg/MSerror df=(k-1, (N-1)(k-1)) Repeated Measures ANOVA - Sphericity E is less than or equal to 1. The closer E is to 1, the smaller the violation of the sphericity assumption. If close to or equal to one, do not adjust F values Nominal alpha Level of significance - type 1 error rate (If assumptions are not violated) Actual alpha Percentage of making a type 1 error when at least 1 assumption has been violated. Test statistic is robust if actual alpha is very close to nominal alpha. Experimental alpha The alpha level set for the whole study (usually 0.05) orthogonal contrast Non-independent contrasts Type II Error Falsely rejecting the a true null hypothesis .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } Authortnrose87 ID202914 Card SetEDRM 711 Midterm Descriptionedrm Updated2013-02-26T06:45:48Z Show Answers