TB8/9 RM Overview

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  • Created by: mint75
  • Created on: 16-01-16 15:59
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  • TB8 RM Overview
    • Lecture 1; One-way ANOVA
      • T-Tests
        • T-Tests allow you to compare 2 means/ *two models*
          • For t-tests, the data is categorical as opposed to continuous on x-axis
        • The regression version
          • Formula
            • A  = B0 + B1 x G + eta
              • A=The DV for each condition
              • G = The 'code' for each condition
          • Two conditions are given a code (0 or 1), called 'dummy variables'
            • Control is usually = 0
          • F-Statistic used to assess regression models appropriate-ness
          • However, T-Tests can ONLY COMPARE 2 MEANS
      • ANOVA
        • Can compare several means
        • 'Omnibus test'
          • Tests for ANY difference between groups and IF the group means are different
            • Does NOT tell you WHICH means differ
        • Output
          • Homogeneity of variance tests
          • Homo-skedasticity
      • Errors
        • Type 1 and 2 errors
        • Familywise errors
      • Post-Hoc Tests and Planned Contrasts
        • Control for the familywise error rate
        • Bonferri method (simplest post hoc)
          • Alpha level / Number of tests
        • REGWQ, Tukey HSD (standard ones), Gabriels, Hochberg GT2, Games-Howell
          • Different criterion!
        • Post hoc tests look for the mean differences and significances
    • Lecture 2; Planned Contrasts and 2-way Independent ANOVAs
      • ANOVA by hand
        • The F Ratio
        • Theory of ANOVA
          • Total variance in the data = Variance explained by the model and unexplained variance
        • The steps for ANOVA by hand
        • 2 Way Independent ANOVA
          • 2 Independent Variables
          • Diferent pps in ALL conditions
          • Several independent variables = a factorial design
            • Can look at how variables interact
          • By hand!
            • Steps involved
        • Can be worked out as long as you have the;
          • Group means
          • Group STDDEVs
          • Number of items
      • Planned Contrasts
        • Explains how is the variance partitioned?
          • Because ANOVA is an omnibus test and doesn't tell you which variable is explaining the variance between groups
        • Rules when CHOOSING  contrasts
          • Independent chunks
          • Only 2 chunks
          • K - 1 aka you should always have one less contrast than the number of groups
          • Control groups; the first contrast should always be a comparison between CONTROL and EXPERI-MENTAL
        • Rules when CODING contrasts
          • Groups coded with + weights compared to groups coded with - weights
          • The sum of weights for a comparison should be 0
          • If a group is NOT involved in a comparison, code it as 0
          • For a given contrast, weights in one chunk should = the opposite chunk
          • If a group is 'singled out' in a comparison, it should NOT be used in further comparisons
        • e.g Helmert Contrast
      • Effect Sizes
        • Eta2, same as R2
        • Partial Eta2; The proportion of variance that a variable explains that is NOT EXPLAINED by other variables
    • Lecture 3; Repeated Measures ANOVA
      • Violation of assumption of independence = core problem
        • Adjust DF
      • (Assumption of) Sphericity
        • "Variances in the differences between conditions are equal"
          • Deviations in sphericity = not enough DFs
            • Correct to make them smaller using 'correction factor'
              • Correction factors (multiply DF by)
                • Greenhouse-Geisser estimate (CON-SERVATIVE)
                • Huynh Feldt Estimate (LIBERAL)
                • Lower bound estimate
        • Mauchlys test
    • Lecture 4; Mixed ANOVA
      • Reduction v.s refinement
        • Use more subjects than pure repeated measures
      • When 1 or more IV uses the same pps, and 1 or more uses different pps
      • Interactions are important
        • Also generate an F-ratio alongside the F for the main effect
  • Errors
    • Type 1 and 2 errors
    • Familywise errors

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