# 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

- A = B0 + B1 x G + eta
- 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

- Formula

- T-Tests allow you to compare 2 means/ *two models*
- 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

- Tests for ANY difference between groups and IF the group means are different
- 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

- T-Tests
- 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

- Explains how is the variance partitioned?
- Effect Sizes
- Eta2, same as R2
- Partial Eta2; The proportion of variance that a variable explains that is NOT EXPLAINED by other variables

- ANOVA by hand
- 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

- Correction factors (multiply DF by)

- Correct to make them smaller using 'correction factor'

- Deviations in sphericity = not enough DFs
- Mauchlys test

- "Variances in the differences between conditions are equal"

- Violation of assumption of independence = core problem
- 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

- Reduction v.s refinement

- Lecture 1; One-way ANOVA
- Errors
- Type 1 and 2 errors
- Familywise errors

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