Master in Psycholog
Erasmus University Rotterdam
Applied Multivariate Data-Analysis
Summary of Course Materials
Contents of this document
• Sec on 1. Recommended Study Approach & Overview of Course Concepts (page 2)
• Sec on 2. Notes of Q&A Lectures (page 5)
• Sec on 3. Tips from Assignments, Exercises & SPSS Sessions (page 21)
• Sec on 4. Notes of Regular Lectures (page 25)
• Sec on 5. Notes of SPSS Lectures (page 71)
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, AMD Notes
Lectures are taught by Bruno Bocanegra and Oliver Lindemann.
This summary covers all regular lectures, general course ps,
Q&A Lectures (in January 2022) and the SPSS Lectures
Recommended Study Approach
Steps
• 1. Skim all book chapters
• 2. Watch the prerecorded lecture
• 3. Read the book chapters
• skim Field & see what he adds that is not covered in the lecture
• Field can have quite of a u y wri ng style (eg when he’s making jokes about his cats)
• Field can have quite of a u y wri ng style (eg when he’s making jokes about his cats)
• Field has a lot of details that do not ma er that much
• 3. do the Tutorial Exercises > will be useful for MDA exam
• 4. watch the tutorial mee ngs
• 5. do the SPSS Exercises > will be useful for the SPSS exam
• 6. ask & review ques ons during Q&A sessions
• 7. work on the assignment
• a lot of things that you need to do in the exercises, are also covered in the assignment
Exam
SPSS Exam
• 20 open ques ons
• you have a small window to only report a corresponding result (eg p-value)
• ques ons will specify whether you need to round o & at how many decimals
• one will examine the SPSS output, not the SPSS syntax
• ques ons ask you to do thing > then you do things in SPSS > you report things from the output
• there won't be mul ple choice ques ons on the exam
• you won’t have to produce ‘graphs’ or sca erplots on the exam – maybe you have to create them, and
report your interpreta on of them (eg when checking assump ons)
• will not cover modera on and media on (it doesn’t cover your knowledge of Process)
AMD Exam
• 40 mul ple choice ques ons
• asks you about techniques & comparisons between techniques, theore cal concepts covered in lectures
& Field
• there will be conceptual ques ons and ques ons related to personal interpreta on (Bruno in Q&A
Session 1)
• there is no formula sheet, because the exam won’t emphasise on formulas. Yet, Bruno expects that you
know how to calculate a mean, standard devia on and standard error, as well as the general structure of
a con dence interval (basically, all the basics that you needed to know for the day of Q&A Session 1,
corresponding with Lecture 1)
• we will give you graphs/histograms/tables/SPSS output, and you will have to do the interpreta on of that
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, AMD exam
• Bruno: it will be described whether a test is one-sided or two-sided (if there is an op on)
• Bruno: in our course & on the exam, there is almost always no op on > there is only a two-sided test
• Bruno: you won't have to look up t-values > we won’t give you tables with t-values (and then you would
have to look up the cri cal value)
• there might be a ques on about the coding of dummy variables
Course Overview
Concepts Readings
C1 intro 2 53 3 43 6 73
• MSS MSE and SSE Simmons
C2 linear & multiple regression • simple regression analysis 8 51 9 79
• model equation, parameters
• model t (R-square and F-test)
• predictors
• multiple regression analysis
• model equation, parameters
• model t (R-square and F-test)
• predictors
• assumptions of regression analysis
C3 multiple & hierarchical • multiple regression analysis 9 79
regression • more assumptions (and conditions) of
regression analysis
• hierarchical regression (another method of
regression)
• unique contribution of predictors
• regression with categorical predictors
(dummies)
C4 dummies & bootstrapping • regression with dummies 11 41
• bootstrapping
• testing moderation and mediation models
with regression
C5 ANOVA & ANCOVA • assumptions 12 60 13 38
C6 ANCOVA • follow-up analyses 13 38 14 45
• factorial designs
C7 rm-ANOVA 15 70 16 31
C8 mixed-ANOVA 16 31 531
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, Overview of exercises and assignments
Exercises
Overview of Exercises
Length Content covered Remarks
E1 19 (1:11, 2:8) samples, sum of squares, standard error, degrees of
freedom
E2 46 (1:8, 2:9, 3:10, b-values, beta-values, R-squared, F-value, case summaries,
4:11, 5:3, 6:5) multi-collinearity, linear regression assumptions, statistical
power
E3 14 (1:7, 2:3, 3:4)
E4 13 (1:7, 2:6)
E5 29 (1:11, 2:11, ANOVA
3:2, 4:5)
E6 23 (1:9, 2:9, 3:3, ANCOVA
4:2)
E7 22 (1:7, 2:6, 3:5, Repeated Measures ANOVA
4:4)
E8 18 (1:5, 2:7, 3:6) Mixed ANOVA
Assignments
• A1 and A2 are very similar
• A3 asks similar ques ons to A1 and A2, but is a bit more elaborate
• all other assignments have again the same structure, but become slightly more elaborate/intense in what
they cover
Assignments – Content Covered
• A1: mul ple regression
• with data screening, inspect outliers, (un)standardised coe cients, standard errors, R2, p-values
• A2: hierarchical mul ple regression
• same as A1
• A3: moderator model, mediator model, bootstrapping > quite di cult
• A4: ANCOVA test > quite di cult
• A5: ANCOVA test with extension (eg table sample characteris cs) > quite di cult
• A6: repeated measures ANOVA
• A7: mixed ANOVA
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