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)
please do not share without permission
ti y
, 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
please do not share without permission
tifi ti ti tititi tiflti ff ti ti titi tt tt ti ti ff ti ti ti ti ti
, 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
please do not share without permission
fi titi ti ti
, 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
please do not share without permission
ti ti ffi ffi ti ffi ffi
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)
please do not share without permission
ti y
, 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
please do not share without permission
tifi ti ti tititi tiflti ff ti ti titi tt tt ti ti ff ti ti ti ti ti
, 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
please do not share without permission
fi titi ti ti
, 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
please do not share without permission
ti ti ffi ffi ti ffi ffi
