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Clear summary of all the lectures from ARMS

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Clear summary of all lectures of the ARMS course

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  • March 12, 2021
  • 14
  • 2019/2020
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Advanced Research Methods and Statistics (ARMS) hoorcolleges:
Algemeen

Tentamen:
· Algemene deel: 60%
o Theorie: geen boek 45% à minimaal 5.0
o SPSS: boek mag mee à minimaal 5.0
o Allebei in week 6
· Studie pad: 40%
· = in Engels
· Hoeven we niet te weten: Equations à some of squares, how to compute … etc à mathematical
things we don’t need to know

Hoorcollege 1: Multiple Lineair Regression

Multiple lineair regression:
 Paper we needed to read before Friday | Birth order effects:
o Galton (1874) noticed that the number of firstborns among eminent scientists was
remarkably large à researchers started to study relation birth order with IQ, and
observed a significant positive relation
o Does this imply a real effect of birth order on IQ?
 Critically review the way the studies were performed
 Representative sample?
 Reliable measures of variables?
 Correct analyses and correct interpretation of results?
 Critically consider alternative explanations for the statistical association
 Association =/= causation
 Does effect remain when additional variables are included?
 Adding variables:
o Simple linear regression: involves 1 outcome (Y) and 1 predictor (X)
 Outcome = DV = dependent variable = e.g. IQ
 Predictor = IV = independent variable = e.g. birth order
 Y = B + BX + e
i 0 1 i i


 Multiple lineair regression: involves 1 outcome and multiple predictors



It’s a lineair line
If the line described the line really well? The dots
resample the …
If the dots are wider around the line? Less …

R2:
First picture: large
Second picture: smaller

, When the slope is steep = big b
When the slope is horizontal = B is close to 0
1




Multiple linear regression (MLR)
 MLR examines a model where multiple predictors are included to check their unique linear
effect on Y
 Things you need to know about MLR
o The model
o Types of variables in MLR
o MLR and Hierarchical MLR
 Hypotheses
 Output
 Model fit: R2, adjusted R2, and R2-change
 Regression coefficients: B and Beta (=standardized B)
o Exploratory MLR (stepwise) versus confirmatory MLR (forced entry)
o Model assumptions important to MLR (see Grasple)

The model:



i= individual scores can differ à without subscribed i
= general
A multiple regression model: could also be named a
additive linear model




Types of variables:
 Formal distinction in 4 measurement levels:
o Nominal
o Ordinal
o Interval
o Ratio
 For choice of analysis we usually distinguish:
o Categorical (qualitative): nominal + ordinal
o Numerical (quantitative or continuous): interval + ratio
 MLR requires continuous outcome and continuous predictors
 If you want to include al categorical predictors: use dummy variables

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