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
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
