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SPPS tentamen samenvatting en Grasples

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Samenvatting stof die handig is voor het SPSS tentamen en Grasples samenvatting. Hoe je bepaalde analyses moet uitvoeren in SPSS en achtergrondinformatie over de analyses

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  • March 18, 2021
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SPSS exam

Goodness of fit: R-squared = how well the fit of the prediction is (verklaarde variantie van de
voorspellende variabele between 0-1). Betekent niet dat het een goede voorspeller is voor
nieuwe observaties.

Strongest predictor: highest standardized coefficient

Residual sum of squares (SSr): how well the model fits the data, the smaller the better

R2 = proportion of improvement due to the model. Proportion of the variation in the
outcome that can be predicted from the model

F-test: a measure of how much the model has improved the prediction compared to the
level of inaccuracy of the model >1 at least! Hoe goed het model kan voorspellen.

Sum of squares: hoe goed het model bij de data past

t-test: kans dat de nul hypothese waar is

z-scores: standardized measure so we can compare different models

Influential cases:
- Cook’s distance: overall influence of one case on the model. >1 is cause for concern!
- Leverage: the influence of the observed value of the outcome variable over the
predicted values (N-1/N=maximum value)
- Mahalanobis distances: measure the distance of cases from the. Mean of the
predictor variable

Assumptions:
- Additivity and linearity: lijn moet linear zijn
- Independent errors: residual terms should be uncorrelated (Durbin Watson Test)
- Homoscedasticity: at each level of the predictors variable, the variance of the
residual terms should be constant (dezelfde error/residual bij alle waardes)
- Normally distributed errors: with a mean of zero
- No perfect multicollinearity: no perfect linear relationship between two or more
predictors
- Absence of outliers

Adjusted R2: how much variance in Y would be accounted for if the model had been derived
from the population from which the sample was taken

R square: the explained variance in the model

Data splitting: a method that can be used to check how well the model generizes
Absence of outliers:
- Standardized residual should be between -3.3 and 3.3

, - Mahalanobis Distance must be lower than 10+2*number of Independent Variables
(outlier in X space)
- Cook’s distance must be lower than 1 (outlier in XY space)

Multicollinearity:
- In coefficients table last 2 columns
- Tolerance <.2: potential problem. <.1: problem
- VIF (variance inflation factor) = 1/tolerance > 10 = problem

Normally distributed à check histogram
Homoscedasticity à check scatter plot


Regressie coefficient interactie:
Y = b0 + b1*X1 + b2*X2 + b3*(X1*X2)

Bonferroni method: alpha/aantal experimenten

Grand variantie: totale variantie binnen alle scores ongeacht de groep
Degree of freedom (df): N-k (totale sample grootte - aantal groepen)

F-test: alleen algemene verschillen tussen de gemiddeldes en niet welke groepen verschillen

Levene’s test mag niet significant zijn! Om homogeniteit mee te testen

Post-hoc: alle groepen gemiddeldes vergelijken

ANOVA: om lineaire modellen te onderzoeken
- Interval/ratio level
ANCOVA: covariaat die de relatie beinvloedt wordt meegenomen

Partial eta squared (partial n2): effect size. Kijkt naar de proportie in totale variantie die een
variabele verklaart. Percentage of total variation that is explained by condition. Tells us
something about the slope; direction and strength.

Explained variance à between variance
Residual variance à within variance

Follow-up testing:
- Post-hoc
- Planned comparisons through specific contrast-tests (Simple in SPSS)

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