Content
Analysis of Variance................................................................................................................................. 3
Conceptual Models.............................................................................................................................. 3
Moderation...................................................................................................................................... 3
Mediation ........................................................................................................................................ 3
ANOVA ................................................................................................................................................. 4
Conditions........................................................................................................................................ 4
Hypothesis ....................................................................................................................................... 4
Test Statistics ................................................................................................................................... 5
Planned Contrast ............................................................................................................................. 9
Post-Hoc Tests ................................................................................................................................. 9
Moderation in ANOVA....................................................................................................................... 10
Factorial ANOVA ................................................................................................................................ 11
Conditions...................................................................................................................................... 11
Mean Squares ................................................................................................................................ 12
F-Test ............................................................................................................................................. 12
Partial Eta Squared (η2) ................................................................................................................. 13
Post-Hoc Tests ............................................................................................................................... 13
Regression ............................................................................................................................................. 14
Assumptions of Regression ........................................................................................................... 15
Model Testing ................................................................................................................................ 16
Categorical PV´s in regression and dummy variables ....................................................................... 18
Interpretation of β-coefficient ...................................................................................................... 19
Multicollinearity ................................................................................................................................ 20
Detecting multicollinearity ............................................................................................................ 20
Rules of thumb .............................................................................................................................. 20
Mediation .......................................................................................................................................... 21
Logistic Regression ............................................................................................................................ 24
Pseude-R2 ...................................................................................................................................... 24
Model Statistics ............................................................................................................................. 24
Coefficients .................................................................................................................................... 25
Factor Analysis (FA) ........................................................................................................................... 26
Principal Component Analysis (PCA) ............................................................................................. 26
Initial checks .................................................................................................................................. 26
Main analysis ................................................................................................................................. 27
1
, Reliability Analysis ............................................................................................................................. 29
Cronbach’s Alpha........................................................................................................................... 29
Further Analysis ............................................................................................................................. 30
References ............................................................................................................................................. 31
2
,Analysis of Variance
Conceptual Models
Conceptual models are visual representations of relations between theoretical constructs (and
variables) of interest.
Outcome variable (OV) = Dependent variable → Dependent upon other variables
Predictor variable (PV) = Independent variable → Not dependent upon other variables
Both OV’s and PV’s can have different measurement scales:
• Categorical
• Quantitative
Moderation
The proposed effect is stronger/weaker in certain settings. One variable moderates the relationship
between two other variables.
For example: “Mobile ownership only leads to higher mobile spending when there are options to pay
via mobile (not when there are no options to pay via mobile).”
Mediation
The proposed effect “goes through” another variable. One variable mediates the relationship
between two other variables.
For example: “The positive effect of mobile ownership on online mobile spending is mediated by
mobile browsing.”
3
, Rules for Conceptual Modals
• The boxes represent variables;
• Arrows represent relationships between variables;
• Arrows go from predictor variables (PV) to outcome variables (OV)
ANOVA
ANOVA is “Analysis of Variance” → Examine how much of the variance in the data can be explained
by the independent variable.
ANOVA is used to test whether statistically significant differences exist in scores on a quantitative
outcome variable, between different levels (groups) of a categorical predictor variable.
Variance: The average of the squared differences from the mean.
Conditions
(Between-subject) ANOVA is used when:
✓ Outcome Variable = Quantitative;
✓ Predictor Variable = Categorical > 2 groups;
✓ Between-subject design (Everyone participates in one experiment group only);
✓ Variance is homogenous across groups;
o Levene Statistic (H0: Variances are homogenous.)
✓ Residuals are normally distributed
Hypothesis
Hypotheses in ANOVA
H0: There is no difference in outcome variable scores between different levels of the independent
variable. → μ1 = μ2 = … = μi
H1: There is a difference in outcome variable scores between at least two levels of the independent
variable. → μ1 ≠ μ2 = … ≠ μi
4
Analysis of Variance................................................................................................................................. 3
Conceptual Models.............................................................................................................................. 3
Moderation...................................................................................................................................... 3
Mediation ........................................................................................................................................ 3
ANOVA ................................................................................................................................................. 4
Conditions........................................................................................................................................ 4
Hypothesis ....................................................................................................................................... 4
Test Statistics ................................................................................................................................... 5
Planned Contrast ............................................................................................................................. 9
Post-Hoc Tests ................................................................................................................................. 9
Moderation in ANOVA....................................................................................................................... 10
Factorial ANOVA ................................................................................................................................ 11
Conditions...................................................................................................................................... 11
Mean Squares ................................................................................................................................ 12
F-Test ............................................................................................................................................. 12
Partial Eta Squared (η2) ................................................................................................................. 13
Post-Hoc Tests ............................................................................................................................... 13
Regression ............................................................................................................................................. 14
Assumptions of Regression ........................................................................................................... 15
Model Testing ................................................................................................................................ 16
Categorical PV´s in regression and dummy variables ....................................................................... 18
Interpretation of β-coefficient ...................................................................................................... 19
Multicollinearity ................................................................................................................................ 20
Detecting multicollinearity ............................................................................................................ 20
Rules of thumb .............................................................................................................................. 20
Mediation .......................................................................................................................................... 21
Logistic Regression ............................................................................................................................ 24
Pseude-R2 ...................................................................................................................................... 24
Model Statistics ............................................................................................................................. 24
Coefficients .................................................................................................................................... 25
Factor Analysis (FA) ........................................................................................................................... 26
Principal Component Analysis (PCA) ............................................................................................. 26
Initial checks .................................................................................................................................. 26
Main analysis ................................................................................................................................. 27
1
, Reliability Analysis ............................................................................................................................. 29
Cronbach’s Alpha........................................................................................................................... 29
Further Analysis ............................................................................................................................. 30
References ............................................................................................................................................. 31
2
,Analysis of Variance
Conceptual Models
Conceptual models are visual representations of relations between theoretical constructs (and
variables) of interest.
Outcome variable (OV) = Dependent variable → Dependent upon other variables
Predictor variable (PV) = Independent variable → Not dependent upon other variables
Both OV’s and PV’s can have different measurement scales:
• Categorical
• Quantitative
Moderation
The proposed effect is stronger/weaker in certain settings. One variable moderates the relationship
between two other variables.
For example: “Mobile ownership only leads to higher mobile spending when there are options to pay
via mobile (not when there are no options to pay via mobile).”
Mediation
The proposed effect “goes through” another variable. One variable mediates the relationship
between two other variables.
For example: “The positive effect of mobile ownership on online mobile spending is mediated by
mobile browsing.”
3
, Rules for Conceptual Modals
• The boxes represent variables;
• Arrows represent relationships between variables;
• Arrows go from predictor variables (PV) to outcome variables (OV)
ANOVA
ANOVA is “Analysis of Variance” → Examine how much of the variance in the data can be explained
by the independent variable.
ANOVA is used to test whether statistically significant differences exist in scores on a quantitative
outcome variable, between different levels (groups) of a categorical predictor variable.
Variance: The average of the squared differences from the mean.
Conditions
(Between-subject) ANOVA is used when:
✓ Outcome Variable = Quantitative;
✓ Predictor Variable = Categorical > 2 groups;
✓ Between-subject design (Everyone participates in one experiment group only);
✓ Variance is homogenous across groups;
o Levene Statistic (H0: Variances are homogenous.)
✓ Residuals are normally distributed
Hypothesis
Hypotheses in ANOVA
H0: There is no difference in outcome variable scores between different levels of the independent
variable. → μ1 = μ2 = … = μi
H1: There is a difference in outcome variable scores between at least two levels of the independent
variable. → μ1 ≠ μ2 = … ≠ μi
4
