| Tag | Value |
|---|---|
| file | Inferential_Statistics_eur-inferential_statistics-211-en_eur-inferential_statistics-211-en |
| name | eur-inferential_statistics-211-en |
| section | Inferential Statistics/Regression/Multiple linear regression/R squared change, Inferential Statistics/Regression/Simple linear regression, Inferential Statistics/Regression/R squared |
| type | schoice |
| solution | TRUE, FALSE, FALSE, FALSE |
| Type | Test choice |
| Program | |
| Language | English |
| Level | Statistical Literacy |
In order to select new personnel the police-force uses a procedure in which applicants are rated on three continuous scales: stress-resistance, sociability, and intelligence. To study the validity of this procedure a random sample of police officers is rated on these three psychological scales. Additionally, their supervisor rates their overall functioning on a scale ranging from 0 (poor functioning) to 100 (excellent functioning).
A researcher wants to know whether stress-resistance and sociability together explain a significant proportion of the variation in overall functioning after controlling for intelligence. Which analysis technique is most suited for this purpose?
Hierarchical regression analysis is the correct answer. You want to know what the effect is of two continuous variables on overall functioning when controlling for a third continuous variable. Out of these options, only a hierarchical regression will give an answer to this question through R-squared change.
A multiple regression analysis would only test for a general effect (explained variance) and would not control for a specific variable.
Mixed ANOVA is irrelevant as there is no mix of between- and within-subjects factors.
An ANCOVA is also not relevant here. You use an ANCOVA when you want to look at the effect of a categorical independent variable on a continuous dependent variable, while controlling for another continuous variable (covariate) that covariates with the dependent variable. In this case, there is no categorical independent variable.