Bivariate Correlational Research
19 important questions on Bivariate Correlational Research
What kind of validity has to do with the following question; how well does the data support the conclusion?
What kind of validity has to do with the following question; how well was the experiment conducted, are there alternative explanations for the statistical effect?
How do we call the concept that describes the strength of a relationship between two or more variables?
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What are the properties of larger effect sizes?
- allow more accurate predictions
- are more important.
How do we call the conclusion that's been made regarding the likelihood of getting a correlation of this size just by chance, assuming there is no correlation in the real world?
Why are outliers problematic?
When is an outlier especially problematic?
How do we call the situation where there's not a full range of scores on one of the variables, in the association which can make the correlation appear smaller than it actually is?
How do we call a correlation that is in fact not linear but forms a parabool?
What is the statistical valid way to analyze a curvilainear association?
What are the three criteria a study has to satisfy to establish causality?
- covariance of cause and effect
- temporal presedence
- internal validity.
Which causal criteria of a study is described; the results must show a correlation or association between the cause- and effect variable.
Which causal criteria of a study is described; the cause variable must precede the effect variable.
How is the temporal precedence criterion sometimes called?
How is the third validity criterion often called?
What is necessary for the third variable problem to occur?
How do we call the phenomenon that occurs when a bivariate correlation is present, but only because of some third variable?
How can you show in a scatterplot that a potential third variable isn't a threat to internal validity?
How do we call a third variable of which level the relationship between two variables depend?
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