L9 Correlation

What is correlation analysis used for?

• To calculate effect size for group differences.

• To identify outliers in the data.

• To determine the variability within a single variable.

• To investigate the relationships that exist between variables.

Correlation analysis is an example of what types of analysis?

• Univariate

• Bivariate (data on two variables is collected from the same participants at the same point in time)

What does a correlation coefficient (r) express?

• The strength of shared variance between groups.

• The probability of rejecting the null hypothesis.

• The direction of causation between two variables.

• The nature and strength of the relationship between two variables.

What are the three critical pieces of information provided by a correlation coefficient (r)?

• Direction, variability, and independence.

• Variability, strength, and sample size.

• Causation, prediction, and confidence intervals.

• Direction, strength, amount of shared variance.

What is the coefficient of determination?

• The standard deviation of the predictor variable.

• The p-value for a correlation test.

• The range of the correlation coefficient.

• The proportion of variance shared between two variables.

In what type of study is a correlation analysis typically used?

• Cross-sectional methodological design

• Experimental Design

• Longitudinal Causal Study

• Case Study

Are any variables manipulated in a correlation analysis?

• yes

• No

• Maybe

• I don’t know

• Are any variables manipulated in a correlation analysis?

What does the phrase 'correlation does not imply causation' mean?

• Correlation cannot establish a cause-and-effect relationship.

• Correlation is a weaker form of regression analysis.

• Correlation tests always assume equal sample sizes.

• Correlation can only test weak relationships.

What are spurious correlations?

• Statistically significant correlations that lack practical significance.

• Apparent correlations between variables that share no actual relationship.

• Correlations that occur only in small datasets.

• Correlations that result from errors in measurement.

What r values represent weak, moderate, and strong relationships, respectively?

• Weak: 0.00–0.49, Moderate: 0.50–0.74, Strong: 0.75–1.0.

• Weak: 0.01–0.09, Moderate: 0.10–0.19, Strong: 0.20–0.29.

• Weak: 0.20–0.39, Moderate: 0.40–0.59, Strong: 0.60–0.79.

• Weak: 0.10–0.29, Moderate: 0.30–0.49, Strong: 0.50–1.0.