Chapter 7: Correlational Research

Correlational Research

Examines the relationship between two or more naturally occurring variables without manipulation.

Correlation Coefficient

A statistic that quantifies the degree of linear relationship between two variables, ranging from -1.00 to +1.00.

Positive Correlation

Indicates that as one variable increases, the other also increases.

Negative Correlation

Indicates an inverse relationship between two variables.

Coefficient of Determination (r-squared)

Indicates the proportion of variance in one variable that can be explained by another variable.

Statistical Significance

A correlation coefficient is considered statistically significant if the probability of it being zero in the population is very low (commonly set at an alpha level of .05).

Factors Affecting Statistical Significance

Include sample size, effect size, and the chosen alpha level.

Correlation vs

Correlation does not imply causation; a correlation between two variables does not mean one causes the other.

Criteria for Inferring Causality

Include covariation, directionality, and controlling for extraneous variables.

Partial Correlation

Measures the relationship between two variables while controlling for the influence of one or more additional variables.

Spearman Rank-Order Correlation

Used for ordinal data, reflecting the rank ordering of participants.

Phi Coefficient

Used for two dichotomous variables, such as gender and dropout rates.

Point-Biserial Correlation

Used when one variable is dichotomous and the other is continuous, such as gender and IQ scores.