PSYC012 Bivariate Correlation

What is a bivariate correlation?

A statistical relationship between exactly two variables.

What types of measures are used in bivariate correlations?

Self-report, observation, or physiological measures.

When is bivariate correlation most appropriate?

When both measures are continuous (ordinal, interval, or ratio).

How are bivariate correlations visually represented?

By scatterplots.

What is an independent samples t-test?

A method to analyze the relationship between one categorical variable and one continuous variable.

What is the difference between correlational studies and experiments?

Correlational studies measure variables without manipulation, while experiments involve manipulation of variables.

What are the five key questions for statistical validity in correlational studies?

1) Effect size (r value), 2) Statistical significance (p value), 3) Outliers, 4) Restriction of range, 5) Curvilinear associations.

What does the effect size (r value) indicate?

The strength and direction of a relationship between two variables.

What does an r value closer to -1 or 1 indicate?

A stronger correlation.

What is considered a small effect size?

An r value of .1 (or -.1).

What is statistical significance?

The likelihood that the observed effect occurred by chance, assuming no real effect exists.

What p-value is considered statistically significant?

A p-value less than .05.

What is an outlier?

An extreme score on either or both variables that can influence the correlation.

What is restriction of range?

When only part of the full scale for one or more variables is represented, reducing correlation strength.

What is a curvilinear relationship?

A relationship that is not well-represented by a straight line.

What are the three requirements to determine causation?

1) Covariance, 2) Temporal precedence, 3) Internal validity.

What is the directionality problem in causation?

Failing to establish that the cause precedes the effect.

What is the third variable problem?

Failing to account for other variables that may explain the relationship.

What is the significance of a strong correlation?

It allows for more accurate predictions.

Can small effect sizes still be important?

Yes, as demonstrated by studies like the aspirin and heart attack correlation.

What happens to p-values with larger samples?

They tend to be smaller, making it less likely to find a strong correlation by chance.

How can outliers affect correlation?

They can distort the correlation strength and significance.

What is the impact of restricted range on correlation?

It can artificially reduce the strength of the correlation.

What is the relationship between correlation and causation?

Correlation does not imply causation.

What is the effect of a curvilinear relationship on correlation testing?

Normal correlation tests only for linear relationships, potentially missing interesting associations.

What does a p-value greater than .05 indicate?

The result is considered non-significant.

What is the importance of effect sizes in research?

They provide insight into the strength of relationships and their predictive power.