Exam 3 Research Methods

Correlation

Predictive relationship between two variables that must be at interval or ratio level.

Correlation Coefficient (r)

Indicates direction and strength of a relationship, ranging from -1.00 to +1.00.

Direction of Correlation

Positive indicates variables move in the same direction; negative indicates they move in opposite directions.

Strength of Correlation

Range of strength: 0.10 (very weak), 0.20 (small), 0.35 (moderate), 0.50+ (strong).

Scatterplot

A visual representation showing the relationship between two variables.

Z-scores

Standardize values to compare across variables, typically with ~95% of data falling within normal distribution.

Degrees of Freedom (df)

Calculated as N − 1, adjusts for sample estimation error.

Statistical Significance

Indicates that an observed effect is unlikely due to chance, with p < .05 considered statistically significant.

Test-retest reliability

Measures the stability of a test over time.

Internal consistency

Measures how well items on a test assess the same construct, often using Cronbach’s alpha.

Independent Variable (IV)

The manipulated variable in an experiment.

Dependent Variable (DV)

The measured outcome that is not manipulated.

Experimental Group

The group receiving the treatment in an experiment.

Control Group

The baseline comparison group that does not receive treatment.

Manipulation check

A method to confirm that the independent variable worked as intended.

Between-Subjects Design

A design in which different participants are assigned to each condition.

Within-Subjects Design

A design where the same participants are used in all conditions.

Order Effects

Potential issues in within-subjects design such as carryover, practice, and fatigue effects.

Confound

A variable that varies with the independent variable, threatening internal validity.

Type I error

Rejecting a true null hypothesis, resulting in a false positive (α = .05).

Type II error

Failing to reject a false null hypothesis, resulting in a false negative.

Post Hoc Tests

Tests used after a significant ANOVA to identify which groups differ.

Factorial Design

An experimental study involving 2 or more independent variables.

Interaction Effect

An effect where the impact of one independent variable depends on the level of another.

Main Effect

The effect of one independent variable while disregarding the others.

Design Advantages

Allows testing of multiple independent variables at once and identifying interactions efficiently.