Multivariate Correlational Research

Vocabulary flashcards covering key statistical concepts from the lecture on multivariate correlational research, including causal criteria, longitudinal correlation types, and multiple-regression interpretation.

Multivariate Designs

Research designs, such as longitudinal and multiple-regression designs, that involve more than two measured variables.

Three Criteria for Causation

The three requirements necessary to establish a causal relationship: covariance, temporal precedence, and internal validity.

Covariance

A causal criterion requiring an observed association or correlation between the proposed cause and the proposed effect.

Temporal Precedence

A causal criterion requiring that the proposed causal variable occurs first in time before the proposed outcome variable.

Internal Validity

A causal criterion requiring that the relationship between two variables is not attributable to an alternative explanation or a third variable.

Longitudinal Design

A correlational research study where the same variables are measured in the same individuals across multiple points in time.

Cross-Sectional Correlations

In a longitudinal design, correlations between two distinct variables that are measured at the exact same point in time, which cannot establish temporal precedence.

Autocorrelations

In a longitudinal design, correlations that assess the consistency of a single variable with itself across two different time points.

Cross-Lag Correlations

In a longitudinal design, correlations between an earlier measurement of one variable and a later measurement of another variable, helping move closer to establishing temporal precedence.

Multiple Regression

A statistical method examining the relationship between one dependent (criterion) variable and multiple independent (predictor) variables, allowing assessment of each predictor's unique contribution while controlling for others.

Criterion Variable

The dependent or outcome variable in a multiple-regression analysis that researchers are interested in understanding or predicting.

Predictor Variable

An independent variable in a multiple-regression analysis used to explain variance in the criterion variable.

Control For

A statistical technique used to account for other variables' effects to isolate the unique impact of the main variable of interest on an outcome.

Beta ($\beta$)

The standardized coefficient in a multiple-regression analysis that denotes the direction and strength of the relationship between a predictor and the criterion variable while holding all other predictors constant.

Evaluating Beta Significance

Assessing whether a standardized regression coefficient is statistically significant using either the $p$-value or determining whether the $95\%$ confidence interval excludes zero.

Comparing Beta Strengths

The rule that standardized beta ($\beta$) magnitudes can only be compared directly to each other within a single multiple-regression table, not across different regression tables.

Nonsignificant Beta

A finding where a predictor's beta fails to reach statistical significance, indicating that the relationship between the predictor and criterion variable goes away or is explained when holding other variables constant.