PSCH 443 Multiple Regression 4 Hierarchical Regression

Simultaneous Regression

A type of regression where all predictor variables are entered into the regression equation at the same time.

Hierarchical Regression

A method of regression where predictors are entered into the model in an order specified by the researcher.

Predictor Variables

Independent variables, labeled as IV1, IV2, IV3, etc., that are used to predict the dependent variable.

Dependent Variable

The outcome variable that researchers are trying to predict or explain.

R-squared

A statistic that indicates the proportion of variance in the dependent variable that is predictable from the independent variables.

Background Variables

Known predictors that are controlled for in a regression model before introducing new predictors.

Variable Entry Order

The sequence in which variables are added to the regression model, often based on theoretical considerations.

Change in R-squared

The difference in R-squared values between models that indicates how much additional variance is explained by new predictors.

B weights

The coefficients in a regression model that represent the change in the dependent variable for a one-unit change in the predictor.

Beta weights

Standardized coefficients that allow for comparison of the strength of predictors in a regression model.

Aggressive Personality

A background factor that may influence aggressive behavior, often controlled for in studies of video game violence.

Video Game Violence Exposure

The extent to which an individual has been exposed to violent content in video games, considered as a key predictor in aggression studies.

Statistically Meaningful

Referring to results that are unlikely to have occurred by chance, typically indicated by a p-value less than 0.05.

Demographic Variables

Variables that describe characteristics of the sample population, such as age, gender, and race, often included in regression models.

Multicollinearity

A situation in regression analysis in which two or more predictor variables are highly correlated, potentially leading to unreliable estimates.

Iterative Process

A method in hierarchical regression where new variables are added one at a time, allowing exploration of their unique contributions.

Correlation Matrix

A table showing the correlation coefficients between multiple variables, helping to understand relationships among them.