Quantitative Research Methods: Multiple & Hierarchical Regression

These flashcards cover key vocabulary and concepts related to multiple and hierarchical regression analysis in quantitative research methods.

Multiple Regression

Statistical technique that uses several independent variables to predict the outcome of a dependent variable.

Hierarchical Regression

A method where predictors are entered into the model in steps to assess their unique contributions.

Regression Coefficient (b1)

The amount that the predicted value increases for each one unit increase in the predictor variable.

Constant Value (b0)

The predicted value of the dependent variable when all independent variables are set to zero.

Homoscedasticity

Assumption that the variance of residual is constant across all levels of the independent variable.

Multicollinearity

Condition where two or more predictor variables in a multiple regression model are highly correlated.

Adjusted R²

An estimate of the population variance explained by the predictors, adjusted for the number of predictors in the model.

ANOVA F-test

Statistical test used to compare the variances between different models or groups.

Standardized Beta Coefficient

Shows the strength of the relationship between the independent variable and the dependent variable while controlling for other variables.

Normality of Residuals

Assumption that the residuals (errors) are normally distributed in regression analysis.

Independence of Errors

Assumption that the residuals are independent of each other in a regression analysis.

Continuous Dependent Variable (DV)

A dependent variable that can take any value within a given range; essential for regression analysis.