chapter 8

3 main usages of factor analysis

1. To study the underlying structure of psychological constructs 2. To reduce a large number of variables to a smaller, more manageable set of data 3. To confirm that self-report measures of attitude and personality are unidimensional (measure only one thing).

What is multiple regression analysis? what are the three types?

Multiple regression analyses use more than one predictor variable. Three types: Standard (or simultaneous) Stepwise Hierarchical

What is stepwise regression analysis? (DO WE NEED TO KNOW THE STEPS)

Stepwise multiple regression - builds the regression equation by entering the predictor variables one at a time based on their ability to predict the outcome variable

what is the y in the regression analysis ?

y - the variable we would like to predict; this is also called the dependent variable, criterion variable, or outcome variable

what is the regression coefficient

Β1 -- the slope of the line that best represents the relationship between the predictor variable (x) and the criterion variable (y); also called the regression coefficient

What is a constant in regression analysis ?

β0 - the y-intercept of the line that best fits the data in the scatter plot; also called the regression constant

what is the X in a regression analysis ?

x - the variable we are using to predict y; also called the predictor variable

what is the purpose of advanced correlational studies

Allow researchers to understand how and why sets of variables are related.

What is factor analysis?

Factor analysis is a class of statistical techniques that are used to analyze the interrelationships among a large number of variables. Factor analysis is used to identify the minimum number of factors needed to account for the relationships among the variables. The presence of correlations among several variables suggests that the variables may all be related to some underlying factors. not controlling or predicting but looking at relationships-->when variables are related we say x, y, and z are loaded

what is hierarchical regression analysis? What is it commonly used to do?

Hierarchical multiple regression - the predictor variables are entered into the equation in an order that is predetermined by the researcher an order that is - hopefully - based on theory As each new variable is entered into the equation, the researcher tests whether the new variable uniquely contributes to variance in the criterion variable. Two common uses: 1. Eliminating confounding variables "over and above" 2. Testing mediational hypotheses The reason why two variables are related is partially or fully explained by how the first variable predicts the second variable

What is structural equation modeling?

In structural equations modeling, the researcher makes a prediction regarding how a set of variables are causally related. This prediction implies that the variables ought to be correlated in a particular pattern. This predicted pattern is then compared to actual pattern of correlations.

What is standard multiple regression analysis?

Standard (or simultaneous) multiple regression - all of the predictor variables are entered into the regression analysis at the same time. The resulting equation provides a regression constant and separate regression coefficients for each predictor. Ex: Predicting job performance based on GPA, test scores, measure for work motivation, index of physical strength, etc

Why is regression analysis used?

The goal of regression analysis is to develop a regression equation from which we can predict one score on the basis of one or more other scores. One dependent variable, predicted by a number of independent variables Regression provides a mathematical description of how the variables are related and allows us to predict one variable from the others. y= Bo + Bx

Describe the multiple correlation coefficient (not on slides)

The multiple correlation coefficient (R) describes the degree of relationship between the criterion variable (y) and the set of predictor variables R can range from 0 to 1.00. The larger the value of R, the better job the regression equation does of predicting the criterion variable from the predictor variables.

What is a cross lagged panel design

Used when the main question is direction of effect: Does x cause y, or does y cause x? To what extent does x cause y, and vice versa? I n a cross-lagged panel design, you measure two things at two different time points-->If x causes y, then the correlation between x at Time 1 and y at Time 2 should be larger than the correlation between y at Time 1 and x at Time 2.