1. Regression analysis is used to develop a regression equation that describes how variables are related and allows researchers to predict people’s scores on one variable based on their scores on other variables. A regression equation provides a regression constant (equivalent to the y-intercept) as well as a regression coefficient for each predictor variable.
   
   
2. When constructing regression equations, a researcher may enter all of the predictor variables at once (simultaneous or standard regression), allow predictor variables to enter the equation based on their ability to account for unique variance in the criterion variable (stepwise regression), or enter the variables in a manner that allows him or her to test particular hypotheses (hierarchical regression).
   
   
3. Multiple correlation expresses the strength of the relationship between one variable and a set of other variables. Among other things, it provides information about how well a set of predictor variables can predict scores on a criterion variable in a regression equation.
   
   
4. Cross-lagged panel correlation designs and structural equations modeling are used to test the plausibility of causal relationships among a set of correlated variables. Both analyses can provide evidence for or against causal hypotheses, but our conclusions are necessarily tentative because the data are correlational.
   
   
5. Factor analysis refers to a set of procedures for identifying the dimensions or factors that account for the observed relationships among a set of variables. A factor matrix shows the factor loadings for each underlying factor, which are the correlations between each variable and the factor. From this matrix, researchers can identify the basic factors in the data.
   
   

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