Regresion Multiple

MULTIPLE REGRESSION MODEL

Multiple regression is a statistical technique that allows us to predict someone’s
score on one variable on the basis of their scores on several other variables. Anexample might help. Suppose we were interested in predicting how much an
individual enjoys their job. Variables such as salary, extent of academic qualifications, age, sex, number of years in full-timeemployment and socioeconomic status might all contribute towards job satisfaction. If we collected data on all of these variables, perhaps by surveying a few hundred members of the public, we would beable to see how many and which of these variables gave rise to the most accurate prediction of job satisfaction. We might find that job satisfaction is most accurately predicted by type ofoccupation, salary and years in full-time employment, with the other variables not helping us to predict job satisfaction. When using multiple regression in psychology, many researchers use the term“independent variables” to identify those variables that they think will influence some other “dependent variable”. We prefer to use the term “predictor variables” for those variables that may be useful inpredicting the scores on another variable that we call the “criterion variable”. Thus, in our example above, type of occupation, salary and years in full-time employment would emerge as significantpredictor variables, which allow us to estimate the criterion variable, how satisfied someone is likely to be with their job. As we have pointed out before, human behaviour is inherently noisy and thereforeit is not possible to produce totally accurate predictions, but multiple regression allows us to identify a set of predictor variables which together provide a useful estimate of a participant’s likelyscore on a criterion variable.

HOW DOES MULTIPLE REGRESSION RELATE TO CORRELATION AND ANALYSIS OF VARIANCE?

If two variables are correlated, then knowing the score on one variable will...