Читать книгу Methods in Psychological Research - Annabel Ness Evans - Страница 100

Structural equation modeling is a complex endeavor that can involve various techniques, including factor analysis, regression models, path analysis, and so on. We know that human behavior can be influenced by many variables. The purpose of structural equation modeling is to test whether data can confirm a model of how a number of variables are associated. These models are a way of testing how well theory can account for available evidence by testing whether the variables are associated in a way that would be predicted by the theory. We will just be able to give you an idea of the purpose of structural equation modeling here.

We hope you will remember what happens when we transform a set of numbers by adding a constant to each or multiplying each by a constant. Let’s say we multiply all the numbers in a list by a constant, c. The mean of that set of transformed numbers will be equal to the old mean times c, the standard deviation of the new set of numbers will equal the old standard deviation times the absolute value (i.e., ignore the sign) of c, and the variance will equal the old variance times c squared. Simple, right?

What is our point, you might be wondering? Well, bear with us. If we suspected that two sets of numbers were related, we could compare the variances of the two sets of numbers, for example, to confirm our suspicions. If one set was related to the other set by the equation Y = 2X, the variance of Y must be four times the variance of X. So we could confirm our hypothesis about the relationship between the two sets of numbers by comparing their variances rather than the numbers themselves. We hope you are not too confused by this somewhat odd way of doing things, but we think it might help you understand structural equation modeling. Two sets of numbers could be related in much more mathematically complex ways than by Y = 2X, but we hope you are getting the idea. You can determine if variables are related by looking at their variances and covariances.

Structural modeling is a way of determining whether a set of variances and covariances fits a specific structural model. In essence, the researcher hypothesizes that the variables are related in a particular way, often with something called a path diagram that shows the interrelationships among the variables. Then the researcher figures out what this model predicts about the values of the variances and covariances of the variables. This is the really complex part of the process, and we just can’t go there in this book! Then the researcher examines the variances and covariances of the variables to see if they fit the predictions of the model. If the model is supported by the statistical evidence, it is one possible way in which these variables are related. It’s important to know that this does not prove that the model is the only way these variables are related, and indeed there may be better models that someone will propose at a later time; however, it does provide researchers with useful information for further study.

As we said earlier, this is a complex procedure well beyond the scope of our book, but we hope our brief discussion gives you some idea of the purpose of structural equation modeling.