![]() *Show variable names, values and labels in output tables. We'll inspect the frequency distributions with corresponding bar charts for our 16 variables by running the syntax below. Now let's first make sure we have an idea of what our data basically look like. which satisfaction aspects are represented by which factors?.which questions measure similar factors?.how many factors are measured by our 16 questions?.So our research questions for this analysis are: We think these measure a smaller number of underlying satisfaction factors but we've no clue about a model. The survey included 16 questions on client satisfaction. The data thus collected are in dole-survey.sav, part of which is shown below. Research Questions and DataĪ survey was held among 388 applicants for unemployment benefits. There's different mathematical approaches to accomplishing this but the most common one is principal components analysis or PCA. The software tries to find groups of variablesĮach such group probably represents an underlying common factor. ![]() The simplest possible explanation of how it works is that That is, I'll explore the data (hence, “exploratory factor analysis”). Exploratory Factor Analysisīut what if I don't have a clue which -or even how many- factors are represented by my data? Well, in this case, I'll ask my software to suggest some model given my correlation matrix. SPSS does not include confirmatory factor analysis but those who are interested could take a look at AMOS. This is known as “ confirmatory factor analysis”. In this case, I'm trying to confirm a model by fitting it to my data. Now I could ask my software if these correlations are likely, given my theoretical factor model. Right, so after measuring questions 1 through 9 on a simple random sample of respondents, I computed this correlation matrix. So if my factor model is correct, I could expect the correlations to follow a pattern as shown below. However, questions 1 and 4 -measuring possibly unrelated traits- will not necessarily correlate. The same reasoning goes for questions 4, 5 and 6: if they really measure “the same thing” they'll probably correlate highly. Now, if questions 1, 2 and 3 all measure numeric IQ, then the Pearson correlations among these items should be substantial: respondents with high numeric IQ will typically score high on all 3 questions and reversely. For measuring these, we often try to write multiple questions that -at least partially- reflect such factors. Such “underlying factors” are often variables that are difficult to measure such as IQ, depression or extraversion.
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