Factor analysis: a primer for asthma researchers.

In the past few years, factor analysis (FA) has been increasingly used to examine the relationship between variables known to be associated with asthma health status including spirometry, atopy, inflammation, symptom frequency, quality of life, b-agonist use, and exacerbations. FA is a multistep analytic process that seeks to define the underlying latent constructs among a larger set of observed variables. Variables that correlate with one another but are distinct from other variables are combined into factors that are assumed to reflect the same underlying disease process. Thus, a primary advantage of FA is that it reduces a large number of disease features to a smaller, more manageable number of independent and analyzable factors. FA does not have any a priori hypotheses about the data structure. As such, the specific variables included in the model highly affect the output of the factor analysis and the interpretation of which latent constructs are represented. Moreover, there are multiple valid options for conducting FA, and there are no tests of significance. As a result, FA is dependent on the researchers’ judgment about (1) the consistency of results using different statistical approaches and rules of thumb, (2) the interpretability of the output, and (3) comparability with previous research. Herein lies the challenge of FA. Because FA is based on the correlation between variables, one of the most important decisions is selecting which variable to include in the model. Ideally the researcher has access to the universe of possible variables from which to choose those that are interrelated and make sense on the basis of known empirical data or hypothesized mechanisms. Unfortunately, for most asthma studies, the FA is a secondary analysis of an existing dataset, and the variables available may not include relevant domains, or domains present may not be measured thoroughly. For example, Holt et al did not have quality-of-life data, and symptom data were based on diary cards, which contrasts with the survey approach used by Shatz et al, which in turn did not have measures of atopy or inflammation that were included to a varying degree in other studies (Table I). This is important because failure to measure an important factor may distort the apparent relationships among the measured factors. The complexity of the variables included in the analysis can also affect the results. In asthma, this is a significant limitation because many of the constructs of interest