As in our previous study (Mattson et al., 2010b), latent profile analysis (LPA) was conducted to derive latent profiles that describe different categorical types of participants. LPA is a person-centered statistical approach that classifies individuals into groups based on their patterns of responses to sets of observed variables (Hagenaars and McCutcheon, 2002, McCutcheon, 1987, Lanza et al., 2003, Lanza et al., 2010, Roesch et al., 2010). The primary goal of LPA is to maximize the homogeneity within groups (i.e., individuals within a class/profile should look similar) and maximize the heterogeneity between groups (i.e., individuals between classes/profile groups should look different). These groups are represented by a categorical latent variable, as they are inferred from the response patterns on observed variables. LPA assumes a simple parametric model and uses the observed data to estimate parameter values for the model. This model-based approach is preferable to more subjective grouping techniques such as cluster analysis due to mathematical strengths, less subjectivity, and the ability to weight independent variables differentially and generate group probability predictions (Vermunt and Magidson, 2002). Model parameters are estimated using
