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Rather, they may be multidimensional, sharing a degree of association with other constructs (Reise, 2012; Morin et al., 2016). Confirmatory bi-factor models are one set of models gaining traction as a technique for accounting for this multidimensionality (Reise, 2012). A bi-factor model partials out covariance that is shared by all scale items into a single ��general�� psychopathology factor (��p��) which reflects individual differences in what is common amongst the items, whilst simultaneously identifying two or more orthogonal sub-factors, or item CAL101 parcels, representing common factors shared by those items that explain variance not accounted for by the general factor. By using bi-factor modeling, we were able to test the hypothesis that the three facets of loneliness and separate positive and negative schizotypy traits existed outside of a single, general psychopathology factor. The final step, in Phase 4, was to explore the pattern of associations between the sub-factors of loneliness and positive and negative schizotypy traits, once the variance attributable to general psychopathology was removed, to determine any differential relationships that exists. In all analyses, a weighted least squares mean and variance adjusted (WLSMV) estimator was used. This robust estimator was developed for use with categorical or ordinal data, and was designed for use with polychoric correlations (Muthen et al., unpublished data2). Model fit was examined with a range of fit statistics. Given that chi-square is highly sensitive to sample size (Marsh et al., 1988), we also report the comparative fit index (CFI: Bentler, 1990) and the Tucker�CLewis Sitaxentan index (TLI: Afatinib in vivo Tucker and Lewis, 1973) where values above 0.90 indicated reasonable fit, and above 0.95 good fit. The TLI compares the fit of each theoretically derived model to a null or baseline model, which assumes no relationships between the variables. This index is less affected by sample size than chi-square (Marsh et al., 1988) and is, therefore, useful for comparing factor models. For the root mean square error of approximation (RMSEA; Steiger, 1990) values