Of a two-factor model with the specification of error covariances for

Of a two-factor model with the specification of error covariances for Items 12, 14, and 16 or (b) the specification of a three-factor model splitting TAF-L into TAF-LS and TAF-LO. The two-factor model with error covariances was pursued given (a) its parsimony with regard to number of factors, (b) the fact that a separate TAF-LS factor would be defined by only three items (and the TAF-LO factor would consist of four items), and (c) the lack of compelling reasons to distinguish TAF-LS and TAF-L as separate, substantively meaningful dimensions. With regard to (c), it was deemed likely that the three TAF-LS items measured the general TAF-L dimension but 5-BrdU site shared Resiquimod site additional covariance because of a method effect (e.g., because of differential item content). Thus, the CFA solution was respecified with correlated measurement errors for Items 12, 14, and 16. Although the revised CFA model provided a better fit to the data than the initial two-factor model: that is, 2(148) = 565.0, p < .001, BIC = 17478.2, modification indices revealed two salient areas of ill fit that corresponded to the error covariances of Items 8 and 11 and Items 10 and 19 (modification indices = 52.44 and 57.35, respectively). Because this unexplained indicator covariance was likely because of the highly similar wording and content of these items (e.g., Items 8 and 11 pertain to harming others, and Items 10 and 19 assess obscene thoughts, remarks, and/or gestures in church), the CFA solution was respecified with correlated measurement errors for these two pairs of items. The second revised CFA model with additional correlated residuals provided an acceptable fit to the data, 2(146) = 454.8; p < .001; RMSEA = .07; 90 CI = .07, .08; SRMR = .05; CFI = .95; TLI = .94; BIC = 17380.0, and fit diagnostics did not detect other salient areas of strain. As hypothesized on the basis of overlapping item content, significant error covariances were observed among Items 12, 14, and 16 (r = .54-.67, ps < .001), between Items 8 and 11 (r = .37, p < .001), and between Items 10 and 19 (r = .39, p < .001). All factor loadings were exceeded .30 (range = .59-.95), and the factor correlation was moderate in strength (r = .55; see Table 2 for details). Based on these CFA results, which corroborated that the majority of items (i.e., 12 of 19) loaded onto the TAF-M factor, the final CFA solution was respecified as a bifactor model (cf. Chen, West, Sousa, 2006; Holzinger Swineford, 1937). One key advantage of the bifactor model is the ability to evaluate the importance of domain-specific factors. In this case, the TAF-M domain-specific factor may not be relevant to the prediction of the TAFS items when a general TAF factor is included in the model. This situation seemed possible because most items loaded onto a broad TAF-M factor (with some additional covariation explained by a smaller TAF-L dimension). Although second-order factor models allow for the inclusion of superordinate factors, these analyses do not permit indicators to load directly on the higher order factors nor do they directly permit the validation of subdomain factors. Bifactor models, on the other hand, allow a general factor to directly account for item covariation, along with possible orthogonal subdomain factors that account forAuthor Manuscript Author Manuscript Author Manuscript Author ManuscriptAssessment. Author manuscript; available in PMC 2015 May 04.Meyer and BrownPageadditional covariance over and beyond the general fact.Of a two-factor model with the specification of error covariances for Items 12, 14, and 16 or (b) the specification of a three-factor model splitting TAF-L into TAF-LS and TAF-LO. The two-factor model with error covariances was pursued given (a) its parsimony with regard to number of factors, (b) the fact that a separate TAF-LS factor would be defined by only three items (and the TAF-LO factor would consist of four items), and (c) the lack of compelling reasons to distinguish TAF-LS and TAF-L as separate, substantively meaningful dimensions. With regard to (c), it was deemed likely that the three TAF-LS items measured the general TAF-L dimension but shared additional covariance because of a method effect (e.g., because of differential item content). Thus, the CFA solution was respecified with correlated measurement errors for Items 12, 14, and 16. Although the revised CFA model provided a better fit to the data than the initial two-factor model: that is, 2(148) = 565.0, p < .001, BIC = 17478.2, modification indices revealed two salient areas of ill fit that corresponded to the error covariances of Items 8 and 11 and Items 10 and 19 (modification indices = 52.44 and 57.35, respectively). Because this unexplained indicator covariance was likely because of the highly similar wording and content of these items (e.g., Items 8 and 11 pertain to harming others, and Items 10 and 19 assess obscene thoughts, remarks, and/or gestures in church), the CFA solution was respecified with correlated measurement errors for these two pairs of items. The second revised CFA model with additional correlated residuals provided an acceptable fit to the data, 2(146) = 454.8; p < .001; RMSEA = .07; 90 CI = .07, .08; SRMR = .05; CFI = .95; TLI = .94; BIC = 17380.0, and fit diagnostics did not detect other salient areas of strain. As hypothesized on the basis of overlapping item content, significant error covariances were observed among Items 12, 14, and 16 (r = .54-.67, ps < .001), between Items 8 and 11 (r = .37, p < .001), and between Items 10 and 19 (r = .39, p < .001). All factor loadings were exceeded .30 (range = .59-.95), and the factor correlation was moderate in strength (r = .55; see Table 2 for details). Based on these CFA results, which corroborated that the majority of items (i.e., 12 of 19) loaded onto the TAF-M factor, the final CFA solution was respecified as a bifactor model (cf. Chen, West, Sousa, 2006; Holzinger Swineford, 1937). One key advantage of the bifactor model is the ability to evaluate the importance of domain-specific factors. In this case, the TAF-M domain-specific factor may not be relevant to the prediction of the TAFS items when a general TAF factor is included in the model. This situation seemed possible because most items loaded onto a broad TAF-M factor (with some additional covariation explained by a smaller TAF-L dimension). Although second-order factor models allow for the inclusion of superordinate factors, these analyses do not permit indicators to load directly on the higher order factors nor do they directly permit the validation of subdomain factors. Bifactor models, on the other hand, allow a general factor to directly account for item covariation, along with possible orthogonal subdomain factors that account forAuthor Manuscript Author Manuscript Author Manuscript Author ManuscriptAssessment. Author manuscript; available in PMC 2015 May 04.Meyer and BrownPageadditional covariance over and beyond the general fact.