Sentences with phrase «comparative fit index»

The goodness of fit of the model was assessed using chi - square and the p - value, the Comparative Fit Index (CFI: Bentler 1989), and the Root Mean Square Error of Approximation (RMSEA: Steiger 1990).

The fit of the measurement models was evaluated using χ2, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA) and its 90 % confidence interval, and factor loadings.

Model fit for this original conceptualization was highly acceptable (χ2 = 1.44, p =.32, comparative fit index [CFI] =.91, root mean square error of approximation [RMSEA] =.07; all paths significance at p <.05).

Overall, this model fits the data extremely well (χ2 = 70.4, P <.001; comparative fit index = 0.99, Tucker - Lewis index = 0.96, root mean square error of approximation = 0.04, standardized root mean square residual = 0.02).

Mplus v7.11 was used for all analyses.23 SDQ items were treated as ordinal, with weighted least - squares means and variance — adjusted estimation used.23 Given the χ2 statistic's propensity to reject good models when samples are large and / or complex, the comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to assess model fit.

Goodness - of - fit was assessed by evaluating the Satorra - Bentler scaled chi - square, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR).

The fit of the model was evaluated in terms of three fit indices: (a) the chi - square statistic, which compares the observed covariance structure to the covariance structure specified by the model; (b) the comparative fit index (CFI), which compares the hypothesized model to a null model with no paths or latent variables; and (c) the root mean square error of approximation (RMSEA), which estimates the degree to which the covariance structure observed in the data deviates from that specified in the model.

Incremental fit measures include the non-normed fit index (NNFI) and the comparative fit index (CFI).

Additionally, when the comparative fit index (CFI) and the incremental fit index (IFI) are greater than.90 the hypothesized model fits the observed data adequately (Browne & Cudek, 1993).

To evaluate model fit, the X2 - test statistic, the comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used.

The global model fit to the data was tested by Chi - square, Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Goodness of Fit Index (GFI).

Several indices of model fit were inspected, including the chi - square statistic, the chi - square to degrees of freedom ratio, the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardised root mean residual (SRMR).

The comparative fit index (CFI) deals with the difference between observations and hypothesized model.

The findings of CFA were assessed on the basis of several goodness - of - fit statistics such as goodness of fit index (GFI), comparative fit index (CFI), standardized root mean square residual (SRMR) and root mean square error of approximation (RMSEA).

GFI = Goodness - of - Fit Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of approximation.

Model fit was evaluated using a chi - square test statistic as well as Comparative Fit Index (CFI), Tucker - Lewis Index (TLI) and root - mean - square error of approximation (RMSEA).

Commonly reported fit indices like the Chi - square (χ2), the comparative fit index (CFI), the

Fit indices used to evaluate the model included a χ2 goodness - of - fit test (nonsignificant values indicate good fits), the comparative fit index (scores of > 0.95 indicate better fits), the root mean square error of approximation (values of < 0.05 indicate good fits), and the standardized root mean square residual (values of < 0.08 indicate good fits).43, 44 Missing values were imputed through multiple imputation by using functions in the missing data library in S - Plus (Insightful Corp, Seattle, WA).45, 46 The combined data for the cross - lagged / survival model converged more quickly with 15 imputed data sets than did the model that used a likelihood - based approach to missing data.

CFA, confirmatory factor analysis; CFI, comparative fit index; RMSEA, root mean square error of approximation; TLI, Tucker - Lewis index.

Goodness of fit was evaluated by the Standardized Root Mean Square Residual (SRMR), the Root Mean Square Error of Approximation (RMSEA, 90 % CI), the Comparative Fit Index (CFI), and finally by the Tucker - Lewis index (TLI).

The most commonly used goodness - of - fit statistics were used in the present study (Byrne, 2016; Laveault & Grégoire, 2014), that is, the chi - square to its degrees of freedom (χ 2 / df; a χ 2 / df close to or less than 2.0 was considered to be indicative of a good model fit, and close to or less than 5.0 as indicative of a satisfying fit); the Root Mean Square Error of Approximation (RMSEA; good fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 1999).

Based on the recommendation by Jackson, Gillaspy, & Purc - Ste - phenson (2009), Study 1 evaluated each model with multiple and different types of model fit indexes: The Tucker - Lewis index (TLI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA).

The minimum fit function χ2 value (CMIN), CMIN / DF, comparative fit index (CFI), incremental fit index (IFI) and root mean square error of approximation (RMSEA) with 90 % confidence intervals were used to estimate the model fit.

Further, the comparative fit index (CFI) and the incremental fit index (IFI) were measured, and values equal to or higher than 0.9 for these indices indicate an acceptable fit to the model.

Model fit was evaluated by a) non-significant chi - square values, b) comparative fit indices (CFI) >.90, c) TLI >.90, and d) squared residual means (SRMR) <.06.

Not exact matches

In this respect, they distinguish, among others, absolute fit indices which compare the hypothesized model with no model at all, comparative or incremental indices of fit which use a baseline model for assessing model fit, and parsimony fit indices which penalize for model complexity (Byrne, 2016).