Sentences with phrase «square goodness of fit»

For a given n (the number of observations) 10,000 simulations were run and the Chi - square goodness of fit test and regression coefficient (Genotype (Postn − / −)-RRB- was calculated for each simulated data set.

Not exact matches

A chi - square test for goodness of fit indicated that the teachers» preference for projection usage was indeed significant, X2 (1, N = 91) = 5.82, p <.05, as opposed to utilizing its interactive tools.

A chi - square test of goodness of fit was performed to determine if the teachers» instructional practice with the IWB differed from their instruction without the IWB.

We include the Wilson HS indicator in our final models (2) and (4) in Table 1, considering the large increase in goodness of fit (R - squared) and outlier nature of boundary participation rates in many of these neighborhoods.

We analyzed data using the LISREL 8.80 analysis of covariance structure approach to path analysis and maximum likelihood estimates.42 We used four goodness - of - fit statistics to assess the fit of our path model with the data: the Root Mean Square Error of Approximation test (RMSEA), the Norm - fit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square Residuagoodness - of - fit statistics to assess the fit of our path model with the data: the Root Mean Square Error of Approximation test (RMSEA), the Norm - fit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square Residual (RMfit statistics to assess the fit of our path model with the data: the Root Mean Square Error of Approximation test (RMSEA), the Norm - fit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square Residual (RMfit of our path model with the data: the Root Mean Square Error of Approximation test (RMSEA), the Norm - fit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square Residual (RMfit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square ResiduaGoodness of Fit index (GFI) and the mean Root Mean Square Residual (RMFit index (GFI) and the mean Root Mean Square Residual (RMR).

I used Excel's curve fitting capability to fit straight lines to the data and to report the equations (i.e., regression equations) and goodness of fit (R - squared).

I have listed all of the coefficients for the curves along with the values of R squared, which indicates the goodness of fit.

I used Excel's curve fitting capability to fit straight lines to the data and report the equations (i.e., regression equations) and goodness of fit (R - squared).

Betas versus Treasury yields declined with credit quality, as did the goodness - of - fit (R - squared).

The goodness - of - fit of the different models were compared using the residual sum - of - squares.

Goodness - of - fit of the polygenic and major gene models were compared using the residual sums - of - squares (SS) as in Palmer et al. (2001).

Using a linear regression model as in Allen and Tett this approach yields an objective measure of model - observation goodness - of - fit (via the residual sum of squares weighted by differences expected due to internal variability).

I calculated the trends and the R ^ 2 for the series and then looked at the goodness of fit of the data using a chi square test.

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, 199fit 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, 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, 199fit, 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, 199fit); 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, 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, 199fit < 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, 199fit < 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, 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, 199fit < 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, 199fit < 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, 199Fit 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, 199fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.90)(Hu & Bentler, 1999).

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 (TLfit 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 (TLFit Index (CFI), and finally by the Tucker - Lewis index (TLI).

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 daFit 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 dafit 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 dafit 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.

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

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).

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

Considering the categorization of participants into groups of rapid regulators and nonregulators, a goodness - of - fit chi - square test (χ2) revealed that older adults were just as likely to be rapid regulators as nonregulators, χ2 (1, N = 34) =.00, p = 1.00; however, for younger adults a trend was found in which they were more likely to be nonregulators than rapid regulators, χ2 (1, N = 25) = 3.24, p =.07.

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 (SRMfit 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 (square, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMFit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (Square Residual (SRMR).

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 199fit 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 square and the p - value, the Comparative Fit Index (CFI: Bentler 1989), and the Root Mean Square Error of Approximation (RMSEA: Steiger 199Fit Index (CFI: Bentler 1989), and the Root Mean Square Error of Approximation (RMSEA: Steiger Square Error of Approximation (RMSEA: Steiger 1990).