Sentences with phrase «goodness of fit test»

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.

Tetrachoric correlation and model goodness of fit tests were used to verify the direction and magnitude of associations between variable constructs and dependent variables.

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 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).

To assess the calibration of each model, we performed Hosmer — Lemeshow goodness - of - fit tests (74).

Janczura, Joanna and Weron, Rafal (2012): Goodness - of - fit testing for the marginal distribution of regime - switching models.

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.

All this means that any scientific test of the goodness of fit that also measures complexity (such as the Akaike Information Criterion) will pick the three trend pattern over the five trend pattern every time.

As the range would include only the 66 % of ensemble members that passed goodness - of - fit test, I would expect it to remain largely unchanged with ensemble size, assuming a close link between goodness - of - fit and forecast warming.

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.

A CFA was performed on the raw data of the ULS - 6 to test goodness of fit of the observed data for the one - factor model suggested by Neto (1992).

Evaluating the fit of structural equation models: Tests of significance and descriptive goodness - of - fit measures.

Testing the goodness of fit in early intervention.

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 (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.

The logistic regression model's χ2 and Hosmer and Lemeshow goodness - of - fit test statistics were respectively significant (χ2 = 31.187, p =.008) and not significant (χ2 = 4.384, p =.821), both indicating a well - fitting model; the Nagelkerke's R 2 was moderate (0.459).

SEM allows the both the assessment of goodness of fit of a specified model and testing of each estimated path coefficient.