Tetrachoric correlation and model
goodness of fit tests were used to verify the direction and magnitude of associations between variable constructs and dependent variables.
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 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 Residua
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 Residual (RM
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 (RM
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 (RM
fit index (NFI), the adjusted
Goodness of Fit index (GFI) and the mean Root Mean Square Residua
Goodness of Fit index (GFI) and the mean Root Mean Square Residual (RM
Fit 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 da
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 da
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 da
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.
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 (GF
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 (GF
Fit Index (CFI) and
Goodness of Fit Index (GF
Fit 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.