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Lisrel 9.1 ##VERIFIED## Full Version Free 32

November 22, 2022

Lisrel 9.1 ##VERIFIED## Full Version Free 32

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Lisrel 9.1 Full Version Free 32

to investigate whether the proposed structural relationships between variables changed when a latent construct, c&l, was added to the cfa model, it was necessary to create latent c&l factors in the model. to enable interpretation of the latent c&l factors we added item response and error covariance thresholds to the model. thus, for the purposes of our research the latent c&l factors were operationalised as latent constructs that measured the constructs of (1) computational time and hardware?, (2) training?, and (3) software support?, each consisting of two indicators (meaning that each latent c&l factor consisted of two items). to ensure that the c&l latent factor measurements had appropriate error structure to represent the variance between organisations, we compared the model with a model that included a free error variance covariance term between the measured error variables of each c&l latent factor [ 32 ]. where the model with free error variance variance accounted for most of the variance of the latent c&l, we concluded that the error terms were appropriately partitioned and the factors were not conceptually overlapping. finally, to ensure that c&l latent factors were sufficiently measured, error correlation terms were then added to the model so that the c&l latent factors were non-correlated (i.e., they have the same factor loading) [ 33 ]. the full cfa model is reported in table 1. resulting from this process was the following three-factor model that best fit the data: (1) computational time and hardware?, (2) training?, and (3) software support?

the imputed datasets for each pair of analyses (same-sex and different-sex) were analysed together in mplus, using the full-information maximum-likelihood estimation (fiml). all models were estimated with the wlsmv (weighted least squares with mean and variance adjustment) estimator. the between-group-differences (bsd) command in mplus was used to test for between-group-differences on the latent c&l factors and their items. a chi-square difference test was used to check whether there was a significant difference between the factor loadings of the same-sex and different-sex imputed datasets. such differences would indicate that the underlying concepts were different in the two subsamples and that the c&l measure was sufficiently unidimensional.

we measured perceived neighbourhood quality by measuring subjective norms using a 3-item scale developed by sallis and colleagues (sallis, clark, & demarree, 2005 ). perceived physical activity facilities was measured by asking participants how often in the past three months they used an outdoor area for physical activity. single items were used to measure perceptions of aesthetics ( ) and threats ( ) (cadwell, cukor, & lewis, 2008 ; lewis & cukor, 2010 ). the items are shown in table 2. cfi is a measure of incremental fit, with values of 1 representing perfect fit. the comparative fit index (cfi) is typically used as a heuristic to evaluate overall model fit. the cfi has a limited capacity to discriminate among nested models (nylund, 2003 ). it is recommended that the value 0.90 or greater should be achieved to discriminate between the fit of the null model and the alternative model (hu & bentler, 1998 ). we used tli to evaluate overall model fit of the bifactor model in the current data. tli is recommended to evaluate the overall goodness of fit of a bifactor model (nylund & asparouhov, 2010 ). smaller chi-squared values may be viewed as an indication of a good fit (hu & bentler, 1998 ). however, large chi-squared values may be acceptable in research that deals with exploratory analyses in which model complexity is not a concern (thode & quensel, 2006 ). the ratio of chi-square to degrees of freedom is a statistic that captures model misspecification. however, normed fit index (nfi) and incremental fit index (ifi) are recommended to compare two nested models. these two indexes compare the best fitting model (a bifactor model) to the next best model (an alternative model). they are equivalent to the chi-square value that results from deleting the level for the subordinate factor from the model (hu & bentler, 1998 ). a value of 0.90 or greater is recommended (cheung & rensvold, 2000 ; hu & bentler, 1998 ).
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