“…However, the v 2 was less than two times the model's degrees of freedom, which roughly indicates a good model fit (Tabachnick and Fidell 2006). The goodness-of-fit indices (Schreiber et al 2006) also indicated a good model fit (the normed fit index, incremental fit index, Tucker-Lewis index and comparative fit index were greater than 0.95 and the root mean square error of approximation was less than 0.06).…”
The use of intensive forestry on part of the forested area in Sweden increases the production of forest biomass and enables an increased use of such biomass to mitigate climate change. However, with increasing conflicting interests in forests and forestry, the success of such a strategy depends on the public acceptance. In this paper,
“…However, the v 2 was less than two times the model's degrees of freedom, which roughly indicates a good model fit (Tabachnick and Fidell 2006). The goodness-of-fit indices (Schreiber et al 2006) also indicated a good model fit (the normed fit index, incremental fit index, Tucker-Lewis index and comparative fit index were greater than 0.95 and the root mean square error of approximation was less than 0.06).…”
The use of intensive forestry on part of the forested area in Sweden increases the production of forest biomass and enables an increased use of such biomass to mitigate climate change. However, with increasing conflicting interests in forests and forestry, the success of such a strategy depends on the public acceptance. In this paper,
“…The evidence from the fit indices was mixed. The CFI (0.853) and TLI (0.836) were below the cut-off of 0.9, but the RMSEA (0.050 90% CI [0.045, 0.055]) was less than the cut-off value of 0.06 (Hu & Bentler, 1999;Schreiber et al, 2006).…”
Section: Baseline Model Fitmentioning
confidence: 88%
“…Model fit was assessed using multiple fit indices including the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), and the Root Mean SquareError of Approximation (RMSEA). Suggested cut-off criteria from Hu and Bentler (1999) and Schreiber, Stage, King, Nora and Barlow (2006) were consulted used to help interpret the fit of the model. However, with complex models, fit statistics are often overly sensitive to small, theoretically insignificant lack of fit (Cheung & Rensvold, 2002).…”
There is increasing concern about the large civic engagement gap between Whites and Latina/o and African American youth. Some suggest this may be because traditional models and measures of civic engagement may not be as applicable for youth from historically marginalized groups. With an urban sample of middle and high school-age youth (n = 903, 52% female), we used structural equation modeling to identify differences in civic pathways between youth from different racial/ ethnic backgrounds. We found significant differences between groups including much stronger relationships between exposure to democratic practices and civic selfefficacy and knowledge for African American and Latina/o youth than for White youth and a stronger relationship between civic knowledge and future civic engagement for Whites and Latina/os than for African Americans. These findings suggest that educators and researchers interested need to take into account the diversity of youths' racial experiences when examining youth civic development.
“…Non-significant chi-square values (p > 0.05) indicate the model fits the data relatively well. We also used a multiple additional indicators of model fits, including: Comparative Fit Index–CFI (values > 0.95 indicate good model fits); root mean square error–RMSE (values < 0.06 indicate good model fits); and weighted root mean square residual–WRMR (values < 0.90 indicate good model fits) [80–83]. …”
Beached bird surveys have been widely used to monitor the impact of oil pollution in the oceans. However, separating the combined effects of oil pollution, environmental variables and methodological aspects of beach monitoring on seabird stranding patterns is a challenging task. The effects of a comprehensive set of oceanographic and climatic variables and oil pollution on seabird strandings in a tropical area of Brazil were investigated herein, using two robust and innovative methods: Generalized Linear Mixed Models and Structural Equation Modeling. We assessed strandings of four resident seabird species along 480 km of beaches divided into 11 sampling areas, between November 2010 and September 2013. We found that increasing the distance from the nearest breeding island reduce the seabird stranding events. Storm activity and biological productivity were the most important factors affecting the stranding events of brown boobies Sula leucogaster, Cabot’s terns Thalasseus acuflavidus and kelp gulls Larus dominicanus. These species are also indirectly affected by warm tropical waters, which reduce chlorophyll-a concentrations. Beach surveys are, thus, useful to investigate the mortality rates of resident species near breeding sites, where individuals are more abundant and exposed to local factors associated with at-sea mortality. In contrast, conservation actions and monitoring programs for far-ranging seabird species are needed in more distant foraging areas. Furthermore, beach monitoring programs investigating the impact of oil pollution on seabirds need to account for the effects of environmental factors on stranding patterns. The present study also demonstrated that seabirds inhabiting tropical coastal waters are sensitive to climate conditions such as adverse weather, which are expected to increase in frequency and intensity in next decades.
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