We examined multiple alternative models, including three-, two-, and one-factor models, to identify the most suitable model for our data. Our analysis showed that the assumed theory-driven four-factor model outperformed the other models based on its lower AIC and BIC values, suggesting that ...
marketing structuration. This reasoning similarly applies to specialization, which refers to the assignment of a narrow scope of responsibilities to subunits (Ruekert et al.,1985; Van de Ven,1976). Moreover, formalization, which refers to the specification and monitoring of job-related rules and c...
We found that the FW + Decay RL model had the lowest AIC score (AIC: 3023.0) compared to all other models tested including a FW (feature-weighting) model with only three parameters lacking the decay parameter (AIC: 3331.1), and a Feature Value Decay RL model (see model 2 ...
You run an AIC test to find out, which shows that model 1 has the lower AIC score because it requires less information to predict with almost the exact same level of precision. Another way to think of this is that the increased precision in model 2 could have happened by chance. From ...
As presented in Table S1, the five-profile solution had lower Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Sample Size Adjusted BIC (SABIC) than did the preceding four models. At the same time, none of the profile sizes were smaller than five percent of the ...
were constrained to be zero. When the fit significantly worsened, the contribution of genetic factors was considered significant. Finally, the Akaike Information Criterion (AIC) was used to determine the best-fitting model, with lower AIC indicating a better fit of the model to the observed data...
How to interpret model fit results is probably one of the most frequently asked questions whenever Confirmatory Factor Analysis and Structural Equation
To probe the interactions, conditional (b1 and b2 estimates) and interaction terms (b3 estimates) were calculated with three values of goal clarification; the lowest occurring score, the mean, and highest score in the dataset (see Table 2 for the range). The nature of all interactions was ...
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The presence of first-order autocorrelation is confirmed by a Durbin-Watson test, and based on this test and model fit (AIC, cf. Judge et al., 1988) a first-order autoregressive term is included in Equation 1.Footnote 10 The dependent variable and all continuous independent variables are ln...