| dc.description.abstract | Dissertation under the direction of Professor Joseph Lee Rodgers
In behavior genetics, like many fields, researchers must decide whether their models adequately explain their data – whether their models “fit” at some satisfactory level. Well-fitting models are compelling, whereas poorly-fitting models are not (Rodgers & Rowe, 2002). Oftentimes, researchers evaluate model fit by employing “universal” rules of thumb (e.g., Hu and Bentler, 1999). However, these rules are not universal, and are – in fact – model specific (Kang et al., 2016). Accordingly, I focused on developing fit criteria emulating Hu and Bentler (1999) for classic univariate models (ACE; CE; AE) by fitting simulated twin data to correctly- and incorrectly-specified models. Ideal criteria should consistently accept correct models and reject incorrect models. Classic ACE models were indistinguishable and virtually all fit indices were non-informative because (or especially when) they are saturated models. For non-ACE models, criteria were informative. Nevertheless, every metric employed, except TLI differed markedly across models and/or conditions. Universal solutions remain elusive, but promising approaches include nested model comparisons, increasing degrees of freedom, and ruthless skepticism. | |
