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  • UC Davis Psychology Quantitative Program

    Quantitative Brown Bag Series

    Location:  Young Hall 166 (Unless specified otherwise)
    Thursdays from 1:35 PM - 2:35 PM (Unless specified otherwise)
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    ACADEMIC YEAR:      2014 - 2015 Print Page
    Fall 2014
    Bayesian versus Frequentist Estimation of Multitrat-Multimethod Confirmatory Factor Models 
    SPEAKER: Dr. Jonathan Helm

    Campbell and Fiske's (1959) separation of trait, method, and unique variance across a set of multitrait-multimethod (MTMM) manifest variables directly translates to a confirmatory factor model, and several reports support this approach for partitioning variance (Cole, 1987; Widaman, 1985; Schmitt & Stults, 1986). However, researchers selecting this approach often encounter estimation problems (i.e., failed convergence or solutions with out-of-bounds estimates; Widaman, 1985). Mathematical investigations have identified several potential sources of these problems (Kenny & Kashy, 1992; Grayson & Marsh, 1994), forcing applied researchers to face an analytic conundrum when performing MTMM data analysis. The advent of Bayesian estimation for structural models offers many new opportunities, including the ability to fit models that would fail to converge when estimated within a frequentist framework (Scheines, Hoijtink, & Boomsma, 1999; Asparouhov & Muthén, 2010). Based on the non-identification problems that typically arise when fitting the CTCM model to MTMM data (Kenny & Kashy, 1992; Grayson & Marsh, 1994), and extra modeling flexibility provided by Bayesian estimation, the current paper examines the differences between maximum-likelihood (ML) and Bayesian estimation of the CTCM model. *Prior to Dr. Helm's presentation Dr. Emilio Ferrer will be introducing our area's new graduate students. He'll also be giving updates on the area search for a new faculty member, with emphasis on opportunities for us to attend prospective candidates' job talks later this quarter and/or early winter. The meeting will conclude with asking for volunteers to present at fall brown bag meetings.
    Modeling Time-varying Interdependence 
    SPEAKER: Dr. Jonathan Helm

    Inter-dependence between two individuals may be estimated using a variety of statistical techniques, but these models typically assume that dependence remains constant across repeated measures. To the extent that theory predicts different patterns of dependence as a function of time, current analytic approaches may not adequately test relevant hypotheses. This dissertation focuses on the development of a novel method for analyzing changes in dependence, as it unfolds over time for a sample of dyads. The first chapter gives detail of a specific theory that may benefit from the new method, explains why several common methods cannot investigate change in dependence, and outlines criteria for an approach to summarize change in dependence appropriately. The second chapter introduces an approach that satisfies the criteria, describes the technique analytically, and tests its mathematical properties via simulation. The third chapter describes an extension of the method that accounts for measurement error, and, via simulation, summarizes situations when the extension out-performs the simpler version. The fourth chapter provides an application of the method to empirical data to inform the theory outlined in the first chapter. The fifth chapter summarizes the benefits gained from the method, describes the limitations and assumptions, and suggests future steps for further innovation to the proposed method.
    Fit Index Sensitivity to Restricted Factor Analysis Model Misspecification 
    SPEAKER: Joseph E. Gonzales

    While there is contention over the use of fit indices (Barret, 2007; Bentler, 2007), they are often used to assess whether models are tenable representations of data, often utilizing rules of thumb (Hu & Bentler, 1999) that, despite being over-generalized (Marsh, Hau, & Wen, 2004), have been widely adopted. Previous work has shown that interindividual models of individual data are relatively insensitive to heterogeneity in intraindividual factor structures with respect to factor loadings (Molenaar, 2004). In the present study I expand on this finding using simulated data to evaluate if fit indices are sensitive to heterogeneity in factor structure when cases generated using either a one or two-factor model are randomly mixed and fit with a one or two-factor CFA model. Results suggest that AIC and BIC were able to discriminate between one and two-factor models with as little as ~10% heterogeneity (9:1 one-factor to two-factor cases), but CFI, TLI, and RMSEA all failed to reject the one-factor model until heterogeneity reached ~20%. SRMR performed the worse; failing to reject the one-factor model until ~55% heterogeneity was present. The most efficient measure of fit was the chi-square, which rejected the one-factor model with the smallest proportion of heterogeneity considered (5%). Note that the two-factor model was not closely evaluated for rejection, as its fit to data was consistently excellent. In the case of the two-factor model, inspection of the correlation between the two factors could be used to justify appropriateness of a one-factor model. However, with greater heterogeneity of one to two-factor model generated data, estimated correlations will be reduced depending on the strength of the correlation in the generating two-factor model. Consequently, inspection of the estimated factor correlations, an obvious indicator for the one-factor model, would likely be less informative, or possibly masked depending on the amount of heterogeneity in the sample.
    Brown Bag Meeting Cancelled 

    No Quantitative Brown Bag Today
    The mad-genius paradox: Can creative people be more mentally healthy but highly creative people more mentally ill? 
    SPEAKER: Dr. Dean K. Simonton

    The persistent mad-genius controversy concerns whether creativity and psychopathology are positively or negatively correlated. Remarkably, the answer can be "both"! The debate has unfortunately overlooked the fact that the creativity-psychopathology correlation can be expressed as two independent propositions: (a) among all creative individuals, the most creative are at higher risk for mental illness than are the less creative and (b) among all people, creative individuals exhibit better mental health than do non-creative individuals. In both propositions, creativity is defined by the production of one or more creative products that contribute to an established domain of achievement. Yet when the typical cross-sectional distribution of creative productivity is taken into account, these two statements can both be true. This potential compatibility is here christened the mad-genius paradox. This paradox can follow logically from the assumption that the distribution of creative productivity is approximated by an inverse power function called Lotka's Law. Even if psychopathology is specified to correlate positively with creative productivity, creators as a whole can still display appreciably less psychopathology than in the general population because the more at risk creative geniuses represent an extremely tiny proportion of those contributing to the domain. The hypothesized paradox has important scientific ramifications.
    SPEAKER: Dr. Paul Hastings & Jonas Miller


    SPEAKER: Nathan Smith

    Thanksgiving Holiday 
    No brown bag this week. Happy Thanksgiving and safe travels.
    SPEAKER: Melissa McTernan
    SPEAKER: Marilu Isiordia

    Finals Week 
    No brown bag this week. Good luck with finals, whether taking or grading them.