Quantitative Brown Bag Series
Location:  Young Hall 166 (Unless specified otherwise)
Thursdays from 1:35 PM  2:35 PM (Unless specified otherwise) 
Calendar Administrator:
ACADEMIC YEAR: 2014  2015 Print Page  

2015  
1/01/1900 


Fall 2014  
10/02/2014 
Bayesian versus Frequentist Estimation of MultitratMultimethod Confirmatory Factor Models
SPEAKER: Dr. Jonathan Helm Campbell and Fiske's (1959) separation of trait, method, and unique variance across a set of multitraitmultimethod (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 outofbounds 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 nonidentification 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 maximumlikelihood (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. 

10/09/2014 
Modeling Timevarying Interdependence
SPEAKER: Dr. Jonathan Helm Interdependence 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 outperforms 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. 

10/16/2014 
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 overgeneralized (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 twofactor model are randomly mixed and fit with a one or twofactor CFA model. Results suggest that AIC and BIC were able to discriminate between one and twofactor models with as little as ~10% heterogeneity (9:1 onefactor to twofactor cases), but CFI, TLI, and RMSEA all failed to reject the onefactor model until heterogeneity reached ~20%. SRMR performed the worse; failing to reject the onefactor model until ~55% heterogeneity was present. The most efficient measure of fit was the chisquare, which rejected the onefactor model with the smallest proportion of heterogeneity considered (5%). Note that the twofactor model was not closely evaluated for rejection, as its fit to data was consistently excellent. In the case of the twofactor model, inspection of the correlation between the two factors could be used to justify appropriateness of a onefactor model. However, with greater heterogeneity of one to twofactor model generated data, estimated correlations will be reduced depending on the strength of the correlation in the generating twofactor model. Consequently, inspection of the estimated factor correlations, an obvious indicator for the onefactor model, would likely be less informative, or possibly masked depending on the amount of heterogeneity in the sample. 

10/23/2014 
Brown Bag Meeting Cancelled
No Quantitative Brown Bag Today 

10/30/2014 
The madgenius paradox: Can creative people be more mentally healthy but highly creative people more mentally ill?
SPEAKER: Dr. Dean K. Simonton The persistent madgenius 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 creativitypsychopathology 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 noncreative 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 crosssectional distribution of creative productivity is taken into account, these two statements can both be true. This potential compatibility is here christened the madgenius 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. 

11/06/2014 
"Issues, Questions, and Tentative Answers in Studying Physiological Synchrony" and "The Autonomic Regulation of Parenting: Parasympathetic Control Supports Positive Parenting"
SPEAKER: Dr. Paul Hastings & Jonas Miller Human biological systems vary, at least in part, according to external stimuli. Accordingly, many research enterprises aim to understand the interwoven nature of environment and physiology. Social interaction represents one specific stimulus of great interest, and many investigations report correspondence between one individual’s behavior and another’s physiology. Yet, given that social interaction requires at least two people, an extended perspective examines the association between individuals’ physiology, often called physiological synchrony. In this case physiological responses are repeatedly measured for both individuals and are modeled as a dependent system (i.e. each individual’s current response predicts the other’s concurrent, or future, response). This perspective offers several testable hypotheses, including the presence of physiological covariation and betweendyad differences in that covariation. Although these tests may be appealing, proper application requires a statistical model that aligns with those hypotheses. The current presentation outlines several issues that arise from the assumptions inherent in the statistical models most often used to test physiological synchrony, questions these raise about our understanding of physiological synchrony, and suggestions for modifications of analytic approaches that may advance this area of work. Raising children can be challenging at times, but reacting to one's child as a threatening stimulus  that is, responding to an interaction as a fightorflight situation  would undermine a parent's ability to manage challenging interactions effectively. Emotion regulation promotes positive parenting, and physiological activity is a critical component of emotion regulation. In this talk, I will present findings from two data sets on the links between autonomic control of cardiac arousal and mothers' warmsupportive versus negativepunitive approaches to socialization. 

11/13/2014 
TBA


11/20/2014 
TBA
TBA 

11/27/2014 
Thanksgiving Holiday
Agenda No brown bag this week. Happy Thanksgiving and safe travels. 

12/04/2014 
TBA


12/11/2014 
TBA
TBA 

12/18/2014 
Finals Week
Agenda No brown bag this week. Good luck with finals, whether taking or grading them. 

2015  
1/15/2015 
Cancelled


1/22/2015 
#BlackLivesMatter when Explaining Disparities in Police Contact
SPEAKER: Melissa McTernan In honor of Martin Luther King, Jr. Day this week, and of the national conversations regarding race and police contact since the events of Ferguson, MO, this presentation will explore racial disparities in the context of communitypolice interactions. While most researchers agree that these disparities can not entirely be explained by criminal behavior, much research still needs to be done to explore the complexities of police interaction with the public and what role race plays during these interactions. I will review existing literature on racial disparities in police contact, and present results from my own analysis of the PolicePublic Contact Survey (PPCS) of 2008. 

1/29/2015 
Cancelled


2/05/2015 
Cancelled


2/12/2015 
Setting the record straight: Models for nonlinearity and for nonnormality
SPEAKER: Melissa McTernan Standard OLS regression is a familiar and straightforward method for studying relations between constructs. However, in psychology research we are often interested in data for which OLS regression is not appropriate. In this talk I focus on the problems of nonlinearity of the parameters and nonnormality of the residuals. OLS regression is not sufficient for data with either of these characteristics, and therefore other methods must be employed. Moving away from a familiar method and making a decision about what alternative method to use can be difficult in practice because there are many methods to choose from and the literature on those methods can be daunting for an unfamiliar reader. A goal of this talk is to describe a few alternative methods to OLS and when they are appropriate to apply in practice. Specifically, I will be reviewing linear regression with OLS, nonlinear regression, generalized linear modeling, generalized nonlinear modeling, and direct likelihood optimization approaches. 

2/19/2015 
Absence Makes the Heart Grow Fonder: An Introduction to Planned Missing Data Designs
SPEAKER: Nathan Smith Missing data is ubiquitous in psychological research. Powerful methods and software have been developed to address this problem and are successful at recovering parameter estimates without bias when modeling from incomplete data sets. These methods are most successful when the data are missing at random (MAR) or missing completely and random (MCAR). In planned missing data designs (PPMD) participants are intentionally assigned to conditions where they will not respond to all items/measures or measurement occasions. This allows experimenters to leverage resources, reduce respondent fatigue, and potentially improve data quality by decreasing unplanned missingness. This talk will provide a general overview of applications of planned missing data designs and address two well researched PMDD’s, the 3Form Design and 2 Method measurement design. 

2/26/2015 
Cancelled


3/05/2015 
Small Sample Inference in Linear Mixed Models
SPEAKER: Timothy Banh Small samples are frequently encountered in psychological research. In this talk, I will discuss the implications of small sample size in regards to linear mixed models. When using SAS PROC MIXED, there are different denominator degrees of freedom options available. I will discuss the motivation for including these degrees of freedom options and talk about one particular method, the KenwardRoger method. This method is a small sample correction that leads to an F distribution for the null distribution of the fixed effects. The motivation behind the KenwardRoger method and simulations exploring its behavior will be discussed. 

3/12/2015 
No Meeting


4/02/2015 
Organizational Meeting
Agenda 

4/09/2015 
Fundamentals of Item Response Theory and its Applications
SPEAKER: Dr. Tim Gaffney California State University, Sacramento 

4/16/2015 
TBA
SPEAKER: Matthew Miller 

4/24/2015 
TBA
SPEAKER: Dr. Joel Steele Portland State University 
