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  • UC Davis Psychology Quantitative Program

    Quantitative Brown Bag Series

    Location:  Young 166 (unless specified otherwise)
    Thursdays 1:30pm - 2:20pm
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    ACADEMIC YEAR:      2013 - 2014 Print Page
    Fall 2013
    SPEAKER: Joseph Gonzales
    Construct Validity of Risk Indices: Analytic Techniques, Costs and Benefits, and Two Examples 
    SPEAKER: Dr. Keith Widaman
    Evaluation of a Bayesian Approach to Estimating Nonlinear Mixed-Effects Mixture Models 
    SPEAKER: Dr. Kevin Grimm
    SPEAKER: Jonas Miller
    Pattern-Mixture Two-Part Mixed Models for Semi-Continuous Data with Nonignorable Missingness 
    SPEAKER: Dr. Shelley Blozis
    SPEAKER: Melissa McTernan
    SAS PROC NLMIXED was developed for the estimation of nonlinear mixed models, including both multilevel and longitudinal data. Nonlinear mixed models are models for which a random coefficient may enter an equation nonlinearly. PROC NLMIXED may be used to estimate models for which a response follows a normal, binomial, or Poisson distribution. For other distributions, the program includes an option for the user to express a likelihood function. This naturally allows for more complex distributions, including joint distributions between variables that follow possibly unique distributions. Thus, this is an advantage to researchers because it allows for a great variety of response functions. In this brief talk, I will describe steps to developing a likelihood function and corresponding NLMIXED syntax to estimate the model. Empirical examples are provided, including specification of a joint longitudinal model for a normal and binomial response.
    SPEAKER: Katerina Marcoulides
    Linking Attentional Training and Performance Across Scales of Time and Methods of Measurement 
    SPEAKER: Stephen Aichele
    Linking Attentional Training and Performance Across Scales of Time and Methods of Measurement The dynamics of psychological processes, as they unfold within individuals over time, are fundamental to understanding behavioral, emotional, and cognitive development. The incorporation of multiple methods of assessment, and their application across different time scales and settings, is essential to the advancement of empirical knowledge through psychological research. But as scientists examine increasingly complex relations between psychological variables, there is an ever greater need to identify potential sources of bias during analysis. Data aggregation within variables (e.g., across persons, attributes, and/or settings) influences patterns of association between variables, and this in turn affects research outcomes. Furthermore, different analytic objectives (e.g., causal inference, valid interpretation of within-person change) may conflict due to experimental and/or statistical constraints. In the present work, I illustrate different strategies for data aggregation and analysis in two observational studies from the Shamatha Project (SP), a longitudinal investigation of intensive meditation practice. The general aim of these studies was to examine person- and training-specific variables in relation to previously reported experimental outcomes. Implications for quantitative theory are discussed in light of these studies' results.
    The Relation of Depressive Symptoms and Socioeconomic Status on Diabetes Self-Care Management and BMI in Rural Latinos with Diabetes 
    SPEAKER: Marilu Isiordia
    This study explored the relationships between depressive symptoms and socioeconomic status (SES) on self-care management (e.g., diet and physical activity) and body weight in 250 rural Latinos with diabetes. I hypothesized there would be a negative correlation between self-reported depressive symptoms and ratings of adherence to (a) healthy diet, (b) physical activity, and (c) a positive correlation with body mass index (BMI). I also hypothesized that SES would have (d) a positive correlation with healthy diet, (e) a positive correlation with physical activity, and (f) a negative correlation with BMI. Additionally, I conducted exploratory analyses to examine the influence of neighborhood perception on diabetes self-care management and BMI. Significant results indicated depressive symptoms were negatively correlated with diabetes self-care management. Contrary to these predictions, negative relationships between annual income and both healthy diet and physical activity were found. Results also showed a positive relationship between education and physical activity and BMI. Finally, neighborhood perception was positively correlated with self-care behaviors and negatively correlated with BMI. Overall, these findings help indicate how the presence of depressive symptoms and SES are associated with diabetes self-care management in Latinos with diabetes.
    SPEAKER: No brown bag
    Winter 2014
    SPEAKER: Michelle Harris
    Global self-esteem is thought to stem, at least partly, from positive parent-child relationships. Previous cross-sectional research supports this hypothesis, but longitudinal studies have failed to find robust evidence of prospective effects. The current study uses data from Germany and the United States to test the prospective relation between parent-child closeness and adolescent self-esteem development. We tested this relation using self, parent, and observer reports of parent-child closeness and self-reported self-esteem from ages 12-16. Results replicated the concurrent correlation, but six types of longitudinal models (regressions, autoregressive cross-lagged models, latent growth curve models, latent profile analyses, latent difference score models, and enduring effects vs. revisionist models) failed to show a prospective relation. Thus, the longitudinal effect of parent closeness on self-esteem is, at best, small and difficult to detect. These findings call into question the purported impact of parent-child relationships on self-esteem, at least during the adolescent years.
    Timing is everything: Ovulation estimation method efficacy 
    SPEAKER: Joseph Gonzales
    Ovulatory effects research in humans is typically conducted by comparing time points within persons that are associated with critical periods of the ovulatory cycle. Specifically, researchers compare observational and self-report data corresponding with the late-follicular and mid-luteal phases to test for shifts corresponding to reproductive fertility or hormonal processes. Critical to this research design is the estimation of ovulation in order to sample correctly from target phases. Typical approaches include the forward and backward counting methods, and hormone detection methods (e.g., LH urine testing). While the forward counting method has largely been dismissed as unreliable due to evidence of follicular phase variability between- and within-women, variability in the still popular backward counting method may also require larger sampling in order to overcome phase-sampling error. In the present study I present four representations of behavioral shifts associated with the ovulatory cycle and evaluate the efficacy of conventional sampling methods with respect to detecting differences between late-follicular and mid-luteal phases.
    Estimation of Factor Scores from Multiple-group Item Response Models: Implications for Integrative Data Analysis 
    SPEAKER: Pega Davoudzadeh
    Data integration refers to obtaining multiple data sets, scaling their measurements, and analyzing them as though they represent a single data set. A first step is to scale measurements to common scales, which is typically done with multiple group item response models. Latent variable scores are then estimated and used in subsequent analyses. This approach was found to produce inconsistencies in latent variable estimates for individuals from different studies with the same response pattern. Monte Carlo Simulations were then conducted to evaluate the accuracy of latent variable estimates from this and other approaches that ignore the nesting of participants in studies. Results suggest that ignoring study differences led to slightly more accurate latent variable estimates that were also consistent. This approach is recommended for data integration and scaling of latent variable scores across studies. Implications for longitudinal data integration are discussed.
    My answer to Tukey's question: Does anyone know when the correlation coefficient is useful? 
    SPEAKER: Dr. Fushing Hsieh
    I will begin with two real examples to illustrate association patterns in bipartite network data. Then the final example is about the drug-gene-cell*line triangular association relationship. The asymmetry of such a relationship reveals where correlation coefficient does and doesn't work
    SPEAKER: No Brown Bag
    Nonlinear Mixed Models: A SAShay 
    SPEAKER: Nathan Smith
    I will be using the PROC NLMIXED option in SAS to compare different estimation techniques (Gaussian Quadrature, First Order Methods, Importance Sampling, and Hardy Quadrature). With data from an empirical study and utilizing a structured latent curve model to fit the data, I test the different options available in SAS for PROC NLMIXED. After providing background for both the empirical data and methods used in my analysis, I will present my findings and introduce a number of questions arising from the differences in results.
    SPEAKER: Dr. Jon Helm
    Quantitative Methods for Agent-Based Models 
    SPEAKER: Matt Miller
    I will discuss some of the goals and results of agent-based models (ABMs) used in psychology and other disciplines in relation to the challenges of characterizing ABM results quantitatively. I will offer some thoughts and solicit suggestions on appropriate quantitative methods for ABMs.
    From Functional to Neuroimaging Data 
    SPEAKER: Dr. Jane-Ling Wang
    Functional data are random functions on an interval or set and have emerged frequently in modern scientific experiments. The analysis of such data is termed "Functional Data Analysis (FDA)" in the statistical literature. In this talk, we first give a brief overview of the various types of functional data and the various approaches to handle them. Then we show how the FDA approaches could be useful to analyze neuroimaging data, which are intrinsically functional data. Two applications will be illustrated, one for PET time course data and the other for fMRI time series.
    Statistics for Individual Change Assessment and their Relationship with Group Change 
    SPEAKER: Eduardo Estrada
    I will describe and briefly discuss some simple statistics for assessing individual change and their relationship with classical group change statistics such as T test and effect size measures.
    Nonignorable Nonresponse, Random Subsamples of Non-respondents and How Best to Adjust for the Missing Data 
    SPEAKER: Dr. Christiana Drake
    Early School Readiness Predictors of Grade Retention from Kindergarten through Eighth Grade 
    SPEAKER: Pega Davoudzadeh & Melissa McTernan
    Later academic success (e.g., 8th grade test scores) is often the criterion used to judge the effectiveness of early schooling interventions and the importance of early school readiness skills (e.g., Duncan et al., 2007). With this key outcome, previous research has found that early academic skills, notably mathematics, and attention as the important school readiness skills. However, another crucial indicator of academic success is whether a child repeats a grade. Studies have found grade retention to be predicted by test scores, behavior, and demographic factors such as gender, age, ethnicity, and socioeconomic status. In the current study, using a multilevel survival analysis, we examined if and when a child is retained and its association with school readiness indicators using data from the Early Childhood Longitudinal Study – Kindergarten cohort. Grade retention was generally a rare occurrence, but found to be associated with child characteristics and early academic skills. The results of the current study add to the body of research on important school readiness skills.
    Ideas for a Developmental Model of Emotion Regulation: Ideas Emerging from Looking at Leopards' Spots 
    SPEAKER: Dr. Nilam Ram
    Emotional experiences are dynamic and idiosyncratic. Inspired by the processes from which patterns emerge on animals' skins, we are exploring how reaction-diffusion models might be used to model both short-term change and long-term development of emotional function.
    A demonstration of basic Sweave skills for an APA style manuscript 
    SPEAKER: Melissa McTernan
    This talk will be two-fold. The primary goal will be to demonstrate the basics of using Sweave (a program that allows for literate programming in R using the LaTex language) to produce an APA style manuscript, including figures and tables. In line with this goal, I will be distributing handouts that will help a novice user get started with Sweave. In order to demonstrate these skills, I will walk through a simulation study that I have been working on that involves mean comparison tests and positively skewed data.
    Validation of the Quad Model among Alternate Model Specifications 
    SPEAKER: Jimmy Calanchini
    The Quadruple process model (Quad model: Conrey et al., 2005) is a multinomial processing tree that specifies the joint contribution of four qualitatively distinct cognitive processes to responses on implicit attitude measures. The relations between the processes were specified based on theory, but alternate specifications of these relations are possible that are equally theoretically defensible. Using large datasets of implicit measures collected from the general population across three different content domains, the present research compares the standard version of the Quad model against two alternate specifications. Theoretical and methodological implications are discussed.
    Biological measures of partnership and social stress in monogamous primate males  
    SPEAKER: Chelsea Muth
    Monogamous adult titi monkeys (Callicebus cupreus) form strong heterosexual pair bonds, observed through attributes of emotional attachment. Previous studies implicate pair bonding as a social stress buffer in adult male titi monkeys. However, these studies have yet to examine the biology of social bonding and stress at different levels of partnership and separation. In a 2011 study led by the Bales lab, 12 male subjects were measured at 5 (long-term and short-term) partnership and separation conditions, to observe changes in brain activity and attachment- and stress-induced hormones. This talk will highlight methodological considerations for this small-sample study and preliminary analyses of hormone data. Limitations and comparisons between RM ANOVA and mixed effects models will be reviewed.
    SPEAKER: No brown bag
    Sensory Symptoms in Children with Autism Spectrum Disorder, Other Developmental Disorders and Typical Development: A Longitudinal Study 
    SPEAKER: Carolyn McCormick
    Children with Autism Spectrum Disorder display a wide range of sensory symptoms, but little is know about the developmental trajectories of these symptoms. This study examined the development of sensory symptoms and the relationship between sensory symptoms and adaptive functioning during early childhood. Three groups of children were followed across three time points from age two to age eight: Autism Spectrum Disorder (ASD), developmental delay (DD) and typical development (TD). Sensory symptoms and adaptive functioning were analyzed using multilevel models. While the TD group decreased their sensory symptoms, the clinical groups demonstrated no significant change across assessment points. Sensory symptoms were not independently predictive of adaptive functioning when verbal mental age was also included in the model. The young age range at the initial assessment and pattern of results suggests that sensory symptoms are present early in the etiology of autism and other developmental disorders, and remain stable over time.