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ItemHow Does It Feel to Be Treated Like an Object? Direct and Indirect Effects of Exposure to Sexual Objectification on Women's Emotions in Daily LifeKoval, P ; Holland, E ; Zyphur, MJ ; Stratemeyer, M ; Knight, JM ; Bailen, NH ; Thompson, RJ ; Roberts, T-A ; Haslam, N (American Psychological Association, 2019-06-01)Exposure to sexual objectification is an everyday experience for many women, yet little is known about its emotional consequences. Fredrickson and Roberts' (1997) objectification theory proposed a within-person process, wherein exposure to sexual objectification causes women to adopt a third-person perspective on their bodies, labeled self-objectification, which has harmful downstream consequences for their emotional well-being. However, previous studies have only tested this model at the between-person level, making them unreliable sources of inference about the proposed intraindividual psychological consequences of objectification. Here, we report the results of Bayesian multilevel structural equation models that simultaneously tested Fredrickson and Roberts' (1997) predictions both within and between persons, using data from 3 ecological momentary assessment (EMA) studies of women's (N = 268) experiences of sexual objectification in daily life. Our findings support the predicted within-person indirect effect of exposure to sexual objectification on increases in negative and self-conscious emotions via self-objectification. However, lagged analyses suggest that the within-person indirect emotional consequences of exposure to sexual objectification may be relatively fleeting. Our findings advance research on sexual objectification by providing the first comprehensive test of the within-person process proposed by Fredrickson and Roberts' (1997) objectification theory.
ItemFrom Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)Zyphur, MJ ; Hamaker, EL ; Tay, L ; Voelkle, M ; Preacher, KJ ; Zhang, Z ; Allison, PD ; Pierides, DC ; Koval, P ; Diener, EF (FRONTIERS MEDIA SA, 2021-02-15)This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.
ItemFrom Data to Causes II: Comparing Approaches to Panel Data AnalysisZyphur, MJ ; Voelkle, MC ; Tay, L ; Allison, PD ; Preacher, KJ ; Zhang, Z ; Hamaker, EL ; Shamsollahi, A ; Pierides, DC ; KOVAL, P ; Diener, E (SAGE Publications, 2020-10-01)This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
ItemFrom Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)Zyphur, MJ ; Allison, PD ; Tay, L ; Voelkle, MC ; Preacher, KJ ; Zhang, Z ; Hamaker, EL ; Shamsollahi, A ; Pierides, DC ; KOVAL, P ; Diener, E (SAGE Publications, 2020-10-01)This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference.