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Psychological Methods

Daniel R Cavagnaro, Clintin P Davis-Stober
Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i...
May 21, 2018: Psychological Methods
Victoria Savalei
It is well known that methods that fail to account for measurement error in observed variables, such as regression and path analysis (PA), can result in poor estimates and incorrect inference. On the other hand, methods that fully account for measurement error, such as structural equation modeling with latent variables and multiple indicators, can produce highly variable estimates in small samples. This article advocates a family of intermediate models for small samples (N < 200), referred to as single indicator (SI) models...
May 21, 2018: Psychological Methods
Kazuhiko Hayakawa
Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the overrejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that one of the tests considered in Browne (1974) can address this long-standing problem. We also propose a simple modification of Satorra and Bentler's mean and variance adjusted test for non-normal data. A Monte Carlo simulation is carried out to investigate the performance of the corrected tests in the context of a confirmatory factor model, a panel autoregressive model, and a cross-lagged panel (panel vector autoregressive) model...
May 17, 2018: Psychological Methods
Dirk Lubbe
Parallel analysis (PA) is regarded as one of the most accurate methods to determine the number of factors underlying a set of variables. Commonly, PA is performed on the basis of the variables' product-moment correlation matrix. To improve dimensionality assessments for dichotomous or ordered categorical variables, it has been proposed to replace product-moment correlations with more appropriate coefficients, such as tetrachoric or polychoric correlations. While similar modifications have proven useful for various factor analytic approaches, PA results were not consistently improved...
May 10, 2018: Psychological Methods
Kirsten Bulteel, Merijn Mestdagh, Francis Tuerlinckx, Eva Ceulemans
In psychology, modeling multivariate dynamical processes within a person is gaining ground. A popular model is the lag-one vector autoregressive or VAR(1) model and its variants, in which each variable is regressed on all variables (including itself) at the previous time point. Many parameters have to be estimated in the VAR(1) model, however. The question thus rises whether the VAR(1) model is not too complex and overfits the data. If the latter is the case, the estimated model will not properly predict new unseen data...
May 10, 2018: Psychological Methods
Rutger van Oest
We derive a general structure that encompasses important coefficients of interrater agreement such as the S-coefficient, Cohen's kappa, Scott's pi, Fleiss' kappa, Krippendorff's alpha, and Gwet's AC1. We show that these coefficients share the same set of assumptions about rater behavior; they only differ in how the unobserved category proportions are estimated. We incorporate Bayesian estimates of the category proportions and propose a new agreement coefficient with uniform prior beliefs. To correct for guessing in the process of item classification, the new coefficient emphasizes equal category probabilities if the observed frequencies are unstable due to a small sample, and the frequencies increasingly shape the coefficient as they become more stable...
May 3, 2018: Psychological Methods
Merijn Mestdagh, Madeline Pe, Wiebe Pestman, Stijn Verdonck, Peter Kuppens, Francis Tuerlinckx
Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked "rate how happy you feel now between 0 and 100?"). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified...
April 12, 2018: Psychological Methods
Robert G Moulder, Steven M Boker, Fabian Ramseyer, Wolfgang Tschacher
Synchrony between interacting systems is an important area of nonlinear dynamics in physical systems. Recently psychological researchers from multiple areas of psychology have become interested in nonverbal synchrony (i.e., coordinated motion between two individuals engaged in dyadic information exchange such as communication or dance) as a predictor and outcome of psychological processes. An important step in studying nonverbal synchrony is systematically and validly differentiating synchronous systems from nonsynchronous systems...
March 29, 2018: Psychological Methods
Charles C Driver, Manuel C Voelkle
Continuous time dynamic models are similar to popular discrete time models such as autoregressive cross-lagged models, but through use of stochastic differential equations can accurately account for differences in time intervals between measurements, and more parsimoniously specify complex dynamics. As such they offer powerful and flexible approaches to understand ongoing psychological processes and interventions, and allow for measurements to be taken a variable number of times, and at irregular intervals...
March 29, 2018: Psychological Methods
David P MacKinnon, Matthew J Valente, Ingrid C Wurpts
This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test...
March 29, 2018: Psychological Methods
Sacha Epskamp, Eiko I Fried
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data...
March 29, 2018: Psychological Methods
Daniel McNeish, Gregory R Hancock
Lance, Beck, Fan, and Carter (2016) recently advanced 6 new fit indices and associated cutoff values for assessing data-model fit in the structural portion of traditional latent variable path models. The authors appropriately argued that, although most researchers' theoretical interest rests with the latent structure, they still rely on indices of global model fit that simultaneously assess both the measurement and structural portions of the model. As such, Lance et al. proposed indices intended to assess the structural portion of the model in isolation of the measurement model...
March 2018: Psychological Methods
Todd E Bodner
Researchers working in the context of randomized trials routinely estimate and test for treatment effects on the study outcomes. This article discusses the merits of assessing differential treatment effects across outcomes and proposes a multivariate approach using standardized outcomes for this purpose. This multivariate approach extends prior approaches to an arbitrary number of treatment groups and outcomes and does not require that the within-group covariance matrix have particular properties (e.g., sphericity)...
March 2018: Psychological Methods
Felix Thoemmes, Yves Rosseel, Johannes Textor
Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data...
March 2018: Psychological Methods
Jennifer J Pokorny, Alex Norman, Anthony P Zanesco, Susan Bauer-Wu, Baljinder K Sahdra, Clifford D Saron
We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures...
March 2018: Psychological Methods
Gwowen Shieh
Moderation analysis is a vital aspect of research in education, management, psychology, and related disciplines. Although several methodological artifacts have been identified and examined, heterogeneity of variance remains one of the unique and problematic factors known as detrimental to statistical power in the detection of moderating effects. To alleviate the difficulty in assessing moderation because of low statistical power, this article describes feasible solutions to sample size calculations for tests of hypothesized interactions between categorical variables under variance heterogeneity...
March 2018: Psychological Methods
Taehun Lee, Robert C MacCallum, Michael W Browne
Extending work by Waller (2008) on fungible regression coefficients, we propose a method for computation of fungible parameter estimates in structural equation modeling. Such estimates are defined as distinct alternative solutions for parameter estimates, where all fungible solutions yield identical model fit that is only slightly worse than the fit provided by optimal estimates. When such alternative estimates are found to be highly discrepant from optimal estimates, then substantive interpretation based on optimal estimates is called into question...
March 2018: Psychological Methods
Michael C Edwards, Carrie R Houts, Li Cai
Item response theory (IRT) is a widely used measurement model. When considering its use in education, health outcomes, and psychology, it is likely to be one of the most impactful psychometric models in existence. IRT has many advantages over classical test theory-based measurement models. For these advantages to hold in practice, strong assumptions must be satisfied. One of these assumptions, local independence, is the focus of the work described here. Local independence is the assumption that, conditional on the latent variable(s), item responses are unrelated to one another (i...
March 2018: Psychological Methods
Mark H C Lai, Oi-Man Kwok, Yu-Yu Hsiao, Qian Cao
The research literature has paid little attention to the issue of finite population at a higher level in hierarchical linear modeling. In this article, we propose a method to obtain finite-population-adjusted standard errors of Level-1 and Level-2 fixed effects in 2-level hierarchical linear models. When the finite population at Level-2 is incorrectly assumed as being infinite, the standard errors of the fixed effects are overestimated, resulting in lower statistical power and wider confidence intervals. The impact of ignoring finite population correction is illustrated by using both a real data example and a simulation study with a random intercept model and a random slope model...
March 2018: Psychological Methods
Yang Tang, Thomas D Cook, Yasemin Kisbu-Sakarya
In the "sharp" regression discontinuity design (RD), all units scoring on one side of a designated score on an assignment variable receive treatment, whereas those scoring on the other side become controls. Thus the continuous assignment variable and binary treatment indicator are measured on the same scale. Because each must be in the impact model, the resulting multi-collinearity reduces the efficiency of the RD design. However, untreated comparison data can be added along the assignment variable, and a comparative regression discontinuity design (CRD) is then created...
March 2018: Psychological Methods
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