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Psychological Methods

Tacksoo Shin, Mark L Davison, Jeffrey D Long
The study examined the performance of maximum likelihood (ML) and multiple imputation (MI) procedures for missing data in longitudinal research when fitting latent growth models. A Monte Carlo simulation study was conducted with conditions of small sample size, intermittent missing data, and nonnormality. The results indicated that ML tended to display slightly smaller degrees of bias than MI across missing completely at random (MCAR) and missing at random (MAR) conditions. Although specification of prior information in the MI imputation-posterior (I-P) phase influenced the performance of MI, especially with nonnormal small samples and missing not at random (MNAR), the impact of this tight specification was not dramatic...
October 6, 2016: Psychological Methods
Laura F Bringmann, Ellen L Hamaker, Daniel E Vigo, André Aubert, Denny Borsboom, Francis Tuerlinckx
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics...
September 26, 2016: Psychological Methods
Thierno M O Diallo, Alexandre J S Morin, HuiZhong Lu
This article evaluates the impact of partial or total covariate inclusion or exclusion on the class enumeration performance of growth mixture models (GMMs). Study 1 examines the effect of including an inactive covariate when the population model is specified without covariates. Study 2 examines the case in which the population model is specified with 2 covariates influencing only the class membership. Study 3 examines a population model including 2 covariates influencing the class membership and the growth factors...
September 19, 2016: Psychological Methods
Bhargab Chattopadhyay, Ken Kelley
The standardized mean difference is a widely used effect size measure. In this article, we develop a general theory for estimating the population standardized mean difference by minimizing both the mean square error of the estimator and the total sampling cost. Fixed sample size methods, when sample size is planned before the start of a study, cannot simultaneously minimize both the mean square error of the estimator and the total sampling cost. To overcome this limitation of the current state of affairs, this article develops a purely sequential sampling procedure, which provides an estimate of the sample size required to achieve a sufficiently accurate estimate with minimum expected sampling cost...
September 8, 2016: Psychological Methods
Oliver Lüdtke, Alexander Robitzsch, Simon Grund
Multiple imputation is a widely recommended means of addressing the problem of missing data in psychological research. An often-neglected requirement of this approach is that the imputation model used to generate the imputed values must be at least as general as the analysis model. For multilevel designs in which lower level units (e.g., students) are nested within higher level units (e.g., classrooms), this means that the multilevel structure must be taken into account in the imputation model. In the present article, we compare different strategies for multiply imputing incomplete multilevel data using mathematical derivations and computer simulations...
September 8, 2016: Psychological Methods
Michael J Brusco, Emilie Shireman, Douglas Steinley
The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous...
September 8, 2016: Psychological Methods
Michael Eid, Christian Geiser, Tobias Koch, Moritz Heene
G-factor models such as the bifactor model and the hierarchical G-factor model are increasingly applied in psychology. Many applications of these models have produced anomalous and unexpected results that are often not in line with the theoretical assumptions on which these applications are based. Examples of such anomalous results are vanishing specific factors and irregular loading patterns. In this article, the authors show that from the perspective of stochastic measurement theory anomalous results have to be expected when G-factor models are applied to a single-level (rather than a 2-level) sampling process...
August 15, 2016: Psychological Methods
Peter M Bentler
Internal consistency reliability coefficients based on classical test theory, such as α, ω, λ4, model-based ρxx, and the greatest lower bound ρglb, are computed as ratios of estimated common variance to total variance. They omit specific variance. As a result they are downward-biased and may fail to predict external criteria (McCrae et al., 2011). Some approaches for incorporating specific variance into reliability estimates are proposed and illustrated. The resulting specificity-enhanced coefficients α+, ω+, λ4+, ρxx+ and ρglb+ provide improved estimands of reliability and thus may be worth reporting in addition to their classical counterparts...
August 15, 2016: Psychological Methods
Margaret L Kern, Gregory Park, Johannes C Eichstaedt, H Andrew Schwartz, Maarten Sap, Laura K Smith, Lyle H Ungar
Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow. Second, we describe how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate...
August 8, 2016: Psychological Methods
Benjamin P Chapman, Alexander Weiss, Paul R Duberstein
Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool...
July 25, 2016: Psychological Methods
Cheng-Hsien Li
Three estimation methods with robust corrections-maximum likelihood (ML) using the sample covariance matrix, unweighted least squares (ULS) using a polychoric correlation matrix, and diagonally weighted least squares (DWLS) using a polychoric correlation matrix-have been proposed in the literature, and are considered to be superior to normal theory-based maximum likelihood when observed variables in latent variable models are ordinal. A Monte Carlo simulation study was carried out to compare the performance of ML, DWLS, and ULS in estimating model parameters, and their robust corrections to standard errors, and chi-square statistics in a structural equation model with ordinal observed variables...
September 2016: Psychological Methods
Ke-Hai Yuan, Wai Chan
Multigroup structural equation modeling (SEM) plays a key role in studying measurement invariance and in group comparison. When population covariance matrices are deemed not equal across groups, the next step to substantiate measurement invariance is to see whether the sample covariance matrices in all the groups can be adequately fitted by the same factor model, called configural invariance. After configural invariance is established, cross-group equalities of factor loadings, error variances, and factor variances-covariances are then examined in sequence...
September 2016: Psychological Methods
Joost C F de Winter, Samuel D Gosling, Jeff Potter
The Pearson product-moment correlation coefficient () and the Spearman rank correlation coefficient () are widely used in psychological research. We compare and on 3 criteria: variability, bias with respect to the population value, and robustness to an outlier. Using simulations across low (N = 5) to high (N = 1,000) sample sizes we show that, for normally distributed variables, and have similar expected values but is more variable, especially when the correlation is strong. However, when the variables have high kurtosis, is more variable than ...
September 2016: Psychological Methods
Charles E Lance, Stefanie S Beck, Yi Fan, Nathan T Carter
Almost all goodness-of-fit indexes (GFIs) for latent variable structural equation models are global GFIs that simultaneously assess the fits of the measurement and structural portions of the model. In one sense, this is an elegant feature of overall model GFIs, but in another sense, it is unfortunate as the fits of the 2 different portions of the model cannot be assessed independently. We (a) review the developing literature on this issue, (b) propose 6 new GFIs that are designed to evaluate the structural portion of latent variable models independently of the measurement model, (c) that are couched within a general taxonomy of James, Mulaik, and Brett's (1982) Conditions 9 and 10 for causal inference from nonexperimental data, (d) conduct a Monte Carlo simulation of the usefulness of these 6 new GFIs for model selection, and (e) on the basis of simulation results provide recommended criteria for 4 of them...
September 2016: Psychological Methods
Heining Cham, Stephen G West
Propensity score analysis is a method that equates treatment and control groups on a comprehensive set of measured confounders in observational (nonrandomized) studies. A successful propensity score analysis reduces bias in the estimate of the average treatment effect in a nonrandomized study, making the estimate more comparable with that obtained from a randomized experiment. This article reviews and discusses an important practical issue in propensity analysis, in which the baseline covariates (potential confounders) and the outcome have missing values (incompletely observed)...
September 2016: Psychological Methods
Siwei Liu, Yang Zhou, Richard Palumbo, Jane-Ling Wang
In this article, we introduce dynamical correlation, a new method for quantifying synchrony between 2 variables with intensive longitudinal data. Dynamical correlation is a functional data analysis technique developed to measure the similarity of 2 curves. It has advantages over existing methods for studying synchrony, such as multilevel modeling. In particular, it is a nonparametric approach that does not require a prespecified functional form, and it places no assumption on homogeneity of the sample. Dynamical correlation can be easily estimated with irregularly spaced observations and tested to draw population-level inferences...
September 2016: Psychological Methods
Royce Anders, F-Xavier Alario, Leendert Van Maanen
We propose and demonstrate the shifted Wald (SW) distribution as both a useful measurement tool and intraindividual process model for psychological response time (RT) data. Furthermore, we develop a methodology and fitting approach that readers can easily access. As a measurement tool, the SW provides a detailed quantification of the RT data that is more sophisticated than mean and SD comparisons. As an intraindividual process model, the SW provides a cognitive model for the response process in terms of signal accumulation and the threshold needed to respond...
September 2016: Psychological Methods
Mijke Rhemtulla
Previous research has suggested that the use of item parcels in structural equation modeling can lead to biased structural coefficient estimates and low power to detect model misspecification. The present article describes the population performance of items, parcels, and scales under a range of model misspecifications, examining structural path coefficient accuracy, power, and population fit indices. Results revealed that, under measurement model misspecification, any parceling scheme typically results in more accurate structural parameters, but less power to detect the misspecification...
September 2016: Psychological Methods
Carl F Falk, Li Cai
We present a flexible full-information approach to modeling multiple user-defined response styles across multiple constructs of interest. The model is based on a novel parameterization of the multidimensional nominal response model that separates estimation of overall item slopes from the scoring functions (indicating the order of categories) for each item and latent trait. This feature allows the definition of response styles to vary across items as well as overall item slopes that vary across items for both substantive and response style dimensions...
September 2016: Psychological Methods
Amanda K Montoya, Andrew F Hayes
Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis...
June 30, 2016: Psychological Methods
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