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Psychological Methods

Herbert Hoijtink, Xin Gu, Joris Mulder, Yves Rosseel
The Bayes factor is increasingly used for the evaluation of hypotheses. These may be traditional hypotheses specified using equality constraints among the parameters of the statistical model of interest or informative hypotheses specified using equality and inequality constraints. Thus far, no attention has been given to the computation of Bayes factors from data with missing values. A key property of such a Bayes factor should be that it is only based on the information in the observed values. This article will show that such a Bayes factor can be obtained using multiple imputations of the missing values...
July 12, 2018: Psychological Methods
Jason D Rights, Sonya K Sterba
Researchers often mention the utility and need for R-squared measures of explained variance for multilevel models (MLMs). Although this topic has been addressed by methodologists, the MLM R-squared literature suffers from several shortcomings: (a) analytic relationships among existing measures have not been established so measures equivalent in the population have been redeveloped 2 or 3 times; (b) a completely full partitioning of variance has not been used to create measures, leading to gaps in the availability of measures to address key substantive questions; (c) a unifying approach to interpreting and choosing among measures has not been provided, leading to researchers' difficulty with implementation; and (d) software has inconsistently and infrequently incorporated available measures...
July 12, 2018: Psychological Methods
Wendy Chan
While research on statistical power for designs with continuous outcomes is extensive, the literature on power for designs with binary outcomes is notably more limited. Because statistical power for continuous outcomes is well known, a natural question is whether power may be estimated in a similar way for the case of binary outcomes. This question involves establishing the appropriate analogy between design parameters in the continuous and binary outcome cases, which consists of an analogy in effect sizes and an analogy in the intraclass correlation coefficient (ICC)...
July 5, 2018: Psychological Methods
Oscar N E Kjell, Katarina Kjell, Danilo Garcia, Sverker Sikström
Psychological constructs, such as emotions, thoughts, and attitudes are often measured by asking individuals to reply to questions using closed-ended numerical rating scales. However, when asking people about their state of mind in a natural context ("How are you?"), we receive open-ended answers using words ("Fine and happy!") and not closed-ended answers using numbers ("7") or categories ("A lot"). Nevertheless, to date it has been difficult to objectively quantify responses to open-ended questions...
July 2, 2018: Psychological Methods
James E Pustejovsky
A wide variety of effect size indices have been proposed for quantifying the magnitude of treatment effects in single-case designs. Commonly used measures include parametric indices such as the standardized mean difference as well as nonoverlap measures such as the percentage of nonoverlapping data, improvement rate difference, and nonoverlap of all pairs. Currently, little is known about the properties of these indices when applied to behavioral data collected by systematic direct observation, even though systematic direct observation is the most common method for outcome measurement in single-case research...
June 18, 2018: Psychological Methods
Daniel McNeish, Ken Kelley
Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medical, and educational fields generally prefer mixed effects models, whereas those working in economics, behavioral finance, and strategic management generally prefer fixed effects models...
June 4, 2018: 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
(no author information available yet)
Reports an error in "Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data" by Siwei Liu, Yang Zhou, Richard Palumbo and Jane-Ling Wang ( Psychological Methods , 2016[Sep], Vol 21[3], 291-308). In the article, there were errors in the R script of Appendix B which could lead to incorrect significance testing results for dynamical correlation. We created an updated R script with corrections. In the updated R script, argument "na" from function "ind_DC" and argument "ms" from function "boot_test_DC" were removed...
June 2018: Psychological Methods
Kimberley A Goldsmith, David P MacKinnon, Trudie Chalder, Peter D White, Michael Sharpe, Andrew Pickles
The study of mediation of treatment effects, or how treatments work, is important to understanding and improving psychological and behavioral treatments, but applications often focus on mediators and outcomes measured at a single time point. Such cross-sectional analyses do not respect the implied temporal ordering that mediation suggests. Clinical trials of treatments often provide repeated measures of outcomes and, increasingly, of mediators as well. Repeated measurements allow the application of various types of longitudinal structural equation mediation models...
June 2018: Psychological Methods
Joyce Lok Yin Kwan, Wai Chan
Mediated moderation (meMO) occurs when the moderation effect of the moderator (W) on the relationship between the independent variable (X) and the dependent variable (Y) is transmitted through a mediator (M). To examine this process empirically, 2 different model specifications (Type I meMO and Type II meMO) have been proposed in the literature. However, both specifications are found to be problematic, either conceptually or statistically. For example, it can be shown that each type of meMO model is statistically equivalent to a particular form of moderated mediation (moME), another process that examines the condition when the indirect effect from X to Y through M varies as a function of W...
June 2018: Psychological Methods
Mark J Lachowicz, Kristopher J Preacher, Ken Kelley
Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirable properties. We also derive an expression for the bias of the sample estimator for the proposed effect size measure and propose an adjusted version of the estimator...
June 2018: Psychological Methods
Sara van Erp, Joris Mulder, Daniel L Oberski
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information...
June 2018: Psychological Methods
Qian Zhang, Lijuan Wang, C S Bergeman
In the current study, extending from the cross-lagged panel models (CLPMs) in Cole and Maxwell (2003), we proposed the multilevel autoregressive mediation models (MAMMs) by allowing the coefficients to differ across individuals. In addition, Level-2 covariates can be included to explain the interindividual differences of mediation effects. Given the complexity of the proposed models, Bayesian estimation was used. Both a CLPM and an unconditional MAMM were fitted to daily diary data. The 2 models yielded different statistical conclusions regarding the average mediation effect...
June 2018: Psychological Methods
Craig K Enders, Brian T Keller, Roy Levy
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential relations at Level-1 and Level-2, and incomplete Level-2 variables. Given the limitations of existing imputation tools, the purpose of this manuscript is to describe a flexible imputation approach that can accommodate a diverse set of 2-level analysis problems that includes any of the aforementioned features...
June 2018: Psychological Methods
Brandon J George, Peng Li, Harris R Lieberman, Greg Pavela, Andrew W Brown, Kevin R Fontaine, Madeline M Jeansonne, Gareth R Dutton, Adeniyi J Idigo, Mariel A Parman, Donald B Rubin, David B Allison
Blinded randomized controlled trials (RCT) require participants to be uncertain if they are receiving a treatment or placebo. Although uncertainty is ideal for isolating the treatment effect from all other potential effects, it is poorly suited for estimating the treatment effect under actual conditions of intended use-when individuals are certain that they are receiving a treatment. We propose an experimental design, randomization to randomization probabilities (R2R), which significantly improves estimates of treatment effects under actual conditions of use by manipulating participant expectations about receiving treatment...
June 2018: Psychological Methods
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