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Psychological Methods

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https://read.qxmd.com/read/30742474/the-unappreciated-heterogeneity-of-effect-sizes-implications-for-power-precision-planning-of-research-and-replication
#1
David A Kenny, Charles M Judd
Repeated investigations of the same phenomenon typically yield effect sizes that vary more than one would expect from sampling error alone. Such variation is even found in exact replication studies, suggesting that it is not only because of identifiable moderators but also to subtler random variation across studies. Such heterogeneity of effect sizes is typically ignored, with unfortunate consequences. We consider its implications for power analyses, the precision of estimated effects, and the planning of original and replication research...
February 11, 2019: Psychological Methods
https://read.qxmd.com/read/30742473/assessing-the-robustness-of-cluster-solutions-obtained-from-sparse-count-matrices
#2
Kathleen M Gates, Zachary F Fisher, Cara Arizmendi, Teague R Henry, Kelly A Duffy, Peter J Mucha
Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging). Increasingly, community detection methods are being used to subset the data in a data-driven manner. While many of these approaches perform well in simulation studies and thus offer some improvement upon traditional clustering approaches, there is no readily available approach for evaluating the robustness of these solutions in empirical data...
February 11, 2019: Psychological Methods
https://read.qxmd.com/read/30742472/a-tutorial-on-testing-hypotheses-using-the-bayes-factor
#3
Herbert Hoijtink, Joris Mulder, Caspar van Lissa, Xin Gu
Learning about hypothesis evaluation using the Bayes factor could enhance psychological research. In contrast to null-hypothesis significance testing it renders the evidence in favor of each of the hypotheses under consideration (it can be used to quantify support for the null-hypothesis) instead of a dichotomous reject/do-not-reject decision; it can straightforwardly be used for the evaluation of multiple hypotheses without having to bother about the proper manner to account for multiple testing; and it allows continuous reevaluation of hypotheses after additional data have been collected (Bayesian updating)...
February 11, 2019: Psychological Methods
https://read.qxmd.com/read/30667242/how-to-determine-the-number-of-factors-to-retain-in-exploratory-factor-analysis-a-comparison-of-extraction-methods-under-realistic-conditions
#4
Max Auerswald, Morten Moshagen
Exploratory factor analyses are commonly used to determine the underlying factors of multiple observed variables. Many criteria have been suggested to determine how many factors should be retained. In this study, we present an extensive Monte Carlo simulation to investigate the performance of extraction criteria under varying sample sizes, numbers of indicators per factor, loading magnitudes, underlying multivariate distributions of observed variables, as well as how the performance of the extraction criteria are influenced by the presence of cross-loadings and minor factors for unidimensional, orthogonal, and correlated factor models...
January 21, 2019: Psychological Methods
https://read.qxmd.com/read/30489099/the-effect-of-publication-bias-on-the-q-test-and-assessment-of-heterogeneity
#5
Hilde E M Augusteijn, Robbie C M van Aert, Marcel A L M van Assen
One of the main goals of meta-analysis is to test for and estimate the heterogeneity of effect sizes. We examined the effect of publication bias on the Q test and assessments of heterogeneity as a function of true heterogeneity, publication bias, true effect size, number of studies, and variation of sample sizes. The present study has two main contributions and is relevant to all researchers conducting meta-analysis. First, we show when and how publication bias affects the assessment of heterogeneity. The expected values of heterogeneity measures H ² and I ² were analytically derived, and the power and Type I error rate of the Q test were examined in a Monte Carlo simulation study...
November 29, 2018: Psychological Methods
https://read.qxmd.com/read/30730193/-parameter-uncertainty-in-structural-equation-models-confidence-sets-and-fungible-estimates-correction-to-pek-and-wu-2018
#6
(no author information available yet)
Reports an error in "Parameter uncertainty in structural equation models: Confidence sets and fungible estimates" by Jolynn Pek and Hao Wu ( Psychological Methods , 2018[Dec], Vol 23[4], 635-653). In the article "Parameter Uncertainty in Structural Equation Models: Confidence Sets and Fungible Estimates," by Jolynn Pek and Hao Wu ( Psychological Methods , 2018, Vol. 23, No. 4, pp. 635-653. http://dx.doi.org/10.1037/met0000163), the copyright attribution was incorrect. The copyright should not have been "In the public domain...
February 2019: Psychological Methods
https://read.qxmd.com/read/30372100/fit-indices-for-mean-structures-with-growth-curve-models
#7
Ke-Hai Yuan, Zhiyong Zhang, Lifang Deng
Motivated by the need to effectively evaluate the quality of the mean structure in growth curve modeling (GCM), this article proposes to separately evaluate the goodness of fit of the mean structure from that of the covariance structure. Several fit indices are defined, and rationales are discussed. Particular considerations are given for polynomial and piecewise polynomial models because fit indices for them are valid regardless of the underlying population distribution of the data. Examples indicate that the newly defined fit indices remove the confounding issues with indices jointly evaluating mean and covariance structure models and provide much more reliable evaluation of the mean structure in GCM...
October 29, 2018: Psychological Methods
https://read.qxmd.com/read/30359043/tutorial-on-removing-the-shackles-of-regression-analysis-how-to-stay-true-to-your-theory-of-binary-response-probabilities
#8
Michel Regenwetter, Daniel R Cavagnaro
Statistical analyses of data often add some additional constraints to a theory and leave out others, so as to convert the theory into a testable hypothesis. In the case of binary data, such as yes/no responses, or such as the presence/absence of a symptom or a behavior, theories often actually predict that certain response probabilities change monotonically in a specific direction and/or that certain response probabilities are bounded from above or below in specific ways. A regression analysis is not really true to such a theory in that it may leave out parsimonious constraints and in that extraneous assumptions like linearity or log-linearity, or even the assumption of a functional relationship, are dictated by the method rather than the theory...
October 25, 2018: Psychological Methods
https://read.qxmd.com/read/30299117/target-rotation-with-both-factor-loadings-and-factor-correlations
#9
Guangjian Zhang, Minami Hattori, Lauren A Trichtinger, Xianni Wang
Factor rotation is conducted to aid interpretation in exploratory factor analysis (EFA). Target rotation allows researchers to directly examine the match between the rotated factor loading matrix and their expected factor loading pattern. In some EFA applications, however, researchers have expectations on both the factor loading pattern and the factor correlation pattern. We propose to extend target rotation such that target values can be specified for both factor loadings and factor correlations. We illustrate extended target rotation with a memory study and a personality study with the multitrait-multimethod design...
October 8, 2018: Psychological Methods
https://read.qxmd.com/read/30265048/statistical-power-in-two-level-models-a-tutorial-based-on-monte-carlo-simulation
#10
Matthias G Arend, Thomas Schäfer
The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a hands-on tutorial illustrating how a priori and post hoc power analyses for the most frequently used two-level models are conducted. We describe how a population model for the power analysis can be specified by using standardized input parameters and how the power analysis is implemented in SIMR, a very flexible power estimation method based on Monte Carlo simulation...
September 27, 2018: Psychological Methods
https://read.qxmd.com/read/30265047/a-tutorial-on-probabilistic-index-models-regression-models-for-the-effect-size-p-y1-y2
#11
Maarten De Schryver, Jan De Neve
The probabilistic index (PI), also known as the probability of superiority or the common language effect size, refers to the probability that the outcome of a randomly selected subject exceeds the outcome of another randomly selected subject, conditional on the covariate values of both subjects. This summary measure has a long history, especially for the 2-sample design where the covariate value typically refers to 1 of 2 treatments. Despite some of the attractive features of the PI, it is often not used beyond the 2-sample design...
September 27, 2018: Psychological Methods
https://read.qxmd.com/read/30188157/measurement-error-and-person-specific-reliability-in-multilevel-autoregressive-modeling
#12
Noémi K Schuurman, Ellen L Hamaker
An increasing number of researchers in psychology are collecting intensive longitudinal data in order to study psychological processes on an intraindividual level. An increasingly popular way to analyze these data is autoregressive time series modeling; either by modeling the repeated measures for a single individual using classic n = 1 autoregressive models, or by using multilevel extensions of these models, with the dynamics for each individual modeled at Level 1 and interindividual differences in these dynamics modeled at Level 2...
September 6, 2018: Psychological Methods
https://read.qxmd.com/read/29172612/more-stable-estimation-of-the-starts-model-a-bayesian-approach-using-markov-chain-monte-carlo-techniques
#13
Oliver Lüdtke, Alexander Robitzsch, Jenny Wagner
The STARTS (Stable Trait, AutoRegressive Trait, and State) model decomposes individual differences in psychological measurement across time into 3 sources of variation: a time-invariant stable component, a time-varying autoregressive component, and an occasion-specific state component. Previous simulation research and applications of the STARTS model have shown that serious estimation problems such as nonconvergence or inadmissible estimates (e.g., negative variances) frequently occur for STARTS model parameters...
September 2018: Psychological Methods
https://read.qxmd.com/read/29172609/linear-mixed-effects-models-and-the-analysis-of-nonindependent-data-a-unified-framework-to-analyze-categorical-and-continuous-independent-variables-that-vary-within-subjects-and-or-within-items
#14
Markus Brauer, John J Curtin
In this article we address a number of important issues that arise in the analysis of nonindependent data. Such data are common in studies in which predictors vary within "units" (e.g., within-subjects, within-classrooms). Most researchers analyze categorical within-unit predictors with repeated-measures ANOVAs, but continuous within-unit predictors with linear mixed-effects models (LMEMs). We show that both types of predictor variables can be analyzed within the LMEM framework. We discuss designs with multiple sources of nonindependence, for example, studies in which the same subjects rate the same set of items or in which students nested in classrooms provide multiple answers...
September 2018: Psychological Methods
https://read.qxmd.com/read/28737413/explaining-general-and-specific-factors-in-longitudinal-multimethod-and-bifactor-models-some-caveats-and-recommendations
#15
Tobias Koch, Jana Holtmann, Johannes Bohn, Michael Eid
An increasing number of psychological studies are devoted to the analysis of g-factor structures. One key purpose of applying g-factor models is to identify predictors or potential causes of the general and specific effects. Typically, researchers relate predictor variables directly to the general and specific factors using a classical mimic approach. However, this procedure bears some methodological challenges, which often lead to model misspecification and biased parameter estimates. We propose 2 possible modeling strategies to circumvent these problems: the multiconstruct bifactor and the residual approach...
September 2018: Psychological Methods
https://read.qxmd.com/read/28557467/thanks-coefficient-alpha-we-ll-take-it-from-here
#16
Daniel McNeish
Empirical studies in psychology commonly report Cronbach's alpha as a measure of internal consistency reliability despite the fact that many methodological studies have shown that Cronbach's alpha is riddled with problems stemming from unrealistic assumptions. In many circumstances, violating these assumptions yields estimates of reliability that are too small, making measures look less reliable than they actually are. Although methodological critiques of Cronbach's alpha are being cited with increasing frequency in empirical studies, in this tutorial we discuss how the trend is not necessarily improving methodology used in the literature...
September 2018: Psychological Methods
https://read.qxmd.com/read/28425729/some-properties-of-p-curves-with-an-application-to-gradual-publication-bias
#17
Rolf Ulrich, Jeff Miller
p-curves provide a useful window for peeking into the file drawer in a way that might reveal p-hacking (Simonsohn, Nelson, & Simmons, 2014a). The properties of p-curves are commonly investigated by computer simulations. On the basis of these simulations, it has been proposed that the skewness of this curve can be used as a diagnostic tool to decide whether the significant p values within a certain domain of research suggest the presence of p-hacking or actually demonstrate that there is a true effect. Here we introduce a rigorous mathematical approach that allows the properties of p-curves to be examined without simulations...
September 2018: Psychological Methods
https://read.qxmd.com/read/28383950/multilevel-factorial-designs-with-experiment-induced-clustering
#18
Inbal Nahum-Shani, John J Dziak, Linda M Collins
Factorial experimental designs have many applications in the behavioral sciences. In the context of intervention development, factorial experiments play a critical role in building and optimizing high-quality, multicomponent behavioral interventions. One challenge in implementing factorial experiments in the behavioral sciences is that individuals are often clustered in social or administrative units and may be more similar to each other than to individuals in other clusters. This means that data are dependent within clusters...
September 2018: Psychological Methods
https://read.qxmd.com/read/28368177/testing-small-variance-priors-using-prior-posterior-predictive-p-values
#19
Herbert Hoijtink, Rens van de Schoot
Muthén and Asparouhov (2012) propose to evaluate model fit in structural equation models based on approximate (using small variance priors) instead of exact equality of (combinations of) parameters to zero. This is an important development that adequately addresses Cohen's (1994) The Earth is Round (p < .05), which stresses that point null-hypotheses are so precise that small and irrelevant differences from the null-hypothesis may lead to their rejection. It is tempting to evaluate small variance priors using readily available approaches like the posterior predictive p value and the DIC...
September 2018: Psychological Methods
https://read.qxmd.com/read/28301199/analyzing-data-from-single-case-alternating-treatments-designs
#20
Rumen Manolov, Patrick Onghena
Alternating treatments designs (ATDs) have received comparatively less attention than other single-case experimental designs in terms of data analysis, as most analytical proposals and illustrations have been made in the context of designs including phases with several consecutive measurements in the same condition. One of the specific features of ATDs is the rapid (and usually randomly determined) alternation of conditions, which requires adapting the analytical techniques. First, we review the methodologically desirable features of ATDs, as well as the characteristics of the published single-case research using an ATD, which are relevant for data analysis...
September 2018: Psychological Methods
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