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Psychological Methods

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https://www.readbyqxmd.com/read/30188157/measurement-error-and-person-specific-reliability-in-multilevel-autoregressive-modeling
#1
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://www.readbyqxmd.com/read/30138004/a-cautionary-note-on-the-finite-sample-behavior-of-maximal-reliability
#2
Miguel I Aguirre-Urreta, Mikko Rönkkö, Cameron N McIntosh
Several calls have been made for replacing coefficient α with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock's H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted differently based on their estimated reliabilities...
August 23, 2018: Psychological Methods
https://www.readbyqxmd.com/read/30124300/uncovering-general-shared-and-unique-temporal-patterns-in-ambulatory-assessment-data
#3
Stephanie T Lane, Kathleen M Gates, Hallie K Pike, Adriene M Beltz, Aidan G C Wright
Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, a subset of the sample, and a given individual...
August 20, 2018: Psychological Methods
https://www.readbyqxmd.com/read/30113184/nonlinear-growth-curve-modeling-using-penalized-spline-models-a-gentle-introduction
#4
Hye Won Suk, Stephen G West, Kimberly L Fine, Kevin J Grimm
This didactic article aims to provide a gentle introduction to penalized splines as a way of estimating nonlinear growth curves in which many observations are collected over time on a single or multiple individuals. We begin by presenting piecewise linear models in which the time domain of the data is divided into consecutive phases and a separate linear regression line is fitted in each phase. Linear splines add the feature that the regression lines fitted in adjacent phases are always joined at the boundary so there is no discontinuity in level between phases...
August 16, 2018: Psychological Methods
https://www.readbyqxmd.com/read/30102054/alternating-optimization-for-g-%C3%A3-e-modelling-with-weighted-genetic-and-environmental-scores-examples-from-the-mavan-study
#5
Alexia Jolicoeur-Martineau, Ashley Wazana, Eszter Szekely, Meir Steiner, Alison S Fleming, James L Kennedy, Michael J Meaney, Celia M T Greenwood
Motivated by the goal of expanding currently existing Genotype × Environment interaction (G × E) models to simultaneously include multiple genetic variants and environmental exposures in a parsimonious way, we developed a novel method to estimate the parameters in a G × E model, where G is a weighted sum of genetic variants (genetic score) and E is a weighted sum of environments (environmental score). The approach uses alternating optimization, an iterative process where the genetic score weights, the environmental score weights, and the main model parameters are estimated in turn, assuming the other parameters are constant...
August 13, 2018: Psychological Methods
https://www.readbyqxmd.com/read/30080056/using-generalizability-theory-with-continuous-latent-response-variables
#6
Walter P Vispoel, Carrie A Morris, Murat Kilinc
In this article, we illustrate ways in which generalizability theory (G-theory) can be used with continuous latent response variables (CLRVs) to address problems of scale coarseness resulting from categorization errors caused by representing ranges of continuous variables by discrete data points and transformation errors caused by unequal interval widths between those data points. The mechanism to address these problems is applying structural equation modeling (SEM) as a tool in deriving variance components needed to estimate indices of score consistency and validity...
August 6, 2018: Psychological Methods
https://www.readbyqxmd.com/read/30070547/statistical-analyses-for-studying-replication-meta-analytic-perspectives
#7
Larry V Hedges, Jacob M Schauer
Formal empirical assessments of replication have recently become more prominent in several areas of science, including psychology. These assessments have used different statistical approaches to determine if a finding has been replicated. The purpose of this article is to provide several alternative conceptual frameworks that lead to different statistical analyses to test hypotheses about replication. All of these analyses are based on statistical methods used in meta-analysis. The differences among the methods described involve whether the burden of proof is placed on replication or nonreplication, whether replication is exact or allows for a small amount of "negligible heterogeneity," and whether the studies observed are assumed to be fixed (constituting the entire body of relevant evidence) or are a sample from a universe of possibly relevant studies...
August 2, 2018: Psychological Methods
https://www.readbyqxmd.com/read/29999379/computing-bayes-factors-from-data-with-missing-values
#8
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
https://www.readbyqxmd.com/read/29999378/quantifying-explained-variance-in-multilevel-models-an-integrative-framework-for-defining-r-squared-measures
#9
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
https://www.readbyqxmd.com/read/29975081/the-relationship-among-design-parameters-for-statistical-power-between-continuous-and-binomial-outcomes-in-cluster-randomized-trials
#10
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
https://www.readbyqxmd.com/read/29963879/semantic-measures-using-natural-language-processing-to-measure-differentiate-and-describe-psychological-constructs
#11
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
https://www.readbyqxmd.com/read/29172612/more-stable-estimation-of-the-starts-model-a-bayesian-approach-using-markov-chain-monte-carlo-techniques
#12
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://www.readbyqxmd.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
#13
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://www.readbyqxmd.com/read/28737413/explaining-general-and-specific-factors-in-longitudinal-multimethod-and-bifactor-models-some-caveats-and-recommendations
#14
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://www.readbyqxmd.com/read/28557467/thanks-coefficient-alpha-we-ll-take-it-from-here
#15
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://www.readbyqxmd.com/read/28425729/some-properties-of-p-curves-with-an-application-to-gradual-publication-bias
#16
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://www.readbyqxmd.com/read/28383950/multilevel-factorial-designs-with-experiment-induced-clustering
#17
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://www.readbyqxmd.com/read/28368177/testing-small-variance-priors-using-prior-posterior-predictive-p-values
#18
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://www.readbyqxmd.com/read/28301199/analyzing-data-from-single-case-alternating-treatments-designs
#19
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
https://www.readbyqxmd.com/read/28301198/a-framework-of-r-squared-measures-for-single-level-and-multilevel-regression-mixture-models
#20
Jason D Rights, Sonya K Sterba
Psychologists commonly apply regression mixture models in single-level (i.e., unclustered) and multilevel (i.e., clustered) data analysis contexts. Though researchers applying nonmixture regression models typically report R-squared measures of explained variance, there has been no general treatment of R-squared measures for single-level and multilevel regression mixtures. Consequently, it is common for researchers to summarize results of a fitted regression mixture by simply reporting class-specific regression coefficients and their associated p values, rather than considering measures of effect size...
September 2018: Psychological Methods
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