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A constrained factor mixture analysis model for consistent and inconsistent respondents to mixed-worded scales.

#1Isa SteinmannH-Index: 1

#2Rolf StrietholtH-Index: 6

Last. Johan BraekenH-Index: 16

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Mixed-worded scales require a more careful reading and answering process than scales with only one type of wording. The present study is about the unintended consequences of using such scales, because typically, not all respondents answer positively and negatively worded items consistently. This population heterogeneity-meaning that there are distinct groups of consistently and inconsistently answering respondents-may arguably underlie wording-related effects in mixed-worded scales. We formulate...

Monte Carlo simulations using extant data to mimic populations: Applications to the modified linear probability model and logistic regression.

Monte Carlo simulations are widely used in the social sciences to explore the viability of analytic methods in the face of assumption violations. Simulation results, however, may not be applicable to substantive research applications because they often are conducted under idealized rather than realistic conditions. Shortcomings of simulation design are discussed using linear equations as a case study, focusing on (a) variable distributions, (b) population level specification error, (c) populatio...

#1Debby ten HoveH-Index: 1

#2Terrence D. JorgensenH-Index: 10

Last. L. Andries van der ArkH-Index: 25

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Current interrater reliability (IRR) coefficients ignore the nested structure of multilevel observational data, resulting in biased estimates of both subject- and cluster-level IRR. We used generalizability theory to provide a conceptualization and estimation method for IRR of continuous multilevel observational data. We explain how generalizability theory decomposes the variance of multilevel observational data into subject-, cluster-, and rater-related components, which can be estimated using ...

#1Christopher J. UrbanH-Index: 1

#2Kathleen M. GatesH-Index: 21

Deep learning has revolutionized predictive modeling in topics such as computer vision and natural language processing but is not commonly applied to psychological data. In an effort to bring the benefits of deep learning to psychologists, we provide an overview of deep learning for researchers who have a working knowledge of linear regression. We first discuss several benefits of the deep learning approach to predictive modeling. We then present three basic deep learning models that generalize ...

#2Woojong YiH-Index: 1

Last. Brandon M. TurnerH-Index: 22

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In a world of big data and computational resources, there has been a growing interest in further validating computational models of decision making by subjecting them to more rigorous constraints. One prominent area of study is model-based cognitive neuroscience, where measures of neural activity are explained and interpreted through the lens of a cognitive model. Although some early work has developed the statistical framework for exploiting the covariation between brain and behavior through fa...

The sensitivity of structural equation modeling with ordinal data to underlying non-normality and observed distributional forms.

#1Njål FoldnesH-Index: 9

#2Steffen GrønnebergH-Index: 6

Structural equation modeling (SEM) of ordinal data is often performed using normal theory maximum likelihood estimation based on the Pearson correlation (cont-ML) or using least squares principles based on the polychoric correlation matrix (cat-LS). While cont-ML ignores the categorical nature of the data, cat-LS assumes underlying multivariate normality. Theoretical results are provided on the validity of treating ordinal data as continuous when the number of categories increases, leading to an...

#1Mark H. C. LaiH-Index: 9

This article shows how the concept of reliability of composite scores, as defined in classical test theory, can be extended to the context of multilevel modeling. In particular, it discusses the contributions and limitations of the various level-specific reliability indices proposed by Geldhof, Preacher, and Zyphur (2014), denoted as ωb and ωw (and also αb and αw). One major limitation of those indices is that they are quantities for latent, unobserved level-specific composite scores, and are no...

#1Daniel J. Schad (University of Potsdam)H-Index: 16

#2Michael BetancourtH-Index: 18

Last. Shravan Vasishth (University of Potsdam)H-Index: 29

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Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's doma...

#1Jacob M. SchauerH-Index: 2

#2Larry V. HedgesH-Index: 77

Some researchers wear yellow pants, but even fewer participants read consent forms: Exploring and improving consent form reading in human subjects research.

#1Benjamin D. Douglas (Stanford University)

#2Emma L. McGorray (NU: Northwestern University)

Last. Patrick J. Ewell (Kenyon College)H-Index: 5

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Though consent forms include important information, those experienced with behavioral research often observe that participants do not carefully read consent forms. Three studies examined participants' reading of consent forms for in-person experiments. In each study, we inserted the phrase "some researchers wear yellow pants" into sections of the consent form and measured participants' reading of the form by testing their recall of the color yellow. In Study 1, we found that the majority of part...

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