Roy Levy
Biographic Data
| ID | 4270392 |
|---|---|
| NAME | Roy Levy |
| GIVEN NAMES | Roy |
| FAMILY NAME | Levy |
| SIGNATURE | LEVY R |
| AFFILIATIONS | Arizona State University |
| ORCID | 0000-0001-7737-9176 |
| VERIFIED | Yes |
| TOTAL WORKS | 18 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 18 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling
Latent variable item response and structural equation models are widely used to model constructs and acknowledge measurement error in research settings and operational assessments. Such work often proceeds in stages, where the results of an analysis from an earlier stage are fed into the analysis at a later stage. However, common practices in single‐stage and multistage estimation have weaknesses, including when viewed from a Bayesian perspective…
Working memory structure in young Spanish–English bilingual children
Working memory encompasses the limited incoming information that can be held in mind for cognitive processing. To date, we have little information on the effects of bilingualism on working memory because, absent evidence, working memory tasks cannot be assumed to measure the same constructs across language groups. To garner evidence regarding the measurement equivalence in Spanish and English, we examined second-grade children with typical develo…
Precluding Interpretational Confounding in Factor Analysis with a Covariate or Outcome via Measurement and Uncertainty Preserving Parametric Modeling
In latent variable models, interpretational confounding occurs when the inclusion of a covariate or outcome when fitting the model alters the results for the measurement model. Commonly used estimation procedures do not preclude this possibility. Multi-stage estimation approaches preclude interpretational confounding, but most are limited in that they do not properly propagate uncertainty from earlier stages to later stages. This work introduces …
Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods
A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make j…
Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables
50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated eff…
Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments
Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, c…
Translating the Icap Theory of Cognitive Engagement Into Practice
ICAP is a theory of active learning that differentiates students’ engagement based on their behaviors. ICAP postulates that I nteractive engagement, demonstrated by co‐generative collaborative behaviors, is superior for learning to C onstructive engagement, indicated by generative behaviors. Both kinds of engagement exceed the benefits of A ctive or P assive engagement, marked by manipulative and attentive behaviors, respectively. This paper disc…
Tests of Simple Slopes in Multiple Regression Models with an Interaction
In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional varianc…
Accuracy of Revised and Traditional Parallel Analyses for Assessing Dimensionality with Binary Data
Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA (T-PA) methods for factor analysis of tetrachoric correlations between items with binary responses. We m…
Bayesian Psychometric Modeling
Presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate--and sometimes conflicting--ideas and activities in psychometrics. This book explains both how to perform psychometrics u…
Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors
Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequent…
Exploratory Data Analysis
In contrast to statistical approaches aimed at testing specific hypotheses, Exploratory Data Analysis (EDA) is a quantitative tradition that seeks to help researchers understand data when little or no statistical hypotheses exist, or when specific hypotheses exist but supplemental representations are needed to ensure the interpretability of statistical results. In this way, EDA seeks to answer the broad scientific questions of “what is going on h…
A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the s…
Extending models of deliberate self-harm and suicide attempts to substance users
The current study examined models of risk for deliberate self-harm (DSH) and suicide attempts (SA) in a sample of 180 inner-city substance users. The factors of childhood physical, sexual, and emotional abuse, posttraumatic stress (PTS) symptoms, and difficulties controlling impulsive behaviors when distressed were examined, with path modeling used to explore the interrelationships between variables. Analyses examined the utility of a model where…
An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures
The model comparison framework of Levy and Hancock for covariance and mean structure models is extended to treat multiple-group models, both in cases in which group membership is known and in those in which it is unknown (i.e., finite mixtures). The framework addresses questions of distinguishability as well as difference in fit of the models with respect to data, first by determining the nature of the models’ relation in terms of the families of…
Evaluation of Parallel Analysis Methods for Determining the Number of Factors
Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria were examined. The 95th percentile criterion was preferable for assessing the first eigenvalue using…
Dispositional happiness and college student GPA
A Framework of Statistical Tests For Comparing Mean and Covariance Structure Models
Although statistical procedures are well-known for comparing hierarchically related (nested) mean and covariance structure models, statistical tests for comparing non-hierarchically related (nonnested) models have proven more elusive. Although isolated attempts at statistical tests of non-hierarchically related models have been made, none exist within the commonly used maximum likelihood estimation framework, thereby compromising these methods' a…
A Framework of Statistical Tests For Comparing Mean and Covariance Structure Models
Although statistical procedures are well-known for comparing hierarchically related (nested) mean and covariance structure models, statistical tests for comparing non-hierarchically related (nonnested) models have proven more elusive. Although isolated attempts at statistical tests of non-hierarchically related models have been made, none exist within the commonly used maximum likelihood estimation framework, thereby compromising these methods' a…
Dispositional happiness and college student GPA
Evaluation of Parallel Analysis Methods for Determining the Number of Factors
Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria were examined. The 95th percentile criterion was preferable for assessing the first eigenvalue using…
Extending models of deliberate self-harm and suicide attempts to substance users
The current study examined models of risk for deliberate self-harm (DSH) and suicide attempts (SA) in a sample of 180 inner-city substance users. The factors of childhood physical, sexual, and emotional abuse, posttraumatic stress (PTS) symptoms, and difficulties controlling impulsive behaviors when distressed were examined, with path modeling used to explore the interrelationships between variables. Analyses examined the utility of a model where…
An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures
The model comparison framework of Levy and Hancock for covariance and mean structure models is extended to treat multiple-group models, both in cases in which group membership is known and in those in which it is unknown (i.e., finite mixtures). The framework addresses questions of distinguishability as well as difference in fit of the models with respect to data, first by determining the nature of the models’ relation in terms of the families of…
Exploratory Data Analysis
In contrast to statistical approaches aimed at testing specific hypotheses, Exploratory Data Analysis (EDA) is a quantitative tradition that seeks to help researchers understand data when little or no statistical hypotheses exist, or when specific hypotheses exist but supplemental representations are needed to ensure the interpretability of statistical results. In this way, EDA seeks to answer the broad scientific questions of “what is going on h…
A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the s…
Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors
Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequent…
Accuracy of Revised and Traditional Parallel Analyses for Assessing Dimensionality with Binary Data
Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA (T-PA) methods for factor analysis of tetrachoric correlations between items with binary responses. We m…
Bayesian Psychometric Modeling
Presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate--and sometimes conflicting--ideas and activities in psychometrics. This book explains both how to perform psychometrics u…
Tests of Simple Slopes in Multiple Regression Models with an Interaction
In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional varianc…
Translating the Icap Theory of Cognitive Engagement Into Practice
ICAP is a theory of active learning that differentiates students’ engagement based on their behaviors. ICAP postulates that I nteractive engagement, demonstrated by co‐generative collaborative behaviors, is superior for learning to C onstructive engagement, indicated by generative behaviors. Both kinds of engagement exceed the benefits of A ctive or P assive engagement, marked by manipulative and attentive behaviors, respectively. This paper disc…
Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments
Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, c…
Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods
A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make j…
Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables
50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated eff…
Precluding Interpretational Confounding in Factor Analysis with a Covariate or Outcome via Measurement and Uncertainty Preserving Parametric Modeling
In latent variable models, interpretational confounding occurs when the inclusion of a covariate or outcome when fitting the model alters the results for the measurement model. Commonly used estimation procedures do not preclude this possibility. Multi-stage estimation approaches preclude interpretational confounding, but most are limited in that they do not properly propagate uncertainty from earlier stages to later stages. This work introduces …
Working memory structure in young Spanish–English bilingual children
Working memory encompasses the limited incoming information that can be held in mind for cognitive processing. To date, we have little information on the effects of bilingualism on working memory because, absent evidence, working memory tasks cannot be assumed to measure the same constructs across language groups. To garner evidence regarding the measurement equivalence in Spanish and English, we examined second-grade children with typical develo…
Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling
Latent variable item response and structural equation models are widely used to model constructs and acknowledge measurement error in research settings and operational assessments. Such work often proceeds in stages, where the results of an analysis from an earlier stage are fed into the analysis at a later stage. However, common practices in single‐stage and multistage estimation have weaknesses, including when viewed from a Bayesian perspective…
Mathematics (11 works) · Statistics (11 works) · Computer Science (10 works) · Econometrics (8 works) · Psychology (6 works) · Psychometric Methodologies and Testing (5 works) · Statistical Methods and Bayesian Inference (5 works) · Advanced Statistical Methods and Models (4 works) · Advanced Statistical Modeling Techniques (4 works) · Artificial Intelligence (4 works)