Christine Distefano
Biographic Data
| ID | 4370103 |
|---|---|
| NAME | Christine Distefano |
| GIVEN NAMES | Christine |
| FAMILY NAME | Distefano |
| SIGNATURE | DISTEFANO C |
| AFFILIATIONS | University of South Carolina |
| ORCID | 0000-0001-7504-6554 |
| VERIFIED | Yes |
| TOTAL WORKS | 38 |
| TOTAL CITATIONS | 13 |
| WORKS AS AUTHOR | 38 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1992 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Investigating and Cross-Validating Alternative Factor Structures of the Basc-3 Bess TRA-C/A
Universal screening of students’ social, emotional, and behavioral needs requires psychometrically sound tools to support timely intervention. The BASC-3 BESS Teacher Form–Child/Adolescent is widely used for this purpose, yet independent evaluations of the form are limited and often do not test alternative factor structures beyond those stated in the manual. This study examined four factorial models using data from 5,150 K–5 students rated by 278…
An Omega-Hierarchical Extension Index for Second-Order Constructs With Hierarchical Measuring Instruments
An index extending the widely used omega-hierarchical coefficient is discussed, which can be used for evaluating the influence of a second-order factor on the interrelationships among the components of a hierarchical measuring instrument. The index represents a useful and informative complement to the traditional omega-hierarchical measure of explained overall scale score variance by that underlying construct. A point and interval estimation proc…
Evaluating Change in Adjusted R -Square and R -Square Indices
A procedure for interval estimation of the difference in the adjusted R -square index for nested linear models is discussed. The method yields as a byproduct confidence intervals for their standard R -square difference, as well as for the adjusted and standard R -squares associated with each model. The resulting interval estimate of the difference in adjusted R -square represents a useful and informative complement to the commonly used R -square …
Identifying Children at Risk for Emotional and Behavioral Problems
Behavioral and emotional disorders in childhood can have lasting impacts in areas such as education and future employment, often extending into adulthood. Identifying the potential disorders in children's early grades is beneficial to provide proactive assistance. In this study, we employed a well‐validated scale ‐ the Strengths and Difficulties Questionnaire (SDQ) to investigate the profiles of five‐dimensional behavioral and emotional problems …
Proportion Explained Component Variance in Second-Order Scales
A procedure for evaluation of the proportion explained component variance by the underlying trait in behavioral scales with second-order structure is outlined. The resulting index of accounted for variance over all scale components is a useful and informative complement to the conventional omega-hierarchical coefficient as well as the proportion of explained component correlation. A point and interval estimation method is described for the discus…
Collapsing Sparse Responses in Likert-Type Scale Data
Applied researchers often encounter situations where certain item response categories receive very few endorsements, resulting in sparse data. Collapsing categories may mitigate sparsity by increasing cell counts, yet the methodological consequences of this practice remain insufficiently explored. The current study examined the effects of response collapsing in Likert-type scale data through a simulation study under the confirmatory factor analys…
Examining the Dynamic of Clustering Effects in Multilevel Designs
This note is concerned with the study of temporal development in several indices reflecting clustering effects in multilevel designs that are frequently utilized in educational and behavioral research. A latent variable method-based approach is outlined, which can be used to point and interval estimate the growth or decline in important functions of level-specific variances in two-level and three-level settings. The procedure may also be employed…
Comparing Methods for Factor Score Estimation in Structural Equation Modeling
Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate factor scores and compared the performance with traditional methods, where both proper and improper …
Overall Model Fit Does Not Imply Linearity in Longitudinal Structural Equation Models
This article is concerned with the assumption of linear temporal development that is often advanced in structural equation modeling-based longitudinal research. The linearity hypothesis is implemented in particular in the popular intercept-and-slope model as well as in more general models containing it as a component, such as longitudinal structural models with covariates, or models for the study of predictors and correlates of change. In empiric…
A Note on Comparing the Bifactor and Second-Order Factor Models
This note demonstrates that the widely used Bayesian Information Criterion (BIC) need not be generally viewed as a routinely dependable index for model selection when the bifactor and second-order factor models are examined as rival means for data description and explanation. To this end, we use an empirically relevant setting with multidimensional measuring instrument components, where the bifactor model is found consistently inferior to the sec…
Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models
Regression mixture models (RMMs) can be used to specifically test for and model differential effects in heterogeneous populations. Based on the results of the Aim 1 simulation study, enumeration conducted with constrained predictor means appears to be advantageous. Furthermore, researchers should estimate the K and K+1 unconditional models (chosen during initial enumeration), adding the C on X paths, to investigate the potential for model instabi…
A randomized controlled trial on the interconnected systems framework for school mental health and PBIS
Design Effect in Multilevel Settings
A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval esti…
A Monte Carlo Study of Confidence Interval Methods for Generalizability Coefficient
Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstra…
On Effect Size Measures for Nested Measurement Models
A class of effect size indices are discussed that evaluate the degree to which two nested confirmatory factor analysis models differ from each other in terms of fit to a set of observed variables. These descriptive effect measures can be used to quantify the impact of parameter restrictions imposed in an initially considered model and are free from an explicit relationship to sample size. The described indices represent the extent to which respec…
Evaluating SEM Model Fit with Small Degrees of Freedom
and to rely more on SRMR and CFI
Collapsing Categories is Often More Advantageous than Modeling Sparse Data
When questionnaires include Likert scales, items endorsed by relatively few respondents may result from characteristics of examinees or the constructs under study. Researchers may collapse categories to increase cell sample size; however, effects of this practice have not been systematically investigated. A five-point ordinal scale was simulated where data included few responses in extreme categories. Different estimators were applied to sparsely…
Examining motivation profiles within and across socioeconomic levels on educational outcomes
Fitting latent growth models with small sample sizes and non-normal missing data
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N 2 test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (…
A Short Note on Optimizing Cost-Generalizability via a Machine-Learning Approach
The costs of an objective structured clinical examination (OSCE) are of concern to health profession educators globally. As OSCEs are usually designed under generalizability theory (G-theory) framework, this article proposes a machine-learning-based approach to optimize the costs, while maintaining the minimum required generalizability coefficient, a reliability-like index in G-theory. The authors adopted G-theory parameters yielded from an OSCE …
Evaluating Restrictive Models in Educational and Behavioral Research
The frequent practice of overall fit evaluation for latent variable models in educational and behavioral research is reconsidered. It is argued that since overall plausibility does not imply local plausibility and is only necessary for the latter, local misfit should be considered a sufficient condition for model rejection, even in the case of omnibus model tenability. The argument is exemplified with a comparison of the widely used one-parameter…
The Complex Nature of Youth Aggression
Youth victimization and aggression are common in adolescents' everday lives. This study examines relations between youth cognition and reasoning around bullying and possible responses to bullying, peer nominations related to youth roles in the bullying ecology and self-report experiences of perceived racial discrimination using latent class analyses. Participants included 6th ( n = 423) and 9th ( n = 392) grade adolescents in the United States (4…
Using Integrative Data Analysis to Investigate School Climate Across Multiple Informants
Survey research frequently involves the collection of data from multiple informants. Results, however, are usually analyzed by informant group, potentially ignoring important relationships across groups. When the same construct(s) are measured, integrative data analysis (IDA) allows pooling of data from multiple sources into one data set to examine information from multiple perspectives within the same analysis. Here, the IDA procedure is demonst…
Measurement invariance of the English and Spanish Basc-3 behavioral and emotional screening system parent preschool forms
Fitting Large Factor Analysis Models With Ordinal Data
A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and misspecification affect parameter estimates, standard errors of parameter estimates, and selected fit indices…
School and Family Factors Predicting Adolescent Cognition Regarding Bystander Intervention in Response to Bullying and Victim Retaliation
Reading Men
Construction and Validation of the National Survey on Recreation and the Environment's Lifestyles Scale
This study sought to construct and validate a lifestyle scale that would capture for leisure professionals a more holistic picture of people's interests. A broader profile of their customers would allow recreation professionals to tailor information of their services. Public sector recreation managers could provide better services by learning about how customers spend their leisure time. With general lifestyle information, public sector recreatio…
Measurement invariance of the English and Spanish Basc-3 behavioral and emotional screening system parent preschool forms
Reading Men
Behavioral Clustering of School Children
The intent of this article is to illustrate how a cluster analysis might be conducted, validated, and interpreted. Data normed for a behavioral assessment instrument with 14 scales on a sample drawn from a nationally representative pool of U.S. school children were utilized. The analysis discussed covers the cluster method, cluster typology, cluster validity, cluster structure, and prediction of cluster membership
The Impact of Categorization With Confirmatory Factor Analysis
This study investigated the impact of categorization on confirmatory factor analysis (CFA) parameter estimates, standard errors, and 5 ad hoc fit indexes. Models were generated that represented empirical research situations in terms of model size, sample sizes, and loading values. CFA results obtained from analysis of normally distributed, continuous data were compared to results obtained from 5-category Likert-type data with normal distributions…
Using Confirmatory Factor Analysis for Construct Validation
This study investigated the psychological assessment literature to determine what applied researchers are using and reporting from confirmatory factor analysis (CFA) studies for evidence of construct validation. One hundred and one articles published in four major psychological assessment journals between 1990 and 2002 were systematically reviewed. Information from each article was collected across four broad areas: Background, Data Screening, Re…
Investigating Subtypes of Child Development
Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures and results were compared. The latent class cluster analysis uncovered three classes representing diff…
Further Investigating Method Effects Associated With Negatively Worded Items on Self-Report Surveys
This article used multitrait-multimethod methodology and covariance modeling for an investigation of the presence and correlates of method effects associated with negatively worded items on the Rosenberg Self-Esteem (RSE) scale (Rosenberg, 1989) using a sample of 757 adults. Results showed that method effects associated with negative item phrasing on the RSE scale were present. Method effects associated with negative item wording were similarly o…
Construction and Validation of the National Survey on Recreation and the Environment's Lifestyles Scale
This study sought to construct and validate a lifestyle scale that would capture for leisure professionals a more holistic picture of people's interests. A broader profile of their customers would allow recreation professionals to tailor information of their services. Public sector recreation managers could provide better services by learning about how customers spend their leisure time. With general lifestyle information, public sector recreatio…
A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data
This study compared diagonal weighted least squares robust estimation techniques available in 2 popular statistical programs: diagonal weighted least squares (DWLS; LISREL version 8.80) and weighted least squares–mean (WLSM) and weighted least squares—mean and variance adjusted (WLSMV; Mplus version 6.11). A 20-item confirmatory factor analysis was estimated using item-level ordered categorical data. Three different nonnormality conditions were a…
Apsa Committees
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Examination of the Weighted Root Mean Square Residual
This study examined the performance of the weighted root mean square residual (WRMR) through a simulation study using confirmatory factor analysis with ordinal data. Values and cut scores for the WRMR were examined, along with a comparison of its performance relative to commonly cited fit indexes. The findings showed that WRMR illustrated worse fit when sample size increased or model misspecification increased. Lower (i.e., better) values of WRMR…
The Relationship Between the Standardized Root Mean Square Residual and Model Misspecification in Factor Analysis Models
We argue that the definition of close fitting models should embody the notion of substantially ignorable misspecifications (SIM). A SIM model is a misspecified model that might be selected, based on parsimony, over the true model should knowledge of the true model be available. Because in applications the true model (i.e., the data generating mechanism) is unknown, we investigate the relationship between the population standardized root mean squa…
Fitting Large Factor Analysis Models With Ordinal Data
A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and misspecification affect parameter estimates, standard errors of parameter estimates, and selected fit indices…
Evaluating Factorial Invariance
In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers t…
School and Family Factors Predicting Adolescent Cognition Regarding Bystander Intervention in Response to Bullying and Victim Retaliation
Using Integrative Data Analysis to Investigate School Climate Across Multiple Informants
Survey research frequently involves the collection of data from multiple informants. Results, however, are usually analyzed by informant group, potentially ignoring important relationships across groups. When the same construct(s) are measured, integrative data analysis (IDA) allows pooling of data from multiple sources into one data set to examine information from multiple perspectives within the same analysis. Here, the IDA procedure is demonst…
Measurement invariance of the English and Spanish Basc-3 behavioral and emotional screening system parent preschool forms
Collapsing Categories is Often More Advantageous than Modeling Sparse Data
When questionnaires include Likert scales, items endorsed by relatively few respondents may result from characteristics of examinees or the constructs under study. Researchers may collapse categories to increase cell sample size; however, effects of this practice have not been systematically investigated. A five-point ordinal scale was simulated where data included few responses in extreme categories. Different estimators were applied to sparsely…
Examining motivation profiles within and across socioeconomic levels on educational outcomes
Fitting latent growth models with small sample sizes and non-normal missing data
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N 2 test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (…
A Short Note on Optimizing Cost-Generalizability via a Machine-Learning Approach
The costs of an objective structured clinical examination (OSCE) are of concern to health profession educators globally. As OSCEs are usually designed under generalizability theory (G-theory) framework, this article proposes a machine-learning-based approach to optimize the costs, while maintaining the minimum required generalizability coefficient, a reliability-like index in G-theory. The authors adopted G-theory parameters yielded from an OSCE …
Evaluating Restrictive Models in Educational and Behavioral Research
The frequent practice of overall fit evaluation for latent variable models in educational and behavioral research is reconsidered. It is argued that since overall plausibility does not imply local plausibility and is only necessary for the latter, local misfit should be considered a sufficient condition for model rejection, even in the case of omnibus model tenability. The argument is exemplified with a comparison of the widely used one-parameter…
The Complex Nature of Youth Aggression
Youth victimization and aggression are common in adolescents' everday lives. This study examines relations between youth cognition and reasoning around bullying and possible responses to bullying, peer nominations related to youth roles in the bullying ecology and self-report experiences of perceived racial discrimination using latent class analyses. Participants included 6th ( n = 423) and 9th ( n = 392) grade adolescents in the United States (4…
Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models
Regression mixture models (RMMs) can be used to specifically test for and model differential effects in heterogeneous populations. Based on the results of the Aim 1 simulation study, enumeration conducted with constrained predictor means appears to be advantageous. Furthermore, researchers should estimate the K and K+1 unconditional models (chosen during initial enumeration), adding the C on X paths, to investigate the potential for model instabi…
A randomized controlled trial on the interconnected systems framework for school mental health and PBIS
Design Effect in Multilevel Settings
A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval esti…
Mathematics (28 works) · Statistics (28 works) · Computer Science (18 works) · Econometrics (18 works) · Psychology (16 works) · Psychometric Methodologies and Testing (15 works) · Structural equation modeling (15 works) · Advanced Statistical Modeling Techniques (12 works) · Sample size determination (9 works) · Artificial Intelligence (8 works)