Sonya K Sterba
Biographic Data
| ID | 6697377 |
|---|---|
| NAME | Sonya K Sterba |
| GIVEN NAMES | Sonya K |
| FAMILY NAME | Sterba |
| SIGNATURE | STERBA S K |
| AFFILIATIONS | Vanderbilt University |
| VERIFIED | No |
| TOTAL WORKS | 23 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 23 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Parceling to Lower Indicator-to-Sample-Size Ratio? Instead Try Correcting Your SEM Chi-Square for High p and Modest N to Obtain a Nominal Type I Error Rate Without Parcel-Allocation Variability
Parceling is routinely recommended to SEM researchers with many items (high p) and modest N in order to “lower indicator-to-sample-size ratio.” Here we show that this directive is a roundabout response to address chi-square distortion at high p and modest N, which in turn leads to egregiously inflated Type I error for the item-level model’s chi-square test of absolute fit. We explain, demonstrate, and provide software to implement two more-direct…
Transfer between reading comprehension and word-problem solving among children with learning difficulty in both domains
0.91 and 0.54. Children's text-structure knowledge in passages and word problems mediated acquisition and cross-domain transfer effects in multilevel models. Findings suggest that text-structure intervention may help address the complex needs of children with comorbid RC and WPS difficulty
R-squared Measures for Multilevel Models with Three or More Levels
Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and l…
On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models
For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random c…
R-squared Measures for Multilevel Mixture Models with Random Effects
Multilevel regression mixtures involving both discrete latent classes and continuous random effects are an increasingly popular approach for accommodating nested data structures. However, their application has outpaced the development of effect size measures to aid model interpretation. In response, we provide a general framework of R-squared measures for multilevel regression mixtures with random effects as well as either classes only at level-1…
Building word-problem solving and working memory capacity
Closing the word-problem achievement gap in first grade
grade; and the added value of language comprehension instruction within word-problem intervention. Results also provide causal evidence on the role of language comprehension in word-problem solving
Effect size measures for longitudinal growth analyses
Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R 2 ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random interce…
New Recommendations on the Use of R-Squared Differences in Multilevel Model Comparisons
When comparing multilevel models (MLMs) differing in fixed and/or random effects, researchers have had continuing interest in using R-squared differences to communicate effect size and importance of included terms. However, there has been longstanding confusion regarding which R-squared difference measures should be used for which kind of MLM comparisons. Furthermore, several limitations of recent studies on R-squared differences in MLM have led …
Quantifying explained variance in multilevel models
that computes all measures in our framework to help researchers in considering effect size and conveying practical significance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Problems with Rationales for Parceling that Fail to Consider Parcel-Allocation Variability
In structural equation modeling applications, parcels—averages or sums of subsets of item scores—are often used as indicators of latent constructs. Parcel-allocation variability (PAV) is variability in results that arises within sample across alternative item-to-parcel allocations. PAV can manifest in all results of a parcel-level model (e.g., model fit, parameter estimates, standard errors, and inferential decisions). It is a source of uncertain…
Accounting for Parcel-Allocation Variability in Practice
Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in s…
Interpreting and Testing Interactions in Conditional Mixture Models
Mixture modeling applications in psychology often include covariates to explain class membership and aid in construct validation of the latent classification variable. These applications tend to use between-class models involving only main effects of predictors. However, a variety of developmental theories posit interactions among risk and protective variables in predicting membership in trajectory classes or behavioral symptom profiles. This art…
Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions
Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional likelihood, treating covariates as fixed and fully obser…
Structural Equation Modeling Approaches for Analyzing Partially Nested Data
Study designs involving clustering in some study arms, but not all study arms, are common in clinical treatment-outcome and educational settings. For instance, in a treatment arm, persons may be nested in therapy groups, whereas in a control arm there are no groups. Methodological approaches for handling such partially nested designs have recently been developed in a multilevel modeling framework (MLM-PN) and have proved very useful. We introduce…
Investigating multilevel mediation with fully or partially nested data
Mediation analysis has become one of the most widely used tools for investigating the mechanisms through which variables influence each other. When conducting mediation analysis with fully nested data (e.g., individuals working in teams) or partially nested data (e.g., individuals working alone in one study arm but working in teams in another arm) special considerations arise. In this article we (a) review traditional approaches for analyzing med…
Understanding Linkages Among Mixture Models
The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear disconnected. This pedagogical review facilitates an integrative understanding of mixture models. First, 5 prototypic mixture models are presented in …
Factors Affecting the Adequacy and Preferability of Semiparametric Groups-Based Approximations of Continuous Growth Trajectories
Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unk…
Variability in Parameter Estimates and Model Fit Across Repeated Allocations of Items to Parcels
Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a single sample are thought not to meaningfully alter model fit-at least when items are normally distrib…
Revealing the form and function of self-injurious thoughts and behaviors
Self-injurious behaviors are among the leading causes of death worldwide. However, the basic nature of self-injurious thoughts and behaviors (SITBs) is not well understood because prior studies have relied on long-term, retrospective, aggregate, self-report assessment methods. The authors used ecological momentary assessment methods to measure suicidal and nonsuicidal SITBs as they naturally occur in real time. Participants were 30 adolescents an…
Estimating and Visualizing Nonlinear Relations Among Latent Variables
The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides…
Alternative Model-Based and Design-Based Frameworks for Inference From Samples to Populations
A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and allow different kinds of inference (descriptive vs. analytic) to different kinds of populations (finite vs. infinite). We describe the extent of each fr…
Evaluating Group-Based Interventions When Control Participants Are Ungrouped
Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as independent, given that these individuals are not placed into groups. Although this kind of design is common i…
Investigating multilevel mediation with fully or partially nested data
Mediation analysis has become one of the most widely used tools for investigating the mechanisms through which variables influence each other. When conducting mediation analysis with fully nested data (e.g., individuals working in teams) or partially nested data (e.g., individuals working alone in one study arm but working in teams in another arm) special considerations arise. In this article we (a) review traditional approaches for analyzing med…
Evaluating Group-Based Interventions When Control Participants Are Ungrouped
Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as independent, given that these individuals are not placed into groups. Although this kind of design is common i…
Revealing the form and function of self-injurious thoughts and behaviors
Self-injurious behaviors are among the leading causes of death worldwide. However, the basic nature of self-injurious thoughts and behaviors (SITBs) is not well understood because prior studies have relied on long-term, retrospective, aggregate, self-report assessment methods. The authors used ecological momentary assessment methods to measure suicidal and nonsuicidal SITBs as they naturally occur in real time. Participants were 30 adolescents an…
Estimating and Visualizing Nonlinear Relations Among Latent Variables
The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides…
Alternative Model-Based and Design-Based Frameworks for Inference From Samples to Populations
A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and allow different kinds of inference (descriptive vs. analytic) to different kinds of populations (finite vs. infinite). We describe the extent of each fr…
Variability in Parameter Estimates and Model Fit Across Repeated Allocations of Items to Parcels
Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a single sample are thought not to meaningfully alter model fit-at least when items are normally distrib…
Factors Affecting the Adequacy and Preferability of Semiparametric Groups-Based Approximations of Continuous Growth Trajectories
Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unk…
Understanding Linkages Among Mixture Models
The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear disconnected. This pedagogical review facilitates an integrative understanding of mixture models. First, 5 prototypic mixture models are presented in …
Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions
Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional likelihood, treating covariates as fixed and fully obser…
Structural Equation Modeling Approaches for Analyzing Partially Nested Data
Study designs involving clustering in some study arms, but not all study arms, are common in clinical treatment-outcome and educational settings. For instance, in a treatment arm, persons may be nested in therapy groups, whereas in a control arm there are no groups. Methodological approaches for handling such partially nested designs have recently been developed in a multilevel modeling framework (MLM-PN) and have proved very useful. We introduce…
Investigating multilevel mediation with fully or partially nested data
Mediation analysis has become one of the most widely used tools for investigating the mechanisms through which variables influence each other. When conducting mediation analysis with fully nested data (e.g., individuals working in teams) or partially nested data (e.g., individuals working alone in one study arm but working in teams in another arm) special considerations arise. In this article we (a) review traditional approaches for analyzing med…
Accounting for Parcel-Allocation Variability in Practice
Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in s…
Interpreting and Testing Interactions in Conditional Mixture Models
Mixture modeling applications in psychology often include covariates to explain class membership and aid in construct validation of the latent classification variable. These applications tend to use between-class models involving only main effects of predictors. However, a variety of developmental theories posit interactions among risk and protective variables in predicting membership in trajectory classes or behavioral symptom profiles. This art…
Quantifying explained variance in multilevel models
that computes all measures in our framework to help researchers in considering effect size and conveying practical significance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Problems with Rationales for Parceling that Fail to Consider Parcel-Allocation Variability
In structural equation modeling applications, parcels—averages or sums of subsets of item scores—are often used as indicators of latent constructs. Parcel-allocation variability (PAV) is variability in results that arises within sample across alternative item-to-parcel allocations. PAV can manifest in all results of a parcel-level model (e.g., model fit, parameter estimates, standard errors, and inferential decisions). It is a source of uncertain…
New Recommendations on the Use of R-Squared Differences in Multilevel Model Comparisons
When comparing multilevel models (MLMs) differing in fixed and/or random effects, researchers have had continuing interest in using R-squared differences to communicate effect size and importance of included terms. However, there has been longstanding confusion regarding which R-squared difference measures should be used for which kind of MLM comparisons. Furthermore, several limitations of recent studies on R-squared differences in MLM have led …
Closing the word-problem achievement gap in first grade
grade; and the added value of language comprehension instruction within word-problem intervention. Results also provide causal evidence on the role of language comprehension in word-problem solving
Effect size measures for longitudinal growth analyses
Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R 2 ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random interce…
R-squared Measures for Multilevel Mixture Models with Random Effects
Multilevel regression mixtures involving both discrete latent classes and continuous random effects are an increasingly popular approach for accommodating nested data structures. However, their application has outpaced the development of effect size measures to aid model interpretation. In response, we provide a general framework of R-squared measures for multilevel regression mixtures with random effects as well as either classes only at level-1…
Building word-problem solving and working memory capacity
R-squared Measures for Multilevel Models with Three or More Levels
Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and l…
On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models
For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random c…
Transfer between reading comprehension and word-problem solving among children with learning difficulty in both domains
0.91 and 0.54. Children's text-structure knowledge in passages and word problems mediated acquisition and cross-domain transfer effects in multilevel models. Findings suggest that text-structure intervention may help address the complex needs of children with comorbid RC and WPS difficulty
Parceling to Lower Indicator-to-Sample-Size Ratio? Instead Try Correcting Your SEM Chi-Square for High p and Modest N to Obtain a Nominal Type I Error Rate Without Parcel-Allocation Variability
Parceling is routinely recommended to SEM researchers with many items (high p) and modest N in order to “lower indicator-to-sample-size ratio.” Here we show that this directive is a roundabout response to address chi-square distortion at high p and modest N, which in turn leads to egregiously inflated Type I error for the item-level model’s chi-square test of absolute fit. We explain, demonstrate, and provide software to implement two more-direct…
Computer Science (17 works) · Mathematics (17 works) · Econometrics (12 works) · Statistics (12 works) · Psychology (10 works) · Statistical Methods and Bayesian Inference (10 works) · Artificial Intelligence (7 works) · Data mining (7 works) · Mental Health Research Topics (7 works) · Artificial Intelligence (6 works)