Patrick J Curran
Biographic Data
| ID | 254530 |
|---|---|
| NAME | Patrick J Curran |
| GIVEN NAMES | Patrick J |
| FAMILY NAME | Curran |
| SIGNATURE | CURRAN P J |
| AFFILIATIONS | University of North Carolina at Chapel Hill |
| ORCID | 0000-0002-5772-5120 |
| VERIFIED | Yes |
| TOTAL WORKS | 35 |
| TOTAL CITATIONS | 290 |
| AUTHOR COUNT | 35 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1993 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 8 |
Children’s strategic memory development: The delayed role of kindergarten teachers’ instructional language
During elementary school, children demonstrate significant growth in an array of cognitive skills, including their ability to use deliberate strategies for remembering. Despite a rich literature documenting age-related changes in these skills (Schneider & Ornstein, 2019), much remains to be learned about contextual factors that support the development of strategic memory. Data from a longitudinal investigation were used to examine the role of kin…
Excess mortality during the Covid-19 pandemic: A geospatial and statistical analysis in Aden governorate, Yemen
BACKGROUND: The burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution sat…
Psychometric models for scoring multiple reporter assessments: Applications to integrative data analysis in prevention science and beyond
Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter assessments, particularly when using integrative data analysis to fit models to data that have been…
Bidirectional Relations between Witnessing Violence, Victimization, Life Events, and Physical Aggression among Adolescents in Urban Schools
The Increasing Diversity and Complexity of Family Structures for Adolescents
The structure of adolescents’ families, and thus parental forms, in the United States, have become more heterogeneous and fluid over the past several decades. These changes are due to increases in never‐married, single parents, divorce, cohabitation, same‐sex parenting, multipartnered fertility, and co‐residence with grandparents. We document current diversity and complexity in adolescents’ families as important context for rethinking future pare…
The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.
OBJECTIVE: Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful t…
A Moderated Nonlinear Factor Model for the Development of Commensurate Measures in Integrative Data Analysis
Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across 2 or more independent sources. IDA offers many potential advantages including increased statistical power, greater subject heterogeneity, higher observed frequencies of low base-rate behaviors, and longer developmental periods of study. However, a core challenge is the estimation of valid and reliable psychometr…
The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change
Longitudinal models are becoming increasingly prevalent in the behavioral sciences, with key advantages including increased power, more comprehensive measurement, and establishment of temporal precedence. One particularly salient strength offered by longitudinal data is the ability to disaggregate between-person and within-person effects in the regression of an outcome on a time-varying covariate. However, the ability to disaggregate these effect…
Twelve Frequently Asked Questions About Growth Curve Modeling
Longitudinal data analysis has long played a significant role in empirical research within the developmental sciences. The past decade has given rise to a host of new and exciting analytic methods for studying between-person differences in within-person change. These methods are broadly organized under the term growth curve models. The historical lines of development leading to current growth models span multiple disciplines within both the socia…
Integrative data analysis: The simultaneous analysis of multiple data sets.
There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the stat…
Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis
There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the poolin…
Linking teachers' memory-relevant language and the development of children's memory skills
This longitudinal study was designed to (a) examine changes in children's deliberate memory across the 1st grade; (b) characterize the memory-relevant aspects of their classrooms; and (c) explore linkages between the children's performance and the language their teachers use in instruction. To explore contextual factors that may facilitate the development of skills for remembering, 107 first graders were assessed 3 times with a broad set of tasks…
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models
This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it joint…
Latent Variable Models Under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators
This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct spe…
Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods u…
Latent Curve Models: A Structural Equation Perspective
"The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters gove…
Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques
Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another.Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed-and random-effects regression.Often, these interactive effects must be further probed to fully explicate the nature of the conditional relation.The most common method for probing interactions is to test simple slop…
The Role of Coding Time in Estimating and Interpreting Growth Curve Models.
The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of receding time in growth curve models. Linear and quadratic growth model examples are provided, and the …
The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities.
Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. W…
An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.
Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA…
Crimes of Opportunity or Crimes of Emotion? Testing Two Explanations of Seasonal Change in Crime
While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year period with a focus on testing the theoretical predictions of two key explanations for seasonal changes …
Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions
Although there are a variety of statistical methods available for the analysis of longitudinal panel data, two approaches are of particular historical importance: the autoregressive (simplex) model and the latent trajectory (curve) model. These two approaches have been portrayed as competing methodologies such that one approach is superior to the other. We argue that the autoregressive and trajectory models are special cases of a more encompassin…
Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that mul…
Have Multilevel Models Been Structural Equation Models All Along
A core assumption of the standard multiple regression model is independence of residuals, the violation of which results in biased standard errors and test statistics. The structural equation model (SEM) generalizes the regression model in several key ways, but the SEM also assumes independence of residuals. The multilevel model (MLM) was developed to extend the regression model to dependent data structures. Attempts have been made to extend the …
Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA
A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fi…
Latent Curve Models: A Structural Equation Perspective
"The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters gove…
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models
This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it joint…
Improper Solutions in Structural Equation Models: Causes, Consequences, and Strategies
In this article, the authors examine the most common type of improper solutions: zero or negative error variances. They address the causes of, consequences of, and strategies to handle these issues. Several hypotheses are evaluated using Monte Carlo simulation models, including two structural equation models with several misspecifications of each model. Results suggested several unique findings. First, increasing numbers of omitted paths in the m…
Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions
Although there are a variety of statistical methods available for the analysis of longitudinal panel data, two approaches are of particular historical importance: the autoregressive (simplex) model and the latent trajectory (curve) model. These two approaches have been portrayed as competing methodologies such that one approach is superior to the other. We argue that the autoregressive and trajectory models are special cases of a more encompassin…
Crimes of Opportunity or Crimes of Emotion? Testing Two Explanations of Seasonal Change in Crime
While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year period with a focus on testing the theoretical predictions of two key explanations for seasonal changes …
Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis
There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the poolin…
The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research
The effectiveness of a prevention or intervention program has traditionally been assessed using time‐specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time‐specific mean levels can be both limiting and potentially mis…
Alcohol Expectancies as Potential Mediators of Parent Alcoholism Effects on the Development of Adolescent Heavy Drinking
This study used latent growth curve modeling to examine adolescent alcohol expectancies as potential mediators of the effects of parent alcoholism on escalation in adolescent heavy drinking. Data were drawn from a 3-year longitudinal study of a community sample of children of alcoholics (COAs) and demographically matched controls. Parent alcoholism had a direct effect on adolescent heavy drinking. Compared to non-COAs, COAs started out at higher …
Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA
A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fi…
Linking teachers' memory-relevant language and the development of children's memory skills
This longitudinal study was designed to (a) examine changes in children's deliberate memory across the 1st grade; (b) characterize the memory-relevant aspects of their classrooms; and (c) explore linkages between the children's performance and the language their teachers use in instruction. To explore contextual factors that may facilitate the development of skills for remembering, 107 first graders were assessed 3 times with a broad set of tasks…
Latent Variable Models Under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators
This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct spe…
Bidirectional Relations between Witnessing Violence, Victimization, Life Events, and Physical Aggression among Adolescents in Urban Schools
Relation of parental alcoholism to early adolescent substance use: A test of three mediating mechanisms.
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
Monte Carlo computer simulations were used to investigate the performance of three X 2 test statistics in confirmatory factor analysis (CFA). Normal theory maximum likelihood)~2 (ML), Browne's asymptotic distribution free X 2 (ADF), and the Satorra-Bentler rescaled X 2 (SB) were examined under varying conditions of sample size, model specification, and multivariate distribution. For properly specified models, ML and SB showed no evidence of bias …
General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation.
The generality of latent variable modeling of individual differences in development over time is demonstrated with a particular emphasis on randomized intervention studies. First, a brief overview is given of biostatistica l and psychometric approaches to repeated measures analysis. Second, the generality of the psychometric approach is indicated by some nonstandard models. Third, a multiple-population analysis approach is proposed for the estima…
The relation between adolescent alcohol use and peer alcohol use: A longitudinal random coefficients model
Alcohol Expectancies as Potential Mediators of Parent Alcoholism Effects on the Development of Adolescent Heavy Drinking
This study used latent growth curve modeling to examine adolescent alcohol expectancies as potential mediators of the effects of parent alcoholism on escalation in adolescent heavy drinking. Data were drawn from a 3-year longitudinal study of a community sample of children of alcoholics (COAs) and demographically matched controls. Parent alcoholism had a direct effect on adolescent heavy drinking. Compared to non-COAs, COAs started out at higher …
The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research
The effectiveness of a prevention or intervention program has traditionally been assessed using time‐specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time‐specific mean levels can be both limiting and potentially mis…
The best of both worlds: Combining autoregressive and latent curve models.
Monte Carlo Experiments: Design and Implementation
The use of Monte Carlo simulations for the empirical assessment of statistical estimators is becoming more common in structural equation modeling research. Yet, there is little guidance for the researcher interested in using the technique. In this article we illustrate both the design and implementation of Monte Carlo simulations. We present 9 steps in planning and performing a Monte Carlo analysis: (1) developing a theoretically derived research…
Improper Solutions in Structural Equation Models: Causes, Consequences, and Strategies
In this article, the authors examine the most common type of improper solutions: zero or negative error variances. They address the causes of, consequences of, and strategies to handle these issues. Several hypotheses are evaluated using Monte Carlo simulation models, including two structural equation models with several misspecifications of each model. Results suggested several unique findings. First, increasing numbers of omitted paths in the m…
The Noncentral Chi-square Distribution in Misspecified Structural Equation Models: Finite Sample Results from a Monte Carlo Simulation
The noncentral chi-square distribution plays a key role in structural equation modeling (SEM). The likelihood ratio test statistic that accompanies virtually all SEMs asymptotically follows a noncentral chi-square under certain assumptions relating to misspecification and multivariate distribution. Many scholars use the noncentral chi-square distribution in the construction of fit indices, such as Steiger and Lind's (1980) Root Mean Square Error …
Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that mul…
Have Multilevel Models Been Structural Equation Models All Along
A core assumption of the standard multiple regression model is independence of residuals, the violation of which results in biased standard errors and test statistics. The structural equation model (SEM) generalizes the regression model in several key ways, but the SEM also assumes independence of residuals. The multilevel model (MLM) was developed to extend the regression model to dependent data structures. Attempts have been made to extend the …
Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA
A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fi…
The Role of Coding Time in Estimating and Interpreting Growth Curve Models.
The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of receding time in growth curve models. Linear and quadratic growth model examples are provided, and the …
The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities.
Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. W…
An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.
Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA…
Crimes of Opportunity or Crimes of Emotion? Testing Two Explanations of Seasonal Change in Crime
While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year period with a focus on testing the theoretical predictions of two key explanations for seasonal changes …
Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions
Although there are a variety of statistical methods available for the analysis of longitudinal panel data, two approaches are of particular historical importance: the autoregressive (simplex) model and the latent trajectory (curve) model. These two approaches have been portrayed as competing methodologies such that one approach is superior to the other. We argue that the autoregressive and trajectory models are special cases of a more encompassin…
Latent Curve Models: A Structural Equation Perspective
"The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters gove…
Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques
Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another.Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed-and random-effects regression.Often, these interactive effects must be further probed to fully explicate the nature of the conditional relation.The most common method for probing interactions is to test simple slop…
Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods u…
Latent Variable Models Under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators
This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct spe…
Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis
There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the poolin…
Linking teachers' memory-relevant language and the development of children's memory skills
This longitudinal study was designed to (a) examine changes in children's deliberate memory across the 1st grade; (b) characterize the memory-relevant aspects of their classrooms; and (c) explore linkages between the children's performance and the language their teachers use in instruction. To explore contextual factors that may facilitate the development of skills for remembering, 107 first graders were assessed 3 times with a broad set of tasks…
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models
This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it joint…
Mathematics (23 works) · Econometrics (21 works) · Statistics (20 works) · Computer Science (19 works) · Psychology (19 works) · Advanced Statistical Modeling Techniques (12 works) · Psychometric Methodologies and Testing (12 works) · Developmental psychology (10 works) · Statistical Methods and Bayesian Inference (8 works) · Structural equation modeling (8 works)