Structural Equation Modeling: A Multidisciplinary Journal, 2013
Little research has examined factors influencing statistical power to detect the correct number o... more Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
The existing missing data literature does not provide a clear prescription for estimating interac... more The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques.
... perfectionism (a maladaptive dimension of HMPS, Hewitt & Flett, 1991) among caree... more ... perfectionism (a maladaptive dimension of HMPS, Hewitt & Flett, 1991) among career mothers. ... time that burnout and engagement are measured together in a Chinese student sample, their ...Academic burnout and engagement are two latent structures rather than a single one of ...
A Monte Carlo simulation was conducted to investigate the robustness of four latent variable inte... more A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance.
Mexican American youth are at greater risk of school failure than their peers. To identify factor... more Mexican American youth are at greater risk of school failure than their peers. To identify factors that may contribute to academic success in this population, this study examined the prospective relationships from 5th grade to 7th grade of family (i.e., human capital [a parent with at least a high school education], residential stability, academically and occupationally positive family role models, and family structure) and individual characteristics (i.e., externalizing symptoms, bilingualism, gender, and immigrant status) to the academic performance of 749 Mexican American early adolescents (average age = 10.4 years and 48.7% were girls in 5th grade) from economically and culturally diverse families as these youth made the transition to junior high school. Results indicated that while controlling for prior academic performance, human capital and positive family role models assessed when adolescents were in 5th grade positively related to academic performance in 7th grade. Further, being a girl also was related to greater 7th grade academic success, whereas externalizing symptoms were negatively related to 7th grade academic performance. No other variables in the model were significantly and prospectively related to 7th grade academic performance. Implications for future research and interventions are discussed.
Participants were 204 academically at-risk Latino students recruited into a study when in first g... more Participants were 204 academically at-risk Latino students recruited into a study when in first grade and followed for 9 years. Using piecewise latent growth curve analyses, we investigated trajectories of teacher-rated behavioral engagement and student-reported school belonging during elementary school and middle school and the association between trajectories and enrollment in bilingual education classes in elementary school and a change in school ethnic congruence across the transition to middle school. Overall, students experienced a drop in school belonging and behavioral engagement across the transition. A moderating effect of ethnic congruence on bilingual enrollment was found. A decline in ethnic congruence was associated with more positive trajectories for students previously enrolled in bilingual classes but more negative trajectories for non-bilingual students.
Traditional statistical analyses can be compromised when data are collected from groups or multip... more Traditional statistical analyses can be compromised when data are collected from groups or multiple observations are collected from individuals. We present an introduction to multilevel models designed to address dependency in data. We review current use of multilevel modeling in 3 personality journals showing use concentrated in the 2 areas of experience sampling and longitudinal growth. Using an empirical example, we illustrate specification and interpretation of the results of series of models as predictor variables are introduced at Levels 1 and 2. Attention is given to possible trends and cycles in longitudinal data and to different forms of centering. We consider issues that may arise in estimation, model comparison, model evaluation, and data evaluation (outliers), highlighting similarities to and differences from standard regression approaches. Finally, we consider newer developments, including 3-level models, cross-classified models, nonstandard (limited) dependent variables, multilevel structural equation modeling, and nonlinear growth. Multilevel approaches both address traditional problems of dependency in data and provide personality researchers with the opportunity to ask new questions of their data.
Journal of Consulting and Clinical Psychology, 2014
A propensity score is the probability that a participant is assigned to the treatment group based... more A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome.
Structural Equation Modeling: A Multidisciplinary Journal, 2013
Little research has examined factors influencing statistical power to detect the correct number o... more Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
The existing missing data literature does not provide a clear prescription for estimating interac... more The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques.
... perfectionism (a maladaptive dimension of HMPS, Hewitt & Flett, 1991) among caree... more ... perfectionism (a maladaptive dimension of HMPS, Hewitt & Flett, 1991) among career mothers. ... time that burnout and engagement are measured together in a Chinese student sample, their ...Academic burnout and engagement are two latent structures rather than a single one of ...
A Monte Carlo simulation was conducted to investigate the robustness of four latent variable inte... more A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance.
Mexican American youth are at greater risk of school failure than their peers. To identify factor... more Mexican American youth are at greater risk of school failure than their peers. To identify factors that may contribute to academic success in this population, this study examined the prospective relationships from 5th grade to 7th grade of family (i.e., human capital [a parent with at least a high school education], residential stability, academically and occupationally positive family role models, and family structure) and individual characteristics (i.e., externalizing symptoms, bilingualism, gender, and immigrant status) to the academic performance of 749 Mexican American early adolescents (average age = 10.4 years and 48.7% were girls in 5th grade) from economically and culturally diverse families as these youth made the transition to junior high school. Results indicated that while controlling for prior academic performance, human capital and positive family role models assessed when adolescents were in 5th grade positively related to academic performance in 7th grade. Further, being a girl also was related to greater 7th grade academic success, whereas externalizing symptoms were negatively related to 7th grade academic performance. No other variables in the model were significantly and prospectively related to 7th grade academic performance. Implications for future research and interventions are discussed.
Participants were 204 academically at-risk Latino students recruited into a study when in first g... more Participants were 204 academically at-risk Latino students recruited into a study when in first grade and followed for 9 years. Using piecewise latent growth curve analyses, we investigated trajectories of teacher-rated behavioral engagement and student-reported school belonging during elementary school and middle school and the association between trajectories and enrollment in bilingual education classes in elementary school and a change in school ethnic congruence across the transition to middle school. Overall, students experienced a drop in school belonging and behavioral engagement across the transition. A moderating effect of ethnic congruence on bilingual enrollment was found. A decline in ethnic congruence was associated with more positive trajectories for students previously enrolled in bilingual classes but more negative trajectories for non-bilingual students.
Traditional statistical analyses can be compromised when data are collected from groups or multip... more Traditional statistical analyses can be compromised when data are collected from groups or multiple observations are collected from individuals. We present an introduction to multilevel models designed to address dependency in data. We review current use of multilevel modeling in 3 personality journals showing use concentrated in the 2 areas of experience sampling and longitudinal growth. Using an empirical example, we illustrate specification and interpretation of the results of series of models as predictor variables are introduced at Levels 1 and 2. Attention is given to possible trends and cycles in longitudinal data and to different forms of centering. We consider issues that may arise in estimation, model comparison, model evaluation, and data evaluation (outliers), highlighting similarities to and differences from standard regression approaches. Finally, we consider newer developments, including 3-level models, cross-classified models, nonstandard (limited) dependent variables, multilevel structural equation modeling, and nonlinear growth. Multilevel approaches both address traditional problems of dependency in data and provide personality researchers with the opportunity to ask new questions of their data.
Journal of Consulting and Clinical Psychology, 2014
A propensity score is the probability that a participant is assigned to the treatment group based... more A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome.
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