A Short Note on Interpretation in the Dual Change Score Model

The latent change score framework allows for estimating a variety of univariate trajectory models, such as the no change, linear change, exponential forms of change, as well as multivariate trajectory models that allow for coupling between two or more constructs. A particularly attractive feature of these models is that it is easy to decompose and interpret aspects of change. One particularly flexible model, the dual change score model, has two components of change: a proportional change component that depends on scores at the previous time point, and a constant change component that is additive. We demonstrate through simulation and an empirical example that in a correctly specified model, the correlation between the proportional change parameter and the mean of the constant change component can approach either ?1 or 1, thus complicating interpretation. We provide recommendations and code to aid researchers’ ability to diagnose this issue in their own data.     

Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.     

Measurement Models,Estimation, and the Study of Change

The study of change is based on the idea that the score or index at each measurement occasion has the same meaning and metric across time. In tests or scales with multiple items, such as those common in the social sciences, there are multiple ways to create such scores. Some options include using raw or sum scores (i.e., sum of item responses or linear transformation thereof), using Rasch-scaled scores provided by the test developers, fitting item response models to the observed item responses and estimating ability or aptitude, and jointly estimating the item response and growth models. We illustrate that this choice can have an impact on the substantive conclusions drawn from the change analysis using longitudinal data from the Applied Problems subtest of the Woodcock–Johnson Psycho-Educational Battery–Revised collected as part of the National Institute of Child Health and Human Development's Study of Early Child Care. Assumptions of the different measurement models, their benefits and limitations, and recommendations are discussed.     

Online News Coverage of Preexposure Prophylaxis

Human immunodeficiency virus, or HIV continues to be a serious issue, especially for men who have sex with men (MSM). A new means of HIV prevention, called preexposure prophylaxis (PrEP), significantly reduces the chance of HIV infection. However, adoption of the drug has been slow. In this study, a content analysis of 234 online news articles about PrEP from popular U.S. news Web sites was conducted, focusing on how articles were framed in terms of benefits to PrEP, barriers to PrEP, the severity of HIV, and the susceptibility of MSM to HIV. Results showed that coverage contained conflicting information about PrEP and failed to cast HIV as a serious problem.     

Comparison of Inverse Wishart and Separation-Strategy Priors for Bayesian Estimation of Covariance Parameter Matrix in Growth Curve Analysis

Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due to its proper and conjugate properties. However, many researchers have pointed out that the inverse Wishart prior might not work as expected. The purpose of this study is to investigate the influence of the inverse Wishart prior and compare it with a class of separation-strategy priors on the parameter estimates of growth curve models. In this article, we illustrate the use of different types of priors with 2 real data analyses, and then conduct simulation studies to evaluate and compare these priors in estimating both linear and nonlinear growth curve models. For the linear model, the simulation study shows that both the inverse Wishart and the separation-strategy priors work well for the fixed effects parameters. For the Level 1 residual variance estimate, the separation-strategy prior performs better than the inverse Wishart prior. For the covariance matrix, the results are mixed. Overall, the inverse Wishart prior is suggested if the population correlation coefficient and at least 1 of the 2 marginal variances are large. Otherwise, the separation-strategy prior is preferred. For the nonlinear growth curve model, the separation-strategy priors work better than the inverse Wishart prior.     

Cast Aside

This paper examines the role of user-generated content in a traditional news organization during the coverage of the Boston marathon bombings. In-depth interviews were conducted with the journalists who made up the interactive team at the Boston Globe. The interactive team was in charge of the live blog during the week-long coverage of the marathon bombings. The study identifies the perceptions journalists hold of user-generated content during crisis reporting and the impact user-generated content had during the tense coverage. The findings suggest that user-generated content has a good way to go before being considered an integral part of breaking news content.     

睡美人

很久很久以前，住着一位国王和一位王后，他们很想有个孩子，却一直未能如愿。有一天．在王后沐浴的时候，一只青蛙从水中跳了出来，它告诉王后：“一年之后，你会有一个女儿。”     

Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling

Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks.     

Observations of teacher-child interactions in classrooms serving Latinos and dual language learners: Applicability of the Classroom Assessment Scoring System in diverse settings

With the rising number of Latino and dual language learner (DLL) children attending pre-k and the importance of assessing the quality of their experiences in those settings, this study examined the extent to which a commonly used assessment of teacher-child interactions, the Classroom Assessment Scoring System (CLASS), demonstrated similar psychometric properties in classrooms serving ethnically and linguistically diverse children as it does in other classrooms. Specifically, this study investigated: (1) whether CLASS observations of teacher-child interactions are organized in three domains across classrooms with varying ethnic and language compositions (measurement invariance) and (2) the extent to which CLASS-assessed teacher-child interactions (emotional support, classroom organization, and instructional support) predict children's social, math, and literacy outcomes equally well for Latino and DLL children (predictive validity). CLASS observations of teacher-child interactions were conducted in 721 state-funded pre-k classrooms across 11 states. Direct assessments and teacher ratings of social, math, and literacy outcomes were collected for four randomly selected children in each classroom. CLASS observations factored similarly across pre-k classrooms with different Latino and DLL compositions and predicted improvements in school readiness regardless of a child's Latino or DLL status. Results suggest CLASS functions equally well as an assessment of the quality of teacher-child interactions in pre-k settings regardless of the proportion of Latino children and/or the language diversity of the children in that setting.     

Elektronische Vertr?ge und ihre verbindliche Aushandlung

Zusammenfassung.   Dieser Artikel erarbeitet eine formale Bestimmung der Begriffe “elektronischer Vertrag”, seine “Ziele”, “Verpflichtungen” und seine “verbindliche Aushandlungsphase”. Die hier erarbeiteten Begriffe dienen erstens der Spezifikation elektronischer Vertr?ge und zweitens der Verifikation lokaler Implementationen von elektronischen Vertragskooperationen. Besonders behandelt werden die lokale Darstellungen von Vertr?gen und ihre vermittelnde Kommunikation, mehrfache sowie sich überlappende Durchl?ufe durch eine verbindliche Phase und die Rolle von Beweismitteln. Die Begriffe beruhen auf der Theorie der formalen Sprachen bzw. der Automaten. Sie werden an einem einfachen Beispiel einer bilateralen Auftragskooperation demonstriert. Eingegangen am 16. Dezember 1999 / Angenommen am 10. M?rz 2000