计量经济分析 = Econometric analysis

The power of feedback.

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural Equation Modeling

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

Testtheorie und Fragebogenkonstruktion

Risk for Permanent Pacemaker After Transcatheter Aortic Valve Implantation.

Background: Permanent pacemaker (PM) requirement is a known complication after transcatheter aortic valve implantation (TAVI). There are, however, no systematic data concerning this complication.

Objective: To determine the incidence and potential predictors of permanent PM requirement after TAVI based on published literature.

Methods: We conducted a MEDLINE search to identify potentially relevant literature dealing with PM requirement after TAVI. Data were collected on paper extraction forms by 2 independent investigators.

Results: There were 32 relevant published studies comprising data of 5,258 patients without an implanted PM before TAVI. An Edwards-Sapiens® prosthesis (ESP) was implanted in 2,887 patients, whereas 2,371 patients received a CoreValve® prosthesis (CVP). The crude incidence of PM implantation after TAVI was 15%. Six hundred and fourteen of 2,371 (25.8%) CVP patients and 189 of the 2,887 (6.5%) ESP patients had to receive a permanent PM (odds ratio [OR] 4.91, 95% confidence interval [CI] 4.12–5.86, P < 0.001). Presence of right bundle branch block (RBBB) before TAVI was a significant predictor for development of complete atrioventricular (AV) block and subsequent PM need (OR 1.358, 95% CI 1.001–1.841, P = 0.02). More than 90% of all AV-block requiring PM implantation occurred immediately or within 7 days after TAVI.

Conclusion: Patients undergoing TAVI with implantation of CVP are at significantly higher risk for development of AV block and subsequent need for permanent PM, particularly if RBBB preexists. Since AV block occurs in >90% within the first week after the procedure, careful monitoring should be performed for at least 7 days after TAVI.

Risk for Permanent Pacemaker After Transcatheter Aortic Valve Implantation: A Comprehensive Analysis of the Literature

Drawing on expectancy-value theory, the present study examined the unique contributions of the four major value beliefs and self-concept on achievement, self-reported effort, and teacher-rated behavioral engagement in mathematics. In particular, we examined the multiplicative effects of self-concept and task values on educational outcomes using the latent moderated structural equation approach. Participants were 1,868 German ninth-grade students. The data analyses relied on a higher-order structure of value beliefs, which is suited to parsing the differential patterns of predictive relations for different value beliefs. The findings revealed that (a) self-concept was more predictive of achievement, whereas value beliefs were more predictive of self-rated effort; (b) self-concept and value beliefs emerged as equally important predictors of teacher-reported engagement; (c) among the four value beliefs, achievement was more associated with low cost, whereas effort was more associated with attainment value; and (d) latent interactions between self-concept and value beliefs predicted the three outcomes synergistically.

https://files.eric.ed.gov/fulltext/EJ1194603.pdf

Probing the Unique Contributions of Self-Concept, Task Values, and Their Interactions Using Multiple Value Facets and Multiple Academic Outcomes.

Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x 1 2, x 1 x 4) to serve as indicators of each nonlinear latent construct. These approaches require the use of complex nonlinear constraints and additional model specifications and do not directly address the nonnormal distribution of the product terms. In contrast, recently developed, easy-to-use distribution analytic approaches do not use product indicators, but rather directly model the nonlinear multivariate distribution of the measured indicators. This article outlines the theoretical properties of the distribution analytic Latent Moderated Structural Equations (LMS; Klein & Moosbrugger, 2000) and Quasi-Maximum Likelihood (QML; Klein & Muthen, 2007) estimators. It compares the properties of LMS and QML to those of the product indicator approaches. A small simulation study compares the two approaches and illustra...

Augustin Kelava

Papers

Testtheorie und Fragebogenkonstruktion

Risk for Permanent Pacemaker After Transcatheter Aortic Valve Implantation: A Comprehensive Analysis of the Literature

Probing the Unique Contributions of Self-Concept, Task Values, and Their Interactions Using Multiple Value Facets and Multiple Academic Outcomes.

Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects

Diagnostic competence of teachers: A process model that accounts for diagnosing learning behavior tested by means of a case scenario