Teacher self-efficacy and enthusiasm: Relations to changes in student-perceived teaching quality at the beginning of secondary education

Teaching quality is a key factor in student academic success, but few studies have investigated how teaching quality changes at the beginning of secondary education and how such changes are predicted by dimensions of teacher motivation. This study investigated the changes in class-level student perceptions of teaching quality over one school year at the beginning of secondary school and examined how teachers' self-efficacy and enthusiasm predicted such changes. Data from 1996 students (53.8% male; mean age: 11.09 years, SD = 0.55) and their homeroom teachers (N = 105), who were surveyed at the beginning of Grades 5 and 6, were analyzed. Results showed a significant decline in class-level student-perceived emotional support, classroom management, and instructional clarity. Teacher-reported self-efficacy was not significantly related to changes in teaching quality. Teacher-reported enthusiasm buffered the decline in students’ class-level classroom management.     

Latent Class Trees with the Three-Step Approach

Latent class (LC) analysis is widely used in the social and behavioral sciences to find meaningful clusters based on a set of categorical variables. To deal with the common problem that a standard LC analysis may yield a large number classes and thus a solution that is difficult to interpret, recently an alternative approach has been proposed, called Latent Class Tree (LCT) analysis. It involves starting with a solution with a small number of “basic” classes, which may subsequently be split into subclasses at the next stages of an analysis. However, in most LC analysis applications, we not only wish to identify the relevant classes, but also want to see how they relate to external variables (covariates or distal outcomes). For this purpose, researchers nowadays prefer using the bias-adjusted three-step method. Here, we show how this bias-adjusted three-step procedure can be applied in the context of LCT modeling. More specifically, an R-package is presented that performs a three-step LCT analysis: it builds a LCT and allows checking how splits are related to the relevant external variables. The new tool is illustrated using a cross-sectional application with multiple indicators on social capital and demographics as external variables and with a longitudinal application with a mood variable measured multiple times during the day and personality traits as external variables.     

Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior and Drug-Using Behaviors

This article proposes a new type of latent class analysis, joint latent class analysis (JLCA), which provides a set of principles for the systematic identification of the subsets of joint patterns for multiple discrete latent variables. Inferences about the parameters are obtained by a hybrid method of expectation-maximization and Newton–Raphson algorithms. We apply JLCA in an investigation of adolescent violent behavior and drug-using behaviors. The data are from 4,957 male high-school students who participated in the Youth Risk Behavior Surveillance System in 2015. The JLCA approach identifies the different joint patterns of 4 latent variables: violent behavior, alcohol consumption, tobacco cigarette smoking, and other drug use. The JLCA uncovers 4 common violent behaviors and 3 representative behavioral patterns for each of 3 other latent variables. In addition, the JLCA supports 3 common joint classes, representing the most probable simultaneous patterns for being violent and being a drug user among adolescent males.     

Longitudinal Measurement Non-Invariance with Ordered-Categorical Indicators: How are the Parameters in Second-Order Latent Linear Growth Models Affected?

Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations.     

Multilevel Rasch Modeling: Does Misfit to the Rasch Model Impact the Regression Model?

Abstract

Multilevel Rasch models are increasingly used to estimate the relationships between test scores and student and school factors. Response data were generated to follow one-, two-, and three-parameter logistic (1PL, 2PL, 3PL) models, but the Rasch model was used to estimate the latent regression parameters. When the response functions followed 2PL or 3PL models, the proportion of variance explained in test scores by the simulated student or school predictors was estimated accurately with a Rasch model. Proportion of variance within and between schools was also estimated accurately. The regression coefficients were misestimated unless they were rescaled out of logit units. However, item-level parameters, such as DIF effects, were biased when the Rasch model was violated, similar to single-level models.     

Evaluation of Multicomponent Measuring Instrument Reliability in Complex Design Studies

A latent variable modeling approach to evaluation of scale reliability in complex design studies is outlined. The procedure is readily applicable in empirical research for the purpose of point and interval estimation of reliability of multicomponent measuring instruments in the presence of probability sampling and possible nesting within higher order units. The method can be used to aid scale construction and development efforts in large-scale studies of substantially heterogeneous populations. The described approach is illustrated with data from an international educational survey.     

农村居民公共文化云使用行为的研究

随着“数字乡村”的推进和公共数字文化的建设,公共文化云日益成为农村居民获取文化资讯的重要渠道。本文基于UTAUT模型,引入年龄、性别、教育背景三个控制变量,探索农村居民公共文化云使用行为的影响因素,分析发现：绩效期望、努力期望、社群影响显著正向影响农村居民公共文化云使用倾向；便利条件显著正向影响农村居民公共文化云使用行为；使用倾向在模型中起中介效应,在绩效期望与使用行为之间的效应占比最大,在努力期望、社群影响与使用行为之间的效应占比次之。在此基础上,提出了促进农村居民公共文化云使用行为的可操作性建议。     

Declining interest in science in lower secondary school classes: Quasi-experimental and longitudinal evidence on the role of teaching and teaching quality

Although promoting student interest is a pivotal educational goal, student interest in science, and particularly in physics, declines substantially during secondary school. This study focused on the long-term development of interest in physics at the lower secondary level (grades 5–7) and examined the role of teaching and teaching quality on the development. In particular, the study investigated the role of whether or not physics was taught in class and the role of perceived teaching quality for classes' interest trajectories. The results provide evidence of declining interest in physics from Grade 5 to 7, with stronger declines from Grade 5 to 6. Whether classes participated in physics teaching or not neither notably reduced nor increased interest in physics. However, several dimensions of perceived teaching quality (in particular, cognitive activation and cognitive support) mitigated the decline in interest.