Components of Variance of Scales With a Bifactor Subscale Structure From Two Calculations of α

Since Cronbach's (1951) elaboration of α from its introduction by Guttman (1945), this coefficient has become ubiquitous in characterizing assessment instruments in education, psychology, and other social sciences. Also ubiquitous are caveats on the calculation and interpretation of this coefficient. This article summarizes a recent contribution (Andrich, 2015) on the use of coefficient α which complements these many caveats. It shows that in the presence of a simple bifactor structure of a scale where unique components of variance are homogeneous in magnitude, three components of variance and the common latent common correlation among the subscales can be calculated from the ratio of two calculations of α, one at the level of the items, the other at the level of the subscales. It was suggested that these two ready calculations and their interpretation, and the reporting of all four indices in the analysis of scales with a subscale structure, would reduce the misinterpretation of this coefficient. An illustrative example of the application of the calculations is also shown.     

论教师专业发展的范式转移

教师专业发展无论在理论上还是实践上都发生了深刻的变化,这种变化可视为教师专业发展的范式转移。教师专业发展的范式转移在教师专业发展的目标、组织、课程和教学等维度均有所体现,并且这种转移有其深层的哲学、社会学和心理学基础。新的教师专业发展范式的出现,并不是对传统的教师专业发展范式的彻底否定,而是"既有又有"、互为补充。     

我国民族地区中学生的学习观念调查——以海南民族地区为例

以海南民族地区为例,通过问卷调查的方式,了解到中学生对学习、成绩、考试和环境的态度和看法,认为素质教育和多元化评价方式对调动学生的学习主动性有积极的推动作用。     

数字城管科学发展的维度设计及理想建构——以杭州为例

2006年3月28日杭州市数字城管的上线运行,翻开了杭州市城市管理的新篇章.四年来,杭州市数字城管以“实际、实用、实效”为发展维度,以“人”为核心要素,走出了一条符合杭州实际、独具杭州特色的数字城管运行之路.     

Using Multidimensional Item Response Theory to Evaluate Educational and Psychological Tests

Many educational and psychological tests are inherently multidimensional, meaning these tests measure two or more dimensions or constructs. The purpose of this module is to illustrate how test practitioners and researchers can apply multidimensional item response theory (MIRT) to understand better what their tests are measuring, how accurately the different composites of ability are being assessed, and how this information can be cycled back into the test development process. Procedures for conducting MIRT analyses–from obtaining evidence that the test is multidimensional, to modeling the test as multidimensional, to illustrating the properties of multidimensional items graphically-are described from both a theoretical and a substantive basis. This module also illustrates these procedures using data from a ninth-grade mathematics achievement test. It concludes with a discussion of future directions in MIRT research.     

PISA式汉语阅读测验的编制与维度评价

PISA测验着眼于学生的终生发展,其测验编制思想给各国教育评价带来了深刻的变革。本研究在PISA阅读测验理论与框架基础上,编制了PISA式汉语阅读测验。该测验包含三篇阅读材料,共18个测验项目。通过对测验难度、区分度、信度、效度的检测,并使用全息Bifactor模型进行维度评价。结果表明,编制的PISA式汉语阅读测验难度适中,具有较好区分度,信效度基本合格。同时,基本达到PISA对阅读测验能力结构的要求,较好地考查了学生的一般阅读理解能力,以及信息提取、文本解释、反思和评价等三个子维度的能力。     

反全球化运动的维度及其价值澄清

厘清反全球化运动的维度不仅是科学对待反全球化运动的前提,而且是不断校正和调节全球化发展方向和进程的调适器。从横向维度看：反全球化以跨国公司为代表的生产全球化为对象;以国际经济组织及其“资本整体利益”为对象;以全球媒体、信息网络技术载体为对象。从纵向维度看：反全球化可分为国别行动和全球化行动两种类型。在此基础上,以全球化发展的“生态价值”取向来澄清全球化与反全球化的关系,充分彰显二者的价值及发展趋势。     

司法权威的和谐维度

从司法权威到社会和谐的逻辑演绎．旨在通过司法的工具性价值来实现司法的目的性价值——社会和谐。司法作为一项社会活动,它的权威是受其它社会活动的影响和评判的,因而它的和谐效应也在全方位展开。司法权威的和谐维度是多方位的,它的政治维度是司法的人民性,法律维度是司法的正义性,道德维度是司法的真实性,经济维度是司法的效益性。     

Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions.     

Evaluating Model Fit and Structural Coefficient Bias: A Bayesian Approach to Multilevel Bifactor Model Misspecification

Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed.