Measuring Teacher Attitudes Toward Instructional Technology: A Confirmatory Factor Analysis of the TAC and TAT

In this article the authors present a confirmatory factor analysis of the Teachers’ Attitudes Toward Computers (TAC) and the Teachers’ Attitudes Toward Information Technology (TAT) scales by Christensen and Knezek (1996, 1998) using large samples from three states. The TAC was reduced from 98 items and nine factors to 35 items and eight factors, while maintaining superior psychometric properties. The TAT was reduced from 50 items and five factors to 20 items measuring five factors, similarly maintaining excellent psychometric properties. The authors also developed a combined short form of both instruments using 42 items to measure 11 factors for use in situations where parsimony is critical.     

Extension Procedures for Confirmatory Factor Analysis

We present factor extension procedures for confirmatory factor analysis that provide estimates of the relations of common and unique factors with external variables that do not undergo factor analysis. We present identification strategies that build upon restrictions of the pattern of correlations between unique factors and external variables. The first restriction minimizes the sum of squared correlations between unique factors and external variables. This approach is similar to the traditional factor extension procedure. The second restriction minimizes the complexity of the pattern of external correlations of unique factors. This approach has similarities with the simple structure ideal imposed on most factor rotation strategies. The procedures are illustrated with a real data example that demonstrates their applicability to real-world research questions.     

Examining the Performance Standards for In-Service Teachers: A Confirmatory Factor Analysis of the Assessment of Teachers' NETS-T Expertise

The survey Performance Standards for In-service Teachers is widely used to help describe teacher skills relating to instructional technology for assessment or planning of professional development. It is based on the six constructs of the National Educational Technology Standards for Teachers, and though used broadly in a variety of contexts, it does not appear to have undergone significant independent evaluation of its psychometric properties. Our analysis found that all six subscales possessed strong internal consistency (all α > 0.93). A subsequent confirmatory factor analysis supported both its current six-factor structure as well as a more parsimonious one-factor model.     

Using Multigroup Confirmatory Factor Analysis to Test Measurement Invariance in Raters: A Clinical Skills Examination Application

Psychometric models based on structural equation modeling framework are commonly used in many multiple-choice test settings to assess measurement invariance of test items across examinee subpopulations. The premise of the current article is that they may also be useful in the context of performance assessment tests to test measurement invariance of raters. The modeling approach and how it can be used for performance tests with less than optimal rater designs are illustrated using a data set from a performance test designed to measure medical students’ patient management skills. The results suggest that group-specific rater statistics can help spot differences in rater performance that might be due to rater bias, identify specific weaknesses and strengths of individual raters, and enhance decisions related to future task development, rater training, and test scoring processes.     

Multigroup Confirmatory Factor Analysis: Locating the Invariant Referent Sets

Multigroup confirmatory factor analysis (MCFA) is a popular method for the examination of measurement invariance and specifically, factor invariance. Recent research has begun to focus on using MCFA to detect invariance for test items. MCFA requires certain parameters (e.g., factor loadings) to be constrained for model identification, which are assumed to be invariant across groups, and act as referent variables. When this invariance assumption is violated, location of the parameters that actually differ across groups becomes difficult. The factor ratio test and the stepwise partitioning procedure in combination have been suggested as methods to locate invariant referents, and appear to perform favorably with real data examples. However, the procedures have not been evaluated through simulations where the extent and magnitude of a lack of invariance is known. This simulation study examines these methods in terms of accuracy (i.e., true positive and false positive rates) of identifying invariant referent variables.     

教师实施课程标准测量工具的研制

学科课程标准是人才培养规格学科化的表述,是国家教学指导性文件,是教师实施教学的重要依据。教师实施课程标准的测量是评估课程改革的效果、分析课程实施影响因素、揭示教学过程质量的必然要求。教师实施课程标准的测量分为理解与动机、行动与策略、结果与反思三个维度,每个维度再分为具体的子维度,然后按照子维度编制大规模调查的问卷。经过严格抽样,获取试测数据,并将试测数据进行非旋转主成分分析、探索性因素分析、效度分析和信度分析,最后形成信效度较好的教师实施课程标准调查问卷。     

教师课改阻抗的原因与启示

教育决策者多从社会发展的角度考虑教育改革,教师多从现实角度考虑教育改革,学校多从声誉考虑教育改革。新课改过高估计了教师的职业素养,教师职业素养在教师培养中其实并没有特殊培养。课程改革应基于教师的现实及发展空间大小而有序进行。     

自我监控量表的探索性和验证性因素分析

该文在指出Snyder(1974)自我监控构念存在的缺陷后,根据自我呈现的动机和自我呈现过程中是否权衡自我和谐与人际和谐,把自我监控分为三种类型:自我导向、他人导向和高自我监控。在分析每种自我监控者行为特征的基础上,编制出新的自我监控量表,探索性因子分析与验证性因子分析显示该量表具有较好的结构效度。     

Confirmatory Factor Analysis of Ordinal Variables With Misspecified Models

Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS), maximum likelihood (ML), weighted least squares (WLS), or diagonally weighted least squares (DWLS). In this simulation evaluation we study the behavior of these methods in combination with polychoric correlations when the models are misspecified. We also study the effect of model size and number of categories on the parameter estimates, their standard errors, and the common chi-square measures of fit when the models are both correct and misspecified. When used routinely, these methods give consistent parameter estimates but ULS, ML, and DWLS give incorrect standard errors. Correct standard errors can be obtained for these methods by robustification using an estimate of the asymptotic covariance matrix W of the polychoric correlations. When used in this way the methods are here called RULS, RML, and RDWLS.     

用多组验证性因素分析模型比较教学评价结果

使用一种新的统计模型——多组验证性因素分析比较学生评教的数据,以判断教师间授课效果的差异。相比于传统T检验的方法,多组验证性因素分析模型消除了测量误差和起评点的影响,得出的结论更为可靠。使用两种方法对两位教师的教学评价的真实数据进行了比较,结果发现,在教学常规因子和总分两项上,两种统计方法得出了相反的结论。     

A Psychometric Investigation of the FACES-III: Confirmatory Factor Analysis with Replication

The Family Adaptability and Cohesion Evaluation Scales—III has been frequently used to measure the family environment. Although some data exist attesting to the reliability and validity of FACES-III data, the underlying factor structure of the instrument has not been assessed using a confirmatory methodology. The present research used data from a sample of 922 families with children having disabilities to conduct a confirmatory factor analysis. Results suggest that the structure of the instrument proposed by the author did not provide an acceptable fit to the data. Post-hoc model fitting identified 3-Factor solutions for the Actual and Ideal Scales as superior based on fit statistics and replication. The Cohesion Scale as originally proposed by the authors of the instrument remained intact. The original Adaptability Scale was divided into two scales, Adaptability, which focused on change in leadership and rules within the family, and Discipline and Control which targeted the role of children in the family system. The present results are consistent with challenges to the adaptability scale, and supports other exploratory analyses of the instrument.     

The Construct Validity of Student Engagement: A Confirmatory Factor Analysis Approach

As institutions seek to promote student engagement on campus, the National Survey of Student Engagement (NSSE) is increasingly being used to chart progress and compare results using the Five Benchmark Scores. While recent research has begun to decompose the five benchmarks in a variety of ways; few research studies have sought to explore the underlying structure of these five benchmarks, their interdependence, and the extent to which the items do reflect those five dimensions. This study begins to address the instrument’s construct validity by submitting a single, first-time freshman cohort’s NSSE responses to a confirmatory factor analysis, and proposes as an alternative, eight “dimensions” of student engagement that fit this set of data slightly better and in a more useful way. Results have practical implications for institutions utilizing NSSE, but also contain conceptual implications pertaining to the application of these benchmarks.     

C.TEST语言能力的验证性因素分析

本文根据前期探索性因素分析成果和相关语言测试理论,提出了两个C.TEST语言能力结构假设模型,分别是C.TEST(A～D级)模型1和C.TEST(E～F级)模型1。然后,使用LISREL统计软件分别对两个假设模型和两个对比模型进行了验证性因素分析。统计数据表明:本研究中提出的两个假设模型都能较好地拟合数据,且优于两个对比模型,这说明研究中提出的两个假设模型是合理的,也说明C.TEST的确具有较高的构想效度。     

解释学视阈中的教师课程观念及其影响因素

课程观念是人们思考课程问题所获得的结果,教师对课程的理解包括对课程目标、课程内容以及学生的理解。教师是课程观念的主体,课程是教学活动的载体。从哲学解释学的理解观出发,教师对课程的理解是一种意义的创造与不断生成的过程。但实际的教育教学中,教师的课程观却被一些外在的因素所制约,如教师的教学经验、教育评价制度以及教科书的设计,等等。     

学校课程改革中的教师参与:调查与分析

在学校课程改革过程中,教师作为课程的主要实施者,其参与学校课程与教学事务的机会和程度,直接关系到学校课程改革的成败.本研究通过对772名中小学教师进行调查,了解在学校课程改革过程中教师参与学校课程与教学事务的基本情况,结果表明,尽管教师对参与学校课程与教学事物的态度比较积极,但在学校课程与教学事务中的实际参与相对不足,不同教师的参与机会和程度存在差异.     

学校课程改革中的教师参与:调查与分析

在学校课程改革过程中,教师作为课程的主要实施者,其参与学校课程与教学事务的机会和程度,直接关系到学校课程改革的成败。本研究通过对772名中小学教师进行调查,了解在学校课程改革过程中教师参与学校课程与教学事务的基本情况,结果表明,尽管教师对参与学校课程与教学事物的态度比较积极,但在学校课程与教学事务中的实际参与相对不足,不同教师的参与机会和程度存在差异。     

A Monte Carlo Study of Recovery of Weak Factor Loadings in Confirmatory Factor Analysis

The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size, loading size, factor correlation, and model specification (correct vs. incorrect). The effects of these variables on goodness of fit and convergence are also examined. Results show that recovery of weak factor loadings, goodness of fit, and convergence are improved when factors are correlated and models are correctly specified. Additionally, unweighted least squares produces more convergent solutions and successfully recovers the weak factor loadings in some instances where maximum likelihood fails. The implications of these findings are discussed and compared to previous research.     

《大学教师教学关键环节行为评价问卷》的验证性因素分析

对某高校942名学生进行了《大学教师教学关键环节行为评价问卷》的测量,采用Anos4．0程序进行验证性因素分析,结果表明,该问卷的模型建构比较理想且各因子指标拟合较好,可以作为下一步研究的良好测量工具。