大学生挫折承受力问卷的编制

在充分研究已有文献的基础上,采用事实驱动的基本方法,结合开放式问卷和专家分析,编制了大学生挫折承受力初测问卷,并对问卷的调查结果进行了探索性因素分析和验证性因素分析,初步确定大学生挫折承受力是包含积极抗挫因子、消极抗挫因子和抗挫特质因子的3维度结构。问卷的各项指标均符合测量学要求,可作为测量和评估当代大学生挫折承受力的工具。     

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.     

A Bootstrap Generalization of Modified Parallel Analysis for IRT Dimensionality Assessment

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA methodology for generation of synthetic datasets. Performance of the bootstrap test was compared with the likelihood ratio difference test and the DIMTEST procedure using a Monte Carlo simulation. The bootstrap test was found to exhibit much better control of the Type I error rate than the likelihood ratio difference test, and comparable power to DIMTEST under most conditions. A major conclusion to be taken from this research is that under many real-world conditions, the bootstrap MPA test presents a useful alternative for practitioners using Marginal Maximum Likelihood factor analysis to test for multidimensional testing data.     

大学生公共安全素质量表的编制

研究者通过访谈调查,编制出80个题目,在737名大学生中进行初测,通过项目鉴别度分析和探索性因素分析,保留了24个题目。24个题目包括安全体质、安全心理、安全技能和安全意识等四个因子,内部一致性系数均大于0.73。在571名大学生中进行了复测,该量表的内部一致性系数为0.88;从复测的大学生中随机抽取51人进行重测,重测信度在0.66~0.85之间。验证性因素分析验证了量表与构想模型拟合较好,具有很好的结构效度。最后,研究者进行了大学生公共安全素质量表的结构分析、信度和效度分析,并讨论了存在的问题。     

师范生择业效能感问卷的初步编制

为了编制师范生择业效能感问卷,并检验所编制问卷的信度和效度,采用探索性因素分析初步探究理论结构,用验证性因素分析验证理论结构的合理性和正确性。验证结果为:探索性因素分析确定该问卷包括5个因子,全问卷的内部一致性系数为0.96,各分问卷的信度均在0.85以上,各分问卷全问卷得分之间的相关系数在0.859~0.918之间。从而得出结论:编制的师范生择业效能感问卷共包括5个因子,量表具有较好的信效度。     

Power and Precision in Confirmatory Factor Analytic Tests of Measurement Invariance

This study investigates the effects of sample size, factor overdetermination, and communality on the precision of factor loading estimates and the power of the likelihood ratio test of factorial invariance in multigroup confirmatory factor analysis. Although sample sizes are typically thought to be the primary determinant of precision and power, the degree of factor overdetermination and the level of indicator communalities also play important roles. Based on these findings, no single rule of thumb regarding the ratio of sample size to number of indicators can ensure adequate power to detect a lack of measurement invariance.     

Likelihood-Based Confidence Intervals in Exploratory Factor Analysis

In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by applying maximum likelihood factor analysis subject to scaling and rotation constraints. As an illustrative example, an oblique 5-factor model will be fitted to the variance-covariance matrix of the 30 personality facets measured by the Revised NEO Personality Inventory, and confidence intervals will be estimated for all factor loadings and factor correlations, as well as for the associated reliability and validity coefficients.     

Application of Exploratory Structural Equation Modeling to Evaluate the Academic Motivation Scale

In this research, the authors examined the construct validity of scores of the Academic Motivation Scale using exploratory structural equation modeling. Study 1 and Study 2 involved 1,416 college students and 4,498 high school students, respectively. First, results of both studies indicated that the factor structure tested with exploratory structural equation modeling provides better fit to the data than the one tested with confirmatory factor analysis. Second, the factor structure was gender invariant in the exploratory structural equation modeling framework. Third, the pattern of convergent and divergent correlations among Academic Motivation Scale factors was more in line with theoretical expectations when computed with exploratory structural equation modeling rather than confirmatory factor analysis. Fourth, the configuration of convergent and divergent correlations connecting each Academic Motivation Scale factors to a validity criterion was more in line with theoretical expectations with exploratory structural equation modeling than with confirmatory factor analysis.     

The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.     

Attitudes towards School,Teacher, and Classmates at Classroom and Individual Levels: An application of two‐level confirmatory factor analysis

Pupils' responses in Grade 6 to a 40‐item questionnaire originally constructed to reveal different school attitudes were re‐analysed using recently developed techniques for latent variable analysis of two‐level data. One aim was to test a model for investigation of classroom environment and another aim was to compare exploratory factor analysis and confirmatory factor analysis when applied at individual and class levels. When using confirmatory factor modelling a separation of the individual and class‐level influences on the between‐group matrix was obtained. At class level three factors could be justified: Teachers and Teaching, Social Relations in Classrooms and Work Atmosphere in Classrooms. We conclude that the present analysis encourages further use of this type of questionnaire when investigating pupils' attitudes in a large number of classes. Two‐level latent variable analysis is useful for comparing pupils' attitudes within and between classes     

教师学习策略结构研究

应用自行研制的教师学习策略调查问卷,通过对523名中小学教师的调查,对教师学习策略结构维度进行了探讨。探索性因素分析和验证性因素分析的结果都表明,教师学习策略是由七个具体的学习行为,即反思实践、专业对话、阅读规划、观摩学习、拜师学艺、记录研思、批判性思维构成的。进一步的研究显示,教师学习策略七因素存在一个二阶的三维结构,即交互学习、探究学习和批判性思维。教师学习策略结构提供了教师培训的内容框架,研究量表可以作为测量中小学教师学习策略的有效工具,并可用于预测教师专业发展水平。     

Toward making the invisible visible using a scale: prospective teachers' thoughts and affective reactions to feedback

This paper presents the development and initial validation of a feedback scale which measures the thoughts and affective reactions of prospective teachers concerning feedback on their teaching experiences. To reach this goal, data from 512 prospective teachers were used to test the internal consistency, exploratory and confirmative factor structure. While exploratory factor analysis was conducted on a random split-half sample of the data to examine the factor structure of the feedback scale items, confirmative factor analysis was conducted in the holdout sample. As a result of these analyses, it has been determined that the scale showed good validity and it has a structure composed of two factors; professional development and anxiety. Also, the reliability of these sub-factors of scale scores was found to be highly reliable. Overall, results suggest that this scale is a valid measurement that should reveal the viewpoints of prospective teachers regarding feedback in the form of observable behaviours for future research.     

Feathers in the nest: Establishing a supportive environment for women researchers

This paper discusses research examining the attitudes and behaviours of researching women in academia and considers the effect of these factors on successful researching outcomes. The results of this exploratory research highlight in particular, a number of interesting environmental influencers which contribute to enhancing successful work outcomes for academic women researchers. Specifically, personal factors such as, marital status, partner support, age, cultural background and level of organisation (in life) coupled with, research defined factors such as incentive for conducting the research and the existence of research partnerships and/or groups are identified as significant performance influencers. These dimensions appear to facilitate the level of research productivity for women academics based on key performance indicators such as journal/conference paper submissions and successful research funding applications. The potential benefits of this exploratory research are that any correlation between specific self-supporting attitudes or behaviours of successful women academics and effective research outcomes could provide important clues to both emerging and continuing researchers for career development and promotion.     

Using Exploratory Bifactor Analysis to Understand the Latent Structure of Multidimensional Psychological Measures: An Example Featuring the WISC-V

Exploratory bifactor analysis (EBFA) represents a methodological advancement for implementing a bifactor model in exploratory factor analysis (EFA). However, little is known about how to properly employ the procedure. The current rotation criteria available for EBFA make it more likely to “get stuck” in local minima, contributing to possible group factor collapse, than more traditional EFA rotations. Thus, getting a proper solution is a more complex and involved process than typical EFA and may require a sensitivity analysis. This article examines EBFA through a sensitivity analysis and subsequent simulation of parameters thought to contribute to group factor collapse. Results support the use of sensitivity analysis, as the problematic variable was shown to greatly increase the likelihood of factor collapse. The hypothesis that estimation start values contribute to factor collapse was not supported. Accompanying R syntax for all analyses are provided to facilitate reproducibility.     

A Cross‐validated Study of the Factorial Structure Underlying Intelligence Tests and Piagetian Formal Operational Tests

Based on a stratified sample of 15‐year‐old students in Singapore schools, exploratory factor analysis and confirmatory factor analysis were used to identify and cross‐validate the factorial structure underlying two group intelligence tests and two group Piagetian tests. The structure of the first‐ and second‐order factors underlying the tests was first identified using exploratory factor analysis on the exploratory sample. The confirmatory approach using LISREL was then used to cross‐validate the factor structure on the validation sample. One second‐ and four first‐order factors were found. To allow for easier interpretation of the factors, a Schmid‐Leiman transformation was carried out on the first‐ and second‐order factor matrices of the pooled sample. A hierarchical factor matrix consisting of a general factor and four group factors was found.     

国际中文教师评价素养构念及影响因素研究

评价素养是国际中文教师基本素养的重要组成部分。文章采用扎根理论及方法,构建了由评价知识、评价观念、评价技术与评价元认知力构成的评价素养概念模型。对352名国际中文教师进行了调查,采用探索性因子分析与验证性因子分析的方法对模型进行了检验与调整。研究提出,国际中文教师评价素 养概念模型包括四个维度,共18项指标。《国际中文教师专业能力标准》中的相关内容可据此进行调整充实。研究发现:专业背景与培训经历对评价素养影响显著,评价观念与评价技术是构成评价素养的关键因素,应成为培养教师评价素养的重要切入点。     

高校毕业生就业公正感问卷的编制

该研究在相关研究与开放式问卷调查的基础上,编制了高校毕业生就业公正感初始问卷.对186名毕业生第一次施测,221名毕业生第二次施测,均进行探索性因素分析,然后对872名高校毕业生正式施测的结果进行验证性因素分析.结果表明,高校毕业生就业公正感问卷包括八个维度：就业机会、信息发布、录用程序、招录依据、所受待遇、潜在干扰、自我展示、综合评价.验证性因素分析的结果表明,各指标均达到可接受水平,八维度模型拟合度较好.问卷具有良好的信效度,可以作为高校毕业生就业公正感的测量工具。     

H-Likelihood Approach to Factor Analysis for Ordinal Data

Marginal likelihood-based methods are commonly used in factor analysis for ordinal data. To obtain the maximum marginal likelihood estimator, the full information maximum likelihood (FIML) estimator uses the (adaptive) Gauss–Hermite quadrature or stochastic approximation. However, the computational burden increases rapidly as the number of factors increases, which renders FIML impractical for large factor models. Another limitation of the marginal likelihood-based approach is that it does not allow inference on the factors. In this study, we propose a hierarchical likelihood approach using the Laplace approximation that remains computationally efficient in large models. We also proposed confidence intervals for factors, which maintains the level of confidence as the sample size increases. The simulation study shows that the proposed approach generally works well.     

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.     

Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data

This study examined the effect of model size on the chi-square test statistics obtained from ordinal factor analysis models. The performance of six robust chi-square test statistics were compared across various conditions, including number of observed variables (p), number of factors, sample size, model (mis)specification, number of categories, and threshold distribution. Results showed that the unweighted least squares (ULS) robust chi-square statistics generally outperform the diagonally weighted least squares (DWLS) robust chi-square statistics. The ULSM estimator performed the best overall. However, when fitting ordinal factor analysis models with a large number of observed variables and small sample size, the ULSM-based chi-square tests may yield empirical variances that are noticeably larger than the theoretical values and inflated Type I error rates. On the other hand, when the number of observed variables is very large, the mean- and variance-corrected chi-square test statistics (e.g., based on ULSMV and WLSMV) could produce empirical variances conspicuously smaller than the theoretical values and Type I error rates lower than the nominal level, and demonstrate lower power rates to reject misspecified models. Recommendations for applied researchers and future empirical studies involving large models are provided.