问卷数据建模前传

问卷法是一种常见的实证研究方法。问卷数据建模之前的工作,就像是一栋大楼的奠基工程,基础是否扎实,影响后续的工程质量。本文专门讨论统计建模之前的工作(重点是量表评价),内容包括:处理缺失值、评价量表的结构效度和题目删除的适当性、多维量表需要合成总分时检验同质性并计算合成信度、检验共同方法偏差和评价(变量)区分效度、题目打包、检验自变量的多重共线性,最后也涉及建模理据和无关变量控制等。     

消费者独特性需求量表的研究

本研究目的是修订消费者独特性需求量表（CNFU）的中文版。研究对两个样本共计918名被试进行了问卷测量。项目分析显示,31个题目均符合心理测量学要求。验证性因素分析表明,26个题目的中文版量表符合原量表的三因素（即标新立异、非大众化和避免雷同）模型。量表的重测信度、内部一致性和分半信度均在0．85以上。量表具有较好的汇聚和区分效度,非学生样本得分显著高于学生样本。本文对量表今后的研究和应用进行了探讨。     

初中学生攻击行为的心理特征测量

本研究通过对浙江省和福建省16所中学的464名被试的调查和测量,构建了初中学生攻击行为的心理特征量表并进行了验证型和诊断型的效度检验,研究结果表明,（1）该量表具有较好的信度和效度;（2）行为、认知、情绪和言语是测量攻击行为的心理特征的良好指标;（3）同学提名法与教师评价法具有较高的鉴别力;（4）问卷自评的鉴别力尚有待改进。     

新世纪20年国内测验信度研究

随着验证性因子分析模型的应用, 信度研究进入了崭新的发展阶段。新世纪前20年国内有关测验信度的研究有三条发展主线。一是基于验证性因子模型的信度发展, 包括同质性系数、合成信度、最大信度等; 二是数据类型的拓展, 包括两水平和追踪数据的信度; 三是信度用途的拓展, 如评分者信度、编码者信度等。对于通常的测验(题目之间的测量误差不相关), 如果α系数够高, 信度就够高; 否则使用合成信度。如果一个统计模型中所有变量的合成信度都很高(超过0.95), 使用显变量建模与使用潜变量建模的结果差别不大; 否则, 使用潜变量建模较好。     

自我报告高敏感儿童量表在中学生群体中的修订

目的：进行自我报告高敏感儿童量表（Highly Sensitive Child Scale,HSC）在中国中学生群体中的修订及信效度检验。方法：通过项目分析和探索性因素分析确定中文版自我报告HSC量表题目，最后进行量表的信效度检验。结果：修订后的中文版自我报告HSC量表包括9道题目，包括低感觉阈限、易受过度刺激和审美敏感性三个维度。该量表的信效度指标符合要求，三因素模型拟合良好。结论：修订后中文版自我报告HSC量表可作为测量我国中学生感觉加工敏感性的可靠工具。     

班级环境与青少年抑郁的关系：核心自我评价的中介与调节作用

通过整群抽样的方法抽取了1404名中学生,对其施测核心自我评价量表,我的班级问卷和抑郁问卷,应用无约束结构方程模型技术和结构方程中介效应检验技术,分别考察了核心自我评价在班级环境与抑郁关系间的调节与中介作用。结果表明：（1）核心自我评价对班级环境中的师生关系、同学关系、竞争三者和抑郁之间的关系起着调节作用,但结构模型的拟合指数不佳;（2）核心自我评价在班级环境与青少年抑郁间起着中介作用,其中核心自我评价在竞争与抑郁间关系起着完全中介作用。     

青少年早期负面评价恐惧的发展轨迹及其影响因素

采用问卷法对977名初中生在校三年间负面评价恐惧的发展状况进行三次追踪测试，通过建构潜变量增长模型，检验初中生负面评价恐惧的变化趋势，并考察学业自尊和社交自尊对负面评价恐惧变化的影响。结果发现：（1）初中青少年负面评价恐惧呈上升趋势；（2）从初一到初三，学生较高的学业自尊和社交自尊显著抑制负面评价恐惧的增长；（3）性别对负面评价恐惧发展轨迹的影响是由于自尊的性别差异引起的。     

生命意义问卷(修订版)在初中生群体中的信效度:留守与非留守学生的比较分析

检验生命意义问卷(修订版)在初中生群体中的信效度,并比较了留守与非留守学生在测量学指标上的差异。采用生命意义问卷(修订版)、超越意义量表、情感调节量表、Rosenberg自尊量表和幸福感指数量表对1300名初中生进行调查,其中有636名留守初中生。探索性因素分析、平行分析和最小平均偏相关分析均表明该量表为双因子结构,验证性因素分析与各类群体拟合良好;与上述效标变量均有显著的正相关;在性别和是否留守学生变量上,个别条目表现出一致性或非一致性条目功能差异;总量表、追寻和拥有意义分量表的δ系数都大于0.9。生命意义问卷(修订版)具有在初中生和留守初中生中均有良好的信效度;可以忽略在性别和是否留守学生变量的条目功能差异;问卷辨识度较高。     

压力性生活事件对青少年烟酒使用的影响：基本心理需要和应对方式的链式中介作用

采用压力性生活事件量表、基本心理需要量表、特质应对方式问卷和烟酒使用问卷对867名青少年进行调查,考察了基本心理需要和积极应对方式、消极应对方式在压力性生活事件与烟酒使用关系中的链式中介效应。结果表明：（1）基本心理需要是压力性生活事件与青少年烟酒使用之间的中介变量;（2）积极应对方式、消极应对方式是基本心理需要与青少年烟酒使用之间的中介变量。因此,基本心理需要和积极应对方式、消极应对方式在压力性生活事件与青少年烟酒使用之间起链式中介作用。研究结论对青少年烟酒使用的预防和干预具有重要价值。     

初中生班级团体依恋、自悯与学习倦怠的关系

本研究采用青少年学习倦怠量表、班级团体依恋问卷、自悯量表、青少年父母同伴依恋问卷、生活满意度量表、和自编学业满意度问题对658名初中生进行问卷调查,考察当代初中生对其所在班级的团体依恋和自悯与学习倦怠之间的关系,并检验自悯在班级团体依恋和学习倦怠关系中的中介作用。结果发现：在控制了人口学变量、父母依恋、同伴依恋、学业满意度和生活满意度之后,（1）班级团体依恋焦虑和回避均能显著正向预测学习倦怠;（2）自悯显著负向预测学习倦怠;（3）自悯在班级团体依恋焦虑和学习倦怠的关系中起到完全中介的作用,而在班级团体依恋回避和学习倦怠的关系中起部分中介的作用。研究结果对于从班级团体依恋和自悯的角度理解中学生学习倦怠具有重要意义。     

结构方程建模中的题目打包策略

结构方程建模中题目打包法的优缺点包括：指标数据质量变好、模型拟合程度提高; 估计偏差不大, 可校正; 估计稳定, 但降低了敏感性与可证伪性。打包法的前提条件是单维、同质, 适合结构模型分析, 不适合测量模型分析。对于单维测验, 给出了一个打包流程。对于通常的多个子量表(多维结构)测验, 推荐在子量表内打包, 每个子量表打包成1个指标或者3个指标, 用于结构方程建模。     

项目组合在结构方程模型中的应用

项目组合（itemparceling）是对同一量表中的若干项目进行整合并形成新的观测指标的过程。虽然一直以来它都是一个有争议的议题,但随着其在结构方程模型中的应用日益广泛,它越来越受到研究者重视。文章从项目组合的基本逻辑、优缺点以及具体方法等方面对项目组合的研究进行了概括,并在此基础上提出了使用的具体建议：（1）根据研究的目的与具体情境选择是否需要组合;（2）组合之前必须首先确定概念的维度性;（3）项目组合最好建立在一定的理论基础上等等。未来的研究可以深入探讨各种组合方法对模型拟合以及参数估计的影响以及项目组合在一些SEM高阶应用中的效果,并进一步与项目反应理论等测量理论相结合     

The Factor Structure and Measurement Invariance of the Daily Spiritual Experiences Scale

The current study has two main goals: (a) to identify a factor structure of the Daily Spiritual Experiences Scale (DSES) on a large archival data, collected from 1,325 adults in the United States (709 women, 616 men) by the U.S. General Social Survey in 2004 and (b) to examine the measurement invariance of the 16 DSES items between women and men in the same data to see whether any of the items are favoring or biased toward either women or men. The one-factor confirmatory factor analysis (CFA) model fit our data better than the two-factor CFA models because of high correlations between the two factors (r > .90). The fit of the one-factor CFA to our sample data was improved when we specified seven correlated residuals suggested by overlapping item content and large modification indices. The ensuing measurement invariance testing of the one-factor CFA model with seven correlated residuals supported full measurement invariance of factor loadings, thresholds, and residual variances, as well as factor variances between the women and the men. Yet the factor mean for the women was .841 units (Cohen’s d = .496) higher than it was for the men, indicating that higher levels of daily spiritual experiences for women reported in gender comparison studies in the United States are not likely to be an artifact of bias in the questionnaire.     

The ubiquity of common method variance: The case of the Big Five

The factor structures of the International Personality Item Pool (IPIP) and NEO-FFI Big Five questionnaires were examined via confirmatory factor analyses. Analyses of IPIP data for five samples and NEO data for one sample showed that a CFA model with three method bias factors, one influencing all items, one influencing negatively worded items, and one influencing positively worded items fit the data significantly better than models without method factors or models with only one method factor . With the method factors estimated, our results indicated that the Big Five dimensions may be more nearly orthogonal than previously demonstrated. Implications of the presence of method variance in Big Five scales are discussed.     

Initial validation of the coach-created Empowering and Disempowering Motivational Climate Questionnaire (EDMCQ-C)

This article employs Duda's (2013) hierarchical conceptualization of the coach-created motivational climate to inform the validation of a questionnaire (Empowering and Disempowering Motivational Climate Questionnaire-Coach; EDMCQ-C) that assesses junior athletes' perceptions of the social environmental dimensions proposed by achievement goal theory and self-determination theory. Confirmatory factor analyses (CFA) were initially employed to reduce the number of items required to measure the targeted climate dimensions. A series of competing models were then tested to determine the best representation of the questionnaire's factor structure. The findings revealed that exploratory structural equation modelling (ESEM) provided a better fit of the data to the hypothesised model than CFA solutions. Specifically, the bi-factor ESEM provided the best fit, although parameter estimates suggest that none of the ESEM solutions replicated the underlying theoretical model of the motivational climate proposed by Duda (2013). The evidence from this study suggests that the EDMCQ-C is a promising, parsimonious questionnaire to assess empowering and disempowering facets of the motivational climate albeit the development of the questionnaire remains a work in progress.     

The use of configural frequency analysis for explorative data analysis

Configural frequency analysis (CFA) is a widely used method of explorative data analysis. It tries to detect patterns in the data that occur significantly more or significantly less often than expected by chance. Patterns which occur more often than expected by chance are called CFA types, while those which occur less often than expected by chance are called CFA antitypes. The patterns detected are used to generate knowledge about the mechanisms underlying the data. We investigate the ability of CFA to detect adequate types and antitypes in a number of simulation studies. The basic idea of these studies is to predefine sets of types and antitypes and a mechanism which uses them to create a simulated data set. This simulated data set is then analysed with CFA and the detected types and antitypes are compared to the predefined ones. The predefined types and antitypes together with the method to generate the data are called a data generation model. The results of the simulation studies show that CFA can be used in quite different research contexts to detect structural dependencies in observed data. In addition, we can learn from these simulation studies how much data is necessary to enable CFA to reconstruct the predefined types and antitypes with sufficient accuracy. For one of the data generation models investigated, implicitly underlying knowledge space theory, it was shown that zero‐order CFA can be used to reconstruct the predefined types (which can be interpreted in this context as knowledge states) with sufficient accuracy. Theoretical considerations show that first‐order CFA cannot be used for this data generation model. Thus, it is wrong to consider first‐order CFA, as is done in many publications, as the standard or even only method of CFA.     

Bias in cross-sectional analyses of longitudinal mediation

Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading.     

Random intercept item factor analysis

The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. For instance, this may occur if participants consistently use the response scale idiosyncratically. To account for this phenomenon, the authors partially relax the fixed coefficients assumption by allowing the intercepts in the factor model to change across participants. The model is attractive when m factors are expected on the basis of substantive theory but m + 1 factors are needed in practice to adequately reproduce the data. Also, this model for single-level data can be fitted with conventional software for structural equation modeling. The authors demonstrate the use of this model with an empirical data set on optimism in which they compare it with competing models such as the bifactor and the correlated trait-correlated method minus 1 models.     

Temporal patterns of variable relationships in person-oriented research: longitudinal models of configural frequency analysis

This article reviews the premises of configural frequency analysis (CFA), including methods of choosing significance tests and base models, as well as protecting alpha, and discusses why CFA is a useful approach when conducting longitudinal person-oriented research. CFA operates at the manifest variable level. Longitudinal CFA seeks to identify those temporal patterns that stand out as more frequent (CFA types) or less frequent (CFA antitypes) than expected with reference to a base model. A base model that has been used frequently in CFA applications, prediction CFA, and a new base model, auto-association CFA, are discussed for analysis of cross-classifications of longitudinal data. The former base model takes the associations among predictors and among criteria into account. The latter takes the auto-associations among repeatedly observed variables into account. Application examples of each are given using data from a longitudinal study of domestic violence. It is demonstrated that CFA results are not redundant with results from log-linear modeling or multinomial regression and that, of these approaches, CFA shows particular utility when conducting person-oriented research.