面向“为学习而测评”的纵向认知诊断模型

基于“为学习而测评”的理念,以促进学生学习为目的,客观量化学习现状并提供诊断反馈的测评模式日益受到重视。相比于横断认识诊断测评,纵向认知诊断测评更有利于实现促进学生发展的目标。为使国内学者系统性地了解纵向认知诊断模型,首先,依据建模逻辑将已有纵向认知诊断模型划分为基于潜在转换分析的和基于高阶潜在结构模型的两类,并逐一介绍和说明两类模型的理论基础和应用情景;然后,通过模拟研究为读者呈现如何使用纵向认知诊断模型进行数据分析及如何解读相应的诊断结果。最后,提炼出四个可进一步研究的议题。     

多变量追踪研究的模型整合与拓展：考察往复式影响与增长趋势

追踪研究当中, 交叉滞后模型可以探究多变量之间往复式影响, 潜增长模型可以探究个体增长趋势。对两类模型进行整合, 例如同时关注往复式影响与个体增长趋势, 同时可以定义测量误差、随机截距等变异成分, 衍生出随机截距交叉滞后模型、特质-状态-误差模型、自回归潜增长模型、结构化残差潜增长模型等。以交叉滞后模型和潜增长模型分别作为基础模型, 从个体间/个体内变异分解的角度对上述各类模型梳理, 整合出此类模型的分析框架, 并拓展建立“因子结构化潜增长模型(factor latent curve model with structured reciprocals)”作为统合框架。通过实证研究(早期儿童的追踪研究-幼儿园版, ECLS-K), 建立21049名儿童的阅读和数学能力的往复式影响与增长趋势。研究发现, 分离了稳定特质的模型拟合最优。研究也对模型建模思路和模型选择提供了建议。     

Examining Spirituality Over Time: Latent Growth Curve and Individual Growth Curve Analyses

SUMMARY

Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques.     

The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research

The effectiveness of a prevention or intervention program has traditionally been assessed using time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost-efficient intervention programs for the future.     

如何描述发展趋势的差异：潜变量混合增长模型

在追踪研究中,研究者不仅关心某一特质随时间的发展趋势,而且关注个体之间发展趋势的差异及其存在差异的原因。在总体发展同质的情形下,多层线性模型和潜变量增长曲线模型为解决这一问题提供了切实有效的方法。但是如果所研究的总体本身不同质,就需要一种能够描述总体中不同质子总体的不同发展特点的方法。该文简要介绍了一种能够描述不同群体不同发展趋势特征的统计模型——潜变量混合增长模型,并通过一个实际例子介绍了这一方法的应用过程,同时说明了潜变量混合增长模型与多层线性模型和潜变量增长曲线模型之间的关系     

A Latent Growth Curve Modeling Approach to Pooled Interrupted Time Series Analyses

This paper presents a latent variable approach for the estimation of treatment effects within a pooled interrupted time series (ITS) design. Although considered quasi-experimental, the ITS design has been noted as representing one of the strongest alternatives to the randomized experiment, making it highly appropriate for use in documenting the presence of effects that might warrant further evaluation in a large-scale randomized study. Results suggest that the latent variable growth modeling (LGM) is capable of detecting simultaneous differences in both level and slope, and provides tests of significance for these two necessary indicators of an ITS intervention effect. As shown in the analyses, the LGM framework provides a comprehensive and flexible approach to research design and data analysis, making available to a wide audience of researchers an analytical framework for a variety of analyses of growth and developmental processes.     

Regularized Finite Mixture Models for Probability Trajectories

Finite mixture models are widely used in the analysis of growth trajectory data to discover subgroups of individuals exhibiting similar patterns of behavior over time. In practice, trajectories are usually modeled as polynomials, which may fail to capture important features of the longitudinal pattern. Focusing on dichotomous response measures, we propose a likelihood penalization approach for parameter estimation that is able to capture a variety of nonlinear class mean trajectory shapes with higher precision than maximum likelihood estimates. We show how parameter estimation and inference for whether trajectories are time-invariant, linear time-varying, or nonlinear time-varying can be carried out for such models. To illustrate the method, we use simulation studies and data from a long-term longitudinal study of children at high risk for substance abuse. This work was supported in part by NIAAA grants R37 AA07065 and R01 AA12217 to RAZ.     

追踪研究在组织行为学中的应用

追踪研究是一种通过引入时滞, 以探索变量动态变化趋势和变量之间相互关系的调查研究方法。近年来, 在组织行为学中, 追踪研究越来越受到学者们的关注, 追踪研究成果也在逐渐增加, 但是大多数研究者尤其是国内研究者, 对追踪研究尚缺乏全面正确的认识。追踪研究设计可以依照重复测量之间的时间间隔长短进行分类, 不同的类型对应不同的样本量、调查方式和统计分析方法, 未来可以拓展追踪研究在团队和领导力、组织文化和变革以及员工激励等方面的应用, 或开展跨文化的追踪研究。     

高中生未来职业规划的发展趋势:基于潜变量增长模型的分析

为揭示高中生未来职业规划发展趋势;探究公正世界信念的影响;通过2年共5次问卷调查;对1646名高中生进行测试。潜变量增长模型分析发现:(1)高中生未来职业探索的发展趋势呈两阶段性;即先下降后增长;(2)高中生未来职业投入的发展趋势可分为2个亚组;高投入下降组和低投入增长组;(3)一般公正世界信念可以预测高中生未来职业探索的发展速率;个人公正世界信念可以预测高中生未来职业投入的发展趋势。研究结果揭示了公正世界信念对高中生未来职业规划发展轨迹的预测作用;验证了公正世界信念理论;对高中职业规划教育具有重要启示。     

3～6岁儿童人格发展的群组序列追踪研究

目的:运用自编幼儿人格发展教师评定问卷对幼儿人格进行追踪测量,探讨其年龄及性别发展特点。方法:用整群抽样法选取3~3.5岁、3.5~4岁、4~4.5岁3个年龄群组幼儿为被试,采用群组序列的追踪设计,进行为期1年半的追踪测量,结合潜变量增长曲线模型和多层线性模型处理数据,探讨幼儿在3~6岁间人格的发展特点。结果:(1)幼儿的智能特征、认真自控、外倾性、亲社会性、情绪稳定性5个人格维度在3~4岁发展最快,4~5岁持续增长但发展速度放缓,到5~6岁时趋于平稳;(2)女孩的认真自控和亲社会性在3岁时显著高于男孩,但在3~6岁间的增长率不存在差异,即女孩的认真自控和亲社会性在幼儿阶段发展水平始终高于男孩。结论:从家庭进入幼儿园的环境变迁促使了幼儿人格的进一步发展,5岁左右幼儿人格开始初步形成;女孩的认真自控和亲社会性水平在幼儿阶段始终高于男孩。     

家人情感卷入对老年自我刻板印象的影响：基于潜变量增长模型的分析

本研究使用问卷法对257名老年人进行了历时1年的3次追踪测试, 采用潜变量增长模型与交叉滞后回归分析考察了家人情感卷入与老年自我刻板印象的变化趋势, 家人情感卷入发展与老年自我刻板印象发展的关系, 以及家人情感卷入对老年自我刻板印象的时序效应。结果发现：(1)老年人感知到的家人情感卷入在1年内呈线性递减, 而老年自我刻板印象呈线性增长; (2)家人情感卷入的初始水平负向预测老年自我刻板印象的初始水平与增长速率; (3)家人情感卷入的下降速率也显著预测了老年自我刻板印象的增长速率; (4)交叉滞后回归分析进一步支持了老年人家人情感卷入对老年自我刻板印象的总体负向预测作用。本研究为老年刻板印象内化的家庭过程提供了理论支持, 并对减少老年刻板印象内化、改善消极老年自我刻板印象的干预具有一定的实践价值。     

Obtaining Systematic Teacher Reports of Disruptive Behavior Disorders Utilizing DSM-IV

This study examines the psychometric properties of the Vanderbilt AD/HD Diagnostic Teacher Rating Scale (VADTRS) and provides preliminary normative data from a large, geographically defined population. The VADTRS consists of the complete list of DSM-IV AD/HD symptoms, a screen for other disruptive behavior disorders, anxiety and depression, and ratings of academic and classroom behavior performance. Teachers in one suburban county completed the scale for their students during 2 consecutive years. Statistical methods included (a) exploratory and confirmatory latent variable analyses of item data, (b) evaluation of the internal consistency of the latent dimensions, (c) evaluation of latent structure concordance between school year samples, and (d) preliminary evaluation of criterion-related validity. The instrument comprises four behavioral dimensions and two performance dimensions. The behavioral dimensions were concordant between school years and were consistent with a priori DSM-IV diagnostic criteria. Correlations between latent dimensions and relevant, known disorders or problems varied from .25 to .66.