项目反应理论观察分数核等值的影响因素

探究带宽选择方法、样本量、题目数量、等值设计、数据模拟方式对项目反应理论观察分数核等值的影响。通过两种数据模拟方式,获得研究数据,并计算局部与全域评价指标。研究发现,在随机组设计中,带宽选择方法表现相似;考生样本量和题目数量影响甚微。在非等组设计中,惩罚法与Silverman经验准则表现优异;增加题目量可降低百分相对误差和随机误差;增加样本量导致百分相对误差变大,随机误差减小。数据模拟方式可影响等值评价。未来应重点关注等值系统评估。     

社会创造性的微观变化：基于虚拟仿真技术的研究

本研究将微观发生法设计和虚拟仿真技术相结合,考察了不同特质社会创造性大学生跨情境、跨时间的状态社会创造性的变化特点。结果表明：（1）在四种具体社会问题情境中,个体策略新颖性的变化表现出了跨情境的多样性特点,而策略实用性的变化则主要表现出了跨情境的一致性特点;（2）个体特质社会创造性对其状态社会创造性的表现的影响主要体现在策略的实用性上。     

Single-case effect size calculation: Comparing regression and non-parametric approaches across previously published reading intervention data sets

Growing from demands for accountability and research-based practice in the field of education, there is recent focus on developing standards for the implementation and analysis of single-case designs. Effect size methods for single-case designs provide a useful way to discuss treatment magnitude in the context of individual intervention. Although a standard effect size methodology does not yet exist within single-case research, panel experts recently recommended pairing regression and non-parametric approaches when analyzing effect size data. This study compared two single-case effect size methods: the regression-based, Allison-MT method and the newer, non-parametric, Tau-U method. Using previously published research that measured the Words read Correct per Minute (WCPM) variable, these two methods were examined by comparing differences in overall effect size scores and rankings of intervention effect. Results indicated that the regression method produced significantly larger effect sizes than the non-parametric method, but the rankings of the effect size scores had a strong, positive relation. Implications of these findings for research and practice are discussed.     

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.     

Within‐subject analysis of a prevention strategy for problem behavior

Although decades of research on functional analysis methodology have identified common contingencies that maintain problem behavior and effective interventions, relatively little research has been conducted on strategies to prevent the initial development of problem behavior. We conducted a 2‐part case study, the purposes of which were to illustrate the use of sensitivity tests as the bases for intervention (Study 1) and subsequently to assess the efficacy of a prevention strategy using a single‐subject design (Study 2). Results showed that the sensitivity tests identified establishing operations that may set the occasion for the development of problem behavior and that interventions based on differential reinforcement prevented increases in the severity of problem behavior relative to untreated and control baselines. Benefits and limitations to this individualized approach to prevention are discussed.     

Effects of perception on public bike-and-ride: A survey under complex,multifactor mode-choice scenarios

Bicycles are an environmentally-friendly, energy-saving mode of transportation. Public bike-and-ride is a term used to describe the use of public bicycles combined with transit as a means of transportation. A new stated preference (SP) design scheme based on the factors respondents felt were most important was proposed to augment the traditional SP design under complex multi-factor and multi-level situations. iPads were used to automatically administer the survey under each design scheme and collect the choice behavior data for public bike-and-ride. The concept of perceived psychological distance was further proposed and used to estimate Logit models. Comparative analysis of these models shows for the traditional SP design scheme, some factors, including perceived psychological distance, have reduced statistically significant effects on public bike-and-ride choice relative to the new SP design scheme. This indicates that in scenarios with complex decisions, the decision-makers would reduce their consideration of some factors and simplify their decision process making the new SP survey design scheme more suitable to analyze their choice behavior in those cases. Model analysis showed that perceived psychological distance for each traveler is the most critical factor during the decision process for travel behavior analysis. Finally, sensitivity analysis was conducted to determine which factors in the new survey design were important to increase the share of public bike-and-ride users. Practical findings for promoting public bike-and-ride use are presented in addition to theoretical references for improving the traditional survey methods.     

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。