元分析的特点、方法及其应用的现状分析

元分析是心理、教育及其他科学领域内新近出现的一种重要研究方法,它主要是借助统计方法,对针对同一问题的大量研究结果进行综合分析与评价,从而概括出其研究结果所反映的共同效应,即普遍性的结论。但这种方法在目前国内心理与教育研究中仍不多见。本文首先将通过与其他整合研究结果的分析方法的比较,归纳出元分析的主要特点及其局限性,其次本文着重介绍目前应用较为广泛的三种重要的元分析方法,并对三种方法作出比较分析;最后本文还将对元分析技术在国内心理学研究中的应用现状作分析与评价。     

眼动记录技术与方法综述

眼动记录法是一种重要的心理学研究方法,目前已被广泛地应用于心理学的基础研究和应用研究领域之中。本文对主要的眼动记录方法进行了总结和分析,对国内外眼动记录仪的研制和发展情况进行了介绍,并就眼动记录技术和方法的前景提出了几点看法。     

Children’s Perspectives of an Enhanced Cognitive-Behavioral Treatment for Child–Parent Dyads With Anxiety Disorders

Prior research has extensively evaluated the efficacy of cognitive-behavioral therapy (CBT) for child anxiety disorders—however, few studies have investigated anxious children’s perspectives and experiences of participating in CBT. This qualitative study explored children’s acceptability of a newly developed enhanced CBT intervention, designed specifically for the treatment of anxiety disorders in children with a clinically anxious parent. The study also explored children’s perceptions and experiences of individual (child only) and joint observational (child–parent) exposure activities that were key to the intervention. Ten children (age range 6–11, M = 8.5 years) and their mothers (age range 34–45, M = 39.5 years) completed in-depth semistructured interviews to investigate child participants’ anticipated and experiential acceptability of the enhanced CBT intervention. Thematic analysis revealed seven major themes broadly reflecting the acceptability, appropriateness, and perceived benefit of the intervention elements, with particular value credited to exposure tasks and the dyadic nature of the intervention. Findings suggest that future experimental evaluation of the enhanced intervention is warranted. Further, the study highlights that CBT for child anxiety disorders, where exposure work is a feature, is acceptable and perceived to be effective by its intended treatment recipients. Trial prospectively registered, preresults, ANZCTR1261900033410.     

EM and beyond

The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques. These techniques—multiple imputation, data augmentation, stochastic relaxation, and sampling importance resampling—combine simulation techniques with complete-data methods to attack problems that are difficult or impossible for EM.A preliminary version of this article was the Keynote Address at the 1987 European Meeting of the Psychometric Society June 24–26, 1987 in Enschede, The Netherlands. The author wishes to thank the editor and reviewers for helpful comments.