Inferential procedures for multifaceted coefficients of generalizability

A two-facet measurement model with broad application in the behavioral sciences is identified, and its coefficient of generalizability (CG) is examined. A normalizing transformation is proposed, and an asymptotic variance expression is derived. Three other multifaceted measurement models and CGs are identified, and variance expressions are presented. Next, an empirical investigation of the procedures follows, and it is shown that, in most cases, Type I error control in inferential applications is precise, and that the estimates are relatively efficient compared with the correlation coefficient. Implications for further research and for practice are noted. In an Appendix, four additional models, CGs, and variance expressions are presented.The research reported herein formed part of a doctoral dissertation conducted by Marsha Schroeder (Schroeder, 1986), under the direction of Ralph Hakstian, at the University of British Columbia. We acknowledge with thanks the contributions to this research of Todd Rogers and James Steiger. We are also very indebted to an mous reviewer who provided some important clarifications in connection with two of the models considered. Some support for this research was provided by a grant to Ralph Hakstian from the Natural Sciences and Engineering Research Council of Canada.     

Spatial and conjoint models based on pairwise comparisons of dissimilarities and combined effects: Complete and incomplete designs

In pairwise multidimensional scaling, a spatial representation for a set of objects is determined from comparisons of the dissimilarity of any two objects drawn from the set to the dissimilarity of other pairs of objects drawn from that set. In pairwise conjoint scaling, comparisons among the joint effects produced by pairs of objects, where the objects in a pair are drawn from separate sets, are used to determine numerical representations for the objects in each set. Monte Carlo simulations of both pairwise dissimilarities and pairwise conjoint effects show that Johnson's algorithm can provide good metric recovery in the presence of high levels of error even when only a small percentage of the complete set of pairwise comparisons are tested.     

Murray Sidman: fostering progress through foundational choices

The contributions of Murray Sidman to the field of behavior analysis have helped to put the field on a progressive path. In this paper we describe three areas as examples, drawn from the larger set of his notable contributions: the analysis of stimulus equivalence in a way that has fostered a behavior-analytic approach to derived stimulus relations and symbolic meaning; the observation and measurement of individual behavior through time; and his stance against punitive applied methods. In each of these areas Sidman was a dedicated behaviorist, avoiding appeals to mentalistic or transcendental forces, opposing hypothetical mediational accounts, and taking a functional and contextual approach. Clarity of assumptions was at the heart of Sidman's effective scientific practices and there is no reason to think that those same assumptions will not carry us further, as evidence mounts in support of these views on psychological research and practice.     

It has been said that, “There are three degrees of falsehoods: Lies,damn lies,and statistics”

Applied behavior analysis (ABA) researchers have historically eschewed population-based, inferential statistics, preferring to conduct and analyze repeated observations of each participant's responding under carefully controlled and manipulated experimental conditions using single-case designs (SCDs). In addition, early attempts to adapt traditional statistical procedures for use with SCDs often involved trade-offs between experimental and statistical control that most ABA researchers have found undesirable. The statistical methods recommended for use with SCDs in the current special issue represent a welcome departure from such prior suggestions in that the current authors are proposing that SCD researchers add statistical methods to their current practices in ways that do not alter traditional SCD practices. Further refinement and use of such methods would (a) facilitate the inclusion of research using SCDs in meta-analyses and (b) aid in the development and planning of grant-funded research using SCD methods. Collaboration between SCD researchers and statisticians, particularly on research that demonstrates the benefit of these methods, may help promote their acceptance and use in ABA.     

Is advance reconfiguration in voluntary task switching affected by the design employed?

In task switching, when the amount of preparation time is increased, a reduction in switch cost or RISC effect is observed. This RISC effect is frequently attributed to advance reconfiguration processes. In the explicit task-cueing procedure, RISC effects are observed when varying the preparation time within participants but not when varying the preparation time across participants—a finding suggesting that RISC effects in the explicit task-cueing procedure are restricted to specific designs. The present study investigated RISC effects in voluntary task switching and compared RISC effects in a within-subjects design with RISC effects in a between-subjects design. Our results indicate that RISC effects are present in both designs. We conclude that advance reconfiguration in voluntary task switching is robust.     

实验设计体系初探

根据对常见实验设计的系统回顾,本文指出名目繁多和缺乏系统性是现有实验设计在名称上存在的问题,据此本文进一步提出了实验设计体系的概念.接着通过对实验设计本质属性的深入讨论,本文认为,基本的实验没计可分为两类,两类基本的实验设计构成了实验设计体系的基础.     

计划缺失设计——通过有意缺失让研究更高效

缺失值是社会科学研究中非常普遍的现象。全息极大似然估计和多重插补是目前处理缺失值最有效的方法。计划缺失设计利用特殊的实验设计有意产生缺失值, 再用现代的缺失值处理方法来完成统计分析, 获得无偏的统计结果。计划缺失设计可用于横断面调查减少(或增加)问卷长度和纵向调查减少测量次数, 也可用于提高测量有效性。常用的计划缺失设计有三式设计和两种方法测量。