The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

Tucker3 hierarchical classes analysis

This paper presents a new model for binary three-way three-mode data, called Tucker3 hierarchical classes model (Tucker3-HICLAS). This new model generalizes Leenen, Van Mechelen, De Boeck, and Rosenberg's (1999) individual differences hierarchical classes model (INDCLAS). Like the INDCLAS model, the Tucker3-HICLAS model includes a hierarchical classification of the elements of each mode, and a linking structure among the three hierarchies. Unlike INDCLAS, Tucker3-HICLAS (a) does not restrict the hierarchical classifications of the three modes to have the same rank, and (b) allows for more complex linking structures among the three hierarchies. An algorithm to fit the Tucker3-HICLAS model is described and evaluated in an extensive simulation study. An application of the model to hostility data is discussed.The first author is a Research Assistant of the Fund for Scientific Research-Flanders (Belgium). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/2000/02). We are grateful to Kristof Vansteelandt for providing us with an interesting data set.     

时间记忆层次网络模型的实验检验

实验采用分类性实验材料,使被试在对词单项目加工时形成较清晰的群集,进而形成时间组块,目的在于对时间记忆层次网络进行直接检验。其中时序判断用反时间时和正确率两种反应指标,时距估计用再现和口头估计两种方法。结果发现,时间信息记忆既存在层次网络的特征,又存在线性结构的特征。     

Training requirements and retention characteristics of serial list organization by macaque monkeys

This work evaluated the prospect that organizational accounts of the retention of list information by monkeys might be an artifact of familiarity with conditional relationships. Seven sophisticated macaques were trained on four five-item lists. Each acquisition selectively excluded one of the internal conditional pairs of the typical four-problem sequence (AB,BC,CD,DE) that defines a five-item serially ordered list. Then, all possible novel pairings and the trained pairs appeared together in a test. After this, the previously omitted pair was trained and animals were retested. On all tasks, initial tests revealed little organization and much intersubject variability of characteristic choice strategies, but subsequent inclusion of all four conditional pairs always yielded organized serial choice. On both the four-problem tests and in a later retention, errors were directly related to interitem distance between the objects paired on test trials. These results helped to specify the conditions required for demonstration of non-human primate analogs of transitivity, and showed that even sophisticated monkeys organize information in retention only if they know all interitem relationships. Received: 7 October 1998 / Accepted after revision: 10 October 1999     

认知控制的层级性：来自任务切换的脑电证据*

认知控制的主要研究范式之一是任务切换。以往研究发现切换代价受到认知控制层级性的调节, 但鲜有研究探索这一调节过程的动态神经机制。本研究通过嵌套的线索-任务切换范式考察不同层级任务切换代价的差异及其神经机制。在实验中, 要求被试完成高低两种层级任务, 低层级任务要求被试判断数字大小(或奇偶); 高层级任务则须先加工数字的某一语义特征(如当前数字是否是偶数), 然后进行大小判断。行为结果表明, 高层级任务切换代价显著大于低层级任务切换代价。线索锁时的脑电结果表明, 层级效应最早出现于P2成分, 切换效应(切换与重复之差)在CNV成分上受到任务层级的调控, 反映了在任务目标重构阶段给予高层级任务更多的选择性注意以及更高的主动性控制。目标锁时的脑电结果表明, 在N2及慢波(SP)成分上, 高层级任务切换与重复的波幅差异相比低层级任务显著更大, 反映了在抑制旧任务集与重构新反应集的过程中增强的反应性控制。这些结果为任务设置重构论和认知控制的层级性提供了新的证据。     

Levels of analysis and sources of data in sales research: a multilevel-multisource review

Sales researchers are increasingly adopting a multilevel-multisource (MLMS) approach to answer many key questions involving sales managers, salespeople and customers. MLMS research involves the acquisition and analysis of data collected from two or more sources pertaining to multiple hierarchical levels and presents a number of opportunities and challenges for sales researchers to consider. The authors highlight the benefits and the drawbacks of MLMS research and describe various approaches for implementing an MLMS collection and analysis. Additionally, a review of the MLMS studies conducted in the sales domain is provided which summarizes and delineates relationships tested in the extant literature. Based on this review, the authors advance a number of underdeveloped areas of research where MLMS approaches can be applied to further the understanding of the dynamic conditions that typify sales research.