A comparison of normative data for the Trail Making Test from several countries: equivalence of norms and considerations for interpretation

The Trail Making Test may not be equivalent across cultures, i.e., differences in the scores across different cultures may not reveal real differences in the ability of the subjects on the construct being measured. In order to assess this hypothesis, normative samples from ten different countries were compared. Age decade subgroups across samples were ranked based on mean time taken to complete each part of the task. Large Z scores differences were found between these samples when comparing the first with the second, and the last in the rank. These differences were significant even when age and education were comparable across samples. Following Van de Vijver & Tanzer (1997), several possible sources of bias were identified. Incomparability of samples and administration differences were the most likely factors accounting for differences. Because of the lack of validity studies in the countries considered, no firm conclusions could be obtained regarding construct bias. Although the TMT may be measuring visual scanning, psychomotor speed and mental flexibility, normative data from different countries and cultures are not equivalent which might lead to serious diagnostic errors.     

From Zero to Sixty: Calibrating Real-Time Responses

Recent advances in data recording technology have given researchers new ways of collecting on-line and continuous data for analyzing input-output systems. For example, continuous response digital interfaces are increasingly used in psychophysics. The statistical problem related to these input-output systems reduces to linking time-varying covariates to a continuous response variate. Using real-time data obtained from an experiment in psychoacoustics, we showcase new statistical tools that incorporate dynamical elements of an input-output system. We employ functional data analysis (FDA) methods and a simple differential equation to analyze and model the continuous responses. Furthermore, we outline the issues involved in analyzing input-output systems when the exact form of the underlying mathematical model is not known. Finally, we develop a calibration method to facilitate inter-subject and intra-subject comparisons. This work was supported by grants from the National Sciences and Engineering Research Council (NSERC) to J. O. Ramsay and to D. J. Levitin, and by a grant from the Social Sciences and Humanities Research Council (SSHRC) to D. J. Levitin. We would like to thank Bennett Smith for designing and implementing the software used to conduct the pitch tracking experiment. Also, we wish to thank the research assistants in the Levitin Laboratory involved in the data collection: Catherine Chapados, Andrew Schaaf and Carla Himmelman. We would also like to acknowledge Giles Hooker’s work on implementing the generalized profiling software used within this paper.     

The CHIC Model: A Global Model for Coupled Binary Data

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may be most appropriate. Such a strategy implies a global model, consisting of different N-way N-mode submodels, and a global loss function that is a (weighted) sum of the partial loss functions associated with the different submodels. In this paper, such a global model for an integrated analysis of a three-way three-mode binary data array and a two-way two-mode binary data matrix that have one mode in common is presented. A simulated annealing algorithm to estimate the model parameters is described and evaluated in a simulation study. An application of the model to real psychological data is discussed. T. Wilderjans 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/2005/04). We are grateful to Kristof Vansteelandt for providing us with an interesting data set. We also thank three anonymous reviewers for their useful comments.     

Disaggregating the Distal,Proximal, and Time-Varying Effects of Parent Alcoholism on Children’s Internalizing Symptoms

We tested whether children show greater internalizing symptoms when their parents are actively abusing alcohol. In an integrative data analysis, we combined observations over ages 2 through 17 from two longitudinal studies of children of alcoholic parents and matched controls recruited from the community. Using a mixed modeling approach, we tested whether children showed elevated mother- and child-reported internalizing symptoms (a) at the same time that parents showed alcohol-related consequences (time-varying effects), (b) if parents showed greater alcohol-related consequences during the study period (proximal effects), and (c) if parents had a lifetime diagnosis of alcoholism that predated the study period (distal effects). No support for time-varying effects was found; proximal effects of mothers' alcohol-related consequences on child-reported internalizing symptoms were found and distal effects of mother and father alcoholism predicted greater internalizing symptoms among children of alcoholic parents. Implications for the time-embedded relations between parent alcoholism and children's internalizing symptoms are discussed.     

不同条件下拟合指数的表现及临界值的选择

在本模拟研究中设计了6种样本容量,6种因子载荷,和4种评分等级,并考察了正态和非正态分布两种情况。采用的错误模型为参数误置（真模型中每个因子各由5个题目来测量,错误模型中则是第一个因子由6个题测量,另两个因子各由4个和5个题来测量,即有一个因子载荷被误置）模型。结果发现（1）样本量、载荷量、评分等级数和分布形态都对GOF的取值确有影响。其中分布形态的影响最大。NNFI、IFI在不同条件下的平均值是最稳定的,其次是CFI、RMSEA和SRMR。它们都算是值得推荐的GOF,尤其是NNFI和IFI。（2）在正态分布中,当样本量≥1000时,根据NNFI、IFI、CFI、RMSEA、SRMR对模型是否拟合做出判断时有很低的两类错误率,在样本量<1000时则不理想。在偏态条件下无论选择哪个GOF两类错误率都很高。（3）采用2指数策略在很多情况下也不能显著降低两类错误率。（4）由于在数据分布非正态,或正态但样本量<1000时是难判断模型是否拟合的。因此我们提出了2界值策略。即为每个GOF确定上下两个界值。低于下界值时可判断模型是不正确的,而高于上界值时则可判断模型是正确的。GOF取值处于上下界值之间时难以判断模型是否拟合,只能说越高拟合的可能性越大。这时就要通过跨样本验证和增加样本量来确定模型是否正确     

主观概率判断的支持理论

支持理论是一个关于主观概率判断的非外延性理论,它有以下几个主要观点：1.主观概率判断受到描述的影响,具有描述的依赖性;2.主观概率判断的结果是判断者对中心假设的相对支持的反映;3.主观概率在二元判断中表现出二元互补性,在多元判断中表现出次可加性;4.主观概率判断存在分解效应和促进效应     

ASSESSING OBSERVER ACCURACY IN CONTINUOUS RECORDING OF RATE AND DURATION: THREE ALGORITHMS COMPARED

The three algorithms most frequently selected by behavior‐analytic researchers to compute interobserver agreement with continuous recording were used to assess the accuracy of data recorded from video samples on handheld computers by 12 observers. Rate and duration of responding were recorded for three samples each. Data files were compared with criterion records to determine observer accuracy. Block‐by‐block and exact agreement algorithms were susceptible to inflated agreement and accuracy estimates at lower rates and durations. The exact agreement method appeared to be overly stringent for recording responding at higher rates (23.5 responses per minute) and for higher relative duration (72% of session). Time‐window analysis appeared to inflate accuracy assessment at relatively high but not at low response rate and duration (4.8 responses per minute and 8% of session, respectively).     

Pooling data versus averaging model fits for some prototypical multinomial processing tree models

For a study with multinomial data where there are n_g individuals and with each person having n_r test trials, the question arises as to how to fit the parameters of a multinomial processing tree (MPT) model. Should each parameter be estimated for each individual and then averaged to obtain a group estimate, or should the frequencies in the multinomial categories be pooled so that the model is fit once for the entire group? This basic question is explored with a series of Monte Carlo simulations for some prototypical MPT models. There is a general finding of a pooling advantage for the case where there is a single experimental condition. Also when there are different experimental conditions, there is reduced bias for detecting condition differences for a method based on the pooled data. Although the focus of the paper is on multinomial models, a general theorem is advanced that establishes a basic condition that determines whether there is or is not a difference between the averaging of individual estimates and the estimate based on the pooled data.     

A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.