Observation procedure, observer gender, and behavior valence as determinants of sampling error in a behavior assessment analogue

Several factors thought to influence the representativeness of behavioral assessment data were examined in an analogue study using a multifactorial design. Systematic and unsystematic methods of observing group behavior were investigated using 18 male and 18 female observers. Additionally, valence properties of the observed behaviors were inspected. Observers' assessments of a videotape were compared to a criterion code that defined the population of behaviors. Results indicated that systematic observation procedures were more accurate than unsystematic procedures, though this factor interacted with gender of observer and valence of behavior. Additionally, males tended to sample more representatively than females. A third finding indicated that the negatively valenced behavior was overestimated, whereas the neutral and positively valenced behaviors were accurately assessed.     

On muthén’s maximum likelihood for two-level covariance structure models

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures that take into account the dependence of such observations have been developed. Among procedures for 2-level covariance structure analysis, Muthén’s maximum likelihood (MUML) has the advantage of easier computation and faster convergence. When data are balanced, MUML is equivalent to the maximum likelihood procedure. Simulation results in the literature endorse the MUML procedure also for unbalanced data. This paper studies the analytical properties of the MUML procedure in general. The results indicate that the MUML procedure leads to correct model inference asymptotically when level-2 sample size goes to infinity and the coefficient of variation of the level-1 sample sizes goes to zero. The study clearly identifies the impact of level-1 and level-2 sample sizes on the standard errors and test statistic of the MUML procedure. Analytical results explain previous simulation results and will guide the design or data collection for the future applications of MUML.This research was supported by NSF Grant DMS04-37167.We thank Dr.Bengt Muthén for providing key references. We are also grateful to three expert reviewers for their constructive comments that have led the paper to an improvement over the previous version.This revised article was published online in August 2005 with the PDF paginated correctly.     

The roots of self-awareness

In this paper we provide an account of the structural underpinnings of self-awareness. We offer both an abstract, logical account – by way of suggestions for how to build a genuinely self-referring artificial agent – and a biological account, via a discussion of the role of somatoception in supporting and structuring self-awareness more generally. Central to the account is a discussion of the necessary motivational properties of self-representing mental tokens, in light of which we offer a novel definition of self-representation. We also discuss the role of such tokens in organizing self-specifying information, which leads to a naturalized restatement of the guarantee that introspective awareness is immune to error due to mis-identification of the subject.     

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients

Principal component regression (PCR) is a popular technique in data analysis and machine learning. However, the technique has two limitations. First, the principal components (PCs) with the largest variances may not be relevant to the outcome variables. Second, the lack of standard error estimates for the unstandardized regression coefficients makes it hard to interpret the results. To address these two limitations, we propose a model-based approach that includes two mean and covariance structure models defined for multivariate PCR. By estimating the defined models, we can obtain inferential information that will allow us to test the explanatory power of individual PCs and compute the standard error estimates for the unstandardized regression coefficients. A real example is used to illustrate our approach, and simulation studies under normality and nonnormality conditions are presented to validate the standard error estimates for the unstandardized regression coefficients. Finally, future research topics are discussed.     

Analysis of residuals for the multionmial item response model

Using the item response model as developed on the multinomial distribution, asymptotic variances are obtained for residuals associated with response patterns and first-, and second-order marginal frequencies of manifest variables. When the model does not fit well, an examination of these residuals may reveal the source of the poor fit. Finally, a limited-information test of fit for the model is developed by using residuals defined for the first-, and second-order marginals. Model evaluation based on residuals for these marginals is particularly useful when the response pattern frequencies are sparse.The author would like to thank Yasuo Amemiya and Joseph Lucke for helpful suggestions. This research was supported by a Research Incentive Grant from Arizona State University.     

THE REPRESENTATIVENESS OF OBSERVATIONAL SAMPLES OF DIFFERENT DURATIONS

The representativeness of behavioral observation samples with durations of less than the whole time of interest was investigated. A real-time recording system was developed to quantify the behavior of 5 profoundly mentally retarded physically handicapped adult students in an institutional training setting. Behavior was observed using six mutually exclusive and exhaustive categories during 2.5-hr observation sessions. Sample observation sessions with durations ranging from 15 to 135 min were computer simulated from the whole-session (150-min) records. It was found that the representativeness of these samples, when compared to whole-session records, was a function of the relative duration of the behavioral categories and of sample duration. The occurrence of relatively high-duration behaviors (lasting for more than 50% of the session) was estimated to within 20% error by samples of less than 60 min, but low-duration behaviors (1 to 3% of the session) were inadequately quantified even from 135-min samples. Increasing irregularity of bouts of behavior in the low-duration behaviors is suggested as the cause of the functions obtained. Implications of the findings for applied behavior analysis are discussed, with the recommendation that the adequacy of observational session durations be empirically assessed routinely.     

RELATIVE EFFECTS OF WHOLE-WORD AND PHONETIC-PROMPT ERROR CORRECTION ON THE ACQUISITION AND MAINTENANCE OF SIGHT WORDS BY STUDENTS WITH DEVELOPMENTAL DISABILITIES

We used an alternating treatments design to compare the effects of two procedures for correcting student errors during sight word drills. Each of the 5 participating students with developmental disabilities was provided daily one-to-one instruction on individualized sets of 14 unknown words. Each week's new set of unknown words was divided randomly into two groups of equal size. Student errors during instruction were immediately followed by whole-word error correction (the teacher stated the complete word and the student repeated it) for one group of words and by phonetic-prompt error correction (the teacher provided phonetic prompts) for the other group of words. During instruction, all 5 students read correctly a higher percentage of whole-word corrected words than phonetic-prompt corrected words. Data from same-day tests (immediately following instruction) and next-day tests showed the students learned more words taught with whole-word error correction than they learned with phonetic-prompt error correction.     

无意识信息引发的认知控制及其神经机制

对无意识信息引发的认知控制的研究是揭示意识的功能与局限的重要方法和步骤.在无意识认知控制研究中主要采用模式掩蔽和偏对比掩蔽创设无意识条件,使用客观测量方法对意识状态进行测量.作者从无意识抑制控制、无意识冲突控制和无意识错误加工三个方面回顾了近年对无意识认知控制及其神经机制的研究.已有研究从行为和认知神经研究两个方面强有力地证明了无意识认知控制的存在,揭示了其神经机制.这些研究拓展了人们对意识信息广度和深度的认识,也为未来进一步揭示意识的起源等问题奠定了一定的基础.未来的研究可以从无意识认知控制和有意识认知控制的相互转化等方面进行进一步研究.     

认知诊断模型的标准误与置信区间估计：并行自助法

认知诊断模型的标准误(Standard Error, SE; 或方差—协方差矩阵)与置信区间(Confidence Interval, CI)在模型参数估计不确定性的度量、项目功能差异检验、项目水平上的模型比较、Q矩阵检验以及探索属性层级关系等领域有重要的理论与实践价值。本研究提出了两种新的SE和CI计算方法：并行参数化自助法和并行非参数化自助法。模拟研究发现：模型完全正确设定时, 在高质量及中等质量项目条件下, 这两种方法在计算模型参数的SE和CI时均有好的表现; 模型参数存在冗余时, 在高质量及中等质量项目条件下, 对于大部分允许存在的模型参数而言, 其SE和CI有好的表现。通过实证数据展示了新方法的价值及计算效率提升效果。     

权力对基本归因错误的影响

归因对理解和预测环境有着重要作用。归因的两阶段过程理论认为,自动化加工会衍生内归因,进一步的控制化加工才会导致外归因。社会权力会提升自动化加工倾向,因此,社会权力可能会提升基本归因错误的发生。通过四个研究,对此进行验证。研究1-3通过问卷测量,以特质性权力感作为社会权力的指标,其中研究1通过情境判断测验对基本归因错误进行测量,研究2-3采用自陈量表对基本归因错误进行测量,并对社会经济地位进行了控制。研究4则采用启动的方法启动临时性社会权力,探求社会权力与基本归因错误之间的因果关系。四个研究均得出了一致的结论：高社会权力个体更倾向于内归因。