多阶段混合增长模型的方法及研究现状

多阶段混合增长模型(PGMM)可对发展过程中的阶段性及群体异质性特征进行分析,在能力发展、行为发展及干预、临床心理等研究领域应用广泛。PGMM可在结构方程模型和随机系数模型框架下定义,通常使用基于EM算法的极大似然估计和基于马尔科夫链蒙特卡洛模拟的贝叶斯推断两种方法进行参数估计。样本量、测量时间点数、潜在类别距离等因素对模型及参数估计有显著影响。未来应加强PGMM与其它增长模型的比较研究;在相同或不同的模型框架下研究数据特征、类别属性等对参数估计方法的影响。     

Progressing toward data intimacy: a review of within-session data analysis

Visual inspection of data is a common method for understanding, responding to, and communicating important behavior-environment relations in single-subject research. In a field that was once dominated by cumulative, moment-to-moment records of behavior, a number of graphic forms currently exist that aggregate data into larger units. In this paper, we describe the continuum of aggregation that ranges from distant to intimate displays of behavioral data. To aid in an understanding of the conditions under which a more intimate analysis is warranted (i.e., one that provides a richer analysis than that provided by condition or session aggregates), we review a sample of research articles for which within-session data depiction has enhanced the visual analysis of applied behavioral research.     

Optimal Partitioning of a Data Set Based on the p-Median Model

Although the K-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The p-median model is an especially well-studied clustering problem that requires the selection of p objects to serve as cluster centers. The objective is to choose the cluster centers such that the sum of the Euclidean distances (or some other dissimilarity measure) of objects assigned to each center is minimized. Using 12 data sets from the literature, we demonstrate that a three-stage procedure consisting of a greedy heuristic, Lagrangian relaxation, and a branch-and-bound algorithm can produce globally optimal solutions for p-median problems of nontrivial size (several hundred objects, five or more variables, and up to 10 clusters). We also report the results of an application of the p-median model to an empirical data set from the telecommunications industry.     

Re-conceptualizing the past: Historical data in vocational interest research

Noteworthy progress has been made in the development of statistical models for evaluating the structure of vocational interests over the past three decades. It is proposed that historically significant interest datasets, when combined with modern structural methods of data analysis, provide an opportunity to re-examine the underlying assumptions of J.L. Holland’s [Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45; Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources] RIASEC model. To illustrate this potential, data obtained from J. P. Guilford’s study of interest structure were re-analyzed using modern circumplex and hierarchical clustering techniques to evaluate Holland’s and I. Gati’s [Gati, I. (1979). A hierarchical model for the structure of interests. Journal of Vocational Behavior, 15, 90–106; Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309–324] interest structures. Obtained results indicate that a circumplex model can be used to effectively represent the structure underlying Guilford’s interest measures. However, hierarchical clustering results suggest that Holland’s RIASEC types may not be the most effective categories for grouping specific interest measures into broader interest areas. The current findings provide support for the continued investigation of alternatives to Holland’s interest categories using modern measures of basic interests.     

进展期胃癌术后生存质量影响因素探讨

胃癌是我国发病率和病死率较高的恶性肿瘤,且住院病例90％以上为中晚期。因此预后不佳。近年来在进展期胃癌诊断和治疗方面有较大进展,尤其是人们对术后生存质量的重视。本文初步探讨了进展期胃癌术后生存质量的影响因素：如患者一般状况、手术方式、术后辅助放化疗和中医药治疗等。     

Lessons learned from pedestrian-driver communication and yielding patterns

Understanding the hidden patterns of tacit communication between drivers and pedestrians is crucial for improving pedestrian safety. However, this type of communication is a result of the psychological processes of both pedestrians and drivers, which are very difficult to understand thoroughly. This study utilizes a naturalistic field study dataset and explores the hidden patterns from successful and failed communication events using a pattern recognition method known as Taxicab Correspondence Analysis (TCA). The successful communication scenarios indicate the combinations of variable attributes such as eye contact, facial expression, the assertion of crossing, and effective traffic control devices are strongly associated with successful scenarios. The patterns for failed scenarios are most likely to be on the roadway with a relatively higher speed limit (e.g., 35 mph) and a relatively lower speed limit (e.g., 15 mph) under different conditions. On roadways with a higher speed limit, the failed scenarios are highly associated with passive and undecisive pedestrians, pedestrians far away from the crosswalk, regardless of pedestrian-driver eye contact and facial expression of the pedestrians. Instead of waiting for pedestrians to making a crossing decision, overspeeding drivers are more likely to speed up and pass the crosswalk. On roadways with a lower speed limit, the failed scenarios are often associated with distracted pedestrians, vehicles having the right of way, and the absence of effective traffic control devices. These findings could help transportation agencies identify appropriate countermeasures to reduce pedestrian crashes. The findings on driver-pedestrian communication patterns could provide scopes for improvement in computer vision-based algorithms designed for autonomous vehicle industries.     

Impacts of school-wide positive behaviour support: Results from National Longitudinal Register Data

Problem behaviour in schools may have detrimental effects both on students' well-being and academic achievement. A large literature has consistently found that school-wide positive behaviour support (SWPBS) successfully addresses social and behavioural problems. In this paper, we used population-wide longitudinal register data for all Norwegian primary schools and a difference-in-difference (DiD) design to evaluate effects of SWPBS on a number of primary and secondary outcomes, including indicators of externalising behaviour, school well-being, pull-out instruction, and academic achievement. Indications of reduced classroom noise were found. No other effects were detected. Analyses revealed important differences in outcomes between the intervention and control schools, independent of the implementation of SWPBS, and that a credible design like DiD is essential to handle such school differences.     

Adjusting for information inflation due to local dependency in moderately large item clusters

When multiple items are clustered around a reading passage, the local independence assumption in item response theory is often violated. The amount of information contained in an item cluster is usually overestimated if violation of local independence is ignored and items are treated as locally independent when in fact they are not. In this article we provide a general method that adjusts for the inflation of information associated with a test containing item clusters. A computational scheme was presented for the evaluation of the factor of adjustment for clusters in the restrictive case of two items per cluster, and the general case of more than two items per cluster. The methodology was motivated by a study of the NAEP Reading Assessment. We present a simulated study along with an analysis of a NAEP data set.The research was supported under the National Assessment of Educational Progress (Grant No. R999G30002) as administered by the Office of Educational Research and Improvement, U.S. Department of Education. This work was started when the author was at the Division of Statistics and Psychometrics at the Educational Testing Service. The author thanks Juliet Shaffer, Bob Mislevy, Eric Bradlow, three reviewers and an associate editor for their helpful comments on the paper.     

An estimate of the covariance between variables which are not jointly observed

Situations sometimes arise in which variables collected in a study are not jointly observed. This typically occurs because of study design. An example is an equating study where distinct groups of subjects are administered different sections of a test. In the normal maximum likelihood function to estimate the covariance matrix among all variables, elements corresponding to those that are not jointly observed are unidentified. If a factor analysis model holds for the variables, however, then all sections of the matrix can be accurately estimated, using the fact that the covariances are a function of the factor loadings. Standard errors of the estimated covariances can be obtained by the delta method. In addition to estimating the covariance matrix in this design, the method can be applied to other problems such as regression factor analysis. Two examples are presented to illustrate the method. This research was partially supported by NIMH grant MH5-4576