A probability-based formula for calculating interobserver agreement

Estimates of observer agreement are necessary to assess the acceptability of interval data. A common method for assessing observer agreement, per cent agreement, includes several major weaknesses and varies as a function of the frequency of behavior recorded and the inclusion or exclusion of agreements on nonoccurrences. Also, agreements that might be expected to occur by chance are not taken into account. An alternative method for assessing observer agreement that determines the exact probability that the obtained number of agreements or better would have occurred by chance is presented and explained. Agreements on both occurrences and nonoccurrences of behavior are considered in the calculation of this probability.     

TEMPORAL AND CLIMATIC VARIABLES IN NATURALISTIC OBSERVATION1

Home-observation data on 5- to 7-yr-old boys collected over 2 yr were examined for systematic variations in rates of desirable and undesirable behaviors associated with several temporal and climatic variables. Significant effects associated with time of day, day of the week, precipitation, and temperature were found. No significant effects on the naturalistic observation data were found for environmental factors associated with lunar phase. It was noted that the correlational nature of the findings did not obviate the necessity for control of the influence of temporal and climatic variables. Several methodological strategies for such control were discussed.     

MSA for a special spearman matrix

Kaiser's measure of sampling adequacy is applied to a special Spearman matrix and a specialq-cluster generalization. The result supports the contention that the measure should be no less than .5 for data to be appropriate for factor analysis.The research reported in this note was supported in part by the Program in Applied Mathematics and Statistics, National Science Foundation, and by the National Institute of Education, Department of Health, Education, and Welfare.     

A comparison of self-instructional checking procedures for remediating mathematical deficits

We examined the relative effectiveness of three procedures for teaching long multiplication/division to seven adolescents with learning disabilities: no-checking, end-checking, and multi-checking. During training, each subject was taught by modelling and imitation to verbalize self-instructions in the form of a strategy while solving the problems. The relative effects of the checking and no-checking procedures on accuracy and rate of problems completed were examined in an alternating treatments design. The best treatment was then given alone and a reversal was implemented six weeks later, followed by a return to the best treatment during a final phase. Irrespective of the procedure used, the subjects' accuracy improved while the rate of problems completed decreased. These effects were greatest with the multi-checking procedure for six of the seven subjects. Generalization to untaught problems of various levels of complexity occurred under all procedures. Though maintenance effects were seen during the follow-up, accuracy was generally higher and more reliable when the subject's best checking procedure was reinstated. It is suggested that error detection and correction were important for maintaining high levels of accuracy and that these operated differentially in the three procedures to produce the differing levels of accuracy. The role of other factors such as pre-skill knowledge, complexity of the problem and prior reinforcement history are also considered.     

EM and beyond

The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques. These techniques—multiple imputation, data augmentation, stochastic relaxation, and sampling importance resampling—combine simulation techniques with complete-data methods to attack problems that are difficult or impossible for EM.A preliminary version of this article was the Keynote Address at the 1987 European Meeting of the Psychometric Society June 24–26, 1987 in Enschede, The Netherlands. The author wishes to thank the editor and reviewers for helpful comments.     

情绪调节灵活性对负性情绪的影响：来自经验取样的证据

情绪调节灵活性是指个体根据不断变化的情境需求灵活部署情绪调节策略的能力。本研究采用经验取样方法,通过拟合个体在日常生活事件(如,未通过考试)和突发公共卫生事件(COVID-19)中的策略使用剖面结构和情境负性程度与策略使用程度的共变关系测量个体的情绪调节灵活性水平,并探讨其对个体后续负性情绪(抑郁和焦虑)的影响。两个独立样本结果表明：单一策略使用偏好(如沉浸偏好和表达抑制偏好)的个体在负性生活事件中和疫情期间经历了更高水平的抑郁和焦虑情绪。此外,当个体随情境负性程度提高使用更多分心策略,而随情境负性程度降低使用更多认知重评策略(意味较高的情绪调节灵活性),其抑郁和焦虑情绪水平更低。以上结果共同证实了情绪调节灵活性有利于减少个体的负性情绪体验。     

Virtue Ethics and Situationist Personality Psychology

In this paper I examine and reply to a deflationary challenge brought against virtue ethics. The challenge comes from critics who are impressed by recent psychological evidence suggesting that much of what we take to be virtuous conduct is in fact elicited by narrowly specific social settings, as opposed to being the manifestation of robust individual character. In answer to the challenge, I suggest a conception of virtue that openly acknowledges the likelihood of its deep, ongoing dependence upon particular social relationships and settings. I argue that holding this conception will indeed cause problems for some important strands of thought in virtue ethics, most notably in the tradition of Aristotle's Nicomachean Ethics. But an approach to virtue ethics modeled on David Hume's treatment of virtue and character in A Treatise of Human Nature promises to escape these problems.     

Bayesian factor analysis for multilevel binary observations

Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropolis-Hastings methods can be used to perform Bayesian inference, model checking and model comparison without the need for multidimensional numerical integration. We illustrate the proposed estimation methods using three simulation studies and an application involving student's achievement results in different areas of mathematics. The authors thank Ian Westbury, University of Illinois at Urbana Champaign for kindly providing the SIMS data for the application.     

Bayesian analysis of order-statistics models for ranking data

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).The author is grateful to K. Lam, K.F. Lam, the Editor, an associate editor, and three reviewers for their valuable comments and suggestions. This research was substantially supported by the CRCG grant 335/017/0015 of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7169/98H). Upon completion of this paper, I became aware that similar work had been done independently by K.G. Yao and U. Böckenholt (1999).     

工作情景中差错概念与差错取向因素分析

研究一调查了解我国工作差错概念以及对待差错的基本态度与行为。研究二通过问卷调查,分析差错取向因素结构及其影响因素,结果发现,差错是一种目标导向的行为;差错取向由差错处理、差错掩盖、差错压力和差错预防四个因素组成,并且管理层次和文化程度对差错取向有较大的影响。