TEACHING RETARDED WOMEN A CLOTHING SELECTION SKILL USING COMMUNITY NORMS

Five institutionalized retarded women were taught a clothing selection skill to coincide with popular fashion. Normative data concerning popular styles of color-coordinated clothing were collected through observations of women's apparel in a local community. Using a puzzle simulation of a woman with alternative pieces of colored clothing, color coordination training was conducted using modelling, instructions, practice, praise, and feedback to teach popular selections of color combinations. A generalization training component with actual clothing was also included. Training was accompanied by large increases in percentage of popular color selections with the puzzle and actual clothing. The increases maintained over a seven- to 14-week followup period. Time efficiency measures supported the use of the simulation procedure as opposed to total reliance on actual clothing. The results underscore the utility of local norms in community preparation programs and the efficacy of instructional procedures for severely and profoundly retarded persons as part of their participation in the deinstitutionalization movement.     

A lower bound to the probability of choosing the optimal passing score for a mastery test when there is an external criterion

Recently there has been interest in the problem of determining an optimal passing score for a mastery test when the purpose of the test is to predict success or failure on an external criterion. For the case of constant losses for the two error types, a method of determining an optimal passing score is readily derived using standard techniques. The purpose of this note is to describe a lower bound to the probability of identifying an optimal passing score based on a random sample ofN examinees.The work upon which this publication is based was performed pursuant to a grant [contract] with the National Institute of Education, Department of Health, Education and Welfare. Points of view or opinions stated do not necessarily represent official NIE position or policy.     

Fisher transformations for correlations corrected for selection and missing data

The validity of a test is often estimated in a nonrandom sample of selected individuals. To accurately estimate the relation between the predictor and the criterion we correct this correlation for range restriction. Unfortunately, this corrected correlation cannot be transformed using Fisher'sZ transformation, and asymptotic tests of hypotheses based on small or moderate samples are not accurate. We developed a Fisherr toZ transformation for the corrected correlation for each of two conditions: (a) the criterion data were missing due to selection on the predictor (the missing data were MAR); and (b) the criterion was missing at random, not due to selection (the missing data were MCAR). The twoZ transformations were evaluated in a computer simulation. The transformations were accurate, and tests of hypotheses and confidence intervals based on the transformations were superior to those that were not based on the transformations.     

Measurement invariance,factor analysis and factorial invariance

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.Presidential address delivered at the Annual Meeting of the Psychometric Society in Berkeley, California, June 18–20, 1993.     

The impact of using forced decision-making strategies on post-decisional confidence

The impact of motivational and cognitive factors on post-decisional confidence (PDC) level was tested in two experiments. In the first experiment, subjects were first identified as having an intuitive preference toward using either a compensatory or a noncompensatory decision strategy and later on were forced to use either a compatible or a non-compatible strategy. PDC level decreased after using a noncompensatory strategy, and the decrease was higher when it was a noncompatible strategy. In a second experiment, subjects received feedback about their preferred strategy but were not later forced to use any specific strategy. Most subjects continued to utilize their preferred strategy and PDC level was not changed. Overall, intuitive PDC was not found to be sensitive to differences between compensatory and noncompensatory strategies. The result suggested that PDC is a function of an internal cost-benefit analysis which includes both cognitive and motivational factors.     

Selecting entrepreneurial employees using a person-group fit perspective

As jobs become unstructured and collective endeavor oriented, it is increasingly being realized that work groups must become more autonomous and entrepreneurial. The selection literature is however silent on the predictive mechanisms that may be leveraged to select group members with the desired competencies. The person-group fit perspective enables us to hypothesize and demonstrate that selection instruments geared toward gauging an individual's fit within a group are likely to manifest entrepreneurial competencies and behaviors in the individual. Further, the nature of the job, in terms of the structuredness of work (task formalization) and the repetitiveness of work activities (task routinization), has a moderating impact on the relationship between overall entrepreneurial competence and selection practices. The study has implications for incorporating the principles of person-group fit into the design of job profiles.     

: Applicants' experiences with discrimination explain their reactions to algorithms in personnel selection

Algorithms might prevent prejudices and increase objectivity in personnel selection decisions, but they have also been accused of being biased. We question whether algorithm-based decision-making or providing justifying information about the decision-maker (here: to prevent biases and prejudices and to make more objective decisions) helps organizations to attract a diverse workforce. In two experimental studies in which participants go through a digital interview, we find support for the overall negative effects of algorithms on fairness perceptions and organizational attractiveness. However, applicants with discrimination experiences tend to view algorithm-based decisions more positively than applicants without such experiences. We do not find evidence that providing justifying information affects applicants—regardless of whether they have experienced discrimination or not.     

Stepwise variable selection in factor analysis

It is very important to choose appropriate variables to be analyzed in multivariate analysis when there are many observed variables such as those in a questionnaire. What is actually done in scale construction with factor analysis is nothing but variable selection.In this paper, we take several goodness-of-fit statistics as measures of variable selection and develop backward elimination and forward selection procedures in exploratory factor analysis. Once factor analysis is done for a certain numberp of observed variables (thep-variable model is labeled the current model), simple formulas for predicted fit measures such as chi-square, GFI, CFI, IFI and RMSEA, developed in the field of the structural equation modeling, are provided for all models obtained by adding an external variable (so that the number of variables isp + 1) and for those by deleting an internal variable (so that the number isp – 1), provided that the number of factors is held constant.A programSEFA (Stepwise variable selection in Exploratory Factor Analysis) is developed to actually obtain a list of the fit measures for all such models. The list is very useful in determining which variable should be dropped from the current model to improve the fit of the current model. It is also useful in finding a suitable variable that may be added to the current model. A model with more appropriate variables makes more stable inference in general.The criteria traditionally often used for variable selection is magnitude of communalities. This criteria gives a different choice of variables and does not improve fit of the model in most cases.The URL of the programSEFA is http://koko15.hus.osaka-u.ac.jp/~harada/factor/stepwise/.     

New approaches to the ‘crucial’ test of the causation and selection hypotheses in a Northern Irish sample

This paper examines the relationship between minority/majority status and psychological adjustment, as measured by the GHQ‐12. Rather than using religious labels in Northern Ireland to define minority/majority status, the authors hypothesized that such status is more contingent upon the local area context. Using a purer operationalization of minority/majority status, the authors found no evidence of an ‘ethnic density effect’. Implications for the questionable validity of the ‘crucial test’ of the selection and causation hypotheses are discussed. Copyright © 2002 John Wiley & Sons, Ltd.     

文化演化的认知视角——从个体社会学习出发探究文化动态性

文化演化是多学科共同关心的文化研究主题。文化演化的认知视角是心理学家切入该研究主题的一种方式, 它着眼于社会文化环境中的个体认知, 研究个体在社会学习过程中对文化信息的加工、改变、记忆与提取。文化演化的认知视角借用达尔文生物演化理论, 涉及文化传承、创新、选择三个子领域, 提出了文化演化的三原则: 遗传、变异、选择。文化传承的路径包括模仿和教导, 类型分为工具性与习俗性文化传承; 文化创新具有层次性, 人类特有的累积性文化演化建立在文化创新的基础之上, 体现了文化创新的代际传递; 基于行为生态学和人类认知机制的文化选择造成了文化信息的差异化适应。未来研究可以从研究概念、研究思路、研究方法三方面推进认知视角下的文化演化研究, 探索更高层次的文化创新, 扩展文化演化的前因变量, 结合新技术加深对文化演化的理解; 发挥文化演化对文化心理学研究的助推作用, 研究文化混搭、个性心理特征对文化演化的影响。