两种条件下的妒忌分离:间接测量的优势

通过间接测量和直接测量考察被试的妒忌感受(实验1),并测量两种测量条件下被试的敏感性和反应偏向的分离(实验2).结果发现:间接测量组的被试比直接测量组报告更多的妒忌感受,两种条件下被试所采用的判定标准没有变化,但间接测量中被试的敏感性高于直接测量,这说明两种测量测得了妒忌的不同层面,间接测量在测量妒忌的无意识方面更具优势;而两种条件下被试对于妒忌者都比被妒忌者要更为敏感,则表明了被试在无意识中对妒忌者更为偏好;两种条件下对于妒忌的评定均无性别差异.     

大学生亲社会倾向、亲社会推理以及它们的相关模式

采用《亲社会倾向测量》和《亲社会推理客观测量》研究了17至20岁大学生的六类亲社会倾向和亲社会推理的状况。发现大学生将利他的亲社会倾向列为六类倾向中的首位,接下来依次是紧急的、情绪性的、依从的、匿名的和公开的亲社会倾向;在亲社会推理五种类型中大学生选择比率最高的是内化价值定向的推理,接下来依次是刻板定向、需要定向和享乐主义定向的推理,所占比率最低的是赞扬定向的推理;六类亲社会倾向与亲社会推理之间具有特定的棚关模式．利他的和匿名的亲社会倾向与内化价值定向和刻板定向的推理正相关、与享乐主义定向及赞扬定向的推理负相关,公开的亲社会倾向与赞扬定向的推理正相关、与需要定向的推理负相关。跨文化比较证实,中美大学生的上述三个方面表现出一定的差异。     

Probability: A new logico-semantical approach

This approach does not define a probability measure by syntactical structures. It reveals a link between modal logic and mathematical probability theory. This is shown (1) by adding an operator (and two further connectives and constants) to a system of lower predicate calculus and (2) regarding the models of that extended system. These models are models of the modal systemS ₅ (without the Barcan formula), where a usual probability measure is defined on their set of possible worlds. Mathematical probability models can be seen as models ofS ₅.     

Caution indices based on item response theory

A new family of indices was introduced earlier as a link between two approaches: One based on item response theory and the other on sample statistics. In this study, the statistical properties of these indices are investigated and then the relationships to Guttman Scales, and to item and person response curves are discussed. Further, these indices are standardized, and an example of their potential usefulness for diagnosing students' misconceptions is shown.This research was sponsored by the Personnel and Training Research Program, Psychological Sciences Division, Office of Naval Research, under contract No. N00014-82-K-0604.     

Denoting the base-free measure of change

Bond criticized the base-free measure of change proposed by Tucker, Damarin, and Messick by pointing to an incorrect derivation which is here viewed instead as a correct derivation entailing an inadequately specified tacit assumption. Bond's revision leads to estimates of the correlation between initial position and change which are negatively biased by correlated errors, whereas the original approach, with the tacit assumption properly denoted, leads to unbiased values.     

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.     

Call for new measures suitable for intensive longitudinal studies: Ideas and suggestions

The intensive longitudinal studies (ILS) has recently gained prominence as one of the most developing and promising approaches in psychological research. It is suitable for studying intraindividual dynamics, since it collects many repeated measurements for multiple persons. However many existing psychometric scales that are suitable for cross-sectional studies cannot be used successfully in ILS. That is why it would be advisable to develop them. This article presents potential solutions and future directions that may be helpful in developing such tools. It focuses on and discusses three issues: (1) appropriate tool length, so that the person completing them multiple times does not feel too tired; (2) parallel forms of questionnaires to reduce the problem of remembering previous answers; (3) complementary types of measure, that would allow approaching certain characteristics in slightly different ways.     

Smartphone use and daily cognitive failures: A critical examination using a daily diary approach with objective smartphone measures

While smartphones have brought many benefits and conveniences to users, there is continuing debate regarding their potential negative consequences on everyday cognition such as daily cognitive failures. A few cross-sectional studies have found positive associations between smartphone use and cognitive failures. However, several research gaps remain, such as the use of cross-sectional designs, confounds related to stable individual differences, the lack of validity in self-report measures of smartphone use, memory biases in retrospective self-reports, and the lack of differentiation between smartphone checking and smartphone screen time. To simultaneously address the aforementioned shortcomings, the current study examined the within-person associations between various objective indicators of smartphone use and daily cognitive failures using a 7-day daily diary study. Multilevel modelling revealed that smartphone checking, but not total smartphone screen time, predicted a greater occurrence of daily cognitive failures at the within-person level. Surprisingly, we also found negative within-person associations between smartphone screen time for social- and tools-related applications and daily cognitive failures, suggesting that some types of smartphone use may temporarily benefit one's cognitive functioning. This finding demonstrates the importance of studying the specific functions of smartphone use and their differential cognitive consequences, as well as highlights the complex relations between smartphone use and cognition.     

An exploratory investigation of the reliability and validity of the Independent-Interdependent Problem-Solving Style Scale

The Independent-Interdependent Problem-Solving Scale is based on Cross et al.'s conceptualisation of relational-interdependent self-construal. The IIPSS provides a relatively context-free measure of people's tendencies to solve problems independently or with the help of others. Because previous investigations have not provided extensive evidence for the reliability and validity of the IIPSS, the current research aimed to test the psychometric properties of this novel measure. Investigations of four student samples (combined N = 1157) and one sample comprised of academic researchers (N = 198) generally supported the reliability and validity of the IIPSS. Exploratory factor analysis of IIPSS items yielded a single factor structure. However, confirmatory factor analyses did not demonstrate good model fit for the one factor solution and instead yielded good model fit for two underlying factors. The IIPSS showed adequate test–retest reliability and predicted positive associations with social personality traits. It also showed no significant associations with measures of demand characteristics and social desirability. Future research needs to be undertaken to further assess the factor structure and address shortcomings of the present research such as utilising objective data in addition to self-reports to assess the scale's validity.     

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/.