Swedish translations of the Suicide Probability Scale, Perceived Social Support from Friends and Family Scales, and the Scale for Interpersonal Behavior: A reliability analysis

The Suicide Probability Scale (SPS), the Perceived Social Support (PSS) from Friends (PSS-Fr) and Family (PSS-Fa) scales, and the Scale for Interpersonal Behavior (SIB) were translated into Swedish and their reliability was estimated in a university student sample. The reliability coefficients indicated that both subscales and the total scales of the SPS, PSS, and SIB possess highly adequate reliabilities. The intercorrelations among the subscales and between the subscales and the total scales were found to be highly significant. The results of the study supported the use of SPS, PSS-Fr and PSS-Fa, and the SIB as reliable methods for assessing suicide risk, perceived social support from friends and family, and assertive behavior.     

Category discrimination by pigeons using five polymorphous features

Eight pigeons were trained to discriminate between sets of color photographs of natural scenes. The scenes differed along five two-valued dimensions (site, weather, camera distance, camera orientation, and camera height), and all combinations of the feature values were used. One value of each dimension was designated as positive, and slides containing three or more positive feature values were members of the positive stimulus set. Thus, each feature had an equal, low, correlation with reinforcement, and all features had zero correlations with each other. Seven of the 8 pigeons learned this discrimination, and their responding came under the control of all five features. Within the positive and negative stimulus sets, response rates were higher to stimuli that contained more positive feature values. Once discrimination had been achieved, reversal training was given using a subset of the slides. In this subset, only a single feature was correlated with reinforcement. All pigeons learned this reversal successfully and generalized it to additional photographs with the same feature content. After reversal, the original reinforcement contingencies were reinstated, and training was continued using all the slides except those that had been used in reversal. Reversal generalized to these slides to some extent. Analysis of the response rates to individual slides showed that, compared with prereversal training, only the feature that had been subjected to reversal contingencies showed a reversed correlation with response rate. The remaining features showed the same correlation with response rate as they had before reversal training. Thus, reversal on some members of a category following category discrimination training led to generalization to stimuli within the category that were not involved in the reversal, but not to features that were not reversed. It is therefore inappropriate to refer to the pigeons as learning a concept.     

Three-term contingency patterns in mother-child verbal interactions during first-language acquisition

Selections from a large longitudinal data set of verbal interactions between a mother and her child are presented. Two sets of three-term contingency sequences that seemed to reflect maternal rewards and corrections were noted. Both the antecedents as well as the immediate consequences of maternal interventions are presented to explore training and learning processes. The observed frequencies of three-step sequences are compared to those expected based upon Markov-chain logic to substantiate the patterning of the interactions. Behavioral conceptualizations of the learning process are supported by these analyses, although their sufficiency is questioned. It is suggested that maternal rewards and corrections should be integrated with perceptual, cognitive, and social learning conceptualizations in a skill-learning approach to explain the complexity of language transmission and acquisition processes.     

Social networks with two sets of actors

Traditional network research analyzes relational ties within a single group of actors: the models presented in this paper involve relational ties exist beteen two distinct sets of actors. Statistical models for traditional networks in which relations are measured within a group simplify when modeling unidirectional relations measured between groups. The traditional paradigm results in a one-mode socionatrix; the network paradigm considered in this paper results in a two-mode socionatrix; A statistical model is presented, illustrated on a sample data set, and compared to its traditional counterpart. Extensions are discussed, including those that model multivariate relations simultaneously, and those that allow for the inclustion of attributes of the individuals in the group.We thank the Editor and two anonymous reviewers for their helpful comments. We are also grateful to George Barnett for allowing us to analyze his data.     

Interpreting canonical correlation analysis through biplots of structure correlations and weights

This paper extends the biplot technique to canonical correlation analysis and redundancy analysis. The plot of structure correlations is shown to the optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression and canonical correlation analysis/redundancy analysis is exploited for producing an optimal biplot that displays a matrix of regression coefficients. This plot can be made from the canonical weights of the predictors and the structure correlations of the criterion variables. An example is used to show how the proposed biplots may be interpreted.     

New lower and upper bounds for communality in factor analysis

We derive several relationships between communalities and the eigenvalues for ap ×p correlation matrix under the usual factor analysis model. For suitable choices ofj, _j (), where _j () is thej-th largest eigenvalue of , provides either a lower or an upper bound to the communalities for some of the variables. We show that for at least one variable, 1 - _p () improves on the use of squared mulitiple correlation coefficient as a lower bound.This research was done while the second author was at Tokyo Institute of Technology.     

Type A behaviour in pre-school children: Interrater reliability, stability over six months and subcomponents

Type A behaviour was measured in 250 boys and 245 girls (ages 3-7) using a Swedish version of Matthews' Youth Test for Health (MYTH). In addition, speech and voice characteristics (speed, loudness, response latency) were measured. Each child was independently rated by two play group leaders and measurements were repeated after a six month interval. Interrater reliability (first session) was 0.76 for both sexes and did not change with the age of the child. Boys obtained significantly (p less than 0.0001) higher scores than girls. Correlations over the six month period were 0.64 and 0.60 (p less than 0.001) for the two possible combinations of different raters and slightly higher (0.75 and 0.68, p less than 0.001) when the same rater was used. Two components of Type A behaviour were identified from a factor analysis of the data: impatience and competitiveness, and they accounted for 57% of the total variance. As for the total scale, boys obtained significantly higher scores than girls on both subscales. Scores on the competitiveness scale increased systematically with age. Speech and voice characteristics correlated significantly with Type A scores (total Type A behaviour: r = 0.50, using different raters, and 0.71, using the same raters, p less than 0.001) and, once again, boys obtained significantly (p less than 0.001) higher scores than girls. The results are in close agreement with the American findings from the original scale. It was concluded that the scale provides a reliable and valid instrument for measuring Type A or Type A-like behaviours in pre-school children.     

Continuous and discrete latent structure models for item response data

Relations are examined between latent trait and latent class models for item response data. Conditions are given for the two-latent class and two-parameter normal ogive models to agree, and relations between their item parameters are presented. Generalizationss are then made to continuous models with more than one latent trait and discrete models with more than two latent classes, and methods are presented for relating latent class models to factor models for dichotomized variables. Results are illustrated using data from the Law School Admission Test, previously analyzed by several authors.     

Finite mixtures in confirmatory factor-analysis models

In this paper, various types of finite mixtures of confirmatory factor-analysis models are proposed for handling data heterogeneity. Under the proposed mixture approach, observations are assumed to be drawn from mixtures of distinct confirmatory factor-analysis models. But each observation does not need to be identified to a particular model prior to model fitting. Several classes of mixture models are proposed. These models differ by their unique representations of data heterogeneity. Three different sampling schemes for these mixture models are distinguished. A mixed type of the these three sampling schemes is considered throughout this article. The proposed mixture approach reduces to regular multiple-group confirmatory factor-analysis under a restrictive sampling scheme, in which the structural equation model for each observation is assumed to be known. By assuming a mixture of multivariate normals for the data, maximum likelihood estimation using the EM (Expectation-Maximization) algorithm and the AS (Approximate-Scoring) method are developed, respectively. Some mixture models were fitted to a real data set for illustrating the application of the theory. Although the EM algorithm and the AS method gave similar sets of parameter estimates, the AS method was found computationally more efficient than the EM algorithm. Some comments on applying the mixture approach to structural equation modeling are made.Note: This paper is one of the Psychometric Society's 1995 Dissertation Award papers.—EditorThis article is based on the dissertation of the author. The author would like to thank Peter Bentler, who was the dissertation chair, for guidance and encouragement of this work. Eric Holman, Robert Jennrich, Bengt Muthén, and Thomas Wickens, who served as the committee members for the dissertation, had been very supportive and helpful. Michael Browne is appreciated for discussing some important points about the use of the approximate information in the dissertation. Thanks also go to an anonymous associate editor, whose comments were very useful for the revision of an earlier version of this article.     

计量测验效度的一种方法——测量目标结构空间的实证性因素分析

该文提出了计量测验效度的一种统一方法。首先,根据测量目标由测验编制者提出一组可能的线性拟合模型,拟合模型的集合称作“测量目标结构空间”。然后运用实证性因素分析对各个假设的拟合模型进行多方面的拟合良好性检验。若其中的某一假设模型能最优地拟合预测资料,则该模型中的公共因素的方差总贡献与观测变量的总方差的比就可定义为该测验的效度