Principals versus osmod: A comment on saito and otsu

Saito and Otsu (1988) compared their OSMOD method of nonmetric principal-component analysis to an early and incorrect implementation of the PRINCIPALS algorithm of Young, Takane, and de Leeuw (1978). In this comment we present results from the current, correct implementations of the algorithm.     

On component analyses

Principal components analysis can be redefined in terms of the regression of observed variables upon component variables. Two criteria for the adequacy of a component representation in this context are developed and are shown to lead to different component solutions. Both criteria are generalized to allow weighting, the choice of weights determining the scale invariance properties of the resulting solution. A theorem is presented giving necessary and sufficient conditions for equivalent component solutions under different choices of weighting. Applications of the theorem are discussed that involve the components analysis of linearly derived variables and of external variables.Preparation of this article was supported in part by National Institute of Aging Grant NIA-AG03164-03 to William Meredith.     

高中生的亲子沟通质量对情绪性刺激注意偏向的影响

为了探讨高中生亲子沟通质量对情绪性刺激注意偏向的影响,实验一采用亲子沟通量表,筛选出亲子沟通高分组和低分组,两组被试参加点探测任务,要求被试迅速判断情绪性图片出现后呈现的圆点的位置。实验为2（组别：高亲子沟通组、低亲子沟通组）×2（情绪类型：负性和正性）×2（探测点位置一致性：一致、不一致）的混合实验设计。结果发现,负性和正性刺激的不一致条件反应时均显著短于一致条件的反应时。实验二采用事件相关电位（ERP）记录了情绪性图片诱发的信号反应。结果发现,亲子沟通高分组对负性刺激所诱发的注意指向和集中（P2成分）及选择和巩固（P3成分）显著高于低分组。以上结果表明,亲子沟通较好,在注意早期定向和选择中会对负性情绪刺激产生注意偏向。亲子沟通较差,则对情绪性的刺激没有明显的偏向性,类似于“漠不关心”的状态。     

The linear regression line as a judgmental aid in visual analysis of serially dependent A-B time-series data

The purpose of this study was to investigate the effects of different types and magnitudes of serial dependence (first-order moving average and autoregression) and of linear regression lines within experimental phases on the agreement between results of visual and results of statistical data analyses. The stimulus material consisted of computer-simulated A-B-design data graphs. The time series were generated with a constant variance, varying degrees of treatment effects (changes in level), five conditions of serial dependency, and with or without linear regression lines. The material was presented to three groups of student raters (n₁=52, n₂=14, n₃=17) who rated the treatment effect in the graphs on a five-point scale. These ratings were compared with statistical results (time-series analyses). Each group had to interpret 70 graphs, 35 of which had regression lines. Data were analyzed by means of two three-factor and one four-factor ANOVA and by graphic display. The linear regression lines generally enhanced the agreement between the raters' estimations and the statistical results. Serial dependency also increased the agreement between the two analysis methods. However, with strong autoregression processes in the data, the raters tended to overestimate treatment effects relative to time-series analysis.Parts of this study were presented at the World Congress on Behavior Therapy, Washington, DC, December 11, 1983. The authors wish to express their appreciation to Christoph Bonk and Willi Ecker for their extensive collaboration in data analysis and for their assistance in carrying out the study.     

Describing the elephant: Structure and function in multivariate data

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain.     

Redundancy analysis an alternative for canonical correlation analysis

A component method is presented maximizing Stewart and Love's redundancy index. Relationships with multiple correlation and principal component analysis are pointed out and a rotational procedure for obtaining bi-orthogonal variates is given. An elaborate example comparing canonical correlation analysis and redundancy analysis on artificial data is presented.A Fortran IV program for the method of redundancy analysis described in this paper can be obtained from the author upon request.     

Some developments in multivariate generalizability

This article is concerned with estimation of components of maximum generalizability in multifacet experimental designs involving multiple dependent measures. Within a Type II multivariate analysis of variance framework, components of maximum generalizability are defined as those composites of the dependent measures that maximize universe score variance for persons relative to observed score variance. The coefficient of maximum generalizability, expressed as a function of variance component matrices, is shown to equal the squared canonical correlation between true and observed scores. Emphasis is placed on estimation of variance component matrices, on the distinction between generalizability- and decision-studies, and on extension to multifacet designs involving crossed and nested facets. An example of a two-facet partially nested design is provided.Appreciation is expressed to the Office of Research in Medical Education, University of Texas Medical Branch, for permitting use of their data.     

Factors influencing responding under multiple schedules of conditioned and unconditioned reinforcement

Two experiments examined pigeons' responses under multiple schedules of conditioned and unconditioned reinforcement. In one component, responses produced food according to a fixed-interval schedule; in a second component, responses produced brief stimuli according to a fixed-ratio schedule. When brief-stimulus presentations were paired with food in the first component, rates in the second component were usually higher than 10 responses per minute. When pairing in the first component was eliminated, responding continued to be maintained in the second component. Elimination of food presentation from the first component substantially decreased responding in the second component, even though the brief stimulus had not been paired with food. Experiment II demonstrated that response rate was affected by the duration of both the second component and the brief stimulus. The results suggest that three conditions are important in maintaining responding with brief-stimulus presentations: (1) pairing the brief stimulus, at least initially, with food, (2) maintaining unconditioned reinforcement in one component, and (3) employing optimal brief-stimulus and component durations.     

The extension of component analysis to four-mode matrices

A model for four-mode component analysis is developed and presented. The developed model, which is an extension of Tucker's three-mode factor analytic model, allows for the simultaneous analysis of all modes of a four-mode data matrix and the consideration of relationships among the modes. An empirical example based upon viewer perceptions of repetitive advertising shows the four-mode model applicable to real data.This research was supported by the University of Kansas School of Business Research Fund provided by the Fourth National Bank & Trust Company, Wichita. The ideas and opinions expressed herein are solely those of the author.     

On the effects of component durations and component reinforcement rates in multiple schedules

Four experiments, each using the same six pigeons, investigated the effects of varying component durations and component reinforcement rates in multiple variable-interval schedules. Experiment 1 used unequal component durations in which one component was five times the duration of the other, and the shorter component was varied over conditions from 120 seconds to 5 seconds. The schedules were varied over five values for each pair of component durations. Sensitivity to reinforcement rate changes was the same at all component durations. In Experiment 2, both component durations were 5 seconds, and the schedules were again varied using both one and two response keys. Sensitivity to reinforcement was not different from the values found in Experiment 1. In Experiment 3, various manipulations, including body-weight changes, reinforcer duration changes, blackouts, hopper lights correlated with keylights, and overall reinforcement rate changes were carried out. No reliable increase in reinforcement sensitivity resulted from any manipulation. Finally, in Experiment 4, reinforcement rates in the two components were kept constant and unequal, and the component durations were varied. Shorter components produced significantly increased response rates normally in the higher reinforcement rate component, but schedule reversals at short component durations eliminated the response rate increases. The effects of component duration on multiple schedule performance cannot be interpreted as changing sensitivity to reinforcement nor to changing bias.