Improving the efficiency and effectiveness of interactively selected MDS data designs

Two revisions of interactive MDS data selection procedures are presented. One revision improves the estimates of the MDS parameters by adding an analysis of the volume of the spatial coordinates of stimuli. Frames of stimuli augmented by an analysis of volume should more nearly surround the swarm of stimulus points. The second revision, based on randomly ordering the list of stimuli, permits more efficient data designs to be selected by reducing the number of judgments collected but never analyzed.This investigation was supported in part by a Research Scientist Development Award (KO2-DA00017), research grants (DA01070 and MH24149) from the U.S. Public Health Service, and a research and instruction award (IUC 2-47622-1990) from the University of California.     

On the precision of a euclidean structure

This paper is concerned with the development of a measure of the precision of a multidimensional euclidean structure. The measure is a precision index for each point in the structure, assuming that all the other points are precisely located. The measure is defined and two numerical methods are presented for its calculation. A small Monte Carlo study of the measure's behavior is performed and findings discussed.The authors are indebted to Bert F. Green, Jr., Ronald Helms, Andrea Sedlak, and three anonymous reviewers for their valuable comments on earlier drafts of this paper.     

A durable recording and feedback system

A durable low-cost data-recording and feedback system is described. Data transcribed with a special pen onto a plastic sheet can be removed only with two solvents. These data can be displayed for feedback purposes without fear of destruction.     

The analysis of group contingency data

The data analyses utilized in group contingency projects are reviewed. Previous studies are cited to emphasize advantages of nonconsolidated ("individual") over consolidated analyses. Several procedures are described that enable applied researchers to incorporate nonconsolidated data analyses in group contingency studies.     

Observer agreement, credibility, and judgment: some considerations in presenting observer agreement data

Graphical and statistical indices employed to represent observer agreement in interval recording are described as "judgmental aids", stimuli to which the researcher and scientific community must respond when viewing observer agreement data. The advantages and limitations of plotting calibrating observer agreement data and reporting conventional statistical aids are discussed in the context of their utility for researchers and research consumers of applied behavior analysis. It is argued that plotting calibrating observer data is a useful supplement to statistical aids for researchers but is of only limited utility for research consumers. Alternatives to conventional per cent agreement statistics for research consumers include reporting special agreement estimates (e.g., per cent occurrence agreement and nonoccurrence agreement) and correlational statistics (e.g., Kappa and Phi).     

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.     

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.     

A thurstonian pairwise choice model with univariate and multivariate spline transformations

A probabilistic choice model is developed for paired comparisons data about psychophysical stimuli. The model is based on Thurstone's Law of Comparative Judgment Case V and assumes that each stimulus is measured on a small number of physical variables. The utility of a stimulus is related to its values on the physical variables either by means of an additive univariate spline model or by means of multivariate spline model. In the additive univariate spline model, a separate univariate spline transformation is estimated for each physical dimension and the utility of a stimulus is assumed to be an additive combination of these transformed values. In the multivariate spline model, the utility of a stimulus is assumed to be a general multivariate spline function in the physical variables. The use of B splines for estimating the transformation functions is discussed and it is shown how B splines can be generalized to the multivariate case by using as basis functions tensor products of the univariate basis functions. A maximum likelihood estimation procedure for the Thurstone Case V model with spline transformation is described and applied for illustrative purposes to various artificial and real data sets. Finally, the model is extended using a latent class approach to the case where there are unreplicated paired comparisons data from a relatively large number of subjects drawn from a heterogeneous population. An EM algorithm for estimating the parameters in this extended model is outlined and illustrated on some real data.The first author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. The authors are indebted to Ulf Böckenholt and Yoshio Takane for useful comments on an earlier draft of this paper.     

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.