THE BEHAVIOR OBSERVATION INSTRUMENT: A METHOD OF DIRECT OBSERVATION FOR PROGRAM EVALUATION1

The background and development of a multicategory direct observation system, the Behavior Observation Instrument (BOI), is described. This time-sampling procedure for recording the behavior of persons is demonstrated in several treatment settings and the results applied to issues of program evaluation. Elements that have prevented direct observation from being widely adopted, such as costs, manpower, and training requirements, are systematically analyzed. A basic psychometric analysis of the instrument is used to determine optimum frequency and duration of observation intervals as well as observer agreement. The results imply that direct observation methods, once assumed by some to belong to the special province of the single-subject design, can be used to assess the effects of programs on groups of psychiatric clients in an efficient and economic manner.     

Tests of symmetry in three-way contingency tables

Three-dimensional contingency tables are analyzed, with one variable (e.g., sex) as a factor, and with a natural relation between the other variables (e.g. left and right eye vision). Models of special interest, like symmetry and proportional symmetry between the related variables, and homogeneity across the factor levels, are investigated. Maximum likelihood estimators of parameters and partitions of chi-square goodness-of-fit statistics are explicitly presented; the independence of certain models is noted, and an example is discussed.     

Seat belt usage: A potential target for applied behavior analysis

Results of 1,579 observations of cars entering or exiting campus parking lots showed direct relationships between seat belt wearing and the intrusiveness of the engineering device designed to induce belt usage, and between device intrusiveness and system defeat. For example, all drivers with working interlocks or unlimited buzzer reminders were wearing a seat belt; but 62% of the systems with interlocks or unlimited buzzers had been defeated, and only 15.9% of the drivers in these cars were wearing a seat belt. The normative data indicated marked ineffectiveness of the negative reinforcement contingencies implied by current seat belt inducement systems; but suggested that unlimited buzzer systems would be the optimal system currently available if contingencies were developed to discourage the disconnection and circumvention of such systems. Positive reinforcement strategies are discussed that would be quite feasible for large-scale promotion of seat belt usage.     

Selective punishment of interresponse times

Lever pressing by two squirrel monkeys was maintained under a variable-interval 60-second schedule of food presentation. When response-dependent electric shock was made contingent on comparatively long interresponse times, response rate increased, and further increases were obtained when the minimum interresponse-time requirement was decreased. When an equal proportion of responses produced shock without regard to interresponse time, rates decreased. Thus, shock contingent on long interresponse times selectively decreased the relative frequency of those interresponse times, and increased the relative frequency of shorter interresponse times, whereas shock delivered independent of interresponse times decreased the relative frequency of shorter interresponse times while increasing the frequency of longer ones. The results provide preliminary evidence that interresponse times may be differentiated by punishment, further supporting the notion that interresponse times may be considered functional units of behavior.     

A bayesian approach to confirmatory factor analysis

Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis. An iterative algorithm is developed to obtain the Bayes estimates. A numerical example based on longitudinal data is presented. A simulation study is designed to compare the Bayesian approach with the maximum likelihood method.Computer facilities were provided by the Computer Services Center, The Chinese University of Hong Kong.     

Testing for independence between pairs of autocorrelated binomial data sequences

A problem arises in analyzing the existence of interdependence between the behavioral sequences of two individuals: tests involving a statistic such as chi-square assume independent observations within each behavioral sequence, a condition which may not exist in actual practice. Using Monte Carlo simulations of binomial data sequences, we found that the use of a chi-square test frequently results in unacceptable Type I error rates when the data sequences are autocorrelated. We compared these results to those from two other methods designed specifically for testing for intersequence independence in the presence of intrasequence autocorrelation. The first method directly tests the intersequence correlation using an approximation of the variance of the intersequence correlation estimated from the sample autocorrelations. The second method uses tables of critical values of the intersequence correlation computed by Nakamuraet al. (J. Am. Stat. Assoc., 1976,71, 214–222). Although these methods were originally designed for normally distributed data, we found that both methods produced much better results than the uncorrected chi-square test when applied to binomial autocorrelated sequences. The superior method appears to be the variance approximation method, which resulted in Type I error rates that were generally less than or equal to 5% when the level of significance was set at .05.     

Maximum likelihood estimation of the joint covariance matrix for sections of tests given to distinct samples with application to test equating

Consider an old testX consisting ofs sections and two new testsY andZ similar toX consisting ofp andq sections respectively. All subjects are given testX plus two variable sections from either testY orZ. Different pairings of variable sections are given to each subsample of subjects. We present a method of estimating the covariance matrix of the combined test (X ₁, ...,X _s ,Y ₁, ...,Y _p ,Z ₁, ...,Z _q ) and describe an application of these estimation techniques to linear, observed-score, test equating.The author is indebted to Paul W. Holland and Donald B. Rubin for their encouragement and many helpful comments and suggestions that contributed significantly to the development of this paper.This research was supported by the Program Statistics Research Project of the ETS Research Statistics Group.     

A more powerful method for testing for agreement between a judge and a known standard

We assume that a judge's task is to categorize each ofN subjects into one ofr known classes. The design of primary interest is employed if the judge is presented withs groups, each containingr subjects, such that each group of sizer consists of exactly one subject of each of ther types. The probability distribution for the total number of correct choices is developed and used to test the null hypothesis that the judge is guessing in favor of the alternative that he or she is operating at a better than chance level. The power of the procedure is shown to be superior to two other procedures which appear in the literature.The authors are grateful for the suggestions of the referees and for computer funding provided by the Northeast Regional Data Center at the University of Florida.     

A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential components of microeconomic theory including convex indifference curves, constrained utility maximization, demand functions, et cetera. This paper presents a new stochastic MDS procedure called MICROSCALE that attempts to operationalize many of these traditional microeconomic concepts. First, we briefly review several existing MDS models that operate on paired comparisons data, noting the particular nature of the utility functions implied by each class of models. These utility assumptions are then directly contrasted to those of microeconomic theory. The new maximum likelihood based procedure, MICROSCALE, is presented, as well as the technical details of the estimation procedure. The results of a Monte Carlo analysis investigating the performance of the algorithm as a number of model, data, and error factors are experimentally manipulated are provided. Finally, an illustration in consumer psychology concerning a convenience sample of thirty consumers providing paired comparisons judgments for some fourteen brands of over-the-counter analgesics is discussed.