Data‐driven approaches to information access

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.     

PROMOTING LAW ENFORCEMENT FOR CHILD PROTECTION: A COMMUNITY ANALYSIS

The Colorado Occupant Protection Project (COPP) intervention provided police with brief instruction concerning the importance of citations for drivers' failure to use child safety seats and special coupons to accompany citations. Coupons were exchangeable by drivers for a safety seat and brief training in its use, plus a waiver of the $50 citation fine. Over 4.5 years of archival records were employed, using an ABA design and a comparison community to evaluate the program. Few tickets were issued for nonuse of safety seats during the 3-year baseline in either community. Citations for nonuse of safety seats increased to over 50 per month during the intervention period at the test site, whereas rates remained essentially zero at the comparison site. After the COPP intervention was removed at the intervention site, citation rates for nonuse of safety seats decreased to about 15 per month. Differences between intervention conditions and settings were statistically significant. During the intervention, officers were 44 times more likely to write citations than were controls. Results suggested that a behavioral program can increase police citation writing for child protection purposes.     

AN ANALYSIS OF MAINTENANCE FOLLOWING FUNCTIONAL COMMUNICATION TRAINING

The multiple and long-term effects of functional communication training relative to a common reductive procedure (time-out from positive reinforcement) were evaluated. Twelve children participated in a functional analysis of their challenging behaviors (Study 1), which implicated adult attention as a maintaining variable. The children were then matched for chronological age, mental age, and language age and assigned to two groups. One group received functional communication training as an intervention for their challenging behavior, and the second group received time-out as a contrast. Both interventions were initially successful (Study 2), but durable results were achieved only with the group that received functional communication training across different stimulus conditions (Study 3). Students whose challenging behaviors were previously reduced with time-out resumed these behaviors in the presence of naive teachers unaware of the children's intervention history. The value of teaching communicative responses to promote maintenance is discussed as it relates to the concept of functional equivalence.     

Imputation of missing categorical data by maximizing internal consistency

This paper suggests a method to supplant missing categorical data by reasonable replacements. These replacements will maximize the consistency of the completed data as measured by Guttman's squared correlation ratio. The text outlines a solution of the optimization problem, describes relationships with the relevant psychometric theory, and studies some properties of the method in detail. The main result is that the average correlation should be at least 0.50 before the method becomes practical. At that point, the technique gives reasonable results up to 10–15% missing data.We thank Anneke Bloemhoff of NIPG-TNO for compiling and making the Dutch Life Style Survey data available to use, and Chantal Houée and Thérèse Bardaine, IUT, Vannes, France, exchange students under the COMETT program of the EC, for computational assistance. We also thank Donald Rubin, the Editors and several anonymous reviewers for constructive suggestions.     

Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations

Millsap and Meredith (1988) have developed a generalization of principal components analysis for the simultaneous analysis of a number of variables observed in several populations or on several occasions. The algorithm they provide has some disadvantages. The present paper offers two alternating least squares algorithms for their method, suitable for small and large data sets, respectively. Lower and upper bounds are given for the loss function to be minimized in the Millsap and Meredith method. These can serve to indicate whether or not a global optimum for the simultaneous components analysis problem has been attained.Financial support by the Netherlands organization for scientific research (NWO) is gratefully acknowledged.     

Robust interactive decision-analysis (RID): Behavioral results and implications

We have proposed a novel interactive procedure for performing decision analysis, called Robust Interactive Decision Analysis (RID), which permits a decision maker (DM) to voluntarily and interactively express strong (viz, sure) binary preferences for actions, partial decision functions, and full decision functions, and only imprecise probability and utility function assessments. These serve as INPUTS TO operators to prune the state probability space and decision space until an optimal choice strategy is obtained. The viability of the RID approach depends on a DM's ability to provide such information consistently and meaningfully. On a limited scale we experimentally investigate the behavioral implications of the RID method in order to ascertain its potential operational feasibility and viability. More specifically, we examine whether a DM can (1) express strong preferences between pairs of vectors of unconditional and conditional payoffs or utilities consistently; (2) provide imprecise (ordinal and interval) state probabilities that are individually as well as mutually consistent with the state probabilities imputed from the expressed strong preferences. The results show that a DM can provide strong individually and mutually consistent preference and ordinal probability information. Moreover, most individuals also appear to be able to provide interval probabilities that are individually and mutually consistent with their strong preference inputs. However, the several violations observed, our small sample size, and the limited scope of our investigation suggest that further experimentation is needed to determine whether and/or how such inputs should be elicited. Overall, the results indicate that the RID method is behaviorally viable.     

Three-way metric unfolding via alternating weighted least squares

Three-way unfolding was developed by DeSarbo (1978) and reported in DeSarbo and Carroll (1980, 1981) as a new model to accommodate the analysis of two-mode three-way data (e.g., nonsymmetric proximities for stimulus objects collected over time) and three-mode, three-way data (e.g., subjects rendering preference judgments for various stimuli in different usage occasions or situations). This paper presents a revised objective function and new algorithm which attempt to prevent the common type of degenerate solutions encountered in typical unfolding analysis. We begin with an introduction of the problem and a review of three-way unfolding. The three-way unfolding model, weighted objective function, and new algorithm are presented. Monte Carlo work via a fractional factorial experimental design is described investigating the effect of several data and model factors on overall algorithm performance. Finally, three applications of the methodology are reported illustrating the flexibility and robustness of the procedure.We wish to thank the editor and reviewers for their insightful comments.     

Iterative least squares estimates of communality: Initial estimate need not affect stabilized value

A common criticism of iterative least squares estimates of communality is that method of initial estimation may influence stabilized values. As little systematic research on this topic has been performed, the criticism appears to be based on cumulated experience with empirical data sets. In the present paper, two studies are reported in which four types of initial estimate (unities, squared multiple correlations, highestr, and zeroes) and four levels of convergence criterion were employed using four widely available computer packages (BMDP, SAS, SPSS, and SOUPAC). The results suggest that initial estimates have no effect on stabilized communality estimates when a stringent criterion for convergence is used, whereas initial estimates appear to affect stabilized values employing rather gross convergence criteria. There were no differences among the four computer packages for matrices without Heywood cases.     

A brief neuropsychological screening battery to detect brain damage in a psychiatric population

Seventy-five patients admitted to the psychiatric unit of a general hospital were administered a brief neuropsychological screening battery. Discharge diagnoses obtained from the medical records revealed that 20% had brainbased pathology. A discriminant function analysis resulted in a five-variable model (Symbol-Digit Modalities Test, educational level, Psychiatric-Organic subscale of the MMPI, and Shipley Vocabulary and Abstraction scores) which correctly classified 84% of the subjects, with 87% correct identification of the brain-damaged group. A jackknife cross-validation procedure resulted in comparable hit rates.     

On structural equation modeling with data that are not missing completely at random

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.The research of the first author was supported by grant No. SES-8312583 from the National Science Foundation and by a Spencer Foundation grant. We wish to thank Chuen-Rong Chan for drawing the path diagram.