Test theory without an answer key

A general model is presented for homogeneous, dichotomous items when the answer key is not known a priori. The model is structurally related to the two-class latent structure model with the roles of respondents and items interchanged. For very small sets of respondents, iterative maximum likelihood estimates of the parameters can be obtained by existing methods. For other situations, new estimation methods are developed and assessed with Monte Carlo data. The answer key can be accurately reconstructed with relatively small sets of respondents. The model is useful when a researcher wants to study objectively the knowledge possessed by members of a culturally coherent group that the researcher is not a member of.This research was supported by NSF Grant No. SES-8320173 to the authors. We gratefully acknowledge comments and suggestions from John Boyd, Tarow Indow, and Kathy Maher as well as the editor and several anonymous referees.     

An availability bias in professional judgment

When branches of a fault tree are pruned, subjects do not fully transfer the probability of those branches to the ‘all other’ category. This underestimation of the catch-all probability has been interpreted as an ‘out of sight, out of mind’ form of the availability bias. The present work replicates this underestimation bias with professional managers. It then demonstrates the effectiveness of a corrective tactic, extending the tree by generating additional causes, and also reveals that more easily retrieved short-term causes dominate the generation process. These results do not differ across managers' culture, education or experience. After evaluating such alternative explanations as category redefinition, we conclude that availability is a major cause, though possibly not the sole cause, of the underestimation bias.     

Maximizing versus matching on concurrent variable-interval schedules

Maximization and matching predictions were examined for a time-based analogue of the concurrent variable-interval variable-ratio schedule. One alternative was a variable interval whose time base operated relatively independent of the schedule chosen, and the other was a discontinuous variable interval for which timing progressed only when selected. Pigeons switched between schedules by pecking a changeover key. The maximization hypothesis predicts that subjects will show a bias toward the discontinuous variable interval and undermatching; however the obtained results conformed closely to the predictions of the matching law. Finally, a quantitative comparison was made of the bias and sensitivity estimates obtained in published concurrent variable-interval variable-ratio analogue studies. Results indicated that only the ratio-based analogue of the concurrent variable interval variable ratio studied by Green, Rachlin, and Hanson (1983) produced significant bias toward the variable-ratio alternative and undermatching, as predicted by reinforcement maximization.     

A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the standard (Two-Way) MDS model. In this Extended Two-Way Euclidean model then stimuli (or other objects) are assumed to be characterized by coordinates onR common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between objecti and objectj then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity,s _j , can be thought of as the sum of squares of coordinates on those dimensions specific to objecti, all of which have nonzero coordinatesonly for objecti. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)We further assume that _ij =F(d _ij ) +e _ij where _ij is the proximity value (e.g., similarity or dissimilarity) of objectsi andj,d _ij is the extended Euclidean distance defined above, whilee _ij is an error term assumed i.i.d.N(0, ²).F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition toR common) dimensions.This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.     

EFFECTS OF WAIT-TIME AND INTERTRIAL INTERVAL DURATIONS ON LEARNING BY CHILDREN WITH MULTIPLE HANDICAPS

We investigated the influence of teacher wait-time and intertrial interval durations on the performance of 4 multiply handicapped students during instruction in 10 skills. Four experimental conditions were evaluated: long wait-time and long intertrial interval, long wait-time and short intertrial interval, short wait-time and long intertrial interval, and short wait-time and short intertrial interval. Instructors attempted to keep short intervals as close as possible to 1 s and long intervals as close as possible to 10 s for both variables. Results showed that student performance was superior under the long wait-time conditions irrespective of the length of the intertrial interval.     

Nonconvergence,improper solutions,and starting values in lisrel maximum likelihood estimation

In the framework of a robustness study on maximum likelihood estimation with LISREL three types of problems are dealt with: nonconvergence, improper solutions, and choice of starting values. The purpose of the paper is to illustrate why and to what extent these problems are of importance for users of LISREL. The ways in which these issues may affect the design and conclusions of robustness research is also discussed.     

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.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

Drug-behavior interaction history: modification of the effects of morphine on punished behavior.

Squirrel monkeys were trained to respond under a multiple fixed-interval, fixed-interval schedule in which the first response after 5 min terminated a visual stimulus in the presence of which electric shocks could occur. During one component of the schedule, correlated with one color of stimulus lights, every 30th response also produced electric shock; responding was suppressed during this component to approximately 10 to 12% of that occurring in the alternate component in which responding was not punished. In contrast to previous research, morphine (0.03 to 1.0 mg/kg) increased punished responding. Unpunished responding, however, was either not affected or decreased at doses of morphine that increased punished responding. Increases in rate of punished responding also occurred when the single-schedule punishment condition was studied alone in these animals. Subsequent experimentation, which systematically analyzed the development of the rate-enhancing effects of morphine on punished responding, involved the study of drug effects in additional monkeys trained initially under a single-schedule punishment condition. The effects of morphine on punished responding were studied before, after, and then during exposure to the multiple schedule that included a component in which responding was not punished. Increases in response rate with morphine did not occur until it was administered during exposure to the multiple schedule that included a component in which responding was not punished. As with the other monkeys, once the rate increases in punished responding occurred under the multiple schedule, these effects of morphine persisted, even when the multiple schedule was removed.(ABSTRACT TRUNCATED AT 250 WORDS)     

INCREASING APPOINTMENT KEEPING BY REDUCING THE CALL-APPOINTMENT INTERVAL

We examined the effect of reducing the interval between a patient's call for an appointment and the appointment itself. In Experiment 1, patients calling a family planning unit of a public health department were assigned appointments within either 1 or 3 weeks of their call. Data on patient “shows” and “no-shows” were recorded weekly for 6 weeks. Show rates for those in the 1-week appointment group were significantly higher than those in the 3-week group. In Experiment 2, patients were assigned to appointment dates either the next operating clinic day (next-day group) or 2 weeks from the call date (2-week group). Show rates for those in the next-day group were significantly better than show rates for patients in the 2-week group. Clinic productivity, time spent with patients, and consumer satisfaction were also assessed. Implications for appointment scheduling are discussed.