Fitting cross-classification table data to models when observations are subject to classification error

Individuals are classified in a cross-classification table where two behavioral observations on each individual determine the classification. The problem is to test certain structural models assumed to underlie the cross-classified observations. A minimum chi-square test procedure is proposed.     

A biphasic 60-Hz synchronizer circuit for the recording of visually evoked responses in high-noise environments

A simple synchronizer clock circuit is described that aids in the rejection of 60-Hz noise from evoked potential measurements.     

Development of an on-line problem-oriented system for the evaluation of mental health treatment services

Current on-line assessment applications are limited to mimicking instruments designed for paper and pencil. The development of an instrument designed specifically for on-line technology is described. The instrument provides research data on treatment effectiveness, clinical information for treatment planning, and management information for resource allocation.     

Vowel recognition: Inferences from studies of forward and backward masking

The recognition of brief vowels was studied in forward and backward masking tasks. In a series of experiments in which both target and mask parameters were systematically varied, two populations of subjects were identified. The majority (Nonmaskers) evidenced little masking at any interstimulus interval, while relatively fewer subjects (Maskers) evidenced backward masking (but not forward masking) over a 100-200 ms interval. Increasing target set size systematically increased masking for the Maskers but not for the Nonmaskers. Adding white noise to the targets increased the extent of masking for both groups. These results suggest (1) that masking does not impose a substantive constraint on vowel perception in running speech, and (2) that multiple strategies may exist for vowel recognition.     

Performing item- and subject-based analyses using ALICE

By using the program ALICE, we are able to perform subject- and item-based analyses of randomly ordered sets of data without the usual computational effort. The program allows us to transform data and combine, partition, or systematically manipulate them by simple, readily interpretable instructions. Of equal importance to us is the ease with which students and staff learn to use the program independently.