Testing the identity of trends under the restriction of monotonicity in repeated measures designs

Procedures are described for the analysis of profiles of means in repeated measures designs under order restriction for patterns of mean change. The exact likelihood ratio is derived for the case of two groups of subjects, and a computationally simple alternative to the exact likelihood ratio test is provided for designs involving more than two groups. Tables of critical values are provided for the case of simple-order alternatives.The author wishes to thank Joseph C. Saunders for his editorial assistance.     

Context specificity of operant discrimination performance in pigeons

In an experiment designed to investigate odors as potential retrieval cues in pigeons' memory (i.e., as conditional stimuli), 8 pigeons first learned to peck a red keylight (S+, reinforced) and not a blue one (S−, extinguished) in the presence of either a eucalyptus oil or isoamyl acetate odor. They were repeatedly switched between two chambers with the same odor to habituate any reaction to the switching that would be required for eventual testing for conditional control by the odors. Next, the birds learned the reversal (blue S+, red S−) in the presence of the alternative odor in one of these chambers. When the birds were then switched to the alternative chamber for additional training, although the odor was still appropriate to the reversal problem, behavior appropriate to the original training condition recurred. Testing indicated that reversal performance was specific to the one chamber in which it had been trained.     

Maximum information per time unit designs for continuous online item calibration

Previous designs for online calibration have only considered examinees’ responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D-optimal per expectation time unit design (D-ET) and the D-optimal per time unit design (D-T). The former method uses the conditional maximum likelihood estimation (CMLE) method to estimate the expected response times, while the latter employs the nonparametric k-nearest-neighbour method to predict the response times. Simulations were conducted to compare the two new designs with the D-optimal online calibration design (D design) in the context of continuous online calibration. In addition, a preliminary study was carried out to evaluate the performance of CMLE prior to its application in D-ET. The results showed that, compared to the D design, the D-ET and D-T designs saved response time and accrued larger calibration information per time unit, without sacrificing item calibration precision.     

Evaluating the duration of the competing response in habit reversal: a parametric analysis.

The effectiveness of habit reversal was compared across three different competing response (CR) durations. Results showed that 1‐min and 3‐min CR durations were associated with short‐term and long‐term increases in nail length for people who bit their nails. A 5‐s CR duration produced immediate increases in nail length that were not maintained. Social validity data were consistent with these findings.     

Minimax d-optimal designs for item response theory models

Various different item response theory (IRT) models can be used in educational and psychological measurement to analyze test data. One of the major drawbacks of these models is that efficient parameter estimation can only be achieved with very large data sets. Therefore, it is often worthwhile to search for designs of the test data that in some way will optimize the parameter estimates. The results from the statistical theory on optimal design can be applied for efficient estimation of the parameters.A major problem in finding an optimal design for IRT models is that the designs are only optimal for a given set of parameters, that is, they are locally optimal. Locally optimal designs can be constructed with a sequential design procedure. In this paper minimax designs are proposed for IRT models to overcome the problem of local optimality. Minimax designs are compared to sequentially constructed designs for the two parameter logistic model and the results show that minimax design can be nearly as efficient as sequentially constructed designs.     

Using the dual‐criteria methods to supplement visual inspection: An analysis of nonsimulated data

The purpose of our study was to examine the probability of observing false positives in nonsimulated data using the dual‐criteria methods. We extracted data from published studies to produce a series of 16,927 datasets and then assessed the proportion of false positives for various phase lengths. Our results indicate that collecting at least three data points in the first phase (Phase A) and at least five data points in the second phase (Phase B) is generally sufficient to produce acceptable levels of false positives.     

Less is better: when low-value options are valued more highly than high-value options

This research demonstrates a less-is-better effect in three contexts: (1) a person giving a $45 scarf as a gift was perceived to be more generous than one giving a $55 coat; (2) an overfilled ice cream serving with 7 oz of ice cream was valued more than an underfilled serving with 8 oz of ice cream; (3) a dinnerware set with 24 intact pieces was judged more favourably than one with 31 intact pieces (including the same 24) plus a few broken ones. This less-is-better effect occurred only when the options were evaluated separately, and reversed itself when the options were juxtaposed. These results are explained in terms of the evaluability hypothesis, which states that separate evaluations of objects are often influenced by attributes which are easy to evaluate rather than by those which are important. © 1998 John Wiley & Sons, Ltd.