A glass rodent enclosure: Gerbil City

A glass housing unit for the study of rodent burrowing behavior is described. The results of 18 months of burrowing for a family ofMeriones unguiculatus are depicted. The system can be used to investigate relationships among the animals’ behaviors, communication processes, and ecology.     

Rank disagreement: A comparison of multi-criteria methodologies

A number of multi-criteria decision support techniques have emerged in recent years that use varying computational approaches to arrive at the most desirable solution and thereby ‘recommend’ a course of action. Decision makers who use the results of this analytic work should be assured that the computational schemes used by their supporting analysts or decision support software produce the appropriate solutions. We conducted a series of simulation experiments that compared the top-ranked options resulting from the computational algorithms that support Multi-Attribute Value Theory (MAVT) and three methods that are reported in the literature that allow rank reversals, the change in rank order of two options when an unrelated option is added or deleted from the analysis: the Analytical Hierarchy Process (AHP), Percentaging and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We also included a Fuzzy algorithm proposed by Yager to gauge its consistency with the other algorithms, even though it is not subject to rank reversals. These experiments demonstrated that the MAVT and AHP techniques, when provided with the same decision outcome data, very often identify the same alternative as ‘best’. The other techniques are noticeably less consistent with MAVT, the Fuzzy algorithm being the least consistent. The situations under which the most frequent and significant differences occurred were dependent upon the method. The results of our experiments indicate that other issues (e.g. the processes used for problem structuring and the elicitation of value weights) are likely to be of greater significance to problem outcome (based on our experience) than the choice between the computational algorithms of MAVT and AHP. The results cause us to be concerned about the use of the other methods.     

Does the effect of familiarity on proofreading change with encoding task and time?

The authors examined whether the beneficial effect of text familiarity on proofreading is dependent on the type of encoding operations performed on the text prior to proofreading (i.e., surface vs. deep), and on the time elapsed between encoding and proofreading. In the present study, familiarity, irrespective of the nature of the encoding operations and delay, improved proofreading times, whereas only surface encoding made proofreading more accurate. These results represent a challenge for the transfer-appropriate processing model of memory.     

Expanding the universe of categorical syllogisms: a challenge for reasoning researchers

Syllogistic reasoning, in which people identify conclusions from quantified premise pairs, remains a benchmark task whose patterns of data must be accounted for by general theories of deductive reasoning. However, psychologists have confined themselves to administering only the 64 premise pairs historically identified by Aristotle. By utilizing all combinations of negations, the present article identifies an expanded set of 576 premise pairs and gives the valid conclusions that they support. Many of these have interesting properties, and the identification of predictions and their verification will be an important next step for all proponents of such theories.     

Concurrent Validity of the Multidimensional Health Profile—Health Functioning Scales (MHP-H) in the Pre-operative Assessment of Applicants for Gastric Bypass Surgery

The concurrent validity of a brief screening test, the Multidimensional Health Profile-Health Functioning (MHP-H) was examined in conjunction with a much more lengthy pre-operative assessment procedure in 449 applicants for gastric bypass surgery (GBS). The MHP-H is a 69-item screening inventory that assesses five areas of health functioning: Adult Health History, Health Habits, Health Care Utilization, Health Beliefs and Attitudes, and Response to Illness. The more lengthy procedure included a comprehensive 273-item structured interview plus the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), Basic Personality Inventory (BPI), and Beck Depression Inventory (BDI). The MHP-H showed significant correspondence with the more comprehensive assessment on measures of potential relevance to outcome in GBS, supporting the use of the MHP-H as a low-cost alternative to the more extensive procedure. The utility of the MHP-Psychosocial Functioning (MHP-P), a companion instrument to the MHP-H, was demonstrated in an earlier study.     

Moderation Analysis Using a Two-Level Regression Model

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.     

The benefit of attention is not diminished when distributed over two simultaneous cues

Recent findings have suggested that transient attention can be triggered at two locations simultaneously. However, it is unclear whether doing so reduces the effect of attention at each attended location. In two experiments, we explored the consequences of dividing attention. In the first experiment, we compared the effects of one or two cues against an uncued baseline to determine the consequences of dividing attention in a paradigm with four rapid serial visual presentation (RSVP) streams. The results indicated that two simultaneous cues increase the accuracy of reporting two targets by almost the same amount as a single cue increases the report of a single target. These results suggest that when attention is divided between multiple locations, the attentional benefit at each location is not reduced in proportion to the total number of cues. A consequent prediction of this finding is that the identification of two RSVP targets should be better when they are presented simultaneously rather than sequentially. In a second experiment, we verified this prediction by finding evidence of lag-0 sparing: Two targets presented simultaneously in different locations were reported more easily than two targets separated by 100 ms. These findings argue against a biased-competition theory of attention. We suggest that visual attention, as triggered by a cue or target, is better described by a convergent gradient-field attention model.     

Exploratory Bifactor Analysis: The Schmid-Leiman Orthogonalization and Jennrich-Bentler Analytic Rotations

Analytic bifactor rotations have been recently developed and made generally available, but they are not well understood. The Jennrich-Bentler analytic bifactor rotations (bi-quartimin and bi-geomin) are an alternative to, and arguably an improvement upon, the less technically sophisticated Schmid-Leiman orthogonalization. We review the technical details that underlie the Schmid-Leiman and Jennrich-Bentler bifactor rotations, using simulated data structures to illustrate important features and limitations. For the Schmid-Leiman, we review the problem of inaccurate parameter estimates caused by the linear dependencies, sometimes called “proportionality constraints,” that are required to expand a p correlated factors solution into a (p + 1) (bi)factor space. We also review the complexities involved when the data depart from perfect cluster structure (e.g., item cross-loading on group factors). For the Jennrich-Bentler rotations, we describe problems in parameter estimation caused by departures from perfect cluster structure. In addition, we illustrate the related problems of (a) solutions that are not invariant under different starting values (i.e., local minima problems) and (b) group factors collapsing onto the general factor. Recommendations are made for substantive researchers including examining all local minima and applying multiple exploratory techniques in an effort to identify an accurate model.