An introduction to Bayesian hierarchical models with an application in the theory of signal detection

Although many nonlinear models of cognition have been proposed in the past 50 years, there has been little consideration of corresponding statistical techniques for their analysis. In analyses with nonlinear models, unmodeled variability from the selection of items or participants may lead to asymptotically biased estimation. This asymptotic bias, in turn, renders inference problematic. We show, for example, that a signal detection analysis of recognition memory data leads to asymptotic underestimation of sensitivity. To eliminate asymptotic bias, we advocate hierarchical models in which participant variability, item variability, and measurement error are modeled simultaneously. By accounting for multiple sources of variability, hierarchical models yield consistent and accurate estimates of participant and item effects in recognition memory. This article is written in tutorial format; we provide an introduction to Bayesian statistics, hierarchical modeling, and Markov chain Monte Carlo computational techniques.     

Omnibus hypothesis testing in dominance-based ordinal multiple regression

Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q2 statistic is introduced for testing the omnibus null hypothesis in DOMR. A simulation study is discussed that examines the actual Type I error rate and power of Q2 in comparison to the LSMR omnibus F test under normality and non-normality. Results suggest that Q2 has favorable sampling properties as long as the sample size-to-predictors ratio is not too small, and Q2 can be a good alternative to the omnibus F test when the response variable is non-normal.     

Category learning and multiple memory systems

Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. As different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks.     

Using the score method to construct asymmetric confidence intervals: An SAS program for content validation in scale development

Expert review sessions are often conducted to determine the content validity of scale items. The accurate quantification of content validity is usually limited by a relatively small number of experts as well as by a small number of rating categories. These factors, combined with the bounded and discrete nature of rating scale categories, hinder use of traditional methods for computing standard errors and confidence intervals. Using an application of the score method, researchers can construct an asymmetric interval that is better suited for these situations. SAS code is provided to automate the computations, and a discussion of two methods for using the obtained results for content validation decisionmaking follows.     

Creating a stem cell donor: a case study in reproductive genetics

During the nearly 10 years since its introduction, preimplantation genetic diagnosis (PGD) has been used predominantly to avoid giving birth to a child with identified genetic disease. Recently, PGD was used by a couple not only to test IVF-created embryos for genetic disease, but also to test for a nondisease trait related to immune compatibility with a child in the family in need of an hematropoetic stem cell transplant. This article describes the case, raises some ethical and policy issues, highlights gaps in U.S. policy, and finally makes some recommendations for addressing advancing genetic and reproductive technologies.     

Retrieval-induced forgetting occurs in tests of item recognition

Using the retrieval-practice paradigm (Anderson, R. A. Bjork, & E. L. Bjork, 1994), we tested whether or not retrieval-induced forgetting could be found in item recognition tests. In Experiment 1, retrieval practice on items from semantic categories depressed recognition of nonpracticed items from the same categories. Similar results were found in Experiment 2 in a more stringent source test for practiced, nonpracticed, and new items. These results conceptually replicate those of previous retrieval-induced forgetting studies done with cued recall (e.g., Anderson et al., 1994). Our findings are inconsistent with the hypothesis that item-specific cues during retrieval will eliminate retrieval interference in the retrieval-practice paradigm (Butler, Williams, Zacks, & Maki, 2001). We discuss our results in relation to other retrieval interference and inhibition effects in recall and recognition.     

Psychological research online: report of Board of Scientific Affairs' Advisory Group on the Conduct of Research on the Internet

As the Internet has changed communication, commerce, and the distribution of information, so too it is changing psychological research. Psychologists can observe new or rare phenomena online and can do research on traditional psychological topics more efficiently, enabling them to expand the scale and scope of their research. Yet these opportunities entail risk both to research quality and to human subjects. Internet research is inherently no more risky than traditional observational, survey, or experimental methods. Yet the risks and safeguards against them will differ from those characterizing traditional research and will themselves change over time. This article describes some benefits and challenges of conducting psychological research via the Internet and offers recommendations to both researchers and institutional review boards for dealing with them. ((c) 2004 APA, all rights reserved)