Creationism in Twentieth-Century America

Creationism in Twentieth Century America: A Ten-Volume Anthology of Documents, 1903–1961 Gen ed. Ronald L. Numbers     

Nuance, Complexity, and Context: Qualitative Methods in Genetic Counseling Research

Because of its potential for capturing complexity and process and its focus on communicating the meaning in human action, qualitative research is rapidly gaining acceptance in a wide variety of disciplines. Various qualitative approaches to research support its potential usefulness in genetic counseling research. Formulation of research questions and selection of appropriate qualitative methods are the first step. Interviewing, narrative analysis, focus groups, ethnography/participant observation, and participatory action research are only some of the most popular techniques that may be suitable in various situations. Processes of evaluation, analysis, and even ethical issues often differ somewhat from quantitative research. This article discusses these issues and the potential value of qualitative methods for research related to genetic counseling.     

A Postmodern Analysis of a Course on Clinical Supervision

A qualitative study of a supervision course included pre-and post-interviews of the participants in addition to in-class participant observation. In an effort to be consistent with a postmodern perspective, the data were reviewed by the four members of the research team, each of whom shares his or her analysis, or story. Common themes which emerged in response to the focus on student change include greater awareness of isomorphic processes, ethical and legal issues, and evaluative processes.     

BASIC AND APPLIED RESEARCH ON CHOICE RESPONDING

Choice responding refers to the manner in which individuals allocate their time or responding among available response options. In this article, we first review basic investigations that have identified and examined variables that influence choice responding, such as response effort and reinforcement rate, immediacy, and quality. We then describe recent bridge and applied studies that illustrate how the results of basic research on choice responding can help to account for human behavior in natural environments and improve clinical assessments and interventions.     

EFFECTS OF REINFORCER QUALITY ON BEHAVIORAL MOMENTUM: COORDINATED APPLIED AND BASIC RESEARCH

The high-probability (high-p) instructional sequence has been an effective treatment for noncompliance. However, treatment failures have also been reported. We hypothesized that the efficacy of the high-p treatment may be improved by using higher quality reinforcers for compliance to high-p instructions. The resistance of compliance to change was tested by varying reinforcer quality in two applied studies and a basic laboratory experiment. Experiment 1 tested the hypothesis that an increase in reinforcer quality for high-p compliance will increase the effectiveness of the high-p treatment when it fails to increase compliance. Experiment 2 assessed the effects of reinforcer quality on resistance of compliance to change by presenting successive low-p requests following the high-p treatment. A basic laboratory study (Experiment 3) was conducted to further isolate the relation between reinforcer quality and behavioral momentum. Two different liquid reinforcers (sucrose and citric acid solutions) were presented in a two-component multiple variable-interval variable-interval schedule followed by a single extinction test session. Results of all three experiments showed a generally consistent relationship between reinforcer quality and behavioral momentum.     

Behavior analysis in consumer affairs: Retail and consumer response to publicizing food price information

A popular program among consumer action groups involves publicizing comparative food price information (CFPI) gathered from retail stores. Its significance is based on the assumption that publishing CFPI maximizes retail competition (i.e., moderates price levels or price increases) and occasions more frugal store selections among consumers. We tested these assumptions during a 2-year analysis. Specifically, we monitored the prices of two distinct market baskets in the supermarkets of two midwestern cities (target and contrast cities). Following a lengthy baseline, we published the prices of only one of the market baskets at stores in the target city in the local newspaper on five different occasions. The results suggested that reductions in price inflation occurred for both market baskets at the independently operated target stores. The corporate chain stores were not similarly affected. In addition, surveys indicated that many consumers used the CFPI as a basis for store selection. Finally, the analysis included a discussion of the politics, economics, and future of CFPI programs.     

Randomization tests in single-subject designs: Illustrative examples

Randomization tests have recently been adapted for use in the analysis of single-subject data. The advantages of these tests lie in their ease of implementation and interpretation as well as their freedom from underlying distributions. Even though numerous articles and books have explicated randomization test procedures, due to the lack of appropriate examples, very little use of these procedures has been made by applied behavior analysts. Data sets reported in a prominent applied behavior journal are used to demonstrate the application of randomization tests to the following three single-subject design models: (a) two-phase random intervention point, (b) multiple phase, and (c) multiple phase with a predicted order of effect size.     

Flexible work schedules and family time allocation: Assessment of a system change on individual behavior using self-report logs

This study assessed the effects of a flexible work schedule ("flextime") on time allocated to children and spouse by federal workers. Direct behavioral observations of family, home, and work functions were precluded because of the cost involved in observing many people for long periods of time. In order to obtain detailed individual data, participants completed hour-by-hour activity logs a mean of twice per week for 35 weeks. Participants received prior training on log completion, initial feedback on the detail of their log entries, and were prompted to complete the forms. Four different procedures assessing reliability indicated a corroboration rate of 80% with other sources. Log data were reliably reduced to nine categories such as "PM time with children" and 37 subcategories such as "time at dinner." The log data were presented in time-series form and the use of a quasi-experimental design showed that participants who altered their work schedule were able to spend more PM time with their families. The log data demonstrated that the capacity exists to assess closely the effects of large-scale changes at a micro-behavioral level, but other methods are needed to make complex self-reporting systems less expensive and more capable of immediate monitoring of the intervention's effects.     

The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research

The effectiveness of a prevention or intervention program has traditionally been assessed using time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost-efficient intervention programs for the future.