The role of pleasantness and activation-based well-being in performance prediction.

This study examined the relationships between 2 measures of psychological well-being and work performance using the circumplex model of emotion as the theoretical framework. Although the pleasantness-based measure of well-being predicted subsequent work performance, the results failed to establish a relationship between the activation-based measure of well-being and work performance. Future directions and implications of the findings regarding the further refinement of the role of psychological well-being in performance prediction are introduced.

Bias reduction for multiple-recapture estimators of closed population size.

A bias correction for multiple-recapture sampling is proposed. The correction is similar to Chapman's (1951, University of California Publication in Statistics 1, 131-160) correction of the Lincoln-Petersen estimator of population size. Small-sample properties of the modified population size estimator are examined using Monte Carlo simulation. Substantial reductions in estimator bias and variance are obtained with the modified estimator.

A general theory for modeling capture-recapture data from a closed population.

A general theory for estimating the size of a closed population from multiple-recapture data is presented. This theory is easily extended to open population models for multiple-recapture data. Estimation is based on a log-linear model developed for modeling dependent capture-recapture data when capture probabilities vary temporally with behavioral response and are heterogeneous among animals. Models for complete capture history data are developed along with the first log-linear models for removal data. The maximum likelihood estimator of the model parameters along with the estimated covariance matrix are presented.

Estimating the size of closed populations using inverse multiple-recapture sampling.

A log-linear model for estimating the size of a closed population is defined for inverse multiple-recapture sampling with dependent samples. Efficient estimators of the log-linear model parameters and the population size are obtained by the method of minimum chi-square. A chi-square test of the general linear hypothesis regarding the log-linear model parameters is defined.

Androstenol, a putative human pheromone, affects human (Homo sapiens) male choice performance.

A natural secretion, 5 alpha-androst-16-en-3 alpha-ol (androstenol) is speculated to function as a spacing pheromone. The effect of the odor of androstenol on restroom-stall choices was investigated over a 5-week period. The first, third, and fifth weeks served as baselines against which the effect of androstenol or a control odor, 3 alpha-hydroxy-5 alpha-androstan-17-one (androsterone) could be evaluated. During the second and fourth weeks, half of the stalls in each restroom were treated with androstenol or androsterone, respectively. As predicted, men avoided the treated stalls during the androstenol week only, and neither odor affected female stall selection, demonstrating a sex differential influence of the experimental odor.

Conditional factors of maturing out: personal resources and preaddiction sociopathy.

The study continues exploration of the conditional nature of the process of maturing out of narcotics addiction over time. It tests hypotheses about the relationship of selected personal resource/sociodemographic variables (including ethnicity and employability) and preaddiction sociopathy characteristics to maturing out. Hypotheses were tested using log-linear models on data from the California Civil Addict Program. Results suggest that while the process of maturing out over time is not substantially different depending upon level of preaddiction sociopathy or ethnicity, the process may be influenced by levels of other personal resource characteristics.

An empirical study of maturing out: conditional factors.

Using data from the California Civil Addict Program, the study tested hypotheses about the possible conditional nature of the process of maturing out of heroin addiction. Hypotheses were tested in a five-way contingency table using the log-linear model. Results show that maturing out of addiction with increasing age is inhibited by high levels of involvement in crime and drug dealing.

Some extensions of a linear model for categorical variables.

The Grizzle-Starmer-Koch (GSK) model is extended to include the traditional log-linear model and a general class of Poisson and conditional Poisson distributions. Estimators of the model parameters are defined under general exact and stochastic linear constraints.

A covariance structure model for quantitative genetic research.

In this paper, we have presented a general covariance structure model for quantitative genetic research that incorporates measurement theory, components of variance, and regression theory for specification of structural relations among variance components. Each portion of the general model addresses specific issues important in delineating the genetic etiology of continuous traits. Through specification of a measurement model, genetic and environmental influences can be estimated independently of measurement error. Genetic and environmental sources of variance are defined from familial sampling designs through the components of variance model. Structural relationships among several traits at the genetic or environmental level can be specified through the regression model. The significance of parameters specified in each part of the model can be tested through model comparisons using a likelihood ratio chi-square test. We utilized the general model to test genetic covariance as a source of observed covariation of obesity and glucose tolerance in the Pima Indians. We initially applied the modeling approach using full-sib, half-sib sampling among the Pima with a second application including offspring as well as parental scores. Our initial applications of the methodology suggest that the covariance structure model can be a useful tool in genetic epidemiological research. Through model comparisons, our findings suggest that the association of glucose tolerance and obesity in the Pima is not due to a common set of genes but rather due to non-familial environmental influences. In conclusion, one task important for future research, as we see it, is the application of covariance structure models to other familial sampling designs and the evaluation of the usefulness of this approach through further applications to data.