Modeling longitudinal binomial responses: implications from two dueling paradigms

The generalized estimating equations (GEEs) and generalized linear mixed-effects model (GLMM) are the two most popular paradigms to extend models for cross-sectional data to a longitudinal setting. Although the two approaches yield well-interpreted models for continuous outcomes, it is quite a different story when applied to binomial responses. We discuss major modeling differences between the GEE- and GLMM-derived models by presenting new results regarding the model-driven differences. Our results show that GLMM induces some artifacts in the marginal models at assessment times, making it inappropriate when applied to such responses from real study data. The different interpretations of parameters resulting from the conceptual difference between the two modeling approaches also carry quite significant implications and ramifications with respect to data and power analyses. Although a special case involving a scale difference in parameters between GEE and GLMM has been noted in the literature, its implications in real data analysis has not been thoroughly addressed. Further, this special case has a very limited covariate structure and does not apply to most real studies, especially multi-center clinical trials. The new results presented fill a substantial gap in the literature regarding the model-driven differences between the two dueling paradigms.     

Monitoring parameter change in time series models

In this paper, we develop a monitoring procedure for an early detection of parameter changes in time series models. We design the monitoring procedure in general time series models and apply it to the changes for the autocovariances of linear processes, GARCH parameters, and underlying distributions. Simulation results are provided for illustration.     

A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting

Econometric methods used in foreign exchange rate forecasting have produced inferior out-of-sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross-validation schemes. The effects of different in-sample time periods and sample sizes are examined. Out-of-sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.     

The mood of a firm

Mood is information. A good mood signals a desire to cooperate; a bad mood warns of a determination to oppose. Firms may communicate by mood. The paper makes three points about the mood of a firm. First, mood can change. A change in mood affects everyone in the market. Second, there exists a strong tendency for a firm frustrated by poor communication to have bad mood. Bad mood amplifies behavioral responses. Third, the attendant risks of bubbles and panics are a concern about policies that encourage firms to communicate by mood.     

Young Infants Detect the Direction of Biological Motion in Point‐Light Displays

In the present study, we examined if young infants can extract information regarding the directionality of biological motion. We report that 6‐month‐old infants can differentiate leftward and rightward motions from a movie depicting the sagittal view of an upright human point‐light walker, walking as if on a treadmill. Inversion of the stimuli resulted in no detection of directionality. These findings suggest that biological motion displays convey information for young infants beyond that which distinguishes them from nonbiological motion; aspects of the action itself are also detected. The potential visual mechanisms underlying biological motion detection, as well as the behavioral interpretations of point‐light figures, are discussed.     

Attitudes and beliefs about mental health among African American older adults suffering from depression

Depression among older adults is a major public health concern leading to increased disability and mortality. Less than 3% of older adults utilize professional mental health services for the treatment of depression, less than any other adult age group. And despite similar rates of depression, African Americans are significantly less likely to seek, engage and be retained in professional mental health services than their white counterparts. Cultural differences in the way depression symptoms are manifested, defined, interpreted and labeled may in part explain some of these racial differences in help-seeking behaviors. Focus group methodology was utilized to identify and explore attitudes and beliefs about depression and mental health treatment utilization among 42 older African Americans who had recently suffered a major depressive episode. Thematic analysis of identified six overarching themes: (a) perceptions of depression, (b) the African American experience, (c) seeking treatment as a last resort, (d) myths about treatment, (e) stigma associated with seeking treatment and (f) culturally appropriate coping strategies. We discuss implications for practice, education and research.     

A life course built for two: Acting,aging, and soap operas

Due to their longevity, daytime soap operas provide a rich entertainment text through which to examine representations of, and experiences of, age and aging. Our exploratory, qualitative project explores how veteran soap actors make sense of their own aging process alongside that of their characters’, and how soaps serve as an unexpected cultural resource for negotiating the varied meanings of aging. Drawing on original interview data with soap actors (n = 11) and other industry experts (n = 4), original survey data with long-term viewers (n = 34), and secondary data as reported in the entertainment and popular presses, we examine actors’ use of fictional narratives to make meaning of their progression through the life course. Our analysis is situated at the intersections of gerontology, media studies, and the sociology of work.