Participatory approach for the identification of dairy industry needs in the design of research, development and extension actions: Australian and Brazilian case studies

In both Australia and Brazil there are rapid changes occurring in the macroenvironment of the dairy industry. These changes are sometimes not noticed in the microenvironment of the farm, due to the labour-intensive nature of family farms, and the traditionally weak links between production and marketing. Trends in the external environment need to be discussed in a cooperative framework, to plan integrated actions for the dairy community as a whole and to demand actions from research, development and extension (R, D & E). This paper reviews the evolution of R, D & E in terms of paradigms and approaches, the present strategies used to identify dairy industry needs in Australia and Brazil, and presents a participatory strategy to design R, D & E actions for both countries. The strategy incorporates an integration of the opinions of key industry actors ( defined as members of the dairy and associated communities), especially farm suppliers ( input market), farmers, R, D & E people, milk processors and credit providers. The strategy also uses case studies with farm stays, purposive sampling, snowball interviewing techniques, semi-structured interviews, content analysis, focus group meetings, and feedback analysis, to refine the priorities for R, D & E actions in the region.

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions

Background and Objective: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. Methods: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. Results: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10: 1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P >.05) in the proportion of articles meeting the criteria across the two journals. Conclusion: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression. (C) 2004 Elsevier Inc. All rights reserved.

Efficiency effects of agricultural economics research in the United States

Allocations of research funds across programs are often made for efficiency reasons. Social science research is shown to have small, lagged but significant effects on U.S. agricultural efficiency when public agricultural R&D and extension are simultaneously taken into account. Farm management and marketing research variables are used to explain variations in estimates of allocative and technical efficiency using a Bayesian approach that incorporates stylized facts concerning lagged research impacts in a way that is less restrictive than popular polynomial distributed lags. Results are reported in terms of means and standard deviations of estimated probability distributions of parameters and long-run total multipliers. Extension is estimated to have a greater impact on both allocative and technical efficiency than either R&D or social science research.

Capturing the complexity of women's experiences: A mixed-method approach to studying incontinence in older women

Capturing the voices of women when the issue is of a sensitive nature has been a major concern of feminist researchers. It has often been argued that interpretive methods are the most appropriate way to collect such information, but there are other appropriate ways to approach the design of research. This article explores the use of a mixed-method approach to collect data on incontinence in older women and argues for the use of a variety of creative approaches to collect and analyze data.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.