This useful book outlines the chief forms and major causes of academic stress. Practical advice shows how to distinguish negative from positive stress and how to deal with negative stressors in life and at work. The book includes exercises to help the academic understand how stress affects him or her, as well as forms to help design programmes for coping with stress.
Using vivid examples, classroom strategies, teaching tips and feedback tools, this book demonstrates how to improve teaching skills. Weimer dissects the elements of good teaching - enthusiasm, organization, clarity, among others - and emphasizes that good teaching can come in a variety of guises.
This useful guide explains the workings of the press and other media, and gives concrete, practical advice on how to work with them effectively. The authors provide examples of all likely media situations and offer clear directions for handling them, showing academics how to use the media rather than be used by them.
This book summarizes the state of our knowledge on the effects of men in women's professions - effects on the men, on their views of masculinity, on the occupations and on the women they work with. Do men get preferential treatment in these positions? Do they receive higher salaries? Or are they treated the same as their women colleagues? Through a series of statistical and demographic analyses, as well as case studies of men in professions such as teaching, secretarial work, care-giving and stripping, the contributors give a glimpse of the role of these men in bolstering or undermining the gendered assumptions of occupational sex segregation in the workplace.
This volume examines the urban underclass from theoretical, empirical and policy perspectives. Focusing strongly on policy, contributors explore such topics as demographic and industrial transitions, family patterns, sexual behaviour, immigration and homelessness. A new introduction updates recent work in the field since publication of the first edition.
This volume explores the impact of social, cultural, structural, network and dynamic transactional processes on the conduct of relationships. In so doing, it makes a compelling case for research to be directed away from over-application of individual perspectives and towards inclusion of contextual factors. Confronting the practical realities against which individuals may struggle to manage relationships, contributors focus on such issues as: limits on opportunity and freedom; coercive family norms; responsibilities; poverty; and prejudice.
This volume explores the impact of social, cultural, structural, network and dynamic transactional processes on the conduct of relationships. In so doing, it makes a compelling case for research to be directed away from over-application of individual perspectives and towards inclusion of contextual factors. Confronting the practical realities against which individuals may struggle to manage relationships, contributors focus on such issues as: limits on opportunity and freedom; coercive family norms; responsibilities; poverty; and prejudice.
In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods.
Bootstrapping, a computational nonparametric technique for `re-sampling', enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percentile, bias-corrected percentile, and percentile-t. The authors conclude with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
