Eugene J. Meehan's normatively driven approach for social inquiry is essential equipment for policy makers, critics, and administrators. Meehan appends illustrations and applications to education and housing, It is useful for methodology courses for graduate or advanced undergraduate students.
ISBN-13: 9781566430067
(Paperback)
Publisher: SAGE PUBLICATIONS Imprint: CHATHAM HOUSE PUBLISHERS INC.,U.S.
What is the current spatial form and structure of our urban environment, and how can we study the factors and forces that account for the specific structure of urban space, its social and political processes, population distribution and land use? Addressing these and other issues, the authors highlight specific research questions and the ways in which they can be approached by offering a framework for considering the various ways in which to do urban research. Covering such topics as how to choose a research design, secondary research methods for data collection and how to enhance research utilization, the authors demonstrate ways to pair research questions with specific levels of analysis, such as neighbourhood, city or national level.
When using the analysis of variance (ANOVA) in an experimental design, how can the researcher determine whether to treat a factor as fixed or random? This book provides the reader with the criteria to make the distinction between fixed and random levels among factors, an important decision that directly reflects the purpose of the research. In addition to exploring the varied roles random factors can play in social research, the authors provide a discussion of the statistical analyses required with random factors and give an overview of computer-assisted analysis of random factor designs using SAS and SPSSX.
The issues of soundness of qualitative research are examined in this book. Contributors discuss how a researcher does qualitative research, considering such questions as: whether one deviates from the developer's protocol and what the ramifications are of such deviation; how students learn to acquire the conceptual skills necessary to conduct qualitative inquiry and how theory `emerges' from the data. The book discusses group effect in focus groups and describes an observational method using videotaped data. The various schools of phenomenology and their major characteristics of excellence are explained, and the Glasserian and Straussian methods of grounded theory are compared. Issues of ethics and scientific integrity are also raised. Each chapter, dealing with a matter that has not yet been resolved or addressed in the literature, is preceded by a dialogue in which contributors raise questions and comment upon the concept presented.
Neuman's Systems Model is a comprehensive conceptual framework, used extensively in nursing education; it reflects nursing's interest in holism and the influence of the environment on health. This volume provides a clear and concise overview of the model, with a brief biography of the theorist and a succinct discussion of the theory itself.
Historical and biographical work is becoming a more common type of qualitative research done by social scientists and usually requires the extensive use of formal archives housed in universities, governments, museums and other institutions. This practical and concise book provides an introduction for the novice on conducting archival research and covers such topics as contacting and preparing to work in archives, the protocol of using archives, and ways of organizing and referencing the useful data from the archive.
In this introduction to understanding, researching and doing case studies in the social sciences, Hamel outlines several differing traditions of case study research including the Chicago School of Sociology, the anthropological case studies of Malinowski, and the French La Play school tradition. He shows how each developed, changed and has been practised over time. Suggestions for the practice of case studies are made for the novice reader and an additional feature is the extensive bibliography on case study methods in social science to allow for further exploration of the topic.
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.
