Bøger i Quantitative Applications in the Social Sciences serien

Derived from engineering literature that uses similar techniques to map electronic circuits and physical systems, this work utilizes a systems approach to modeling that offers social scientists a variety of tools that are both sophisticated and easily applied. It introduces a modeling tool to researchers in the social sciences.

Explores even the fundamental assumptions underlying mediation analysis

Introduces the basis of the confidence interval framework and provides the criteria for 'best' confidence intervals, along with the trade-offs between confidence and precision. This book covers topics such as the transformation principle, confidence intervals, and the relationship between confidence interval and significance testing frameworks.

Quantile Regression establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.

Meta-Analysis shows concisely, yet comprehensively, how to apply statistical methods to achieve a literature review of a common research domain. It demonstrates the use of combined tests and measures of effect size to synthesize quantitatively the results of independent studies for both group differences and correlations. Strengths and weaknesses of alternative approaches, as well as of meta-analysis in general, are presented.

Panel data - information gathered from the same individuals or units at several different points in time - are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions.

Where an assumption of unidirectionality in causal effects is unrealistic, 'recursive' models cannot be used, and complex 'nonrecursive' models are necessary. But, many nonrecursive models are 'unidentified', which makes meaningful parameter estimation impossible. This book explains the concept of identification and the factors that lead to it.

This book explores the issues underlying the effective analysis of interaction in factorial designs. It includes discussion of: different ways of characterizing interactions in ANOVA; interaction effects using traditional hypothesis testing approaches; and alternative analytic frameworks that focus on effect size methodology and interval estimation.

With empirical examples which are both plentiful and well chosen to teach the technique, this book provides a thorough guide to latent class scaling models for binary response variables.

Reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. This book focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. It also reviews arguments for and against using multi-equation time series models.

Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise `limited' dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.

Offers an in-depth treatment of robust and resistant regression. This work, which is geared toward both future and practicing social scientists, takes an applied approach and offers readers empirical examples to illustrate key concepts. It includes a web appendix that provides readers with the data and the R-code for the examples used in the book.

This volume offers social scientists a concise overview of multiple attribute decision making (MADM) methods, their characteristics and applicability and methods for solving MADM problems. Real world examples are used to introduce the reader to normative models for optimal decisions.The authors explore how MADM methods can be used for descriptive purposes to model: the existing decision-making process; noncompensatory and scoring methods; accommodation of soft data; construction of a multiple-decision support systems; and the validity of methods. The advanced procedures of TOPSIS and ELECTRE are also presented.

Discusses data access, transformation and preparation issues, and how to select the appropriate analytic graphics techniques through a review of various GIS and common data sources, such as census products, Tiger files, and CD-ROM access. It provides illustrative output for sample data using selected software.

Using an expository style that builds from simpler to more complex topics, this text explains how to measure the centre and variation on a single variable. It also considers ways to examine the distribution of variables and measure the spread of a variable.

Feiring provides a well-written introduction to the techniques and applications of linear programming. He shows readers how to model, solve, and interpret appropriate linear programming problems. His carefully-chosen examples provide a foundation for mathematical modelling and demonstrate the wide scope of the techniques.

This excellent introduction to stochastic parameter regression models is more advanced and technically difficult than other papers in this series. These models allow relationships to vary through time, rather than requiring them to be fixed, without forcing the analyst to specify and analyze the causes of the time-varying relationships. This volume will be most useful to those with a good working knowledge of standard regression models and who wish to understand methods which deal with relationships that vary slowly over time, but for which the exact causes of variation cannot be identified.

The great advantage of time series regression analysis is that it can both explain the past and predict the future behavior of variables. This volume explores the regression (or structural equation) approach to the analysis of time series data. It also introduces the Box-Jenkins time series method in an attempt to bridge partially the gap between the two approaches.

Examines ways to analyze surveys, and focuses on the problems of weights and design effects. This edition incorporates practice of analyzing complex survey data, introduces the analytic approach for categorical data analysis, reviews new software and provides an introduction to the model-based analysis that can be useful analyzing social surveys.

Presents a discussion of fundamental statistical modeling concepts in a multiple regression framework. This book provides an introduction to GLM, exponential family distribution, and maximum likelihood estimation. It is useful for social science researchers who like to learn about advanced techniques.

Secondary analysis has assumed a central position in social science research as existing survey data and statistical computing programmes have become increasingly available. This volume presents strategies for locating survey data and provides a comprehensive guide to US social science data archives, describing several major data files. The book also reviews research designs for secondary analysis.

The Internet has emerged as a popular medium for collecting data because of its ability to access millions of users, facilitate an array of research designs, and efficiently deliver and compile questionnaires.

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Fuzzy set theory deals with sets or categories whose boundaries are blurry or, in other words, 'fuzzy.' This book presents an introduction to fuzzy set theory, focusing on its applicability to the social sciences. It provides a guide for researchers wishing to combine fuzzy set theory with standard statistical techniques and model-testing.

The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition:┬╖ More detailed consideration of grouped as opposed to casewise data throughout the book┬╖ Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency┬╖ Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data┬╖ Updated coverage of unordered and ordered polytomous logistic regression models.