Bücher der Reihe Quantitative Applications in the Social Sciences

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.

In recent years the loglinear model has become the dominant form of categorical data analysis as researchers have expanded it into new directions. This book shows researchers the applications of one of these new developments - how uniting ordinary loglinear analysis and latent class analysis into a general loglinear model with latent variables can result in a modified LISREL approach. This modified LISREL model will enable researchers to analyze categorical data in the same way that they have been able to use LISREL to analyze continuous data. Beginning with an introduction to ordinary loglinear modelling and standard latent class analysis, the author explains the general principles of loglinear modelling with latent variables, the application of loglinear models with latent variables as a causal model as well as a tool for the analysis of categorical longitudinal data, the strengths and limitations of this technique, and finally, a summary of computer programs that are available for executing this technique.

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.

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.

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.

Covering the basics of the cohort approach to studying aging, social, and cultural change, this volume also critiques several commonly used (but flawed) methods of cohort analysis, and illustrates appropriate methods with analyses of personal happiness and attitudes toward premarital and extramarital sexual relations. Finally, the book describes the major sources of suitable data for cohort studies and gives the criteria for appropriate data.The Second Edition features:- a chapter on the analysis of survey data, which includes a discussion of the problems posed by question order effects when data from different surveys are used in a cohort analysis. - an emphasis on the difference between linear and nonlinear effects. - instruction on how to use available data from cohort studies.

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.

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.

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.

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.

Combines time series and cross-sectional data to provide the researcher with an efficient method of analysis and improved estimates of the population being studied. With more relevant data available this analysis technique allows the sample size to be increased, which ultimately yields a more effective study.

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.

Applied demography is a technique which can handle small geographic areas -- an approach which allows market segments and target populations to be studied in detail. This book provides the essential elements of applied demography in a clear and concise manner. It details the kind of information that is available; who produces it; and how that information can be used. The sources mentioned are American, but the techniques for estimating have universal application. A background in elementary algebra is sufficient for this book.'This is a handy, concise primer that summarizes various fundamentals of particular interest to the field (estimating total populations, household sizes, etc.)' -- Population Today, October 1984

Considers how "real-world" observations can be interpreted to convert them into data to be analyzed, so as to facilitate more effective use of scaling techniques. The text introduces the most appropriate scaling strategies for different research situations.

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.

In the QUANTITATIVE APPLICATIONS IN THE SOCIAL SCIENCES series, an introduction to correspondence analysis which outlines the history and logic behind the technique. It explains the analysis of large contingency tables and survey data and compares correspondence analysis with log linear models.

Proving a non-technical introduction to probability theory, this book covers topics including: the concept of probability and its relation to relative frequency, the properties of probability, discrete and continuous random variables, and binomial, uniform, normal and chi-squared distributions.

Provides applied researchers in the social, educational, and behavioural sciences with comprehensive coverage of analyses for ordinal outcomes. This book includes content that builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data.

Interpreting and Using Regression sets out the actual procedures researchers employ, places them in the framework of statistical theory, and shows how good research takes account both of statistical theory and real world demands. Achen builds a working philosophy of regression that goes well beyond the abstract, unrealistic treatment given in previous texts.

Demonstrating the application of this analysis technique to the social sciences, the author explains how it can be used to determine the number of factors to be retained in a factor analysis; in selecting a subset of variables to represent a larger set; and much more.

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.

Explores even the fundamental assumptions underlying mediation analysis

Effect Size for Anova Designs lays out the computational methods for 'd' with a variety of designs including factorial ANOVA, ANCOVA and repeated measures ANOVA; 'd' divides the observed effect by the standard deviation of the dependent variable.

Provides easy-to-follow, didactic examples of several common growth modeling approaches

This book presents a technique for analyzing the effects of variables, groups, and treatments in both experimental and observational settings. It considers not only the main effects of one variable upon another, but also the effects of group cases.

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.