Bøger i Quantitative Applications in the Social Sciences serien

An elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests.

This book builds on John Fox's previous volume in the QASS Series, Non Parametric Simple Regression. In this book, the reader learns how to estimate and plot smooth functions when there are multiple independent variables.

This book provides a nontechnical introduction to Multiple Correspondence Analysis

Offers a unified framework to help students and researchers to analyze and understand any social science data that are organized in cross-classified formats.

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

Introduces the concept of expert systems development as a model for the acquisition, representation and validation of knowledge about relatively limited domains. Case studies derive from the authors' own development experiences in the social sciences.

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume. Mohr first reviews what is meant by sampling and probability distributions and then examines in-depth normal and t-tests of significance. The uses and misuses of significance testing are also explored.

Krippendorff introduces social scientists to information theory and explains its application for structural modeling. He discusses key topics such as: how to confirm an information theory model; its use in exploratory research; and how it compares with other approaches such as network analysis, path analysis, chi square and analysis of variance.Information Theory simplifies and clarifies a complex but powerful statistical method for analysing multivariate qualitative data. It will serve both as a textbook and as a sourcebook for researchers in communication theory, information theory and systems theory.

Explains the most widely used methods for analyzing cross-classified data on occupational origins and destinations. Hout reviews classic definitions, models, and sources of mobility data, as well as elementary operations for analyzing mobility tables. Tabular and graphic displays illustrate the discussion throughout.

Clearly reviews the properties of important contemporary measures of association and correlation. Liebetrau devotes full chapters to measures for nominal, ordinal, and continuous (interval) data, paying special attention to the sampling distributions needed to determine levels of significance and confidence intervals. Valuable discussions also focus on the relationships between various measures, the sampling properties of their estimators and the comparative advantages and disadvantages of different approaches.

This book explores a variety of graphical displays that are useful for visualizing multivariate data. The basic problem involves representing information that varies along several dimensions when the display medium is inherently two-dimensional. In order to address this problem, Jacoby introduces and explores the concept of a `data space'.

Groups - organizations, corporations and governments - have formal rules for the allocation of their resources and in democratic societies the decisions about allocation are generally made by simple majority voting. But does majority rule always improve social well-being? Could it sometimes lead to collective irrationality? In this thought-provoking book, Paul E Johnson considers the key questions and concepts in social choice theory.

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.

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.

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.

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.

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.

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.

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.

Offers students a brief and accessible approach to systematically quantifying various types of narrative data they can collect during a research process.

Provides readers with a clear and concise introduction to the why, what, and how of the comparative method

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.

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

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.