With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.
When analyzing data, how should the relationship between two or more sets of observations be described, that is, values of two or more variables, when the variables are ordinal and not bivariate normal? Aimed at helping the researcher select the most appropriate measure of association for two or more variables, the author clearly describes such techniques as Spearman's rho, Kendall's tau, Goodman and Kruskals' gamma and Somer's d and carefully explains the calculation procedures as well as the substantive meaning of each measure. In addition, each technique is illustrated by one or more examples from recent social or behavioural science studies. Finally, Gibbons provides information on the strengths and weaknesses of leading statistical packages for calculating these measures.
Making Sense of Multivariate Data Analysis is a short introduction to multivariate data analysis (MDA) for students and practitioners in the behavioral and social sciences. It provides a conceptual overview of the foundations of MDA and of a range of specific techniques including multiple regression, logistic regression, discriminant analysis, multivariate analysis of variance, factor analysis, and log-linear analysis. As a conceptual introduction, the book assumes no prior statistical knowledge, and contains very few symbols or equations. Its primary objective is to expose the conceptual unity of MDA techniques both in their foundations and in the common analytic strategies that lie at the heart of all of the techniques. Although introductory, the book encourages the reader to reflect critically on the general strengths and limitations of MDA techniques. Each chapter includes references for further reading accessible to the beginner. This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource.
Aimed at researchers across the social scien ces, this book explains the logic behind the Monte Carlo sim ulation method and demonstrates its uses for social and beha vioural research. '
Probability Theory: A Primer intends to give a non-technical introduction to probability theory, as it is used in the social sciences. The topics covered include 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 ......
What log-linear models can social scientists use to examine categorical variables whose attributes may be logically rank-ordered? In this book, the author presents a technique that is often overlooked but highly advantageous when dealing with such ordered variables as social class, political ideology and life satisfaction attitudes. Beginning with an introduction to the concept and measurement of ordinal models and a brief review of nominal log-linear analysis, the book provides a detailed description of the various ordinal models, including row effects, column effects, uniform association and uniform interaction models. Each model is illustrated with data from the National Survey of Families and Households, with which Ishii-Kuntz discusses the fit of the models, how alternative models compare and odds ratios. Additionally, statistical computer software packages that can be used to estimate these models are presented.
This book provides practical guidance for using MMR to better assess whether the relationship between two quantitative variables is moderated by group membership.
Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully understand this phenomenon and avoid common errors in interpretation.
Designed to help beginners estimate and test structural equation modelling (SEM) using the EQS approach, this book demonstrates a variety of SEM/EQS applications that include both partial factor analytic and full latent variable models. Beginning with an overview of the basic concepts of SEM and the EQS program, the author works through applications starting with a single sample approach through to more advanced applications, such as a multi-sample approach. The book concludes with a section on using EQS for modelling with Windows.
