How can social scientists assess the reliability of the measures derived from tests and questionnaires? Through an illustrative review of the principles of classical reliability theory, Ross E Traub explores some general strategies for improving measurement procedures. Beginning with a presentation of random variables and the expected value of a random variable, the book covers such topics as: the definition of reliability as a coefficient and possible uses of a coefficient; the notion of parallel tests so as to make possible the estimation of a reliability coefficient for a set of measurements; what to do when parallel tests are not available; what factors affect the reliability coefficient; and how to estimate the standard error of measurement. Aimed at giving readers a nontechnical treatment of classical reliability theory, the book also includes end of chapter exercises as well as boxes that give more in-depth coverage of major topics or that provide algebraic proofs.
In this volume, Clark Moustakas clearly discusses the theoretical underpinnings of phenomenology, based on the work of Husserl and others, and takes the reader step-by-step through the process of conducting a phenomenological study.
Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests. In addition, it covers such topics as: joint stationarity; testing for cointegration; testing for causality; and model order and forecast accuracy. Related models explained include transfer function, vector autoregression and error correction models.
Logit, Probit, and Other Generalized Linear Models
What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.
Critical Theory traces its roots from Marxism, through the renowned Frankfurt School, to a wide array of national and cultural traditions. Raymond Morrow's book traces the history and outlines the major tenets of critical theory for an undergraduate audience. He exemplifies the theory through an analysis of two leading social theorists: J[um]urgen Habermas and Anthony Giddens. Unique to this volume is the emphasis on the link between Critical Theory and empirical research and social science methodology, often thought to be incompatible.
A new, comprehensive framework for programme evaluation designed to bridge the gap between the method- and theory-oriented perspectives, is presented in this book, newly available in paper. Chen provides an intensive discussion of the nature and functions of programme theory, approaches to constructing programme theories, and the integration of programme theory with evaluation processes. Specific types of theory-driven evaluations, as well as principles and guidelines for application, are developed for meeting different policy purposes. Application of systematic strategies is illustrated by concrete examples from a variety of evaluation studies in different fields.
Content Design and Intrinsic Data Analysis in Behavioral Research
Using detailed examples, the authors introduce readers to the use of facet theory as a method for integrating content design with data analysis. They show how facet theory provides a strategy for conceptualizing a study, for formulating the study's variables in terms of its purposes, for systematic sampling of the variables and for formulating hypotheses. The first part of the book introduces mapping with specific emphasis on mapping sentences. Part Two explores procedures for processing multivariate data. In conclusion there is a discussion of the nature of scientific enquiry and the difference between research questions and observational questions.
This book makes clear to researchers what item-bias methods can (and cannot) do, how they work and how they should be interpreted. Advice is provided on the most useful methods for particular test situations. The authors explain the logic of each method - from item-response theory to nonparametric, categorical methods - in terms of how differential item functioning (DIF) is defined by the method and how well the method can be expected to work. A summary of findings on the behaviour of indices in empirical studies is included. The book concludes with a set of principles for deciding when DIF should be interpreted as evidence of bias.
By emphasizing how to think strategically about a research project, the author of this innovative book shows readers the important steps of a scientific study - from the formulation of the study to the write-up of results. Illustrative examples from the social, health and behavioural sciences are used throughout to illustrate 40 principles of good research practice.
