Bøger i Quantitative Applications in the Social Sciences serien

PLEASE UPDATE SAGE UK AND SAGE INDIA ADDRESSES ON IMPRINT PAGE.

This book offers an overview of the central ideas in calculus and gives examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus - differentiation and integration - Gudmund R Iversen illustrates how calculus is used in statistics: to distinguish between the mean and the median; to derive the least squares formulas for regression co-efficients; to find values of parameters from theoretical distributions; and to find a statistical p-value when using one of the continuous test variables such as the t-variable.

How to collect, describe, compare and analyze data.

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.

This book introduces readers to the basic models of neural networks and compares and contrasts these models using other statistical models. Through the use of examples that can be computed by hand or with a simple calculator, the authors describe and explain the various models.

These procedures, collectively known as discriminant analysis, allow a researcher to study the difference between two or more groups of objects with respect to several variables simultaneously, determining whether meaningful differences exist between the groups and identifying the discriminating power of each variable.

An introduction to a variety of techniques that may be used in the analysis of data from a panel study -- information obtained from a large number of entities at two or more points in time. The focus of this volume is on analysis rather than problems of sampling or design, and its emphasis is on application rather than theory.

Introduction to Power Analysis: Two-Group Studies provides readers with the background, examples, and explanation they need to read technical papers and materials that include complex power analyses.  This clear and accessible guide explains the components of test statistics and their sampling distributions, and author Eric Hedberg walks the reader through the simple and complex considerations of this research question. Filled with graphics and examples, the reader is taken on a tour of power analyses from covariates to clusters, seeing how the complicated task of comparing two groups, and the power analysis, can be made easy.

Presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style.

A fully updated and accessible overview of randomized response

Approaching the topic from the perspective of the social scientist interested in hypothesis-testing, this volume is an introduction to time-series methods and their application in social science research.

This book provides a systematic and practical guide for researchers wishing to set up their own factorial survey design or to analyze factorial survey data.

Explaining the techniques and applications of exponential random graph modeling (ERGM) for social scientists, this is a uniquely sophisticated volume for examining social systems.

Drawing on recent "event history" analytical methods from biostatistics, engineering and sociology, this book explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human-related events.

This book provides a conceptual systematization and a practical tool for the randomization of between-subjects and within-subjects experimental designs.

With the format of the text mirroring the steps needed to be taken to solve multivariate general linear model problems, this clear and accessible guide introduces readers to this area of statistics.

Part of the 'little green books' QASS series, this text provides a clear introduction to ordinal item response theory.

As one of the only texts introducing fractal analysis and the social processes involved to social science readers, this is a must-have book for those looking to gain an understanding of this area of analysis.

This text guides readers through the specification and identification of simultaneous equation models, how to assess the quality of the estimates and how to correctly interpret results.

Defines the distinctive set of psychometric and operational principles which, when combined with specialized statistical applications of correlation and factor-analysis techniques, provide researchers with a systematic and rigorously quantitative means for examining human subjectivity.

This text covers the consequences of violating one of the key assumptions of Ordinary Least Squares regression (equal error variances), diagnostic tools to assess the existence of the problem of heteroskedasticity, and statistical techniques to analyse the data correctly.

Looking at the multigraph representations of loglinear models, this is a clear, introductory text on the area of graphical models and is an ideal text for those new to the field.

This new edition presents an up-to-date description of differential item functioning. It describes varying procedures for addressing this process in practical testing contexts and presents useful examples and studies that readers may employ as a guide in their own work

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts.

Through developing a decomposition analysis of the inequality measures and promoting their effective use in research, this book provides readers with a step-by-step understanding of the inequality measures that are currently used.

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.

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.