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Fixed Effects Regression Models

Fixed Effects Regression Models

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Pages: 136
Year: 2009-04-20
This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here
Multilevel Modeling

Multilevel Modeling

Author: Douglas A. Luke
Publisher: SAGE
ISBN: 0761928790
Pages: 79
Year: 2004-07-08
A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.
Interaction Effects in Multiple Regression

Interaction Effects in Multiple Regression

Author: James Jaccard, Robert Turrisi
Publisher: SAGE Publications
ISBN: 1544332572
Pages: 104
Year: 2003-03-05
Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here
Causal Analysis with Panel Data

Causal Analysis with Panel Data

Author: Steven E. Finkel
Publisher: SAGE
ISBN: 0803938969
Pages: 98
Year: 1995-01-17
Panel data — information gathered from the same individuals or units at several different points in time — are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions.
Spatial Regression Models

Spatial Regression Models

Author: Michael D. Ward, Kristian Skrede Gleditsch
Publisher: SAGE
ISBN: 1412954150
Pages: 99
Year: 2008-02-29
Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.
Spline Regression Models

Spline Regression Models

Author: Lawrence C. Marsh, David R. Cormier
Publisher: SAGE
ISBN: 0761924205
Pages: 69
Year: 2001-09-14
Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For some researchers this will involve situations where the number and location of the spline knots are known in advance, while others will need to determine the number and location of spline knots as part of the estimation process. Through the use of a number of straightforward examples, the authors will show readers how to work with both types of spline knot situations as well as offering practical, down-to-earth information on estimating splines.
Understanding Regression Assumptions

Understanding Regression Assumptions

Author: William D. Berry
Publisher: SAGE Publications
ISBN: 1506315828
Pages: 104
Year: 1993-02-25
Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.
Event History Analysis

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 0803920555
Pages: 87
Year: 1984-11-01
Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.
Interpreting Probability Models

Interpreting Probability Models

Author: Tim Futing Liao
Publisher: SAGE
ISBN: 0803949995
Pages: 88
Year: 1994-06-30
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.
Longitudinal and Panel Data

Longitudinal and Panel Data

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 0521535387
Pages: 467
Year: 2004-08-16
An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.
Applied Panel Data Analysis for Economic and Social Surveys

Applied Panel Data Analysis for Economic and Social Surveys

Author: Hans-Jürgen Andreß, Katrin Golsch, Alexander W. Schmidt
Publisher: Springer Science & Business Media
ISBN: 3642329144
Pages: 327
Year: 2013-01-24
Many economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation. ​
Heteroskedasticity in Regression

Heteroskedasticity in Regression

Author: Robert L. Kaufman
Publisher: SAGE Publications
ISBN: 1483322513
Pages: 112
Year: 2013-06-28
Heteroskedasticity in Regression: Detection and Correction, by Robert Kaufman, covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction. Emphasizing how to apply diagnostic tests and corrections for heteroskedasticity in actual data analyses, the monograph offers three approaches for dealing with heteroskedasticity: (1) variance-stabilizing transformations of the dependent variable; (2) calculating robust standard errors, or heteroskedasticity-consistent standard errors; and (3) generalized least squares estimation coefficients and standard errors. The detection and correction of heteroskedasticity is illustrated with three examples that vary in terms of sample size and the types of units analyzed (individuals, households, U.S. states). Intended as a supplementary text for graduate-level courses and a primer for quantitative researchers, the book fills the gap between the limited coverage of heteroskedasticity provided in applied regression textbooks and the more theoretical statistical treatment in advanced econometrics textbooks.
Missing Data

Missing Data

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1452207909
Pages: 104
Year: 2001-08-13
Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.
Analysis of Variance

Analysis of Variance

Author: Gudmund R. Iversen, Helmut Norpoth
Publisher: SAGE
ISBN: 0803930011
Pages: 94
Year: 1987
The second edition of this book provides a conceptual understanding of analysis of variance. It outlines methods for analysing variance that are used to study the effect of one or more nominal variables on a dependent, interval level variable. The book presumes only elementary background in significance testing and data analysis.
Multilevel Analysis for Applied Research

Multilevel Analysis for Applied Research

Author: Robert Bickel
Publisher: Guilford Press
ISBN: 1609181069
Pages: 355
Year: 2007-03-19
This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual-level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, Robert Bickel emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps.