Buch, Englisch, Band 97, 72 Seiten, Format (B × H): 140 mm x 216 mm, Gewicht: 109 g
Buch, Englisch, Band 97, 72 Seiten, Format (B × H): 140 mm x 216 mm, Gewicht: 109 g
Reihe: Quantitative Applications in the Social Sciences
ISBN: 978-0-8039-4376-6
Verlag: Sage Publications
Which log-linear models can social scientists use to examine categorical variables whose attributes may be logically rank ordered? Ordinal Log-Linear Models 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, this 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 the author 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.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Demographie, Demoskopie
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
Weitere Infos & Material
INTRODUCTION
Ordinal Measures
Log-Linear Models for Nominal Variables
A Review
ORDINAL LOG-LINEAR MODELS
Row Effects Models
Column Effects Models
Uniform Association Models
Assignment of Scores
Row and Column Effects Models
Odds Ratios for Two-Way Tables
Summary
Ordinal Log-Linear Models for Higher-Ordered Tables
Multidimensional Log-Multiplicative Models
Odds Ratios for Three-Way Log-Linear Models
Summary
Selection for Ordinal Log-Linear Models
Advantages of Using Ordinal Log-Linear Models
Summary
CONCLUSION
