Handbook for Applied Modeling: Non-Gaussian and Correlated Data

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs. Data, R, and SAS scripts can be found online at http://www.spesi.org.

[1]  Andreas Ziegler,et al.  Generalized Estimating Equations , 2011 .

[2]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[3]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[4]  A. Agresti.  An introduction to categorical data analysis , 1997 .

[5]  Paul D. Allison,et al.  Survival analysis using sas®: a practical guide , 1995 .

[6]  David W. Hosmer,et al.  Applied Survival Analysis: Regression Modeling of Time-to-Event Data , 2008 .

[7]  J. Faraway.  Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models , 2005 .

[8]  J. Twisk,et al.  Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide , 2003 .

[9]  A. Agresti,et al.  Statistical Methods for the Social Sciences , 1979 .

[10]  Donald Hedeker,et al.  Longitudinal Data Analysis , 2006 .

[11]  Joseph Hilbe,et al.  Practical Guide to Logistic Regression , 2015 .

[12]  J. Ware,et al.  Applied Longitudinal Analysis , 2004 .

[13]  David R. Cox.  The analysis of binary data , 1970 .

[14]  J. Dwyer,et al.  Statistical Models for the Social and Behavioral Sciences , 1983 .

[15]  D. Collett.  Modelling Binary Data , 1991 .

[16]  John Verzani,et al.  Using R for introductory statistics , 2018 .

[17]  J. Hardin,et al.  Generalized Estimating Equations , 2002 .

[18]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[19]  Michael J. Crawley,et al.  The R book , 2022 .

[20]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[21]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[22]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[23]  William S. Cleveland,et al.  Visualizing Data , 1993 .

[24]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[25]  David G. Kleinbaum,et al.  Logistic Regression. A Self- Learning Text , 1994 .

[26]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[27]  Joseph M. Hilbe,et al.  Modeling Count Data , 2014, International Encyclopedia of Statistical Science.

[28]  Jos W. R. Twisk.  Applied Multilevel Analysis: A Practical Guide for Medical Researchers , 2006 .

[29]  A. Dobson.  An introduction to generalized linear models , 1990 .

[30]  Paul D. Allison,et al.  Logistic regression using sas®: theory and application , 1999 .

[31]  W. Nelson.  Statistical Methods for Reliability Data , 1998 .

[32]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[33]  M. Schemper,et al.  A solution to the problem of separation in logistic regression , 2002, Statistics in medicine.

[34]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[35]  J. Hilbe.  Negative Binomial Regression: Preface , 2007 .

[36]  P. Diggle,et al.  Analysis of Longitudinal Data , 2003 .

[37]  K Y Liang,et al.  An overview of methods for the analysis of longitudinal data. , 1992, Statistics in medicine.

[38]  G. Molenberghs,et al.  Longitudinal data analysis , 2008 .

[39]  Steve McKillup,et al.  Statistics Explained: An Introductory Guide for Life Scientists , 2006 .

[40]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[41]  G. W. Snedecor.  Statistical Methods , 1964 .

[42]  A. Agresti.  Analysis of Ordinal Categorical Data , 1985 .

[43]  A. Alexander Beaujean,et al.  Latent Variable Modeling Using R: A Step-by-Step Guide , 2014 .

[44]  G. Glass,et al.  Statistical methods in education and psychology , 1970 .

[45]  Charles E Ebeling,et al.  An Introduction to Reliability and Maintainability Engineering , 1996 .

[46]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[47]  M. Karim.  Generalized Linear Models With Random Effects , 1991 .