Statistics for Social and Behavioral Sciences

By: Scott M. Lynch

Write A Review

Hardcover | 15 August 2007

At a Glance

Hardcover
388 Pages

Dimensions(cm)
23.39 x 15.6 x 2.24

Hardcover

$259.01

or 4 interest-free payments of $64.75 with

Ships in 5 to 7 business days

"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.

The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.

Industry Reviews

From the reviews:

"The book ... contains a very detailed and comprehensive description of MCMC methods useful for applied researchers. ... Undoubtedly the book is interesting ... . The reader will gain an extensive knowledge of the issues covered ... ." (Dimitris Karlis, Zentralblatt MATH, Vol. 1133 (11), 2008)

"This new offering adds to our burgeoning Bayesian bookshelves a text directed at social scientists ... . To summarize, this a very useful text for a tightly bounded semester-long introduction to Bayesian statistics in the social sciences. The text is distinguished by its hands-on practical orientation which many readers will find very appealing. ... In addition, the book is handy for self-study ... ." (Jeff Gill, Journal of the American Statistical Association, Vol. 103 (483), September, 2008)

"This book introduces readers to the world of Bayesian analysis and MCMC methods through brief discussions of theory, examples, and programming computations for pplications. ...The potential users of the book are students or researchers in the social sciences, or anyone that is interested in learning Bayesian techniques and MCMC methods and applying them to their practice. The book is geared... towards practical applications. ... I recommended this book to anyone who is interested in learning about Bayesian inference and MCMC methods." (Journal of Educational Measurement . Summer 2010, Vol. 47, No 2, pp. 250-254)

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $89.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Sociology & Anthropology Sociology Social Research & Statistics Mathematics Probability & Statistics Applied Mathematics Population & Demography

Other Editions and Formats

Paperback

Published: 19th November 2010

View Product

Preface	p. vii
List of Figures	p. xix
List of Tables	p. xxvii
Introduction	p. 1
Outline	p. 3
A note on programming	p. 5
Symbols used throughout the book	p. 6
Probability Theory and Classical Statistics	p. 9
Rules of probability	p. 9
Probability distributions in general	p. 12
Important quantities in distributions	p. 17
Multivariatedistributions	p. 19
Marginal and conditional distributions	p. 23
Some important distributions in social science	p. 25
Thebinomialdistribution	p. 25
Themultinomialdistribution	p. 27
The Poisson distribution	p. 28
The normal distribution	p. 29
The multivariate normal distribution	p. 30
t and multivariate t distributions	p. 33
Classicalstatistics in social science	p. 33
Maximumlikelihood estimation	p. 35
Constructingalikelihoodfunction	p. 36
Maximizingalikelihoodfunction	p. 38
Obtainingstandarderrors	p. 39

A normal likelihood example	p. 41
Conclusions	p. 44
Exercises	p. 44
Probability exercises	p. 44
Classicalinferenceexercises	p. 45
Basics of Bayesian Statistics	p. 47
Bayes' Theorem for point probabilities	p. 47
Bayes' Theorem applied to probability distributions	p. 50
Proportionality	p. 51
Bayes' Theorem with distributions: A voting example	p. 53
Specification of a prior: The beta distribution	p. 54
An alternative model for the polling data: A gamma prior/Poisson likelihood approach	p. 60
A normal prior-normal likelihood example with ¿² known	p. 62
Extending the normal distribution example	p. 65
Someusefulpriordistributions	p. 68
The Dirichlet distribution	p. 69
Theinversegammadistribution	p. 69
Wishart and inverse Wishart distributions	p. 70
Criticism against Bayesian statistics	p. 70
Conclusions	p. 73
Exercises	p. 74
Modern Model Estimation Part 1: Gibbs Sampling	p. 77
What Bayesians want and why	p. 77
The logic ofsampling from posterior densities	p. 78
Two basic sampling methods	p. 80
Theinversionmethodofsampling	p. 81
The rejection method of sampling	p. 84
Introduction to MCMC sampling	p. 88
Generic Gibbs sampling	p. 88
Gibbs sampling example using the inversion method	p. 89
Example repeated using reeection sampling	p. 93
Gibbs sampling from a real bivariate density	p. 96
Reversing the process: Sampling the parameters given the data	p. 100
Conclusions	p. 103
Exercises	p. 105
Modern Model Estimation Part 2: Metroplis-Hastings Sampling	p. 107
A generic MH algorithm	p. 108
Relationship between Gibbs and MH sampling	p. 113
Example: MH sampling when conditional densities are difficult to derive	p. 115
Example: MH sampling for a conditional density with an unknown form	p. 118
Extending the bivariate normal example: The full multiparameter model	p. 121
The conditionals for ¿_x and ¿_y	p. 122
The conditionals for ¿_x², ¿_y², and ¿	p. 123
Thecomplete MH algorithm	p. 124
A matrix approach to the bivariate normal distribution problem	p. 126
Conclusions	p. 128
Exercises	p. 129
Evaluating Markov Chain Monte Carlo Algorithms and Model Fit	p. 131
Why evaluate MCMC algorithm performance?	p. 132
Some common problems and solutions	p. 132
Recognizing poor performance	p. 135
Trace plots	p. 135
Acceptance rates of MH algorithms	p. 141
Autocorrelation of parameters	p. 146
""""&Rcirc;"""" and other calculations	p. 147
Evaluating model fit	p. 153
Residual analysis	p. 154
Posteriorpredictivedistributions	p. 155
Formal comparison and combining models	p. 159
Bayes factors	p. 159
Bayesianmodelaveraging	p. 161
Conclusions	p. 163
Exercises	p. 163
The Linear Regression Model	p. 165
Developmentof the linear regression model	p. 165
Sampling from the posterior distribution for the model parameters	p. 168
Sampling with an MH algorithm	p. 168
Sampling the model parameters using Gibbs sampling	p. 169
Example: Are people in the South """"nicer"""" than others?	p. 174
Results and comparison of the algorithms	p. 175
Model evaluation	p. 178
Incorporatingmissing data	p. 182
Types of missingness	p. 182
A generic Bayesian approach when data are MAR: The """"niceness"""" example revisited	p. 186
Conclusions	p. 191
Exercises	p. 192
Generalized Linear Models	p. 193
The dichotomous probit model	p. 195
Model development and parameter interpretation	p. 195
Sampling from the posterior distribution for the model parameters	p. 198
Simulating from truncated normal distributions	p. 200
Dichotomous probit model example: Black-white differences in mortality	p. 206
The ordinal probit model	p. 217
Model development and parameter interpretation	p. 218
Sampling from the posterior distribution for the parameters	p. 220
Ordinal probit model example: Black-white differences in health	p. 223
Conclusions	p. 228
Exercises	p. 229
Introduction to Hierarchical Models	p. 231
Hierarchical models in general	p. 232
The voting example redux	p. 233
Hierarchicallinearregressionmodels	p. 240
Random effects: The random intercept model	p. 241
Random effects: The random coefficient model	p. 251
Growth models	p. 256
A note on fixed versus random effects models and other terminology	p. 264
Conclusions	p. 268
Exercises	p. 269
Introduction to Multivariate Regression Models	p. 271
Multivariate linear regression	p. 271
Model development	p. 271
Implementing the algorithm	p. 275
Multivariate probit models	p. 277
Model development	p. 278
Step 2: Simulating draws from truncated multivariate normal distributions	p. 283
Step 3: Simulation of thresholds in the multivariate probit model	p. 289
Step 5: Simulating the error covariance matrix	p. 295
Implementing the algorithm	p. 297
A multivariate probit model for generating distributions	p. 303
Model specification and simulation	p. 307
Life table generation and other posterior inferences	p. 310
Conclusions	p. 315
Exercises	p. 317
Conclusion	p. 319
Background Mathematics	p. 323
Summary ofcalculus	p. 323
Limits	p. 323
Differential calculus	p. 324
Integral calculus	p. 326
Finding a general rule for a derivative	p. 329
Summary ofmatrixalgebra	p. 330
Matrix notation	p. 330
Matrix operations	p. 331
Exercises	p. 335
Calculusexercises	p. 335
Matrixalgebraexercises	p. 335
The Central Limit Theorem, Confidence Intervals, and Hypothesis Tests	p. 337
A simulation study	p. 337
Classicalinference	p. 338
Hypothesistesting	p. 339
Confidence intervals	p. 342
Some final notes	p. 344
References	p. 345
Index	p. 353
Table of Contents provided by Publisher. All Rights Reserved.

Statistics for Social and Behavioral Sciences

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

More in Population & Demography

Going On and On

Why Our Longevity Threatens the Future

The Science of Racism

Everything you need to know but probably don't - yet

The Fourth Turning

An American Prophecy: What the Cycles of History Tell Us About America's Next Rendezvous with Destiny

The Origin of Politics

How Evolution and Ideology Shape the Fate of Nations

University of Miami School of Education and Human Developmen

Rethinking Data Practices and Inspiring Collective Dialogue

Bias and Beyond

Rethinking Data Practices and Inspiring Collective Dialogue

The Times Reference Atlas of the World

10th Edition

Scales of Transformation

How the Irish Became White

Routledge Classics

The Fourth Turning Is Here

What the Seasons of History Tell Us about How and When This Crisis Will End

Population

13th edition - An Introduction to Concepts and Issues

After the Spike

The Risks of Global Depopulation and the Case for People

The Best We Could Do

An Illustrated Memoir

The Science of Racism

Everything you need to know but probably don't - yet

The Great Leveler

Violence and the History of Inequality from the Stone Age to the Twenty-First Century

Third Culture Kids

Growing Up Among Worlds

Diversity Explosion

How New Racial Demographics Are Remaking America

Diversity Explosion

How New Racial Demographics Are Remaking America

Human Migration and the Refugee Crisis

Origins and Global Impact

The International Handbook of Women and Poverty

Global Essays and Social Justice Initiatives

The Fourth Turning Is Here

What the Seasons of History Tell Us about How and When This Crisis Will End

Changing Mobilities

Explorations of Exclusionary Design and Beyond

Routledge Revivals

Routledge Revivals

Population Geography

Tools and Issues

This product is categorised by