Hierarchical Linear Models: Applications and Data Analysis Methods: 1 (Advanced Quantitative Techniques in the Social Sciences) - Image 1

£76.71

Sage Publications Hierarchical Linear Models: Applications and Data Analysis Methods: 1 (Advanced Quantitative Techniques in the Social Sciences)

Name: Hierarchical Linear Models: Applications and Data Analysis Methods: 1 (Advanced Quantitative Techniques in the Social Sciences)
Brand: Sage Publications
Price: 76.71 GBP
Availability: InStock

Reference

This is the usual price. Wait for it to drop, or tell us your number.

£77 today · usual range £71–£83 · best ever £71

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 7 days • 7 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 7 days • 4 price levels

Days at Price

Current Price

Price Analysis

Most common price: £71 (2 days, 28.6%)

Price range: £71 - £83

Price levels: 4 different prices over 7 days

Description

"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high." --Short Book Reviews from the International Statistical Institute "The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research." --TED GERBER, Sociology, University of Arizona "Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems." --PAUL SWANK, Houston School of Nursing, University of Texas, Houston Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: * An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3 * New section on multivariate growth models in Chapter 6 * A discussion of research synthesis or meta-analysis applications in Chapter 7 * Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III: * New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case * New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model * New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13) The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.

Product Specifications

Brand: Sage Publications
Format: hardcover
ASIN: 076191904X
Category: Books > Subjects > Reference
Domain: Amazon UK
Release Date: 19 December 2001
Listed Since: 14 February 2007

Barcode

No barcode data available

Sage Publications Hierarchical Linear Models: Applications and Data Analysis Methods: 1 (Advanced Quantitative Techniques in the Social Sciences)

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science)

Applied Missing Data Analysis, Second Edition (Methodology in the Social Sciences)

Handbook of Advanced Multilevel Analysis (European Association of Methodology Series)

Practical Applications of Bayesian Reliability (Quality and Reliability Engineering Series)

Bayesian Hierarchical Models: With Applications Using R, Second Edition

Latent Markov Models for Longitudinal Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)

Latent Markov Models for Longitudinal Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)

Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

Exercises and Solutions in Statistical Theory: 99 (Chapman & Hall/CRC Texts in Statistical Science)

Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

Latent Variable Modeling with R - Routledge Statistical Guide

An Introduction to Generalized Linear Models (Chapman & Hall/CRC Texts in Statistical Science)

Sage Handbook of Quantitative Methods in Psychology - Book

Linear Models in Statistics

Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Linear Mixed Models: A Practical Guide Using Statistical Software

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Foundations of Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)

Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R (Chapman & Hall/CRC Texts in Statistical Science)

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)