--- product_id: 5853984 title: "Data Analysis Using Regression and Multilevel/Hierarchical Models" price: "C$5294" currency: NIO in_stock: true reviews_count: 13 url: https://www.desertcart.ni/products/5853984-data-analysis-using-regression-and-multilevel-hierarchical-models store_origin: NI region: Nicaragua --- # R-oriented with practical coding examples Comprehensive regression & multilevel models Extensive real-world data applications Data Analysis Using Regression and Multilevel/Hierarchical Models **Price:** C$5294 **Availability:** βœ… In Stock ## Summary > πŸ“ˆ Elevate your data game with the ultimate regression & multilevel modeling toolkit! ## Quick Answers - **What is this?** Data Analysis Using Regression and Multilevel/Hierarchical Models - **How much does it cost?** C$5294 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.ni](https://www.desertcart.ni/products/5853984-data-analysis-using-regression-and-multilevel-hierarchical-models) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Key Features - β€’ **Applied Research Ready:** Benefit from real-world examples and practical tips that bridge theory with impactful application. - β€’ **R-Powered Practicality:** Leverage hands-on R code snippets to seamlessly implement models and boost your workflow. - β€’ **Data Visualization Focus:** Explore graphical tests and visualizations that transform raw data into compelling insights. - β€’ **Master Multilevel Modeling:** Unlock advanced hierarchical regression techniques essential for cutting-edge data analysis. - β€’ **Bayesian Foundations Included:** Gain a conceptual edge with Bayesian statistics insights, elevating your analytical rigor. ## Overview This used book offers a comprehensive, R-oriented guide to regression and multilevel/hierarchical models, blending theoretical rigor with practical application. Packed with real data examples, coding recipes, and a focus on Bayesian methods and data visualization, it’s a must-have resource for applied researchers and social scientists aiming to master advanced statistical modeling. ## Description Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/ Review: If I were shipwrecked ... - If I were shipwrecked and had only one statistics book with me,* this would be the one. Why? 1. For most applied uses of multi-level (mixed effects) regression in the social sciences, this book is appropriately comprehensive. You will want for little. 2. The book is R oriented. Though R might not be sufficient for all your needs, it is necessary. R has become the coordination point. 3. The book deals with basic concepts in probability, simulation, inference, and causation. The focus is on understanding what you are doing, not simply applying standard recipes. That's important because you can't competently apply the tools you will learn from this book without understanding these basic concepts. There are no shortcuts. 4. Nevertheless, the book contains a bunch of recipes, which I found helpful, for example, when learning how to simulate in R. Also, the authors write using a compact coding style. I'm grateful to have learned some simplifying tricks. 5. The authors focus on graphical tests and visualizing data. That's how you ought to be exploring data and testing/interpreting your results. 6. The book is oriented to generalized linear mixed effects (multi-level) modelling. When you learned ordinary regression you learned a special case. If you haven't learned ordinary regression, start with GLMMs. 7. The book is oriented to Bayesian statistics. Whether you use Bayesian statistics is up to you, however you owe it to yourself not to make embarrassing objections. Gelman and Hill do a fine job of explaining the motivations. Downsides 1. The individual sentences of this book are clear, however I felt that some sections could have had fuller explanations. Perhaps I'm a slow learner, but I had to move even slower than usual in some places, not because there was a thicket of mathematical detail (there's refreshingly little extraneous maths) but because the explanations were brief. For example, the sections on simulating data took me a couple of reads. 2. Software development is moving fast, and this book is already a little stale. That said, it is far from outdated. All the tools still work, and *most* are the same you'd be using now. That said some very good new statistical and graphical packages are available now (such as MCMCglmm, ggplot2, Rstan, blme, and others) and many will want to be running and interpreting their models using these. Note Gelman is involved in developing the latter two, and a bunch of others. No matter. This book is all most applied researchers will ever need, and again, you need to know the conceptual underpinnings. The tools will always be changing. *.. and with me: my compute, power, the motivation to work, abundant coffee, a fine cafe to work in... & etc. Review: Statistics in a box - I'm a social sciences PhD student and this is the book I keep going back to. There are a huge number of texts that you will find useful, but this one stands out for being particularly useful from cover to cover. A few of the advantages: - theoretically rigorous, but done by example and counter-example vs. mathematical proofs - tremendous number of examples with code and interpretation - didactic approach yet organized for quick reference - oriented toward practice vs. theory Some other things I like that others might not: Gelman is not a big fan of NHST inference and so he does not emphasize it. Nor does he stress jargony interpretation of tables of regression coefficients. Rather he emphasizes interpretation by simulation and counterfactuals. In that way he lays the groundwork for Bayesian analysis. Gelman is one of the developers of the R package lmer which estimates multilevel models. As such, it is the best reference for doing multilevel models in R. But realize that it is so much more. They spend the first half of the book reviewing single-level (?) regression and so the transition to multilevel is intuitive. You will understand it as an extension of what you already know. And (I keep saying this) you will find yourself going back to reference their coverage of regression when you have a question. The book is brilliant. ## Features - Used Book in Good Condition ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #830,944 in Books ( See Top 100 in Books ) #350 in Statistics (Books) #507 in Probability & Statistics (Books) #47,304 in Politics & Social Sciences (Books) | | Customer Reviews | 4.4 out of 5 stars 173 Reviews | ## Images ![Data Analysis Using Regression and Multilevel/Hierarchical Models - Image 1](https://m.media-amazon.com/images/I/61gjV8wBgpL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ If I were shipwrecked ... *by P***A on May 25, 2013* If I were shipwrecked and had only one statistics book with me,* this would be the one. Why? 1. For most applied uses of multi-level (mixed effects) regression in the social sciences, this book is appropriately comprehensive. You will want for little. 2. The book is R oriented. Though R might not be sufficient for all your needs, it is necessary. R has become the coordination point. 3. The book deals with basic concepts in probability, simulation, inference, and causation. The focus is on understanding what you are doing, not simply applying standard recipes. That's important because you can't competently apply the tools you will learn from this book without understanding these basic concepts. There are no shortcuts. 4. Nevertheless, the book contains a bunch of recipes, which I found helpful, for example, when learning how to simulate in R. Also, the authors write using a compact coding style. I'm grateful to have learned some simplifying tricks. 5. The authors focus on graphical tests and visualizing data. That's how you ought to be exploring data and testing/interpreting your results. 6. The book is oriented to generalized linear mixed effects (multi-level) modelling. When you learned ordinary regression you learned a special case. If you haven't learned ordinary regression, start with GLMMs. 7. The book is oriented to Bayesian statistics. Whether you use Bayesian statistics is up to you, however you owe it to yourself not to make embarrassing objections. Gelman and Hill do a fine job of explaining the motivations. Downsides 1. The individual sentences of this book are clear, however I felt that some sections could have had fuller explanations. Perhaps I'm a slow learner, but I had to move even slower than usual in some places, not because there was a thicket of mathematical detail (there's refreshingly little extraneous maths) but because the explanations were brief. For example, the sections on simulating data took me a couple of reads. 2. Software development is moving fast, and this book is already a little stale. That said, it is far from outdated. All the tools still work, and *most* are the same you'd be using now. That said some very good new statistical and graphical packages are available now (such as MCMCglmm, ggplot2, Rstan, blme, and others) and many will want to be running and interpreting their models using these. Note Gelman is involved in developing the latter two, and a bunch of others. No matter. This book is all most applied researchers will ever need, and again, you need to know the conceptual underpinnings. The tools will always be changing. *.. and with me: my compute, power, the motivation to work, abundant coffee, a fine cafe to work in... & etc. ### ⭐⭐⭐⭐⭐ Statistics in a box *by M***Y on December 6, 2011* I'm a social sciences PhD student and this is the book I keep going back to. There are a huge number of texts that you will find useful, but this one stands out for being particularly useful from cover to cover. A few of the advantages: - theoretically rigorous, but done by example and counter-example vs. mathematical proofs - tremendous number of examples with code and interpretation - didactic approach yet organized for quick reference - oriented toward practice vs. theory Some other things I like that others might not: Gelman is not a big fan of NHST inference and so he does not emphasize it. Nor does he stress jargony interpretation of tables of regression coefficients. Rather he emphasizes interpretation by simulation and counterfactuals. In that way he lays the groundwork for Bayesian analysis. Gelman is one of the developers of the R package lmer which estimates multilevel models. As such, it is the best reference for doing multilevel models in R. But realize that it is so much more. They spend the first half of the book reviewing single-level (?) regression and so the transition to multilevel is intuitive. You will understand it as an extension of what you already know. And (I keep saying this) you will find yourself going back to reference their coverage of regression when you have a question. The book is brilliant. ### ⭐⭐⭐⭐ An excellent contribution but . . . *by J***S on March 29, 2011* Pros: They tackle a complex topic from many different angles. They present enough code and theory to get people up and running with the techniques, assuming some prior familiarity with likelihood based inference and R. Otherwise you might need to dig through some of the references to understand everything. Regardless, this book is a valuable reference to keep in your library. They use matrix notation sparingly and this helps the reader focus on the important concepts of multilevel modeling. I am not even remotely a statistician so my attention would have been lost if I had to sort through a bunch of matrix transpositions and inversions in addition to all of the multilevel notation. The authors provide many useful references that help reinforce difficult ideas/concepts and that elaborate on topics that are not explored in depth. I had no prior experience using WinBUGS and the authors provided enough information for me to successfully execute some models that integrate R and WinBUGS. That is no small feat and the authors should be commended because somehow I understood what was going on. Cons: The organization of the book seems scattered and could be a little more consistent. On pp 245-246, the authors go on a diatribe about "fixed" and "random" effects terminology, claim that much of the literature that applies these terms does so inconsistently, disown these terms by saying they will avoid using them entirely, and then continue using these terms throughout the book. The website needs some work. You need to already know how to use R to open different types of files (and maybe some basics of variable assignment)in order to reproduce all of their examples. This book will not hold your hand through the steps like many R books. ## Frequently Bought Together - Data Analysis Using Regression and Multilevel/Hierarchical Models - Bayesian Data Analysis (Chapman & Hall / CRC Texts in Statistical Science) - Regression and Other Stories (Analytical Methods for Social Research) --- ## Why Shop on Desertcart? - πŸ›’ **Trusted by 1.3+ Million Shoppers** β€” Serving international shoppers since 2016 - 🌍 **Shop Globally** β€” Access 737+ million products across 21 categories - πŸ’° **No Hidden Fees** β€” All customs, duties, and taxes included in the price - πŸ”„ **15-Day Free Returns** β€” Hassle-free returns (30 days for PRO members) - πŸ”’ **Secure Payments** β€” Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** β€” Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.ni/products/5853984-data-analysis-using-regression-and-multilevel-hierarchical-models](https://www.desertcart.ni/products/5853984-data-analysis-using-regression-and-multilevel-hierarchical-models) --- *Product available on Desertcart Nicaragua* *Store origin: NI* *Last updated: 2026-06-26*