SCS Reads Nominations

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SCS Reads Nominations, 2014-2015

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  • Latent Variable Models and Factor Analysis: A Unified Approach. by David J. Bartholomew, Martin Knott, and Irini Moustaki [1]
    • The discussion moves from more traditional latent variable models for continuous data (i.e., factor analysis) to continuous and discrete latent variable models for ordered and unordered categorical data. It's a little on the technical side, but I think the topics covered (although cursory at times, especially the MCMC sections) provide decent in depth look at latent variable models for educational, psychological, and survey research. -- Phil
  • Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (Quantitative Methodology Series) edited by John J McArdle and Gilbert Ritschard [2]
    • Data mining seems to be getting more popular in this "big data" era. This book covers it from a behavioural/social science perspective, seems heavy with several applied examples.
  • Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer Harrell, F.E. Jr. (2001).
    • We've considered this one before, but the problem continues to arise in consulting: I have many potential predictors, how do I decide which ones to include?
    • Great book. I used it as a textbook for a course in Applied Stats many years ago. It announces early and clearly that it's about prediction -- specifically not causality. So I knew I wouldn't find answers to the questions that were really bothering me! For what it aims to do, it's an excellent book.
  • Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models Chapman & Hall/CRC web page
    • This one is comprehensive and interesting but may be too technical.
    • I think it's an outstanding book by outstanding authors. We would need access to Stata to really benefit from it. I've always been intrigued with the idea of trying to implement 'gllamm' in R. Maybe we could figure out how to do that too as we work through the book. -- Georges
  • Jim Albert (2009) Bayesian Computation with R, 2nd ed., Springer
    A relatively small book in the Use R! series. What seems nice about this book is that it would provide an introduction to Bayesian analysis, MCMC, Gibbs sampling, convergence diagnostics, etc. in a context in which we can learn the methods by using them. We could supplement the book with other readings on Bayesian methods. Or, since it is relatively short we could devote time to other topics.
  • Exploratory Factor Analysis by Farigar & Wegener [3]
    • You can add comments here
  • Handbook of structural equation modeling by Hoyle. [4]
    • and here
  • Hancock, G.R. & Mueller, R.O. (2010). The reviewer's guide to quantitative methods in the social sciences. New York Routledge.
    • This book was nominated by Manolo a few year's ago. I had a chance to read the chapter on mixed models recently and I was quite impressed. There is no such thing as an authoritative guide on reporting analyses with methodologies that keep changing and about which there is considerable controversy. However, a thoughtful and well-written book that purports to be a guide can serve as a starting point for a good discussion. -- Georges

SCS Reads Nominations, 2013

JUNE. 28, 2013: NEW ONLINE POLL: PLEASE VOTE HERE

SCS Reads Nominations, 2012

SEPT. 11, 2012: NEW ONLINE POLL: PLEASE VOTE HERE -- [8]

  • 'Little Green Books' from Sage.
    You can see the list at http://srmo.sagepub.com/browse?doctype=qass. The idea would be to select a few books to read over the year, covering various topics. (This was nominated by Matt and Carrie if I remember correctly)
  • Jim Albert (2009) Bayesian Computation with R, 2nd ed., Springer
    A relatively small book in the Use R! series. What seems nice about this book is that it would provide an introduction to Bayesian analysis, MCMC, Gibbs sampling, convergence diagnostics, etc. in a context in which we can learn the methods by using them. We could supplement the book with other readings on Bayesian methods. Or, since it is relatively short we could devote time to other topics. (nominated by Georges)
  • Structural Equation Modeling: Concepts, Issues, and Applications by Rick Hoyle (nominated by Constance)
    Each chapter is written by a different contributor (e.g., Bentler and Hu)
    We haven't done a SEM book since I've been at York (so at least 4 years). Given its popularity among applied researchers, would be great to have some discussion of this modeling technique! We could supplement with a few more recent papers on SEM as well.
  • Multivariate Generalized Linear Mixed Models Using R by Damon M. Berridge and Robert Crouchley. CRC Press, 2011. (nominated by Hugh)
    The extension of Generalized Linear Mixed Models to handle multivariate dependent variables is, of course, a very valuable addition to our tools for multi-level modelling. This book uses the SabreR package in R.

SCS Reads Nominations, 2011

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