Book-wise, there is

Gries, S. Th. 2009. Statistics for linguistics using R: A practical introduction. Mouton de Gruyter. (more corpus-oriented),

Baayen, R. H. 2008. Analyzing linguistic data: A practical introduction to statistics using R. Cambridge University Press. (more psycholinguisticky), and

Johnson, K. 2008. Quantitative methods in linguistics. Blackwell.

Some of my favorite papers on mixed effects:

Clark, H. H. 1973. The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335-359.

Raaijmakers, J. G. W., J. M. C. Schrijnemakers, & F. Gremmen. 1999. How to deal with "The Language-as-Fixed-Effect Fallacy": Common misconceptions and alternative solutions. Journal of Memory & Language, 41, 416-26.

Barr, D. J., R. Levy, C. Scheepers, & H. J. Tily. 2013. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255-78.

Dixon, P. 2008. Models of accuracy in repeated-measures designs. Journal of Memory & Language, 59, 447-56. (other papers in the same special issue are also worth reading)

Bresnan, J., A. Cueni, T. Nikitina, & R. H. Baayen. 2007. Predicting the dative alternation. In G. Bouma, I. Kraemer, & J. Zwarts, eds. Cognitive foundations of interpretation, 69-94. Royal Netherlands Academy of Science.

These two papers on multimodel inference (of course, I like my own):

Kuperman, V., & J. Bresnan. 2012. The effects of construction probability on word durations during spontaneous incremental sentence production. Journal of Memory and Language, 66, 588-611.

Barth, D., & V. Kapatsinski. 2014. A multimodel inference approach to categorical variant choice: construction, priming and frequency effects on the choice between full and contracted forms of am, are and is. Corpus Linguistics and Linguistic Theory, http://dx.doi.org/10.1515/cllt-2014-0022

I also liked this paper using generalized additive mixed models:

Martijn Wieling, Simonetta Montemagni, John Nerbonne and R. Harald Baayen (2014). Lexical differences between Tuscan dialects and standard Italian: Accounting for geographic and socio-demographic variation using generalized additive mixed modeling. Language, 90(3), 669-692.

And these papers comparing tree-based and regression methods:

Tagliamonte, S. A., & Baayen, R. H. (2012). Models, forests, and trees of York English: Was/were variation as a case study for statistical practice. Language Variation and Change, 24(02), 135-178.

Baayen, R. H., Endresen, A., Janda, L. A., Makarova, A., & Nesset, T. (2013). Making choices in Russian: pros and cons of statistical methods for rival forms. Russian linguistics, 37(3), 253-291.

(Of course, I like my manuscript on this topic too:

Kapatsinski, V. (2013). Towards a de-ranged study of variation: Estimating predictor importance with multimodel inference. Available at http://blogs.uoregon.edu/ublab/files/2014/11/Range-comparisons-Revised-SgSp-20wy623.pdf)

Hi Matt Jans,

I can recommed the following book for an overview on quantitative and qualitative research methods:

Breakwell, G. M., Smith, J. A., & Wright, D. B. (2012). Research methods in Psychology. Los Angeles: Sage.

And here is the one I found very helpful for my dissertation (1. Designing an experiment, 2. Analysing and interpreting data, 3. Writing up your research):

Field, A., & Hole, G. (2010). How to design and report experiments. Los Angeles: Sage.

Best, Jeanette

Well I know that it may sound unpopular but I would be very cautions with books especially in our admittedly matophobic field :-). It seems that it takes too long to write a book and when it's done it is outdated. Statistics are rapidly becoming more and more powerful but also harder and harder to master. For example the paper by authored by Barr et al (2013) and cited by Vsevolod, substantially contradict the chapters on mixed models in the book by Baayen (also cited by  Vsevolod, at leas in the version I have in my library). Now that does not mean that the book by Bayeen is not a good one, it just means that few years later, things changed. 

Probably the best practice would be to start from books aiming at introducing the methods regardless of the field of application and then to move toward very recent journal papers with a tutorial style. Also I generally find papers in other domain (e.g.: genetics, ecology) very useful.

For mixed models I would stat from

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. Springer Science & Business Media.

and I would soon after read the excellent (and evil :-) paper cited by Vsevolod and authored by Barr et al. (2013) to which I would add this very nice paper:

Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in ecology & evolution, 24(3), 127-135.

(Bodo Winters oriented me toward this paper a couple of years ago).

For Bayesian approaches to hierarchical modeling I would read:

Kruschke, J. (2010). Doing Bayesian data analysis: A tutorial introduction with R. Academic Press.

Which has a wonderful support blog, with examples exercises and programs to do your own stuff:

http://doingbayesiandataanalysis.blogspot.fr/

If you want more theory then the must read is:

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Chapman & Hall/CRC.

Since cognitive processes and behavioral data unfold over time many researchers find important to conduct statistics on time series

Founding titles in this domain are

Silverman, B. W., & Ramsay, J. O. (2005). Functional Data Analysis. Springer. (more theoretical)

Ramsay, J. O., & Silverman, B. W. (2002). Applied functional data analysis: methods and case studies (Vol. 77). New York: Springer. (lot of examples and link to code and data)

Graves, S., Hooker, G., & Ramsay, J. (2009). Functional Data Analysis with R and MATLAB. (to do your own stuff with their code)

However things change rapidly and nowaday it is possible to do Functional Data mixed models trough baysian analysis:

Morris, J. S., & Carroll, R. J. (2006). Wavelet‐based functional mixed models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(2), 179-199.

which comes together with its own matlab code to be found here:

https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware.aspx?Software_Id=70

The flexibility of this approach also allows to apply this framework to mode images via mixed models. Here is our tutorial paper (well it's phonetics stuff but you can also apply this approach to FMRI images etc.):

Lancia, L., Rausch, P., & Morris, J. S. Automatic quantitative analysis of ultrasound tongue contours via wavelet-based functional mixed models. Journal of the Acoustical society of America, 137 , EL178. http://dx.doi.org/10.1121/1.4905881 

Then there is the whole world of dynamical systems theory applied to speech processing, requiring a very different approach. But I am not sure this relevant here.

Best,

Leonardo

Hello!

I think the most useful books mentioned here are Baayen and Gries. Both of them use R, which in my opinion, is a great tool for quantitative analysis, data representation, etc.

Good luck!

Best,

Marcos

My absolute favorite book on mixed effects regression is Gelman and Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models.  It walks through what each part of a mixed effects model _means_, and how to interpret the output of a model.  It's a bit math heavy, but not excessively and in my experience working through it things are presented in a very digestible way if you're patient and willing to think through it.  Most importantly, it's not only theoretical but has a lot of practical suggestions and examples, too, and even though they're not geared explicitly towards socio-/psycholinguistics it's easy to draw the parallels.

... looks like you can spend the rest of the year reading on statistics :-)  - some of these have got me curious, too.  I'd just like to add:

For Gries 2009, there is now a second edition (2014), and it's definitely improved from the first.

A good overview is found in the chapters "Basic significance testing" (Gries) and "Multivariate statistics" (Baayen) in: Podesva, Robert J. & Devyani Sharma. 2013. Research Methods in Linguistics. Cambridge university Press.

Also, what has helped me understand logistic regression models are the Sage University Papers: "Logistic Regression: A Primer" (2000) by Fred C. Pampel, and "Interaction Effects in Logistic Regression" (2001) by James Jaccard.

And all in all, I'm still a fan of Johnson (2008) (which the others have already mentioned).

Best,

David

This is a great thread! Thanks all! I have a pretty good grasp (at least applied) on stats in general, but my original motivation was trying to find leads into the quantitative/statistical aspects of psycho and sociolinguistics. This gives me a lot of great resources to look through, and I'll try to remember my favorites within them as I review. A research assistant was taking an conversation analysis class with John Heritage here at UCLA a couple years ago and told me that there's  a lot more quantitative going on there now than the qualitative ethnomethodology I was familiar with. 

Let me give a major thumbs up for the Clark paper mentioned by Vsevolod, which my PhD advisor gave to me years ago. 

Clark, H. H. 1973. The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335-359.

Baayen's book is my favorite too. 

hi

I like the Baayen book too, and can recommend any papers coming from the Baayen group. The paper by Martijn Wieling is good too. He is btw a very good lecturer on statistics, I followed a course by him. So any publications were he is a co-author are bound to have good parts on statistics too.

Esther

Thanks Sascha, Marina, and Esther for the additional answers. The Baayen book looks like a clear winner (or at least one I should check out)

Some of my work in education involves nonrandomized samples, and I found Guo and Fraser's book Propensity Score Analysis: Statistical Methods and Applications to be very helpful.

Dear Matt,

would you be interested in single-case studies (as opposed or in combination to group studies) you could have a look at the work of John R. Crawford, Paul H. Garthwaite and colleagues. For example:

Crawford, J. R., & Garthwaite, P. H. (2005). Evaluation of criteria for classical dissociations in single-case studies by Monte Carlo simulation. Neuropsychology, 19(5), 664.

Crawford, J. R., & Garthwaite, P. H. (2002). Investigation of the single case in neuropsychology: Confidence limits on the abnormality of test scores and test score differences. Neuropsychologia, 40(8), 1196-1208.

Crawford, J. R., Garthwaite, P. H., & Porter, S. (2010). Point and interval estimates of effect sizes for the case-controls design in neuropsychology: rationale, methods, implementations, and proposed reporting standards. Cognitive Neuropsychology, 27(3), 245-260.

They also have a website, where you can download some of their programs and articles.

Regards,

Adrià

http://homepages.abdn.ac.uk/j.crawford/pages/dept/