Ling 621

Empirical Methods in Linguistics

Fall 2010

MW 11-11:50, R 17-17:50 Straub 145

Course goals:

This course introduces students to experimental design and basic issues in quantitative analysis of linguistic data.

Books:

Baayen, R. H. 2008. Analyzing linguistic data: A practical introduction to statistics using R. Cambridge University Press. RECOMMENDED

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

Requirements:

10% Pass CITI Human subjects training

10% Discussion Questions

50% Homeworks

30% Final paper

Preliminary schedule:

1.1-1.2: Why care?

                Gries, S. Th. 2009. Statistics for linguistics using R: A practical introduction. Mouton de Gruyter. pp.1-7.

                Berez, A. L., & S. Th. Gries. 2010. Correlates to middle marking in Denai’na iterative verbs. International Journal of American Linguistics, 76, 145-65.

Myers, J. 2009. Syntactic judgment experiments. Language & Linguistics Compass, 3, 406-23.

Labov, W. 1996. When intuitions fail. Chicago Linguistic Society, 32, 77-106.

1.3: Choosing and operationalizing a dependent variable

                Gries, S. Th. 2009. Statistics for linguistics using R: A practical introduction. Mouton de Gruyter. pp.7-29.

  2.1-2.2 Linking hypotheses

Tanenhaus, M. K., J. S. Magnuson, D. Dahan, & C. Chambers. 2000. Eye movements and lexical access in spoken-language comprehension: Evaluating a linking hypothesis between fixations and linguistic processing. Journal of Psycholinguistic Research, 29, 557-80.

2.3 When linking hypotheses fail to convince

Schütze, C. T. 2005. Thinking about what we are asking speakers to do. In S. Kepser & M. Reis (eds.), Linguistic evidence: Empirical, theoretical, and computational perspectives,  457–484. Mouton de Gruyter. (pp.457-469 only)

Kapatsinski, V. 2010. Modularity in the channel: A response to Moreton (2008). Research in Spoken Language Processing Progress Report 29, 297-305. (and reviews on Blackboard)

3.1: Choosing a dependent variable: Sensitivity/power Project proposals due

                Gerrits, E., & M. E. H. Schouten. 2004. Categorical perception depends on the discrimination task. Perception & Psychophysics, 66, 363-76.

3.2-3.3: Intro to R Homework 1 assigned

                Baayen, Chapter 1

4.1-4.2: Basic logic of hypothesis testing

                Johnson, Chapter 1, 2.1-2.3

                Baayen, Chapter 4.6

4.3: Who is your population? Homework 1 due; Homework 2 assigned

Nisbett, R. E., K. Peng, I. Choi, & A. Norenzayan. 2001. Culture and systems of thought: Holistic vs. analytic cognition. Psychological Review, 108, 291-310.

5.1-5.2: Distributions: A cautionary tale

                Yang, C. 2010. Who is afraid of George Kingsley Zipf? Ms. U Penn.

McMurray, B. 2007. Defusing the childhood vocabulary explosion. Science, 317, 631.

5.3:         hw 1 review

6.1: Some common statistical tests and their nonparametric versions Homework 2 due; Homework 3 assigned

Baayen, Chapter 4.0-4.2, 4.5

6.2: Sensitivity and bias: Signal Detection Theory

http://wise.cgu.edu/sdtmod/overview.asp

6.3: Experimental design

Gries, S. Th. 2009. Statistics for linguistics using R: A practical introduction. Mouton de Gruyter. pp.48-57.

http://www.experiment-resources.com/counterbalanced-measures-design.html

7.1: Methods of control

Chan, K.Y., & M. S. Vitevitch. (2010). Network structure influences speech production. Cognitive Science, 34, 685-97.

Bien, H., W. J. M. Levelt, & R. H. Baayen. 2005. Frequency effects in compound production. Proceedings of the National Academy of Sciences, 102, 17876-81.

7.2: Dichotomization and sensitivity

http://psych.colorado.edu/~mcclella/MedianSplit/

                Baayen, R. H. 2010. A real experiment is a factorial experiment? The Mental Lexicon, 5, 149-57.

7.3: Correlation and regression

Johnson, Chapters 2.4, 3.2

8.1-8.3: Multiple variables, interactions; Regression and ANOVA Homework 3 due; Homework 4 assigned

               Johnson, Chapters 4.1-4.3

9.1-9.2

No class. Work on your projects.

10.1: The evils of by-items tests

Johnson, Chapter 4.4

Raaijmakers, J. G. W. 2003. A further look at the “language-as-a-fixed-effect” fallacy. Canadian Journal of Experimental Psychology, 57, 141-51.

10.2-10.3: Problems with standard hypothesis testing; The Bayesian perspective Homework 4 due

              Kruschke, J. K. 2010. Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 658-76.

Finals week: Project presentations

OPTIONAL readings:

Linking hypotheses:

Yurovsky, D., S. Hidaka, C. Yu, & L. B. Smith. 2010. Linking learning to looking: Habituation and association in infant statistical language learning. Proceedings of the Annual Meeting of the Cognitive Science Society, 1589-94.

Spivey, M. J., M. Grosjean, & G. Knoblich. 2005. Continuous attraction toward phonological competitors. Proceedings of the National Academy of Sciences, 102, 10393-8.

The virtues of mixed effects

Johnson, Chapter 7

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.

Beyond means: The whole distribution

Balota, D.A., M. J. Yap, M.J. Cortese, & J.M. Watson. 2008. Beyond mean response latency: Response time distributional analyses of semantic priming. Journal of Memory & Language, 59, 495-523.

Ioannidis, J. P. A. 2005. Why most published research findings are false. PLoS Medicine, 2(8), e124.

Boucher, L., & Z. Dienes. 2003. Two ways of learning associations. Cognitive Science, 27, 807-42.