1887
  1. Home
  2. e-Journals
  3. Linguistic Approaches to Bilingualism
  4. Online First Articles
  5. Online First Article
image of A gentle introduction to Bayesian statistics, with applications to bilingualism research
USD
Buy:$35.00 + Taxes

Abstract

Abstract

Bayesian analyses have been increasingly adopted in psychology and linguistics as an addition (or replacement) to traditional frequentist methods. However, Bayesian methods are not yet widely applied in bilingualism research, possibly because existing introductions and tutorials have not been directed specifically at our field. The current paper highlights the advantages of Bayesian statistics to the bilingualism researcher, by providing both an introduction to its foundational principles and a practical tutorial on estimation and hypothesis testing using the R package. The examples build up from simple linear regression to more advanced mixed-effects models and showcase different aspects of a Bayesian workflow. All data, code, models, and supplementary materials are publicly available at https://osf.io/n3jgm/.

© John Benjamins Publishing Company
Loading

Article metrics loading...

/content/journals/10.1075/lab.24027.ver
2025-05-20
2025-06-21

  • From This Site

    /content/journals/10.1075/lab.24027.ver
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Baayen, R. H., Davidson, D. J., & Bates, D. M.
    (2008) Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, (), –. 10.1016/j.jml.2007.12.005
     https://doi.org/10.1016/j.jml.2007.12.005 [Google Scholar]
  2. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J.
    (2013) Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, (), –. 10.1016/j.jml.2012.11.001
     https://doi.org/10.1016/j.jml.2012.11.001 [Google Scholar]
  3. Bayarri, M. J., & Berger, J. O.
    (2004) The interplay of Bayesian and frequentist analysis. Statistical Science, (). 10.1214/088342304000000116
     https://doi.org/10.1214/088342304000000116 [Google Scholar]
  4. Broos, W. P. J., Bencivenni, A., Duyck, W., & Hartsuiker, R. J.
    (2021) Delayed picture naming in the first and second language. Bilingualism: Language and Cognition, (), –. 10.1017/S1366728920000620
     https://doi.org/10.1017/S1366728920000620 [Google Scholar]
  5. Bürki, A., Elbuy, S., Madec, S., & Vasishth, S.
    (2020) What did we learn from forty years of research on semantic interference? A Bayesian meta-analysis. Journal of Memory and Language, , . 10.1016/j.jml.2020.104125
     https://doi.org/10.1016/j.jml.2020.104125 [Google Scholar]
  6. Bürkner, P.-C.
    (2017) brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, (), –. 10.18637/jss.v080.i01
     https://doi.org/10.18637/jss.v080.i01 [Google Scholar]
  7. Bylund, E., Antfolk, J., Abrahamsson, N., Olstad, A. M. H., Norrman, G., & Lehtonen, M.
    (2023) Does bilingualism come with linguistic costs? A meta-analytic review of the bilingual lexical deficit. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑022‑02136‑7
     https://doi.org/10.3758/s13423-022-02136-7 [Google Scholar]
  8. Ciaccio, L. A., & Veríssimo, J.
    (2022) Investigating variability in morphological processing with Bayesian distributional models. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑022‑02109‑w
     https://doi.org/10.3758/s13423-022-02109-w [Google Scholar]
  9. Clark, M.
    (2022) Bayesian demonstration. RetrievedNovember 29, 2024, fromhttps://micl.shinyapps.io/prior2post/
  10. Cohen, J.
    (1994) The earth is round (p .05). American Psychologist, (), –. 10.1037/0003‑066X.49.12.997
     https://doi.org/10.1037/0003-066X.49.12.997 [Google Scholar]
  11. Coretta, S., Casillas, J. V., & Roettger, T.
    (2023) learnB4SS: Learning materials for the learnB4SS workshop. RetrievedNovember 29, 2024, fromhttps://learnb4ss.github.io/learnB4SS/
  12. Cumming, G.
    (2014) The new statistics: Why and how. Psychological Science, (), –. 10.1177/0956797613504966
     https://doi.org/10.1177/0956797613504966 [Google Scholar]
  13. Davis, C. J., & Lupker, S. J.
    (2006) Masked inhibitory priming in English: Evidence for lexical inhibition. Journal of Experimental Psychology: Human Perception and Performance, , –. bgjcrp
     https://doi.org/bgjcrp [Google Scholar]
  14. Dickey, J. M., & Lientz, B. P.
    (1970) The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain. The Annals of Mathematical Statistics, (), –. 10.1214/aoms/1177697203
     https://doi.org/10.1214/aoms/1177697203 [Google Scholar]
  15. Dienes, Z., & Mclatchie, N.
    (2018) Four reasons to prefer Bayesian analyses over significance testing. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑017‑1266‑z
     https://doi.org/10.3758/s13423-017-1266-z [Google Scholar]
  16. Feldman, L. B., Kostić, A., Basnight-Brown, D. M., Đurđević, D. F., & Pastizzo, M. J.
    (2010) Morphological facilitation for regular and irregular verb formations in native and non-native speakers: Little evidence for two distinct mechanisms. Bilingualism: Language and Cognition, (), –. 10.1017/S1366728909990459
     https://doi.org/10.1017/S1366728909990459 [Google Scholar]
  17. Fernandes, A. I., Luna, K., Soares, A. P., & Comesaña, M.
    (2023) Is there an early morphological decomposition during L2 lexical access? A meta-analysis on the morphological priming effect. Brain Sciences, (), . 10.3390/brainsci13010127
     https://doi.org/10.3390/brainsci13010127 [Google Scholar]
  18. Foote, R.
    (2017) The storage and processing of morphologically complex words in L2 Spanish. Studies in Second Language Acquisition, (), –. 10.1017/S0272263115000376
     https://doi.org/10.1017/S0272263115000376 [Google Scholar]
  19. Forster, K. I., Mohan, K., & Hector, J.
    (2003) The mechanics of masked priming. InS. Kinoshita & S. J. Lupker (Eds.), Masked priming: The state of the art (pp.–). Psychology Press.
     [Google Scholar]
  20. Garcia, G. D.
    (2024) Bayesian estimation in multiple comparisons. 10.31219/osf.io/wzqxg
     https://doi.org/10.31219/osf.io/wzqxg [Google Scholar]
  21. Gelman, A., & Tuerlinckx, F.
    (2000) Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational Statistics, (), –. 10.1007/s001800000040
     https://doi.org/10.1007/s001800000040 [Google Scholar]
  22. Gibson, E., & Fedorenko, E.
    (2013) The need for quantitative methods in syntax and semantics research. Language and Cognitive Processes, (), –. 10.1080/01690965.2010.515080
     https://doi.org/10.1080/01690965.2010.515080 [Google Scholar]
  23. Kass, R. E., & Raftery, A. E.
    (1995) Bayes factors. Journal of the American Statistical Association, (), –. 10.1080/01621459.1995.10476572
     https://doi.org/10.1080/01621459.1995.10476572 [Google Scholar]
  24. Kruschke, J. K.
    (2015) Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (Edition 2). Academic Press.
     [Google Scholar]
  25. Kruschke, J. K., & Liddell, T. M.
    (2018) The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑016‑1221‑4
     https://doi.org/10.3758/s13423-016-1221-4 [Google Scholar]
  26. Lago, S., Stone, K., Oltrogge, E., & Veríssimo, J.
    (2023) Possessive processing in bilingual comprehension. Language Learning, (), –. 10.1111/lang.12556
     https://doi.org/10.1111/lang.12556 [Google Scholar]
  27. Lakens, D., Scheel, A. M., & Isager, P. M.
    (2018) Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, (), –. 10.1177/2515245918770963
     https://doi.org/10.1177/2515245918770963 [Google Scholar]
  28. Lee, M. D., & Wagenmakers, E.-J.
    (2013) Bayesian cognitive modeling: A practical course. Cambridge University Press.
     [Google Scholar]
  29. Linck, J. A., & Cunnings, I.
    (2015) The utility and application of mixed-effects models in second language research. Language Learning, (), –. 10.1111/lang.12117
     https://doi.org/10.1111/lang.12117 [Google Scholar]
  30. Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., & Lüdecke, D.
    (2019) Indices of effect existence and significance in the Bayesian framework. Frontiers in Psychology, . 10.3389/fpsyg.2019.02767
     https://doi.org/10.3389/fpsyg.2019.02767 [Google Scholar]
  31. Marsman, M., & Wagenmakers, E.-J.
    (2017) Three insights from a Bayesian interpretation of the one-sided P value. Educational and Psychological Measurement, (), –. 10.1177/0013164416669201
     https://doi.org/10.1177/0013164416669201 [Google Scholar]
  32. Matzke, D., & Wagenmakers, E.-J.
    (2009) Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, (), –. 10.3758/PBR.16.5.798
     https://doi.org/10.3758/PBR.16.5.798 [Google Scholar]
  33. Maxwell, S. E., Lau, M. Y., & Howard, G. S.
    (2015) Is psychology suffering from a replication crisis? What does “failure to replicate” really mean?American Psychologist, (), –. 10.1037/a0039400
     https://doi.org/10.1037/a0039400 [Google Scholar]
  34. Mayo, D. G.
    (2018) Statistical inference as severe testing: How to get beyond the statistics wars (1st ed.). Cambridge University Press. 10.1017/9781107286184
     https://doi.org/10.1017/9781107286184 [Google Scholar]
  35. McElreath, R.
    (2020) Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). CRC Press. 10.1201/9780429029608
     https://doi.org/10.1201/9780429029608 [Google Scholar]
  36. Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J.
    (2016) The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑015‑0947‑8
     https://doi.org/10.3758/s13423-015-0947-8 [Google Scholar]
  37. Morey, R. D., Rouder, J. N., Verhagen, J., & Wagenmakers, E.-J.
    (2014) Why hypothesis tests are essential for psychological science: A comment on Cumming (2014). Psychological Science, (), –. 10.1177/0956797614525969
     https://doi.org/10.1177/0956797614525969 [Google Scholar]
  38. Nicenboim, B., Schad, D. J., & Vasishth, S.
    (2024) An introduction to Bayesian data analysis for cognitive science. Retrieved fromhttps://vasishth.github.io/bayescogsci/book/
     [Google Scholar]
  39. Nickerson, R. S.
    (2000) Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, (), –. 10.1037/1082‑989X.5.2.241
     https://doi.org/10.1037/1082-989X.5.2.241 [Google Scholar]
  40. Norouzian, R., De Miranda, M., & Plonsky, L.
    (2018) The Bayesian revolution in second language research: An applied approach. Language Learning, (), –. 10.1111/lang.12310
     https://doi.org/10.1111/lang.12310 [Google Scholar]
  41. O’Hagan, A.
    (2019) Expert knowledge elicitation: Subjective but scientific. The American Statistician, , –. 10.1080/00031305.2018.1518265
     https://doi.org/10.1080/00031305.2018.1518265 [Google Scholar]
  42. Pliatsikas, C., Johnstone, T., & Marinis, T.
    (2014) fMRI evidence for the involvement of the procedural memory system in morphological processing of a second language. PLoS ONE, (), . 10.1371/journal.pone.0097298
     https://doi.org/10.1371/journal.pone.0097298 [Google Scholar]
  43. Plonsky, L., Mohebbi, H., & Coombe, C. A.
    (2021) Quantitative research methods and the reform movement in applied linguistics. InResearch questions in language education and applied linguistics: A reference guide (pp.–). Springer. 10.1007/978‑3‑030‑79143‑8_130
     https://doi.org/10.1007/978-3-030-79143-8_130 [Google Scholar]
  44. Rastle, K., & Davis, M. H.
    (2008) Morphological decomposition based on the analysis of orthography. Language and Cognitive Processes, (), –. 10.1080/01690960802069730
     https://doi.org/10.1080/01690960802069730 [Google Scholar]
  45. Rouder, J. N., Haaf, J. M., & Vandekerckhove, J.
    (2018) Bayesian inference for psychology, part IV: Parameter estimation and Bayes factors. Psychonomic Bulletin & Review, (), –. 10.3758/s13423‑017‑1420‑7
     https://doi.org/10.3758/s13423-017-1420-7 [Google Scholar]
  46. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G.
    (2009) Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, (), –. 10.3758/PBR.16.2.225
     https://doi.org/10.3758/PBR.16.2.225 [Google Scholar]
  47. Schad, D. J., Betancourt, M., & Vasishth, S.
    (2021) Toward a principled Bayesian workflow in cognitive science. Psychological Methods, (), –. 10.1037/met0000275
     https://doi.org/10.1037/met0000275 [Google Scholar]
  48. Schad, D. J., Nicenboim, B., Bürkner, P.-C., Betancourt, M., & Vasishth, S.
    (2022) Workflow techniques for the robust use of Bayes factors. Psychological Methods. gtbjhb
     https://doi.org/gtbjhb [Google Scholar]
  49. Schad, D. J., Nicenboim, B., & Vasishth, S.
    (2024) Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance. Psychological Methods. 10.1037/met0000621
     https://doi.org/10.1037/met0000621 [Google Scholar]
  50. Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R.
    (2020) How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, , . 10.1016/j.jml.2019.104038
     https://doi.org/10.1016/j.jml.2019.104038 [Google Scholar]
  51. Schmalz, X., Biurrun Manresa, J., & Zhang, L.
    (2023) What is a Bayes factor?Psychological Methods, (), –. 10.1037/met0000421
     https://doi.org/10.1037/met0000421 [Google Scholar]
  52. Silva, R., & Clahsen, H.
    (2008) Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language & Cognition, , –. 10.1017/S1366728908003404
     https://doi.org/10.1017/S1366728908003404 [Google Scholar]
  53. Sinharay, S., & Stern, H. S.
    (2002) On the sensitivity of Bayes factors to the prior distributions. The American Statistician, (), –. 10.1198/000313002137
     https://doi.org/10.1198/000313002137 [Google Scholar]
  54. van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Van Aken, M. A. G.
    (2014) A gentle introduction to bayesian analysis: Applications to developmental research. Child Development, (), –. 10.1111/cdev.12169
     https://doi.org/10.1111/cdev.12169 [Google Scholar]
  55. Van De Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S.
    (2017) A systematic review of Bayesian articles in psychology: The last 25 years. Psychological Methods, (), –. 10.1037/met0000100
     https://doi.org/10.1037/met0000100 [Google Scholar]
  56. Vasishth, S., Nicenboim, B., Beckman, M. E., Li, F., & Kong, E. J.
    (2018) Bayesian data analysis in the phonetic sciences: A tutorial introduction. Journal of Phonetics, , –. 10.1016/j.wocn.2018.07.008
     https://doi.org/10.1016/j.wocn.2018.07.008 [Google Scholar]
  57. Vehtari, A., Gelman, A., & Gabry, J.
    (2017) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, (), –. 10.1007/s11222‑016‑9696‑4
     https://doi.org/10.1007/s11222-016-9696-4 [Google Scholar]
  58. Veríssimo, J.
    (2021a) Analysis of rating scales: A pervasive problem in bilingualism research and a solution with Bayesian ordinal models. Bilingualism: Language and Cognition, (), –. 10.1017/S1366728921000316
     https://doi.org/10.1017/S1366728921000316 [Google Scholar]
  59. (2021b) BLC mini-series: New statistical approaches and research practices for bilingualism research. Bilingualism: Language and Cognition, (), –. 10.1017/S1366728921000365
     https://doi.org/10.1017/S1366728921000365 [Google Scholar]
  60. Veríssimo, J., Heyer, V., Jacob, G., & Clahsen, H.
    (2018) Selective effects of age of acquisition on morphological priming: Evidence for a sensitive period. Language Acquisition, (), –. 10.1080/10489223.2017.1346104
     https://doi.org/10.1080/10489223.2017.1346104 [Google Scholar]
  61. Wagenmakers, E.-J., Gronau, Q. F., Dablander, F., & Etz, A.
    (2022) The support interval. Erkenntnis, (), –. 10.1007/s10670‑019‑00209‑z
     https://doi.org/10.1007/s10670-019-00209-z [Google Scholar]
  62. Wagenmakers, E.-J., Lee, M., Lodewyckx, T., & Iverson, G. J.
    (2008) Bayesian versus frequentist inference. InH. Hoijtink, I. Klugkist, & P. A. Boelen (Eds.), Bayesian evaluation of informative hypotheses (pp.–). Springer New York. 10.1007/978‑0‑387‑09612‑4_9
     https://doi.org/10.1007/978-0-387-09612-4_9 [Google Scholar]
/content/journals/10.1075/lab.24027.ver
Loading
A gentle introduction to Bayesian statistics, with applications to bilingualism research
John Benjamins ; https://doi.org/10.1075/lab.24027.ver
/content/journals/10.1075/lab.24027.ver
/content/journals/10.1075/lab.24027.ver
Loading

Data & Media loading...

  • Article Type: Research Article
Keywords: tutorial ; bilingualism ; Bayes factors ; Bayesian statistics ; brms

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error