The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Hierarchical diffusion models for two-choice response times.
Open accessAbstract
Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times-the Wiener diffusion process-with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology · doi:10.2307/2075823 · Cited by 18,724
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,723
Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A., & Hill, J. (2006) · doi:10.1017/cbo9780511790942 · Cited by 13,597
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
Mixed-effects modeling with crossed random effects for subjects and items
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540
Human Memory: A Proposed System and its Control Processes
Atkinson, R. C., & Shiffrin, R. M. (1968) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60422-3 · Cited by 7,216
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility
Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000) · Statistics and Computing · doi:10.1023/a:1008929526011 · Cited by 5,368
Cited by
(2013) · Cited by 1,351
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00014 · Cited by 1,047
The drift diffusion model as the choice rule in reinforcement learning
Pedersen, M. L., Frank, M. J., & Biele, G. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1199-y · Cited by 283
Bayesian inference for psychology, part IV: parameter estimation and Bayes factors
Rouder, J. N., Haaf, J. M., & Vandekerckhove, J. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1420-7 · Cited by 102
Self-citationFrom bad to worse: Symbolic equivalence and opposition in fear generalisation
Bennett, M., Hermans, D., Dymond, S., Vervoort, E., & Baeyens, F. (2015) · Cognition and Emotion · doi:10.1080/02699931.2014.973833 · Cited by 27
Metadata
- Authors
- Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D.
- Venue
- Psychological Methods
- Published
- 2011
- Volume
- 16
- Issue
- 1
- Pages
- 44-62
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2160627480
- Cited by
- 323
- References
- 95
- FWCI
- 21.24
- Type
- journal article
- Language
- en
- Open access
- green
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