Multilevel modelling: Beyond the basic applications

British Journal of Mathematical and Statistical Psychology

Volumn 62, Issue 2, 2009, Pages 439-456

  Wright, Daniel B ^a   London, Kamala ^b  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

^b UNIVERSITY OF TOLEDO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ANXIETY DISORDER; ARTICLE; COMPUTER PROGRAM; FEMALE; HUMAN; LONGITUDINAL STUDY; MATHEMATICAL COMPUTING; METHODOLOGY; NORMAL DISTRIBUTION; PERCEPTIVE DISCRIMINATION; PERSONALITY TEST; PREGNANCY; PSYCHOLOGY; PSYCHOMETRY; PUERPERAL DISORDER; REACTION TIME; RECALL; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ANALYSIS OF VARIANCE; ANXIETY DISORDERS; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; LONGITUDINAL STUDIES; MATHEMATICAL COMPUTING; MENTAL RECALL; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PERSONALITY INVENTORY; PREGNANCY; PSYCHOLOGY; PSYCHOMETRICS; PUERPERAL DISORDERS; REACTION TIME; REPRODUCIBILITY OF RESULTS; SIGNAL DETECTION, PSYCHOLOGICAL; SOFTWARE; STATISTICS AS TOPIC;

EID: 67849103812     PISSN: 00071102     EISSN: None     Source Type: Journal    
DOI: 10.1348/000711008X327632     Document Type: Article

References (43)

1. Ayers, S. E. (1999). Post-traumatic stress disorder following childbirth. PhD dissertation. University of London.
2. 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.
3. Banks, W. P. (1970). Signal detection theory and human memory. Psychological Bulletin, 74, 81-99.
4. Bates, D. (2007). lme4: Linear mixed-effects models using S4 classes. R package version 0.99875-9.
5. Browne, W., Subramanian, S., Jones, K., & Goldstein, H. (2005). Variance partitioning in multilevel logistic models that exhibit over-dispersion. Journal of the Royal Statistical Society: A, 168, 599-613.
6. Bryk, A. S., & Raudenbush, S. W. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
7. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33, 261-304.
8. Chambers, J. M. (2008). Software for data analysis: Programming with R. London: Springer.
9. 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.
10. Cohen, J. (1976). Random means random. Journal of Verbal Learning and Verbal Behavior, 15, 261-262.
11. Crawley, M. J. (2005). Statistics: An introduction using R. Chichester, UK: Wiley.
12. Crawley, M. J. (2007). The R book. Chicester, UK: Wiley.
13. DeCarlo, L. T. (1998). Signal detection theory and generalized linear models. Psychological Methods, 3, 186-205.
14. Faraway, J. J. (2004). Linear models with R. Boca Raton, FL: Chapman and Hall/CRC.
15. Faraway, J. J. (2006). Extending the linear model with R: Generalized linear, mixed effects and nonparameteric regression models. Boca Raton, FL: Chapman and Hall/CRC.
16. Fox, J. (2002). An R and S-Plus companion to applied regression. Thousand Oaks, CA: Sage Publications.
17. Goldstein, H. (2003). Multilevel statistical methods (3rd ed.). London: Edward Arnold.
18. Hoffmann, J. P. (2004). Generalized linear models: An applied approach. Boston, MA: Pearson Education.
19. Hoffman, L., & Rovine, M. J. (2007). Multilevel models for the experimental psychologist: Foundations and illustrative examples. Behavior Research Methods, 39, 101-117.
20. Horry, R., & Wright, D. B. (2008). I know your face but not where I saw you: Context memory is impaired for other race faces. Psychonomic Bulletin and Review, 15(3), 604-609.
21. Hox, J. (2002). Multilevel analysis: Techniques and applications. London: Erlbaum.
22. Kreft, I. I., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage Publications.
23. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.
24. Lockhart, R. S., & Murdock, B. B. (1970). Memory and the theory of signal detection. Psychological Bulletin, 74, 100-109.
25. MacMillan, N. A., & Creelman, C. D. (2005). Detection theory: A user's guide. Mawhaw, NJ: Erlbaum.
26. Ng, E. S. W., Carpenter, J. R., Goldstein, H., & Rasbash, J. (2006). Estimation in generalised linear mixed models with binary outcomes by simulated maximum likelihood. Statistical Modelling, 6, 23-42.
27. Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-Plus. New York: Springer.
28. Pinheiro, J. C., Bates, D. M., DebRoy, S., & Sarkar, D. (2008). nlme: Linear and nonlinear mixed effects models. R package version 3.1-87.
29. R Development Core Team (2008). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, http://www.R-project.org.
30. Rasbash, J., Steele, F., Browne, W., & Prosser, B. (2005). A user's guide to MLwiN. http://www.cmm.bristol.ac.uk/MLwiN/download/userman_2005.pdf.
31. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford: Oxford University Press.
32. Singer, J. D., & Willett, J. B. (2009). Applied multilevel data analysis. Manuscript in preparation.
33. Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, and Computers, 31, 137-149.
34. Venables,W. N., Smith, D. M., & the R Development Core Team (2008). An introduction to R. ISBN 3-900051-12-7. http://cran.r-project.org/doc/manuals/R-intro.pdf.
35. Weber, N., Brewer, N., Wells, G. L., Semmler, C., & Keast, A. (2004). Eyewitness identification accuracy and response latency: The unruly 10-12 second rule. Journal of Experimental Psychology: Applied, 10, 139-147.
36. Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton, FL: Chapman and Hall/CRC.
37. Wright, D. B. (1997). Extra-binomial variation in multilevel logistic models with sparse structures. British Journal of Mathematical and Statistical Psychology, 50, 21-29.
38. Wright, D. B. (1998). Modelling clustered data in autobiographical memory research: The multilevel approach. Applied Cognitive Psychology, 12, 339-357.
39. Wright, D. B., Boyd, C. E., & Tredoux, C. G. (2003). Inter-racial contact and the own race bias for face recognition in South Africa and England. Applied Cognitive Psychology, 17, 365-373.
40. Wright, D. B., Horry, R., & Skagerberg, E. M. (in press). Functions for traditional and multilevel approaches to signal detection theory. Behavior Research Methods.
41. Wright, D. B., & London, K. (2009). Modern regression techniques using R: A practical guide for students and researchers. London: Sage Publications.
42. Wright, D. B., & McDaid, A. T. (1996). Comparing system and estimator variables using data from real line-ups. Applied Cognitive Psychology, 10, 75-84.
43. Zho, X.-H., Obuchowski, N. A., & McClish, D. K. (2002). Statistical methods in diagnostic medicine. New York: Wiley.