Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power

Annual Review of Psychology

Volumn 68, Issue , 2017, Pages 601-625

  Judd, Charles M ^a   Westfall, Jacob ^b   Kenny, David A ^c  

^a UNIVERSITY OF COLORADO   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Effect size; Experimental design; Mixed models; Random factors; Sample size; Statistical power

Indexed keywords

HUMAN; METHODOLOGY; PSYCHOLOGY; STATISTICAL MODEL;

HUMANS; MODELS, STATISTICAL; PSYCHOLOGY; RESEARCH DESIGN;

EID: 85009483629     PISSN: 00664308     EISSN: 15452085     Source Type: Book Series    
DOI: 10.1146/annurev-psych-122414-033702     Document Type: Review

References (23)

1. Baayen RH, Davidson DJ, Bates DM. 2008. Mixed-effects modeling with crossed-random effects for subjects and items. J. Mem. Lang. 59:390-412
2. Barr DJ, Levy R, Scheepers C, Tily HJ. 2013. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68:255-78
3. Bates D, Kliegl R, Vasishth S, Baayen H. 2015. Parsimonious mixed models. arxiv:1506.04967v1 [stat.ME]
4. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24(3):127-35
5. Bond CF, DePaulo B. 2008. Individual differences in judging deception: accuracy and bias. Psychol. Bull. 134:477-92
6. ClarkHH.1973. The language-as-fixed-effect fallacy: a critique of language statistics in psychological research. J. Verb. Learn. Verb. Behav. 12:335-59
7. Cohen J. 1988. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Erlbaum
8. GoldsteinH, BrowneW, Rasbash J. 2002. Partitioning variation in multilevel models. Underst. Stat. 1:223-31
9. Hönekopp J. 2006. Once more: Is beauty in the eye of the beholder? Relative contributions of private and shared taste to judgments of facial attractiveness. J. Exp. Psychol. Hum. Percept. Perfor. 32:199-209
10. Hox JJ. 2010. Multilevel Analysis: Techniques and Applications. New York: Routledge
11. Judd CM,McClelland RG, Ryan CS. 2008. Data Analysis: AModel Comparison Approach.New York: Routledge
12. Judd CM, Westfall J, Kenny DA. 2012. Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem. J. Pers. Soc. Psychol. 103:54-69
13. Kenny DA, Kashy DA, Cook WL. 2006. Dyadic Data Analysis. New York: Guilford Press
14. Raudenbush SW, Bryk AS. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage
15. Satterthwaite FE. 1946. An approximate distribution of estimates of variance components. Biom. Bull. 2(6):110-14
16. Smith ER. 2014. Research design. In Handbook of Research Methods in Social and Personality Psychology, ed.HT Reis, CM Judd, pp. 27-48. Cambridge, UK: Cambridge Univ. Press
17. Snijders T, Bosker R. 2011. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks, CA: Sage
18. Stroup WW. 2012. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. New York: CRC Press
19. Toma C, Corneille O, Yzerbyt V. 2012. Holding a mirror up to the self: egocentric similarity beliefs underlie social projection in cooperation. Pers. Soc. Psychol. Bull. 38:1259-71
20. Welch BL. 1947. The generalization of 'Student's' problem when several different population variances are involved. Biometrika 34:28-35
21. Westfall J, Judd CM,Kenny DA. 2015. Replicating studies in which samples of participants respond to samples of stimuli. Pers. Psychol. Sci. 10:390-99
22. Westfall J, Kenny DA, Judd CM. 2014. Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. J. Exp. Psychol. Gen. 143:220-45
23. Winer BJ. 1971. Statistical Principles in Experimental Design. New York: McGraw-Hill.