선형 혼합효과 모형에서 실험조건 효과 검정을 위한 절차 비교
Comparison of procedures for detecting the experimental condition effect in a linear mixed-effects model
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초록
심리학 실험자료 분석을 위한 선형 혼합효과 모형에서 실험자료의 특성에 따라서 모형설정 방법이 실험조건의 효과를 탐지하는데 미치는 영향을 몬테카를로 시뮬레이션 방법을 통해 조 사하였다. 구체적으로 자료생성모형의 무선효과의 복잡성을 변화시키면서 모형을 선택하는 전략에 따른 1종 오류비율과 검정력을 비교하였다. 그 결과, 최대모형 접근(또는 무선기울기 모형)은 자료의 무선효과 구조가 단순한 조건에서 실험조건 효과를 지나치게 엄격하게 검증 하여 상대적으로 낮은 검정력을 보였다. 반면, 모형비교 접근을 사용했을 때는 본 연구의 모 든 시뮬레이션 조건에서 1종 오류비율이 유의수준에 근접하면서도 최대모형 접근에서보다 우 수하거나 그에 상당하는 검정력을 나타냈다. 실험 연구자가 선형 혼합효과 모형을 분석도구 로 사용할 때 고려해야 할 점을 논의하였다.
Abstract
In a linear mixed-effects model for psychological experimental data analysis, the effect of the model selection procedure on detecting the experimental condition effect was investigated through a Monte Carlo simulation study. Specifically, while changing the complexity of the random effect structure of the data-generating model, the type I error rate and power were compared between the model selection strategies. As a result, the maximal model approach (or the random slope model) showed relatively low statistical power under the condition that the structure of the random component of the data was simple. On the other hand, when the model comparison approach was used, the Type I error rate approached the significance level, and the power was superior to or equivalent to that of the maximal model approach in all simulation conditions of this study. Finally, we discussed the points that experimental researchers should consider when using the linear mixed-effects model as an analysis tool.
참고문헌 (38)
- Baayen, H., "Mixed-effects modeling with crossed random effects for subjects and items", Journal of Memory and Language, 59(4), pp.390~412 (2008)[DOI]
- Barr, D. J., "Random effects structure for confirmatory hypothesis testing: Keep it maximal", Journal of Memory and Language, 68(3), pp.255~278 (2013)[DOI]
- "Parsimonious mixed models" (2015)[URL]
- Bates D, "Fitting linear mixed-effects models using lme4", Journal of Statistical Software, 67(1), pp.1~48 (2015)[DOI]
- Brown, V. A., "An introduction to linear mixed-effects modeling in R", Advances in Methods and Practices in Psychological Science, 4(1) (2021)[DOI]
- Brown, M. B., "Robust tests for equality of variances", Journal of the American Statistical Association, 69, pp.364~367 (1974)[DOI]
- Cohen J., "Statistical power analysis for the behavioral sciences" (1988), Routledge Academic
- Enders, C. K., "A fully conditional specification approach to multilevel imputation of categorical and continuous variables", Psychological Methods, 23(2), pp.298~317 (2018)[DOI]
- Fisher, R. A., "Studies in crop variation: 1. An examination of the yield of dressed grain from broadbalk", The Journal of Agricultural Science, 11(2), pp.107~135 (1921)[DOI]
- hierarchical models, "Data analysis using regression and multilevel", pp.2007, Gelman, A.
- Gurka, M. J., "Selecting the best linear mixed model under REML", The American Statistician, 60(1), pp.19~26 (2006)[DOI]
- Gurka, M. J., "Avoiding bias in mixed model inference for fixed effects", Statistics in Medicine, 30, pp.2696~2707 (2011)[DOI]
- Henderson, D. A., "Stepwise regression in social and psychological research", Psychological Reports, 64(1), pp.251~257 (1989)[DOI]
- Judd, C. M., "Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem", Journal of Personality and Social Psychology, 103, pp.54~69 (2012)[DOI]
- Kwok, O. -M., "The impact of misspecifying the within-subject covariance structure in multiwave longitudinal multilevel models: A Monte Carlo study", Multivariate Behavioral Research, 42, pp.557~592 (2007)[DOI]
- LeCam, L., "On some asymptotic properties of maximum likelihood estimates and related Bayes' estimates", pp.277~330 (1953)
- 이우열, "실험데이터 분석을 위한 선형 혼합효과 모형", 한국심리학회지: 인지 및 생물, 32(2), pp.197~211 (2020), 한국인지및생물심리학회[DOI]
- Lix, L. M., "Consequences of assumption violation revisited. A quantitative review of alternatives to the one-way analysis of variance F test", Review of Educational Research, 66(4), pp.579~619 (1996)[DOI]
- Luke, S. G., "Evaluating significance in linear mixed-effects models in R", Behavioral Research, 49, pp.1494~1502 (2017)[DOI]
- Maas, C. J. M., "Sufficient sample sizes for multilevel modeling", Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3), pp.86~92 (2005)[DOI]
- Magezi, D. A., "Linear mixed-effects models for within-participant psychology experiments:An introductory tutorial and free, graphical user interface (LMMgui)", Frontiers in Psychology, 6 (2015)[DOI]
- Matuschek, H., "Balancing Type I error and power in linear mixed models", Journal of Memory and Language, 94, pp.305~315 (2017)[DOI]
- Maxwell, S. E., "Designing experiments and analyzing data" (2017), Lawrence Erlbaum Associates
- Meteyard, L., "Best practice guidance for linear mixed-effects models in psychological science", Journal of Memory and Language, 112, pp.104092~ (2020)[DOI]
- Metropolis, N., "The beginning of the Monte Carlo method", Los Alamos Science, 15, pp.125~130 (1987)[DOI]
- Molenberghs, G., "Likelihood ratio, score, and Wald tests in a constrained parameter space", The American Statistician, 61(1), pp.22~27 (2007)[DOI]
- Park, J., "Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data", Methodology, 16(2), pp.92~111 (2020)[DOI]
- Pinheiro, J., "Mixed-effects models in S and S-PLUS" (2000), Springer
- Raaijmakers, J. G. W., "How to deal with “The language-as-fixed-effect fallacy”:Common misconceptions and alternative solutions", Journal of Memory and Language, 41(3), pp.416~426 (1999)[DOI]
- Rao, P., "Some notes on misspecification in multiple regressions", The American Statistician, 25(5), pp.37~39 (1971)[DOI]
- Raudenbush, S. W., "Hierarchical linear models: Applications and data analysis methods" (2002), Sage
- Scariano, S. M., "The effects of violations of independence assumptions in the One-way ANOVA", The American Statistician, 41(2), pp.123~129 (1987)[DOI]
- Singmann, H., "New Methods in Cognitive Psychology" (2020), Psychology Press
- Snijders, T. A. B., "Multilevel analysis: An introduction to basic and advanced multilevel modeling" (2011), Sage
- Tomarken, A. J., "Comparison of ANOVA alternatives under variance heterogeneity and specific noncentrality structures", Psychological Bulletin, 99(1), pp.90~99 (1986)[DOI]
- Verbeke, G., "A linear mixed-effects model with heterogeneity in the random-effects population", Journal of the American Statistical Association, 91, pp.217~221 (1996)[DOI]
- Wald, A., "Note on the consistency of the maximum likelihood estimate", The Annals of Mathematical Statistics, 20(4), pp.595~601 (1949)[DOI]
- Zhang, D., "Random effect and latent variable model selection", pp.19~36 (2008), Springer