Implementation of multivariate linear mixed-effects models in the analysis of indoor climate performance experiments

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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The aim of the current study was to apply multivariate mixed-effects modeling to analyze experimental data on the relation between air quality and the performance of office work. The method estimates in one step the effect of the exposure on a multi-dimensional response variable, and yields important information on the correlation between the different dimensions of the response variable, which in this study was composed of both subjective perceptions and a two-dimensional performance task outcome. Such correlation is typically not included in the output from univariate analysis methods. Data originated from three different series of experiments investigating the effects of air quality on performance. The example analyses resulted in a significant and positive correlation between two performance tasks, indicating that the two tasks to some extent measured the same dimension of mental performance. The analysis seems superior to conventional univariate statistics and the information provided may be important for the design of performance experiments in general and for the conclusions that can be based on such studies.
Original languageEnglish
JournalInternational Journal of Biometeorology
Publication date2011
Volume56
Issue1
Pages129-136
ISSN0020-7128
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 0

Keywords

  • Indoor air quality, Multivariate mixed-effects modeling, Statistical analysis, Experimental design, Performance
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