Abstract
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.
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Acknowledgments
Associate Professor Pawel Wargocki from the International Centre for Indoor Environment and Energy (ICIEE) is acknowledged for providing us with the data we used for this study. The study was part of the research programme of ICIEE and was supported by the Danish Ministry of Science, Technology and Innovation, and the Birch and Krogboe Foundation as part of an Industrial PhD program.
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Jensen, K.L., Spiild, H. & Toftum, J. Implementation of multivariate linear mixed-effects models in the analysis of indoor climate performance experiments. Int J Biometeorol 56, 129–136 (2012). https://doi.org/10.1007/s00484-011-0404-y
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DOI: https://doi.org/10.1007/s00484-011-0404-y