International Journal of Biometeorology

, Volume 56, Issue 1, pp 129–136 | Cite as

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

Original Paper

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.

Keywords

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

Supplementary material

484_2011_404_MOESM1_ESM.doc (28 kb)
ESM 1(DOC 28 kb)

References

  1. Bako-Biro Z, Wargocki P, Weshler CJ, Fanger PO (2004) Effects of pollution from personal computers on perceived air quality, SBS symtoms and productivity in offices. Indoor Air 14:178–187CrossRefGoogle Scholar
  2. Demidenko E (2004) Mixed models: theory and applications, 1st edn. Wiley, New JerseyCrossRefGoogle Scholar
  3. Gunnarsen L, Fanger PO (1992) Adaptation to indoor air pollution. Environ Int 18:43–54CrossRefGoogle Scholar
  4. Jensen KL, Toftum J, Friis-Hansen P (2009) A Bayesian Network Approach to the evaluation of building design and its consequences for employee performance and operational cost. Build Environ 44:456–462CrossRefGoogle Scholar
  5. Kolarik K, Toftum J, Olesen BW, Shitzer A (2009) Human perceptions and office work performance in environments with moderately drifting operative temperatures (1269-RP). Int J Heat Ventilation Air Cond Refrigeration Res 15(5):931–960Google Scholar
  6. Lagercrantz L, Wistrand M, Willen U, Wargocki P, Witterseh T, Sundell J (2000) Negative impact of air pollution on productivity: previous Danish findings repeated in new Swedish test. In: Proceedings of the Healthy Buildings 2000 Conference, vol 2, pp 653–658Google Scholar
  7. Shafer J, Yucel R (2002) Computational strategies for multivariate linear mixed-effects model with missing values. J Comput Graph Stat 11(2):421–442Google Scholar
  8. Shah A, Laird N, Schoenfeld D (1997) A random-effects model for multiple characteristics with possible missing data. J Am Stat Assoc 92(438):775–779CrossRefGoogle Scholar
  9. The R Development Core Team (2004) R reference manual base package, 2. Network Theory Limited, BristolGoogle Scholar
  10. Thiébaut R, Jacqmin-Gadda H, Chêne G, Leport C, Commenges D (2002) Bivariate linear mixed models using SAS proc MIXED. Comput Meth Programs Biomed 69:249–256CrossRefGoogle Scholar
  11. Toftum J, Kolarik J, Belkowska D, Olesen BW (2010) Influence on occupant responses of behavioral modification of clothing insulation in non-steady thermal environments—RP-1269. Int J Heat Ventilation Air Cond Refrigeration Res 16(1):59–74Google Scholar
  12. Wargocki P, Wyon DP, Baik YK, Clausen G, Fanger PO (1999) Perceived air quality, Sick Building Syndrome (SBS) symptoms and productivity in an office with two different pollution loads. Indoor Air 9:165–179CrossRefGoogle Scholar
  13. Wargocki P, Wyon DP, Sundell J (2000) The effects of outdoor air supply rate in an office on perceived air quality, sick building syndrome (SBS) symptoms and productivity. Indoor Air 10:222–236CrossRefGoogle Scholar
  14. Wargocki P, Seppänen O (ed) (2006) Andersson J, Boerstra A, Clements-Croome D, Fitzner K, Hanssen SO. Indoor Climate and Productivity in Offices—how to integrate productivity in life-cycle cost analysis of building services, REHVA Guidebook no 6, REHVA, FinlandGoogle Scholar
  15. Witterseh T, Wyon DP, Clausen G (2004) The effects of moderate heat stess and open-plan office noise distraction on SBS symptoms and on the performance of office work. Indoor Air 14(8):30–40CrossRefGoogle Scholar

Copyright information

© ISB 2011

Authors and Affiliations

  • Kasper L. Jensen
    • 1
  • Henrik Spiild
    • 2
  • Jørn Toftum
    • 3
  1. 1.ALECTIA A/SVirumDenmark
  2. 2.Department of Infomatics and Mathematical ModelingTechnical University of DenmarkVirumDenmark
  3. 3.International Centre for Indoor Environment and Energy, Department of Civil EngineeringTechnical University of DenmarkVirumDenmark

Personalised recommendations