Skip to main content
SpringerLink
Log in

Application of statistical analysis of sample size: How many occupant responses are required for an indoor environmental quality (IEQ) field study

  • Research Article
  • Indoor/Outdoor Airflow and Air Quality
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Determining required sample size is one of the critical pathways to reproducible, reliable and robust results in human-related studies. This paper aims to answer a fundamental but often overlooked question: what sample size is required in surveys of occupant responses to indoor environmental quality (IEQ). The statistical models are introduced in order to promote determining required sample size for various types of data analysis methods commonly used in IEQ field studies. The Monte Carlo simulations are performed to verify the statistical methods and to illustrate the impact of sample size on the study accuracy and reliability. Several examples are presented to illustrate how to determine the value of the parameters in the statistical models based on previous similar research or existing databases. The required sample size including “worst” and “optimal” cases in each condition is obtained by this method and references. It is indicated that 385 is a “worst case” sample size to be adequate for a subgroup analysis, while if the researcher has an estimate of the study design and outcome, the “optimal case” sample size can potentially be reduced. When the required sample size is not achievable, the uncertainty in the result can properly interpret via a confidence interval. It is hoped that this paper would fill in the gap between statistical analysis of sample size and IEQ field research, and it can provide a useful reference for researchers when planning their own studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Akhter S, Pauyo T, Khan M (2019). What is the difference between a systematic review and a meta-analysis? In: Basic Methods Handbook for Clinical Orthopaedic Research. Berlin: Springer Berlin Heidelberg.

    Google Scholar 

  • Arens E, Humphreys MA, de Dear R, et al. (2010). Are ‘class A’ temperature requirements realistic or desirable? Building and Environment, 45: 4–10.

    Article  Google Scholar 

  • ASHRAE (2010). ASHRAE 90329: Performance measurement protocols for commercial buildings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air conditioning Engineers.

    Google Scholar 

  • ASHRAE (2020). ASHRAE 55: Thermal Environmental Conditions for Human Occupancy. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air conditioning Engineers.

    Google Scholar 

  • Basner M, Brink M (2013). Sample size estimation for field studies on the effects of aircraft noise on sleep. Applied Acoustics, 74: 812–817.

    Article  Google Scholar 

  • Borenstein M, Hedges LV, Higgins JPT, et al. (2021). Introduction to Meta-Analysis. Chichester, UK: John Wiley & Sons.

    Book  MATH  Google Scholar 

  • Cao T, Lian Z, Ma S, et al. (2021). Thermal comfort and sleep quality under temperature, relative humidity and illuminance in sleep environment. Journal of Building Engineering, 43: 102575.

    Article  Google Scholar 

  • Cao T, Lian Z, Zhu J, et al. (2022). Parametric study on the sleep thermal environment. Building Simulation, 15: 885–898.

    Article  Google Scholar 

  • Chalmers TC, Levin H, Sacks HS, et al. (1987). Meta-analysis of clinical trials as a scientific discipline. I: Control of bias and comparison with large co-operative trials. Statistics in Medicine, 6: 315–325.

    Article  Google Scholar 

  • Chen L, Kántor N, Nikolopoulou M (2022a). Meta-analysis of outdoor thermal comfort surveys in different European cities using the RUROS database: The role of background climate and gender. Energy and Buildings, 256: 111757.

    Article  Google Scholar 

  • Chen Y, Chen B, Deng J, et al. (2022b). The integration model of objective and subjective data of residential indoor environment quality in Northeast China based on structural equation modeling. Building Simulation, 15: 741–754.

    Article  Google Scholar 

  • Cheung T, Schiavon S, Parkinson T, et al. (2019). Analysis of the accuracy on PMV - PPD model using the ASHRAE global thermal comfort database II. Building and Environment, 153: 205–217.

    Article  Google Scholar 

  • Dattalo P (2009). A review of software for sample size determination. Evaluation and the Health Professions, 32: 229–248.

    Article  Google Scholar 

  • de Dear R (2004). Thermal comfort in practice. Indoor Air, 14: 32–39.

    Article  Google Scholar 

  • de Dear RJ, Akimoto T, Arens EA, et al. (2013). Progress in thermal comfort research over the last twenty years. Indoor Air, 23: 442–461.

    Article  Google Scholar 

  • Dong B, Yan D, Li Z, et al. (2018). Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation, 11: 899–921.

    Article  Google Scholar 

  • Du H, Lian Z, Lai D, et al. (2021). Method of determining acceptable air temperature thresholds in Chinese HVAC buildings based on a data-driven model. Energy and Buildings, 241: 110920.

    Article  Google Scholar 

  • Du H, Lian Z, Lai D, et al. (2022a). Comparison of thermal comfort between radiant and convective systems using field test data from the Chinese Thermal Comfort Database. Building and Environment, 209: 108685.

    Article  Google Scholar 

  • Du H, Lian Z, Lai D, et al. (2022b). Evaluation of the accuracy of PMV and its several revised models using the Chinese thermal comfort Database. Energy and Buildings, 271: 112334.

    Article  Google Scholar 

  • Elnaklah R, Fosas D, Natarajan S (2020). Indoor environment quality and work performance in “green” office buildings in the Middle East. Building Simulation, 13: 1043–1062.

    Article  Google Scholar 

  • EN (2007). EN 15251: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Brussels: European Committee for Standardization.

  • Fan X, Shao H, Sakamoto M, et al. (2022). The effects of ventilation and temperature on sleep quality and next-day work performance: pilot measurements in a climate chamber. Building and Environment, 209: 108666.

    Article  Google Scholar 

  • Fleiss JL, Levin B, Paik MC (2013). Statistical Methods for Rates and Proportions. Hoboken, NJ, USA: John Wiley & Sons

    MATH  Google Scholar 

  • Fong KF, Lee CK, Lau KK-L, et al. (2023). Residential building performance analysis at near extreme weather conditions in Hong Kong through a thermal-comfort-based strategy. Building Simulation, 16: 13–25.

    Article  Google Scholar 

  • Földváry Ličina V, Cheung T, Zhang H, et al. (2018). Development of the ASHRAE global thermal comfort database II. Building and Environment, 142: 502–512.

    Article  Google Scholar 

  • GB (2012). GB 50736: Design Code for Heating Ventilation and Air Conditioning of Civil Buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • Geng Y, Lin B, Yu J, et al. (2019). Indoor environmental quality of green office buildings in China: Large-scale and long-term measurement. Building and Environment, 150: 266–280.

    Article  Google Scholar 

  • Ghaddar D, Itani M, Ghaddar N, et al. (2021). Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces. Building Simulation, 14: 1757–1771.

    Article  Google Scholar 

  • Hahn GJ, Meeker WQ (2011). Statistical Intervals: A Guide for Practitioners (Vol. 92). Hoboken, NJ, USA: John Wiley & Sons.

    Google Scholar 

  • Hegazy M, Yasufuku K, Abe H (2022). An interactive approach to investigate brightness perception of daylighting in Immersive Virtual Environments: Comparing subjective responses and quantitative metrics. Building Simulation, 15: 41–68.

    Article  Google Scholar 

  • Hickey GL, Grant SW, Dunning J, et al. (2018). Statistical primer: Sample size and power calculations—why, when and how? European Journal of Cardio-Thoracic Surgery, 54: 4–9.

    Article  Google Scholar 

  • Hong T, Langevin J, Sun K (2018). Building simulation: Ten challenges. Building Simulation, 11: 871–898.

    Article  Google Scholar 

  • Hsieh FY, Bloch DA, Larsen MD (1998). A simple method of sample size calculation for linear and logistic regression. Statistics in Medicine, 17: 1623–1634.

    Article  Google Scholar 

  • Hou D, Wang L, Katal A, et al. (2023). Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools. Building Simulation, 16: 133–149.

    Article  Google Scholar 

  • Hui PS, Wong LT, Mui KW (2007). Sampling strategies of indoor air quality assessment for offices. Facilities, 25: 179–184.

    Article  Google Scholar 

  • ISO (2005). ISO 7730: Ergonomics of the thermal environment: Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD Indices and local thermal comfort criteria. Geneva: International Organization for Standardization.

    Google Scholar 

  • ISO (2012). ISO 28802: Ergonomics of the physical environment—Assessment of environments by means of an environmental survey involving physical measurements of the environment and subjective responses of people. Geneva: International Organization for Standardization.

    Google Scholar 

  • ISO (2019). ISO 10551: Ergonomics of the thermal environment—Assessment of the influence of the thermal environment using subjective judgement scales. Geneva: International Organization for Standardization.

    Google Scholar 

  • JGJ (2014). JGJ/T 347: Standard of Test Methods for Thermal Environment of Building. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • Karjalainen S (2012). Thermal comfort and gender: a literature review. Indoor Air, 22: 96–109.

    Article  Google Scholar 

  • Lan L, Lian Z (2010). Application of statistical power analysis—How to determine the right sample size in human health, comfort and productivity research. Building and Environment, 45: 1202–1213.

    Article  Google Scholar 

  • Li Y, Rezgui Y, Guerriero A, et al. (2020). Development of an adaptation table to enhance the accuracy of the predicted mean vote model. Building and Environment, 168: 106504.

    Article  Google Scholar 

  • Ma KW, Wong HM, Mak CM (2018). A systematic review of human perceptual dimensions of sound: Meta-analysis of semantic differential method applications to indoor and outdoor sounds. Building and Environment, 133: 123–150.

    Article  Google Scholar 

  • Mishra AK, Ramgopal M (2013). Field studies on human thermal comfort—An overview. Building and Environment, 64: 94–106.

    Article  Google Scholar 

  • Newcombe RG (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine, 17: 857–872.

    Article  Google Scholar 

  • Norman G, Monteiro S, Salama S (2012). Sample size calculations: should the emperor’s clothes be off the peg or made to measure? BMJ, 345: e5278.

    Article  Google Scholar 

  • Open Science Collaboration (2015). Estimating the reproducibility of psychological science. Science, 349(6251): aac4716.

    Article  Google Scholar 

  • Peduzzi P, Concato J, Feinstein AR, et al. (1995). Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates. Journal of Clinical Epidemiology, 48: 1503–1510.

    Article  Google Scholar 

  • Peduzzi P, Concato J, Kemper E, et al. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49: 1373–1379.

    Article  Google Scholar 

  • Pisello AL, Pigliautile I, Andargie M, et al. (2021). Test rooms to study human comfort in buildings: A review of controlled experiments and facilities. Renewable and Sustainable Energy Reviews, 149: 111359.

    Article  Google Scholar 

  • Porras-Salazar JA, Schiavon S, Wargocki P, et al. (2021). Meta-analysis of 35 studies examining the effect of indoor temperature on office work performance. Building and Environment, 203: 108037.

    Article  Google Scholar 

  • Schulz KF, Grimes DA (2005). Sample size calculations in randomised trials: mandatory and mystical. The Lancet, 365: 1348–1353.

    Article  Google Scholar 

  • Shafavi NS, Zomorodian ZS, Tahsildoost M, et al. (2020). Occupants visual comfort assessments: A review of field studies and lab experiments. Solar Energy, 208: 249–274.

    Article  Google Scholar 

  • Song W, Zhang Z, Chen Z, et al. (2022). Thermal comfort and energy performance of personal comfort systems (PCS): A systematic review and meta-analysis. Energy and Buildings, 256: 111747.

    Article  Google Scholar 

  • Su X, Wang Z, Zhou F, et al. (2022). Comfortable clothing model of occupants and thermal adaption to cold climates in China. Building and Environment, 207: 108499.

    Article  Google Scholar 

  • Tang M-L (2010). Sample size determination for clinical trials. Encyclopedia of Statistical Sciences. Hoboken, NJ, USA: John Wiley & Sons.

    Book  Google Scholar 

  • Torresin S, Pernigotto G, Cappelletti F, et al. (2018). Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works. Indoor Air, 28: 525–538.

    Article  Google Scholar 

  • Uttley J (2019). Power analysis, sample size, and assessment of statistical assumptions—Improving the evidential value of lighting research. Leukos, 15: 143–162.

    Article  Google Scholar 

  • Wang J, Wang Z, de Dear R, et al. (2018). The uncertainty of subjective thermal comfort measurement. Energy and Buildings, 181: 38–49.

    Article  Google Scholar 

  • Wang Z, Wang J, He Y, et al. (2020a). Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning. Journal of Building Engineering, 29: 101120.

    Article  Google Scholar 

  • Wang Z, Zhang H, He Y, et al. (2020b). Revisiting individual and group differences in thermal comfort based on ASHRAE database. Energy and Buildings, 219: 110017.

    Article  Google Scholar 

  • Williams MS, Ebel ED, Wagner BA (2007). Monte Carlo approaches for determining power and sample size in low-prevalence applications. Preventive Veterinary Medicine, 82: 151–158.

    Article  Google Scholar 

  • Xia X, Chan KH, Lam KBH, et al. (2021). Effectiveness of indoor air purification intervention in improving cardiovascular health: A systematic review and meta-analysis of randomized controlled trials. Science of the Total Environment, 789: 147882.

    Article  Google Scholar 

  • Xu X, Lian Z, Shen J, et al. (2021a). Experimental study on sleep quality affected by carbon dioxide concentration. Indoor Air, 31: 440–453.

    Article  Google Scholar 

  • Xu X, Lian Z, Shen J, et al. (2021b). Environmental factors affecting sleep quality in summer: a field study in Shanghai, China. Journal of Thermal Biology, 99: 102977.

    Article  Google Scholar 

  • Yan D, Zhou X, An J, et al. (2022). DeST 3.0: A new-generation building performance simulation platform. Building Simulation, 15: 1849–1868.

    Article  Google Scholar 

  • Yao H, Cheng X, Wei S, et al. (2022). Sampling method for long-term monitoring of indoor environmental quality in residential buildings. Building and Environment, 215: 108965.

    Article  Google Scholar 

  • Zhang H, Arens E, Pasut W (2011). Air temperature thresholds for indoor comfort and perceived air quality. Building Research & Information, 39: 134–144.

    Article  Google Scholar 

  • Zhang S, Lin Z (2020). Predicted Mean Vote with skin temperature from standard effective temperature model. Building and Environment, 183: 107133.

    Article  Google Scholar 

  • Zhou X, Xu L, Zhang J, et al. (2022). Development of data-driven thermal sensation prediction model using quality-controlled databases. Building Simulation, 15: 2111–2125.

    Article  Google Scholar 

  • Zhu H, Wang H, Liu Z, et al. (2018). Experimental study on the human thermal comfort based on the heart rate variability (HRV) analysis under different environments. Science of the Total Environment, 616–617: 1124–1133.

    Article  Google Scholar 

  • Zhu P, Tao W, Lu X, et al. (2022). Optimisation design and verification of the acoustic environment for multimedia classrooms in universities based on simulation. Building Simulation, 15: 1419–1436.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China (2022YFC3803201). The authors wish to thank Dr. Jingyun Shen from the Shanghai Jiao Tong University for the useful suggestions.

Author information

Authors and Affiliations

  1. Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China

    Heng Du, Zhiwei Lian, Li Lan & Dayi Lai

Authors
  1. Heng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiwei Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dayi Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiwei Lian.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, H., Lian, Z., Lan, L. et al. Application of statistical analysis of sample size: How many occupant responses are required for an indoor environmental quality (IEQ) field study. Build. Simul. 16, 577–588 (2023). https://doi.org/10.1007/s12273-022-0970-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-022-0970-4

Keywords

Search

Navigation