AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

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

Heng Du, Zhiwei Lian( ), Li Lan, Dayi Lai
Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Show Author Information

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.

Keywords

indoor environmental qualitybuilt environmentfield studysample sizethermal comfort

Electronic Supplementary Material

Download File(s)
bs-16-4-577_esm.pdf (174.2 KB)

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.
Crossref
 

Arens E, Humphreys MA, de Dear R, et al. (2010). Are 'class A' temperature requirements realistic or desirable? Building and Environment, 45: 4–10.
Crossref Google Scholar
 
ASHRAE (2010). ASHRAE 90329: Performance measurement protocols for commercial buildings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air conditioning Engineers.
 
ASHRAE (2020). ASHRAE 55: Thermal Environmental Conditions for Human Occupancy. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air conditioning Engineers.
 

Basner M, Brink M (2013). Sample size estimation for field studies on the effects of aircraft noise on sleep. Applied Acoustics, 74: 812–817.
Crossref Google Scholar
 
Borenstein M, Hedges LV, Higgins JPT, et al. (2021). Introduction to Meta-Analysis. Chichester, UK: John Wiley & Sons.
Crossref
 

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.
Crossref Google Scholar
 

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

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

de Dear R (2004). Thermal comfort in practice. Indoor Air, 14: 32–39.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
 
Fleiss JL, Levin B, Paik MC (2013). Statistical Methods for Rates and Proportions. Hoboken, NJ, USA: John Wiley & Sons
 

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

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.
Crossref 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.
Crossref Google Scholar
 

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

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

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

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

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

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

Schulz KF, Grimes DA (2005). Sample size calculations in randomised trials: mandatory and mystical. The Lancet, 365: 1348–1353.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
 
Tang M-L (2010). Sample size determination for clinical trials. Encyclopedia of Statistical Sciences. Hoboken, NJ, USA: John Wiley & Sons.
Crossref
 

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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
 

Zhang S, Lin Z (2020). Predicted Mean Vote with skin temperature from standard effective temperature model. Building and Environment, 183: 107133.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
Building Simulation
Volume 16 Issue 4,
April 2023
Pages 577-588
DOI: 10.1007/s12273-022-0970-4
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. Building Simulation, 2023, 16(4): 577-588. https://doi.org/10.1007/s12273-022-0970-4

623

Views

9

Crossref

9

Web of Science

9

Scopus

0

CSCD

Altmetrics
Received: 30 June 2022
Revised: 15 November 2022
Accepted: 27 November 2022
Published: 09 January 2023
© Tsinghua University Press 2023
Return