Weight and height

Subjective methods to assess weight and height can be used instead of objective measurement, when that is not practical.

Individuals either self-report their weight/height or it is reported on their behalf by a proxy-reporter.

Examples of frequently used questions to measure weight subjectively:

  1. "How much do you weigh now?"
  2. "What is your current weight?"

Examples of frequently used questions to collect subjectively measured height:

  1. "How tall are you now?"
  2. "What is your current height?"

Selection of data source

Data can be collected by interview, by self-administered questionnaire, or by an independent observer (e.g. friend or family member). Questions can be administered using pen and paper or an electronic device such as a mobile phone, tablet or computer, either face-to-face or remotely (e.g. by post or internet)

Validation study

When studies rely on subjective methods, a validation study in a subset of the main study cohort may be informative to quantify potential sources of errors/bias and to calculate correction factors that can be applied at the analysis stage (see below).

If collecting repeated subjective data at different time points e.g. in longitudinal studies, their validity must be monitored by collecting direct measures at regular intervals as validity may vary overtime.

Units of measurement

Unit of the measurement (e.g. metric units or imperial scale) should be clearly indicated. Incorrect conversion may lead to substantial errors. Units should be appropriate for the population of interest, possibly providing a choice of alternatives.

Subjective assessment of weight and height are often used in large-scale population studies, and can be useful options for individuals who are reluctant to be measured.

Caution should be used when interpreting results from subjective assessment of weight and height as misreporting could result in BMI misclassification and lead to inaccurate estimates of the prevalence of overweight and obesity. It is possible to apply statistical methods that correct for or take into account such errors if relevant validation data are available.  

Analysis could be performed by ranking study participants in quantiles of BMI calculated from subjective height and weight; the ‘true’ mean or median value of BMI in each quantile can then be calculated from the objectively measured values for a random sample of participants and used to estimate the association of BMI with disease risk.

Adjustment of subjective BMI scores, based on easily gathered socio-demographic characteristics (gender, age, race/ethnicity, marital and pregnancy status, and household income) can also be used. Predictive equations for weight and height using objective data from a random sample of the main cohort can also be derived and applied to the whole cohort. There are published equations for a number of different populations (1, 5, 7, 12). However, as the pattern of over- or under-reporting may be unique to each population, specific population correction factors may be required.

An overview of subjective weight and height methods is outlined in Table A.2.7.


  1. Low cost
  2. Practical tool as it can be included in study questionnaires and be self-administered
  3. Information can be obtained via mail, face-to-face or telephone interviews or the Internet
  4. Can be obtained retrospectively
  5. Low respondent burden
  6. Large number of individuals can be approached
  7. Non-intrusive
  8. It could increase recruitment rates and participation retention, especially by individuals who are reluctant to be measured
  9. No fieldwork required if self-administered or data collected online


  1. Prone to systematic errors in reporting by different body size and socio-demographic characteristics
  2. Prone to response bias e.g. social desirability
  3. May not be feasible in certain population where recall bias is high (e.g. misreporting of this measure is higher in older individuals when compared to younger individuals).
  4. Corrections may be necessary at analysis point

Table A.2.7 Characteristics of subjective weight and height methods.

Characteristic Comment
Number of participants High
Relative cost Low
Participant burden Low
Researcher burden of data collection Low
Researcher burden of coding and data analysis Low
Risk of reactivity bias No
Risk of recall bias Yes
Risk of social desirability bias Yes
Risk of observer bias Yes
Space required Low
Availability High
Suitability for field use High
Participant literacy required Yes, if self-administered
Cognitively demanding Yes

Considerations relating to the use of subjective weight and height methods in specific populations are described in Table A.2.8. Mis-reporting occurs across all level of body sizes.

Table A.2.8 Use of subjective weight and height methods in different populations.

Population Comment
Infancy and lactation
Toddlers and young children
Adults Underestimation of height parameter is generally higher in women than in men, probably due to a social desirability response. Men tend to overestimate height far more frequently than women.
Older Adults Overestimation of height is greater in older populations.
Ethnic groups Perception of body weight in relation to socially-defined weight norms may be different in middle income countries to high income countries. Cultural differences in awareness of body size, different cultural norms for social desirability, or differing views of body image may influence validity.
Other Overweight individuals tend to underreport weight than lighter individuals.
Under-reporting of weight has also been observed in individuals with higher socio-economic status and education level.
Overestimation of height is greater in overweight people.
Shorter and thinner individuals also tend overestimate their heights, whereas tall people underestimate their height.
Overestimation of height has also been observed more frequently in individuals with lower socio-economic status and lower education level.
  1. Minimal resources are generally required with subjective methods as field work may not be necessary
  2. Questionnaire via paper and pen or electronic device
  3. Instructions for completion and return
  4. For mailed questionnaires a pre-paid stamped address envelope
  5. Trained interviewers for interviewer-administered tools, plus standard operating procedures for interviewers
  6. Data entry cleaning code
  7. Standard operating procedures for data entry errors/extreme values/data cleaning
  8. Statistical knowledge may be required if correction factors are applied to the data

A method specific instrument library is being developed for this section. In the meantime, please refer to the overall instrument library page by clicking here to open in a new page.

  1. Connor Gorber S, Shields M, Tremblay MS, McDowell I. The feasibility of establishing correction factors to adjust self-reported estimates of obesity. Health Rep. 2008;19(3):71-82.
  2. Connor Gorber S, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev. 2007;8(4):307-26.
  3. Craig BM, Adams AK. Accuracy of body mass index categories based on self-reported height and weight among women in the United States. Matern Child Health J. 2009;13(4):489-96.
  4. Dekkers JC, van Wier MF, Hendriksen IJ, Twisk JW, van Mechelen W. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Methodol. 2008;8:69.
  5. Dutton DJ, McLaren L. The usefulness of "corrected" body mass index vs. self-reported body mass index: comparing the population distributions, sensitivity, specificity, and predictive utility of three correction equations using Canadian population-based data. BMC Public Health. 2014;14:430.
  6. Gildner TE, Barrett TM, Liebert MA, Kowal P, Snodgrass JJ. Does BMI generated by self-reported height and weight measure up in older adults from middle-income countries? Results from the study on global AGEing and adult health (SAGE). BMC Obes. 2015;2:44.
  7. Hayes AJ, Kortt MA, Clarke PM, Brandrup JD. Estimating equations to correct self-reported height and weight: implications for prevalence of overweight and obesity in Australia. Aust N Z J Public Health. 2008;32(6):542-5.
  8. Kuczmarski MF, Kuczmarski RJ, Najjar M. Effects of age on validity of self-reported height, weight, and body mass index: findings from the Third National Health and Nutrition Examination Survey, 1988-1994. J Am Diet Assoc. 2001;101(1):28-34; quiz 5-6.
  9. Nawaz H, Chan W, Abdulrahman M, Larson D, Katz DL. Self-reported weight and height: implications for obesity research. Am J Prev Med. 2001;20(4):294-8.
  10. Park JY, Mitrou PN, Keogh RH, Luben RN, Wareham NJ, Khaw KT. Effects of body size and sociodemographic characteristics on differences between self-reported and measured anthropometric data in middle-aged men and women: the EPIC-Norfolk study. Eur J Clin Nutr. 2011;65(3):357-67.
  11. Park JY, Mitrou PN, Keogh RH, Luben RN, Wareham NJ, Khaw KT. Self-reported and measured anthropometric data and risk of colorectal cancer in the EPIC-Norfolk study. Int J Obes (Lond). 2012;36(1):107-18.
  12. Shields M, Connor Gorber S, Janssen I, Tremblay MS. Bias in self-reported estimates of obesity in Canadian health surveys: an update on correction equations for adults. Health Rep. 2011;22(3):35-45.
  13. Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr. 2002;5(4):561-5.
  14. Stommel M, Schoenborn CA. Accuracy and usefulness of BMI measures based on self-reported weight and height: findings from the NHANES & NHIS 2001-2006. BMC Public Health. 2009;9:421.