Data quality: total survey error
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Total survey error (TSE) refers to all errors that occur during the survey process that contribute to estimates deviating from true population parameters. TSE results from both sampling errors and non-sampling errors. The goal of TSE framework is to minimize all sources of bias and variance through optimal survey design, implementation, data collection, processing, analysis and modelling. However, minimizing all errors fully is impossible due to budget constraints, so trade-offs must be made to focus on reducing the most influential errors. Continuous quality improvement efforts such as redesigns, nonresponse reduction, quality monitoring and data quality indicators can help gradually reduce biases and unwanted variations in survey estimates over time.
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Data quality: total survey error
- 1. Data Quality: Total Survey Error (TSE) Dr Olga Maslovskaya NCRM National Centre for Research Methods University of Southampton
- 2. Survey Data • Vast amounts of survey data are collected for many purposes, including governmental information, public opinion and election surveys, advertising and market research as well as scientific research • Survey data underlie many public policy and business decisions • Good quality data reduces the risk of poor policies and decisions and is of crucial importance
- 3. Total Survey Quality (TSQ) Total Survey Quality (TSQ) Statistical Dimension Non-statistical Dimension
- 4. TSQ: Quality Dimensions –Statistical • Accuracy of estimates is the difference between the estimate and the true parameter value • Accuracy is most important concept of TSQ X = T + e Observed item True value Error Variance (random error) Bias (systematic error)
- 5. Total Survey Error (TSE) (1) •TSE concept was developed by Robert Groves (1989) in book on Survey Errors and Survey Costs •Survey estimates are derived from complex survey data •Published estimates may differ from their true parameter values due to survey errors •Total Survey Error is the difference between a population mean, total, or other population parameter and the estimate of the parameter based on the sample survey (Biemer and Lyberg, 2003)
- 6. Total Survey Error (TSE) (2) •Survey error is any error arising from the survey process that contributed to the deviation of an estimate from its true parameter value (Biemer, 2016) •Survey error diminishes the accuracy of inferences derived from the survey •TSE is the accumulation of all errors that may arise in the design, collection, processing, and analysis of survey data (Biemer, 2016)
- 7. TSE framework (1) • Set of principles, methods and processes that minimise TSE within the budget allocated for accuracy, timing and other constrains • Non-statistical dimensions of TSQ can be viewed as constrains: timeliness and comparability constrain the design; accessibility, relevance and completeness constrain the budget (Biemer 2017)
- 8. TSE framework (2) TSE framework provides principles that guide stages of survey process: • Survey design • Survey implementation • Data collection • Data processing • Data analysis • Modelling and estimation Each stage of survey process provides opportunities for errors which add up to TSE
- 9. TSE TSE= sampling errors + non-sampling errors Survey errors: •Sampling errors – can be computed for probability samples and are due to selecting a sample instead of the entire population
- 10. TSE •Non-sampling errors (including measurement error – cannot be formally estimated but can be improved by interviewing procedures and question wordings etc.) - are errors due to mistakes or system deficiencies, also from incomplete responses to the survey or its questions, etc. In many cases non-sampling error can be much more damaging than sampling error to estimates from surveys
- 11. Sources of Sampling Error • Sampling scheme • Stratification • Clustering • Selection probabilities • Sample size • Overall sample size • Effective sample size • Estimator choice • Simple • Use of auxiliary information • Model-based • Model-assisted
- 12. Components of Non-sampling Error 1. Specification error 2. Frame error 3. Nonresponse error 4. Measurement error 5. Processing error 6. Modelling/Estimation error Biemer (2017)
- 13. Specification Error •Refers to a question on the questionnaire •Occurs when the concept implied by the survey question and the concept that should be measured in the survey differ (Biemer and Lyberg, 2003)
- 14. Frame Error •Arises from construction of the sampling frame for the survey •The sampling frame might have missing elements (units), duplicates or erroneous inclusions (nonpopulation units)
- 15. Nonresponse Error • Unit nonresponse occurs when a sample unit (individual, household or organisation) does not response to any part of the questionnaire, • Item nonresponse occurs when the questionnaire is only partially completed and some items are not answered • Incomplete response occurs when the response to open-ended question is incomplete or very short and inadequate • Panel attrition occurs when a sample unit is lost over the period of a longitudinal study
- 16. Measurement error •Measurement errors pose a serious limitation to the validity and usefulness of the data collected •Most damaging source of error •Without reliable measurements, analysis of data hardly make any sense
- 17. Sources of measurement error •Respondents •May deliberately or unintentionally provide incorrect information •Response style behaviours (agree with everything, do not know to every question or choose extreme response options); through social desirability bias •Satisficing (less efforts to provide optimal responses) •Interviewers - enumerators •May falsify data •May inappropriately influence responses
- 18. •May have negative impact on responses to sensitive questions •May record responses incorrectly •May fail to comply with the survey protocol
- 19. •Questionnaire - design •Bad design •Ambiguous questions •Confusing instructions •Unclear terms •Mode of administration •Online mode •Non-optimised questionnaire for smartphones
- 20. Processing Error Contributes to measurement error • Occurs during data processing stage • Errors in data editing • Errors in data entry • Errors in coding • Errors in outlier editing • Errors in assignment of survey weights • Errors in non-response imputing
- 21. Modelling and Estimation Error •Occurs during data analysis stage (modelling) •Errors in weight adjustments, •Errors in imputation, •Errors in modelling process
- 22. Types of Errors • Systematic Error – bias -errors that tend to agree – results in biased estimates (strengthen the relations between variables, leading to false conclusions) – e.g. response styles or other stable behaviours - bias the results, distorting the mean value on variables – does not cancel out • Random Error – variance - errors that tend to disagree (unintended mistakes made by respondents) – affects the variance of estimates (may weaken the relations between variables), vary from case to case but are expected to cancel out
- 23. Mean Squared Error (MSE) • Total survey error (TSE) is a term that is used to refer to all sources of bias (systematic error) and variance (random error) that may affect accuracy of survey data. • Mean Squared Error (MSE) – metric for measuring TSE • MSE is the sum of the total bias squared plus the variance components for all the various sources of error in the survey design.
- 24. MSE •MSE cannot be calculated directly but useful conceptually to consider how large the different components of error can be and how much they add to the total survey error •MSE is a great guide for optimal survey designs
- 25. MSE • Survey design goal is to minimise the MSE • When two designs are similar on other quality dimensions, the optimal design is the one achieving the smallest MSE • Working to reduce the measurement error on one set of questions could increase the error for a different set of questions in the same survey • Also, reducing one error could increase another error in the survey
- 26. Survey designers face the following questions: • Where should additional resources be directed to generate the greatest improvement to data quality: extensive interviewer training for nonresponse reduction, greater nonresponse follow up intensity, or by offering larger incentives to sample members to encourage participation? • Should a more expensive data collection mode be used, even if the sample size must be reduced significantly to stay within budget?
- 27. TSE in Practice •Idea is to minimise all these error sources •Minimising all of these errors would require an unlimited budget (impossible) •Cost-benefit trade-offs are needed to decide which errors to minimise
- 28. TSE in Practice (1) •Realistic scenario is to work on continuous improvement of various survey processes so that biases and unwanted variations are gradually reduced •Redesign of surveys if needed •Non-response bias reduction through real time responsive and adaptive survey designs •Quality monitoring strategies, e.g., paradata •Data quality indicators application in data analysis
- 29. TSE in Practice (2) Decisions are needed: •To ignore some errors •To measure and to control/adjust for some (data analysis stage: complex designs, measurement errors, missing data, sampling errors)
- 30. Conclusions •Data accuracy is of crucial importance •Single score or measure of data quality (Total Survey Quality) is not available •TSE framework was developed and adopted •Cost-benefit trade-offs to minimise different errors of TSE depending on survey aims •TSE helps keeping data quality standards high and in line with survey aims under financial constrains
- 31. References • Biemer (2010) Total survey error: Design, implementation, and evaluation. Public Opinion Quarterly, 74(5): 817-848. • Biemer (2016) Total Survey Error Paradigm: Theory and Practice. In The Sage handbook of survey methodology by Wolf, Joye, Smith and Fu. London: SAGE publications. • Biemer (2017) Total survey error: A Framework for censuses and surveys. Presentation at the University of Southampton. • Biemer and Lyberg (2003) Introduction to survey quality. New York: John Wiley & Sons. • Groves and Heeringa (2006) Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society Series A, 169 (3): 439-457. • Lyberg and Weisberg (2016) The SAGE handbook of survey methodology. London: SAGE publications. • Lynn (2004) Editorial: Measuring and communicating survey quality. Journal of the Royal Statistical Society Series A, 167 (4): 575-578. • Schouten et al. (2013) Optimizing quality of response through adaptive survey designs. Survey Methodology, 39 (1): 29-39. • Weisberg (2005) The total survey error approach. Chicago: University of Chicago Press.
Editor's Notes
- So data quality is crucial
- TSQ – survey quality is more than its accuracy or statistical dimension. It also includes among other factors producing results that fit the needs of the survey users and providing results that users will have confidence in. Usability of results is of crucial importance. (Eurostat, Statistics Canada and Statistics Sweden) Statistics Canada: Relevance Accuracy Timeliness Accessibility Interpretability Coherence Statistics Sweden: Content Accuracy Timeliness Comparability/coherence Availability/clarity
- Bias – mean of errors is not equal to 0, does not cancel out; variance – mean of error is equal to 0, does cancel out Accuracy is The larger concept of Total Survey Quality (TSQ) Broader that accuracy definition is needed as users are not just interested in the accuracy of the estimates provided. Accuracy is the cornestone of quality, since without it, sruvey data are of little use. If the data are erroneous, it does not help much if relevance, timeliness, accessibility, comparability, coherence and completeness are sufficient.
- Simple random sampling is often neither possible nor cost-effective. Stratifying the sample can reduce the sampling error, clustering the sample can reduce costs but would increase the sampling error.
- Idea is to minimize the errors
- Biemer and Lyberg in their book Introduction to Survey Quality introduced devision between sampling and non-sampling errors Roots in cautioning against sole attention to sampling error Framework contains statistical and nonstatistical notions
- There are different components of non-sampling error Errors can be systematic or random and correlated or uncorrelated. Uncorrelated (e.g., interviewer mistakenly records a “yes” answer as a “no” Correlated (when interviewers take multiple interviewers and when cluster sampling is used – correlated errors increase the variance of estimates due to an effective sample size that is smaller than the intended one and thereby make it more difficult to achieve statistically significant results) Measurement errors pose a serious limitation to the validity and usefulness of the information collected via survey. Having excellent samples representative of the target population, having high response rates, having complete data, etc. does us little good if our measurement instruments evoke responses that are fraught with error. Measurement error is distinct from other survey errors and it is error that occurs when the recorded or observed value is different from the true value of the variable. Reliability and validity are important in measurement error. Reliability is “agreement between two efforts to measure the same thing, using maximally similar methods” How was the survey administered (e.g. in person, by telephone, online, multiple modes, etc.)? (sensitive questions) Were the questions well constructed, clear, and not leading or otherwise biasing? (satisficing) What steps, if any, were taken to ensure that respondents were providing truthful answers to the questions, and were any respondents removed from the final dataset (e.g., identifying speeders, satisficers, multiple completions)? (in-survey behaviour)
- Having excellent samples representative of the target population, high response rates, complete data, etc. does us little good if our measurement instruments evoke responses that are fraught with error Response errors or response styles are measurement errors and found in the answers respondents give to survey Response styles: Acquiescence response style – tendency to agree with items regardless of content Disacquiescence reponse style – tendency to disagree with items regardless of content Mid-point response style – tendency to use the middle response category of a rating scale regardless of content Extreme response style – tendency to select most extreme response option regardless of content Straightlining – tendency to rush through the survey clicking on the same response every time regarding of content Tendency to select “do not know” options regardless of content Reliability and validity are important concepts in measurement error. Reliability is “agreement between two efforts to measure the same thing, using maximally similar methods” (in Alwin, 2016). The score for reliability is called the coefficient of precision Validity is an agreement or consistency between two efforts to measure the same thing using maximally different measurements (in Alwin, 2016) Satisfising behaviour increases measurement error (when respondents give answers that sound plausible so as to get through the task quickly Improving survey question wording might minimise the likelihood of satisficing
- Interviewers can cause errors in a number of ways Acquiescence response style – tendency to agree with items regardless of content Disacquiescence reponse style – tendency to disagree with items regardless of content Mid-point response style – tendency to use the middle response category of a rating scale regardless of content Extreme response style – tendency to select most extreme response option regardless of content Straightlining – tendency to rush through the survey clicking on the same response every time regarding of content Tendency to select “do not know” options regardless of content
- MSE is hypothetical
- Clients need to wiegh these trade offs deciding how they want to spend limited resources to minimize the potential survey errors
- Cost-benefit trade-offs are needed to decide which errors to minimize Quality frameworks were developed and adopted and provided statistics producers with clear description of how certain dimensions of quality can be measured and why it might be important to do so. The survey community needs to find ways of ensuring that as broad a range as possible of relevant indicators and information is made available routinely (Lynn 2004) The chances of users misusing the data or misinterpreting published statistics will be reduced if they understand better the strengths and limitations of the data. The publication of data quality measures itself represent an improvement in the quality of a survey