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Repeated surveys

Repeated surveys. Presented by. Eva Elvers Statistics Sweden. Outline. Definition and related concepts Why treat in a separate module? Some typical issues Frames, sampling, and estimation Data collection and data processing Time series issues Tests, experiments, and evaluation

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Repeated surveys

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  1. Repeated surveys

  2. Presented by • Eva Elvers • Statistics Sweden

  3. Outline • Definition and related concepts • Why treat in a separate module? • Some typical issues • Frames, sampling, and estimation • Data collection and data processing • Time series issues • Tests, experiments, and evaluation • Design issues

  4. A repeated survey • A repeated survey is • carried out more than once • mostly/often with a regular frequency. • Most surveys in a statistical office are repeated, e.g. STS and SBS.

  5. A few related concepts • Some concepts and terms: • Longitudinal survey; longitudinal data • Panel survey; panel, rotation panel • Survey with overlap over time • Coordinated samples; independent samples • Why such data and surveys/studies? • Study on unit level • Accuracy reasons

  6. Why a separate Memobust module? • Discuss possible reasons to consider and describe Repeated surveys separately

  7. Why enhance in a separate module Some major reasons: • Possibilities to make improvements over time. • Possibilities to utilise previous data if a unit is included repeatedly. • Issues related to time series breaks. • ?

  8. Note • Many issues are mentioned here. • For most of them there will be more information in other topics and parts of the course (later today or tomorrow). • Some issues are considered here only.

  9. Accuracy (and response burden) Normally • Measures of change are important. • Higher accuracy requested for estimators of change than for estimators of level. • Accuracy, in general: • considerable overlap between samples over time • include and handle population changes: births, deaths, splits, change of industry, …

  10. (Accuracy and) response burden • Few surveys and few times! • Not jump in and out of a survey. • Be selected for a survey during a period and then not at all for a period.

  11. Frames and sampling • Sample design with overlap • Participation over a time period for each business (statistical unit: enterprise, …) • Frame construction: up-to-date information, as far as possible • Draw the sample • Reality is a bit different due to deaths, splits, …

  12. Some issues for frames & sampling • How often do you construct a new frame and draw a new sample? • Advantages of frequent updates? • Disadvantages of frequent updates? • Do you feed back information about changes to the Business Register? • Do you update the frame? When? • Are there risks/disadvantages of updates?

  13. Sampling and estimation (1) • Sample, consider e.g. industry and size • Changes are due to • Population changes (births, deaths, …) • Variable values change • Estimation methods • Changes, organisational • Coverage • Auxiliary information, calibration

  14. Sampling and estimation (2) • Point estimation • Interval estimation, e.g. confidence intervals • Outliers • True value • Highly influential • Bias and variance • Learn

  15. Data collection & processing (1) • Providing previous values • Assistance to respondent. • May discover previous error. • Previous error may remain.

  16. Data collection & processing (2) • Corrections of previous micro-data • Possibly effects on macro-data • Size? • Correction of statistics needed? • Revisions are planned

  17. Data collection & processing (3) • Using previous values in imputation • Model • Similarity? • Group average for level • Own value and group change

  18. Time series issues (1) • Comparability over time • Definitions • Methodology • Reality and user needs change

  19. Time series issues (2) • Reasons for breaks, e.g. • New definitions due to new needs • Align with other statistics • Better methodology

  20. Time series issues (3) • Methods to overcome breaks • Double period • Planned, study • Model, e.g. when NA change a source

  21. Time series issues (4) • Revision of a classification, e.g. a new version of NACE • Two major approaches • Micro • Macro • Time series, back-casting • Assumption about relationships

  22. Tests and experiments • Pilot study • qualitative; • quantitative. • Tests and experiments • Embedded experiment • Would … mean an improvement? E.g. contact strategy • Can … be changed without effects on the estimates?E.g. data collection mode

  23. Evaluation • For this repeated survey and possibly others • Teamwork to summarise and suggest actions for future rounds • Quality indicators, e.g. • Accuracy planned and achieved. • Response rates, over time, on item level etc. • Signals in editing and debriefing with staff • Revision sizes

  24. Design issues • New repeated survey(groups 1, 4) • Frame, sampling, estimation(groups 2, 5) • Improvements of repeated surveys(groups 3, 6)

  25. Conclusions • Many surveys are repeated • Design from the beginning, as repeated • Utilise the advantages, with data, … • Evaluate and feed back • Improve in a controlled way; consider time series, effects

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