Adjustment methods for differential measurement errors in multimode surveys

Transcription

1 Adjustment methods for dfferental measurement errors n multmode surveys Salah Merad UK Offce for Natonal Statstcs ESSnet MM DCSS, Fnal Meetng Wesbaden, Germany, 4-5 September 2014

2 Outlne Introducton Stablsng bas - CBS contrbuton Unt level adjustment for bas removal ONS contrbuton Concluson/Further work

3 Introducton Measurement errors n surveys have always been a problem, but generally gnored Problem becomes more apparent when addtonal modes are used When share of web responses vares over tme (CBS) Impact may be more mportant n atttudnal questons Measurement effects and selecton effects are dffcult to separate complex experments are needed (Vanneuwenhuyze and Revlla, 2013, and Schouten et al., 2013) Focus s on dfferental measurement errors only Results of contrbutons from CBS and ONS

4 Stablsng bas CBS contrbuton In the context of varyng take-up of modes over tme If non-zero mode-dependent measurement errors are at play, the varatons n mode composton wll lead to a varaton n total bas n the estmates Objectve of method: stablse mode-specfc measurement bas Method developed for sequental multmode desgns Appled to Dutch LFS

5 Mode composton n wave 1 of Dutch LFS

6 Stablsng bas by constranng mode mxture Correct for selectvty as usual (standard calbraton) In addton, calbrate response modes to fxed proportons Do not choose extreme calbraton proportons Here: CAWI 44%, CATI 26%, CAPI 30% Calbraton occurs once and apples to all target varables Standard errors ncrease Detals of method n Buelens and Van den Brakel, 2014, SMR

7 Evaluatng alternatve mxtures Alternatve Mode composton* Explanaton regular varable GREG n current use n producton calbalanced CAWI 44 CATI 26 CAPI 30 mode calbrated to average levels callesscapi CAWI 35 CATI 60 CAPI 5 mode calbrated, neglectng CAPI callesscati CAWI 35 CATI 5 CAPI 60 mode calbrated, neglectng CATI

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9 Alternatve method Combned Predcton estmator Developed by Suzer-Gurtekn, 2013, PhD thess, Unv. of Mchgan Estmate measurement error explctly usng a model: regress y on x and mode Use ftted model to predct responses under alternatve modes Use these to produce mode specfc estmators Combne these estmators to obtan a fnal estmate Here: use combnaton same as calbraton levels Varance estmates through bootstrappng Needs to be done for every target varable

10 Comparng methods (1)

11 Comparng methods (2) Alternatve Estmated number unemployed Standard error regular 641,021 6,947 calbraton Balanced combned predcton 636,276 6, ,672 6,749

12 Removng bas va unt level adjustment - ONS contrbuton Sample survey to estmate total/mean of y Two modes are used Sample 1: data collected face to face (FtF) Sample 2: data collected onlne (web) Assume one of the modes s subject to lttle or no measurement error for y; assume t s FtF (reference mode) Assume that some varables are not subject to measurement effects; x=(x1,x2,...,xp) Want to compute condtonal dstrbuton ( FtF Web,,x) f y web y We attempt to extend method of Km (ESRA, 2013) based on statstcal measurement models

13 Approach Use Bayes theorem ( FtF Web,,x) f y web y ( FtF ) ( Web FtF x ) ( FtF mode =, x) f y g y y P web y Assume gnorable mode selecton ( mode = FtF, x) = ( mode = x) P web y P web Assumng gnorable mode selecton yelds ( FtF mode =, Web, x ) ( FtF x ) ( Web FtF ) f y web y f y g y y

14 Contnuous varables case Overvew of soluton n Km (2013) Normal dstrbuton case Structural model of truth Measurement error model ( σ ) = x ' +, 0, 2 e y β e e N y FtF = y (FtF same as truth) Web = + Web Web 2 uw y y u, u N(0, σ ) Assumes measurement error leads to no bas but ncreases varance

15 Overvew of J-K Km s soluton (2) ( Web ) ( Web W 2 ) y y,x N y, α σ e where y ( x ' ) ( 1 ) Web = α W β + α W y Web W ( ) uw uw e α = σ σ + σ For non-normal dstrbuton case, parametrc fractonal mputaton (Km, 2011) was used Have extended method to case where measurement error leads to bas soluton has same form

16 Extendng method to categorcal varables Bnary case Consder a bnary varable ( FtF Web mode =,, x) f y web y usng can be expressed ( FtF 1 x ) P y = ( Web FtF 1 0) = y = P y ( Web FtF 0 1) = y = P y Error probabltes

17 Adjustng for bnary varables (1) ( ) FtF We propose to estmate P y = 1 x usng data from the FtF sample by fttng a logstc regresson model However, we cannot estmate error probabltes unquely from regular survey data We would need a valdaton sample/experment to estmate the error probabltes Costly and estmaton not so straghtforward Estmates may become out of date

18 Adjustng for bnary varables - consstency Can estmate overall measurement bas from regular survey data n a sequental desgn use Pr ed Uˆ = ω Pr ed Pr ed ( ) 1,x 1,x Web Web FtF s 1,x s the predcted value obtaned by applyng the model ftted usng web data Pr ed Web FtF 1,x s the predcted value obtaned by applyng the model ftted usng FtF data ω Survey weghts need to be calbrated wth respect to x Can wrte an equaton relatng overall measurement bas and the two error probabltes - no unque soluton Error probabltes estmated from experment may not satsfy equaton consstency problem s ω

19 Adjustng for bnary varables A heurstc soluton for consstency When nconsstency s severe, we need to amend estmates of error probabltes Construct solutons n neghbourhood of the soluton obtaned from experment and satsfyng the equaton Select soluton that leads to smallest ncrease n varance of estmates Bas should be removed at the overall level but estmates of subpopulatons may be slghtly based

20 Applcaton to onlne Opnons plot (1) 2010 onlne Plot November and December Splt sample desgn FtF Opnons survey as control Demographc questons; some LFS and OPN module questons One-person ntervewed n each selected HH Response for web survey poor n November (8%); better n December (17%) letter amended and snow 54% response rate to FtF survey

21 Applcaton to onlne Opnons plot (2) Estmates of proporton of adults n employment Calbraton on age, sex and geography FtF sample: 59% Web sample: 66% Bg part of dfference should be due web mode selecton Used age, sex, regon, martal status, level of educaton and tenure as covarates n logstc models Estmate of overall measurement bas found to be 4% Rather large: self-selecton not completely elmnated? Survey weghts calbrated only on age, sex and geography

22 Unt level adjustments n onlne OPN plot

23 Concluson /Further work Method for bas stablsaton s promsng but needs further evaluaton Not clear we can adjust for measurement bas usng current survey data only for categorcal data - Need a sutable experment to estmate error probabltes - Need to nvestgate further mpact of usng varance mnmsaton over neghbourng solutons that are consstent wth survey data Ignorable mode selecton s assumed n all methods but avalablty of covarates to control for self-selecton s a problem n many countres Adjustment methods have lmtatons: need to desgn questons that are not mode-senstve

24 References Buelens, B. and van den Brakel J. (2014) Measurement error calbraton n mxed-mode sample surveys. Socologcal Methods & Research, publshed onlne May 12, do: / Km, J-K (2011) Parametrc fractonal mputaton for mssng data analyss, Bometrca, Vol. 98, Issue 1, pp Km, J-K (2013) An mputaton approach for analysng mxed-mode survey, ESRA 2013 conference, July 2013, Lujbana, Slovena. Schouten, B., Jan van den Brakel, J V D, Buelens, B., Laan, J V D, Klausch, T. (2013) Dsentanglng mode-specfc selecton and measurement bas n socal surveys, Socal Scence Research, Volume 42, Issue 6, November 2013, pp Vanneuwenhuyze, J. T. A. and Revlla, M, (2013) Evaluatng Relatve Mode Effects on Data Qualty n Mxed-Mode Surveys, Survey Research Methods, Vol. 7, N0. 3, pp