Guidelines on Use of Electronic Data Collection in Censuses: Multi-Mode Data Collection Approach

Transcription

1 Guidelines n Use f Electrnic Data Cllectin in Censuses: Multi-Mde Data Cllectin Apprach

2 Multi-mde data cllectin Objectives f the use f multi-mde apprach: t imprve cverage, in a cst effective manner - Especially t reach peple difficult t enumerate, such as peple living alne, living in buildings difficult t access - Prvide an alternative t peple reluctant t participate Decisin shuld be given thrugh decisin-making prcess t make sure it is cst- effective apprach

3 Multi-mde data cllectin Factrs affecting decisin: Ppulatin f interest: Each methd f enumeratin prvides better access t different ppulatin grups - The nature and gegraphical lcatin f the ppulatin f interest may have a significant impact n selectin f mde Operatinal and resurce burdens: Every additinal methd will create additinal peratinal and resurce burdens, including financial csts, human resurce requires, data prcessing, etc. Time needed: Planning and designing f each mde may require additinal time Respnse rate: Each mde shuld add significant value n respnse rate

4 Appraches fr implementatin f multi-mde data cllectin Multi-mde data cllectin can be designed using tw different appraches: Sequential apprach: All respndents are first requested t prvide infrmatin in ne particular mde nly then are ffered ther mdes t increase the respnse rate. One cmmn practice is t start with the least expensive mde and then prgress t mre expensive and mre persuasive mdes Cncurrent apprach: Respndents are ffered, at the utset, the chice f ne f tw r mre mdes by which they can prvide infrmatin. An example f a cncurrent mixed-mde design is ffering respndents the ptin f cmpleting a paper questinnaire r prviding infrmatin nline via the Internet

5 Hw t decide multi-mdes CAPI CASI CATI PAPI/PASI What gegraphical areas/ppulatin grups are targeted with additinal mde? Sequential r cncurrent apprach will be used What is the expected prprtin f ppulatin wh will be enumerated with specific methd? What are additinal burdens n peratin? Is this apprach cst-effective? What extend respnse rate will increase Other???

6 Pssible ptins fr multi-mde data cllectin Paper-based questinnaire Electrnic questinnaire Multi-mde paper-based data cllectin PAPI PASI CAPI CASI Multi-mde electrnic data cllectin Mixed electrnic /paper data cllectin CATI

7 Management f multi-mdes Census Cmpleteness Management Operatinal Micr Database *Reliable address frame is necessary *During enumeratin, there shuld be a mechanism fr identifying husing units enumerated by a specific mde nt t allw any duplicatin *Clear prcedures fr nn-respnse fllw up CAPI CASI CATI PAPI/PASI

8 Final Recrd Generatin Data frm different mdes Integratin with Census Frame Validatin during the field enumeratin (nnrespnse, missing, incnsistency) If PAPI/PASI used, data capture, cding, editing Imputatin Operatinal Micr Database Data validatin/analysis Final Data

9 Mde effect in multi-mde data cllectin Multi-mde data cllectin has implicatins fr the quality f the cllected data, particularly fr data cmparability Mde effect means the bias caused by the mde f the data cllectin due t the delivery f different results as a cnsequence f using different means f cllectin Mde effect creates artificial differences in the ppulatin Mde effect varies depending n the type f multi mde system

10 Multi-mde data cllectin- Mde effect Fur factrs cncerned with data quality and assciated with mde-effect include: Cverage, Respnse rate, Item nn-respnse and Measurement differences a. Cverage: Cverage differs depending n the methd f data cllectin - It is higher fr face-t-face interview cmpared t self-enumeratin methd - A respndent wh fills in the questinnaire may create a different type f errr in listing members f the husehlds due t misinterpretatin f census questins r instructins

11 Multi-mde data cllectin- Mde effect b. Respnse rate: Respnse rates may vary with mde f cntact and mde f data cllectin - Face-t-face interviews are mre effective at securing high levels f participatin, with a generally equal cperatin rate acrss different ppulatin grups, cmpared t the ther mdes (telephne interviews and self-respnse via pstal mail r Internet) - Mdes f data cllectin are mre r less likely t encurage different members f the ppulatin t participate

12 Multi-mde data cllectin- Mde effect Item nn-respnse: Item nn-respnse varies with mde f data cllectin - It is widely recgnised that the use f electrnic data cllectin will reduce significantly item nn-respnse cmpared t paperbased data cllectin, due t the autmatic cntrl f nnrespnse and cnsistency checks during questinnaire cmpletin - Item nn-respnse rate tends t be lwer with the use f electrnic data cllectin mdes including self- respnse via Internet, face-t-face interview with hand-held devices and telephne interview, cmpared t paper-based data cllectin methds

13 Multi-mde data cllectin- Mde effect Measurement differences: One f the majr challenges mixed-mde data cllectin presents is the fact that peple tend t give different answers t questins (especially f a sensitive nature) depending n the mde in which they are asked - Measurement differences typically result frm either the design f the questinnaire and the wrding f particular questins being asked f the respndents, r in the case f face-t-face and telephne interviewing the cmpetence f the interviewer

14 Multi-mde data cllectin- Mde effect Minimizing mde effect n data quality Optimize design - Sequential design- ne mde is used as main data cllectin methd supprted by additinal data cllectin methd fr nn-respnse fllw up Main data cllectin shuld be used t its maximum ptential t enumerate the majrity f ppulatin - Cncurrent design - the different methds are equally imprtant and respndents are given a chice There is a risk f nt using a mde t its fullest ptential. Fr example, the minimal use f editing and validatin in electrnic questinnaires In general, sequential design has less prnunced mde-effect as cmpared t cncurrent design

15 Multi-mde data cllectin- Mde effect Minimizing mde effect n data quality Cnduct empirical studies- understanding causes f mde effects and assessing the magnitude f the mde effect - This kind f experimental study can be cnducted with pre-tests and pilt censuses t understand mde effects n the data quality especially n item-nn-respnse and measurement errr - Findings frm such experimental studies can be used t develp editing and imputatin strategies in a way f decreasing mde effect n the data quality. - Where experiments are nt pssible, matching studies is anther ptin t assess mde effects

16 Cllectin Respnse Rates - Canada 2011 Census 2016 Census Cllectin Rate Actual Planned Actual Census Cllectin rate 98.1% 98% 98.4% Internet 53.8% 65% 68.3% Paper 31.3% 20% 20.5% Self-Respnse 85.2% 85% 88.8% NRFU 12.9% 13% 9.7% Wrklad at start f NRFU 4.8 M 4.5M 3.7 M Surce: MCS f August 4, 2016 ccupied private dwellings Surce :

17 THANK YOU