The e-census challenge: how web affects quality

The e-census challenge: how web affects qualityThe Italian experience with a local focus on Tuscany

L. Porciani; L. Faustini; A. Valentini; B.M. MartelliIstat

Session 31 - Quality Aspects when using Administrative Sources04 June 2014, Vienna

Summary

The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

Official statistics and web data collection in Italy

Population and Housing Census & Enterprises Census

The Italian response to web data collection mode

Data quality management in e-census

Discussion and future research

1

2

3

4

5

Official statistics and web data collection


Social environment

Statistical environment

1

• Evolution of technology

• Changes in lifestyles

• Widespread use of Internet by people and entreprises

• Development of e-Government

• Cutting down expenses (mainly human costs)

• Decreasing statistical burden

• Increasing timeliness

• Reducing workload of the local actors

Promotion of web data collection tecniques in Official Statistics

2011 Italian CensusPopulation and Housing / Enterprises Census


2

Main innovations:

From a totally door-to-door census to an assisted [by census lists] e-census

Mixed- mode channel of restitution [paper, web, enumerator and postal office]

Sampling strategy

Survey Management System [SGR]

2011 Italian CensusPopulation and Housing / Enterprises Census


National Local

2

• Mass media communication [Journal/Radio/Television]

• Promotional contest [for children and teenagers]

• Differentiated payment strategies + web response - > + payment

• Local mass media communication [ethnic journal/radio/television]

• Web promotional contest [I-phone prize]

• Open informative meeting

The Italian response to web DCM


3

Abruzzo

Basilicata

CalabriaCampania

Emilia Romagna

Friuli V.G.Lazio

Liguria

Lombardy

Marche

Molise

Piedmont

Apulia

Sardinia

Sicily

TuscanySüdtirol

Umbria Aosta Valley

Veneto

R = -0.38

20

25

30

35

40

45

50

40 45 50 55 60 65



3

n

1 io (WRR)logit iix

WRR = web response rate

• Observation units are Italian NUTS-2 (regions: [20 units])

• Covariates (x) are:• 6 for Population and Housing Census; • 3 for Enterprises Census

• Dichotomization of covariates [based on median values]

• Model and all covariates are statistically significant (p< 0.0001)



Population and Housing Census

3

• Ageing Index [Age]

• Foreigner quota [Foreigners]

• Average family size [Family]

• Quota of municipalities over than 20.000 inhab. - [Large Munic]

• ICT users quota [ICT]

• Ex-post evaluation level of web mode data collection [Evaluation]

• Foreigner quota of entrepreneur [Foreigners]

• Quota of enterprises with 10 or more persons employed [Large Enter]

• ICT users quota [ICT]

Enterprises Census

The Italian response to web DCM3

Population and Housing Census


The Italian response to web DCM3

Enterprises Census


Internet penetration is not the only factor which affects the web response [coverage issue]

Socio-demographic characteristics seems to be more crucial to determine the web response quota

• Regarding population, an aged sociey with smaller families settled in smaller cities is less confortable with web tools

• Regarding enterprises, the habits of using Internet and working in large enterprises push them towards a wider use of web response

Ex post evaluation instruments confirm their central role for future surveys and census planning

Web response analysis: main results3


Focus on Tuscany PHC web response


3

Tuscany PHC web response is the lowest in the whole country, the ICT penetration rate is above the average national level (54.7% versus 52.5%)

24.3%

Regional web response rates distribution

Focus on Tuscany PHC web response


3

n

1 io (WRR)logit iix WRR = quota of web respondent

• Observation units are Municipalities of Tuscany [287 units] – LAU2

• 4 covariates

• Ageing Index [Age]• Foreigner quota [Foreigners]• Average family size [Family]• Municipalities size [Size Munic]

• Dichotomization of covariates [based on median values]

• Model and all covariates are statistically significant (p< 0.0001)

Focus on Tuscany PHC web response3




3

Web responses are linked to some characteristics of population and enterprises

The presence of foreigners has a negative impact both for population and enterprises. On the other side, the higher is the habit to use ICT the higher is the propensity to web response.

And specifically

in case of population, a more traditional society (aged and with bigger family size) living in smaller municipalities is less attracted by web tools. Nevertheless, a good appreciation of web instruments by census operators promotes a better performance of e-census

in case of enterprises, larger enterprises are more confortable to work with web tools

Top-down approachResearcher selects

DQ attributes

Top-down approach How data become weak

during data collection process

Bottom up approachInvestigation of

users/researcher opinions about DQ attributes

Field work

Paradata tool

Ad hoc survey on operators

Intuitive

Theoretical

Empirical

Data quality management in e-census4


Thereotical approach: paradata


Dashboard indicators

4

Real time -> to realize an immediate set of interventions to increase general census performance

Road-map indicators

Indicator Description Target

value (%)

QUES.LAC Percentage ratio between number of questionnaires filled in and number of questionnaires in the population registry lists (LAC) at 31.12.2010

≥ 97 ≤ 103

QUES.CA Percentage ratio between number of questionnaires processed and number of questionnaires enabled to the comparison (CA) between census and population registers.

100

DIARIO.LIFA Percentage ratio between processed rows and total rows of potential undercoverage derived from administrative archives (LIFA)

100

DIARIO.NETTO Percentage ratio between rows processed and total number of rows excluding (NETTO) rows of potential undercover derived from LIFA

100

CFR01.EDI Percentage ratio between number of surveyed building (EDI) and number of surveyed buildings in 2001 census

≥ 100 ≤ 150

CFR01.ABI Percentage ratio between number of surveyed unoccupied dwellings (ABI) and number of surveyed unoccupied dwellings in 2001 census

≥ 85 ≤ 115

Ex post indicator -> actions planned to prevent critical situations and to reengineer the process

- Rate of postal delivery (percentages)

- Questionnaires by channel of answer (percentages)

The set of tools introduced is valid to

easily point out the criticalities through simple indicators

promote best practices in data collection

more consciously monitor the field work and, finally

assess data and process quality

Discussion and proposal5


Ex post and in itinere analysis of results could be a key factor for better planning and realizing the continuous [rolling] censuses, which will be paperless

Discussion and proposal5


Availability of census micro data could represent a good opportunity to include other covariates in the web response rate model [age; work activities and level of education of the family head; innovation propensity, localization, outsourcing quota of enterprises…]

Detailed information about the response process [place of compilation? help from who?] could represent useful information to enrich the understanding of web data collection mode

Thanks for your attention!

Linda Porciani, [email protected]


The e-census challenge: how web affects quality

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