Getting Data for Business Statistics

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2/10/2012 1

Getting Data for (Business) Statistics:

What¶s new? What¶s next?



2/10/2012 2

Getting Data for Business Statistics

How do we get the data we need

for business statistics?

Yesterday, today, tomorrow

Data In time

Complete

Correct

Statistical

picture of

a country

NSI

Survey Parameters

in and out of control

Respondent

Parameters



2/10/2012 3


Over the years:

1. Yesterday: ICES-I* 1993ICES-II 2000CASM** 1980¶s

2. Today: ICES-III 2007 Challenges and developments A few examples

3. Tomorrow What¶s next ?

* International Conference on Establishment Surveys

** Cognitive Aspects of Survey Methodology



2/10/2012 4


Yesterday

ICES-I (1993):

1. Surveying various branches of industry:

agriculture, energy, health care, trade, finance,education, manufacturing industry

2. Quality of business frames & sampling

3. Data analysis & Estimation

4. Data collection methodology:data quality, registers, non-response, Q-design

µStove-pipe¶ approach

Single-mode survey designs



2/10/2012 5

Response In time

Complete

Correct

D e ci si on

t o p ar t i ci p

a t e

An

sw

er i n

g b eh

avi o

ur

Motivation

Respondent

burden De facto

Perception

Internal

business factors

Policy

Data

Resources Market position

Informant:

Mandate

Data knowledge

Job priority

External

business factors

Econ. climate

Regulatory

requirements Political climate

The survey:

Topic

Population and sample

Sponsor /

Survey organisation Resources

Planning

Authority/confidentiality

The survey design

C on

t a c t

s t r a t e g y

Q u e s t i onn

ai r e

M o d e s of

d a

t a

c ol l e

c t i on

NSI

Black box

A business

P a p er

D a t aWE w

a

n t

L e t t er s:

M an

d a t or y

Survey designs

not coordinated:


NSI

Single mode



2/10/2012 6


Yesterday

CASM (started in 1980¶s; USA, Germany):

Cognitive Aspects of Survey Methodology

From simple stimulus-response model tomodelling Question-Answer Process:- comprehension

- retrieval

- evaluation- response

Pre-testing facilities



2/10/2012 7


Today

ICES-III (2007):

1. Survey data collection methodology:

questionnaire design & pre-testing survey participation: non-response reduction,

response burden, bias

mixed-mode designs & e-data collection

understanding the response process in bus¶s

2. Using administrative data

3. Business frames & Sampling

4. Weighting, Outlier detection, Estimation &

Data analysis



2/10/2012 8

Response In time

Complete

Correct

D e ci si on

t o p ar t i ci p

a t e

An

sw

er i n

g b eh

avi o

ur

Motivation

Respondent

burden De facto

Perception More than one survey More than once In other ways: Registers

EDI

Internal

business factors

Policy

Data

Resources Market position

Informant:

Mandate

Data knowledge

Job priority

External

business factors

Econ. climate

Regulatory

requirements Political climate

I m a g e

The survey:

Topic


Sponsor /

Survey organisation Resources

Planning


The survey design

C on

t a c t

s t r a t e g y

Q u e s t i onn

ai r e

M o d e s of

d a

t a

c ol l e

c t i on

NSI NSI

Register

data

Statistical

picture of

a country

Black box

A business



2/10/2012 9


Over the years

General picture:

1993:

2007:


Single-mode designs Survey organisation is central

Systematisation and

standardisation of methods

Towards

multi-source/mixed-mode designs

Respondent is central: tailoring

2000: Transition



2/10/2012 10


The data collection design today

Challenges:

Good statistics: relevant

more & integrated information

faster

Less money

Less compliance costs: providing data only once to government

New technologies: powerful computers, access to the internet

Consequences for the data collection «



2/10/2012 11



Use of administrative data: Coordination of definitions:

- variables

- units

Quality of register data:

- timeliness

Data collection without questionnaires: EDI: XBRL

GPS

Surveys: If other sources are not possible or insufficient

Process measurement and quality control Getting insight in the data collection process



2/10/2012 12



Surveys:

Sampling:- controlling for overlap across surveys

- controlling for rotation over time (survey holiday) one statistical business register

In order to avoid this:



2/10/2012 13



Surveys:

Sampling:- controlling for overlap across surveys

- controlling for rotation over time (survey holiday) one statistical business register

Mode:- Mixed-mode designs: paper, internet, CATI

- Computer-assisted

Questionnaires for web data collection:- Customization (tailoring)

- Controlling the completion process (routing, checks)



2/10/2012 14



Surveys:

Contact strategy:

- Mixed-mode:.. paper letters, brochures, telephone,.. e-mails, website information

- Message:.. Cooperation = mandatory!

.. What, how, who, when?

- Cooperation no longer taken for granted:.. Motivating and stimulating respondents:

. Cialdini: Compliance (persuasion) principles

. Dillman: Social Exchange Theory

- Two-way communication via the internet



2/10/2012 15



Process measurement and quality control:

Paradata ± process data:

- Macro paradata (survey process data):.. Process summaries:

response rates, timeliness of response,

quality of response over time

- Micro paradata (process data at R level):.. Completion process: audit trails



2/10/2012 16


Macro paradata

Timeliness of response (Monthly Survey)

120

117

114

111

108

105

102

99

96

93

90

87

84

81

78

75

72

69

66

63

60

57

54

51

48

45

42

39

36

33

30

27

24

21

18

15

12

9630

responstijd afgerond in dagen

10000

8000

6000

4000

2000

0

f r e q

u e n t i e

Responstijd voor papieren vragenlijsten

2

1

0

aantal keer gerappelleerd

120

117

114

111

108

105

102

99

96

93

90

87

84

81

78

75

72

69

66

63

60

57

54

51

48

45

42

39

36

33

30

27

24

21

18

15

12

9630

responstijd afgerond in dagen

10000

8000

6000

4000

2000

0

f r e q

u e n t i e

Responstijd voor html-vragenlijstenPaper (letter + Q) Online (e-mail + e-Q)

N u m b e r o f r e

s p o n s e s

Days Days

Reminder 1 Reminder 1

Reminder 2 Reminder 2



2/10/2012 17


Macro paradata

R-indicator to monitor fieldwork of business surveys The representativity of the Monthly Survey for

industry and retail trade by number of fieldwork days.

0,5

0,6

0,7

0,8

0,9

1

0 10 20 30 40 50 60 70

Days

R - i n

d i c a t o r

0,0

0,1

0,2

0,3

0,4

0,5

0 10 20 30 40 50 60 70Days

M a x i m u m a b s o l u t e b i a s

Retail

IndustryIndustry

Industry

Retail

Retail



2/10/2012 18


Micro paradata ± audit trails

Completion process e-SBS: conscientious R

0

100

200

300

400

500

600

700

800

0:00 0:17 0:22 0:30 1:31 1:55 2:02 2:06 2:19 2:26 2:42 2:53time (hour:min)

' q u e s t i o n n u m b e r '

session no.

day no.

help

info

calculator

save

print

send

action



2/10/2012 19


Micro paradata: audit trails

Completion process e-SBS: quick µn¶ dirty R

0

100

200

300

400

500

600

700

800

00:00 02:17 02:57 03:46 03:51 04:02 04:16

time (min)

' q u e s t i o n n u m b e r '

session no.

day no.

help

info

calculator

save

print

send

action



2/10/2012 20

Completion Min. Max. Average

N times started 1 37 1.7

Completion time 00:01:27 11:29:51 01:07:30

Used

functionalities of

the Questionnaire

Used by

%

of R¶s

How many times

used

mean min.-max.

Print button 43 1.7 1-21

Save button 28 2.6 1-95



2/10/2012 21



More complex than yesterday:

More data sources Dependent on providers of registers

Integration of sources

Mixed-mode surveys Coordinated developments over modes

Tailoring to mode

Tailoring to respondents Tailoring to target populations

Coordination over surveys (samples and Q¶s)

Tomorrow, even more complex



2/10/2012 22


Tomorrow

Multi-source/mixed-mode data collection Managing integrated sets of statistics (not stove-pipes) Advanced statistical modelling and estimation Coordinated data collection designs:

- not single-purpose, but multi-purpose surveys Advanced questionnaire design:

- images, spoken language, animations, video pictures

Methodologists: competent in all modes

Opening the survey process Process measurement and quality control:

- continuous measurement using paradata- responsive adaptive designs

Tailoring to the internal business¶s processes Improved communication with businesses

Opening the businesses Insight in the internal response processes



2/10/2012 23


What¶s next?

Opening the businesses Insight in the response processes

A Business CASM movement:Communicative Aspects of Business Survey Methodology

Communication sciences

Administrative sciences Organisational sciences

Psychology (organisational, work and social, cognitive)



2/10/2012 24

Response In time

Complete

Correct

D e ci si on

t o p ar t i ci p

a t e

An

sw

er i n

g b eh

avi o

ur

Motivation

Respondent

burden De facto

Perception More than one survey More than once In other ways: Registers

EDI

Internal

business factors

Policy

Data

Resources

Market position

Informant:

Mandate

Data knowledge

Job priority

External

business factors

Econ. climate

Regulatory

requirements

Political climate

I m a g e

The survey:

Topic


Sponsor /

Survey organisation

Resources

Planning


The survey design

C on

t a c t

s t r a t e g y

Q u e s t i onn

ai

r e

M o d e s of

d a

t a

c ol l e

c t i on

Statistical

picture of

a country

NSI NSI

Register

data

Black box

A business



2/10/2012 25


Communication model

Direct communication

Indirect communication

Image NSI

Response in time,

complete,

correct

Decision toparticipate

One coherent strategy

with regard totone-of-voice, lay-out, and

compliance principles

Communicationwe cannot control



2/10/2012 26

References

in addition to proceedings paper

Bethlehem, J., F. Cobben, and B. Schouten (2008), Indicators for the Represen-

tativity of Survey Response. Presentation at the 24th International

Methodology Symposium of Statistics Canada: ³Data Collection:

Challenges, Achievements and New Directions´, 28-31 October 2008,

Gatineau, Canada.

De Nooij, G. (2008), Representativity of Short Term Statistics. Statistics

Netherlands, The Hague.

Groves, R.M. (2008), Dynamic Survey Design managed by modelled Paradata.

Presentation at the 24th International Methodology Symposium of Statistics

Canada: ³Data Collection: Challenges, Achievements and New Directions´,

28-31 October 2008, Gatineau, Canada.

Scheuren, F. (2001), Macro and Micro Paradata for Survey Assessment .

Urban Institute: unpublished paper, Washington D.C., USA.Snijkers, G. (2007), Collecting Data for Business Statistics: Yesterday, Today,

Tomorrow. Presentation at 56th Meeting of the ISI, 22-29 August 2007,

Lisbon, Portugal.

Snijkers, G. (2008), Getting Data for Business Statistics: A Response Model for

Business Surveys. Presentation at the 4th European Conference on Quality

in Official Statistics, 8-11 July 2008, Rome, Italy.

Getting Data for Business Statistics

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