#### Transcript Il progetto RELAIS

```Data integration: an overview on
statistical methodologies and
applications.
Mauro Scanu
Istat
Central Unit on User Needs, Integration and Territorial Statistics
[email protected]
Poznan 20 October 2010
Summary
• In what sense methods for integration are “statistical”?
• Record linkage: definition, examples, methods, objectives and
open problems
• Statistical matching: definition, examples, methods, objectives
and open problems
• Micro integration processing: definition, examples, methods,
objectives and open problems
• Other statistical integration methods?
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Methods for integration 1
Generally speaking, integration of two data sets is understood as a
single unit integration: the objective is the detection of those
records in the different data sets that belong to the same statistical
unit. This action allows the reconstruction of a unique record of
data that contains all the unit information collected in the different
data sources on that unit.
On the contrary: let’s distinguish two different objectives - micro and
macro
Micro: the objective is the “development” of a complete data set
Macro: the objective is the “development” of an aggregate (for
example, a contingency table)
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Methods for integration 2
Further, the methods of integration can be split in automatic and
statistical methods
The automatic methods take into account a priori rules for the linkage
of the data records
The statistical methods include a formal estimation or test procedure
that should be applied on the available data: this estimation or test
procedure
1. can be chosen according to optimality criteria,
2. and are associated with an estimate error.
This talk restricts the attention on the (micro and macro) statistical
methods of integration
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Statistical methods
Classical inference
1) There exists a data
generating model
2) The observed sample is an
image of the data
generating model
3) We estimate the model from
the observed sample
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Statistical methods of integration
If a method of
integration is
used, it is
necessary to
include an
intermediate
phase.
The final data set
is a blurred
image of the data
generating
model
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Statistical methods of integration
Statistical methods for integration can be organized according to the
available input
Input
Output
Metodo
Two data sets that observe (partially)
overlapping groups of units
Micro
Record linkage
Two independent samples
Macro/micro
Statistical matching
Sets of estimates from different
surveys, that are not coherent
Macro
Calibration methods
Graphical methods
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Record linkage
Input: two data sets on overlapping sets of units.
Problem: lack of a unique and correct record identifier
Alternative: sets of variables that (jointly) are able to identify units
Attention: variables can have “problems”!
Objective: the largest number of correct links, the lowest number of
wrong links
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Book of life
Dunn (1946)* describes record linkage in this way:
…each person in the world creates a book of life. The
book starts with the birth and ends with the death. Its
pages are made up of all the principal events of life.
Record linkage is the name given to the process of
assembling the pages of this book into one volume. The
person retains the same identity throughout the book.
Except for advancing age, he is the same person…
*Dunn (1946) "Record Linkage". American Journal of Public Health 36 (12): 1412–1416.
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When there is the lack of a unique identifier
If a record identifier is missing or cannot be used, it is necessary to
use the common variables in the two files.
The problem is that these variables can be “unstable”:
1.
2.
3.
4.
Time changes (age, address, educational level)
Errors in data entry and coding
Correct answers but different codification (e.g. address)
Missing items
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Main motivations for record linkage
According to Fellegi (1997)*, the development of tools for
integration is due to the intersection of these facts:
• occasion: construction of big data bases
• tool: computer
• need: new informative needs
*Fellegi (1997) “Record Linkage and Public Policy: A Dynamic Evolution”. In
Alvey, Jamerson (eds) Record Linkage Techniques, Proceedings of an
international workshop and exposition, Arlington (USA) 20-21 March 1997.
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Why record linkage? Some examples
1. To have joint information on two or more variables
observed in distinct data sources
2. To “enumerate” a population
3. To substitute (parts of) surveys with archives
4. To create a “list” of a population
5. Other official statistics objectives (imputation and
editing / to enhance micro data quality; to study the risk
of identification of the released micro data)
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Example 1 – analysis of mortality
Problem: to analyze jointly the “risk factors” with the event
“death”.
A) The risk factors are observed on ad hoc surveys (e.g. those on
nutrition habits, work conditions, etc.)
B) The event “death” (after some months the survey is conducted)
can be taken from administrative archives
These two sources (survey on the risk factors and death
archive) should be “fused” so that each unit observed in
the risk factor survey can be associated with a new
dichotomous variable (equal to 1 if the person is dead
and zero otherwise).
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Example 2 – to enumerate a population
Problem: what is the number of residents in Italy?
Often the number of residents is found in two steps, by
means of a procedure known as “capture-recapture”.
This method is usually applied to determine the size of
animal populations.
A) Population census
B) Post enumeration survey (some months after the census) to
evaluate Census quality and give an accurate estimate of the
population size
USA - in 1990 Post Enumeration Survey, in 2000 Accuracy and
Coverage Evaluation
Italy - in 2001 “Indagine di Copertura del Censimento”
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Example 2 – to enumerate a population
The result of the comparison between Census and post
enumeration survey is a 22 table:
Obs. Post Non obs
Post
Obs.
Cens.
noo
non
Non obs
Cens
nno
??
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Example 2 - to enumerate a population
For short, for any distinct unit it is necessary to
understand if it was observed
1) both in the census and in the PES
2) only in the census
3) only in the PES
These three values allow to estimate (with an appropriate
model) the fourth value.
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Example 3 – surveys and archives
Problem: is it possible to use jointly administrative
archives and sample surveys?
At the micro level this means: to modify the questionnaire
of a survey dropping those questions that are already
available on some administrative archives (reduction of
the response burden)
E.g., for enterprises:
Social security archives, chambers of commerce, …
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Example 4 – Creation of a list
Problem: what is the set of the active enterprises in Italy?
In Istat, ASIA (Archivio Statistico delle Imprese Attive) is the most
important example of a creation of a list of units (the active
enterprises in a time instant) “fusing” different archives.
It is necessary to pay attention to:
• Enterprises which are present in more than one archives
(deduplication)
• Non active enterprises
• New born enterprises
• transformations (that can lead to a new enterprise or to a
continuation of the previous one)
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Example 5 – Imputation and editing
Problem: to enhance microdata quality
Micro Integration in the Netherlands (virtual census, social
statistical data base)
It will be seen later, when dealing with micro integration
processing
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Example 6 - Privacy
Problem: does it exist a “measure” of the degree
of identification of the released microdata?
In order to evaluate if a method for the protection of data disclosure is
good, it is possible to compare two datasets (the true and the
protected ones) and detect how many modified records are “easily”
linked to the true ones.
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Record linkage steps
The record linkage techniques are a multidisciplinary set of
methods and practices
SEARCH SPACE REDUCTION
• Sorted Neighbourhood Method
• Blocking
• Hierarchical Grouping
•…
......
......
RECORD
LINKAGE
DECISION MODEL CHOICE
• Fellegi & Sunter
• exact
• Knowledge – based
• Mixed
•…
......
PRE-PROCESSING
• Conversion of upper/lower cases
• Replacement of null strings
• Standardization
• Parsing
•…
COMPARISON FUNCTION
CHOICE
• Edit distance
• Smith-Waterman
• Q-grams
• Jaro string comparator
• Soundex code
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Tiziana Tuoto, FCSM 2007, Arlington, November 6 2007
•…
21
Example (Fortini, 2008)*
Census is sometimes associated with a post enumeration surveys, in
order to detect the actual census coverage.
To this purpose, a “capture-recapture” approach is generally
considered.
It is necessary to find out how many individuals have been observed:
• in both the census and the PES
• Only in the census
• Only in the PES
These figures allow to estimate how many individuals have NOT been
observed in both the census and the PES
* In ESSnet Statistical Methodology Project on Integration of Survey and Administrative Data “Report
of WP2. Recommendations on the use of methodologies for the integration of surveys and
administrative data”, 2008
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CENSUS
Record linkage workflow for
Census - PES
PES
Step 1
Unmatched
households
Matched
households
Step 2
Matched
households
Unmatched
households
Step 3.a
Step 3.b
Matched
people
Unmatched
people
Unmatched
people
Step 4.b
Step 4.a
Matched
people
Matched
people
Matched
people
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Unmatched
people
Matched
people
Step 5
Unmatched
people
Unmatched
people
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Problem: Lack of identifiers
Difference between step 1 and step 2 is that:
Step 1 identifies all those households that coincide for all these
variables:
• Name, surname and date of birth of the household head
• Address
• Number of male and female components
Step 2 uses the same keys, but admits the possibility of differences of
the variable states for modifications of errors
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Probabilistic record linkage
For every pairs of records from the two data sets, it is necessary to
estimate
• The probability that the differences between what observed on the
two records is due to chance, because the two records belong to
the same unit
• The probability that the two records belong to different units
These probabilities are compared: this comparison is the basis for the
decision whether a pair of records is a match or not
Estimate of this probability is the “statistical step” in the probabilistic
record linkage method
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Statistical step
Key variables
Data set A with na units.
Data set B with nb units.
K key variables (they jointly make
an identifier)
a
X1
X2
…
Xk
1
x11A
x12A
…
x1Ak
XA1
2
x 21A
x 22A
…
x 2Ak
XA2
…
…
…
…
…
…
nA
x naA 1
A
xna
2
…
A
xnak
XAk
Key variables
b
X1
X2
…
Xk
1
x11B
x12B
…
x1Bk
XB1
2
B
x 21
B
x 22
…
x 2Bk
XB2
…
…
…
…
nb
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B
x nb
1
…
B
xnb
2
…
B
xnbk
XBk
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Statistical procedure
The key variables of the two records in
a pair (a,b) is compared:
yab=f(xAa,xBb)
The function f(.) should register how
much the key variables observed in
the two units are different.
For instance, y can be a vector with k
components, composed of 0s
(inequalities) or 1s (equalities)
(a,b)
comparisons
(1,1)
f(XA1,XB1)= y11
(1,2)
f(XA1,XB2)= y12
…
…
…
…
…
…
…
…
…
(na,nb) f(XAna,Xb1)= ynanb
The final result is a data set of na x nb
comparisons
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Statistical procedure
The na x nb pairs are split in two sets:
M: the pairs that are a match
U: the unmatched pairs
Likely, the comparisons y will follow this situation:
• Low levels of diversity for the pairs that are match, (a,b)M
• High levels of diversity for the pairs that are non-match, (a,b)U
For instance: if y=(sum of the equalities for the k key variables), y
tends to assume large values for the pairs in M with respect to
those in U
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Statistical procedure
If y=(sum of the equalities), the distribution of y is a mixture of the
distribution of y in M (right) and that in u (left)
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Statistical procedure
Inclusion of a pair (a,b) in M or U is a missing value (latent variable).
Let C denote the status of a pair (C=1 if (a,b) in M; C=0 if (a,b) in U)
Likelihood is the product on the na x nb pairs of
P(Y=y, C=c) = [p m(y)]c [(1-p) u(y)](1-c)
Parameters
data
p: fraction of matches among the
na x nb pairs
Y: observed
m(y): distribution of y in M
C: missing (latent)
u(y): distribution of y in U
Estimation method: maximum likelihood on a partially observed data
set (EM algorithm – Expectation Maximization)
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Statistical procedure
A pair is assigned to M or U in the
following way
1) For every comparison y assign a
“weight”:
t(y)=m(y)/u(y)
where m and u are estimated;
2) Assign the pairs with a large weight to
M and the pairs with a small weight
to U.
3) There can be a class of weights t
where it is better to avoid definitive
decisions (m and u are similar)
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Statistical procedure
The procedure is
the following.
Note that,
generally,
probabilities of
mismatching are
still not
considered
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Open problems
Different probabilistic record linkage aspects should still be better
investigated. Two of them are related to record linkage quality
a) What model should be considered
– a1) on the pairs relationship (Copas and Hilton, 1990)
– a2) on the key variables relationship (Thibaudeau, 1993)
b) How probabilities of mismatching can be used for a statistical analysis
of a linked data file? (Scheuren and Winkler, 1993, 1997)
Copas J.R., Hilton F.J. (1990). “Record linkage: statistical models for matching computer
records”. Journal of the Royal Statistical Society, Series A, 153, 287-320.
Thibaudeau Y. (1993). “The discrimination power of dependency structures in record
linkage”. Survey Methodology, 19, 31-38.
Scheuren F., Winkler W.E. (1993). “Regression analysis of data files that are computer
matched”. Survey Methodology, 19, 39-58
Scheuren F., Winkler W.E. (1997). “Regression analysis of data files that are computer
matched - part II”. Survey Methodology, 23, 157-165.
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Statistical matching
What kind of integration should be considered if the analysis involves
two variables observed in two independent sample surveys?
• Let A and B be two samples of size nA and nB respectively, drawn
from the same population.
• Some variables X are observed in both samples
• Variables Y are observed only in A
• Variables Z are observed only in B.
Statistical matching aims at determining information on (X;Y;Z), or at
least on the pairs of variables which are not observed jointly (Y;Z)
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Statistical matching
It is very improbable that the two samples observe the same units,
hence record linkage is useless.
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Some statistical matching applications 1
The objective of the integration of the Time Use Survey (TUS) and of the
Labour Force Survey (LFS) is to create at a micro level, a synthetic file of
both surveys that allows the study of the relationships between variables
measured in each specific survey.
By using together the data relative to the specific variables of both surveys,
one would be able to analyse the characteristics of employment and the
time balances at the same time.
Information on labour force units and the organisation of her/his life
times will help enhance the analyses of the labour market
The analyses of the working condition characteristics that result from
the labour force survey will integrate the TUS more general analysis of
the quality of life
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Some statistical matching applications 1
The possibilities for a reciprocal enrichment have been largely recognised
(see the 17th International Conference of Labour Statistics in 2003 and the
2003 and 2004 works of the Paris group). The emphasis was indeed put on
how the integration of the two surveys could contribute to analysing the
different participation modalities in the labour market determined by hour
and contract flexibility.
Among the issues raised by researchers on time use, we list the following
two:
the usefulness and limitations involved in using and combining various
sources, such as labour force and time-use surveys, for improving data
quality
Time-use surveys are useful, especially for measuring hours worked of
workers in the informal economy, in home-based work, and by the
hidden or undeclared workforce, as well as to measure absence from
work
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Some statistical matching applications 1
Specific variables in the TUS (Y ): it enables to estimate the time
dedicated to daily work and to study its level of "fragmentation"
(number of intervals/interruptions), flexibility (exact start and end of
working hours) and intra-relations with the other life times
Specific variables in the LFS (Z): The vastness of the information
gathered allow us to examine the peculiar aspects of the Italian
participation in the labour market: professional condition, economic
activity sector, type of working hours, job duration, profession carried
out, etc. Moreover, it is also possible to investigate dimensions
relative to the quality of the job
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Some statistical matching applications 2
The Social Policy Simulation Database and Model (SPSD/M) is a
micro computer-based product designed to assist those interested in
analyzing the financial interactions of governments and individuals in
Canada (see http://www.statcan.ca/english/spsd/spsdm.htm).
It can help one to assess the cost implications or income redistributive
effects of changes in the personal taxation and cash transfer system.
The SPSD is a non-confidential, statistically representative database
of individuals in their family context, with enough information on each
individual to compute taxes paid to and cash transfers received from
government.
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Some statistical matching applications 2
The SPSM is a static accounting model which processes each
individual and family on the SPSD, calculates taxes and transfers
using legislated or proposed programs and algorithms, and reports on
the results.
It gives the user a high degree of control over the inputs and outputs
to the model and can allow the user to modify existing tax/transfer
programs or test proposals for entirely new programs. The model can
be run using a visual interface and it comes with full documentation.
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Some statistical matching applications 2
In order to apply the algorithms for microsimulation of tax–transfer benefits
policies, it is necessary to have a data set representative of the Canadian
population. This data set should contain information on structural (age,
sex,...), economic (income, house ownership, car ownership, ...), health–
related (permanent illnesses, child care,...) social (elder assistance,
cultural–educational benefits,...) variables (among the others).
• It does not exist a unique data set that contains all the variables that can
influence the fiscal policy of a state
• In Canada 4 samples are integrated (Survey of consumers finances, Tax
return data, Unemployment insurance claim histories, Family expenditure
survey)
• Common variables: some socio-demographic variables
• Interest is on the relation between the distinct variables in the different
samples
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Example (Coli et al, 2006*)
The new European System of the Accounts (ESA95) is a detailed
source of information on all the economic agents, as households
and enterprises. The social accounting matrix (SAM) has a
relevant role.
Module on households: it includes the amount of expenditures and
income, per typology of household
Coli A., Tartamella F., Sacco G., Faiella I., D’Orazio M., Di Zio M., Scanu M., Siciliani I., Colombini
S., Masi A. (2006). “La costruzione di un Archivio di microdati sulle famiglie italiane ottenuto
integrando l’indagine ISTAT sui consumi delle famiglie italiane e l’Indagine Banca d’Italia sui
bilanci delle famiglie italiane”, Documenti ISTAT, n.12/2006.
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Example
Problem:
1) Income are observed on a Bank of Italy survey
2) Expenditures are observed on an Istat survey
3) The two samples are composed of different households, hence
record linkage is useless
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Adopted solutions 1
The first statistical matching solution was imputation of missing data.
Usually, “distance hot deck” was used.
In pratice, this method “mimics” record linkage: instead of matching
records of the same unit, this approach “matches” records of
similar units, where similarity is in terms of the common variables
in the two files.
The procedure is
1) Compute the distances between the matching variables for every
pair of records
2) Every record in A is associated to that record in B with minimum
distance
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Adopted solutions 1
The inferential
path is the
following
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Adopted solutions 2
It is applied an estimate procedure under specific models that
considers the presence of missing items. The easiest model is:
conditional independence of the never jointly observed variables (e.g.,
income and expenditures) given the matching variables.
Example:
Y = income, Z = expenditures, X = house surface
(X,Y,Z) is distributed as a multivariate normal with parameters:
Mean vector = 
Variance matrix = 
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Adopted solutions 2
1)
2)
3)
4)
Estimate the regression equation on A: Y=+X
Impute Y in B: Yb=+Xb , b=1,…,nB
Estimate the regression equation in B: Z=+X
Impute Z in A: Za= +Xa , a=1,…,nA
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Adopted solutions 2
The inferential
mechanism
assumes that
Y and Z are
independent
given X
(there is not
the regression
coefficient of Z
on Y
given X)
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Adopted solutions 2
This method
can be
applied also
with this
inferential
scheme: the
problem is
what
hypotheses
are before
the analysis
phase
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Adopted solutions 3
We do not hypothesize any model. It is estimated a set of values, one
for every plausible model given the observed data
Example
When matching two sample surveys on farms (Rica-Rea - FADN and
SPA - FSS), it was asked the following contingency table for farms
Y = presence of cattle (FSS)
Z = class of intermediate consumption (from FADN)
Using the common variables
X1 = Utilized Agricultural Area (UAA) ,
X2 = Livestock Size Unit (LSU)
X3 = geographical characteristics
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Example
We consider all the models that we cam estimate from the observed
data in the two surveys
In practice, the available data allow to say that the estimate of the
number of farms with at least one cow (Y=1) in the lowest class of
intermediate consumption (Z=1) is between 2,9% and 4,9%
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Inferential machine
The inferential machine does
not use any specific model
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It is possible to simulate data including
uncertainty on the data generation model
(e.g. by multiple imputation)
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Quotation (Manski, 1995*)
…”The pressure to produce answers, without qualifications, seems
particularly intense in the environs of Washington, D.C. A perhaps
apocryphal, but quite believable, story circulates about an
economist’s attempt to describe his uncertainty about a forecast to
President Lyndon Johnson. The economist presented his forecast
as a likely range of values for the quantity under discussion.
Johnson is said to have replied, “Ranges are for cattle. Give me a
number”
*Manski, C. F. (1995) Identification problems in the Social Sciences,
Harvard University Press.
Manski and other authors show that in a wide range of applied areas
(econometrics, sociology, psychometrics) there is a problem of
identifiability of the models of interest, usually caused by the
presence of missing data. The statistical matching problem is an
example of this.
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Why statistical matching?
Applications in Istat
SAM
Joint analysis FADN / FSS
Joint use of Time Use / Labour force
Objectives
Estimates of parameters of not jointly observed parameters
Creation of synthetic data (e.g. data set for microsimulation)
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Open problems
1) Uncertainty estimate (D’Orazio et al, 2006)
2) Variability of uncertainty (Imbens e Manski, 2004)
3) Use of sample drawn according to complex survey designs (Rubin, 1986;
Renssen, 1998)
4) Use of nonparametric methods (Marella et al, 2008; Conti et al 2008)
Conti P.L., Marella D., Scanu M. (2008). “Evaluation of matching noise for imputation techniques based
on the local linear regression estimator”. Computational Statistics and Data Analysis, 53, 354-365.
D’Orazio M., Di Zio M., Scanu M. (2006). “Statistical Matching for Categorical Data: Displaying
Uncertainty and Using Logical Constraints”, Journal of Official Statistics, 22, 137-157.
Imbens, G.W, Manski, C. F. (2004). "Confidence intervals for partially identified parameters".
Econometrica, Vol. 72, No. 6 (November, 2004), 1845–1857
Marella D., Scanu M., Conti P.L. (2008). “On the matching noise of some nonparametric imputation
procedures”, Statistics and Probability Letters, 78, 1593-1600.
Renssen, R.H. (1998) Use of statistical matching techniques in calibration estimation. Survey
Methodology 24, 171–183.
Rubin, D.B. (1986) Statistical matching using file concatenation with adjusted weights and multiple
imputations. Journal of Business and Economic Statistics 4, 87–94.
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Micro integration processing
It can be applied every time it is produced a complete data set (micro
level) by any kind of method. Up to now, applied after exact record
linkage
Micro integration processing consists of putting in place all the
necessary actions aimed to ensure better quality of the matched
results as quality and timeliness of the matched files. It includes
• defining checks,
• editing procedures to get better estimates,
• imputation procedures to get better estimates.
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Micro integration processing
It should be kept in mind that some sources are more reliable than
others.
Some sources have a better coverage than others, and there may
even be conflicting information between sources.
So, it is important to recognize the strong and weak points of all the
data sources used.
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Micro integration processing
Since there are differences between sources, a micro integration
process is needed to check data and adjust incorrect data. It is
believed that integrated data will provide far more reliable results,
because they are based on an optimal amount of information. Also
the coverage of (sub) populations will be better, because when
data are missing in one source, another source can be used.
Another advantage of integration is that users of statistical
information will get one figure on each social phenomenon, instead
of a confusing number of different figures depending on which
source has been used.
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Micro integration processing
During the micro integration of the data sources the following steps have to be
taken (Van der Laan, 2000):
a. harmonisation of statistical units;
b. harmonisation of reference periods;
c. completion of populations (coverage);
d. harmonisation of variables, in case of differences in definition;
e. harmonisation of classifications;
f. adjustment for measurement errors, when corresponding variables still do
not have the same value after harmonisation for differences in definitions;
g. imputations in the case of item nonresponse;
h. derivation of (new) variables; creation of variables out of different data
sources;
i. checks for overall consistency.
All steps are controlled by a set of integration rules and fully automated.
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Example: Micro integration processing
From Schulte Nordholt, Linder (2007) Statistical Journal of the IAOS
24,163–171
Suppose that someone becomes unemployed at the end of November
and gets unemployment benefits from the beginning of December.
The jobs register may indicate that this person has lost the job at
the end of the year, perhaps due to administrative delay or
because of payments after job termination. The registration of
benefits is believed to be more accurate. When confronting these
facts the ’integrator’ could decide to change the date of termination
of the job to the end of November, because it is unlikely that the
person simultaneously had a job and benefits in December. Such
decisions are made with the utmost care. As soon as there are
convincing counter indications of other jobs register variables,
indicating that the job was still there in December, the termination
date will, in general, not be adjusted.
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Example: Micro integration processing
Method: definition of rules for the creation of a usable complete data
set after the linkage process.
If these approaches are not applied, the integrated data set can
contain conflicting information at the micro level.
These approaches are still strictly based on quality of data sets
knowledge.
Proposition for a possible next ESSnet on integration: study the links
between imputation and editing activities and
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Other supporting slides
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Macro integration: coherence of estimates
Sometimes it is useful to integrate aggregate data, where aggregates
are computed from different sample surveys.
For instance: to include a set of tables in an information system
A problem is the coherence of information in different tables.
The adopted solution is at the estimate level: for instance, with
calibration procedures (e.g.: the Virtual census in the Netherlands)
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Project
The objective of a project is to gather the developments in two distinct
areas
Probabilistic expert systems: these are graphical models,
characterized by the presence of an easy updating system of the
joint distribution of a set of variables, once one of them is updated.
These models have been used for a class of estimators that
includes poststratification estimators
Statistical information systems: SIS for the production of statistical
output (Istar) with the objective to integrate and manage statistical
data given and validated by the Istat production areas, in order to
produce purposeful output for the end users
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Objectives and open problems
Objectives
To develop a statistical information system for agriculture data,
managing tables from FADN. FSS, and lists used for sampling
(containing census and archive data)
To manage coherence bewteen different tables
To update information on data from the most recent survey and to
visualize what changes happen to the other tables
To allow simulations (for policy making)
Problems
Use of graphical models for complex survey data
To link the selection of tables to the updating algorithm
To update more than one table at the same time
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Some practical aspects for integration: Software
There exist different software tools for record linkage record linkage
and statistical matching
Relais: http://www.istat.it/strumenti/metodi/software/analisi_dati/relais/
R package for statistical matching:
http://cran.r-project.org/index.html
Look for Statmatch
Probabilistic expert systems: Hugin (it does not work with complex
survey data)
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Bibliography
Batini C, Scannapieco M (2006) Data Quality, Springer Verlag,
Heidelberg.
Scanu M (2003) Metodi statistici per il record linkage, collana Metodi e
Norme n. 16, Istat.
D’Orazio M., Di Zio M., Scanu M. (2006) Statistical matching: theory
and practice, J. Wiley & Sons, Chichester.
Ballin M., De Francisci S., Scanu M., Tininini L., Vicard P. (2009)
Integrated statistical systems: an approach to preserve coherence
between a set of surveys based on the use of probabilistic expert
systems, NTTS 2009, Bruxelles.
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Is this conditional independence?
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And this?
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Statistical methods of integration
Sometimes a
“shorter track” is
used.
Note! The
“automatic
methods”
correspond to
specific data
generating
model
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Statistical methods of integration
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Statistical methods of integration
The last approach is very appealing:
1) Estimate a data generating model from the two data samples at
hand
2) Use this estimate for the estimation of aggregate data (e.g.
contingency tables on non jointly observed variables)
3) If necessary, develop a complete data set by simulation from the
estimated model: the integrated data generating mechanism is the
“nearest” to the data generating model, according to the optimality
properties of the model estimator
Attention! Issue 1 includes hypothesis that cannot be tested on the
available data (this is true for record linkage and, more
“dramatically”, for statistical matching)
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