1 1 a two-phase life-cycle model of integrated statistical micro data li-chun zhang statistics...
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A two-phase life-cycle model of A two-phase life-cycle model of integrated statistical micro dataintegrated statistical micro data
Li-Chun Zhang
Statistics Norway
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Register-based statistics & early years of survey sampling
N. Kiær (1895).The
representative method.
ISI Session, Bern.
A. Jensen (ISI-committee, 1924): “When ISI discussed the matter twentytwo years ago, it was the question of the
recognition of the method in principle that claimed most interest. Now it is otherwise. I think I may venture to say that nowadays there is hardly one statistician, who in principle will contest the
legitimacy of the representative method. Nevertheless, I believe that the representative method is capable of being used to a
much greater extent than now is the case.”
20??
J. Neyman (1934). On the two different aspects of the
representative method: The method of stratified
sampling and the method of purposive selection.
JRSS 97, 558-606.
(Source: UNECE 2007)
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Survey life cycle from a quality perspective (Groves et al., 2004, Survey Methodology, Figure 2.5)
Construct
Measurement
Response
Edited Response
Target Population
Sampling frame
Sample
PostsurveyAdjustments
Survey Statistic
Measurement Representation
Validity
MeasurementError
ProcessingError
CoverageError
SamplingError
AdjustmentError
Respondents
NonresponseError
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A two-phase life-cycle model
-Secondary use-Combination of sources
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Single-source primary-phase statistical micro data
Target Concept
Measurement
Response/Registration
Editing
Target Set
Accessible Set
Accessed Set
Observed/Validated Set
Single-sourceMicro Data(Primary)
Measurement(Variables)
Representation(Objects)
Validity
Measurement
Processing
Frame
Selection
Missing/Redundancy
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Integrated secondary-phase statistical micro data
Target Concept
Harmonization
Classification
Adjustment
Target Population
Data Linkage
Alignment
Statistical Units
IntegratedMicro Data
(Secondary)
Measurement(Variables)
Representation(Units)
Relevance
Mapping
Compatibility
Coverage
Identification
Unit
Transformation(Object to Unit)
Unit vs. ObjectMeasurement vs. Representation
Missing Values vs. Coverage
Base Unit No. 1
Base Unit No. 2
Base Unit No. N
Composite Unit No. 1
Composite Unit No. 2
Composite Unit No. M
Composite Unit No. 1
Composite Unit No. 2
Composite Unit No. K
m:1 m:1
Composite Unit No. 1 Composite Unit No. 2 Composite Unit No. H
m:1
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An illustration of register-based household data:Kongsvinger at the time point of census 2001
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Representing unit error by allocation matrix
(Equivalence on row permutation & sequential upper-triangular by definition)
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Value matrix (or vector): XStatistics: y = A X
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Two more examples of statistics
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Results: Statistical uncertainty w.r.t. unit errors
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The 20th Century = Survey Sampling
The 21th Century = Data Integration
Welcome to a new age!