The Use of Administrative Sources for Statistical Purposes

The Use of Administrative Sources for Statistical Purposes Matching and Integrating Data from Different Sources

What is Matching? • Linking data from different sources • Exact Matching - linking records from two or more sources, often using common identifiers • Probabilistic Matching - determining the probability that records from different sources should match, using a combination of variables

Why Match? • Combining data sets can give more information than is available from individual data sets • Reduce response burden • Build efficient sampling frames • Impute missing data • To allow data integration

Models for Data Integration • Statistical registers • Statistics from mixed source models – Split population model – Split data approach – Pre-filled questionnaires – Using administrative data for nonresponders – Using administrative data for estimation • Register-based statistical systems

Statistical Registers

Mixed Source Models • Traditionally one statistical output was based on one statistical survey • Very little integration or coherence • Now there is a move towards more integrated statistical systems • Outputs are based on several sources

Split Population Model • One source of data for each unit • Different sources for different parts of the population

Split Population Model

Split Data Approach • Several sources of data for each unit

Pre-filled Questionnaires • Survey questionnaires are pre-filled with data from other sources where possible • Respondents check that the information is correct, rather than completing a blank questionnaire • This reduces response burden. . . but may introduce a bias!

Example

Using Administrative Data for Non-responders • Administrative data are used directly to supply variables for units that do not respond to a statistical survey • Often used for less important units, so that response-chasing resources can be focused on key units

Using Administrative Data for Estimation • Administrative data are used as auxiliary variables to improve the accuracy of statistical estimation • Often used to estimate for small subpopulations or small geographic areas

Registerbased Statistical Systems

Matching Terminology

Matching Keys • Data fields used for matching e. g. • Reference Number • Name • Address • Postcode/Zip Code/Area Code • Birth/Death Date • Classification (e. g. ISIC, ISCO) • Other variables (age, occupation, etc. )

Distinguishing Power 1 • This relates to the uniqueness of the matching key • Some keys or values have higher distinguishing powers than others • High - reference number, full name, full address • Low - sex, age, city, nationality

Distinguishing Power 2 • Can depend on level of detail – Born 1960, Paris – Born 23 June 1960, rue de l’Eglise, Montmartre, Paris • Choose variables, or combinations of variables with the highest distinguishing power

Match • A pair that represents the same entity in reality A A

Non-match • A pair that represents two different entities in reality A B

Possible Match • A pair for which there is not enough information to determine whether it is a match or a non-match A a

False Match • A pair wrongly designated as a match in the matching process (false positive) A = B

False Non-match • A pair which is a match in reality, but is designated as a non-match in the matching process (false negative) A A

Matching Techniques

Clerical Matching • Requires clerical resources - Expensive - Inconsistent - Slow - Intelligent

Automatic Matching • Minimises human intervention - Cheap - Consistent - Quick - Limited intelligence

The Solution • Use an automatic matching tool to find obvious matches and no-matches • Refer possible matches to specialist staff • Maximise automatic matching rates and minimise clerical intervention

How Automatic Matching Works

Standardisation • Generally used for text variables • Abbreviations and common terms are replaced with standard text • Common variations of names are standardised • Postal codes, dates of birth etc. are given a common format

Blocking • If the file to be matched against is very large, it may be necessary to break it down into smaller blocks to save processing time – e. g. if the record to be matched is in a certain town, only match against other records from that town, rather than all records for the whole country

Blocking • Blocking must be used carefully, or good matches will be missed • Experiment with different blocking criteria on a small test data set • Possible to have two or more passes with different blocking criteria to maximise matches

Parsing • Names and words are broken down into matching keys e. g. Steven Vale stafan val Stephen Vael stafan val • Improves success rates by allowing matching where variables are not identical

Scoring • Matched pairs are given a score based on how closely the matching variables agree • Scores determine matches, possible matches and non-matches

How to Determine X and Y • Mathematical methods e. g. Fellegi / Sunter method • Trial and Error • Data contents and quality may change over time so periodic reviews are necessary

Enhancements • Re-matching files at a later date reduces false non-matches (if at least one file is updated) • Link to data cleaning software, e. g. address standardisation

Matching Software • Commercial products e. g. SSAName 3, Trillium, Automatch • In-house products e. g. ACTR (Statistics Canada) • Open-source products e. g. FEBRL • No “off the shelf” products - all require tuning to specific needs

Internet Applications • Google (and other search engines) – www. google. com • Cascot – an automatic coding tool based on text matching – http: //www 2. warwick. ac. uk/fac/soc/ier/publicati ons/software/cascot/choose_classificatio/ • Address finders e. g. Postes Canada – http: //www. postescanada. ca/tools/pcl/bin/adva nced-f. asp

Software Applications • Trigram method applied in SAS code (freeware) for matching in the Eurostat business demography project • Similar approach in UNECE “Data Locator” search tool • Works by comparing groups of 3 letters, and counting matching groups

Trigram Method • Match “Steven Vale” – Ste/tev/eve/ven/en /n V/ Va/Val/ale • To “Stephen Vale” – Ste/tep/eph/phe/hen/en /n V/ Va/Val/ale – 6 matching trigrams • And “Stephen Vael” – Ste/tep/eph/phe/hen/en /n V/ Va/Vae/ael – 4 matching trigrams • Parsing would improve these scores

Matching in Practice

Matching Records Without a Common Identifier The UK Experience by Steven Vale (Eurostat / ONS) and Mike Villars (ONS)

The Challenge • The UK statistical business register relies on several administrative sources • It needs to match records from these different sources to avoid duplication • There is no system of common business identification numbers in UK

The Solution • Records are matched using business name, address and post code • The matching software used is Identity Systems / SSA-NAME 3 • Matching is mainly automatic via batch processing, but a user interface also allows the possibility of clerical matching

Batch Processing 1 • Name is compressed to form a namekey, the last word of the name is the major key • Major keys are checked against those of existing records at decreasing levels of accuracy until possible matches are found • The name, address and post codes of possible matches are compared, and a score out of 100 is calculated

Batch Processing 2 • If the score is >79 it is considered to be a definite match • If the score is between 60 and 79 it is considered a possible match, and is reported for clerical checking • If the score is <60 it is considered a non-match

Clerical Processing • Possible matches are checked and linked where appropriate using an on-line system • Non-matches with >9 employment are checked - if no link is found they are sent a Business Register Survey questionnaire • Samples of definite matches and smaller non-matches are checked periodically

Problems Encountered 1 • “Trading as” or “T/A” in the name e. g. Mike Villars T/A Mike’s Coffee Bar, Bar would be the major key, but would give too many matches as there are thousands of bars in the UK. • Solution - split the name so that the last word prior to “T/A” e. g. Villars is the major key, improving the quality of matches.

Problems Encountered 2 • The number of small non-matched units grows over time leading to increasing duplication • Checking these units is labour intensive • Solutions – Fine tune matching parameters – Re-run batch processes – Use extra information e. g. legal form / company number where available

Future Developments • Clean and correct addresses prior to matching using “Quick. Address” and the Post Office Address File • Links to geographical referencing • Business Index - plans to link registers of businesses across UK government departments • Unique identifiers?

One Number Census Matching by Ben Humberstone (ONS)

One Number Census • Aim: To estimate and adjust for underenumeration in the 2001 Census • Census Coverage Survey (CCS) - 1% sample stratified by hard-to-count area – 320, 000 households – 500, 000 people • 101 Estimation Areas in England Wales

ONC Process Census CCS Matching Dual System Estimation Imputation Adjusted Census DB Quality Assurance

ONC Matching Process CCS Census Exact Matching Probability Matching Key Green = CCS Blue = Census Red = Matched pair Italics = Automated Clerical Review Clerical Matching Quality Assurance Matched Records

Data Preparation • Names – Soundex used to bring together different spellings of the same name • Anderson, Andersen = A 536 • Smith, Smyth = S 530 • Addresses – Converted to a numeric/alpha string • 12 a Acacia Avenue = 12 AA • Top Flat, 12 Acacia Ave. = 12 AA

Exact Matching • Data “blocked” at postcode level • Households matched on key variables – surname, address name/number, accommodation type, number of people • Individuals from within matched households matched – forename, surname, day of birth, month of birth, marital status, relationship to head of household

Probability Matching • Block by postcode • Compare CCS with all Census households in postcode + neighbouring postcodes using key variables • Create matrix according to match weight • Repeat for people within matched households

Probability Matching • Matching weights • Apply threshold to cumulative weights • 2 thresholds – High probability matches – Low probability matches

Automatic Match Review • Clerical role • Matchers presented with all low probability matches – Household matches – Matched individuals within matched households • Access to form images to check scanning • Basic yes/no operation

Clerical Matching • Clerical matching of all unmatched records • Matchers - perform basic searches, match or defer • Experts - carry out detailed searches on deferred records and review matches • Quality assurance staff - review experts work including all unmatchable records using estimation area wide searches

Quality Assurance • Experts and Quality Assurance staff • Double Matching – Estimation area matched twice, independently – Outputs compared, discrepancies checked • Matching protocol – Based on best practice

Resources • • • 8 - 10 Matchers 4 - 5 Expert Matchers 2 - 3 Quality Assurance staff 3 Research Officers/Supervisors 1 Senior Research Officer Computer Assisted Matching System

Quality Assurance England & Wales Automatically Matched Clerically Resolved Clerically Matched Unmatched CCS Excluded CCS Unmatched Census Excluded Census Household 58. 8% 13. 7% 22. 3% 5. 0% 0. 2% 12. 8% 0. 0% Person 51. 1% 11. 4% 30. 7% 6. 4% 0. 3% 11. 7% 0. 0% • False negative rate: < 0. 1% • 1 Estimation area matched per day

Group Discussion Practical experiences of data matching