DLI Workshop -- Mar 2000 1 Hosted by Dalhousie University March 2000 DLI Training Workshop.

transcript

DLI Workshop -- Mar 2000

Hosted by

Dalhousie UniversityMarch 2000

DLI Training Workshop

• Review data service models within the framework of: access and dissemination aggregate data and microdata statistics versus data

Data Service Models

• Models were presented as a continuum during the 1997 DLI workshop“Order & Pass-through” Service

Install Data and Provide Access

Treat as a Collection and Provide Reference

Data Service Models

• Choose a model that matches your staff and computing resources

AcquisitionFill a Request

Locate dataOrder data & documentation

Collection DevelopmentSelect & Locate dataOrder data & documentationCatalogue data & documentationInstall & Store (data & documentation)

ReferenceSearch for dataInterpret documentationRetrieve or download dataProcess data

change formatssubset cases or variablesaggregate casesmerge filesanalyze data

AcquisitionFill a Request

Locate dataOrder data & documentation

Collection DevelopmentSelect & Locate dataOrder data & documentationCatalogue data & documentationInstall & Store (data & documentation)

Acquisition Fill a Request Locate data Order data & documentationCollection Development

Select & Locate dataOrder data & documentationCatalogue data & documentationInstall & Store (data & documentation)

Select & Locate dataOrder data & documentationCatalogue data & documentation

Install & Store (data & documentation)Reference

Search for dataInterpret documentationRetrieve or download dataProcess data

Search for data Interpret documentation Retrieve or download data Process data

Acquisition Fill a Request Locate data Order data & documentation Collection Development Select & Locate data Order data & documentation Catalogue data & documentation Install & Store (data & documentation)

Reference Search for data Interpret documentation Retrieve or download data Process data

Reference Search for data Interpret documentation Retrieve or download data Process data change formats subset cases or variables

aggregate casesmerge filesanalyze data

Find a referral partner on campus

The Inventory Model

• In the traditional inventory model, roughly half of the support goes to putting items on the shelf, while the other half goes to finding and getting the items off the shelf.

Source: Darlene Fichter

The Access Model

• With the access model, support is split between getting information into a deliverable state and finding appropriate ways of retrieving and disseminating the information.

Access/Dissemination Issues

•managing vendor licenses are the license conditions realistic?

what type of identification or authentication is required?

•matching products with technology is the product dependent on a specific operating system?

is the product software dependent?

•determining access methods stand-alone, lan or wan? what are the finding tools?

•determining dissemination options what are the output formats? does the output require special storage considerations?

The Access Model

• These issues and others about access and dissemination will continue in our discussions.

Data Types

In the 1997 DLI workshop time was spent discussing differences between aggregate data and microdata.

Each type has an impact on data access models.

Aggregate Data

Aggregate data consist of statistical summaries derived from original data collections and organized in tables according to the following properties:

•socio-economic phenomena•spatial representation•time

Aggregate Data

Statistical summaries

•these summaries take the form of counts, totals, sums, averages or percentages

Age and Sex are displayed

Spatial representation and Time are fixed

Cells contain counts

Spatial representation and Age are fixed

Year and Sex are displayed

Geography and Sex are displayed

Age and Time are fixed

Aggregate Data

Aggregate data products•usually stored as a series of related tables in some type of database structure requiring special retrieval software (examples from STC include C86, C91, CBP, CANSIM, etc.)

Microdata

Microdata are•usually anonymised records of actual respondents from a survey

•unsummarized, i.e, observations in the form in which the data were collected

•in a raw format requiring some form of processing, typically a flat ASCII file

Microdata: Cases 3 & 4 from the GSS 2 Main File

000031214110011982001212222221002098200121222222401121111241112121112205020197111971021212222225211026121204300140955720411313022111999901978787879702221411271412400315000616611232222222221111172626162212222666666636212000000020320222224222000022204141101101102111111122111000000210000000002100000000010000000000200000423300200200100000100200000041100110011101102122222221002009200212222222021111111231212111211208120193811938044122222221111052201203901007504721031191012233520406058787870304221303420708300400001420007111222122211721575656565555555666666656565000555500210222111111110000001111100001101112212122111011010110000110101100000000000000000000000000000000000000000000000000

000041144504000800024010000000012518733000041144308000900006011222220012518733000041141709000930003031222220012518733000041141709301100009031222220012518733000041141211001330015011222220012518733000041149113301630018011222220012518733000041141216301800009011222220012518733000041143018002000012031222220012518733000041147920002015001541222220012518733000041143720152130007531222220012518733000041147921302145001542221220012518733000041144321452200001512221220012518733000041147522002300006012221220012518733000041144523002800030010000000012518733

Microdata: First 14 Cases from the GSS 2 Episode File

Impact on Data Access

Aggregate data have been processed and organized in a database structure

•must locate the table with desired data

•must deal with each database structure

•must deal with accompanying retrieval software

Microdata data must be processed or subset for subsequent processing•must identify desired variables and cases (data documentation)

•must deal with the raw data file structure

•must address the issue of desired formats

•These and others differences between aggregate data and microdata will be part of our discussions about data access, also.

Statistics versus Data

The term statistics is commonly used to describe the numeric summaries, such as counts, totals, sums and averages, that people use to make a point in a study or report.

The term data refers to numeric files containing a collection of raw information with many observations that can be analyzed from a variety of perspectives.

Typically, generalizations are drawn from analyses of a data file.

The information provided by all of the individuals in a survey is considered to be data, while the percent of respondents in a survey with a university degree is a statistic.

Blurring Statistics and Data

In the print world, statistical information is usually found in statistical abstracts, census monographs and serial publications by government agencies.

In the digital world this numeric information is now appearing with electronic table access on CD-ROM, the Internet, or in electronic journals.

Many aggregate data products now fall in this category.

In other instances, the responses in the microdata file of a survey may provide the answer to a statistical question.

•For example, the percentage of the population in Canada with high blood pressure may be determined from the National Population Health Survey.

•The use of aggregate data products and microdata files to answer statistical questions will also contribute to our discussions about data access.

Context for Aggregate Data

•The motivation to simplify access to aggregate data products exists because of their utility in answering general statistics questions.

•The demand for facts and figures at the reference desk steadily increases.

Aggregate Data Challenges

•The challenges of creating access to aggregate data were summarized earlier. how to find the table with the desired

statistics how to deal with each database

structure how to cope with the retrieval software

DLI Aggregate Data Sources

•Four major DLI aggregate data sources have been chosen for this workshop.

•CANSIM is a major source for economic data and social data reported over time.

•E-STAT is a popular resource for simple access to selected CANSIM series and some aggregate data sources viewed useful in teaching.

•The 1996 Census aggregate data products are a valuable collection of electronic tables.

•The Health Indicators Database is a compilation of tables from several sources to provide a single-access tool about health status in Canada.

Hands-on work with aggregate data•CANSIM •E-STAT•Census ‘96 •Health Indicators Database

DLI Workshop -- Mar 2000 1 Hosted by Dalhousie University March 2000 DLI Training Workshop.

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