project? microdata? say what? try conference may 5, 2008 suzette giles, ryerson university laine...
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![Page 1: Project? Microdata? Say what? TRY Conference May 5, 2008 Suzette Giles, Ryerson University Laine Ruus, University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022081821/56649f275503460f94c3f7ff/html5/thumbnails/1.jpg)
<odesi> project? Microdata? Say what?
TRY ConferenceMay 5, 2008Suzette Giles, Ryerson UniversityLaine Ruus, University of Toronto
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• Acronym of “Ontario Data Documentation, Extraction Service and Infrastructure Initiative”
• A new product delivered through Scholars Portal• Collaboration between OCUL and Ontario Buys• Web-based resource discovery to a growing
collection of Canadian data • 3rd generation statistics and data extraction
system• Equalizes access to selected statistics and data
for all Ontario universities
![Page 3: Project? Microdata? Say what? TRY Conference May 5, 2008 Suzette Giles, Ryerson University Laine Ruus, University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022081821/56649f275503460f94c3f7ff/html5/thumbnails/3.jpg)
• A web-based extraction system
• Provides equal access to these resources for all Ontario universities
• Contains diverse, quality, numeric (microdata) data sets
• Allows data resource discovery, extraction and analysis
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First some background about <odesi>
• Why is this resource so important that Ontario Buys and OCUL (Ontario Council of Ontario Libraries) are investing over $1.4 million?
• How will it support teaching and research in quantitative methods and contribute to statistical literacy?
• How will it help me at the Reference Desk?
![Page 5: Project? Microdata? Say what? TRY Conference May 5, 2008 Suzette Giles, Ryerson University Laine Ruus, University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022081821/56649f275503460f94c3f7ff/html5/thumbnails/5.jpg)
Why is this resource so important that Ontario Buys and OCUL (Ontario Council of Ontario Libraries)
are investing over $1.4 million?
• 23 universities & colleges in Ontario belong to DLI, only 7 have full-time staff ‘doing’ data
• therefore, large differences in data access by faculty and students
• Goes some way to making access to data and statistics more easily findable.
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How will it support teaching and research in quantitative methods and contribute to statistical
literacy?• As a resource discovery tool, ability to search across
collections at a more finely-grained level than Stats Can provides
• Access to resources that have not previously been readily accessible, eg Canadian Gallup polls
• Ability to quickly and easily display descriptive statistics, regardless of whether the resource is aggregate statistics or microdata.
• Web-based access supports use in classroom as well as 24x7 access for research.
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How will it help me at the Reference Desk?
• Easy access to a collection of statistics and data in a uniform interface
• Blurs the distinctions between aggregate statistics and microdata
• Enable the creation, on the fly, of aggregate statistics that have not been published elsewhere
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What are microdata and why do I need to know about them?
• Microdata are the actual responses that survey or census respondents give to a questionnaire,
• Usually translated into a numeric format so that one can do arithmetic with them
For example:• Income: (1) high, (2) medium, (3) low
– Average = ????
• Income: (1) $13,725 (2) $118,297 (3) $63,958– Average = $65,327
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What are microdata?
• A person’s sex or gender could be recorded as Male or Female, but instead it will be coded 1 or 2.
• 1 = male 2 = female or the other way
• 1 and 2 are the VALUE given to the VARIABLE in this case sex (or gender)
• A microdata file consists of numbers
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From microdata are generated descriptive statistics
• Microdata – A person is working or not working
• Aggregate statistics– A count of the number of persons not working
(in a geographic area) – A count of the number of persons not working
divided by the number of persons in the labour force = the unemployment rate
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…and more descriptive statistics
• Microdata– A family has a gross annual income in year 2005
• Aggegregate (descriptive) statistics– Families in a geographic area have an average
income– 50% of families in a geographic area have an income
above (or below) the median income– LICO is the % of families in a geographic area that
have an income below the low income cut-off for that geographic area and family size
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Two systems
• <odesi>• Available to all Ontario
universities• Searching metadata
across files• Descriptive statistics only• Download system files
(users needs to have software)
• SDA• 10 universities subscribe,
incl Ryerson & York• More advanced statistical
analysis functions• Download raw data &
syntax files (user needs to create system files)
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What’s changed?
• In the ‘bad old days’– Statistics were published in books/periodicals, data
were ‘published’ mainly as files of microdata or very extensive aggregate statistics
– You needed access to a mainframe or PC.Mac– You needed special software (SAS, SPSS, Stata)– You needed training in the production of descriptive
statistics (weighting, types of data and appropriate types of descriptive statistics)
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But nowadays!
• Statistics are published as Excel files, Beyond 20/20 files, and microdata are available in 3rd generation interfaces
• All you need is a computer and a web browser• Generate descriptive statistics with a few mouse
clicks – tho’ you still need to know how to interpret them (and knowing about weighting is a good idea too!)
• Users can download data 24x7 for further processing on their own workstations with appropriate software