NAICS YIKES

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An Introduction to
OLA Conference February 2008 Session 1022 Jeff
Moon Head, Maps, Data, Government Information
Centre (MADGIC) Queens University
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Flowchart Do I want to use statistics?
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• What well cover
• What is survey data, and whats the big deal?
• Whats happening in Ontario on the data front?
• Show me the goods
• Why is this important at my library?

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What is Survey Data and whats the big deal?
Data continuum
Survey Data
Tables, Charts, Graphs
A number
(Microdata)
(in Books, CD-ROM, the WWW)
(machine-readable)
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What is Survey Data and whats the big deal?
Descriptive Statistics
Inferential Statistics
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What is Survey Data and whats the big deal?
Statistics
Survey Data
Tables, Charts, Graphs
A number
(Microdata)
(in Books, CD-ROM, the WWW)
(machine-readable)
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What is Survey Data and whats the big deal?
Survey Data
Aggregate Data
Postcard
Camera
Fixed
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Well look at the flexibility of survey data a
bit later on In the mean time, lets look at
the situation in Ontario right now
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1990s Home-grown survey data systems

• Guelph, Western, Queens
• No cataloguing standard
• Varying features/capabilities
• Served a purpose at the time

2000s Emerging data cataloguing standards
Data Documentation Initiative -- an
international standard for describing survey
data. Like MARC, only for data
Mature commercial software solutions
Software such as Nesstar, SDA, and others
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In 2005, the Data IN Ontario (DINO) working group
of OCUL (Ontario Council of University Libraries)
started thinking about moving beyond home-grown
data solutions, adopting the DDI standard, and
building a province-wide data solution. A
discussion paper followed
In 2007, with funding from OCUL and Ontario
Buys, a Project Director was hired, and
hardware/software purchased through Scholars
Portal.
Ontario Data Documentation, Extraction Service
and Infrastructure Initiative
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Lead institutions in ltODESIgt are Carleton and
Guelph, with in-kind assistance from Queens
University. First step was developing a Canadian
best practices document for cataloguing data
files using DDI analogous to AACR2 for MARC.
Next, survey files were marked up
(catalogued) and loaded onto a test server at
Guelph. The team at Scholars Portal is working
with ltODESIgt to establish a data server and load
data files.
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Use of the Data Documentation Initiative standard
facilitates

• Interoperability. XML-compliant DDI Codebooks
  can exchanged and transported seamlessly, and
  applications can be written to work with these
  homogeneous documents.
• Richer content. The DDI encourages better
  description of social science datasets, providing
  researchers with a better window into what is
  available
• Single document - multiple purposes. DDI
  codebook contain all of the information necessary
  to produce several different types of output,
  including a traditional social science codebook,
  a bibliographic record, and SAS/SPSS/Stata data
  definition statements. Thus, the document may be
  repurposed for different needs and applications.
• On-line subsetting and analysis. Because the DDI
  markup extends down to the variable level and
  provides a standard uniform structure and content
  for variables, DDI documents are easily imported
  into on-line analysis systems, rendering datasets
  more readily usable for a wider audience.
• Precision in searching. Since each of the
  elements in a DDI-compliant codebook is tagged,
  searches across documents and studies are
  possible.

www.ddialliance.org
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• SOFTWARE CHOSEN ? NESSTAR
• Developed by the Norwegian Social Science Data
  Services -- Networked Social Science Tools and
  Resources
• In use internationally (Europe, UK, US, Canada)
• In Ontario Queens, Guelph, Carleton, Windsor,
  Ottawa, U. of T. and Statistics Canada use
  Nesstar
• DDI compliant
• Search by keyword for surveys and survey
  questions
• Do basic data exploration and analysis on the
  web
• Download full datasets or subsets in popular
  formats
• Export tables and charts

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http//nesstar.esds.ac.uk/webview/
http//www.nsd.uib.no/cessda/extcessda.jsp
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Nesstar Publisher produces DDI-compliant metadata
using a set of structured tags, grouped into
tabs in Publisher.
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Document Description Tab
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Study Description Tab
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Other Study Materials Tab
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File Description Tab
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Variables Tab
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Variable Groups Tab
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Data Entry Tab
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Other Materials Tab
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Once ready, a marked up survey file is
published to the Nesstar Server where it
becomes available through Nesstar Webview.
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Lets take a look at how ltODESIgt can be used to
answer a research question.
How do men and women differ in perceptions of
their health (using weight as an example).
Concepts? Health Body Mass Index
(BMI) Weight Males/Females
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Starting point A simple search on the Statistics
Canada web site
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Fixed
Flexible
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Basic frequencies or marginals for
categorical variables
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Descriptive statistics for continuous variables
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But what if we want to look at more than one
variable at a time? Say, for instance, the issue
of weight and gender?
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OK now we want to add gender as a variable.
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Opinion of own weight, by sex
Proportionally, more women than men had the
opinion that they were Overweight.
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OK, but how does this change if we add an
objective measure of weight, such as Body Mass
Index (BMI)?
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Start where we left off opinion of
own weight, by sex
But add another variable as a layer
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Add BMI class as a layer
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Layer those with a BMI indicating underweight
Of respondents who were objectively
underweight, proportionally more women than men
had the subjective opinion that they were Just
About Right.
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Layer those with a BMI indicating normal
weight
Of respondents who were objectively normal
weight, proportionally more women than men had
the subjective opinion that they were
Overweight.
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Layer those with a BMI indicating overweight
Of respondents who were objectively overweight,
proportionally more MEN than women had the
subjective opinion that they were Just About
Right.
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OK, I have an confession to make
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Statistical Weight All the previous slides
ignored an important concept that of weight. Not
weight in kilograms but rather statistical
weight. We dont want to describe the sample
we want to describe the population at large (in
this case, Canadians 18). Statistical weights
are assigned by statisticians, not surprisingly,
to each individual in a sample, based on a
variety of demographic and sampling
considerations. These weights reflect how many
people a given respondent represents in the
population being studied.
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In general, you must apply the Statistical Weight
in order to get valid results.
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They say a picture is worth a thousand words
If this is true, then a good chart has to be
worth at least a couple of hundred Lets
revisit our data visually using the bar chart
feature of Nesstar.
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Barcharts showing weighted results
Proportionally, of those who are objectively
underweight, more women than men think they are
just about right
Weight is on
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Barcharts showing weighted results
Proportionally, of those who are objectively
normal weight, more women than men think they are
overweight
Weight is on
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Barcharts showing weighted results
Proportionally, of those who are objectively
overweight, more men than women think they are
just about right
Weight is on
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Searching for questions in Nesstar Simple
Search
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Search results Simple search
You get all the surveys that have the keyword
you searched for but specific questions
(variables) are NOT highlighted.
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Searching for questions in Nesstar Advanced
Search
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Advanced Search Screen
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Search results Advanced search
Here, specific variables that meet the search
criteria are shown, with the option of opening
in context
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Menu options
Table
Download
Barchart
Export to spreadsheet
Time series graph
Export PDF
Map
Print
Clear
Create bookmark
Weight
Help
Subset
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• OK, so what kind of data can I expect to find
  using ODESI?
• Statistics Canada survey files released through
  the Data Liberation Initiative (Census PUMFs,
  Special Surveys, General Social Surveys, and
  more)
• Public Opinion Polls (e.g. Gallup)
• Survey files from other sources (academics)
• These surveys and polls include questions on all
  manner of topics (politics, health, work,
  leisure, education, drug use, aging, spending,
  internet use, and many more)

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Lets take a look at some Gallup
questions Dataset Canadian Gallup Poll, August
1951, 212 In some cities in Canada, horsemeat
is now being sold, because of the high price of
other meats.  If horsemeat were available here,
would you be willing to try it? 35.9 of
respondents said Yes theyd be willing.
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Dataset Canadian Gallup Poll, September 1956,
251 WOULD YOU FAVOR REQUIRING EVERY ABLE-BODIED
YOUNG MAN IN THIS COUNTRY, WHEN HE REACHES THE
AGE OF 18, TO SPEND ONE YEAR IN MILITARY TRAINING
AND THEN JOIN THE RESERVES OR MILITIA? 65.7
favoured this.
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Dataset Canadian Gallup Poll, August 1953, 231
HOW MUCH DO YOU THINK A YOUNG MAN SHOULD BE
EARNING PER WEEK BEFORE HE GETS MARRIED?
41 - 50 per week equals roughly 2100 - 2600
annually.
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Dataset Canadian Gallup Poll, August 1953, 231
THERE'S AN ATTEMPT BEING MADE BY SOME FASHION
LEADERS TO SHORTEN WOMEN'S SKIRTS. DO YOU THINK
THAT WOMEN SHOULD  FOLLOW THIS LEAD - AND WEAR
SKIRTS SHORTER THAN THEY ARE NOW?
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Tracking Opinions over time
DO YOU APPROVE OF THE USE OF BIRTH CONTROL?
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• Researchers can search across all surveys in a
  collection.
• Researchers have the ability to explore surveys
  in more detail (e.g. looking at questions by
  gender, province, age group, income, etc.).
• Tables can be saved in Excel or Adobe format.
• Researchers can download data for use in more
  powerful statistical packages (SPSS, SAS, etc.)

Key points about survey data in ltODESIgt
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• In conclusion, ODESI will
• Provide a more level data playing field for
  Ontario Universities.
• Provide students and researchers with access to a
  substantial and growing body of survey and
  polling data, both current and historical.
• Provide an easy, yet powerful, search and
  exploration tool (Nesstar) that will serve both
  beginners and power users.
• Encourage cooperation and sharing of data and
  metadata in Ontario.
• Serve as a potential model for other
  jurisdictions.
• ltodesi.cagt

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