DLI Training – Ontario RegionApril 3, 2008Carleton University

An Introduction to

No statisticsDo I want to

use Statistics?NO

Flowchart: ‘Do I want to use statistics?’

Lead institutions in <odesi> are Carleton and Guelph, with in-kind assistance from Queen’s 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 <odesi> 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 be 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 NESSTARDeveloped 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

http://www.esds.ac.uk/

http://www.nsd.uhttp://www.nsd.uib.no/cessda/home.html

http://zacat.gesis.org/webview/index.jsp

http://ess.nsd.uib.no/webview/index.jsp

ZA Online Study Catalogue

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Nesstar Publisher produces DDI-compliant metadata using a set of structured tags, grouped into ‘tabs’ in Publisher.

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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The “<odesi> Best Practices Document” is designed to guide

you through the ‘cataloguing’ process.

It is available on the <odesi> WIKI at URL: odesi.ca

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Once ready, a ‘marked up’ survey file is ‘published’ to the

Nesstar Server where it becomes available through

Nesstar Webview.

It is at this point that most of you will walk on stage…

Let’s take a look at how <odesi> 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

Starting point: A simple search on the Statistics Canada web site…

“Fixed”

“Flexible”

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Variable ‘groups’ Variables

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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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Of respondents who were ‘objectively’ underweight, proportionally more women than men had the ‘subjective’ opinion that they were “Just About Right”.

Layer = those with a BMI indicating ‘underweight’

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Of respondents who were ‘objectively’ normal weight, proportionally more women than men had the ‘subjective’ opinion that they were “Overweight”.

Layer = those with a BMI indicating ‘normal weight’

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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”.

OK, I have an confession to make…

Statistical Weight…All the previous slides ignored an important concept… that of weight.

Not ‘weight in kilograms’ but rather ‘statistical weight’.

We don’t 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.

Sample count Population EstimateStatistical weight

Weight ‘off’: Note the sample sizes

Weight ‘on’: Note the sample sizes

But also note the differences in percentages…

In general, you must apply the Statistical Weight in order to get valid results.

It is easy to turn weight ‘on’ in Nesstar ( ), or other statistical packages (e.g. SPSS, SAS, STATA).BUT READ THE DOCUMENTATION

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…

Let’s revisit our data visually using the ‘bar chart’ feature of Nesstar.

Weight is on

Barcharts showing weighted results:

Proportionally, of those who are objectively underweight, more women than men think they are ‘just about right’

Weight is on

Barcharts showing weighted results:

Proportionally, of those who are objectively normal weight, more women than men think they are overweight

Weight is on

Barcharts showing weighted results:

Proportionally, of those who are objectively overweight, more men than women think they are ‘just about right’

Searching for ‘questions’ in Nesstar: Simple Search

Search results – Simple search

You get all the surveys that have the ‘keyword’ you searched for… but specific questions (variables) are NOT highlighted.

You have to open each survey (click on the icon: ) and look for the question(s) containing your keyword. Again, specific questions containing your keyword are NOT highlighted.

Searching for ‘questions’ in Nesstar: Advanced Search

Advanced Search

Advanced Search Screen

Search results – Advanced search

Here, specific variables that meet the search criteria are shown, with the option to “Open in context”

If you “Open in context”, a new “Nesstar” window will open, specific to the chosen survey, and highlighting the selected question. Closing this new window will take you back to your results list.

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Barchart

Table

Time series graph

Map

Clear

Weight

Subset

Export to spreadsheet

Download

Export PDF

Print

Create bookmark

Help

Menu options:

OK, so what kind of data can I expect to find using ODESI?

1. Statistics Canada survey files released through the Data Liberation Initiative (Census PUMF’s, Special Surveys, General Social Surveys, and more)

2. Public Opinion Polls (e.g. Gallup, CRIC, Ipsos Reid)3. Survey files from other sources (academics, government)

… coming soon.

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)…

Let’s 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” they’d be willing.

Of course, this questions begs for a yea or ‘neigh’ answer

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.

$41-50

UP TO $40

OVER $100

$71-80

$81-100

$61-70

$51-60

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.

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?

13% Shorter

82 % About the same

5 % Longer

Year % in FavourApprove of Birth Control? 1960 66.4%

1964 82.1%1965 78.7%

Approve of Male Sterilization? 1971 48.6%

DO YOU APPROVE OF THE USE OF BIRTH CONTROL?

Tracking Opinions over time

1. Researchers can search across all surveys in a collection.

2. Researchers have the ability to explore surveys in more detail (e.g. looking at questions by gender, province, age group, income, etc.).

3. Tables can be saved in Excel or Adobe format.4. Researchers can download data for use in more

powerful statistical packages (SPSS, SAS, etc.)

Key points about survey data in <ODESI>

In conclusion, <odesi> will:

1. Provide a more level ‘data’ playing field for Ontario Universities.

2. Provide students and researchers with access to a substantial and growing body of survey and polling data, both current and historical.

3. Provide an easy, yet powerful, search and exploration tool (Nesstar) that will serve both beginners and ‘power users’.

4. Encourage cooperation and sharing of data and metadata in Ontario.

5. Serve as a potential model for other jurisdictions.

<odesi.ca>

Description: The Health Status Index or Health Utility INDEX (HUI) is a generic health status index that is ableto synthesize both quantitative and qualitative aspects of health. The index, developed at McMaster University’sCentre for Health Economics and Policy Analysis, is based on the Comprehensive Health Status MeasurementSystem (CHSMS). It provides a description of an individual’s overall functional health, based on eight attributes:vision, hearing, speech, mobility (ability to get around), dexterity (use of hands and fingers), cognition (memoryand thinking), emotion (feelings), and pain and discomfort.

http://www.statcan.ca/english/sdds/document/3226_D5_T9_V3_E.pdf

HUI ranges from zero to one, with zero being ‘death’ and one being ‘perfect health’. Statistics Canada has yet to explain those folks who have negative scores… ‘die hard, with a vengeance’…