DLI Training – Ontario Region April 3, 2008 Carleton University

An Introduction to

No statistics Do 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 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

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…

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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 Estimate Statistical 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 Favour

Approve 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 able

to synthesize both quantitative and qualitative aspects of health. The

index, developed at McMaster University’s

Centre for Health Economics and Policy Analysis, is based on the

Comprehensive Health Status Measurement

System (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 (memory

and 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’…