1 ola conference february 2008 session 1022 jeff moon head, maps, data, & government information...

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OLA Conference February 2008Session 1022Jeff MoonHead, Maps, Data, & Government Information Centre (MADGIC)Queen’s University

An Introduction to

No statisticsDo I want to

use Statistics?NO

Flowchart: ‘Do I want to use statistics?’

What we’ll cover:

• What is survey data, and what’s the big deal?

• What’s happening in Ontario on the ‘data front’?

• Show me the goods…

• Why is this important at my library?

What is Survey Data and what’s the big deal?

Tables, Charts, Graphs

(in Books, CD-ROM, the WWW)

A ‘number’ Survey Data

(machine-readable)

Data continuum…

(Microdata)

Age Sex MarStat Children Income Occ Educ

Person 1 24 M 1 1 5 1 7Person 2 34 F 1 0 3 5 3Person 3 52 F 2 2 4 3 3Person 4 64 F 1 3 6 4 4Person 5 23 M 3 1 7 2 6Person 6 63 F 4 1 5 6 3………Person "n" 29 M 1 0 5 2 2

What is Survey Data and what’s the big deal?

Percentages

Counts

Standard Deviations

Cross-tabs

More advanced

AnalysisMeans

Statistical Analysis continuum…

Descriptive Statistics Inferential Statistics

What is Survey Data and what’s the big deal?

Tables, Charts, Graphs

(in Books, CD-ROM, the WWW)

A ‘number’ Survey Data

(machine-readable)

Statistics…

Percentages

Counts

Standard Deviations

Cross-tabs

More advanced

AnalysisMeans

Statistical Analysis…

(Microdata)

Survey DataAggregate DataPostcard Camera

“Fixed”

“Flexible”

What is Survey Data and what’s the big deal?

We’ll look at the flexibility of survey data a bit later on…

In the mean time, let’s look at the situation in Ontario

right now…

1990’sHome-grown survey data

systems

- Guelph, Western, Queen’s- No ‘cataloguing’ standard- Varying features/capabilities- Served a purpose at the time

2000’s 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

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.

OCUL & Ontario Buys

Commercial

SoftwareScholars

Portal

DDI Standard

Ontario Data Documentation, Extraction Service and Infrastructure Initiative

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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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

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

Nesstar Server where it becomes available through

Nesstar Webview.

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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Before proceeding, you must log into the Nesstar System

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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.

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 of “opening in context”

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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)3. 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)…

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>