Busy Business Bees
~or~
The Z-Z-Z-Zen of
Asking and Answering
with LFS Data
WHY Zen?
Because Business Data
can make your head spin …
Sometimes we need
to take a slow stroll
to take it all in …
Business Data: Many Meanings
• Commerce, Finance, Economics …
• Entrepreneurship
• Labour
Wages
Companies
• Trade
Banks
Products
Inflation
Stock Market
Labour
Consumers
Trade
House $$
Credit
Global
Unemployment
Industries
Today’s Assignment:
Labour Force Survey Data Stroll
• Exercise 1:
Researchers are wondering …
• Exercise 2:
− We’ve been wondering …
− And wondering, some more …
Researchers are wondering …
Look at one or more of the citation & abstract sheets*
Each article has used LFS data.
1. What’s the story here? Identify the basic LFS data
elements that are key to this story?
2. What’s the relationship between the data elements? i.e.
when one measure goes up, the other goes up too, or the
opposite.
3.
Does the story draw on additional datasets, beyond the
LFS? What data elements do they add to the story?
*(provided in class)
Researchers are wondering …
What did we find?
We’ve been wondering … 1
Browse the Variable Descriptions*
• Notice the detailed values listed for Industries &
Occupations
• Notice some of the other variables
-
Do any of them surprise you?
Do any of them make you curious?
Identify a couple of elements that you want to look at further.
Keep it simple …
*provided in class, printed from Labour Force Survey, October 2015 [Canada] Study Documentation
We’ve been wondering … 2
• Discuss: Use the suggestions from datatherapy.org “Find
the … story” sheet to “find” a story / question.
•~Write it down!
• Use NESSTAR to browse a few of the data points that you
identified in the variables list. (Use a 2015 dataset)
• Create a VERY SIMPLE data table to see what’s going on
with the data point(s) you’ve chosen.
• Share: What interesting “story” have you found?
Simple Examples
Example: Highest Education, Filtered by age:
University Bachelor’s degree holders: 21% of 30-34yrs vs 12% 55-59 yrs …hmm
Simple Examples
• Example: Employed and unemployed persons X province;
Different ways to see the story:
• Consider “Row Percentage” vs “Column Percentage”
Simple Examples
• Example: Union membership X province
Simple Examples
• Example: Multiple job holders X gender
• What happens if you filter this data by 5-year age groups?
Simple Examples
• Example: Job Status:
− Permanent vs Temporary employment, X sex (Row %)
What happens when you filter by 5-year age groups?
To Sum Up:
Next time you’re buzzing like a
busy reference bee,
remember:
Festina lente
(Make haste, slowly)
• “Business” Data – Just another feature on the
Information Landscape, not some alien world.
• Slow Data is a good thing!
• Taking time to think about the “stories” and ideas,
instead of spreadsheets and math, is one way to
“normalize” this tool in everyday Reference work.
• Emphasizing the “small stories” and simple
calculations can smooth the path for staff and
students to adopt these resources.
• Whets the appetite to explore more datasets, and to
increase analytical skills.
• These ideas are entirely inspired by the folks at
datatherapy.org and databasic.io. Check them out!
Questions?
Thank-you!
Joyce Thomson,
Digital Services Librarian, Patrick Power Library
Saint Mary’s University, Halifax, NS
DLI Atlantic, April 11, 2017
[email protected]