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 Trade Consumers Wages Labour Global Inflation Industries Companies Trade Products Stock Market House $$ Credit Banks Unemployment

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. What’s the story here? Identify the basic LFS data elements that are key to this story? What’s the relationship between the data elements? i.e. when one measure goes up, the other goes up too, or the opposite.  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 ~Write it down! 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)

Slow Data is a good thing! “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 Joyce.Thomson@smu.ca