1. Introducing Data to History Students A. Michelle Edwards, Ph.D. University of Guelph

2. Setting the stage Data traditionally used in Social Sciences:  Psychology  Sociology  Any department with quantitative methods research

3. Setting the stage Today we have new faculties coming online with the introduction of data via new quantitative or research method courses. Examples include:  History at University of Guelph  There may be more…

4. How do we introduce data? On one side - students who may be very tentative, timid when it comes to numbers and may have little or no training. On the other side - faculty who are also hesitant, may also be timid when it comes to working with numbers and may not know where to start.

5. How do we introduce data? On one side - those faculty who see a benefit to teaching data to students…. On the other side – students who may fight it all the way with: “I’m a history/english major I don’t need to understand data!”

6. “History by Numbers” at UG New course offered by History (CoA) and Economics (CSAHS) departments. Available to 4 th year students No Statistics background required

7. “History by Numbers” at UG Course objective: “…This course introduces advanced students to the use of quantitative evidence in historical research and explores some of the ways in which quantitative information can illuminate or distort the past. Along the way we learn some basic statistical concepts even though the course itself is more concerned with critical thinking about their use. If the course is successful, you will improve your ability (i) to read critically literature that relies on quantitative evidence and (ii) to use such data in your own research”

8. “History by Numbers” at UG “Although the course adopts a critical approach to data, it is not intended to explore the important epistemological debates surrounding method and source. We have the more immediate objective of improving our understanding of and ability to use quantitative evidence.”

9. “History by Numbers” at UG Evaluation included assignments and a project.  The course divides into three sections: a survey/review of basic statistical concepts data exercises and discussion of case studies presentation and discussion of student work

10. “History by Numbers” at UG “All students should keep in mind that while some familiarity with basic statistical concept and method is indispensable, an advanced knowledge of statistics is not needed for an appropriate and successful use of quantitative evidence”

11. “History by Numbers” at UG DRC was looking for an oppurtunity to beta test our Nesstar implementation So - we used our Nesstar service as the basis for the data assignment  easy interface and intuitive.  Want to see how a group of non-quantitative users worked with service

12. Assignment Components Students were asked to work with the 1871 Canadian Census file. Start basic: “Determine and report the number of observations, mean and standard deviation of age, separately, for males and for females”

13. Assignment Components Create a subset of individuals over the age of 30 years. Create frequency distributions of marital status for males and females for each of 2 different religions.

14. Assignment Components Straight forward calculations and subsetting Students were provided with basic Excel training and a handout on how to use the Nesstar Webview.

15. Sample shot of Data - Gender

16. Very straightforward Variable called Sex Two levels: male and female NO confusion on the student’s part here

17. Sample shot of data - Religion

18. A bit more confusing… 2 Variables to choose from  Religion – Census Var 12  Religion Code Religion Code showed labels –  students picked up on this

19. Student feedback Overall navigation in Webview was great There were some questions about variables:  religion is an example – which do you choose?  Marital status – differentiation between widow and widower

20. Student feedback Creating and looking at frequencies – easy on Nesstar – a bit more challenging in Excel Subsetting was a challenge for many  Challenge was learning how to accomplish this in Nesstar rather than understanding the task at hand. File needed for Excel.

21. Results “Expected results” or “Correct answers” – we retrieved and calculated on Nesstar We had 1 student who matched ALL the answers – approximately 20 students Majority came close – why???

22. Results - challenges Subsetting – individuals who are older than 30 years...  Number of students chose 30 and over  Number of students chose 31 and over This caused the biggest difference in results.

23. Results - Challenges Definition of the variables and value labels – also lead to confusion –  This was an ‘older’ file with limited metadata. Many found the assignment a bit challenging but seemed to enjoy it.

24. Conclusion Very successful course assignment and we look forward to running it again. Shows that an intuitive interface can make the introduction to data easier.

25. Conclusion One of the greatest rewards was having a student at the beginning of the semester who asked “How do you calculate percentage?”, asking to have several datasets loaded onto Nesstar for their project at the end of the semester, because they understood how “quantitative information can illuminate”.

26. Acknowledgements Kris Inwood – for including the Data Resource Centre in this course Bo Wandschneider – for presenting this paper in my absence Michelle Edwards