University of Kentucky Gatton College of Business. Archival Data. Scott Soltis 2016 Links Workshop Lexington, Kentucky

Transcription

1 Archival Data Scott Soltis 2016 Links Workshop Lexington, Kentucky

2 Goals for this mini-module Demonstrate how useful and rewarding the use of archival data can be Generate ideas for you to go out and get data Help you to collect some network data of your own (hopefully two datasets)

3 What is Archival Data and Why Collect It? Data that already exists out there somewhere Archival network data is everywhere, although often not quickly recognizable Typically we want attribute data to complement networks One benefit of collecting data from archival sources is frequent accessibility of attribute data Primary benefits of this process: At a minimum, you get some data to play with while you are learning network analysis At a maximum, you get some data to publish

4 Conventional Places to Find Data Organization-Provided Archives Communication Networks Project Team Co-membership Online Archives Board of Director Interlocks Citation/Co-Authorship Movie Co-Stars Governmental Data Lawsuits Patent Citations Tax Records Govt. Reports

5 Less Conventional Places to Find Publishable Data Music Video Lyrics: Dissing Networks of Rappers (Halgin) Artists: 1950 s Jazz Collaborations (Hannibal) Soccer Match Passing Networks (Doreian) Speeches Developmental Networks from MLB Hall of Fame Speeches (Cotton et al., 2011) Video Games Sports Second Life Networks (Burt) Disease Spread in World of Warcraft (Fefferman) Very rich area for collecting data (Day et al., 2012)

6 Worst Case Scenario Data to play and learn with

7 Best Case Scenario Data that you can publish ts/2013/pennstate-34

8 Potential Pitfalls Missing Data Accessibility may bound your study moreso than theory Entity Resolution Issues What is USC? (Southern Cal or South Carolina) UCLA vs U.C.L.A., U C L A, or ucla (four different entities in UCINET) Inconsistency of Data Format Requires greater cleaning i.e. Soltis, S.M Archival Data. vs Soltis, S.M. (2016). Archival Data. Can be very time consuming for large networks if data is not clean and/or you do not know programming (i.e. Perl)

9 Data that will make you sound smart at happy hour NFL Transactions Track player movements between teams Courtesy of spotrac.com U.S. Senatorial Voting Records Explore hidden coalitions Courtesy of the Library of Congress

10 Importance of Documentation In any network study, documenting your steps is useful (i.e. how did you define a tie, did you symmetrize, what options did you choose in your analyses) When collecting/analyzing archival data documenting your steps is a necessity You might want others to help you collect data You might want to collect more data later You might need to re-collect data

11 How to gather/clean/input the NFL data we collected in class 1. Go to select filter to Signed. Select all data from the first player name in the top left of the table to the final cell in the bottom right of the data and copy (Ctrl+C) 2. Open Microsoft Excel and paste copied data (Ctrl+V) 3. Clean Data in Excel (if needed) 4. Once data is clean, copy all of the data under columns D and E (should be two columns that have team abbreviations). 5. Open UCINET, and select the DL Editor (the button looks like this: ) 6. Highlight (do not click into) the first cell (top left) and paste your data. 7. On the right hand side of the DL Editor in the Data Format drop down menu, select Edgelist1. Click save, name your data, and enjoy!

12 Documentation for U.S. Senate voting data we collected SOURCE: Go to click the Legislation and Records link near the top-middle of the page. Next under Recent Floor Activity on the page you will see several links click on Roll Call Votes. You should now see a list of votes (i.e , 00115) in the US senate with each vote having a hyperlink - select an individual vote. GETTING DATA INTO EXCEL: From here there are three ways to get the data into Excel (repeating for each vote data you want to collect): I. Highlight and copy each senator name and vote, paste into Excel, run Data Text to Columns selecting a comma delimiter to place name and vote II. III. into two columns Right click somewhere on the page to view the source data and then highlight and copy each name and vote, paste into Excel, run Text to Columns, clean code out with Find and Replace (Ctrl+F) Click the link near the top right that says XML. This will open a new page, copy the URL on this page. In Excel run Data From Other Sources From XML Data Import and in the dialog box past the XML URL you copied, click okay a few times and see what happens!

13 Brief version of the U.S. Senate voting data we collected cont. CLEANING DATA: For each vote we want to code each vote type (i.e. yea = 2, nay = 1, not voting = 0 or blank if not interested). The data should have this structure: Name SV00115 SV00116 SV00117 SenatorA SenatorB SenatorC etc GETTING DATA INTO UCINET: Open the Matrix Spreadsheet Editor, paste your data (starting at Cell A1) from Excel into UCINET and save. Congrats, you have two mode data!

14 Brief version of the U.S. Senate voting data we collected cont. TO EXPLORE CO-VOTING PATTERNS: Run Tools Similarities select your new data in the Input Dataset field, select Matches under Measure of Profile Similarity, select Rows under Compute Similarities Among and Run. This will yield a Senator by Senator network of how frequently they vote together (as a percentage of all votes). Your Similarities dialog box should look like this: