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Developing a Mixed Qualitative and Quantitative Research Design to Inform Library Policy Decision-making

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Presented at QQML 2013: Qualitative and Quantitative Methods in Libraries International Conference. Rome, Italy.

Presented at QQML 2013: Qualitative and Quantitative Methods in Libraries International Conference. Rome, Italy.

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  • 1. Developing a Mixed Qualitative andQuantitative Research Design toInform Library Policy Decision-makingDavid P. KennedyRAND CorporationMarie R. KennedyLoyola Marymount UniversityQQML 2013: Qualitative and QuantitativeMethods in Libraries International Conference
  • 2. Outline• Background• Overview of Cultural Domain Analysis• Results of use of two methods that combinequalitative and quantitative approaches– Free Listing– Pile Sorting
  • 3. Systematic Data Collection
  • 4. Cultural Domain Analysis• 2 Key Methods–Free Listing–Pile Sorting
  • 5. What is a Cultural Domain?• Cognitive Domain:– How people think (cognition) about categories(domains)– Typically named• examples: illnesses, vegetables, countries– Categories contain items– Items have semantic relationships with each other• Examples: X is part of Y, X is similar to Y, X causes Y, etc.• Some may be categories themselves– For example– Books -> reference books -> dictionaries -> types of dictionaries ->and so on• When it is Cultural?– Shared
  • 6. Cultural Domain Analysis atWilliam H. Hannon Library
  • 7. Cultural Domain Analysis atWilliam H. Hannon Library• The WHH Library is the library atLoyola Marymount University (LMU)• Private, Catholic• In Los Angeles, California (USA)• Primary attention given to undergraduates• Strong student employee program in the library
  • 8. Goal: Efficiently and effectively determineitems in a domain and understand howthey are related to each other.• Step One: Free ListsEvaluating a Cultural Domain
  • 9. Free Listing• Goal: Native categories and terminology• Basic idea:– “Tell me all the <category name> you can think of”– Typically loosely timed, no questions allowed– “Grand tour” question• Contrasts with survey open-ended question– Open-ended is typically about the respondent:• What do you like about this product? What ice-cream flavors doyou like? What illnesses have you had?– Free list is about the domain:• What ice-cream flavors are there? What illnesses exist? Whatare all of the fruits and vegetables?
  • 10. Free Listing:Cultural Domain of Library Usage• Systematic: Everyone Given the SameQuestion:• “Think about all of the things that people onLMU campus do when they use libraryservices. Off the top of your head, list all ofthe ways that people on LMU campus use thelibrary.” NOTE: Not just what they do!• Exploratory: How they answer is upto them
  • 11. Free List Analysis• Different ways to analyze free list data– Sort in descending order of frequency mentioned– Tally average position in lists– Combine frequency and position to create“salience” measure• Software: Anthropac (Visual Anthropac 1.0)• Goal: Identify core set of items– Explore patterns in what people say
  • 12. FREE LIST RESULTS
  • 13. Free List Results• 4 Interviewers– Library Staff– 3 Female, 1 male• 21 Respondents– Student Workers– 16 Freshman/Sophomores– 13 Had worked in library less than 1 year– 18 Female• 324 responses– Collapsed into 62 Unique responses
  • 14. Free List Results: > 50%Item Freq. % Rank Avg. SalienceUse printers 19 86.4 6.6 0.5Use computers 17 77.3 6.8 0.5Use group study rooms 15 68.2 5.9 0.5Borrow books 15 68.2 5.3 0.5Study 14 63.6 4.1 0.5Attend classes 13 59.1 12.1 0.2Use copier 11 50.0 11.4 0.2Get research help 11 50.0 8.0 0.3Spend time in a quiet place 11 50.0 7.1 0.3Do research 11 50.0 3.3 0.4
  • 15. Free lists: Scree Plot, % mentioned0.010.020.030.040.050.060.070.080.090.0100.0• No sharp scree fall• No strong core set of Items• Short Tail• Relatively fewidiosyncratic answers• Top electronic resource13.6%
  • 16. Goal: Efficiently and effectively determineitems in a domain and understand howthey are related to each other.• Step Two: Pile Sorting items identified infree list interviewsEvaluating a Cultural Domain
  • 17. Pile Sorting Example• Fruits and Vegetables
  • 18. Free List Text to CardsApplePear13CornBanana24
  • 19. Cards to PilesAppleBananaBreadfruitKumquatCurrantPile 1: “Fruit I eatat lunch”Pile 2: “Fruit Idon’t like”Pile 3: “Fruit I’m not surewhat they are”OrangeCoconut
  • 20. Pile Sort Analysis• Different ways to analyze pile sort data– Produce Aggregate Proximity Matrix• How often did each item end up in the same pileacross all pile sorters– Multivariate Analysis Techniques• Multidimensional Scaling (MDS)• Cluster Analysis• Consensus Analysis– Compare results of quantitative analysis toqualitative descriptions of piles
  • 21. Pile Sort Analysis• Software: Anthropac (Visual Anthropac1.0)• Goal: Identify Patterns in How PeopleGroup Items• 35 items from free list – top frequency– 5 additional items not named: electronicresources• Better understand how electronic resources fit intocultural domain• Provide insight into how to better market electronicresources• Increase their salience
  • 22. PILE SORT RESULTS
  • 23. Pile Sort Results• 4 Interviewers– Library Staff– 3 Female, 1 male• 21 Respondents– Student Workers– 12 Freshman/Sophomores– 13 Had worked in library less than 1 year– 18 Female• 102 piles total across respondents– Nearly 5 piles on average– Range 2 to 8 piles
  • 24. Use computersUse printersBorrow booksUse group study roomsAttend classesStudyGet research helpUse copierDo researchSpend time in a quiet placeGo to the CaféUse scannersReadWork on group projectsConduct archival researchAttend eventsAccess the InternetDo homeworkEmploymentBorrow DVDs / moviesWatch moviesBorrow technical equipmentMeeting placeAccess course reservesUse presentationequipment/roomUse media roomsUse fax machineRead newspapersGet technical helpAsk questionsLook at art/exhibitsSee the viewUse databasesRead periodicals/magazinesHang outProQuestEBSCOGoogle ScholarWikipedia LibGuidesResearchBorrow things and get helpSocial and enjoymentrelated activitiesStress: .133High Consensus : Strongevidence of one cultureMDS and Cluster Analysis
  • 25. Reactions from Data Collectors• “I was also surprised by students who did notimmediately identify functions in their own areasof work as things that people do when they cometo the library! (For example, ref desk studentswho listed research help only at the very end oftheir list, if at all.)”• “Not a single student separated the ideas of usingthe library to explore and gratify personal orintellectual curiosities or independentlybroadening their knowledge of their own majorareas of study as separate from the overall ideaof research.”
  • 26. Conclusions and Recommendations• Three primary sub-domains– Research– Getting help / borrow things– Socialize• Electronic resources are not highly salientelements of the cultural domain of library activities– Loyola Marymount Campus– Student workers• Market resources as aspects of other maindomains– Peer educators
  • 27. Thank You!davidk@rand.orgmarie.kennedy@lmu.edu