My Dissertation Defense
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ATPI Doctoral Dissertation Defense of Laura A. Pasquini Department of Learning Technologies, College of Information University of North Texas June 12, 2014
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My Dissertation Defense
- 1. 1 Organizational Identity and Community Values: Determining Meaning in Post-Secondary Education Social Media Guideline and Policy Documents ATPI Dissertation of Laura A. Pasquini Department of Learning Technologies College of Information, University of North Texas June 12, 2014 Co-Major Professors: Dr. Jeff M. Allen & Dr. Nick Evangelopoulos; Dissertation Committee: Dr. Kim Nimon & Dr. Mark Davis
- 2. Research Study To examine and define the semantic structure of a corpus-creating community of practice and to establish a common reference point for post-secondary education (PSE) social media guideline and policy documents. 2 pp. 2-3
- 3. How is Social Media Being “Guided” in Higher Ed? •Mergel et al. (2012) Create a social media policy before using social media or experimentation with social media within the organization to generate and apply guidance. •Wandel (2009) and Joosten et al. (2013) Security and privacy are two of the primary concerns •Rodriguez (2011) Deal with challenges related to privacy, ownership of intellectual property, legal use, identity management, and literacy development pp. 31-32 3
- 4. Background • Social media use has increased in higher education (Brenner & Smith, 2013); however guideline and policy documents have rarely been examined (Joosten, 2012; Joosten et al., 2013; Reed, 2013) • Institutions direct & moderate how students, staff, faculty & administrators use social media on campus (Blankenship, 2011; Moran, Seaman, & Tinti-Kane, 2011) pp. 4-54
- 5. Research Questions R1. What content related factors are relevant to structuring the body of textual data in retrieved electronic social media guideline and policy documents from the PSE sector? R2. Does the distribution of topics analyzed in the corpus differ by PSE institution geographic location? pp. 9-85
- 6. Theoretical Development The cycle of Wenger’s (1998) participation and reification in the community of practice is assessed through a distributed repository of documents, which for the purpose of this study is called a corpus. pp. 8-16 6
- 9. Theory Building Following Evangelopoulos & Polyakov (2014) this research focused on a special kind of community of practice, the corpus-creating community, where the body of social media guideline and policy documents is a distributed corpus. Specifically this corpus contains meaning, values and identity. 9 pp. 14-16
- 10. Assumption 1. The community of social media guideline and policy administrators in PSE is a community of practice. 10 p. 9
- 11. Assumption 2. The community of practice, social media guideline and policy administrators in PSE, have built a semantic structure with a shared understanding of how social media guidelines and policies should be. 11 p. 10
- 12. Assumption 3. Published and accessible social media guideline and policy documents are artifacts that reify the ideas from the community of practice. 12 p. 11
- 13. Assumption 4. Analysis of the collection of social media guideline and policy documents by an appropriate text analytic method uncovers the components of the semantic structure of meaning. 13 p. 13
- 15. Limitations The research method LSA is: •Dimension reduction of the dataset •Orthogonal (Lee, Song & Kim, 2010) •Polysemy issues (Li & Joshi, 2012) pp. 19-20 15
- 16. Delimitations • Published, online accessible • Text only in artifacts • Organizational focus: PSE sector • English-speaking countries • No indicates bound of the study controlled might influence validity • Follow LSA methodological recommendations for this type of text mining procedures (Evangelopoulos, Zhang, & Prybutok, 2012) pp. 20-2116
- 17. Methodology The research design for this study was a semi-automatic approach to reviewing the semantic structure and terms in the social media guideline and policy documents. This particular text mining procedure required a large matrix of term-document data to construct a semantic space in which the closely associated terms and documents were place near one another. (Deerester, Dumais, Landauer, & Harshman, 1990) p. 3617
- 18. Research Methods Latent Semantic Analysis (LSA) • a computational research method that simulates human like analysis with language (Landauer, 2011) • originally used for information retrieval query optimization (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990; Dumais, 2004) • topic extraction using LSA (Sidorova, Evangelopoulos, Valacich & Ramakrishnan, 2008; Li & Joshi, 2012) • rotated LSA (Evangelopoulos & Polyakov, 2014) pp. 41-4518
- 20. Step 1: Establish the Corpus
- 22. Sample: 250 PSE Institutions from 10 Countries pp. 53-54 & Appendix A (p.103) 22 24,243 atomic social media guideline & policy documents
- 23. pp. 53-54 & Appendix A (p.103) 23 17,429 1071 13 3771 314 1121 189 109 160 62 # of atomic social media documents
- 24. Step 2: Pre-Process the Data
- 25. Text Document Preparation for LSA pp. 47-48 & Appendix C 25 PassageID PassageText SMP00001 Social Networking/Social Utilities SMP00002 The following recommendations were discussed in the context of the social media that are most popular now, mainly Facebook, LinkedIn, and Twitter, but were drafted to be fluid enough to apply to social networks and utilities that will emerge in the future. The IMG recommends the following best practices guidelines. SMP00003 Do: SMP00004 Use social media to stay in touch with friends and make new ones. SMP00005 Use social media to create your best image, since your page is likely visible to more people than just your selected friends, followers, or subscribers. SMP00006 Type your name into a search engine (i.e., Google, Bing, Facebook, YouTube) every once in a while to check on your public image. SMP00007 Use social media to get involved with the campus community and learn what's happening. SMP00008 Use social media to advertise your organization's events. SMP00009 Make sure you understand and use the privacy settings on your social media accounts to monitor who can look at your profile.
- 26. LSA Input Data: Term Frequency Matrix Input data for LSA is the term frequency matrix X. This matrix quantifies the collection of documents by recording the occurrence of each term in each document. documents terms X pp. 42-4426 PassageID PassageText SMP00001 Social Networking/Social Utilities SMP00002 The following recommendations were discussed in the context of the social media that are most popular now, mainly Facebook, LinkedIn, and Twitter, but were drafted to be fluid enough to apply to social networks and utilities that will emerge in the future. The IMG recommends the following best practices guidelines. SMP00003 Do: SMP00004 Use social media to stay in touch with friends and make new ones. SMP00005 Use social media to create your best image, since your page is likely visible to more people than just your selected friends, followers, or subscribers.
- 27. Step 3: Extract Knowledge
- 28. LSA Step 1: Singular Value Decomposition Latent Semantic Analysis (LSA) starts with the Singular Value Decomposition (SVD) of matrix X: X = U Σ VT where U is the term eigenvector matrix, V is the document eigenvector matrix, and Σ is the diagonal matrix of singular values (square roots of eigenvalues). SVD performs a semantic decomposition of the discourse in X. documents terms X dimensions terms dimensionsdimensions = documents dimensions · · VT ΣU pp. 42-4428
- 29. LSA Step 2: Truncated SVD The truncated term frequency matrix is obtained by retaining the first k SVD dimensions: Xk = Uk Σk Vk T The truncation of the SVD components corresponds to a semantic abstraction of the discourse in X. documents terms Xk dimensions terms dimensions dimensions= documents dimensions · · Vk T ΣkUk pp. 44-4529 24,24 3 664
- 30. How Many Dimensions? pp. 63-6430 Eigenvalues obtained by squaring the singular values in matrix and using iterative methods to obtain the scree plot elbow, and the profile likelihood test (Zhu & Ghodsi, 2006).
- 31. Contractual vs. Promotional View pp. 73-79 31
- 32. Appendix E (p. 129) and pp. 68-71 32 Document Count Topics Label Contractual View: 36 Universal Topics for 250 PSE Institutions
- 33. 33 Document Count Topics Label Appendix E (p. 129) and pp. 68-71 Contractual View: Universal Topics for 250 PSE Institutions
- 34. Appendix E (p. 131) 34 Factors Labels High-Loading Terms with TF-IDF F36.1 Facebook facbook (15.3), page (0.8) F36.2 Twitter twitter (15), account (0.83), tweet (0.76) F36.3 Engagement engag (6.68), share (3.32), convers (2.61), onlin (2.15), user (2.14), peopl (2.13), more (2.09), audienc (2.04), help (1.93), social (1.91), inform (1.87), network (1.81), don (1.66), creat (1.65), commun (1.6), follow (1.57), activ , (1.46), group (1.4), tool (1.37), keep (1.37),post (1.34), facebook (1.34), us (1.22), content, 1.21), comment, (1.17), presenc (1.16), photo (1.16), event (1.13), profession (1.11), provid (1.08), blog (1.08), on (1.05), connect (1.03), encourag (1.03), public (1.02), platform (1.02), twitter (1.01), new (0.99), allow (0.98), tweet (0.94), re (0.92), build (0.91), discuss (0.86), respond (0.86), learn (0.84), valu (0.83), organ (0.82), effect (0.82), fan (0.82), respect (0.81), channel (0.81), site (0.81), promot (0.8), page (0.8), person (0.77), particip (0.77), friend (0.77), linkedin (0.77), relev (0.76), student (0.76), not (0.75), best (0.75) F36.4 Best Practices practic (9.79), best (9.51) F36.5 Content content (12.9), share (1.54), creat (1.27), web (1.1), comment (0.93), manag (0.88), copyright (0.76) F36.6 YouTube youtub (12.8), channel (1.33), photo (1.16), video (0.9) For Example – F36.3: Engagement
- 35. Appendix E (p. 131) 35 F36.3: Engagement The high-loading terms for F36.2 include:
- 36. Promotional View: Common Topics Among 250 PSE Institutions Appendix G (pp. 136-137)36 Topic Very High Clarity By Region Observed Topic Label Topic Non-USUS Coln 1 Coln 2 Total Facebook F36.1 21 68 22.372 66.628 89 Twitter F36.2 20 61 20.361 60.639 81 Best Practices F36.4 12 59 17.847 53.153 71 Content F36.5 9 41 12.569 37.431 50 YouTube F36.6 18 53 17.847 53.153 71 Posting F36.8 12 43 13.825 41.175 55 Comments F36.9 5 21 6.536 19.464 26 Institutional Users F36.12 12 6 4.525 13.475 18 Account Management F36.13 4 19 5.782 17.218 23 Use of Platforms F36.14 13 21 8.547 25.453 34 Respect F36.15 19 57 19.104 56.896 76 Blogs F36.16 10 33 10.809 32.191 43 Copyright & Fair Use F36.17 7 22 7.290 21.710 29 Social Networking F36.19 6 14 5.027 14.973 20 Audience F36.21 6 23 7.290 21.710 29 Site Maintenance F36.22 5 8 3.268 9.732 13 Link F36.27 7 15 5.530 16.470 22 Privacy F36.28 5 18 5.782 17.218 23 Naming Conventions F36.29 5 17 5.530 16.470 22 Flickr F36.33 11 31 10.558 31.442 42 LinkedIn F36.34 10 33 10.809 32.191 43 Responsibility F36.35 12 19 7.793 23.207 31 229 682 911 Expected DESCRIPTION VALUE c2 * 30.919727 p-value 0.075004 Critical value 32.670573 a 0.05 df 21 Calculation of the Chi-Square Test
- 37. Promotional View: Most Diverging Topics Between the US & Non-US PSE Institutions pp. 79-82 37 Step Topic Chi-sq Value P-value Most Diverging Topics Factor Chi-sq 1 36 324.41 0.000 Institutional Users F36.12 81.74 2 35 242.39 2.76E-33 Page & Group Administration F36.10 55.17 3 34 182.82 1.15E-22 Information Management F36.7 27.21 4 33 155.6 3.55E-18 Privacy F36.28 25.74 5 32 130.07 4.26E-14 Facebook F36.1 17.48 6 31 111.17 2.96E-11 Social Networking F36.19 13.84 7 30 97.37 2.56E-09 Audience F36.21 12.38 8 29 84.18 1.57E-07 Posting F36.8 10.72 9 28 72.52 4.87E-06 Personal Use F36.18 8.05 10 27 63.8 5.06E-05 Support at Institution F36.11 6.1 11 26 56.99 0.000268 Time & Resource Management F36.25 5.73 12 25 50.83 0.00111 Followers F36.24 6.04 13 24 44.32 0.00481 Institutional Identity F36.23 5.21 14 23 38.64 0.01551 Flickr F36.33 4.95
- 39. Promotional View: Canada p. 8839 F28 see SMP01268 passage example from Brock University : ”Brock University protects your privacy and your personal information. The personal information requested on this form is collected under the authority of The Brock University Act, 1964, and in accordance with the Freedom of Information and Protection of Privacy Act (FIPPA) for the administration of the University and its programs and services. Direct any questions about this collection to the Social Media coordinator in University Marketing and Communications.” F28 = Privacy F07 = Information Management F07 see SMP16534 passage example from Thompson River University : “The first, and probably most important, privacy tool or protocol you can engage is to prepare or provide a brief privacy seminar for students that informs them about existing privacy legislation in BC and Canada and highlights the importance of fundamental privacy principles, such as knowledge, notice and informed consent. Most younger students have grown up in a culture of mass information-- sharing and are not yet old enough-or simply have been fortunate enough-to never have suffered the serious negative consequences for sharing too much of their or other people's personal information.”
- 41. Recommendations for Social Media Guideline and Policy Development 41 Appendix H (pp. 138-169)
- 42. Bonus: Q-Sort Method 42 Conducted a Q-Sort Method for Categories
- 43. 43 Q-Sort Inter Rater Reliability Matrix Inter-Rater Reliability Coefficients and Associated Standard Errors Condition al/Rater Sample Condition al/Subject Sample Unconditi onal Method Estimate Std Error 95%C.I. Std Error 95% C.I. Std Error 95% C.I. AC1 0.679709 0.057206 (0.564 : 0.796) 0.049043 (0.58 : 0.779) 0.064524 (0.549 : 0.811) AC1C 0.680354 0.056997 (0.565 : 0.796) 0.048832 (0.581 : 0.779) N/A N/A Kappa 0.6726 0.057481 (0.556 : 0.789) 0.049882 (0.571 : 0.774) 0.065012 (0.541 : 0.805) KappaC 0.671924 0.058116 (0.554 : 0.79) 0.0501 (0.57 : 0.774) N/A N/A BP 0.679012 0.057226 (0.563 : 0.795) 0.049125 (0.579 : 0.779) 0.064564 (0.548 : 0.81) Conger 0.672937 0.057863 (0.555 : 0.79) 0.049738 (0.572 : 0.774) N/A N/A (Landis & Koch, 1997; Gwet, 2008)
- 44. Updated Categories from Q-Sort 44
- 45. Social Media Guideline and Policy Document Database 45 Appendix B (pp. 116-117) Pasquini, Laura A. (2014). Appendix B: Social media guideline and policy document database. figshare. http://dx.doi.org/10.6084/m 9.figshare.1050571
- 47. Summary of Research 47 • Compiled recommendations for developing social media guidelines and policies from 250 PSE institutions representing 10 countries after extracting key topics. • Developed a common reference for social media guideline and policy documents research to inform the PSE sector. • Compared the distribution of the 36 topics (factors) across two geographic regions to determine importance. • Theorized the semantic structure created by an organization in relation to Wenger’s (1998) Community of Practice framework with corpus-creating community.
- 48. 48 Questions?
Hinweis der Redaktion
- State title of dissertation: Organizational Identity and Community Values: Identifying Meaning in Post-Secondary Education Social Media Guideline and Policy Documents
- GOAL: to define and identify the common structure to establish a common reference for social media guideline and policy documents Research Study: To examine and define the common semantic structure of a corpus-creating community of practice and to establish a common reference for post-secondary education (PSE) social media guideline and policy documents.
- Motivation for this research: There is a need to create a standard for social media guidance, with respect to guideline and policy development in higher education.
- Over 75% of the incoming 2013 class use social media for enrollment decisions (Uversity, 2013) 41% of faculty use social media for teaching (Seaman & Tinti-Kane, 2013; Pearson, 2012) Social media guideline and policy document analysis has the potential to inform use (e.g. teaching, engagement, etc.), implementation, and policy design in higher education On-going and current concerns: December 2013 Kansas Board of Regents; NYU policies for community May 2014; Chicago faculty blog ban October/November 2013
- These are the questions to guide my research investigation.
- To build theory around organizational identity, this study applied Wenger’s (1998) community of practice theory to a distributed repository of documents known as a corpus in this study. THIS is my focal point of the dissertation – in assessing organizations and their identity development in a community of practice.
- The community of practice is involved in participation on a regular basis as PSE administrators of social media are aware of these documents, read these documents, contribute and edit these documents, and share these documents between PSE institutions to deal with legal requirements within their region (state, territory or country), at professional associations, among accrediting bodies, via association involvement, and through online communities and networks – thanks to social media and electronic sharing on websites.
- With REIFICATION, members of the community of practice REFLECT and REIFY social media guideline and policy documents as they have reviewed and see this body of knowledge as both the inspiration and authority of reference for social media use and guidance in the PSE sector. Members of the community who are craft social media policy, look up into those documents and they see something in them and they believe they have value; symbolic value and higher status– content is no longer part of the individuals or PSE institution, those documents and points of references/objects/texts become associated with “the standard” for social media guidance --others make reference and comparison to the corpus during reification ---Isn’t this interesting? Good – this is what this dissertation is about because we are going to do it
- In creating this new theory related to communities of practice, I focused this research investigation on a particular group: A CORPUS-CREATING COMMUNITY identified by Evangelopoulos & Polyakov in a recent article. Specifically this theory identify meaning, value and identify from the corpus.
- How does this make sense we propose that…
- Therefore…
- This also means that the artifacts reify the ideas from the community of practice
- And this finally means that, since the corpus has a semantic structure it will reflect the meaning, values, and identity of the community (or organizations) in the PSE sector.
- As people look up to the common documents from the community of users with the particular discussion of social media guideline and policy of users. This theoretical framwork will help to uncover how the knowledge sharing in the corpus makes meaning from a corpus-creating community of practice.
- Latent semantic analysis (LSA) is dimension reduction of the original dataset; determination of dimension factors is based on a subjective researcher judgment. LSA has orthogonal characteristics, which means multiple occurrences of words from different factors (topics) are usually prevented and words in a certain topics will have a high relation with words in that topic, whereas will be limited in connection to other topics. LSA will not be able to resolve polysemy issues (coexistence of many possible word or phrase meanings). It makes no use of word order, thus of syntactic relations or logic, or of morphology. Remarkably, it manages to extract correct reflections of passage and word meanings quite well without these aids, but it must still be suspected of incompleteness or likely error on some occasions.
- I have selected to review online published and publically accessible text artifacts from English speaking PSE institutions. Analysis of textual content only i.e. no images, screenshots, videos, photos, or URLs. The organizational focus on the PSE sector was due to the abundance of documents that could be gathered to build a sufficient database (corpus) for text analysis. Text is optimal since we are applying LSA and there is a large amount of text in the corpus.
- Data mining to both predict clusters and results for a significant amount of data (Romero, Ventura, & Garcia, 2008). Text mining extracting interesting and non-trivial patterns or knowledge from unstructured text documents (Hearst, 1997; Feldman & Dagan, 1995; Fayyad, Piatesky-Shapiro, & Smyth, 1996; Simoudis, 1996). uses fast processing by consolidating a vase amount of data, reduce coding bias, and limit researcher influence (Cronin, Stiffler, & Day, 1993; Litecky, Aken, Ahmad, & Nelson, 2010). “text classification, text clustering, ontology and taxonomy creation, document summarization and latent corpus analysis” (Feinerer, Hornik, & Meyer, 2008) Semi-automatic approach, i.e. the Text Document Preparation for LSA can be found in Appendix C pp. 118-119; offers coding validity and member checking for data manipulation
- LSA is a text mining approach to index words and concepts. Essentially, LSA is a computational model that learned word meanings from vast amounts of text and identified the degree to which two words or passages have the same meaning (Landauer, 2011). ORIGINALLY used for information retrieval; NOW a new methods developed to allow for topic identification through rotated LSA (Siddorova et. al 2008) and updated rLSA (Evangelopoulos & Polyakov, 2014 The latest version of LSA is rotated LSA (rLSA) to extract topics – none of the previous LSA methods extracted topics using LSA after 2008 only
- 10 seconds “More schematically… “ with 5 second to pause (show in segments) -goal is to define and identify the common structure to establish a common reference for social media guideline and policy documents This study will follow established text mining procedures as discussed in prior studies (Evangelopoulos et al., 2010; Hossain et al, 2011; Li & Joshi, 2012) and utilize the following three-step process of text mining using LSA as described in Elder, Hill, Delen, and Fast’s (2012) methodology as outlined in Figure Step 1: Establish the Corpus - search online, website gathering, social media Step 2: Pre-Process the Data - Word (carriage returns), to Excel docs (macros), combine all - clean URLs, videos, images, etc text only —pre-processing and term reduction; SVD; term frequency matrix Step 3: Extract Knowledge
- Intro to step 1
- STEP 1: ESTABLISH THE CORPUS Try to get all of it. The random sample is the classic statistic; downloaded from databases and search engines To ensure the corpus for this study would be robust for latent semantic analysis procedures, the researcher conducted a preliminary online search of social media guideline and policy documents to form the database from October 2013 until January 2014. The database currently contains at least 20, 000 documents from approximately 240 post-secondary education institution representing various geographic locations (countries), size of campus (by student population), and institutional types (e.g. public, private, bachelor’s and associate degrees, etc.). The researcher will continue to solicit for submissions for social media guidelines and policy documents that are directed at students, staff, faculty, researchers, and campus stakeholders from the post-secondary education sector via an online form (http://socialmediaguidance.wordpress.com/submit-a-social-media-guideline/) embedded into a research website
- Activity 1: show the segment For the purpose of this study, publicly accessible social media guideline and policy documents were the target sample. Although a growing number of institutions were guiding social media use, the researcher only reviewed documents retrieved online as accessible to any visitor of a PSE institution website. To be eligible for this study, all social media guideline and policy documents had to be available electronically and accessible through PSEs’ institutional websites or a general web search. The text documents would guide social media from departmental or institutional levels within the PSE sector. .
- Here are the 24,243 atomic documents representing the following countries: Canada, the United States of America, Australia, New Zealand, Norway, the Netherlands, Austria, Ireland, South Africa, and Great Britain (until Scotland has their vote in September).
- Intro to Step 2
- STEP 2; PRE-PROCESS THE DATA Prepare the text. See guidelines for text document preparation for LSA for this semi-automatic approach in Appendix C (pp. 117-118); discusses the segmentation of documents, validity and member checking among multiple coders
- STEP 2; PRE-PROCESS THE DATA Input data for LSA is the term frequency matrix X. This matrix quantifies the collection of documents by recording the occurrence of each term in each document
- Intro to step 3
- 664 terms by 24,243 documents X = the quantification of the 24,243 atomic documents
- By keeping this truncated model- it promotes different term frequencies -This is an improved version of LSA from the “stereotypical interpretation” -The assumption is that these documents are talking about topics (factors) and each term is mentioned at least once in ALL the documents -Rather than looking at 664 dimensions, we examine 36 to understand the semantic structure of the documents from this truncated term frequency matrix…
- But how many topics (or factors) should we keep from this matrix? Script log reviewing eigenvalues; look for elbow points (similar to bootstrapping by Efron, 2005) Zhu & Ghodsi, 2006 – use profile likelihood loading; I am fortunate to have the macro provided by my co-chair Dr. E to conduct this analysis Explored a bit and found 664 terms by 24, 243 documents, because it is like that, the eigenvalues it can extract is the maximum by the dimensions Dimensionality detection was determined by the change-point and profile likelihood test (Zhu & Ghodsi, 2006).
- Contractional = normative Transcendent topics or Converge – if the document mentions something it is there and it doesn’t matter if it is present one or many times, just as it is written in a contract (maximum) Promotional = what should be heavily promoted through repetition; some guideline & policy documents mention online one term once or multiple times via the count function here the views differ Divergent Differences: Neighborhoods in the global village of the 250 PSE institutions have slightly different values depending on their region.
- Here are the first 18 factors: The 6 high-loading terms extracted using LSA was Institutional Users, Information Management, Page & Group Administration, Account Management, Support at Institution, Comments, and Content
- The next 18 factors: The lowest factors: Respect, Privacy, Responsibility, Advising Resources and Questions, Flickr and LinkedIn
- Table 9 on pp. 69-71 and Appendix E outlines the high-loading terms with the term frequency – inverse document frequency (TF-IDF) Let’s take a look at one example
- Separate – zoom in to the top factors Highlight a few key factors for the extraction
- Identify the 22 topics that converge and are present in all geographic regions From help of the chi square test… that make a difference across geo regions; the remaining 22 do not make a difference 1Promotional = what should be heavily promoted through repetition; here the views differ Divergent **Topics of divergent importance -- -Chi-square statistic compares distributions of documents across the 36 topics in the US and Non-US universally -Homogeneous and heterogeneous – not evenly distributed
- Identify the 14 that diverge from the topics From help of the chi square test… that make a difference across geo regions; the remaining 22 do not make a diff 1Promotional = what should be heavily promoted through repetition; here the views differ Divergent **Topics of divergent importance -- -Chi-square statistic compares distributions of documents across the 36 topics in the US and Non-US universally -Homogeneous and heterogeneous – not evenly distributed
- F12 Institutional Users – discussed more among Commonwealth countries and Ireland F28 Privacy is is closer to Canada US (and close by the NLD) seem to cluster around similar topics including F36.21 Audience and F36.11 Support at Institution following in very close proximity. Other factors, F36.10 Page & Group Administration, F36.1 Facebook, and F36.8 Posting
- For example, passages for F28 and F07 being close to Canada are… F07 have 141 out of 735 originate from Canadian PSE institutions about the topic of information management F28 have 98 out of 349 originate from Canadian PSE institutions about the topic of Privacy – with Thompson River University leading with this
- Topics discussed in social media policies – Q sort: verify # of people; your task is to match the 36 topics in 9 categories To be recorded on a matrix (confederates – no IRB application) Factor by Rater matrix developed
- Contribution to research findings: Recommendations for developing social media guidelines and policies FINAL Q-sort of classifications to group the topics (factors)
- Topics discussed in social media policies – Q sort: verify # of people; your task is to match the 36 topics in 9 categories To be recorded on a matrix (confederates – no IRB application) Factor by Rater matrix developed
- Fliess’ kappa – macro provided by Dr. E to compute this statistics for inter rater reliability 9 categories to cluster the 36 topics (factors) for social media guideline and policy recommendations to validate own classification Bonus: outside the scope of the dissertation however useful for future publications The inter-rater reliability is at 0.68. This is not bad at all! Landis and Koch (1977) – see p. 165 of the attached paper – consider this to be in the “substantial agreement” range, which is the (0.60, 0.80) range. Ran the Excel macro AgreeStat, created by Gwet (reference his 2008 paper in Psychometrika, also attached here.) I attach a document that discusses IRR, has some references, and explains how to use the AgreeStat macro.
- Contribution to research findings: Recommendations for developing social media guidelines and policies FINAL Q-sort of classifications to group the topics (factors)
- Database shared on figshare
- Future research implications & applications: Review difference further between countries; linguistics; potential to see how these factors are implemented at PSE institutions for teaching, research, and service scholarship -Industry review for other organization -application for policy to institutional and organizational culture and identity, with regards to common values -Continue research to identify differences between countries additional languages and linguistically assessment.
- Contributions from this investigation include… Motivated the need for social media g & p comparison & research Theorized the semantic structure of the community that builds them in relation to the cop framework Compiled a set of 250 PSE from 10 countries to extract the topics Compared distributions of these topics across two geographic regions & found that some are more important than others
- Any Questions? Thanks QUESTION from Nick: What’s the meaning of reification from Wenger’s Community of Practice theory? How does it apply to this study?