From data to endgame for dissertation and theses: what does it take
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Both studies analyzed large amounts of qualitative and quantitative data to understand organizational change and cross-cultural mentoring relationships. The first study examined technology changes in renewable energy companies using interviews, public records, and statistical analysis to determine how cognitive flexibility at the team and organization levels impacted the likelihood and scale of changes. The second study used grounded theory and interviews to develop a model of effective practices for mentors in cross-cultural relationships. Both analyzed data dimensions and levels to answer research questions and proposed theoretical frameworks to understand their topics of study.
From data to endgame for dissertation and theses: what does it take
- 1. Exploiting Rapid Change in Technology Enhanced Learning … for Post Graduate Education From Data to Endgame – What does it take?
- 2. Students Experience the Endgame differently I like being near the top of a mountain. One can't get lost here. Wislawa Szymborska When you're passionate about something, you want it to be all it can be. But in the endgame of life, I fundamentally believe the key to happiness is letting go of that idea of perfection. Debra Messing If there's not any endgame, we're in quicksand. We take one more step, and we're still there, and there's no way out. Richard Shelby
- 3. Agenda 1. Endgame backwards – qualitative 2. Endgame backwards – quantitative 3. What they have in common – the beginning of a map through from data to the end Dissertations discussed today from PQDT Open.
- 4. The Documents – both from Stanford Large scale empirical (2009) • 212 pages • 6 chapters – used #3 for theory • 50 pages of tables at the end Grounded Theory (2008) • 440 pages • 8 chapters – 3 for 3 different dimensions of the results then 7 for a model and 8 for conclusion
- 5. Study #1
- 6. Abstract Cognitive flexibility: The adaptive reality of concrete organization change by Furr, Nathan R., Ph.D., Stanford University, 2009, 201; 3382938 Abstract (Summary) The question of why some organizations change when others do not is of central interest to the organization change literature. While large-scale, empirical studies have emphasized organization demographic characteristics such as size, age and resources, a recent body of case-based literature has begun to emphasize the role of cognition in determining whether organizations change. In particular, the literature suggests that organizations characterized by cognitive flexibility are more likely to change than those characterized by cognitive rigidity. This study investigates the impact of three core constructs that contribute to cognitive flexibility—variety, novelty, and framing—on when organizations change their technology. I examine the impact of these three constructs at both the team and organization level on the likelihood of three degrees of technical change: major, moderate, and minor. I find that all three constructs: variety, novelty, and framing contribute to technology change. Furthermore, I find that the greater the scale of change, the greater the impact of cognitive flexibility on change. Finally, I investigate the link between change and performance, and find that, in fact, large changes actually improve performance more than small changes but only after an initial adjustment delay.
- 7. How Much Data Was Collected? Pilot interviews (12) 1 hour each – analyzed whether the questions made sense… - yes they did so he progressed. The study – public and private data on the PV industry from 1992 – 2007 Data from Venture Xpert, Dept of Energy and Renewable Energy Lab – (70 lists total) Data was filled in by interviews (4) Data measured in quarterly intervals – all changes in technology recorded in public documentation
- 8. How Was It Analyzed? The analysis conducted as part of this study consists of three parts. First, conducted a pilot inductive study in which he validated the research question. Second, he conducted a deductive analysis to statistically test the effects of cognitive flexibility on change. As part of this analysis, he also tested the effects of change on performance. Third, to more robustly illustrate the statistical analysis he constructed three cases of organizations from the sample that illustrate the change process Dependent variable change – major, moderate, minor Independent variables – team experience, (career histories were extracted from biographies on the web) science or business – who came and who went in each company each quarter?
- 9. Other Things Measured Structure variety Technical novelty Outward opportunity Measured it all through content analysis Texts were scored on whether motives for change were economic vs sustainability and then commercial vs technical Statistical analysis on cognitive flexibility and change and on effect of change on performance through correlations and descriptive statistics.
- 10. Where did he get to in the end? Additional exploratory analysis of CEO’s – as to their adaptive flexibility and what that meant for their organizations. The results suggest that CEO-level cognitive flexibility does impact change although the management team as a whole has a greater impact on change than the CEO alone, particularly for large changes And then this requires the results are gone through carefully for each level of change etc.
- 11. Study #2
- 12. Abstract Best practices of outstanding mentors in psychology: An ecological, relational, and multicultural model by Chan, Anne, Ph.D., Stanford University, 2008, 430; 3313810 Abstract (Summary) Recent studies have pinpointed disconcerting trends regarding the recruitment, retention, and graduation rates of ethnic minorities in doctoral programs. Mentoring has been touted as part of the strategy to address this problem. However, there is a paucity of research on the mentoring of ethnic minorities in academia, particularly with regard to how mentors tackle cross-cultural differences in mentoring relationships. This dissertation addresses this gap in the literature by examining the practices of outstanding mentors in cross-cultural mentoring relationships. The sample consisted of 9 mentors nominated for being outstanding mentors and 17 doctoral-level psychology protégés—all the mentoring relationships diverged along racial and/or cultural lines. Grounded theory was used in this study to uncover mentor practices as well as to discern a theory of cross-cultural mentoring. Data was collected from semi-structured interviews with mentors and their proteges, audiotapes of two actual mentoring sessions, as well as archival materials such as e-mail exchanges. The data showed that the mentors engaged in a wide variety of practices targeted at three key areas: individual career development of the protégés, relationship/trust building, and socialization/organizational development of the protégés. These mentor practices were found to address the special concerns and challenges faced by ethnic minority and culturally different protégés. This dissertation contributes to the literature on mentoring by proposing an ecological model of mentoring that emphasizes the contextual, relational, and multicultural nature of cross-cultural mentoring relationships. The findings from this study contribute to our understanding of the processes within a mentoring relationship and the ways in which mentors can successfully negotiate differences.
- 13. How Much Data Was Collected? Grounded theory – requirement for looking at evidence from several angles in order to build theory 26 participants, in depth interviews over 9 months Document analysis as well: emails, between mentors and protégé’s Audio taped sessions (2) were also analyzed Pilot done to verify ideas – opened ideas about mentor access that were key at the end
- 14. How Was It Analyzed? Triangulation, negative case analysis, member checking, Reflexive journaling, reactivity Multiple readings of data and examination over the course of collection – analysis throughout Field Notes and memos: observational notes, theoretical notes, methodological notes, and open and selective coding Constant comparison of previous ideas to new ones, reflecting back, integrating categories, delimiting theoretical options, then writing theory
- 15. Constructing similarities and differences in order to propose a map through from data to the end
- 16. Process comparison #1 • Purpose to extend what is known with broad question: When do organizations change? • Lit set up dimensions and levels • Results in one chapter - 2 dimensions discussed each across 3 levels of change • Outcomes across each of 3 types of literature #2 Both had LOTS of data and complex system for analysis Similarities: 1. Both had complex system to discuss results across a number of dimensions • RQ to be answered • Lit set up the dimensions • Each dimension became its own results chapter (= 200 pages) • Outcomes results taken together- a model for cross-cultural mentoring
- 17. Where #1 get to in the end? The descriptive statistics suggest that entrepreneurial organizations are in fact highly adaptive. The frequency of technical change suggests that change inside entrepreneurial organizations is a central feature of early organizational life. When do organizations change? 1. Cognitive flexibility influences whether /how much 2. That is true at multiple levels within organization 3. CF has a different effect depending on size of change – more the bigger it is 4. Change improved performance overall
- 18. Where #2 get to in the end? Model for mentoring… The major mentor functions identified in this model are: • Building trust in the mentoring relationship • Building community and supportive networks • Providing access to the inside story • Providing validation • Providing support for career development • Providing protection Each conceptualized across individual, relationship, and organizational contexts
- 19. http://pqdtopen.proquest.com/search.html Suggested Map for those with Data What might these comparisons suggest to others? 1. Lit sets up the sort mechanism for your data 2. Questions determine where you want to end up 3. Steps might include: 1. Beginning, during, after analysis sort, sort, sort your ideas into the different strata determined throught the lit. 2. Keep asking – how do these ideas answer my questions? 3. Keep journal of the progress so you can go back and double check. 4. Use models from others – as your ideas progress search again.
- 20. What’s Up at DoctoralNet? Continuing development in two areas: 1) Support, 2) Non Academic career tracks and transferable skills Webinars Opt-ins For Summer Track your milestones 30 day challenges – writing and work/life balance Top 10 motivations April 11th Read/Listen/Comment Reading Group https://www.bigmarker.com/doctoralnet/Copy-of-Read-Listen- Comment-Academic-Writing-Help April 13th Critical Analysis Part 1 with exercise https://www.bigmarker.com/doctoralnet/Critical-analysis-part-one-with- exercise April 18th Preparing for your Final Defense/VIVA https://www.bigmarker.com/doctoralnet/Preparing-for-your-Final- Defence-VIVA April 18th Lingerers Group: Picking up the pace on literature work https://www.bigmarker.com/doctoralnet/Lingerers-Second-Quarter-2- 2017-Picking-up-the-pace April 20th Critical analysis part two from exercise https://www.bigmarker.com/doctoralnet/Critical-analysis-part-two-from- exercise April 24th IRB/Ethics: Ins, Outs, What to do and What to avoid https://www.bigmarker.com/doctoralnet/IRB-Ethics-Ins-Outs-What-to-do- and-What-to-avoid