Archive for Research

Research Tips: What Worked for Me

In preparing to defend my dissertation in July, I collected several notes in case others asked me what went well, or what advice I might have for others. I thought I’d share them here in case they are useful to others.

  1. Read a dissertation in your intended field as soon as you can. A month before I started the Leadership PhD I found one of the key dissertations that I quoted throughout my study. It was very helpful to see how the dissertation was put together, to get a map for what was ahead.
  2. Create a research journal. My research journal at first contained my search methods for finding articles and dissertations on my topics. During my statistics classes I recorded questions and thoughts about my own research ideas. During the writing stage, I recorded notes, questions, learning from talking with my committee members, and lists of things to fix in my dissertation. It ended up about 30 pages long, full of notes & ideas.
  3. Email Summaries. Another useful practice was my email summaries. Soon after I talked to a committee member, usually within an hour, I wrote a list of things I learned and things to change. This helped me rephrase my learning, “catch it out of the air” and nail it down to words that I could re-read later. Some of the statistical procedures and knowledge I would have immediately forgotten if I hadn’t written it down. My committee members also found this habit very helpful as they also had a record of my understanding of what was said. They corrected me as needed.
  4. Adapt to your advisor. I thought at first that I wanted to work via email because I want to be able to read things over & over. Yet one committee member wanted to work via phone. I found that adapting to this method was wise because of the give & take that occurred in real time.
  5. Ask questions. If you don’t understand something, don’t just say “uh, huh.” Ask! Clarify. Check for understanding. It’s your own learning! Take control of it!
  6. Take the initiative. After getting feedback on one set of multiple regression analysis, I did all the rest of them. I didn’t wait to be told to do the next analysis. Playing with my data helped me know it better, even when I had to redo analyzes.
  7. Know your data well enough to find errors. Several times I found odd results that didn’t look right. Upon further examination they weren’t right! Understanding your data and the analysis makes it much easier to reflect on the results and implications.
  8. Psych yourself up to spend time on formatting. The formatting will take longer than you expect and can get annoying. Just psych yourself up to know it’s going to be a pain and slog through it! It’s part of the process and makes for a clean nice looking document when you’re done.

What tips do you have for doctoral students writing a dissertation? Feel free to comment and share!


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Stigmergic collaboration: A theoretical framework for mass collaboration

Continuing work on my 5th competency:  Servant Leadership in Technology Facilitation and Collaboration.

Elliott, M. A. (2007). Stigmergic collaboration: A theoretical framework for mass collaboration (Doctoral Dissertation, University of Melbourne, 2007). Retrieved from

Notes (instead of a summary)

This work is incredible, detailed, and presented in an attractive way. I feel that I can’t do it justice, but here are a few notes.

Definitions: “Collaboration is the process of two or more people collectively creating emergent, shared representations of a process and or outcome that reflects the input of the total body of contributors” (Elliott, 2007, p. 31).
“co-created emergent shared representation” (p. 45).
“Stigmergy is a class of behavior in which collective activity is coordinated through the individuals’ response to and modification of their local environment—one agent’s modification becomes another’s cue (p. 8). (swarm intelligence)

Some principles: Non zero sum outcome (i.e. win win)
Includes creative activity (not just cooperating together); must create (p. 40).
Generate multiple solutions to a problem and one is selected by the collective (p. 40)
Collaboration transcends and includes cooperation which transcends and includes coordination (p. 41)
The Internet is fundamentally a stigmergic system in that it supports mediated indirect communication and inspires users to respond to its encoding by further encoding it (p. 92).

What does it take to create the environment for it?

The individual must relinquish some control to the collective, including sole authorship (p. 49)

Collaborative output requires “constant attention and redevelopment” through out the process, and the purpose needs redefining daily, each moment (p. 50)

Procedures must be previously agreed upon (p. 51).

What are the participants like?

Multiple participants with varying social capacities, personalities, histories and relationships (p. 53)

An ideal prospective collaborator…
1. is enthustiastic about the subject of our collaboration
2. is open-minded and curious
3. speaks their mind even if it’s an unpopular viewpoint.
4. gets back to me and others in a timely way.
5. is willing to enter into difficult conversations
6. is a perceptive listener. (p. 54)

Communication happens through all types of mediums, and may not stay in the same medium. It also isn’t two-way, it has multiple paths and multiple participants.

What does it take for digital stigmergic collaboration to happen?

Someone has to:

“Define an objective for which collective creative contribution is required in order to build value through user contribution.

Define a set of procedures designed to provide the capacity for participants to make such contributions.

Develop an online environment which caters for these contributions and enables the emergence of collectively created shared representations, and cultivate a community which supports the objectives.

[Then]  “Compliant participants make creative contributions and benefit from collective efforts.” (p. 104)



Wow. This deserves much further thought! Preferably stigmergic collaborative thought.

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Checking Sources

This post is part of a series of my notes on articles provided by Dr. Newman in his Regression workshop at Roundtable 2008 at Andrews University, summer of 2008.

O’Neill, B. (1994, March 6). The history of a hoax. The New York Times, pp. 46-49.

The full text of this article is actually posted online here too.

This little article traces the origin of comparing the top discipline problems in the 1940s and those “today”. It’s a really interesting read, and these are some of the lessons for research that I see:

  • Roots & Fruits. Last summer at Roundtable, Dr. Covrig emphasized the importance of following the “roots and fruits” of research. i.e. where did it come from? what is it based on? (roots) and who else has quoted it? what other research has it inspired? (fruits). It’s pretty clear that it’s important to find out the source(s) of what you’re quoting.
  • “In a study” Credibility. Seems like we all believe something once someone says the words “research study.” I’ve been reading Neil Postman’s book Technopoly this summer. I figure if I’m so heavily involved in technology I should read a dissenting voice once in a while. In the chapter on “invisible technologies” he discusses statistics and polls. “Public opinion is a yes or no answer to an unexamined question” (p. 134). He suggests that we are quick to believe anyone who can quote a study. But do we take the time to examine the questions and answers?
  • Cautious Comparison. One of the flaws of the comparison of these two lists (if quoted as research), is that the question is unknown for the second list “today”. So if we don’t know what the question was, how can we compare to another? How can they be compared if they aren’t at least answers to the same questions?

A great article and interesting read. Shakes you up a bit and helps you realize the important of thinking critically about the information you consume.

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Newman’s Low R-Squares Article

This next series of posts are my notes on articles provided by Dr. Newman in his Regression workshop at Roundtable 2008 at Andrews University, summer of 2008.

Newman, I., & Newman, C. (2000). A discussion of low r-squares: Concerns and uses. Educational research quarterly, 24(2), 3-9.

This article is available in the OCLC Article First database.

The point of this article is to suggest that low R-squares shouldn’t be thrown out without consideration. They may have value under certain circumstances, so should be considered carefully.

What is an R Square?
So first let’s remind ourselves what an R squared is. Wikipedia has a very detailed overview if you want to read that. Basically when you’re using linear regression, the R squared is the explained variance. It is the percent of variance that can be explained by the variables you’re examining. I.e. why do some people get a higher  or lower score on your measurement than others? Your variables may explain some of that variance in scores.

Why are R Squares low?
The article suggests some reasons why R Squares can be low.

  • They can be low (and are appropriately low) in the early stages of research. i.e. not enough research has been done to identify all the variables that would account for the variance.
  • In social sciences, the predictor variables tend to have small effects.
  • There might be some measurement error. It is very difficult in social sciences to measure a construct such as intelligence, attitude, etc. So it’s pretty common to have some measurement error. This is where the reliability and validity scores come into the picture.

How do you know your research is any good?
From what I’ve learned about stats so far, there are a few ways we can look at our data to see what it tells us and if the results are useful.

  • Tests of significance tells is if the effect happened by chance or not.
  • Effect size is another important measurement, which used to not be reported, but really is a critical piece of information to help others interpret your results.
  • Replicability is also important. In fact, Dr. Newman suggests that a measure of replicability is more useful that mere significance. Maybe it’s significant with this set of respondents, but does it hold up with another set?

Under what circumstances is a low R square “ok”?
The article suggests some examples and things to consider when looking at low R squares. There are several examples in the article of places where a low effect size or R square is still helpful.

  • A drug that explains less than 1% of the variance may still impact 60,000 lives in a population of 1 million.
  • The odds ratio at casinos may be just slightly in advantage for the house over players, but that adds up to billions of dollars over time.
  • When looking at groups of people vs. individuals, the smaller R squared still has value.
  • If the small R square is consistent and replicable, it still has value.
  • A low R square may not necessarily be a wrong path (as suggested by McNeil quoted in the article), it may only be a partial explanation of the variance and further research will improve it by adding additional predictor variables.
  • It may be better to have a smaller R square that is replicable vs. a higher R square that isn’t replicable.

In summary, the point seems to be that it’s ok early on in research to have a smaller R square when the goal is to hopefully eventually get to a larger R square. This article seems really useful to use in interpreting research that results in a low R square!

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Checking Your Reference List for APA

This video is part of a series of informal tutorials related to literature reviews, proposal and dissertation formatting, etc. for the Leadership Program at Andrews University.

This tutorial covers how to quickly check your reference list in Endnote for APA format.

Please comment if you have additional tips or suggestions, or even questions.

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The AU Template, Headings, Table of Contents

This video is part of a series of informal tutorials related to literature reviews, proposal and dissertation formatting, etc. for the Leadership Program at Andrews University.

This tutorial covers starting with the AU template, and tips on headings and the table of contents.

Please comment if you have additional tips or suggestions, or even questions.

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APA Electronic References: ProQuest Dissertations

This video is part of a series of informal tutorials related to literature reviews, proposal and dissertation formatting, etc. for the Leadership Program at Andrews University.

This tutorial covers how to make sure your Endnote references are listed right for ProQuest Digital Dissertations.

Please comment if you have additional tips or suggestions, or even questions.

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