Reflections on critiquing the writing of others

Giving feedback about the paper is a way to show who you are and how much you care about the author.

Suppose, in your opinion,  the author has made a glaring error in logic or has not supported the thesis, or mischaracterized an opposing view, and because you are concerned about hurting their feelings, you don’t say anything.

How are you helping them? By letting their paper out into the world?

If you were right about the paper, and didn’t tell them, shame on you.

If you were wrong about the paper, that should emerge in the continued dialogue between professionals and now you have a chance to sharpen your own tool. You miss that chance if you don’t CRITIQUE THE PAPER, NOT THE PERSON.

If you comment on the paper without regard for the human who offered their vulnerability, their knowledge, their insights, THEMSELVES to you, try remembering to walk a mile in their shoes and ask yourself, before sending, have I been fair? have I been constructive? What is the tone of voice I used?

If you would say things anonymously about the paper in a double blind, but not to their face, that says more about you than about the paper you are critiquing.

Envision the paper as it leaves their hand and lands on a community table of knowledge for consideration. Focus on the paper on the table, not the person who offered it. The paper is not the person; restrict yourself to examining what has been offered. Don’t assume you know anything about their feelings or how they might take it. They have offered a piece of academic writing. Your duty is to evaluate it academically, while remembering there is a person on the other end, eventually.

The author has given us all a gift. Respect the gift by giving it your best critique: with support, with care, with your best work.  Respect the author for their gift and vulnerability. The critiques we offer are more important than anything we are likely to write on our own, and we will do a lot more of them than our own writing.

If you are an author, recognize the boundary between your Self and your paper. Be clear about what you are asking for when you offer it for review. If you want self-esteem more than honesty, you’ll get both, but not as you might want it.

A reflection on action research “storytelling”

What follows is a 1st person, stream of consciousness  reflection written to my mentor & committee chair.  

I describe  what it was like to record a 10 min video “telling the story” of  some preliminary findings emerging from my action research cycles into curriculum and adult learning. 

The video is hosted  at YouTube.

It will be shown at an international conference in Athens, as part of the Collaborative Action Research Network (CARN) annual conference, as part of a bundle of reports from the Future(s) of Education project, an international  participatory action research network.  

Dr Alana:  

i am just glad to get it out of my head  

i had a real out of body experience recording that one;  

i  am a very effective briefer in person, because i can read the audience pretty well.  

i have recorded hundreds of mini lectures etc for my business and for use here at the college on various topics.

i have never, ever needed more than a single take to record, decent and sometimes even inspired voice-overs  until  last night and that briefing.  

I literally needed about 30 takes to get thru it; most i stopped when less than a minute into it because the tone just didn’t feel right

 i think it has to do with being a fish out of water, and the difficulty i felt in trying to tune my story for an audience i couldn’t see, but more importantly didn’t have empathy for

because the audience characteristics still feel fuzzy to me, i couldn’t call up the right tone, voice, persona to apply  

 this caused me to have almost a split personality in the moment, when i am ordinarily dialed in

 i had a “talking part” and a “look ahead part” that is concerned with shaping the transition to the next point/slide  

but now i had a disconcerting 3rd part that was trying to anticipate the possible reactions of an unfamiliar, and hard to imagine audience  

this is what made me feel so out of sorts

 until i “wore out” the last, 3d part and was able to trust in just telling the story, and accepting the vulnerability of knowing that i couldn’t know the audience, i found i just couldn’t get thru it.  

this is the same phenomenon I spoke with Prof Mike Wesch, the digital anthropologist at Kansas State University, and world thought leader on social dynamics in social media: the camera eye represents the unlimited, unfathomable infinite future of all possible audiences across time and space who can be looking in on the “telling moment”.  

in a sense, its like coming face to face with the unblinking eye of God and wondering what she is thinking  

 it is trust that lets us get thru that moment, the accepting of vulnerability, that creates the empathy that hopefully fills the story, as told, with hope.  

that’s a clumsy way of trying to express my meaning of the risk and vulnerability to “telling” and why it can be such a powerful learning moment, and why we need to model it, embrace it, encourage it, and support it. 

Your “producer’s draft” was exactly what i needed to be able to get out of my own comfortable fishbowl; 

you gave me a bridge to the audience that i could not create on my own.  

this has become an interesting reflection to me already  

please put the video on the website, and any or all of this reflection as you deem suitable  

have a great time at the conference!

Management games for deep insight

Peter Checkland’s Soft Systems Methodology (SSM) describes the use of models to help us frame questions to ask of the world, and which help us become explicit about our world views, assumptions, frames of reference, theories of cause and effect, values, and desired outcomes.

Checkland, P. (2006) Learning for action: A short definitive account of soft systems methodology and its use for practitioners, teachers and students.  Chichester, England, Oxford Press

 We’ve developed a deceptively simple Force Mgt practical exercise in the form of a card game. The complete rule set is simple; takes 5 min to scan and understand. 

Rapid rule summary:

1. Students buy forces (5 cards) from a production table (a limited deck) and in each of 5 rounds,  deploy them into 5 regions to compete for Victory Points

2. Win:  first one to 51 victory points OR most points after 5 rounds

3. Game: lasts up to 5 rounds

4. Each round has 5 hands , each hand is worth Victory Points (VP)

5. Hand 1 is worth 6 VP, hand 2 is worth 5 VP etc…

6. Player 1 buys from the red deck, player 2 from the blue deck)

7. After you buy your 5 cards, you place 1 card face down in each region (hand)

8. Once all cards are placed,  cards are flipped over and you determine results

9. If your card wins the hand you get the victory points and keep you card; if you lose the hand, you get no victory points and lose your card. If it’s a tie, you keep your card and no one gets points.

10. Each player has an identical deck to buy from.

 It turns out that the development of strategy and then fielding an appropriate force really matters, AND there are distinct choices that are meaningful, available and feasible.

If you are interested, we’d like you to review the rules, and :

• 1. Buy your first round of forces
• 2. Deploy them into the 5 regions for turn 1.
• 3. Send your “Round 1” move to long-kenneth@conus.army.mil, along with a short description of your strategy

 We are interested in examining the variety of forces and the strategy employed in round 1.  Do you, for example:

1. Buy 4 ea 10s and a Joker to kill any enemy aces and retain max budget  flexibility to see what he has remaining?

2. Buy aces early to get a lead on victory points and then protect them?

3. Buy Jacks to kill 10s while still preserving SOME budgetary flexibility?

4. How do you balance economy of force with winning victory points? (efficiency vs effectiveness)

5. Variations?

 And then tactically employing forces, do you:

1. Put aces against 6 and 5 victory point regions?

2. Put 10s against 6 and 5s to hunt aces?

3. Aim for maximum victory points each round?

4. Aim to capture 11 of the 20 available points each round? (ie bluff on 6 and 3, but try to win 5,4,2?)

 In the actual play of the game we’ll look for adaptability and learning, and how strategies change after teams have played each other a couple times etc. 

We’ve play tested it enough to know there is a rich source of insights available in the game and that it is simple to play. We’ll  play it with decks of cards in the classroom 

We prototyped the game in our Force Management elective and are satisfied that that we generate student interest and insight into broader questions of Army force management in an interesting way.

 Here are some student insights gleaned from our playtesting:

1.  Round 1 results dominate the rest of your strategic choices, so getting Round 1 is crucial.

2.  Round 1 strategies are dominated by uncertainty because you have no information about your opponent’s strategy or adaptive style yet.

3. You have to decide when you want to buy strength: early and aim for quick wins, or later after you have seen pieces of the opponents forces and strategy.

4. Forecasting your opponents moves is problematic and make this more like poker than chess or bridge.

5. Aces are like the FCS: dominating until low-cost alternatives found the weakness. It wasn’t unit Aces were developed that the 10s became meaningful, so be alert to deep flaws in complex technologies.

6. Kings are costly but dominate the field; An opponent with Kings drives you to buy Aces but make you vulnerable to 10s.

6. Jacks (J) are a low cost success strategy against 10s, but can be incrementally be defeated by other mid-weight forces.

8. The costs of transforming cards between rounds is significant but manageable and may lead to strategic advantage. Scenario: You buy Aces on the first round and are successful, opponent buys 10s to kill your aces in the second round, but you trade down to Kings which dominate, and which remain difficult to defeat in subsequent rounds.

9. Deciding where (in what regions) to selectively deploy strength

10. Tactical results can overcome strategic insights and strategic failures. Tacrtics can be game changing.

11. What if the enemy has different victory conditions? Price points? Has different rules?

12. What if new cards are introduced after the first rule set is established?

13. How much would you pay to see the opponents’ hands?

14. What if there are partial wins? Or more than 2 teams playing?

15. Simple games can be powerful learning strategies

 Conclusions: the game serves as a way to dramatize very clearly many of our force management challenges and is a useful way to create rapid, deep awareness of prime issues in this domain.

 Here are some insights from a dedicated gamer and management game modeler:

I suspect that for most people’s first play they are strongly influenced by a form of Confirmation Bias: the As are priced higher, therefore new players conduct their analysis from the assumption that As are more valuable. Depending on the goals of your concrete experience, that may be the best argument for keeping the current price structure. However, an ace of spades loses to seven cards, including four cheap ones, where a KH loses to only four cards that are both expensive and vulnerable — the KH is easily the strongest card in the deck.

I assume trade-ins are secret — in fact that for all practical purposes players are operating behind a screen during their setup phase — because knowing whether your opponent has made any trade-ins is very valuable information. You may want to specify that in the rules.

 Given the prevalence of 10s in everyone’s first turn strategies, it seems like the second-cheapest strategy is far more optimal than the cheapest — that is four tens and a jack of spades. That marginal $15 gives you a pretty good shot at a victory somewhere, and a decent chance of carrying more net capital forward.

 Here are a selection of previously submitted moves for  Round 1: (* = Joker)

Strategy 1
Region	Cards	Strategy:                  Cost:  102   Carry forward: 48
6	10h	I’m trying to kill aces while creating and deploying one, but putting it where it is unlikely to run into an ace-killer unless the other guys is trying an ace-killer strategy like mine.  I’ve got cheap on the ace I bought, which is a risk that may not be worthwhile. I’m expecting to kill an ace in either 6 or 5, win 4 outright, and lose in 3 and 2.  Expected results are thus 9.5 points to me, 10.5 points to the bad guys, I will lose approx $35 worth of cards and kill approx $70 worth.  The enemy is expected to have spent rather more than me, so I will have more cash with which to restructure in light of what I find out. Cost: 102
5	10c
4	As
3	10d
2	10s
Strategy 2
Region	Cards	Strategy:                   Cost:  150   Carry forward: 0
6	10s	10 is the ace killer on 6, then we try to overpower each successive category on the way down.  Assumes aces go to 6, which rapidly becomes a tail-chasing assumption.
5	As
4	Ks
3	Qs
2	Js
Strategy 3
Region	Cards	Strategy:                   Cost:  123   Carry forward: 27
6	Jh	Hunting the ace-killers, retaining some flexibility, winning early points
5	10h
4	Ah
3	Jc
2	*
Strategy  4
Region	Cards	Strategy:                   Cost:  150   Carry forward: 0
6	Ah	Maximum strength in every region
5	Ac
4	Qs
3	*
2	*
Strategy  5
Region	Cards	Strategy:                   Cost:  145   Carry forward: 5
6	Ah	Maximum strength in main regions, try to hunt an ace and kill 10s; accept risk in small region
5	Ac
4	10s
3	Js
2	*
Strategy  6
Region	Cards	Strategy:                   Cost:  149   Carry forward: 1
6	10s	Hunt aces and accept risk in regions 5,6, steal points with aces & J in regions 2,3,4

A reflection on mixed methods research in adult education

Introduction

The Research Problem

The purpose of this paper is to offer one vision of developing a methodological theory of mixed methods research co-equal with that of quantitative and qualitative research. I use a case study of the US Army Command & General Staff College engaged in a redesign of its curriculum, its teaching practices and its design process itself in a period of revolutionary change while supporting a nation at war. I describe circumstances and worldviews in which I argue that only mixed methods research may be employed to simultaneously develop a deep appreciation of uncertainty, improve decision making through an appropriate gathering, mixing and analyzing of quantitative and qualitative data, and applying “learning in action” as a strategy to manage success.  I contrast the view of research  as a process of increasing knowledge for control  with a worldview of research as a learning-in-action that allows for deep appreciation of complexity but without the assertion that appreciation and research can lead to prescriptive measures of control.  I examine the merging feedback system of the CGSC curriculum redesign as a mixture of qualitative and quantitative data. The concept of “Voice” that emerges from the CGSC action research process will be described, along with a multi-phased, multi-year research plan that demonstrates the practical development of an interactive dynamic research plan that is also adaptive to interim and periodic results. The paper  reflects a pragmatic worldview as it focuses on practical outcomes inside an organization concerned with real-world results, but acknowledges the importance and utility of the other 3 worldviews described by Creswell (2007, p.6), namely advocacy/participatory, post-positivist and constructivist.

Waldrop (1992, 2008) described the emerging science of complexity in a rich description of the inter-disciplinary work developing at The Sante Fe Institute. Sixteen years later (Waldrop, 2008) he found that the pioneer days of complexity research had evolved into a rich diversity of programs in major and minor universities worldwide, with lines of business and cognitive domains each finding ways to apply the ideas of emergence, uncertainty and complexity in new and profound ways. What remained unchanged from the origins of the research were the questions of what next and so what and how much more is there and what does it mean to apply an appreciation of complexity to everyday problems and opportunities.  The field of education is only beginning to appreciate how complexity and uncertainty may change the dynamics, structure, content and practice of adult education (Siemens, 2004). Professions in particular will be challenged by educating for complexity, since deep, profound, and reliable bodies of knowledge are at the center of professional practice. Educators, themselves members of a profession, are examining what it means to educate, teach and instruct in light of an emerging awareness of complexity.

The US Army Command & General Staff College (CGSC) is a self-described “learning organization” (Senge, 2000), engaged in a revolutionary re-design of curriculum and teaching practice, with a mission to educate 1500 US Army Majors for uncertainty and complexity, while engaged in a global war on terror and in direct combat in Iraq and Afghanistan (Long, 2009). This provides an opportunity to examine reflective learners and practitioners in action (Schon, 1987) using mixed methods research and using multiple worldviews (Creswell, 2009; Creswell & Plano Clark, 2007).

Past Research on the Problem

Edmondson and McManus (2003) propose a structured approach to selecting research methods that fit the state of theory in a given field. Their 3 archetypes of the state of theory: nascent, intermediate and mature  are connected to qualitative, mixed methods, and quantitative research methods, resulting in an appropriate methodological fit that aims to meet the needs of researchers worldwide. Creswell(2009) offers a systematic approach to analyzing: researcher worldview, research purpose,  research questions, the state of theory, data collection, populations and situations to be studied, and data analysis in order to further refine the methodological fit  and better connect purpose with practice across all 3 methods. Creswell and Plano Clark (2007, p.8) offers a functional working description of the state of mixed methods research, which proceeds from a deep review of current field practice, establishes a superb framework for classifying current choices of mixed methods research design and the means by which methodological fit may be refined, but stops at the boundary of developing a deep theory of mixed methods of research.

Deficiencies in past research and Need for Mixed Methodology

Conventional professional education processes have been adapting at an increasingly frequent rate as a consequence of Army senior leader directives and direct field feedback. The adaptive processes and decisions to date have been single issue, single iteration problem solving exercises inherited from an environment in which incremental change was the norm and most appropriate. These processes are less and less suitable as rates of required change increase and the relevance of existing processes and curriculum are increasingly called into question (Long, 2009)

Audience

The audience for this research include: staff, faculty and students of CGSC; educators of military professional schools; curriculum developers in graduate schools and organizations engaged in preparing leaders for uncertainty; scholar-practitioners of mixed methods looking to adapt practical field methods of for mixing qualitative and quantitative data; scholars examining the deep theory of the methodology of mixed methods.

Purpose

Purpose of the study, and reasons for a mixed methods study

The purpose of this study is to examine the CGSC curriculum redesign project and the emerging feedback system that guides design decisionmaking, which incorporates both quantitative and qualitative data. The project can be described as intermediate theory in the Edmondson  and McManus ontology, and therefore suitable for mixed methods research since I am introducing an emerging concept (“Voice”), focusing on the exploration of theoretical propositions (the theory of mixed methods), the availability of sets of rich theory that inform the research (adult learning, decisionmaking, complexity, design, learning organizations, narrative inquiry and action research), and incorporating multiple data types and analysis (Edmondson& McManus, 2007, p.1165).

Research Questions and Hypotheses

Quantitative Questions

H₁: Student satisfaction measured on the Noel-Levin Adult Learner Satisfaction Survey is not different than their reported overall satisfaction

H₂: Student education priorities measured on the Noel-Levin Adult Learner Satisfaction Survey are not different than those of faculty and college leadership as measured on the same instruments

H₃: Student education priorities measured on the Noel-Levin Adult Learner Satisfaction Survey do not vary through time in the course of the academic year

H₄: Student education priorities measured on the Noel-Levin Adult Learner Satisfaction Survey do not vary after graduation and reassignment to field units

H₅: Student satisfaction measured on the Noel-Levin Adult Learner Satisfaction Survey do not vary from satisfaction as measured by existing CGSC Quality Assurance  surveys

Qualitative Questions

What are the dominant and subordinate narratives that emerge from focus group discussions on educational priorities and practice and environment within CGSC?

How does the curriculum design decisionmaking process respond to similarities and differences in narratives that emerge from groups of students, faculty and senior leaders?

Describe the development, emergence of the construct of “Voice” from the CGSC PAR cycles, and how this prototype construct is evolving and being applied by various sub-groups within and associated with CGSC, by applying various interpretive methods of the narrative inquiry tradition.

Mixed Methods Questions

1. To what extent are qualitative insights generated from PAR cycles, focus groups, and individual interviews supported by quantitative data generated from surveys and actual use data of digital communication and collaboration mediums?

2. How are various organizational narratives constructed by sub-groups within the CGSC curriculum design process in order to make sense of quantitative data?

3.  What insights are offered by the application of  various narrative inquiry traditions? Which traditions are favored or overlooked or rejected by curriculum design decisionmakers?

4. What happens within CGSC when students and faculty are given opportunities to exercise “Voice”?

Philosophical Foundations for Mixed Methods Research

Quantitative research literature review

Student satisfaction surveys built on consumer theory (Watkins, 2009) are broadly applied in colleges and universities, and  treat students as  free-willed individuals that choose between alternatives for an educational institution and particular fields of study. They are seen as rational actors with definite expectations about what they want in their educational experience, and that satisfaction occurs when their expectations are met or exceeded. Smart, Feldman and Ethington (2006) note a decline in the attention being paid to the attitudes and behaviors of faculties, administrators, and the college and university environments as contributors to student success.(p.2.). These insights are related to the “college impact” model of student success. Applying the Noel-Levitz  Adult Student Priorities Survey leverages a robust, nationally recognized, validated research instrument whose dimensions reflect the areas of importance emerging from the CGSC Participatory action research  (PAT) study (Long, 2009) and enables  quantitative research into the existing database of historical satisfaction measures currently applied in the college’s curriculum design process.

Qualitative research literature review

The James, Milenkiewicz and Buchnam (2008) application of Participatory Action Research (PAR) develops measureable action steps that can lead to  revolutionary transformations within educational institutions.  The use of measureable qualitative and quantitative data gives power and legitimacy to the insights it generates inside an organization that values rigor and validity, while respecting the intuitive insights of qualitative research.  Prasad describes many techniques of Narrative Inquiry that offer many techniques for interpreting and making sense of qualitative and quantitative data.  Reason & Bradbury, (2008) and Clandinin, (2007), describe these disciplines and crafts of action research and narrative inquiry as having a relatively mature foundation of theory and best practices, with enough variation between sub-disciplines as to create real and significant choices for researchers . Various methodologies in each discipline can be characterized according to their own logic that connects their particular world view (Creswell, 2009), ontology, research technique, data requirements, classification and analysis protocols, and strategies for sense-making of the results of inquiry. The combination of PAR  and narrative inquiry offer a robust set of strategies for generating insightful qualitative data with connections to quantitative data sets, which make them especially useful and practical to mixed methods researchers.

Mixed methods research literature review

Creswell and Plano Clark (2007) provide a broad yet detailed overview of the current state of the art of mixed methods research. They offer a working definition of the field derived from a survey of practice which proceeds from a deep understanding of high quality methods of practice, through choices of design and point to potentials  for the development of deep methodological theory. They offer mixed methods as an appropriate research strategy as a way to improve on the use of either qualitative or quantitative research alone.  In their view, mixed methods are more comprehensive, can answer more  types questions, encourages collaboration  and the deliberate incorporation of more than 1 worldview and is especially well suited for situations where practicality and pragmatism are prized (Creswell & Plano Clark 2007, p8-11).

Methods

A definition of mixed methods research

Creswell and Plano Clark (2007, p.5) define mixed methods research in the following way:

“As a method, it focuses on collecting, analyzing and mixing both quantitative and qualitative data in a single study or series of studies. Its central premise is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems than either approach alone”.

The type of design used and its definition

In this section I will briefly describe the theoretical shortcoming of treating mixed methods merely as a practical solution to improving upon either the qualitative or quantitative approach alone, and why a broader and deeper theory of mixed methods is appropriate for developing deep appreciation of complexity and uncertainty. I will briefly describe two different designs that would pass the test of the Creswell and Plano Clark ontology of mixed methods designs. With appropriate development, either would be approved for research within CGSC.

Comparison table  (adapted from Creswell & Plano Clark (2007)

	Design 1	Design 2
Choices	Explanatory	Exploratory
Theoretical Description	A 2 phase  design, where qual helps explain or build upon initial quan results (p.71)	2 phase design where qual results help develop or inform 2d phase quan inquiry
Description of application to CGSC	Round 1: the Noel-Levitz Adult Learner Satisfaction Survey is applied to a population, and results are tabulated, analyzed, compared against national  graduate student norms and in  a time series from the beginning, midpoint and endpoint of the academic year. Insights are developed   Round 2:  A series of focus groups and individual interviews are used to develop qualitatitive insights to  make sense of the quantitative findings	Round 1: a  set of participatory action research cycles identify areas of pressing concern to leaders, faculty and students within the college. A grounded theory is developed and constructs are defined by the community of practice, informed by theory from PAR outsiders.   Round 2: A quant survey is developed to explore deeply into issues and constructs developed by the PAR teams to cross check for validity, to confirm or deny, to support or modify the emerging grounded theory and provide the basis for future inquiries as selected by PAR teams. (Note: this is s summary of the actual process used at CGSC as the basis for this case study. The various tangents deriving from the initial rounds of inquiry generated my epistemological concerns with the pragmatic assumptions of mixed emthods)
Design notation	QUAN->QUAL	QUAL->QUAN
Justification	Needs qual to help explain significant, , non-significant, outlier or surprising quant results	Exploration is needed because:   1. no existing instrument 2. unknown variables 3. immature theory or framework Well suited for exploring a phenomenon or when researcher wants to generalize to other populations, test emerging theory or classifications (p. 75)
Variants	1. Follow-up explanations (quan results, insights need additional explanation)   2. Participant selection  (where a sampling of representative outliers are selected for follow-on inquiry)	1. instrument development model   2. taxonomy development model
Strengths	Straightforward implementation   Feasible for single researcher 2 section report of results Supports both single and multiple phase studies Appealing to quan researchers	1. easy to design, describe, implement and report   2. although initial emphasis is on qual, the quan phase makes it easier to appeal to quan audience 3. both variants supports multiphase studies well
Challenges	1. Time consuming   2. Decisions on which individuals to use by phase w/justification 3. Difficulties with IRBs 4. Deciding which results to explore 5. Specifying criteria for follow-on inquiry (before or after results?)	1. time consuming   2. difficult to specify phase 2 construct for IRB prior to phase 1 results 3. deciding up front which individuals to use in phase 2 4. which data to use in phase 2 instrument 5. deciding relevancy of phase 1 results for phase 2 taxonomy
Timing	2 phase sequential model	2 phase sequential model
Weighting	I think the QUAL(quan) model is more likely. This design relies on at least a mature enough state of theory to allow for initial quan inquiry, but we are more concerned with the interpretation and application of insights than in model or theoretical validation	the equal weighted choice is more logical; the desired outcome is an improvement to state of theory (quan) by either a better instrument or by an improved taxonomy (ontology). Yet the reliance on initial qual inquiry as a guide makes it at least co-equal to quan.
Mixing the data	Either Merging or Connecting is more likely than embedding.  Embedding implies a single phase, whereas this is defined as a 2phase design.  The improved explanation of initial quan findings is how the design could be “connected”. If the interpretation or meaning making is intended to create “rich description” then either variant of merging is logical	Connecting is by far the most logical design choice, as the 2 phases are explicitly linked; quan follows qual and the connection is either an instrument or a taxonomy.   There is a distinct “manufacturing or processing” aspect to this design, which does not seek to produce a rich description that is a blend, but rather produces a better quan framework as guided by the initial qual inquiry

Diagrams:

Research model 1: Explanatory:

Research model 2: Exploratory:

Analyzing the data:

In both models of mixed methods design the quantitative data would be subjected to power analysis, tests for relationship and causality.  The quantitative hypotheses are framed in the form of null hypotheses in order to determine if there were differences that could be attributed to a difference in instruments and what they are measuring (existing survey vs the Noel-Levitz survey); through time series tests to see if there is a treatment effect, and with the samples subjected to control variables to examine the effects of demographic, career experience, educational goals and faculty specific effects on the measures of satisfaction and importance.

Qualitative data would be subjected to thematic analysis according to the practices of the grounded theory, narrative inquiry and PAR traditions/  Narrative inquiry traditions are especially important here as outlined in Boje (2001).

Analyzing the mixed data would be drivcefn by the specific design selected as noted above.

Theoretical analysis of the consequences of choice in mixed methods design

Both designs would be interpretable as providing a deeper insight and understanding than a study restricted  to either of their individual qualitative or quantitative components. The functional definition of mixed methods (Creswell & Plano Clark, 2007, p.5) would lead decisionmakers, particularly of the pragmatic worldview, to ‘receive the wisdom of experts” and seek to straightforwardly apply the insights based on a justifiable belief that they now knew more about what was going on, and had in some fashion reduced the amount of uncertainty about the world around them. My central argument is that there are situations so complex and uncertain that no amount of research and conclusions drawn from best practices of the traditions of both quantitative and qualitative inquiry, and the best practices emerging from mixed methods as described by Creswell and Plano Clark. In fact my use of the word “situation” in the preceding sentence, is an implied assumption that there is such a thing in the real world as a definable “situation” or problem set  which may be bounded and contained by a problem solving, decisionmaking entity. While this construct is the basis for the post positivist tradition, which has endlessly proven its utility in countless settings, it is normal for pragmatists to conflate utility with reality.

It can be argued that there is could be a tacit agreement between constructionists and post-positivists to allow each other the primacy of method and interpretation based on typical problems, and indeed much work is being done to increase cross-discipline understanding, cooperation and integration. The common assumption between these two worldviews is that the end product of such effort is a measureable increase in our knowledge of the world as it is, from which we may exert more control and prediction by having reduced the amount of uncertainty by some amount. This tacit shared assumption I submit is expressed through the research practice of pragmatists who are “naturally” drawn to the mixed methods designs and practices described so well by Creswell and Plano Clark. Given the fertile and as yet only partially explored areas best suited for mixed method research it would be natural for the deeper philosophical theory or theories of mixed methods to be postponed, much as Smart, Feldman and Ethington (2006) found a willingness for researchers to revert to their preferred and more easily measured  research domains and begin to neglect the messy and challenging issues of environmental factors affecting student success. One is reminded of the story of the man who’d lost his key in a dark alley but was searching for it under the streetlight because the light was better there.

I am arguing that there are situations where even mixed methods are properly and rigorously applied, and interpreted in best professional practice, that the insights may serve only to help decision-makers appreciate the vastness of what they do not understand, and better act within an uncertain environment, humble in their ignorance, yet moved to action from values and on the basis of principles informed by the best practice of inquiry.

It is my contention that in those situations described so aptly as “wicked problems” by Rittel & Webber, (1973) that a  deep theory of mixed methods may be developed that is co-equal to that of qualitative and quantitative methods. I argue that mixed methods not only are useful in solving less-than-wicked problems, as described by Creswell and Plano Clark,  but most appropriate to engage with uncertainty and complexity for the express purpose of appreciating deeply the current situation. The deep theory of mixed methods I anticipate would require explicit inclusion of all 4 world views, since there is no a priori basis for excluding any of the 4. I thinkit quite likely that a reasonable assumption of a deep theory of mixed methods in fact could require an explicit inclusion of the best practices of each world view in some fashion, details to be determined, of course.

The shift in epistemological  perspective seems important to me,  and which should be developed in tandem to the directions for improvements in design and pure method described by Creswell and Plano Clark.  Checkland’s application of soft systems methodology,  artfully describes  “learning towards success” in a satisfying way (Checkland & Poulter, 2006).

The best expression of the theoretical stance towards irreducible complexity intersecting the human need for the state of nature or through any objective criteria (Boje, 2001) and in the work of Hayden White (1987) concerning the relationship between narrative discourse and the historical representation. These insights are causing me to reflect deeply on my own essentially pragmatic worldview and its underlying assumptions, and lead me inevitably back to the proposition that we need the methodological theory of mixed methods developed simultaneously with that of its design choices and specific methods.

References

Argyris, C.  (2008). Teaching smart people how to learn. Boston, Harvard Business Press.

Astin, A. (1999) Student involvement: A developmental theory for higher  education.  Journal of College Student Development (Sep/Oct, 19990 (Vol 40, No 5)

Beltyukova, S. & Fox, C. (2002) Student satisfaction as a measure of student development: Towards a universal metric.  Journal of College Student  Development (Mar/Apr 2002)

Checkland, P. & Poulter, J. (2006). Learning for action: A short definitive account of soft systems methodology and its use for practitioners, teachers, and students. West Sussex, England,  John Wiley & Sons Ltd.

Clark, M. & Rossiter, M.  (2008). Narrative learning in adulthood.  In S. Merriam (Ed) Third Update On Adult Learning Theory (pp 61-70). San Francisco,  Josey-Bass.

Creswell, J. (2009). Research design: Qualitative, quantitative and mixed methods approaches (Third ed.). Thousand Oaks, CA: Sage Publications, Inc.

Dorner, D. (1996). The logic of failure: Recognizing and avoiding error in complex situations. New York, Metropolitan Books.

Edmondson, A., and McManus, S. (2007). Methodological fit in management field research. Academy of Management review 32: 1159-1176

Fenwick, T.  (2008). Workplace learning: Emerging trends and new perspectives.  In S. Merriam (Ed) Third Update On Adult Learning Theory (pp 17-28). San Francisco,  Josey-Bass.

James, E. A., Milenkiewicz, M., Buchnam, A., (2008).  Participatory action research: Data driven decision making for school leadership. Thousand Oaks, CA: Sage Publications

Kearsley, G. (1997). The Virtual Professor: A Personal Case Study. Retrieved from http://home.sprynet.com/~gkearsley/virtual.htm

Lincoln, Y. (1983) Expectancy theory as a predictor of grade-point averages, satisfaction and participation in the college environment.  Annual meeting paper, Association for the Study of Higher Education, 1983.

Long, K. (2009). Participatory Action Research pilot study notes. Ft Leavenworth, KS: CGSC (unpublished).

March, J. (1994). A primer on decision making: How decisions happen. New York, The Free Press (Simon & Schuster Inc).

Prasad, P. (2005). Crafting qualitative research: Working in the postpositivist traditions. Armonk, New York, M. E. Sharpe.

Rittel, H., and M. Webber (1973). Dilemmas in a general theory of planning.   Policy Sciences, Vol. 4, pp 155-169.

Schon, D. (1987). Educating the reflective practitioner. San Francisco, Jossey-Bass.

Senge, P., McCabe,N., Lucas, T., & Kleiner, A. (2000). Schools that learn: A fifth discipline fieldbook for eductaors, parents and everyone who cares about education. New York, Doubleday.

Siemens, G., (2005). Connectivism: A learning theory for the digital age.  International Journal of Instructional Technology and Distance Learning 2 (10), 2005.

Smart, J., Feldman, K., & Ethington, C. (2006). Holland’s theory and patterns of college student success. Commissioned report for the national symposium on postsecondary student success: Spearheading a dialog on student success

Taylor, K. & Lamoreaux, A.  (2008). Teaching with the brain in mind.  In S. Merriam (Ed) Third Update On Adult Learning Theory (pp 49-60). San Francisco,  Josey-Bass.

TRADOC Pam 525-5-500. (2008).  Commander’s Appreciation and Campaign Design.

Trochim, W. Research methods knowledge base (2006). Types of designs.  Retrieved from http://www.socialresearchmethods.net/kb/desdes.php

Waldrop, M. (1992,2008). Complexity: The emerging science at the edge of order and chaos. New York, Simon & Schuster, Inc.

Watkins, T, (2009). Consumer theory in economics. San Jose State University Economics Department website. Retrieved from http://www.sjsu.edu/faculty/watkins/constheo.htm.

White, H.  (1987). The content of the form: narrative discourse and historical representation. Baltimore, The Johns Hopkins University Press.

Traders Roundtable: Maximum Compounded Return Versus Fear of Drawdown

In a recent traders roundtable discussion on the subject of balancing maximum gain with risk management the question was posed how to reconcile these two issues. A few definitions for the purposes of this essay are in order.

First let’s consider maximum compounded return to be defined as a system that is traded at every viable opportunity at the maximum level of acceptable risk with profits that are rolled into the total portfolio and with maximum acceptable risk in leveraging the markets money. This definition allows us to stay within natural risk limits but operate the machine as close to the red line of sustainable performance as possible.

Now let’s consider the fear of drawdown to be defined as the natural human response to operating power tools or heavy equipment in a dangerous setting as close to the edge as possible with full recognition of the consequences implied of potentially catastrophic failure. The human mind is capable of creating shades of distinction between various perceived levels of risk and these degrees of perception vary by individual. Some people are fully physically capable of walking within 5 feet of the edge of the balcony where as others can go right up to the edge and peer over and still remain in full normal natural control of their reactions. For each person though there is a line beyond which you judge that you are in dangerous territory in that extraordinary measures of protection and care must be taken because of the increased risk.

I am certain that there is a lot of evolutionary biology involved inside our brains which had to face this exact challenge on every front in prehistoric times or more technically, in the era of evolutionary adaptation. Even the most primitive caveman was certainly capable of appreciating the pay off of killing a mammoth for the future of survival needs of the tribe. And yet that same caveman was fully aware of the danger to himself that he took by stocking the huge beast. Sharpened by fear and hunger in the visions of a plentiful tomorrow, each caveman had to reconcile distention in some fashion and the successful ones passed on their adaptations to countless generations. This manifests itself in modern times in any place where risk and reward are brought together and we asked humans to make a balanced trade-off decision.

In every significant situation, and by significant I mean where impactful money is being rest for an impactful reward, the brain is flooded with chemicals which trigger flight or fight responses and invoke millions of years of stored up collective unconsciousness which shape and color our decisions and effectiveness of implementing those decisions. Trading is no different.

My sense is that for long-term safety and survival and given a trading system that generates a significant number of opportunities that long-term survival should drive us towards finding the minimum level of risk with which to trade to meet our specific financial goals. For those whose goal is expressed as maximum compounded rate of return, I suggest they are more likely than not to push it past the red line and come crashing down.

Without an appreciation of the real cost and friction associated with long-term trading in multiple market conditions that require constant adaptation is too easy to extrapolate the results of a few successful trades into next taxation that is far from being achievable. These false expectations are more likely than not to add to the level of stress and further degrade performance. My sense is that minimum risk levels rather than maximum risk levels are appropriate place to begin your inquiry into long-term trading as a potential career.

Therefore, I strongly suggest that you start with ways to use part-time small position sizing trading and learn to supplement your current income at your current standard of living and proceed in small stages, and getting larger only when supported by the evidence of long-term performance. It’s an incremental approach, it does not generate lottery size winds, but it will keep you in the game while you’re learning and keep your feet planted firmly on the ground.

Testing the Word 2007 Blog publishing template

This is to see how things come through when written in Word 2007 and pushed to the blog site.

Here is a sample picture:

 

Am wondering how tags and categories are handled?

Traders roundtable discussion: the issue of optimization in research

We were having a traders’ roundtable discussion on the topic of researching potential trading systems and the issue of optimization came up. This is a very important topic for traders who want to apply a systematic approach to trading markets. Here are some of the highlights of that discussion for you to consider as you prepare your trading strategies.

Typically when we think of optimization for a trading system we are looking at a process that incorporates multiple variables, parameters with different settings possible and perhaps a number of market filters or conditions which taken together with an exit strategy give us a multitude of ways to trade a particular concept or idea.

It normally begins with an idea the trader has based on insights fromtheory or from reflective practice where he believes the system gives a persistent advantage compared to the average market return  of simply buying and holding. It is also possible however that the edge may come from a brute force data mining operation that finds a statistical edge in some combination of market conditions. In other words, the insight comes from the result of massive computations and not from an intuitive or academic insight.

In either case , what we have is a system of multiple components, each of which can vary, and on an initial pass through with middle-of-the-road parameter settings we find a persistent edge in multiple markets with a statistical significance. Human nature being what it is, we would want to start testing different parameter settings for each component in order to determine the best mix for the most robust return and to find which of the parameters seem to have the most power when it comes to influencing the results. In statistics, this general approach is called factor analysis or principal component analysis.

In theory, you would want to find the absolute maximum return by finding the absolute optimum setting for each of the possible parameters and then take that into the market to begin trading. Taken to an extreme, this can produce a phenomenon of curve fitting or over optimization. What you can end up with is a system that would be perfect for the unique set of data conditions of the test. The problem of course is that the future may not ever show you that same data set again in your over optimize system will under perform much to your surprise.

The usual response to this phenomenon is to conduct testing with out of sample data. In other words designing the system on one data set and refining it to a certain degree and then testing it on a completely new set of market data to see if the edge persists. If you were systems development practice find you always discarding your systems after the out of sample test, it probably occurs as a result of over optimization.

In practice then, we want to find the trade-off between robustness of performance in multiple market conditions with out of sample testing that yields a persistent advantage in multiple market conditions but without trying to overturn the system for ideal conditions.

A way to keep this systematic approach in tune is to continue to monitor performance in a feed-forward approach that examines actual trading results to see how the performance results compare to the test and confirmation performance curves.

The bottom line: the more you rely on automatic trading systems, the more important your research and validation process becomes since you will not be using inexperienced traders override protocol to keep you from going off the deep end.

The future of education in a net-centric world

reinventinglife.org 

Surveys of CEOs and futurists alike all agree that the future is calling to us with a need for open systems, open thinking, collaborative organizations, workplaces and attitudes.  Net-centric  democratic education,  public and private movements for social justice, moral and honorable business enterprises,  and cross cultural communications all place a premium on skills, tools, and attitudes that encourage and multiply the network effect.

Doctoral research should reflect this same willingness to push boundaries, and encourage the use of collaborative teams of researchers and practitioners to explore collaborative theory and practice. If collaboration works in practice, there needs to be a theory of collaborative action, and the research in support of that practice should reflect the same principles as the work itself.

Like all new movements, there is a bootstrap problem when you try to go beyond conception into first action.  How do you create the initial conditions to generate the spark of collaboration which can ignite the passion and energy of research teams? The words sound good, but we need a place to try it on for size; We need supportive yet challenging places to discover, invent , develop and propagate the  best practices and techniques of collaborative knowledge creation.  We need places to test our theories, tell our stories and plan for the next cycle.

Dr Alana James’ growing community of practice at reinventinglife.org, centered on the principles and techniques of Participatory Action Research, is just such a node. It serves as an incubator for researchers and doctoral students seeking to develop the net-centric research effect. It goes beyond dry academic reports of practice, by being a live and vibrant laboratory for discovering, applying and reporting on the “theory-in-use” of open education.

It is a combination of challenging questions, a tool repository for new applications of digital learning and research, and is creating a circle of multiple competencies that will support  spin-off collaborations in a variety of settings that will propagate waves of change throughout the network like waves in a pond.

Dr James’ leadership in this area is paving a way for new approaches to research and practice and is exactly the kind of practice which needs support and recognition. She has the vision to imagine revolutionary positive changes in the world around her, the moral courage to invest in encouraging the Voices of students and co-researchers as we work to define and realize that future, and the skills to lead and encourage that change.

when post-positivism meets uncertainty

I am thinking about trying to figure out how to conduct research “properly” in the regions beyond the boundary of post-positivism.

Think of a farm that now borders the deep dark woods.

In the beginning we have a farm, surrounded by other farms, and over time, we haved figured out through trial and error, what works, even considering how variable the weather may be. The trial and error has been so successful that we have codified it into formal science and our success grew by leaps and bounds.

our farm now has expanded and on the border we encounter a deep, dark forest, whose boundaries we cannot see, only glimpse, and in whose depths we can see only shadows.

The farmland is the land where positivism and post-positivism excel. We are able to reduce and analyze problems and rely on the rules of cause and effect and the power of math to find answers and produce reliable solutions. It’s a land wherein all things are knowable.

In the forest, however, things change so much that our old rules don’t seem to apply. Things change faster there, unpredicatably, sometimes as if by chance, and at other times by rules that only sometimes seem to make sense, and the ruules seem to change as soon as we write them down. There are plenty of things we don’t yet understand. There seem to be things that are essentially unknowable. This is the land of complexity and chaos, where new rules sets are tentative and unproven. William Poundstone has written powerfully about these kinds of paradoxes in his book Labyrinths of Reason. The Sante Fe Institute in New Mexico researches this area in a scholarly manner.

It turns out that the epistemology of the farmland breaks down in the land of the forest. At the same time, the emerging rulesets of the forest don’t perform as well when you bring them into the farmland, because they are too tentative and uncertain, and in the farmland efficiency and certainty can be achieved and are rewarded.

People who would explore the forest know a lot less about that region than the people who explore the farmland know about the farms, and sometimes they talk past each other because they are coming from 2 different epistemologies, which in turn can create a disconnect at the ontological level itself (ie the nature of the world).

My sense is that in the “forest” of complexity, chaos and uncertainty, that mixed methods are going to be absolutely essential when it comes to developing an understanding of how to stay alive and thrive out there.

As we grow in knowledge we are able to push the boundaries of the farm out into the forest. everything that is coming out of the study of the science of complexity and uncertainty and chaos, however, suggests that there is some vast amount of forest that must remain out there beyond our ability to “domesticate” with the power of post-positivism.

This doesn’t disrespect post-positivism and all the good that it can do, it only says that the world is stranger than we CAN imagine so far, and that in the forest a different world view is useful.

The Sante Fe folks have a combination of pragmatism and constructionism threaded throughout their work, while at the same time having a deep and abiding respect for science and scientific method, as evidenced by the Nobel laureates they have on staff

what's your judgement on the state of the farm vs forest discussion? i pick #2

reflecting on the forest past the boundary of post-positivism

Creswell talks about the 4 worldviews that shape the  research goals, objectives, methods, interpretive measures, and sense making of researchers.  I inherited a post-positivist outlook from my formative years working in a machine shop with designers, engineers, craftsmen,  and other shop rats. My years of experience as a planner in the Army helped me appreciate the pragmatic worldview, which drives you to focus on results by any means necessary and without an overly strict compulsion to stay committed to a particular method, at the cost perhaps of sacrificing deep understanding or the pursuit of Truth.

I have rejected advocacy as a legitimate worldview for most of my life, with the exception of the values of freedom and democracy, as I am unwilling to committ on faith to untested beliefs, and have witnessed how advocacy can lead people to distort findings and spin results.

Constructivism seems more and more useful to me as I investigate social situations and complex human problems and opportunities.

The doctoral program is really helping me examine my beliefs and knowledge claims, and I have seen the positive results in my teaching that have emerged fron the cycles of reflective learning.

I have hit a tipping point where I must begin the convergent writing and thinking that will result in the dissertation, and it is with some sadness that I recognize that I have to put some of the divergent thinking aside in order to focus on task accomplishment.  This part of the journey wont be as much fun. I wish I could just push a button and get thru it and get back to scouting new knowledge areas which is how i prefer to spend every waking moment, in order to feed my dreams.