Dilemmas for Doctoral candidates

October 4, 2014

Doctoral candidates face the two challenges of making a contribution to knowledge and of defending their claims against the toughest of scrutiny. The methodology of conceptual mapping and examination of dilemmas offers an additional research approach

The principles were outlined in 2006 in the first edition of the book Dilemmas of Leadership, a post-graduate teaching text. An earlier LWD post gives a brief overview.

The approach

The approach draws on a social constructional treatment of knowledge generation and validity testing. In its initial use, it was offered to business executives to assist in their evaluation of leadership texts. In this post, it illustrates a way of simplifying the epistemology offered on doctoral courses in business and the social sciences. In its earlier application, executive MBA students are encouraged to study emerging leadership news stories, deriving a conceptual map from each. This ‘map reading’, like any life skill, improves with active and regular practice. ‘Map-testing’ includes processes found in research methods courses for investigating the reliability of the information and its validity. These two processes feed into the third, in which the derived and tested maps of a story are examined and compared with the personal map of the student. This process permits personal and experiential learning. Termed ‘map making’ this is the revised map of the student beliefs about leadership for personal reflection and class discussion.

Beyond the basic system A range of additional procedures are introduced to support the basic system. These include a search for dilemmas as significant hard-to-resolve decisions confronting the actors in the stories, these include the personal dilemmas for the student (‘the most important leader you study is yourself’).

Extending the process to doctoral research The process offers possibilities for modification for direct application in research studies even at the level of doctoral investigations. A workshop opportunity has arisen which will be reported here in a future post.

Update for Doctoral students The brief for the doctoral workshop was The Evolution of Leadership and Management and its links with Theories of Organisation: Bringing it all together. The syllabus indicated that the workshop follows the student’s journey through different perspectives on organisation and management theory (modernism, scientific management & Bureaucracy); neo-modernism (human relations and culture management); critical perspectives; postmodernist organisation theory). Students were advised to revise these topics to be prepared for discussion at the workshop.

Further updates

Further updates will report on the workshop and add discussion points from subscribers.

October 24th 2014

An illustration of the mapping approach applied to a leadership text which asks the question ‘are managers sacked for breaking the rules and leaders sacked for not breaking them?’

November 1st, 2014

Bridging the gap between the empirical and the social

One substantial difficulty for doctoral students is the gulf between the methods of enquiry in the empirical sciences and the social sciences. The former retains the methodology of the dominant rational model. This perspective is one I acquired in my schooldays and have retained as a technical manager trained to examine technical and economic problems through the methodology of scientific inquiry.

My attraction to a second approach involving the methodology of the social sciences grew, as I became familiar with the ideas of the social construction of reality. Nevertheless, I felt that moving completely from a scientific to a social scientific approach was likely to be switching from one horn of a dilemma to another.

November 3rd 2014

Two authors helped me find a way of bridging the gap.

The first was Professor Gail Fairhurst in her book Discursive Leadership in which she shows how social constructionist approaches are able to co-exist successfully with the more dominant model of cognitive psychology.

The second insight came from the work into what Jim Collins called ‘the  Genius of the And’.  Fairhurst and Collins had in quite different ways addressed a way of dealing with dilemmas. In each case, the approach was a form of creativity to escape from ‘either-or’ thinking.  The outcome is a bridging of the gap between the dominant rational model of the sciences and the social constructionist approach of the social scientist

January 5th 2015

This leadership case is a nice way to test understanding of ways of applying a qualitative analysis


Towards a Better-informed Climate Debate

December 7, 2009

Informed debate on climate change is hindered by naïve interpretation of trend data. I have more confidence in the understanding of stock-market traders than in that of most climate change commentators

The more I look at the arguments on climate change, the more concerned I am at the crude misapplication of statistical methods that I find in them. This note is at least as reliable as views being expressed by many major contributors to the climate debate.

Lies, damn lies and crappy statistics

How much faith do you have in statistics? If you have formed a view on climate change, you should at least also have a view on what can and can not be concluded from a statistical examination of a database. And on the credibility of the analyses made by other commentators. My position is this. Statistics are generally considered to be the collection, analysis and interpretation of data applying various mathematically derived methods. That will do as a working definition. Data is plural of a datum. Data are the bits of stuff collected – the daily temperature readings from a weather station for example, or for a patient in hospital, or the price of a Corporate stock. As a matter of fact all of these data look rather similar when visualized as a time series.

We are pretty sure that the fluctuations of the patient’s temperature will ‘spike’ but in general will return to a standard basal level. Stock-prices as we know too well are less predictable. And as for charts of data about climate change, well these are in a different class altogether.

The First law of statistics

The first law of statistics for me is that statistical interpretation has to follow statistical understanding. Opinion is OK, informed judgement is different from opinions. Public debate tends respects the rights of individuals to express honestly expressed views. Phone-in programmes and the majority of blogs are not much more than that.

The second law of statistics

There is no safety in numbers if you don’t know what they mean. This brings me to one difference between climate debate numbers and those stock market and patient temperature charts. Climate debate numbers are increasingly highly complex abstractions which have been developed to examine a theoretical model.

The third law of statistics

Correlation is not the same as causality. My old statistics teacher used to quote the example of storks found perching on houses with the greater number of children born in them in one study. There was a correlation (perhaps because big warm houses attracted storks) but not one which can confirm a causal relationship between stork presence and human birth data.

The fourth law of statistics

Trend lines are treacherous.

I base this on the mathematical fact that there are a very large number of ways of drawing a line through thirty of so data-points. The zigzags of the data points are smoothed out in all sorts of cunning ways. The simplest smoothing is a straight line. It invites you to decide what all the deviations from that straight line mean.

A simple illustration

I have been playing around with a simple way of visualizing trend data. Let’s take the yearly data on temperature changes over a thirty year time period, the recent battle-ground in the climate debate

Many charts have appeared showing a trend as a straight line (so we have to beware of the fourth law). I find the following little thought-experiment revealing. Take a thirty point trend chart and select the highest and lowest items on the chart.

Mentally add each point in turn as the first datum of the chart. Almost always, one or both new visualizations change your perspective of the trend line and where the chart is going into the future.

Now repeat the experiment with each of the same two points in turn at the end of the trend chart. You will again find the visualizations offering one or maybe two fresh perspectives

Doesn’t that just confuse the issue?

Maybe it does. It weakens confidence in just what the trend-line might be telling you. But perhaps a bit more confusion and a bit less conviction is what is needed at the moment. Anyway, I do hope you will be able to contribute to more productive discussions on climate change in future.

Notes

I am no Fellow of the Royal Society of Statisticians, but I check my views from time to time with someone who is. The post captures my beliefs as a relative outsider to the Climate debate. I am suggesting the ‘laws of statistics’ in the sense of guiding principles on which I develop my case, rather than universal truths. Nor am I suggesting that visual inspection of trends is a substitute for careful application of statistical testing. But developing skills of visual inspection may enable more people to develop a sense of what a trend-curve might be signalling, and have a more informed discussion with those generating and interpreting such data.