Michael
Wood: academic interests
michael.wood@port.ac.uk
or michaelwoodslg@gmail.com
I am now
retired but am keen to carry on working on a few of the themes covered by the
links below. The main ones are the virtues of simplifying
academic knowledge, especially statistics,
and the peer review system.
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Making sense of statistics: a
non-mathematical approach (Book published by
Palgrave, 2003) |
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Simple knowledge: why academic knowledge should be simplified |
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Brief notes on
statistics: old version, new version |
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Brief notes on research methods
(without the pointless jargon) |
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Other teaching
notes, slides, etc |
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A few
conference presentations |
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Times Higher Scholarly Web column (26 June, 2014) on
my blog post about the
closure of Cambridge University |
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Resample.xlsx: a
spreadsheet for resampling, bootstrapping, etc |
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Ancestral memoirs: grandfather,
great aunt |
Selected articles
Simple Methods for Estimating
Confidence Levels, or Tentative Probabilities, for Hypotheses Instead of P
Values, Methological Innovations,
2019. (A similar argument is presented in earlier papers: P
values, confidence intervals, or confidence
levels for hypotheses? and Liberating research from null
hypotheses: confidence levels for substantive hypotheses instead of p values.)
How sure are we? Two approaches to statistical inference. (2018). arXiv:1803.06214 [stat.OT] or click here.
Making
statistical methods more useful: some suggestions from a case study. Sage Open,
vol. 3, no. 1, 2013 or click here. Spanish version.
Journals,
repositories, peer review, non-peer review, and the future of scholarly
communication (2013), arXiv:1311.4566 [cs.DL] or click here.
Maths should not be hard: the case for making academic knowledge more palatable. Higher Education Review, 34(3), 3-19, 2002. Click here for a copy.
I’ll make
it simple. Times
Higher, 30 August 2002.
Simplifying Academic Knowledge: Opportunities,
Benefits and Barriers (2015). https://ssrn.com/abstract=2687046 or click here.
The Pros and Cons of Using Pros and Cons for
Multi-Criteria Evaluation and Decision Making (2009). https://ssrn.com/abstract=1545189 or http://dx.doi.org/10.2139/ssrn.1545189
The role of simulation
approaches in statistics. Journal of Statistics Education, 13(3), http://www.amstat.org/publications/jse/v13n3/wood.html, 2005.
Bootstrapped confidence
intervals as an approach to statistical inference. Organizational
Research Methods, 8(4), 454‑470, 2005. click here
The reliability of peer reviews of papers on
information systems. Journal of Information Science, 30(1), 2-11
(with Martyn Roberts and Barbara Howell, 2004). click here
The journal
of everything. Times Higher, 22 April, 2010.
Citation
games: comments on the paper by Annette Risberg. Notework: Newsletter of the Standing Conference on
Organization and Symbolism, May 2005, 26-8.
The case for crunchy methods in practical mathematics. Philosophy of Mathematics Education Journal, 14, 2001. click here or here
The Pros and Cons of Using Pros and Cons for
Multi-Criteria Evaluation and Decision Making (2009). https://ssrn.com/abstract=1545189 or http://dx.doi.org/10.2139/ssrn.1545189
Are “qualitative” and “quantitative” useful terms for describing research? Methodological Innovations Online5(1) 56-71 (with Christine Welch, 2010). Copy in SSRN
Anecdote,
fiction and statistics - the three poles of empirical methodology. Click here.
Prospecting research:
knowing when to stop. Marketing Letters, 12(4), 299-313 (with
Richard Christy, 2001). click
here
Sampling for
possibilities. Quality & Quantity, 33, 185-202 (with Richard
Christy, 1999). click
here
Researching possibilities
in marketing. Qualitative Market Research, 2(3), 189-196 (with
Richard Christy, 1999). click
here
The notion of the
customer in total quality management. Total Quality Management, 8(4),
181-194, 1997. click
here
Computer packages as
cognitive paradigms: implications for the education of accountants. Journal
of Accounting Education, 15(1), 53-69 (with Philip Cahill and James Hicks,
1997). click
here
Statistical inference using bootstrap confidence
intervals. Significance, Volume 1 (4), 180-182, 2004. click here
Statistical methods for monitoring service processes (1994). International Journal of Service Industry Management, 5(4), 53-68. click here
Bootstrapping confidence
levels for hypotheses about regression models (2012). arXiv:0912.3880v4 [stat.ME]
Statistical process monitoring in the 21st century (2002). In J. Antony & D. Preece (eds), Understanding, managing and implementing quality: frameworks, techniques and cases (pp 103-119). London: Routledge. click here
The use of resampling for estimating control chart limits. Journal of the Operational Research Society, 50, 651-659 (with Mike Kaye and Nick Capon, 1999). click here
User-friendly statistical concepts for process
monitoring. Journal of the Operational Research Society, 49(9),
976-985 (with Nick Capon and Mike Kaye, 1998). click here