Introducing the mean absolute deviation ‘effect’ size

International Journal of Research and Method in Education

Volumn 38, Issue 2, 2015, Pages 105-114

  Gorard, Stephen ^a  

^a DURHAM UNIVERSITY   (United Kingdom)

Author keywords

absolute correlation; analysing population data; effect size; mean absolute deviation; randomised trials

Indexed keywords

EID: 84922224537     PISSN: 1743727X     EISSN: 17437288     Source Type: Journal    
DOI: 10.1080/1743727X.2014.920810     Document Type: Article

References (33)

1. Amir, E. 2012. “On Uses of Mean Absolute Deviation: Decomposition, Skewness and Correlation Coefficients.” METRON – International Journal of Statistics LXX (23): 145–164.
2. Anand, M., and Y. Narasimha. 2013. “Removal of Salt and Pepper Noise from Highly Corrupted Images Using Mean Deviation Statistical Parameter.” International Journal on Computer Science and Engineering 5 (2): 113–119.
3. APA (American Psychological Association). 2009. Publication Manual of the APA. 6th ed. Washington, DC: American Psychological Association.
4. Barnett, V., and T. Lewis. 1978. Outliers in Statistical Data. Chichester: John Wiley and Sons.
5. Cahan, S., and E. Gamliel. 2011. “First among Others? Cohen's d vs. Alternative Standardized Mean Group Difference Measures.” Practical Assessment, Research and Evaluation 16 (10): 1–6.
6. Coe, R. 2002. “It’s the Effect size, Stupid.” Presentation to British Annual Research Association Conference, Exeter, September 2002.
7. Cooper, L., and F. Shore. 2008. “Students’ Misconceptions in Interpreting Center and Variability of Data Represented via Histograms and stEm-and-Leaf Plots.” Journal of Statistics Education 16 (2): 1–13.
8. Eddington, A. 1914. Stellar Movements and the Structure of the Universe. London: Macmillan.
9. Fama, E. 1963. “Mandelbrot and the Stable Paretian Hypothesis.” Journal of Business 36 (4): 420–429.
10. Fisher, R. 1920. “A Mathematical Examination of the Methods of Determining the Accuracy of Observation by the Mean Error and the Mean Square Error.” Monthly Notes of the Royal Astronomical Society 80: 758–770.
11. Gorard, S. 1999. “Keeping a Sense of Proportion: The ‘Politician's Error’ in Analysing School Outcomes.” British Journal of Educational Studies 47 (3): 235–246.
12. Gorard, S. 2005. “Revisiting a 90-Year-Old Debate: The Advantages of the Mean Deviation.” The British Journal of Educational Studies 53 (4): 417–430.
13. Gorard, S. 2006. Using Everyday Numbers Effectively in Research: Not a Book about Statistics. London: Continuum.
14. Gorard, S. 2010. “All Evidence is Equal: The Flaw in Statistical Reasoning.” Oxford Review of Education 36 (1): 63–77.
15. Gorard, S. 2013. Research Design: Robust Approaches for the Social Sciences. London: Sage.
16. Gorard, S. 2014a. “The Widespread Abuse of Statistics by Researchers: What is the Problem and what is the Ethical Way Forward?” Psychology of Education Review 38 (1): 3–10.
17. Gorard, S. 2014b. “Confidence Intervals, Missing Data and Imputation.” International Journal of Research in Educational Methodology 5 (3): 693–698.
18. Gorard, S. 2014c. “A ‘New’ Correlation Coefficient.” Accessed March 25, 2014. https://www.dur.ac.uk/education/research/current_research/quantitative-methods/resources/
19. Gorard, S., and C. Taylor. 2002. “What is Segregation? A Comparison of Measures in Terms of Strong and Weak Compositional Invariance.” Sociology 36 (4): 875–895.
20. Gorard, S., G. Rees, and J. Salisbury. 2001. “The Differential Attainment of Boys and Girls at School: Investigating the Patterns and their Determinants.” British Educational Research Journal 27 (2): 125–139.
21. Hampel, F. 1997. Is Statistics Too Difficult? Research Report 81, Seminar fur Statistik, Eidgenossiche Technische Hochschule, Switzerland.
22. Hao, Y., H. Flowers, M. Monti, and J. Qualters. 2012. “U.S. Census Unit Population Exposures to Ambient Air Pollutants.” International Journal of Health Geographics 11 (3). doi: 10.1186/1476-072x-11-3
23. Hinton, P. 1995. Statistics Explained. London: Routledge.
24. Hižak, J., and R. Logoža. 2011. “A Derivation of the Mean Absolute Distance in One-Dimensional Random Walk.” Technical Journal 5 (1): 10–16.
25. Hoaglin, D., F. Mosteller, and J. Tukey. 1983. Understanding Robust and Exploratory Data Analysis. San Francisco: John Wiley & Sons.
26. Huber, P. 1981. Robust Statistics. New York: John Wiley and Sons.
27. Lipsey, M., K. Puzio, C. Yun, M. Hebert, K. Steinka-Fry, M. Cole, M. Roberts, K. Anthony, and M. Busick. 2012. Translating the Statistical Representation of the Effects of Education Interventions Into more Readily Interpretable Forms. Washington, DC: Institute of Education Sciences.
28. Murtonen, M., and E. Lehtinen. 2003. “Difficulties Experienced by Education and Sociology Students in Quantitative Methods Courses.” Studies in Higher Education 28 (2): 171–185.
29. Sari, S., H. Roslan, and T. Shimamura. 2012. “Noise Estimation by Utilizing Mean Deviation of Smooth Region in Noisy Image.” Fourth international conference on computational intelligence, modelling and simulation. http://eprints.uthm.edu.my/3265/1/4871a232.pdf
30. Science, Math and Philosophy. 2014. “Formula to Quickly Convert Standard Deviation to Absolute Mean Deviation.” Accessed April 22, 2014. http://forumserver.twoplustwo.com/47/science-math-philosophy/formula-quickly-convert-standard-deviation-absolute-mean-deviation-1195849
31. Stigler, S. 1973. “Studies in the History of Probability and Statistics XXXII: Laplace, Fisher and the Discovery of the Concept of Sufficiency.” Biometrika 60 (3): 439–445.
32. Tukey, J. 1960. “A Survey of Sampling from Contaminated Distributions.” In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, edited by I. Olkin, S. Ghurye, W. Hoeffding, W. Madow, and H. Mann. Stanford: Stanford University Press.
33. Watts, D. 1991. “Why is Introductory Statistics Difficult to Learn?” The American Statistician 45 (4): 290–291.