Introducing the mean absolute deviation ‘effect’ size

Gorard, Stephen

doi:10.1080/1743727x.2014.920810

Cited by 24 publications

(20 citation statements)

References 18 publications

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“…Using the mean absolute deviation as a preferred measure of dispersion, it is then possible to modify other forms of analysis to make them easier to portray and more robust in face of extreme scores. There is a mean absolute deviation version of the effect size, correlation coefficient and regression model, for example (Gorard, , ,b). However, useful though these may be for the future, not all are yet ready for widespread use and none of them leads to the kind of simplification that follows from simply not using significance testing (and all its disguised forms).…”

Section: What Are the Alternatives?mentioning

confidence: 99%

Rethinking ‘quantitative’ methods and the development of new researchers

Gorard

2015

Review of Education

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The argument in this paper, supported by literature, evidence, illustrations and simulations, is as follows. The general standard of social science research still needs improvement, even after a number of attempts have been made to improve it. The most recent attempt in the UK is the Quantitative Methods Initiative, based on widening the appeal of social science that involves numbers. However, before any initiative like this could be successful what is envisaged as 'quantitative' methods needs to be radically altered to make it simpler and more logical. All work based on standard errors-significance testing, confidence intervals, power calculations, multilevel modelling and so on -requires complete random samples/groups as a mathematical necessity. Since complete random samples are so rare in social science, work based on standard errors is largely irrelevant and should not define what 'quantitative' methods are. The paper then shows that the logic of significance testing, power and confidence intervals does not work anyway (even with a perfect random sample). Also, estimated standard errors are often inaccurate, and that inaccuracy propagates into the calculation of test statistics. The inaccuracy is worsened by missing data and sometimes even more so by attempting to compensate for that missing data. The ensuing results cannot be trusted. Once released from consideration of probability distributions and the like, 'quantitative' methods can be further simplified. The consequence would be a social science in which the logic of working with numbers would be the same as the logic of working with any other kind of data. This would then have formative implications for so-called 'qualitative' methods and leads to the realisation that there are, in fact, just methods. The things that make good research or trustworthy results are the same, and independent of the methods used-rigorous design, pre-specification as far as possible, full reporting, full consideration of missing data and lost cases, and setting out the complete logical argument from research findings to implications.

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Section: What Are the Alternatives?mentioning

confidence: 99%

Rethinking ‘quantitative’ methods and the development of new researchers

Gorard

2015

Review of Education

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“…Clearly more work would be needed than presented in this introductory paper to make either RA1 or RA2 something that could be used safely in practice, and offered as a genuinely simpler alternative to R. Analysts would also need more practice in assessing the meaning of a new coefficient value. It is not as easy, for example, as using the mean absolute deviation to create a new effect size [11]. But the work so far shows that in principle the mean absolute deviation can be used instead of the standard deviation in a variety of settings, probably throughout descriptive statistics [12].…”

Section: Resultsmentioning

confidence: 99%

“…The paper forms part of an attempt to simplify the use of numeric analysis, to make it more 'everyday', for the benefit of both analysts and the consumers of evidence. The approach includes replacing the standard deviation (and its needless squaring and square rooting) with the absolute deviation where possible, introducing a robust absolute deviation effect size [11,12], and absolute deviation regression models, and of course the removal of significance testing and all of its components from everyday analysis [13].…”

Section: Introductionmentioning

confidence: 99%

An Absolute Deviation Approach to Assessing Correlation

Gorard

2015

BJESBS

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTThis paper describes two possible alternatives to the more traditional Pearson's R correlation coefficient, both based on using the mean absolute deviation, rather than the standard deviation, as a measure of dispersion. Pearson's R is well-established and has many advantages. However, these newer variants also have several advantages, including greater simplicity and ease of computation, and perhaps greater tolerance of underlying assumptions (such as the need for linearity). The first alternative approach simply divides the co-variance by the mean absolute deviation(s) instead of the standard deviation as in Pearson's R. The second alternative uses the sum of each pair of deviations in x and y instead of the covariance, and again uses the mean absolute deviation(s) as the denominator. All three are compared to one another using 30,000 simulations based on 100 pairs of random numbers. The substantive findings are the same for each approach, and the 'coefficients' correlate with each other (using R) at +0.99 to 1.00. The three approaches also give the same substantive findings when trialled with real-life secondary datasets. This introduction of simpler kinds of correlation forms part of an attempt to simplify the use of numeric analysis, to make it more 'everyday', for the benefit of both analysts and consumers of evidence.

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“…Among them are overfitting (Allamy, 2014;Zhang et al, 2018) and underfitting (Allamy, 2014), data scarcity, the need for normalization, data imbalance and outlier influence (Khamis, Ismail, Khalid, & Tarmizi Mohammed, 2005). These issues were addressed using methods such as dropout (Park & Kwak, 2017), augmentation (jitter (pure Gaussian noise) and warp (Gaussian noise on Bezier-Curves))(Le Guennec, Malinowski, & Tavenard, 2016;Um et al, 2017;Velasco, Garnica, Lanchares, Botella, & Ignacio Hidalgo, 2018;Xiao & Xu, 2012), synthetic minority oversampling technique (SMOTE) (Fernández, García, Herrera, & Chawla, 2018), interquartile range (IQR) scaling (Mizera et al, 2004) and median absolute deviation (MAD) (Gorard, 2013) based Gaussian noise data completion. The complete process is shown in Figure 2.…”

Section: Annsmentioning

confidence: 99%

The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network

et al. 2020

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Reservoirs are installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs' sustainability because it significantly reduces the reservoirs' original capacity. The present paper attempts to evaluate the global reservoir siltation problem with the optimism bias theorem introduced by Kahneman and Tversky and applied to infrastructural mega-projects by Flyvbjerg and Ansar using artificial neural networks (ANNs) algorithms for large Japanese reservoirs. Japan possesses suitable long-term data and a legal directive concerning the sediment capacity siltation duration, which serves as a valid guide to check whether, over the past 100 years, engineers, planners and managers were capable of judging the sediment input correctly. Various ANN models were established to emulate Japanese reservoir siltation behavior. The networks demonstrate that reservoirs in Japan suffer from optimism bias. In contrast to the law, the dead storage volume of an average dam is supposed to reach capacity after 52 years. This finding joins the overall observation that mega-projects generally and globally suffer from optimism bias. The emulations were subsequently screened for a presumed influence of governance actions, namely, indicating plus monitoring and the change in the market competition situation. While reservoir siltation appears to continue regardless of the level of competition in public procurement, monitoring directives appear to have a considerable impact on improved siltation management, which demonstrates that dedicated governance action can significantly strengthen the sustainable behavior of key infrastructure elements such as reservoirs.

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Introducing the mean absolute deviation ‘effect’ size

Cited by 24 publications

References 18 publications

Rethinking ‘quantitative’ methods and the development of new researchers

Rethinking ‘quantitative’ methods and the development of new researchers

An Absolute Deviation Approach to Assessing Correlation

The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network

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