data analysis in psychological studies


ability to concentrate or deal with routine tasks, which in turn damage recuperation. Tis study is concerned with ameliorative melatonin treatment, but Asma, a GP with a large number of patients in a socially deprived area of a European conurbation for whom chronic sleep disruption is a common factor in such recuperation problems, uses free online statistical analysis tools, such as those for nonparametric testing at Vassar College’s website, to refine non-pharmaceutical approaches to breaking similar cycles. Tough it is a natural extension


Further information


Advise Analytics adviseanalytics.com


Guardian data www.guardian.co.uk/data


Statsoft www.statsoſt.co.uk


Systat Software www.systat.com


Tableau Software www.tableausoſtware.com


of the same thinking as the sleep deprivation issue, I was separately set off along the psychosomatic track by an email enquiry about statistical methods. Twenty-year-old student Aqib Ashraf is interested in the association between psychological state and resistance to viral infection in his peers, and wants to design his research with sound data analysis in mind. Tere is a lot of assumption and anecdotal


report in this area, but some quantitative work has also been done. Sheldon Cohen of Carnegie


Mellon University’s Department of Psychology, who has, with various collaborators, pursued this line over a period of 20 years or so, shows that subjects show a statistically significant relation between positive emotional style (PES) and not only reduced reporting of symptoms, but objectively verifiable rates of rhinovirus and influenza infection. Cohen concludes[12]


on the basis of a


trial with almost 200 subjects that ‘positive emotions play a larger and more important role in disease risk and health


complaints than previously believed.’ Taking a longer and wider view of linkage


between psychological states and bodily wellbeing, Schafer and Ferraro (University of Toronto and Purdue University, respectively) conducted a multivariate statistical analysis of longitudinal data from just under 3,000 adults. Teir results show[13]


a high degree


of correspondence between incidence of declared childhood stressors, such as abuse or financial strain, and probability of a range of


physical illnesses later in life. Aſter allowing for confounding factors, there is no statistically sustainable evidence of influence on levels of cancers, strokes or heart problems, but 15 identified linkages with probabilities below five per cent include conditions as diverse as haemorrhoids, sciatica and emphysema. Te authors draw an approximate equivalence between the aggregate psychosomatic effects of childhood misfortune on adult disease avoidance and ‘the combined effect of moderate lifetime smoking and obesity.’ I opened by saying that statistical data analysis


is vital to psychology, but the traffic is not one way. Te psychology of data producers, users and consumers is an inescapable factor in the manner and quality of analytic process, which in turn affects the scientific validity and value. Tis realisation has (see box: Psyching out the data) strongly shaped the presentation of data and lies behind the evolution of exploratory data analysis which so oſten opens up avenues of research for more formal and traditional methods.


References and Sources For a full list of references and sources cited in this article, visit www.scientific-computing.com/ features/referencesaug12.php


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