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Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab(R) based toolbox () that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand.

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BackgroundOlder people are at risk for health decline and loss of independence. Lifestyle interventions offer potential for reducing such negative outcomes. The aim of this study was to determine the effectiveness and cost-effectiveness of a preventive lifestyle-based occupational therapy intervention, administered in a variety of community-based sites, in improving mental and physical well-being and cognitive functioning in ethnically diverse older people.MethodsA randomised controlled trial was conducted comparing an occupational therapy intervention and a no-treatment control condition over a 6-month experimental phase. Participants included 460 men and women aged 60–95 years (mean age 74.9±7.7 years; 53% <$12 000 annual income) recruited from 21 sites in the greater Los Angeles metropolitan area.ResultsIntervention participants, relative to untreated controls, showed more favourable change scores on indices of bodily pain, vitality, social functioning, mental health, composite mental functioning, life satisfaction and depressive symptomatology (ps<0.05). The intervention group had a significantly greater increment in quality-adjusted life years (p<0.02), which was achieved cost-effectively (US $41 218/UK £24 868 per unit). No intervention effect was found for cognitive functioning outcome measures.ConclusionsA lifestyle-oriented occupational therapy intervention has beneficial effects for ethnically diverse older people recruited from a wide array of community settings. Because the intervention is cost-effective and is applicable on a wide-scale basis, it has the potential to help reduce health decline and promote well-being in older people.Trial Registrationclinicaltrials.gov identifier: NCT0078634.

Various statistical methods, developed after 1970, offer the opportunity to substantially improve upon the power and accuracy of the conventional t test and analysis of variance methods for a wide range of commonly occurring situations. The authors briefly review some of the more fundamental problems with conventional methods based on means; provide some indication of why recent advances, based on robust measures of location (or central tendency), have practical value; and describe why modern investigations dealing with nonnormality find practical problems when comparing means, in contrast to earlier studies. Some suggestions are made about how to proceed when using modern methods.

This paper reviews and offers tutorials on robust statistical methods relevant to clinical and experimental psychopathology researchers. We review the assumptions of one of the most commonly applied models in this journal (the general linear model, GLM) and the effects of violating them. We then present evidence that psychological data are more likely than not to violate these assumptions. Next, we overview some methods for correcting for violations of model assumptions. The final part of the paper presents 8 tutorials of robust statistical methods using R that cover a range of variants of the GLM (t-tests, ANOVA, multiple regression, multilevel models, latent growth models). We conclude with recommendations that set the expectations for what methods researchers submitting to the journal should apply and what they should report. Keywords Robust statistical methods, assumptions, bias ROBUST ESTIMATION 3 Robust statistical methods: a primer for clinical psychology and experimental psychopathology researchers OverviewThe general linear model (GLM), which is routinely used in clinical and experimental psychopathology research, was once thought to be robust to violations of its assumptions. However, based on hundreds of journal articles published during the last fifty years, it is well established that this view is incorrect. Moreover, modern methods for dealing with the violations of these assumptions can result in substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. We begin with an overview of the key assumptions underlying the GLM. We then review various misconceptions about how robust the GLM is to violations of those assumptions and look at the effects that violations can have. We end the first section by looking at the evidence that psychological data, in general, are likely to violate the assumptions of the GLM.In part 2 of the paper we overview a selection of ways to deal with violations of assumptions that fall under the headings of data transformation, adjustments to standard errors, and robust estimation. In the final part, we present 8 tutorials that use datasets relevant to this journal to show how to implement a selection of techniques (robust estimators for model parameters and standard errors) for designs common to this journal (comparing dependent and independent means, predicting continuous outcomes from continuous predictors and longitudinal designs). ROBUST ESTIMATION 4The assumptions of the general linear model Critical assumptionsPsychology researchers (generally) and those with interests in psychopathology (specifically) typically apply variants of the general linear model to their data. In this model, an outcome variable (Y) is predicted from a linear and additive combination of one or more predictor variables (X). For each predictor there is a parameter that is estimated from the data (!) that represents the relationship between the predictor and outcome variable if the effects of other predictors in the model are held constant. There ...

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