Modern robust data analysis methods: measures of central tendency

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.     

Animals and the agency account of moral status

Philosophical Studies - In this paper, I aim to show that agency-based accounts of moral status are more plausible than many have previously thought. I do this by developing a novel account of...     

Attitude Toward Christianity and Psychological Type: A Survey Among Religious Studies Students

A sample of 367 students studying religion at A-level completed the Keirsey Temperament Sorter together with the Francis Scale of Attitude Toward Christianity. The data demonstrated that sensing types hold a more positive attitude toward Christianity than intuitive types and that judging types hold a more positive attitude toward Christianity than perceiving types. No significant differences in attitude toward Christianity were found either between extroverts and introverts, or between thinking types and feeling types.     

Knowledge and perceived risk of major diseases in middle-aged and older women.

Women's (N = 200; 41-95 years) knowledge of mortality risks and their perceived general risk, personal risk, control, and preventability of coronary heart disease (CHD) and breast, colon, and lung cancer were examined. Middle-aged (MA) women were more accurate in their mortality knowledge for MA men than for MA women and were more accurate for MA than for older (OA) men and women. OA women, in contrast, were least accurate in their mortality knowledge for OA women compared with all other target groups; only 34% knew that CHD is the leading cause of death in OA women. Participants also overestimated a woman's risk of death from breast cancer and underestimated the risk from lung and colon cancer. Ratings of perceived risk, control, and preventability varied as a function of disease. OA women in particular appear to lack knowledge regarding women's risk of major diseases. Results have implications for women's health behaviors and medical decisions.     

Personal and environmental factors associated with physical inactivity among different racial-ethnic groups of U.S. middle-aged and older-aged women.

Personal, program-based, and environmental barriers to physical activity were explored among a U.S. population-derived sample of 2,912 women 40 years of age and older. Factors significantly associated with inactivity included American Indian ethnicity, older age, less education, lack of energy, lack of hills in one's neighborhood, absence of enjoyable scenery, and infrequent observation of others exercising in one's neighborhood. For all ethnic subgroups, caregiving duties and lacking energy to exercise ranked among the top 4 most frequently reported barriers. Approximately 62% of respondents rated exercise on one's own with instruction as more appealing than undertaking exercise in an instructor-led group, regardless of ethnicity or current physical activity levels. The results underscore the importance of a multifaceted approach to understanding physical activity determinants in this understudied, high-risk population segment.     

New Methods for Comparing Groups

Abstract— A commonly used method for comparing groups of individuals is the analysis of variance (ANOVA) F test. When the assumptions underlying the derivation of this test are true, its power, meaning its probability of detecting true differences among the groups, competes well with all other methods that might be used. But when these assumptions are false, its power can be relatively low. Many new statistical methods have been proposed—ones that are aimed at achieving about the same amount of power when the assumptions of the F test are true but which have the potential of high power in situations where the F test performs poorly. A brief summary of some relevant issues and recent developments is provided. Some related issues are discussed and implications for future research are described.     

The new and improved two-sample T test

Abstract This article considers the problem of comparing two independent groups in terms of some measure of location. It is well known that with Student's two-independent-sample t test, the actual level of significance can be well above or below the nominal level, confidence intervals can have inaccurate probability coverage, and power can be low relative to other methods. A solution to deal with heterogeneity is Welch's (1938) test. Welch's test deals with heteroscedasticity but can have poor power under arbitrarily small departures from normality. Yuen (1974) generalized Welch's test to trimmed means; her method provides improved control over the probability of a Type I error, but problems remain. Transformations for skewness improve matters, but the probability of a Type I error remains unsatisfactory in some situations. We find that a transformation for skewness combined with a bootstrap method improves Type I error control and probability coverage even if sample sizes are small.     

Comparing one-step m-estimators of location corresponding to two independent groups

Experience with real data indicates that psychometric measures often have heavy-tailed distributions. This is known to be a serious problem when comparing the means of two independent groups because heavy-tailed distributions can have a serious effect on power. Another problem that is common in some areas is outliers. This paper suggests an approach to these problems based on the one-step M-estimator of location. Simulations indicate that the new procedure provides very good control over the probability of a Type I error even when distributions are skewed, have different shapes, and the variances are unequal. Moreover, the new procedure has considerably more power than Welch's method when distributions have heavy tails, and it compares well to Yuen's method for comparing trimmed means. Wilcox's median procedure has about the same power as the proposed procedure, but Wilcox's method is based on a statistic that has a finite sample breakdown point of only 1/n, wheren is the sample size. Comments on other methods for comparing groups are also included.