The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.

The aim was to analyse the psychometric properties of the 10-item version of the Connor-Davidson Resilience Scale (CD-RISC 10©) in breast cancer patients. A sample of 169 Spanish women who had undergone surgery for breast cancer completed the CD-RISC 10©, along with questionnaires assessing life satisfaction, emotional intelligence, self-esteem, and positive and negative affect. Confirmatory Factor Analysis supported a single-factor structure with adequate fit indices. Reliability was analysed by calculating McDonald's omega coefficient, which yielded a value of .83. Validity evidence based on relationships with other variables was provided by positive and significant correlations between scores on the CD-RISC 10© and scores on emotional intelligence (clarity and repair), life satisfaction, self-esteem and positive affect, and by a negative and significant correlation with negative affect. The majority of these correlations were above |.50|. The CD-RISC 10© has satisfactory psychometric properties and is a suitable tool for measuring resilience in patients with cancer. The instrument is quick and easy to apply and may be used in both clinical and research contexts.

Breast cancer diagnosis is one of the most difficult events that a woman can experience during her life and it usually produces high levels of stress. Global measures of perceived stress are useful for screening and for comparing stress levels between cancer patients and other clinical and nonclinical populations. One such instrument that is widely used is the Perceived Stress Scale (pss‐10), but its psychometric properties have scarcely been analysed with breast cancer patients. The aim of this study was to provide validity evidence regarding the use of the 10‐item version of the pss‐10 as a tool for measuring perceived stress in this context. Participants were 215 Spanish breast cancer patients who completed the PSS‐10 and the DASS‐21, a measure of affective distress (depression, anxiety, and stress). The internal structure of the PSS‐10 was examined through confirmatory factor analysis (CFA), and the reliability of test scores was estimated using McDonald's omega coefficient. Validity evidence based on relationships with other variables was also obtained using correlation analysis. The CFA supported a correlated two‐factor structure: perceived helplessness (six negatively worded items) and perceived self‐efficacy (four positively worded items). Reliability coefficients for scores on these two factors were 0.87 and 0.73, respectively. Scores on affective distress (DASS‐21) were strongly and positively correlated with perceived helplessness and moderately and negatively correlated with perceived self‐efficacy. The PSS‐10 is an adequate tool for measuring perceived stress in the breast cancer context and it may be useful for identifying women at risk of psychological maladjustment.

Several measures of skewness and kurtosis were proposed by Hogg (1974) in order to reduce the bias of conventional estimators when the distribution is non-normal. Here we conducted a Monte Carlo simulation study to compare the performance of conventional and Hogg’s estimators, considering the most frequent continuous distributions used in health, education, and social sciences (gamma, lognormal and exponential distributions). In order to determine the bias, precision and accuracy of the skewness and kurtosis estimators for each distribution we calculated the relative bias, the coefficient of variation, and the scaled root mean square error. The effect of sample size on the estimators is also analyzed. In addition, a SAS program for calculating both conventional and Hogg’s estimators is presented. The results indicated that for the non-normal distributions investigated, the estimators of skewness and kurtosis which best reflect the shape of the distribution are Hogg’s estimators. It should also be noted that Hogg’s estimators are not as affected by sample size as are conventional estimators.