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      Recreational Drug Use and Fluctuating Asymmetry: Testing the Handicap Principle

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          Abstract

          Zahavi's handicap principle suggests that only organisms with good genetic quality can afford to engage in costly behaviors. Recreational drug use can be harmful to one's health and therefore might be viewed as a costly signal of one's genetic quality. One of the measurements of genetic quality is bodily symmetry assessed by fluctuating asymmetry. If unhealthy drug use is a behavioral example of Zahavi's handicap principle, then men who use different stimulants or recreational drugs should be more symmetrical than men who do not use them at all or use them only in low quantity. The aim of this study was to examine the relationships between drug use and fluctuating asymmetry. The subjects were 190 young women and 202 young men. Six bilaterally symmetrical traits were measured: length of II–V digits, wrist breadth, and ear height. Questionnaires included questions about smoking, alcohol drinking, drug use, and designer drug use. There was no relationship between bodily symmetry and smoking frequency, alcohol drinking frequency, drug or designer drug use, total substance use, age of smoking initiation, or reason of this initiation. The results indicate that drug use does not reflect genetic quality and does not necessarily relate to the handicap hypothesis.

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          Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

          As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n 2.1 Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper) and West et al. (1996) proposed a reference of substantial departure from normality as an absolute kurtosis (proper) value > 7.1 For some practical reasons, most statistical packages such as SPSS provide 'excess' kurtosis obtained by subtracting 3 from the kurtosis (proper). The excess kurtosis should be zero for a perfectly normal distribution. Distributions with positive excess kurtosis are called leptokurtic distribution meaning high peak, and distributions with negative excess kurtosis are called platykurtic distribution meaning flat-topped curve. 2) Normality test using skewness and kurtosis A z-test is applied for normality test using skewness and kurtosis. A z-score could be obtained by dividing the skew values or excess kurtosis by their standard errors. As the standard errors get smaller when the sample size increases, z-tests under null hypothesis of normal distribution tend to be easily rejected in large samples with distribution which may not substantially differ from normality, while in small samples null hypothesis of normality tends to be more easily accepted than necessary. Therefore, critical values for rejecting the null hypothesis need to be different according to the sample size as follows: For small samples (n < 50), if absolute z-scores for either skewness or kurtosis are larger than 1.96, which corresponds with a alpha level 0.05, then reject the null hypothesis and conclude the distribution of the sample is non-normal. For medium-sized samples (50 < n < 300), reject the null hypothesis at absolute z-value over 3.29, which corresponds with a alpha level 0.05, and conclude the distribution of the sample is non-normal. For sample sizes greater than 300, depend on the histograms and the absolute values of skewness and kurtosis without considering z-values. Either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality. Referring to Table 1 and Figure 1, we could conclude all the data seem to satisfy the assumption of normality despite that the histogram of the smallest-sized sample doesn't appear as a symmetrical bell shape and the formal normality tests for the largest-sized sample were rejected against the normality null hypothesis. 3) How strict is the assumption of normality? Though the humble t test (assuming equal variances) and analysis of variance (ANOVA) with balanced sample sizes are said to be 'robust' to moderate departure from normality, generally it is not preferable to rely on the feature and to omit data evaluation procedure. A combination of visual inspection, assessment using skewness and kurtosis, and formal normality tests can be used to assess whether assumption of normality is acceptable or not. When we consider the data show substantial departure from normality, we may either transform the data, e.g., transformation by taking logarithms, or select a nonparametric method such that normality assumption is not required.
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            A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors

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              Mate selection-a selection for a handicap.

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                Author and article information

                Journal
                Evol Psychol
                Evol Psychol
                EVP
                spevp
                Evolutionary Psychology
                SAGE Publications (Sage CA: Los Angeles, CA )
                1474-7049
                1 October 2014
                October 2014
                : 12
                : 4
                : 769-782
                Affiliations
                Department of Human Biology, University of Wroclaw, Wroclaw, Poland
                Author notes
                [*]Email: barbara.borkowska@ 123456antropo.uni.wroc.pl (Corresponding author)
                Article
                10.1177_147470491401200407
                10.1177/147470491401200407
                10524073
                25300053
                2a89412b-7799-44e4-bfcd-68505e548728
                © 2014 SAGE Publications Inc.

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License ( https://creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 15 August 2013
                : 15 June 2014
                Categories
                Original Article
                Custom metadata
                ts19

                genetic quality,body symmetry,fluctuating asymmetry,handicap principle,smoking,drinking

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