H Cohen Quotes

Why did the burst of activity fade so rapidly? The specific explanations diverge in their particulars, but they agree on the central point that, as H. Floris Cohen, put it, “the root cause of its decline is to be found in the Faith, and in the ability of its orthodox upholders to stifle once-flowering science.”16 To Islamic scholar G. E. von Grunebaum, Islam was never able to accept that scientific research is a means of glorifying God.

The differences in the two thinking styles, as Baron-Cohen describes them, are intriguing. “Systematizing involves exactness, excellent attention to local detail,” and an attraction to fixed rules independent of context, he says. “To systematize, you need detachment.” 21 (Baron describes autism as an “extreme” male brain.)

paramour.

What really corrupts is not power, but a sense of powerlessness.

(Irigaray was my favourite. She denounced Einstein’s E = mc2 as a sexist equation which ‘privileges the speed of light’ over more feminine speeds ‘which are vitally necessary to us’. Presumably, light might have appeased her if it had shown its feminine side by slowing down to 30 m.p.h. in built-up areas.)

In Depth Types of Effect Size Indicators Researchers use several different statistics to indicate effect size depending on the nature of their data. Roughly speaking, these effect size statistics fall into three broad categories. Some effect size indices, sometimes called dbased effect sizes, are based on the size of the difference between the means of two groups, such as the difference between the average scores of men and women on some measure or the differences in the average scores that participants obtained in two experimental conditions. The larger the difference between the means, relative to the total variability of the data, the stronger the effect and the larger the effect size statistic. The r-based effect size indices are based on the size of the correlation between two variables. The larger the correlation, the more strongly two variables are related and the more of the total variance in one variable is systematic variance related to the other variable. A third category of effect sizes index involves the odds-ratio, which tells us the ratio of the odds of an event occurring in one group to the odds of the event occurring in another group. If the event is equally likely in both groups, the odds ratio is 1.0. An odds ratio greater than 1.0 shows that the odds of the event is greater in one group than in another, and the larger the odds ratio, the stronger the effect. The odds ratio is used when the variable being measured has only two levels. For example, imagine doing research in which first-year students in college are either assigned to attend a special course on how to study or not assigned to attend the study skills course, and we wish to know whether the course reduces the likelihood that students will drop out of college. We could use the odds ratio to see how much of an effect the course had on the odds of students dropping out. You do not need to understand the statistical differences among these effect size indices, but you will find it useful in reading journal articles to know what some of the most commonly used effect sizes are called. These are all ways of expressing how strongly variables are related to one another—that is, the effect size. Symbol Name d Cohen’s d g Hedge’s g h 2 eta squared v 2 omega squared r or r 2 correlation effect size OR odds ratio The strength of the relationships between variables varies a great deal across studies. In some studies, as little as 1% of the total variance may be systematic variance, whereas in other contexts, the proportion of the total variance that is systematic variance may be quite large,

Cohen P, Longo VD. “Fasting and cancer treatment in humans: A case series report.” Aging (Albany NY) 1.12 (2009): 988–1007. Dorff TB, Groshen S, Garcia A, Shah M, Tsao-Wei D, Pham H, Cheng CW, Brandhorst S, Cohen P, Wei M, Longo V, Quinn DI. “Safety

DID is misrepresented in the ICD-10 as a rare disorder. The prevalence of DID found in community samples was between 0.4% (Akyuz, Dogan, Sar, Yargic, & Tutkun, 1999) and 1.5% (Johnson, Cohen, Kasen, & Brook, 2006), whereas its prevalence in psychiatric samples falls in the range of 1 % (Rifkin, Ghisalbert, Dimatou, Jin, & Sethi, 1998), and 2% (Friedl & Draijer, 2000), to 5.4% (Tutkun et al., 1998) and 6% (Foote et al., 2006). One North American study found a prevalence of 12% (Latz, Kramer, & Hughes, 1995). Although the latter finding is exceptional and possibly due to site-specific ascertainment biases, it seems safe to conclude that the prevalence of the disorder is probably at least as high as that of schizophrenia, which is not a rare disorder.