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Journal ArticleDOI

Gender differences in risk taking: A meta-analysis.

TLDR
This paper conducted a meta-analysis of 150 studies in which the risk-taking tendencies of male and female participants were compared and found that the average effects for 14 out of 16 types of risk taking were significantly larger than 0 (indicating greater risk taking in male participants).
Abstract
The authors conducted a meta-analysis of 150 studies in which the risk-taking tendencies of male and female participants were compared. Studies were coded with respect to type of task (e.g., self-reported behaviors vs. observed behaviors), task content (e.g., smoking vs. sex), and 5 age levels. Results showed that the average effects for 14 out of 16 types of risk taking were significantly larger than 0 (indicating greater risk taking in male participants) and that nearly half of the effects were greater than .20. However, certain topics (e.g., intellectual risk taking and physical skills) produced larger gender differences than others (e.g., smoking). In addition, the authors found that (a) there were significant shifts in the size of the gender gap between successive age levels, and (b) the gender gap seems to be growing smaller over time. The discussion focuses on the meaning of the results for theories of risk taking and the need for additional studies to clarify age trends.

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Posted Content

Risk as Feelings

TL;DR: It is shown that emotional reactions to risky situations often diverge from cognitive assessments of those risks, and when such divergence occurs, emotional reactions often drive behavior.
Journal ArticleDOI

Gender Differences in Preferences

TL;DR: This paper reviewed the literature on gender differences in economic experiments and identified robust differences in risk preferences, social (other-regarding) preferences, and competitive preferences, speculating on the source of these differences and their implications.
Journal ArticleDOI

Risk as feelings.

TL;DR: This article proposed the risk-as-feelings hypothesis, which highlights the role of affect experienced at the moment of decision making, and showed that emotional reactions to risky situations often diverge from cognitive assessments of those risks.
Journal ArticleDOI

Cognitive Reflection and Decision Making

TL;DR: This paper introduced a three-item Cognitive Reflection Test (CRT) as a simple measure of one type of cognitive ability, i.e., the ability or disposition to reflect on a question and resist reporting the first response that comes to mind.
Journal ArticleDOI

A Social Neuroscience Perspective on Adolescent Risk-Taking.

TL;DR: This article proposes a framework for theory and research on risk-taking that is informed by developmental neuroscience, and finds that changes in the brain's cognitive control system - changes which improve individuals' capacity for self-regulation - occur across adolescence and young adulthood.
References
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Journal ArticleDOI

A power primer.

TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.
Book ChapterDOI

Prospect theory: an analysis of decision under risk

TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Journal ArticleDOI

The Framing of Decisions and the Psychology of Choice

TL;DR: The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways.
Book

Statistical Methods for Meta-Analysis

TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Journal ArticleDOI

Statistical Methods for Meta-Analysis.

TL;DR: In this paper, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
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