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Seda Can

Researcher at İzmir University of Economics

Publications -  20
Citations -  2091

Seda Can is an academic researcher from İzmir University of Economics. The author has contributed to research in topics: Medicine & Mate choice. The author has an hindex of 6, co-authored 15 publications receiving 1914 citations. Previous affiliations of Seda Can include Gediz University.

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Journal ArticleDOI

Contrasting Computational Models of Mate Preference Integration Across 45 Countries

Daniel Conroy-Beam, +111 more
- 15 Nov 2019 - 
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
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Sex Differences in Mate Preferences Across 45 Countries: A Large-Scale Replication

Kathryn V. Walter, +112 more
TL;DR: Using a new 45-country sample (N = 14,399), this work attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives, finding neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.
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Affective Interpersonal Touch in Close Relationships: A Cross-Cultural Perspective.

Agnieszka Sorokowska, +104 more
TL;DR: For instance, this article found that affective touch was most prevalent in relationships with partners and children, and its diversity was relatively higher in warmer, less conservative, and religious countries, and among younger, female, and liberal people.
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Assortative mating and the evolution of desirability covariation

Daniel Conroy-Beam, +116 more
TL;DR: This work uses agent-based models to demonstrate that assortative mating causes the evolution of a positive manifold of desirability, d, such that an individual who is desirable as a mate along any one dimension tends to be desirable across all other dimensions.
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Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations.

TL;DR: The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood, and Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation.