Yulia Kovas

8.1k total citations · 1 hit paper
151 papers, 4.8k citations indexed

About

Yulia Kovas is a scholar working on Experimental and Cognitive Psychology, Developmental and Educational Psychology and Statistics and Probability. According to data from OpenAlex, Yulia Kovas has authored 151 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Experimental and Cognitive Psychology, 43 papers in Developmental and Educational Psychology and 41 papers in Statistics and Probability. Recurrent topics in Yulia Kovas's work include Cognitive Abilities and Testing (46 papers), Cognitive and developmental aspects of mathematical skills (41 papers) and Reading and Literacy Development (26 papers). Yulia Kovas is often cited by papers focused on Cognitive Abilities and Testing (46 papers), Cognitive and developmental aspects of mathematical skills (41 papers) and Reading and Literacy Development (26 papers). Yulia Kovas collaborates with scholars based in United Kingdom, Russia and United States. Yulia Kovas's co-authors include Robert Plomin, Philip S. Dale, Stephen A. Petrill, Claire M. A. Haworth, Kaili Rimfeld, Nicole Harlaar, Brian Butterworth, Lee A. Thompson, Sara A. Hart and Maria Grazia Tosto and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Personality and Social Psychology.

In The Last Decade

Yulia Kovas

143 papers receiving 4.6k citations

Hit Papers

True grit and genetics: Predicting academic achievement f... 2016 2026 2019 2022 2016 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yulia Kovas United Kingdom 39 1.8k 1.5k 1.4k 1.1k 710 151 4.8k
Stephen A. Petrill United States 48 2.2k 1.3× 2.3k 1.5× 2.2k 1.5× 1.2k 1.1× 784 1.1× 170 7.2k
Nicole Harlaar United States 34 972 0.5× 849 0.6× 1.0k 0.7× 402 0.4× 405 0.6× 67 3.4k
Kevin S. McGrew United States 29 2.3k 1.3× 878 0.6× 1.8k 1.3× 900 0.8× 431 0.6× 97 4.4k
Irene C. Mammarella Italy 40 1.4k 0.8× 1.1k 0.7× 1.5k 1.0× 1.4k 1.3× 656 0.9× 144 4.4k
Richard Lynn United Kingdom 39 3.4k 1.9× 759 0.5× 806 0.6× 491 0.4× 853 1.2× 244 5.6k
Sara A. Hart United States 30 965 0.5× 1.4k 0.9× 1.1k 0.8× 904 0.8× 395 0.6× 122 2.9k
Michael S. C. Thomas United Kingdom 39 734 0.4× 713 0.5× 2.0k 1.4× 463 0.4× 376 0.5× 175 5.3k
John H. Kranzler United States 28 1.5k 0.8× 662 0.4× 1.1k 0.8× 386 0.3× 547 0.8× 78 3.1k
Claire M. A. Haworth United Kingdom 35 1.2k 0.7× 788 0.5× 511 0.4× 312 0.3× 443 0.6× 107 5.2k
Frank M. Spinath Germany 46 3.2k 1.8× 1.4k 0.9× 1.1k 0.7× 314 0.3× 1.8k 2.5× 163 7.0k

Countries citing papers authored by Yulia Kovas

Since Specialization
Citations

This map shows the geographic impact of Yulia Kovas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yulia Kovas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yulia Kovas more than expected).

Fields of papers citing papers by Yulia Kovas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yulia Kovas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yulia Kovas. The network helps show where Yulia Kovas may publish in the future.

Co-authorship network of co-authors of Yulia Kovas

This figure shows the co-authorship network connecting the top 25 collaborators of Yulia Kovas. A scholar is included among the top collaborators of Yulia Kovas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yulia Kovas. Yulia Kovas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
3.
Chapman, Robert M., et al.. (2024). Are we ready for the genomic era? Insights from judges and lawyers. New Genetics and Society. 43(1). 1 indexed citations
4.
Likhanov, Maxim, et al.. (2023). Persistent gender differences in spatial ability, even in STEM experts. Heliyon. 9(4). e15247–e15247. 8 indexed citations
5.
Chapman, Robert M., et al.. (2023). Consensus too soon: judges’ and lawyers’ views on genetic information use. New Genetics and Society. 42(1). 2 indexed citations
6.
Казанцева, А. В., Р. Н. Мустафин, Zalina Takhirova, et al.. (2022). The role of inflammatory system genes in individual differences in nonverbal intelligence. Vavilov Journal of Genetics and Breeding. 26(2). 179–187.
7.
Likhanov, Maxim, et al.. (2022). This is the way: Network perspective on targets for spatial ability development programmes. British Journal of Educational Psychology. 92(4). 1597–1620. 4 indexed citations
8.
Мустафин, Р. Н., А. В. Казанцева, Yulia Kovas, & Э. К. Хуснутдинова. (2022). Role Of Retroelements In The Development Of COVID-19 Neurological Consequences. SHILAP Revista de lepidopterología. 11(3). 3 indexed citations
9.
Luo, Yu L. L., Yulia Kovas, Lizhong Wang, et al.. (2022). Sex differences in the Dark Triad are sensitive to socioeconomic conditions: the adaptive value of narcissism in the UK, Greece, and China. Current Psychology. 42(26). 22436–22448. 14 indexed citations
11.
Likhanov, Maxim, et al.. (2020). Ordinary extraordinary: Elusive group differences in personality and psychological difficulties between STEM‐gifted adolescents and their peers. British Journal of Educational Psychology. 91(1). 78–100. 22 indexed citations
12.
Malanchini, Margherita, Kaili Rimfeld, Zhe Wang, et al.. (2020). Genetic factors underlie the association between anxiety, attitudes and performance in mathematics. Translational Psychiatry. 10(1). 12–12. 22 indexed citations
13.
Stumm, Sophie von, Emily Smith‐Woolley, Rosa Cheesman, et al.. (2020). School quality ratings are weak predictors of students’ achievement and well‐being. Journal of Child Psychology and Psychiatry. 62(3). 339–348. 13 indexed citations
14.
Papageorgiou, Kostas Α., et al.. (2018). Prenatal testosterone does not explain sex differences in spatial ability. Scientific Reports. 8(1). 13653–13653. 19 indexed citations
15.
Smith‐Woolley, Emily, Jean‐Baptiste Pingault, Saskia Selzam, et al.. (2018). Differences in exam performance between pupils attending selective and non-selective schools mirror the genetic differences between them. npj Science of Learning. 3(1). 3–3. 43 indexed citations
16.
Skalny, Anatoly V., et al.. (2018). The impact of lifestyle factors on age-related differences in hair trace element content in pregnant women in the third trimester [pdf]. Acta Scientiarum Polonorum Technologia Alimentaria. 17(1). 83–89. 4 indexed citations
17.
Tosto, Maria Grazia, Stephen A. Petrill, Sergey Malykh, et al.. (2017). Number sense and mathematics: Which, when and how?. Developmental Psychology. 53(10). 1924–1939. 25 indexed citations
18.
Kovas, Yulia, Sergey Malykh, & Darya Gaysina. (2016). Behavioural Genetics for Education. Palgrave Macmillan UK eBooks. 13 indexed citations
19.
Rimfeld, Kaili, Yulia Kovas, Philip S. Dale, & Robert Plomin. (2016). True grit and genetics: Predicting academic achievement from personality.. Journal of Personality and Social Psychology. 111(5). 780–789. 277 indexed citations breakdown →
20.
Kovas, Yulia, et al.. (2007). Generalist Genes, Specialist Environments and the Internet Generation: Etiology of Learning Abilities Using Web-based Testing at 10 Years. Twin Research and Human Genetics. 10. 27. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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