Maria Bolsinova

775 total citations
41 papers, 492 citations indexed

About

Maria Bolsinova is a scholar working on Management Science and Operations Research, Statistics and Probability and Artificial Intelligence. According to data from OpenAlex, Maria Bolsinova has authored 41 papers receiving a total of 492 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Management Science and Operations Research, 15 papers in Statistics and Probability and 13 papers in Artificial Intelligence. Recurrent topics in Maria Bolsinova's work include Psychometric Methodologies and Testing (21 papers), Statistical Methods and Bayesian Inference (9 papers) and Advanced Statistical Modeling Techniques (9 papers). Maria Bolsinova is often cited by papers focused on Psychometric Methodologies and Testing (21 papers), Statistical Methods and Bayesian Inference (9 papers) and Advanced Statistical Modeling Techniques (9 papers). Maria Bolsinova collaborates with scholars based in Netherlands, United States and Slovakia. Maria Bolsinova's co-authors include Jesper Tijmstra, Dylan Molenaar, Paul De Boeck, Gunter Maris, Minjeong Jeon, Jeroen K. Vermunt, Yigal Rosen, Alina A. von Davier, Michael Yudelson and Sándor Rózsa and has published in prestigious journals such as Scientific Reports, Computers in Human Behavior and Frontiers in Psychology.

In The Last Decade

Maria Bolsinova

36 papers receiving 469 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maria Bolsinova Netherlands 12 236 173 107 101 85 41 492
Esther Ulitzsch Germany 16 209 0.9× 129 0.7× 113 1.1× 69 0.7× 67 0.8× 39 498
Peida Zhan China 14 184 0.8× 91 0.5× 86 0.8× 83 0.8× 193 2.3× 46 478
Laine Bradshaw United States 14 293 1.2× 73 0.4× 190 1.8× 107 1.1× 218 2.6× 18 698
Peter W. van Rijn United States 13 127 0.5× 62 0.4× 64 0.6× 145 1.4× 59 0.7× 42 455
Timo Bechger Netherlands 11 144 0.6× 84 0.5× 60 0.6× 35 0.3× 54 0.6× 28 365
David J. Scrams United States 6 209 0.9× 90 0.5× 79 0.7× 52 0.5× 44 0.5× 15 340
André A. Rupp United States 10 131 0.6× 52 0.3× 64 0.6× 192 1.9× 193 2.3× 20 521
Paul D. Nichols United States 10 120 0.5× 49 0.3× 43 0.4× 108 1.1× 116 1.4× 20 517
Cynthia G. Parshall United States 9 224 0.9× 43 0.2× 60 0.6× 68 0.7× 34 0.4× 26 433
Gautam Puhan United States 11 247 1.0× 56 0.3× 120 1.1× 85 0.8× 21 0.2× 53 464

Countries citing papers authored by Maria Bolsinova

Since Specialization
Citations

This map shows the geographic impact of Maria Bolsinova'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 Maria Bolsinova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Bolsinova more than expected).

Fields of papers citing papers by Maria Bolsinova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maria Bolsinova. 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 Maria Bolsinova. The network helps show where Maria Bolsinova may publish in the future.

Co-authorship network of co-authors of Maria Bolsinova

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Bolsinova. A scholar is included among the top collaborators of Maria Bolsinova 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 Maria Bolsinova. Maria Bolsinova 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
1.
Kruis, Joost, et al.. (2025). Psychometrics of an Elo-based large-scale online learning system. Computers and Education Artificial Intelligence. 8. 100376–100376.
2.
Bolsinova, Maria, et al.. (2025). Keeping Elo alive: Evaluating and improving measurement properties of learning systems based on Elo ratings. British Journal of Mathematical and Statistical Psychology. 79(1). 95–110.
3.
Tijmstra, Jesper & Maria Bolsinova. (2025). Modeling Within- and Between-Person Differences in the Use of the Middle Category in Likert Scales. Applied Psychological Measurement. 49(6). 266–281.
4.
Tijmstra, Jesper, et al.. (2023). Correcting for Extreme Response Style: Model Choice Matters. Educational and Psychological Measurement. 84(1). 145–170. 4 indexed citations
5.
Bolsinova, Maria, et al.. (2021). Improving the Precision of Ability Estimates Using Time-On-Task Variables: Insights From the PISA 2012 Computer-Based Assessment of Mathematics. Frontiers in Psychology. 12. 579128–579128. 10 indexed citations
6.
Kruis, Joost, Gunter Maris, Maarten Marsman, Maria Bolsinova, & Han L. J. van der Maas. (2020). Deviations of rational choice: an integrative explanation of the endowment and several context effects. Scientific Reports. 10(1). 16226–16226. 9 indexed citations
7.
Bolsinova, Maria, et al.. (2020). A Rasch Model and Rating System for Continuous Responses Collected in Large-Scale Learning Systems. Frontiers in Psychology. 11. 500039–500039. 6 indexed citations
8.
Rosen, Yigal, et al.. (2019). Gamified performance assessment of collaborative problem solving skills. Computers in Human Behavior. 104. 106036–106036. 31 indexed citations
9.
Simmering, Vanessa R., Lu Ou, & Maria Bolsinova. (2019). What Technology Can and Cannot Do to Support Assessment of Non-cognitive Skills. Frontiers in Psychology. 10. 2168–2168. 6 indexed citations
10.
Molenaar, Dylan, Sándor Rózsa, & Maria Bolsinova. (2019). A heteroscedastic hidden Markov mixture model for responses and categorized response times. Behavior Research Methods. 51(2). 676–696. 8 indexed citations
11.
Tijmstra, Jesper, Maria Bolsinova, & Minjeong Jeon. (2018). General mixture item response models with different item response structures: Exposition with an application to Likert scales. Behavior Research Methods. 50(6). 2325–2344. 37 indexed citations
12.
Yudelson, Michael, et al.. (2018). Assessment meets Learning: On the relation between Item Response Theory and Bayesian Knowledge Tracing. arXiv (Cornell University). 1 indexed citations
13.
Bolsinova, Maria & Dylan Molenaar. (2018). Nonlinear Indicator-Level Moderation in Latent Variable Models. Multivariate Behavioral Research. 54(1). 62–84. 6 indexed citations
14.
Tijmstra, Jesper & Maria Bolsinova. (2018). On the Importance of the Speed-Ability Trade-Off When Dealing With Not Reached Items. Frontiers in Psychology. 9. 964–964. 11 indexed citations
15.
Bolsinova, Maria, et al.. (2017). Using expert knowledge for test linking.. Psychological Methods. 22(4). 705–724. 4 indexed citations
16.
Molenaar, Dylan & Maria Bolsinova. (2017). A heteroscedastic generalized linear model with a non‐normal speed factor for responses and response times. British Journal of Mathematical and Statistical Psychology. 70(2). 297–316. 5 indexed citations
17.
Bolsinova, Maria, Paul De Boeck, & Jesper Tijmstra. (2016). Modelling Conditional Dependence Between Response Time and Accuracy. Psychometrika. 82(4). 1126–1148. 63 indexed citations
18.
Bolsinova, Maria & Jesper Tijmstra. (2016). Posterior Predictive Checks for Conditional Independence Between Response Time and Accuracy. Journal of Educational and Behavioral Statistics. 41(2). 123–145. 15 indexed citations
19.
Bolsinova, Maria, Gunter Maris, & Herbert Hoijtink. (2016). Unmixing Rasch scales: How to score an educational test. The Annals of Applied Statistics. 10(2). 3 indexed citations
20.
Bolsinova, Maria & Gunter Maris. (2016). Can IRT Solve the Missing Data Problem in Test Equating?. Frontiers in Psychology. 6. 1956–1956. 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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