Duanli Yan

866 total citations
29 papers, 441 citations indexed

About

Duanli Yan is a scholar working on Artificial Intelligence, Management Science and Operations Research and Education. According to data from OpenAlex, Duanli Yan has authored 29 papers receiving a total of 441 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Management Science and Operations Research and 4 papers in Education. Recurrent topics in Duanli Yan's work include Bayesian Modeling and Causal Inference (7 papers), Psychometric Methodologies and Testing (5 papers) and Advanced Statistical Modeling Techniques (3 papers). Duanli Yan is often cited by papers focused on Bayesian Modeling and Causal Inference (7 papers), Psychometric Methodologies and Testing (5 papers) and Advanced Statistical Modeling Techniques (3 papers). Duanli Yan collaborates with scholars based in United States, Belgium and Netherlands. Duanli Yan's co-authors include Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, David M. Williamson, Alina A. von Davier, David Magis, Peter W. Foltz, André A. Rupp, Charles Lewis and Joris Mulder and has published in prestigious journals such as The American Statistician, Journal of Educational and Behavioral Statistics and International Journal of Science and Mathematics Education.

In The Last Decade

Duanli Yan

28 papers receiving 391 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Duanli Yan United States	10	216	119	99	81	80	29	441
André A. Rupp United States	10	193 0.9×	131 1.1×	192 1.9×	128 1.6×	128 1.6×	20	521
Peida Zhan China	14	193 0.9×	184 1.5×	83 0.8×	58 0.7×	82 1.0×	46	478
Louis V. DiBello United States	7	99 0.5×	82 0.7×	102 1.0×	56 0.7×	174 2.2×	16	380
Luca Stefanutti Italy	18	591 2.7×	149 1.3×	104 1.1×	119 1.5×	64 0.8×	66	807
Laine Bradshaw United States	14	218 1.0×	293 2.5×	107 1.1×	54 0.7×	165 2.1×	18	698
S. Hunka Canada	4	131 0.6×	124 1.0×	80 0.8×	33 0.4×	56 0.7×	13	317
Esther Ulitzsch Germany	16	67 0.3×	209 1.8×	69 0.7×	58 0.7×	82 1.0×	39	498
Ali Ünlü Germany	11	111 0.5×	61 0.5×	29 0.3×	28 0.3×	39 0.5×	38	288
Kyung T. Han United States	13	62 0.3×	215 1.8×	34 0.3×	33 0.4×	45 0.6×	32	379
Maria Bolsinova Netherlands	12	85 0.4×	236 2.0×	101 1.0×	55 0.7×	60 0.8×	41	492

Countries citing papers authored by Duanli Yan

Since Specialization

Citations

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

Fields of papers citing papers by Duanli Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Duanli Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Duanli Yan. A scholar is included among the top collaborators of Duanli Yan 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 Duanli Yan. Duanli Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Jin, Hui, Duanli Yan, & Joseph Krajcik. (2024). Handbook of Research on Science Learning Progressions. 2 indexed citations

Deane, Paul, Duanli Yan, Katherine E. Castellano, et al.. (2024). Modeling Writing Traits in a Formative Essay Corpus. ETS Research Report Series. 2024(1). 1–62. 2 indexed citations

Jin, Hui, et al.. (2020). An Empirically Grounded Framework That Evaluates Argument Quality in Scientific and Social Contexts. International Journal of Science and Mathematics Education. 19(4). 681–700. 10 indexed citations

Yan, Duanli. (2020). Bayesian inference for Gaussian models : Inverse problems and evolution equations. Data Archiving and Networked Services (DANS). 1 indexed citations

Magis, David, Duanli Yan, & Alina A. von Davier. (2018). Computerized Adaptive and Multistage Testing with R: Using Packages catR and mstR. 6 indexed citations

Magis, David, Duanli Yan, & Alina von Davier. (2017). mstR: An R package to generate multistage testing designs. Open Repository and Bibliography (University of Liège).

Magis, David, Duanli Yan, & Alina A. von Davier. (2017). Computerized Adaptive and Multistage Testing with R. CERN Document Server (European Organization for Nuclear Research). 47 indexed citations

Magis, David, Duanli Yan, & Alina von Davier. (2016). Computerized adaptive testing and multistage testing with R. Open Repository and Bibliography (University of Liège). 1 indexed citations

Almond, Russell G., Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, & David M. Williamson. (2015). Bayesian Networks in Educational Assessment. CERN Document Server (European Organization for Nuclear Research). 95 indexed citations

10.

Almond, Russell G., et al.. (2009). Bayesian Network Models for Local Dependence Among Observable Outcome Variables. Journal of Educational and Behavioral Statistics. 34(4). 491–521. 22 indexed citations

11.

Almond, Russell G., et al.. (2008). Parameter Recovery Studies With a Diagnostic Bayesian Network Model. Behaviormetrika. 35(2). 159–185. 9 indexed citations

12.

Almond, Russell G., et al.. (2006). Bayesian Network Models for Local Dependence among Observable Outcome Variables. Research Report. ETS RR-06-36.. ETS Research Report Series. 3 indexed citations

13.

Sinharay, Sandip, Russell G. Almond, & Duanli Yan. (2004). Assessing Fit of Models with Discrete Proficiency Variable in Educational Assessment. Research Report. RR-04-07.. 1 indexed citations

14.

Yan, Duanli, Russell G. Almond, & Robert J. Mislevy. (2004). A Comparison of Two Models for Cognitive Diagnosis. Research Report. ETS RR-04-02.. ETS Research Report Series. 5 indexed citations

15.

Yan, Duanli, Robert J. Mislevy, & Russell G. Almond. (2003). DESIGN AND ANALYSIS IN A COGNITIVE ASSESSMENT. ETS Research Report Series. 2003(2). 20 indexed citations

16.

Mislevy, Robert J., Russell G. Almond, Frank Jenkins, et al.. (2002). Modeling Conditional Probabilities in Complex Educational Assessments. CSE Technical Report.. 3 indexed citations

17.

Almond, Russell G., Frank J. Jenkins, Deniz Şentürk, et al.. (2001). Models for Conditional Probability Tables in Educational Assessment.. International Conference on Artificial Intelligence and Statistics. 1–7. 29 indexed citations

18.

Almond, Russell G., Charles Lewis, John W. Tukey, & Duanli Yan. (2000). Displays for Comparing a Given State to Many Others. The American Statistician. 54(2). 89–93. 7 indexed citations

19.

Mislevy, Robert J., Russell G. Almond, Duanli Yan, & Linda S. Steinberg. (1999). Bayes nets in educational assessment: Where the numbers come from. arXiv (Cornell University). 437–446. 75 indexed citations

20.

Yan, Duanli, Charles Lewis, & Martha L. Stocking. (1998). Adaptive Testing without IRT.. 1998(1). 1 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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