John T. Willse

33 papers receiving 1.9k citations

John T. Willse's Hit Papers

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables 2008 · 404 citations
4040+6+12Years since publication200400600

Peers

John T. Willse
Comparison fields: 5 of 143
  • Experimental and Cognitive Psychology 732
  • Management Science and Operations Research 324
  • Statistics and Probability 171
  • Social Psychology 342
  • Cognitive Neuroscience 306
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Countries citing papers authored by John T. Willse

Since Specialization
Citations

This map shows the geographic impact of John T. Willse'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 John T. Willse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John T. Willse more than expected).

Fields of papers citing papers by John T. Willse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John T. Willse. 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 John T. Willse. The network helps show where John T. Willse may publish in the future.

Co-authors

The 25 scholars most cited alongside John T. Willse, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John T. Willse Line = papers co-authored together John T. Willse links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods.
Hit paper breakdown →
2008636
2 2006427
3
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
Hit paper breakdown →
2008404
4 2015135
5 201057
6 201257
7 201143
8 201335
9 201034
10 201421
11 201519
12 201019
13 201018
14 200816
15 201015
16 201315
17 201715
18 200814
19 202112
20 200811

About John T. Willse

John T. Willse is a scholar working on Management Science and Operations Research, Education, Computer Networks and Communications, Social Psychology and Experimental and Cognitive Psychology, having authored 34 papers that have together received 2.1k indexed citations. Recurring topics across this work include Psychometric Methodologies and Testing (9 papers), Advanced Statistical Modeling Techniques (8 papers), School Choice and Performance (4 papers), Counseling Practices and Supervision (4 papers), Creativity in Education and Neuroscience (3 papers), Multi-Criteria Decision Making (2 papers), Statistical Methods and Bayesian Inference (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (732 citations), Management Science and Operations Research (324 citations), Statistics and Probability (171 citations), Social Psychology (342 citations) and Cognitive Neuroscience (306 citations). John T. Willse has collaborated with scholars based in United States, Norway and Ghana. Frequent co-authors include Robert A. Henson, Jonathan Templin, Xitao Fan, E. Lea Witta, Stephen A. Sivo, Paul J. Silvia, Beate P. Winterstein, Nina Arshavsky, Julie Edmunds and Jane E. Myers. Their work appears in journals such as Counselor Education and Supervision, Educational and Psychological Measurement, Journal of Educational Measurement, PLoS ONE and Psychology of Aesthetics Creativity and the Arts.

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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