Daniel Jurich

27 papers receiving 344 citations

Peers

Daniel Jurich
Comparison fields: 5 of 63
  • Health Informatics 28
  • Family Practice 22
  • Management Science and Operations Research 57
  • Statistics and Probability 30
  • Social Psychology 71
Replace Kimberly A. Swygert with:
Kimberly A. Swygert United States
Michael J. Zieky United States
Maria Kambouri United Kingdom
Chad W. Buckendahl United States
Kayley Lyons Australia
Aurélien Allard Switzerland
Jay Blanchard United States
Michael Parker United Kingdom
Sébastien Béland Canada
Sen‐Chi Yu Taiwan
Daniel Jurich relative to Kimberly A. Swygert United States Kimberly A. Swygert's profile →
Citations per field
00.5×1.5×2.5×
Kimberly A. Swygert · 1×
Citations per year

Countries citing papers authored by Daniel Jurich

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Jurich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Jurich, 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 Daniel Jurich Line = papers co-authored together Daniel Jurich links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011113
2 201732
3 202329
4 201827
5 201327
6 202024
7 201917
8 201217
9 201815
10 202111
11 202111
12 20148
13 20208
14 20226
15 20124
16 20204
17 20214
18 20123
19 20233
20 20182

About Daniel Jurich

Daniel Jurich is a scholar working on Management Science and Operations Research, Computer Networks and Communications, Statistics and Probability, Public Health, Environmental and Occupational Health and Statistics, Probability and Uncertainty, having authored 30 papers that have together received 374 indexed citations. Recurring topics across this work include Psychometric Methodologies and Testing (10 papers), Advanced Statistical Modeling Techniques (7 papers), Advanced Statistical Methods and Models (5 papers), Innovations in Medical Education (3 papers), Reliability and Agreement in Measurement (2 papers), Disability Education and Employment (1 paper), Statistics Education and Methodologies (1 paper) and Behavioral and Psychological Studies (1 paper). The work is most often cited by research in Health Informatics (28 citations), Family Practice (22 citations), Management Science and Operations Research (57 citations), Statistics and Probability (30 citations) and Social Psychology (71 citations). Daniel Jurich has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Jason P. Kopp, Tracy E. Zinn, Sara J. Finney, Richard A. Feinberg, Laine Bradshaw, Miguel Paniagua, Christine E. DeMars, Sally A. Santen, Arnyce R. Pock and Victoria Yaneva. Their work appears in journals such as Academic Medicine, Applied Psychological Measurement, Applied Measurement in Education, Educational Measurement Issues and Practice and Educational and Psychological Measurement.

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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