Julia Kopf
Impact in
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- Psychometric Methodologies and Testing
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
Papers in
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- Advanced Statistical Modeling Techniques 3
- Distributed and Parallel Computing Systems 1
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- Psychometric Methodologies and Testing 3
- Co-authors
- Carolin Strobl (8 shared papers)Achim Zeileis (7 shared papers)Florian Wickelmaier (3 shared papers)Thomas Augustin (1 shared paper)P. J. Plauger (1 shared paper)
- Journals
- Applied Psychological Measurement (1 paper)Psychometrika (1 paper)Educational and Psychological Measurement (1 paper)Zurich Open Repository and Archive (University of Zurich) (2 papers)Open access LMU (Ludwid Maxmilian's Universitat Munchen) (1 paper)
- Partner nations
- GermanyAustriaSwitzerland
In The Last Decade
Julia Kopf
9 papers receiving 239 citations
Peers
Comparison fields: 5 of 59
- Management Science and Operations Research 132
- Statistics and Probability 72
- Computer Networks and Communications 98
- Experimental and Cognitive Psychology 49
- Applied Psychology 8
Countries citing papers authored by Julia Kopf
This map shows the geographic impact of Julia Kopf'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 Julia Kopf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Kopf more than expected).
Fields of papers citing papers by Julia Kopf
This network shows the impact of papers produced by Julia Kopf. 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 Julia Kopf. The network helps show where Julia Kopf may publish in the future.
Co-authors
The 5 scholars most cited alongside Julia Kopf, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 106 | |
| 2 | 2014 | 83 | |
| 3 | 2014 | 35 | |
| 4 | 2011 | 6 | |
| 5 | 2010 | 5 | |
| 6 | 2013 | 5 | |
| 7 | Infrastructure for Psychometric Modeling | 2015 | 5 |
| 8 | 1968 | 1 | |
| 9 | Recursive Partitioning Based on Psychometric Models | 2015 | 1 |
About Julia Kopf
Julia Kopf is a scholar working on Computer Networks and Communications, Management Science and Operations Research, Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 9 papers that have together received 247 indexed citations. Recurring topics across this work include Psychometric Methodologies and Testing (3 papers), Advanced Statistical Modeling Techniques (3 papers), Scientific Computing and Data Management (1 paper), Behavioral and Psychological Studies (1 paper), VLSI and Analog Circuit Testing (1 paper), Distributed and Parallel Computing Systems (1 paper), Mental Health Research Topics (1 paper) and Context-Aware Activity Recognition Systems (1 paper). The work is most often cited by research in Management Science and Operations Research (132 citations), Statistics and Probability (72 citations), Computer Networks and Communications (98 citations), Experimental and Cognitive Psychology (49 citations) and Applied Psychology (8 citations). Julia Kopf has collaborated with scholars based in Germany, Austria and Switzerland. Frequent co-authors include Carolin Strobl, Achim Zeileis, Florian Wickelmaier, Thomas Augustin and P. J. Plauger. Their work appears in journals such as Applied Psychological Measurement, Psychometrika, Educational and Psychological Measurement, Zurich Open Repository and Archive (University of Zurich) and Open access LMU (Ludwid Maxmilian's Universitat Munchen).
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.