Grace E. Cho
Impact in
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
Papers in
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- Educator Training and Historical Pedagogy 1
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- Matrix Theory and Algorithms 3
- Co-authors
- Carl D. Meyer (2 shared papers)Peggy J. Miller (7 shared papers)Todd L. Sandel (3 shared papers)Jeana Bracey (2 shared papers)Suhua Wang (1 shared paper)Cara Wong (1 shared paper)Su‐hua Wang (1 shared paper)Ilse C. F. Ipsen (1 shared paper)
- Journals
- Human Development (2 papers)Linear Algebra and its Applications (2 papers)Social Development (1 paper)Journal of Family Communication (1 paper)Psychological Assessment (1 paper)
- Partner nations
- United States
In The Last Decade
Grace E. Cho
12 papers receiving 458 citations
Peers
Comparison fields: 5 of 91
- Computational Mathematics 4
- Statistics and Probability 48
- Social Psychology 109
- Computational Theory and Mathematics 81
- Linguistics and Language 20
Countries citing papers authored by Grace E. Cho
This map shows the geographic impact of Grace E. Cho'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 Grace E. Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Grace E. Cho more than expected).
Fields of papers citing papers by Grace E. Cho
This network shows the impact of papers produced by Grace E. Cho. 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 Grace E. Cho. The network helps show where Grace E. Cho may publish in the future.
Co-authors
The 12 scholars most cited alongside Grace E. Cho, 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 | 2001 | 122 | |
| 2 | 2002 | 111 | |
| 3 | 2005 | 79 | |
| 4 | 2000 | 52 | |
| 5 | 2005 | 51 | |
| 6 | 1998 | 28 | |
| 7 | 2005 | 26 | |
| 8 | 2006 | 18 | |
| 9 | 2017 | 11 | |
| 10 | 2020 | 5 | |
| 11 | 2005 | 5 | |
| 12 | 2017 | 1 |
About Grace E. Cho
Grace E. Cho is a scholar working on Sociology and Political Science, Computational Theory and Mathematics, Education, Social Psychology and Management Information Systems, having authored 12 papers that have together received 509 indexed citations. Recurring topics across this work include Early Childhood Education and Development (3 papers), Matrix Theory and Algorithms (3 papers), Advanced Queuing Theory Analysis (2 papers), Literacy, Media, and Education (2 papers), Markov Chains and Monte Carlo Methods (2 papers), Attachment and Relationship Dynamics (1 paper), Discourse Analysis in Language Studies (1 paper) and Educator Training and Historical Pedagogy (1 paper). The work is most often cited by research in Computational Mathematics (4 citations), Statistics and Probability (48 citations), Social Psychology (109 citations), Computational Theory and Mathematics (81 citations) and Linguistics and Language (20 citations). Grace E. Cho has collaborated with scholars based in United States. Frequent co-authors include Carl D. Meyer, Peggy J. Miller, Todd L. Sandel, Jeana Bracey, Suhua Wang, Cara Wong, Su‐hua Wang, Ilse C. F. Ipsen, Elizabeth A. Nick and Farrah Jacquez. Their work appears in journals such as Human Development, Linear Algebra and its Applications, Social Development, Journal of Family Communication and Psychological Assessment.
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.