The statistical sleuth : a course in methods of data analysis

929 indexed citations

Abstract

loading...

About

This paper, published in 2002, received 929 indexed citations. Written by Fred L. Ramsey and Daniel W. Schafer covering the research area of . It is primarily cited by scholars working on Ecology (281 citations), Nature and Landscape Conservation (191 citations) and Global and Planetary Change (164 citations). Published in .

In The Last Decade

doi.org/w10060542 →

Countries where authors are citing The statistical sleuth : a course in methods of data analysis

Specialization
Citations

This map shows the geographic impact of The statistical sleuth : a course in methods of data analysis. 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 The statistical sleuth : a course in methods of data analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The statistical sleuth : a course in methods of data analysis more than expected).

Fields of papers citing The statistical sleuth : a course in methods of data analysis

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of The statistical sleuth : a course in methods of data analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The statistical sleuth : a course in methods of data analysis.

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.

This paper is also available at doi.org/w10060542.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026