Linear Mixed Models: A Practical Guide Using Statistical Software
- Journal
- TU Digital Collections (Thammasat University)
In The Last Decade
doi.org/w68835157 →Countries where authors are citing Linear Mixed Models: A Practical Guide Using Statistical Software
This map shows the geographic impact of Linear Mixed Models: A Practical Guide Using Statistical Software. 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 Linear Mixed Models: A Practical Guide Using Statistical Software with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linear Mixed Models: A Practical Guide Using Statistical Software more than expected).
Fields of papers citing Linear Mixed Models: A Practical Guide Using Statistical Software
This network shows the impact of Linear Mixed Models: A Practical Guide Using Statistical Software. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Linear Mixed Models: A Practical Guide Using Statistical Software.
About Linear Mixed Models: A Practical Guide Using Statistical Software
This paper, published in 2006, received 1.1k indexed citations . Written by Brady T. West, Kathleen B. Welch and Andrzej T. Gałecki. It is primarily cited by scholars working on Cognitive Neuroscience (133 citations), Social Psychology (127 citations) and Clinical Psychology (124 citations). Published in TU Digital Collections (Thammasat University).
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/w68835157.