Principal-components analysis and exploratory and confirmatory factor analysis.
Impact in
- Authors
- Fred B. BryantPaul R. Yarnold
In The Last Decade
doi.org/w72787040 →Countries where authors are citing Principal-components analysis and exploratory and confirmatory factor analysis.
This map shows the geographic impact of Principal-components analysis and exploratory and confirmatory factor 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 Principal-components analysis and exploratory and confirmatory factor analysis. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Principal-components analysis and exploratory and confirmatory factor analysis. more than expected).
Fields of papers citing Principal-components analysis and exploratory and confirmatory factor analysis.
This network shows the impact of Principal-components analysis and exploratory and confirmatory factor analysis.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Principal-components analysis and exploratory and confirmatory factor analysis..
About Principal-components analysis and exploratory and confirmatory factor analysis.
This paper, published in 1995, received 882 indexed citations . Written by Fred B. Bryant and Paul R. Yarnold. It is primarily cited by scholars working on Social Psychology (159 citations), Sociology and Political Science (155 citations), Clinical Psychology (151 citations), Education (115 citations) and Experimental and Cognitive Psychology (66 citations).
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/w72787040.