A General Model for Testing Mediation and Moderation Effects
Impact in
Classified as
- Journal
- Prevention Science
In The Last Decade
doi.org/10.1007/s11121-008-0109-6 →Countries where authors are citing A General Model for Testing Mediation and Moderation Effects
This map shows the geographic impact of A General Model for Testing Mediation and Moderation Effects. 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 A General Model for Testing Mediation and Moderation Effects with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A General Model for Testing Mediation and Moderation Effects more than expected).
Fields of papers citing A General Model for Testing Mediation and Moderation Effects
This network shows the impact of A General Model for Testing Mediation and Moderation Effects. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A General Model for Testing Mediation and Moderation Effects.
About A General Model for Testing Mediation and Moderation Effects
This paper, published in 2008, received 654 indexed citations . Written by Amanda J. Fairchild and David P. MacKinnon covering the research area of Clinical Psychology, Statistics and Probability and Management Science and Operations Research. It is primarily cited by scholars working on Clinical Psychology (159 citations), Social Psychology (119 citations), Sociology and Political Science (101 citations), General Health Professions (60 citations) and Experimental and Cognitive Psychology (53 citations). Published in Prevention Science.
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/10.1007/s11121-008-0109-6.