Michael R. Healy

977 total citations
12 papers, 564 citations indexed

About

Michael R. Healy is a scholar working on Statistics and Probability, Developmental and Educational Psychology and Cognitive Neuroscience. According to data from OpenAlex, Michael R. Healy has authored 12 papers receiving a total of 564 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Statistics and Probability, 4 papers in Developmental and Educational Psychology and 3 papers in Cognitive Neuroscience. Recurrent topics in Michael R. Healy's work include Statistics Education and Methodologies (5 papers), Innovations in Educational Methods (3 papers) and Memory Processes and Influences (2 papers). Michael R. Healy is often cited by papers focused on Statistics Education and Methodologies (5 papers), Innovations in Educational Methods (3 papers) and Memory Processes and Influences (2 papers). Michael R. Healy collaborates with scholars based in United States. Michael R. Healy's co-authors include Victoria Romero, Christopher L. Aberson, Leah L. Light, Christie Chung, Dale E. Berger, Robert F. Kennison, Stanley Krippner, Robert A. Silverman and Robert A. Silverman and has published in prestigious journals such as Journal of Experimental Psychology Learning Memory and Cognition, Personality and Social Psychology Review and Memory & Cognition.

In The Last Decade

Michael R. Healy

12 papers receiving 510 citations

Peers

Michael R. Healy
Victoria Romero United States
Sebastiano A. Fisicaro United States
Erin Sparks Canada
Wendian Shi China
Faria Sana Canada
Paula J. Waddill United States
Marguerite P. Ford United States
Sylvia Kenig United States
Miriam W. Schustack United States
Jennifer Hindman United States
Victoria Romero United States
Michael R. Healy
Citations per year, relative to Michael R. Healy Michael R. Healy (= 1×) peers Victoria Romero

Countries citing papers authored by Michael R. Healy

Since Specialization
Citations

This map shows the geographic impact of Michael R. Healy'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 Michael R. Healy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael R. Healy more than expected).

Fields of papers citing papers by Michael R. Healy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael R. Healy. 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 Michael R. Healy. The network helps show where Michael R. Healy may publish in the future.

Co-authorship network of co-authors of Michael R. Healy

This figure shows the co-authorship network connecting the top 25 collaborators of Michael R. Healy. A scholar is included among the top collaborators of Michael R. Healy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael R. Healy. Michael R. Healy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Healy, Michael R., Leah L. Light, & Christie Chung. (2005). Dual-Process Models of Associative Recognition in Young and Older Adults: Evidence From Receiver Operating Characteristics.. Journal of Experimental Psychology Learning Memory and Cognition. 31(4). 768–788. 82 indexed citations
2.
Light, Leah L., et al.. (2004). Effects of repetition and response deadline on associative recognition in young and older adults. Memory & Cognition. 32(7). 1182–1193. 97 indexed citations
3.
Aberson, Christopher L., Dale E. Berger, Michael R. Healy, & Victoria Romero. (2003). Evaluation of an Interactive Tutorial for Teaching Hypothesis Testing Concepts. Teaching of Psychology. 30(1). 75–78. 35 indexed citations
4.
Light, Leah L., Robert F. Kennison, & Michael R. Healy. (2002). Bias effects in word fragment completion in young and older adults. Memory & Cognition. 30(8). 1204–1218. 5 indexed citations
5.
Healy, Michael R., et al.. (2002). Evaluating Java Applets for Teaching on the Internet. Scholarship - Claremont (Claremont Colleges). 4 indexed citations
6.
Aberson, Christopher L., Dale E. Berger, Michael R. Healy, & Victoria Romero. (2002). An Interactive Tutorial for Teaching Statistical Power. Journal of Statistics Education. 10(3). 27 indexed citations
7.
Aberson, Christopher L., Dale E. Berger, Michael R. Healy, & Victoria Romero. (2001). Teaching Statistics with Web Technology: The WISE Project.. 14(7). 43–45. 3 indexed citations
8.
Romero, Victoria, Dale E. Berger, Michael R. Healy, & Christopher L. Aberson. (2000). Using cognitive learning theory to design effective on-line statistics tutorials. Behavior Research Methods, Instruments, & Computers. 32(2). 246–249. 11 indexed citations
9.
Aberson, Christopher L., Michael R. Healy, & Victoria Romero. (2000). Ingroup Bias and Self-Esteem: A Meta-Analysis. Personality and Social Psychology Review. 4(2). 157–173. 237 indexed citations
10.
Aberson, Christopher L., et al.. (2000). Evaluation of an Interactive Tutorial for Teaching the Central Limit Theorem. Teaching of Psychology. 27(4). 289–291. 54 indexed citations
11.
Krippner, Stanley, et al.. (1974). Stimulant drugs and hyperkinesis: A question of diagnosis. Reading World. 13(3). 198–222. 4 indexed citations
12.
Krippner, Stanley, et al.. (1973). A Study of "Hyperkinetic" Children Receiving Stimulant Drugs. Academic Therapy. 8(3). 261–269. 5 indexed 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.

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