John M. Ferron

150 papers receiving 5.2k citations

Peers

John M. Ferron
Comparison fields: 5 of 190
  • Developmental and Educational Psychology 2.0k
  • Statistics and Probability 765
  • Management Science and Operations Research 817
  • Clinical Psychology 1.0k
  • Cognitive Neuroscience 870
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S. Natasha Beretvas United States
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Citations per year

Countries citing papers authored by John M. Ferron

Since Specialization
Citations

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

Fields of papers citing papers by John M. Ferron

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John M. Ferron. 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 John M. Ferron. The network helps show where John M. Ferron may publish in the future.

Co-authors

The 25 scholars most cited alongside John M. Ferron, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John M. Ferron Line = papers co-authored together John M. Ferron links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 155 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005406
2 2011273
3 2016218
4 2002194
5 2012161
6 2009153
7 1994153
8 2009139
9 2013123
10 2002116
11
Cluster Size in Multilevel Models: The Impact of Sparse Data Structures on Point and Interval Estimates in Two-Level Models
2008104
12 199497
13 201392
14 201691
15 200283
16
The Impact of Small Cluster Size on Multilevel Models: A Monte Carlo Examination of Two-Level Models with Binary and Continuous Predictors
201082
17 201078
18 200678
19 201276
20 200276

About John M. Ferron

John M. Ferron is a scholar working on Developmental and Educational Psychology, Statistics and Probability, Management Science and Operations Research, Education and Clinical Psychology, having authored 155 papers that have together received 5.5k indexed citations. Recurring topics across this work include Behavioral and Psychological Studies (62 papers), Optimal Experimental Design Methods (28 papers), Autism Spectrum Disorder Research (21 papers), Early Childhood Education and Development (19 papers), Statistical Methods and Bayesian Inference (19 papers), Child and Adolescent Psychosocial and Emotional Development (16 papers), Advanced Causal Inference Techniques (13 papers) and Psychometric Methodologies and Testing (12 papers). The work is most often cited by research in Developmental and Educational Psychology (2.0k citations), Statistics and Probability (765 citations), Management Science and Operations Research (817 citations), Clinical Psychology (1.0k citations) and Cognitive Neuroscience (870 citations). John M. Ferron has collaborated with scholars based in United States, Belgium and Canada. Frequent co-authors include Jeffrey D. Kromrey, Wim Van Den Noortgate, S. Natasha Beretvas, Mariola Moeyaert, Shannon M. Suldo, Bethany A. Bell, Joel R. Levin, Kristine Y. Hogarty, Nicholas Ngʼandu and Maaike Ugille. Their work appears in journals such as The Journal of Experimental Education, Behavior Research Methods, Educational and Psychological Measurement, Journal of School Psychology and Multivariate Behavioral Research.

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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