Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Multidimensional Item Response Theory
2009678 citationsMark D. ReckaseCERN Document Server (European Organization for Nuclear Research)profile →
Unifactor Latent Trait Models Applied to Multifactor Tests: Results and Implications
1979640 citationsMark D. ReckaseJournal of Educational Statisticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mark D. Reckase
Since
Specialization
Citations
This map shows the geographic impact of Mark D. Reckase'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 Mark D. Reckase with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark D. Reckase more than expected).
This network shows the impact of papers produced by Mark D. Reckase. 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 Mark D. Reckase. The network helps show where Mark D. Reckase may publish in the future.
Co-authorship network of co-authors of Mark D. Reckase
This figure shows the co-authorship network connecting the top 25 collaborators of Mark D. Reckase.
A scholar is included among the top collaborators of Mark D. Reckase 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 Mark D. Reckase. Mark D. Reckase is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Guarino, Cassandra M., et al.. (2014). A Comparison of Growth Percentile and Value-Added Models of Teacher Performance. RePEc: Research Papers in Economics.1 indexed citations
6.
Reckase, Mark D., et al.. (2014). Optimal item pool design for computerized adaptive tests with polytomous items using GPCM. 56(3). 255.2 indexed citations
Guarino, Cassandra M., Mark D. Reckase, & Jeffrey M. Wooldridge. (2011). Evaluating Value-Added Methods of Estimating of Teacher Performance.. Society for Research on Educational Effectiveness.2 indexed citations
Reckase, Mark D.. (1985). Models for Multidimensional Tests and Hierarchically Structured Training Materials.. Defense Technical Information Center (DTIC).5 indexed citations
McKinley, Robert L. & Mark D. Reckase. (1982). Multidimensional Latent Trait Models..2 indexed citations
17.
McKinley, Robert L. & Mark D. Reckase. (1980). A Successful Application of Latent Trait Theory to Tailored Achievement Testing.. Defense Technical Information Center (DTIC).4 indexed citations
18.
Reckase, Mark D., et al.. (1979). Operational Characteristics of a One-Parameter Tailored Testing Procedure.. Defense Technical Information Center (DTIC).1 indexed citations
Reckase, Mark D.. (1979). Unifactor Latent Trait Models Applied to Multifactor Tests: Results and Implications. Journal of Educational Statistics. 4(3). 207–230.640 indexed citations breakdown →
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.