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.
Hierarchical Linear Models: Applications and Data Analysis Methods.
199316.1k citationsNicholas T. Longford et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Nicholas T. Longford
Since
Specialization
Citations
This map shows the geographic impact of Nicholas T. Longford'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 Nicholas T. Longford with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas T. Longford more than expected).
Fields of papers citing papers by Nicholas T. Longford
This network shows the impact of papers produced by Nicholas T. Longford. 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 Nicholas T. Longford. The network helps show where Nicholas T. Longford may publish in the future.
Co-authorship network of co-authors of Nicholas T. Longford
This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas T. Longford.
A scholar is included among the top collaborators of Nicholas T. Longford 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 Nicholas T. Longford. Nicholas T. Longford is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Longford, Nicholas T.. (2010). Small-sample inference about variance and its transformations. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 34(1). 3–20.6 indexed citations
9.
Longford, Nicholas T.. (2008). An alternative analysis of variance. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 32(1). 77–92.2 indexed citations
10.
Longford, Nicholas T.. (2001). Estimation of the Rates and Composition of Employment in Norwegian Municipalities. Journal of Official Statistics. 17(3). 391–406.1 indexed citations
Longford, Nicholas T.. (2000). Multiple imputation in an international database of social science surveys. Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences). 72–95.2 indexed citations
Longford, Nicholas T., et al.. (1997). Monitoring the University Admissions Process in Spain. Repositori digital de la UPF (Universitat Pompeu Fabra).
16.
McDonald, Roderick P. & Nicholas T. Longford. (1997). Models for Uncertainty in Educational Testing.. Journal of the American Statistical Association. 92(437). 386–386.31 indexed citations
17.
Longford, Nicholas T.. (1996). Small-area estimation using adjustment by covariates. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 20(2). 187–212.2 indexed citations
Longford, Nicholas T.. (1994). A Case for Adjusting Subjectively Rated Scores in the Advanced Placement Tests. Program Statistics Research. Technical Report No. 94-5..3 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.