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.
This map shows the geographic impact of Ian Plewis'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 Ian Plewis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Plewis more than expected).
This network shows the impact of papers produced by Ian Plewis. 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 Ian Plewis. The network helps show where Ian Plewis may publish in the future.
Co-authorship network of co-authors of Ian Plewis
This figure shows the co-authorship network connecting the top 25 collaborators of Ian Plewis.
A scholar is included among the top collaborators of Ian Plewis 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 Ian Plewis. Ian Plewis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Marshall, Alex, Patricia M. Norman, & Ian Plewis. (2013). Development of a relational model of disability (in press).1 indexed citations
3.
Pina-Sánchez, José, Johan Koskinen, & Ian Plewis. (2013). Implications of Retrospective Measurement Error in Event History Analysis. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 15(15). 5–25.6 indexed citations
4.
Mason, Alexina J., Sylvia Richardson, Ian Plewis, & Nicky Best. (2012). Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods. Journal of Official Statistics. 28(2). 279–302.33 indexed citations
5.
Plewis, Ian, Sosthenes Ketende, & Lisa Calderwood. (2012). Assessing the accuracy of response propensities in longitudinal studies. Research Explorer (The University of Manchester). 38(2). 167–171.5 indexed citations
Hawkes, Denise, Ian Plewis, & Georgia Verropoulou. (2008). Missing Income Data in the Millennium Cohort Study: Evidence from the First Two Sweeps. Greenwich Academic Literature Archive (University of Greenwich).5 indexed citations
8.
Plewis, Ian, et al.. (2008). The Contribution of Residential Mobility to Sample Loss in a Birth Cohort Study: Evidence from the First Two Waves of the UK Millennium Cohort Study. Journal of Official Statistics. 24(3). 365–385.18 indexed citations
9.
Plewis, Ian & Sosthenes Ketende. (2006). Millennium Cohort Study: Technical Report on Response, First Edition.11 indexed citations
10.
Plewis, Ian, Lisa Calderwood, Denise Hawkes, & Gad Nathan. (2004). National Child Development Study and 1970 British Cohort Study Technical Report: Changes in the NCDS and BCS70 Populations and Samples over Time.117 indexed citations
11.
Joshi, Heather, et al.. (2002). Mobile Families and Other Challenges in the Design of the Millennium Cohort Study. IOE EPrints.2 indexed citations
12.
Plewis, Ian. (2002). Modelling Impact Heterogeneity. Journal of the Royal Statistical Society Series A (Statistics in Society). 165(1). 31–38.13 indexed citations
Plewis, Ian & John Preston. (2001). Evaluating the Benefits of Lifelong Learning: A Framework. OpenGrey (Institut de l'Information Scientifique et Technique).6 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.