Laine Bradshaw

1.0k total citations
18 papers, 698 citations indexed

About

Laine Bradshaw is a scholar working on Management Science and Operations Research, Statistics and Probability and Computer Networks and Communications. According to data from OpenAlex, Laine Bradshaw has authored 18 papers receiving a total of 698 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Management Science and Operations Research, 8 papers in Statistics and Probability and 7 papers in Computer Networks and Communications. Recurrent topics in Laine Bradshaw's work include Psychometric Methodologies and Testing (11 papers), Statistics Education and Methodologies (7 papers) and Advanced Statistical Modeling Techniques (7 papers). Laine Bradshaw is often cited by papers focused on Psychometric Methodologies and Testing (11 papers), Statistics Education and Methodologies (7 papers) and Advanced Statistical Modeling Techniques (7 papers). Laine Bradshaw collaborates with scholars based in United States and Türkiye. Laine Bradshaw's co-authors include Jonathan Templin, Andrew Izsák, Erik Jacobson, Ashley J. Harrison, Jonathan M. Campbell, Daniel Jurich, Sedat Şen, Ren Liu, Anne Corinne Huggins‐Manley and Roy Levy and has published in prestigious journals such as Journal of Autism and Developmental Disorders, Frontiers in Psychology and Psychometrika.

In The Last Decade

Laine Bradshaw

17 papers receiving 677 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Laine Bradshaw United States 14 293 218 190 165 139 18 698
Gunter Maris Netherlands 18 312 1.1× 203 0.9× 191 1.0× 121 0.7× 138 1.0× 57 1.1k
Judith A. Spray United States 13 373 1.3× 91 0.4× 116 0.6× 132 0.8× 199 1.4× 39 829
G. Gage Kingsbury United States 11 362 1.2× 139 0.6× 85 0.4× 224 1.4× 147 1.1× 30 879
Peida Zhan China 14 184 0.6× 193 0.9× 86 0.5× 82 0.5× 70 0.5× 46 478
Maria Bolsinova Netherlands 12 236 0.8× 85 0.4× 107 0.6× 60 0.4× 74 0.5× 41 492
Deborah L. Schnipke United States 9 300 1.0× 58 0.3× 99 0.5× 99 0.6× 113 0.8× 23 575
Seang‐Hwane Joo United States 14 202 0.7× 66 0.3× 74 0.4× 53 0.3× 60 0.4× 46 494
James A. Wollack United States 14 481 1.6× 140 0.6× 292 1.5× 87 0.5× 249 1.8× 42 779
Esther Ulitzsch Germany 16 209 0.7× 67 0.3× 113 0.6× 82 0.5× 54 0.4× 39 498
Richard J. Patz United States 8 642 2.2× 146 0.7× 525 2.8× 77 0.5× 342 2.5× 12 1.0k

Countries citing papers authored by Laine Bradshaw

Since Specialization
Citations

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

Fields of papers citing papers by Laine Bradshaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laine Bradshaw. 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 Laine Bradshaw. The network helps show where Laine Bradshaw may publish in the future.

Co-authorship network of co-authors of Laine Bradshaw

This figure shows the co-authorship network connecting the top 25 collaborators of Laine Bradshaw. A scholar is included among the top collaborators of Laine Bradshaw 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 Laine Bradshaw. Laine Bradshaw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Bradshaw, Laine, et al.. (2023). Approaches to estimating longitudinal diagnostic classification models. Behaviormetrika. 51(1). 7–19. 3 indexed citations
2.
Lee, Hollylynne S., et al.. (2023). Validating a concept inventory for measuring students’ probabilistic reasoning: The case of reasoning within the context of a raffle. The Journal of Mathematical Behavior. 71. 101081–101081.
3.
Bradshaw, Laine, et al.. (2021). A Semi-supervised Learning-Based Diagnostic Classification Method Using Artificial Neural Networks. Frontiers in Psychology. 11. 618336–618336. 7 indexed citations
4.
Wang, Shiyu, et al.. (2020). Flexible Computerized Adaptive Tests to Detect Misconceptions and Estimate Ability Simultaneously. Applied Psychological Measurement. 45(1). 3–21. 4 indexed citations
5.
Bradshaw, Laine & Roy Levy. (2019). Interpreting Probabilistic Classifications From Diagnostic Psychometric Models. Educational Measurement Issues and Practice. 38(2). 79–88. 15 indexed citations
6.
Izsák, Andrew, Erik Jacobson, & Laine Bradshaw. (2019). Surveying Middle-Grades Teachers' Reasoning About Fraction Arithmetic in Terms of Measured Quantities. Journal for Research in Mathematics Education. 50(2). 156–209. 23 indexed citations
7.
Bradshaw, Laine, et al.. (2018). Assessing Growth in a Diagnostic Classification Model Framework. Psychometrika. 83(4). 963–990. 49 indexed citations
8.
Bradshaw, Laine, et al.. (2018). Evaluating Intervention Effects in a Diagnostic Classification Model Framework. Journal of Educational Measurement. 55(1). 32–51. 17 indexed citations
9.
Harrison, Ashley J., et al.. (2017). Development and Psychometric Evaluation of the Autism Stigma and Knowledge Questionnaire (ASK-Q). Journal of Autism and Developmental Disorders. 47(10). 3281–3295. 85 indexed citations
10.
Şen, Sedat & Laine Bradshaw. (2017). Comparison of Relative Fit Indices for Diagnostic Model Selection. Applied Psychological Measurement. 41(6). 422–438. 28 indexed citations
11.
Liu, Ren, Anne Corinne Huggins‐Manley, & Laine Bradshaw. (2016). The Impact of Q-Matrix Designs on Diagnostic Classification Accuracy in the Presence of Attribute Hierarchies. Educational and Psychological Measurement. 77(2). 220–240. 36 indexed citations
12.
Bradshaw, Laine, et al.. (2015). Invariance Properties for General Diagnostic Classification Models. International Journal of Testing. 16(2). 99–118. 22 indexed citations
13.
Templin, Jonathan & Laine Bradshaw. (2014). Hierarchical Diagnostic Classification Models: A Family of Models for Estimating and Testing Attribute Hierarchies. Psychometrika. 79(2). 317–339. 111 indexed citations
14.
Bradshaw, Laine, et al.. (2014). The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model. Educational and Psychological Measurement. 75(3). 491–511. 45 indexed citations
15.
Bradshaw, Laine & Jonathan Templin. (2013). Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions. Psychometrika. 79(3). 403–425. 46 indexed citations
16.
Templin, Jonathan & Laine Bradshaw. (2013). Measuring the Reliability of Diagnostic Classification Model Examinee Estimates. Journal of Classification. 30(2). 251–275. 104 indexed citations
17.
Jurich, Daniel & Laine Bradshaw. (2013). An Illustration of Diagnostic Classification Modeling in Student Learning Outcomes Assessment. International Journal of Testing. 14(1). 49–72. 27 indexed citations
18.
Bradshaw, Laine, Andrew Izsák, Jonathan Templin, & Erik Jacobson. (2013). Diagnosing Teachers’ Understandings of Rational Numbers: Building a Multidimensional Test Within the Diagnostic Classification Framework. Educational Measurement Issues and Practice. 33(1). 2–14. 76 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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