Katherine E. Castellano

490 total citations
29 papers, 341 citations indexed

About

Katherine E. Castellano is a scholar working on Education, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, Katherine E. Castellano has authored 29 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Education, 11 papers in Statistics and Probability and 7 papers in Management Science and Operations Research. Recurrent topics in Katherine E. Castellano's work include School Choice and Performance (10 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (7 papers). Katherine E. Castellano is often cited by papers focused on School Choice and Performance (10 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (7 papers). Katherine E. Castellano collaborates with scholars based in United States and Norway. Katherine E. Castellano's co-authors include Andrew Ho, Anders Skrondal, Sophia Rabe‐Hesketh, J. R. Lockwood, Daniel F. McCaffrey, Sean F. Reardon, Michael D. Bates, Mark Wilson, Kip Téllez and Mikyung Kim Wolf and has published in prestigious journals such as Journal of Research in Science Teaching, BMC Medical Research Methodology and Educational and Psychological Measurement.

In The Last Decade

Katherine E. Castellano

24 papers receiving 292 citations

Peers

Katherine E. Castellano

EDUCA

MSOR

ISM

SPS

Kilchan Choi United States

Fiona M. Hollands United States

Roberto Agodini United States

Richard G. Wolfe Canada

Nancy L. Allen United States

Anna E. Bargagliotti United States

Spencer S. Swinton United States

Mdutshekelwa Ndlovu South Africa

F. Joe Crosswhite United States

Gina Schuyler Ikemoto United States

Kilchan Choi United States

Katherine E. Castellano

202 ×1.0

EDUCA

43 ×0.6

MSOR

41 ×0.7

61 ×1.3

ISM

28 ×0.9

SPS

Citations per year, relative to Katherine E. Castellano Katherine E. Castellano (= 1×) peers Kilchan Choi

Countries citing papers authored by Katherine E. Castellano

Since Specialization

Citations

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

Fields of papers citing papers by Katherine E. Castellano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katherine E. Castellano. 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 Katherine E. Castellano. The network helps show where Katherine E. Castellano may publish in the future.

Co-authorship network of co-authors of Katherine E. Castellano

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

All Works

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20 of 20 papers shown

Mikeska, Jamie N., et al.. (2025). Score Reports for Teacher Educators: Informing Assessment Results for Content Knowledge for Teaching. Educational Assessment. 30(1). 41–65.

Castellano, Katherine E., Daniel F. McCaffrey, Ning Xi, Yue Jia, & Laura S. Hamilton. (2025). Exploring differential change in student performance during the COVID-19 pandemic across the United States. Large-scale Assessments in Education. 13(1).

Deane, Paul, Duanli Yan, Katherine E. Castellano, et al.. (2024). Modeling Writing Traits in a Formative Essay Corpus. ETS Research Report Series. 2024(1). 1–62. 2 indexed citations

Coffman, Donna L., Haoyu Zhou, Katherine E. Castellano, Megan S. Schuler, & Daniel F. McCaffrey. (2024). Sampling weighting strategies in causal mediation analysis. BMC Medical Research Methodology. 24(1). 133–133. 1 indexed citations

Castellano, Katherine E., et al.. (2024). Are Large-Scale Test Scores Comparable for At-Home Versus Test Center Testing?. Applied Psychological Measurement. 48(6). 295–299. 1 indexed citations

Castellano, Katherine E. & Jamie N. Mikeska. (2023). Developing and using a scalable assessment to measure preservice elementary teachers' content knowledge for teaching about matter. Journal of Research in Science Teaching. 61(6). 1389–1426. 3 indexed citations

Castellano, Katherine E., et al.. (2022). Examining Preservice Elementary Teachers’ Answer Changing Behavior on a Content Knowledge for Teaching Science Assessment. Journal of Science Education and Technology. 31(4). 528–541.

Castellano, Katherine E., Sandip Sinharay, Jiangang Hao, & Chen Li. (2022). An Investigation Into the Impact of Test Session Disruptions for At-Home Test Administrations. Applied Psychological Measurement. 47(1). 76–82.

Bergner, Yoav, et al.. (2019). Item Response Models for Multiple Attempts With Incomplete Data. Journal of Educational Measurement. 56(2). 415–436. 10 indexed citations

10.

Castellano, Katherine E. & Daniel F. McCaffrey. (2019). Estimating the Accuracy of Relative Growth Measures Using Empirical Data. Journal of Educational Measurement. 57(1). 92–123. 3 indexed citations

11.

Lockwood, J. R., et al.. (2018). Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data. Journal of Educational and Behavioral Statistics. 43(6). 663–692. 7 indexed citations

12.

Castellano, Katherine E., et al.. (2016). Assessing Academic Language in an Elementary Mathematics Teacher Licensure Exam.. Teacher education quarterly (Claremont, Calif.). 43(1). 3–27. 6 indexed citations

13.

Lockwood, J. R. & Katherine E. Castellano. (2016). Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models. Educational and Psychological Measurement. 77(6). 917–944. 6 indexed citations

14.

Lockwood, J. R. & Katherine E. Castellano. (2015). Alternative Statistical Frameworks for Student Growth Percentile Estimation. 2(1). 1–9. 17 indexed citations

15.

Castellano, Katherine E., Sophia Rabe‐Hesketh, & Anders Skrondal. (2014). Composition, Context, and Endogeneity in School and Teacher Comparisons. Journal of Educational and Behavioral Statistics. 39(5). 333–367. 47 indexed citations

16.

Bates, Michael D., Katherine E. Castellano, Sophia Rabe‐Hesketh, & Anders Skrondal. (2014). Handling Correlations Between Covariates and Random Slopes in Multilevel Models. Journal of Educational and Behavioral Statistics. 39(6). 524–549. 22 indexed citations

17.

McCaffrey, Daniel F. & Katherine E. Castellano. (2014). A Technical Evaluation of the Student Growth Component of the Georgia Teacher and Leader Evaluation System.

18.

Castellano, Katherine E., et al.. (2014). Examining the Internal Structure Evidence for the Performance Assessment for California Teachers. Journal of Teacher Education. 65(5). 402–420. 26 indexed citations

19.

Castellano, Katherine E. & Andrew Ho. (2013). A Practitioner's Guide to Growth Models. 41 indexed citations

20.

Castellano, Katherine E. & Andrew Ho. (2012). Contrasting OLS and Quantile Regression Approaches to Student “Growth” Percentiles. Journal of Educational and Behavioral Statistics. 38(2). 190–215. 30 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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