David M. Klieger

1.0k total citations · 1 hit paper
27 papers, 699 citations indexed

About

David M. Klieger is a scholar working on Public Health, Environmental and Occupational Health, Education and Management Science and Operations Research. According to data from OpenAlex, David M. Klieger has authored 27 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Public Health, Environmental and Occupational Health, 7 papers in Education and 6 papers in Management Science and Operations Research. Recurrent topics in David M. Klieger's work include Medical Education and Admissions (11 papers), Psychometric Methodologies and Testing (6 papers) and Higher Education Research Studies (3 papers). David M. Klieger is often cited by papers focused on Medical Education and Admissions (11 papers), Psychometric Methodologies and Testing (6 papers) and Higher Education Research Studies (3 papers). David M. Klieger collaborates with scholars based in United States, Canada and Netherlands. David M. Klieger's co-authors include Nathan R. Kuncel, Thomas W. Britt, Matthew R. Grossman, Winny Shen, Robert R. Sinclair, Deniz S. Öneş, Brian S. Connelly, Marcus Credé, Sang Eun Woo and Lisa L. Thomas and has published in prestigious journals such as Journal of Applied Psychology, Learning and Individual Differences and American Journal of Pharmaceutical Education.

In The Last Decade

David M. Klieger

24 papers receiving 660 citations

Hit Papers

How Much Do We Really Know About Employee Resilience? 2016 2026 2019 2022 2016 100 200 300

Peers

David M. Klieger
Phillip J. Decker United States
Denise Reyes United States
Gary W. Carter United States
Taehun Lee United States
Jennie Weiner United States
Lisa M. Kath United States
Richard Hermida United States
Joanne Lyubovnikova United Kingdom
Phillip J. Decker United States
David M. Klieger
Citations per year, relative to David M. Klieger David M. Klieger (= 1×) peers Phillip J. Decker

Countries citing papers authored by David M. Klieger

Since Specialization
Citations

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

Fields of papers citing papers by David M. Klieger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Klieger. 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 David M. Klieger. The network helps show where David M. Klieger may publish in the future.

Co-authorship network of co-authors of David M. Klieger

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

All Works

20 of 20 papers shown
1.
Robertson, Christopher T., et al.. (2023). The Association of Participation in a Summer Prelaw Training Program and First-Year Law School Students’ Grades. ScholarWorks (Walden University). 13(1).
2.
Klieger, David M., et al.. (2022). Using third-party evaluations to assess socioemotional skills in graduate and professional school admissions. International Journal of Testing. 22(1). 72–99. 1 indexed citations
4.
Klieger, David M., et al.. (2018). Development of the Behaviorally Anchored Rating Scales for the Skills Demonstration and Progression Guide. ETS Research Report Series. 2018(1). 1–36. 11 indexed citations
5.
Klieger, David M., et al.. (2017). Perceptions and Uses of "GRE"® Scores after the Launch of the "GRE"® Revised General Test in August 2011. ETS GRE® Board Research Report. ETS GRE®-17-01. ETS Research Report. RR-17-03.. ETS Research Report Series. 1 indexed citations
6.
Klieger, David M., et al.. (2017). Perceptions and Uses of GRE® Scores After the Launch of the GRE® revised General Test in August 2011. ETS Research Report Series. 2017(1). 1–49. 4 indexed citations
7.
Liu, Ou Lydia, et al.. (2016). An Investigation of the Use and Predictive Validity of Scores from the "GRE"® revised General Test in a Singaporean University. ETS GRE® Board Research Report. ETS GRE®-16-01. ETS Research Report. RR-16-05.. ETS Research Report Series. 2 indexed citations
8.
Britt, Thomas W., Winny Shen, Robert R. Sinclair, Matthew R. Grossman, & David M. Klieger. (2016). How Much Do We Really Know About Employee Resilience?. Industrial and Organizational Psychology. 9(2). 378–404. 300 indexed citations breakdown →
9.
Liu, Ou Lydia, et al.. (2016). An Investigation of the Use and Predictive Validity of Scores From the GRE® revised General Test in a Singaporean University. ETS Research Report Series. 2016(1). 1–24. 6 indexed citations
10.
Klieger, David M., et al.. (2014). New Perspectives on the Validity of the "GRE"® General Test for Predicting Graduate School Grades. ETS GRE® Board Research Report. ETS GRE®-14-03. ETS Research Report. RR-14-26.. ETS Research Report Series. 4 indexed citations
11.
Young, John W., et al.. (2014). The Validity of Scores from the "GRE"® revised General Test for Forecasting Performance in Business Schools: Phase One. ETS GRE® Board Research Report. ETS GRE®-14-01. ETS Research Report. RR-14-17.. ETS Research Report Series. 4 indexed citations
12.
Hülsheger, Ute R., et al.. (2014). Supervisor ratings of students' academic potential as predictors of citizenship and counterproductive behavior. Learning and Individual Differences. 35. 62–69. 8 indexed citations
13.
Klieger, David M., et al.. (2014). Testing the Invariance of Interrater Reliability Between Paper-Based and Online Modalities of the SIR II™ Student Instructional Report. 4 indexed citations
14.
Klieger, David M., et al.. (2014). New Perspectives on the Validity of the GRE® General Test for Predicting Graduate School Grades. ETS Research Report Series. 2014(2). 1–62. 18 indexed citations
15.
Kuncel, Nathan R., David M. Klieger, & Deniz S. Öneş. (2014). In hiring, Algorithms beat instinct. 21 indexed citations
16.
Klieger, David M., et al.. (2013). Asking Differently about Race and Ethnicity: New Needs for a Changing Population. ETS GRE® Board Research Report. ETS GRE® GREB-11-01. ETS Research Report. RR-13-37.. ETS Research Report Series.
17.
Kuncel, Nathan R., David M. Klieger, Brian S. Connelly, & Deniz S. Öneş. (2013). Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis.. Journal of Applied Psychology. 98(6). 1060–1072. 158 indexed citations
18.
Klieger, David M., et al.. (2009). The Predictive Power of Personal Statements in Admissions: A Meta-Analysis and Cautionary Tale. College and university. 84(4). 83–43. 23 indexed citations
19.
Kuncel, Nathan R. & David M. Klieger. (2007). Application patterns when applicants know the odds: Implications for selection research and practice.. Journal of Applied Psychology. 92(2). 586–593. 14 indexed citations
20.
Kuncel, Nathan R., et al.. (2005). A Meta-Analysis of the Validity of the Pharmacy College Admission Test (PCAT) and Grade Predictors of Pharmacy Student Performance. American Journal of Pharmaceutical Education. 69(3). 51–51. 99 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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