George B. Macready

1.7k total citations
40 papers, 1.2k citations indexed

About

George B. Macready is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, George B. Macready has authored 40 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Statistics and Probability, 13 papers in Management Science and Operations Research and 8 papers in Artificial Intelligence. Recurrent topics in George B. Macready's work include Psychometric Methodologies and Testing (12 papers), Statistical Methods and Bayesian Inference (8 papers) and Advanced Statistical Methods and Models (6 papers). George B. Macready is often cited by papers focused on Psychometric Methodologies and Testing (12 papers), Statistical Methods and Bayesian Inference (8 papers) and Advanced Statistical Methods and Models (6 papers). George B. Macready collaborates with scholars based in United States and Netherlands. George B. Macready's co-authors include C. Mitchell Dayton, Karen L. Soeken, Henk Kelderman, Ruth Garner, Beth Davey, Hong Jiao, Jeffrey R. Harring, Carol LaSasso, Ming Li and William D. Schafer and has published in prestigious journals such as Psychological Bulletin, Journal of the American Statistical Association and Biometrics.

In The Last Decade

George B. Macready

40 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
George B. Macready United States 14 507 374 316 191 156 40 1.2k
Rolf Langeheine Germany 15 386 0.8× 264 0.7× 211 0.7× 157 0.8× 120 0.8× 37 1.4k
Theodore Micceri United States 6 612 1.2× 311 0.8× 92 0.3× 168 0.9× 54 0.3× 21 1.2k
Marcus Lieberman United States 7 241 0.5× 309 0.8× 47 0.1× 200 1.0× 106 0.7× 14 1.0k
Reinhold Hatzinger Austria 13 188 0.4× 175 0.5× 86 0.3× 80 0.4× 67 0.4× 38 933
Richard J. Patz United States 8 525 1.0× 642 1.7× 146 0.5× 342 1.8× 49 0.3× 12 1.0k
Yanyan Sheng United States 16 215 0.4× 183 0.5× 119 0.4× 95 0.5× 227 1.5× 55 820
Alina A. von Davier United States 22 457 0.9× 722 1.9× 280 0.9× 381 2.0× 352 2.3× 105 1.6k
Jorge N. Tendeiro Netherlands 18 180 0.4× 235 0.6× 88 0.3× 78 0.4× 71 0.5× 63 978
Ab Mooijaart Netherlands 16 200 0.4× 130 0.3× 81 0.3× 83 0.4× 32 0.2× 36 1.1k
Steffi Pohl Germany 18 290 0.6× 393 1.1× 76 0.2× 134 0.7× 136 0.9× 53 965

Countries citing papers authored by George B. Macready

Since Specialization
Citations

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

Fields of papers citing papers by George B. Macready

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by George B. Macready. 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 George B. Macready. The network helps show where George B. Macready may publish in the future.

Co-authorship network of co-authors of George B. Macready

This figure shows the co-authorship network connecting the top 25 collaborators of George B. Macready. A scholar is included among the top collaborators of George B. Macready 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 George B. Macready. George B. Macready 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.
Li, Ming, Jeffrey R. Harring, & George B. Macready. (2014). Investigating the Feasibility of Using Mplus in the Estimation of Growth Mixture Models. Journal of Modern Applied Statistical Methods. 13(1). 484–513. 11 indexed citations
2.
Jiao, Hong, George B. Macready, Junhui Liu, & Youngmi Cho. (2012). A Mixture Rasch Model–Based Computerized Adaptive Test for Latent Class Identification. Applied Psychological Measurement. 36(6). 469–493. 8 indexed citations
3.
Jiao, Hong, et al.. (2011). Exploring levels of performance using the mixture Rasch model for standard setting 1. 13 indexed citations
4.
Macready, George B. & C. Mitchell Dayton. (1992). The Application of Latent Class Models in Adaptive Testing. Psychometrika. 57(1). 71–88. 11 indexed citations
5.
Dayton, C. Mitchell & George B. Macready. (1988). Concomitant-Variable Latent-Class Models. Journal of the American Statistical Association. 83(401). 173–173. 60 indexed citations
6.
Dayton, C. Mitchell & George B. Macready. (1988). Concomitant-Variable Latent-Class Models. Journal of the American Statistical Association. 83(401). 173–178. 334 indexed citations
7.
Soeken, Karen L. & George B. Macready. (1986). Application of setwise randomized response procedures for surveying multiple sensitive attributes.. Psychological Bulletin. 99(2). 289–295. 5 indexed citations
8.
Davey, Beth & George B. Macready. (1985). Prerequisite relations among inference tasks for good and poor readers.. Journal of Educational Psychology. 77(5). 539–552. 10 indexed citations
9.
Davey, Beth & George B. Macready. (1985). Prerequisite relations among inference tasks for good and poor readers.. Journal of Educational Psychology. 77(5). 539–552. 10 indexed citations
10.
Macready, George B., et al.. (1985). An Investigation of Hierarchic Structure of Acquisition for Left-Right Identification Tasks. Perceptual and Motor Skills. 61(3_suppl). 1163–1170. 1 indexed citations
11.
Soeken, Karen L. & George B. Macready. (1985). Randomized Response Parameters as Factors in Frequency Estimates. Educational and Psychological Measurement. 45(1). 89–100. 5 indexed citations
12.
Dayton, C. Mitchell & George B. Macready. (1983). Latent structure analysis of repeated classifications with dichotomous data. British Journal of Mathematical and Statistical Psychology. 36(2). 189–201. 8 indexed citations
13.
Davey, Beth, Carol LaSasso, & George B. Macready. (1983). Comparison of Reading Comprehension Task Performance for Deaf and Hearing Readers. Journal of Speech Language and Hearing Research. 26(4). 622–628. 13 indexed citations
14.
Macready, George B. & C. Mitchell Dayton. (1980). A Two-Stage Conditional Estimation Procedure for Unrestricted Latent Class Models. Journal of Educational Statistics. 5(2). 129–129. 4 indexed citations
15.
Dayton, C. Mitchell & George B. Macready. (1980). A Scaling Model with Response Errors and Intrinsically Unscalable Respondents. Psychometrika. 45(3). 343–356. 52 indexed citations
16.
Macready, George B. & C. Mitchell Dayton. (1980). The Nature and Use of State Mastery Models. Applied Psychological Measurement. 4(4). 493–516. 32 indexed citations
17.
Macready, George B. & C. Mitchell Dayton. (1980). A Two-Stage Conditional Estimation Procedure for Unrestricted Latent Class Models. Journal of Educational Statistics. 5(2). 129–156. 10 indexed citations
18.
Macready, George B. & C. Mitchell Dayton. (1977). Statistical Comparisons Among Hierarchies Based on Latent Structure Models. Research Monograph 77-1.. 1 indexed citations
19.
Dayton, C. Mitchell & George B. Macready. (1976). A Probabilistic Model for Validation of Behavioral Hierarchies. Psychometrika. 41(2). 189–204. 97 indexed citations
20.
Macready, George B., et al.. (1973). Homogeneity Within Item Forms in Domain Referenced Testing. Educational and Psychological Measurement. 33(2). 351–360. 13 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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