Kevin J. Grimm
Management Science and Operations Research · with Woods · 2011
A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Management Science and Operations Research — 8 citations into it; #3,406 of 11,946 by trust here, #10,392 by citations
- Computer Networks and Communications — 7 citations into it; #2,728 of 9,886 by trust here, #8,844 by citations
- Statistics and Probability — 6 citations into it; #4,482 of 12,932 by trust here, #10,855 by citations
Whom Kevin J. Grimm trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andreas G. Klein
- Wen-Chung Wang
- Holmes Finch
- Carol M. Woods
- Ching-Lin Shih
- Melanie M. Wall
- Yoav Benjamini
- Herbert W. Marsh
- B. Muthén
- Y. Hochberg
Who trusts Kevin J. Grimm overall
Papers from Kevin J. Grimm's perspective
Ordered by how strongly the researchers Kevin J. Grimm trusts endorse each paper. Same corpus, a different order for every researcher.
- Generalized Appended Product Indicator Procedure for Nonlinear Structural Equation Analysis
- Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction. (Psychological methods)
- The MIMIC Model as a Method for Detecting DIF: Comparison With Mantel-Haenszel, SIBTEST, and the IRT Likelihood Ratio
- Empirical Selection of Anchors for Tests of Differential Item Functioning
- Maximum likelihood estimation of latent interaction effects with the LMS method
- Statistical aspects of the analysis of data from retrospective studies of disease.
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Quasi-Maximum Likelihood Estimation of Structural Equation Models With Multiple Interaction and Quadratic Effects
- Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study
- Differential Item Functioning Detection Using the Multiple Indicators, Multiple Causes Method with a Pure Short Anchor