Laura M. Stapleton

Statistics and Probability · 2002–2018

A researcher in the Science corpus: 11 papers; trusts 63 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 76 citations into it; #2,278 of 12,932 by trust here, #1,962 by citations
• Education — 41 citations into it; #443 of 11,309 by trust here, #2,921 by citations
• Sociology and Political Science — 20 citations into it; #9,450 of 22,756 by trust here, #12,387 by citations
• Clinical Psychology — 18 citations into it
• Developmental and Educational Psychology — 16 citations into it
• Economics and Econometrics — 16 citations into it; #7,994 of 23,481 by trust here, #13,168 by citations
• Management Science and Operations Research — 16 citations into it; #551 of 11,946 by trust here, #2,016 by citations
• Artificial Intelligence — 14 citations into it; #7,970 of 35,345 by trust here, #23,731 by citations

Whom Laura M. Stapleton trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Joop J. Hox
2. K. Liang
3. Daniel M. McNeish
4. Andrew Gelman
5. Halbert White
6. Gary A. Ballinger
7. Sophia Rabe-Hesketh
8. Donald B. Rubin
9. Akihito Kamata
10. Cora J. M. Maas

Who trusts Laura M. Stapleton overall

1. Daniel L. Oberski
2. T. Asparouhov
3. Mei-Shiu Chiu
4. Matthew A. Diemer
5. Daniel M. McNeish
6. Francis L. Huang
7. Frederick L. Philippe
8. Xin Li
9. Chiara Consiglio
10. G. Caprara

Papers from Laura M. Stapleton's perspective

Ordered by how strongly the researchers Laura M. Stapleton trusts endorse each paper. Same corpus, a different order for every researcher.

1. Generalized Linear Models
2. INFERENCE AND MISSING DATA
3. Multilevel analysis : an introduction to basic and advanced multilevel modeling
4. Longitudinal data analysis using generalized linear models
5. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
6. Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models
7. Specification tests in econometrics
8. Are multilevel techniques necessary?: An overview, including simulation studies
9. Generalized, Linear, and Mixed Models (Technometrics)
10. The Accuracy of Multilevel Structural Equation Modeling With Pseudobalanced Groups and Small Samples

Their papers

• The Incorporation of Sample Weights Into Multilevel Structural Equation Models (2002) — 103 citations
• On the Unnecessary Ubiquity of Hierarchical Linear Modeling (2017) — 685 citations
• An Assessment of Practical Solutions for Structural Equation Modeling with Complex Sample Data (2006) — 171 citations
• The Reviewer’s Guide to Quantitative Methods in the Social Sciences (2018) — 880 citations
• The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration (2014) — 671 citations
• Modeling Clustered Data with Very Few Clusters (2016) — 372 citations
• Distinguishing Between Cross- and Cluster-Level Mediation Processes in the Cluster Randomized Trial (2012) — 91 citations
• Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016) — 114 citations
• The Role of Parent Education and Parenting Knowledge in Children's Language and Literacy Skills among White, Black, and Latino Families (2016) — 158 citations
• Factor Analysis: Exploratory and Confirmatory (2010) — 464 citations
• Home‐based Parental Involvement in Young Children’s Education: Examining the effects of maternal education across U.S. ethnic groups (2007) — 157 citations