A tale of two effects: Using longitudinal data to compare within‐ and between‐firm effects

By S. Certo, Michael C. Withers, Matthew Semadeni. 2017

258 citations recorded by the source corpus.

Subfields: Accounting, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d7b2e3963ffa2cc16856c44f7e7f804f4c504288

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Markku V. J. Maula
2. W. Stam
3. Jeffery S. McMullen
4. Karl J. Wennberg
5. A. Hill
6. Ernest H. O’Boyle
7. Donald J. Schepker
8. B. S. Anderson

In Finance

1. Jeffery S. McMullen
2. Markku V. J. Maula
3. W. Stam
4. Karl J. Wennberg
5. Robert E. Ployhart

In Management Science and Operations Research

1. Robert E. Ployhart
2. P. Bliese
3. Jeffery S. McMullen
4. Ernest H. O’Boyle
5. Donald J. Schepker
6. B. S. Anderson
7. S. Essman
8. Markku V. J. Maula

Citations in the corpus

• National Business Regulations and City Entrepreneurship in Europe: A Multilevel Nested Analysis (2018, unknown): “Third, we make a methodological contribution to entrepreneurship research by using a multilevel model comprising both country level and city level characteristics (Certo et al., 2016).”
• Editorial: Enhancing quantitative theory-testing entrepreneurship research (2019, background): “Consider another example: in a panel model with higher-order (j) fixed effects, the coefficient estimate is the expected within-entity (i) change in the dependent variable over repeated observations of the independent variable, and not the ‘overall effect’ of the independent variable on the dependen”
• Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research (2020, methodology): “Medical experts (i.e., methodologists) have studied how various medicines are being used and have found they are often administered incorrectly (e.g., Antonakis et al., 2010; Certo et al., 2016; Semadeni et al., 2014; Wolfolds & Siegel, 2019).”
• Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “Missing data may severely affect research findings because it can easily lead to sample attrition and associated sample selection biases (Aguinis et al., 2018; Certo et al., 2016; Enders, 2010; Newman, 2014).”
• Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data (2020, methodology): “It is not clear how often emergent effects exist because they are rarely tested; however, Certo et al. (2017) noted that 66% of studies they reviewed reported a significant Hausman (1978) test.”