Organizational relationships are complex. Employees do their work as individuals, but also as members of larger teams. They exist within various social networks, both within and spanning organizations. Multilevel theory is at the core of the organizational sciences, and unpacking multilevel relationships is fundamental to the challenges faced…

One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts…

This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical…

In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…