Measurement Bias Detection Through Factor Analysis

Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.     

Accountability in an Information Age: Opportunities and Risks for Records Management

Electronic records of government organizations are becoming increasingly important for accountability. Managing electronic records, however, proves to be difficult since information and communication technologies confront organizations with various opportunities and risks. In this paper the findings of an international expert survey on electronic records management are presented. These findings identify opportunities and risks for e-mail systems, database management systems, office systems, web technology systems and smart systems. They also point to five crucial questions that must be addressed in order to guarantee the availability of electronic records for accountability.This revised version was published online in October 2005 with corrections to the Cover Date.     

Universities as partners in U.S. research joint ventures

This paper investigates the conditions when a research joint venture (RJV) will involve a university as a research partner. We hypothesize that larger RJVs are more likely to invite a university to join the venture as a research partner than smaller RJVs because larger ventures are less likely to expect substantial additional appropriability problems to result because of the addition of a university partner and because the larger ventures have both a lower marginal cost and a higher marginal value from university R&D contributions to the ventures’ innovative output. We test this hypothesis using data from the COoperative REsearch (CORE) database, and those data confirm the hypothesis.