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Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ--a methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results.

Conducting good research is critical to any student today. Writing good research papers is equally important--yet many students have not been given the proper tools to convey cogently the results of their research. This is intended to address and redress this need. This book is literally a step-by-step approach to the writing of an undergraduate or graduate level research paper in the field of economics.

The use of the Internet to gather data, produce and report research has changed the face of the fields of education and research. This paper will present a method for combining electronic on-line media and Delphi methodology to begin the process of ethnographic research with participant inclusion, informed consent, data gathering by discourse facilitation, and preparation for coding. The use of a reflecting team by the research group provides impetus for second round responses by participants. Methods, format, a case study and an evaluation of the process will be presented.

Most of empirical modeling involves the use of Ordinary Least Squares regression where the residuals are assumed normal, independent, and identically distributed. In finite samples, these assumptions becomes critical for accurate estimations, however, in macroeconomics in particular, these assumptions are rarely tested. This study addresses the applications of statistical testing methods and model respecification within the context of applied macroeconomics. The first application is a statistical comparison of Gregory Mankiw, David Romer and David Weils A Contribution to the Empirics of Economic Growth, and Nazrul Islams Growth Empirics: A Panel Data Approach. This analysis shows that the models in both papers are statistically misspecified. When respecified, the functional forms of Mankiw, Romer, and Weils models change considerably whereas Islams retain the theoretical structure. The second application is a study of the impact of inflation on investment and growth. After instrumenting for inflation with a set of political variables, I find that between approximately 1% and 9% inflation, there is a positive correlation between inflation and investment--the Mundell-Tobin effect may be a valid explanation. I further this analysis to show that treating investment as an exogenous variable may be problematic in empirical growth models. ; Ph. D.

Theory development is a high priority in organizational and management research. However, theory development is often equated with building new theory, a practice that is rewarded in the publication process and encouraged by norms that pervade the field. This practice has produced a proliferation of theories, most of which are not exposed to rigorous empirical research that probes core propositions and puts theories at risk. In the interest of theory development, management and organizational research would make better progress if we devoted more attention to theoretical refinement, conducting research that identifies the boundaries and limitations of theories, stages competitive tests between rival theories, and increases the precision of theories so they yield strong predictions that can be falsified. These issues are addressed by the articles that constitute this feature topic, with the goal of enhancing theoretical progress in management and organizational research.

In management research, there is a growing trend toward formative measurement, in which measures are treated as causes of constructs. Formative measurement can be contrasted with reflective measurement, in which constructs are specified as causes of measures. Although recent work seems to suggest that formative measurement is a viable alternative to reflective measurement, the emerging enthusiasm for formative measurement is based on conceptions of constructs, measures, and causality that are difficult to defend. This article critically compares reflective and formative measurement on the basis of dimensionality, internal consistency, identification, measurement error, construct validity, and causality. This comparison leads to the conclusion that the presumed viability of formative measurement is a fallacy, and the objectives of formative measurement can be achieved using alternative models with reflective measures.

During the past decade, the use of polynomial regression has become increasingly prevalent in congruence research. One drawback of polynomial regression is that it relies on the assumption that variables are measured without error. This assumption is relaxed by structural equation modeling with latent variables. One application of structural equation modeling to congruence research is the latent congruence model (LCM). Although the LCM takes measurement error into account and allows tests of measurement equivalence, it is framed around the mean and algebraic difference of the components of congruence (e.g., the person and organization), which creates various interpretational problems. This article discusses problems with the LCM and shows how these problems are resolved by a linear structural equation model that uses the components of congruence as predictors and outcomes. Extensions of the linear model to quadratic equations used in polynomial regression analysis are discussed.

Difference scores have been widely used in studies of fit, similarity, and agreement. Despite their widespread use, difference scores suffer from numerous methodological problems. These problems can be mitigated or avoided with polynomial regression analysis, and this method has become increasingly prevalent during the past decade. Unfortunately, a number of potentially damaging myths have begun to spread regarding the drawbacks of difference scores and the advantages of polynomial regression. If these myths go unchecked, difference scores and the problems they create are likely to persist in studies of fit, similarity, and agreement. This article reviews 10 difference score myths and attempts to dispel these myths, focusing on studies conducted since polynomial regression was formally introduced as an alternative to difference scores.

Multidimensional constructs are widely used to represent several distinct dimensions as a single theoretical concept. The utility of multidimensional constructs relative to their dimensions has generated considerable debate, and this debate creates a dilemma for researchers who want the breadth and comprehensiveness of multidimensional constructs and the precision and clarity of their dimensions. To address this dilemma, this article presents an integrative analytical framework that incorporates multidimensional constructs and their dimensions, using structural equation modeling with latent variables. This framework permits the study of broad questions regarding multidimensional constructs along with specific questions concerning the dimensions of these constructs. The framework also provides tests of issues underlying the multidimensional construct debate, thereby allowing researchers to address these issues on a study-by-study basis. The framework is illustrated using data from studies of the effects of personality on responses to conflict and the effects of work attitudes on employee adaptation.