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Recently, we (Judiesch, Schmidt, & Mount, 1992) concluded that the Schmidt et al. (1979) SDy estimation procedure results in downwardly biased estimates of utility. This conclusion led us to propose a modification of the Schmidt et al. method that involves estimating SDy as the product of estimates of the coefficient of variation (SDy/ Y) and an objective estimate of the average value of employee output (Y). The present article reviews the rationale underlying our conclusion that this modification of the Schmidt et al. method of estimating SDy results in more accurate estimates of SDy, and hence, utility.

Meta-regression is widely used and misused today in meta-analyses in psychology, organizational behavior, marketing, management, and other social sciences, as an approach to the identification and calibration of moderators, with most users being unaware of serious problems in its use. The purpose of this paper is to describe nine serious methodological problems that plague applications of meta-regression.

This paper is methodological in nature and is based on well-established principles of measurement and statistics. These principles are used to illuminate the potential pitfalls in typical applications of meta-regression.

The analysis in this paper demonstrates that many of the nine statistical and measurement pitfalls in the use of meta-regression are nearly universal in applications in the literature, leading to the conclusion that few meta-regressions in the literature today are trustworthy. A second conclusion is that in almost all cases, hierarchical subgrouping of studies is superior to meta-regression as a method of identifying and calibrating moderators. Finally, a third conclusion is that, contrary to popular belief among researchers, the process of accurately identifying and calibrating moderators, even with the best available methods, is complex, difficult, and data demanding.

This paper provides useful guidance to meta-analytic researchers that will improve the practice of moderator identification and calibration in social science research literatures.

Today, many important decisions are made on the basis of the results of meta-analyses. These include decisions in medicine, pharmacology, applied psychology, management, marketing, social policy, and other social sciences. The guidance provided in this paper will improve the quality of such decisions by improving the accuracy and trustworthiness of meta-analytic results.

This paper is original and valuable in that there is no similar listing and discussion of the pitfalls in the use of meta-regression in the literature, and there is currently a widespread lack of knowledge of these problems among meta-analytic researchers in all disciplines.

I reexamine the conflicting results in Frank, Lynch, and Rego (2009) and Lennox, Lisowsky, and Pittman (2013). Frank et al. (2009) conclude that firms can manage book income upward and taxable income downward in the same period, implying a positive relation between aggressive book and tax reporting. Lennox et al. (2013) conclude the relation is negative and aggressive book reporting informs users that aggressive tax reporting is less likely. I identify four key differences in the research designs across the two studies, including measures of aggressive book reporting, measures of aggressive tax reporting, sample time periods, and empirical models. I systematically examine whether each of these differences is responsible for the conflicting results by altering the key difference while holding other factors as constant as possible. I find the relation between aggressive book and tax reporting is driven by the measure of aggressive book reporting, as the relation is positive for some subsets of firms and negative for others. Firms accused of financial statement fraud have a negative relation while nonfraud firms exhibit a positive relation. Using discretionary accruals, I also look for, but do not find a “pivot point” in the relation between aggressive book and tax reporting. I provide a better understanding of the relation between aggressive book and tax reporting by identifying research design choices that are responsible for prior results. I show that measures of both discretionary accruals and financial statement fraud are necessary to gain a more complete picture of the relation between aggressive book and tax reporting.

Professional school counselors have been in the public schools since the early 1900s. Fueled by the industrial revolution, the vocational guidance movement spawned the creation of high school guidance counseling programs. In 1907, Jesse B. Davis created one of the first vocational guidance programs at Central High School in Detroit, Michigan (Schmidt, 2014). In 1908, Frank Parsons, the father of vocational guidance, founded the Vocations Bureau that eventually became part of the Division of Education at Harvard University. These early efforts helped students develop vocationally, morally, and intellectually, and it would take nearly 70 years for children with exceptionalities to be similarly served in the public schools.

The purpose of this chapter is to explain the role of counselors in assisting students with exceptionalities. This will be examined by better understanding the counseling history, defining the terms of exceptionalities and transdisciplinary collaboration, and showcasing the many benefits of individual, group, and brain-based interventions.

Auditing is seasonal, with the majority of U.S. public companies having a December fiscal year-end. This results in an audit “busy season” and “off-season” with a non-trivial seasonal impact on the pricing of audit services. We apply an economic framework that explains how audit seasonality affects both the magnitude and the price elasticity of audit demand and audit supply. We find that the audit busy season is associated with an audit fee premium of approximately 10% based on a meta-analysis of 97 analyses from 18 audit fee studies of U.S public companies. A meta-regression of the contextual differences in research design between studies reveals that examining only Big N attenuates the busy season effect size but does not eliminate it, and that the busy season effect size may be larger post-SOX.