Data Sparseness and Variance in Accounting Profitability

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Dimitris SkurasDepartment of Economics, University of Patras, 26504 Rio, Greece. E-mail: skuras@econ.upatras.gr Forthcoming in Organizational Research Methods AbstractA central question in strategic management is why some firms perform better than others. One approach to addressing this question empirically is to decompose the variance in firm-level profitability into firm, industry, location, and year components. Although it is well established that data sparseness in variance decomposition studies can lead to overestimating particular variance components, little attention has been paid to sample size requirements in strategic management studies that have examined the nature of differences in firm profitability. We conduct a meta-regression and variance decomposition study and conclude that the variation in the results from previous studies is driven-to a considerable extent-by the number of observations per group within a component. Based on these findings, we draw conclusions regarding the validity and reliability of previous variance decomposition studies and provide implications for current debates in the strategic management literature.

How Much Do Industry, Corporation, and Business Matter, Really? A Meta-Analysis

“…The correlation between relative degrees of freedom and effect sizes is weak for the variance but is strong for the sum-of-squares measure (see the d f columns in Table 5). 11 The "data sparseness" problem mentioned before with respect to the variance measure may manifest itself not with relative but rather with absolute degrees of freedom (Stavropoulos et al 2015). However, correlations between effect sizes and the number of years, industries, multibusiness corporations, and businesses (column n k ) or their natural logarithms (column ln(n k )) remain weak.…”

“…Variance Stavropoulos et al 2015). Note that this problem arises only for a small number of levels and appears to be modest in size compared to the sum of squares.…”

“…Studies have found that three factors typically account for most of the performance differences between such businesses (Schmalensee 1985, Hansen and Wernerfelt 1989, Rumelt 1991, Roquebert et al 1996, McGahan and Porter 1997: the industry in which the business operates (here, baby care versus defense industry); the corporate parent, if any, to which the business belongs (e.g., Procter & Gamble versus Boeing); and the business itself (i.e., the specifics of each business). Although consensus exists that industry, corporate, and business effects together explain most differences in performance, there is ongoing debate about how much each of these factors explains (Hough 2006, Misangyi et al 2006, Bou and Satorra 2010, Karniouchina et al 2013, Stavropoulos et al 2015, Zavosh and Dibiaggio 2015.…”

Variance decomposition of firm performance: past, present and future

“…However, issues of definitions, data and statistical methods (McNamara et al , 2005; Stavropoulos et al , 2015; Vanneste, 2017) have affected results and findings of variance decomposition of firm performance. Although the impact of statistical techniques has received the most attention in the literature, issues of performance measures and the definition of industry, samples and effects, complicate direct and inter-study comparisons of effects.…”

“…Despite the foundational importance of these studies to strategic management, and the insights it has provided on sources of firm performance variance, there has been no comprehensive review. Stavropoulos et al (2015) and Vanneste (2017) conducted meta-analyzes of a few studies to highlight the influence of statistical methods on results. However, what do we really know about the sources of differential firm performance?…”