Enyi Patrick Enyi
Babcock University
e.p.enyi@gmail.com
Department of Accounting, School of Management Sciences
Accounting and finance-based researchers often face the problem of using multiple surrogates to capture the true essence of a dependent variable in order to study its predictive relationship with explanatory variables. The major pitfall with this, is the inability to directly connect the study results with the major objective of the research forming the topic. This study explores the various methods that can be adopted to unify the engaging properties of the multiple parameters used in identifying a dependent variable. Using logistic regression, the study converted the a priori expectations of 30 Ph.D research theses in finance and accounting with four dependent surrogates into a probabilistic log values and compared them with the individual surrogate performance on the one hand and the surrogates geometric mean on the other hand. While the geometric mean showed no significant difference between itself and the probabilistic expectation (β = .278,t(30) = .695, R2 = .077,p > .10), the individual surrogates results revealed both combined and individual significant differences with the thesesa priori expectations (Adj. R2 = .0291,F (4, 25) = 22.598, p < .05). This paper recommends unifying multivariate dependent variables with geometric means for successful financial performance relational studies.
Keywords : performance, dependent variable, surrogate, proxy, logistic regression
Researchers and scholars in the social and management sciences are often faced with the problem of identifying and operationalizing the main ingredients forming the dependent or outcome variable in performance assessment research. This is particularly a big issue when such a study is extended in scope or necessitated by the requirements to fulfil the conditions for the award of a degree. For those studying behavioral traits and similar psychological phenomena that has no accounting or financial data foundation, this will not present a problem as there are several methods already developed to tackle the issue. Some of these methods include the use of general linear model (GLM) multivariate and repeated measures procedures using computer statistical software, while others may include the use of canonical correlations and specially constructed regression equations (Helwig, 2017; NCSS, 1989; Steiger, n.d.). The use of means as a converging point was also advocated by other researchers especially when multiple dependent variables represent different measures of the same construct. When such differing variables are measured on the same scale, researchers have the option of combining them into a single measure of that construct. The use of binary response model (BRM) was also in the picture, but as posited by Cameron (2015), the description of the use of means looks more practical and less ambiguous to comprehend (Cameron, 2015; Gruszczy, 2009; Price, Jhangiani, & Chiang, 2015). However, the imprecise and unsure nature of the various methods in current use makes most research findings and conclusions meaningless and unrelated to the major objective of the research. This is very worrisome especially to graduate level research students and their advisors and has proven to be of particular negative implications in the fields of accounting and finance in management sciences where precision in conclusions is of utmost importance in guiding investors and decision makers correctly.