Analyzing Dependent Variables with Multiple Surrogates in Financial Performance Research

Enyi, E. P. (2019). Analysing Dependent Variables with Multiple Surrogates in Financial Performance Research, European Journal of Accounting, Auditing and Financial Research, Vol.7(1), pp. 1-11

20 Pages Posted: 1 May 2019

See all articles by Enyi Patrick Enyi

Enyi Patrick Enyi

Babcock University - School of Management Sciences

Date Written: March 31, 2019

Abstract

Accounting and finance-based researchers often use multiple surrogates to capture the properties of a dependent variable (DV) when studying its predictive relationship with predictors. This often fail to directly connect the study results with the major objective of the research. This paper compares the existing practice with a plausible and less complicated alternative. 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 revealed close connection with the theses’ probabilistic expectations (β = .278, t(30) = .695, R2 = .077, p > .10), the individual surrogates results showed singular and combined significant differences with the theses’ a priori expectations (Adj. R2 = .0291, F(4, 25) = 22.598, p < .05). The paper recommends unifying multivariate DVs with geometric means for better conclusion in financial performance relational studies

Keywords: performance, dependent variable, surrogate, proxy, logistic regression

JEL Classification: M40, M41, M21, G30

Suggested Citation

Enyi, Enyi Patrick, Analyzing Dependent Variables with Multiple Surrogates in Financial Performance Research (March 31, 2019). Enyi, E. P. (2019). Analysing Dependent Variables with Multiple Surrogates in Financial Performance Research, European Journal of Accounting, Auditing and Financial Research, Vol.7(1), pp. 1-11, Available at SSRN: https://ssrn.com/abstract=3363075 or http://dx.doi.org/10.2139/ssrn.3363075

Enyi Patrick Enyi (Contact Author)

Babcock University - School of Management Sciences ( email )

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