Using a Rule Developing Experimentation Approach to Study Social Problems: The Case of Corruption in Education

Using a Rule Developing Experimentation Approach to Study Social Problems: The Case of Corruption in Education

Attila Gere, Petraq Papajorgji, Howard R. Moskowitz, Veljko Milutinovic
Copyright: © 2019 |Pages: 26
DOI: 10.4018/IJPAE.2019070103
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Abstract

This article presents the first in a series of studies on the corruption of various types, assessed through an online experiment known as mind genomics. The data allows for the creation of simple models from regression, showing the part-worth contribution of every element to perceived corruption, and to perceived positive, neutral or negative emotion. The authors use ordinary least squares regression models and advanced data mining techniques to analyze the data and classify the users accordingly. They present the results from four countries (Albania, Hungary, India, and the USA), looking at the linkages between corruption by country, and by other factors such as social class. Based on the collected data a model is generated for each group (country, type of person), showing how the person in the group is likely to call a description ‘corrupt,' and how each particular element from the set of 20 elements related to education adds or subtracts to that basic proclivity to call a situation or behavior corrupt.
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1. Introduction

Corruption as a phenomenon has accompanied the development of the human society during the entire its history. As such, corruption has long been subject of many studies in different countries around the world. There is an abundant list of studies on corruption. Some authors have tried to have a holistic approach and have included in their studies different perspectives: social, economic and political ((Vito Tanzi, 1998; Tullock, 1996) to name a few). Others have focused mostly on one dimension of the problem such as growth and investment (Mauro, 1997, 2008; Rose-Ackerman, 1998; Tanzi & Davoodi, 1997), education (Feoktistova, 2014; Heyneman, Anderson, & Nuraliyeva, 2008; Osipian, 2012, 2017; Rumyantseva, 2009; Sabic-El-Rayess & Mansur, 2016; Tierney & Sabharwal, 2017), low wages in the civil service (Rijckeghem, Caroline, & Weder, 1997) and political regimes (Bosco, 2016; Méndez & Sepúlveda, 2006).

It is not casual that corruption is treated with such a focus from researchers in the ex-communist countries and in under-developed countries. Most of the publications in specialized literature comes from studies occurred in ex-communist countries (Osipian, 2017; Sabic-El-Rayess & Mansur, 2016), and from under-developed countries such as India (Tierney & Sabharwal, 2017). Some authors have tried to deal with much larger areas such as Central and Easter Europe (Bosco, 2016; Sabic-El-Rayess & Mansur, 2016).

Social scientists have long since worked on a variety of issues dealing both with education on the one hand, and with corruption on the other. Combining the two issues into one study adds to a large and growing literature. There is a strong focus in the specialized literature on corruption in higher education (Feoktistova, 2014; Heyneman et al., 2008; Osipian, 2012, 2017; Rumyantseva, 2009; Sabic-El-Rayess & Mansur, 2016; Tierney & Sabharwal, 2017). The motivating topic is what to do for students for whom, it is clear, that they are not particularly qualified, and will ultimately face the job market wherein the competition is very strong. A number of authors have focused their studies particularly of corruption on the doctoral programs (Osipian, 2012, 2017). Their conclusions are rather sad and reason for caution. They report, in general, that corruption has already infiltrated even the elite preparation of some societies, making it even a more dangerous symptom of an underlying social problem.

The large number of papers published on corruption could be characterized in two groups: papers that use mathematical/statistical models and papers that do not use any mathematical tools. Few authors have used a statistical or a combination of different approaches to analyse the data such as (Sabic-El-Rayess & Mansur, 2016). The authors have dressed initially a number of objectives to verify and use a combination of methods to analyse the results such as content analysis, binary logistic regression and sampling. The initial objectives are: 1) presence of corruption, 2) pecuniary versus non-pecuniary corruption and 3) student’s traits and non-pecuniary corruption. Data are collected using surveys and interviews. Although the paper provides a good level of description, it is very difficult to reproduce the study.

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