Quantitative Methods in Comparative Education and Other Disciplines: are they valid?

Steven J Klees

doi:10.1590/2175-623664816

Abstract

Abstract1: Comparison is the essence of science and the field of comparative and international education, like many of the social sciences, has been dominated by quantitative methodological approaches. This paper raises fundamental questions about the utility of regression analysis for causal inference. It examines three extensive literatures of applied regression analysis concerned with education policies. The paper concludes that the conditions necessary for regression analysis to yield valid causal inferences are so far from ever being met or approximated that such inferences are never valid. Alternative research methodologies are then briefly discussed.

Highlights

Comparison is a fundamental part of science and the social sciences
They argued that the cultural and historical methods of comparison that had been used in comparative education were generally not sufficiently scientific or precise and that was needed was the widespread application of the quantitative methods that were being used in social sciences like economics and sociology
There has been a resurgence of interest in qualitative methods, even in comparative education (Bray; Adamson; Mason, 2007), quantitative methods still dominate, especially in the policy arena

Summary

Introduction

Comparison is a fundamental part of science and the social sciences. The field of comparative education was initially based on more qualitative cultural and historical approaches to making comparisons (Edwards; Holmes; Van de Graff, 1973; Schriewer; Holmes, 1992). The estimated impacts of education on earnings and associated rates of return are basically arbitrary, the result of ad hoc empiricism run rampant Another very common use of regression analysis is to estimate what are called education production or input-output functions (Levin, 1976; Hanushek, 1986). Given these fundamental problems with fulfilling the conditions for regression analysis to yield accurate estimates of causal impact (discussed earlier), it is no wonder that consistent results of the impact of education on GNP are not found Reviews of this literature report a bewildering array of idiosyncratic methodological choices resulting in a bewildering array of different results (Stevens; Weale, 2004).

Findings

Discussion

Conclusions