GENDER INEQUALITIES

Gender Inequalities

Abstract

This paper argues that concerns that the feminist agenda is better served by qualitative not quantitative methodology were based on a rather narrow definition of feminism and a somewhat misleading portrayal of quantitative research. Using exemplar studies undertaken as part of the ESRC Research Priority Network on Gender Inequalities in Production and Reproduction (GeNet), I show how quantitative analysis can forward our understanding of the processes that underlie gender inequalities. Quantitative approaches are essential to examine the processes of selection and exclusion that reflect and create gender inequalities as manifest in changing lives and structures. Quantitative analysis of longitudinal data is used for investigating dynamic processes and different patterns of gendered resource allocation in productive and reproductive activities; whereas in-depth qualitative analysis is used to unpick the different national policy contexts for work-family balance. This can help inform how quantitative researchers (some of whom are feminists) interpret what they count.

Gender Inequalities

Introduction

Quantitative research that uses numerical or statistical information is commonplace in virtually all branches of social science. However, the widespread use of quantitative information in areas such as gender inequalities does not of itself guarantee universal acceptance of the importance of quantitative and statistical methods in gender-related research. Within the social sciences, there has been a lively and ongoing debate about the respective strengths and weaknesses of quantitative and qualitative approaches. Some of the debate has been based on an exaggerated notion of the different epistemologies. There are very few adherents to epistemologies of objective knowledge. Quantitative researchers are not nave positivists. They acknowledge the role of social construction in measures and are wary of quantification being seen as the equivalent of scientific reasoning. They know better than most that 'statistics can lie'.

Quantitative research like qualitative research can be well or poorly designed and implemented. In the war-of-words about research methodologies, poor research design is often confused with inherent weaknesses of the method. Good quantitative research avoids 'statisticism', that puts together some arbitrarily and haphazardly assembled collection of variables that are supposed to justify a 'causal model' or, even worse, a 'measurement model' (Duncan, 1984, p. 226). The great strength of quantitative research is that it allows the understanding of patterns. As Thomas Kuhn (1961, p. 174) wrote 'Numbers gathered without some knowledge of the regularity to be expected almost never speak for themselves. Almost certainly, they remain just numbers'. Fortunately, there are good quantitative researchers who successfully walk the tightrope of balancing theory and data, and produce inspiring analysis that advances our social understanding.

The strengths of quantitative methods for furthering our understanding of gender inequalities by drawing on work that is part of the Research Priority Network on Gender Inequalities in Production and Reproduction (GeNet). I examine how different research questions point to the use of different methods and how, in seeking best methodological practice, GeNet uses a range of techniques including statistical methods, state-of-the-art longitudinal analysis, as well as qualitative interviews and comparative analysis. The fact that GeNet regards quantitative methods as essential (although ...