Brightening the Bulb

Abstract

We thank the several commentators for their careful reading of our paper (this volume, 2012:43-76, hereafter WJP) and, even more, for their insightful reactions to our work and ideas. The range of issues raised in these commentaries is quite broad-from specific nitty-gritty details of software design and utilization to deep questions about epistemology-and so range our responses below. In our response, we remark on issues raised at several levels. In all cases our vantage point is with a look to the future. Even though we questioned in our original title whether the bulb might be dim, we are also of the view that the bulb's luminosity will no doubt increase in the years to come.To put it most succinctly, we are very much of the view that Qualitative Data Analysis Software (hereafter QDAS) is of great value to a wide range of social scientists. We see QDAS as being of use to a spectrum of research investigative styles, from quantitative to qualitative. Applications should expand in conventional mainline social science disciplines, and also within applied and interstitial fields, such as education, and health, and public policy. (The commentaries also reflect upon the QNA paper [Franzosi, De Fazio, and Vicari, this volume, 2012:1-42], of which we are not a part and have not read as of this writing.) As background, readers may find it of value to know that ours is a mixed disciplinary team of authors (one sociologist; two cultural anthropologists); furthermore, the overarching project from which WJP is drawn is a mixed-methods research investigation that involved anthropologists, sociologists, and demographers. We would like to think that whatever strengths or weaknesses characterize WJP, they are not attributable simply to the wearing of disciplinary methodological or epistemological blinders. Our motivation for producing the WJP manuscript grew out of our project efforts and our attempts to understand very low fertility in Italy using both (now-conventional?) longitudinal survey data with multivariate methods (e.g., see Kertzer et al. 2009), and the challenge of dealing