Graduate students’ modes of engagement in computer-assisted qualitative data analysis

Abstract

ABSTRACT Computer-Assisted Qualitative Data Analysis (CAQDAS) is becoming increasingly prevalent in the social and health sciences and an expected skill for many researchers. CAQDAS is much less structured than the use of quantitative analysis software. However, actual CAQDAS practices and challenges have been insufficiently studied, especially among graduate students. The study addresses this gap through analysis of 15 semi-structured interviews with graduate students from different fields in a Canadian university. Analysis was guided by concepts from the Technology Acceptance Model (TAM) and the CAQDAS Postgraduate Learning Model (CPLM). Findings suggest that graduate students held high expectations of the analytic affordances of CAQDAS, yet experienced significant challenges in actualizing those expectations, due to the perceived complexity of CAQDAS, coupled with insufficient training. In this context, students manifested two divergent modes of engagement in CAQDAS – focused or comprehensive – each of which entailed substantial implications for their education and practice as emerging qualitative researchers.