Textual data analysis to interpret focus group interviews: an evaluation of two encoding techniques

The purpose of this paper is to discuss the advantages of using software for textual data analysis of focus groups (FG) interviews. In particular, we examine the technical issues faced by a researcher when analysing a corpus resulting from FG conducted in different countries. These issues are illustrated by the results of research carried out on European people’s attitudes and behaviours toward Personal Identity Data Management (PIDM) funded by the European Commission IPTS Joint Research Centre. Two focus group interviews (one with youngsters, the other with adults) were carried in different EU27 countries including France and Germany. The results obtained were compared with Alceste Software using two different encoding techniques: de-crowded and crowded, in order to analyse the advantages and drawbacks of these two techniques. The de-crowded technique, which consists of regrouping the discourse of each individual, is more suitable when the researcher wishes to analyse the discourse of each participant in relation to his characteristics and when many short answers are given during the focus group. The crowded technique takes into account the specificities of a focus group as the technique is concerned with the interactions of individuals and allows the researcher to respect the different phases of the focus group interview. However it is difficult in this case to analyse the characteristics of each participant in relation to his discourse and problems can occur when many short answers are given