Petra Rodik Faculty of Social Sciences and Humanities , Zagreb, Croatia Jaka Primorac

1. Information and Communication Technologies within the Sociological Research: The Implications of Computer-Assisted Qualitative Data Analysis Software (CAQDAS) Petra Rodik Faculty of Social Sciences and Humanities, Zagreb, Croatia Jaka Primorac Institute for International Relations (IMO), Zagreb, Croatia

2. Development of CAQDAS I • history of CAQDAS begins in the eighties • from the mid eighties till early nineties: development of separate CAQDAS, connected to specific projects/researchers • 1990ies: connecting of CAQDAScommunity (CAQDAS Networking Project, University of Surrey); comparability and development of new software attributes, development of CAQDAS towards software for general purpose, market competition of software developers • 2000s: metaperspectives in QDAS ( development of new generic possibilities, integration with other IT possibilities; software convergence) • current developments: towards CAQDAS 2.0 (web), that is, QDAS 2.0 (di Gregorio, 2010)

3. Development of CAQDAS II • Number of users is rising (according to number of course attendandees of different CAQDAS); more non-academic users (commerical agencies, public sector, NGOs) (Mangabeira, 2004: 172) • In Croatia: in the last 2-3 years the beginning ot CAQDAS usage; small community of users; insufficient inclusion in (post)graduate curricula (problems of small research community; Slovenian example: Adam and Podmetnik, 2005)

4. Types of CAQDAS users • Mangabeira et al. (2004) "Computers and Qualitative Research“ • 4 types of users: 1. Loyalists 2. Critical Appropriators (including the Experts) 3. Experienced Hands 4. Instrumental Adopters

5. Case study research methods ‘CAQDAS usage patterns among postgraduate sociology students in Croatia’ • Research executed June-September 2010 • Short online survey among postgraduate sociology students (31 sent, 19 responded) • Snowball method • In-depth interviews with six researchers who have used CAQDAS • For analysis: MAXQDA

6. Survey results Short survey was conducted among 19 postgraduate students who attended CAQDAS workshop during the doctoral program • None of the institutions have institutional licence, 1 group (project) licence (2 persons); 1 individual licence (funded by research project) • Eight of them confirmed that they would use CAQDAS if the institution where they worked had the licence (six noted ‘maybe’) • Seven students used CAQDAS after the workshop, but only two outside of doctoral courses obligations (for Phd thesis) • Eight students noted that there was no need or opportunity to use CAQDAS, while four of them noted that although there was an opportunity they decided not to use CAQDAS • Decision for the analysis without CAQDAS was due to: software is too expensive; it takes too much time to learn how to work with it, other

8. Outline of case study results • CAQDAS rarely used, but it is spreading • Not many users; Atlas, Nvivo, and MaxQDA mainly used • Software is not easily available (price, institution does not want to acquire it, etc.) • External factors are crucial in software choice: recommendation; exposure to particular software during postgraduate studies (abroad) or workshops • Ony one interviewee had specific requirements in advance, regarding the software capabilities • Collaborative options that CAQDAS bring are rarely used • Many survey respondents highlighted an intent to use CAQDAS in future and interest for workshops

9. Advantages of CAQDAS usage • Highlighted advantages are mainly of general nature: • Simplicity of data handling • Good data overview, searchability, organization of data • Easier overview of the whole research data • Easier recoding • Focus on data (Grounded theory approach) • Higher professionalism, appearence of exactness /similar as in: Gibbs, Lewins and Silver, 2005; Lewins and Silver, 2009 (from Silva and Ramos, 2010)

10. “With qualitative data it is always a question of their value in relation to quantitative data. So I think that this usage of software, however awkward it may sound, but in a way this gives a credibility to it.” Respondent, nVivo user “Atlas helps with the exactness of data results.” Respondent, Atlas user “So these methods that I used before... Maybe there were other ways, but these that I was acquainted with, were really unpractical... All these papers and cutting, and gluing them...It was like some process in primary school.” Respondend, nVivo user

11. Obstacles, limitations, problems with CAQDAS usage • Majority of problems were connected to a particular software package • Technical problems: crashing of the program; bad layout (nVivo), bad format for printing of code tree (MaxQDA); code tree organization (MaxQDA); unpracticality of families for categorization (Atlas.ti); problems with file paths (Atlas.ti); lack of intuitiveness in complex functions (Atlas.ti) • Learning time • Great expectations I want software to do the analysis, but it does not! :@

12. “Basically, it cannot do anything. It cannot do for you the most important things. Unlike SPSS that does a certain data processing.” Respondent, nVivo user “I have expected more from nVivo, and it drove me crazy for months. I thought to myself – why on earth I haven’t taken a normal survey, put the results in SPSS, take variables out and said, ‘folks, here are the results’.” Respondent, nVivo user

13. Quantitative vs. qualitativeand software “I think the main difference is that, lets say, statistical software can be used as a crutch, and qualitative cannot. Lets say, all work that I do in qualitative software, I have to do it myself. It cannot do anything instead of me. It helps me organize my thoughts, my documents... And that’s about it.” Atlas.ti user "The first and foremost point to make about the use of computers in qualitative analysis is that computers do not and cannot analyse qualitative data" (Roberts and Wilson 2002: 21).

14. Quantitative vs. qualitativeand software The most important difference Quant.: conceptualisation outside of software Qualit.: work with CAQDAS involves (almost) whole research project, including conceptualisation/theory building (1) The need for more reflexive usage (2) Can software usage really add to the research quality and credibility?

15. Ways to improve credibility? (Bringer, 2004; Friese, 2010) • Validity • inter-subjectivity and transparency • Trustworthiness • Analysis of negative cases • Seeking feedback from colleagues • Credibility • Writing notes during the entire research process (research diary) • Synthesis of the developed concepts with existing literature • Second reader • Audit trail / external auditor

16. Ways to improve credibility? (Friese, 2010) • Dependability • Are the results grounded in the data? • Is the coding system transparent and comprehensible? • Generalizability • Systematic and comparative analysis • A well thought through integration of results

17. Can CAQDAS improve credibility?(reliability, generalizability)

18. Can CAQDAS improve credibility?(validity)

19. Conclusion • In Croatia – not many users, but on the rise; wow-factor >> need for >> training, reflexivity • Mostly non-experienced users, education/user support not yet institutionalized • Too great epectations • Technology > credibility assumption • Possible problem of non-critical usage • CAQDAS facillitates research but software does not do the analysis • CAQDAS can improve credibility

20. Thank you :-)