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Qualitative Content Analysis

Qualitative Content Analysis. Hanna Järvenoja. Who I am ?. Researher at Learning and Eductaional Technology Research Unit --- LET. Research Interest. Conceptual analysis of motivation and self-regulation in learning.

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Qualitative Content Analysis

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  1. Qualitative Content Analysis Hanna Järvenoja

  2. Who I am? • Researher at Learning and Eductaional Technology Research Unit --- LET

  3. Research Interest • Conceptual analysis of motivation and self-regulation in learning. • Empirical studies integrated to the individual, social, shared and interactive processes of learning with and without technology. • Development and validation of theory-based process oriented methods and analyses.

  4. Qualitative Content Analysis • Is a systematic way to analyze data • Object of the analysis can be all sort of recorded communication that is in textual form (transcriptions of video data and interviews, virtual conversations…) • Proceeds through specific step by step phases In Content Analysis data is divided into pieces, conceptualized and reorganized according to research questions and theoretical framework

  5. When to useContentAnalysis? IfyouAim to… • categorize data according to certain theoretical perspectives • find similarities/differences from data • a compact description of the data that can be related to the wider context, theoretical framework and results from former empirical research • quantify qualitative data -> enables statistical treatment

  6. Ifyoudecide to useContentAnalysis Consider… • Inductive category development or deductive category application? • Unit of the analysis? • Definition of categories? – has to be clear also for other coders

  7. How to proceed with Content Analysis • Define research questions, object of the study • Operationalise concepts that you study “How you can recognize the things you focus on from the whole data set?” • Define unit of the analysis • Determine coding category definitions and coding rules • Test coding categories (May include several rounds of coding) • If necessary revise/modify/change coding categories and/or rules • Formativecheck of reliability (aftercoding 10-50% of data) • If necessary revise/modify/change coding categories and/or rules • Definitive coding • Summative check of reliability (e.g. inter-coder reliability: two independent coders code same segments of data) • If necessary, revise and repeat coding • Report results

  8. Example 1Deductive Approach • The study investigated students’ motivational goal orientations • Motivational orientation is a well established theoretical approach in motivation research • Interview data • The unit of the analysis was defined as one utterance • The categories and they definitions derived from theory Järvelä & Salovaara, 2004

  9. 5. Reliability was ensured with inter-coder procedure and negotiation 6. Results were reported

  10. Example 2Deductive-Inductive The study investigated students’ regulation of motivation in collaborative learning task Regulation of motivation is studied within the framework of self-regulated learning theory. However, there is not research on regulation strategies that are shared between the collaborative group members Videodata The unit of the analysis was first defined as a sequence of a meaningful motivational contribution The categories and they definitions derived from theory and was modified to adapt to a socially shared learning situation during the first analysing phase. Järvelä, Järvenoja & Veermans, 2008

  11. 5. Reliability was ensured with inter-coder procedure and negotiation 6. Results were reported Group A Group B 1 3 6 4 1 TASK 1 1 8 social reinforcing efficacy management 4 3 4 self-handicapping interest enhancement shared goal talk task structuring 4 5 5 TASK 2 8 1 1 3 3 1 2 1 4 11 TASK 3 7 1 8 3 4 3 1 5

  12. Categorising the data • Do you categories come from theory (deductive) or data (inductive)? • How many categories is enough/too much? • Can there be many levels of categories (subcategories)? • Another (trained) person shouldbeable to codeyour data into categories • Cleardefinitions <-> theory • Descriptions • Examples

  13. Reliability of the Coding • At least 10 % of data should be coded by another independent coder • How much data you have? • Do you need to train the inter-coder? • Several possibilities to check inter-coder , e.g. • percent of agreement • CohensKappa • Agreement after discussion • Inter-coding by two or more independent coders can open new perspectives

  14. Whenyoureportyouranalysis • Describe the phases of procedure • Provide general results (e.g. frequencies), but also enough data examples!!! • Qualitative examples • Descriptions • Frequencies • Distributions, percentage values… • Quantitative statistics • Report reliability • Link your results to research questions, theory and former research! • Considerusingmulti-methodapproach

  15. Advantages and disadvantages of Content Analysis • Systematic, Clear procedure • Describes data well • Repeatability • Reliability and validity • Provides possibilities for generalisation • Works best with well defined, specific research questions with solid theoretical framework

  16. Advantages and disadvantages of Content Analysis • Don’t reach the process, non-verbal cues, individual differences • Don’tworkwithresearchquestionsthatare ”open” and/oraim for detaileddescriptions of process/phenomenon • Researchers that emphasize quantitative may consider too qualitative, qualitative researchers may consider too quantitative

  17. Task-Find out fromarticle • Define research questions, object of the study • Operationalise concepts that you study “How you can recognize the things you focus on from the whole data set?” • Define unit of the analysis • Determine coding category definitions and coding rules • Test coding categories (May include several rounds of coding) • If necessary revise/modify/change coding categories and/or rules • Formativecheck of reliability (aftercoding 10-50% of data) • If necessary revise/modify/change coding categories and/or rules • Definitive coding • Summative check of reliability (e.g. inter-coder reliability: two independent coders code same segments of data) • If necessary, revise and repeat coding • Report results

  18. References and Readings • Bluemink, J. & Järvelä, S. (in press). Elements of collaborative discussion and shared problem-solving in a voice-enhanced multiplayer game. Journal of Interactive Learning Research. • Chi, M. (1997). Quantifying qualitative analysis of verbal data: A practical guide. Journal of the Learning Sciences, 6(3), 271–315. • Järvenoja, H. & Järvelä, S. (2005). How the students explain their social, emotional and motivational experiences during their learning processes. Learning and Instruction, 15, 465-480. • Järvelä, S. & Salovaara, H. (2004). The interplay of motivational goals and cognitive strategies in a new pedagogical culture – a context oriented and qualitative approach. European Psychologist, 9, 4, 232-244. • Mayring, P. (2000). Qualitative Content Analysis. Forum: Qualitative Social Research, 1(2), Art. 20 – June 2000 • Strijbos, J. W., Martens, R. L., Jochems, W. M. G. (2004). Designing group based learning: Six steps to designing computer-supported group based learning. Computers & Education 42, 403-424.

  19. QUESTIONS?

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