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Using Mixed Methods Research Designs for Research in Teaching and Learning. Dr. Elizabeth G. Creamer, Virginia Tech Dr. Beth Mac Donald, Utah State University Julaine Fowlin , Virginia Tech. Goals for the Session. Model ways to design a feasible mixed methods research study.
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Using Mixed Methods Research Designs for Research in Teaching and Learning Dr. Elizabeth G. Creamer, Virginia Tech Dr. Beth Mac Donald, Utah State University Julaine Fowlin, Virginia Tech
Goals for the Session • Model ways to design a feasible mixed methods research study. • Review strategies to maximize the benefits of a mixed methods approach. • Consider challenges to designing and conducting a mixed methods dissertation. 2014 CIDER Conference, February 5-7, 2014
Agenda • Introductions and introductory remarks – Creamer Model 1: Exploratory/Confirmatory Study • Model 2 and Example from Math Education – MacDonald • Model 3 and Example from Distance Education – Fowlin • Challenges to Using Mixed Methods in Dissertation Research 2014 CIDER Conference, February 5-7, 2014
Key Elements of the Definition of MMR • Both a method and a methodology • Contains both a qualitative (e.g. emergent or inductive) and a quantitative (e.g. deductive, hypothesis testing) strand • Requires MIXING or the integration of the qualitative and quantitative strands in on or more phases of the research process: • Design • Sampling • Data Collection • Analysis • Inferences and conclusions 2014 CIDER Conference, February 5-7, 2014
Arches as a Metaphor for Mixing 2014 CIDER Conference, February 5-7, 2014
Combining QUANT and QUAL Strengths for a Stronger Study From E. G. Creamer (in progress), Introduction to fully integrated mixed methods research. SAGE Publisher. 2014 CIDER Conference, February 5-7, 2014
TRIANGULATION: The Most Common MM Design DATA COLLECTION IN ONE PHASE – OFTEN A SURVEY QUANT DATA- Likert Items QUAL DATA – open ended questions 2014 CIDER Conference, February 5-7, 2014
TRIANGULATION DESIGN – TAKING ADVANTAGE OF STRENGTHS • DESIGN – • Adds potential to ask how or why questions • ANALYSIS • If open-ended questions are analyzed inductively, adds an exploratory component • Potential to show relationships between QUAN and QUANT data • Potential to analyze potential predictive power of QUAL variables 2014 CIDER Conference, February 5-7, 2014
The MM Development Design QUAL INTERVIEWS DEVELOP A SURVEY PILOT TEST THE SURVEY Dissertation Your Next Life 2014 CIDER Conference, February 5-7, 2014
Mixed Methods Research and Mathematics Education • Rely on pragmatic paradigms which offer a blend of worldviews (i.e. radical and social constructivism). • Explains learning a process, not a product • Learning is investigated through interactions between: • Student and Student • Students and Teachers • Students and Tasks 2014 CIDER Conference, February 5-7, 2014
Blended Conclusions describe how differing experiences changed PST content knowledge and PST beliefs Model #2: Mathematics Education Analysis of QUAL Survey Data Analysis of QUAN Survey Data QUAN and QUAL Survey given to all groups Blended Experiences to influence PST knowledge and beliefs Treatment Groups and Control Group Research Purpose and Paradigms are blended to support Pragmatic perspective 2014 CIDER Conference, February 5-7, 2014
Mixed Methods Mathematics Education Research: Maximizing the Benefits of Mixed Methods Research 2014 CIDER Conference, February 5-7, 2014
What Mathematical Knowledge Matters and What Evidence Counts? • “Qualitative researchers have much to learn from large-scale test developers; large-scale test developers need lessons learned in qualitative research to succeed. More crossover needs to occur between these two separate enterprises” (Hill, Sleep, Lewis, & Ball, 2007, pp. 151). Hill, Sleep, Lewis, & Ball (2007). Assessing teachers’ mathematical knowledge. In F. K. Lester, Jr. (Ed.) Second handbook of research on mathematics teaching and learning (pp. 111-156). Charlotte, NC: Information Age Publishing 2014 CIDER Conference, February 5-7, 2014
Mixed Methods and Distance Education • Provides a more holistic picture of the learning situation • Allows for advancement to the field through instrument and framework development • Allows for comprehensive program evaluations 2014 CIDER Conference, February 5-7, 2014
Model # 3 : Distance Education Research Quant Data Collection Survey N=278 RQ: What factors? QUAL Data Analysis QUAL Data Collection Case Study N=4 RQ: Why those factors? Quant Data Analysis Mixing Interpretation using Quant and Qual results Mixing Quant results used to select Qual participants and create interview protocol Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3-20. 2014 CIDER Conference, February 5-7, 2014
Challenges in Mixed Methods Research • Meaningful Mixing- Creamer • Blending of Paradigms-MacDonald • Issues with Priority in MM Designs-Fowlin 2014 CIDER Conference, February 5-7, 2014