Further analysis revealed statistically significant changes in teachers’ views, suggesting the need for in-depth analysis of the qualitative data collected in response to an open survey question. Employing a serial, cross-sectional, design, SPSS was used initially to conduct item-matching across the two surveys to identify common themes. The context for this case study is a research project that used secondary data from two large-scale studies of primary (elementary) teachers’ attitudes to, and uses of, standardized achievement tests to assess the nature and extent, if any, of changes and trends in teachers’ views over a 40-year period.
This case study details the opportunities, challenges, and lessons learned when two colleagues, neither of whom had used NVivo software previously, nor, indeed, ever met face-to-face due to the lockdown imposed by the COVID-19 pandemic, collaborated on a mixed-methods research project using both SPSS and NVivo. When mixed methods are employed, and researchers attempt to master computer software as they conduct analysis and interpretation of large data sets in order to make the process more efficient and reliable, unexpected difficulties can arise. A key challenge researchers face is mining and reporting these data appropriately and promptly to inform research publications and presentations. An advantage of well-designed survey research is that, typically, it yields large volumes of quantitative and qualitative data.
0 kommentar(er)
