4.1.1 Technology Skills for Research Data Management and Analysis

Equipping Doctoral Researchers for Effective Data Handling and Analysis

Introduction

In the contemporary research landscape, proficiency in technology-based data management and analytical tools is essential for doctoral students. These skills enhance data integrity, reproducibility, and the robustness of research findings. This article provides an overview of key technology skills necessary for effective data management and analysis.

Research Data Management (RDM)

Importance of RDM

  • Ensures data is organised, accessible, secure, and preserved for future use (Borgman, 2015).
  • Complies with institutional and funder requirements for data sharing and transparency.

Key RDM Practices

  • Develop a Data Management Plan (DMP) detailing data collection, storage, sharing, and archiving strategies (Tenopir et al., 2011).
  • Use version control systems (e.g., Git) to track changes in data and code.
  • Implement secure storage solutions with regular backups, including encrypted cloud services and institutional repositories.

Data Analysis Tools

Quantitative Analysis Software

  • SPSS, R, Stata: Statistical packages for descriptive and inferential analysis (Field, 2013).
  • MATLAB: Used for numerical computing and algorithm development in engineering and sciences.

Qualitative Analysis Software

  • NVivo, Atlas.ti: Tools for coding, categorising, and analysing qualitative data such as interviews and textual documents (Woods et al., 2015).

Mixed Methods Integration

  • Use appropriate software combinations or bespoke workflows to integrate qualitative and quantitative data.

Additional Technical Skills

  • Proficiency in programming languages such as Python or R facilitates data manipulation, automation, and advanced analytics.
  • Familiarity with database management systems (e.g., SQL) enhances handling of large datasets.
  • Visualization tools (e.g., Tableau, ggplot2 in R) support effective presentation of findings.

Training and Resources

  • Universities often provide workshops and online courses on data management and analysis tools.
  • Open educational resources (e.g., Coursera, edX) offer accessible training options.

Conclusion

Developing robust technology skills in data management and analysis is critical for doctoral researchers. These competencies support research quality, compliance, and scholarly communication.

References

  • Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, MA: MIT Press.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). London: Sage.
  • Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., … & Frame, M. (2011). Data Sharing by Scientists: Practices and Perceptions. PLoS ONE, 6(6), e21101. https://doi.org/10.1371/journal.pone.0021101
  • Woods, M., Paulus, T., Atkins, D. P., & Macklin, R. (2015). Advancing Qualitative Research Using Qualitative Data Analysis Software (QDAS)? Reviewing Potential versus Practice in Published Studies Using ATLAS.ti and NVivo, Social Science Computer Review, 33(5), 597–617. https://doi.org/10.1177/0894439315596311
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