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Building over 1000 data management expertise in 2 years

Failure to understand or acknowledge data analysis issues presented can compromise data integrity. Our considerations for the trainees focus on developing expert who can rightfully distinguish, interpret and analyze data.

Course Introduction

Global Research Development Empire sees the scope of Data Analysis as the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. More often than not, various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”.

The major contraband in research is the data integrity, while data analysis in qualitative research includes statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase.

The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, unobtrusive research) and the form of the data (field notes, documents, audiotape, and videotape).

An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well “Doing the right things demand knowing the right steps to take”.

Our considerations for the trainees focus on developing 1000 data management expertise before the end of 2018 regardless of area of specialization and business industry. Thus, there are numerous analytic procedures specifically designed for qualitative material including content, thematic, and ethnographic analysis. Regardless of whether one studies quantitative or qualitative phenomena, researchers use a variety of tools to analyze data in order to test hypotheses, discern patterns of behaviour, and ultimately answer research questions. Failure to understand or acknowledge data analysis issues presented can compromise data integrity.

What you are expected to learn during the course session?

Fundamental and practical application of statistical software for academics, business marketing, and organization problem identification and solving

Training Software & Resources

SPSS, Math Lab, and Qualitative Data Analysis (Interpretative Phenomenological Analysis)

Additional Resources

Self Learning Material PDF (e-Version), SPSS Self Training Videos, Math Lab Training Resources

Target Audience

  • PG Students
  • Consultants
  • Graduates and Under-graduates
  • Research Team / Organisation
  • Marketers
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