Understanding the Applications, Strengths and Limitations of Automatic Qualitative Coding

Location

DeSantis Room 1047

Format Type

Plenary

Format Type

Paper

Start Date

15-1-2020 9:15 AM

End Date

15-1-2020 9:35 AM

Abstract

We live in a world where a huge amount of data is being collected each day. Technological innovations have made it possible for us to collect massive amounts of data, as the results of our daily interaction with people and things around us develop. As social scientists, this data can be a great source of rich information for research.

It is humanly challenging and time consuming to code all the data. However, we can make good use of machine learning tools within some of the qualitative data analysis software tools to automatically code the data. Nevertheless, most qualitative researchers are not familiar with the machine learning tools and how to effectively use them. They are not aware of what happens behind the scenes of most of the automatic coding – making it difficult to trust the outcome of the analysis.

In this presentation, I will be addressing the applications of qualitative analysis software with machine learning tools in conducting automatic coding. Questions to be addressed are:

  • What is machine learning and how is it related to autocoding of qualitative data?
  • What is the rationale behind autocoding?
  • When it is appropriate to automatically code your data?
  • What role can autocoding play in your data analysis process?

Keywords

Machine learning, autocoding, qualitative data analysis software, QDAS

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Jan 15th, 9:15 AM Jan 15th, 9:35 AM

Understanding the Applications, Strengths and Limitations of Automatic Qualitative Coding

DeSantis Room 1047

We live in a world where a huge amount of data is being collected each day. Technological innovations have made it possible for us to collect massive amounts of data, as the results of our daily interaction with people and things around us develop. As social scientists, this data can be a great source of rich information for research.

It is humanly challenging and time consuming to code all the data. However, we can make good use of machine learning tools within some of the qualitative data analysis software tools to automatically code the data. Nevertheless, most qualitative researchers are not familiar with the machine learning tools and how to effectively use them. They are not aware of what happens behind the scenes of most of the automatic coding – making it difficult to trust the outcome of the analysis.

In this presentation, I will be addressing the applications of qualitative analysis software with machine learning tools in conducting automatic coding. Questions to be addressed are:

  • What is machine learning and how is it related to autocoding of qualitative data?
  • What is the rationale behind autocoding?
  • When it is appropriate to automatically code your data?
  • What role can autocoding play in your data analysis process?