•  
  •  

Home > HCAS > HCAS_PUBS > HCAS_JOURNALS > TQR Home > TQR > Vol. 23 > No. 7 (2018)

 

Abstract

Sequence analysis has been widely used to investigate the patterns of similarities and differences of sequential data in biology and sociology. However, the debate on the usage of sequence analysis in social sciences has not been settled yet. Among a long list, sequence analysis methods have been criticized for ignoring the qualitative information behind the sequences. This paper presents a new instrument for inspecting sequential data visually in qualitative studies. The method includes building a hierarchical tree of relations among the categories which is then used to recode the categories systematically. The recoding process is meant to give meaning to the differences among categories and, therefore, increases our ability to see the differences. The instrument is a fruit of a qualitative study carried out to explore student’s learning patterns. The focus in this paper will be on the algorithm of recoding the categories and how the emergent codes can be plotted to generate insights for further qualitative investigation.

Keywords

: Sequence Analysis, Qualitative Studies, Learning Patterns, Data Analysis, Categorization, Sequences

Author Bio(s)

Alaa Aldahdouh is a PhD candidate at University of Minho, Portugal. He has a computer engineering degree with more than 10 years of experience in software development. His major area of interest resides in investigating online learning, digital literacy, online experience, learning theories, connectivism, and MOOC. Correspondence regarding this article can be addressed directly to: AlaaAlDahdouh@gmail.com

Acknowledgements

I am grateful to Professor António J. Osório and Professor Susana Caires for their valuable advice and encouragement throughout the qualitative study in which this instrument has been developed.

Publication Date

7-15-2018

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2018.3295

Download

Share

 
COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.