Qualitative Method Research

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Data treatment and analysis

Because the questionnaire data comprised both Likert scales and open questions, they were analyzed quantitatively and qualitatively. Textual data (open responses) were qualitatively analyzed by coding: each segment (e.g. a group of words) was assigned to a semantic reference category, as systematically and rigorously as possible. For example, “Using an iPad in class really motivates me to learn” was assigned to the category “positive impact on motivation.” The qualitative analysis was performed using an adapted version of the approaches developed by L’Écuyer (1990) and Huberman and Miles (1991, 1994). Thus, we adopted a content analysis approach using QDAMiner software, which is widely used in qualitative research (see Fielding, 2012; Karsenti, Komis, Depover, & Collin, 2011). For the quantitative analysis, we used SPSS 22.0 software to conduct descriptive and inferential statistics. We also conducted inferential statistics to further explore the iPad’s role in teaching and learning, along with its motivational effect. The results will be presented in a subsequent report (Fievez, & Karsenti, 2013)

Fievez, A., & Karsenti, T. (2013). The iPad in Education: uses, benefits and challenges. A survey of 6057 students and 302 teachers in Quebec, Canada (p. 51). Canada Research Chair in Technologies in Education. Retrieved from https://www.academia.edu/5366978/The_iPad_in_Education_uses_benefits_and_challenges._A_survey_of_6057_students_and_302_teachers_in_Quebec_Canada

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– RQDA (the small one): http://rqda.r-forge.r-project.org/ (see on youtube the tutorials of Metin Caliskan); one active developper.
– GATE (the large one): http://gate.ac.uk/ | https://gate.ac.uk/download/

text mining: https://en.wikipedia.org/wiki/Text_mining
Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
https://ischool.syr.edu/infospace/2013/04/23/what-is-text-mining/
Qualitative data is descriptive data that cannot be measured in numbers and often includes qualities of appearance like color, texture, and textual description. Quantitative data is numerical, structured data that can be measured. However, there is often slippage between qualitative and quantitative categories. For example, a photograph might traditionally be considered “qualitative data” but when you break it down to the level of pixels, which can be measured.
word of caution, text mining doesn’t generate new facts and is not an end, in and of itself. The process is most useful when the data it generates can be further analyzed by a domain expert, who can bring additional knowledge for a more complete picture. Still, text mining creates new relationships and hypotheses for experts to explore further.

quick and easy:

intermediate:

advanced:

http://tidytextmining.com/

Introduction to GATE Developer  https://youtu.be/o5uhMF15vsA

use of RapidMiner:

https://rapidminer.com/pricing/

– IBM Watson Conversation
– IBM Watson Text to Speech
– Google Translate API
– MeTA
– LingPipe
– NLP4J
– Timbl
– Colibri Core
– CRF++
– Frog
– Ucto
– CRFsuite