Content analysis

Draft

Definition

Content analysis refers to a family of qualitative data analysis methods or to various forms of quantitative analysis.

“Content analysis (sometimes called textual analysis when dealing exclusively with text) is a standard methodology in the social sciences for studying the content of communication. Earl Babbie defines it as "the study of recorded human communications, such as books, websites, paintings and laws." Harold Lasswell formulated the core questions of content analysis: "Who says what, to whom, why, to what extent and with what effect?." Ole Holsti (1969) offers a broad definition of content analysis as "any technique for making inferences by objectively and systematically identifying specified characteristics of messages."” (Wikipedia, retrieved nov 1 2007)

See also:

• Computer assisted qualitative research analysis software
• Methodology tutorial - qualitative data analysis

This entry should be split into two different articles: qualitative content analysis and machine analysis (e.g. text mining) - Daniel K. Schneider 14:01, 12 March 2012 (CET).

Links

(Semi-) manual qualitative data analysis

• The Qualitative Report (both a journal and an index)
• QualPage by Judy Norris. Very good site (traditional academic build)
• The qualitative Research Page by Bobbi Kerlin, also maintains the Qualitative ResearchWebring
• Grounded Theory Methodology Karl "Chuck B." Freiherr von Manteuffel ...
• Qualnet, in particular Qualitative Research Resources
• Action Research Sources (by Bob Dick, Southern Cross University)
• Daniel K. Schneider once made some slides for a crash course on research design in educational technology PDF (ok for starters, but would need some extra work). See the chapter on qualitative data analysis.

Quantitative analysis of large corpus

• Text mining (Wikipedia)

• Text mining (German Wikipedia). Better, if you speak German.

• Web mining (Wikipedia)

• Web Mining Research - Pointers by Pranam Kolari, UMBC Baltimore.

• Text Insight. serves as a research and academic portal for those doing qualitative analysis and text analytics. Main focus of the site is the Leximancer tool However, all researchers, students, academics, and commercial entities are welcome to use this portal and its resources.

• Research and Technology Planning with Textmining. At Fraunhofer Institute for Technological Trend Analysis. Includes a publication list.

• Text Mining at Toolbox.com

• R, RapidMiner, Statistica, SSAS or WEKA AI Computer Vision blog (April 29, 2010).

• Data mining tools comparison - Summary Pharmine blog (data mining in pharmacy), May 18th, 2008

Software

See other wiki pages of interest

These pages include specialised technologies

• For computer-assisted manual analysis, see Computer assisted qualitative research analysis software

• For wikis analytics (various methods, techniques and tools), see Wiki metrics, rubrics and collaboration tools

• latent semantic analysis and indexing, a family of analysis techniques that that assume that a text contains a semantic structure through a kind vector space model and some kind of factor analysis that identifies relationships between terms.

List of tools

Tools that fit nowhere else for the moment, in particular general purpose text mining instruments ...

• Leximancer, allows to summarize and navigate large text data (e.g. a wiki site) with various visualization tools. (commercial, $750 AUD single license or $150 one-month online)

• CRAN Task View: Natural Language Processing contains a list of packages useful for natural language processing.

• RapidMiner. Quote: “is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products.”. The system is based on the earlier YALE system. Commercial versions of RapidMiner can do more than the free community edition.
  • See RapidMiner

• Weka 3: Data Mining Software in Java is quote: “a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.”

• GATE is quote an “open source software capable of solving almost any text processing problem”. See the 2 Minute Guide. It can be compared to RapidMiner.

More should be added here ! In the meantime, see Text Mining at Wikipedia.

Bibliography

(to do )

Text and data mining

• Xiaojun Chen, Yunming Ye, Graham Williams, and Xiaofei Xu, A Survey of Open Source Data Mining Systems. PDF

Analysis of text quality

....

Analysis of on-line interactions

• De Wever, B., Schellens, T., Valcke, M., and Van Keer, H. 2006. Content analysis schemes to analyze transcripts of online asynchronous discussion groups: a review. Comput. Educ. 46, 1 (Jan. 2006), 6-28. DOI= http://dx.doi.org/10.1016/j.compedu.2005.04.005

• Naidu, S. and Järvelä, S. 2006. Analyzing CMC content for what?. Computers and Education 46, 1 (Jan. 2006), 96-103. DOI=http://dx.doi.org/10.1016/j.compedu.2005.04.001

• Pena-Shaff, J. B. and Nicholls, C. 2004. Analyzing student interactions and meaning construction in computer bulletin board discussions. Computers and Education 42, 3 (Apr. 2004), 243-265. DOI= http://dx.doi.org/10.1016/j.compedu.2003.08.003

• Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological Issues in the Content Analysis of Computer Conference Transcripts. International Journal of Artificial Intelligence in Education, 12(1), 8-22. PDF

• Schrire, S. 2006. Knowledge building in asynchronous discussion groups: going beyond quantitative analysis. Comput. Educ. 46, 1 (Jan. 2006), 49-70. DOI= http://dx.doi.org/10.1016/j.compedu.2005.04.006

• Strijbos, J., Martens, R. L., Prins, F. J., and Jochems, W. M. 2006. Content analysis: what are they talking about?. Computers and Education 46, 1 (Jan. 2006), 29-48. DOI= http://dx.doi.org/10.1016/j.compedu.2005.04.002