Experiments, survey research, and content analysis are different methods of conducting a research. As a researchers, we need to understand which method best applies to which situation in order to defend the findings of our studies. For instance, taking a snapshot and then talking about a process is not proper way of conducting a research.
In this chapter, Neuman elaborates the content analysis and existing statistics (secondary survey data) which he briefly described in Chapter 2. According to Neuman, both content analysis and existing statistics can be used for exploratory and explanatory purposes, but most often used for descriptive purposes. In this context, I like to approach content analysis, which is concerned with data reduction from SWOT analysis perspective.Strengths:
- Content analysis describes what is in the text.
- The data are in permanent form and hence can be subject to re-analysis, allowing reliability checks and replication studies.
- It is based on existing documents. Hence, a researcher can observe without being observed.
- It may provide a low cost form of longitudinal analysis when a run or series of documents of a particular type is available.
- Content analysis can not reveal the intentions of those who created the text or the effects that messages in the text have on those who receive them.
- The documents available may be limited or partial.
- The documents have been written for some purpose other than for the research.
- It is difficult to assess causal relationships. Are the documents causes of the social phenomena the researcher interested in, or reflection of them? (Robson, 358)
Opportunities:
- A researcher can measure large amounts of text with sampling and multiple coders.
- It is helpful when a topic must be studied at a distance.
- Content analysis can reveal messages in a text that are difficult to see with causal observation.
Threats:
- Intercoder reliability. The researcher must check the degree of consistency among coders.
- Visual text communicates messages or emotional content indirectly through images, symbols, and metaphors. Hence, it is difficult to measure.
In this context, as Nicole indicated, I wonder how the PhD students in our school approach to content analysis other than literature reviews. I have not heard a student conducting a meta-analysis in our school. It seems to that most of them prefer conducting surveys. As for me, the findings from a meta-analysis paper can be as interesting as a survey research.
For instance: Dubé and Peré's Rigor in Information Systems Positivist Case Research: Current Practices, Trends, and Recommendations paper or the working paper I cited last week. These papers showed me how other researcher conduct research and the mistakes that they made.
The data is already available .Why not use it? Isn't anyone interested in examining existing data?
Comments
Well Evryn, wonder no more! I have done a research paper using meta-analysis - even got it published. It can be a very tedious process and you will definitely want the right software tool to pull it all together. I can tell you a couple of big advantages it has over surveys:
1. No IRB!
2. You don't have to rely on people to participate!
I agree with you Lori when you talk about the advantages that it has over the surveys. However, how do the researcher deals with the problems of intercoder reliability and the different analysis that you could get form a visual material?