Yes, that is a good point. That is a good example of discrepancy between interests of government officials and normal customers. As a former economist of the bank of Korea, I heard that question whenever CPI was released. In brief, components that consists of CPI were decided based on through considerations. Once the components are fixed, the components are not easily changed because of reliability and validity.

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This chapter got me thinking about the problem with governmentally collected statistics, specifically the “Official inflation rate” and how it is used for a wide variety of purposes.  We are told that in the past few years, the beast of inflation has been conquered by careful planning by our central bank, with inflation now around 3% a year.  CPI (Consumer Price Index) is used to determine pay raises, cost of living increases, etc., but I believe it is often a poor measure of the increases that consumers actually are feeling.  The cost of gasoline has increased more than 3% a year, and so has the cost of housing, but somehow during the years when these two key expenses were spiking up, the CPI seemed to keep falling.  This discrepancy between the experience of the population and the “official” rate are determined to suit the purposes of government.  The CPI is an abstraction of the true rate of inflation with some spin, but it seems that research with these politically tied numbers might suffer from problematic conclusions. 

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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?

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Evren, I also like the flow chart.  The only thing to consider with secondary data, is the research question.  Researchers, do not get to ask whatever they want.  They have to take into considerations the reasons and must know what was the true purpose.  In that sense, they are limited as to how they can conceptualize and operationalize their definitions, since that has already been done in the original research.  However, they might be able to extend and improve any aspect of it (Action Research!).

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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! 

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You did a good job on explaining content analysis from the SWOT analysis. I think that a few research use content analysis because it's difficult to do in the IS field and its validity is hard to verify. It may be more suitable for other fields than the IS field. I'm also interested in doing this kind of research. We may work together in the future. :)

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Thank you for your sharing. I really like this flowchart. It makes concepts of content analysis clearer.

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The SWOT analysis for Content Analysis is an interesting way to frame the short comings and strengths of this approach. In general, nonreactive research has a certain appeal for me, in that the subjects are completely unaware of their participation in a research project (measuring "natural" wear of some kind or observing people) and therefore their actions are unaffected by any bias tht may be present when there is awareness of research.

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The flowchart in the link below show the steps a researcher follows when conducting a research. The flowchart is not very different than other research methods. It begins with a theory and rationale which is followed by conceptualization, operationalization. However, as Robson stated the important thing in content analysis is that instead of desiging study then collecting data, the researcher starts by finding out what data are available and then identifies a model. 

http://academic.csuohio.edu/kneuendorf/content/resources/flowc.htm

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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.

Weaknesses:

• 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?

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