Page 261 - DLIS402_INFORMATION_ANALYSIS_AND_REPACKAGING

Information Analysis and Repackaging

Notes Relational Analysis

As stated above, relational analysis builds on conceptual analysis by examining the relationships
among concepts in a text. And as with other sorts of inquiry, initial choices with regard to what is
being studied and/or coded for often determine the possibilities of that particular study. For rela-
tional analysis, it is important to first decide which concept type(s) will be explored in the analysis.
Studies have been conducted with as few as one and as many as 500 concept categories. Obviously,
too many categories may obscure your results and too few can lead to unreliable and potentially
invalid conclusions. Therefore, it is important to allow the context and necessities of your research
to guide your coding procedures.
There are many techniques of relational analysis available and this flexibility makes for it’s popularity.
Researchers can devise their own procedures according to the nature of their project. Once a
procedure is rigorously tested, it can be applied and compared across populations over time.
The process of relational analysis has achieved a high degree of computer automation but still is,
like most forms of research, time consuming. Perhaps the strongest claim that can be made is that it
maintains a high degree of statistical rigor without losing the richness of detail apparent in even
more qualitative methods.

Issues of Reliability and Validity

The issues of reliability and validity are concurrent with those addressed in other research methods.
The reliability of a content analysis study refers to its stability, or the tendency for coders to consis-
tently re-code the same data in the same way over a period of time; reproducibility, or the tendency
for a group of coders to classify categories membership in the same way; and accuracy, or the extent
to which the classification of a text corresponds to a standard or norm statistically.
The overarching problem of concept analysis research is the challengeable nature of conclusions
reached by its inferential procedures. The question lies in what level of implication is allowable, i.e.
do the conclusions follow from the data or are they explainable due to some other phenomenon?
For occurrence-specific studies, for example, can the second occurrence of a word carry equal weight
as the ninety-ninth? Reasonable conclusions can be drawn from substantive amounts of quantitative
data, but the question of proof may still remain unanswered.
The generalizability of one’s conclusions, then, is very dependent on how one determines concept
categories, as well as on how reliable those categories are. It is imperative that one defines categories
that accurately measure the idea and/or items one is seeking to measure. Akin to this is the
construction of rules. Developing rules that allow one, and others, to categorize and code the same
data in the same way over a period of time, referred to as stability, is essential to the success of a
conceptual analysis. Reproducibility, not only of specific categories, but of general methods applied
to establishing all sets of categories, makes a study, and its subsequent conclusions and results,
more sound.

Advantages of Content Analysis

Content analysis offers several advantages to researchers who consider using it. In particular, content
analysis:
• looks directly at communication via texts or transcripts, and hence gets at the central aspect of
social interaction
• can allow for both quantitative and qualitative operations

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