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논문 상세정보

소비자 집단 인터뷰에서 의미 네트워크 응집 구조의 이해

Understanding the Semantic Network Structure in the Consumer Group Interview with the Subnetwork Analysis

소비자학연구 v.23 no.2 , 2012년, pp.249 - 272  
이동일, 이혜준
초록

Researchers have performed the qualitative studies utilizing consumer group interviews to reveal consumers’s responses in depth. In general, most of the qualitative studies have analyzed by researchers' examination every aspect of participants’ opinions and interpretation of the distinctive responses. There is concern in this approach that the results may have validity issues, because it is difficult for researcher to keep objective viewpoint. Some studies suggest an alternative methodology like content analysis and LIWC, but these approaches still remain unsolved subjectivity issues. First, content analysis has some disadvantages. Coders encode the qualitative data so the researcher could analyze in the further analysis in a rather quantitative ways. But it is possible that the encoded data may contain the coders' subjectivity according to the coders’ understanding of coding schemes. And sometimes the coding scheme implies the researcher’s hypothesis implicitly. Second, LIWC is a kind of text analysis software program which is designed to calculate the degree people use different categories of words across a wide array of texts. It categorizes about 3,000 words into 72 categories. LIWC helps researchers understand the qualitative data more objectively in some degree. Nevertheless it has limit to interpret the words beyond the pre-defined ones designated in the categories.This research proposes a semantic network analysis as an alternative method for the existing qualitative methodologies. The purpose of network analysis is to investigate the relation between linked nodes. If it is applied to the relationship between shared meanings in linguistic communications, then it represents semantic network. In this case, a word becomes a node and a cooccurrance of two words in a sentence becomes a link. Linked nodes are the basic element of semantic network.Semantic network analysis uses words which are included in a qualitative data, and does not exclude any of them by the subjective judgement basis. Thus it can overcome validity issues previously associated with content analysis and LIWC. Semantic network analysis is relatively less likely to be reflected the subjective viewpoint of the researcher and gives us a chance to find out inherent contents structure of qualitative data.Semantic network analysis produces several network indexes which reflect the characteristics of network or node of interest such as density and degree centrality. Network density is calculated the number of connected links in a network compared to the number of possible links. High density implies that people are very interested in the topic in the qualitative data, because very dense network is resulted from highly related topics and keywords in the context of the semantic network. Degree centrality is the number of nodes directly connected to an individual node. A word with a high degree centrality is able to activate many other words, thus it functions as a keyword within the semantic cluster.In this study, we try to make the theoretical background of semantic network analysis base on the existing theories of cognitive linguistics, cognitive psychology, and sociolinguistics. A word which conveys a concept in individual cognitive structure is represented as a node in a semantic network analysis, and researchers may interpret the relation between linked words.One semantic network is composed of several subnetworks which imply the cohesive group of similar concepts within cognitive structure. Moreover language has common meaning between individuals who are belonging to the same social groups through interaction of group members.Therefore we can interpret the subnetwork of semantic network in the group interview as the clique of words which are about one topic. As a result, we can understand the entire semantic network contents structure through interpreting subnetworks. Subnetwork also can be represented by the indices such as density and deg...

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