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  • 标题:Beyond Words: Using Nonverbal Communication Data in Research to Enhance Thick Description and Interpretation
  • 作者:Magdalena A. Denham ; Anthony John Onwuegbuzie
  • 期刊名称:International Journal of Qualitative Methods
  • 印刷版ISSN:1609-4069
  • 电子版ISSN:1609-4069
  • 出版年度:2013
  • 卷号:12
  • 期号:1
  • 页码:670-696
  • DOI:10.1177/160940691301200137
  • 语种:English
  • 出版社:SAGE Publications
  • 摘要:Interviews represent the most common method of collecting qualitative data in both qualitative research and mixed research because, potentially, they provide researchers with opportunities for collecting rich data. Unfortunately, when collecting and analyzing interview data, it appears that researchers tend to pay little attention to describing nonverbal communication data and the role that these data played in the meaning-making process. Thus, in this mixed methods research-based systematic review, we examined the prevalence and use of nonverbal communication data throughout the phases of all qualitative research studies published in a reputable qualitative journal—namely The Qualitative Report—since its inception in 1990 (n = 299) to the mid-year point (i.e., June 30) of 2012—representing approximately 22 years. Overall, nonverbal communication was evidenced in only 24% (N = 299, n = 72) of qualitative research studies involving design and instruments suitable for collection of nonverbal communication. Moreover, the degree of discussion varied greatly from a mere mention to substantive integration and interpretation. Nonverbal discussion was least frequent in the data analysis phase of research and most underutilized in case studies. The essential functions of nonverbal discussion across the stages of research were identified as clarification, juxtaposition, discovery, confirmation, emphasis, illustration, elaboration, complementarity, corroboration and verification, and effect. Implications are discussed.
  • 关键词:nonverbal communication ; thick description ; interpretation ; systematic review ; mixed methods research ; mixed analysis
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