{"id":425,"date":"2017-10-23T09:01:19","date_gmt":"2017-10-23T13:01:19","guid":{"rendered":"http:\/\/blogs.shu.edu\/lmlc\/?p=425"},"modified":"2019-06-03T10:20:37","modified_gmt":"2019-06-03T14:20:37","slug":"qualitative-research-challenges-we-encounter","status":"publish","type":"post","link":"https:\/\/blogs.shu.edu\/lmlc\/2017\/10\/23\/qualitative-research-challenges-we-encounter\/","title":{"rendered":"Qualitative Research: Challenges We Encounter"},"content":{"rendered":"

One of the biggest challenges in working with qualitative data such as the very self-directed and open ended responses that our participants provided, is interpreting said statements in a way that generates useful data. I have come to observe that in this particular study, the relatively vague direction prompt that was used when administering the survey (something to the effect of \u201cmake a statement about each language that you\u2019re aware of\u201d) yielded responses that were either very informative or very (very) vague. Because we asked participants to hand write their responses on index cards, as opposed to having someone else interview and record their answers, or having them use a digital answer form (like the one found elsewhere on this blog), we also had to contend with some instances of unclear or illegible handwriting. Though deciphering somebody\u2019s handwriting ranks relatively low on the scale of challenges that crop up with qualitative research, it can be nonetheless frustrating. \u00a0\"Card\"Card<\/p>\n

The images pictured above are digital scans of one of the index cards from our pool of responses; the participant\u2019s name has been redacted for privacy. This card was the most notable example of the difficulties that come with dealing with qualitative data, especially when it\u2019s collected through an analog medium. The participant made a statement about English, but due to the illegibility of his handwriting that information is lost to us. However, because we know that he was a student in Dr. Quizon\u2019s Intro to Linguistic Anthropology class at the time, we felt comfortable enough to make certain inferences about the respondent\u2019s proficiencies in the English language: that the participant can read, write, speak, understand and identify it. It is worth noting that this assessment on our part does not make a statement about the degree of proficiency (at least not without more information, such as the \u201cdomain\u201d of the language); just that there is a<\/em> proficiency.<\/p>\n

\"\"<\/p>\n

This type of response was, thankfully, not the norm. There were more than a few that were brief, but straightforward, which were full of information that we could use to make more informed inferences:<\/p>\n

\"\"<\/p>\n

Now that we know that Bembe is the respondent’s native<\/em> language in Zambia, we can infer that Zambia is the participant’s country of origin, and that Bembe is more than likely, the language of choice in that particular household. Based on personal experience, with a non-English native language which is the language of choice in my household, I was able to infer that the particular respondent was likely fluid in Bembe, and could read, write, speak, understand and identify the language.<\/p>\n","protected":false},"excerpt":{"rendered":"

One of the biggest challenges in working with qualitative data such as the very self-directed and open ended responses that our participants provided, is interpreting said statements in a way that generates useful data. I have come to observe that<\/p>\n","protected":false},"author":3832,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[43,3,10,1],"tags":[],"class_list":["post-425","post","type-post","status-publish","format-standard","hentry","category-blog","category-coding","category-data","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/posts\/425","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/users\/3832"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/comments?post=425"}],"version-history":[{"count":4,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/posts\/425\/revisions"}],"predecessor-version":[{"id":1112,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/posts\/425\/revisions\/1112"}],"wp:attachment":[{"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/media?parent=425"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/categories?post=425"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.shu.edu\/lmlc\/wp-json\/wp\/v2\/tags?post=425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}