Systematic Data Collection (Qualitative Research Methods Series 10)

Systematic Data Collection (Qualitative Research Methods Series 10)

Susan C. Weller

Language: English

Pages: 96

ISBN: 0803930747

Format: PDF / Kindle (mobi) / ePub

Data collection in the field, whether by interviewing or other methods, can be carried out in a structured, systematic and scientific way. This volume compels field researchers to take very seriously not only what they hear, but what they ask. Ethnographers have often discovered too late that the value of their interview information is discounted as a consequence of poor sampling (of both questions and informants) and poor elicitation techniques.

The authors focus on the importance of establishing the right questions to ask through the use of free listing techniques; then they describe in practical terms the administration of an impressive array of alternative kinds of informant task. They conclude with a discussion of reliability and validity of various methods which can be used to generate more systematic, culturally meaningful data.

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subject matter. A domain may be defined as an organized set of words, concepts, or sentences, all on the same level of contrast, that jointly refer to a single conceptual sphere. The items in a domain derive their meanings, in part, from their position in a mutually interdependent system reflecting the way in which a given language or culture classifies the relevant conceptual sphere. For example, the concept of "shape," may have category members such as "round," "square," "rectangular," and so

"Hierarchical clustering schemes." Psychometrika 32:241-254. KIRK, J. and M. L. MILLER (1986) Reliability and Validity in Qualitative Research. Newbury Park, CA: Sage. KIRK, L. and M. BURTON (1977) "Meaning and context: a study in contextual shifts in meaning of Maasai personality descriptors." Amer. Ethnologist 4,4: 734-761. KNUTH, D. E. (1973) Sorting and Searching. Reading: Addison Wesley. KRACKHARDT, D. and KILDUFF (1987) "Diversity is strength: a social network approach to the

with a descriptive method such as hierarchical clustering or multidimensional scaling. Lumper versus splitter differences in sorting patterns are sometimes counteracted with a constrained pile sort. Here subjects are told how many or how few piles they should make. For example, in the success and failure study, respondents in one sample were asked to sort characteristics into 7 to 9 piles. The results, however, were the same as when informants were allowed to make as many piles as they

unnormalized pile sort data performed quite well. TABLE 3.1 Pile Sort Tabulation Other variations on the pile sort include "splitting items" so items can be in more than one pile, collecting successive sorts from a single individual, and creating "piles" by selecting items that are most similar to a target item. Stefflre et al. (1971: 86) asked respondents if they thought any items should go in more than one group. If the respondent thought that an item should go into two groups, then the

most like it in meaning. White (1978) used this procedure to collect similarity data among 37 personality descriptors. Data were collected by selecting a target item, shuffling the remaining cards, and asking informants to select five items that were most similar in meaning to the target item. When five items had been matched to a given target item, another target item was selected for consideration, the remaining items were reshuffled and five new items selected. Responses are tabulated into the

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