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Unformatted text preview: Exam 3 Review: Chapter 12 • Editing – examining forms and taking corrective action. 2 stages of editing (what they are, when you conduct them, how they differ, etc.) o Examining forms and taking corrective action. o 2 stages: 1 st : Preliminary (field) edit 2 nd : Office (final) edit Example: A respondent indicated that he was 14 years old and had an annual income of $100,000 + o Can help uncover: Improper field procedures Incomplete interviews Improperly conducted interviews Technical problems with the questionnaire or interview Respondent rapport problems Consistency problems that can be isolated and reconciled • Coding – Transforming edited responses into a form that is ready for analysis o Steps: Transforming responses into meaningful categories Assigning numerical codes to the categories Creating a data set suitable for computer analysis • Handling missing data o A missing value can stem from A respondent's refusal to answer a question An interviewer's failure to ask a question or record an answer or a "don't know" that does not seem legitimate o Prevention of missing value responses Sound questionnaire design Tight control over fieldwork o Handling missing data : code questions for which answers should have been, but were not, obtained for whatever reason, as a separate category (“missing value category ”) • Data set, case, observation – understand these terms, e.g., what is a “data set”? o Organized collection of data records o Each sample unit within the data set is called a Case or Observation o Structure of a data set The number of observations = n The total number of variables embedded in the questionnaire is m , then o Data set = n x m matrix of numbers • Raw vs. transformed variables o Raw variables: variables that are directly defined by the questionnaire data o Transformed variables: new variables that are constructed from data on raw variables • Central tendency measures (mean, median, mode) o Preliminary data analysis examines the central tendency and the dispersion of the data on each variable in the data set o Central tendency measures: Mean = arithmetic average Median = category into which the 50 th percentile response falls Mode = most frequent response • Dispersion measures o Range : Difference between the largest and smallest value o Variance : Deviation of the data around the mean o Standard deviation : Square root of the variance; Puts dispersion in same units as mean • Frequency distribution o Table showing the distribution of data pertaining to categories of a single variable Helps identify coding errors Most useful for nominal or ordinal data Shows skewed response distributions o A clear understanding of the distribution of responses can help a researcher avoid erroneous inferences...
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This note was uploaded on 02/22/2011 for the course MKTG 352 taught by Professor ? during the Summer '10 term at South Carolina.
- Summer '10