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Published byBritton Fleming Modified over 9 years ago
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Dr. Michael R. Hyman, NMSU Data Preparation
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2 File, Record, and Field
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3 Data Matrix
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4 Data Entry Process of transforming data from research projects to computers
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5 (1) Validation (2) Editing (3) Coding (4) Data entry/transcription (5) Machine cleaning of data Five Steps for Data Preparation
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6 Check that interviews conducted as specified Ensure respondent qualified Interviewer looked/acted professionally Interview conducted in proper environment All appropriate questions asked Validation
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7 Check for: Omissions Ambiguities Inconsistencies Proper skip patterns Properly recorded answers, especially to open-ended questions Editing: Personal Interviews
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8 Check for: All questionnaire sections and key questions answered Respondents understood instructions and took task seriously No missing pages Questionnaire returned before cutoff date Editing: Self-Administered Questionnaires
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9 Solutions for Editing Problems Re-contact respondent Discard questionnaire Use only good items –Data analysis implications (beyond scope of class)
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10 Coding Process of grouping and assigning numeric codes to different question responses Closed-ended questions easier because pre-coded
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11 Pre-coding Example
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12 Coding an Open-Ended Question Generate list of responses Consolidate responses (subjective judgment) Set response category codes Assign independent response category and record associated numeric code
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13 Portion of Travel Study Code Book
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14 Validated, edited, and coded questionnaires given to data entry operator More accurate and efficient to go directly from questionnaire to data entry device and storage medium Skip coding sheets Data Entry Process
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15 Data Transcription
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16 Checking entered data for internal logic by either the data entry device or another connected device Excel/Quattro and SPSS rely on dumb data entry Require data cleaning Intelligent Data Entry
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17 Machine Cleaning of Data Computerized error check –Identifies and suggests fixes for logical errors Marginal report –Computer-generated table of response frequencies for questions –Monitor entry of valid codes and skip patterns
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18 Machine Cleaning Instructions
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19 Recoding Data
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20 Recoding Data Using computers to convert original codes used for raw data into codes that are more suitable for analysis Var1 = 8 - Var1
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21 Collapsing a Five-Point Likert Scale
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22 Coping with Missing Data
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24 Item Non-response to Questions of Fact
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25 Ways to Handle Missing Responses Leave blank Case-wise deletion Pair-wise deletion Mean response Imputed response
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