Dr. Michael R. Hyman, NMSU Data Preparation

2 File, Record, and Field

3 Data Matrix

4 Data Entry Process of transforming data from research projects to computers

5 (1) Validation (2) Editing (3) Coding (4) Data entry/transcription (5) Machine cleaning of data Five Steps for Data Preparation

6 Check that interviews conducted as specified Ensure respondent qualified Interviewer looked/acted professionally Interview conducted in proper environment All appropriate questions asked Validation

7 Check for: Omissions Ambiguities Inconsistencies Proper skip patterns Properly recorded answers, especially to open-ended questions Editing: Personal Interviews

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

9 Solutions for Editing Problems Re-contact respondent Discard questionnaire Use only good items –Data analysis implications (beyond scope of class)

10 Coding Process of grouping and assigning numeric codes to different question responses Closed-ended questions easier because pre-coded

11 Pre-coding Example

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

13 Portion of Travel Study Code Book

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

15 Data Transcription

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

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

18 Machine Cleaning Instructions

19 Recoding Data

20 Recoding Data Using computers to convert original codes used for raw data into codes that are more suitable for analysis Var1 = 8 - Var1

21 Collapsing a Five-Point Likert Scale

22 Coping with Missing Data

23

24 Item Non-response to Questions of Fact

25 Ways to Handle Missing Responses Leave blank Case-wise deletion Pair-wise deletion Mean response Imputed response