1. McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 9 Processing the Data

2. 9-2 Initiating Data Processing of Paper Questionnaires Prepare the Processing System While Survey Is Still in the Field For Large Surveys, Arrange for Table, Shelf and File Space for Questionnaires Monitor Collection and Receipt of Finished Work Daily Record Non-deliverables and Reason for Failure to Deliver Record Date of Receipt and Insert Case Number on Each Questionnaire Keep Records of Who Has What Documents for Editing, Postcoding and Data Entry

3. 9-3 Sight-Editing Completed Paper Questionnaires Sight-edit Early Returns to Catch Problems Early Discard Obviously Incomplete Questionnaires Record Discards and Any Comments on Them Establish Criteria for Disqualifying Returns Examine Each Page and Section Carefully Sort Into Three Piles: Okay, Maybe, and Reject

4. 9-4 Editing Questionnaires with Branching Make up a "Key" Showing Each Branch Area Determine How Much Missing Data Is Okay Check Each Area for Completion or Omission Identify Sections That Shouldn't Be Completed Mark Out All Superfluous Data From Them Note Problems on Returns for the Maybe Pile

5. 9-5 Objectives Of Postcoding Assign Codes to Unstructured Responses Assign Codes to "Other" Category Answers Reassign Codes to Incorrectly Coded Items Reassign Incorrectly Recorded Responses Assign One Value to Invalid Multiple Response Assign Codes to Any Notes or Comments

6. 9-6 The Postcoding Process Two Things Must Be Recorded: The code values, themselves must be recorded on the questionnaire The actual answers or category labels must be recorded in the codebook There Are Two Key Terms: A codelist is a list of postcode values and labels for any one item The codebook is a series of pages, each containing a single codelist

7. 9-7 Postcoding Requirements Some postcoding is almost always required Postcode values should be easily recognizable Use a separate codelist page for each postcoded item It's vital to record every new code on the codelist Each code must be unique to one answer category Multiple editors must all work with one codebook Simple postcoding can be done with sight-editing Make a key showing items that need postcoding Editors need specific guidelines on how to postcode Too many, small categories are better than too few, large The codebook must be very neat, clean and orderly

8. 9-8 Spreadsheet Data Entry The Most Common Document for Data Entry Each Row Contains the Data for One Questionnaire or Case Each Column Contains the Data for One Variable Case Numbers Are Usually Entered in the Last Column

9. 9-9 Database Documents Database Programs Allow Users to Structure and Format Files Data Entry Procedures Depend on How Files Are Set Up Programs Can Edit and Screen Data to Accept Only Correct Values Correct character type (e.g., numerics only) Correct variable range (e.g., 1 to 5 plus 0) After Variable Entry, Program Moves to Next Entry Field Automatically

10. 9-10 Dedicated Data Entry Programs Dedicated Programs Are Designed Specifically for Data Entry Input Screens Appear As an Image of the Questionnaire Page Only Permissible Values Are Accepted During Data Entry On Rejection of Data, the Program Prompts for Acceptable Values Especially Useful for Large Surveys or Frequent Projects

11. 9-11 Processing Terminology Code A unique numeric (or alphameric) value for each response alternative Record Format Specification of the position in the record where the data are located Case The entire set of data obtained from one respondent or pertaining to one sampling unit

12. 9-12 Processing Terminology Data Record One line or row of data for a case in the data entry spreadsheet Record Range Total number of columns in a spreadsheet assigned to one record or case Variable Range Spectrum of values from low to high which any one variable may take on legitimately

13. 9-13 1 2 4 5 6 7 Nominal Scale Data Numbers Identify Categories Values Make Categories Distinct Values Don’t Show Magnitudes Values Don’t Show Relationships No Valid Arithmetic Operations Statistics Are Very Limited

14. 9-14 123456 Ordinal Scale Data Numbers Show Order or Sequence Values Do Not Show Magnitudes Intervals Don’t Show Magnitudes Values Do Show Relationships Values Can Not Be Used in Equations Statistics Are Somewhat Limited

15. 9-15 123456 (Equal) Interval Scale Data Numbers Indicate Magnitudes Intervals Between Values Are Equal Intervals Reflect Magnitudes Values Show Order, Sequence Values Can Be Used in Equations Statistics Are Not Restricted

16. 9-16 0123456 Ratio Scale Data Numbers Indicate Magnitudes Intervals Values Are Equal Zero Means Nothing Is Present Intervals Reflect Magnitudes Values Show Order, Sequence Values Can Be Used in Equations Statistics Are Not Restricted

17. 9-17 Common Examples of Scale Data Types Nominal Scale Data A person’s social security or phone number Ordinal Scale Data Ordinal position in the family (e.g., 3rd child) (Equal) Interval Scale Data Temperature in degrees Fahrenheit Ratio Scale Data Distance in miles from one point to another

18. 9-18 Identifying Scale Data Types Nominal Scales The values are only names to designate categories. Values don’t indicate magnitudes or relationships. Ordinal Scales Values indicate only sequence or order of magnitude? They don't indicate the magnitude or interval between values.

19. 9-19 Identifying Scale Data Types Interval Scales The values indicate actual magnitudes. There are equal intervals between scale points. Zero does not indicate absence of what’s measured. Ration Scales The values indicate actual magnitudes. There are equal intervals between scale points. Zero indicates an absence of what’s measured

20. 9-20 Data Transformation Recode the Data Fewer, larger categories More meaningful categories Transform the Data Simple arithmetic operations Conditional transformations Select Subsamples Based on specific strata Based on variable values

21. 9-21 Reasons to Recode Data The New Categories Would Be More Meaningful Than the Originals Larger Cell Sizes (Number Per Category) Are Needed for Analysis Continuous Data Must Be Recoded Into Categories for Some Forms of Analysis New Categories Are Needed to Portray the Distributions in Tables or Charts

22. 9-22 Report Labeling Two Kinds of Labels May Be Needed Labels identifying variables Labels identifying categories Labels in Analysis Programs Save Time They need only be entered once Each report will then show the label Labels Should Be Abbreviated Analysis programs may limit label length Short labels fit better on tables and charts

23. McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. End of Chapter 9