Chapter 9

1. Chapter 9 Processing the Data

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 7 4 5 6 2 1

14. 1 2 3 4 5 6 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. 1 2 3 4 5 6 (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. 0 1 2 3 4 5 6 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. 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. 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. 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. 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. 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. 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. End of Chapter 9