1. Data Analysis: Preliminary Steps
Chapter 19

2. Stages in the Research Process
Formulate Problem
Determine Research Design
Determine Data Collection Method
Design Data Collection Forms
Design Sample and Collect Data
Analyze and Interpret the Data
Prepare the Research Report
SLIDE 19-1

3. Editing
Editing involves the inspection and, if necessary, correction of each questionnaire or observation form.
FIELD EDIT
CENTRAL-OFFICE EDIT
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4. Coding
The process of transforming raw data into symbols (usually numbers) that can be utilized for analysis.
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5. Coding Closed-ended Items
What is your overall opinion of TARGET department stores?
unfavorable
favorable
(Typical coding: )
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6. Coding Closed-ended Items: Check All That Apply
How did you learn about Brown Furniture Company? (check all that apply)
newspaper advertising
radio advertising
billboard advertising
recommended by others
drove by store
other: _______________
Typical coding:
6 different variables
(1 if checked; 0 if not)
SLIDE 19-5

7. Coding Open-ended Items
Open-ended items seeking concrete, or factual, responses are relatively easy to code: numeric answers are typically recorded as given by the respondent, while other types of responses are given a specific code number.
In what year were you born? (code year)
How many times have you eaten at Streeter’s Grill in the last month? (code number)
(3) Name the first 3 coffee shops located in Tampa that come to mind. (code as 3 separate variables; assign numbers to represent each coffee shop mentioned)
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8. Coding Open-ended Items
Open-ended items seeking less structured responses are much more difficult to code.
In your own words, give us two or three reasons why you prefer to leave the state after graduation.
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9. Process for Coding (Abstract) Open-ended Questions
Develop initial response categories (before reading responses)
Identify usable responses
Review responses; add, delete, revise categories
Sort responses into categories, using multiple coders; compare results
Repeat #3 and #4 if one or more categories are too broad
Assign code numbers for each category; use these codes to represent responses in the data file
Assess interrater reliability (the degree of agreement between coders); low interrater reliability suggests that the categories are not well-defined, and #3-6 should be repeated
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10. Not at all willing Somewhat willing Very willing
Developing a Codebook
SPORTING GOODS SURVEY
Please answer the following questions about buying sporting goods over the internet:
During the past year, what percentage of the sporting goods you purchased were ordered through the internet?
________ percent
2. How willing are you to purchase merchandise offered through the Avery Sporting Goods web site?
Not at all willing Somewhat willing Very willing
3. Please provide some reasons why someone might not want to purchase sporting goods over the internet:
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11. Avery Sporting Goods – CODEBOOK (partial)
Developing a Codebook
Avery Sporting Goods – CODEBOOK (partial)
Col. Var. Name Description
1-3 ID questionnaire identification number
4-6 PERCENT % products purchased through internet (record response)
WILLING willingness to purchase through web site 1=not at all willing =somewhat willing =very willing
REASON1 first reason for not purchasing over internet 1=security issues (open ended) 2=no internet access =can’t examine goods =difficult to return =don’t want to wait =prior bad exper. w/internet =other
REASON2 second reason SAME
REASON3 third reason SAME
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12. Building the Data File: First Two Records in Avery Sporting Goods Data File
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13. How to identify blunders:
Cleaning the Data
A BLUNDER is an error that occurs during editing, coding, or data entry. Blunders are usually due to researcher carelessness in coding or data entry.
How to identify blunders:
Run frequency analysis on all variables
Check a sample of questionnaires against the data file
Double-entry of data (preferred)
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14. Handling Missing Data
If a particular case has a significant amount of missing data (unanswered questions), it should probably be eliminated during the editing process.
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15. Strategies for Handling Missing Data
Report “missing” as separate category
Eliminate cases with missing data from all analyses
Eliminate cases with missing data from analyses involving the variables on which data are missing, but keep cases for other analyses
Substitute values for the missing data based on (a) responses to other variables given by the same individual, or (b) responses to the same variable given by other individuals
Eliminate the variable (for that respondent) from a multi-item scale
Recontact the respondent
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