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CHAPTER 10
Data Issues and Inputting Data into
XL Data Analyst
LEARNING OBJECTIVES
To learn about the data matrix, data coding, and the data code book
To understand errors that occur during data collection
To become knowledgeable of various types of nonresponse errors
To become acquainted with data quality errors and how to handle them
To learn about the XL Data Analyst, including the Data and Define Variables worksheets
To read about special operations possible with the XL Data Analyst
CHAPTER OUTLINE
Data Matrix, Coding Data, and the Data Code Book
Errors Encountered During Data Collection
Types of Nonresponse Errors
Refusals to Participate in the Survey
Break-Offs During the Interview
Refusals to Answer Specific Questions (Item Omission)
Preliminary Data Screening
What to Look for in Raw Data Inspection
Incomplete Response
Nonresponses to Specific Questions (Item Omissions)
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Yea-Saying or Nay-Saying Patterns
Middle-of-the-Road Patterns
Other Data Quality Problems
How to Handle Data Quality Issues
What Is an “Acceptable Respondent”?
Introduction to Your XL Data Analyst
The Data Set and Data Code Book Are in the XL Data Analyst
Case Datasets and Building Your Own XL Data Analyst Data Set
Special Operations and Procedures with XL Data Analyst Data Sets
Selecting Subsets of the Data for Analysis
Computing or Adding Variables
KEY TERMS
Acceptable respondent
Break-off
Data coding
Data entry
Data matrix
Data worksheet
Date code book
Middle-of-the-road pattern
Nay-saying
Nonresponse
Nonsampling errors
Refusal
Respondent errors
Unintentional fieldworker errors
Unintentional respondent errors
Value code
Value label
Variable description
Variable label
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Define variables worksheet
Fieldworkers
Incomplete response
Intentional fieldworker errors
Intentional respondent errors
Item omission
Yea-saying
TEACHING SUGGESTIONS
1. This is a new chapter not found in the 1st or 2nd editions of our textbook. We have addressed
data quality and XL Data Analyst specifics for the following reasons. First, data quality
issues are a constant concern, and they persist even with the use of panel data, which is
becoming prevalent. Case 10.2, in fact, makes students realize that panelists can commit
these errors. Second, the handling of suspicious responses and missing data is important
because there is considerable potential for adverse impacts on data analysis. Last, we have
found from our own teaching, that despite its Excel platform, the XL Data Analyst requires
more introduction and description for students to understand its structure. Plus, we have
included descriptions of data selection (via filtering) and the computation or addition of new
variables in an XL Data Analyst dataset.
2. Implicit in this discussion of fieldworker errors is the message that professional data
collection companies will control for field work errors. If you have a nearby data collection
company, invite the manager to your class to talk about the company’s operations,
particularly training, orientation sessions, and other quality control procedures. Alternatively,
a field trip to a data collection company may be feasible.
3. Students may not appreciate the need for interviewer training. A way to combat this belief is
to bring a (complicated) telephone or personal interview questionnaire to class and require
two students to role play the interviewer and the respondent. It will become apparent that
reading the instructions, following skip patterns, and making notations of the respondent’s
answers on the questionnaire takes practice.
4. There is a danger when talking about respondent and interviewer errors that students will
come away with a negative opinion about surveys. That is, class discussion sometimes dwells
on the cheating, falsehoods, and other misrepresentations that occur in surveys and students
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may acquire a belief that these problems are rampant. The point to be made is that there may
be significant problems if controls are not used, but most researchers will apply controls even
if an ad hoc group is used. So the errors are kept
5. An experiential way to have students learn about the need for preliminary questionnaire
screening is to bring a stack of completed questionnaires to class, pass them out to students,
and have them inspect each for problems such as incomplete questionnaires, nonresponses to
specific questions, yea-saying, nay-saying, or middle-of-the-road patterns. Outlier
questionnaires can be spotted, and class discussion can be used to determine how to handle
the outliers. Alternatively, because hard copy questionnaires are not common, the Instructor
may have a dataset for students to examine. With a single worksheet, one can orient student
on data labels, and the code book for variable descriptions, value codes, and value labels can
be learned and used in this exercise as will.
6. It is strongly urged that students have hands-on experience with one or more of the XL Data
Analyst datasets that accompany the textbook. The Advanced Automobile Concepts dataset is
used in the Figures, so if possible, have students bring laptops (or use a PC lab) to inspect
and closely examine the structure of the XL Data Analyst. Although students are not formally
oriented on data analysis, an Instructor can use simple summarization with percents to show
how data selection (filtering) can be used.
7. Instructors who want a different data set or who want to have students learn first-hand
how to build an XL Data Analyst data set should strongly consider visiting the XL Data Analyst
(www.xldataanalyst.com) website for updated versions, posted instructions, help, and FAQs.
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