Exploring the Structure and Contents of a Data Set

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Exploring the Structure and Contents of a Data Set
If you are using someone else's data, as we are
now, our first task is to examine the structure
and contents of the data set. SPSS provides us
with two tools for reviewing the contents of the
data set  the Variables utility and the File
Info utility which produces a data dictionary or
code book listing of the data set. The Variables
utility supports interactive checking about a
variable during analysis. The File Info utility
produces an output listing that can be printed
for reference. Both utilities list information
about each variable in the data set its mnemonic
name, the text label for the variable, the
measurement level of the variable, the labels for
the different values if the variable is
nonmetric, and the format that will be used for
printing data for the variable, i.e. the number
of decimal places displayed.  The level of
measurement, which is designated by the creator
of the data set, can be a source of confusion
because there are multiple terminologies for
typing variables.  Using the terminology of Hair,
a variable is either metric or nonmetric.  Metric
variables include the traditional categories of
interval level and ratio level variables.  SPSS
refers to metric variables as scale level
variables.  Hair's nonmetric category includes
the traditional measurement levels of ordinal and
nominal. SPSS uses the traditional ordinal and
nominal designation these variables. Note that
the default data type specified by SPSS is
Scale.  If the data set creator does not specify
a level for each variable, or if the data set was
created before SPSS supported typing of
variables, the variable will be listed as Scale. 
The data sets that we will use have not been
typed. Metric variables include both continuous
variables for which the measurement scale
includes decimal values, and discrete variables
only contain whole number measurements.  Examples
of continuous measurements are height, weight,
temperature, etc.  Examples of discrete variables
include age, highest grade level attained, number
of children, etc.  Likert-scale scores are
usually treated as discrete metric variables by
convention, even though they are only designed to
be ordinal scale.
Exploring the Structure and Contents of a Data Set
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1. Downloading the Data Set
Create a directory on your computer's hard disk
named C\SW388R7.  We will download all data sets
to this directory.  Download the HATCO data set
from the course web page.
Exploring the Structure and Contents of a Data Set
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2. The Data Dictionary
The data dictionary for a data set contains all
the code book specifications for each variable 
the mnemonic variable name, its associated text
label, the measurement scale of the variable, as
well as a list of codes for nonmetric variables. 
Exploring the Structure and Contents of a Data Set
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The File Info Output
Exploring the Structure and Contents of a Data Set
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The Data Dictionary
The data dictionary information is written to the
Output1 window in the SPSS Viewer. Scroll through
the output. Can you identify the SPSS data type
and the measurement level (metric or nonmetric)
for each variable? Compare your answer to the
listing in Table 1.3 in the text. ID is included
as a reference number so that we can refer back
to the questionnaire for a specific subject if
necessary. Only in very rare circumstances is it
included in any analysis. If it should ever be
used in an analysis, it is a label for the case
and nonmetric by definition. We will get the
additional information about our variables in the
next section.
Exploring the Structure and Contents of a Data Set
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3. Online Information in the Variables Dialog Box
In addition to the complete listing in the data
dictionary, information about each variable can
be found in SPSS in the variables dialog box.
Exploring the Structure and Contents of a Data Set
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Accessing the Variables Dialog
Exploring the Structure and Contents of a Data Set
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Information about Other Variables
Exploring the Structure and Contents of a Data Set
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Requesting Frequencies and Histograms
Exploring the Structure and Contents of a Data Set
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Request the Histogram Charts
Exploring the Structure and Contents of a Data Set
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Request the Output
Exploring the Structure and Contents of a Data Set
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Viewing the Output
Exploring the Structure and Contents of a Data Set
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Histograms for Nonmetric Variables
Exploring the Structure and Contents of a Data Set
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Histograms for Metric Variables
Exploring the Structure and Contents of a Data Set