2. Aggregate Data
• Aggregate Data aggregates groups of cases in the active dataset into single
cases and creates a new, aggregated file or creates new variables in the active
dataset that contain aggregated data. Cases are aggregated based on the
value of zero or more break (grouping) variables. If no break variables are
specified, then the entire dataset is a single break group.
– If you create a new, aggregated data file, the new data file contains one
case for each group defined by the break variables. For example, if there
is one break variable with two values, the new data file will contain only
two cases. If no break variable is specified, the new data file will contain
one case.
– If you add aggregate variables to the active dataset, the data file itself is
not aggregated. Each case with the same value(s) of the break variable(s)
receives the same values for the new aggregate variables. For example,
if gender is the only break variable, all males would receive the same
value for a new aggregate variable that represents average age. If no
break variable is specified, all cases would receive the same value for a
new aggregate variable that represents average age.
3. Aggregate Data
• Break Variable(s). Cases are grouped together based on the values of the
break variables. Each unique combination of break variable values defines a
group. When creating a new, aggregated data file, all break variables are
saved in the new file with their existing names and dictionary information.
The break variable, if specified, can be either numeric or string.
• Aggregated Variables. Source variables are used with aggregate functions to
create new aggregate variables. The aggregate variable name is followed by
an optional variable label, the name of the aggregate function, and the
source variable name in parentheses.
4. Aggregate Data
• Aggregate Data: Aggregate Function
• This dialog box specifies the function to use to calculate aggregated data
values for selected variables on the Aggregate Variables list in the Aggregate
Data dialog box. Aggregate functions include:
• Summary functions for numeric variables, including mean, median, standard
deviation, and sum
• Number of cases, including unweighted, weighted, nonmissing, and missing
• Percentage or fraction of values above or below a specified value
• Percentage or fraction of values inside or outside of a specified range
• Aggregate Data: Variable Name and Label
• Aggregate Data assigns default variable names for the aggregated variables in
the new data file. This dialog box enables you to change the variable name
for the selected variable on the Aggregate Variables list and provide a
descriptive variable label. See the topicVariable names for more information.
8. SELECT Case
• Select Cases provides several methods for selecting a subgroup of cases
based on criteria that include variables and complex expressions. You can
also select a random sample of cases. The criteria used to define a subgroup
can include:
• • Variable values and ranges
• • Date and time ranges
• • Case (row) numbers
• • Arithmetic expressions
• • Logical expressions
• • Functions
9. SELECT Case
• Output
• This section controls the treatment of unselected cases. You can choose one
of the following alternatives for the treatment of unselected cases:
• • Filter out unselected cases. Unselected cases are not included in the
analysis but remain in the dataset. You can use the unselected cases later in
the session if you turn filtering off. If you select a random sample or if you
select cases based on a conditional expression, this generates a variable
named filter_$ with a value of 1 for selected cases and a value of 0 for
unselected cases.
• Copy selected cases to a new dataset. Selected cases are copied to a new
dataset, leaving the original dataset unaffected. Unselected cases are not
included in the new dataset and are left in their original state in the original
dataset.
• Delete unselected cases. Unselected cases are deleted from the dataset.
Deleted cases can be recovered only by exiting from the file without saving
any changes and then reopening the file. The deletion of cases is permanent
if you save the changes to the data file.
10. SELECT Case:if
• This dialog box allows you to select subsets of cases using conditional
expressions. A conditional expression returns a value of true,false,
or missing for each case.
• • If the result of a conditional expression is true, the case is included in the
selected subset.
• • If the result of a conditional expression is false or missing, the case is not
included in the selected subset.
• • Most conditional expressions use one or more of the six relational
operators (<, >, <=, >=, =, and ~=) on the calculator pad.
• • Conditional expressions can include variable names, constants, arithmetic
operators, numeric (and other) functions, logical variables, and relational
operators.
14. Select Cases: Random Sample
• This dialog box allows you to select a random sample based on an
approximate percentage or an exact number of cases. Sampling is performed
without replacement; so, the same case cannot be selected more than once.
• Approximately. Generates a random sample of approximately the specified
percentage of cases. Since this routine makes an independent pseudo-
random decision for each case, the percentage of cases selected can only
approximate the specified percentage. The more cases there are in the data
file, the closer the percentage of cases selected is to the specified
percentage.
• Exactly. A user-specified number of cases. You must also specify the number
of cases from which to generate the sample. This second number should be
less than or equal to the total number of cases in the data file. If the number
exceeds the total number of cases in the data file, the sample will contain
proportionally fewer cases than the requested number.
17. Select Cases: Range
• This dialog box selects cases based on a range of case numbers or a range of
dates or times.
• • Case ranges are based on row number as displayed in the Data Editor.
• • Date and time ranges are available only for time series data with defined
date variables (Data menu, Define Dates).
22. Weight Cases
• Weight Cases gives cases different weights (by simulated replication) for statistical
analysis.
• • The values of the weighting variable should indicate the number of
observations represented by single cases in your data file.
• • Cases with zero, negative, or missing values for the weighting variable are
excluded from analysis.
• • Fractional values are valid and some procedures, such as Frequencies,
Crosstabs, and Custom Tables, will use fractional weight values. However, most
procedures treat the weighting variable as a replication weight and will simply
round fractional weights to the nearest integer. Some procedures ignore the
weighting variable completely, and this limitation is noted in the procedure-
specific documentation.
• Once you apply a weight variable, it remains in effect until you select another
weight variable or turn off weighting. If you save a weighted data file, weighting
information is saved with the data file. You can turn off weighting at any time,
even after the file has been saved in weighted form.
• Weights in Crosstabs. The Crosstabs procedure has several options for handling
case weights. See the topic Crosstabs cell display for more information.
• Weights in scatterplots and histograms. Scatterplots and histograms have an
option for turning case weights on and off, but this does not affect cases with a
zero, negative, or missing value for the weight variable. These cases remain
excluded from the chart even if you turn weighting off from within the chart.
24. Transpose
• Transpose creates a new data file in which the rows and columns in the
original data file are transposed so that cases (rows) become variables and
variables (columns) become cases. Transpose automatically creates new
variable names and displays a list of the new variable names.
– A new string variable that contains the original variable name, case_lbl,
is automatically created.
– If the active dataset contains an ID or name variable with unique values,
you can use it as the name variable, and its values will be used as variable
names in the transposed data file. If it is a numeric variable, the variable
names start with the letter V, followed by the numeric value.
– User-missing values are converted to the system-missing value in the
transposed data file. To retain any of these values, change the definition
of missing values in the Variable View in the Data Editor.
