*Using
the New Variable Function *

The NEW VARIABLE function in EZAnalyze allows you to easily create new variables from your existing data - you can create several different kinds of variables with the NEW VARIABLE function.

To create a new variable with your data, select the "New Variable . . ." option from the EZAnalyze menu in Excel. Then choose one of the following:

**Summary
Variable.** Select this option if you would like to create
a new variable that is the sum or mean of several variables. Common
reasons to create a new summary variable are to create an average
GPA from students' English, math, social studies, and science GPA's,
or to obtain the total number of days a student was absent by adding
up the number of absences for each academic quarter.

**Difference
Variable. **Select this option if you would like to create
a new variable that is a *difference score* - simply put,
one variable subtracted from another variable. Difference variables
are useful for showing changes over time - for example, if you started
a new program designed to increase attendance in your school, you
could create a difference score to show how effective the program
was by subtracting the attendance rate after the program (a posttest)
from the attendance rate before the program (pretest).

**Percent
Change****.** This
function is useful for determining the difference between two variables
in terms of the percent of change from baseline - for example, the
amount of change that occurred between a pretest and posttest. This
option is similar to the Difference Variable function, except that
it provides a more standardized way of reporting the difference between
the two variables.

**Standarized
(Z) Score****.** Select this option if you
would like to convert your data to 'standardized scores', or Z scores.
Standardizing your variables is useful for putting things 'on the
same metric'. For example, if you have two variables, and one is
scored on a 5 point scale and the other is scored on a 7 point scale,
they are difficult to compare side by side. If you standardize both
variables, you can compare the standardized scores side by side easily.
You can choose to create standardized scores based on a mean and
standard deviation from your own data, or if the population parameters
are known, you may choose to use those.

**Percentile
Rank**. With this function, you can convert
your data to their percentile rank equivalent. For example, if you
want to know who is in the top 10% of the senior class at your high
school, you can convert their overall GPA into a percentile rank
variable to help you see who is in the 90th percentile or higher.

**Binary
Variable**. This function creates new
variables that are scored as either a 0 or a 1 - a process also known
as 'dummy coding'. This is a very useful, and probably underutilized
tool. For example, if you wanted to create a disaggregation graph
using the percent of people who 'agreed' or 'strongly agreed' with
your survey question, you can create a new binary variable that is
scored a 1 if people selected 'agree' or 'strongly agree', and a
0 if they did not. This is useful because binary variables that are
scored as a 0 or a 1 have a 'special property', and that special
property is that the mean of a binary variable with values of 0 or
1 is the percent of people who scored a one.

**Random
Numbers**. Using this function, you can
quickly create random numbers to demonstrate various statistical
problems and concepts. You can set the mean and standard deviation,
or generate completely random numbers within a specified range.