Data & Intelligence

# Dummy Coding with IBM SPSS

Dummy Coding with IBM SPSS

To understand what is meant by dummy coding, you need to understand 2 forms of data:

Qualitative or Quantitative?

“Qualitative data describes items in terms of some quality or categorization while Quantitative data are described in terms of quantity (and in which a range of numerical values are used without implying that a particular numerical value refers to a particular distinct category).” To better understand the differences, always remember that qualitative data is more of an observance, while quantitative is measurable.

## Your Morning Latte…

So!

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If we consider a morning latte example, we might note the following:

Qualitative Examples

• robust aroma
• frothy appearance
• strong taste
• burgundy cup

Quantitative Examples

• 12 ounces of latte
• Serving temperature 150º F.
• serving cup 7 inches in height
• cost \$4.95

Statistical Analysis often includes variables in which the numbers represent qualitative categories (such as gender, ethnicity or political affiliation).

Including these variables in an analytical model requires special steps to ensure the results can be interpreted properly. These steps involve coding a categorical variable into multiple dichotomous variables, in which variables take the value of “1” or zero.

For clarity, a dichotomous variable is defined as a variable that splits or groups data into 2 distinct categories. An example would be employed and unemployed.

This process is known as “dummy coding.” IBM SPSS makes dummy coding an unpretentious practice. Let’s walk through the steps!

## Dummy Coding Step by Step

1. Select the categorical variable that you want to dummy code. (Note the number of categories, remembering that dummy coding transforms a variable with “n” categories into “n-1” categories. For example, a categorical variable on political affiliation with three categories — Democrat, Republican and Independent — would be dummy coded into two dichotomous variables, such as Democrat and Republican. A person who identifies as one of these would be coded a “1” in the data set. A person with a zero in these categories would be counted as independent).
2. Click the “Transform” menu at the top of the SPSS data sheet, then select “Recode Into Different Variable,” because you will transform the categorical variable into one or more dichotomous or dummy variables. This opens a window that displays the variables in your data set. Select the variable you want to recode, and then click the arrow, which moves the variable name into the box labeled “Numeric Variable.”
3. Click the “Output Variable” name box and type a name for your new dichotomous variable. Click “Change.” Click “Old and New Values,” which opens a new display, showing old and new values for the variable you want to transform.
4. Recode the values of the variable by coding one category as a “1” and the others as zero. Under “Old Value,” enter the category value to be recoded. Under “New Value,” type a “1,” then click “Add.” On the “Old Value” side, select the “All Other Values” button and type “0” as the new value. For example, the political affiliation example that codes Democrat as a “1,” Republican as a “2” and Independent as a “3” could be recoded into the dichotomous variable Republican, with all “2s” recoded as “1” and other values coded as zero.Click “Continue” after entering the old and new values for your dummy codes, then click “OK.” SPSS will then recode the categorical variable as you have specified.

Done Deal!

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##### Jim Miller

Mr. Miller is an IBM certified and accomplished Senior Project Leader and Application/System Architect-Developer with over 30 years of extensive applications and system design and development experience. His current role is National FPM Practice Leader. His experience includes BI, Web architecture & design, systems analysis, GUI design and testing, Database modeling and systems analysis, design, and development of Client/Server, Web and Mainframe applications and systems utilizing: Applix TM1 (including TM1 rules, TI, TM1Web and Planning Manager), dynaSight - ArcPlan, ASP, DHTML, XML, IIS, MS Visual Basic and VBA, Visual Studio, PERL, Websuite, MS SQL Server, ORACLE, SYBASE SQL Server, etc. His Responsibilities have included all aspects of Windows and SQL solution development and design including: analysis; GUI (and Web site) design; data modeling; table, screen/form and script development; SQL (and remote stored procedures and triggers) development and testing; test preparation and management and training of programming staff. Other experience includes development of ETL infrastructure such as data transfer automation between mainframe (DB2, Lawson, Great Plains, etc.) systems and client/server SQL server and Web based applications and integration of enterprise applications and data sources. In addition, Mr. Miller has acted as Internet Applications Development Manager responsible for the design, development, QA and delivery of multiple Web Sites including online trading applications, warehouse process control and scheduling systems and administrative and control applications. Mr. Miller also was responsible for the design, development and administration of a Web based financial reporting system for a 450 million dollar organization, reporting directly to the CFO and his executive team. Mr. Miller has also been responsible for managing and directing multiple resources in various management roles including project and team leader, lead developer and applications development director. Specialties Include: Cognos/TM1 Design and Development, Cognos Planning, IBM SPSS and Modeler, OLAP, Visual Basic, SQL Server, Forecasting and Planning; International Application Development, Business Intelligence, Project Development. IBM Certified Developer - Cognos TM1 (perfect score 100% on exam) IBM Certified Business Analyst - Cognos TM1

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