Introduction to SPSS by yaoyufang

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									Introduction to SPSS
           (Version 16)




         LINDSEY BREWER

 CSSCR (CENTER FOR SOCIAL SCIENCE
   COMPUTATION AND RESEARCH)

    UNIVERSITY OF WASHINGTON

        September 17, 2009
Topics we will cover today


 SPSS at a glance
 Basic Structure of SPSS
 Cleaning your data
 Descriptive Statistics
 Charts
 Data manipulation
 Other Resources
               SPSS at a glance


 SPSS stands for Statistical Package for the Social
 Sciences

 SPSS was made to be easier to use then other
 statistical software like S-Plus, R, or SAS.

 The newest version of SPSS is SPSS 17.0. Today we
 will be working on SPSS 16.0.
                How to open SPSS

 Go to START


 Click on PROGRAMS


 Click on SPSS INC


 Click on SPSS 16.0


 The computers in the CSSCR lab typically have SPSS on
 the desktop. It is a red box that says SPSS on the top.
        Opening a data file

 Click on FILE  OPEN  DATA


 Click MY COMPUTER  LOCAL DISK C:/


 Click PROGRAM FILES  SPSS


 Click TUTORIAL  SAMPLE FILES


 Select CATALOG.SAV
               Basic structure of SPSS


 There are two different windows in SPSS


 1st – Data Editor Window - shows data in two forms
     Data view
     Variable view


 2nd – Output viewer Window – shows results of data
  analysis

 *You must save the data editor window and output viewer window
  separately. Make sure to save both if you want to save your changes in
  data or analysis.*
           Data view vs. Variable view


 Data view
   Rows are cases

   Columns are variables



 Variable view
   Rows define the variables
       Name, Type, Width, Decimals, Label, Missing, etc.
         Scale – age, weight, income

         Nominal – categories that cannot be ranked (ID number)

         Ordinal – categories that can be ranked (level of satisfaction)
   Cleaning your data – missing data

 There are two types of missing values in SPSS:
  system-missing and user-defined.

 System-missing data is assigned by SPSS when a
  function cannot be performed.

 For example,
  dividing a
  number by zero.
  SPSS indicates
  that a value is
  system-missing
  by one period in
  the data cell.
    Cleaning your data – missing data

 User-defined missing data are values that the researcher can tell SPSS
  to recognize as missing. For example, 9999 is a common user-defined
  missing value. To define a variable’s user-defined missing value…

Look at your variables in VARIABLE VIEW
Find the column labeled MISSING
Find the variable that you would like to work
with.
Select that variable’s missing cell by clicking
on the gray box in the right corner.
click DISCRETE MISSING VALUES
enter 9999 to define this variable’s missing
value

 A range can also be used if you only
want to use half of a scale.
  Cleaning your data – missing data cont.

 When you have missing data in your data set, you
 can fill in the missing data with surrounding
 information so it does not affect your analysis.
 click TRANSFORM
 click REPLACE MISSING VALUES
 select the variable with missing
  values and move it to the right
  using the arrow
 SPSS will rename and create a new
  variable with your filled in data.
 click METHOD to select what type
  of method you would like SPSS to
  use when replacing missing values.
 click OK and view your new data in
  data view
               Descriptive Statistics

 Lets say we are interested in
  learning more about the
  number of customer service
  representatives (service).

 Click ANALYZE


 Click DESCRIPTIVE
  STATISTICS

 Click FREQUENCIES


 Choose service from the list.
      Descriptive Statistics continued

 Lets learn more about the number of
  catalogs mailed (mail).

 Click ANALYZE

 Click DESCRIPTIVE STATISTICS

 Click DESCRIPTIVES

 Move MAIL over with the arrow

 Click OPTIONS – we can choose which statistics we are interested in
  looking at

 We should remember that these descriptive statistics will not always
  make sense for every variable. For example, we should not be asking
  for the mean of nominal variables like gender or race.
                          Graphing Data

 Click GRAPH

 Click CHART BUILDER

 Click HISTOGRAM

 Put MEN on the X axis.

 Click ELEMENT PROPERTIES. Check
  the box labeled DISPLAY NORMAL
  CURVE. This will impose a normal
  curve onto your graph. You can also
  change the style of your graph in this
  element properties window.

 You can copy and paste these graphs
  into word and excel files.
              Graphing Continued

 There are other ways to make
    graphs.

 Click ANALYZE
 Click DESCRIPTIVE
    STATISTICS
   Click FREQUENCIES
   Click services
   Click CHART
   Click BAR CHART
   Click PERCENTAGES
    Data manipulation – select cases

 By selecting cases,
    the researcher can
    select only certain
    cases for analysis
   click DATA
   click SELECT CASES
   click RANDOM
    SAMPLE OF CASES
   select your
    preferences
Data manipulation – compute new variable

 Computing new variables – create a
    new variable from multiple variables

 click TRANSFORM
 click COMPUTE
 fill in the new target variable
    TOTALSALES
   fill in numeric expression =
    men+women+jewel
   create an IF statement by clicking on
    the IF button
   click INCLUDE IF CASE SATISFIES
    CONDITION
   enter condition MAIL>10000

 This new variable TOTALSALES tells us what the total sales are for
    catalogs which mailed over 10,000 catalogs.
    Data manipulation in action!

 Try creating another variable for
 TOTALSALES2 for catalogs which mailed
 under 10,000 catalogs.

 Try comparing the descriptive statistics of
 TOTALSALES and TOTALSALES2.

 What did you find?
 Data manipulation – recode a variable

 Recoding allows a researcher to create a new
  variable with a different set of parameters
 click TRANSFORM
 click RECODE INTO DIFFERENT VARIABLE
 move mail over to
 the right
 create a name for
 the new variable
 mailcategories
 click OLD AND
 NEW VALUES
Data manipulation – recode a variable cont.



 click RANGE
  to create
  ranges of old
  values
 click VALUE to
  create a new
  value for that
  range
    Data manipulation in action!


 Try recoding another variable on your
 own.

 Try finding the descriptive statistics of
 your new variable.
Data manipulation – create a dummy variable


 Dummy variables is a variable that has a value of
 either 0 or 1 to show the absence or presence of some
 categorical effect
  To create a dummy
     variable…
    click TRANSFORM
    click RECODE INTO
     DIFFERENT VARIABLE
    click OLD AND NEW
     VALUES
    click RANGE to create
     range of old values
    click VALUE to set new
     value to 0 or 1
     What we have learned!

 SPSS at a glance
 Basic Structure of SPSS
 Cleaning your data – missing data
 Descriptive Statistics – frequencies,
  descriptive statistics
 Charts
 Data manipulation – select cases,
  recoding, dummy variables
               Other Resources

 There are many resources online to help you learn
 SPSS (tutorials, blogs, etc.)

 CSSCR has a Quicktime SPSS class on its website


 CSSCR offers SPSS handouts which are also on its
 website

 CSSCR offers classes on SPSS each quarter – come
 back for the SPSS Beyond the Basics class!

								
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