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LINDSEY BREWER

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SPSS stands for Statistical Package for the Social Sciences ... SPSS will rename and create a new variable with your filled in data. ... – PowerPoint PPT presentation

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Title: LINDSEY BREWER


1
Introduction to SPSS (Version 16)
  • LINDSEY BREWER
  • CSSCR (CENTER FOR SOCIAL SCIENCE COMPUTATION AND
    RESEARCH)
  • UNIVERSITY OF WASHINGTON
  • September 17, 2009

2
Topics we will cover today
  • SPSS at a glance
  • Basic Structure of SPSS
  • Cleaning your data
  • Descriptive Statistics
  • Charts
  • Data manipulation
  • Other Resources

3
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.

4
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.

5
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

6
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.

7
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)

8
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.

9
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 variables
    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 variables missing cell by clicking
    on the gray box in the right corner.
  • click DISCRETE MISSING VALUES
  • enter 9999 to define this variables missing
    value
  • A range can also be used if you only want to use
    half of a scale.

10
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

11
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.

12
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.

13
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.

14
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

15
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

16
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 menwomenjewel
  • create an IF statement by clicking on the IF
    button
  • click INCLUDE IF CASE SATISFIES CONDITION
  • enter condition MAILgt10000
  • This new variable TOTALSALES tells us what the
    total sales are for catalogs which mailed over
    10,000 catalogs.

17
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?

18
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

19
Data manipulation recode a variable cont.
  • click RANGE to create ranges of old values
  • click VALUE to create a new value for that range

20
Data manipulation in action!
  • Try recoding another variable on your own.
  • Try finding the descriptive statistics of your
    new variable.

21
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

22
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

23
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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