Date post: | 29-Dec-2015 |
Category: |
Documents |
Upload: | ashlyn-lamb |
View: | 214 times |
Download: | 0 times |
LINDSEY BREWER
CSSCR (CENTER FOR SOCIAL SCIENCE COMPUTATION AND RESEARCH)
UNIVERSITY OF WASHINGTON
September 17, 2009
Introduction to SPSS(Version 16)
Topics we will cover today
SPSS at a glanceBasic Structure of SPSSCleaning your dataDescriptive StatisticsChartsData manipulationOther 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.
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…
Cleaning your data – missing data
Look at your variables in VARIABLE VIEWFind the column labeled MISSINGFind 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 VALUESenter 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 ANALYZEClick DESCRIPTIVE
STATISTICSClick FREQUENCIESClick servicesClick CHARTClick BAR CHARTClick PERCENTAGES
By selecting cases, the researcher can select only certain cases for analysis
click DATAclick SELECT
CASESclick RANDOM
SAMPLE OF CASESselect your
preferences
Data manipulation – select cases
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?
Recoding allows a researcher to create a new variable with a different set of parameters
click TRANSFORMclick RECODE INTO DIFFERENT VARIABLE
Data manipulation – recode a variable
move mail over to the right
create a name for the new variable mailcategories
click OLD AND NEW VALUES
click RANGE to create ranges of old values
click VALUE to create a new value for that range
Data manipulation – recode a variable cont.
Data manipulation in action!
Try recoding another variable on your own.
Try finding the descriptive statistics of your new 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
Data manipulation – create a dummy variable
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 glanceBasic Structure of SPSSCleaning your data – missing dataDescriptive Statistics –
frequencies, descriptive statisticsChartsData manipulation – select cases,
recoding, dummy variables