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introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
introduction to spss
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introduction to spss

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  • 1. Introduction to SPSS (Version 16) OMID MINOOEE MBA (ABM),second semester Institute of development study Mysore university
  • 2. content:SpssSPSS at a glanceBasic Structure of SPSSCleaning your dataDescriptive StatisticsChartsData manipulationOther Resources
  • 3. spssSPSS is a computer program used for survey authoring  and deployment (IBM SPSS Data Collection), data  mining (IBM SPSS Modeler), text analytics, statistical  analysis, and collaboration and deployment (batch and  automated scoring services).
  • 4. Ownership history: Between 2009 and 2010, the premier vendor for SPSS was called PASW  (Predictive Analytics Software) Statistics. The company announced on  July 28, 2009 that it was being acquired by IBM for US$1.2 billion.[2] As of  January 2010, it became "SPSS: An IBM Company". Complete transfer of  business to IBM was done by October 1, 2010. By that date, SPSS: An IBM  Company ceased to exist. IBM SPSS is now fully integrated into the IBM  Corporation, and is one of the brands under IBM Software Groups  Business Analytics Portfolio, together with IBM Cognos.
  • 5. Statistics program: SPSS (originally, Statistical Package for the Social Sciences)  was released in its first version in 1968 after being developed  bynoH. Nie and C. Hadlai Hull. SPSS is among the most widely  used programs for statistical analysis in social science. It is  used by market researchers, health researchers, survey  companies, government, education researchers, marketing  organizations and others. The original SPSS manual (Nie,  Bent & Hull, 1970) has been described as one of "sociologys  most influential books
  • 6. 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.
  • 7. How to open SPSSGo to STARTClick on PROGRAMSClick on SPSS INCClick on SPSS 16.0
  • 8. 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
  • 9. 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.*
  • 10. 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)
  • 11. 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.
  • 12. 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 workwith.Select that variable’s missing cell by clickingon the gray box in the right corner.click DISCRETE MISSING VALUESenter 9999 to define this variable’s missingvalueA range can also be used if you only wantto use half of a scale.
  • 13. 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
  • 14. 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.
  • 15. 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.
  • 16. 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.
  • 17. 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
  • 18. 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
  • 19. 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.
  • 20. 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?
  • 21. 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
  • 22. Data manipulation – recode a variable cont.click RANGE to create ranges of old valuesclick VALUE to create a new value for that range
  • 23. Data manipulation in action!Try recoding another variable on your own.Try finding the descriptive statistics of your new variable.
  • 24. 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
  • 25. 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
  • 26. Thank you

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