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An Introduction to SPSS


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Basic use of SPSS (Statistical Package for Social Sciences). Measurement scales and data entry. Various types of tests and techniques.

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An Introduction to SPSS

  1. 1. An Introduction to Statistical Package for Social Sciences
  2. 2. Types of questions1 Dichotomous (Yes/No, Male/Female)2 Multiple choice (select only one from several options)3 Likert scale (e.g. Strongly disagree … Strongly agree)4 Rating (numerical scale, e.g. from 1 to 10)5 Multiple response (checklist)6 Ranking7 Open-ended (qualitative – not to be analyzed by SPSS)
  3. 3. Hot TipBranching should be avoided as far as possible:Example:If ‘Yes’, go to question 14;If ‘No’, go to question 16.
  4. 4. QuestionnaireOnce your questionnaire is designed, perform a pilottesting with the initial version by distributing it among15 – 20 people. There are no restrictions as to how youmay choose these people (they can be your friends,members of your family). This exercise will help in Detecting any misprints, grammatical mistakes Broadening your perspective with respect to the range of expected answers to your proposed multiple-choice questionsAmend or redesign your questionnaire (if necessary)before it is finally approved.
  5. 5. Set up the Questionnaire in SPSSThere are two views in SPSS: Variable view Data viewIn ‘Variable view’, you declare all your variables(questions) whilst in ‘Data view’, you simply enter thedata from your collected questionnaires.For example, ‘Gender’ is a variable with values ‘Male’and ‘Female’.
  6. 6. Variable ViewThe first variable to be declared is ‘Questionnaire ID’.This is an extremely important declaration, especially ifthere are data entry errors.In SPSS, we often have recourse to data sorting foranalysis purposes. Should we save and close a file aftersorting, the file will next be opened in this last state(sorted data). In such a case, if an entry error is located,there would be no other option but to physically verifyeach collected questionnaire to be able to correct thatentry!
  7. 7. Hot TipPhysically number your questionnaires beforeentering data in SPSS.
  8. 8. Variable ViewVariable nameThe name of a variable can be generic or chosen atyour own convenience.For example, if the first question of your questionnaire is Gender (choose between ‘Male’ and ‘Female’),you may opt for the generic name ‘gender’ itself or ‘q1’.However, if you choose ‘q1’, make sure that you labelthis variable properly!
  9. 9. Variable ViewLabelThe label for a variable has to be written in the sameway as you wish it to appear (as a heading) in an outputtable or chart.If the label is omitted for a variable that has been named‘q1’, then ‘q1’ will appear as the heading for itscorresponding output table or chart. That will obviouslynot be understood by another reader.
  10. 10. Variable ViewValuesThese are to be assigned to variables which arecategorical in nature (variables that have options, e.g.dichotomous, Likert scale, multiple-choice). Forexample, ‘Male’ and ‘Female’ would be assigned values1 and 2 respectively for the variable ‘gender’.Numerical variables like rating scales, age last birthday,etc are not assigned any values because these areentered directly as chosen by the respondent in thequestionnaire.
  11. 11. Variable ViewValuesMultiple response questions are assigned values in adifferent manner for analysis purposes.For example, to the question ‘How did you come toknow about this product?’, a respondent may choosemore than one answer to the options:□ Radio □ TV □ Newspapers/Magazines□ Billboard □ Internet □ Other (please specify)
  12. 12. Variable ViewValuesIn such a case, we have to break down this questioninto 6 variables, namely Radio, TV,Newspaper/Magazines, Billboard, Internet and Other.These variables will be declared as dichotomous withvalues 0 (if chosen) and 1 (if not chosen).Now make sure that you don’t include too many multipleresponse questions in your questionnaire because youmight end up with more than 100 variables in SPSS!!!
  13. 13. Data ViewThis is meant for data entry – each row represent theentries for a collected questionnaire.The declared variables are now column headings underwhich the corresponding chosen response (by eachrespondent) has to be entered as a number, whetherthe variable is numerical or categorical.Once all the collected data have been entered andverified to be correct, we may proceed to descriptive orinferential analysis.
  14. 14. Descriptive StatisticsFor each variable, there should be commentsaccompanying its chart and frequency table.Pie charts are usually very explicit – remember theyonly display percentages! Bar charts displayfrequencies – comments must be made in terms ofskewness, that is, where its peak lies (middle, to the leftor to the right). Middle: symmetrical distribution To the left: positively skewed distribution To the right: negatively skewed distribution
  15. 15. Inferential StatisticsThis part of the analysis is directly related with theobjectives of the research.Here, we perform all kinds of testing (mostly, the testingof research hypotheses in order to achieve the researchobjectives).It is important that we include both categorical andnumerical variables in our questionnaire so as to beable to use the various tests as prescribed by Curtin(see the Marketing Research 200 unit outline)
  16. 16. Inferential StatisticsTests to be used:1 Chi-Squared test of Independence2 Independent Samples T-test3 ANOVA (ANova Of VAriance)4 Correlation5 Factor Analysis6 Multiple Regression Analysis
  17. 17. Hot TipIrrespective of whether you used or did not usea specific test in your research project, you maybe examined on all of them!
  18. 18. Inferential StatisticsTests for categorical variables1 Chi-Squared test of Independence2 Correlation3 Factor Analysis4 Multiple Regression Analysis
  19. 19. Hot TipFor multiple regression analysis, thedependent variable has to be numerical.
  20. 20. Inferential StatisticsTests for numerical variables 1 Independent Samples T-test 2 ANOVA (ANova Of VAriance) 3 Correlation 4 Factor Analysis 5 Multiple Regression Analysis
  21. 21. Hot TipIn this module, for multiple regression analysis,the independent variables may be eithercategorical or numerical or both.