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SPSS: Beyond the Basics

SPSS: Beyond the Basics. A “next steps” class on SPSS 16 for PCs Consultant: Betty Zou. What we will cover:. PART 1 Open Excel files in SPSS Merge SPSS files Multiple Response Questions PART 2 The Wonders of Statistics Coach Bivariate Correlation T-Tests One-Sample Independent

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SPSS: Beyond the Basics

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  1. SPSS: Beyond the Basics A “next steps” class on SPSS 16 for PCs Consultant: Betty Zou

  2. What we will cover: • PART 1 • Open Excel files in SPSS • Merge SPSS files • Multiple Response Questions • PART 2 • The Wonders of Statistics Coach • Bivariate Correlation • T-Tests • One-Sample • Independent • Paired

  3. PART 1

  4. Open an Excel file in SPSS • In your Excel document • Set-up should be like in SPSS • Variable names in the first row • Each row is a case • In SPSS • File > open data • Specify “Files of type” • (notice all the file types) • In pop-up window--specify worksheet Open: “Beyond_ExampleData_1.xls”

  5. Merge SPSS files • Some sources will give you data sets in separate files • For example: • New Immigrant Survey: http://nis.princeton.edu/ • Same respondents, different questions • Income, Assets, Employment all in separate files • Merge • Sort data in all files in ascending order • Keep one file as working file and merge into that file • Data > Merge Files > Add Variables • Match cases on key variables: “ID” Merge: “Sample_Data_2” to “Sample_Data_Main”

  6. Multiple response questions • Respondents might give more than one answer to a question. • What is your favorite beer? • PBR b. Corona c. Fat Tire d. Mannies • e. Budweiser f. Other g. Don’t drink beer • What is your favorite genre of music? • Hip-hop b. Rock c. Punk d. Folk • e. Country f. Other (please specify) _________

  7. Multiple Response Questions (cont’d) • 2 Options when entering data • 1. Multiple category method (Beer 1, Beer 2, Beer 3) • 2. Multiple dichotomy method Create a variable for each answer choice (Hip-hop, Rock, Punk…) Enter “1” if the answer choice was chosen, “0” for not • Other (fill-in answer) • Enter data & automatic recode OR • Code the data yourself

  8. Define Multiple Response Sets • Which beer is more popular? PBR or Bud? • Analyze > Multiple Response > Define Variable Sets • Set Variables Beer 1 – Beer 3 • Set Variables are coded as “Categories” • Range: 1 through 7 (the value labels) • Analyze > Multiple Response > Frequencies/ Crosstabs

  9. More on Multiple Response • Are men or women more likely to listen to hip-hop? • Same process as before • Set Variables are coded as “Dichotomies” • Counted Value: 1 • Go to crosstabs • It may ask you to define range for “gender” • Enter 1 for Min, 2 for Max

  10. PART 2

  11. Statistics Coach • Help > Statistics Coach • What do you want to do? • Ex: Compare groups for significant differences • Data in categories • Show Crosstabs Case Studies

  12. Bivariate Correlation • Is there a correlation between age and income? • 2 scale variables: “age” and “income” • Analyze > Correlate > Bivariate • Check Pearson, Flag significant correlations • Interpret: Pearson (0.665), Sig (0.00) • There is a positive relationship between age and income. The higher the age, the higher the wage. This correlation is highly significant. • Scatter Plot Using dataset: “Sample_Data_Main”

  13. T-Test: One Sample • You know that the average number of hours people work per week is 40. • You want to know if the average number of hours worked in your sample is different from the known value. • Analyze > Compare Means > One-Sample T Test • Enter “WorkHrs” ; Test value: 40 • Interpret: The sample mean is 44.6750 • Sig. 0.014 significant at the 5% significance level • t = 2.564 • The sample mean could be 44.675 +2.564 or -2.564 • These people are generally over worked • The difference is significant

  14. T-Test: Independent • Is the average annual income of females different from males? • You want to compare the mean of two unrelated groups. • Analyze > Independent-Samples T Test • Test variables: Income • Grouping variables: Gender • Define Groups: 1, 2 • Interpret: Sig: 0.301 The difference is not significant.

  15. T-Test: Paired • A soda with ginko biloba in it claims to improve test scores. • Your sample was given a general knowledge test before and after drinking the soda. (Test score out of 100) • You want to test if drinking the soda significantly improves test scores. • Use Paired because you are comparing two measurements for the same individuals.

  16. T-Test: Paired (cont’d) • Analyze> Paired-Samples T Test • Variable 1: PreTest • Variable 2: PostTest • Notice that you can compare more pairs • Interpret: • Drinking the soda actually reduces test scores by an average of about 40 points! • Sig: 0.000, it is highly significant

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