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Internet Usage Statistical Data Analysis
 

Internet Usage Statistical Data Analysis

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Internet Usage Statistical Data Analysis

Internet Usage Statistical Data Analysis

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    Internet Usage Statistical Data Analysis Internet Usage Statistical Data Analysis Presentation Transcript

    • Internet UsageStatistical DataAnalysis Edgardo Donovan RES 610 – Dr. Joshua Shackman Module 5 – Session Long Project Monday, September 19, 2011
    • Overview 1. Study Background 2. Top/Lowest Uses 3. Top/Lowest Uses Chart 4. InterSurvey 5. Sampling Issues 6. Hypotheses 7. Case Processing 8. Reliability 9. Item Statistics 10. Item Statistics (cont.) 11. Summary Item Stats
    • Overview (cont.) 12. Inter-Item Correlations 13. More Quantitative Analysis 14. Item Statistics 15. Item Statistics (cont.) 16. Improving the Original 17. Improved USC Model 18. Autocorrelations 19. Hours on the Internet 20. Hours on Internet (cont.) 21. Positive Correlations 22. Positive Correlations (cont.)
    • Overview (cont.) 23. Conclusion 24. Questions?
    • 1. Study Background 2000 UCLA study surveying the digital future Limited to WebTV users Initially started at Stanford, then UCLA, then USC
    • 2. Top/Lowest Uses Top Uses:  Learning  Surfing (overlap?)  Reading about products Surprising Lowest Uses:  Schoolwordk  Banking  Job Search
    • 3. Top/Lowest Uses Chart
    • 4. InterSurvey Relied upon a form application tool named “Intersurvey” Survey had to be done online Low interest in effectively sampling the US Internet user population
    • 5. Sampling Issues WebTV Set Top Boxes  Limited to low end income demographic Poor attempt at sampling External validity problematic
    • 6. Hypotheses “Negative correlation between Internet and TV use Negative correlation between Internet Use and traditional social activity and shopping No insight on survey questions
    • 7. Case Processing Case Processing Summary N % Cases Valid 1241 100.0 Excludeda 0 .0 Total 1241 100.0 a. Listwise deletion based on all variables in the procedure.
    • 8. Reliability Reliability Statistics Cronbachs Alpha Based on Cronbachs Alpha Standardized Items N of Items .816 .809 17
    • 9. Item Statistics
    • 10. Item Statistics (cont.)
    • 11. Summary Item Stats
    • 12. Inter-Item Correlations
    • 13. More Quantitative Analysis
    • 14. Item Statistics
    • 15. Item Statistics (cont.)
    • 16. Improving the Original USC to improve the Stanford/UCLA study  Auto, Pharms, and groceries were removed  Smoothing effect  10-25 questions that delve deeper into issues
    • 17. Improved USC Model
    • 18. Autocorrelations
    • 19. Hours on the Internet
    • 20. Hours on Internet (cont.)
    • 21. Positive Correlations Positive correlation between hours spent on the Internet and amount of online purchases Significant deviation between males and females concerning when purchasing sporting goods
    • 22. Positive Correlations (cont.)
    • 23. Conclusion Stanford/UCLA Study  WebTV and InterSurvey Limitations  Extra Variables  Measured “strange” usage USC  Eliminated Unnecessary Variables  No WebTV InterSurvey Limitations  Hypothetical Correlations Have Value  More advanced Stage of Internet Use
    • 24. Questions? Questions?  Edgardo Donovan  Trident University  edonovan@tuiu.edu