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Introduction to Statistical Analysis

Introduction to Statistical Analysis. Yale Braunstein School of Information. Approximate (!) Schedule. Today Data, data collection instruments (e.g., surveys) Focus is on descriptive statistics Research design Sample size, sources of error (maybe) Thursday Sample size, sources of error

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Introduction to Statistical Analysis

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  1. Introduction to Statistical Analysis Yale Braunstein School of Information

  2. Approximate (!) Schedule • Today • Data, data collection instruments (e.g., surveys) • Focus is on descriptive statistics • Research design • Sample size, sources of error (maybe) • Thursday • Sample size, sources of error • Measures of central tendency • Demos of Excel & SPSS • Discussion of statistics assignment • Next Tuesday • More on SPSS with lots of examples • Q & A on the assignment

  3. Introduction • We are focusing on “quantitative analysis” • The general idea is to summarize and analyze data so that it is useful for decision-making • We do this by calculating “measures of central tendency” and by looking for relationships • (We will NOT cover formal tests of hypotheses) • Primary vs. secondary data sources • Data on uses (system) vs. data on users (people)

  4. Data • Data may be continuous or discrete • Just looking at the data often does not enable one to ascertain what is actually happening • Solution: Use appropriate descriptive statistics to summarize and present results Another Data 

  5. Analysis--Introduction • The BIG Questions: • What are you trying to discover or show? • How will you present the results? • From survey to report • Flow of information • Sample survey of California ISPs • Brief comparison of Excel & SPSS

  6. Data Collection Instruments • Questionnaires & surveys • Transactions logs • Experimental observation • Bills & invoices • Census forms & reports • Pre-packaged data sets Interviewing & designing surveys requires skill & experience. It is often useful to get professional help.

  7. Issues in Research Design • Case study vs. statistical sample • What is the universe ? (uses, users, etc.) • Example: political debate over “average tax cut” vs. “tax cut for the average family” • Is the sample representative ? • Volumes vs. titles in the library • Does correlation imply causality? • Do we need to identify the pathogen? • Controlling for outside factors

  8. Sample Size • How large a sample is needed? • The larger the sample the more accurate the results (unless the response rate becomes very low) • The larger the sample the more the cost/effort • Sample size does NOT depend on the size of the population • Rules of thumb • 100 for 95% confidence, 5% tolerance, 90-10 expected split • 400 for 95% confidence, 5% tolerance, 50-50 expected split • 30 – 50 in each cell on n x m discrete classes • Exact formula (use with care): • Size = 0.25 * (certainty factor/acceptable error)^2 • Where the certainty factor = 1.96 for 95%; 2.576 for 99% [Alternate approach: hire a statistical consultant.]

  9. Sources of Error • The respondent • The investigator • Sampling error • Change in the system itself • Coding & analysis • Other

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