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 Solution 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 Flow of information  Sample survey of California ISPssurvey of California ISPs  Brief comparison of Excel & SPSScomparison

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, expected split  400 for 95% confidence, 5% tolerance, 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%; 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