Introduction to Statistical Analysis

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Introduction to Statistical Analysis

• Yale Braunstein
• School of Information Management Systems

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Approximate (!) Schedule

• Today
• Data, data collection instruments (e.g., surveys)
• Research design
• Sample size, sources of error (maybe)
• Wednesday
• Sample size, sources of error
• Measures of central tendency
• Demos of Excel SPSS
• Discussion of statistics assignment
• Next Wednesday
• More on SPSS with lots of examples
• Q A on the assignment

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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)

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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 ?
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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

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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.
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Issues in Research Design

• Case study vs. statistical sample
• What is the universe ? (uses, users, etc.)
• Current 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

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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.

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Sources of Error

• The respondent
• The investigator
• Sampling error
• Change in the system itself
• Coding analysis
• Other