Proposed Empirical Study -Theory of four-paradigm in information system development

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Proposed Empirical Study-Theory of four-paradigm
in information system development

• Presented by Jing Hou
• February 10, 1999

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Problem Description

• The hypothesis to be tested The implicit
  assumptions that information system developers
  make are based on one of the four broad
  categories of paradigms
• Functionalism
• Social relativism
• Radical structuralism
• Neohumanism

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Methodology Description

• Step 1 Conduct a survey
• Step 2 Analyze the sample data from the survey
  to base the foundation of the empirical study
• Step 3 Compare the survey result with the
  hypothesis

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Survey Elements

• Sampling Design
• Instrumentation
• Data Collection
• Data Processing
• Result Interpretation

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Sampling Design

• Strive for reliability and validity
• Identify components quite precisely
• Determine sample size carefully
• Depend upon random sampling
• Use trial when necessary
• Identify sources of bias

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Instrumentation

• Composing questions
• Focus very precisely
• Keep each item brief
• Strive for clarity
• Use core vocabulary
• Use simple sentences

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Instrumentation

• Creating item scales
• Keep it simple
• Respect the respondent
• Dimension the response
• Pick the denominations
• State instructions clearly

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Instrumentation

• Building Questionnaire
• Emphasize the introduction
• Check sequence carefully
• Group items into sections
• Limit and control branching
• Dont overestimate respondents
• Precode responses
• Pretest the entire questionnaire

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Data Collection

• Choose e-mail over personal interview
• The task doesnt require interaction with the
  respondents
• E-mail is free, therefore no cost of travel or
  long distance telephone calls
• Respondents can record their own responses
• E-mail can reach respondents in a broad
  geographic area and ensure timely response

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Data Processing

• Keep it neat and orderly
• Maintain adequate records
• Postcode questionnaires carefully
• Monitor data transfer
• Edit the computer data files
• Identify scale data types

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Result Interpretation

• Analyzing The Results
• Remember the objectives
• Provide data description first
• Identify the scale types
• Portray information when feasible
• Determine the data type
• Assess causality carefully
• Select the simplest tool

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Result Interpretation

• Interpreting Statistics
• Begin with data description
• Choose the best average
• Recode data when necessary
• Make inferences carefully
• Depend on cross-tabulation
• Use prediction cautiously
• Check causality carefully

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Result Interpretation

• Report The Information
• Use an executive summary
• Include a response summary
• Compose tables first
• Display the information
• Write a simple narrative
• Conclude the project