Info 271B Lecture 3

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Preparing for Research

• Info 271B Lecture 3
• Coye Cheshire

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Todays Topics

• Constructs, Variables and Causation
• Research Problems

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Types of Variables

• Nominal
• Categorical
• Dichotomous, Binary, Dummy Variables
• Qualitative Variables
• Ordinal
• Rank Variables
• Metric
• Interval Variables
• Ratio Variables

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Nominal Variables

• Binary/dichotomous
• Example Gender, event occurred or did not
  occur, etc.
• When coded as 0/1, also called dummy variables
• Nominal/non-ordered polytomous
• Example Employment Status
• 1 Employed
• 2 Unemployed
• 3 Retired

Three New Dummy Variables Employed
(0,1) Unemployed (0,1) Retired (0,1)
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Ordinal Variables

• Ordered polytomous
• Example Likert scales
• Any ordered, categorical variable where the
  distance between categories may not be equal and
  meaningful

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Metric Variables

• Interval
• Distance between attributes has meaning
• Example Celsius temperature, likert-scale
  questions
• Ratio
• Distance between attributes has meaning, and
  there can be a meaningful zero.
• Example Kelvin temperature, Count variables

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Constructs, Variables and Causation
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An example theoretical model
Socioeconomic Status
Academic Achievement
Academic Ability
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Theoretical Model with Variables
Income
Job Prestige
Socioeconomic Status
Academic Achievement
Academic Ability
Grades
Level of Schooling attained
Math skills
Language skills
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Causation and Causal Paths

• Direct causal paths
• Reciprocal causation
• Indirect causation

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Conditions of Causality

• Covariation
• Non-spurious relationship
• Logical time ordering
• Mechanism to explain how X causes Y

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Hypothesis

• hypothesis statements contain two or more
  variables that are measurable or potentially
  measurable and that specify how the variables are
  related (Kerlinger 1986)

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Propositions and Hypotheses

• Propositions link concepts together with specific
  relationships
• Hypotheses link variables together with specific
  relationships

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Next Class Criteria for a Research Problem

• What are we going to learn as the result of the
  proposed project that we do not know now?
• Why is it worth knowing?
• How will we know that the conclusions are valid?