Research Design: Developing Concepts, Operationalization, and Measuring Variables, Slides of Research Methodology

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Designing Research Concepts,

Hypotheses, and Measurement

Research Design

• Must create a Research Design
• Questions are composed of concepts
  • Must start with a research question

Operationalization

• It is critical to survey research to

understand how to go from ideas to

concepts to variables – operationalization.

Concepts

• Concept (p.35): an idea, a general mental formulation summarizing specific occurrences
• A label we put on a phenomenon, a matter, a “thing” that enables us to link separate observations, make generalizations, communicate and inherit ideas.
• Concepts can be concrete, abstract, tangible or intangible.
  • Concrete: Height, Major
  • Abstract: Happiness, Love

Example: Concept and Variable

• Concept:

Political participation

• Variables:
  • Voted or not
  • How many times a person has voted
  • What party a person votes for

How to be measured?

• Conceptualization: The process of conceptualization includes coming to some agreement about the meaning of the concept
• In practice, you often move back and forth between loose ideas of what you are trying to study and searching for a word that best describes it.
• Sometimes you have to “make up” a name to encompass your concept.

Operationalizing Choices

• You must operationalize : process of converting concepts into measurable terms - The process of creating a definition(s) for a concept that can be observed and measured
• The development of specific research procedures that will result in empirical observations - SES is defined as a combination of income and education and I will measure each by… - The development of questions (or characteristics of data in qualitative work) that will indicate a concept

Variable Attribute Choices

• Variable attributes need to be exhaustive and

exclusive

• Represent full range of possible variation
• Degree of Precision
  • selection depends on your research interest
• Is it better to include too much or too little?

Variables

• Qualitative Variable : Composed of categories which are not comparable in terms of magnitude
• Quantitative Variable: Can be ordered with respect to magnitude on some dimension
• Continuous Variable: A quantitative variable, which can be measured with an arbitrary degree of precision. Any two points on a scale of a continuous variable have an infinite number of values in between. It is generally measured.
• Discrete Variable: A quantitative variable where values can differ only by well-defined steps with no intermediate values possible. It is generally counted.

Level of Measurement

• Nominal
• Ordinal
• Interval
• Ratio

Ordinal Measures

• Variables with attributes that can be rank ordered
• Can say one response is more or less than another
• Distance between does not have meaning
  • lower class, middle and upper class
• Note: Scales and indexes are ordinal measures, but conventions for analysis allow us to assume equidistance between attributes (if it makes logical sense); treat them like “interval” measures; and subject them to statistical tests

Interval Measures

• Distance separating attributes has meaning and is

standardized (equidistant)

• “ 0 ” value does not mean a variable is not present
• Score on an ACT test 50 vs. 100
  • does not mean person is twice as smart

Hypotheses

• Hypotheses : (pg. 36) Untested statements

that specify a relationship between 2 or more

variables.

• Example: Milk Drinkers Make Better Lovers

Characteristics of a Hypothesis

• States a relationship between two or more variables
• Is stated affirmatively (not as a question)
• Can be tested with empirical evidence
• Most useful when it makes a comparison
• States how multiple variables are related
• Theory or underlying logic of the relationship makes sense