Variables and Measurement in Research

Chapter 5: Variables and Measurement in Research

Steps in Research Process

  • Step 1: Choosing a research question
  • Step 2: Conducting a literature review
  • Step 3: Developing a hypothesis
  • Step 4: Designing the study
  • Step 5: Conducting the study
  • Step 6: Analyzing the data
  • Step 7: Reporting the results

Dependent Variables

  • Definition: A variable that is measured in a study.
  • Can be operationally defined in various ways.

Scales of Measurement for Dependent Variables

  1. Nominal Scales

    • Simplest scale of measurement
    • Nonordered categorical responses
    • Qualitative data
    • Examples:
      • What is your gender? (M/F)
      • What is your hair color? (Brown, Black, Blonde, Gray, Other)

  2. Ordinal Scales

    • Involves order but not necessarily equal spacing
    • Qualitative data
    • Examples:
      • Rankings (1st, 2nd, 3rd)
      • Ratings (Best, Medium, Worst)

  3. Interval Scales

    • Scores are equally spaced but not ratios of one another
    • Examples:
      • IQ scores, temperatures in Celsius, PH levels, Likert scales without a true zero.
    • Key Point: 20C is not twice as hot as 10C (no true zero).

  4. Ratio Scales

    • Continuous scales with a natural zero value
    • Scores express ratios of one another
    • Examples:
      • Kelvin scale (e.g., 100K is twice as hot as 50K)
      • # of recalled words, # of divorces, # of drinks per week
      • Likert scale with a zero.

Reliability of Measurements and Response Scales

  • Affects the reliability of data.
  • Types of reliability:
    • Inter-rater/inter-observer reliability: Measure of agreement between different observers or raters.
    • Test/Retest Reliability: Consistency of scores when the same test is repeated (e.g., IQ, GRE).

Independent Variables

  • Definition: A variable manipulated by the researcher to see its effect on the dependent variable.

  • Types of Manipulations:

    1. Presence/Absence Variable
      • Also called bivalent IVs, simplest form.
    2. Type Variable
      • Involves different types of treatments.
    3. Amount Variable
      • Multivalent variable with three or more levels.
    • Examples:
      • Presence of drug vs. absence of drug (bivalent).
      • Different drug types or amounts (multivalent).

Validity and Sources of Bias

  • Internal Validity: Ensures that the study is measuring what it is intended to.
    • Threats to Internal Validity:
      • Order effects, experimenter bias, social desirability
  • External Validity: The applicability of study results to wider populations and scenarios
    • Example: The Hawthorne Effect where participants change behavior due to observation.

Practice Questions

  • Improve internal and external validity in the following examples:
    1. Eyewitness Study: Assessing if confidence indicates accuracy after witnessing a staged crime.
    2. Happiness and Income: Surveying participants on happiness ratings and income levels.
    3. Driving Study: Comparing near collision rates during day versus night driving.

Summary of Concepts

  • Variables:
    • Independent Variables vs. Dependent Variables
  • Scales of Measurement: Nominal, Ordinal, Interval, and Ratio
  • Validity: Importance of assessing internal and external validity
  • Threats to Validity:
    • Group differences, order effects, testing effects, regression to the mean, experimenter bias, social desirability, attrition/mortality, Hawthorne effect.