BS

Experimental Designs and Causal Claims

Research Goals and Claims

  • Describe behavior: Make a claim about frequency.
  • Example: How prevalent is depression?
  • Predict behavior: Make a claim about associations.
  • Example: Is depression related to spending time alone?
  • Explain behavior: Make a causal claim.
  • Example: Does social isolation make people depressed?

Elements of an Experiment

  • Independent Variable (IV): The variable that the experimenter manipulates.
  • e.g., Treatment Group, Control Group
  • Levels or Conditions: Must have at least two levels.
    • Example Levels: Low intensity, Medium intensity, High intensity.
  • Dependent Variable (DV): The variable that is measured.

Experimental Design Elements

  • Experimental Control: Holding all variables constant except for the IV to ensure a fair test.
  • Random Assignment: Participants are randomly assigned to any given condition, ensuring equal probability among conditions.

Criteria for Making Causal Claims

  1. Covariance: Cause and effect must co-occur.
  2. Temporal Precedence: The cause must precede the effect.
  3. Internal Validity: The experiment must control for third variables.

Establishing Covariance

  • Use a comparison group to address the question, "Compared to what?"

Establishing Temporal Precedence

  • The manipulation of the IV must occur before measuring the DV.

Establishing Internal Validity

  • Confounds: A variable that may systematically vary with the levels of the IV and affect the DV.
  • Noise Variables: Variables that do not systematically vary with the IV but can still affect the DV.

Selection Effects

  • Occurs when participant types differ systematically between conditions, often resulting from self-selection.

Random Assignment & Matched Groups Design

  • Random assignment turns potential confounds into noise variables.
  • Matched Groups Design: Pair participants based on a variable (e.g., GPA) and randomly assign them to groups.

Types of Experimental Designs

  • Independent-groups designs:
  • Posttest only
  • Pretest & posttest
  • Matched-groups design
  • Within-groups designs:
  • Repeated measures
  • Concurrent measures

Evaluating Causal Claims

  1. Internal Validity: Measures if the IV truly affects the DV.
  2. Statistical Validity: Considers sample size effect and power.
  3. External Validity: Questions generalizability to real-life scenarios.
  4. Construct Validity: Checks if the manipulation effectively changes the IV and accurately measures the DV.

Example Study - Latané and Darley (1969)

  • Aim: Study the bystander effect.
  • Method: 120 male participants completed a survey. Hear a recording of a female experimenter supposedly falling and needing help.
  • IV: Survey condition (alone or with a confederate)
  • DV: Whether participants helped or not.
  • Findings: 70% helped when alone; only 7% helped with a passive confederate.

Evaluation Questions

  • Identify IV, DV, and controls.
  • Type of study: Posttest only independent groups design.
  • Evaluating based on four validities:
  • Construct Validity: How well the IV and DV are measured.
  • External Validity: Generalizability beyond the sample.
  • Statistical Validity: Includes sample size and power analysis.
  • Internal Validity: Control of confounding variables.