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Research Questions and Experimental Design

Jun 16, 2024

Research Questions and Experimental Design

Four Types of Research Questions

  1. Making an Estimate

    • Example: Estimating the average age of statistics students in California.
    • Method: Take a sample of students.
  2. Testing a Claim

    • Example: Testing if the average age of statistics students in California is greater than 19 when research shows it is exactly 19.
    • Method: Hypothesis testing to verify the claim.
  3. Comparing Two Populations

    • Example: Comparing statistics success rates between students at Napa Valley College and Solano College.
    • Method: Estimate or test the differences between the populations.
  4. Investigating a Relationship

    • Example: Relationship between the number of hours watching study videos and course grades.
    • Method: Analyze the correlation between variables.

Components of Research Questions

  1. Population

    • Example: Napa Valley College students.
  2. Variable

    • Example: Attendance status (full-time or not).
  3. Numerical Characteristic

    • Example: Proportion or percentage of students attending full-time.

Identifying Research Question Components

  • Example Question: Do the majority of Napa Valley College students attend full-time?
    • Population: Napa Valley College students.
    • Variable: Attendance status (full-time or not).
    • Numerical Characteristic: Percentage of full-time students.

Observations vs. Experiments

Cause-and-Effect Relationships

  • Explanatory Variable: Variable that is changed to measure its effects.
  • Response Variable: Result of the change in the explanatory variable.

Types of Studies

  • Observational Study

    • No variables are manipulated.
    • Example: Recording hours of video watched without altering study habits.
    • Keywords: Connected, associated, correlated, likelihood.
  • Experimental Study

    • Variables are manipulated to observe effects.
    • Example: Testing the effect of different cooking temperatures on turkey doneness.

Explanatory vs. Response Variables

  • Explanatory Variable: Variable assumed to cause an effect (e.g., smoking).
  • Response Variable: Outcome being measured (e.g., cancer development).

Tips for Identifying Variables

  • Consider the logical direction of causation (e.g., Does smoking cause cancer? Does weight affect blood pressure?).
  • Focus on what is being measured.

Confounding and Lurking Variables

  • Confounding Variable: Variable whose effects cannot be separated from the explanatory variable.
    • Example: Sunlight in a fertilizer experiment.
  • Lurking Variable: Variable not accounted for in the study that still affects the response variable.
    • Example: Moisture level in a plant growth study.