Notes on Research Methods: Methods of Knowing, Scientific Method, Research Process, Variables, Measurement, Scales, and Hypotheses

Methods of Knowing

  • There are 5 methods of knowing to answer: How do we know what we know?
    • Tenacity: Info is accepted because it has been around for a long time or due to superstition.
    • Examples: opposites attract, you can catch a cold by going out in the cold.
    • Intuition: Info is accepted due to a hunch or gut feeling.
    • Examples: gambling, walking down a dark alley.
    • Authority: Info is accepted because it comes from an expert or person of status/power.
    • Examples: movie critic, celebrity.
    • Faith: Info is blindly accepted because of trust, without challenging it; a variant of the authority method.
    • Examples: young children’s reliance on parents, religious beliefs.
    • Empirical: Info is accepted because it comes from direct observation or the senses.
    • Required for the scientific method.
    • Examples: directly seeing, tasting.

The Scientific Method

  • Science is characterized by its method, not the subject matter.
  • Scientific method elements:
    • Empirical: Information can be observed.
    • Objective: The researcher’s biases and beliefs do not influence observations.
    • Public: Observations can be evaluated by others.
  • Replication: A study can be repeated by others.
  • Pseudoscience: Disguised as science and lacks 3 elements of science.
    • Example: psychic readings.

The Research Process

  • The process comprises 10 steps:
    1. Research idea (based on literature search)
    2. Form a hypothesis
    3. Operationalize your variables
    4. Select population/participants
    5. Research strategy (what type of study)
    6. Research design (how you will collect data)
    7. Do the data (collect data)
    8. Evaluate the data (check for outliers/errors and analyze)
    9. Report
    10. Repeat with more refined research questions

Variable and Concepts

  • What is a variable?
    • A variable is a characteristic or condition that has different values for different individuals.
    • Examples: political party affiliation, gender, happiness, IQ score, attractiveness, self-esteem.

3 Components of a Variable

1) Variable name (concept/construct): the label, e.g., intelligence, altruism
2) Conceptual definition: the meaning, often from dictionary or accepted definitions.

  • Intelligence: “cognitive capacity”
  • Altruism: “unselfish concern for the welfare of others”
    3) Operational definition: how the variable is measured; procedures, operations, instruments, and units needed to produce and measure a concept.
  • Intelligence: how fast a 3-D puzzle task is completed
  • Altruism: answering “yes” to the question,
    "Did you give money to a charity in the past year?"

Measurement Modalities

  • Ways of operationally defining a variable: 1) Self-report indicator: Survey/Interview
    • Example: Participant answers “yes” to the question "Are you intoxicated with alcohol?"
      2) Physiological indicator: Biological assessments
    • Example: Heart rate/Blood pressure; e.g., achieving a blood alcohol concentration of 0.15\% or higher on a breathalyzer
      3) Behavioral indicator: Direct observation
    • Example: counting and coding drunken behaviors at a bar

Scales of Measurement

  • Nominal: categories with different names
    • Examples: Race (White, Latino, Asian, Black), Gender (male, female)
  • Ordinal: rank-ordered
    • Examples: Marathon place (1^{st}, 2^{nd}, 3^{rd}, etc), Cap size (small, medium, large)
  • Interval: intervals of equal width, with no true zero point
    • Example: Fahrenheit temperature scale
  • Ratio: intervals of equal width and a true zero point
    • Examples: Time spent studying (minutes), Number of sexual partners

Hypothesis

  • Definition: A predictive statement that describes or explains a relation between two variables.
  • Complementary concepts:
    • Research question example: Does practicing a sport using imagery lead to the same outcomes as practice in physical space?
    • Hypothesis example: Practice via imagery will lead to less measurable improvement in tennis performance than physical practice.

A Good Hypothesis

  • Logical: The statement should make sense within theory or prior knowledge.
    • Logical but weak example: Reduction in class size leads to higher GPA.
    • Not logical example: Higher GPA leads to reduction in class size.

A Good Hypothesis: Testable

  • Definition: Variables can be observed and measured.
  • Testable example: Drug is effective on dogs.
  • Not testable example: Drug is effective on space aliens.

A Good Hypothesis: Refutable (Falsifiable)

  • Definition: Data may or may not support the hypothesis.
  • Refutable example: Praying will speed up recovery after surgery.
  • Not refutable example: Praying will send you to heaven.

A Good Hypothesis: Positive vs Negative

  • Positive (affirmatively stated):
    • Example: Tutoring increases academic grades.
  • Negative (no relationship found):
    • Example: No relationship is found between tutoring and academic grades.

Connections and Implications (Foundational and Real-World)

  • Emphasizes the need for empirical verification and replication to establish knowledge.
  • Highlights the risk of accepting information from non-empirical sources (Tenacity, Intuition, Authority, Faith).
  • Demonstrates how clear definitions and operationalization are essential for measurement and comparison across studies.
  • Underlines the importance of falsifiability in scientific inquiry and the practical implications for education, psychology, and health research.
  • Ethical and philosophical notes:
    • Distinguishing science from pseudoscience protects decision-making from unfounded claims.
    • Transparent methods enable peer evaluation and public scrutiny.
    • Clear hypotheses guide ethical considerations around data collection and interpretation.

Quick Reference: Key Terms to Memorize

  • 5 methods of knowing
  • Empirical method requires direct observation and senses
  • Replication is essential for verification
  • Pseudoscience lacks one or more elements of science
  • Variables have 3 components: name, conceptual definition, operational definition
  • Measurement modalities: self-report, physiological, behavioral
  • Scales: nominal, ordinal, interval, ratio
  • Hypothesis should be logical, testable, falsifiable, and clearly positive or negative