Notes on Psychodynamic, Humanistic, Scientific Method, Correlation, and Causation

Psychodynamic, Humanistic, and Social-Contextual Approaches

• Psychodynamic approach (Freud): focuses on unconscious conflicts and drives.
  • Dreams as signs of unconscious impulses and conflicts that drive behavior.
  • Therapeutic method: talk therapy to reveal unconscious material; dreams may reflect unconscious issues.
• Humanistic approach: emphasizes growth, personal agency, and free will.
  • Not primarily driven by unconscious conflicts.
  • Behavior is influenced by both the individual and the social/contextual factors around them.
  • Focus on personal growth, decision-making, and the interaction between person and context; considers the broader social environment and relationships with others, institutions, etc.
• Social-contextual/sociocultural approach: stresses the social context and interactions with the world.
  • Behavior and thought emerge from ongoing feedback loops between the person and their environment.
  • Contexts include family, peers, institutions, culture, and social norms.
• Key contrast and takeaway:
  • Psychodynamic = internal unconscious conflicts.
  • Humanistic = growth and autonomy within context.
  • Social-contextual = bidirectional influence between person and environment.

The Five Steps in the Scientific Method (as discussed)

• Step 1: Theory development
  • Science uses a theory: a set of related ideas/concepts about mental processes and/or behavior that connect and explain phenomena and make predictions.
  • In psychology, theories explain mental processes and behaviors and generate testable predictions.
• Step 2: Form a hypothesis
  • A hypothesis is a specific, testable prediction derived from the theory.
• Step 3: Observing/Identifying phenomena for testing
  • Use observational or correlational methods to examine relationships between variables.
• Step 4: Data collection and numerical expression of relationships
  • Example topic: correlational study between two variables.
  • Express association with a correlation coefficient: r.
  • The correlation coefficient measures the strength and direction of a linear relationship.
• Step 5: Evaluate and interpret results; consider causality
  • Correlation is not causation; results lead to evaluation of the theory and consideration of causation via experimental methods.

Correlation Studies: Interpretation, Examples, and Limitations

• What the correlation coefficient r expresses:
  • Direction: the sign of r indicates positive or negative association.
  • Strength: the absolute value |r| indicates how strong the association is.
  • Range: -1 \le r \le 1.
  • Formula for Pearson correlation (illustrative):
    r = \frac{\operatorname{cov}(X,Y)}{\sigmaX \ \sigmaY}
• Example framework: parental behavior and child behavior
  • Variable X: parental harshness/strictness
  • Variable Y: child rebellious behavior
  • Positive correlation example: higher harshness associated with higher rebellion.
  • Negative correlation example: higher harshness associated with lower rebellion (hypothetical alternative scenario).
  • No correlation example: no systematic association between parental harshness and rebellion.
• Why correlation does not imply causation
  • Core issue: correlation alone does not reveal the causal direction or underlying mechanism.
  • Third-variable problem: a third variable may influence both X and Y, creating a spurious association.
  • Directionality problem: even if X and Y are related, it’s unclear whether X causes Y, Y causes X, or a third factor affects both.
  • Example: stress could simultaneously increase parental harshness and child behavior problems, making stress the potential third variable.
• Third-variable example discussed in class
  • Third variable hypothesis: stress influences both parenting style (harshness) and child rebellion, creating an observed correlation between harshness and rebellion.
  • Without ruling out third variables, causation cannot be established.
• Summary of what a correlational study can and cannot do
  • Can identify associations and quantify them with r.
  • Cannot determine causation or direction of causality on its own; requires experimental manipulation to infer causation.

Moving from Correlation to Causation: Experimental Studies

• Why experimental studies are used to argue for causation
  • Control over variables: manipulate an independent variable (IV) and observe its effect on a dependent variable (DV).
  • Random assignment: reduce preexisting differences between groups, limiting confounds.
  • Control conditions: compare to a baseline or alternative condition to isolate the effect of the IV.
• Key components of an experimental design
  • Independent Variable (IV): the variable deliberately manipulated by the experimenter.
  • Dependent Variable (DV): the variable measured to assess the effect of the IV.
  • Random assignment: participants are allocated to conditions by chance to ensure equivalence.
  • Control group: a baseline condition used for comparison.
• How experiments address causality
  • If manipulation of the IV leads to systematic differences in the DV while controlling for other factors, this supports causal inference.
  • Demonstrates that changes in the IV precede changes in the DV under controlled conditions.
• Quick illustrative example (hypothetical)
  • IV: level of parental warmth vs. harshness (manipulated through intervention or scripted scenarios).
  • DV: child rebellious behavior measured after exposure.
  • If increased warmth (vs. harshness) leads to reduced rebellion in the DV, with random assignment and controls, this supports a causal interpretation.

Connections, Implications, and Real-World Relevance

• Integrating approaches
  • Psychodynamic insights can inform understanding of deep-seated motivations and conflicts.
  • Humanistic perspectives highlight personal agency and growth potential within social contexts.
  • Sociocultural and contextual factors remind us to consider environments, institutions, and relationships when interpreting behavior.
• Practical implications
  • Therapeutic approaches may combine insights about unconscious processes, personal growth, and contextual factors.
  • Educational and policy interventions can target both individual factors (skills, resilience) and context (family, schools, communities).
• Ethical and philosophical considerations
  • Causality in human behavior is complex; overinterpreting correlations can mislead policy and therapy.
  • Emphasis on agency and context requires respect for autonomy, cultural variation, and social justice considerations.
• Formulas and numerical references to remember
  • Correlation coefficient range: -1 \le r \le 1
  • Sign of r indicates direction; absolute value |r| indicates strength.
  • Pearson correlation (illustrative): r = \dfrac{\operatorname{cov}(X,Y)}{\sigmaX \sigmaY}
• Quick recap of key differences
  • Psychodynamic: unconscious conflicts, dream interpretation, talk therapy.
  • Humanistic: growth, free will, personal responsibility, context-aware.
  • Sociocultural/Contextual: bidirectional influence with environment and social systems.
  • Scientific method: theory → hypothesis → observation/correlational testing → data analysis (r) → experimental testing for causation.