Comprehensive Notes: Chest Pain Anatomy and Psychology 204 — Dual Process Model, RWA/SDO, and Multiple Regression

Medical discussion: Chest pain and related anatomy

• Heart pain sensing and referred pain

  • The heart pain sensing is not very exquisite; most heart pain is referred, meaning it’s felt away from the actual site (e.g., in the arm or shoulder).
  • When pain is felt in the shoulder area, it tends to indicate a pericardial problem (pericarditis or irritation of the pericardium).
  • Chest pains felt on the inside of the arm usually imply something wrong with the heart itself, which is not a good sign.
  • If someone touches the chest and experiences pain, that could be a prelude to a heart attack, but not a definitive diagnosis.
  • Practical advice given: if you’re experiencing chest pains, it’s important to get checked out rather than assuming it’s nothing.

• Personal anecdote and differential diagnoses related to shoulder/lung symptoms

  • A student mentions running with their dad and noticing shoulder pain; the instructor notes this could be pericardial pain, pleural irritation, or lung-related problems.
  • If someone is breathing heavily or the lungs are inflamed (pleura rubbing), the nerves can be confused with the dermatomes of the shoulder, leading to referred-like shoulder pain.

• Pulmonary vessels labeling discussion (pulmonary veins vs. pulmonary arteries)

  • There is a quick class exercise about labeling pulmonary vessels on a diagram.
  • Student asks: “Would I be correct in saying these are your pulmonary veins?”
  • Instructor responds (in sequence):
  • “Yeah.”
  • “Sweet. So those are the pulmonary arteries?”
  • “Yep.”
  • The exchange illustrates common confusion between pulmonary veins and arteries and the need to correctly identify which vessels are which on diagrams.
  • Note: The conversation highlights that one label may correspond to pulmonary veins and another to pulmonary arteries, demonstrating anatomy labeling challenges in a teaching context.

• Audio check and course intro (brief procedural notes)

  • A quick audio check is performed to ensure the audience can hear from the back rows.
  • The rest of the segment transitions to a course introduction (Psych 204) by Chris, the course coordinator and lecturer on ideology and prejudice.

• Quick wrap-up of the medical segment

  • Acknowledgement of the practical relevance of understanding chest pain symptoms and their differential diagnoses for patient safety.

Psychology 204: Ideology and prejudice — regression, theory, and interpretation of statistics

• Course introduction and context

  • Chris, course coordinator and professor of social psychology, will teach the next block on ideology and prejudice.
  • The block focuses on statistical analysis as a foundation for testing theories about prejudice.
  • The material will cover predicting prejudice toward different groups, and the statistical modeling behind testing those predictions.
  • The block is designed as a stepping stone to doing research, not just repeating textbook assertions.
  • Readings related to the assignment will be emphasized; some questions (e.g., multiple-choice) are based on readings rather than lectures.

• Learning objectives

  • Understand and describe threat-based and competition-based processes proposed by the dual process model of prejudice.
  • Describe the sequence of causal effects in the model for predicting individual differences in prejudice.
  • Explain contexts and targets for two key predictors: right-wing authoritarianism (RWA) and social dominance orientation (SDO), and how they predict prejudice separately or jointly.
  • Describe and interpret different research designs used to test the dual process model.
  • Describe and interpret results from a multiple regression analysis.
  • Recognize that content may be over-assessed due to scope of the course, and understand the importance of readings for comprehensive understanding.
  • Appreciate how lectures complement Eden’s (sexual prejudice) block as foundational for the assignment.

• Core terms and definitions (foundational concepts introduced at the start)

  • Dangerous worldview: view that the social world is dangerous, threatening, and unpredictable, as opposed to safe, secure, stable, and predictable.
  • Competitive welfare: a measure of how much people view the social environment as a ruthless competition in which the strong win and the weak lose; related to zero-sum thinking in economic/game-theory terms.
  • Right-Wing Authoritarianism (RWA): traditionally defined as social attitudes that favor coercive social control, obedience and respect for existing authorities, and conformity to traditional moral/religious norms; in the dual process model, RWA expresses threat-driven values for collective security, control, stability, and order.
  • Social Dominance Orientation (SDO): social attitudes favoring group dominance and inequality; in the dual process model, SDO reflects competition-driven values for power and hierarchy.
  • Generalized prejudice: the overall tendency to be negative toward almost all outgroups, minorities, and disadvantaged groups; prejudice tends to generalize across domains (strong correlations across many racial/ethnic groups) though not equally across all domains (e.g., less consistently with religious prejudice or attitudes toward sexual minorities).
  • Dual process model (background): explains how threat and competition drive different pathways (biases) for prejudice through RWA and SDO.
  • Founders and key figures: Bob Altemeyer (RWA), Jim Sidanius and Felicia Pratto (SDO), John Duckitt (dual process model).

• The dual process model, RWA, and SDO in context

  • RWA is linked to threat-driven motives for security, order, and conformity under authoritative structures.
  • SDO is linked to competition-driven motives for dominance, hierarchy, and inequality.
  • Together, RWA and SDO predict prejudice across contexts and groups; they can be additive or interact, depending on the context.
  • The model helps explain why people vary in prejudice across individuals and situations.

• General framing about research design and readings

  • Readings for the course complement lectures; the upcoming midterm and final may include questions not fully covered in lectures, requiring engagement with readings.
  • The three-week structure of readings is considered heavy relative to typical introductory content.

• Introduction to multiple regression (the statistical backbone of the course’s empirical work)

  • Purpose: to predict an outcome score from a set of predictor variables (and to control for other variables to isolate unique effects).
  • Basic regression idea: adding predictors can improve prediction beyond the mean; this is the essence of regression as a building block for predictive modeling.

• Conceptual example: vitality in a romantic partner (regression illustration)

  • A scale measures vitality/attractiveness desirability in a partner, using items such as adventurous, nice body, outgoing, sexy, attractive, good lover.
  • Scale of measurement: each item rated on a 1–7 scale; the mean is computed (example dataset mean provided is 4.64).
  • Distribution assumption: in the absence of more information, one would expect a normal distribution around the mean (bell curve), making the mean a naive prediction for everyone.
  • Regression adds information by including other variables that relate to the outcome, thereby allowing better predictions for individuals.

• Simple regression example (one predictor)

  • Outcome: desired vitality in a romantic partner (on a 1–7 scale).
  • Predictor: openness to experience (a Big Five personality trait).
  • Basic idea: test whether openness predicts desired vitality beyond the mean.
  • Visual metaphor: R^2 represents the overlap between predicted values and actual outcomes (variance explained by the predictor).
  • In the example: R^2 ≈ 0.02 (2% variance explained) — a very small overlap, meaning the model explains little of the variance; the rest (~98%) is unexplained.
  • Model significance: assessed with an F-test; if p < .05, the model is considered significant relative to predicting the mean; in the example, p ≈ 0.004, indicating statistical significance despite a small effect size.
  • Practical takeaway: statistical significance does not imply a strong or practically important effect.

• Interpreting regression output (key components)

  • Intercept (β0): the predicted outcome when all predictors are zero; in practice, for predictors on a limited scale (like 1–7), the intercept may have limited practical meaning.
  • Unstandardized beta (β1): the change in the outcome for a one-unit increase in the predictor; e.g., if openness to experience is on a 1–7 scale and β1 = -0.195, a one-unit increase in openness predicts a 0.195 unit decrease in the vitality-desire score.
  • Standardized beta (β*: beta coefficient in standard deviation units): scales predictors to make them comparable; ranges roughly from -1 to 1 and allows comparison of effect sizes across predictors. For a simple model, the standardized beta equals the Pearson correlation between predictor and outcome.
  • P-values: indicate statistical significance for the predictor's effect in the model; in the example, p ≈ 0.004 for openness, meaning the predictor is significantly related to the outcome when controlling for the model.
  • Standard error (SE) and t-value: provide the precision of the beta estimates and the test statistic for significance.
  • R^2 (coefficient of determination): proportion of variance in the outcome explained by the model; in the one-predictor example, R^2 ≈ 0.02.

• Interpreting a one-predictor example (openness → vitality desirability)

  • Unstandardized β1 = -0.195: each one-unit increase in openness predicts a 0.195 decrease in the vitality-desire score (on the 1–7 scale).
  • Standardized β* ≈ -0.143: a small-to-moderate negative effect in standard deviation units (rough guideline: -0.1 to 0.1 = small; -0.3 ≈ medium; -0.5 or more = large).
  • Significance: p = 0.004 (significant) despite a small effect size; highlights the distinction between statistical significance and practical significance.

• Extending to multiple predictors (adding extroversion and gender)

  • Adding predictors typically increases R^2, i.e., explains more variance.
  • In the extended model, R^2 ≈ 0.1079 (10.79% of the variance in desired vitality explained by openness, extroversion, and gender).
  • Updated standardized betas:
  • Openness: β* ≈ -0.153 (still negative and significant, shows unique predictive value after accounting for extroversion and gender).
  • Extroversion: β* ≈ 0.144 (positive association; more extroverted individuals rate vitality as more important).
  • Gender: β* ≈ 0.245 (strong positive effect; coded gender as female = 2 and male = 1 — indicates women rate vitality as more important than men in this sample).
  • Unstandardized betas (β):
  • The unstandardized β for gender is ≈ 0.711; in standardized units this corresponds to β* ≈ 0.245.
  • Interpretation of gender effect in this NZ undergraduate sample: women rated vitality/attractiveness as more important than men, which contradicts some traditional evolutionary predictions (which often forecast men to prioritize attractiveness and women to prioritize status/resources). The presenter suggests cultural moderation: university-educated young women may have direct access to status/resources, potentially shifting preferences toward attractiveness.
  • The model is still significant (p < .05) with the added predictors, and the effect of openness remains significant but attenuated when controlling for extroversion and gender.

• Conceptual takeaways about regression in this context

  • Regression allows testing whether a predictor remains associated with an outcome after controlling for other variables (i.e., testing unique effects).
  • The strength of a predictor is best compared via standardized betas when multiple predictors are present.
  • The presence of a significant predictor does not imply a large effect; real-world effects in psychology are often small.
  • There can be strong moderator effects (culture, context) that alter the size or even the direction of effects across different samples.
  • The constants and intercepts can be less meaningful when predictors have limited ranges (e.g., 1–7 scales).

• Practical implications for the assignment and research practice

  • Students will be given a printed output from a real regression analysis and will be asked to interpret it and write up a results section.
  • The emphasis will be on standardized betas for comparing predictor strengths and on understanding how controlling for additional variables changes estimates.
  • It’s important to distinguish statistical significance from practical significance and to consider potential cultural/contextual moderators when interpreting results.

• Final notes on the course trajectory

  • The next lecture (Thursday evening) will walk through the regression for the assignment in more depth and then introduce broader theory.
  • The block is designed to build toward understanding how to test theory with data and how to interpret statistical outputs in the context of ideology and prejudice.