Expressions of Dispositions — Vocabulary Flashcards
Key Concepts
- Cross-situational consistency: the assumption that personality traits predict behavior across different situations, not just within a single context.
- Empirical tests challenged this assumption: Hartshorne & May (1928) provided early cross-situational data on honesty.
- Mischel’s 1968 challenge argued that correlations between personality measures and specific behaviors were consistently low (r < .3), raising questions about the stability of personality across situations.
- Contemporary responses propose that small but persistent effects can be meaningful when aggregated, that behavior should be studied via aggregation, density distributions of states (DDS), and behavioral signatures (If…then profiles).
- Two types of stability in personality: (1) mean trait level differences (between-person differences) and (2) if…then patterns (within-person patterning across situations).
- Traits (general tendencies) vs social-cognitive variables (goals, expectations, strategies) that contribute to behavioral signatures.
- Person × Situation (P × S) interactions: the idea that both the person and the situation influence each other rather than simply summing to behavior; the same situation may yield different behaviors for different people, and different situations may evoke different patterns for the same person.
- Behavioral signatures: stable, meaningful patterns of variability across situations that reflect an underlying personality system rather than random error.
Hartshorne & May (1928): An Empirical Test of Trait Consistency
- Focus: Trait-based honesty across multiple contexts in children (academic settings, classroom behavior, etc.).
- Research questions:
- What’s the correlation in honest behaviors across situations?
- Can we predict cheating on a test from other dishonest behaviors (e.g., lying to teacher, stealing, etc.)?
- Key findings:
- Situation-specific correlation was r = 0.79, indicating strong consistency within a given type of situation (e.g., cheating across similar contexts).
- Cross-situation correlation was r = 0.20, indicating substantial variation in honesty across different situations.
- Implications:
- There is meaningful cross-situational variability; strong cross-situational consistency is not the norm.
- The data demonstrated that honesty is not perfectly stable across contexts, challenging a simple, unitary trait model.
Mischel’s 1968 Challenge: The Personality Debate
- Core claim: Consistency in self-reports of personality is limited; self-/peer-reports and time-separated measures show modest correlations with specific behaviors (r < .3).
- Across-situations correlations are also < .3, echoing Hartshorne & May’s cross-situational finding.
- Implication: The classic view of stable, broad personality traits predicting behavior in all situations is problematic; behavior appears to be more situation-specific than widely assumed.
- Position: This sparked a crisis in trait-based theories to account for observed variability.
Contemporary Responses to the Crisis (Ways to Reconcile Findings)
- 1) Small but persistent effects are consequential:
- The claim is not that personality effects are large on a single occasion, but that they are ubiquitous across many occasions and, when aggregated at the population level, can be practically consequential. (Ozer & Benet-Martínez, 2006)
- 2) Aggregation:
- Single responses or one-off behaviors are noisy. Aggregate across multiple instances to obtain a more reliable measure of behavior.
- Principle of averaging: because noise is random, averaging across occasions reduces noise and yields a more true score.
- 3) Density Distribution of States (DDS):
- Traits predispose general tendencies, but behavior varies across states; individuals show a distribution of state manifestations across time and contexts.
- DDS captures the distribution of how often a person exhibits certain states (e.g., talkativeness) across time.
- 4) Behavioral Signatures (If…then… profiles):
- Variability across situations is meaningful, not just noise.
- People exhibit stable intraindividual variability, forming characteristic profiles that reflect the personality system and social-cognitive processes (goals, values, expectancies, strategies) activated in particular situations.
Small and Persistent is Consequential
- The effect of personality is ubiquitous and, when aggregated, has real-world consequences for behavior and life outcomes.
- Quotation (paraphrased): Personality effects are not necessarily large on a single occasion, but they accumulate to produce meaningful population-level patterns.
Compounding Effects: An Analogy for Aggregation
- Example: $1000 today at 7% interest, in 50 years.
- Formula: FV = PV(1 + r)^n
- Calculation: FV = 1000(1 + 0.07)^{50} \approx 3.31 \times 10^4
- Takeaway: Small, consistent effects, when aggregated over time (or across many occasions), can yield substantial differences.
Aggregation: Improving Reliability of Measurements
- Principle: Single responses are noisy; averaging across multiple observations reduces random error.
- Result: A more reliable average score correlates more strongly with other constructs and outcomes.
Density Distributions of States (DDS)
- Core idea: Traits predict average tendencies, but the actual distribution of states (how often someone is, e.g., talkative) varies around that mean.
- Evidence: Fleeson & Gallagher (2009) conducted a mega-analysis across 15 studies with Big-5 traits and experience sampling (multiple states per day, across days).
- Findings:
- DDS shows distinct distributions for Extraverts vs Introverts; the high- vs low-trait groups exhibit different means in their state distributions.
- Correlations between trait means and the mean of the DDS range approximately from r \approx 0.38 to r \approx 0.53.
- Visual intuition: The DDS plots show density curves for high vs low trait groups; the means differ, and there is considerable overlap, illustrating both trait-based predictability and within-person variability.
- For Extraverts (high on extraversion): the distribution of momentary talkativeness tends to be higher on average than for Introverts, but there is still variability within each group.
- For Introverts (low on extraversion): the distribution centers lower with overlap.
- The key point: Traits predict the location (mean) of the distribution, not the exact state on any given moment.
Within-Person Variability Across Individuals
- Important empirical pattern: People vary a lot from themselves across time; you can differ more from your own typical behavior than you differ from another person in a single snapshot.
- Across studies, more than half of the variability in behavior occurs within-person rather than between-person (i.e., intraindividual variability dominates).
- Sources citing this pattern include Fournier, Moskowitz, & Zuroff (2008); Miner, Glomb, & Hulin (2005); Rocke, Li & Smith (2009); Nesselroade & Salthouse (2004).
- Intra-individual variability is often 2–3 times larger than inter-individual variability (Minbashian, Wood, & Beckmann, 2009).
- Important interpretation: Much of the apparent variability is structured and meaningful (not random noise). It reflects changes in states and their regulation by context.
Behavioral Signatures: Explore Variability Across Situations
- Core idea: There are stable patterns of behavior across contexts that constitute a person’s signature, even if behaviors vary by situation.
- Wediko Study (Mischel, Shoda, et al.):
- Sample: 84 children (ages 6–11) observed over 6 weeks; 77 adult observers; ~167 hours of behavioral observation per child.
- Situations tracked: Interpersonal contexts (peer positive contact, peer teased/threatened), adult contexts (praised, warned, punished).
- Behaviors tracked: Prosocial talk, whine, comply, physical aggression, verbal aggression, etc.
- Findings: Example profiles show varying stability in behavior across situations; e.g., Child #9 had a high stability r = 0.89, while Child #28 had r = 0.49 for the same cross-situational mapping (situation → behavior).
- Interpretation: Some individuals exhibit strong, consistent if…then patterns, while others show more context-sensitive profiles.
P × S Interactions: Interdependence of Person and Situation
- The traditional view of a simple additive model B = P + S is inadequate.
- Key idea: The effect of the person depends on the situation, and the effect of the situation depends on the person.
- Examples:
- If in a game, Child 5 cheats; if in an exam, they do not cheat.
- Child 8 may cheat in some contexts but not in others; different children respond differently to the same situation.
- Conclusion: Personality and situational factors interact to shape behavior; not all people react the same way to a given situation.
Behavioral Signatures: From Error to Essence
- Traditional view treated cross-situational differences as error in the measurement of a stable trait.
- Mischel & Shoda (1995) reframed this: cross-situational variability can be a meaningful part of personality, constituting a behavioral signature that reflects the latent system of traits and social-cognitive processes.
Two Types of Stability (Recap)
- 1) Mean trait level differences (between-person stability): what you generally do on average.
- 2) If…then patterns (within-person stability): how you behave in a given situation relative to the general tendency.
Conclusion and Implications
- The Hartshorne & May findings and Mischel’s critique opened a debate about cross-situational consistency and the nature of personality.
- What remains robust:
- Traits do predict general tendencies (typical levels) with moderate effects (often r > .4 in some meta-analytic summaries).
- There is substantial within-person variability, but this variability is not pure noise; it forms stable behavioral signatures related to individuals’ goals, values, expectations, abilities, and strategies.
- A modern view of adult personality: it is a system that includes traits plus dynamic social-cognitive processes and situationally activated strategies; behavior is best understood through P × S interactions and the distribution of states across time (DDS).