Maximisation Scale

1. Abstract

The Maximisation Scale (MS), developed by Barry Schwartz and colleagues (2002), is a foundational psychometric instrument designed to assess individual differences in decision-making orientation, specifically contrasting the tendency to maximise—striving to identify and select the absolute best possible alternative—against the tendency to satisfice—settling for an option that meets an acceptable threshold of adequacy. Grounded in Herbert A. Simon’s seminal conceptualization of bounded rationality, the instrument operationalizes the psychological mechanisms underlying what Schwartz famously termed the “paradox of choice.” Comprising 13 self-report items evaluated on a 7-point Likert scale (ranging from 1 = Completely disagree to 7 = Completely agree), the scale captures three correlated latent dimensions: Alternative Search, Decision Difficulty, and High Standards.

Extensive psychometric investigations have revealed adequate internal consistency across diverse adult and student populations (overall scale Cronbach’s α typically ranging from .68 to .75). Although individuals scoring high on maximisation frequently attain objectively superior outcomes through exhaustive pre-decisional search and comparative evaluation, empirical studies demonstrate that they paradoxically experience diminished subjective wellbeing, characterized by elevated post-decisional regret, pervasive counterfactual rumination, decision fatigue, and vulnerability to depressive symptoms. This article delivers an exhaustive academic analysis of the Maximisation Scale, examining its theoretical lineage, latent factor dimensionality, construct validity, reliability parameters, administration protocol, and enduring legacy across behavioral economics, consumer psychology, and clinical decision research.

2. Keywords

Maximisation Scale, satisficing, bounded rationality, decision making, paradox of choice, regret, counterfactual thinking, consumer behavior, psychometrics, decision difficulty, alternative search, high standards

3. Authors

The Maximisation Scale was conceptualized, operationalized, and psychometrically validated by a collaborative team of researchers in personality, social psychology, and behavioral economics:

• Barry Schwartz, Ph.D. — Department of Psychology, Swarthmore College, Swarthmore, Pennsylvania, United States. (Emeritus Professor of Social Theory and Social Action; currently Visiting Professor at the Haas School of Business, University of California, Berkeley).
• Andrew Ward, Ph.D. — Department of Psychology, Swarthmore College, Swarthmore, Pennsylvania, United States.
• John Monterosso, Ph.D. — Department of Psychology, University of Southern California, Los Angeles, California, United States.
• Sonja Lyubomirsky, Ph.D. — Department of Psychology, University of California, Riverside, Riverside, California, United States.
• Katherine White, Ph.D. — Sauder School of Business, University of British Columbia, Vancouver, British Columbia, Canada.
• Darrin R. Lehman, Ph.D. — Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada.

Primary Citation: Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197. https://doi.org/10.1037/0022-3514.83.5.1178

4. Purpose

The overarching purpose of the Maximisation Scale (MS) is to quantify individual differences in decision-making orientation along the continuum between maximizing and satisficing. While classical economic frameworks, such as expected utility theory, operate under the axiomatic premise that rational economic agents inevitably strive to optimize every decision by identifying the choice that delivers maximum utility, human cognitive constraints render comprehensive optimization impossible in complex environments. Schwartz and colleagues designed the MS to transition satisficing from an abstract normative model of computational limitation into an empirically measurable individual-difference trait with substantive psychological and affective ramifications.

From an applied research perspective, the scale serves critical functions across diverse behavioral disciplines:

• Consumer Psychology and Behavioral Economics: The scale explains why an abundance of consumer choice can paralyze prospective buyers (the choice overload hypothesis) and how digital interfaces, recommendation engines, and algorithmic aggregators interact with trait maximisation to exacerbate decision-making latency and dissatisfaction.
• Clinical and Well-Being Research: Researchers utilize the MS to examine non-adaptive decision styles linked to perfectionism, rumination, depression, and generalized anxiety. The scale provides an explanatory bridge connecting cognitive evaluation strategies with chronic affective discontent and self-blame.
• Organizational Behavior and Career Trajectories: In organizational contexts, the MS illuminates vocational search patterns, showing that while maximizing job seekers often secure positions with higher starting salaries, they report lower subjective job satisfaction and higher turnover intentions due to persistent counterfactual speculation regarding unchosen opportunities.

In essence, the instrument was established not merely to measure how people decide, but to investigate the theoretical puzzle of why individuals who invest the greatest cognitive effort into achieving optimal outcomes frequently end up psychologically worse off than those who settle for “good enough.”

5. Psychological Construct

The psychological construct assessed by the Maximisation Scale is the individual disposition toward maximizing versus satisficing in choice environments. Maximizing represents an exhaustive behavioral and cognitive tendency to evaluate all available options, entertain hypothetical alternatives, and demand the superlative outcome. Satisficing, by contrast, denotes the pragmatic heuristic of establishing an internal threshold of acceptability and terminating the decision process as soon as an encounter matches or exceeds that benchmark.

Through psychometric exploration and subsequent structural analyses (e.g., Nenkov et al., 2008), the construct is understood to manifest across three interrelated dimensions:

1. Alternative Search

The Alternative Search facet reflects an obsessive, exhaustive exploration of existing and prospective alternatives prior to, during, and even following a decision. Individuals high in this dimension continuously scan their environment, engage in extensive information foraging, and manifest restlessness even when their current situation is objectively pleasant. For example, a driver high in alternative search will continuously scan car radio frequencies or streaming playlists despite currently listening to an engaging song, driven by the lingering belief that an even superior option exists elsewhere. Items 2, 3, 5, 11, 12, and 13 capture this propensity to explore options across diverse domains such as careers, relationships, daily entertainment, and personal consumption.

2. Decision Difficulty

The Decision Difficulty dimension measures the subjective burden, cognitive paralysis, and psychological distress experienced during choice selection. Faced with multiple attractive alternatives, maximizers experience profound decision fatigue, ambivalence, and dread regarding the opportunity costs inherent in choosing one option over another. This manifests behaviorally as prolonged deliberation times, avoidance of commitment, and struggle over routine selections, such as choosing a movie to watch, picking an item of clothing, or selecting a gift for a friend (captured by Items 4, 6, 7, and 8).

3. High Standards

The High Standards dimension operationalizes the respondent’s aspiration level—the refusal to accept mediocrity or “second best.” It reflects an intrinsic demand for perfection and superlative standards across personal performance and acquisition choices. Driven by an aspiration to achieve the absolute peak of utility, individuals scoring high on this dimension frequently rely on external benchmarks, curated lists, and expert ratings (e.g., “Best 10” lists) to validate that their choices represent the absolute zenith of quality (represented by Items 1, 9 [reversed], and 10).

6. Theoretical Framework

The theoretical framework of the Maximisation Scale integrates foundational tenets of Herbert Simon’s behavioral economics, prospect theory, and the social psychology of counterfactual thinking.

Bounded Rationality and Satisficing

In 1956, Herbert A. Simon challenged the neoclassical economic paradigm of Homo economicus—the fully rational decision-maker equipped with infinite computational capacity, perfect information, and invariant preference orders. Simon posited that human beings possess physiological and cognitive constraints (bounded rationality), rendering optimal calculation computationally intractable in real-world environments. Consequently, humans rely on satisficing: setting aspiration levels and terminating search heuristics upon reaching an alternative that is “good enough.” Schwartz et al. (2002) extended Simon’s model by postulating that satisficing is not merely a universal computational necessity, but an individual-difference trait that varies systematically across persons.

The Paradox of Choice

Schwartz combined this bounded-rationality framework with the realities of modern consumer culture, characterized by exponential increases in product variety, vocational fluidity, and personal freedom. While traditional economic theory posits that expanding choice sets monotonically increases welfare by maximizing the likelihood that an individual’s idiosyncratic preferences are met, Schwartz argued that beyond a modest threshold, choice abundance produces adverse psychological consequences: paralysis, elevated expectations, regret, and diminished self-esteem. When an individual attempts to maximize in an environment featuring dozens or hundreds of options, the cognitive demand escalates exponentially, culminating in decision fatigue and cognitive strain.

Counterfactual Thinking and Regret Theory

The affective deterioration experienced by maximizers is heavily mediated by counterfactual thinking—the cognitive process of imagining hypothetical alternatives to actual events (e.g., “If only I had selected Option B instead of Option A”). In large choice sets, every chosen feature is contrasted against the most attractive attributes of the rejected alternatives. Maximizers synthesize an idealized, composite alternative that does not exist in reality, ensuring that any real-world outcome inevitably pales in comparison. Consequently, maximizers experience heightened post-decisional regret and assign personal culpability to themselves for failing to achieve the imagined ideal.

7. Validity

The Maximisation Scale has undergone extensive psychometric evaluation, establishing robust convergent, discriminant, construct, and predictive validity across numerous empirical studies.

Construct and Convergent Validity

In their initial validation studies, Schwartz et al. (2002) administered the MS alongside an array of established personality and well-being inventories. Maximisation correlated positively and significantly with:

• Regret: Substantial positive correlation with the Regret Scale (r ≈ .40 to .50), affirming that the pursuit of perfection is deeply entangled with anticipation and experience of post-choice regret.
• Perfectionism: Positive associations with the Multidimensional Perfectionism Scale, specifically with maladaptive perfectionist dimensions such as concern over mistakes and doubts about actions.
• Depression: Statistically significant positive correlations with the Beck Depression Inventory (BDI; r = .34, p < .001).
• Neuroticism: Moderate positive correlation with trait neuroticism as indexed by five-factor inventories.

Conversely, the scale correlated negatively with measures of positive psychological functioning, including subjective happiness (Lyubomirsky’s Subjective Happiness Scale; r = -.25), life satisfaction (Diener’s Satisfaction with Life Scale; r = -.25), optimism (LOT-R; r = -.27), and self-esteem (Rosenberg Self-Esteem Scale; r = -.24).

Discriminant Validity

Discriminant validity was established by demonstrating that the MS measures a cognitive decision-making orientation distinct from standard cognitive ability, need for cognition, and general perfectionism. While perfectionists strive for flawlessness in their own performance across specific tasks, maximizers specifically struggle with choice sets and the comparative evaluation of external options. Furthermore, factor analyses demonstrate that the Maximisation Scale separates cleanly from scales measuring impulsivity, locus of control, and cognitive reflection.

Predictive Validity

The behavioral predictive validity of the MS is well documented. In experimental decision paradigms, participants scoring high on maximisation:

• Examine significantly more product alternatives and review more consumer ratings prior to making a purchase.
• Engage in elevated social comparison, constantly benchmarking their selections against those of peers.
• Experience greater post-purchase dissatisfaction and return merchandise more frequently despite selecting products with objectively higher specifications or lower price-to-performance ratios.
• In career transition studies (e.g., Iyengar, Wells, & Schwartz, 2006), graduating college maximizers secured jobs offering salaries that were an average of 20% higher than their satisficing peers, yet were significantly less satisfied with the jobs they accepted and experienced higher daily stress.

8. Reliability

Psychometric evaluations of the 13-item Maximisation Scale indicate acceptable to moderate internal consistency and test-retest stability, although the multifaceted nature of the construct has prompted continuous discussion regarding subscale reliability.

Internal Consistency

In the original validation studies conducted by Schwartz et al. (2002), the full 13-item composite scale exhibited a Cronbach’s alpha (α) of .71 across a normative sample of 1,833 college students and community adults. Subsequent international replications have reported full-scale alphas generally clustering between .68 and .75:

• Schwartz et al. (2002): Total Scale α = .71.
• Nenkov et al. (2008): Sample 1 (Undergraduates, N = 1,440) α = .71; Sample 2 (Adult consumers, N = 735) α = .70.
• Subscale reliabilities identified by Nenkov et al. (2008) in their structural evaluation: Alternative Search (α ≈ .65–.70), Decision Difficulty (α ≈ .67–.74), and High Standards (α ≈ .60–.66).

Test-Retest Stability

Longitudinal stability assessments indicate that maximisation operates as a relatively stable dispositional trait rather than a transient state. Test-retest reliability evaluations over intervals ranging from 4 to 12 weeks have yielded Pearson stability coefficients (r) between .72 and .81, confirming that individuals consistently approach decision domains with an enduring maximizing or satisficing orientation.

9. Factor Analysis

The internal dimensionality of the Maximisation Scale has served as a catalyst for rigorous psychometric debate. While originally treated by Schwartz et al. as a unidimensional composite index of maximizing tendency, subsequent exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) have firmly demonstrated that the instrument is multidimensional.

Exploratory Factor Analysis (EFA)

EFA employing principal axis factoring with oblique (Promax or Oblimin) rotation consistently reveals a three-factor solution that accounts for approximately 42% to 48% of the total variance:

• Factor 1 (Alternative Search): Explains the largest proportion of common variance, with primary loadings from items emphasizing ongoing exploration and refusal to close search horizons (Items 2, 3, 5, 11, 12, 13; loadings range from .45 to .72).
• Factor 2 (Decision Difficulty): Captures subjective paralysis, shopping distress, and selection struggle (Items 4, 6, 7, 8; factor loadings range from .52 to .78).
• Factor 3 (High Standards): Comprises items reflecting perfectionistic aspiration and preference for top-tier rankings (Items 1, 9 [reversed], 10; loadings range from .40 to .68).

Confirmatory Factor Analysis (CFA) and Model Fit

Nenkov, Morwitz, Schwartz, and Ward (2008) conducted extensive structural equation modeling across multiple independent samples to evaluate competing structural models. The original single-factor model demonstrated inadequate fit to the data (χ²/df > 4.5, RMSEA > .08, CFI < .80, TLI < .75). By contrast, a correlated three-factor model yielded vastly superior fit indices:

• Chi-Square / Degrees of Freedom: χ²/df ≈ 2.1–2.6.
• Root Mean Square Error of Approximation (RMSEA): .046 to .055 (90% CI [.039, .061]).
• Comparative Fit Index (CFI): .91 to .94.
• Tucker-Lewis Index (TLI): .89 to .92.
• Standardized Root Mean Square Residual (SRMR): .042 to .051.

Crucially, psychometricians have noted that the three subscales relate differently to well-being: Decision Difficulty and Alternative Search drive the negative associations with happiness, self-esteem, and life satisfaction, whereas High Standards frequently correlates positively or neutrally with academic achievement and subjective fulfillment. This finding prompted subsequent psychometric refinements in the field, including the 6-item short form (MS-S; Nenkov et al., 2008) and alternate instruments by Diab, Gillespie, and Highhouse (2008) and Turner et al. (2012).

10. Instrument / Measurement Tool

• Instrument Name: Maximisation Scale (MS) (alternatively spelled Maximization Scale)
• Authors: Barry Schwartz, Andrew Ward, John Monterosso, Sonja Lyubomirsky, Katherine White, and Darrin R. Lehman (2002)
• Target Population: Adolescents, university students, and adult populations across commercial, organizational, and clinical settings
• Format: Self-administered paper-and-pencil or digital self-report survey
• Number of Items: 13 items
• Response Scale: 7-point Likert scale (1 = Completely disagree, 2 = Disagree, 3 = Somewhat disagree, 4 = Neither agree nor disagree, 5 = Somewhat agree, 6 = Agree, 7 = Completely agree)
• Subscale Breakdown:
  • Alternative Search: Items 2, 3, 5, 11, 12, 13
  • Decision Difficulty: Items 4, 6, 7, 8
  • High Standards: Items 1, 9 (reversed), 10
• Reverse-Scoring Rules: Item 9 is reverse-scored (1 → 7, 2 → 6, 3 → 5, 4 → 4, 5 → 3, 6 → 2, 7 → 1).
• Scoring Procedure: The overall maximisation score is calculated either as the sum (ranging from 13 to 91) or the arithmetic mean (ranging from 1.0 to 7.0) of all 13 items after recoding Item 9. Subscale scores are derived by calculating the sum or mean of the corresponding item subsets. Higher scores denote a stronger disposition toward maximizing, whereas lower scores reflect a satisficing orientation.
• Administration Time: Approximately 3 to 5 minutes

11. Permissions & Fee and Test Year

The Maximisation Scale was originally published in 2002 in the Journal of Personality and Social Psychology, a journal published by the American Psychological Association (APA). In accordance with standard academic psychometric conventions, the instrument is available free of charge for non-commercial educational, scientific, and scholarly research purposes. Formal permission from the authors or the APA is generally not required for academic research when the publication is properly cited. However, commercial utilization, reproduction within assessment software for profit, or organizational consulting platforms requires formal licensing and copyright clearance through the American Psychological Association’s Permissions Desk.

12. References

• Diab, D. L., Gillespie, M. A., & Highhouse, S. (2008). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making, 3(5), 364–370. https://doi.org/10.1017/S193029750000088X
• Iyengar, S. S., Wells, R. E., & Schwartz, B. (2006). Doing better but feeling worse: Looking for the “best” job impairs satisfaction. Psychological Science, 17(2), 143–150. https://doi.org/10.1111/j.1467-9280.2006.01677.x
• Nenkov, G. Y., Morwitz, V. G., Schwartz, B., & Ward, A. (2008). A short form of the Maximization Scale: Factor structure, reliability and validity studies. Journal of Consumer Psychology, 18(4), 371–388. https://doi.org/10.1016/j.jcps.2008.09.009
• Schwartz, B. (2004). The paradox of choice: Why more is less. Ecco/HarperCollins Publishers.
• Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197. https://doi.org/10.1037/0022-3514.83.5.1178
• Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
• Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138. https://doi.org/10.1037/h0042769
• Turner, B. M., Rim, H. B., Betz, N. E., & Nygren, T. E. (2012). The maximization inventory. Judgment and Decision Making, 7(1), 48–60. https://doi.org/10.1017/S1930297500001550

13. Items of the Scale