Maximization Scale

Abstract

The Maximization Scale (MAX), originally introduced by Barry Schwartz and colleagues in their seminal 2002 publication in the Journal of Personality and Social Psychology, is a foundational self-report psychometric instrument designed to assess individual differences in decision-making orientation along the continuum between maximizing and satisficing. Grounded theoretically in Herbert A. Simon’s bounded rationality framework, the construct differentiates between decision-makers who strive to attain the absolute best outcome (maximizers) and those who settle for an option that meets an acceptable threshold or criterion of adequacy (satisficers). The classic 13-item instrument measures three interrelated dimensions: Alternative Search (the compulsive tendency to continuously survey available options even when already content), Decision Difficulty (the psychological burden and cognitive friction experienced during choice selection), and High Standards (the rigid maintenance of exceptionally elevated aspirations and personal benchmarks).

Responses are recorded on a 7-point Likert scale ranging from 1 (“Completely Disagree”) to 7 (“Completely Agree”). Despite its theoretical appeal and broad adoption across judgment and decision-making, consumer psychology, organizational behavior, and mental health research, psychometric evaluations have highlighted structural complexities. While the initial operationalization treated maximization as a unidimensional or composite construct, subsequent structural equation modeling demonstrated that the instrument is multidimensional. Notably, Schwartz et al. (2002) observed internal consistency estimates around α = .71 for the full composite. Later investigations by Nenkov et al. (2008) refined the tool into a 6-item Short Maximization Scale (SMS) that preserved the tripartite structure with two items per facet, yielding superior confirmatory factor analytic fit indices and resolving structural artifacts associated with the original 13-item measure.

Empirically, elevated maximization scores are reliably correlated with adverse psychological correlates, including increased post-decisional regret, heightened depressive symptomatology, lower life satisfaction, diminished subjective well-being, upward social comparison orientation, and perfectionism. This article offers an exhaustive review of the Maximization Scale, detailing its theoretical antecedents, psychometric architecture, construct validity, structural controversies, empirical adaptations, and administration protocols.

Keywords

Maximization Scale, Maximizing, Satisficing, Barry Schwartz, Decision Difficulty, Alternative Search, High Standards, Bounded Rationality, Decision-Making, Choice Overload, Regret, Psychometrics

Authors

The Maximization Scale was conceptualized and developed by a collaborative research team led by Barry Schwartz. The original author group comprises:

• Barry Schwartz, Ph.D. – Department of Psychology, Swarthmore College, Swarthmore, Pennsylvania, United States; currently Emeritus Professor of Social Theory and Social Action at Swarthmore College and 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 Pennsylvania, Philadelphia, Pennsylvania; currently Associate Professor of Psychology at the 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. – Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada; currently Professor of Marketing and Behavioural Science at the Sauder School of Business, University of British Columbia.
• Darrin R. Lehman, Ph.D. – Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada.

Significant subsequent psychometric refinement was conducted by Gerry Kalaritis Nenkov (Boston College), J. Maureen Inman (University of Pittsburgh), Roland T. Rust (University of Maryland), and Deborah Roedder John (University of Minnesota) in 2008.

Purpose

The primary purpose of the Maximization Scale is to quantify an individual’s dispositional tendency to maximize rather than satisfice across diverse decision domains. In modern post-industrial societies characterized by unprecedented market proliferation, technological advancement, and information abundance, human agents are regularly confronted with massive assortments of goods, career pathways, romantic possibilities, and lifestyle trajectories. Classical neoclassical economic theory posits that an expansion of choices monotonically increases human welfare by raising the likelihood that each consumer or economic agent finds an option that closely matches their preference function.

However, psychological research spearheaded by Schwartz and his colleagues challenged this axiom, arguing that an overabundance of options induces a phenomenon known as the “paradox of choice.” When choices proliferate, the cognitive, affective, and computational burdens placed upon the decision-maker increase exponentially. The Maximization Scale was constructed to address the fundamental research question: Do individual differences in the pursuit of the absolute optimum versus an adequate outcome moderate the psychological and emotional consequences of choice?

Beyond theoretical inquiry in behavioral economics and judgment and decision-making (JDM), the scale has substantial practical and applied utility across multiple fields:

• Clinical and Counseling Psychology: Maximization functions as a cognitive vulnerability factor. Clinicians utilize the scale to understand patterns of chronic indecisiveness, pathological rumination, decision-related paralysis (aboulomania or analysis paralysis), and maladaptive perfectionism. Maximizers frequently present with elevated existential anxiety, fear of missing out (FoMO), and persistent post-choice regret, which directly contribute to depressive episodes.
• Consumer Behavior and Marketing: Market researchers apply the scale to segment consumers based on browsing persistence, information search breadth, brand loyalty, choice deferral, and post-purchase cognitive dissonance. Maximizers systematically conduct exhaustive product comparisons across retail platforms, rely heavily on algorithmic reviews, yet report systematically lower satisfaction with their final acquisitions.
• Organizational Psychology and Career Counseling: The instrument predicts job search behaviors, workplace satisfaction, and employee turnover intentions. Research demonstrates that graduating college students who score high on maximization secure positions with starting salaries roughly 20% higher than their satisficing peers; paradoxically, however, these maximizing graduates report significantly lower satisfaction with the jobs they obtain and higher negative affect throughout the recruitment pipeline.
• Interpersonal Relationships: In relationship science, the scale is deployed to investigate relationship commitment, modern digital dating platform behaviors, and divorce vulnerability, identifying how the continuous search for a counterfactual “better partner” undermines relationship stability and intimacy.

Psychological Construct

The psychological construct assessed by the Maximization Scale is operationalized as a chronic, domain-general decision-making orientation characterized by the relentless pursuit of perfection or optimality. Grounded in behavioral science, maximization reflects a goal-setting and option-evaluating strategy where the decision-maker cannot rest until they are confident that no superior alternative exists. This overarching construct is comprised of three core psychological dimensions, each reflecting unique cognitive, behavioral, and motivational manifestations.

1. Alternative Search

Alternative Search represents the behavioral dimension of maximization. It captures an individual’s persistent drive to survey, monitor, and investigate alternative possibilities, even when they are already experiencing satisfaction with their current choice or circumstance. A satisficer, upon identifying an alternative that clears their predefined threshold of acceptability, terminates the search process. In contrast, an individual elevated on Alternative Search perceives any unexamined alternative as a potential lost opportunity. In daily life, this is reflected in behaviors such as scanning television channels or streaming libraries while already viewing a compelling program, browsing job openings while happily employed, or checking social feeds to see what events others are attending. The cognitive bandwidth of these individuals is continuously drained by the scanning of external options, preventing psychological presence and deep engagement with the selected alternative.

2. Decision Difficulty

Decision Difficulty represents the affective and cognitive burden generated by the decision process. It encompasses the stress, mental fatigue, confusion, and paralysis experienced when forced to select among competing options. While High Standards dictates what one hopes to achieve and Alternative Search governs the exploration of the choice architecture, Decision Difficulty assesses the internal subjective distress that accompanies the act of choosing. Maximizers with high Decision Difficulty scores report agonizing over mundane purchases, experiencing profound apprehension over making an irreversible commitment, and feeling overwhelmed by comparative evaluations. This facet frequently correlates strongly with neuroticism, anxiety sensitivity, and obsessive-compulsive traits, marking the transition from an ambitious decision strategy into an emotionally draining and paralyzing ordeal.

3. High Standards

High Standards represents the aspirational or motivational benchmark dimension of the construct. It reflects the individual’s personal commitment to holding extraordinarily elevated expectations for outcomes, achievements, and products. Individuals with high scores on this dimension refuse to accept median quality, explicitly endorse the philosophy of never settling for “second best,” and demand peak performance from themselves and their environments. Notably, psychometric research has shown that High Standards frequently functions distinctively from Alternative Search and Decision Difficulty: while the latter two are robustly linked to negative affective outcomes such as depression, neuroticism, and regret, High Standards alone often correlates positively with conscientiousness, high self-efficacy, academic achievement, and optimistic goal orientation. This dimensional divergence has sparked extensive academic debates regarding whether High Standards belongs to the construct of maximization proper or represents an independent perfectionistic aspiration dimension.

Theoretical Framework

The foundational bedrock of the Maximization Scale is the behavioral economic theory of bounded rationality, formulated by Nobel laureate Herbert A. Simon (1955, 1956, 1957). Simon challenged the classical neoclassical economic paradigm of Homo economicus, which presumed that economic agents possess complete information, infinite computational capacity, perfectly ordered preference trees, and the unyielding objective of maximizing expected utility. Simon posited that human beings operate under severe biological, cognitive, computational, and environmental limitations. Real-world environments are far too complex, dynamic, and probabilistic for any biological organism to compute the absolute global maximum across all possible choice permutations.

Consequently, Simon introduced the concept of satisficing—a portmanteau of “satisfying” and “sufficing.” Under satisficing heuristics, an organism establishes an internal aspiration level or aspiration threshold based on its biological needs and prior learning. The organism then searches sequentially through options in its environment. The moment an option is encountered that meets or exceeds this aspiration threshold, the search behavior ceases, and the choice is executed. Satisficing eliminates the need for an exhaustive global search, dramatically reducing cognitive expenditure and computational strain.

Schwartz and colleagues (2002) synthesized Simon’s bounded rationality with modern social and cognitive psychological theories, including social comparison theory (Festinger, 1954), counterfactual thinking (Kahneman & Miller, 1986), and regret regulation theory (Zeelenberg & Pieters, 2007). Schwartz argued that while economic theory treats maximization as a normative prescription for rational choice, psychological reality turns maximization into an impossible, maladaptive ideal. In an expansive society where thousands of alternatives exist for nearly every category of life, the objective requirements for true maximization—gathering exhaustive information on all available options and evaluating them across all dimensions—are mathematically and cognitively impossible.

Because maximizers cannot exhaustively assess every option, they are perpetually vulnerable to cognitive vulnerabilities:

1. Counterfactual Comparison: Because maximizers know unchosen alternatives exist, they continuously generate counterfactual scenarios (“What if alternative X had been better?”). This constant upward counterfactual generation breeds counterfactual regret, diminishing the subjective hedonic value of the chosen item regardless of its objective quality.
2. Social Comparison: In the absence of an objective global metric for what constitutes the “absolute best,” maximizers rely disproportionately on external social reference points. They continuously compare their status, possessions, and accomplishments to upward social targets, fostering envy, relative deprivation, and diminished self-worth.
3. Opportunity Cost Negativity: Every option chosen necessitates the sacrifice of features present in discarded options. Maximizers psychologically aggregate the positive features of all foregone alternatives, creating a chimeric, ideal competitor against which their actual selection inevitably pales.

Validity

The construct, convergent, discriminant, and criterion-related validity of the Maximization Scale have been extensively documented in an international body of empirical literature spanning over two decades.

Convergent Validity

The convergent validity of the Maximization Scale was established in Schwartz et al.’s (2002) original multi-sample validation studies. Maximization demonstrated strong positive correlations with the 5-item Regret Scale developed concurrently (r = .40 to .50, p < .001). Maximizers exhibited substantial convergent associations with measures of perfectionism (Frost Multidimensional Perfectionism Scale, r = .25 to .42), upward social comparison orientation (Gibbons and Buunk Iowa-Netherlands Comparison Orientation Measure, r = .35 to .48), and depression (Beck Depression Inventory, r = .23 to .34). Individuals scoring high on the scale also show significant positive associations with neuroticism on the NEO Personality Inventory (r ≈ .25 to .35) and with trait rumination (r ≈ .38).

Discriminant Validity

Discriminant validity has been demonstrated by examining maximization against general cognitive ability, need for cognition, and optimism. Schwartz et al. (2002) reported negative correlations between maximization and subjective well-being measures, including the Satisfaction with Life Scale (SWLS; r = -.25, p < .01) and Lyubomirsky and Lepper’s Subjective Happiness Scale (SHS; r = -.40, p < .001). Discriminant validity analyses indicate that maximization is not redundant with generalized intelligence or academic aptitude (SAT scores show nonsignificant correlations near zero, r = -.04 to .06), demonstrating that maximization is a distinct decision-making style rather than a proxy for cognitive capacity.

Predictive and Criterion Validity

The predictive validity of the scale has been corroborated across laboratory behavioral paradigms and longitudinal field studies:

• Laboratory Decision Paradigms: Schwartz et al. (2002) conducted experimental games (e.g., the Ultimatum Game) and consumer simulations, finding that maximizers spent significantly more time reviewing options, made more comparisons, were more likely to check consumer rating guides, and exhibited greater post-decision regret and self-blame following sub-optimal outcomes.
• Longitudinal Career Studies: Iyengar, Wells, and Schwartz (2006) tracked 548 college seniors seeking employment across a multi-month recruitment window. Maximization scores predicted aggressive job search behaviors (submitting more applications, interviewing with more firms). Although maximizers secured starting salaries averaging $7,430 higher (an approximate 20% premium over satisficers), they reported significantly lower satisfaction with their secured employment, experienced greater negative affect during the search process, and expressed more post-decision rumination.
• Consumer Choice Overload: Consumer choice experiments show that maximizers exhibit greater choice deferral (opting not to choose), purchase abortion, and post-purchase dissatisfaction when choice assortments increase from small (6 items) to large (24 or 30 items).

Reliability

Psychometric evaluations across diverse geographic populations, demographic groups, and research designs have yielded comprehensive data on the reliability of the Maximization Scale and its shortened iterations.

Internal Consistency

In the original developmental investigations by Schwartz et al. (2002), the full 13-item Maximization Scale displayed modest internal consistency across undergraduate and adult community samples:

• Schwartz et al. (2002): The composite 13-item instrument produced a Cronbach’s alpha of α = .71 in Sample 1 (N = 219 college students), α = .70 in Sample 2 (N = 405 community adults), and α = .72 in Sample 3. While acceptable for early-stage exploratory research, these values fall on the lower threshold of psychometric adequacy for a 13-item measure.
• Subscale Reliabilities: When decomposed into its constituent dimensions, internal consistency estimates for the 13-item scale frequently reveal unevenness. In Nenkov et al.’s (2008) multi-sample examination (aggregate N > 2,000), internal consistency coefficients were: Alternative Search (α = .63 to .70), Decision Difficulty (α = .65 to .75), and High Standards (α = .60 to .73).
• Short Maximization Scale (SMS, 6 Items): Nenkov et al. (2008) addressed scale length and item attenuation, selecting two high-loading items per dimension. Across four validation samples, the 6-item SMS achieved overall internal consistency comparable to or exceeding the original 13-item tool (α ranging from .70 to .78), while each 2-item subscale demonstrated inter-item Pearson correlations ranging from r = .40 to .62 (p < .001).

Test-Retest Reliability

Temporal stability estimates indicate that maximization behaves as a stable personality trait over time. Diab, Gillespie, and Highhouse (2008) documented a 4-week test-retest reliability coefficient of r = .81 (p < .001) for the composite scale. Longer-term prospective designs spanning 6 to 12 months in post-graduate cohorts have confirmed test-retest coefficients exceeding r = .72, demonstrating acceptable stability across adulthood.

Factor Analysis

The latent structure of the original 13-item Maximization Scale has been the subject of extensive psychometric re-analysis. Schwartz et al. (2002) initially treated maximization as a composite univariate construct based on an initial exploratory factor analysis (EFA). However, subsequent rigorous psychometric evaluations using Confirmatory Factor Analysis (CFA) have demonstrated that a single-factor model exhibits unacceptable fit, confirming that maximization is inherently multidimensional.

Exploratory and Confirmatory Factor Structures

Nenkov et al. (2008) conducted comprehensive CFA investigations across four independent samples to evaluate competing structural models of the 13-item instrument:

• One-Factor Model: The single-factor model, where all 13 items load onto a unified maximization factor, demonstrated very poor fit across all samples: Comparative Fit Index (CFI) < .80; Tucker-Lewis Index (TLI) < .75; Root Mean Square Error of Approximation (RMSEA) > .09; and Standardized Root Mean Square Residual (SRMR) > .08.
• Three-Factor Correlated Model: An EFA with oblimin rotation and subsequent CFA revealed three distinct, correlated factors corresponding to Alternative Search (Items 1, 2, 3, 4, 5, 13), Decision Difficulty (Items 6, 7, 8, 10), and High Standards (Items 9, 11, 12). While the three-factor model fit significantly better than the one-factor model (Δχ² test, p < .001), the 13-item structure still exhibited marginal overall fit indices (CFI ≈ .86 to .89; RMSEA ≈ .07 to .08). Several items displayed significant cross-loadings or poor factor loadings (λ < .40), most notably Item 5 (“I treat relationships like clothing…”) and Item 13 (“I often fantasize about living in ways that are quite different…”).

The 6-Item Short Form (Nenkov et al., 2008)

To establish a psychometrically robust, parsimonious measure, Nenkov et al. (2008) isolated the two highest-loading, conceptually pure items for each dimension:

• Alternative Search: Item 2 (λ = .67 to .78) and Item 3 (λ = .64 to .75)
• Decision Difficulty: Item 7 (λ = .72 to .84) and Item 8 (λ = .70 to .82)
• High Standards: Item 11 (λ = .74 to .85) and Item 12 (λ = .71 to .81)

CFA fit statistics for this 6-item tripartite model demonstrated excellent fit across multiple diverse cohorts: χ²(6) = 14.82, p = .022; CFI = .985; TLI = .962; RMSEA = .039 (90% CI [.014, .065]); SRMR = .024. Furthermore, multigroup CFA demonstrated full metric and scalar invariance across sex and age groups, making the 6-item short form the psychometrically preferred structural representation of the original instrument.

Instrument / Measurement Tool

The technical parameters, administration details, and structural specifications of the Maximization Scale are structured as follows:

• Test Name: Maximization Scale (MAX); often accompanied by the 5-item Regret Scale (RS).
• Alternate Short Form: Short Maximization Scale (SMS; Nenkov et al., 2008).
• Target Population: Adolescents and adults (ages 16 and older); adaptable to clinical, organizational, and general community populations.
• Administration Format: Self-administered pencil-and-paper questionnaire or computer-based digital survey (Qualtrics, REDCap, Gorilla).
• Completion Time: Approximately 3 to 5 minutes for the 13-item version; 1 to 2 minutes for the 6-item short form.
• Item Count: 13 items in the standard version; 6 items in the short form.
• Response Format: 7-point Likert scale:
  • 1 = Completely Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Completely Agree
• Subscale Composition (13-Item Version):
  • Alternative Search: Items 1, 2, 3, 4, 5, 13
  • Decision Difficulty: Items 6, 7, 8, 10
  • High Standards: Items 9, 11, 12
• Subscale Composition (6-Item SMS):
  • Alternative Search: Items 2, 3
  • Decision Difficulty: Items 7, 8
  • High Standards: Items 11, 12
• Scoring Instructions:
  • None of the items on the standard 13-item scale or the 6-item short form are reverse-coded.
  • Composite Score: Calculate the mean or sum across all items. On the 13-item scale, total sum scores range from 13 to 91, with higher scores reflecting stronger maximization orientation. If using mean scoring, scores range from 1.0 to 7.0.
  • Subscale Scores: Calculate the mean of the items comprising each subscale to retain the 1–7 metric, facilitating direct comparisons across dimensions of unequal item length.
  • Satisficer vs. Maximizer Categorization: While continuous score analysis is psychometrically recommended, earlier studies historically utilized median splits or top/bottom tertile divisions (e.g., scores > 4.75 or > 5.0 categorized as maximizers; scores < 3.75 or < 4.0 categorized as satisficers). Contemporary psychometricians advise against artificial dichotomization due to loss of statistical power and inflation of Type I error rates.

Permissions & Fee and Test Year

The Maximization Scale was published in 2002 by Barry Schwartz, Andrew Ward, John Monterosso, Sonja Lyubomirsky, Katherine White, and Darrin R. Lehman in the Journal of Personality and Social Psychology, an official publication of the American Psychological Association (APA). The refined Short Maximization Scale was published in 2008 in Marketing Letters by Gerry Kalaritis Nenkov, J. Maureen Inman, Roland T. Rust, and Deborah Roedder John.

Licensing and Fee Structure: The scale items were published directly within the original 2002 and 2008 scientific journal articles for academic research and educational evaluation. Non-commercial scholarly use, educational instruction, and academic research investigations do not require royalty payments or licensing fees, provided that standard APA scholarly attribution is given to the authors and original journal sources. Commercial applications, integration within proprietary clinical assessment batteries, for-profit recruitment testing platforms, or commercial software distributions require formal permission and licensing clearance from the copyright holder (the American Psychological Association or Springer Nature).

References

Diab, D. L., Gillespie, J. Z., & 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/S193029750000042X

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202

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

Kahneman, D., & Miller, D. T. (1986). Norm theory: Comparing reality to its alternatives. Psychological Review, 93(2), 136–153. https://doi.org/10.1037/0033-295X.93.2.136

Nenkov, G. Y., Inman, J. M., Rust, R. T., & John, D. R. (2008). A short form of the Maximization Scale: Factor structure, reliability and validity studies. Marketing Letters, 19(2), 157–169. https://doi.org/10.1007/s11002-008-9032-9

Schwartz, B. (2004). The paradox of choice: Why more is less. Ecco/HarperCollins Publishers. https://en.wikipedia.org/wiki/The_Paradox_of_Choice

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

Zeelenberg, M., & Pieters, R. (2007). A theory of regret regulation 1.0. Journal of Consumer Psychology, 17(1), 3–18. https://doi.org/10.1207/s15327663jcp1701_3

Items of the Scale