We conducted an analysis of the 13-item Maximization Scale (Schwartz et al., 2002) with the goal of establishing its factor structure, reliability and validity. We also investigated the psychometric properties of several proposed refined versions of the scale. Four sets of analyses are reported. The first analysis confirms the 3-part factor structure of the scale and assesses its reliability. The second analysis identifies those items that do not perform well on the basis of internal, external, and judgmental criteria, and develops three shorter versions of the scale. In the third analysis, the three refined versions of the scale are cross-validated to confirm dimensionality, reliability, and validity. The fourth analysis uses an experiment in an investment decision making context to assess the reliability and nomological validity of the refined scales. These analyses lead us to conclude that a shorter, 6-item Maximization Scale performs best and should be used by future researchers. It is hoped that clarification of the conceptual underpinnings of the maximization construct and development of a refined scale will enhance its use among researchers across several of the social science disciplines.

Building on Herbert Simon’s critique of rational choice theory, Schwartz et al. (2002) proposed that when making choices, some individuals — maximizers — search extensively through many alternatives with the goal of making the best choice, whereas others — satisficers — search only until they identify an option that meets their standards, which they then choose. They developed the Maximization Scale (MS) to measure individual differences in maximization, and a substantial amount of research has now examined maximization using the MS, painting a picture of maximizers that is generally negative. Recently, however, several researchers have criticized the MS, and almost a dozen new measures of maximization have now been published, resulting in a befuddling and contradictory literature. We seek to clarify the confusing literature on the measurement of maximization to help make sense of the existing findings and to facilitate future research. We begin by briefly summarizing the understanding of maximizers that has emerged through research using Schwartz et al.’s MS. We then review the literature on the measurement of maximization, attempting to identify the similarities and differences among the 11 published measures of maximization. Next, we propose a two-component model of maximization, outlining our view of how maximization should be conceptualized and measured. Our model posits that maximization is best understood as the pursuit of the maximization goal of choosing the best option through the maximization strategy of alternative search; other constructs such as decision difficulty and regret are best considered outcomes or causes — rather than components — of maximization. We discuss the implications of our review and model for research on maximization, highlighting what we see as pressing unanswered questions and important directions for future investigations.

Despite their efforts to choose the best of all available solutions, maximizers seem to be more inclined than satisficers to regret their choices and to experience post-decisional dissonance. Maximizers may therefore be expected to change their decisions more frequently and hence exhibit lower customer loyalty to providers of products and services compared to satisficers. Findings from the study reported here (N = 1978) support this prediction. Maximizers reported significantly higher intentions to switch to another service provider (television provider) than satisficers. Maximizers’ intentions to switch appear to be intensified and mediated by higher proneness to regret, increased desire to discuss relevant choices with others, higher levels of perceived knowledge of alternatives, and higher ego involvement in the end product, compared to satisficers. Opportunities for future research are suggested.

This article presents several studies that replicate and extend previous research on maximizing. A modified scale for measuring individual maximizing tendency is introduced. The scale has adequate psychometric properties and reflects maximizers’ aspirations for high standards and their preference for extensive alternative search, but not the decision difficulty aspect included in several previous studies. Based on this scale, maximizing is positively correlated with optimism, need for cognition, desire for consistency, risk aversion, intrinsic motivation, self-efficacy and perceived workload, whereas the association with regret is inconsistent. Analysis of correlates of the difficulty dimension suggests that decision difficulty should be conceptualized as a separate dimension rather than as a sub-dimension of maximizing. Opportunities for future research are suggested.

We examined choice behavior in younger versus older adults using a medical decision-making task similar to Medicare Part D. The study was designed to assess age differences in choice processes in general and specifically designed to examine the effect of choice set size on performance. Data are drawn from a larger study on choice and aging, in which ninety-six younger adults (ages 18–64) and 96 older adults (ages 65–91) selected a prescription drug plan from either 6 or 24 different options. As hypothesized, choice set size was a significant predictor of individuals’ ability to choose the best plan. Participants who were presented with 24 plans were less likely to choose the correct prescription drug plan. Age did not have a negative effect on decision performance; however numeracy and speed of processing significantly affected performance across groups. Older adults were more likely to be characterized as satisficers on a decision personality measure, but this categorization did not predict performance on the choice task.

This series of four studies was designed to clarify the underlying dimensionality and psychological well-being correlates of the major extant measures of the maximization tendency: the Maximization Scale (MS; Schwarz et al., 2002) and the Maximization Tendency Scale (MTS; Diab et al., 2008). Four studies using psychometric and factor analysis, item response theory (IRT), and an experimental manipulation all supported the following conclusions. The MS does measure three separate factors as postulated by its authors, but only two of them (alternative search and decisional difficulty) are correlated with each other and (negatively) with indices of well-being as postulated by the scale authors; high standards, the third factor, correlated strongly with the MTS, and both of these were strongly correlated with positive indices of well-being (optimism and happiness) and functioning (e.g., self-esteem and self-efficacy). The high standards subscale and MTS were related to analytical decision making style, while alternative search and decision difficulty were related to the regret-based decision making style and to procrastination. The IRT analysis indicated serious weaknesses in the measurement capabilities of existing scales, and the findings of the experimental study confirmed that alternative search and decision difficulty are related to the maximization tendency while high standards and MTS are not.Implications for further research and scale development are discussed.

We present the Maximization Inventory, which consists of three separate scales: decision difficulty, alternative search, and satisficing. We show that the items of the Maximization Inventory have much better psychometric properties when compared to the original Maximization Scale (Schwartz et al., 2002). The satisficing scale is a new addition to the study of maximization behavior, and we demonstrate that this scale is positively correlated with positive adaptation, whereas the decision difficulty and alternative search scales are positively correlated with nonproductive decisional behavior. The Maximization Inventory was then compared to previous maximization scales and, while the decision difficulty and alternative search scales are positively correlated with similar previous constructs, the satisficing scale offers a dimension entirely different from maximization.

There is a lively debate about the effect of maximizing and satisficing tendencies on well-being. The question is, whether maximizing and satisficing have an adaptive or maladaptive effect on well-being. There are also issues regarding the conceptualization and measurement of maximizing and satisficing tendencies. In a sample of 514 subjects from the general population in Slovakia, a two-component model of maximizing was examined. Satisficing tendency was measured as a separate construct. The results show the usefulness of a two-component model (maximizing as a strategy and maximizing as a goal) in measuring maximizing tendency. Maximizing as a strategy (measured as alternative search) turned out to be maladaptive (positively related to depression and negatively related to happiness), whereas maximizing as a goal (measured as high standards) had no maladaptive effect (no relation with well-being). In addition, the two components were differently associated with personality factors, which strengthens the need to distinguish between them. However, the satisficing tendency measured separately from maximizing tendency was not related to anything which raises a question about the conceptualization and validity of this tendency. The results of the current study, therefore, indicate that the (mal)adaptive effect of these tendencies depends on their conceptualization as well as on how these tendencies are measured, and also on their different relationship with personality factors. However, results also point to the importance of considering the cultural context that may have an effect on the relationship between maximizing and well-being. Therefore, the results may vary due to different cultures.

Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Contrary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables.

There is now a significant body of literature on consequentialist ethics that propose satisficing instead of maximizing accounts. Even though epistemology recently witnessed a widespread discussion of teleological and consequentialist theories, a satisficing account is surprisingly not developed yet. The aim of this paper is to do just that. The rough idea is that epistemic rules are justified if and only if they satisfice the epistemic good, i.e., reach some threshold of epistemic value (which varies with practical context), and believing is justified if and only if it follows said rules.

I argue that this alternative to the implicitly established way of thinking in maximizing terms has significant advantages. First, maximizing epistemic value can be unreasonably demanding; second, a satisficing theory can make finding reasonable rules for belief formation and sustenance much more accessible; and third, a satisficing approach is a better alternative to both general subjectivist and maximizing objectivist attempts to spell out epistemic blame.

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase nonstrategic misreporting. We provide the first empirical assessment of this trade-off between strategic and nonstrategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents’ true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of nonstrategic reporting errors. For both list experiments, partition validation reveals nonstrategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.

Political scientists rely heavily on survey research to gain insights into public attitudes and behaviors. Over the past decade, survey data collection has moved away from personal face-to-face and telephone interviewing towards a model of computer-assisted self-interviewing. A hallmark of many online surveys is the prominent display of the survey’s sponsor, most often an academic institution, in the initial consent form and/or on the survey website itself. It is an open question whether these displays of academic survey sponsorship could increase total survey error. We measure the extent to which sponsorship (by a university or marketing firm) affects data quality, including satisficing behavior, demand characteristics, and socially desirable responding. In addition, we examine whether sponsor effects vary depending on the participant’s experience with online surveys. Overall, we find no evidence that response quality is affected by survey sponsor or by past survey experience.