Delay of gratification and adult outcomes: The Marshmallow Test does not reliably predict adult functioning

Sperber, J. F., et al. (2024).

Child Development.

https://doi.org/10.1111/cdev.14129

Abstract

This study extends the analytic approach conducted by Watts et al. (2018) to examine the long-term predictive validity of delay of gratification. Participants (n = 702; 83% White, 46% male) completed the Marshmallow Test at 54 months (1995–1996) and survey measures at age 26 (2017–2018). Using a preregistered analysis, Marshmallow Test performance was not strongly predictive of adult achievement, health, or behavior. Although modest bivariate associations were detected with educational attainment (r = .17) and body mass index (r = −.17), almost all regression-adjusted coefficients were nonsignificant. No clear pattern of moderation was detected between delay of gratification and either socioeconomic status or sex. Results indicate that Marshmallow Test performance does not reliably predict adult outcomes. The predictive and construct validity of the ability to delay of gratification are discussed.

A pdf of the research is here.

Here are some thoughts:

This study is part of the replication crisis in psychology. This study examined the long-term predictive validity of the Marshmallow Test, a widely used measure of delay of gratification. The test, administered to 702 participants at age 4, was found to have limited predictive power for adult outcomes, such as achievement, health, and behavior, at age 26. While modest associations were detected with educational attainment and body mass index, these correlations were largely explained by demographics and home life factors. The study's findings question the construct validity of the Marshmallow Test, suggesting it may not accurately measure delay of gratification. Instead, it may screen for broader developmental advantages in early childhood. These results have implications for intervention strategies, highlighting the need to focus on broader capacities for lasting impact.

Measuring Implicit Intergroup Biases.

Lai, C. K., & Wilson, M. 

(2020, December 9).

https://doi.org/10.1111/spc3.12573

Abstract

Implicit intergroup biases are automatically activated prejudices and stereotypes that may influence judgments of others on the basis of group membership. We review evidence on the measurement of implicit intergroup biases, finding: implicit intergroup biases reflect the personal and the cultural, implicit measures vary in reliability and validity, and implicit measures vary greatly in their prediction of explicit and behavioral outcomes due to theoretical and methodological moderators. We then discuss three challenges to the application of implicit intergroup biases to real‐world problems: (1) a lack of research on social groups of scientific and public interest, (2) developing implicit measures with diagnostic capabilities, and (3) resolving ongoing ambiguities in the relationship between implicit bias and behavior. Making progress on these issues will clarify the role of implicit intergroup biases in perpetuating inequality.

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Predictive Validity

Implicit intergroup biases are predictive of explicit biases,  behavioral outcomes,  and regional differences in inequality. 

Relationship to explicit prejudice & stereotypes. 

The relationship  between implicit and explicit measures of intergroup bias is consistently positive, but the size  of the relationship depends on the topic.  In a large-scale study of 57 attitudes (Nosek, 2005), the relationship between IAT scores and explicit intergroup attitudes was as high as r= .59 (Democrats vs. Republicans) and as low as r= .33 (European Americans vs. African Americans) or r = .10 (Thin people vs. Fat people). Generally, implicit-explicit relations are lower in studies on intergroup topics than in other topics (Cameron et al., 2012; Greenwald et al., 2009).The  strength  of  the  relationship  between  implicit  and explicit  intergroup  biases  is  moderated  by  factors which have been documented in one large-scale study and  several meta-analyses   (Cameron et al., 2012; Greenwald et al., 2009; Hofmann et al., 2005; Nosek, 2005; Oswald et al., 2013). Much of this work has focused  on  the  IAT,  finding  that  implicit-explicit  relations  are  stronger  when  the  attitude  is  more  strongly elaborated, perceived as distinct from other people, has a  bipolar  structure  (i.e.,  liking  for  one  group  implies disliking  of  the  other),  and  the  explicit  measure  assesses a relative preference rather than an absolute preference (Greenwald et al., 2009; Hofmann et al., 2005; Nosek, 2005).

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Note: If you are a healthcare professional, you need to be aware of these biases.