A Meta-Analysis of the Effects of Mental Contrasting With Implementation Intentions on Goal Attainment

Abstract

Mental contrasting with implementation intentions (MCII) is a self-regulation strategy that enhances goal attainment. This meta-analysis evaluated the efficacy of MCII for goal attainment and explored potential moderators. A total of 21 empirical studies with 24 independent effect sizes (15,907 participants) were included in the analysis. Results showed that MCII to be effective for goal attainment with a small to medium effect size (g = 0.336). The effect was mainly moderated by intervention style. Specifically, studies with interventions based on interactions between participants and experimenters (g = 0.465) had stronger effects than studies with interventions based on interactions between participants and documents (g = 0.277). The results revealed that MCII is a brief and effective strategy for goal attainment with a small to moderate effect; however, because of some publication bias, the actual effect sizes may be smaller. Due to small number of studies in this meta-analysis, additional studies are needed to determine the role of moderator variables.

Goal motives and mental contrasting with implementation intentions facilitate strategic goal persistence and disengagement

“…MCII has been widely used as an intervention to encourage goal persistence in a variety of applied settings (for reviews, see Cross & Sheffield, 2019; Wang et al, 2021). On the practical side, we develop and test an MCII protocol for encouraging strategic goal pursuit through persistence, disengagement, and reengagement.…”

“…We derived β coefficient estimates for the relations among goal motives, appraisals, coping strategies, and persistence from Ntoumanis, Healy, Sedikides, Duda, et al (2014). We based β coefficient estimates for the effect of MCII and its interaction with controlled goal motives on meta‐analytic data showing small to medium effect sizes for MCII interventions (Wang et al, 2021). We extracted variance parameter estimates by simulating data with the estimated β coefficients in R. The projected sample size required to detect the hypothesized effects in this model with 80% power at α = .05 was N = 260.…”

“…Similarly to Experiment 1, we derived β coefficient estimates for the power analysis from primary (Ntoumanis, Healy, Sedikides, Smith, et al, 2014) and meta‐analytic (Wang et al, 2021) research. We extracted variance parameter estimates by simulating data with the estimated β coefficients.…”

“…To detect significant interactions with these coefficients would have required an unfeasibly large sample ( N > 5000). A second power analysis mirroring the procedure of Experiments 1 and 2 (i.e., using coefficients derived from Ntoumanis, Healy, Sedikides, Duda, et al, 2014; Ntoumanis, Healy, Sedikides, Smith, et al, 2014; Wang et al, 2021) indicated that a sample size of 430 participants would provide at minimum 87% power for all hypothesized effects, including the interactions. Hence, we expected that an N = 430 would entail adequate statistical power to detect meaningful effects.…”

“…MCII has been implemented in several applied contexts, such as emotion regulation (Schweiger et al, 2018), increasing hours of study for individuals in higher education (Clark et al, 2020), collaborative problem solving (Kirk et al, 2013), and encouraging healthy behavior (Adriaanse et al, 2010; Cross & Sheffield, 2019; Mutter et al, 2020). Wang et al (2021) meta‐analyzed 21 studies using MCII and found a small to medium effect size ( g = 0.336) for improving goal attainment. They also obtained evidence of publication bias in the literature, which highlights the need to publish sound research on MCII irrespective of the statistical significance of the identified effects.…”