Person-centered analytic approaches (e.g., latent profile analysis, cluster analysis) have been offered as a potential solution to measurement issues associated with the Five Facet Mindfulness Questionnaire (FFMQ). Yet, extant literature utilizing person-centered approaches reveals a lack of consistency in the identified mindfulness profiles, especially in non-college samples. The present study tested the generalizability of FFMQ profiles in an adult life span, community sample using latent profile analysis and cluster analysis. Furthermore, the study explored whether mindfulness profiles related to age and well-being.
Age-diverse participants (N = 715) recruited through Amazon’s Mechanical Turk completed the FFMQ and numerous measures of well-being.
Cluster analysis revealed four mindfulness profiles: (1) high mindfulness, (2) low mindfulness, (3) judgmentally observing, and (4) nonjudgmentally aware. Latent profile analysis indicated four profiles, but only two profiles resembled profiles resulting from the cluster analysis, and two of the profiles comprised less than 9% of the sample combined. Using profiles identified by cluster analysis, older age was associated with increased likelihood of classification into a high mindfulness profile and decreased likelihood of classification into a low mindfulness profile. Furthermore, the high mindfulness profile showed the best well-being and the low mindfulness profile showed the worst.
Overall, these findings demonstrate that the type of analytic method and sample characteristics, such as age, may affect the makeup of resulting mindfulness profiles. Implications for the state of this literature are discussed.