Philosophy of Education in a Poor Historical Moment: A Personal Account

Under the post-metaphysical sky “old” humanistic-oriented education is possible solely at the cost of its transformation into its negative, into a power that is determined to diminish human potentials for self-exaltation. Nothing less than total metamorphosis is needed to rescue the core of humanistic genesis: the quest for edifying Life and resistance to the call for “home-returning” into the total harmony that is promised to us within nothingness.     

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model

A three-level piecewise growth model (3L-PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L-PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals (level 2) who are members of a single cluster (level 3). However, in longitudinal research, it is sometimes difficult for data structures to remain purely clustered during a study, such as when some students change classrooms or schools over time. One resulting data structure in this situation is known as a multiple membership structure, where some lower-level units are members of more than one higher-level unit. The new multiple membership PGM (MM-PGM) extends the 3L-PGM to handle multiple membership data structures frequently found in the social sciences. This study sought to examine the consequences of ignoring individual mobility across clusters when estimating a 3L-PGM in comparison to estimating a MM-PGM. MM-PGM estimates were less biased (especially in the cluster-level coefficient estimates), although we found substantial bias in cluster-level variance components across some conditions for both models.     

Psychometric Properties and Factor Structure of the French Version of the Behavioral Activation for Depression Scale (BADS) in Non-Clinical Adults

Behavioral activation (BA) is a well-established empirical treatment for depression that aims to improve depressive mood by increasing activation and reducing avoidance. Therefore, it is essential to evaluate activation and avoidance when a BA treatment is applied. The Behavioral Activation for Depression Scale (BADS) was developed to measure the changes in activation and avoidance over the course of BA treatment of depression. This study aims to validate the French version of this scale. In a first study, 131 bilingual adults were recruited to explored internal consistency, test-retest reliability and construct validity of the final French version. In a second study, 409 non-clinical adults completed an online survey assessing concurrent measures. Results of the first study suggested good internal consistency, test-retest reliability and construct validity. The second study revealed a confirmatory factor analysis supporting the original four-factor structure, with Activation, Avoidance/Rumination, Work/School Impairment, and Social Impairment subscales. Results also revealed that a 5-factor model distinguishing Behavioral Avoidance and Rumination had a better fit than the original four-factor structure. All subscales showed adequate internal consistency and good construct validity with evidence of convergent validity with depressive symptoms, brooding, psychological flexibility, negative automatic thought, behavioral inhibition and activation system. Furthermore, the French BADS total scale and subscales showed a good ability to predict depressive symptoms. The French version of the BADS appears to be a reliable tool for clinician and researchers to assess mechanisms of change in BA interventions.