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Are contextual rather than personal factors at the basis of an anti-school culture? A Bayesian analysis of differences in intelligence, overexcitability, and learning patterns between (former) lower and higher-track students

Abstract

Research indicates that educational stratification may lead to a lower-track school culture of futility and a less academically-oriented culture among lower-track teachers, leading to both reduced study involvement and lower educational achievement among their students. This study investigated whether an anti-school culture in the lower tracks [in this study, in technical secondary education (TSE; N = 132) in comparison with general secondary education (GSE; N = 356)] has a solid basis that is supported by personal, ontological differences in intelligence and developmental potential [i.e., overexcitability, according to the theory of positive disintegration (TPD)]. In addition, this study examined the consistency of these results with differences in mathematical and verbal achievement, the use of cognitive processing and metacognitive regulation strategies, and study motivation, as well as differences in the influence of personal competence indicators on the learning approach, all suggesting contextual, educational influences. A Bayesian analysis was applied to address the problem of a frequentist approach in complex statistical models. This study does not primarily reveal competence differences between both tracks (as indicated by no substantive differences in overexcitability and intelligence between respectively former GSE and TSE students and GSE and TSE boys), but rather substantial differences in verbal and mathematical performance, as well as regulatory/motivational problems among former TSE students, corroborating to some extent the abovementioned consequences of academic differentiation. The results are further elucidated from the perspective of self-determination theory and the TPD.

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Fig. 1

Notes

  1. The TPD distinguishes five levels of development, which are not sequential, age-related, or universal (Mendaglio 2008): primary integration, unilevel disintegration, spontaneous multilevel disintegration, organized multilevel disintegration, and secondary integration (for full explanation see Dabrowski 2015). Personality is only achieved at the level of secondary integration.

  2. Subject-object in oneself refers to the process of looking at oneself critically and objectively and approaching the other subjectively, with empathy and compassion (Dabrowski 2015).

  3. At higher levels of development, the individual becomes aware of his/her own personality ideal and the necessity of approaching this ideal. Through critical and objective self-examination and the conscious perception of the higher and lower within herself/himself, while simultaneously becoming aware of a higher, true reality (through intuition), the individual can construct a personal hierarchy of values, which is derived from universal, objective moral values (Dabrowski 2015).

  4. The personality ideal is activated by means of the Third Factor, which can be considered a highly conscious, high value-based self-determinism that rejects the lower, instinctive dimension and affirms the higher, authentic one. The First Factor refers to the constitutional endowment, while the Second Factor points to the social environment (Dabrowski 2015).

  5. In contrast to frequentist approaches, the population parameter is treated as random in Bayesian statistics. This makes it possible to make probability statements about the value of this parameter, based on substantive theories or previous empirical findings, as reflected in its prior probability distribution. Drawing on Bayes’ theorem, observed sampling data will revise this prior knowledge, thereby resulting in the posterior probability distribution of the parameter (which is proportional to the product of the likelihood and the prior distribution) (Bolstad 2007; Kaplan and Depaoli 2012; Lee 2007). Strong prior knowledge regarding the population parameter value (applied to CFA and scalar MI, it reflects the requirement for cross-loadings, correlated errors, and differences in factor loadings and intercepts across groups to be approximately zero) is indicated by a small variance of its prior distribution (allowing the aforementioned parameters in CFA and MI to deviate from zero to a very limited extent). In this condition, the data have less impact on the posterior distribution (Asparouhov and Muthén 2014; Muthén and Asparouhov 2012).

  6. Bayesian analysis uses Markov chain Monte Carlo (MCMC) algorithms to iteratively extract random samples from the posterior distribution of the model parameters (Muthén and Muthén 1998–2017). MCMC convergence of posterior parameters, which denotes that sufficient samples have been extracted from the posterior distribution to precisely estimate the posterior parameter values, is assessed using the potential scale reduction (PSR) convergence criterion (Gelman and Rubin, 1992). The PSR criterion compares within- and between-chain variation of parameter estimates. When a single MCMC chain is used, the PSR compares variation within and between the third and fourth quarters of the iterations. A PSR value of 1.000 indicates perfect convergence (Kaplan and Depaoli 2012; Muthén and Muthén 1998–2017).

  7. Dabrowski’s concept of multilevelness refers to the various vertical levels in the external and internal reality of which developing individuals become aware during the multilevel disintegration phase, the attainment of which depends largely on the presence of a high level of overexcitability (Dabrowski, 2015; Mendaglio, 2008). The level of organized multilevel disintegration is characterized by the structuring of a universal (and consciously derived personal) hierarchy of values (through creativity, intuition, and higher-level emotions) and by the conscious, autonomous self-organization of the course of development (by means of the Third Factor) (Dabrowski 1970b, 1972, 2015).

  8. The Bayesian credibility interval can be derived directly from the percentiles of the posterior distribution, allowing probability statements about the parameter. In this study, a (null) hypothesis testing perspective (Arbuckle 2017; Zyphur and Oswald 2015) was used in parameter estimation by evaluating whether the 95% credibility interval of the model parameters included zero. If the 95% Bayesian credibility interval of a parameter does not cover zero, the null (condition) can be rejected as improbable, and as a result, the parameter is considered significant (which is indicated by a one-tailed Bayesian p value below 0.05). A hypothesis testing perspective was also used to assess the model fit (Levy 2011).

  9. BSEM CFA models with small-variance priors for cross-loadings and residual covariances—according to the LEMO’s hypothesized factor loading pattern for the 49 observed variables—yielded a satisfactory fit, as indicated by PPps of 0.542, 0.548, 0.508, and 0.508 for the meaning-, reproduction-, application-, and undirected-learning pattern, respectively. All intended factor loadings—with the exception of the loading of item y19 and y27 on, respectively, the latent variable of concrete processing and external regulation—were substantive, with no significant (at the 5% level) cross-loadings and 15 (i.e., 4%) non-trivial correlated errors.

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De Bondt, N., Donche, V. & Van Petegem, P. Are contextual rather than personal factors at the basis of an anti-school culture? A Bayesian analysis of differences in intelligence, overexcitability, and learning patterns between (former) lower and higher-track students. Soc Psychol Educ 23, 1627–1657 (2020). https://doi.org/10.1007/s11218-020-09597-5

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  • DOI: https://doi.org/10.1007/s11218-020-09597-5

Keywords

  • Educational stratification
  • Learning patterns
  • Overexcitability within the theory of positive disintegration
  • Bayesian structural equation modeling
  • Approximate measurement invariance
  • Self-determination theory