[Article Review] Nurturing Caregiving: A Key to Mitigate Early Adversities and Boost Adolescent Human Capital

Reference

Trude, A. C. B., Richter, L. M., Behrman, J. R., Stein, A. D., Menezes, A. M. B., Black, M. M., et al. (2021). Effects of responsive caregiving and learning opportunities during pre-school ages on the association of early adversities and adolescent human capital: an analysis of birth cohorts in two middle-income countries. The Lancet Child & Adolescent Health, 5(1), 37-46. https://doi.org/10.1016/S2352-4642(20)30309-6

Review

The study by Trude et al. (2021) investigates the impact of responsive caregiving and learning opportunities during preschool ages on the relationship between early adversities and adolescent human capital in two middle-income countries, Brazil and South Africa. The researchers analyzed longitudinal birth cohort data from the 1993 Pelotas Birth Cohort (Brazil) and the Birth to Twenty Plus (Bt20+) Birth Cohort (South Africa), focusing on three human capital indicators: intelligence quotient (IQ), psychosocial adjustment, and height.

The study found that an increase in cumulative adversities negatively impacted adolescent IQ in both cohorts. However, the negative effects of early adversities on IQ were attenuated by highly nurturing environments. Responsive caregiving and learning opportunities during preschool ages had a significant positive impact on adolescent IQ in the Brazilian cohort, while responsive caregiving played a more significant role in the South African cohort.

These findings emphasize the importance of nurturing care during early childhood in mitigating the effects of early adversities on adolescent human capital. By providing responsive caregiving and learning opportunities, caregivers can foster a protective environment that promotes positive cognitive and psychosocial development in children facing adversity. The study underscores the need for policies and interventions aimed at supporting nurturing care in middle-income countries, to bolster human capital and improve long-term outcomes for adolescents.

[Article Review] Revolutionizing Need for Cognition Assessment: Unveiling the Efficient NCS-6

Reference

Coelho, G. L. d. H., Hanel, P. H. P., & Wolf, L. J. (2018). The Very Efficient Assessment of Need for Cognition: Developing a Six-Item Version. Assessment, 27(8), 1870-1885. https://doi.org/10.1177/1073191118793208

Review

In the article, "The Very Efficient Assessment of Need for Cognition: Developing a Six-Item Version" by Coelho, Hanel, and Wolf (2018), the authors introduce a shortened version of the Need for Cognition Scale (NCS-18) called the NCS-6. The need for cognition refers to people's tendency to engage in and enjoy thinking, which has become influential across social and medical sciences. Using three samples from the United States and the United Kingdom (N = 1,596), the researchers reduced the number of items from 18 to 6 based on various criteria such as discrimination values, threshold levels, measurement precision, item-total correlations, and factor loadings.

The authors then confirmed the one-factor structure and established measurement invariance across countries and gender. They demonstrated that while the NCS-6 provides significant time savings, it comes at a minimal cost in terms of its construct validity with external variables such as openness, cognitive reflection test, and need for affect. This suggests that the NCS-6 is a parsimonious, reliable, and valid measure of the need for cognition.

In conclusion, Coelho et al.'s (2018) article provides valuable insights into the development of a more efficient measure of the need for cognition. The NCS-6 not only reduces the time required for assessment but also maintains the validity and reliability of the original scale. This study contributes to the understanding and measurement of need for cognition, which has implications for various fields, including social and medical sciences.

[Article Review] Decoding Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures

Reference

Liang, X. (2020). Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures. Educational and Psychological Measurement, 80(6), 1025-1058. https://doi.org/10.1177/0013164420906449

Review

Liang's (2020) article, "Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures," delves into the application of Bayesian structural equation modeling (BSEM) with small-variance normal distribution priors (BSEM-N) for the examination and estimation of sparse factor loading structures. The author conducts a two-part investigation, consisting of a simulation study (Study 1) and an empirical example (Study 2), to explore the prior sensitivity in BSEM-N. The results reveal that the optimal balance between true and false positives is achieved when the 95% credible intervals of shrinkage priors barely cover the population cross-loading values.

In Study 1, the author examines the prior sensitivity in BSEM-N using model fit, population model recovery, true and false positive rates, and parameter estimation. The study assesses seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters. Study 2 provides a real data example to demonstrate the impact of different priors on model fit and parameter selection and estimation. The empirical findings suggest that a sparse cross-loading structure with a minimal number of nontrivial cross-loadings and relatively high primary loading values is ideal for variable selection.

The article's conclusion emphasizes the importance of considering the study's goal when selecting priors for BSEM-N. To improve parameter estimates, a relatively large prior variance is preferred. The author advises against using BSEM-N with zero-mean priors for the estimation of cross-loadings and factor correlations when cross-loadings are relatively large. This comprehensive review of Liang's (2020) work highlights the practical implications and methodological considerations for researchers employing BSEM-N in their studies.