Lord's paradox in latent change score modeling: An example involving facilitating longitudinal effects between intelligence and academic achievement

A New School, a Fresh Start? Change and Stability in Peer Relationships and Academic Performance in the Transition from Primary to Secondary School

Previous studies on peer relationships in school transitions neglected individual differences, or did not examine the relation with academic performance in secondary school. This study followed 649 students from their last year of primary school to their first year in secondary school (Mage at T1 = 11.6 (SD = 0.6); 53.6% girls). Results revealed that students became more attached to peers, less lonely, and were stable in victimization across the transition. Particularly students with more negative peer experiences in primary school enjoyed a "fresh start" in terms of peer experiences in secondary school. Students who had more co-transitioning peers experienced greater reductions in loneliness. Changes in peer experiences over the transition did not relate to academic performance in secondary school.

Dangers of including outcome at baseline as a covariate in latent change score models: Results from simulations and empirical re-analyses

Latent change score modeling is a type of structural equation modeling used for estimating change over time. Often change is regressed on the initial value of the outcome variable. However, similarly to other regression analyses, this procedure may be susceptible to regression to the mean. The present study employed simulations as well as re-analyses of previously published data, claimed to indicate reciprocal promoting effects of vocabulary and matrix reasoning on each other's longitudinal development. Both in simulations and empirical re-analyses, when adjusting for initial value on the outcome, latent change score modeling tended to indicate an effect of a predictor on the change in an outcome even when no change had taken place. Furthermore, analyses tended to indicate a paradoxical effect on change both forward and backward in time. We conclude that results from latent change score modeling are susceptible to regression to the mean when adjusting for the initial value on the outcome. Researchers are recommended not to regress change on the initial value included in the calculation of the change score when employing latent change score modeling but, instead, to define this parameter as a covariance.

Corrigendum: Curiosity does help to protect against anxiety and depression symptoms but not conversely

INTRODUCTION

This was a re-analysis of Zainal and Newman (2022) correcting for critiques by Sorjonen, Nilsonne, et al. (2022). As recommended by these authors, in this reanalysis we no longer adjust for baseline measures of the outcome variable in our examination of the longitudinal within-person relations between need for cognition (NFC) and anxiety and depression symptoms (ADS).

METHODS

Using random intercept cross-lagged panel model (RI-CLPM), and bivariate dual latent change score (BLCS) approaches, we separated between- and within-person effects. RI-CLPM and BLCS have advantages of adjusting for regression to the mean, autoregressive and lagged effects, and minimizing measurement error. Community-dwelling adults (n = 6750) completed the Mental Health Inventory-5 and trait-level NFC scales approximately every year across 10 years.

RESULTS

Consistent with the original results, a lower level of NFC within persons predicted a higher future level of ADS and vice versa (d = -0.885 to -0.476). Likewise, BLCS demonstrated that within persons, a smaller change in NFC forecasted a larger subsequent increase in ADS (d = -0.316). However, unlike our original findings, change in ADS did not predict future change in NFC (d = 0.136). The pattern of findings remained after adjusting for socio-demographic covariates.

CONCLUSION

Results support our original finding that cultivating curiosity may help prevent the development of future anxiety and depressive symptoms. Importantly the results refute Sorjonen, Nilsonne, et al. (2022)'s simulated findings regarding the predictive value of NFC. Targeting NFC may treat or prevent the emergence of depression and anxiety disorders.

Questioning the vulnerability model: Prospective associations between low self-esteem and subsequent depression ratings may be spurious

BACKGROUND

According to the vulnerability model, low self-esteem makes people more depressed. Support for the vulnerability model comes almost exclusively from analyses using cross-lagged panel models, showing a negative effect of initial self-esteem on subsequent depression ratings when adjusting for initial depression. However, it is well known that such adjusted effects are susceptible to regression toward the mean.

METHODS

Data from four waves of measurements in five different samples (total N = 2703) were analyzed with two different cross-lagged panel models, two different random intercept cross-lagged panel models, and two different latent change score models, predicting change forwards as well as backwards in time.

RESULTS

High initial self-esteem predicted both decreased and increased depression ratings between measurements and an increase in self-esteem between measurements predicted a concurrent decrease in depression ratings.

LIMITATIONS

Only data from two western countries, Switzerland and USA, were analyzed. Whether the main finding, that a prospective effect of self-esteem on subsequent depression ratings might be spurious, applies to other countries and cultures remains an open question.

CONCLUSIONS

Due to the incongruent results, any causal effect of self-esteem on depression ratings, and thus the vulnerability model as such, cannot be corroborated by the data and models analyzed here. Instead, we propose, tentatively, that prospective associations between self-esteem and depression ratings may be spurious due to a combination of reasons, including regression toward the mean. The indication that depression might not be affected by measures to improve individuals' self-esteem is of clinical relevance.

Regression to the mean in latent change score models: an example involving breastfeeding and intelligence

Background

Latent change score models are often used to study change over time in observational data. However, latent change score models may be susceptible to regression to the mean. Earlier observational studies have identified a positive association between breastfeeding and child intelligence, even when adjusting for maternal intelligence.

Method

In the present study, we investigate regression to the mean in the case of breastfeeding and intelligence of children. We used latent change score modeling to analyze intergenerational change in intelligence, both from mothers to children and backward from children to mothers, in the 1979 National Longitudinal Survey of Youth (NLSY79) dataset (N = 6283).

Results

When analyzing change from mothers to children, breastfeeding was found to have a positive association with intergenerational change in intelligence, whereas when analyzing backward change from children to mothers, a negative association was found.

Conclusions

These discrepant findings highlight a hidden flexibility in the analytical space and call into question the reliability of earlier studies of breastfeeding and intelligence using observational data.