Meta-analytic standardized regression effects between general and eight types of domain-specific self-esteem measured at two occasions.

Meta-analytic standardized regression effects between general and eight types of domain-specific self-esteem measured at two occasions.</p

Predicted sign of effects between general and domain-specific self-esteem according to a hypothesis of true increasing reciprocal effects and a hypothesis of spuriousness.

Predicted sign of effects between general and domain-specific self-esteem according to a hypothesis of true increasing reciprocal effects and a hypothesis of spuriousness.</p

Predicted social and general self-esteem.

Predicted social (A-C) and general (D-F) self-esteem at two occasions, separately for those with high (Z = 1), average, and low (Z = -1) general (A-C) and social (D-F) self-esteem at T1. Separately for situations when conditioning on average level of the outcome at T1 (A and D), average level of the outcome at T2 (B and E), and when not conditioning on the outcome (C and F).</p

Meta-analytic cross-lagged panel analyses, and their conclusions, that we have reanalyzed and challenged.

Meta-analytic cross-lagged panel analyses, and their conclusions, that we have reanalyzed and challenged.</p

PRISMA 2020 checklist.

A recent meta-analysis, of 38 studies with data from 43 independent samples (total N = 24,668), claimed evidence for positive reciprocal prospective effects, and hence for both top-down and bottom-up processes, between general and domain-specific self-esteem. However, the meta-analytic cross-lagged effects were estimated while adjusting for a prior measurement of the outcome variable and it is known that such adjusted cross-lagged effects may be spurious due to correlations with residuals and regression to the mean. In the present reanalyses, we found all of the prospective effects to be spurious. Consequently, claims about increasing prospective effects and top-down and bottom-up processes between general and domain-specific self-esteem can be questioned. It is important for researchers to be aware of the limitations of cross-lagged panel analyses, and of analyses of correlational data in general, in order not to overinterpret findings.</div

R simulated close-to-real data with linear and nonlinear effects from A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data

ESM file

R simulated data toy model imperfect reliability from A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data

ESM file

R real data from A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data

ESM file

R simulated data toy model perfect reliability from A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data

ESM file

Averages of intelligence and psychosocial functioning across levels of socioeconomic status.

<p>ρ = Spearman's correlation coefficient; ρ_adj = partial Spearman's, i.e. adjusted for PF/IQ; *** = p<0.001; NMH = Non-manual workers, higher level; NMI = Non-manual workers, intermediate level; NML = Non-manual workers, lower level; SMW = Skilled manual workers; UMW = Unskilled manual workers; ^a Skewness = −0.191 (p<0.001); ^b Skewness = −0.073 (p<0.001). Mean and standard deviation (SD) of intelligence (IQ) and psychosocial functioning (PF) across levels of SES indicators; </p