Balance in Covariates Before and After Matching between the Low Maternal Expectations Group and the High Maternal Expectations Group.

<p><i>Note</i>. Each standardized mean difference is obtained by: mean in the low expectation group minus mean in the high expectation group, divided by the standard deviation in the low expectation group.</p><p>¹Mahalanobis distance used.</p><p>²Exact matching used.</p><p>³The average absolute standardized mean difference is the average of the absolute values of standardized mean differences for all covariates.</p><p>Balance in Covariates Before and After Matching between the Low Maternal Expectations Group and the High Maternal Expectations Group.</p

Demographic Characteristics of the Study Sample.

<p>Demographic Characteristics of the Study Sample.</p

Distributions of the propensity score.

<p>Treatment Units correspond to children with low maternal expectations whereas Control Units correspond to children with high maternal expectations. The left part of the figure shows the overlap between the Matched Treatment Units versus the Matched Control Units. The Unmatched Control Units correspond to children with high maternal expectations who were discarded from the analyses (note the high density of children with low propensity scores in this group, meaning that, given their characteristics, these children had a very low probability of having a mother with low expectations and thus represented poor matches). The right part of the figure represents the matching procedure when the discard Treatment Units option was used (see <i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119638#sec030" target="_blank">Discard option</a></i>, in the <i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119638#sec029" target="_blank">Complementary analyses</a></i> section): some Matched Treatment Units had a propensity score close to one, which made it hard to find equivalent Controls. As such, they were discarded in this complementary analysis and are plotted as Unmatched Treatment Units.</p

Predicted percentages of failure to graduate from high school according to sex and maternal expectations.

<p>Predicted percentages of failure to graduate from high school according to sex and maternal expectations.</p

Longitudinal model of inattention-hyperactivity symptoms emergence (4^th step).

<p>*Prenatal tobacco and maternal age at birth were proxies of SES. Italicized coefficients are bivariate correlation coefficients. Bold coefficients are standardized path coefficients. A solid arrow indicates a statistical significance (p<0.05) of the multivariate path coefficient A dotted arrow indicates a bivariate significance that disappears in the multivariate model.</p

Study population.

<p>Study population.</p

Risk factors classification.

<p>A and B are the risk factors. O is the outcome. The single/double-headed arrow between A and B indicates a correlation. The solid (or dotted) arrow between a risk factor and O indicates a significant (or insignificant) association adjusting for the other risk factor.</p

Survival Models Predicting the Age at First Infraction based on Official Court Records.

<p><i>Note</i>. The table presents the results of a Cox model (with robust variance) predicting the age at the first infraction documented in the court records. The first column shows the percentages of participants in each trajectory (e.g. 9.5% of the participants were classified in the High mother/teacher trajectory of physical aggression). The second column reports the percentage of events, i.e. whether one crime was recorded or not, irrespective of the age at which it was committed (e.g. of the 9.5% participants in the High mother/teacher trajectory of physical aggression, 43.4% had a criminal record by age 25 years). The last columns present unadjusted Hazard Ratios (uHR) as well as adjusted Hazard Ratios (aHR) based on the multivariate survival models. Low trajectories and Females are the contrast. Regarding adversity, we used the continuous variable in the analyses but, in order to better understand the data, we present in the second column the percentage of crimes in the highest decile (25.3%). ^***p<.001; ^**p<.01; ^*p<.05; ^†p<.10.</p

Survival Models: Contributions of Hyperactivity and Physical Aggression to the Development of Criminality in Males.

<p>The bivariate contributions are based on Kaplan-Meier plots. The adjusted contributions were plotted from multivariate Cox models. The values for covariates were: 1 for sex (i.e. male); mean adversity level; second trajectory (High mother only) for hyperactivity and physical aggression; low trajectory for inattention.</p

REVIEW2_Supplementary_material_corrections – Supplemental material for Longitudinal and Sex Measurement Invariance of the Affective Neuroscience Personality Scales

<p>Supplemental material, REVIEW2_Supplementary_material_corrections for Longitudinal and Sex Measurement Invariance of the Affective Neuroscience Personality Scales by Massimiliano Orri, Alexandra Rouquette, Jean-Baptiste Pingault, Caroline Barry, Catherine Herba, Sylvana M. Côté, Sylvie Berthoz in Assessment</p