Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Table 1.

Information on the items used to construct latent variables for socioeconomic stress, family support, peer problems and emotional symptoms.

More »

Table 1 Expand

Fig 1.

Flow chart showing the derivation of the sample used for the analysis.

More »

Fig 1 Expand

Fig 2.

Path diagrams of the structural equation models tested.

Note: Single-headed arrows show the hypothesised direction of the relationship. Double-headed arrows show covariance. Latent variables are presented in circles; observed variables are in squares. Separate models were run for males and females. Covariates and indicator variables for the latent variables are not shown for simplicity.

More »

Fig 2 Expand

Table 2.

Descriptive statistics for the continuous variables in the sample with complete data, separately for males and females (N = 1950).

More »

Table 2 Expand

Table 3.

Count data for the categorical and ordinal variables separately for males and females, expressed as both frequency and row percentage.

More »

Table 3 Expand

Table 4.

Robust fit statistics for the nested models, including chi-square statistic, df, chi-square difference tests, CFI and RMSEA with 90% CI (N = 1950).

More »

Table 4 Expand

Fig 3.

Results for models 1 and 2 for the male sample.

Note. *** = p < .001, ** = p < .01, * = p < .05. Estimates are unstandardised path coefficients (standardised in parentheses). Amygdala and vmPFC GMV values were divided by 1,000, and WBV was divided by 1,000,000, so that the values were closer in magnitude to other variables in the model.

More »

Fig 3 Expand

Fig 4.

Results for models 1 and 2 for the female sample.

Note. *** = p < .001, ** = p < .01, * = p < .05. Estimates are unstandardised path coefficients (standardised in parentheses). Amygdala and vmPFC GMV values were divided by 1,000, and WBV was divided by 1,000,000, so that the values were closer in magnitude to other variables in the model.

More »

Fig 4 Expand