The association between two ordinal variables can be expressed with a polychoric correlation coefficient. This coefficient is conventionally based on the assumption that responses to ordinal variables are generated by two underlying continuous latent variables with a bivariate normal distribution. When the underlying bivariate normality assumption is violated, the estimated polychoric correlation coefficient may be biased. In such a case, we may consider other distributions. In this paper, we aimed to provide an illustration of fitting various bivariate distributions to empirical ordinal data and examining how estimates of the polychoric correlation may vary under different distributional assumptions. Results suggested that the bivariate normal and skew-normal distributions rarely hold in the empirical datasets. In contrast, mixtures of bivariate normal distributions were often not rejected.

The filtered monotonic polynomial (FMP) model is a semi-parametric item response model that allows flexible response function shapes but also includes traditional item response models as special cases. The flexmet package for R facilitates the routine use of the FMP model in real data analysis and simulation studies. This tutorial provides several code examples illustrating how the flexmet package may be used to simulate FMP model parameters and data (both for dichotomous and polytomously scored items), estimate FMP model parameters, transform traditional item response models to different metrics, and more. This tutorial serves as both an introduction to the unique features of the FMP model and as a practical guide to its implementation in R via the flexmet package.

Examining interactions among predictors is an important part of a developing research program. Estimating interactions using latent variables provides additional power to detect effects over testing interactions in regression. However, when predictors are modeled as latent variables, estimating and testing interactions requires additional steps beyond the models used for regression. We review methods of estimating and testing latent variable interactions with a focus on product indicator methods. Product indicator methods of examining latent interactions provide an accurate method to estimate and test latent interactions and can be implemented in any latent variable modeling software package. Significant latent interactions require additional steps (plotting and probing) to interpret interaction effects. We demonstrate how these methods can be easily implemented using functions in the semTools package with models fit using the lavaan package in R, and we illustrate how these methods work using an applied example concerning teacher stress and testing.

Family socioeconomic status (SES) is closely associated with cognitive ability in children and adolescents. However, most of the research has come from high-income countries. There is only limited research on ‘street children’, who represent an aspect of low-SES particularly associated with low- and middle-income counties. The current research in Quito, Ecuador, compared a group of street-connected youth with a not street-connected control group on two different measures of theory of mind ability and verbal comprehension. Initial analysis revealed that the street-connected sample scored significantly below the level of the control sample for verbal comprehension. For the main analysis, street-connected youth were matched to control participants for age, sex, and verbal comprehension scores. The street-connected sample was found to perform significantly below the control sample on both measures of theory of mind. Furthermore, worse performance appeared to be linked to severity of symptoms of post-traumatic stress disorder (PTSD) within the street-connected sample. In conclusion, the association of relatively poor verbal comprehension with street-connectedness is consistent with existing research from high-income countries on SES gradients and cognitive development. In contrast, theory of mind ability, a core aspect of social cognition, may be particularly linked to the street-connectedness form of low SES that exists in many low- and middle-income countries.

(1) Background: Numerous research studies have investigated and identified the high prevalence of poor mental health status among the Chinese population, for example suffering from a high level of anxiety and depression. Moreover, the Chinese population are shown to have a conservative attitude towards mental health problems and a passive help-seeking behaviour when suffering from psychological distress. Therefore, it is important to assess the levels of self-care among the Chinese population, which is considered as the basis for tackling psychological well-being issues by the World Health Organization (WHO). Self-care was conceptualised and measured by the Mindful Self-Care Scale (MSCS), but without being translated and adapted to the Chinese population. The purpose of this study is to translate the scale into Chinese and to conduct a pilot test to validate the scale among the Chinese population. (2) Methods: Participants (N = 161), with 96 females and 65 males, with an age range from 18 to 62, mage = 26.1 (SD = 6.64), were recruited from Hong Kong (n = 63), Taiwan (n = 44), and mainland China (n = 54) using convenience sampling. (3) Results: The results show a satisfying internal consistency (Cronbach’s α = 0.915) without items that need to be deleted as well as an adequate validity, with Kaiser–Mayer–Olkin (KMO) value of 0.856 and significant correlation between self-care and the two variables: self-compassion and well-being (r = 0.403, r = 0.172, p < 0.001). (4) Conclusions: The results preliminarily indicate that the Chinese version of MSCS is reliable and valid. However, test–retest reliability and confirmatory factor analysis should be performed with a larger sample size for further validation.

Contextual reactivity refers to the degree in which personality states are affected by contextual cues. Research into contextual reactivity has mainly focused on repeated measurement designs. In this paper, we propose a cross-sectional approach to study contextual reactivity. We argue that contextual reactivity can be operationalized as different response processes which are characterized by different mean response times and different measurement properties. We propose a within-person mixture modeling approach that adopts this idea and which enables studying contextual reactivity in cross-sectional data. We applied the model to data from the Revised Temperament and Character Inventory. Results indicate that we can distinguish between two response specific latent states. We interpret these states as a high contextual reactive state and a low contextual reactive state. From the results it appears that the low contextual reactive state is generally associated with smaller response times and larger discrimination parameters, as compared to the high contextual reactivity state. The utility of this approach in personality research is discussed.

Association studies between social capital and health point out that a high level of social capital can act as a protector for mental health. The growing interest in social risk factors for mental health coincides with the development of social capital research. Higher levels of social capital available through social networks can act as a protector for mental health. This study investigates gender differences in the association between social capital and common mental disorders (CMD) and depression. We analyzed 15,052 participants in the baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). CMD and depression were assessed by Clinical Interview Schedule-Revised (CIS-R) and social capital by the Resource Generator scale. We used Logistic regression models stratified by sex. Women with lower social capital in the social support dimension had a greater chance of presenting CMD (OR = 1.36; CI 95%: 1.16–1.60) and depression (OR = 2.07; CI 95%: 1.57–2.72) when compared to women with higher social capital. No association was identified among men, or among women in the “prestige and education” dimension. The differences found between the dimensions of social capital support its multidimensionality, as well as the differences found between sexes, confirm the need to approach gender in its association with mental health.

South Africa represents one of the most rapidly aging countries in sub-Saharan Africa with a rising burden of age-related psychological morbidities. Despite having one of the highest human development scores in the region, the country faces serious poverty and food insecurity related challenges. Previous studies have shown a positive association between food insecurity and poor mental health among the adult population, however there is no systematic evidence on this association among the elderly population in an African setting. In the present study, we aimed to address this research gap by analyzing cross-sectional data (n = 931) on the over-50 population (>50 years) from the SAGE (Study on global AGEing and adult health) Well-Being of Older People Study (WOPS) of the World Health Organization, conducted between 2010 and 2013. The outcome variable was perceived depression and the explanatory variables included several sociodemographic factors including self-reported food insecurity. The independent associations between the outcome and explanatory variables were measured using multivariable regression analysis. Results showed that close to a quarter of the population (22.6%, 95% CI = 21.4, 24.7) reported having depression in the last 12 months, with the percentage being markedly higher among women (71.4%). In the multivariable regression analysis, self-reported food insecurity was found to be the strongest predictor of depression among both sexes. For instance, severe food insecurity increased the odds of depression by 4.805 [3.325, 7.911] times among men and by 4.115 [2.030, 8.341] times among women. Based on the present findings, it is suggested that national food security programs focus on promoting food security among the elderly population in an effort to improve their mental health status. Nonetheless, the data were cross-sectional and the associations can’t imply causality.

Using data from the Philadelphia Neurodevelopmental Cohort, we examined whether European ancestry predicted cognitive ability over and above both parental socioeconomic status (SES) and measures of eye, hair, and skin color. First, using multi-group confirmatory factor analysis, we verified that strict factorial invariance held between self-identified African and European-Americans. The differences between these groups, which were equivalent to 14.72 IQ points, were primarily (75.59%) due to difference in general cognitive ability (g), consistent with Spearman’s hypothesis. We found a relationship between European admixture and g. This relationship existed in samples of (a) self-identified monoracial African-Americans (B = 0.78, n = 2,179), (b) monoracial African and biracial African-European-Americans, with controls added for self-identified biracial status (B = 0.85, n = 2407), and (c) combined European, African-European, and African-American participants, with controls for self-identified race/ethnicity (B = 0.75, N = 7,273). Controlling for parental SES modestly attenuated these relationships whereas controlling for measures of skin, hair, and eye color did not. Next, we validated four sets of polygenic scores for educational attainment (eduPGS). MTAG, the multi-trait analysis of genome-wide association study (GWAS) eduPGS (based on 8442 overlapping variants) predicted g in both the monoracial African-American (r = 0.111, n = 2179, p < 0.001), and the European-American (r = 0.227, n = 4914, p < 0.001) subsamples. We also found large race differences for the means of eduPGS (d = 1.89). Using the ancestry-adjusted association between MTAG eduPGS and g from the monoracial African-American sample as an estimate of the transracially unbiased validity of eduPGS (B = 0.124), the results suggest that as much as 20%–25% of the race difference in g can be naïvely explained by known cognitive ability-related variants. Moreover, path analysis showed that the eduPGS substantially mediated associations between cognitive ability and European ancestry in the African-American sample. Subtest differences, together with the effects of both ancestry and eduPGS, had near-identity with subtest g-loadings. This finding confirmed a Jensen effect acting on ancestry-related differences. Finally, we confirmed measurement invariance along the full range of European ancestry in the combined sample using local structural equation modeling. Results converge on genetics as a potential partial explanation for group mean differences in intelligence.

It is my great pleasure, on behalf of the editorial board and myself, to introduce Psych, MDPI’s new multidisciplinary, open-access journal for the social and behavioral sciences [...]