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Using data sets consisting of cross-sectional surveys drawn from nationally representative samples of adolescents in the U.S. and official sources of crime statistics, a portrait of aggression and violence among adolescents is presented. Fluctuations in self-reported and official sources of data are examined by year, gender, grade, and race. Both distal and contemporary risk factors for aggression and violence are discussed. Distal risk factors for violence in adolescence are presented using longitudinal studies. The General Aggression Model provides the framework for exploring which contemporary personal and situational factors increase or decrease risk for aggression and violence. Dating aggression in adolescence is placed in the context of normal development and variable according to individual partner and relationship factors. This book presents rigorously tested scientific prevention programs for adolescents with violent and aggressive behavior.

The aim of the study was to compare the inter-rater reliability, concurrent validity, completion time, and ease of use of two methodological quality (MQ) assessment tools for cross-sectional studies: an adapted Newcastle–Ottawa Scale (NOS) and the Appraisal Tool for Cross-Sectional Studies (AXIS). Two raters applied the NOS and AXIS to 63 cross-sectional studies of health-related quality of life and breast cancer. AXIS demonstrated poor inter-rater reliability (intraclass correlation coefficient [ICC] = 0.49) and required more than double the amount of time to complete compared with the NOS, which demonstrated moderate reliability (ICC = 0.73). For concurrent validity, weak and moderate positive relationships existed between NOS and AXIS (rater 1: r = 0.26; rater 2: r = 0.45). Ease of using the tools was affected by the indirectness of MQ assessments, perceived thoroughness of the tools’ content, and user experience. This study was the first to assess the psychometric properties of a cross-sectional NOS and AXIS. The results did not support a clear choice between selecting either tool for evaluating MQ in cross-sectional studies.

The paper deals with the estimation of a high dimensional covariance with a conditional sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the principal orthogonal complement thresholding method 'POET' to explore such an approximate factor structure with sparsity. The POET-estimator includes the sample covariance matrix, the factor-based covariance matrix, the thresholding estimator and the adaptive thresholding estimator as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the effect of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings in which variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2 statistic and Newey-West corrected Fama-MacBeth standard errors do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the implied cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.

We propose a novel technique to boost the power of testing a high-dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated by only a few components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a \"power enhancement component,\" which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. The proposed methods are then applied to testing the factor pricing models and validating the cross-sectional independence in panel data models.

Purpose We aimed to explore the relationship between the systemic immune-inflammation index (SII) and rheumatoid arthritis (RA) using NHANES from 1999 to 2018. Methods We collected data from the NHANES database from 1999 to 2018. The SII is calculated from the counts of lymphocytes (LC), neutrophils (NC), and platelets (PC). The RA patients were derived from questionnaire data. We used weighted multivariate regression analysis and subgroup analysis to explore the relationship between SII and RA. Furthermore, the restricted cubic splines were used to explore the non-linear relationships. Result Our study included a total of 37,604 patients, of which 2642 (7.03%) had rheumatoid arthritis. After adjusting for all covariates, the multivariate logistic regression analysis showed that high SII (In-transform) levels were associated with an increased likelihood of rheumatoid arthritis (OR=1.167, 95% CI=1.025–1.328, P =0.020). The interaction test revealed no significant effect on this connection. In the restricted cubic spline regression model, the relationship between ln-SII and RA was non-linear. The cutoff value of SII for RA was 578.25. The risk of rheumatoid arthritis increases rapidly when SII exceeds the cutoff value. Conclusion In general, there is a positive correlation between SII and rheumatoid arthritis. Our study shows that SII is a novel, valuable, and convenient inflammatory marker that can be used to predict the risk of rheumatoid arthritis in US adults.

The cross-sectional research design, especially when used with self-report surveys, is held in low esteem despite its widespread use. It is generally accepted that the longitudinal design offers considerable advantages and should be preferred due to its ability to shed light on causal connections. In this paper, I will argue that the ability of the longitudinal design to reflect causality has been overstated and that it offers limited advantages over the cross-sectional design in most cases in which it is used. The nature of causal inference from a philosophy of science perspective is used to illustrate how cross-sectional designs can provide evidence for relationships among variables and can be used to rule out many potential alternative explanations for those relationships. Strategies for optimizing the use of cross-sectional designs are noted, including the inclusion of control variables to rule out spurious relationships, the addition of alternative sources of data, and the incorporation of experimental methods. Best practice advice is offered for the use of both cross-sectional and longitudinal designs, as well as for authors writing and for reviewers evaluating papers that report results of cross-sectional studies.

Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.