Childhood maltreatment and adulthood victimization: An evidence-based model.

There is ample evidence showing that childhood maltreatment increases two to three fold the risk of victimization in adulthood. Various risk factors, including posttraumatic stress disorder (PTSD) symptoms, dissociation, self-blame, and alcohol abuse are related to revictimization. Although previous research examined associations between risk factors for revictimization, the evidence is limited and the proposed models mostly include a handful of risk factors. Therefore, it is critical to investigate a more comprehensive model explaining the link between childhood maltreatment and adulthood (re)victimization. Accordingly, this study tested a data-driven theoretical path model consisting of 33 variables (and their associations) that could potentially enhance understanding of factors explaining revictimization. Cross-sectional data derived from a multi-wave study were used for this investigation. Participants (N = 2156, age mean = 19.94, SD = 2.89) were first-year female psychology students in the Netherlands and New Zealand, who responded to a battery of questionnaires and performed two computer tasks. The path model created by structural equation modelling using modification indices showed that peritraumatic dissociation, PTSD symptoms, trauma load, loneliness, and drug use were important mediators. Attachment styles, maladaptive schemas, meaning in life, and sex motives connected childhood maltreatment to adulthood victimization via other factors (i.e., PTSD symptoms, risky sex behavior, loneliness, emotion dysregulation, and sex motives). The model indicated that childhood maltreatment was associated with cognitive patterns (e.g., anxious attachment style), which in turn were associated with emotional factors (e.g., emotion dysregulation), and then with behavioral factors (e.g., risky sex behavior) resulting in revictimization. The findings of the study should be interpreted in the light of the limitations. In particular, the cross-sectional design of the study hinders us from ascertaining that the mediators preceded the outcome variable.

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.

Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R 2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.

Kappa Coefficients for Missing Data.

Cohen's kappa coefficient is commonly used for assessing agreement between classifications of two raters on a nominal scale. Three variants of Cohen's kappa that can handle missing data are presented. Data are considered missing if one or both ratings of a unit are missing. We study how well the variants estimate the kappa value for complete data under two missing data mechanisms-namely, missingness completely at random and a form of missingness not at random. The kappa coefficient considered in Gwet (Handbook of Inter-rater Reliability, 4th ed.) and the kappa coefficient based on listwise deletion of units with missing ratings were found to have virtually no bias and mean squared error if missingness is completely at random, and small bias and mean squared error if missingness is not at random. Furthermore, the kappa coefficient that treats missing ratings as a regular category appears to be rather heavily biased and has a substantial mean squared error in many of the simulations. Because it performs well and is easy to compute, we recommend to use the kappa coefficient that is based on listwise deletion of missing ratings if it can be assumed that missingness is completely at random or not at random.

Transforming intraclass correlation coefficients with the Spearman-Brown formula.

OBJECTIVES: De Vet, Mokkink, Mosmuller, and Terwee (2017) discovered that the Spearman-Brown formula (SB formula) can be used to transform certain intraclass correlation coefficients (ICCs) for single measurements into the corresponding ICCs for average measurements, without knowledge of the variance components. RESULTS: A formal proof of the discovery by De Vet et al. is presented. It is also proved that, vice versa, the SB formula can be used to transform certain ICCs for average measurements into the corresponding ICCs for single measurements. Furthermore, it is specified to which type of ICC the transformations can be applied. Finally, several illustrations of the transformations are presented. CONCLUSION: The discovery by De Vet et al. is quite remarkable because ICCs and the SB formula come from quite different scientific disciplines.

Chance-corrected measures for 2 × 2 tables that coincide with weighted kappa.

Cohen's kappa is presently a standard tool for the analysis of agreement in a 2 × 2 reliability study, and weighted kappa is a standard statistic for summarizing a 2 × 2 validity study. The special cases of weighted kappa, for example Cohen's kappa, are chance-corrected measures of association. For various measures of 2 × 2 association it has been observed in the literature that, after correction for chance, they coincide with a special case of weighted kappa. This paper presents the general function, linear in both numerator and denominator, that becomes weighted kappa after correction for chance.

On Similarity Coefficients for 2x2 Tables and Correction for Chance.

This paper studies correction for chance in coefficients that are linear functions of the observed proportion of agreement. The paper unifies and extends various results on correction for chance in the literature. A specific class of coefficients is used to illustrate the results derived in this paper. Coefficients in this class, e.g. the simple matching coefficient and the Dice/Sørenson coefficient, become equivalent after correction for chance, irrespective of what expectation is used. The coefficients become either Cohen's kappa, Scott's pi, Mak's rho, Goodman and Kruskal's lambda, or Hamann's eta, depending on what expectation is considered appropriate. Both a multicategorical generalization and a multivariate generalization are discussed.

On Association Coefficients for 2x2 Tables and Properties That Do Not Depend on the Marginal Distributions.

We discuss properties that association coefficients may have in general, e.g., zero value under statistical independence, and we examine coefficients for 2x2 tables with respect to these properties. Furthermore, we study a family of coefficients that are linear transformations of the observed proportion of agreement given the marginal probabilities. This family includes the phi coefficient and Cohen's kappa. The main result is that the linear transformations that set the value under independence at zero and the maximum value at unity, transform all coefficients in this family into the same underlying coefficient. This coefficient happens to be Loevinger's H.

Priming and binding in and across perception and action: a correlational analysis of the internal structure of event files.

Individual performance was compared across three different tasks that tap into the binding of stimulus features in perception, the binding of action features in action planning, and the emergence of stimulus-response bindings ("event files"). Within a task correlations between the size of binding effects were found within visual perception (e.g., the strength of shape-location binding correlated positively with the strength of shape-colour binding) but not between perception and action planning, suggesting different, domain-specific binding mechanisms. To some degree, binding strength was predicted by priming effects of the respective features, especially if these features varied on a dimension that matched the current attentional set.