Critics of positive psychology have questioned the validity of positive psychological assessment measures (PPAMs), which negatively affects the credibility and public perception of the discipline. Psychometric evaluations of PPAMs have shown that various instruments produce inconsistent factor structures between groups/contexts/times frames, that their predictive validity is questionable, and that popular PPAMs are culturally biased. Further, it would seem positive psychological researchers prioritize date-model-fit over measurement quality. To address these analytical challenges, more innovative and robust approaches toward the validation and evaluation of PPAMs are required to enhance the discipline\'s credibility and to advance positive psychological science. Exploratory Structural Equation Modeling (ESEM) has recently emerged as a promising alternative to overcome some of these challenges by incorporating the best elements from exploratory- and confirmatory factor analyses. ESEM is still a relatively novel approach, and estimating these models in statistical software packages can be complex and tedious. Therefore, the purpose of this paper is to provide novice researchers with a practical tutorial on how to estimate ESEM with a convenient online tool for Mplus. Specifically, we aim to demonstrate the use of ESEM through an illustrative example by using a popular positive psychological instrument: the Mental Health Continuum-SF. By using the MHC-SF as an example, we aim to provide (a) a brief overview of ESEM (and different ESEM models/approaches), (b) guidelines for novice researchers on how to estimate, compare, report, and interpret ESEM, and (c) a step-by-step tutorial on how to run ESEM analyses in Mplus with the De Beer and Van Zy ESEM syntax generator. The results of this study highlight the value of ESEM, over and above that of traditional confirmatory factor analytical approaches. The results also have practical implications for measuring mental health with the MHC-SF, illustrating that a bifactor ESEM Model fits the data significantly better than any other theoretical model.

Traditional diagnostic systems went beyond empirical evidence on the structure of mental health. Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited. The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence. It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability. We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework. The system is not yet comprehensive, and we describe the processes for improving and expanding it. We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response. We describe progress in the development of HiTOP-based measures and in clinical implementation of the system. Finally, we review outstanding challenges and the research agenda. HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology.

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. One critical unmet challenge is that molecular disease subtypes characterized by relevant clinical differences, such as survival, are difficult to differentiate. With the advancement of multi-omics technologies, subtyping methods have shifted toward data integration in order to differentiate among subtypes from a holistic perspective that takes into consideration phenomena at multiple levels. However, these integrative methods are still limited by their statistical assumption and their sensitivity to noise. In addition, they are unable to predict the risk scores of patients using multi-omics data. Here, we present a novel approach named Subtyping via Consensus Factor Analysis (SCFA) that can efficiently remove noisy signals from consistent molecular patterns in order to reliably identify cancer subtypes and accurately predict risk scores of patients. In an extensive analysis of 7,973 samples related to 30 cancers that are available at The Cancer Genome Atlas (TCGA), we demonstrate that SCFA outperforms state-of-the-art approaches in discovering novel subtypes with significantly different survival profiles. We also demonstrate that SCFA is able to predict risk scores that are highly correlated with true patient survival and vital status. More importantly, the accuracy of subtype discovery and risk prediction improves when more data types are integrated into the analysis. The SCFA software and TCGA data packages will be available on Bioconductor.

OBJECTIVE: The MATRICS Consensus Cognitive Battery (MCCB) assesses seven cognitive domains with 10 subtests. This domain structure has not been demonstrated. Three factors have been produced in US samples. We examined the dimensional structure of the Norwegian MCCB. In addition, we studied the contribution of each subtest to the battery sum score.
METHODS: The participants were 131 patients with schizophrenia spectrum disorders and 300 healthy controls. Their Norwegian MCCB test scores were subject to exploratory and confirmatory factor analysis and regression analysis.
RESULTS: The theoretical MCCB factor structure was not shown. In the patient group, three-factor and two-factor models had acceptable fit. In both groups, the Symbol Coding, Spatial Span, Letter-Number Span, and Visual Learning subtests contributed most to the sum score.
CONCLUSIONS: The theoretical domain structure of the MCCB could not be demonstrated in these Norwegian participants. Consonant with US studies, models with three and two factors had mediocre fit, and in the schizophrenia spectrum disorder group only. In both groups, the subtests Symbol Coding, Working Memory, and Learning were the most sensitive in tapping general neurocognitive performance, supporting US results. We conclude that in both Norway and the USA, the MCCB generates the same cognitive domains through factor analysis, but that these domains are not the ones suggested by the MATRICS project.

BACKGROUND: Selecting a measure for oncology distress screening can be challenging. The measure must be brief, but comprehensive, capturing patients\' most distressing concerns. The measure must provide meaningful coverage of multiple domains, assess symptom and problem-related distress, and ideally be suited for both clinical and research purposes.
METHODS: From March 2006 to August 2012, the James Supportive Care Screening (SCS) was developed and validated in three phases including content validation, factor analysis, and measure validation. Exploratory factor analyses were completed with 596 oncology patients followed by a confirmatory factor analysis with 477 patients.
RESULTS: Six factors were identified and confirmed including (i) emotional concerns; (ii) physical symptoms; (iii) social/practical problems; (iv) spiritual problems; (v) cognitive concerns; and (vi) healthcare decision making/communication issues. Subscale evaluation reveals good to excellent internal consistency, test-retest reliability, and convergent, divergent, and predictive validity. Specificity of individual items was 0.90 and 0.87, respectively, for identifying patients with DSM-IV-TR diagnoses of major depression and generalized anxiety disorder.
CONCLUSIONS: Results support use of the James SCS to quickly detect the most frequent and distressing symptoms and concerns of cancer patients. The James SCS is an efficient, reliable, and valid clinical and research outcomes measure.