Correction: Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

[This corrects the article DOI: 10.2196/45407.].

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

Background: Advancements in mobile health technologies and machine learning approaches have expanded the framework of behavioral phenotypes in obesity treatment to explore the dynamics of temporal changes.

Objective: This study aimed to investigate the dynamics of behavioral changes during obesity intervention and identify behavioral phenotypes associated with weight change using a hybrid machine learning approach.

Methods: In total, 88 children and adolescents (ages 8-16 years; 62/88, 71% male) with age- and sex-specific BMI ≥85th percentile participated in the study.

Regularized Exploratory Bifactor Analysis With Small Sample Sizes.

Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling.

A Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing (CAT) Using a Medical Licensing Examination.

Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize observed standard errors (OSEs) to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (<40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean when mean of ability population distribution is zero.

Measuring theory of mind in schizophrenia research: Cross-cultural validation.

Theory of mind (ToM) is the ability to understand mental states of others and it is crucial for building sensitivity to other persons or events. Measuring ToM is important for understanding and rehabilitating social cognitive impairments in persons with schizophrenia. The Social Attribution Task-Multiple Choice (SAT-MC) has been successfully employed to measure ToM between individuals with schizophrenia (SZ) and healthy controls (HC) in North America.

Consistent Partial Least Squares Path Modeling via Regularization.

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables.

Characteristics of Lithium Ions and Superoxide Anions in EMI-TFSI and Dimethyl Sulfoxide.

To clarify the microscopic effects of solvents on the formation of the Li(+)-O2(–) process of a Li–O2 battery, we studied the kinetics and thermodynamics of these ions in dimethyl sulfoxide (DMSO) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-TFSI) using classical molecular dynamics simulation. The force field for ions–solvents interactions was parametrized by force matching first-principles calculations. Despite the solvation energies of the ions are similar in both solvents, their mobility is much higher in DMSO.

Exploratory factor analysis with small sample sizes: a comparison of three approaches.

Exploratory factor analysis (EFA) has emerged in the field of animal behavior as a useful tool for determining and assessing latent behavioral constructs. Because the small sample size problem often occurs in this field, a traditional approach, unweighted least squares, has been considered the most feasible choice for EFA. Two new approaches were recently introduced in the statistical literature as viable alternatives to EFA when sample size is small: regularized exploratory factor analysis and generalized exploratory factor analysis.

Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation.

In covariance structure analysis, two-stage least-squares (2SLS) estimation has been recommended for use over maximum likelihood estimation when model misspecification is suspected. However, 2SLS often fails to provide stable and accurate solutions, particularly for structural equation models with small samples. To address this issue, a regularized extension of 2SLS is proposed that integrates a ridge type of regularization into 2SLS, thereby enabling the method to effectively handle the small-sample-size problem.

Exploratory factor analysis for small samples.

Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature.