Evaluating the Quality of Classification in Mixture Model Simulations.

The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations, it was determined that models without a covariate better predicted the number of classes.

The Impact of Imposing Equality Constraints on Residual Variances Across Classes in Regression Mixture Models.

The purpose of this study is to explore the impact of constraining class-specific residual variances to be equal by examining and comparing the parameter estimation of a free model and a constrained model under various conditions. A Monte Carlo simulation study was conducted under several conditions, including the number of predictors, class-specific intercepts, sample size, class-specific regression weights, and class proportion to evaluate the results for parameter estimation of the free model and the restricted model. The free model yielded a more accurate estimation than the restricted model for most of the conditions, but the accuracy of the free model estimation was impacted by the number of predictors, sample size, the disparity in the magnitude of class-specific slopes and intercepts, and class proportion.

Mechanical and Electrical Characteristics of Lightweight Aggregate Concrete Reinforced with Steel Fibers.

There is increased interest in applying electromagnetic (EM) shielding to prevent EM interference, which destroys electronic circuits. The EM shielding's performance is closely related to the electrical conductivity and can be improved by incorporating conductive materials. The weight of a structure can be reduced by incorporating lightweight aggregates and replacing the steel rebars with CFRP rebars.

Multiple Group Analysis in Multilevel Data Across Within-Level Groups: A Comparison of Multilevel Factor Mixture Modeling and Multilevel Multiple-Indicators Multiple-Causes Modeling.

The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of procedures testing weak and strong invariance models and the latent group mean differences testing after holding for factorial invariance. Two Monte Carlo simulation studies were conducted under the following conditions: number of clusters, cluster size, and the design type in groups.

Adequate Sample Sizes for a Three-Level Growth Model.

Multilevel models have been developed for addressing data that come from a hierarchical structure. In particular, due to the increase of longitudinal studies, a three-level growth model is frequently used to measure the change of individuals who are nested in groups. In multilevel modeling, sufficient sample sizes are needed to obtain unbiased estimates and enough power to detect individual or group effects.

Growth Mixture Modeling With Nonnormal Distributions: Implications for Data Transformation.

This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality assumption and using data transformation on repeated measures. Based on unconditional GMM with two latent trajectories, data were generated under different sample sizes (300, 800, and 1500), skewness (0.

Evaluation on the Microstructure and Durability of High-Strength Concrete Containing Electric Arc Furnace Oxidizing Slag.

The application of electric arc furnace oxidizing slag (EAS) in high strength concrete (HSC) as the cementitious material is investigated in this study. The microstructure and mechanical properties of HSC with four different replacement ratios of EAS were evaluated and HSC with two replacement ratios of ground granulated blast furnace slag (GBS) was used for performance comparison. The results show that the HSC with EAS replacement ratios smaller than 15% undergo similar hydration processes and result in a similar final product when compared with those of NC-NN.

The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling.

The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects.

Profiles of Working Moms' Daily Time Use: Exploring Their Impact on Leisure.

This paper identified latent profiles depending on the patterns of daily time usage amongst working moms in Korea and tested their relations to family- and work-related characteristics. The consequent differences in the levels of leisure attributes were further investigated. Taking a holistic approach, latent profile analysis, one of the person-centered methods, was conducted using data drawn from the seventh year of the Korean Longitudinal Survey of Women and Families (KLoWF), with a sample of 1074 women.

Effect of Strengthening Methods on Two-Way Slab under Low-Velocity Impact Loading.

In this study, the performance of reinforced concrete slabs strengthened using four methods was investigated under impact loads transferred from the top side to bottom side. The top and bottom sides of test slabs were strengthened by no-slump high-strength, high-ductility concrete (NSHSDC), fiber-reinforced-polymer (FRP) sheet, and sprayed FRP, respectively. The test results indicated that the test specimens strengthened with FRP series showed a 4% increase in reaction force and a decrease in deflection by more than 20% compared to the non-strengthened specimens.

Bond Strength and Flexural Capacity of Normal Concrete Beams Strengthened with No-Slump High-Strength, High-Ductility Concrete.

This study investigates the flexural behavior of normal-strength concrete (NSC) beams that were strengthened with no-slump, high-strength, high-ductility concrete (NSHSDC). A set of slant shear tests was performed to investigate the initial performance of the NSC substrate strengthened with NSHSDC. Slant shear tests considered two types of roughness of interface and five angles of the interface between NSC and NSHSDC.

Effects of Steelmaking Slag and Moisture on Electrical Properties of Concrete.

Strain sensors can indicate the conditions of concrete structures, but these sensors are only capable of measuring local behaviors of materials. To solve this problem, researchers have introduced conductive materials that can monitor the overall behavior of concrete structures. Steelmaking slag, which contains large amounts of iron oxide (FeO), is conductive, and researchers have considered the addition of this material to improve concrete monitoring.

Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes.

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means.

A Comparison of Different Nonnormal Distributions in Growth Mixture Models.

The purpose of the present study is to compare nonnormal distributions (i.e., , skew-normal, skew- with equal skew and skew- with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts.

A Comparison of Mixture Modeling Approaches in Latent Class Models With External Variables Under Small Samples.

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions.

Comparisons of Multilevel Modeling and Structural Equation Modeling Approaches to Actor-Partner Interdependence Model.

There are basically two modeling approaches applicable to analyzing an actor-partner interdependence model: the multilevel modeling (hierarchical linear model) and the structural equation modeling. This article explains how to use these two models in analyzing an actor-partner interdependence model and how these two approaches work differently. As an empirical example, marital conflict data were used to analyze an actor-partner interdependence model.

Multivariate autoregressive cross-lagged modeling of the reciprocal longitudinal relationship between perceived control and academic achievement.

Although many studies have demonstrated positive associations between perceived control and academic achievement, few studies have actually explored which of the two constructs is the determinant of the other. There are only a few longitudinal studies on the relationship of perceived control and academic achievement. The present study examined the reciprocal longitudinal relation between perceived control and academic achievement.