Does modality matter? A latent profile and transition analysis of sedentary behaviours among school-aged youth: The UP&DOWN study

ABSTRACT

The aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8–18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time.     

公安类考生体能测试考前心理状况的调整

运用问卷、文献资料和调查访问等研究方法，对浙江省近两年来公安类考生的考前心理状态进行了分析研究，结果表明，有针对性地从意识和行为上加强应考前心理状态的调整对发挥考生潜能，取得优异成绩有重要意义，针对考生的不利心理状态，提出了消除的方法。     

外来词产生及流行的修辞学理据

外来词属于词汇学的范畴。外来词通过潜词显化、显意符号化、显词重叠化三种途径产生，其中的显词重叠化造成了外来词与汉语词的同义关系，而外来词独特的修辞效果也使人们在同义手段选择活动中更青睐于外来词。     

校园文化中的隐性课程对职业素质养成的作用

校园文化中的隐性课程会让学生淡化或摆脱课业的压抑,使学生的兴趣、爱好得到某种程度的张扬,心理压力得到一定的舒缓,文明、进步、健康、蓬勃、富有特色、奋斗不息的校园精神是校园文化的核心,具有启迪、感化学生的作用,对学生身心健康的影响是深刻而持久的.     

Latent Profiles of Reading and Language and Their Association With Standardized Reading Outcomes in Kindergarten Through Tenth Grade

The objective of this study was to determine the latent profiles of reading and language skills that characterized 7,752 students in kindergarten through tenth grade and to relate the profiles to norm-referenced reading outcomes. Reading and language skills were assessed with a computer-adaptive assessment administered in the middle of the year and reading outcome measures were administered at the end of the year. Three measures of reading comprehension were administered in third through tenth grades to create a latent variable. Latent profile analysis (LPA) was conducted on the reading and language measures and related to reading outcomes in multiple regression analyses. Within-grade multiple regressions were subjected to a linear step-up correction to guard against false-discovery rate. LPA results revealed five to six profiles in the elementary grades and three in the secondary grades that were strongly related to standardized reading outcomes, with average absolute between-profile effect sizes ranging from 1.10 to 2.53. The profiles in the secondary grades followed a high, medium, and low pattern. Profiles in the elementary grades revealed more heterogeneity, suggestive of strategies for differentiating instruction.     

Statistical Power of the Multiple Domain Latent Growth Model for Detecting Group Differences

The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological performance is very limited. Hence, the present study compared the power of the MDLGM with that of a set of univariate LGMs for detecting group differences in growth rates over time using a Monte Carlo study with a two-group and two-domain design. The results indicated that there were different scenarios where the power rates for the MDLGM were greater than that of the set of LGMs (and vice versa) due to a joint function of the two domains’ intercorrelation size and the group difference effect size.     

由潜抑到彰显——论老舍的民族归属意识

满族作家老舍的民族归属意识并非像沈从文和张承志等少数民族作家一样,始终自觉彰显,而是以解放前后为界,经历了由潜抑到彰显的转变历程。老舍一生都有着始终如一的满族归属意识,但在解放前一直处于一种压抑状态。直到解放后才开始彰显自己的民族归属意识。     

Proportionality Assumption in Latent Basis Curve Models: A Cautionary Note

Latent basis curve models (LBCMs) have been popular in modeling change when the change trajectories are unknown or nonlinear. The estimated change trajectories from LBCMs are often viewed as optimal and used as reference points against which other change trajectories are tested. However, there is a proportionality assumption underlying LBCMs that has received little attention from researchers. This study uses a Monte Carlo simulation to show that violation of this assumption can potentially result in substantially biased estimates of the means and variances of changes and covariate effects on these changes, leading to incorrect statistical inference. The implications of the simulation study are discussed and alternatives to LBCMs are suggested for use when the proportionality assumption is likely to be violated.     

由书法创作到潜意识的阐释

书法书写中潜意识的形成以扎实的基本功和渊博的字外涵养为支撑点,经典的“过去经验”是书法创作者潜意识形成的基本的材料。书法创作者的心态环境是潜意识外化的基本条件。成功的书法创作是书法作者潜意识充分外化的表现。     

Fitting Unstructured Finite Mixture Models in Longitudinal Design: A Recommendation for Model Selection and Estimation of the Number of Classes

In longitudinal design, investigating interindividual differences of intraindividual changes enables researchers to better understand the potential variety of development and growth. Although latent growth curve mixture models have been widely used, unstructured finite mixture models (uFMMs) are also useful as a preliminary tool and are expected to be more robust in identifying classes under the influence of possible model misspecifications, which are very common in actual practice. In this study, large-scale simulations were performed in which various normal uFMMs and nonnormal uFMMs were fit to evaluate their utility and the performance of each model selection procedure for estimating the number of classes in longitudinal designs. Results show that normal uFMMs assuming invariance of variance–covariance structures among classes perform better on average. Among model selection procedures, the Calinski–Harabasz statistic, which has a nonparametric nature, performed better on average than information criteria, including the Bayesian information criterion.