Adjustment to college and prediction of depression during post-secondary transition

Many studies have reported an increase in mental health problems during post-secondary transition, often originating from high school years. The present study examined how depressive symptoms during the 2 years following the post-secondary transition could be predicted by, on the one hand, school performance, externalized and learning difficulties, and depressive symptoms before the post-secondary transition, and on the other hand, personal factors, family functioning, and adjustment to college after the transition. From a sample of 438 participants (M = 16.20, SD = 0.87) at time point 1, an integrated model was elaborated using structural equation modeling. The statistical analyses showed that the five constructs fit the data well. The path coefficients showed a negative relationship between externalized and learning problems as perceived by the teacher (ELPT) before the transition and academic performance (AP). Personal characteristics (PC) negatively predicted academic adjustment (AC) over time, whereas the path coefficients between the family factor (FF) and AC were not significant over time. ELPT and AP negatively predicted depression at time point 1. At time points 2 and 3, PC positively predicted depression, and depressive symptoms were positively related over time. The percentage of variance accounted for by the depressive symptoms increased over time.     

Effects of Different Durations of Physical Training on Cardiorespiratory Function,Body Composition,and Serum Lipids

Abstract

The purpose of the study was to determine the effects of 15, 30, and 45 min of conditioning on maximum performance and cardiorespiratory fitness variables, body composition, pulmonary function, and serum lipids. Subjects, ages 20 to 35, were 59 inmates at a California state prison. The conditioning included running and walking and was performed three days/week at approximately 85 to 90% max HR for 20 weeks. The distance covered per exercise session was approximately 1.75, 3.25, and 5.1 miles for the 15-, 30-, and 45-min groups, respectively.

Improvements in treadmill performance time. [Vdot]O₂ max, max O₂ pulse, diastolic blood pressure, and total skinfold fat were proportional to duration of the training session; i.e., the subjects training in the longer duration sessions showed the greater improvements. The control group showed no significant change during the same period. Compared to the control group, the 15-min group made significant reductions in resting HR, total skinfold fat, percent fat, and waist girth and increases in [Vdot]O₂ max, max O₂ pulse,[Vdot]_E max, and treadmill performance time. Vital capacity, FEV_1.0, and % FEV_1.0 remained relatively unchanged in all four groups, as did serum cholesterol and triglycerides. The 45-min group improved significantly more than the 15-min group in treadmill time, [Vdot]O₂ max, max O₂ pulse, resting systolic and diastolic blood pressures, and percent fat. In general, comparisons between the 15- and 30-min groups and between the 30- and 45-min groups failed to yield statistically significant differences.     

A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

A comprehensive model covering prospective teachers’ technology use: the relationships among self,teaching and learning conceptions and attitudes

ABSTRACT

This study was conducted to propose and examine a comprehensive model embracing prospective teachers’ beliefs about self, teaching and learning conceptions, and attitudes towards using instructional technologies. Prospective or inservice teachers’ beliefs (from central to peripheral) and attitudes may empower the support of teacher training related to using instructional technologies. Participants were 1208 Turkish prospective teachers from five different universities who participated voluntarily and were selected using convenience sampling. Structural equation modelling analysis showed that prospective teachers’ beliefs about the autonomous self and autonomous–related self positively related to both their constructivist beliefs about teaching and learning and attitudes towards using instructional technologies, whereas the same beliefs negatively related to traditional conceptions of teaching and learning. Moreover, participants’ constructivist beliefs about teaching and learning positively predicted their attitudes towards using instructional technologies. Furthermore, their beliefs about related self positively and unexpectedly predicted their constructivist conceptions and attitudes. Implications are presented considering these results.     

High school students’ scientific epistemological beliefs,self-efficacy in learning physics and attitudes toward physics: a structural equation model

Background: There are some theoretical evidences that explain the relationships between core beliefs (i.e., epistemological beliefs) and peripheral beliefs (self-efficacy in learning) in the literature. The close relationships of such type of beliefs with attitudes are also discussed by some researchers. Constructing a model that investigates these relationships by considering theoretical and empirical evidences can empower researchers to discuss these relationships more comprehensively.

Purpose: The purpose of this study is to explore the relationships among Turkish high school students’ scientific epistemological beliefs, self-efficacy in learning physics and their attitudes toward physics.

Sample: A total of 632 high school students participated in this study; however, 269 female and 229 male (a total of 498) high school students’ data were used.

Design and methods: Three distinct instruments that measure scientific epistemological beliefs, self-efficacy in learning physics and attitudes toward physics were combined into a unique questionnaire form and it was distributed to high school students. To explore the relationships among these variables, structural equation modeling was used.

Results: The results showed that scientific epistemological belief dimensions uncovered by the nature of knowing (source and justification) significantly and positively related to both self-efficacy in learning physics and attitudes toward other important physics dimensions. Additionally, self-efficacy in learning physics significantly and positively predicted attitudes toward multiple physics dimensions (importance, comprehension and requirement). However, epistemological belief dimensions related to the nature of knowledge (certainty and development) did not have significant impact on self-efficacy in learning physics or attitudes toward physics.

Conclusions: This study concludes that there are positive and significant relationships among Turkish high school students’ scientific epistemological beliefs, self-efficacy in learning physics and their attitudes toward physics.     

On the Application of the Three-Step Approach to Growth Mixture Models

This series of simulation studies evaluate, in the context of applied research settings, the impact of the parameterization of the covariance structure of the growth mixture model (GMM) on the regression coefficient and standard error estimates in the 3-step method. The results show that the 1-step approach performs better than the 3-step method across the simulation studies. However, the performance of the 3-step method depends slightly or importantly on the parameterization of the GGM from the first step, on the inclusion or not of the predictor at the first step of the analysis, on the population model, and on the type (i.e., logit vs. linear) and size of the regression coefficient estimates.     

A multidimensional model of school dropout from an 8-year longitudinal study in a general high school population

This study tests an empirical multidimensional model of school dropout, using data collected in the first year of an 8-year longitudinal study, with first year high school students aged 12–13 years. Structural equation modeling analyses show that five personal, family, and school latent factors together contribute to school dropout identified at 19 years of age: poor parent–teenager relationships, youth depression and family difficulties, negative classroom climate, negative school interactions, and poor academic achievement. This model increases our understanding of the dropout process in the general population and has direct implications for the development of high school dropout prevention programs.