Observed classroom quality during teacher education and two years of professional practice.

The aims of the present study are to investigate whether and how teachers change in their observed classroom quality (emotional and instructional support, classroom organization, and students' engagement; measured with the Classroom Assessment Scoring System observation measure for secondary school [CLASS-S]; Pianta, La Paro, & Hamre, 2006) during their teacher education year and the first 2 years of professional practice, as well as the effects of personal characteristics and contextual factors on observed classroom quality. Confirmatory factor analyses (CFAs) with a reduced number of indicators per a priori construct fitted the data well. Using factor scores in 3-level multilevel models for change (376 lesson segments 20 min in duration nested within 10 time points, within 17 teachers), we found more variability over time than within lessons and the least variability between teachers. Emotional support showed an inverted U-shaped change over time, whereas classroom organization increased linearly over time. Emotional support was lower for older students, and students were more engaged in larger classes. A higher level of classroom organization during lessons was related to less variability in student engagement, but variability of instructional support was unrelated to variability of student engagement within each lesson. The findings are discussed within the context of teachers' supported transitions into professional practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from MRI appearances

Several MRI features of supratentorial astrocytomas are associated with high histologic grade by statistically significant p values. We sought to apply this information prospectively to a group of astrocytomas in the prediction of tumor grade. We used 10 MRI features of fibrillary astrocytomas from 52 patient studies to develop neural network and multiple linear regression models for practical use in predicting tumor grade. The models were tested prospectively on MR images from 29 patients studies. The performance of the models was compared against that of a radiologist. Neural network accuracy was 61% in distinguishing between low and high grade tumors. Multiple linear regression achieved an accuracy of 59%. Assessment of the images by a radiologist yielded 57% accuracy. We conclude that while certain MRI parameters may be statistically related to astrocytoma histologic grade, neural network and linear regression models cannot reliably use them to predict tumor grade.     

Simulating Activated Sludge System by Simple-to-Advanced Models

Models ranging through simple, intermediate, and International Water Association complex activated sludge models (ASMs) were evaluated to compare their ability to describe biomass growth and substrate removal in an activated sludge system. A membrane-activated sludge bench-scale system was used to treat a complex synthetic wastewater over a wide range of operating conditions, ranging from 1 to 15 days solids retention time and 4 to 12 h hydraulic retention time. Total suspended solids, volatile suspended solids (VSSs), and total and soluble chemical oxygen demands (CODs) were monitored in the influent, the reactor, and the effluent. A variety of substrate removal formulations were used with the simple and intermediate models. Although all models provide excellent prediction of biomass growth, the intermediate model was best. Prediction of substrate removal was good with models that incorporated a nonbiodegradable component in the influent. ASM3 was the best model for predicting effluent soluble COD, but overall, the intermediate model was judged best for prediction of mixed liquor VSS and effluent soluble COD.     

Models of individual trajectories in computer-assisted instruction for deaf students.

Describes a quantitative theory of student trajectories in a computer-assisted instruction course. The theory rests upon qualitative assumptions about information processing, from which a stochastic differential equation can be derived. The differential equation is characteristic of the course, but the constants of integration are estimated separately for each student. The fit of data from 297 deaf 1st-7th graders to theory is reported in terms of the standard scale of grade placement. The mean of the mean standard errors averaged across Ss was .046, which represents a relatively close fit of data to theory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于多类别生产状态的烧结矿转鼓指数预测模型

将烧结生产大数据与机器学习算法相结合,提出了一种多类别生产状态下预测烧结矿转鼓指数的研究方法.通过数据采集、整合及预处理操作,共获得特征参数65种.以烧结终点位置(BTP)为基础,采用试验研究及可视化分析等方法将转鼓指数划分为2个类别.基于分类别转鼓指数数据集,使用特征选择算法计算了特征参数的重要排序,确定最佳特征参数...     

Impact of work experience on the validity of student evaluations of teaching effectiveness.

Examined whether student evaluations of teaching effectiveness changed significantly once the students had left the university and had gained work experience. Students in their final year at a university were asked to grade lecturers on 9 items relating to teaching effectiveness and course effectiveness; 723 students responded. After the students had graduated and had been working for 1 yr, they were asked again to give a rating of teaching effectiveness for their former lecturers and to nominate the "best" and "worst" teachers; 506 replied. Comparisons of the results show that there was little difference in the 2 ratings of lecturers by their teaching scores. Increasing maturity and job experience did not significantly alter student evaluations of relative teaching effectiveness. (22 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Developing Regression Models for Predicting Pan Evaporation from Climatic Data—A Comparison of Multiple Least-Squares, Principal Components, and Partial Least-Squares Approaches

Regression models for predicting daily pan evaporation depths from climatic data were developed using three multivariate approaches: multiple least-squares regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. The objective was to compare the prediction accuracies of regression models developed by these three approaches using historical climatic datasets of four Indian sites that are located in distinctly different climatic regimes. In all cases (three approaches applied to four climatic datasets), regression models were developed using a part of the data and subsequently validated with the remaining data. Results indicated that although performances of the regression models varied from one climate to another, more or less similar prediction accuracies were obtained by all three approaches, and it was difficult to identify the best approach based on performance statistics. However, the final forms of the regression models developed by the three approaches differed substantially from one another. In all cases, the models derived using PLS contained the smallest number of predictor variables; between two to three out of a possible maximum of six predictor variables. The MLR approach yielded models with three to six predictor variables, and PCR models included all six predictor variables. This implies that the PLS regression models are the most parsimonious in terms of input data required for estimating epan from climate variables, and yet yield predictions that are almost as accurate as the more data-intensive MLR and PCR models.     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrological simulations.For modeling daily precipitation in weather generators,first-order Markov chain-dependent exponential,gamma,mixed-exponential,and lognormal distributions can be used.To examine the performance of these four distributions for precipitation simulation,they were fitted to observed data collected at 10 stations in the watershed of Yishu River.The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms.Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately.Bayesian information criterion,simulated monthly mean,maximum daily value,and variance were tested and compared to evaluate the fitness and performance of these models.The results indicate that the lognormal and mixed-exponential distributions give smaller BICs,but their stochastic simulations have overestimation and underestimation respectively,while the gamma and exponential distributions give larger BICs,but their stochastic simulations produced monthly mean precipitation very well.When these distributions were fitted using Fourier series,they all underestimated the above statistics for the months of June,July and August.     

Classroom Social Experiences as Predictors of Academic Performance.

A model linking children's peer acceptance in the classroom to academic performance via academic self-concept and internalizing symptoms was tested in a longitudinal study. A sample of 248 children was followed from 4th to 6th grade, with data collected from different informants in each year of the study to reduce respondent bias. A path analysis supported the model; a lack of peer acceptance in the classroom in 4th grade predicted lower academic self-concept and more internalizing symptoms the following year, which in turn, predicted lower academic performance in 6th grade. An alternative path with internalizing symptoms predicting declines in peer acceptance was tested and received some support as well. Implications of the findings for schools are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Time management practices in an African culture: Correlates with college academic grades.

Examined the construct and predictive validity of Western process models of time management in an African culture. 472 Zimbabwean student teachers of a Shona cultural background took part in the study. Ss varied by gender, age, college entry grade point and year of study. Students completed the Britton and Tesser (1991) Time Management Questionnaire. Factor analysis revealed that the time management processes of short term planning, perceived control of time, time attitude, and long term planning were valid constructs for the sample of African students. However, when the students' self report time behaviors, time attitudes and demographic variables were regressed on college GPAs, only perceived control of time, short term planning, college entry grade point and gender singularly accounted for a significant proportion of the variance in college GPA. (French abstract) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The role of self-control in early escalation of substance use: A time-varying analysis.

The research tested prediction about the role of temperament and self-control in early substance use (tobacco, alcohol, and marijuana). A sample of 1,526 participants was assessed in 6th grade (mean age=11.5 yrs) and followed with yearly assessments through 9th grade. Latent growth models showed temperament dimensions were related to early substance use, and their effects were mediated through generalized self-control ability. Time-varying effects indicated rate of growth in substance use was higher among participants who showed increases in poor self-control and lower among participants who showed increases in good self-control. Results in self-report data were corroborated by independent teacher ratings. Findings are discussed with reference to epigenetic models of protection and vulnerability. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Monte–Carlo Based Method for Predicting Extreme Value Statistics of Uncertain Structures

In the present paper, a simple method is proposed for predicting the extreme response of uncertain structures subjected to stochastic excitation. Many of the currently used approaches to extreme response predictions are based on the asymptotic generalized extreme value distribution, whose parameters are estimated from the observed data. However, in most practical situations, it is not easy to ascertain whether the given response time series contain data above a high level that are truly asymptotic, and hence the obtained parameter values by the adopted estimation methods, which points to the appropriate extreme value distribution, may become inconsequential. In this paper, the extreme value statistics are predicted taking advantage of the regularity of the tail region of the mean upcrossing rate function. This method is instrumental in handling combined uncertainties associated with nonergodic processes (system uncertainties) as well as ergodic ones (stochastic loading). For the specific applications considered, it can be assumed that the considered time series has an extreme value distribution that has the Gumbel distribution as its asymptotic limit. The present method is numerically illustrated through applications to a beam with spatially varying random properties and wind turbines subjected to stochastic loading.     

Confirmatory factor analysis models of information processing stages: An alternative to difference scores.

Presents a general approach based on ANCOVA structures that can be used by cognitive psychologists to calculate correlations involving a component of information processing time that cannot be directly measured. The usual procedure has been to express this component as a difference between 2 times that can be directly measured. But correlations involving difference scores are notably attenuated by the presence of measurement error, and the substantive assumptions implicit in the calculation of difference scores may not be plausible. The recommended approach begins with reasonable statements of how components of processes are structured in confirmatory factor analysis models, which can be estimated by LISREL or COFAMM. In the process of fitting such models, the proper disattenuated correlation is estimated as part of a set of parameters implied by substantive assumptions. The validity of these assumptions can be tested by comparing the fit of the model to observed data. Such a comparison may suggest how assumptions should be modified to increase the plausibility of the model. This model-fitting approach is illustrated with data relating information-processing tasks to ability measures. (20 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Academic and social motives and drinking behavior.

This longitudinal study of 1,447 first-time college students tested separate time-varying covariate models of the relations between academic and social motives/behaviors and alcohol use and related problems from senior year of high school through the end of the second year in college. Structural equation models identified small but significant inverse relations between academic motives/behaviors and alcohol use across all time points, with relations of somewhat larger magnitude between academic motives/behaviors and alcohol-related problems across all semesters other than senior year in high school. At all time points, there were much larger positive relations between social motives/behaviors and alcohol use across all semesters, with smaller but significant relations between social motives/behaviors and alcohol-related problems. Multi-group models found considerable consistency in the relations between motives/behaviors and alcohol-related outcomes across gender, race/ethnicity, and family history of alcohol problems, although academic motives/behaviors played a stronger protective role for women, and social motives were a more robust risk factor for Caucasian and Latino students and individuals with a positive family history of alcohol problems. Implications for alcohol prevention efforts among college students are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction of Carbon BTEX Adsorption Capacity Using Field Monitoring Data

The need for predicting adsorption capacity for benzene, toluene, ethylbenzene, and xylenes (BTEX) onto granular-activated carbon (GAC) is a problem commonly associated with petroleum-spill remediation. In this study, monitoring data are compiled from operational records of ground-water pump and treat remediation sites where GAC adsorption is utilized as a primary treatment mechanism for BTEX. The monitoring data are reduced to adsorbed and equilibrium concentrations from which Freundlich isotherms and various linear and multivariate models are calibrated for prediction of BTEX capacity on GAC. The models are employed by themselves and with Ideal Adsorbed Solution Theory to predict capacity for total BTEX and benzene. Several models are selected based on prediction ability and are tested with independent data. Two simple models, a multivariate model and a Freundlich isotherm, are recommended. Complex empirical models and Ideal Adsorbed Solution Theory did not perform as well as the selected models and were rejected. From the Freundlich isotherm, new Freundlich constants are reported that describe adsorption of total BTEX on GAC from gasoline-contaminated ground water.     

Cervical endometriosis: a potential pitfall in the evaluation of glandular cells in cervical smears

More reliable prediction of outcome would be helpful for clinicians who treat severely head-injured patients. To determine if neural network modeling would improve outcome prediction compared with standard logistic regression analysis and to determine if data available 24 h after severe head injury allows better prediction than data obtained within 6 h, we tested the ability of both techniques at these two times to predict outcome (dead versus alive) at 6 months. One thousand sixty-six consecutive patients with Glasgow Coma Scale scores of 8 or less during the first 24 h after injury were randomly divided into two groups. Data from the first group (n = 799) were used to develop the models; data from the second group (n = 267) were used to test the accuracy, sensitivity, and specificity of the models by comparing predicted and actual outcomes. The 6-month mortality rate was 63.5%. Our findings confirm the importance of age, Glasgow Coma Scale scores, and hypotension in predicting outcome. Using data available at 24 h improved the predictive power of both models compared with admission data; at both time points, however, the differences in the results obtained with the two models were negligible. We conclude that outcome (dead versus alive) at 6 months after severe head injury can be predicted with logistic regression or neural network models based on data available at 24 h. Critical therapeutic decisions, such as cessation of therapy, should be based on the patient's status 1 day after injury and only rarely on admission status alone.     

基于FA-PSO-RBF神经网络的富氧底吹铜锍品位预测模型

铜锍品位是富氧底吹铜熔炼过程中的一个关键工艺参数,针对铜锍品位实时检测困难、检测结果滞后时间长、指导生产工艺参数优化滞后等问题,基于生产数据深入挖掘及处理,提出了一种基于FA-PSO-RBF神经网络的铜锍品位预测模型。首先为了降低模型的预测误差,利用FA分析方法对原始生产数据进行降维处理,确定主要因子数量为6个,并计算因子得分,然后针对RBF神经网络模型对关键参数依赖性较大的不足,利用改进PSO算法对网络结构中的关键参数进行寻优,最后,以因子得分为输入,铜锍品位值为输出,通过实际生产数据验证模型的准确性,并与RBF、标准PSO-RBF预测模型进行对比,结果表明,本文构建的铜锍品位预测模型预测精度更高,与标准PSO-RBF预测模型相比,RMSE和MAE的值分别降低了17.2%和21.2%,该预测模型对富氧底吹铜熔炼生产过程参数优化控制提供了一种方法借鉴。     

Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students.

This study was conducted to determine the validity of noncognitive and cognitive predictors of the performance of college students at the end of their 4th year in college. Results indicate that the primary predictors of cumulative college grade point average (GPA) were Scholastic Assessment Test/American College Testing Assessment (SAT/ACT) scores and high school GPA (HSGPA) though biographical data and situational judgment measures added incrementally to this prediction. SAT/ACT scores and HSGPA were collected and used in various ways by participating institutions in the admissions process while situational judgment measures and biodata were collected for research purposes only during the first few weeks of the participating students’ freshman year. Alternative outcomes such as a self-report of performance on a range of student performance dimensions and a measure of organizational citizenship behavior, as well as class absenteeism, were best predicted by noncognitive measures. The racial composition of a student body selected with only cognitive measures or both cognitive and noncognitive measures under various levels of selectivity as well as the performance of students admitted under these scenarios is also reported. The authors concluded that both the biodata and situational judgment measures could be useful supplements to cognitive indexes of student potential in college admissions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Make or buy: HMOs'' contracting arrangements for mental health care

Optimal statistical design strategies are applied to toxicokinetic experiments, for determining proper allocations of subjects and/or spacings of sampling times under a variety of nonlinear concentration-time models. The strategies include: (i) optimal allocations of subjects assuming the placement of time points is fixed, (ii) optimal spacing of design time points while assuming an equal allocation of subjects per time points and (iii) allocations/time-point spacings optimized jointly. Emphasis is placed on the first case, where a variance-minimization method is illustrated for optimizing the allocations when estimating specific toxicokinetic parameters. Appeals to forms of D-optimality are also considered, for cases when no specific toxicokinetic parameter is of specialized interest.