Two-stage Selection Procedure Research Articles

PBL METHOD AND LEARNING MOTIVATION ARE INFLUENCED STUDENTS READING COMPREHENSION SKILL

his study aims to find out whether problem-based learning methods and learning motivation have an impact on the reading comprehension skills of eighth-grade students at SMPN 39 Palembang. 60 students from SMPN 39 Palembang were selected as samples for this study using a two-stage random selection procedure during the academic year 2022–2023. A factorial research design was used in this investigation. Tests and questionnaires were used to get the data.. When the data were studied using paired sample t-test, independent sample t-test, and two-way ANOVA test findings of 0.000 0.05, it was discovered that problem-based learning methods had a substantial impact on students' reading comprehension skills. Second, problem-based learning techniques have a considerable impact on students with low learning motivation who struggle with reading comprehension. Thirdly, students who use problem-based learning strategies and have high levels of learning motivation have a major impact on their ability to comprehend what they read. Finally, significant interactions between motivation and learning affect how well students understand recount texts taught by PBL and how well they understand SMPN 39 Palembang. Hence, both high and low-learner-motivation students benefited from the PBL approach.

On the Determinants and Outcomes of IMF Loans in Low- and Middle-Income Countries: Do Politics Matter?

ABSTRACT The objective of this paper is to analyze the economic and political determinants of IMF loans in low- and middle-income countries and their impact on economic growth. Our contribution is threefold. First, we use the IMF Monitoring of Fund Agreements database along with international political economy factors to analyze IMF lending determinants through a Heckman two-stage selection procedure. Second, we use the predicted values of determinants of IMF lending to explain the consequences of this lending on growth. We also investigate how the domestic political regime of the recipient country would affect the outcomes of these loans. Third, we study the dynamic effects of IMF loans on economic growth using the local projection method. Our main findings show that economic and political proximity to the IMF major shareholders matter for the likelihood of obtaining an IMF non-concessional loan. Furthermore, most of the loans exert a negative effect on the trend component of GDP, confirming that such loans can stabilize the economies in the short term without improving the long-run steady growth. The analysis of the dynamic effects of loans also confirmed these findings. Finally, democratic regimes compared to autocratic ones improve the effects of these loans on economic growth.

A two-stage design for comparing binomial treatments with a standard

We propose a two-stage selection and testing procedure for comparing success rates of several populations among each other and against a desired standard success rate to identify which treatment has the highest rate of success that is also higher than the standard. The design combines elements of both hypothesis testing and statistical selection. As a hybrid two-stage procedure, it allows for dropping the poorly performing treatments early on the basis of interim analysis results or for early termination if none of the experimental treatments seems promising. Because this procedure is not a pure hypothesis testing procedure, power and size are redefined to account for its hybrid nature. Using these definitions, we determine the design parameters for given size and power values. When multiple designs meet these requirements, we will recommend the set of design parameters that produces the lowest expected sample size.

Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images: A Cohort Data Study.

Existing methods for muscle atrophy evaluation based on muscle size measures from ultrasound images are inadequate in precision. Radiomics has been widely used in various medical studies, but its validity for the evaluation of muscle atrophy has not been fully explored. This study presents a radiomics analysis for muscle atrophy evaluation using ultrasound images. The hindlimb unloading rat model was developed to simulate weightlessness muscle atrophy and ultrasound images of the hind limbs were acquired for both the hindlimb unloaded (HU) and control groups during a 21-day HU period. A total of 368 radiomics features were extracted and the stable and informative features were selected through a two-stage feature selection procedure. The feature change trajectory of the stable features was analyzed using the hierarchical clustering method. Finally, an adaptive longitudinal feature selection and grading network, ALNet, was developed to evaluate muscle atrophy. The clustering trajectories of ultrasound image features showed similar trends to the changes in muscle atrophy at the molecular level. The best grading accuracy achieved by the ALNet was 79.5% for the Soleus (Sol) muscle and 82.6% for the Gastrocnemius (Gas) muscle. The test-retest is essential in performing radiomics analysis on ultrasound images. The longitudinal feature selection is important for muscle atrophy grading. The ultrasound image features of the Gas muscle have better discrimination ability than that of the Sol muscle. This study proves for the first time the capability of ultrasound image features for muscle atrophy evaluation.

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.

High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies

We present a fully automatic system that can produce high-fidelity, photo-realistic three-dimensional (3D) digital human heads with a consumer RGB-D selfie camera. The system only needs the user to take a short selfie RGB-D video while rotating his/her head and can produce a high-quality head reconstruction in less than 30 s. Our main contribution is a new facial geometry modeling and reflectance synthesis procedure that significantly improves the state of the art. Specifically, given the input video a two-stage frame selection procedure is first employed to select a few high-quality frames for reconstruction. Then a differentiable renderer-based 3D Morphable Model (3DMM) fitting algorithm is applied to recover facial geometries from multiview RGB-D data, which takes advantages of a powerful 3DMM basis constructed with extensive data generation and perturbation. Our 3DMM has much larger expressive capacities than conventional 3DMM, allowing us to recover more accurate facial geometry using merely linear basis. For reflectance synthesis, we present a hybrid approach that combines parametric fitting and Convolutional Neural Networks (CNNs) to synthesize high-resolution albedo/normal maps with realistic hair/pore/wrinkle details. Results show that our system can produce faithful 3D digital human faces with extremely realistic details. The main code and the newly constructed 3DMM basis is publicly available.

A Map of the Poor or a Poor Map?

This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study (LSMS) surveys. The estimation methods considered are that of Elbers, Lanjouw and Lanjouw (2003), the empirical best predictor of Molina and Rao (2010), the twofold nested error extension presented by Marhuenda et al. (2017), and finally an adaptation, presented by Nguyen (2012), that combines unit and area level information, and which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared error (MSE). Results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias is unknown ex ante, methods relying only on aggregated covariates should be used with caution, but may be an alternative to traditional area level models when these are not applicable.

How Do Middle-Aged Chinese Men and Women Balance Caregiving and Employment Income?

Unpaid family caregivers might suffer losses in income as a result of care provision. Here we used data from the baseline survey of the China Health and Retirement Longitudinal Study to assess the relationship between hours of weekly caregiving provided to grandchildren/parents/parents-in-law and individual’s monthly employment income. Our study sample comprised 3718 middle-aged Chinese adults who were of working age (45–60 years). For women and men separately, we used a likelihood-based method to determine a caregiving threshold in a two-stage Heckman selection procedure. Instrumental variables were used to rule out the endogeneity of caregiving hours. Our analysis revealed a negative association between caregiving and income for women that depended on a caregiving threshold of 63 h per week. There was an absence of caregiving-income relationship among men. These results offer new insights into the opportunity costs of unpaid caregiving and support tailored policies to protect the financial well-being of female caregivers.

Model identification and selection for single-index varying-coefficient models

Single-index varying-coefficient models include many types of popular semiparametric models, i.e., single-index models, partially linear models, varying coefficient models, and so on. In this paper, a two-stage efficient variable selection procedure is proposed to select important nonparametric and parametric components and obtain estimators simultaneously. We also find that the proposed procedure can separate predictors into varying-coefficient and constant-coefficient predictors automatically. Theoretically, it has the selection and estimation consistency properties. Simulation studies and a real data application are conducted to evaluate and illustrate the proposed methods.

Do Remittances reduce poverty in Kosovo? - A counterfactual analysis

Abstract Migration and remittances are argued to be an effective mechanism for mitigating poverty, as well as a coping mechanism for disadvantaged households with no or little employment and earning opportunities in Kosovo. A considerable part is reported to be directed towards consumption and very little for investment or enterpreneurship purposes. The high dependence of households on remittances suggests that poverty rates would be much higher without the safety net provided through migration and remittances. The conventional approach of empirically estimating determinants of remittances, including those focusing on Kosovo, treats both remittance and migration behaviour as independent decisions. Empirically estimating determinants of remittances while overlooking the importance of variables that influenced the decision to migrate will leave out these determinants and also bias the results. Hence, this study treats migration and remittance decision as a joint process and focuses on the household. More precisely, it analyses the impact that remittances and migration have on the poverty in Kosovo, in a hypothetical case, without remittances and migration using data from the Household Budget Survey 2011. Due to the potential presence of selection bias, this study uses a two-stage Heckman-type selection procedure which suggests that there is no selection bias. The study develops counterfactual consumption estimates for remittance recipient households through the use of survey bootstrap procedure to predict the consumption of households in the case of no remittances. The results support the hypothesis that remittances increase the consumption of recipient households. The poverty rate would be higher for a considerable proportion of households in the case of no remittances. The poverty rates would increase particularly in rural areas. The novelty of this study lies on the methodological approach chosen to investigate the impact of remittances on poverty in Kosovo. In contrast to previous analysis, this study controls for potential selection bias and empirically assesses whether the expectations on the poverty reducing effect of remittances in Kosovo hold.

Distributionally Robust Selection of the Best

Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the presence of such input uncertainty. We model such uncertainty by an ambiguity set consisting of a finite number of plausible input distributions and aim to select the alternative with the best worst-case mean performance over the ambiguity set. We refer to this problem as robust selection of the best (RSB). To solve the RSB problem, we develop a two-stage selection procedure and a sequential selection procedure; we then prove that both procedures can achieve at least a user-specified probability of correct selection under mild conditions. Extensive numerical experiments are conducted to investigate the computational efficiency of the two procedures. Finally, we apply the RSB approach to study a queueing system’s staffing problem using synthetic data and an appointment-scheduling problem using real data from a large hospital in China. We find that the RSB approach can generate decisions significantly better than other widely used approaches. This paper was accepted by Noah Gans, stochastic models and simulation.

Qualitative Studies on Implicit Criteria during the Individualized Selection Procedure for Medical Studies at Witten/Herdecke University (UW/H).

Objective: The individualized two-stage selection procedure for medical studies at Witten/Herdecke University (UW/H) has been in use for more than 30 years and comprises explicit and implicit selection criteria. This analysis aims at identifying the implicit criteria and answering the question whether an internal consistency of these implicit criteria may be verified for the different phases of the selection procedure (when evaluating the statements of purpose, during the selection weekend and during the concluding discussions of assessors). Methodology: Three qualitative studies on all phases of the selection procedure at UW/H have been conducted for determining the implicit assessment criteria of assessors: statements of purpose in extreme group comparison (12 admissions versus 18 rejections); semi-structured expert interviews (N=25) on the selection weekend; focus group analysis of the concluding discussions on two selection weekends (N=16). Results: The content analysis of the statements of purpose yielded 14 main categories with significant deviations between extreme groups in the categories school career, reasons for application and reflections as well as regarding the higher education entrance qualification grade. Based on the expert interviews, three main categories could be identified: intellectual ability, motivation and social competence, and the ability to reflect as a cross-content category. The focus group analysis yielded four main categories: performance, personality, personal growth potential and ability to reflect. Most frequently, the ability to reflect was mentioned as an assessment criterion. Conclusion: The main assessment categories are: motivation for the medical profession and starting studies at UW/H; performance and scholastic aptitude; personality, personal growth potential and social competence, as well as the ability to reflect as the most important basic competence and general category. Assessors consider the ability to reflect as a predictor of lifelong professional development as a physician.

Directed growth of hydrogen lines on graphene: High-throughput simulations powered by evolutionary algorithm

We set up an evolutionary algorithm combined with density functional tight-binding (DFTB) calculations to investigate hydrogen adsorption on flat graphene and graphene monolayers curved over substrate steps. During the evolution, candidates for the new generations are created by adsorption of an additional hydrogen atom to the stable configurations of the previous generation, where a mutation mechanism is also incorporated. Afterwards a two-stage selection procedure is employed. Selected candidates act as the parents of the next generation. In curved graphene, the evolution follows a similar path except for a new mechanism, which aligns hydrogen atoms on the line of minimum curvature. The mechanism is due to the increased chemical reactivity of graphene along the minimum radius of curvature line (MRCL) and to sp$^3$ bond angles being commensurate with the kinked geometry of hydrogenated graphene at the substrate edge. As a result, the reaction barrier is reduced considerably along the MRCL, and hydrogenation continues like a mechanical chain reaction. This growth mechanism enables lines of hydrogen atoms along the MRCL, which has the potential to overcome substrate or rippling effects and could make it possible to define edges or nanoribbons without actually cutting the material.

Structural holes, exploratory innovation and exploitative innovation

PurposeExisting research has demonstrated that the innovation implications of structural holes are inconsistent. The diverse and broad resources associated with structural holes facilitate innovation. On the contrary, brokerage will also hinder trust and increase the opportunism behaviors among partners, which will damage innovation. Inspired by the conflicting conclusions, the purpose of this paper is to analyze the roles of structural holes on exploratory innovation and exploitative innovation.Design/methodology/approachTo test the model, the paper used a panel of 305 US computer focal firms and 6,894 alliances from the period spanning 1993 to 2004, and adopted the Heckman two-stage selection procedure in predicting the results.FindingsThe results show that structural holes help firms to develop exploratory innovation while negatively impacting exploitative innovation.Originality/valueThis study offers precise insights on inconsistent understandings between structural holes and innovation by differentiating exploratory innovation from exploitative innovation. Furthermore, it contributes to the burgeoning literature on exploration and exploitation from the network perspective.

Model forecasting based on two-stage feature selection procedure using orthogonal greedy algorithm

Currently, forecasting and feature selection tasks are attracting considerable attention from various scientific fields including global solar radiation forecasting, signal processing, microarray data analysis, finance, medicine and others. However, both selection inconsistency and the intractable computational cost pose critical difficulties when implementing forecasting tasks. Although artificial neural networks (ANNs) are useful for forecasting, a large number of nuisance features are employed. To establish an interpretable forecasting model, feature selection techniques are combined with ANNs to reduce the number of inputs and the complexity of network structures. However, these approaches shrink the estimate, which results in inaccurate forecasting results. To overcome these drawbacks, this paper successfully investigates a novel soft computing approach referred to as a two-stage feature selection procedure using the orthogonal greedy algorithm (TSOGA) to select the important features as inputs of ANNs. A simple-to-implement and efficient computational algorithm is designed, and the theoretical analysis is also provided. Furthermore, the high dimensional Bayesian information criterion (HDBIC) is utilized to select the optimal forecasting model. Real data experiments directly demonstrate the outstanding forecasting performances of the proposed TSOGA approach compared with other state-of-the-art techniques.

Adaptive LASSO for linear mixed model selection via profile log-likelihood

ABSTRACTMixed model selection is quite important in statistical literature. To assist the mixed model selection, we employ the adaptive LASSO penalized term to propose a two-stage selection procedure for the purpose of choosing both the random and fixed effects. In the first stage, we utilize the penalized restricted profile log-likelihood to choose the random effects; in the second stage, after the random effects are determined, we apply the penalized profile log-likelihood to select the fixed effects. In each stage, the Newton–Raphson algorithm is performed to complete the parameter estimation. We prove that the proposed procedure is consistent and possesses the oracle properties. The simulations and a real data application are conducted for demonstrating the effectiveness of the proposed selection procedure.

Seeing is not necessarily the truth: Do institutional investors' corporate site visits reduce hosting firms' stock price crash risk?

This study examines the impact of institutional investors' corporate site visits (CSVs) on stock price crash risk. We find that the frequency of institutional investors' CSVs is positively associated with hosting firms' future stock price crash risk. This association is robust to an array of sensitivity checks, including alternative measures of interaction intensity, alternative samples, additional control variables, firm fixed effects model, two-stage instrumental approach, Heckman two-stage sample selection procedure and propensity score matching (PSM) procedure. Further analysis shows that the impact of institutional investors' CSVs on future crash risk is more pronounced when firm managers have higher bad-news hoarding incentives and less information disclosure monitoring, and when firms are plagued more by information asymmetry. Moreover, the economic mechanism analysis shows that the average cumulative abnormal return (CAR) around the announcement day of institutional investors' CSVs is significantly positive, that CSVs have larger effect for firms with higher CARs and that firms visited more by institutional investors are more likely to make financial restatement in future. Overall, our findings are consistent with the notion that institutional investors' CSVs exacerbate managers' incentives to withhold bad news, which leads to bad news accumulation and future stock price crash risk.

Gender equality legislation and foreign direct investment

PurposeThe purpose of this paper is to examine the differences in the effects of gender equality legislation on employment outcomes among female and male workers in industries with different intensity of foreign investment (namely, foreign direct investment (FDI)-intensive industries and non–FDI–intensive industries). The specific employment outcomes that were studied to compare the effects of the legislation are the working hours, employment opportunities, and wages of female and male workers in Taiwan.Design/methodology/approachUsing data from the annual Manpower Utilization Survey, the authors applied a differences-in-differences-in-differences estimation method to test the effect of gender equality legislation on employment outcomes. By using multinomial logit, the authors measured the effect of the legislation on employment opportunities. To correct for simultaneity and selectivity problems/biases, the authors adopted Heckman two-stage selection procedures. Likewise, the authors used weighted least squares to solve heteroskedasticity in the wage and working hour equations. Further, the instrumental variable (IV) method was used to correct for simultaneity bias in the equation on working hour. The authors applied three stages estimation method following Killingsworth’s (1983) approach to measure the effect of the legislation on wages and working hours.FindingsThe authors found the restrictions enforced by the gender equality legislation (namely the Gender Equal Employment Act (GEEA), enacted in 2002) in Taiwan to have made certain impact on the workers’ working conditions in FDI-intensive industries. The major finding indicated that in a country like Taiwan, where the legislature tried tilling the perpetual gender gap in its labour market, by passing a law to counter inequality, could finally narrow the gender gap in wages among workers in the FDI-intensive industries. Although initially after the enactment of the GEEA (between 2002 and 2004), the gender gap in part-timers’ wages has widened, yet over a period of time the gap in their wages too has narrowed down, particularly during 2005-2006. The legislation, however, could not improve the job opportunities for full-time female workers’ in FDI-intensive industries. Besides, post 2002, the female workers were found to have worked for shorter hours than male workers, which according to us, could be largely attributed to the enforcement of the GEEA.Practical implicationsAn in-depth analysis of the labour market effects of gender equality legislation should be useful to policymakers, especially those interested in understanding the impact of legislative measures and policy reforms on labour market and employment outcomes across industry types. If enforcement of a gender equality legislation has succeeded in reducing the gender gap more in one set of industries than the others (e.g. foreign owned instead of domestic industries), as the authors noticed in this study, then the same should have a bearing on revamping of future enactment and enforcement too.Originality/valueCurrent study findings would not only provide the broad lessons to the policymakers in Taiwan, but the results that have emerged from a country case study could be referred by other growing economies who are enthusiastic about improving female workers’ working conditions through legislative reforms.

Knowledge, Attitude and Practices of Vector-Borne Disease Prevention during the Emergence of a New Arbovirus: Implications for the Control of Chikungunya Virus in French Guiana.

BackgroundDuring the last decade, French Guiana has been affected by major dengue fever outbreaks. Although this arbovirus has been a focus of many awareness campaigns, very little information is available about beliefs, attitudes and behaviors regarding vector-borne diseases among the population of French Guiana. During the first outbreak of the chikungunya virus, a quantitative survey was conducted among high school students to study experiences, practices and perceptions related to mosquito-borne diseases and to identify socio-demographic, cognitive and environmental factors that could be associated with the engagement in protective behaviors.Methodology/Principal FindingsA cross-sectional survey was administered in May 2014, with a total of 1462 students interviewed. Classrooms were randomly selected using a two-stage selection procedure with cluster samples. A multiple correspondence analysis (MCA) associated with a hierarchical cluster analysis and with an ordinal logistic regression was performed. Chikungunya was less understood and perceived as a more dreadful disease than dengue fever. The analysis identified three groups of individual protection levels against mosquito-borne diseases: “low” (30%), “moderate” (42%) and “high” (28%)”. Protective health behaviors were found to be performed more frequently among students who were female, had a parent with a higher educational status, lived in an individual house, and had a better understanding of the disease.Conclusions/SignificanceThis study allowed us to estimate the level of protective practices against vector-borne diseases among students after the emergence of a new arbovirus. These results revealed that the adoption of protective behaviors is a multi-factorial process that depends on both sociocultural and cognitive factors. These findings may help public health authorities to strengthen communication and outreach strategies, thereby increasing the adoption of protective health behaviors, particularly in high-risk populations.

An effective powerful test for one-sided supplier selection problem with multiple independent characteristics

In this paper, we use the index to investigate the supplier selection problem for one-sided processes with multiple independent characteristics. We first review the existing approach, which we refer to as the division method, then develop a new exact approach called the subtraction method. A two-stage selection procedure is developed based on the subtraction method for practical applications. We then compared the two methods with regard to selection power. The results show that the subtraction method we propose is indeed more powerful than the existing division method. A thin-film transistor type liquid-crystal display (TFT-LCD) application example is provided to illustrate the testing procedure.