Motivations: The purpose of this research is to develop adaptive nonparametric statistical methods for 2 × 2 cross over designs with repeated measures. Methods: The statistics and their asymptotic distributions are established for testing: (1) the equality for carry-over effects; (2) direct treatment effect when carry-over effects are equal; (3) equality of carry-over effect over time; (4) equality of direct treatment effect over time when carry-over effect over time is equal; (5) the average response for carry-over effects; and (6) the average response for direct treatment effects when average response for carry-over effects are equal. Findings: The methods are applied to the slopes of systolic and diastolic pressures in the study regarding to sleep apnea severity. Implications: Using the sleep apnea severity study as an example, we show that adaptive procedures had advantage of detecting the significance of test while the ranks-only based methods by Johnson and Grender (1993) failed to detect such significant result.
Purpose: This study explores an integrated approach to identify enemy items in item bank management in a medical licensure examination. Method: The integrated approach utilizes item bank analysis, natural language processing methods by using Cosine Similarity Index, and content analysis and review by subject matter experts. Results: Results from an empirical study indicate that the integrated approach is efficient in identifying enemy items. Content review of the flagged enemy item pairs is a necessary step to confirm enemy items.
Purpose: In recent years, smart retailing has been gradually acknowledged and has slowly impacted countless industries. This study takes smart retailing as the starting point and explores the increased attention on the perishable food (fresh food) market in convenience stores. Remarkable improvements in the general food consumer population in Taiwan and the advent of an aging society with changes in social structure and consumer style are also taken into consideration. As perishable goods (fresh food products) easily spoil, the scrap rate of fresh food in convenience stores has always been a topic of great importance. Design/methodology/approach: Perishable food from well-known convenience store chains in Taiwan is taken as the subject of this study, and sales data of stores in different regions are used to establish sales forecast models for the convenience store chains. The optimal sales forecast model for each store's products was established through a data analysis, and the characteristics and differences of all products are explored to establish decision-making advices for enterprises in ordering perishable food. Multiple stores and perishable foods with two-year sales data of 11 perishable foods in every six stores in different county are adopted to establish the predictive models in this study, including time series, support vector machine (SVM), Lagrangian support vector machine (LSVM), random forest, neural network, generalized linear, and generalized linear mixed models. Findings: Results show that the time series model and SVM have lower prediction error values and better prediction results among all the established sales forecast models. Given the influence of region and population characteristics, varying models are applicable to stores in different regions. Thus, the difficulty of prediction will also vary. Practical implications: Different commodities will have varying levels of prediction difficulty due to dissimilarities in commodity attributes. Convenience stores are generally willing to predict the sales of fresh food through an artificial intelligence model. Different forecasting models should be selected by stores. If one or more forecasting models are used for prediction, the model with a stable forecasting error should be selected for implementation. Originality/value: This research analyzes the products sold in multiple stores and applies different time series and machine learning algorithms to build predictive models. The results show that the most suitable algorithms for stores of products are different. If a convenience store wants to build a predictive model, differentiated models must be established for different stores and commodities.
Purpose: The present study explored the influence mechanism of the organic part of moral quality by validating if moral knowledge mediates the relation between moral awareness and moral behavior. Design: The present study used randomly stratified sampling method to sample 1,140 Chinese sports practitioners in southwest China and administrated the self-composed sports practitioner moral quality (SPMQ) questionnaire to the subjects for data collection. In addition, the present study applied regression analysis and structural equation modeling (SEM) techniques to explore the relationships among moral awareness, moral knowledge, and moral behavior. Findings: The present study showed two major findings. First, the SPMQ questionnaire showed good internal consistency since the overall Cronbach's α coefficient is 0.85 and the corresponding values of three sub-dimension were over 0.60. Also, it showed that the instrument had good construct validity because all factor loadings were higher than 0.70, all average extracted variances were higher than 0.60, and all composite reliability coefficients were higher than 0.80. Second, the results showed that moral knowledge played intermediate role between moral awareness and moral behavior in the relationships among moral awareness, moral knowledge, and moral behavior. Practical implications: The study is one of the very few research that discuss the mechanism of moral quality from the perspective of the mediation, especially explore the potential mediation effect of moral knowledge in conjunction with the relevant theories and practices.
The a priori procedure (APP) was designed as a pre-data procedure whereby researchers could find the sample sizes necessary to ensure that sample statistics to be obtained are within particular ranges of corresponding population parameters with known probabilities. Although the APP has been devised for a variety of experimental paradigms, these have all concerned parameters in classical statistics. The present work extends a priori thinking to an important case not addressed previously, where the researcher is interested in estimation in normal Bayes models. Computer simulations support the equations presented, along with a real data example for illustration of our main results.
In this paper, we discuss how to determine the optimum sample size by pre-specified confidence interval for estimating the scale parameter from a family of skew normal distributions. We also obtain its specified confidence interval. Subsequently, we extend the mathematics to apply to two independent samples from normal distributions and feature the scale ratio in the process. Finally, simulation work and real data application support our main results.
This paper seeks to understand the effects of formal and informal economic sectors to Cambodian economy as a small developing country. To accomplish this goal, a small simple small new Keynesian dynamic stochastic general equilibrium (DSGE) model has been constructed featuring characteristic of developing economy such as price nominal rigidity, monopolistic competition, and fixed exchange rate regime. The model is estimated by using Bayesian estimation with annual Cambodian data from 1995 to 2016. The estimation results and impulse response function (IRF) of shocks such as formal non-tradable productivity shock, domestic tradable productivity shock, monetary policy shock, imported inflation shock, and foreign demand shock show that there is no shock absorbing role evidence of informality can be found in foreign demand shock, yet it can be found partially in imported inflation shock. However, there are shock-absorbing role of informality in found in informal productivity shocks.
The objective of this study is to investigate the impact of over-the-top (OTT) media streaming services on consumers' demand for fiber-to-the-home (FTTH) service. A double-hurdle model is applied to decompose consumers' willingness-to-adopt (WTA) and willingness-to-pay (WTP) for FTTH by OTT media streaming services, individual characteristics, experience of connection problem, and expectation of media consumption through the Internet. A respondent is allowed to express his or her WTP in an interval in order to truly reflect the willingness and flexibility to pay. The results show that consumers are willing to adopt and pay for FTTH for their quality of experience from OTT media streaming consumption, particularly movie services. This study contributes to the projection of market value and the effect of OTT media streaming services along the diffusion of the FTTH market. Policy implications are discussed to set regulations and incentives that increase broadband penetration at the right price.
One of the biggest concerns in the implementation of computerized adaptive tests (CAT) is that some items in the item pool are overexposed while some items are underexposed or never administered. The purpose of this study is to present the conditional randomesque (CR) method and assess its performance using simulation based on real item pool data under various study conditions. The CR method is compared to the other two item selection methods: the none_IEC method and the Stocking and Lewis Conditional Multinomial method. The results show that there is some precision loss. However, the conditional and overall maximum item exposure rates are much smaller. In addition, the pool usage is greatly improved.
The purpose of this study is to investigate whether the school size would inf luence the principal’s distributed leadership, teacher’s emotional labor and teaching ef fectiveness. The structural equation model was used. The results show that, the SEM hypothesis for the ef fects distributed leadership employed by elementary school principals and teacher’s emotional labor exert on teacher’s teaching ef fectiveness was supported. The mediating effects of teacher’s emotional labor differed according to the size of school. In other words, principal’s distributed leadership must first influence teacher’s emotional labor in order to enhance teacher’s teaching effectiveness. Moreover, principal’s distributed leadership directly enhances teacher’s teaching effectiveness in large schools only, not for medium or small size school.
This paper applies the multilevel linear regression (MLM) model to investigate factors that affect international tourists' spending per day when travelling in the northern region of Thailand. Four hundred questionnaires were collected by a convenience sampling method in 9 provinces of northern Thailand (Chiang Mai, Chiang Rai, Lamphun, Lampang, Uttaradit, Pitsanulok, Sukhothai, Kampaeng Phet and Nakonsawan) from October, 2015 to December, 2015. The results represent the mixed affected model which contains significant fixed effect explanatory variables (age, intention of revisit, and the attitude to reuse domestic land transportation) and random variance components, including individual income. In addition, the MLM can explain that the overall international tourists' spending per day in the region level (level 1) statistically depends on the individual income in the province level (level 2).
In this paper, three new confidence intervals for the location parameter of skew normal family with known coefficient of variation and skewness are established. Corresponding with these new intervals, three confidence intervals with shortest lengths are obtained also. Monto Carlo simulations of these confidence intervals are discussed towards their coverage probabilities and average lengths. Finally, comparisons of these estimation methods are studied.
This paper develops the model that can be used to analyze income inequality through mathematical and statistical approaches and the mode is introduced to income inequality in Thailand. The study precisely constructs the model displaying a structural change in income distribution among household groups as there is a growth of production in each economic sector. Based on the model, there was still the problem of income inequality in Thailand but the situation was gradually but slowly solved in the past 30 years. However, Thailand's distribution of income is likely to be negatively affected from exogenous shocks, for example, an economic crisis and the natural disaster. Moreover, the results from this model had both conformity and unconformity with the Gini coefficient as a key indicator of income inequality.
This paper focuses on the determinants and practices of Arabica coffee growers in Pang Ma-O and Pamiang villages for planning and developing green cluster supply chains (GCSC). Samples selected in this paper are coffee growers in two villages, staff of the Highland Research and Development Institution (HDRI), and the Royal Project Foundation (RPF), and officers of the Pang Ma-O Extension Project, and the Pamiang Royal Project Development Center. Multivariate probit (MVP) model, cluster mapping and modified GEM model were used as tools for analyzing. The findings revealed that education, age and input cost have significantly positive influences on the green adoption whereas farm size has a negative impact on it. Considering cluster mapping, it is not complicated so the plan and development of GCSC is not too difficult. For the determinants involving in GCSC, the results not only showed the macroeconomic environmental determinants such as government policies, geographical position and total domestic demand, but also the microeconomic environmental determinants, namely, green relation, structure and strategies, and resources which are the strength factors confirming that it is possible to develop the GCSC. However, the external market is a relative weakness determinant that is implied as the drawback of GCSC development. The findings of this paper are useful for planning and developing GCSC to enhance the competitiveness of Arabica coffee farmers in the highland area bringing about the strength of community, the ability to be self-reliance, and the enhancement of income and well-being of farmers in the selected area, and as a helpful resource for expanding the similar projects in other areas.
