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The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

The purpose of this paper is to search for the critical-scenarios of autonomous vehicles (AVs) quickly and comprehensively, which is essential for verification and validation (V&V).

The author adopted the index F1 to quantitative critical-scenarios' coverage of the search space and proposed the improved particle swarm optimization (IPSO) to enhance exploration ability for higher coverage. Compared with the particle swarm optimization (PSO), there were three improvements. In the initial phase, the Latin hypercube sampling method was introduced for a uniform distribution of particles. In the iteration phase, the neighborhood operator was adapted to explore more modals with the particles divided into groups. In the convergence phase, the convergence judgment and restart strategy were used to explore the search space by avoiding local convergence. Compared with the Monte Carlo method (MC) and PSO, experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the method.

Results show that IPSO can search for multimodal critical-scenarios comprehensively, with a stricter threshold and fewer samples in the experiment on critical-scenario search, the coverage of IPSO is 14% higher than PSO and 40% higher than MC.

The critical-scenarios' coverage of the search space is firstly quantified by the index F1, and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs, which shows application potential for V&V.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

In many security domains, the ‘human in the system’ is often a critical line of defence in identifying, preventing and responding to any threats (Saikayasit, Stedmon, & Lawson, 2015). Traditionally, such security domains are often focussed on mainstream public safety within crowded spaces and border controls, through to identifying suspicious behaviours, hostile reconnaissance and implementing counter-terrorism initiatives. More recently, with growing insecurity around the world, organisations have looked to improve their security risk management frameworks, developing concepts which originated in the health and safety field to deal with more pressing risks such as terrorist acts, abduction and piracy (Paul, 2018). In these instances, security is usually the specific responsibility of frontline personnel with defined roles and responsibilities operating in accordance with organisational protocols (Saikayasit, Stedmon, Lawson, & Fussey, 2012; Stedmon, Saikayasit, Lawson, & Fussey, 2013). However, understanding the knowledge that frontline security workers might possess and use requires sensitive investigation in equally sensitive security domains.

This chapter considers how to investigate knowledge elicitation in these sensitive security domains and underlying ethics in research design that supports and protects the nature of investigation and end-users alike. This chapter also discusses the criteria used for ensuring trustworthiness as well as assessing the relative merits of the range of methods adopted.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Purpose – The purpose of this research is to examine the diversity of macrozoobenthos as well as its relationship with water quality and substrate in the pond culture area.

Design/Methodology/Approach – The method of sampling area is on five observation stations by purposive sampling. The research was done indirectly (ex situ) for macrozoobenthic identification at the Ecology Laboratory, Faculty of Mathematics and Natural Sciences.

Findings – The fairness/uniformity index obtained from the five research stations ranging from 0.483 to 0.923 indicates a high degree of uniformity. This indicates that the macrozoobenthos biological index at the study site can be used as an indicator that water quality is in good condition and has the potential to be developed into an aquaculture area as well as supporting the success of the shrimp farming as one of the sub-systems of the shrimp agribusiness.

Research Limitations/Implications – This research can be a source of information for the management and utilization of environment in the research area, so that shrimp harvest can be optimized in the pond farming area.

Originality/Value – This research has found that macrozoobenthos included 61 species.

Purpose – The purpose of this research is to identify Tuah Teng fishing techniques in food security to facing ASEAN economic community (AEC) and to give description about Tuah Teng fishing techniques and its relationship with food security of coastal society in face of AEC era.

Design/Methodology/Approach – The method of sampling is by snowball technique, because every generation of fisherman community has used this since a long time ago. The method of analysis is done by descriptive qualitative based on primary data by observation and secondary data from the literature study.

Findings – The technique of fishing Tuah Teng is done by attracting the fish relying on simple equipment consisting of stereofoam, plastic cans, vats with cement and rubber wheel, and fish bait from dried coconut leaves tied to the rope. Availability of fish can support the food security. During season, the prices of fish can be very cheap or even just distributed free to the community.

Research Limitations/Implications – Food security in the era of ASEAN economic community encourages food self-sufficiency and ultimately realizes food sovereignty. The community no longer imports the fish, even they can export because the needs of fish in domestic has been fulfilled.

Practical Implications – The Office of Marine and Fisheries (DKP) has provided assistance in the form of radar and a more modern computer to be able to detect the fish. But fishermen still survive with this traditional method.

Originality/Value – This research identifies Tuah Teng as a traditional of fishing technique in Aceh Utara.

The COVID-19 pandemic has significantly affected how consumers make payment choices. This study aims to develop a comprehensive model explaining customers’ continuance usage of mobile payment during the COVID-19 pandemic by investigating both the pull (positive) factors of mobile payment and the push (negative) factors of cash payment.

A survey was conducted on 508 mobile payment users. A quota sampling method was applied to collect the data. Then, the data were analyzed using structural equation modeling. This study employed SPSS and LISREL software.

This study reveals that four antecedent factors: favorable attitude toward mobile payment, social influence, facilitating conditions and unfavorable attitude toward cash payment, positively affect the continuance intention to use mobile payment during the COVID-19 pandemic. The finding also corroborates the effect of continuance intention on the actual use of mobile payment.

This research provides valuable insights for formulating business strategies. The results indicate that mobile payment providers should not only consider the positive aspects of mobile payments but also the negative aspects of cash payment when encouraging the continuance usage of mobile payments to customers.

This study is among the first to empirically test the effect of unfavorable attitudes toward cash payment on the continuing use of mobile payment. Specifically, the research extends the unified theory of acceptance and use of technology by adding the push–pull-mooring model to enhance the explanatory power.

La pandemia de COVID-19 ha afectado significativamente a la forma en que los consumidores toman sus decisiones de pago. Este estudio pretende desarrollar un modelo completo que explique el uso continuado del pago por móvil por parte de los clientes durante la pandemia COVID-19, investigando tanto los factores de atracción (positivos) del pago por móvil como los factores de empuje (negativos) del pago en efectivo.

Se realizó una encuesta a 508 usuarios de pago por móvil. Se aplicó un método de muestreo por cuotas para recoger los datos. A continuación, los datos se analizaron mediante un modelo de ecuaciones estructurales. En este estudio se empleó el software SPSS y LISREL.

El estudio revela que cuatro factores antecedentes: la actitud favorable hacia el pago por móvil, la influencia social, las condiciones facilitadoras y la actitud desfavorable hacia el pago en efectivo; afectan positivamente a la intención de permanencia en el uso del pago por móvil durante la pandemia COVID-19. El hallazgo también corrobora el efecto de la intención de permanencia en el uso real del pago por móvil.

Esta investigación aporta valiosas ideas para la formulación de estrategias comerciales. Los resultados indican que los proveedores de pagos por móvil no sólo deben tener en cuenta los aspectos positivos de los pagos por móvil, sino también los aspectos negativos del pago en efectivo a la hora de fomentar el uso continuado de los pagos por móvil entre los clientes.

Este estudio es uno de los primeros en comprobar empíricamente el efecto de las actitudes desfavorables hacia el pago en efectivo en el uso continuado del pago por móvil. En concreto, la investigación amplía la teoría unificada de la aceptación y el uso de la tecnología (UTAUT) añadiendo el modelo push-pull-mooring (PPM) para mejorar el poder explicativo.

COVID-19疫情对消费者的支付方式产生了重大影响。本研究旨在通过研究移动支付的拉动（积极）因素和现金支付的推动（消极）因素, 建立一个综合模型来解释客户在COVID-19疫情期间持续使用移动支付的情况。

本研究采用配额抽样方法, 对508位移动支付用户进行了调查。然后通过SPSS和LISREL软件, 运用结构方程模型对数据进行了分析。

研究结果揭示了四个前因因素对COVID-19疫情期间持续使用移动支付的意愿有积极影响, 这四个因素分别是：对移动支付的有利态度、社会影响、便利条件和对现金支付的不利态度; 这一发现也证实了持续使用意愿对移动支付实际使用的影响。

这项研究为制定商业战略提供了宝贵的见解。结果表明, 移动支付供应商在鼓励客户持续使用移动支付时, 不仅要考虑移动支付的积极方面, 还要考虑现金支付的消极方面。

本研究首次通过实证检验了消费者对现金支付的不利态度对移动支付持续使用的影响。具体而言, 本研究通过加入推拉式模型（PPM）扩展了技术接受和使用的统一理论（UTAUT）, 从而增强了该理论的解释力。