# Advancements in Data Analysis for the Work-Sampling Method
## 1 Introduction
The need for observations represents a considerable cost factor, which is why [PERSON] et al. introduced self-reporting. They described a self-reported work-sampling approach developed and adapted for production development and the application of the approach in an automotive supplier company [6]. The results provide insight into the engagement of group members at work and how their activity was related to the context, mode, and type of information transaction used. Work sampling is mainly used as a stand-alone method but can also be used in combination with other methods. This is how [PERSON] et al. conducted the study, where three different methods were integrated: work sampling, computer simulation, and biomechanical modeling to investigate the physical demands of the job [7]. A work-sampling method was used to quantify the proportion of time spent on specific tasks. Work sampling can also provide key data in ergonomic studies. [PERSON] et al. collected data on the ergonomic strain of workers using the work-sampling method and identified several risk factors in the observed tasks [8]. Similarly, [PERSON] et al. assessed the workload of workers in collaborative workplaces under different workload conditions [9; 10]. The results indicate the need for the individualized treatment of individuals to increase productivity and job satisfaction at the same time. Work-study is the most important, but not the only, application of work sampling. [PERSON] et al. [11] investigated work sampling in product development. They introduced a work-sampling application for cell phones that can greatly simplify and popularize the use of the method. [PERSON] et al., on the other hand, reported on the development of a special tool for workplace analysis [12]. In the field of construction, [PERSON] et al. introduced a hierarchical classification for activity recognition and used a hybrid deep learning model as an alternative to the work sampling method. Based on the activity recognition results, a discrete event simulation was used to predict the progress of the process [13]. To improve the efficiency of the maintenance department, [PERSON] et al. analyzed the current workload of technicians and employees using the work-sampling method [14]. The study found that the workload of the maintenance department was so low that a reduction in the number of employees, the creation of more efficient work processes, and an expansion of the workplace were suggested. [PERSON] and [PERSON] conducted a work-study using work sampling in the garment industry [15]. They integrated work sampling and fuzzy logic. Since safety and productivity are critical in evaluating performance management in maritime transport and port management, [PERSON] and [PERSON] investigated activity analysis methods and selected work sampling as a suitable method for improvement policy [16]. [PERSON] et al. presented a good overview of the novelties regarding the application of the work-sampling method in construction [17]. Their article contributes to the state of knowledge in construction management through a thorough understanding of the current state-of-the-art activity sampling techniques and research gaps. Their analysis includes a qualitative synthesis of the contributions of the reviewed articles. [PERSON] et al. developed a procedure to evaluate the statistical properties of work-sampling strategies that estimated categorical exposure variables and illustrated the applicability of this procedure to investigate the bias and accuracy of exposure estimates from different sample sizes [18]. [PERSON] et al. presented a case study on the SME food industry [19]. The results of this study show that work-sampling data can be used as reliable estimates to identify potential bottlenecks and idle times in a factory. Similarly, [PERSON] and [PERSON] proposed a framework that extends the applicability of event data collection and process models by converting them into DES models for predicting future performance [20].