Evaluation Of Statistical Methods Research Articles

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

Optimizing ESG Management in Enterprises Using 'Internet +' and Big Data Technologies

The in-depth development of "Internet +" has made the application of data information technology in ESG (Environmental, Social and Governance) management more extensive and has made the optimization of ESG management the focus of listed companies. The original statistical evaluation method cannot solve the problem of improving the level of ESG management, and the ability to improve the quality of the environment, society and the company is weak, increasing ESG management's difficulty. Therefore, this paper proposes a method based on big data technology to optimize ESG management. First, the "Internet +" technology is used to analyze environmental, social and company quality data, and ESG management classification is carried out according to the requirements of listed companies. Then, according to the classification results, the "Internet+" technology forms different ESG management sets and iteratively mines different sets to obtain the optimal optimization strategy. After MATLAB testing, the effect of ESG management can be improved based on Internet + technology. The effect improvement rate reaches 92.6%, the ESG management process is improved, the improvement is 25%, and the ESG management time is reduced to 15 seconds, which meets the ESG management needs of listed companies.

Management of sustainable investments: A comprehensive financial evaluation of wind energy facilities in Kastamonu

The rich renewable sources of nature, such as water, wind, solar, geothermal, and biomass, are converted into energy, revitalized into the economy without causing pollution, and ensure sustainability in energy management. Wind power plants have been among the most preferred renewable energy sources in recent years since they are harmless to the environment and easy to install in places with consistent and sufficient wind speed. This study focuses on conducting a feasibility analysis of a Wind Power Plant (WPP) in Kastamonu, a region known for its persistent wind patterns. The analysis aims to contribute to the existing literature by examining the potential of a WPP investment project within the region. Utilizing wind speed data collected from 23 observation stations for six years, starting from 2017 to 2022, a total of 1,200,600 h, a statistical evaluation method known as the Weibull probability density function is employed to indicate the wind energy potential. The parameters are determined by applying the maximum likelihood method. The findings indicate that it is feasible to establish Wind Energy Power Plants in specific locations, namely İnebolu, İnebolu Kar, Küre, and Tosya stations. The cost estimation for establishing a 10 MW power plant with three different turbine powers across these four locations ranges from 10 million USD to 20,8 million USD. Furthermore, the analysis indicates that the net profit for producing 1 kW of electrical energy falls from 9.39 to 50.65 USD. The projected payback period for the investment ranges from 2 to 25 years, depending on factors such as the exact site of the facility and the size of the turbines. Some investments are more feasible than others, with economic viability and potential return on investment varying significantly within the specified locations.

The Application of Statistical Methods in Social Media Marketing Effectiveness Evaluation

The Application of Statistical Methods in Social Media Marketing Effectiveness Evaluation

Expert Evaluation of the Organizational Technology for Prolonging Doctors’ Professional Activity

Background. Healthcare professionals fall into the professions that significantly strengthen national economy. In order to avoid national economic losses, we should protect older healthcare professionals as a source of national workforce by developing and implementing a health-saving technology that prolongs their professional effective activity. Based on the previously conducted sociological survey of doctors providing medical care in the polyclinics of Moscow and Moscow Oblast, an assessment of the doctors’ professional competencies was carried out [1] and an organizational technology was formed [2] and then submitted to an expert evaluation. Objective. To expertly evaluate the importance of the activities that are included in the organizational technology prolonging the professional effective activity of older doctors. Materials and methods. The methods of sociological, statistical and expert evaluation were used for studying the experts’ opinions on the importance of each activity of organizational technology for prolonging the professional effective activity of healthcare professionals. Experts (n = 50) were asked to rank the activities according to the importance for achieving the result – support for the professional effective activity of doctors - in the case of the implementation of this organizational technology. The experts were specialists in the field of healthcare and public health: chief physicians, heads of departments of polyclinics in Moscow and Moscow Oblast, accredited in the specialty of the Higher Attestation Commission 03.02.03 "Public health, organization and sociology of healthcare, medical and social expertise" with leadership experience in healthcare from 1 to 29 years. The objectivity of experts’ judgment is based on their knowledge and expertise in healthcare and public health. Findings. The experts progressively evaluated the organizational technology, firstly by each stage and then as a whole. Thus, it was made a conclusion on the effectiveness of organizational technology preserving medical competencies and prolonging the professional effective activity of older doctors.

Assessing the validity of a Parkinson’s care evaluation: the PRIME-NL study

IntroductionThe PRIME-NL study prospectively evaluates a new integrated and personalized care model for people with parkinsonism, including Parkinson’s disease, in a selected region (PRIME) in the Netherlands. We address the generalizability and sources of selection and confounding bias of the PRIME-NL study by examining baseline and 1-year compliance data.MethodsFirst, we assessed regional baseline differences between the PRIME and the usual care (UC) region using healthcare claims data of almost all people with Parkinson’s disease in the Netherlands (the source population). Second, we compared our questionnaire sample to the source population to determine generalizability. Third, we investigated sources of bias by comparing the PRIME and UC questionnaire sample on baseline characteristics and 1-year compliance.ResultsBaseline characteristics were similar in the PRIME (n = 1430) and UC (n = 26,250) source populations. The combined questionnaire sample (n = 920) was somewhat younger and had a slightly longer disease duration than the combined source population. Compared to the questionnaire sample in the PRIME region, the UC questionnaire sample was slightly younger, had better cognition, had a longer disease duration, had a higher educational attainment and consumed more alcohol. 1-year compliance of the questionnaire sample was higher in the UC region (96%) than in the PRIME region (92%).ConclusionThe generalizability of the PRIME-NL study seems to be good, yet we found evidence of some selection bias. This selection bias necessitates the use of advanced statistical methods for the final evaluation of PRIME-NL, such as inverse probability weighting or propensity score matching. The PRIME-NL study provides a unique window into the validity of a large-scale care evaluation for people with a chronic disease, in this case parkinsonism.

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

For complex equipment, it is easy to over-evaluate the impact of failure on production by estimating the reliability level only through failure probability. To remedy this problem, this paper proposes a statistical evaluation method based on fuzzy failure data considering the multi-state characteristics of equipment failures. In this method, the new reliability-evaluation scheme is firstly presented based on the traditional statistical analysis method using the Weibull distribution function. For this scheme, the failure-grade index is defined, and a fuzzy-evaluation method is also proposed by comprehensively considering failure severity, failure maintenance, time, and cost; this is then combined with the time between failures to characterize the failure state. Based on the fuzzy failure data, an improved adaptive-failure small-sample-expansion method is proposed based on the classical bootstrap method and the deviation judgment between distributions of the original and newborn samples. Finally, a novel reliability-evaluation model, related to the failure grade and its membership degree, is established to quantify the reliability level of equipment more realistically. Example cases for three methods of the scheme (the failure-grade fuzzy-evaluation method, the sample-expansion method, and the reliability-evaluation modeling method) are presented, respectively, to validate the effectiveness and significance of the proposed reliability-evaluation technology.

A performance evaluation method for energy storage systems adapted to new power system interaction requirements

In recent years, China’s new energy storage application on a large scale has shown a good development trend; a variety of energy storage technologies are widely used in renewable energy development, consumption, integrated intelligent energy systems, distribution grids, and microgrids; and substantial progress has been made in the research and development of technology and equipment, construction of demonstration projects, exploration of business models, and construction of standard systems. Up to now, a unified statistical index system and evaluation method standard for new energy storage has not yet been formed domestically or even internationally. The work takes the status quo of the new power system construction of the Hebei South Network as the research object and carries out research on the new energy storage statistical index system and evaluation method. It constructs a new energy storage power station statistical index system centered on five primary indexes: energy efficiency index, reliability index, regulation index, economic index, and environmental protection index; proposes Analytic Hierarchy Process (AHP)–coefficient of variation combination assignment method; and evaluates the development level of the new energy storage power station by adopting a comprehensive evaluation model based on the object element topology method. The new energy storage statistical index system and evaluation method are designed to provide a scientific index system and evaluation method for comprehensively monitoring, assessing and measuring the comprehensive performance and effect of new energy storage power plants in the process of operation and development, and optimizing the operation strategy of new energy storage power plants as well as the development and promotion of energy storage technology.

Economic and statistical methods of estimating and forecasting the enterprise production activity and realization of oilseed crops

Enhancing food security of Kazakhstan, increasing food products is one of the priority tasks of the country’s economic policy. The processing industry is one of the most important links of the agro-industrial complex as a whole, being an integral part of the country’s food security. The processing industry provides the population with quality food products, income from the sale of products, as well as jobs. Therefore, it is necessary to pay special attention to its qualitative development and efficiency improvement by means of economic and statistical methods of evaluation and forecasting of enterprise activity. The research was performed at the micro level in the districts of Kostanay region. A number of typical farms of the region engaged in production of grain legumes and oilseed crops, processing of oilseed crops and sale of finished products were involved. The article considers economic and statistical methods of estimating the change in cash flows from the sale of oilseed linen, as well as forecasting the transformation of sales revenue up to 2026. The work presents several new aspects of the study, including adaptation to government subsidies and market demand, characterisation of the life cycle stage of the company, application of statistical analysis, and a detailed economic sense and methodological calculation of the company’s financial health. The following research methods were applied in the article: statistical method of dynamic series, interval level of the series of dynamics of cash receipts, absolute growth at prices and base values, considered the impact of unaccounted factors, growth and acceleration rates by chain and base method. As a result, the data of the study showed the instability of changes in cash from the sale of oilseed linen.

Role of National Cinematographic Industry in National Economy. Scientometric and Statistical Evaluation Methods

Role of National Cinematographic Industry in National Economy. Scientometric and Statistical Evaluation Methods

Leaching of catalyst platinum from cured silicone elastomers: A preliminary study for comparing reagents

Addition curing systems involve two-part silicones which require the mixture of a silicone polymer with a catalyst to initiate the cure. Platinum is the most commonly used metal catalyst for addition curing of silicones by hydrosilylation which involves the crosslinking by the addition reaction of silicon hydride species to unsaturated bonds, mainly CC, but also CO or CN double bonds. After crosslinking of the polymers, the platinum catalyst cannot be recovered but remains in the silicone materials throughout the entire product life. In the end, platinum is disposed of together with the silicones and is thus lost to the value chain. The overall objective of this work was to develop a recycling process for the recovery of platinum from addition-cured silicone elastomers. In the first step, this was achieved by efficient digestion methods and by optimizing the leaching processes for exemplary commercial silicone elastomer products. Two different silicone materials were investigated, both of which were crosslinked with a platinum catalyst. The initial Pt content in the tested samples was 12.6 ± 0.2 mg/kg for a commercial silicone impression material and 6.3 ± 0.5 mg/kg for a silicone baking mold, measured by graphite furnace atomic absorption spectrometry (GF-AAS). Samples were first frozen with liquid nitrogen to improve brittleness and then crushed with a simple food processor to obtain a silicone granule. Various acid mixtures, mainly based on sulfuric acid, were investigated as digestion methods in order to extract platinum from the silicone network. These had different effects on the dissolution behavior of silicone samples and the amount of platinum extracted in each case. The amount of platinum leached from the filtrate of the digested samples in each case was measured by ICP-OES to evaluate the efficiency of different leaching mixtures. In addition, the dissolved platinum species present in the solutions was identified by UV/VIS as tetrachloridoplatinate(II) complex. The best platinum leaching results so far were obtained with two methods, both of which used a leaching mixture based on sulfuric acid and hexamethyldisiloxane (M2). In the presence of hydrochloric acid, 9.6 ± 1.6 mg platinum/kg was leached from the silicone impression material and 4.2 ± 0.8 mg platinum/kg from the silicone baking mold. With the additional use of aqua regia instead of hydrochloric acid, 10.4 ± 2.8 mg platinum/kg was extracted from the silicone impression material and 4.8 ± 1.0 mg platinum/kg was extracted from the silicone baking mold. These methods were replicated with n = 3. Using statistical evaluation methods (F-test, t-test, and confidence interval), no significant difference was found between these two best methods. Recovery of platinum(0) from leach mixtures has not yet been achieved due to high dilution and very low platinum concentration in samples and will be part of another study.

ВЫБОР МЕТОДА СТРАТЕГИЧЕСКОГО ПЛАНИРОВАНИЯ ПРОМЫШЛЕННОГО ПРЕДПРИЯТИЯ В УСЛОВИЯХ ВЫСОКОТУРБУЛЕНТНОЙ СРЕДЫ

The increasing speed of changes in the environment of industrial enterprises has led to the more frequent use of the term ‘turbulence’ and to the search for a method of strategic planning in this context. The hypothesis of the study is that this method should be based on two key criteria: firstly, a quantitative assessment of turbulence in the industrial environment of the enterprise and, secondly, the degree of resistance. The study develops a matrix for selecting the most effective method of strategic planning, depending on the selected criteria. The methodological basis of the study was the theory of organization and the theory of strategic management. The study used structural and logical analysis of information, and methods of statistical evaluation, matrix construction, and graphics. A Stacey matrix is developed, in which four possible zones of industrial enterprises are conditionally allocated: the zone of ‘conditional comfort’, the zone of ‘additional opportunities’, the zone of ‘threats’, the zone of ‘main risk’. The higher the level of turbulence in the industry and the lower the level of resistance of the in-dustrial enterprise, the more effective the application of integrated methods of strategic planning. An al-gorithm for assessing the effectiveness of accelerating the response to environmental changes is pro-posed. The key conclusion of the research is the confirmation of the hypothesis. The results can be used in the strategic management systems of industrial enterprises.

Multifactor model for assessing innovative potential based on fuzzy set theory.

The author analyses the classification of the methods for the evaluation of an enterprise’s innovative potential. According to the author, the most effective model taking into account the uncertainty factor is the model based on the theory of fuzzy sets. The model has obvious advantages in comparison with the expert and statistical methods of evaluation, since it allows us to minimize the evaluation errors. The scientific-practical value of the results consists in the possibility of their application in combination with the analysis of the official statistical data in the course of perfection of the state scientific and technical and innovative policy in the direction of a more intensive use of the scientific knowledge and achievements in the interests of modernization of the economy of Azerbaijan. The proposed approach can ensure an information integration of the subjects of the scientific organizations and be used for a complex research of the industrial, innovative and economic-administrative processes within the framework of the development of science.

Using photodiodes and statistical evaluation methods for process monitoring tasks during the laser beam welding of stainless-steel foils

Besides battery-based mobility solutions, polymer electrolyte membrane fuel cells (PEMFCs) can be utilized to electrify powertrains. Metallic bipolar plates, usually consisting of two thin micro-formed metal foils, are a crucial component of PEMFCs. For the joining of the foils, laser beam welding is the state-of-the-art manufacturing process. Within the scope of this work, a stable parameter window for the welding of AISI 316L was identified. Therefore, an adjustable ring-mode laser beam source in combination with a photodiode-based sensor system for process monitoring was applied. During the experiments, artificial process faults (e.g., contamination of the samples) were introduced to understand their impact on the welding process and the photodiode signals. The welded samples were evaluated using metallographic cross-sections and surface topography scans. Finally, a statistical signal analysis was employed to interpret the data obtained from the photodiodes to distinguish between fault-free and defective weld seams based on specific signal features.

Experimental Evaluation of Surface Roughness, Burr Formation, and Tool Wear during Micro-Milling of Titanium Grade 9 (Ti-3Al-2.5V) Using Statistical Evaluation Methods

Titanium grade 9 (Ti-3Al-2.5V) stands out as a preferred material in various industrial applications because of its suitable properties. Its applications span diverse sectors, including precision manufacturing, where it is utilized to produce honeycomb structures for advanced aeronautics, as well as for certain biomedical components. In parallel, micro-milling has gained widespread utilization across medical, aerospace, and electronic industries due to the increasing demand for miniature products in these domains. This current research study aims to explore the impact of various micro-milling process parameters—specifically, feed rate, cutting speed, and depth of cut—on the surface quality, burr formation, and tool flank wear of titanium grade 9. Research findings reveal that the feed rate plays a major role in influencing surface roughness (contribution ratio (CR): 62.96%) and burr formation (CR: 55.20%). Similarly, cutting speed and depth of cut significantly affect surface roughness, contributing 20.32% and 9.27%, respectively, but are insignificant factors for burr width. Tool flank wear is primarily influenced by cutting speed (CR: 54.02%), with feed rate contributing 33.18%. Additionally, the feed rate and cutting speed are significant factors in determining the length of the burr, with contribution ratios of 77.70% and 7.77%, respectively. Confirmatory tests conducted at optimum parameters selected from the main effects plot validated the experimental results.

SDE method for cosmic rays modulation in the heliosphere statistical error and solution uniqueness

The cosmic ray modulation in the heliosphere models comes to a new phase of their evolution, where the still higher number of models will be public with published source code. At the dawn of this period, we want to address two topics, the statistical error of their results and the uniqueness of their solutions.We present a method for the evaluation of statistical error for the numerical stochastic differential equation method, which is probably the most used method to numerically solve Parker's equation. We defined a limit of statistical error, for which we present a method to estimate the number of particles needed to be simulated to reach this limit. The estimation of statistical error from a scan of parametric space of two currently available models with public code, for the SOLARPROP model and Geliosphere 2D model, is presented.We present a test of the uniqueness of the solution of Parker's equation for 1D and 2D models of heliospheric modulation. Namely for the 1D B-p model and Geliosphere 2D model. The dependence of solution uniqueness on the selected model's input parameters is presented and discussed. Program summaryProgram Title: CudaHelioCommanderLightCPC Library link to program files:https://doi.org/10.17632/cptpkwphn4.1Developer's repository link:https://github.com/gladiatus55/CudaHelioCommanderLightLicensing provisions: GPLv3Programming language: C#, .NET 6.0Nature of problem: Despite the decades-long history of cosmic ray modulation in the heliosphere models, the topics of the statistical error of their results, and the uniqueness of their solutions were in published articles addressed only in a very limited way. A precise description of the method for statistical error evaluation, with examples applied to available models with published source code, is needed. The uniqueness of the solution of Parker's equation was not addressed in published literature.Solution method: The estimation of statistical error was evaluated for two currently available models with public code, the SOLARPROP model and Geliosphere 2D model, by a scan of their parametric space. The dependence of solution uniqueness on the selected model's input parameters was also done by scanning their parametric space.

Study on the Flow Behavior of 5052 Aluminum Alloy over a Wide Strain-Rate Range with a Constitutive Model Based on the Arrhenius Model Extension

The formability at room temperature and low speed limits the application of aluminum alloy, while high strain rates positively improve the formability of materials. The constitutive behaviors of materials under high strain rates or impact loadings are significantly different from those under quasi-static conditions, while few constitutive models consider the effect of the mobile dislocation and forest dislocation evolution on the dynamic strain aging (DSA) over a wide strain-rate range. The 5052 aluminum alloy, of which the primary source of strain-hardening is dislocation–dislocation interaction, is widely used in manufacturing automotive covering parts and is considered one of the most promising alloys. Therefore, this study conducts uniaxial tensile tests on AA5052-O under conditions of temperatures ranging from 293 K to 473 K and strain rates ranging from 0.001 s−1 to 3000 s−1, and compares the stress–strain relationships of AA5052-O under different conditions to illustrate the constitutive relationship affected by the dislocation evolution over a wide strain-rate range. The Arrhenius model based on the thermal activation mechanism is modified and extended by considering the effects of dynamic strain aging (DSA), drag stress, and the evolution of mobile dislocation and forest dislocation. Thus, a new physics-based constitutive model for AA5052-O is proposed, which can well reflect the change in strain-rate sensitivity with the strain rate increasing. The mobile dislocation density and total dislocation density are predicted with a modified Kubin–Estrin (KE) model, and the influences of variable mobile dislocation on DSA and dislocation drag are discussed as well. In order to verify the reliability of the new constitutive model, the dislocation densities of the specimens before and after deformation are obtained with TEM and XRD, which are in good agreement with the predicted values. This study also compares the newly proposed model with classic constitutive models using multiple statistical evaluation methods, which shows that the new physics-based constitutive model has not only more clear physical meanings for its parameters but also has a higher prediction accuracy.

International commodity market and stock volatility predictability: Evidence from G7 countries

The existing literature has extensively documented the close connection between commodity and stock markets. This empirical study investigates the predictive role of seven metal commodities (gold, silver, aluminum, lead, copper, zinc, and nickel) in G7 stock volatility. The results of the individual factor analysis indicate that gold and silver outperform their peers in both statistical and economic evaluation methods. To extract comprehensive predictive content, we further investigate the forecasting performance using combination forecast, dimension reduction, and shrinkage methods. Moreover, our empirical results indicate that commodity volatility information can transmit to international equity markets through the ratio of gold to platinum prices (GP) and the ratio of silver to platinum prices (SP).

Pharmacological correction of adaptive asthenia in high-class athletes

BACKGROUND: Metabolic protectors containing (Mexicor, Cytoflavin, Metaprot Plus) or not containing (Emoxipin, Riboxin, Metaprot) succinate in their structure showed a sufficiently high clinical efficacy in eliminating or reducing the asthenic symptom complex in athletes with overtraining.
 AIM: The aim of the study was to develop the principles of rational pharmacological correction of asthenic symptom complex in high performance athletes using succinate-free and succinate-containing metabolic protectors (Mexicor, Riboxin/Cytoflavin, Metaprot/Metaprot Plus).
 MATERIALS AND METHODS: The study included 104 high-class athletes of various specializations, which was conducted during the period of the athletes training camps in the pre-competitive period. Due to the high physical and mental stress in the pre-competition period and the focus on competitive selection for responsible international competitions, a short (15 days) study period was used. The maximum loads in athletes were achieved by the end of the 7th day of training, followed by stabilization of the loads and its optimization by the 15th day. The work was performed in compliance with all the rules of evidence-based medicine and ethical standards of work (obtaining informed consent, randomization, the use of a double-blind placebo-controlled method of treatment, valid methods of statistical evaluation).
 RESULTS: The course prescription of succinate-containing metabolic protectors during the period of preparation for competitions had a favorable effect on the restoration of high functional indicators, which is confirmed by an objective assessment of the functional state of athletes, data from biochemical studies of metabolism (carbohydrate, protein and fat), as well as the oxidative status of athletes. In the groups of athletes who received metabolic protectors, recovery was more active than in the placebo group, this was especially noticeable in the groups that received Metaprot plus, Metaprot and Сytoflavin, to a lesser extent Мexicor and Riboxin. In the latter case, the indicators in the stress tests of Stange and Gench were not fully restored, which was also reflected in the reduced Bogomazov index. It is important to emphasize that the indicators of physical endurance (PWC170) reached the values of the initial period, which indicates the optimization of loads under the influence of metabolic agents.
 CONCLUSIONS: The most effective in restoring the functional state of high-class athletes are succinate-containing metabolic agents (Metaprot plus, Cytoflavin, Mexicor) and to a lesser extent (in comparison with them) drugs that do not contain succinic acid (Riboxin, partly Metaprot). LPO indicators, initially somewhat elevated in athletes, showed a tendency to normalize under the influence of the appointment of metabolic protectors. Almost all studied preparations showed activity according to these tests, although the most pronounced positive changes were registered after the appointment of Мetaprot plus, Мetaprot and Сytoflavin. Mexicor and Riboxin were somewhat less active in all studied parameters.

To Check or Not to Check? A Comment on the Contemporary Psychometrics (ConPsy) Checklist for the Analysis of Questionnaire Items.

In a recent paper, the first version of the contemporary psychometrics (ConPsy) checklist for assessing measurement tool quality has been published. This checklist aims to provide guidelines and references to researchers to assess measurement properties for newly developed measurement instruments. The ConPsy checklist recommends appropriate statistical methods for measurement instrument evaluation to guide researchers in instrument development and to support peer review. In this opinion article, I critically review some aspects of the checklist and question the usefulness of certain psychometric analyses in research practice.