Innovative method to evaluate quality management system audit results’ using single value neutrosophic number

Using Quality Management System (QMS) enables the organization to fulfil its purpose and mission to meet customers and regulatory requirements and improve its effectiveness and efficiency on a continuous basis, Quality Audit is very critical tool to ensure that the standards, policies and/or procedures are applied in the organization as planned in QMS, in real world applications, the results of the Quality Audit process depend on the auditors’ human judgments that characterized with doubts sometimes, estimating values another times, and using general impressions in his judgment, which motivate to introduce for the first time the Neutrosophic Quality Audit (NQA) model which can be extended to manage and audit in the future. NQA will be used to enhance the way of evaluating the quality audit results while many uncertainty aspects and indeterminacy do exist, the proposed model using Single Value Neutrosophic Numbers (SVNN) which is an instance of neutrosophic set, to overcome the weakness of using a crisp value to present the result of the audit findings, in addition, using Simplified Neutrosophic Weighted Geometric Average Operator (SNWGAO), and cosine similarity degree, an illustrative example is provided to validate the proposed model.     

Multiple-attribute decision making based on single-valued neutrosophic Schweizer-Sklar prioritized aggregation operator

Single-valued neutrosophic (SVN) sets can successfully describe the uncertainty problems, and Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) can build the information aggregation process more flexible by a parameter. To fully consider the advantages of SVNS and SS operations, in this article, we extend the SS TN and TCN to single-valued neutrosophic numbers (SVNN) and propose the SS operational laws for SVNNs. Then, we merge the prioritized aggregation (PRA) operator with SS operations, and develop the single-valued neutrosophic Schweizer-Sklar prioritized weighted averaging (SVNSSPRWA) operator, single-valued neutrosophic Schweizer-Sklar prioritized ordered weighted averaging (SVNSSPROWA) operator, single-valued neutrosophic Schweizer-Sklar prioritized weighted geometric (SVNSSPRWG) operator, and single-valued neutrosophic Schweizer-Sklar prioritized ordered weighted geometric (SVNSSPROWG) operator. Moreover, we study some useful characteristics of these proposed aggregation operators (AOs) and propose two decision making models to deal with multiple-attribute decision making (MADM) problems under SVN information based on the SVNSSPRWA and SVNSSPRWG operators. Lastly, an illustrative example about talent introduction is given to testify the effectiveness of the developed methods.     

Relating Quine's NF to Feferman's EM

We show that, if non-uniform impredicative stratified comprehension is assumed, Feferman's theories of explicit mathematics are consistent with a strong power type axiom. This result answers a problem, raised by Jäger. The proof relies upon an interpretation into Quine's set theory NF with urelements.     

Inferential procedures for correlation coefficients corrected for attenuation

A model and computational procedure based on classical test score theory are presented for determination of a correlation coefficient corrected for attenuation due to unreliability. Next, variance-covariance expressions for the sample estimates defined earlier are derived, based on application of the delta method. Results of a Monte Carlo study are presented in which the adequacy of the derived expressions was assessed for a large number of data forms and potential hypotheses encountered in the behavioral sciences. It is shown that, based on the proposed procedures, confidence intervals for single coefficients are reasonably precise. Two-sample hypothesis tests, for both independent and dependent samples, are also accurate. However, for hypothesis tests involving a larger number of coefficients than two—both independent and dependent—the proposed procedures require largens for adequate precision. Results of a preliminary power analysis reveal no serious loss in efficiency resulting from correction for attenuation. Implications for practice are discussed.Support for the research reported in this article was provided by the Natural Sciences and Engineering Research Council of Canada. The authors acknowledge with thanks the constructive comments of the editor and three anonymous reviewers.     

The robustness of estimates of total indirect effects in covariance structure models estimated by maximum

The large sample distribution of total indirect effects in covariance structure models in well known. Using Monte Carlo methods, this study examines the applicability of the large sample theory to maximum likelihood estimates oftotal indirect effects in sample sizes of 50, 100, 200, 400, and 800. Two models are studied. Model 1 is a recursive model with observable variables and Model 2 is a nonrecursive model with latent variables. For the large sample theory to apply, the results suggest that sample szes of 200 or more and 400 or more are required for models such as Model 1 and Model 2, respectively.For helpful comments on a previous draft of this paper, we are grateful to Gerhard Arminger, Clifford C. Clogg, and several anonymous reviewers.     

A stochastic multidimensional unfolding approach for representing phased decision outcomes

This paper presents a stochastic multidimensional unfolding (MDU) procedure to spatially represent individual differences in phased or sequential decision processes. The specific application or scenario to be discussed involves the area of consumer psychology where consumers form judgments sequentially in their awareness, consideration, and choice set compositions in a phased or sequential manner as more information about the alternative brands in a designated product/service class are collected. A brief review of the consumer psychology literature on these nested congnitive sets as stages in phased decision making is provided. The technical details of the proposed model, maximum likelihood estimation framework, and algorithm are then discussed. A small scale Monte Carlo analysis is presented to demonstrate estimation proficiency and the appropriateness of the proposed model selection heuristic. An application of the methodology to capture awareness, consideration, and choice sets in graduate school applicants is presented. Finally, directions for future research and other potential applications are given.     

Learn locally, act globally: Learning language from variation set cues

Variation set structure - partial overlap of successive utterances in child-directed speech - has been shown to correlate with progress in children’s acquisition of syntax. We demonstrate the benefits of variation set structure directly: in miniature artificial languages, arranging a certain proportion of utterances in a training corpus in variation sets facilitated word and phrase constituent learning in adults. Our findings have implications for understanding the mechanisms of L1 acquisition by children, and for the development of more efficient algorithms for automatic language acquisition, as well as better methods for L2 instruction.     

Factors affecting responses to Likert type questionnaires: introduction of the ImpExp,a new comprehensive model

The major purpose of this paper is to propose a comprehensive model describing the effects of response sets within the theory framework of the stages of responding to questionnaires, and taking into account the effects of collectivist and individualist attributes within cross-cultural contexts. The introduction of this model aims to provide a construct that may help minimize biases in questionnaire-based research as well as providing new directions for theoretical and empirical research in the field of response sets.