The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Charles S Tapiero - One of the best experts on this subject based on the ideXlab platform.
-
consumers risk and quality Control in a collaborative supply chain
European Journal of Operational Research, 2007Co-Authors: Charles S TapieroAbstract:Abstract The purpose of this paper is to provide a strategic collaborative approach to risk and quality Control in a cooperative supply chain by using a Neyman–Pearson quantile risk framework for the Statistical Control of risks. The paper is focused on the Statistical quality Control of a supplier and a producer, applying the traditional Neyman–Pearson theory to the Control of quality in a supply chain environment. In our framework, the risks assumed by the parties in the supply chain depend on the organizational structure, the motivations and the power relationships that exist between members of the supply chain.
Cengiz Kahraman - One of the best experts on this subject based on the ideXlab platform.
-
fuzzy exponentially weighted moving average Control chart for univariate data with a real case application
Applied Soft Computing, 2014Co-Authors: Sevil şenturk, Nihal Erginel, Ihsan Kaya, Cengiz KahramanAbstract:Statistical process Control (SPC) is an approach to evaluate processes whether they are in Statistical Control or not. For this aim, Control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular Control charts, exponentially weighted moving average Control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA Control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the Control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.
Kevin D Carlson - One of the best experts on this subject based on the ideXlab platform.
-
Statistical Control in correlational studies 10 essential recommendations for organizational researchers
Journal of Organizational Behavior, 2016Co-Authors: Thomas E Becker, Kevin D Carlson, Guclu Atinc, James A Breaugh, Jeffrey R Edwards, Paul E SpectorAbstract:Summary Statistical Control is widely used in correlational studies with the intent of providing more accurate estimates of relationships among variables, more conservative tests of hypotheses, or ruling out alternative explanations for empirical findings. However, the use of Control variables can produce uninterpretable parameter estimates, erroneous inferences, irreplicable results, and other barriers to scientific progress. As a result, methodologists have provided a great deal of advice regarding the use of Statistical Control, to the point that researchers might have difficulties sifting through and prioritizing the available suggestions. We integrate and condense this literature into a set of 10 essential recommendations that are generally applicable and which, if followed, would substantially enhance the quality of published organizational research. We provide explanations, qualifications, and examples following each recommendation. Copyright © 2015 John Wiley & Sons, Ltd.
-
the illusion of Statistical Control Control variable practice in management research
Organizational Research Methods, 2012Co-Authors: Kevin D CarlsonAbstract:The authors extend previous recommendations for improved Control variable (CV) practice in management research by mapping the objectives for using Statistical Control to recommendations for research practice. Including CVs in research designs to permit Statistical Control of “nuisance” variance is a common research practice that is subject to well-documented and potentially serious problems. Yet because CVs are frequently weakly related to focal variables, they rarely influence the interpretation of results. As a result, current practice offers an illusion of Statistical Control when in fact little Control actually occurs. The authors extend the growing literature on CV practice by examining the ambiguity of researchers' stated purposes for using Statistical Control that makes it difficult to determine whether common CV practice accomplishes any of these intents effectively. Guidelines for improving research practice are offered, including adopting a conservative stance toward the inclusion of CVs in the ...
-
the illusion of Statistical Control Control variable practice in management research
Organizational Research Methods, 2012Co-Authors: Kevin D CarlsonAbstract:The authors extend previous recommendations for improved Control variable (CV) practice in management research by mapping the objectives for using Statistical Control to recommendations for researc...
Sevil şenturk - One of the best experts on this subject based on the ideXlab platform.
-
fuzzy exponentially weighted moving average Control chart for univariate data with a real case application
Applied Soft Computing, 2014Co-Authors: Sevil şenturk, Nihal Erginel, Ihsan Kaya, Cengiz KahramanAbstract:Statistical process Control (SPC) is an approach to evaluate processes whether they are in Statistical Control or not. For this aim, Control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular Control charts, exponentially weighted moving average Control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA Control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the Control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.
Paul E Spector - One of the best experts on this subject based on the ideXlab platform.
-
Statistical Control in correlational studies 10 essential recommendations for organizational researchers
Journal of Organizational Behavior, 2016Co-Authors: Thomas E Becker, Kevin D Carlson, Guclu Atinc, James A Breaugh, Jeffrey R Edwards, Paul E SpectorAbstract:Summary Statistical Control is widely used in correlational studies with the intent of providing more accurate estimates of relationships among variables, more conservative tests of hypotheses, or ruling out alternative explanations for empirical findings. However, the use of Control variables can produce uninterpretable parameter estimates, erroneous inferences, irreplicable results, and other barriers to scientific progress. As a result, methodologists have provided a great deal of advice regarding the use of Statistical Control, to the point that researchers might have difficulties sifting through and prioritizing the available suggestions. We integrate and condense this literature into a set of 10 essential recommendations that are generally applicable and which, if followed, would substantially enhance the quality of published organizational research. We provide explanations, qualifications, and examples following each recommendation. Copyright © 2015 John Wiley & Sons, Ltd.
-
methodological urban legends the misuse of Statistical Control variables
Organizational Research Methods, 2011Co-Authors: Paul E Spector, Michael T BrannickAbstract:The automatic or blind inclusion of Control variables in multiple regression and other analyses, intended to purify observed relationships among variables of interest, is widespread and can be cons...
