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Dr. Larry H. Crow is an independent consultant as well as an instructor and consultant in the areas of reliability growth and repairable system data analysis. Previously, Dr. Crow served as Vice President, Reliability and Sustainment Programs at Alion Science and Technology in Huntsville, Alabama. He held this position at IIT Research Institute before Alion was established in 2002 by 1600 former IITRI employees. Prior to that, Dr. Crow was Director, Reliability at General Dynamics Advanced Technology Systems (formerly Bell Laboratories ATS). Before joining Bell Laboratories in 1985, Dr. Crow was chief of the Reliability Methodology Office at the US Army Materiel Systems Analysis Activity (AMSAA). He developed the Crow (AMSAA) model and the Crow Projection model, which have been incorporated into US DoD military handbooks as well as national and international standards and service regulations on reliability. Dr. Crow chaired the Tri-Service Committee to develop US MIL-HDBK-189, and is the principal author of that document. He is also the principal author of the IEC 61164, . He developed the widely used N.H.P.P. Power Law model for analyzing repairable systems reliability, which is featured in the new IEC 61710, .
Reliability of Wikipedia - Wikipedia
Dan Farley is Director of Product Management - Training & Education with HBM Prenscia. Dan held the positions of Associate Director Continuous Improvement at Kraft Foods Group and Engineering Manager/Competency Owner Reliability and Innovation Continuous Improvement Methodologies at Delphi Thermal Systems. Dan's a visionary leader with extensive experience in product and process improvement, having led the creation and deployment of Customer-based Engineering, Six Sigma, Design for Six Sigma and Design for Reliability at Delphi Thermal Systems. Dan has trained and coached global engineers and leaders in Design for Reliability, Life Data Analysis, Accelerated Testing, FMEA, Problem Solving, Statistics, Change Management, Design of Experiments, Lean, Six Sigma, Design for Six Sigma, QFD and Robust Engineering. He is a Certified Reliability Professional (CRP), Design for Six Sigma and Lean Six Sigma Master Black Belt, Certified Change Management Practitioner and Trainer, and Certified Brain-based Coach from the NeuroLeadership Group. Dan received his M.A. in Business and Policy Studies, and B.P.S. in Industrial Technology and Leadership from the State University of New York Empire State College.
David Groebel serves as Director of Software Development with HBM Prenscia. Since 1997, he has been integral in the development of ReliaSoft software. In his previous role as the Director QA & Support, he was instrumental in formalizing the customer support and software quality assurance procedures at ReliaSoft. He has conducted seminars across different industries including Aerospace, Defense, Automotive and Medical. In addition, he has been the project lead for the ReliaSoft RGA software since 2003. His areas of interest include life data analysis, accelerated life testing, system reliability, reliability growth analysis and software quality assurance. Mr. Groebel holds a B.S. in Aerospace Engineering from the University of Arizona. He is a Senior Member of ASQ and a Certified Reliability Professional (CRP).
Cronbach's Alpha | Real Statistics Using Excel
The higher the Alpha is, the more reliable the test is. There isn'ta generally agreed cut-off. Usually 0.7 and above is acceptable(Nunnally, 1978). It is a common misconception that if the Alpha islow, it mustbe a bad test. Actually your test may measure severalattributes/dimensions rather than one and thus the Cronbach Alpha isdeflated. For example, it is expected that the scores of GRE-Verbal,GRE-Quantitative, and GRE-Analytical may not behighly correlated because they evaluate different types of knowledge.
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However, in the preceding aproach, even if the test taker or thesurvey participant skips one question, his entire test will be ignoredby SAS. In a speeded test where test taker or the survey participantsmay not be able to complete all items, the use of"nomiss" will lead to some loss of information. One way to overcomethis problem is to set a criterion for a valid test response. Assumethat 80 percent of test items must be answered in order to beincluded into the analysis, the following SAS code should beimplemented:
Survey Development and Analysis | Precision Consulting
If your test is not internally consistent, you may want to performfactor analysis to combine items into a few factors. You may also dropthe items that affect the overall consistency, which willbe discussed next.