Employee Motivation & Performance Assessment, Inc.(EMPA) has designed Employee Assessments and has provided Survey Research Services for more than 7 million employees, including Performance / Skill Assessments, Employee Surveys, and Program Assessments. Its domestic and international clients span 45 countries and include large companies, non-profit organizations, and government agencies that have 1000 or more employees.
Dr. Palmer Morrel-Samuels is EMPA's founder and CEO. He is a Research Psychologist with over 25 years of extensive training and experience in Statistical Analysis and Assessment Design. He received a Master of Arts degree in Research Methodology from the University of Chicago, a Master of Philosophy degree and a Ph.D. in Experimental Social Psychology from Columbia University. A well sought out expert, Dr. Morrel-Samuels has
Testified before the U.S. Congress on employee motivation and its linkage to objective performance metrics,
Provided assessment services for non-profit organizations (e.g., Blue Cross-Blue Shield, University of Michigan), government agencies (e.g., the Department of Justice), and Fortune 500 companies (e.g., FedEx, IBM, GM, California Edison, Bank of America, Xerox, Coca-Cola, and Disney),
Been an expert resource for the New York Times, the Washington Times, and the Wall Street Journal,
Conducted research for IBM, EDs, the University of Chicago and Yale University,
Been a faculty member at Antioch Graduate School and the University of Michigan Business School
Lectured extensively on survey design and organizational communication,
Authored several patented employee and leadership assessments, and
Published articles in The Journal of Experimental Psychology, Behavioral Research Methods, The Journal of Personality & Social Psychology, California Management Review, Physician Executive, and Harvard Business Review, among others.
Dr. Morrel-Samuels currently teaches courses on survey design and research methodology at the University of Michigan’s Institute for Social Research and the School of Public Health. He is also serving as the President of the Workplace Research Foundation, a nonprofit responsible for the National Benchmark Study® - a nationwide survey that sets BenchmarksTM and measures the causal linkage between employee motivation and subsequent stock return.
This might come as a shock: Employees in large corporations sometimes mistakenly believe that they have been discriminated against. Admittedly, discrimination does occur, both in society and in the workplace. And as most attorneys know, many discrimination cases concern claims of either adverse treatment or adverse impact. In both types of litigation, employees believe that they have been discriminated against because of their minority status. In disparate treatment cases, plaintiffs must show that they were treated differently because of that status, and incriminating statements-express or implied-must be admitted as evidence to suggest a discriminatory intent. In contrast, disparate impact cases typically address the discriminatory impact of an ostensibly neutral policy, decision, or program, so plaintiffs rely upon objective data from the entire corporation to prove a discriminatory outcome.
THERE'S NO DOUBT that companies can benefit from workplace surveys and questionnaires. A GTE survey in the mid-1990s, for example} revealed that the performance of its different billing operations, as measured by the accuracy of bills sent out, was closely tied to the leadership style of the unit managers.
This might come as a shock: Employees in large corporations sometimes mistakenly believe that they have been discriminated against. Admittedly, discrimination does occur, both in society and in the workplace. And as most attorneys know, many discrimination cases concern claims of either adverse treatment or adverse impact.
Effective evidence-based managelnent requires analyzing data from a broad array of sources and conducting carefully designed pretest-posttest comparisons. However, our experience suggests that few businesses take that process to the next level by building merged datasets that can be used for rigorous pretest-posttest comparisons and meaningful statistical analyses.
No contemporary guide exists for using statistics to prove causality in court. We outline a new theory explaining comprehension of causal graphs, and claim four hallmarks of causality are critical: Association, Prediction, Exclusion of Alternative Explanations, and Dose Dependence.
We distinguish between reverse discrimination and over-correction, arguing that the former should be used only to describe cases where well-qualified non-minority applicants are unjustifiably denied positions in organizations run by and/or staffed by minorities. Similarly, we argue over-correction should be used to describe well-qualified non-minority applicants who are unjustifiably denied positions in organizations run by non-minorities.