Dr. Palmer Morrel-Samuels is a Research Psychologist with extensive training and experience in Statistical Analysis and Assessment Design. He has done a considerable amount of research and applied work on communication, testified to the U.S. congress on employee motivation and its linkage to objective performance metrics, published several articles on survey design in Harvard Business Review, among others, and wrote several patents to assist in the administration and analysis of workplace assessments. Dr. Morrel-Samuels currently teaches graduate courses on survey design and research methodology at the University of Michigan.
Litigation Services: Dr. Morrel-Samuels' education, practical experience, and distinguished authorship have made him a valuable resource for providing expert services in legal cases where workplace surveys or assessments are at issue, including:
Statistical analysis of very large datasets, measuring the impact of corporate culture on performance and race discrimination.
Analysis of a survey's validity, reliability, objectivity, fairness, accuracy, confidentiality, freedom from response bias, and conformance to The Uniform Guidelines pertaining to all workplace assessments.
Statistical analyses of performance-related and survey-related data.
Desiging and conducting employee and workplace surveys, including Electronic surveys.
Program evaluations, especially when used in hiring, firing, or other job actions.
Expert Witness Experience includes: Assisted the NAACP in its amicus brief for the Ricci discrimination case. Was the sole statistician in a successful $100M breach of contract case (Tower Automotive v. UNOVA) that required analyzing 4 million rows of data. Testified for the ICC’s International Court of Arbitration in The Hague. Has successfully withstood Daubert challenges - most recently from the City of Indianapolis in a large discrimination case involving the city's fire department.
For more information visit our website at www.ExpertWitnessPsychology.com
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.