Feb 28, 2020
The TTI SI Stress Assessment is a diagnostic tool that is focused on workplace culture. The tool exposes potential sources of stress in the following categories: Workplace Demands, Effort/reward balance, Control, Organization change, Management/supervisor issues, social support, and job security. While we all can, and from time to time do, bring our personal issues into the workplace, work-related problems are the only ones that management has control over and therefore must be aware. This brief paper explains the purpose of the stress assessment and provides the research underpinnings for how it was developed.
Feb 28, 2020
Technological advancements throughout society are occurring at an ever-increasing rate. Neurological discoveries and associated investigative tools are leading to new understandings of human behavior never before explored. While the future of artificial intelligence for hiring and professional development is making headlines, this science requires time for peer review and refinement of new technologies.
Jan 29, 2020
This is the first in a series of monthly articles outlining the International Test Commission’s (ITC) Guidelines for Translating and Adapting Tests. The ITC guidelines are provided in a six-category format that includes the topics pre-condition, test development, confirmation, administration, scoring and interpretation, and documentation. It is the goal of this series to provide the motivation behind and an outline for the TTI Success Insights translation protocol.
Jan 28, 2020
This is the second in a series of monthly articles outlining the International Test Commission’s (ITC) Guidelines for Translating and Adapting Tests. The ITC guidelines are provided in a six-category format that includes the topics pre-condition, test development, confirmation, administration, scoring and interpretation, and documentation. It is the goal of this series to provide the motivation behind and an outline for the TTI Success Insights translation protocol.
Sep 27, 2019
This study establishes relationships between several external variables based on demographic information obtained using the O*Net job classification model and the scales of the TTI Success Insights TriMetrix EQ assessment. The work uses a logistic regression modeling approach to derive statistically significant functional relationships between the TriMetrix EQ scales and membership in the job classification group of interest. ROC curve analysis is used to show the classification algorithm outperforms the standard random selection technique.
