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Hpt validating performance measures

In addition, if the structure of communication is not clearly delineated in the procedures manual, transmission of any change in procedures to staff members can be compromised Quality control also identifies the required responses, or ‘actions’ necessary to correct faulty data collection practices and also minimize future occurrences.

These actions are less likely to occur if data collection procedures are vaguely written and the necessary steps to minimize recurrence are not implemented through feedback and education (Knatterud, et al, 1998) Examples of data collection problems that require prompt action include: In the social/behavioral sciences where primary data collection involves human subjects, researchers are taught to incorporate one or more secondary measures that can be used to verify the quality of information being collected from the human subject.

Each approach is implemented at different points in the research timeline (Whitney, Lind, Wahl, 1998): Since quality assurance precedes data collection, its main focus is 'prevention' (i.e., forestalling problems with data collection).

Prevention is the most cost-effective activity to ensure the integrity of data collection.

These failures may be demonstrated in a number of ways: An important component of quality assurance is developing a rigorous and detailed recruitment and training plan.

Implicit in training is the need to effectively communicate the value of accurate data collection to trainees (Knatterud, Rockhold, George, Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, 1998).

This study was conducted to contribute to the field of Human Performance Technology (HPT) through the validation of the performance analysis process of the International Society for Performance Improvement (ISPI) HPT model, the most representative and frequently utilized process model in the HPT field.

The study was conducted using content analysis as the research methodology to investigate thirty HPT business cases.While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.Regardless of the field of study or preference for defining data (quantitative, qualitative), accurate data collection is essential to maintaining the integrity of research.While quality control activities (detection/monitoring and action) occur during and after data collection, the details should be carefully documented in the procedures manual.A clearly defined communication structure is a necessary pre-condition for establishing monitoring systems.Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring.and the nature of investigation, there is the potential to cause disproportionate harm when these research results are used to support public policy recommendations.Thus, data quality should be addressed for each individual measurement, for each individual observation, and for the entire data set. Each field of study has its preferred set of data collection instruments. The hallmark of laboratory sciences is the meticulous documentation of the lab notebook while social sciences such as sociology and cultural anthropology may prefer the use of detailed field notes. Regardless of the discipline, comprehensive documentation of the collection process before, during and after the activity is essential to preserving data integrity.

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