Academic Analytics Data Principles and Guidelines

Person-level data in Academic Analytics may be used for:

• Identifying relevant grant opportunities for faculty
• Facilitating collaboration of faculty engaged in similar research themes
• Identifying potential mentors to support emerging scholars and mid-career faculty
• Identifying high-performing faculty for proactive retention
• Identifying of meritorious or under-recognized faculty for honorific award nominations

Aggregate, program-level data in Academic Analytics may be used for:

• Identifying university-wide areas of scholarly excellence
• Facilitating scholarly activity, impact and career success
• Analysis of department-level strengths and weaknesses regarding peer departments
• Benchmarking units against peers to draw strengths and opportunities for growth
• Identifying strengths and weaknesses of programs or departments in extramural grants and funding areas relative to peer programs or departments
• Identification of peer and aspirational peer institutions and disciplines
• Academic planning

WSU administration at the university, school/college, or department level should NOT use Academic Analytics for:

• Setting salary or merit increases
• Decisions over retention offers
• Internal funding decisions
• Decisions on allocations of faculty lines
• Promotion and tenure decisions
• Annual faculty performance evaluations
• Decisions about teaching loads
• Faculty termination
• Program elimination

Limitations of the data

• The Academic Analytics database captures measures of research activity; other critical activities of faculty members are not measured, including teaching, service, and engagement.
• In certain disciplines – especially the arts and humanities – there are forms of faculty scholarly activity that are not captured in the Academic Analytics database