ĐĎॹá>ţ˙ }ţ˙˙˙z{|€˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ěĽÁ` řżĽbjbjć‡ć‡ Ť¨„í„ío5˙˙˙˙˙˙¤       4@hƒhƒhƒhЃD…Ü@Ž ü…^Z†"|†|†|†W‡.…‡™‡ ăĺĺĺĺĺĺ$.h–†  –W‡W‡––   |†|†Ű4°›°›°›–l |† |†ă°›–ă°›°›&Üó  Ë |†đ… @ t7ńbĹhƒr—ęĎdotR<Ž3˜\™üČË Ë ô ż°Ľ‡žCŒL°›¤3’ÓĽ‡Ľ‡Ľ‡  X›XĽ‡Ľ‡Ľ‡Ž––––@@@„CÄJ¤8@@@ÄJ@@@      ˙˙˙˙  Position Paper on Faculty Activity Data for the AAU Institutional Data Committee Prepared by the Association of American Universities Data Exchange Dennis Hengstler, Bill Hayward, and Rana Glasgal April 2005 Executive SUMMARY The AAU Executive Committee has charged the Institutional Data Committee (IDC) to evaluate university data collection efforts in the areas of Graduate Education, Undergraduate Education, and Faculty Activities and to identify what data collection procedures should be implemented to produce relevant and comparable data across AAU universities. This paper addresses Faculty Activities data, as companion papers will address the other areas of graduate and undergraduate education. Specifically, the paper discusses: a) the types of data being collected; b) the major issues and problems associated with the data collection efforts; and c) recommendations for future data collection activities. Among the topics covered are: Number of Faculty and Faculty Profiles Faculty Salaries and Benefits Faculty Honors and Awards Faculty Activity (Publications/Citations) Faculty Patents and Licenses Research Activity Research Space Post Doctorates Faculty Surveys on Quality of Life, Campus Climate Teaching Activity Other In reviewing the various data on faculty activities, the IDC will need to consider why it is important to collect such information and what questions could be answered with the data collected. It is assumed that the primary focus of the data collection efforts is to provide data to AAU member institutions for internal policy decisions and that the data could greatly facilitate the government relations and administration (e.g., member selection) of the AAU organization. Other important questions, issues, and principles that will need to be addressed are: Should the data be collected by discipline/program or by organizational unit? If so, what level of detail should be collected (e.g., broad discipline, sub-discipline), and what taxonomy of disciplines (discipline classifications) should be reported (CIP, NRC, NSF, ISI, etc)? If discipline level data is desired the AAU Data exchange recommends using CIP codes and that appropriate crosswalks be developed between discipline taxonomies. How should data be treated / released when there is a small number (N) to ensure the privacy of individuals and as well as the reliability / generality of the data? What constitutes a small N (e.g., 1, 3, 5, 10)? Should historical data be included if comparable? How should data be collected (unit-record data vs. aggregate or summary data), how often should they be collected, who should collect and check the data, how should the data be maintained (e.g., should the data reside the AAU Data Exchange data warehouse), what standard reports should be generated from the data, what principles and guidelines should be developed for downstream reporting of the data (e.g., who has access to what data; under what conditions can data be released and to whom)? What infrastructure and funding will be necessary to support the desired data collection and reporting efforts? Below is a summary of the recommendations for each of the above topics: RECOMMENDATONS: 1. Number of Faculty and Faculty Profiles The AAUDE Faculty Profile Survey provides the most comprehensive and detailed information about faculty. This survey should be collected for all AAU institutions. Questions the IDC will need to consider are: What guidelines should be developed for the release of faculty demographic characteristics when there are small Ns that potentially could identify a faculty member (e.g., cases where there is only one faculty member of a given race or gender)? What level of detail is desired in the reporting of faculty by discipline? Should reports include both the total faculty paid and the total budgeted FTE for the discipline (department)? Should other useful characteristics of the faculty (e.g., citizenship, age) be reported? 2 Faculty Salary and Benefits The AAUDE Faculty Salary Survey provides the most comprehensive and detailed salary information for faculty. This survey should be collected for all AAU institutions. Questions the IDC will need to consider are: What guidelines should be developed for the release of faculty salaries when there are small Ns that potentially could identify a faculty member (e.g., cases where there is only one faculty member). What level of detail is desired in the reporting of faculty salaries by discipline? Should salary data be reported by gender or other characteristics? 3 Faculty Honors and Awards AAUDE has initiated a project to collect and maintain faculty awards and honors in the AAUDE Data Warehouse. It is recommended that faculty honors and awards continue to be collected and maintained in the AAUDE warehouse. Questions the IDC will need to consider are: Which honors and awards should be collected and maintained? Should the AAUDE database track the awards by the name of the recipient and their current institution? Are there other fields (e.g., discipline, date and year) that should be included in the database? How to fund the collection and maintenance cost associated with the faculty honors and awards database? The IDC / AAU should also explore the possibility of working with the various awarding organizations to make their data available in a more user-friendly format, such as adding listings of awards by institution name and making their lists available for electronic download. Another avenue of exploration is the work of the International Congress of Distinguished Award (ICDA). If some of the faculty awards tracked by ICDA are of interest, perhaps AAU or AAUDE could collaborate with this group to obtain their data. 4. Citations & Publications The ISI dataset provides the most comprehensive and detailed information about faculty publications and citations. It is recommended that publication and citation data be collected for all AAU institutions from the ISI dataset. Questions the IDC will need to consider are: Which disciplines should to be reported and what taxonomy should be used (e.g., ISI taxonomy, CIP taxonomy, NRC taxonomy, other taxonomies). It becomes more expensive as the number of broad disciplines is expanded. Should the reporting of publications and citations be normalized to account for the size of faculty at the AAU institutions? Raw (un-normalized) data would be collected from the ISI dataset and additional work would be required for the normalization. Should AAU work with Thomson Scientific to address some of the issues listed above in order to improve their product? For example, to address the issue that ISI is not a good indicator in the Humanities --- perhaps Thomson Scientific could be persuaded to compile a database of books written by university faculty and published by the top publishing houses (e.g., Oxford University Press). Should AAU work with Thomson Scientific to refine their list of “acceptable journals and publications?” 5. Faculty Patents / Licenses Patent and licensure information is collected annually from a survey of the Association of University Technology Managers (AUTM). If patent and licensure information is desired, the IDC and AAU should try to encourage 100% participation in the AUTM survey. Universities that currently report data at the system level will need to report individual campus data in the AUTM survey or provide such data to the AAUDE representative. AAUDE could work with AUTM to receive special extracts of their database for incorporation into the warehouse. It is assumed that there would be a nominal charge to access this information from AUTM. IDC will also need to ascertain what specific types of information (e.g., patent applications filed, patents issued, licenses and options executed, license income received, start-up companies, etc.) from the AUTM survey should be routinely collected and maintained in the warehouse and what type of reports from the warehouse should be produced. 6. Research Activity As recommended in the AAU Membership Taskforce report, the best measure of research activity at particular institutions is the NSF R&D Expenditure Survey. We recommend that this measure continue to be used and that refinements be made in standard reports to account for the research nature of individual campuses (i.e., the influence of medical and agricultural research in the total research support of a campus). AAU may wish to work with the NSF to expand the number of disciplines and/or sub-disciplines reported as the level of detail is fairly restricted. 7. Research Space If the IDC desires to collect research space information, the NSF Scientific and Engineering Research Facilities survey is the only available source of information. This information could be housed in the AAUDE data warehouse. AAUDE would need to make arrangements with NSF to obtain the data in electronic format. If more detailed information is required (e.g., data for disciplines such as physics and chemistry), AAU may wish to pursue discussions with NSF to modify future surveys. 8. Post Doctorates The postdoctoral data from the NSF Survey on Graduate Students and Postdoctorates in Science and Engineering provides the only source of information about postdocs. Because NSF postdoctoral data are also readily available, additional analyses by discipline, gender, and individual citizenship could be provided. For example, two potentially useful indicators calculated from these data would be the percentage of postdoctorates who are U.S. citizens and the ratio of postdoctorates to graduate students or faculty by discipline. 9. Faculty Surveys on Quality of Life, Campus Climate Although AAU institutions participate in periodic surveys of their faculty, there is no systematic/consistent source of data on the Quality of Life/Campus Climate. If the IDC would like to collect survey information from faculty, it would be possible to maintain such data in the AAUDE data warehouse. Questions the IDC will need to consider are: What information would AAU like to receive from faculty (i.e., what questions to ask on the survey)? How often would the AAU like to receive such information (i.e., how often to administer the survey—one a year, once every two or three years)? Who should coordinate the survey (i.e., should this be contracted out, should one of the AAUDE representatives be asked to coordinate the survey)? What resources will be provided to support such a survey? 10. Teaching Activity From time to time, AAUDE has discussed the idea of collecting Student Credit Hour (SCH) data by level, most recently at its 2005 meeting. Previous discussions have also included the idea of collecting faculty teaching workload data (i.e., number primary classes (excluding non-independent study and thesis/dissertation courses) taught per ladder faculty member. If the IDC wishes to collect such information, AAUDE could initiate formal discussions and develop recommendations for collecting this information 11. Other Are there other data the IDC may wish to pursue such as faculty turnover, retention, and retirement rates, mandatory retirement and promotion and tenure policies, etc? (Note: faculty teaching activity, such as the number of classes taught per faculty and student credit hours per faculty, will be addressed in the companion paper on undergraduate education.) Position Paper on Faculty Activity Data for the Institutional Data Committee Prepared by the AAU Data Exchange (AAUDE) Dennis Hengstler, Bill Hayward, Rana Glasgal The AAU Executive Committee has charged the Institutional Data Committee (IDC) to evaluate university data collection efforts in the areas of Graduate Education, Undergraduate Education, and Faculty Activities. Specifically, the IDC has been asked to identify the following questions: What data are currently collected, whether there are systematic flaws or redundancies in the data collected, and how any such problems could be rectified? What data are not being collected that would be useful for both institutional and national planning and policy development, and what data collection procedures should be implemented to produce relevant and comparable data across AAU universities? This paper will address the above questions related to Faculty Activities, as companion papers will address the other areas of graduate and undergraduate education. It is understood that the primary focus of the data collection efforts is to obtain a better understanding of the AAU institutions for use in internal planning and for legislative and lobbying activities. The first section of this paper discusses the various types of faculty activity data being collected. Specific attention will be devoted to: a) the data sources (who collects the data, how the data are similar/redundant or different); b) the major issues and problems associated with the data collection efforts; and c) recommendations for future data collection activities. Among the topics to be covered are: Number of Faculty and Faculty Profiles Faculty Salaries and Benefits Faculty Honors and Awards Faculty Activity (Publications/Citations) Faculty Patents and Licenses Research Activity Research Space Post Doctorates Faculty Surveys on Quality of Life, Campus Climate Teaching Activity Other In considering the various data on faculty activities, the IDC will need to address a number questions and issues: (e.g., why it is important to collect such information and what questions could be answered with the data that is collected?). It is assumed that the primary focus of the data collection efforts is to provide data to AAU member institutions for internal policy decisions and that the data could greatly facilitate the government relations and administration (e.g., member selection) of the AAU organization. Other important questions, issues, and principles that will need to be addressed are: Should the data be collected by discipline/program or by organizational unit? If so, what level of detail should be collected (e.g., broad discipline, sub-discipline), and what taxonomy of disciplines (discipline classifications) should be reported (CIP, NRC, NSF, ISI, etc)? If discipline level data is desired the AAU Data exchange recommends using CIP codes and that appropriate crosswalks be developed between discipline taxonomies. How should data be treated / released when there are a small number (N) to ensure the privacy of individuals and as well as the reliability / generality of the data? What constitutes a small N (e.g., 1, 3, 5, 10)? Should historical data be included if comparable? How should data be collected (electronic vs. hardcopy; unit-record data vs. aggregate or summary data), how often should they be collected, who should collect and check the data, how should the data be maintained (e.g., should the data reside the AAU Data Exchange data warehouse), what standard reports should be generated from the data, what principles and guidelines should be developed for downstream reporting of the data (e.g., who has access to what data; under what conditions can data be released and to whom)? What infrastructure and funding will be necessary to support the desired data collection and reporting efforts? The remaining section of the paper addresses the second charge of the Institutional Data Committee and will be based on future discussion and recommendations of the group. 1. NUMBER OF FACULTY AND FACULTY PROFILES Data Sources A variety of surveys collect information on faculty. Most, however, collect faculty headcount data in conjunction with the reporting of faculty salaries (see section on Faculty Salaries). The following three surveys (two IPEDS surveys from the federal government and one from the AAU Data Exchange) collect faculty information independently of the faculty salary data: Only AAUDE collects faculty information by discipline. IPEDS EAP (Employee by Assigned Position) Survey: This survey annually reports the total number (headcount) of faculty and staff employees on the payroll as of November 1. It excludes faculty on leave without pay, casual and contracted employees, hospital employees, undergraduate and work-study students. Information is reported by: Primary Function/Occupation Activity (instruction/research/public service, instruction, research, public service, executive, technical, clerical, etc), Faculty Status (tenure, tenure-track, no tenure track, without faculty status, graduate students), Full vs. Part Time Status; and Medical vs. Non-Medical affiliation. IPEDS FALL STAFF SURVEY: This survey, conducted biennially reports the same information as in the IPEDS EAP survey (headcount of faculty and staff employees as of November 1) but in a different format. Information on total campus headcount is reported by: Primary Function/Occupation Activity (full-time faculty by 9/10-month, 11/12-month, and <9-month appointments, part-time faculty, executive, technical, clerical, etc); Ethnicity; Gender; and by Salary (<$30k, $30-39k, $40-49k, $50-64k, $65-79k, $80-99k, $100k +). AAUDE FACULTY PROFILE SURVEY. This is a fairly recent survey implemented by the AAU Data Exchange (AAUDE). For the faculty included in the IPEDS EAP (Employee by Assigned Position) survey, the AAUDE Faculty Profile survey collects faculty headcount data by: discipline (6-digit CIP code), department name, academic rank, tenure status, full/part-time status, gender, ethnicity, medical school affiliation, EAP category (i.e., primarily instruction, research, public service, all three areas, other), and track (i.e., regular, research, clinical, public service, librarian, other). This and other AAUDE data are maintained in a data warehouse located on a secured server at MIT. At the present time, access to the warehouse is restricted to AAUDE members only. Data Issues AVAILABILITY: The IPEDS data are easily available via the web for all AAU institutions. Although relative new, the AAUDE Faculty Profile Survey elicited a 44% response rate for 2003-04 (10 AAU private institutions and 17 public AAU institutions). COMPREHENSIVENESS: The number and profile of the faculty from the IPEDS and AAUDE Faculty Profile survey include only faculty who are being paid as of November 1. Faculty who are on unpaid leave, faculty who start in the spring semester, and faculty who are teaching by agreement (no salary) are excluded in the faculty counts. Total FTE by discipline (department) must be imputed from the full-time and part-time faculty. AAUDE institutions attempt to reconcile and match faculty for the various IPEDS and AAUDE surveys. CONSISTENCY: There are also issues in the IPEDS EAP survey of consistency of categorization across institutions. For example, about half of the AAUs report tenured and tenure-track faculty as “primarily instruction” while the other institutions report such faculty as “instruction/research/service. INTERDISCIPLINARY FACULTY: Also excluded from the discipline counts in the AAUDE Faculty Profile Survey are faculty who may hold affiliated appointments in the discipline (department) but who are paid by another department. Often these faculty members teach interdisciplinary courses or cross-listed courses. Recommendations The AAUDE Faculty Profile Survey provides the most comprehensive and detailed information about faculty. This survey should be collected for all AAU institutions. Questions the IDC will need to consider are: What guidelines should be developed for the release of faculty demographic characteristics when there are small Ns that potentially could identify a faculty member (e.g., cases where there is only one faculty member of a given race or gender)? What level of detail is desired in the reporting of faculty by discipline? Should reports include both the total faculty paid and the total budgeted FTE for the discipline (department)? Should other useful characteristics of the faculty (e.g., citizenship, age) be reported? 2. FACULTY SALARIES AND BENEFITS Data Sources There are six major faculty salary surveys that AAU institutions may participate in. As indicated below, four surveys (AAUDE, Oklahoma State, College and University Personnel Association, and Consortium on Financing Higher Education) collect faculty salary data by discipline in various degrees. AAUDE FACULTY SALARY SURVEY BY DISCIPLINE: The AAUDE faculty survey is the most comprehensive survey on faculty salaries and has been in existence for many years and is very similar to the Oklahoma State Faculty Salary Survey. In the past, almost all AAU public institutions and a few AAU private institutions participated in the AAUDE survey. In 2004-05, the MIT faculty salary survey was discontinued and institutions were encouraged to participate in the AAUDE survey. (The MIT survey included a small subset of AAU institutions and Georgia Tech.) It appears that almost all of the privates and publics will be participating in the 2004-05 AAUDE survey. The AAUDE survey collects annualized full-time equivalent contract high, low, and average salaries as well as FTEs and length of appointment (9/10 vs. 11/12 month) by rank (including lecturers and instructors) and by discipline using the 6-digit Classification of Instructional Programs (CIP) code. Collecting by tenure statues (and possible inter-quartile range) will be added to the next collection cycle. It also collects the average faculty salary for new hires and the new hire FTE. Faculty included in the survey are those who are employed full-time at the institution and on the payroll at the time of the data collection (December 15 is requested due date). This includes faculty on paid leave, extension field staff or agricultural experiment station faculty who hold academic rank, lecturers, instructors, visiting faculty, and replacement faculty for those on leave without pay. Faculty excluded from the survey are faculty on leave without pay, faculty with administrative appointments (e.g., assistant deans or higher), and clinical departments of Medicine, if the clinical practice portion cannot be excluded from the institutional portion of their salaries. Salary data are maintained in the AAUDE data warehouse on a secured server at MIT. Data for the private institutions are masked for the year they are “in effect” and the following year (e.g., Fall 2004 data will be masked until July 1, 2006). Only institutions who participate in the survey are allowed to view the results. Standard reports are under development. OKLAHOMA STATE FACULTY SALARY SURVEY BY DISCIPLINE: The Faculty Salary Survey by Discipline is a project instituted in 1974 by Oklahoma State University. A select group of doctoral degree-granting institutions from each state are invited to participate in the survey. Last year (2003-04), 92 institutions participated including 26 public AAU institutions and one private AAU institution (Cornell). Survey results are published for the total sample, for Carnegie Research 1, II, and Doctoral I & II institutions, and by geographical region. No individual institutional data are published CUPA NATIONAL FACULTY SALARY SURVEY: The CUPA salary survey is primarily used by non-AAU universities, although a few AAU institutions do participate. Base salaries, effective October 1 converted to 9-month appointments, are reported only for full-time faculty. CUPA differs from the IPEDS and AAUP salary surveys in that the CUPA survey reports the number of faculty, average salaries, lowest salary, and highest salary by discipline using only the 4-digit Classification of Instructional Programs (CIP) code. There are some slight differences in the definitions of the faculty to be included in the survey. Faculty are to be included if their teaching/research appointments represents more than half of their duties and their contract is for at least 9 months. Clinical faculty and coaches may be included if their teaching/research activities constitute more than half of their duties. CUPA excludes visiting faculty and replacements for faculty on sabbatical. The survey also includes various questions related to faculty pay practices (e.g., compensation methods, summer salaries, department chairs, adjunct faculty). CONSORTIUM ON FINANCING HIGHER EDUCATION (COFHE) FACULTY SALARY SURVEY: Each year COFHE invites it member schools  (31 private colleges and universities) to submit data on faculty salaries as part of the COFHE Institution Profiles Project (CIPP).  These salary data are not collected by discipline, but rather by broad areas (Humanities, Social Sciences, Physical & Life Science, and Engineering).  Salaries of faculty in professional schools or departments outside of these four areas are excluded.  The survey collects nine month salary data for three ranks (Professor, Associate Professor and Assistant Professor).  The collection includes as instructional faculty all those members of the instructional/research staff who are employed full-time and whose major (at least 50%) regular assignment is instruction (including released time for research) -- regardless of whether they are formally designated "faculty."  Faculty on sabbatical leave are reported at their regular salaries even though they may be on reduced salary while on leave. Replacements for those on leave with pay should not be reported; replacements for those on leave without pay should be. Department heads with faculty rank and no other administrative title should be reported at their instructional salary (i.e., excluding administrative stipends).  For each rank and major discipline category, COFHE collects the number of people, median age, minimum, maximum and mean salaries and salaries by percentiles.  Once the data are collected and complied, they are sent to the General Counsels of the participating schools, unmasked.   IPEDS FACULTY SALARY SURVEY: This federal survey reports the total number of full-time faculty and their salaries. It is similar to the IPEDS Staff survey (by 9/10-month, 11/12-month, and <9-month appointments) but includes data by rank (professors, associate, assistant, lecturers, and no academic rank). Campuses are instructed to report only the full-time faculty who are classified in the IPEDS EAP survey as primary instruction, or instruction combined with research and public service. It includes visiting and adjunct faculty and department chairs (at regular faculty salary excluding stipends for being a chair). Deans and administrative officers are excluded, even though they may devote part of their time to instruction. It also excludes the medical faculty but includes the other health or allied health schools such as dentistry, nursing, veterinary medicine, dental hygiene, etc. The data are reported by gender, but not by ethnicity. The salary information includes the salary outlays and average salary for each group as well as data on fringe benefits (number of faculty covered and dollar expenditures for a variety of fringe benefit categories). AAUP FACULTY COMPENSATION SURVEY: This survey is widely known and the results are published annually in the Chronicle of Higher Education. It is similar, in many respects, to the IPEDS Faculty Salary Survey and includes only full-time faculty with at least 50% of their assignment in instruction. The number of faculty and total contracted salaries are reported by rank, tenure status, gender, and appointment (e.g., 9-month, 12-month). Similar data are also reported for fringe benefit data (i.e., number covered and dollar expenditure) for a number of benefit categories. The AAUP salary survey differs from that IPEDS faculty survey in that faculty on sabbatical leave are included at their regular salaries even though they may be receiving a reduced salary while on leave. Replacements for those on leave with pay are also not reported in AAUP survey. Data Issues AVAILABILITY: The IPEDS and AAUP salary data are maintained in and are easily available in the AAUDE data warehouse. In 2004-05, almost all AAU institutions will participate in the AAUDE Faculty Salary Survey. Due to the consent decree for the private institutions regarding the sharing of data, there are time delays in the submittal of the AAUDE and COFHE faculty salary data. Some private institutions define historical data, 3-6 months after the beginning of the academic year, while other private institutions require a full year to define historical data. This means that the complete AAUDE faculty salary data for the 2004-05 academic year will not be available until July 2005. Data for the private institutions in the AAUDE data warehouse will remain masked for the year the salaries are in effect and one year after that (e.g., Fall 2004 data will remain masked until July 1, 2006). COMPREHENSIVENESS: As noted above, there are slight variations across the surveys in the types of faculty included in the cohorts. Most of the variations deal with the appointment status (e.g., medical, administrators, faculty on leave/sabbatical, etc). Several institutions may report instruction, research, and public service faculty, while other may be reporting only instructional faculty in a given salary survey.  Recommendations The AAUDE Faculty Salary Survey provides the most comprehensive and detailed salary information for faculty. This survey should be collected for all AAU institutions. Questions the IDC will need to consider are: What guidelines should be developed for the release of faculty salaries when there are small Ns that potentially could identify a faculty member (e.g., cases where there is only one faculty member). What level of detail is desired in the reporting of faculty salaries by discipline? Should salary data be reported by gender or other characteristics? 3. FACULTY HONORS AND AWARDS Data Sources Data on faculty awards and honors capture elements of faculty activities and achievements not reflected in the data on research expenditures, publications, and citations. This is particularly true in the arts and humanities, where awards and honors may be used to more fully portray faculty activities in the scholarship and artistic arena. The primary data sources for major faculty honors and awards are the awarding agencies themselves, the International Congress of Distinguished Awards (ICDA), individual campus or systems, TheCenter’s “Top American Research Universities” Project at the University of Florida, and the AAUDE data collection effort. Awarding Agencies: Most major awarding agencies publish on their web sites, the name and institutional affiliation of the faculty member receiving the honor or award bestowed by the agency or organization (e.g., The Academy of Sciences at  HYPERLINK "http://www4.nationalacademies.org" http://www4.nationalacademies.org). A few agencies maintain an updated directory of the members, their institutional affiliation, and their status if a faculty member moves to another institution, retires, or dies. Most agencies and organizations, however, only record the institutional affiliation at the time of the award. Collecting, sorting, proofing, and entering these data from all the agencies and organizations into the database each year is a very onerous task Individual Universities/ Systems: Most AAU universities track and publish data on faculty awards and honors for their own faculty. Of course, there are differences in the awards and honors comprising these lists, as well as in the methodology for reporting the data. Some institutions include the awards for emeriti faculty and some may include faculty who are no longer employed or active at the institution. MIT is a good example of a university Website that displays awards and honors data for their faculty:  HYPERLINK "http://web.mit.edu/ir/pop/awards/index.html" \o "http://web.mit.edu/ir/pop/awards/index.html" http://web.mit.edu/ir/pop/awards/index.html This listing clearly indicates they do not limit their list to current faculty. Emeritus, deceased, and former professors are included and their status is clearly indicated. The University System of Maryland also collects and makes available faculty awards and honors data for the three National Academies as well as seven award programs on their Website:  HYPERLINK "http://homepages.usmd.edu/berthold/" http://homepages.usmd.edu/berthold/ The listings here report data for all four-year colleges and universities in the U.S. and data can be downloaded to Excel for further analysis. Data for a five-year period are currently available (2001-2005). The International Congress of Distinguished Award (ICDA): The ICDA is a non-profit educational corporation established in 1994 with the mission of creating “an ongoing forum for the exploration of issues relating to award giving and nature of the world of awards.” They maintain a database and website ( HYPERLINK "http://www.icda.org/" http://www.icda.org/) on awards and award programs and also publish an annual “Roster of Distinguished Awards.” ICDA’s focus is on awards by field/discipline (e.g., chemistry), and the data they collect are determined in part by asking the people in each field to name the premier awards for their field. The awards they track and report on encompass the broad fields of Arts, Culture, Economics, Education, Engineering, Environment, Exploration, Humanitarian, Humanities, Innovation, Literature, Medicine, Organizational Excellence, Peace, Psychology, Religion, Science, and Technology. Their data include both U.S. and international awards. A key factor is that ICDA’s database does NOT currently contain the name of the university with which each award recipient is affiliated. Their focus has been the award itself and tracking recipients of each award over time. Only a few of the 160 award programs tracked by ICDA are also tracked by TheCenter and AAUDE. TheCenter’s “Top American Research Universities” Project: TheCenter, at the University of Florida, uses two measures of faculty quality: 1) membership in the three National Academies (National Academy of Sciences, National Academy of Engineering, and Institute of Medicine); and 2) the number of faculty receiving awards in 24 programs (e.g., American Council of Learned Societies Fellows, Beckman Young Investigators, Fulbright American Scholars, Getty Scholars in Residence, Guggenheim Fellows, Howard Hughes Medical Institute Investigators, etc). Currently, TheCenter database contains five years of faculty awards and honors data (1999-2003) and their listing of award programs has remained unchanged during this period of time. Faculty counts per university are available for downloading on TheCenter website. (See  HYPERLINK "http://thecenter.ufl.edu/" http://thecenter.ufl.edu/). The Association of American Universities Data Exchange (AAUDE): Within the last year, data on selected faculty awards and honors for a five-year period have been loaded to the AAUDE Data Warehouse. The current list of 44 honors and awards (see Appendix A) was based on feedback from the AAUDE members and recommendations from the AAU Membership Committee. AAUDE continues to explore possible additions to the list and/or additional descriptive information regarding awards. History: Following the close of the Assessing Quality in University Education and Research (AQUER) project, AAUDE and the AAU Executive Office continued to have discussions revolving around faculty awards and honors data. This discussion led to the formation of an AAUDE awards and honors working group in 2002. The group’s charge was to examine and recommend which faculty awards and honors data should be tracked, determine criteria for including them, investigate technical and procedural issues with the data collection, and solicit input from the AAUDE membership. As a first step, the AAUDE group developed a table combining TheCenter awards with the list of awards and honors last used by the AAU Membership Committee. AAUDE members were then given an opportunity to review and comment on the combined list of awards and honors and provide input regarding other awards they believed should be included. The group then added the fields below on specific attributes of the awards to provide a better understanding of the properties of each award program and an indication of their level of competitiveness (i.e., disciplines covered (if appropriate), frequency of award, number of awards given, and target group). Since the AAUDE working group has focused their efforts so far on using the considerable data already available from TheCenter and others, some of the potential data issues have been resolved by also adopting the methodology that was used to collect these data. For example: Only current faculty members are included. Former and emeritus faculty are counted only if their names are included on current membership rosters of the organizations. In that case, the faculty member is linked to the university that is listed as their “primary work institution.” Campus-specific data are used rather than including multiple campuses in a university system. Only award counts made at a set point in time each year are counted, versus maintaining a cumulative count of awards over time. Award counts are tracked, not the accompanying recipient names. Based on the above, five years of honors and awards have been incorporated in the AAUDE data warehouse. Data Issues WHICH HONORS AND AWARDS TO TRACK: When considering disciplinary, professional associations, and institutional awards, the number of national and international honors awards is well into the hundreds if not thousands. Ascertaining which honors and awards to track will require consensus among the IDC and AAU institutions. MEASUREMENT ISSUES: There are a number of measurement issues associated with the reporting of faculty honors and awards. Such issues include: a) inclusion of emeriti and former faculty; b) counting awards at a set point in time versus a cumulative count over time; c) reporting of awards by academic year versus calendar year; d) identifying “home” institution for faculty with joint appointments (e.g., a federal research lab and a given AAU campus); and e) identifying the “home” institution when faculty are reported at the system level and not a specific campus of the system. WORKLOAD ASSOCIATED WITH COLLECTING HONORS AND AWARDS: Depending upon the number of honors and awards to track as well as the availability and the quality of the data, the maintenance of an honors and awards database can be very resource intensive, particularly if the database includes the recipient name and associated information (e.g., employment status, discipline, etc.). For example, the format of awards data on organizations’ websites varies greatly from one to another. Sometimes awards data are available only in alphabetical order by recipient name or by state. Also, most awarding organizations do not make their data available for downloading, necessitating considerable manual effort to transfer and check information from many different websites and many formats to a standard electronic format. Regardless of how the data are displayed, extracting data for a subset of institutions is a labor-intensive process. Recommendations AAUDE has made good progress toward collecting and making available faculty awards and honors data through the AAUDE Data Warehouse. It is recommended that faculty honors and awards continue to be collected and maintained in the AAUDE warehouse. Questions the IDC will need to consider are: Which honors and awards should be collected and maintained? Should the AAUDE database track the awards by the name of the recipient and their current institution? Are there other fields (e.g., discipline, date and year) that should be included in the database? How to fund the collection and maintenance cost associated with the faculty honors and awards database. The IDC / AAU should also explore the possibility of working with the various awarding organizations to make their data available in a more user-friendly format, such as adding listings of awards by institution name and making their lists available for electronic download. Another avenue of exploration is the work of the ICDA. If some of the faculty awards tracked by ICDA are of interest, perhaps a collaboration with this group can be investigated. 4. FACULTY ACTIVITY (PUBLICATIONS / CITATIONS) Data Sources Thomson Scientific is the only comprehensive source of data on citations and publications. Their Institute for Scientific Information (ISI) Database is a summary of citation statistics and is region-specific – U.S. University Indicators, Canadian University Indicators, UK University Indicators, Australian University Indicators, and National Science Indicators (note – this product covers citations and publications from all regions). Many ranking systems (e.g., NRC) are planning to include or have included ISI’s citations and publications data in their methodologies. The ISI Database contains counts of publications and citations taken from over 8,500 peer-reviewed journals. ISI currently indexes approximately 5,500 journals in the Sciences, 1,800 in the Social Sciences, and 1,200 in the Arts and Humanities. The citation impact represents the number of citations received per paper published, in a specified time period (note: their data currently covers the period from 1981 to 2003, and will shortly be updated to include 2004). The ISI Database counts only articles, notes, reviews, and proceedings papers. A paper is attributed to an institution only if it carries at least one author address of that institution. In the Deluxe version, papers are divided into 105 fields in the Sciences, Social Sciences, and Arts & Humanities; in the Standard version these 105 fields are aggregated into 24 fields (excluding the Arts & Humanities categories). ISI assigns individual papers into a given field based on the journal in which the paper appeared. In Fall 2004, a member of the AAU Data Exchange purchased the Deluxe version of the ISI data. With approval of Thomson Scientific, a condensed version of the ISI data was made available on the AAUDE website. Available data includes the number of citations and publications as well as the citation impact for all disciplines combined, arts and humanities, life sciences, physical sciences, math and engineering, and the social and behavioral sciences. Data Issues COMPREHENSIVENESS. Not all journals are included - the ISI data is not a comprehensive cataloguing of all the world’s research journals – instead, the journals selected by Thomson Scientific are supposed to represent an elite body of internationally influential research publications. At the same time, ISI may include some journals that an individual faculty member or department may feel are not “elite” or are less reputable journals and should not be included in the publication and citation count. The same journal may be used for several different fields (i.e. journal lists for each of the 105 fields are not mutually exclusive). This can lead to double counting when data is aggregated; however Thomson Scientific reports that this will have minimal effect on the overall counts and citation impacts. Discipline-specific citation rates vary greatly because the number of journals varies greatly among disciplines. For instance, smaller fields like botany or mathematics do not generate as many articles or citations as do larger fields, such as biotechnology or genetics. In some areas, such as Arts & Humanities, it may take a relatively long time for an article to attract a meaningful number of citations, while in others, such as the Life Sciences, citations tend to peak after only a few years. In fact, in the Humanities there tends to be a focus on publishing books over journal articles; for this reason the ISI data is not a very good indicator of research performance in the Humanities. INSTITUTIONAL AFFILIATION: The ISI methodology has some difficulty in assigning articles to the correct institution. In the case of faculty members associated with affiliates (such as hospitals), unless these faculty members include the university name in their address, their paper/article will not be attributed to the university. Similarly, depending on how the faculty member lists their affiliation, their publication may be assigned to the wrong campus of a multi-campus system. The ISI database also includes those papers authored by students, because the status of an author (faculty member vs. student) is not recorded in journal articles and hence cannot be ascertained by Thomson Scientific. DISCIPLINE AND CLASSIFICATIONS: There is a lack of clear mapping between the ISI fields and university departments. Due to the current structure of the database, the data are not available by department or by individual faculty member. Although raw data can be purchased, the amount of manipulation required to alter the database to enable analysis at the department or faculty level would be prohibitive. This is due largely to the fact that there is a disconnect between the variables collected. For instance, in the fictitious example shown below it is not clear which author has an affiliation with the Hospital for Sick Children (it could be either J. Smith or S. Young). TitleDateAuthorsAddresses/AffiliationsJournalIncidence of Hemophelia in a small Italian village2001J. Smith, S. YoungUC Berkeley, Hospital for Sick Children, U of ArizonaJ of Genetic Diseases Recommendations The ISI dataset provides the most comprehensive and detailed information about faculty publications and citations. Publication and citation data should continue be collected for all AAU institutions. Questions the IDC will need to consider are: Which disciplines should be reported and what taxonomy should be used (e.g., ISI’s 105 fields, 24 fields, or 6 fields, the CIP taxonomy, NRC taxonomy, other taxonomies). Expense increases with level of detail Should the reporting of publications and citations be normalized to account for the size of faculty at the AAU institutions? Raw (unnormalized) data would be collected from the ISI dataset and additional work would be required for the normalization. Should AAU work with Thomson Scientific to address some of the issues listed above in order to improve their product? For example, to address the issue that ISI is not a good indicator in the Humanities -- perhaps Thomson Scientific could be persuaded to compile a database of books written by university faculty and published by the top publishing houses (e.g., Oxford University Press). Should AAU work with Thomson Scientific to refine their list of “acceptable journals and publications?” 5. FACULTY PATENTS / LICENSES Data Sources The Association of University Technology Managers (AUTM) conducts a Licensing Survey on an annual basis. The survey collects a variety of information related to technology transfer including: the Total Sponsored Research Expenditures, Invention Disclosures, Patent Applications Filed, Patents Issued, Licenses and Options Executed, License Income Received, Legal Fees, and Start-up Companies. The results are published around the first of March. Data Issues Data Availability: Not all AAU institutions participate in the AUTM survey. The university office which responds to the survey receives the report and circulation of the data is at their discretion. Utility of the information: Much of the data is not comparable across universities as many universities report data at the system-level only and not at the campus level. Universities also include medical center or hospital data. Recommendations If patent and licensure information is desired, the IDC and AAU should try to encourage 100% participation in the AUTM survey. Universities that currently report data at the system level will need to report individual campus data in the AUTM survey or provide such data to the AAUDE representative. The AUTM data can be incorporated into the AAUDE data warehouse, and AAUDE could work with AUTM to receive special extracts of their database for incorporation into the warehouse. It is assumed that there would be a nominal charge to access this information from AUTM. IDC will also need to ascertain what specific types of information from the AUTM survey should be routinely collected and maintained in the warehouse and what type of reports from the warehouse should be produced. 6. RESEARCH ACTIVITY Data Sources There are three primary sources of information on research activity at colleges and universities: IPEDS Finance Survey of Colleges and Universities, NSF Survey of Research and Development Expenditures, and the NSF Survey of Federal Science and Engineering Support. All three sources are available on the AAUDE warehouse. IPEDS Finance Survey of Colleges & Universities: The IPEDS Finance Survey captures all direct research expenditures for an institution. Indirect costs are reported under other categories. The IPEDS Finance Survey data cannot be examined by discipline or by major sponsor or sponsoring agency. These data are submitted to NCES by individual institutions and represent expenditures made within a specific fiscal year. NSF Survey of Research and Development Expenditures at Universities and Colleges: This survey historically captured all research expenditures in science and engineering for an institution. Between FY 1997 and 2002, institutions had the option of reporting R&D expenditures in the non-science and engineering fields (e.g., education, law, humanities, arts, business, communication, social work, etc). For FY 2003, the non-science and engineering fields were not listed as optional and the survey now includes all current fund research expenditures. Both total and federal research dollars (including indirect costs) are reported by discipline. Federal research expenditures are also reported by federal agency by discipline. The campus total research expenditures are broken down by source of funds (federal, industry, state, institutional and other). Campuses are further asked to report how much of the total expenditures pass through to other sub-recipients or are received as a sub-recipient. These data are submitted to NSF by individual institutions and represent expenditures made within a specific fiscal year. Research from Federally Funded Research Labs (e.g., Jet Propulsion Lab at Cal Tech) are excluded. Survey of Federal Science and Engineering Support to Universities, Colleges, and Nonprofit Institutions: This survey, previously known as the NSF Obligations survey, captures all federal research by sponsoring federal agency. The data are submitted by the agencies for each higher education institution and represent obligations for a specific year. In addition to the total research obligations, the federal agency reports the dollars for: 1) research and development; 2) fellowships, traineeships, and training grants; 3) R&D facilities and fixed equipment; 4) facilities and equipment for instruction; 5) general support for science and engineering; and 6) other science and engineering support. The data are available by institution for all agencies except the National Institute of Education, which no longer provides the institutional detail. Survey of Federal Funds for Research and Development: This survey captures all federal research by discipline and sponsoring federal agency. These data are submitted by the sponsoring agency and represents obligations (funded research) for a specific year. Note: No data are reported for individual higher education institutions. In addition to reporting by discipline, the data are reported by: type of research (basic, applied, and development), types of research performers (colleges and universities, Federally Funded Research and Development Centers, industrial firms, nonprofit, state and local government, etc.), by state and foreign performers, and individual Federally Funded Research and Development Centers. Data Issues Obligations vs. Expenditure data: The primary weakness of the NSF obligations data is that it does not always reflect the actual expenditures of an institution and, thus, its research activity. Obligation data sometimes include multi-year obligations, some of which are never funded or expended. The NSF obligations data do not breakout subcontracting activities that often occurs as part of multi-institution research projects nor allow for the inclusion of any non-federal research indices (e.g., foundation funding). Also, institutions cannot verify the NSF obligations data. Obligations data can vary dramatically from year to year for a particular institution, while the expenditure data tends to be more consistent over time; this consistency allows for more meaningful trend analysis. More importantly, the obligation data cannot be broken out by discipline for individual institutions. Medical and Clinical Research: Institutions that do not offer medical degrees have raised concerns about the legitimacy of research comparisons when no adjustments are made for the medical research dollars, as a significant portion of sponsored research is dedicated to the medical field. Also, one of the issues articulated concerning the current research funding data is the need to understand the effect of clinical research on the total research funding. There is no easy way to analyze this issue given the current public sources of data available to AAU (IPEDS, NSF Obligations and NSF Expenditures). The data by discipline is not specific enough to identify clinical trials. Agricultural Research: Similar to medical research, some institutions have raised concerns regarding the inclusion of agricultural research when comparisons are made across AAU institutions as several institutions do not offer agricultural programs. Also, concerns have been raised regarding the inclusion of research expenditures in support of the agricultural experiment stations and USDA funds that are not subject to peer reviews. On the other hand, if these data were excluded, the research activity at land-grant institutions would be severely underreported Recommendations As recommended in the AAU Membership Taskforce report, the best measure of research activity at particular institutions is the NSF R&D Expenditure Survey. We recommend that this measure continue to be used and that refinements be made in standard reports to account for the research nature (i.e., medical and agricultural programs) of individual campuses. AAU may wish to work the NSF to expand the number of disciplines and/or sub-disciplines reported as the level of detail is fairly restricted. 7. RESEARCH SPACE Data Sources There are two major data sources for campus facilities and research space: NSF Scientific and Engineering Research Facilities Survey and the Society of College and University Planning (SCUP) Campus Facilities Inventory Survey. NSF Scientific and Engineering Research Facilities Survey: Since 1988, NSF has conducted a biennial survey of Scientific and Engineering Research Facilities. This survey asks institutions to report the net assignable square feet dedicated to instruction and research in 14 disciplines as well the total research space for all Science & Engineering (S&E) fields and non- S&E fields (e.g., law, business, humanities, arts). In addition, institutions are asked to evaluate the adequacy of the S&E research space for each of the 14 disciplines. In 2001, the survey was sent to 580 research-performing colleges and universities in the U.S. and to U.S. biomedical research institutions that received NIH funding. Research-performing academic institutions are defined as colleges and universities reporting more than $150,000 in research and development (R&D) expenditures. Approximately 90% of the colleges and universities participated in the 2001 survey. Society of College and University Planning (SCUP) Campus Facilities Inventory (CFI) Survey: In 2003, SCUP began work to fill a significant gap in higher education facilities information. Since 1974 there had been no public or private agency responsible for the comprehensive collection of data, facts, and statistics about the physical size and growth patterns of colleges and universities. In 2003, SCUP initiated a survey that collects net assignable square feet (NASF) for educational and general space, auxiliary space, and total space by room use (e.g., classroom, class labs, research labs, offices, libraries, offices, athletic, residential, support, etc) for the entire campus. No facility information is collected by discipline for this survey. Independent operations and hospital space are excluded in the CFI survey. In 2004, 204 institutions participated in the CFI survey including 17 public AAU institutions and six private AAU institutions. The major differences between the two surveys is that NSF collects only instructional and research space by discipline while the SCUP survey collects space data for the entire campus by both room use and function (e.g., education and general vs. auxiliary). Issues DATA AVAILABILITY: Currently these data are not available by institution from NSF or SCUP. However, the NSF or SCUP CFI data could be collected from participating institutions through AAUDE. COMPREHENSIVENESS: Only 14 broad disciplines are represented on the NSF survey; consequently, information on other disciplines (e.g., physics, chemistry) or sub-disciplines is not available. No discipline data are available from the SCUP survey. COMPLETENESS: Several institutions do not complete Section 2 of the NSF survey related to the adequacy of the research space. Interpretation of the “adequacy” may be different from institution to institution. Only 23 the AAU institutions participated in the 2003-04 SCUP survey. Recommendations If the IDC desires to collect research space information, the NSF Scientific and Engineering Research Facilities survey or SCUP’s Campus Facilities Inventory are the only available sources of information. Either information could be housed in the AAUDE data warehouse. AAUDE would need to make arrangements with NSF or SCUP to obtain the data in electronic format. If more detailed information is required (data by sub-discipline) AAU may wish to pursue discussions with NSF or SCUP to modify future surveys. 8. POST DOCTORATES Data Sources Data on postdoctoral appointments are only available from the NSF Survey on Graduate Students and Postdoctorates in Science and Engineering. These data have been collected every year since 1972 (except 1978) from individual institutions and are available by discipline. It is further broken down by foreign and domestic and by gender. These data are often collected directly from Graduate School Deans who rely upon local administrators. Most data are available through WebCaspar and a crosswalk for WebCaspar and CIP codes is available. Data Issues DATA RELIABILITY: The primary weakness of these data is that several, if not most, institutions cannot readily identify post docs. On some campuses, post doc information is provided by local department administrators and is reviewed by administrators within graduate schools. It is usually less reliable than surveys such as IPEDS because coordination varies by institution. In some institutions, departments may complete the data with little or no central oversight of the data submission. In some cases, the postdoctoral data may only be available at the system level. INSTITUTIONAL FOCUS: This indicator favors institutions with emphasis on science unless it is analyzed by discipline. Engineering departments have fewer postdoctoral appointments, while science doctorates tend to spend at least two years in a postdoctoral appointment as part of their training. Postdoctoral data in non-science and engineering fields may not be reported. APPOINTMENT PERIOD: One important variable that can not be controlled for is the length of time a postdoctoral appointment remains at an institution in the “postdoctoral” capacity. Like time to degree, time to permanent/academic job might be an indicator of quality. Recommendations The postdoctoral data from the NSF Survey on Graduate Students and Postdoctorates in Science and Engineering are readily available and standard reports can be generated. Because NSF postdoctoral data are also available by discipline, gender, and individual citizenship, additional analyses are possible. For example, two potentially useful indicators calculated from these data would be the percentage of postdoctorates who are U.S. Citizens and the ratio of postdoctorates to graduate students or faculty by discipline. 9. FACULTY SURVEYS Data Sources Several AAU institutions participate in periodic surveys of their faculty that allow for inter-institutional comparisons. The various surveys have a different focus and purpose (e.g., undergraduate education, quality of work life, campus climate, etc). Higher Education Research Institute (HERI), UCLA, Faculty Survey: This survey is administered every three years (2004-2005) beginning in the fall of the year. This survey began in 1989 and includes faculty from about 1,000 two-year and four-year colleges and universities. It is currently a paper only survey. The survey is mailed by the participating institution, but all surveys are returned to the Institute. HERI provides each participating institutions with frequencies. The cost of participation is $375 plus $3.75 per returned survey. For an additional fee, HERI will provide the raw data file. The HERI faculty survey is built upon the same social science research as the HERI freshman survey. As a result, the overall survey is very useful for institutions whose faculty focus on undergraduate education. It is less appropriate for faculty at research universities who teach graduates students and are involved in sponsored research. Nevertheless, many institutions have participated and make use of the comparative national data. Harvard Graduate School of Education, Collaborative on Academic Career in Higher Education (COACHE): Many AAU institutions have been invited to join the Collaborative on Academic Career in Higher Education and administer a survey to junior/non tenured/probationary faculty. According to the invitation, “the goals of the Collaborative are: to improve the quality of work life for junior faculty and thus make the profession more attractive; and to provide institutions and prospective junior faculty with the data to inform important decision about working in the academy.” This survey builds upon work begun by the Study of New Scholars, in which a number of AAU institutions participated. The cost of participating in COACHE is $20,000 per administration. Each participating institution receives a customized report that includes comparative data and a data file. Because the number of non tenured faculty at some institutions is small the examination of outcomes by discipline may only be possible for aggregate data. Because the invitation was sent in March 2005, we do not know how many members of AAU will decide to participate. There are some concerns about surveying junior faculty while excluding senior faculty. Faculty Quality of Life Surveys: A number of AAU institutions have been administering surveys to faculty that cover a broad range of topics including quality of life, work load, department interactions, promotion and tenure, resources for teaching and research, satisfaction and stress. Informal collaborations have resulted in the sharing of questions and results. In April 2004, presidents, provosts and senior women faculty from nine AAU institutions (Harvard, Massachusetts Institute of Technology, Yale, Stanford, California Institute of Technology, Princeton, University of Pennsylvania, University of Michigan, and the University of California, Berkeley) met to discuss the issue of women in science and engineering. At that meeting they agreed in principle to explore the possibility of administering a common faculty survey. There are several survey instruments in use at one or more institutions including instruments developed by Berkeley, University of Michigan, MIT, Duke and Stanford. Several other institutions have begun exploring and/or planning the administration of similar instruments on their campuses. Data Issues DATA SENSITIVITY: As mentioned at the first meeting of the IDC, there are certain types of sensitive data and information that campuses may be reluctant to share. Perceptions of faculty about their institution would likely fall under this category. MEASUREMENT ISSUES: Surveys of this nature require substantial discussion to reach consensus on what questions to ask, the response options (e.g., excellent to poor), the population, sampling methods and size, and the administration of the survey. Some campuses have been administering internal surveys to their faculty that allow for comparisons over time and may be reluctant to change their wording of the questions and response options (e.g., 5-pt scale to a 4-pt. scale) to conform to an inter-institutional (AAU) survey. CAMPUS PARTICIPATION: The utility of the survey data is in many ways contingent upon the comparisons that can be made with the data. Achieving a large participation rate among the institutions could be problematic. Recommendations If the IDC would like to collect survey information from faculty, it would be possible to maintain such data in the AAUDE data warehouse. Questions the IDC will need to consider are: What information would AAU like to receive from faculty (i.e., what questions to ask on the survey)? How often would the AAU like to receive such information (i.e., how often to administer the survey—one a year, once every two or three years)? Who should coordinate the survey (i.e., should this be contracted out, should one of the AAUDE representatives be asked to coordinate the survey)? What resources will be provided to support such a survey? 10. TEACHING ACTIVITY Data Sources Delaware Teaching Load and Cost Study - This data collection is voluntary and is presently in its thirteenth year of operation. Originally funded by a FIPSE (Fund for the Improvement of Post-Secondary Education) grant, the purpose of the data collection is to collect data from institutions on matters related to teaching load, faculty productivity, and instructional cost. Institutions are asked to report the following information by discipline (academic department): faculty FTE by type (regular, other regular, supplemental, teaching assistances), student credit hours by level, organized class sections by level, and direct expenditures for salaries, benefits, other personnel expenditures, as well as the separately budgeted research activity and public service activity. Although several AAU campuses currently participate in the Delaware study, some have opted not to submit the expenditure data. The Delaware Study does not attempt to capture data on standard teaching loads, credit hour equivalents and course reductions. Data Issues The Delaware Study has been widely recognized as a primary source of comparative data related to faculty teaching workload. The study often requires extensive campus resources to develop the reports needed to answer the survey. For this reason, several AAU institutions have declined further participation having once participated in the study. Also, many objections have been raised regarding the instruction cost portion of the Delaware study (e.g., failure to include the indirect instructional costs). There are numerous issues related to the identifying the cost of instruction and education per student FTE or by credit hour. Obtaining consensus among the AAU group on this issue will be problematic. Recommendations From time to time, AAUDE has discussed the idea of collecting Student Credit Hour (SCH) data by level, most recently at its 2005 meeting. Previous discussions have also included the idea of collecting faculty teaching workload data (i.e., number primary classes (excluding non-independent study and thesis/dissertation courses) taught per ladder faculty member. If the IDC wishes to collect such information, AAUDE could initiate formal discussions and develop recommendations for collecting this information. 11. OTHER Data Sources Occasionally, the AAU Data Exchange has received requests asking for information related to faculty and staff turnover, retention and retirement rates as well as mandatory retirement policies and tenure policies and practices. Would this type of information, or other types of data, be helpful to collect on a routine basis by AAUDE or other AAU entity. (Note: faculty teaching activity, such as the number of classes taught per faculty and student credit hours per faculty, will be addressed in the companion paper on undergraduate education.) APPENDIX A: FACULTY HONORS AND AWARDS MAINTAINED IN THE AAUDE DATAWAREHOUSE National Academy of Sciences Members National Academy of Engineering Members Institute of Medicine Members American Council of Learned Societies Fellowships Beckman Young Investigators Awards Burroughs Wellcome Fund Career Awards NSF Faculty Early Career Development Awards Cottrell Scholar Awards Fulbright American Scholars Getty Scholars in Residence Guggenheim Fellows Howard Hughes Medical Institute Investigators Lasker Medical Research Awards MacArthur Foundation Fellows National Medal of Science National Medal of Technology Andrew W. Mellon Foundation Distinguished Achievement Awards National Humanities Center Fellows National Endowment for the Humanities Fellowships Newberry Library Long-term Fellows NIH MERIT (R37) and Outstanding Investigator (R35) Presidential Early Career Awards for Scientists and Engineers Pew Scholars in Biomedicine Robert Wood Johnson Health Policy Fellows Searle Scholars Sloan Research Fellows US Secretary of Agriculture Honor Awards Woodrow Wilson Fellows American Academy in Rome American Academy of Arts and Sciences Membership American Antiquarian Society Fellowships American Philosophical Society American School of Classical Studies at Athens Fellowships Fields Medal Folger Library Postdoctoral Fellowships Ford Foundation Post-Doctoral Fellowships Huntington Library Research Fellowships National Academy of Education Nobel Prize Packard Fellowships Residency at the Center for Advanced Studies in the Visual Arts Residency at the Institute for Advanced Study Rhodes Scholars American Association for the Advancement of Science Fellows     DRAFT PAGE  PAGE 25 Page 59V˜ÉĘËĚ×ŮŰÜîď’ Ą Ő é ě đ ó ý  # őęőŢŇĆŢż¸Ť¸œtmfm_XmQJQCQ hěMúhľ_Q hěMúhŹç hěMúh§[ hěMúhóX hěMúhNd hěMúh[N hěMúhG;hěMúhŰl56;hěMúhG;5;CJaJhěMúhoCY56;CJ$aJ$hěMúhŰl56;CJ$aJ$hěMúhŰlOJQJ^J hěMúhŰl hěMúhK KhěMúh@Ľ6CJaJhěMúhK K6CJaJhěMúhŰl6CJaJhěMúhŹçCJ(aJ(hěMúhŰlCJ(aJ(V™ĘËĚ×ŘŮÚŰÜîďÉ Ę ń  * T q úúúúúúúúőőőőőííőőíőäÜÜÜÜÜ & F gděMú„Đ^„ĐgděMú$a$gděMúgděMúgděMúo¤ýýq ƒ ’ ˘ Ő č ď đ ÎĎ&ŢľčŘHI‘Ą˘Í÷÷÷÷ââŮĚĚĚŔŔŔŔŔłĚŽŽŽgděMú Ć,@„h^„hgděMú & F( Ć,@gděMú Ć,@„œ˙^„œ˙gděMú„œ˙^„œ˙gděMú & F Ć x„x„pţ^„x`„pţgděMú & F gděMú# % : C Abkm˜ąÎĎčęđ&Xr‘ĽĎ:ÝŢűý%N]ruˆžłźçč˙7[nx€‚‘™$Zerv‚žŸŘGHIůňëůňůňůňůňäÝňÝňäÖäÖäÖäÖňÖäÖäÖäÖäĎäÖäČÁČݺݳȳȳÝȺݳݏݳݳČÝČÝĽ hěMúh'_ hěMúhă'ó hěMúhK K hěMúh6ŕ hěMúhń$­ hěMúh8/ hěMúhŰwî hěMúhFX hěMúh…! 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„ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’ź „°„L˙Ć°^„°`„L˙‡hˆH.ź „€„˜ţĆ€^„€`„˜ţ‡hˆH.ź „P„˜ţĆP^„P`„˜ţ‡hˆH.’ź „ „L˙Ć ^„ `„L˙‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.h „p„˜ţĆp^„p`„˜ţ‡hˆH.’h „@ „L˙Ć@ ^„@ `„L˙‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.h „ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’h „°„L˙Ć°^„°`„L˙‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.h „P„˜ţĆP^„P`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.h „p„˜ţĆp^„p`„˜ţ‡hˆH.’h „@ „L˙Ć@ ^„@ `„L˙‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.h „ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’h „°„L˙Ć°^„°`„L˙‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.h „P„˜ţĆP^„P`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.ź „ „L˙Ć ^„ `„L˙‡hˆH.ź „:„˜ţĆ:^„:`„˜ţ‡hˆH.’ź „ „L˙Ć ^„ `„L˙‡hˆH.ź „Ú „˜ţĆÚ ^„Ú `„˜ţ‡hˆH.ź „Ş„˜ţĆŞ^„Ş`„˜ţ‡hˆH.’ź „z„L˙Ćz^„z`„L˙‡hˆH.ź „J„˜ţĆJ^„J`„˜ţ‡hˆH.ź „„˜ţĆ^„`„˜ţ‡hˆH.’ź „ę„L˙Ćę^„ę`„L˙‡hˆH. „T„L˙ĆT^„T`„L˙‡hˆH. „"„˜ţĆ"^„"`„˜ţ‡hˆH. „ň„L˙Ćň^„ň`„L˙‡hˆH. „ „˜ţĆ ^„ `„˜ţ‡hˆH. „’„˜ţĆ’^„’`„˜ţ‡hˆH.’ „b„L˙Ćb^„b`„L˙‡hˆH. „2„˜ţĆ2^„2`„˜ţ‡hˆH. „„˜ţĆ^„`„˜ţ‡hˆH.’ „Ň„L˙ĆŇ^„Ň`„L˙‡hˆH.h „Œ„˜ţĆŒ^„Œ`„˜ţ‡hˆH.h „\„˜ţĆ\^„\`„˜ţ‡hˆH.’h „, „L˙Ć, ^„, `„L˙‡hˆH.h „ü „˜ţĆü ^„ü `„˜ţ‡hˆH.h „Ě„˜ţĆĚ^„Ě`„˜ţ‡hˆH.’h „œ„L˙Ćœ^„œ`„L˙‡hˆH.h „l„˜ţĆl^„l`„˜ţ‡hˆH.h „<„˜ţĆ<^„<`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH. „h„˜ţĆh^„h`„˜ţOJQJo(ˇđ „h„˜ţĆh^„h`„˜ţOJQJo(ˇđ „Đ„˜ţĆĐ^„Đ`„˜ţo(.€ „ „˜ţĆ ^„ `„˜ţ‡hˆH.‚ „p„L˙Ćp^„p`„L˙‡hˆH.€ „@ „˜ţĆ@ ^„@ `„˜ţ‡hˆH.€ „„˜ţĆ^„`„˜ţ‡hˆH.‚ „ŕ„L˙Ćŕ^„ŕ`„L˙‡hˆH.€ „°„˜ţĆ°^„°`„˜ţ‡hˆH.€ „€„˜ţĆ€^„€`„˜ţ‡hˆH.‚ „P„L˙ĆP^„P`„L˙‡hˆH.h„Đ„˜ţĆĐ^„Đ`„˜ţOJQJo(‡hˆHˇđh„ „˜ţĆ ^„ `„˜ţOJQJ^Jo(‡hˆHoh„p„˜ţĆp^„p`„˜ţOJQJo(‡hˆH§đh„@ „˜ţĆ@ ^„@ `„˜ţOJQJo(‡hˆHˇđh„„˜ţĆ^„`„˜ţOJQJ^Jo(‡hˆHoh„ŕ„˜ţĆŕ^„ŕ`„˜ţOJQJo(‡hˆH§đh„°„˜ţĆ°^„°`„˜ţOJQJo(‡hˆHˇđh„€„˜ţĆ€^„€`„˜ţOJQJ^Jo(‡hˆHoh„P„˜ţĆP^„P`„˜ţOJQJo(‡hˆH§đ „h„˜ţĆh^„h`„˜ţOJQJo(ˇđh „đ„˜ţĆđ^„đ`„˜ţ‡hˆH.h „Ŕ„˜ţĆŔ^„Ŕ`„˜ţ‡hˆH.’h „ „L˙Ɛ ^„ `„L˙‡hˆH.h „`„˜ţĆ`^„``„˜ţ‡hˆH.h „0„˜ţĆ0^„0`„˜ţ‡hˆH.’h „„L˙Ć^„`„L˙‡hˆH.h „Đ„˜ţĆĐ^„Đ`„˜ţ‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.’h „p„L˙Ćp^„p`„L˙‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.h „p„˜ţĆp^„p`„˜ţ‡hˆH.’h „@ „L˙Ć@ ^„@ `„L˙‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.h „ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’h „°„L˙Ć°^„°`„L˙‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.h „P„˜ţĆP^„P`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.h „Đ„˜ţĆĐ^„Đ`„˜ţ‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.’h „p„L˙Ćp^„p`„L˙‡hˆH.h „@ „˜ţĆ@ ^„@ `„˜ţ‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.’h „ŕ„L˙Ćŕ^„ŕ`„L˙‡hˆH.h „°„˜ţĆ°^„°`„˜ţ‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.’h „P„L˙ĆP^„P`„L˙‡hˆH. „h„˜ţĆh^„h`„˜ţOJQJo(ˇđ„$„˜ţĆ$^„$`„˜ţo()€ „ô„˜ţĆô^„ô`„˜ţ‡hˆH.‚ „Ä „L˙ĆÄ ^„Ä `„L˙‡hˆH.€ „” „˜ţĆ” ^„” `„˜ţ‡hˆH.€ „d„˜ţĆd^„d`„˜ţ‡hˆH.‚ „4„L˙Ć4^„4`„L˙‡hˆH.€ „„˜ţĆ^„`„˜ţ‡hˆH.€ „Ô„˜ţĆÔ^„Ô`„˜ţ‡hˆH.‚ „¤„L˙Ƥ^„¤`„L˙‡hˆH.„Đ„˜ţĆĐ^„Đ`„˜ţo()€ „ „˜ţĆ ^„ `„˜ţ‡hˆH.‚ „p„L˙Ćp^„p`„L˙‡hˆH.€ „@ „˜ţĆ@ ^„@ `„˜ţ‡hˆH.€ „„˜ţĆ^„`„˜ţ‡hˆH.‚ „ŕ„L˙Ćŕ^„ŕ`„L˙‡hˆH.€ „°„˜ţĆ°^„°`„˜ţ‡hˆH.€ „€„˜ţĆ€^„€`„˜ţ‡hˆH.‚ „P„L˙ĆP^„P`„L˙‡hˆH.ź „p„L˙Ćp^„p`„L˙‡hˆH.ź„ „˜ţĆ ^„ `„˜ţOJQJo(‡hˆHˇđ’ź „p„L˙Ćp^„p`„L˙‡hˆH.ź „@ „˜ţĆ@ ^„@ `„˜ţ‡hˆH.ź „„˜ţĆ^„`„˜ţ‡hˆH.’ź „ŕ„L˙Ćŕ^„ŕ`„L˙‡hˆH.ź „°„˜ţĆ°^„°`„˜ţ‡hˆH.ź „€„˜ţĆ€^„€`„˜ţ‡hˆH.’ź „P„L˙ĆP^„P`„L˙‡hˆH.h „l„˜ţĆl^„l`„˜ţ‡hˆH.h „<„˜ţĆ<^„<`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.h „Ü „˜ţĆÜ ^„Ü `„˜ţ‡hˆH.h „Ź „˜ţĆŹ ^„Ź `„˜ţ‡hˆH.’h „|„L˙Ć|^„|`„L˙‡hˆH.h „L„˜ţĆL^„L`„˜ţ‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.’h „ě„L˙Ćě^„ě`„L˙‡hˆH.h„Đ„˜ţĆĐ^„Đ`„˜ţOJQJo(‡hˆHˇđh„ „˜ţĆ ^„ `„˜ţOJQJ^Jo(‡hˆHoh„p„˜ţĆp^„p`„˜ţOJQJo(‡hˆH§đh„@ „˜ţĆ@ ^„@ `„˜ţOJQJo(‡hˆHˇđh„„˜ţĆ^„`„˜ţOJQJ^Jo(‡hˆHoh„ŕ„˜ţĆŕ^„ŕ`„˜ţOJQJo(‡hˆH§đh„°„˜ţĆ°^„°`„˜ţOJQJo(‡hˆHˇđh„€„˜ţĆ€^„€`„˜ţOJQJ^Jo(‡hˆHoh„P„˜ţĆP^„P`„˜ţOJQJo(‡hˆH§đh „đ„˜ţĆđ^„đ`„˜ţ‡hˆH.h „Ŕ„˜ţĆŔ^„Ŕ`„˜ţ‡hˆH.’h „ „L˙Ɛ ^„ `„L˙‡hˆH.h „`„˜ţĆ`^„``„˜ţ‡hˆH.h „0„˜ţĆ0^„0`„˜ţ‡hˆH.’h „„L˙Ć^„`„L˙‡hˆH.h „Đ„˜ţĆĐ^„Đ`„˜ţ‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.’h „p„L˙Ćp^„p`„L˙‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.h „p„˜ţĆp^„p`„˜ţ‡hˆH.’h „@ „L˙Ć@ ^„@ `„L˙‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.h „ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’h „°„L˙Ć°^„°`„L˙‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.h „P„˜ţĆP^„P`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.h „ „˜ţĆ ^„ `„˜ţ‡hˆH.h „p„˜ţĆp^„p`„˜ţ‡hˆH.’h „@ „L˙Ć@ ^„@ `„L˙‡hˆH.h „„˜ţĆ^„`„˜ţ‡hˆH.h „ŕ„˜ţĆŕ^„ŕ`„˜ţ‡hˆH.’h „°„L˙Ć°^„°`„L˙‡hˆH.h „€„˜ţĆ€^„€`„˜ţ‡hˆH.h „P„˜ţĆP^„P`„˜ţ‡hˆH.’h „ „L˙Ć ^„ `„L˙‡hˆH.h „Œ„˜ţĆŒ^„Œ`„˜ţ‡hˆH.h „\„˜ţĆ\^„\`„˜ţ‡hˆH.’h „, „L˙Ć, ^„, `„L˙‡hˆH.h „ü „˜ţĆü ^„ü `„˜ţ‡hˆH.h „Ě„˜ţĆĚ^„Ě`„˜ţ‡hˆH.’h 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Ť˜Ťú<Ť8}ŤăWŹż­ń$­{&­ś ŽŚMŽů°8d°ě˛W˛x+˛ąy˛gł1(łżV´H.š|jšbJşí˝ÇAż%Eż€sŔ™*ĂhBĂw@Äě|ĹW'ÇŤbÇ7ČpČÍqČ^ĘUË d˂̂*ĚĚxÍ|ÍĎQÎĎąNŃU Ó:ÓăvÔ¸I֍;מUŘáaŘŮVŮ'ÚÔ(ÚDSŰ?ÜýIÜOÜĄzÜŻŢş3ŢťzŢgßI8ßł ŕ6ŕzŕYă(Bä8?ĺçmĺŹç.čSOé"ëŰwîFUďńnPňgňă'óY2ófô@Cö› ÷éů˛úě;úěMúa ű÷nüŢ(ýÜţÜţ˛˛¸˛˝˛Ĺ˛Ü˛ä˛ĺ˛łł0łfł|ł}łŚž–˙@€nn0Rë nnĽp@˙˙UnknownRebecca Care\r˙˙˙˙˙˙˙˙˙˙˙˙G‡: ˙Times New Roman5€Symbol3& ‡: ˙Arial5& ‡za€˙Tahoma7& ‡ ŸVerdana?5 ‡: ˙Courier New;€Wingdings"1ˆđĐhŰ•Ś•Ű•Śó9”†Ž)áěŽůŽ)áěŽů!đ d´‚‚4dáá 2ƒQđHX đ˙?ä˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙:Ó2˙˙Faculty ActivitiesDennis Hengstler Rebecca CarrČ/                           ! " # $ % & ' ( ) * + , - . ţ˙ŕ…ŸňůOhŤ‘+'łŮ0¤˜ źČäđü (4 T ` l x„Œ”œäFaculty ActivitiesDennis Hengstler Normal.dotRebecca Carr3Microsoft Office Word@¤“Ö@Rćrp;Ĺ@:„đbĹ@v 7ńbĹŽ)áěţ˙ŐÍ՜.“—+,ůŽDŐÍ՜.“—+,ůŽd  hp ¨°¸ ŔČĐŘ ŕ ˙ä(University of California at BerkeleyůŽáŘ Faculty Activities Title˜ 8@ _PID_HLINKSäAP P http://thecenter.ufl.edu/ZU http://www.icda.org/:-$http://homepages.usmd.edu/berthold/&7,http://web.mit.edu/ir/pop/awards/index.html)s#http://www4.nationalacademies.org/  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ Ą˘Ł¤ĽŚ§¨ŠŞŤŹ­ŽŻ°ą˛ł´ľśˇ¸šşťź˝žżŔÁÂĂÄĹĆÇČÉĘËĚÍÎĎĐŃŇÓÔţ˙˙˙Ö×ŘŮÚŰÜţ˙˙˙Ţßŕáâăäĺćçčéęëěíîďđńňóôőö÷řůúűüýţ˙      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghiţ˙˙˙klmnopqţ˙˙˙stuvwxyţ˙˙˙ý˙˙˙ý˙˙˙ý˙˙˙~ţ˙˙˙ţ˙˙˙Root Entry˙˙˙˙˙˙˙˙ ŔFđč—7ńbŁ€Data ˙˙˙˙˙˙˙˙˙˙˙˙Ő1Table˙˙˙˙ÝäWordDocument˙˙˙˙Ť¨SummaryInformation(˙˙˙˙˙˙˙˙˙˙˙˙jDocumentSummaryInformation8˙˙˙˙˙˙˙˙rCompObj˙˙˙˙˙˙˙˙˙˙˙˙q˙˙˙˙˙˙˙˙˙˙˙˙ţ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ý˙˙˙ţ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ţ˙ ˙˙˙˙ ŔFMicrosoft Office Word Document MSWordDocWord.Document.8ô9˛q