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Books on the topic 'Admission Prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Harris, Vincent F. Analysis of differential prediction of law school performance by gender subgroups. Law School Admission Council, 2006.

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2

Sheldon, Zedeck, ed. Final report: Identification, development, and validation of predictors for successful lawyering. [s.n.], 2008.

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3

Anthony, Lisa C. Analysis of differential prediction of law school performance by racial/ethnic subgroups based on the 1996-1998 entering law school classes. Law School Admission Council, 2003.

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4

Duffy, Jennifer R. Analysis of differential prediction of law school performance by gender subgroups based on the 1996-1998 entering law school classes. Law School Admission Council, 2006.

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5

Wightman, Linda F. Beyond FYA: Analysis of the utility of LSAT scores and UGPA for predicting academic success in law school. Law School Admission Council, 2000.

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6

Stilwell, Lisa Anthony. Predictive validity of the LSAT: A national summary of the 1997-1998 correlation studies. Law School Admission Council, 1999.

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7

Rowsome, Wayne. From high school to university: An alternate method for selecting university entrants. Department of Geography, Faculty of Sciences, Carleton University, 1987.

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8

Dalessandro, Susan P. Predictive validity of the LSAT: A national summary of the 2003-2004 correlation studies. Law School Admission Council, 2005.

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9

Ontario. Ministry of Colleges and Universities. A study of the impact of OS:IS implementation on the post-secondary system in Ontario : phase IV report. Ministry of Colleges and Universities, 1989.

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10

Alison, Hegarty, ed. A study of the impact of OS:IS implementation on the post-secondary system in Ontario: Phase II report. Ministry of Colleges and Universities, 1987.

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11

E, Thornton Andrea, and Law School Admission Council, eds. Predictive validity of the LSAT: A national summary of the 1999-2000 correlation studies. Law School Admission Council, 2005.

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12

Stilwell, Lisa Anthony. Predictive validity of the LSAT: A national summary of the 2001-2002 correlation studies. Law School Admission Council, 2005.

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13

E, Thornton Andrea, and Law School Admission Council, eds. Predictive validity of accommodated LSAT scores. Law School Admission Council, 2002.

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14

Norton, Lynne L. Analysis of differential prediction of law school performance by racial/ethnic subgroups based on 2002-2004 entering law school classes. Law School Admission Council, 2006.

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15

Stilwell, Lisa Anthony. Analysis of differential prediction of law school performance by racial/ethnic subgroups based on the 1996-1998 entering law school classes. Law School Admission Council, 2003.

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16

Wightman, Linda F. An analysis of differential validity and differential prediction for black, Mexican American, Hispanic, and white law school students. Law School Admission Council/Law School Admission Services, 1990.

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17

Hegarty, Alison. A study of the impact of OS:IS implementation on the post-secondary system in Ontario: Phase III report. Ministry of Colleges and Universities, 1987.

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18

Hegarty, Alison. A study of the impact of OS:IS implementation on the post-secondary system in Ontario: Phase II report. Ministry of Colleges and Universities, 1987.

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19

Bowman, Phillip J., and Phyllis Kreger Stillman. Diversity, merit, and higher education: Toward a comprehensive agenda for the 21st century. AMS, 2011.

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20

W, Luebke Stephen, and Law School Admission Council, eds. Final report: LSAC skills analysis law school task survey. Law School Admission Council, 2003.

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21

Freeberg, Norman E. Analysis of the revised student descriptive questionnaire. College Entrance Examination Board, 1988.

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22

Pelavin, Sol H. Changing the odds: Factors increasing access to college. College Entrance Examination Board, 1990.

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23

Noble, Julie. Effects of differential prediction in college admissions for traditional- and nontraditional-aged students. ACT, Inc., 2000.

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24

Mehdizadeh, Shahla A. Predicting nursing home length of stay: Implications for targeting pre-admission review efforts. Scripps Gerontology Center, Miami University, 2001.

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25

Cullinan, T. R. Predicting infant illness in East London: An analysis of social factors associated with hospital admission. Queen Mary College, Department of Computer Science and Statistics, 1987.

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26

Tsai, Wei-Der. Hospital affiliation and the proportion of patient discharges to long-term care--best probit predictor estimation of the binary response model with an endogeneous treatment effect. Institute of Economics, Academia Sinica, 1996.

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27

Henderson, Mary G. Predicting costs of hospitalization for cancer care: Final report to HCFA : a DRG-based casemix for cancer care. Bigel Institute for Health Policy, etc.?], 1990.

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28

Njabili, Agnes F. Predictive validity of the 1995 IGCSE mock results as a criterion for provisional admission to the University of Namibia (UNAM), 1996 academic year: A report prepared for the Executive Committee of Senate, University of Namibia. University of Namibia, Research and Publication, 1997.

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29

Outcome Prediction of Eating Disorders: Can Admission Data Forecast Outcome Needs at Discharge. Storming Media, 1997.

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30

Choosing elites. Basic Books, 1985.

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31

Townsend, William M., and Emma C. Morris. ICU selection and outcome of patients with haematological malignancy. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0374.

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Patients with haematological malignancies require admission to the intensive care unit (ICU) due to the underlying disease, as a consequence of treatment with chemotherapy or after haematopoietic stem cell transplantation. With an increasing numbers of patients being diagnosed with these diseases and longer survival as treatments improve, the burden on ICU is anticipated to increase. There is compelling evidence that patients should not be denied admission to ICU based on the presence of a haematological malignancy. In this chapter the disease- and treatment-related reasons for ICU admission,

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32

Kane, Michael, and Sol H. Palavin. Changing the Odds: Factors Increasing Access to College. College Board, 1990.

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33

The college entrance predictor. S. Green Press, 1988.

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34

District of Columbia) Staff American Association of Collegiate Registrars and Admissions Officers (Washington. Holistic Admissions: Predicting the Likelihood for Student Success. Unknown Publisher, 2020.

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35

Dodds, Chris, Chandra M. Kumar, and Frédérique Servin. Intensive care and the elderly. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198735571.003.0012.

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Age is not an independent predictor for poor outcome from intensive care. This chapter reviews admission criteria for the elderly and the assessment of likely outcome including the differences between traumatic or surgical admissions against medical ones. Pre-existing comorbidities all limit functional recovery, and only about 60% of elderly patients get back to their preadmission level of activity, although this may not detract from their perceived quality of life. Potential bias in the use of quality-of-life measures by clinical staff is discussed. Information on the identification of futili

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36

W. G. (William Gerald) 1924 Fleming. Use of Predictive Factors for the Improvement of University Admission Requirements. Creative Media Partners, LLC, 2021.

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37

Schmitt, Neal. Combining Cognitive and Noncognitive Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.003.0012.

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The use of noncognitive measures enhances the prediction of various individual outcomes. Although structured measures of noncognitive constructs are routinely used in employee selection, they are rarely used to predict college student success. Situational judgment, biodata measures, and other methods of measurement address constructs that add to the prediction of grade point average and are major correlates of other student outcomes. Employers and college administrators indicate that outcomes other than task performance and grades are important. Because noncognitive attributes are the best pre

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38

Fuller, Kathleen Mary. IDENTIFICATION OF PRE-ADMISSION CRITERIA PREDICTIVE OF SUCCESS IN GRADUATE NURSE ANESTHESIOLOGY PROGRAMS. 1995.

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39

The Predictive Validity of the AFIT Graduate Engineering and Environmental (GEEM) Admission Requirements. Storming Media, 2002.

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40

Brosnan, James Gerard. Predicting vocational success at a vocational technical high school through eighth-grade admission data. 1989.

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41

The Predictive Validity of the AFIT Graduate Management Program Admission Requirements: A Reassessment and Extension. Storming Media, 2004.

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42

Torres, Antoni, and Adamantia Liapikou. Diagnosis and management of community-acquired pneumonia. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0116.

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Severe community-acquired pneumonia (SCAP) remains the most common infectious reason for admission to the intensive care unit (ICU), reaching a mortality rate of 30–40%. The microbial pattern of the SCAP has changed with S. pneumoniae still the leading pathogen, but a decrease of atypical pathogens, especially Legionella and an increase of viral and polymicrobial pneumonias. IDSA/ATS issued guidelines on the management of CAP including specific criteria to identify patients for ICU admission with good predictive value. The first selection of antimicrobial therapy should be started early coveri

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43

Ace the TEAS® test: Study guide and practice tests for the TEAS® V (Version 5) exam. Exam Review Press, 2013.

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44

Thompson, Charles Richard. SUCCESS IN U.S. ARMY SCHOOLS OF PRACTICAL NURSING: THE PREDICTIVE VALIDITY OF SELECTED ADMISSIONS CRITERIA AND THE ARMED SERVICES VOCATIONAL APTITUDE BATTERY (UNITED STATES). 1988.

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