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J Dent Educ. 71(10): 1314-1321 2007
© 2007 American Dental Education Association
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Critical Issues in Dental Education

Correlation of Admissions Criteria with Academic Performance in Dental Students

Donald A. Curtis, D.M.D.; Samuel L. Lind, Ph.D.; Octavia Plesh, D.D.S., M.S.; Frederick C. Finzen, D.D.S.

Key words: Dental Admission Test, dental school performance, admissions criteria, underachieving students

Submitted for publication 03/29/07; accepted 07/23/07


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 Conclusions
 References
 
Our purpose was to compare admissions criteria as predictors of dental school performance in underachieving and normally tracking dental students. Underachieving dental students were identified by selecting ten students with the lowest class grade point average following the first year of dental school from five classes, resulting in a pool of fifty students. Normally tracking students served as a control and were randomly selected from students who had completed their first year of dental school not in the underachieving group. Admission measures of college grade point average (GPA), science grade point average (SGPA), academic average (AA), Perceptual Ability Test (PAT), college rigor, and academic load in college were evaluated with descriptive statistics, correlation, and regression analysis with first-year and graduating GPA as the dependent variables. Admissions criteria were generally weak predictors of first-year and graduating GPA. However, first-year dental school GPA was a strong predictor (R2=0.77) of graduating GPA for normally tracking students and a moderate predictor (R2=0.58) for underachieving students. Students who completed the first year of dental school having a low GPA tended to graduate with a low GPA. Therefore, remediation and monitoring would be important during the dental school experience for these students.

The dental school admissions process involves establishing criteria for evaluating applicants, weighting the various admissions criteria, and then comparing applicants based on selected criteria and weighting. The predictive value of admissions criteria has been shown to vary by year in dental school,1–3 can change dramatically between classes,1,2,4 and tends to vary over time,4 making uniform criteria difficult to establish. Although individual admissions criteria have been shown to help predict specific aspects of dental school performance, using multiple criteria has been shown to better characterize the academic and psychomotor skills required for success in dental school.1,5,6 Which criteria are used and how the criteria are weighted have been ongoing debates in dental education.

Admissions criteria have traditionally included college grade point average (GPA), science grade point average (SGPA), the overall Dental Admission Test (DAT), and components of the DAT including academic average (AA) and the Perceptual Ability Test (PAT).1–3,6,7 Less frequently used criteria have included the academic rigor of the college attended,8 the average academic load while in college,6 psycho-motor assessments such as chalk carving,9 waxing tests,10,11 and various behavioral measures of empathy and skill in verbal communication.11,12

Standardized tests generally predict other standardized tests better than they predict noncognitive outcome measures because similar testing formats generally increase the validity of compared assessments.5,13 This is also true in evaluating admissions criteria commonly used in dental school admissions, which often fail to strongly predict both cognitive and psychomotor achievement.1,2,6 For example, the Perceptual Ability Test (PAT) of the DAT is a better predictor of preclinical technique course grades than college SGPA or GPA.14,15 However, the SGPA is a much stronger predictor of performance on National Board Dental Examination (Parts I and II) scores than the PAT.1 Similarly, the AA has been shown to be predictive of the didactically oriented first year of dental school but not of performance in the clinically oriented fourth year of dental school.1

Admissions information has historically been used as a predictor of academic success in dental school. However, the admissions information can also be evaluated to help identify students that may be at risk for low academic performance, and the factors that predict success may not be factors that predict low performance.1,4 Sandow et al.1 evaluated 459 dental students over a four-year period and determined that the percentage of students who graduated with difficulty was 8.9 percent. These authors found that the students in academic trouble consumed a disproportionate amount of institutional resources and were shown to have lower undergraduate GPAs, lower DAT academic scores, and lower PMAT scores compared with students who graduated without difficulty. Likewise, the MCAT and college GPA have been shown to be predictive of medical students who either had a delayed graduation or were dismissed.16

At the University of California, San Francisco, we have constantly evaluated and reviewed admissions criteria in order to select a class that reflects our mission of excellence and scholarship. When a dental student is having academic difficulty, we will often evaluate the student’s college background to determine if a predictive pattern could be established. In that process, we have observed that some dental students having academic difficulty had college backgrounds that included a light academic load and often attended less academically rigorous colleges. We therefore sought to determine if the academic load and the rigor of the college attended, as well as more traditional admissions criteria, had predictive value in identifying underachieving dental students.

Our purpose was to determine if a group of underachieving dental students had scores on admissions criteria (GPA, SGPA, AA, PAT, college rigor, and academic load while in college) that differed from the scores of normally tracking dental students. Additionally, we sought to determine if students’ GPA at the end of the first year of dental school predicted their cumulative GPA at graduation.


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 Conclusions
 References
 
Underachieving students were identified by selecting ten students from each class who had the lowest grade point average at the completion of the first year of dental school. This process, conducted for five graduating classes (2001–05), resulted in a pool of fifty underachieving students. Normally tracking students served as a control and were identified by randomly selecting ten students from each class who were not in the underachieving group. This was completed for five graduating classes (2001–05) and resulted in a pool of fifty normally tracking students.

Admissions data collected included science grade point average (SGPA), overall grade point average (GPA), two sub-scores of the DAT (the academic average, AA, and the Perceptual Ability Test, PAT), the ranking of the college attended, and the college academic load. The college ranking was an estimate of the academic rigor of the college attended, determined by using the 2006 online Princeton Review guide that lists the relative selectivity of colleges and universities. College rankings are listed on a scale from 60 to 100, and ranking is determined primarily by the average standardized test scores such as the Scholastic Aptitude Test (SAT) and GPA of the entering class. When a college does not submit profile information to the Princeton Review, the scores are automatically listed as a 60.

The college academic load was determined by reviewing the student’s college transcript and determining the percent of time the student completed a full academic load while in college. The college academic load was calculated by determining the percentage of quarters or semesters that the student completed equal to or more than sixteen quarter or twelve semester units. Summers were not counted.

Dental school GPA was determined by calculating the GPA at the end of the first year of dental school as well as a cumulative GPA at graduation.

Descriptive measures for all variables were prepared to assess general characteristics of the data and to observe variation among students’ performance, especially with respect to differences between the targeted groups and their corresponding admissions criteria and dental school outcomes. To identify if statistically significant performance differences existed between normally tracking and underachieving students, t-tests compared mean differences in admissions variables and dental school outcomes for both study groups.

Graphic and numeric measures of association were developed to understand general relationships among all of the variables and specifically to measure the level of correlation among various admissions criteria and dental school performance outcomes for normally tracking and underachieving students. Matrices with Pearson’s multiple correlation coefficients were used to describe the individual bivariate associations among admissions criteria and first-year dental school GPA and graduation GPA. Scatter diagrams were also completed to visually illustrate the association between first-year GPA and GPA at graduation for the two groups under study.

Multiple regression models using SPSS software were developed to predict the individual effects of six admissions criteria upon dental school performance at first year and at graduation for normally tracking and underachieving students. Similarly, regression models for both student groups were developed to determine if dental school GPA at first year predicted graduating GPA. SPSS modeling included manual, stepwise, remove, backward, and forward entry procedures for independent variables, and all models were tested for multicollinearity.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 Conclusions
 References
 
In the normally tracking group, one student who finished the first year of dental school did not complete the fourth year for nonacademic reasons, resulting in a pool of forty-nine students for analysis. In the underachieving student group, two students withdrew for nonacademic reasons and three departed for academic reasons, leaving a pool of forty-five underachieving study participants.

Descriptive measures and statistical t-test results presented in Table 1Go and Table 2Go, respectively, show that the normally tracking dental student group had statistically significantly higher scores than the underachieving student group for all admissions variables evaluated except PAT. While PAT differences were nearly statistically significant (p=0.07), all remaining comparisons were significant at least at p<0.05 with five of the eight (63 percent) total difference comparisons significant at p<0.001. Compared to underachieving dental students, the normally tracking dental student group had significantly higher first-year dental school GPA (3.43 ±0.28 vs. 2.47 ±0.21; p<0.001) and GPA at graduation (3.34 ± 0.27 vs. 2.55 ± 0.19; p<0.001).


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  		Table 1. Descriptive measures for admissions criteria and dental school performance of normally tracking and underachieving students{dagger}
 

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  		Table 2. T-tests for admissions criteria and dental school performance comparing mean differences between normally tracking and underachieving students{dagger}
 
The multiple correlation matrix in Table 3Go shows that, for normally tracking and underachieving student groups, all of the associations were weak when comparing the six admissions criteria with first-year and graduating GPA. While seven of twenty-four (29 percent) of the correlation coefficients for admissions criteria in Table 3Go were significantly different from zero (p<0.05), their relatively low values imply limited predictive power. In contrast, correlations were relatively strong between first-year and fourth-year dental school GPA for normally tracking students (0.88, p<0.01) and underachieving students (0.76, p<0.01) (Table 3Go). Similarly, the scatter diagrams in Figures 1Go and 2Go visually demonstrate that fourth-year dental school GPA increases concurrently as first-year dental school GPA increases.


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  		Table 3. Correlation matrix for admissions criteria and dental school performance of normally tracking and underachieving students
 

Figure 1
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  		Figure 1. Scatter diagram for dental school performance of normally tracking students

 

Figure 2
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  		Figure 2. Scatter diagram for dental school performance of underachieving students

 
Our regression models provided mixed findings for the predictive power and lack thereof among admissions variables and first-year and fourth-year dental school GPAs. To consider the simultaneous unique effects of six admissions criteria on dental school performance at first-year and graduating GPA, six regression models were estimated. Results of these analyses for normally tracking and underachieving students shown in Table 4Go indicate the dependent variables in the columns and the independent variables in the rows. First-year and fourth-year dental school GPAs are the predicted variables, while the six admissions variables and first-year dental school GPA are the predictor variables.


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  		Table 4. Multiple regression analysis of admissions criteria and dental school performance for normally tracking and underachieving students{dagger}
 
Although the overall fit in four models was relatively good (F-statistic p<0.05), only two of the six regression models that predicted fourth-year GPA with first-year GPA for normal and underachieving students achieved statistical significance (t-statistic p<0.001) in the independent variable. The two statistically significant regression models for normally tracking and underachieving students explained 77 percent and 58 percent, respectively, of the variability in fourth-year GPA with variability in first-year GPA. Regression models for admissions criteria failed to achieve statistical significance for any of the independent variables to predict either first- or fourth-year GPA.


   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
Conclusions
 References
 
Our primary finding was that first-year dental school GPA was a statistically significant predictor for continued success in dental school performance as measured by GPA at graduation for both normally tracking and underachieving students. Our analysis demonstrates that, in both groups, fourth-year GPA increases concomitantly as first-year GPA increases. First-year to fourth-year correlations of dental school GPAs are relatively strong, with Pearson correlation coefficients of 0.88 and 0.76 for normally tracking students and underachievers, respectively.

Regression modeling, Pearson correlation matrices, and graphic means of association indicate that, as measured by GPA, those students who performed well in their first year of dental school continued with similar performance in their graduating GPA and, conversely, students who underperform can expect comparably below average results. Explicitly, students’ first-year GPAs are strongly associated with their graduating GPAs, such that high/low achievers at the end of their first year of dental school tended to remain high/low performers at graduation.

Our finding of first-year dental school GPA being a significant predictor for the GPA at graduation is consistent with investigators who have found that early assessments within an educational program are often strong predictors of later performance.8,17 In a retrospective study of 352 medical students, Croen et al. found marginal performance on exams early in the first year were more predictive in identifying students at risk academically than college GPA, the MCAT, or both.17 Croen et al. attributed this finding to the similar environment of assessments and outcome measurements, which they felt afforded higher predictive validity. Our finding of first-year dental school GPA predicting graduating GPA for normally tracking and underperforming dental students (R2=0.88, 0.76) is consistent with Croen et al.’s findings, as was our finding that admissions criteria were substantially weaker than first-year GPA in predicting graduating GPA. It has also been shown that students having difficulty in one first-year class tended to have difficulty in subsequent classes.8 In a study of 420 medical students, Hall and Bailey found that students who had difficulty in one first-year course tended to have academic problems in subsequent classes, and students who did well in early coursework tended to continue with high grades.8 Our finding of first-year dental school GPA predicting GPA at graduation better than admissions criteria tends to be supported by other investigators who have shown assessments and outcomes completed in a similar environment tend to afford stronger predictive validity.5,13

Our findings of generally weak correlations between admissions criteria and first-year GPA and GPA at graduation were consistent with other investigators who have compared average and underperforming dental students.1,4 We found that SGPA, AA, and the academic rating of the school were significantly correlated to first-year GPA in normally tracking students, while only overall college GPA was significantly correlated to first-year GPA in underachieving dental students. Our finding of only overall GPA being correlated to first-year GPA in underachieving students was similar to Sandow et al., who found that students dismissed for academic reasons had a much lower non-science GPA than average dental students.1 Additionally, in a study of dental students over a four-year period, Kramer and DeMarais found that students who withdrew for academic reasons tended to have lower academic qualifications.4

The correlations we determined between admissions criteria and dental school performance were generally lower than reported by several investigators1,6 but were similar to other investigators.3,12 Sandow et al.1 found admissions correlations to first-year GPA that averaged higher than we determined for SGPA (0.41 vs. 0.27) and AA (0.47 vs. 0.36), while our results averaged closer to Kramer, who determined correlations of 0.37 for SGPA, and Chamberlain et al., who found 0.37 for AA.3,12

While statistical significance affirms only that coefficient values are not zero due to random variation, the essence of the Pearson statistical measure is to communicate a degree of individual association among variables. None of the correlation coefficients for admissions variables approached 0.50, and most were well below 0.50, leading to our observation that admissions criteria are weak predictors for first- or fourth-year academic performance in dental school as measured by GPA. It is important to remember that correlations that are below .40 have limited predictive value, even if statistically significant.5 Our findings included a number of statistically significant correlations that, with the regression analysis, were shown not to be predictive in explaining the variance in first-year GPA or GPA at graduation. This need for caution in interpreting the predictive value of correlations has been emphasized by several investigators.3,5 Mitchell, in an example of correlations when evaluating admissions criteria, explained that with a perfect correlation of 1.0 all students in the top fifth of an applicant pool would graduate in the top fifth of the class.5 With a correlation of .40, only 28 percent of the students in the top fifth of the applicant pool would graduate in the top fifth of the class, while random selection would result in 20 percent of students in the top fifth of the class. Although a correlation may be statistically significant from zero, the correlation may not be predictive when tested with the more rigorous regression analysis. Kramer has stated that the degree of correlation does not necessarily indicate an acceptable level of predictive validity,3 which was certainly the situation when evaluating our data.

Our finding of the weak predictive value of admissions criteria to dental school performance is consistent with the general education literature.18–22 In a study of over 77,000 students enrolled within the University of California system over a four-year period (1996–99), it was shown that high school GPA, SAT I, SAT II, parental income, and parental education only predicted 19.4 percent (R2=0.194) of the variance in the first-year college GPA.19 The individual admissions criteria were even weaker, with high school GPA predicting 12.6 percent of the variance in first-year college GPA, SAT (8.4 percent), SAT writing (11 percent), and the SAT combined with SAT II (12.6 percent).19 This means over 80 percent of what explains GPA in the first year of college is not explained by the admissions criteria according to Zwick et al.19 The ability of the Graduate Record Examination (GRE) to predict grades in the first year of graduate school is also weak, showing correlations of between .15 and .32 depending on the subdiscipline.20,21 The correlations of standardized tests such as the Graduate Management Admission Test (GMAT) (.20 to .32 depending on subscale) or Law School Admission Test (LSAT) (.44) are similar to or slightly stronger than the correlations we determined.18,22

We have found that pre-admissions criteria traditionally used to evaluate applicants for dental school programs are not accurate predictors of dental school performance as measured by first-year or graduating GPA. We also argued that our results are similar to findings in the education literature in which the criteria often used to evaluate college applicants lack predictive validity for academic success in college. We have also argued that, in the dental admissions literature, the degree of validity often ascribed to Pearson product moment correlations demonstrating bivariate association is probably overstated.

These findings and arguments beg the question of what criteria should be used to review applicants for admission to dental school. While we do not have a definitive answer, the recent work by Poole et al. offers a promising direction.23 In a longitudinal study of 373 Canadian dental students, Poole et al. found that data gathered from structured interviews showed the applicants’ "conscientiousness" correlated significantly with all four years of dental school performance (r=0.24, 0.47, 0.32, 0.39). Although Poole et al. stated that the sample size was too small to produce a stable regression model, the correlation of r=0.47 for second-year dental school performance looks especially promising. We hope similar investigations of behavioral measures will be forthcoming and add to the criteria currently being used to evaluate applicants.

The academic load while in college was not found to help predict first-year GPA or graduating GPA. This finding is consistent with similar investigations. Staat and Yancey6 looked at the college credit hours in seventy-seven students at one dental school and found a slight negative correlation between college credit hours and first-year dental school performance.


   Conclusions
Top
 Abstract
 Methods
 Results
 Discussion
 Conclusions
References
 
Our study has led us to the following conclusions:

  1. 1) Students’ first-year GPAs are strongly associated with their graduating GPAs, such that high/low achievers at the end of their first year of dental school tended to remain high/low performers at graduation.
  2. 2) Admissions criteria were weak predictors for first-year success in dental school as measured by GPA.


   Footnotes
 
Dr. Curtis is Professor, Department of Preventive and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco; Dr. Lind is Associate Professor, School of Economics and Business Administration, Saint Mary’s College of California; Dr. Plesh is Professor, Department of Preventive and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco; and Dr. Finzen is Health Sciences Clinical Professor and Chair of Prosthodontics, School of Dentistry, University of California, San Francisco. Direct correspondence and requests for reprints to Dr. Donald A. Curtis, Department of Preventive and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco, 707 Parnassus Avenue, D-3212, San Francisco, CA 94143-0758; 415-476-5827 phone; 415-476-0858 fax; Don.Curtis{at}ucsf.edu.


   REFERENCES
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 Abstract
 Methods
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 Discussion
 Conclusions
 References
 

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  5. Mitchell KJ. Traditional predictors of performance in medical school. Acad Med 1990; 65:149–58.[Medline]
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  8. Hall FR, Bailey BA. Correlating students’ undergraduate science GPAs, their MCAT scores, and the academic caliber of their undergraduate colleges with their first-year academic performances across five classes at Dartmouth Medical School. Acad Med 1992; 67:121–3.[Medline]
  9. Oudshoorn WC. The utility of Canadian DAT Perceptual Ability and Carving Dexterity scores as predictors of psychomotor performance in first-year operative dentistry. J Dent Educ 2003; 67:1201–8.[Abstract]
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