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Logo of bmjThis ArticleThe BMJ
BMJ. 2003 February 22; 326(7386): 429–432.
PMCID: PMC163931

Pilot study of the roles of personality, references, and personal statements in relation to performance over the five years of a medical degree

Eamonn Ferguson, reader in health psychology,a David James, professor of fetomaternal medicine,b Fiona O'Hehir, MSc student in occupational psychology,a and Andrea Sanders, MSc student in occupational psychologya



To compare the power of three traditional selection procedures (A levels, personal statements, and references) and one non-traditional selection procedure (personality) to predict performance over the five years of a medical degree.


Cohort study over five years.


Nottingham medical school.


Entrants in 1995.

Main outcome measures

A level grades, amounts of information contained in teacher's reference and the student's personal statement, and personality scores examined in relation to 18 different assessments.


Information in the teacher's reference did not consistently predict performance. Information in the personal statement was predictive of clinical aspects of training, whereas A level grades primarily predicted preclinical performance. The personality domain of conscientiousness was consistently the best predictor across the course. A structural model indicated that conscientiousness was positively related to A level grades and preclinical performance but was negatively related to clinical grades.


A teacher's reference is of no practical use in predicting clinical performance of medical students, in contrast to the amount of information contained in the personal statement. Therefore, simple quantification of the personal statement should aid selection. Personality factors, in particular conscientiousness, need to be considered and integrated into selection procedures.


A recent review of published research of predictors of performance of students at medical school highlighted several issues requiring further study.1 Specifically the relative contribution of references, personal statements, and personality traits to predict performance across the medical degree has not been examined in single study. Work that has been conducted on each of these factors is consistent with studies in other occupations.2 It shows that references do not predict performance; the value of personal statements is mixed, and the personality domain of conscientiousness predicts preclinical performance but not performance as qualified general practitioners.35

We examined the role of four variables (A levels, the applicant's personal statement, the teacher's reference, and personality) in all stages of undergraduate medical training. We also examined how these variables are interrelated with performance by using a structural equation.6,7

Personality domains of the big five8

Emotional stability—high scores equate to being relaxed and unemotional (mean score 43.1 (SD 8.4), Cronbach's α=0.79)

Surgency—high scores equate to extroversion (44.4 (7.7), 0.82)

Intellect—high scores equate to being creative, reflective, and imaginative (47.2 (6.6), 0.71)

Agreeableness—high scores equate to cooperativeness (49.3 (5.8), 0.74)

Conscientiousness—high scores equate to being hardworking and organised (45.5 (9.1), 0.86)


We followed the 1995 entry cohort at Nottingham medical school over the five years of training. The mean age of the 176 entrants was 19.7 (SD 2.11) years (range 18-35). Of these, 102 (58%) were women. We coded the students' A level grades and the contents of their UCAS personal statements and their references. Two and a half years into the course, 67% of the original cohort gave consent for their personality scores to be assessed. We recorded the performance of the students in 18 formal assessments over the preclinical (years 1 and 2; four assessments), BMedSci (year 3; four assessments), and clinical (years 4 and 5; 10 assessments) components of the course.


A level points score

Overall, 86% of the students had taken A levels. We recorded points scores for A levels for each student (10 points for A grade, 8 for B grade, 6 for C grade, 4 for D grade, and 2 for E grade).

Personal statement and reference coding

We analysed the content of the free response personal statement and reference in the student's UCAS application form by using manifest coding.w2 w3 Most of the personal statement categories covered motivation and hobbies whereas the reference categories covered character and social skills. See for details of the coding scheme and procedures.


We used Goldberg's bipolar adjectives to measure the “big five” domains for personality (see box).8

Outcome measures

Overall, four themes are recorded for the preclinical years: A (the cell: mean score 65.3%, range 50-80%), B (the person: 61.4%, 61-80%), C (the community: 65.7%, 49-86%), and D (the doctor—personal and professional development: 63%, 42-86%).

Four assessments make up the BMedSci year: marks for a project (mean score 65.3%, range 36-85%), a viva voce (65.3%, 30-90%), a data analysis paper (64.2%, 48-82%), and a theoretical paper (53%, 6-74%).

We made 10 assessments during the clinical years. Seven of these were scored as a grade and converted by standard α numeric conversion: D=0, C=1, B=2, and A=3.911 The results of these clinical assessments comprised junior surgery (median grade 2, range 1-3), junior medicine (2, 1-3), psychiatry (2, 0-3), obstetrics and gynaecology (2, 0-3), dermatology (mean score 73.6%, range 46-86%), child health (median grade 2, range 0-3), general practice (2, 1-3), ophthalmology (mean score 55%, range 27-83%), ear, nose, and throat (50%, 28-75%), and senior medicine and surgery (60%, 43-77%). We calculated the average scores for the main course components by summing the four preclinical assessments, the four BMedSci assessments, and the 10 clinical assessments.


We analysed the data with a mixture of univariate (zero order correlations, t test, χ2 test) and multivariate methods (multivariate analysis of variance; hierarchical multiple linear regression, and structural equations modelling).12 w4 w5 See for details on the structural modelling.


Potential sampling bias

Students who completed the personality questionnaire did not differ significantly from those who did not for age (t1,173=1.1, P=0.27), sex (χ2=3.06 (df=1), P=0.08), preclinical, BMedSci, and clinical performance (multivariate F3,140=0.007, P=0.79), and whether or not they obtained honours (χ2=1.6 (df=1), P=0.20).

Univariate analyses

Table Table11 shows the zero order correlations (Pearson's r) between each of the predictors and each of the 18 assessments. Better A level grades significantly predicted better performance in six of the 18 assessments (33%). Three of these six assessments were the preclinical marks (themes A, B, and C).

Table 1
 Zero order bivariate associations between predictors and outcomes

More information in the personal statement was predictive of 33% of the assessments and specifically better clinical performance (theme D, junior surgery, senior surgery, obstetrics and gynaecology, ophthalmology, and dermatology). Higher scores on conscientiousness were significantly related to better performance across most (78%) of the assessments. Students scoring higher on agreeableness performed better on 33% of the assessments. Those scoring higher on emotional stability or lower on surgency performed better on 17% of the assessments. Finally, the amount of information in the reference and scores on intellect were both correlated with 0.055% of the assessments (chance level).

Multivariate analyses

To examine the relative predictive power of the traditional and non-traditional predictors for preclinical, BMedSci, or clinical performance, we conducted a series of three hierarchical multiple linear regression analyses. The traditional selection measures were entered at step 1 and personality at step 2 (table (table2).2).

Table 2
 Hierarchical multiple linear regression analyses for predictors and aggregate outcome measures

A level points predicted assessment scores across the course. The amount of information contained within the personal statement was a significant predictor of clinical performance. The addition of the personality scores significantly improved the fit of the regression models. Conscientiousness was the only personality variable that showed a consistent pattern of significant effects across all three general assessments.

Model fitting

The figure presents the best fitting structural model (χ2=5.82, df=6), P=0.44, comparative fit index=1.0, root mean square approximation of error=0.0 (90% confidence interval 0 to 0.14), n=87). Higher scores on the preclinical assessments significantly predicted better clinical performance. Higher scores on A levels were directly related to better preclinical performance. Higher scores for conscientiousness were significantly correlated with better A level scores and related to better scores on preclinical assessments and to worse performance on clinical assessments.


The amount of information contained in a teacher's reference does not reliably predict performance of a student at medical school. A level scores were a good predictor of performance, and the amount of information in the personal statements related to clinical performance. The personality domain of conscientiousness showed the most consistent pattern of significant relations with the outcome measures, a finding that is consistent with results reported for other occupations.13 The structural model indicated that those scoring high for conscientiousness were more likely to have better A levels grades and to do better on preclinical assessments but less well in clinical assessments. It may be that the behaviours (for example, organised, methodical) associated with high scores for conscientiousness are more suited to the factual nature of preclinical learning; which in turn is related to better clinical performance. Once any benefit from preclinical learning has been accounted for, these same behaviours may, however, be less well suited to clinical learning, such as strategic problem solving. Practically, our results suggest a combination of A level scores, an index of the amount of information in the personal statement, and scores for conscientiousness could aid selection for interview.


The small sample size limits the statistical power of our study, and this may account for null results seen for the reference. However, the findings reported for the reference are consistent with previous work.1,2 That the results are based on a single cohort may introduce sample bias and limit the generalisability of our findings. The effects of conscientiousness, however, are consistent with a large body of findings outside medicine and a small, but growing, body of findings reported in medicine.3,4,13 Indeed, we see our study as a pilot investigation, and we are currently following a second cohort, where the results show conscientiousness as the main personality predictor of preclinical performance (data not shown).

Best fitting structural model (*P<0.05)

Supplementary Material

[extra: Tables and other details]


We thank Jane Schroeder for additional help with data input.


Funding: None.


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2003 February 22; 326(7386): 429–432.

Commentary: How to derive causes from correlations in educational studies

I C McManus, professor of psychology and medical education

Ferguson and colleagues' paper contains much of methodological and substantive interest. Many BMJ readers will be unaware of structural equation modelling, a widespread technique in social research, the synonyms for which include path analysis, covariance modelling, latent variable modelling, and causal modelling. Structural equation modelling uses programs such as LISREL, EQS, and AMOS to fit models that combine multiple regression, factor analysis, psychometrics, and multigroup modelling and which answer subtle statistical questions.1-1

Hermann Goering famously (but erroneously) reached for his revolver on hearing the word culture. Causal modelling may have a similar effect in students taught in elementary statistics classes that “correlation does not imply causation.” It doesn't, but that doesn't mean statisticians don't infer causation. They can, and they do, for science is about understanding causes.1-2

The problem of inferring causation is that if A and B correlate, then this may be because A causes B, B causes A, or that something else, X, causes both A and B. Although often presented as an intractable problem, it is far from that. David Hume in his Treatise of Human Nature of 1739, described the principle of priority of time whereby cause comes before effect. In the present case, the correlation between A level scores and preclinical performance cannot reasonably be interpreted as preclinical performance causing A level scores, since that would mean performing well at university caused students earlier in their lives to achieve better A level grades, which is nonsense. And so we infer A level scores cause preclinical performance.1-3 The third option, that some third factor (X) causes both A level scores and preclinical performance, is directly testable if X has been measured (and so A level scores causing preclinical performance cannot result from both correlating with conscientiousness). If X has not been measured then the claim is not falsifiable, but structural equation modelling does help design the study which would make it testable.

Hume talked also of distant objects being “link'd by a chain of causes,” the principle of contiguity. That can be seen in the academic backbone of this model. A level scores cause better performance indirectly, by causing better preclinical performance, which in turn causes better BMedSci performance and better clinical performance. A similar chain can be seen in a drinking song by Henry Purcell:

'Tis women make us love,

'Tis loving makes us sad,

'Tis sadness makes us drink,

And drinking makes us mad.

Ferguson and colleagues' paper has two important educational messages. Preclinical performance is predicted by conscientiousness, one of the “big five” personality dimensions, which meta-analysis confirms is often a predictor of job performance and job trainability.1-4,1-5 Individuals with high conscientiousness see themselves as practical, thorough, and hardworking, rather than disorganised, lazy, and careless, and not surprisingly such individuals do better in preclinical examinations. Less obvious is the diminishing impact of conscientiousness on later performance, particularly clinical performance, perhaps because conscientiousness is less important for the self directed, more conceptual, less fact dominated learning required of clinical students. That is problematic for those wishing to use conscientiousness as a basis for student selection. Conscientiousness may be predictive of job performance only for repetitive, well organised, relatively closed tasks, and not for the more imaginative, thoughtful, open thinking required of an actor, an artist, a research scientist, or a creative clinician.

The other important result concerns the personal statement on the UCAS form, which although often claimed to have little validity, may be predictive but only for clinical performance. Once again, what is good for preclinical may not be good for clinical. Studies of student selection have to consider long term outcomes, not just the first one or two undergraduate years.


Competing interests: None declared.

Tables and details of coding and structural modelling appear on


1-1. Maruyama GM. Basics of structural equation modelling. Thousand Oaks, CA: Sage; 1998.
1-2. Pearl J. Causality: models, reasoning, and inference. Cambridge: Cambridge University Press; 2000.
1-3. Davis JA. The logic of causal order. London: Sage; 1985.
1-4. Matthews G, Deary IJ. Personality traits. Cambridge: Cambridge University Press; 1998.
1-5. Schmidt FL, Hunter JE. The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 85 years of research findings. Psychol Bull. 1998;124:262–274.

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