lnf. J. Nurs. Stud. Vol. 16, pp. 59.63. GPergamon
Press Ltd., 1979. Printed in Great Britain.
0020-4878/79/0301M)59$02.00/0
Predictors of practical nursing state board examination scores NICHOLAS
DI MARCO
and STEVEN University
D. NORTON School ofBusiness, of Missouri-S1 Loud, Sl Louis, Missouri 63 12 I,
U.S.A. and
DELORES Saint Mary’s
FENDLER Health
Center. Sl Louis, Missouri 63 I 17, U.S.A.
The purpose of this study is to examine the roles of pre-admission ability tests and nursing course exams in predicting practical nursing state board scores. Specifically, the study shows that a multiple regression approach can offer an improvement over judgemental prediction.
Method
Subjects and setting The subjects were 400 applicants to a I-yr program from 1971 to 1976. The program was program was designed to prepare the students nurse (LPN). Of these 4OOapplicants, 221 were
hospital based licensed practical nursing in a 568 bed hospital in the midwest. The to assume the role of a licensed practical accepted and 179 were rejected.
The measures fall in three categories: (I) pre-admission ability tests-Statewide Testing Service I.Q.; National League for Nursing tests in Science and Health, General Information, Arithmetic, Vocabulary and Reading; (2) Course Mastery tests-examinations during the training program measuring knowledge of what has been taught and (3) the practical nursing state board examination. The various measures are presented in Table 1, with their mcans. standard deviations and their correlations with the state board examination scores. Analysis
The analysis
I-Multiple
of the data obtained
regression on students
from 221 students 59
involved
three different
prediction
60
NICHOLAS Table
DI MARCO,
I. Means. standard
Variables
STEVEN
D. NORTON
AND DELORES
deviations of test scores and correlation state board scores (N = 140)
Mean
S.D.
FENDLER
coefficients
with
r with state board score
I. Preadmission
ability tests State-wide testing Service I.Q. National League for Nursing Science and health General information Arithmetic Vocabulary Reading
2. Tests during nursing training Standardized course mastery Medical - surgical Maturation -child Pharmacology Body structure Basic nursing Nutrition - diet
State board
9.66
0.38t
69.59 68.32 71.36 73.70 78.28
18.45 19.13 18.84 16.67 15.22
0.56t 0.38t 0.34t 0.49t 0.53t
61.80 63.49 53.58 56.03 42.66 52.46
22.23 25.04 25.58 23.25 24.87 27.17
0.53t 0.53t 0.48t 0.54t 0.57-t 0.60t
91.44 90.46 86.01 88.80 90.61 90.35 88.64 87.96 87.93 88.09 86.49 86.32 85.28 90.65 82.23
2.95 4.53 4.87 5.06 4.16 3.70 3.89 3.96 6.01 6.21 5.58 5.26 5.70 3.92 6.35
0.45t 0.39t 0.36-t 0.46t 0.21: 0.29-t 0.38t 0.54t 0.18* 0.31t 0.5lf 0.46t 0.52t 0.40t o.sot
90.13 89.06 88.34 89.39 89.13
4.65 6.06 5.21 4.53 5.16
0.38t 0.10 0.24t 0.27t 0.35t
550.04
64.67
tests
Theory course mastery tests Personal vocational relations Communications Community health Body structure & function Medical ethics Normal nutrition & diet Nursing skills Medical-surgical nursing Drugs & solutions Administration of medicines Pharmacology Pediatrics Obstetrics Legal aspects Mental health 3. Clinical course mastery Pediatrics Obstetrics Surgical speciality Medicine Surgery
109.32
tests
*P < 0.05: j-P < 0.01.
equations: (I) the prediction of state board examination scores by ability test scores; (2) the prediction of state board examination scores by scores on course mastery tests and (3) the prediction of state board examination scores by ability test scores and course mastery tests combined. These three prediction equations served three different purposes. Prediction by ability
PREDICTORS
OF-PRACTICAL
NURSING
STATE BOARD
EXAMINATION
SCORES
61
test scores is useful in selecting students who will do well on the state board examinations and become LPNs. Prediction by scores on course mastery tests is useful in identifying students who need to review certain courses in order to improve the probability that they will do well on state board examinations. Prediction by ability test scores and course mastery tests combined is useful in understanding the factors which lead to high scores on the state board examinations. Results
1. Prediction by pre-admission ability tests
Two test scores, the Science and Health Scale of the National League for Nursing PreAdmission Test, and the Reading Scale of the same test, accounted for about 45% of the variance in state board examination score. A third test, the I.Q. Scale of the Statewide Testing Service, added a statistically significant (P< 0.01) but very small (0.016) amount of variance accounted for. The regression equation using standardized regression coefficients is as follows: State Board (R2 = 0.45) = 0.43 Science and Health + 0.39 Reading. 2. Prediction by course mastery tests Five course mastery tests accounted for about 62% of the variance in state board score. They were: Nutrition-Diet, Basic Nursing, Personal Vocational Relations, Surgery and Maturation-Child. A sixth test, Legal Aspects, added a statistically significant (P< 0.001) but very small (0.011) amount of the variance accounted for. The regression equation is as follows: State Board (R* = 0.62) = 0.28 Nutrition-Diet + 0.29 Basic Nursing + 0.24 Personal Vocational Relations + 0.20 Medical Surgical Nursing. A student who receives low scores on any of these five tests should be particularly thorough in reviewing the appropriate material before taking the State Boards. 3. Prediction by ability tests and course mastery tests combined The successful student (with a high state board examination score) enters the program with good reading skills and good general knowledge of science and health. She or he does well in Nutrition-Diet, Basic Nursing, Surgery and Medical-Surgical Nursing. Reading skill is obviously important to success in the program because of the large amount of written material to be mastered. General knowledge of science and health is probably related to success for two reasons: providing a background for the nursing courses and indicating an interest in the subject matter of nursing.
Analysis II--Improvement
over judgemental
prediction
The second part of the study investigated the improvement in state board examination scores that would have resulted from using the regression equation instead of the judgemental approach that was actually used. The first step in this analysis was to determine the cut-off score on the predictor equation that would maximize the effectiveness of the selection process. Assuming that approximately the same number of applicants were chosen, a cut-off score of 521.5 would maximize the selection decision. By examining the distributions of state board examination scores for
62
NICHOLAS
DI MARCO,
D. NORTON AND DELORES
STEVEN
the group
that was actually
accepted
the group
that was rejected
it was determined
end of the rejected number
and the lower
rejected
and accepted
have maximized
Thus by selecting
groups
the state board
state board examination
that at a predictor
end of the accepted
of cases (62 and 60, respectively).
the original would
group
and the predicted
with
a predictor
examination
Table 2. Means and standard deviation\
I-ENDLER
scores for
score of 521.5 the upper group
had about
the same
all the applicants
from
both
score of 521.5 and above
scores for a group
for state hoard examination
we
of 223 applicants. xx~rc’\
for Ihe various group