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

Predictors of practical nursing state board examination scores.

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...
372KB Sizes 0 Downloads 0 Views