Nursing Education: Developing Specification Equations for Selection and Retention MARY H. HUCH, RN, PHD,*

REX L. LEONARD, PHD,“~ AND

KENNETH U. GUTSCH,

Because nursing is a professional area in which costs continue to rise faster than in other academic areas, it is imperative to provide quality education that is wlthin the financial reach of those students who can benefit from it. This study sought to develop a specification equation that could be used to predict retention in the academic area and workplace. (Index words: Nursing education; Predictors; Retention; Selection) 3 Prof Nurs 8:170-775, 7992. Copyright 0 7992 by W.B. Saunders Company

B

ECAUSEof the many and varied expenses incurred

perative

in educating

nursing

students,

to look for ways to improve

it is im-

selection

and

PHD$

formance,

frequently

sets out to predict majoring

remains attrition

in a highly specialized

or if one elects to predict educated lege,

a question.

professionals

it is essential

biographical predictive

attrition

three approaches

rates among

who have graduated

data on which

validity

highly

from col-

interviews,

content,

has been to establishing

est is predictive

if one

area, such as nursing,

to use tests,

validity

Thus,

rates for college students

and/or

concurrent,

established. validity,

or

Of these the strong-

because it mandates

that the

test, interview, or biographical data gathered be collected on a sample of the students coming into the specialty area and then correlated with specific criterion-related contingencies, eg, graduation within the

retention procedures so that those most likely to succeed are selected for admission. Attrition rates for nursing students have ranged from a low of 20 per

area of the specialty selected and/or high grades in the areas that precede graduation. Of the three approaches

cent to a high of 4 1 per cent in various national

to predicting-testing,

ies (Munro,

1980; Rosenfeld,

not an easy task to predict

stud-

1988). Althoughit is

those who will be success-

ful, there are at least three approaches through which the future performance of college students can be predicted:

test data,

interview

data, and/or

biographical

data. Naturally, predictors must be both reliable, ie, they must measure with consistency that which is to be measured, they purport

and valid, ie, they must measure to measure.

what

Although test reliability usually has been well established on standardized tests, the problem of test validity,

ie, validity

as a predictor

of successful

per-

*Professor of Nursing, University of Southern Mississippi, Hattiesburg, MS. tProfessor of Educational Leadership and Research, University of Southern Mississippi, Hattiesburg, MS. $Professor of Counseling Psychology, University of Southern Mississippi, Hattiesburg, MS. Phase I of this study was funded by grants from the University of Southern Mississippi Department of Research and Sponsored Programs and the Gamma Lambda Chapter of Sigma Theta Tau. Address correspondence and reprint requests to Dr Huch: University of Souther Mississippi, Southern Station. Box 5095, Hattiesburg, MS 39406-5095. Copyright 0 1992 by W.B. Saunders Company 8755-7223/92/0803-0009$03.00/O

170

biographical

data-the

interviewing,

and gathering

best predictors

of performance

for most jobs have been schenbaum & Weisberg,

tests (Howell, 1976; Kir1990). Among the tests

most frequently used to determine potential job performance are aptitude and ability tests. These are sometimes recognized as “maximum performance” tests because they are designed to tease out the maximum performance of those who take them. Yet, in the plethora of studies sponsored by the National League for Nursing in which researchers deal with attrition (Levine, 1957; Saleh, Lee, 81 Prier-r, 1965; Schwirian,

1977,

1978a,

on the use of maximum at best, quite poor.

1978b),

predictions

performance

based

tests have been,

Purpose This study sought to develop a specification equation that could be used to predict retention of nursing students in the selected program of study. Selected

Literature

The prediction studies found in the nursing literature focus on a variety of variables ranging from stan-

Journal of Professional Nursing,

Vol 8, No 3 (May-June),

1992: pp 170-175

DEVELOPING

dardized

SPECIFICATION

tests to grade point

that can be used for prediction points

in the student’s

McKinney, Sharp, 1986;

(Glick, Small,

1984).

The variables

are available

Data available

was used to predict McClelland,

O’Dell

at varying success in

& Young,

& Coonrod,

1986;

1988;

Other

researchers & Whitley,

1985) used data

before entry

into nursing

courses and there-

after. Yet another end of a program

(Payne

and

Krupa,

Quick,

available

averages.

matriculation.

at the time of admission some studies

171

EQUATIONS

time of data availability of study (Breyer,

& Duffy,

capacity”

programs,

provide quality all students Cattell,

will eventually

education

within

Eber, and Tatsouka

jective formulas for prediction

(1970)

linked to personality

posit that ob-

tests can be used

rather than merely relying

Furthermore,

they contend

be developed

for a given occupational

is at the

on intuition.

that a typical

profile

can

group.

Problem

1984). In seeking

Nursing is a professional area in which educational costs continue to rise faster than they do in other academic areas.

evidence

bearing

among

directly

nursing

used are per-

sonality tests. They are sometimes referred to as “typmeasures because they do not necical performance” essarily challenge the person to perform at a maxi-

this study

into two phases, ie, phase I and phase II,

and sought

evidence

1.

regarding

Phase I: Do personality who drop

three questions. profiles

out of a nursing

of students

program

differ

from the program?

2. Phase II*: Once graduated less frequently

on the high

students,

was divided

from those who graduate the measures

to

reach of

who desire it.

rate of attrition

Among

find it difficult

the financial

from the school of

nursing, do personality profiles of those students who become long-work tenure nurses, ie, nurses who stay in the profession after

mum level. Unless they are used in creative ways, the

receiving an undergraduate degree, from those who become short-work

results of these tests have no specific outcome contingency against which measurement means anything.

nurses, ie, those who drop out of the nursing profession after they graduate from college as

However, when used for predicting they seem to have practical value and now seem to have potential for the

nurses?

study of attrition among ple, in the Sloat, Gutsch, which

Sixteen

nursing students. For examand Leonard study (1983) in

Personality

Factor

Questionnaire

(16PF) profiles of drug users were compared with those of nonusers, they used Y as the predicted discriminate score and X as the 16PF sten score for each personality factor. Ultimately, they found nine factors that discriminated between the two groups. They then constructed an extrapolation table based on discriminate weights to show movement toward or away from drug use. It was their contention that the statistical procedures incorporated in studies they did with drug users versus nonusers and alcoholics versus nondrinkers indicated substantial support for the idea that when clear-cut behavior was defined as a criterion against which personality measurements could be made, the specification equation they designed could be used for prediction. Furthermore, it was their contention that similar equations could be developed to predict potential dropouts in nursing. Nursing is a professional area in which educational costs continue to rise faster than they do in other academic areas. Thus, nursing programs, as “limited

3. Phases I and II: When applying

differ tenure

the statistical

techniques developed by Sloat, Gutsch, and Leonard (1983): (a) Can the retention of nursing students be predicted more accurately than it has been in the past? (b) Can longtenure nurses be identified through cessing of personality test results?

the pro-

Procedure SAMPLE

Subjects for this longitudinal study consisted of 15 1 nursing majors at a southern public university who agreed to participate. All entering students during 1983 to 1984 were approached to participate in the study. Those who agreed to participate signed a consent form after the purpose of the study was fully *Long-work tenure versus short-work tenure comparisons will be made at a later date when the emphasis for the study will be on phase II of the research, and will include the characteristics that differentiate between nurses who stayed in the profession of nursing once they received their undergraduate degree and those who dropped out after entering the field.

HUCH, LEONARD, AND GUTSCH

172

explained.

No special inducements

to participate

were

coursework.

Stepwise

was then

provided. Subjects

were divided

into

groups

phase I based on successful completion of study (graduates, nursing

program

the university

N =

(transfers,

tinuation

N =

completely

of studies

of the program

121), subjects

and transferred

left the university

at the end of who left the

to another

major in

16), and subjects and reported

(inactives,

N =

who

noncon-

14).

differences

that

could

nursing

differentiate

students

graduation

of specification between

who remained

(1) undergraduate until

out before gradu-

of the program

of nurs-

been used in over 2,000 reported research studies. At least 15,000 adults were tested in the latest standardization of the instrument. Factor analysis was used to establish construct validity of the instrument, which yielded 16 personality factors. Scale reliabilities (testretest) for the 16PF range from .6 1 to .9 1 across all Equivalence

coefficients

be-

tween the various forms taken pairwise range from .15 to .82 across each scale trait (Cattell, Eber, & Tatsouka, 1970). DESIGN

Once data were collected,

estab-

lishes the best possible discrimination

using a forward variables

procedure.

At each step,

were

added only when they made the best contribution discrimination

to

in the presence of those already in the

model.

Then all of those variables

tested,

and the ones that were deleted

a data

ing who rendered short-tenure service (s2 years) to their profession and those who rendered long-tenure service (>2 years) to their profession after receiving their undergraduate degree in nursing. The 16PF has

forms of the instrument.

This technique

selection

in the model were

did not make

significant

at the conclusion

of each

Step 2

equations

in the program

and those who dropped

ation and (2) undergraduates

analysis

step.

Form A of the 16PF was used to establish base for the development

discriminate

a data base from which

could be measured.

contributions INSTRUMENT

multiple

used to establish

raw scores for all groups

The structure tions

between

function,

coefficients,

which

each variable

and

are the correlathe discriminant

were used to help select the principal

factors

of the 16PF. Only those personality factors that remained in the final discriminant model and had the largest structure coefficients were used for study. These factors, which discriminated between groups, became the data base from which predictions were made.

Using

Y as the predicted

discriminate

score

and X as the sten score for each personality factor, the equation for prediction was constructed as follows: Y = WiXi where Wi is the ich discriminant

function

weight.

Group membership, ie, those who stayed versus those who dropped out, was determined by the proximity of a predicted score to the centroid of that group. In other words, if the two groups were of equal size, half the linear distance between the two centroids would become a cutting score for prediction preference

of a

were converted to stens, and comparisons were made for phase I of the study. This included a 5-year study

student to initially undertake a study of nursing dicates some predisposition to success, a weighted

inav-

to determine the differences between those subjects who graduated with an undergraduate major in nursing and those who dropped out of nursing before re-

erage of the group centroids discriminant cutting score.

ceiving their undergraduate degree. Comparisons for phase I of the study, ie, looking at students who graduated from nursing school versus those who dropped out, was done through the use of a two-step design. Step 1

Univariate F tests on each of the 16PF factors were used to test for significance between the students who graduated and those who became inactive, ie, those who dropped out, or transferred, ie, those who transferred out of nursing and into another area of academic

purposes.

However,

since the personal

provided

a more optimal

Findings Discriminant analyses results, which are shown in Tables 1, 2 and 3, showed factors with significant discriminations for the comparisons mentioned. These factors were the ones chosen for the discriminant function through stepwise discriminant analysis. Discussions about each table or comparison focus on those factors having both significance as well as higher structure coefficients, ie, correlations between the factor and the discriminant function (Hair, Anderson, & Tatham, 1987).

DEVELOPING SPECIFICATION EQUATIONS

1.

TABLE

173

Average Sten Scores of Significant Factors and Structure Coefficients of G Versus T Students

3.

TABLE

Average Sten Scores of Significant Factors and Structure Coefficients of G Versus T and I Students

Sten Score

Sten Score Structure Coefficient

G

Factor

(N = 21)

(N =’ 16)

I3 C E

1.8 5.4 5.7 5.5 1.9

2.0 4.4 4.3 6.6 2.0

I Q4

A comparison

of personality

of the school of nursing those students

factors of graduates

with inactive

who stopped

school of nursing,

,336 ,433 ,610 - ,482 174

attending

and transfer

(G)

(I) subjects,

ie, those

students who transferred out of the school of nursing to other academic areas of the university, showed the following results. The profiles of G subjects subjects

differed

from

I or T

scored significantly

tors E and C and significantly

cients of the greatest

did I subjects

I L M 0

Q3 Q,

terested

(Table

-

career and wanted

to know who

the standardized sten score for each personality factor in Table 3, computer analysis yields the following classification

student file--F

equation:

of the equation

proceeds

advisor intended

Suppose

to work with entering

fication

in-

Average Sten Scores of Significant Factors and Structure Coefficients of G Versus I Students Sten Score G (N = 121)

5.6 70 6.0 5.5 4.4 5.7 5.9 61

(N :

14)

4.0 6.4 6.1 6.6 3.6 6.2 5.2 5.3

best done by first standard-

Structure Coefficient ,459 192 - ,025 - ,369 ,256 -.I43 -.014 ,287

by constructing

the profile are into the classi-

a table

(Table

4).

is used because results from discrimie, discriminant scores and coefficients,

are more convenient to plot when standardized. On Table 4 the adviser first locates factor F on the left side and follows the factor F row across the table until value

2.

equation

Standardization inant analysis,

that an

freshmen

= 4, Q4 = 3-is

izing scores. Once the scores forming standardized, they can be substituted

the development

as follows.

these factors to the profile of an entering

with the following 16PF personality pro= 2, G = 3, I = 7, L = 6, M = 7,0 =

2, Qj

2).

Once the results were determined,

Q3 Q,

in a nursing

Applying

magnitude.

Qi (Table 3) than did the combined group of dropouts, ie, T plus inactive subjects.

I

-

,314 ,176 ,481 042 ,331 ,148 ,282 ,269

had potential to complete the program of study. Denoting Y as the predicted discriminant score and X as

lower on Factor

3. G subjects scored significantly higher on factors F, M, and Q4 and lower on factors I and

M 0

4.8 6.6 66 5.4 3.6 6.1 7.6 5.5

Structure Coefficient

Y = (.398)X, + (.529)X, - (.560)X, - (.251)X, + (.528)X, - (.378)X, - (.551)X,, + (.432)X,,.

2. G subjects scored significantly higher on factor F and significantly lower on factor I than

F G H

56 70 5.5 5.3 4.4 5.7 6.9 6.1

F G

higher on fac-

I of the 16PF than did T subjects (Table 1). These factors also showed structure coeffi-

Factor

T and I (N = 30)

in three areas:

1. G subjects

TABLE

G (N = 121)

ie,

classes in the

(T) subjects,

Factor

the column

under

of - .608. This

discriminant

weight

sten 2 is reached value

to find the

is the product

.398 times the standardized

of the sten

of - 1.528 and thus becomes the contribution of factor F in the calculation of Y. Similarly, the adviser continues this process of gathering the products for each of the predictors. Once assembled, products are placed into the equation and substituted for the student’s original sten profile: Y = -.608 - .896 - .372 - .085 + .733 + .654 + .875 - .683. Adding the positive numbers yields a sum of 2.262, and adding the negative numbers yields a result of - 1.961. Combining these two numbers yields a score of .301, ie, the predicted discriminant score.

174

TABLE

HUCH, LEONARD, AND GUTSCH

Example of Standardization Respective Weights

4.

Table With Products

of Factors,

Stens, and

Sten 1

2

3

4

5

6

7

- ,785 i .a30 1.347 ,578 - .a49 ,826 1.733 -1.136

- ,608 -1.519 1.061 ,445 - ,937 ,654 1.447 -.910

- ,430 - ,896 ,774 ,313 - ,321 ,486 1.161 - ,683

- ,253 - ,896 ,487 ,180 - ,058 309 .a75 - ,456

- ,075 - ,585 201 ,047 ,206 ,137 ,589 - ,230

,102 - ,274 - ,085 - 085 ,469 - 036 ,303 - ,003

,280 ,037 - .372 -.218 733 - ,208 ,017 ,224

Factor

F G

I L M 0

Q, Q,

-

8

-

,457 ,344 ,658 ,350 997 380 269 450

9

10

,635 ,659 - 945 - 483 1.260 - ,552 - ,555 ,667

al2 970 - 1.232 -.616 1.524 - ,725 - .a41 ,904

NOTE: Factor F-low score (L): sober, taciturn, serious; high score (H): happy-go-lucky, enthusiastic, impulsive, lively. Factor G-L: expedient, disregards rules; H: conscientious, perservering, moralistic. Factor I-L: tough-minded, self-reliant, realistic H: tender-minded, intuitive, unrealistic. Factor L-L: trusting, adaptable, free of jealousy; H: suspicious, self-opinionated, hard to fool. Factor M-L: practical, careful, conventional; H: imaginative, careless of practical matters. Factor O-L: unperturbed, self-assured, confident. H: apprehensive, self-reproaching, worrying. Factor Q,-L: undisciplined self-conflict, careless of protocol, H: controlled, socially precise, compulsive. Factor Q,-L: relaxed, tranquil, torpid; H: tense, frustrated, driven. From Cattell, R. B., Eber, H. W., & Tatsouka, M. M., Handbook for the Sixteen Personality Factor Quesfionnaire (l.PF), copyright 1986 by the Institute for Personality and Ability Testing, Inc Reproduced by permission.

To develop the meaning now needs to establish two centroids

of such a score, the adviser

a cutting

within

score between

the two groups.

for this data were .233 and - .940. represent the averages of discriminant

the

The centroids These points scores for the

two groups. The cutting score is the criterion score against which each individual’s discriminant score is judged

to determine

into which group the individual

ample,

both groups,

ie, G versus T and I, were com-

posed of people (primarily

and I subjects situations rewarded.

were probably

in which

In reviewing

significant

as more “down-to-earth,”

=

+

realistic,

N42

3OC.233) + (121) (-

.940)

121 + 30 =

6.99

-

113.74

151

=

- .707.

Note that Z, is the centroid for group 1, and N, is the number of students in group 1. Computation of this equation shows that the sample profile score is greater than - .707 and thus falls into the graduation group. Knowing this, the adviser can discuss entrance into the nursing program from a more specific perspective.

Discussion In synthesizing this material, it appears that when comparing G with T and I subjects some common characteristics reflected well on both groups. For ex-

differences

between

were G and

patient, general,

lower on factor I could best be described

tough

jects tended

NI + Nz

to seek

behaviors

I subjects, (Table 2), one might conclude that the G subjects who were significantly higher on factor F and

score: N2Z1

who might

more inclined

these particular

significantly

=

young women)

well be described as enterprising, incisive, and resilient in their relationship with others. However, the T

should be classified. Because the groups were not of equal size, the weighted average of the group centroids is then calculated to provide an optimal cutting

z,

1970,

minded,

independent, and responsible,

to be more demanding,

self-reliant, and I sub-

dependent,

im-

temperamental, unrealistic, and fussy. In I subjects seemed less capable of becoming

team players. When reviewing differences between G & T subjects, (Table 1) it becomes obvious that the G subjects scored significantly higher on factors E and C and significantly lower on Factor I of the 16PF. From these differences one might conclude that G subjects were probably more mature, stable, calm, assertive, self-assured, self-reliant, and realistic; that they were no-nonsense people with a tough minded, competitive, but down-to-earth approach to life. On the other hand, T subjects were more dependent, docile, submissive, and easily led. They seemed to be more impatient and temperamental, more easily annoyed, and more easily frustrated than were those who stayed in the program.

DEVELOPING

SPECIFICATION

Interestingly

enough,

low on factor students

EQUATIONS

both G and I subjects

M, which

could

who go into nursing

over details,

conventional

tical matters,

175

indicate

are probably

thinkers,

many

concerned

attentive

and show good stability

scored

that

to prac-

during

emer-

gencies. Perhaps

the

greatest

was reflected to be lively,

and cheerful, mistic,

the personality

students

to understand

Baccalaureate centrated

divergence

in personality

on factor F where G subjects enthusiastic,

and T and I subjects

sober, restrained,

reticent,

talkative,

carefree,

were more pessiintrospective,

and

students lem.

to begin

families,

money

data such as has been discussed

can be

used by an adviser to guide students using something more than intuition as the student makes career choices. dent

Personality

and thus

traits

relate to needs of the stu-

advisement

can occur based on hard

is spent

Attrition

of their

and other sources in By identi-

who are more likely to transfer to the resources of the

can be saved and directed

toward

may have a greater

success. Additionally,

the

is a costly prob-

education.

major or become inactive,

an area

likelihood

for

the resources of the school can

be put to more effective use in concentrating most likely to persist. Although using equation will not remove all uncertainty, vide an objective

are con-

by the students,

to secure a nursing

where the student Gathering

courses.

the school of nursing,

attempting

student

nursing

from the school of nursing

Much

another

Implications

of study

which means the stu-

2 years of study before having

fying those students

deliberate.

better.

programs

in the upper division,

opportunity

test data can assist

more clearly as well as the

themselves

nursing

dent has completed

makeup seemed

data. Furthermore,

advisers to know students

measure

for student

on those

a prediction it does pro-

selection.

References Breyer, F. J. (1984). The comprehensive nursing achievement test as a predictor of performance on the NCLEX-RN. NzlrJing and Health Care, 5, 193- 195. Catell, R. B., Eber, H. W., & Tatsouka, M. M. ( 1970). Handbook for the sixteenpersonalityfactor questionnaire (16PF). Champaign, IL: Institute for Personality and Ability Testing. Glick, 0. J., McClelland, E., & Young, J. C. (1986). NCLEX-RN: Predicting the performance of graduates of an integrated baccalaureate nursing program. Journal of Professional Nursing. 2, 98- 103. Hair, J. F., Anderson, R. E., & Tatham, R. L. (1987). Mu&variate duta analysis with readings (2nd ed.). New York: Macmillan. Howell, W. C. (1976). Essentials of industria/ and organization&psychology. Homewood, IL: Dotsey. Kirschenbaum, A., & Weisberg, J. (1990). Predicting worker turnover: An assessment of intent on actual separations, Human Relations, 43(9), 829-847.

Levine, E. (1957). Turnover among nursing personnel in general hospitals. Hospitals, 3 1, 5 1. McKinney, J., Small, S., O’Dell, N., & Coonrod, B. A. (1988). Identification of success for the NCLEX and students at risk for NCLEX failure in a baccalaureate nursing program. Journal of ProfarsionalNursing, 4, 55-59. Munro, B. (1980). Drop-outs from nursing education: A path analysis. Nursing Research, 29, 37 l-377. Payne, M. A., & Duffy, M. A. (1986). An investigation of the predictability of NCLEX scores of BSN graduates using academic predictors. Journal ofProfessionalNursing. 2 I 326-332.

Quick, N. M., Krupa, K. C., & Whitley, T. W. (1985). Using admission data to predict success on the NCLEX-RN in a baccalaureate program. Journal of Professional Nursing, 1, 364-367. Rosenfeld, P. (1988). Measuring student retention: national analysis, Nursing and Health Care, 9, 199-202.

A

Saleh, S. D., Lee, R. J., & Prien, E. P. (1965). Why nurses leave their jobs: An analysis of female turnover. Personnel Administration. 28, 25-28. Schwirian, P. M. (1977). Prediction of successful nursing performance. Parts I & II-U.S. Health Resources Administration, Nursing Division. (DHEW Publication No. HRA 77-27). Washington, DC: U.S. Government Printing Office. Schwirian, P. M. (1978a). of nurses: A multidimensional 27, 347-35 1.

Evaluating approach.

the performance Nursing Research,

Schwirian, P. M. (1978b). Prediction of successfulnursing pwformunce. Part III-Evaluation and prediction of performance of recent graduates. (Report #783970). Columbus, OH: The Ohio State University Research Foundations. Sharp, T. G. (1984). An analysis of the relationship of seven selected variables to state board test pool examinations performance of the University of Tennessee, Knoxville College of Nursing. Journal of Nursing Education, 23, 57-64. Sloat, D., Leonard, R., & Gutsch, K. (1983). Discriminant analysis for measuring psychotherapeutic change. Measurementand Evaluation in Guidance, I6( l), 36-42.