Ne

A MULTIPLE REGRESSION ANALYSIS OF THE RELATIONSHIPS

BETWEEN APPLICATION BLANK DATA

AND JOB TENURE

THESIS

Presented to the Graduate Council of the

North Texas State University in Partial

Fulfillment of the Requirements

For the Degree of

MASTER OF ARTS

By

Nancy W. Newton, B. A.

Denton, Texas

August, 1976

Newton, Nancy W., A Multiple Regression Analysis of

the Relationships Between Application Blank Data and Job

Tenure. Master of Arts (Industrial Psychology), August,

1976, 29 pp., 5 tables, bibliography, 18 titles.

One technique being used to reduce employee turnover

is the Weighted Application Blank. Data obtained from

application blanks are analyzed and weights are assigned

to each item. Utilizing these weights, predicted scores

are derived and compared to each person's actual tenure to

determine the effectiveness of the model.

The present study analyzed application blank data

from the files of 93 currently employed and 69 terminated

female clerical workers. Twelve items were analyzed by

means of a stepwise multiple linear regression procedure,

with months of tenure being the dependent variable. The

five most significant items yielded a multiple correlation

of .54. The total sample also was divided randomly into

two groups, and cross-group analyses resulted in simple

correlations of .56 and .29.

LIST OF TABLES

Table Page

1. Variable Categories and Assigned Scores . . . 13

2. Standard Deviations and Means Used for

Weighting Procedures . . . . . . . . * . 14

3. Five Variable Regression Analysis for Job

Longevity, All Subjects . . . . . . . . . 16

4. Five Variable Regression Analysis for Job

Longevity, Sub-Samples . . . . . . . . . 18

5. Comparison of Standard Weights from Two

Sub-Samples . . . . . . . . . . . . . . . 20

iii

A MULTIPLE REGRESSION ANALYSIS OF THE RELATIONSHIPS

BETWEEN APPLICATION BLANK DATA

AND JOB TENURE

One of the most persistent and costly problems in busi-

ness today is employee turnover. It is estimated to cost

American industries billions of dollars each year in de-

creased productivity, efficiency, and profits. It is ap-

parent that every position necessitates initial expenditures

in recruiting and training, even when experienced applicants

are available and only a minimum amount of training is re-

quired. Thus, even the most able worker represents a finan-

cial loss to the company if he or she leaves the organiza-

tion soon after employment and before the initial invest-

ments can be recovered.

A method being used currently to remedy the problem

of turnover is the Weighted Application Blank (WAB). One

of the earliest reported uses of this technique was in 1919

for the selection of insurance salesmen at Phoenix Mutual

Life Insurance Company (Guion, 196$). Since that time the

WAB has been used to predict various criteria, but is most

often utilized to predict tenure.

After determining a definition of a short-term and

long-term employee, the application blanks of such employ-

ees are examined. Following England's (1961) procedure,

each item is categorized, and differences between the

1

2

percentage of responses for long-term and short-term work-

ers are obtained. For convenience, these differences are

converted to integer weights. By summing the weighted

scores for all items, a predicted score is obtained for

each individual. This predicted score is compared to the

person's actual tenure to ascertain the hit rate of the

technique. If the results indicate a differentiation be-

tween the long-tenure and short-tenure workers, a cut-off

score may be determined for the selection of future appli-

cants. As an alternate method, the weighted value of each

item may be obtained by the stepwise multiple linear

regression technique.

The use of the WAB as a selection device may be attri-

buted to a need to express selection methods in more

objective and quantitative terms. There is also an in-

creasing recognition that the application blank may contain

valuable information about one's past history which may be

predictive of one's future behavior. It seems reasonable

to assume that such data as previous employment history and

earnings, education, specific skills, etc. may reflect an

individual's motives, abilities, skills, level of aspira-

tion, and adjustment to particular working situations.

England (1961) presents a detailed account of the

methodology of the development of a WAB, as well as an

extensive list of refrences concerning its application. He

states that 16 out of 18 WAB studies showed the method to

3

be a significant predictor of the criterion with such

diverse groups as sales clerks, engineers, factory workers,

district managers, female clerical employees, draftsmen,

and technicians. These studies involved the three

different criteria of tenure, salary increase, and success

ratings, but tenure was the most-utilized criterion. Addi-

tional occupations in which the WAB procedure has proved to

be a valid predictor of job success are those of physician,

Army officer, scientist, salesman, middle manager, and

production employee (McKillip & Clark, 1974).

In a review of the literature by Schuh (1967) of 21

studies using biographical data as predictors of turnover,

only two failed to find at least one item to be related to

the criterion. These studies covered a wide range of occu-

pations, such as bus drivers, male route salesmen, male

semiskilled workers, factory workers, and farm workers.

The findings indicated again that items in an applicant's

personal history can be found to relate to tenure in

several occupational fields.

It should be emphasized, however, that items which

are found to be predictive of success on one job may not

be successful predictors for a similar job in a similar

company, even within the same city. In a study conducted

by Mosel and Wade (1951), women sales clerks in a large

department store were the subjects. At that time, the

National Dry Goods Association stated that an acceptable

4

turnover rate for such employees should be between 8 and

10%, although this particular store had an annual rate of

79%. The study revealed 12 items which were significantly

related to job tenure, and a cross-validation study showed

a significant relationship between tenure and WAB score.

However, when the same scoring weights were used with other

personnel in the same locale, the results showed only three

out of a possible eight items proved to be significant

predictors.

Although it is believed that some items do have a high

degree of general applicability, the specific situation

must be evaluated before using any biographical items to

predict a criterion. One study by Cassens (McKillip &

Clark, 1974) utilized factor analytic studies of petro-

chemical executives in three samples: Latin American

executives, American overseas executives, and American

executives in the United States. The conclusions were

that the same factors were predictive of performance for

all the groups, but that specific items within each factor

were different among the groups. It appeared that cultur-

ally based items could be measures of the same construct

for different cultural groups. Thus, the conclusions

imply that a specific construct could underlie executive

performance, but that the particular items by which the

construct is measured may vary from one cultural group to

another.

5

In a study conducted by Kriedt and Gadel (1953), a

battery of tests and questionnaires were used to predict

turnover among female high school graduate clerical workers

in a large insurance company. Included in the battery were

an intelligence test, a clerical aptitude test, an interest

questionnaire, a biographical data blank, and a job prefer-

ence questionnaire. The results showed that the biographi-

cal data were the best predictors of tenure, yielding point

biserial correlations of .37 (for three-month tenure) and

.29 (for 12-month tenure). The other measures only contri-

buted slightly to the effectiveness of the prediction as

estimated by multiple correlation.

Minor (1958) also utilized a battery of tests and a

WAB to predict turnover among female clerical workers in a

large midwestern insurance company. Of the 32 variables

employed, 24 were from application blanks, and the remain-

ing eight were from the test battery. The results showed

ten of the application blank items were critical in pre-

dicting tenure and could be used effectively in conjunction

with a properly weighted test score.

Until 1955, research on the application blank had in-

volved primarily the selection of salesmen and clerical

employees. To determine the generality of the method,

Dunnette and Maetzold (1955) applied the technique to the

selection of seasonal cannery production workers who

performed a wide variety of jobs. The resulting difference

6

between the scores of workers classified as good-turnover

risks and those classified as poor-turnover risks was

statistically significant in a cross-validation study.

This suggests that the WAB is applicable to employees

involved in different types of blue-collar occupations.

The authors concluded that the WAB could be a feasible

selection tool for a wide variety of industrial jobs.

Findings in support of Dunnette and Maetzold, were

obtained by Scott and Johnson (1967), who studied perma-

nently employed unskilled cannery workers. Using the same

data to predict tenure, the researchers compared the effec-

tiveness of the traditional WAB weighting procedure to the

multiple regression technique. Factor analysis was also

used to determine which biographical items were associated

with tenure. The results showed the WAB to be an effec-

tive predictor of tenure in selecting unskilled workers,

as a Pearson correlation of .45 (p-<.01) was obtained with

the holdout group. In addition, the traditional weighting

procedure was as efficient as the multiple regression

equation in assigning weights to the variables. The

authors suggested that this surprising result may have

been due to the small sample size (N = 100) and a possible

nonlinearity of the relationship between some of the vari-

ables and the criterion. They indicated that future stud-

ies should use larger samples and curvilinear multiple

regression techniques. However, they felt the traditional

7

method was the recommended weighting procedure due to its

simplicity and ease in development and utilization.

The applicability of the technique to different job

skills was again demonstrated in a study by Kirchner and

Dunnette (1957). The subjects were female office employees

who held and performed a wide variety of jobs which includ-

ed clerical, stenographic, secretarial, and personal

contact duties. From an initial list of 40 variables, 15

were found to differentiate sharply between long-tenure and

short-tenure applicants in a cross-validation study which

was conducted one year after the original study. The

authors also concluded that the technique could be extended

to cover a broad range of occupational groups rather than

being restricted to one particular type of job.

In 1962, Wernimont published the results of a re-

evaluation of the WAB developed by Kirchner and Dunnette.

The subjects were female office personnel who held essen-

tially the same type of job and were from the same large

midwestern manufacturing company as those in the 1957

study. The results showed that the new total WAB scores

did differentiate effectively between long-tenure and

short-tenure groups, as a significant Pearson correlation

of .39 was obtained. One important revelation of this

study was that some items which earlier were effective

discriminators were not effective in the follow-up study;

however, some of the same variables proved to be effective

8

predictors in both studies. In addition, some items had

to be assigned different weights in the follow-up study.

Thus, it is apparent that the scoring weights based on the

application-blank responses should be re-evaluated periodi-

cally in order to maintain validity.

The effect of automation on predicting tenure was

studied by Shott, Albright, and Glennon (1963). In this

study, subjects were male and female clerical workers in a

highly automated petroleum products credit card issuing and

billing office. Data were obtained from application

blanks, Wonderlic Personnel Test scores, Card Punch Apti-

tude Test scores, and reference inquiries. The results

showed seven of the application-blank items were signifi-

cant predictors for the women, and six were for the men.

These results extended the WAB use for both men and women

performing a variety of office jobs in an automated envi-

ronment.

Studies by Black and MacKinney (1963), and Fleishman

and Berniger (1960) sought to reduce turnover by using the

WAB technique with women who performed clerical duties on

university campuses. In the former study, the results

showed nine application-blank items which were statisti-

cally significant as predictors of turnover (whether or not

the person was still employed on a particular date). In

addition, correlations between the application-blank scores

and the tenure criterion (the number of months employed at

9

time of termination) yielded ten significant correlations

in the cross-validation study. Fleishman and Berniger

were also able to predict tenure through the use of such

biographical items as local address, age, previous salary,

and age of children.

A study presented by Buel (1964) was unique in that

it reported both concurrent and predictive validity using

the same WAB in two different environments for two differ-

ent samples. The subjects were female clerical workers in

a major petroleum company, and the initial cross-validation

study using the WAB to predict tenure yielded results sig-

nificant at the .01 level. Shortly after this WAB was

developed, the organization announced it was relocating

in a suburban area 25 miles from the city. Turnover was

expected to increase sharply due to the move, so use of

the WAB was discontinued. Almost four years after the

original validation was completed, another study was con-

ducted using the files of employees hired at least one

year previously. The authors doubted whether the original

WAB would maintain its validity with this group due to

the change in job applicants from an urban to a suburban

environment. However, the results showed that only

three items of the original 16 were no longer useful, and

the biserial correlation obtained between WAB scores and

the tenure dichotomy was significant (r = .33, .p <.02).

10

Thus, the WAB appeared to retain validity over time, in

spite of intervening variables which could be expected

to reduce or destroy its usefulness.

Robinson (1972) demonstrated the use of the WAB as a

predictor of tenure among clerical workers in a chain of

western banks. The results showed that through the use

of the WAB, correct identification could be made of 80%

of the short-term employees at the expense of rejecting

30% of the long-term employees. The authors also ascer-

tained that the items differentiating long-term employees

from short-term employees were similar to those found by

Fleishman and Berniger (1960) and Kirchner and Dunnette

(1957), but were also different enough to emphasize the

importance of validating the WAB in each specific area

in which it is used.

One of the most recent WAB studies (Lee & Booth, 1974)

should be of interest to many companies, as the method

again demonstrated its ability to predict turnover for

clerical employees. Additionally, a utility analysis

indicated substantial savings could be realized if the

technique were used. The Pearson correlation obtained in

the cross-validation sample was significant at the .001

level. The authors then estimated the cost of hiring 200

long-term employees with the aid of the WAB and without its

use. The savings resulting from the use of the WAB were

estimated to be $250,000 in a 25-month period. The results

11

imply that even with low-level jobs requiring a minimum of

hiring and training costs, substantial monetary savings

may be realized with the implementation of a valid WAB

selection technique.

Thus, the literature indicates that the WAB tech-

nique is an effective method for differentiating between

long-tenure and short-tenure employees. It has been shown

to be useful in a wide range of occupational groups. In

addition, the WAB is a relatively inexpensive technique

to develop, and its use could be expected to reduce sub-

stantially organizational training costs due to turnover.

The purpose of this research is to develop a WAB by

investigating the relationships between personal history

data and job tenure with female clerical employees of a

municipal government organization.

Method

Subjects

The subjects were 162 female clerical workers em-

ployed by the municipal government in a large southwestern

city. Both current and terminated employees were included

in the sample. The current employees included women who

were hired from November 1953 to January 1975 with a min-imum of 16 months of tenure. The terminated employees were

hired from January 1972 to February 1976, none having

12

remained on the job longer than 20 months. The total group

consisted of 93 current and 69 terminated employees.

Procedure

The following application blank data were obtained

from the files of the current and terminated employees (by

assigned identification number): (1) Months of tenure on

present job, (2) Age at application, (3) Number of previous

jobs, (4) Average tenure on previous jobs, (5) Number of

months on last job, (6) Type of work done on last job, (7)

Reason for leaving last job, (8) Salary on last job, (9)

Salary increase over all previous jobs, (10) Age at first

job, (11) College credits, and (12) Business school

courses. Variables 6, 7, 11, and 12 were categorized as

presented in Table 1.

The data, for all subjects, were analyzed by the in-

cremental stepwise multiple linear regression procedure,

with Months of tenure on present job representing the de-

pendent variable. A second analysis, designed to test the

generality of the data, was performed. The total group

was divided randomly into two groups of 81 each to compare

the order and contribution of each predictor variable, and

to determine whether the multiple regression weights ob-

tained in one group could be effective in accounting for

tenure in the opposite group.

The means and standard deviations shown in Table 2 for

the variables in both groups were used to convert each

13

Table I

Variable Categories and Assigned Scores

Variable Assigned Score

Type work last job

General office 1

Switchboard 2

Cashier 3

Secretary 4

Other 5

Reason for leaving

Job terminated 1

Person moved 2

Better job 3

Returned to school 4

Pregnancy 5

Other 6

College

30 hrs. max. 1

31-60 hrs. 2

61-90 hrs. 3

91 hrs. plus 4

Business school

Key punch training 1

Bookkeeping, business secretary 2

Both of the above 3

Other 4

14

person's score for each variable to a z-score, rounded to

the nearest standard deviation. The following weighting

procedure was used: minus one standard deviation = 1,

mean = 2, plus one standard deviation = 3, etc. This ap-

proximated the development of a table-based scoring method.

Table 2

Standard Deviations and Means Used

for Weighting Procedures

Group A

(N = 81)

Variable Mean S.D.

(2) Age at application 30 11

(3) Number of previous jobs 3 1

(6) Type of work last job 3 2

(7) Reason left last job 2 2

(8) Salary last job 74 35

Group B

(N = 81)

Variable Mean S.D.

(2) Age at application 29 12

(4) Average job tenure 17 16

(7) Reason left last job 2 2

(8) Salary last job 60 40

(10) Age at first job 19 11

15

Each person's z-score for each variable then was

multiplied by the appropriate standard regression coef-

ficient (beta weight), and the variables were totaled to

obtain each worker's predicted longevity score. In addi-

tion, a mean predicted longevity score was calculated for

both groups. The statistics and weights for both groups

were used to obtain a predicted longevity score for the

other group. Each individual's cross-group value was

compared to the opposite group's mean predicted longevity

score and also to the individual's own group actual mean

longevity. This was done to determine if the cross-group

analysis was correct in its predictions and to obtain a

hit rate. The hit rate was the sum of the true positives

and true negatives divided by the total number of individ-

uals in the sub-sample. A true positive represented one

who scored above the mean on the predictor and the criter-

ion. A true negative was one whose scores fell below both

means.

Correlational analysis was conducted to determine the

degree of association between regression scores extrapo-

lated to another sample from the same population.

Results

The results from the stepwise multiple linear regres-

sion of the application blank scores onto the longevity

variable for all subjects are presented in Table 3. As

can be seen from these results, five of the independent

16

Table 3

Five Variable Regression Analysis for Job Longevity

All Subjects

(N = 162)

Analysis of Variance

Source df S MS F

Regression 5 118,792 23,758 12.54 .00001

Residual 156 295,586 1,895

Total 161 414,378

Standard Beta Weights

Variable Coefficient F

(2) Age at application

(8) Salary last job

(6) Type work last job

(7) Reason left

(11) College

(Tenure)

0.2925

0.1618

-0.2681

-0.2518

-0.2232

(2)

1.00

0.12

-0.00

0.18

-0.10

.31

.30

.20

.20

-.16

Simple Correlations

(6) (7)

)00 0.1283 -0.0061

83 1.0000 0.0634

61 0.0634 1.0000

X92 0.2309 0.2415

30 -0.0200 0.0620

19.3

17.2

8.3

7.0

5.6

(8)

0.1892

0.2309

0.2415

1.0000

0.0454

.00001

.0001

.005

.006

.02

(11)

-0.1030

-0.0200

0.0620

0.0454

1.0000

Var.

(2)

(6)

(7)

(8)

(11)

mm"m...... ,w.....W.

i a - - - - - M -__ - - I ---- i - 14 - - -- -

r

17

variables produced a multiple correlation (R) of .54,

indicating a coefficient of determination of 29%. Two

more variables could have been added at 5% significance

levels, but these resulted in an increase of only 3% of

variance accounted for, and were omitted due to their

small contribution (see Appendix A for the full "best" 11

variable models). These variables were respectively,

Attendance at business school (standard beta = -.13) and

Age at first job (standard beta =.18). The total sample

was divided randomly into two groups and analyzed to com-

pare the order and contribution of the variables. The

results are in Table 4.

In Group A, R was .52, and a hit rate of 69% was

demonstrated. When the statistics and weights from Group

A were used to compute predicted longevity scores in Group

B, a correlation of .56 was obtained between tenure and

predicted score. A hit rate of 68% was shown.

The five most significant variables associated with

tenure in Group B resulted in a multiple correlation of

.62, and a hit rate of 69% was again demonstrated. In the

cross-group analysis, when Group B's statistics and weights

were applied to Group A, a correlation of .29 was obtained,

and the hit rate decreased to 63%.

The simple correlations of .29 and .56 showed the

degree of association between regression scores extrapo-

lated to another sample from the same population. As

Variable

(3) Number o

(2) Age at a

(7) Reason 1

(8) Salary 1

(6) Type wor

Variable

(10) Age at f

(8) Salary 1

(4) Average

(7) Reason 1

(2) Age at a

Table 4

Five Variable Regression Analysis

For Job Longevity, Sub-Samples

Standard Beta Weights

Group A

(N = 81)

Coefficient

f previous jobs -. 19 3

Application .26 6

eft -. 23 4

ast job -.25 5k last job .15 2

Group B

(N = 81)

Coefficient

'irst job .36 9

.ast job -. 45 16

job tenure .25 5

eft -.16 2

Application .16 I

18

F

*13

.57

.88

.47

.11

.0806

.0124

.0302

.0220

.1502

F

.46

.32

"73

.53

.99

3

.003

.0001

.0192

.1161

.1621

0

- . . MWAVM al

. ., ..... w.....r

19

might be expected, a certain amount of "validity shrinkage"

(Anastasi, 1976) was found when these correlations were

compared to the within-group Rs of .62 and .52.

The total sample had been divided randomly into two

groups in the attempt to demonstrate the possible problems

in eventual cross-validation procedures. Under the con-

straint that the same five variables from the overall

analysis were used, the two group Rs obtained were .51

(Group A) and .57 (Group B). While the order of these

five variables was somewhat different, their relative

contribution to the whole regression in each case was

similar. The standard weights are presented in Table 5.

Discussion

As opposed to England's (1961) use of polychotomous

predictor variables, the present use of the multiple re-

gression technique considered the relative contribution of

each variable (eight continuous and four categorized) to

the whole regression. This provided greater interpret-

ability concerning which variables were the best predictors

in the group and did not require a separation of terminated

and current employees for analysis. However, the multiple

regression technique considered the overlap among the pre-

dictor variables; therefore, it is more likely to capital-

ize on sample-specific group characteristics and to result

in greater validity shrinkage during cross-validation.

Table 5

Comparison of Standard Weights

From Two Sub-Samples

Group A

(N = 81)

Variable Coefficient

(2) Age at application .23

(8) Salary last job -.29

(6) Type work last job .13

(7) Reason left -.25

(i) College -.13

Vari~

(2)

(8)

(6)

(7)

(11)

able

Age at application

Salary last job

Type work last job

Reason left

College

Group B

(N =81)

Coefficient

.34

.32

.24

.16

.19

20

F

5.31

8.67

1.51

6.36

1.63

*024

.004

.223

*014

.210

F

10.28

8.61

5.12

2.44

4.29

.002

.004

.027

.122

.042

Now.. r. r..re..r ,r~w,,..

kwma*,. .......... .. r

. ,., ,

21

Another possible limitation of this technique is the

assumption of linearity between the predictor variables

and the criterion. The extent to which relationships

among predictors deviate from a straight line will lower

the accuracy of the predictions. Age is one variable in

this research which could be expected to demonstrate a

curvilinear relationship with the tenure criterion.

In the present study, it appears that a female cler-

ical applicant would be more likely to remain on the job

if she were older, had left her last job involuntarily,

received a low salary on her last job, had secretarial

experience, and had little or no college education.

When the total group was divided randomly into halves

for analysis, three of the original predictors remained

consistently significant. These were Age at application,

Reason for leaving last job, and Salary on last job. In

addition, Type of last job was a significant predictor for

Group A, as was Number of previous jobs. Thus for Group

A, a long-tenured applicant would be older, have had few

previous jobs, left her last job due to job termination or

moving, and had a low previous salary.

Group B's significant variables indicated the tenured

applicant would also be older, have made a low salary on

her last job, had a relatively high average tenure on pre-

vious jobs, and left her last job either because she moved

or the job ended. Thus, the most significant predictors

22

did change somewhat from the total group to the separate

group analyses. This lends support to previous studies

indicating application-blank predictors may be sample-

specific and not generalized readily from one group to

another, even in the same organization or locale.

This lack of generality can be attributed to various

external factors, one of which may be the current labor

market. Due to cyclical swings in economic conditions,

caution should be exercised in interpreting the results of

any method used to predict tenure. During economic reces-

sion, for example, some very effective workers may be

terminated who would normally have remained on the job for

many years. In addition, the selection ratio necessary to

fulfill organizational needs must be a consideration in

determining which applicants will be accepted or rejected.

Thus, when several people must be hired over a short period

of time in order to meet increased business demands, the

result may be to employ individuals regardless of their

expected tenure. The above considerations indicate the

need to re-examine any application-blank selection tech-

nique every one or two years. This revalidation would

help to reveal possible population-specific changes which

could affect its efficacy.

Another factor affecting the use of application blank

data to predict tenure is the frequently changing

- _

23

application blank form. Due to Equal Employment Opportu-

nity Commission regulations, the newer forms contain only

job-pertinent items, thus eliminating many possible predic-

tors such as marital status, age, socioeconomic level,

interests, and hobbies.

Despite the limitations in the WAB technique, this

study has provided additional support for its effective-

ness. Three out of five of the original application-blank

items remained significant in all analyses. In addition,

the hit rates indicated that selection decisions based on

these weighted items would probably be correct two out of

three times. Overall, the results offer strong support to

the previous research of the usefulness of the WAB as a

selection device.

Appendix A

Table 6

Eleven Variable Regression Analysis

for Job Longevity

All Subjects

_(N 162)

Standard Beta Weights

Variable Coefficient

(2) Age at application .27 .

(8) Salary last job -.31 12

(6) Type work last job .18

(7) Reason left .18

(11) College .1514

(12) Business school -.13

(10) Age first job .19 4

(3) Number previous jobs -.13 1

(k) Average tenure -.09

(5) Months last job .08

(9) Salary increase .04

24

F

.55.9

6.3

r.5

.6

i.6

.9

.69

.49

.20

.20 .65

.0018

.0005

.016

.013

.035

.059

.035

*16

.41

,48

.65

Variable

(3) Number p

(2) Age at a

(7 ) Reason

(8) Salary 1

(6) Type won

(4) Average

(9) Salary i

(11) College

(12) Business

(5) Months l1

Table 7

Ten Variable Regression Analysis

for Job Longevity, Sub-Sample

Group A

(N = 81)

Standard Beta Weights

Coefficient

previous jobs -.21

pplication .33

eft .23

as t job - . 30

k last job .19

tenure -. 25

n crease .1

-.12

school -.12

ast job .11

25

F

2.5

9.1

4.6

6.3

3.4

1.9

1.1

1.3

1.1

.38

.38 .54

.1149

.0037

"035

.014

.068

.167

.306

.267

.293

"54

Note . Variable 10 was not entered as it allowed no

improvement in the F.

owirmompm

.- "-Awwwwx.

26

Table 8

Variable

(10) Age firs

(8) Salary 1

(4) Average

(7) Reason l

(2) Age at a

(12) Business

(1i) College

(9) Salary i1

(6) Type wor

(5) Months l~

Ten Variable Regression Analysis

for Job Longevity, Sub-Sample

Group B

(N = 81)

Standard Beta Weights

Coefficient

t job .29

ast job -. 38

tenure .26

eft -.14

pplication .18

school-.15

-. 17

increase -.09

k last job .09

ast job -. 02

Note. Variable three was not entered

improvement in the F.

as it allowed

F

4.7

0.04

1.8

1.7

2.5

2.4

2.9

.67

.63

. o1

.01 .91

P

.0339

.0023

.185

.201

.121

129

.09

.42

.43

. 91

no

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