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Prediction of individual job termination from measured job satisfaction and biographical data
Journal of Vocational Behavior, 2,123-132
(1972)
Prediction of Individual Job Termination from Measured Job Satisfaction and Biographical Data1
KENNETH E. TAYLOR and DAVID J. WEISS2 University of Minnesota
The Minnesota Satisfaction Questionnaire (MSQ) was administered to a group of 475 employees of a discount store chain at the same time that biographical data were collected. After a lapse of one year, personnel records indicated about 20% of the employees had terminated. “Leavers” were significantly less satisfied on 10 of the 27 MSQ scales and differed from “stayers” on 3 of the 11 biographical items. Several discriminant functions were developed using sets of biographical data alone, the MSQ scales alone and both sets of predictors in combination, to predict termination. The MSQ scales alone resulted in the greatest improvement in the hit rate for predicting “leave” in the cross-validation group.
Schuh (1967b) has reviewed a large number of studies relating job tenure to various predictors. He found that job satisfaction data and biographical data were the variables most predictive of tenure. Measurements derived from intelligence and aptitude tests, and interest and personality inventories, had less frequent relationships with tenure. Schuh’s review included a large number of weighted application blank studies, six of which have been concerned with the stability of the predictions over time. In all six studies he found a drop in validity over time, but predictions still remained at statistically significant levels. In his own study, Schuh (1967a) used 19 biographical items and found only one-church attendance-to be correlated with tenure at more than a chance level for five successive one-year samples. Brayfield and Crockett (1955) and Herzberg, Mausner, Peterson and Capwell (1957) did early reviews of the relationship of satisfaction and other variables. Brayfield and Crockett (1955) found that tenure and absenteeism were the only variables consistently related to satisfaction. Most support for this conclusion came from studies in which the group was the unit of observa1This study was supported, in part, by Research Grant RD1613-G from the Social and Rehabilitation Service, U. S. Department of Health, Education, and Welfare. The authors wish to thank Marvin D. Dumrette for his careful review of an earlier version of this paper and his many helpful suggestions. 2Requests for reprints should be sent to the Work Adjustment Project, 447 B. A. Building, University of Minnesota, Minneapolis, MN 55455.
123 Copyright 01972
by Academic Press, Inc.
124
TAYLOR
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tion as opposed to studies of individual workers. Herzberg et al. (1957) found significant negative relationships-high morale or satisfaction related to less turnover-in 33 of the 37 studies they reviewed. More recently, Fournet, DiStefano and Pryer (1966) indicate that a negative relationship between satisfaction and turnover has been found in a number of occupations. Vroom (1964) reviewed seven studies, all of which show a relationship between satisfaction and turnover. Vroom (1964) suggests that job satisfaction is one factor related to turnover-the other being external forces leading away from the job, such as other job opportunities. Likert (1961) treats job satisfaction as an intervening variable which affects end result variables including turnover. The Theory of Work Adjustment (Dawis, Lofquist & Weiss, 1968) specifically states that voluntary turnover is a function of job satisfaction. Vroom (1964) and Hulin (1966) point out that many of the studies relating satisfaction to turnover have used group comparisons. Hulin indicates there may be other group differences which are causing the differences in turnover rather than satisfaction. Hulin found differences in both satisfaction and age between terminators and non-terminators at both 5 month and 1 year followups. Using data from the one-year followup, he formed a control group matched on age and other variables with the terminators. Using the control group and the terminators, Hulin found a significant multiple correlation (.34) between satisfaction and turnover. Overall or general satisfaction also correlated significantly (- .27) with turnover. In a later study, Hulin (1968) showed a dynamic correlation (Vroom, 1966) between turnover and job satisfaction. In this study, which covered a period of two years, he found a decline in turnover accompanied a significant increase on four of five satisfaction scales. Both changes were attributed to new procedures instituted by management. There are important differences between the studies using biographical data and those using job satisfaction as predictors of termination which make direct comparisons of the efficacy of the two sets of predictors difficult from existing studies. Biographical data are generally collected on the application blank prior to the hiring decision. On the other hand, job satisfaction data are obtained from employees some time after they are hired. This could lead to greater restriction of range for the studies using satisfaction data, as those who are most dissatisfied and hence would leave most quickly would be less likely to be included in the sample. An additional difference, which makes direct comparison of the results difficult, is that the results of weighted application blank predictions are often reported in terms of hit rates and individual predictions (Blum & Naylor, 1968; Scott & Johnson, 1967) while the results of studies using job satisfaction as a predictor generally report their results in terms of correlations or group differences (Vroom, 1964; Hulin, 1966). The present study was designed to compare the effectiveness of satis-
PREDICTING
JOB TERMINATION
125
faction and biographical data in predicting turnover when all the data were collected at the same time from employees currently on force. Discriminant function analysis was used with both sets of predictors to permit direct comparison of the predictors on their ability to make individual predictions of job termination. METHOD Subjects. The subjects were 475 regular employees of a discount store chain in Minnesota from five different store locations. Jobs varied from stock boy to cashier. About 75% of the group was female. The mean number of years of education was 12. Average tenure at the beginning of the study was slightly over 2 years. Procedure. In August of 1967 the Work Adjustment Project at the University of Minnesota administered a 27-scale revision of the Minnesota Satisfaction Questionnaire (MSQ; Weiss, Dawis, England & Lofquist, 1967) to all regular employees as part of a larger testing program. The 27 scales of the MSQ are listed in Table 1; scores for each scale have a range of 5 to 25. At the same time the biographical data indicated in Table 2 were collected. In August 1968, company records were consulted to determine which of the employees tested in August 1967 were still employed. Of 475 employees tested, 93 had terminated by August 1968. One of the terminated employees and 7 of those who remained were eliminated from the analysis for incomplete data, leaving 467 employees for the actual analysis. No distinction was made between voluntary and involuntary turnover as company records were not available for these data and Lopez (1965) has indicated that reasons given for termination are frequently in error. The total group was split into development and cross-validation samples using a table of random numbers, on an odd-even basis. A comparison of the two groups on the demographic-biographical items showed no significant differences on either means or variances. In addition, the percentage of terminators in the two groups was not significantly different (20% in each group). Analysis. Discriminant function analyses (Rulon, Tiedeman, Tatsuoka & Langmuir, 1967) were performed using the stayed-left dichotomous criterion. The weights obtained from each discriminant equation on the development group were applied to the data of the cross-validation group to obtain a hit-miss prediction table. To test the differential predictability of biographical and job satisfaction data, seven different discriminant function equations were developed and cross-validated on the dichotomous criterion using the following predictor sets: (1) the 27 MSQ scales; (2) all 11 biographical items; (3) the 27 MSQ scales and 11 biographical items combined; (4) a sub-set of four biographical items (age, number of dependents, education and sex) which
126
TAYLOR AND WEISS TABLE 1 Development Group Means and Standard Deviations on the 27 MSQ Scales for “Leavers” and “Stayers”, and Discriminant Weights Leavers (N=44)
MSQ Scale
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 2 1. 22. 23. 24. 25. 26. 27.
Ability utilization Achievement Activity Advancement Authority Company policies Compensation Co-workers Creativity Independence Moral values Recognition Responsibility Security Social service Social status Supervision-human relations Supervision-technical Variety Working conditions Company image Feedback Physical facilities Work relevance Company prestige Company goals Closure
Stayers (N=174)
Weight
M
SD
M
SD
12.34** 14.84* 16.07 11.59 13.98** 14.55 11.84 17.18 13.61 14.52** 16.95 13.16 14.14* 14.57** 15.20* 13.66** 15.02 14.86 14.30* 13.30 17.68 13.32 12.77 15.07 15.68** 14.68 14.11
4.04 3.54 3.52 4.32 3.61 4.11 3.37 3.57 3.88 1.97 3.26 3.84 3.44 3.34 3.59 3.25 4.09 4.07 4.61 4.45 3.98 3.43 4.14 2.93 3.09 3.74 3.52
15.01 16.23 17.00 12.68 15.38 15.18 12.53 17.85 14.65 15.82 17.59 13.82 15.59 16.24 16.74 14.92 15.82 15.58 15.99 13.62 18.70 13.61 12.45 15.70 17.37 15.75 14.39
4.00 3.54 3.46 4.43 2.76 4.00 3.90 3.79 3.51 2.97 3.26 3.98 3.48 3.46 3.51 2.76 4.27 4.06 3.87 3.98 3.61 3.94 3.84 3.56 3.43 3.58 3.53
-.45 .16 .14 .14 -.07 -.04 -.09 -.09 .27 -.24 .15 -.20 -.22 -.24 -.12 .05 -.ll -.02 -.Ol -.03 .21 .40 .I1 .15 -.26 -.14 .24
*Group mean differences statistically significant at the .05 level. **Group mean differences statistically significant at the .Ol level.
the most commonly used predictors of turnover (England, 1969); (5) the 27 MSQ scalesplus the four biographical items; (6) the four biographical items plus job tenure, on the hypothesis that tenure would be an important predictor of termination; and (7) the 27 MSQ scalesplus tenure and the four selectedbiographical items.
are among
RESULTS Table 1 presents the development group means for leavers and stayers on the 27 satisfaction scalesas well as the weights derived from the dis-
PREDICTING JOB TERMINATION
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TABLE 2 Development Group Means and Standard Deviations on the BiographicalData for “Leavers” and “Stayers”, and Discriminant Weights Biographical item
Leavers (N=44) M
1. Tenure in months 2. Hours worked per week 3. Year of birth. 4. Sex (l=male, 2=female) 5. Minutes to work from home 6. Number of dependents 7. Years of schooling 8. Another job? (l=yes, 2=no) 9. Portion of income earned on job (l=all, to 6=less than %) 10. Family financial condition (l=low, to 6=high) 11. Present financial condition (l=better than most people 2=same, 3=worse)
Stayers
(N=179)
SD
M
SD
36.07** 1.64*
16.80 8.54 14.37 .48
29.35 35.49 30.55
18.37 7.83 11.73
14.98
10.89
21.64* 35.18
Weight
.03
.Ol -.04 .82
1.79 14.31
.41 9.73 2.13 .38
.08 .40 .18
.Ol
1.75 11.80 1.82
2.07 .44
2.04 12.03 1.87
3.55
2.06
4.04
2.01
.05
2.30
1.37
2.49
1.28
.15
.52
1.88
.70
1.95
1.59
1.49
-.33
*Group mean differences statistically significant at the .05 level. **Group mean differences statistically significant at the .Ol level. criminant function using only the MSQ scales. Ten of the 27 scales showed significant differences between leavers and stayers in the expected directionsix at the .Ol level and four at the .05 level. Group centroids on the discriminant function were -2.84 for “leavers” and -4.91 for “stayers”; individuals with discriminant scores of -1 A9 and above were predicted to “leave” while those with scores of less than - 1.49 were predicted to “stay.” Highest discriminant weights were for Ability Utilization (-0.45) and Feedback (0.40). Table 2 presents the development group means for leavers and stayers on the 11 biographical items as well as the weights derived from the discriminant function using only the biographical data. Three of the items showed significant differences-one at the .05 level and two at the .Ol level. Group centroids were 5.69 for the “leavers” and 6.44 for the “stayers.” Largest discriminant weights were for sex (0.82) and years of schooling (0.40). Table 3 shows the hit-miss tables for the above two discriminant function analyses and the five additional analyses. Predicted and actual classifications were significantly related for both development and cross-validation groups for predictions made from the 27 MSQ scales, as indicated by chisquare values of 26.15 and 11.94, respectively. Prediction from the 11 bio-
128
TAYLOR AND WEISS TABLE 3 Hit-Miss Tables from the Seven Discriminant Function Predictions of Termination, for Development and Cross-Validation Groups Development group Predicted Leave Stay
Chisquare0
Cross-validation group Predicted Leave Stay
Chisquare
Predictors
Actual
27 MSQ scales
left stayed
11 5
33 174
26.15
7 5
41 191
11.94
11 Biographical items
left stayed
3 1
41 178
7.86
left stayed left stayed
23 20
21 159
38.34
47 191 33 168
.04
11 Biographical items plus 27 MSQ scales
1 5 15 28
0 0
44 179
0 0
4 Biographical items plus 27 MSQ scales
left stayed
17 18
27 161
21.81
12 18
48 196 36 178
4 Biographical items plus tenure
left stayed
0 0
44 179
0
0 1
48 195
.25
5 Biographical items plus tenure plus 27 MSQ scales
left stayed
25 23
19 156
40.44
15 22
33 174
12.02
4 Biographical items
0
7.64 0 8.94
eChi-square statistic, with 1 degree of freedom, for the relationship between predicted and actual termination. A chi-square value of 3.84 is statistically significant at the .05 level.
graphical items yielded a significant chi-square between observed and predicted scores for the development group, but on cross-validation the relationship was not significant. The four discriminant functions involving the 27 MSQ scales all showed a significant relationship between predicted and actual termination on cross-validation. All prediction equations using biographical data alone did not give significant &i-square values on cross-validation. A comparison of the total and differential hit rates for the 7 discriminant equations, in both development and cross-validation groups, with the hit rates based only on base rates is presented in Table 4. The differential hit rate for “leave” is computed as the percentage of individuals predicted to leave who actually did leave; the hit rate for “stay” is computed in an analogous manner, while the total hit rate is the total number of correct predictions combining predictions of both “leave” and “stay” divided by total number of predictions made. Comparison of the “total” hit rates and the hit rate for “stay” show little improvement over the base rate of 80% for any of the discriminant equations. “Total” hit rates were highest in both development and cross-validation groups for the discriminant equation involving the
129
PREDICTING JOB TERMINATION TABLE 4
Comparison of Differential and Total Hit Rates (Percent Accurate Predictions) for the Seven Discriminant Function Equations with Hit Rates Based Solely on Base Rates Development group
Cross-validation group
Prediction
Prediction
Source of Prediction
Leave
Stay
Total
Base rates only
20%
80%
69% 75% 27 MSQ scales 53% 0% 27 MSQ scales 49% tenure 0% tenure 52%
Discriminant Equations 21 MSQ scales 11 Biographical items 11 Biographical items plus 4 Biographical items 4 Biographical items plus 4 Biographical items plus 5 Biographical items plus plus 27 MSQ scales
Leave Stay
Total
80%
20%
80%
80%
84% 81% 88% 80% 86% 80%
83% 81% 82% 80% 80% 80%
58% 17% 35% 0% 40% 0%
82% 80% 84% 80% 83% 80%
81% 79% 75% 80% 78% 80%
89%
81%
41%
84%
17%
27 MSQ scales only. “Stay” hit rates were above base rates in both development and cross-validation only for the four discriminant equations which included the 27 MSQ scales; equations involving only biographical items did not consistently improve over base rates for predictions of “stay” or for the total group. Results for the prediction of “leave” reflected the major improvement over the base rate predictions. While only 20% of those predicted to leave would have actually left using base rate predictions, hit rates for the “leave” prediction using discriminant function equations ranged from 49% to 75% for the development group where any workers were predicted to leave. On crossvalidation, only the four discriminant functions using the MSQ continued to exceed the base rate for the “leave” prediction. Predictions from the MSQ alone showed the highest cross-validation hit rate for the “leave” prediction (58%). In an attempt to ascertain some utility for biographical data in predictions relating to turnover, biographical data were considered as variables related to mis-prediction of tenure outcomes. For this analysis the development and cross-validation groups were combined and a one-way analysis of variance was performed on the biographical data using the four cells of the hit-miss table based on the discriminant analysis using only the 27 MSQ scales as the independent variable. Table 5 presents the results of this analysis. As Table 5 shows, three variables-tenure in months at the beginning of the study, year of birth and sex-showed significant differences between the hits and misses. These are the same three varilables that had been statistically
130
TAYLOR AND WEISS TABLE 5 Means on Biographical Items for Groups Cross-Classified by Predicted and Actual Tenure Outcomes Predicted leave
Biographical Item 1. 2. 3. 4. 5. 6. 7. 8. 9.
Tenure in months Hours worked per week Year of birth Sex (l=male, 2=female) Minutes to work from home Number of dependents Years of schooling Another job? (l=yes, 2=no) Portion of income earned on job (l=all, to 6=less than %) 10. Family financial condition (l=low, to 6=high) 11. Present financial condition (l=better than most people 2=same, 3=worse)
Predicted stay
Left (N=18)
Stayed (N=lO)
Left (N=74)
Stayed (N=365)
18.56* 36.82 41.23* 1.50* 12.50 2.08 12.44 1.89
20.40 36.50 41.20 1.40 14.00 3.25 12.40 1.67
22.01 35.86 37.14 1.68 13.92 2.58 12.24 1.86
30.65 35.48 30.60 1.80 13.61 2.16 11.93 1.90
3.53
3.44
4.19
4.42
2.53
2.60
2.54
2.56
2.22
2.00
2.04
1.98
*Group mean differences statistically significant at the .05 level.
significant in prediction of staying and leaving. Comparison of these variables within the “predicted stay” groups show significant differences on these three variables between those who stayed and those who left. A discriminant function was computed using the eleven biographical items to predict the accuracy of the “stay” prediction but the discriminant equation was not statistically significant.
SUMMARY AND CONCLUSIONS In the present study, satisfaction data alone were the most stable predictors of termination. Satisfaction also showed the highest “total” and “leave” hit rates in both the development and cross-validation groups. Based on the number of predictors usedthe biographical items would be expected to hold up better in cross-validation than the satisfaction data, but in these analysesthe opposite result was obtained. One possibleexplanation for the failure of the biographical items to be stable predictors of tenure outcomes might be in the variablesincluded in this study. Some of the factors England (1969) has found to be often effective in
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weighted application blank prediction, such as data on previous jobs, club memberships and location of residence, were not included in this study. The expected relationship between job satisfaction and termination was confirmed. Use of the MSQ data in conjunction with the results of the discrirninant function analysis permits the organization to make predictions about individuals that it would be unable to make without it. By using the discriminant function information with MSQ data, the organization can predict that the individuals with scores above -1.49 will leave and have a hit rate for this group of 64% (both samples combined). Neither base rate information nor demographic information provide this degree of accuracy for identifying potential terminators. The practical value of the prediction of “leave” would be in identifying those individuals who might remain on the job if satisfaction could be increased. Manipulation of the job environment or a change in the job reward system might increase an individual’s satisfaction with a consequent accrual of benefits to the organization and the individual simultaneously. Examination of the satisfaction scales which showed a significant difference between stayers and leavers indicates that a majority of them are intrinsic to the work itself. Of these ten MSQ scales, all but two-Security and Company Prestige-had their heaviest loading on the Intrinsic satisfaction factor in a factor analysis of the MSQ (Weiss ef al., 1967). The Theory of Work Adjustment (Dawis et al., 1968) suggests two possible remedies in this situation. One would be provision of increased intrinsic rewards, which would probably involve job redesign. The second would involve selection of those with low need for the intrinsic reinforcers which differed between stayers and leavers in this study. Despite the fact that the biographical information did not increase predictive accuracy in this study, it does seem to have some value. The analysis of the mis-predictions indicates greater confidence might be placed in a prediction of “stay” for an older female worker who has been with the company for some time. This finding appears to be consistent with the hypothesis of Vroom (1964), who points out that the probability of termination is dependent on satisfaction-attraction to the job and those forces pulling the individual elsewhere. Probably the most important force pulling elsewhere would be alternative employment. The young male worker would obviously be at an advantage in this area and would be expected to be less likely to stay due to this factor. The above may suggest a strong relationship between satisfaction and the two variables of sex and age. Analysis of the intercorrelations of the predictors in the development group points to a lack of such relationship. The correlations of the sex variable with the 27 satisfaction scales showed no correlations over 0.20. For the year of birth variable the only correlation over 0.20 was with Social Status (-0.22); those who are older being more satisfied
132
TAYLOR AND WEISS
with their social status. With a larger sample use of sex and age as moderator variables might have been profitable. The results of this study suggest that prediction of job termination from measured job satisfaction is likely to be more fruitful than the use of biographical data for the same prediction; how the individual feels about his job is a better predictor of termination than “who he is.” These data are consistent with theories of motivation in industry and suggest the use of environmental manipulation to increase job satisfaction in order to maintain low rates of job termination. REFERENCES Blum, M. L. & Naylor, J. C. Industrial Psychology: Its theoretical and socinl foundations. New York: Harper and Row, 1968. Brayfield, A. H. & Crockett, W. H. Employee attitudes and employee performance. Psychological Bulletin, 1955, 52, 396-424. Dawis, R. V., Lofquist, L. H., & Weiss, D. J. A theory of work adjustment (a revision). Minnesota Studies in Vocational Rehabilitation, 1968, 23. England, G. W. Development and use of weighted application blanks. Minneapolis: Industrial Relations Center, University of Minnesota, 1969. Fournet, G. P., DiStefano, M. K., & Pryer, M. W. Job satisfaction: issues and problems. Personnel Psychology, 1966, 19, 165-184. Herzberg, F., Mausner, B., Peterson, R. O., & Capwell, D. Job attitudes: Review of research and opinion. Pittsburgh: Psychological Service, 1957. Hulin, C. L. Job satisfaction and turnover in a female clerical population. Journal of Applied Psychology, 1966, 50, 280-285. Hulln, C. L. Effects of changes in job satisfaction levels on employee turnover. Journal of Applied Psychology, 1968, 52, 122-126. Like& R. New Patterns of Management. New York: McGraw-Hill, 1961. Lopez, F. M. Jr. Personnel interviewing: Theory and practice. New York: McGraw-Hill, 1965. Rulon, P. J., Tiedeman, D. V., Tatsuoka, M. M., & Langmuir, C. R. Multivariate statistics for personnel classification. New York: Wiley, 1967. Schuh, A. J. Application blank items and intelligence as predictors of turnover. Personnel Psychology, 1967, 20, 59-63. (a). Schuh, A. J. The predictability of employee tenure: A review of the literature. Personnel Psychology, 1967,20, 133-152. (b). Scott, R. D. & Johnson, R. W. Use of the weighted application blank in selecting unskilled employees. Journal of Applied Psychology, 1967, 51, 393-395. Vroom, V. H. Work and motivation. New York: Wiley, 1964. Vroom, V. H. A comparison of static and dynamic correlational methods in the study of organizations. Organizational Behavior and Human Performance, 1966, 1, 55-70. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. Manual for the Minnesota Satisfaction Questionnaire. Minnesota Studies in Vocational Rehabilitation, 1967, 22. Received: March 18, 1971
(1972)
Prediction of Individual Job Termination from Measured Job Satisfaction and Biographical Data1
KENNETH E. TAYLOR and DAVID J. WEISS2 University of Minnesota
The Minnesota Satisfaction Questionnaire (MSQ) was administered to a group of 475 employees of a discount store chain at the same time that biographical data were collected. After a lapse of one year, personnel records indicated about 20% of the employees had terminated. “Leavers” were significantly less satisfied on 10 of the 27 MSQ scales and differed from “stayers” on 3 of the 11 biographical items. Several discriminant functions were developed using sets of biographical data alone, the MSQ scales alone and both sets of predictors in combination, to predict termination. The MSQ scales alone resulted in the greatest improvement in the hit rate for predicting “leave” in the cross-validation group.
Schuh (1967b) has reviewed a large number of studies relating job tenure to various predictors. He found that job satisfaction data and biographical data were the variables most predictive of tenure. Measurements derived from intelligence and aptitude tests, and interest and personality inventories, had less frequent relationships with tenure. Schuh’s review included a large number of weighted application blank studies, six of which have been concerned with the stability of the predictions over time. In all six studies he found a drop in validity over time, but predictions still remained at statistically significant levels. In his own study, Schuh (1967a) used 19 biographical items and found only one-church attendance-to be correlated with tenure at more than a chance level for five successive one-year samples. Brayfield and Crockett (1955) and Herzberg, Mausner, Peterson and Capwell (1957) did early reviews of the relationship of satisfaction and other variables. Brayfield and Crockett (1955) found that tenure and absenteeism were the only variables consistently related to satisfaction. Most support for this conclusion came from studies in which the group was the unit of observa1This study was supported, in part, by Research Grant RD1613-G from the Social and Rehabilitation Service, U. S. Department of Health, Education, and Welfare. The authors wish to thank Marvin D. Dumrette for his careful review of an earlier version of this paper and his many helpful suggestions. 2Requests for reprints should be sent to the Work Adjustment Project, 447 B. A. Building, University of Minnesota, Minneapolis, MN 55455.
123 Copyright 01972
by Academic Press, Inc.
124
TAYLOR
AND
WEISS
tion as opposed to studies of individual workers. Herzberg et al. (1957) found significant negative relationships-high morale or satisfaction related to less turnover-in 33 of the 37 studies they reviewed. More recently, Fournet, DiStefano and Pryer (1966) indicate that a negative relationship between satisfaction and turnover has been found in a number of occupations. Vroom (1964) reviewed seven studies, all of which show a relationship between satisfaction and turnover. Vroom (1964) suggests that job satisfaction is one factor related to turnover-the other being external forces leading away from the job, such as other job opportunities. Likert (1961) treats job satisfaction as an intervening variable which affects end result variables including turnover. The Theory of Work Adjustment (Dawis, Lofquist & Weiss, 1968) specifically states that voluntary turnover is a function of job satisfaction. Vroom (1964) and Hulin (1966) point out that many of the studies relating satisfaction to turnover have used group comparisons. Hulin indicates there may be other group differences which are causing the differences in turnover rather than satisfaction. Hulin found differences in both satisfaction and age between terminators and non-terminators at both 5 month and 1 year followups. Using data from the one-year followup, he formed a control group matched on age and other variables with the terminators. Using the control group and the terminators, Hulin found a significant multiple correlation (.34) between satisfaction and turnover. Overall or general satisfaction also correlated significantly (- .27) with turnover. In a later study, Hulin (1968) showed a dynamic correlation (Vroom, 1966) between turnover and job satisfaction. In this study, which covered a period of two years, he found a decline in turnover accompanied a significant increase on four of five satisfaction scales. Both changes were attributed to new procedures instituted by management. There are important differences between the studies using biographical data and those using job satisfaction as predictors of termination which make direct comparisons of the efficacy of the two sets of predictors difficult from existing studies. Biographical data are generally collected on the application blank prior to the hiring decision. On the other hand, job satisfaction data are obtained from employees some time after they are hired. This could lead to greater restriction of range for the studies using satisfaction data, as those who are most dissatisfied and hence would leave most quickly would be less likely to be included in the sample. An additional difference, which makes direct comparison of the results difficult, is that the results of weighted application blank predictions are often reported in terms of hit rates and individual predictions (Blum & Naylor, 1968; Scott & Johnson, 1967) while the results of studies using job satisfaction as a predictor generally report their results in terms of correlations or group differences (Vroom, 1964; Hulin, 1966). The present study was designed to compare the effectiveness of satis-
PREDICTING
JOB TERMINATION
125
faction and biographical data in predicting turnover when all the data were collected at the same time from employees currently on force. Discriminant function analysis was used with both sets of predictors to permit direct comparison of the predictors on their ability to make individual predictions of job termination. METHOD Subjects. The subjects were 475 regular employees of a discount store chain in Minnesota from five different store locations. Jobs varied from stock boy to cashier. About 75% of the group was female. The mean number of years of education was 12. Average tenure at the beginning of the study was slightly over 2 years. Procedure. In August of 1967 the Work Adjustment Project at the University of Minnesota administered a 27-scale revision of the Minnesota Satisfaction Questionnaire (MSQ; Weiss, Dawis, England & Lofquist, 1967) to all regular employees as part of a larger testing program. The 27 scales of the MSQ are listed in Table 1; scores for each scale have a range of 5 to 25. At the same time the biographical data indicated in Table 2 were collected. In August 1968, company records were consulted to determine which of the employees tested in August 1967 were still employed. Of 475 employees tested, 93 had terminated by August 1968. One of the terminated employees and 7 of those who remained were eliminated from the analysis for incomplete data, leaving 467 employees for the actual analysis. No distinction was made between voluntary and involuntary turnover as company records were not available for these data and Lopez (1965) has indicated that reasons given for termination are frequently in error. The total group was split into development and cross-validation samples using a table of random numbers, on an odd-even basis. A comparison of the two groups on the demographic-biographical items showed no significant differences on either means or variances. In addition, the percentage of terminators in the two groups was not significantly different (20% in each group). Analysis. Discriminant function analyses (Rulon, Tiedeman, Tatsuoka & Langmuir, 1967) were performed using the stayed-left dichotomous criterion. The weights obtained from each discriminant equation on the development group were applied to the data of the cross-validation group to obtain a hit-miss prediction table. To test the differential predictability of biographical and job satisfaction data, seven different discriminant function equations were developed and cross-validated on the dichotomous criterion using the following predictor sets: (1) the 27 MSQ scales; (2) all 11 biographical items; (3) the 27 MSQ scales and 11 biographical items combined; (4) a sub-set of four biographical items (age, number of dependents, education and sex) which
126
TAYLOR AND WEISS TABLE 1 Development Group Means and Standard Deviations on the 27 MSQ Scales for “Leavers” and “Stayers”, and Discriminant Weights Leavers (N=44)
MSQ Scale
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 2 1. 22. 23. 24. 25. 26. 27.
Ability utilization Achievement Activity Advancement Authority Company policies Compensation Co-workers Creativity Independence Moral values Recognition Responsibility Security Social service Social status Supervision-human relations Supervision-technical Variety Working conditions Company image Feedback Physical facilities Work relevance Company prestige Company goals Closure
Stayers (N=174)
Weight
M
SD
M
SD
12.34** 14.84* 16.07 11.59 13.98** 14.55 11.84 17.18 13.61 14.52** 16.95 13.16 14.14* 14.57** 15.20* 13.66** 15.02 14.86 14.30* 13.30 17.68 13.32 12.77 15.07 15.68** 14.68 14.11
4.04 3.54 3.52 4.32 3.61 4.11 3.37 3.57 3.88 1.97 3.26 3.84 3.44 3.34 3.59 3.25 4.09 4.07 4.61 4.45 3.98 3.43 4.14 2.93 3.09 3.74 3.52
15.01 16.23 17.00 12.68 15.38 15.18 12.53 17.85 14.65 15.82 17.59 13.82 15.59 16.24 16.74 14.92 15.82 15.58 15.99 13.62 18.70 13.61 12.45 15.70 17.37 15.75 14.39
4.00 3.54 3.46 4.43 2.76 4.00 3.90 3.79 3.51 2.97 3.26 3.98 3.48 3.46 3.51 2.76 4.27 4.06 3.87 3.98 3.61 3.94 3.84 3.56 3.43 3.58 3.53
-.45 .16 .14 .14 -.07 -.04 -.09 -.09 .27 -.24 .15 -.20 -.22 -.24 -.12 .05 -.ll -.02 -.Ol -.03 .21 .40 .I1 .15 -.26 -.14 .24
*Group mean differences statistically significant at the .05 level. **Group mean differences statistically significant at the .Ol level.
the most commonly used predictors of turnover (England, 1969); (5) the 27 MSQ scalesplus the four biographical items; (6) the four biographical items plus job tenure, on the hypothesis that tenure would be an important predictor of termination; and (7) the 27 MSQ scalesplus tenure and the four selectedbiographical items.
are among
RESULTS Table 1 presents the development group means for leavers and stayers on the 27 satisfaction scalesas well as the weights derived from the dis-
PREDICTING JOB TERMINATION
127
TABLE 2 Development Group Means and Standard Deviations on the BiographicalData for “Leavers” and “Stayers”, and Discriminant Weights Biographical item
Leavers (N=44) M
1. Tenure in months 2. Hours worked per week 3. Year of birth. 4. Sex (l=male, 2=female) 5. Minutes to work from home 6. Number of dependents 7. Years of schooling 8. Another job? (l=yes, 2=no) 9. Portion of income earned on job (l=all, to 6=less than %) 10. Family financial condition (l=low, to 6=high) 11. Present financial condition (l=better than most people 2=same, 3=worse)
Stayers
(N=179)
SD
M
SD
36.07** 1.64*
16.80 8.54 14.37 .48
29.35 35.49 30.55
18.37 7.83 11.73
14.98
10.89
21.64* 35.18
Weight
.03
.Ol -.04 .82
1.79 14.31
.41 9.73 2.13 .38
.08 .40 .18
.Ol
1.75 11.80 1.82
2.07 .44
2.04 12.03 1.87
3.55
2.06
4.04
2.01
.05
2.30
1.37
2.49
1.28
.15
.52
1.88
.70
1.95
1.59
1.49
-.33
*Group mean differences statistically significant at the .05 level. **Group mean differences statistically significant at the .Ol level. criminant function using only the MSQ scales. Ten of the 27 scales showed significant differences between leavers and stayers in the expected directionsix at the .Ol level and four at the .05 level. Group centroids on the discriminant function were -2.84 for “leavers” and -4.91 for “stayers”; individuals with discriminant scores of -1 A9 and above were predicted to “leave” while those with scores of less than - 1.49 were predicted to “stay.” Highest discriminant weights were for Ability Utilization (-0.45) and Feedback (0.40). Table 2 presents the development group means for leavers and stayers on the 11 biographical items as well as the weights derived from the discriminant function using only the biographical data. Three of the items showed significant differences-one at the .05 level and two at the .Ol level. Group centroids were 5.69 for the “leavers” and 6.44 for the “stayers.” Largest discriminant weights were for sex (0.82) and years of schooling (0.40). Table 3 shows the hit-miss tables for the above two discriminant function analyses and the five additional analyses. Predicted and actual classifications were significantly related for both development and cross-validation groups for predictions made from the 27 MSQ scales, as indicated by chisquare values of 26.15 and 11.94, respectively. Prediction from the 11 bio-
128
TAYLOR AND WEISS TABLE 3 Hit-Miss Tables from the Seven Discriminant Function Predictions of Termination, for Development and Cross-Validation Groups Development group Predicted Leave Stay
Chisquare0
Cross-validation group Predicted Leave Stay
Chisquare
Predictors
Actual
27 MSQ scales
left stayed
11 5
33 174
26.15
7 5
41 191
11.94
11 Biographical items
left stayed
3 1
41 178
7.86
left stayed left stayed
23 20
21 159
38.34
47 191 33 168
.04
11 Biographical items plus 27 MSQ scales
1 5 15 28
0 0
44 179
0 0
4 Biographical items plus 27 MSQ scales
left stayed
17 18
27 161
21.81
12 18
48 196 36 178
4 Biographical items plus tenure
left stayed
0 0
44 179
0
0 1
48 195
.25
5 Biographical items plus tenure plus 27 MSQ scales
left stayed
25 23
19 156
40.44
15 22
33 174
12.02
4 Biographical items
0
7.64 0 8.94
eChi-square statistic, with 1 degree of freedom, for the relationship between predicted and actual termination. A chi-square value of 3.84 is statistically significant at the .05 level.
graphical items yielded a significant chi-square between observed and predicted scores for the development group, but on cross-validation the relationship was not significant. The four discriminant functions involving the 27 MSQ scales all showed a significant relationship between predicted and actual termination on cross-validation. All prediction equations using biographical data alone did not give significant &i-square values on cross-validation. A comparison of the total and differential hit rates for the 7 discriminant equations, in both development and cross-validation groups, with the hit rates based only on base rates is presented in Table 4. The differential hit rate for “leave” is computed as the percentage of individuals predicted to leave who actually did leave; the hit rate for “stay” is computed in an analogous manner, while the total hit rate is the total number of correct predictions combining predictions of both “leave” and “stay” divided by total number of predictions made. Comparison of the “total” hit rates and the hit rate for “stay” show little improvement over the base rate of 80% for any of the discriminant equations. “Total” hit rates were highest in both development and cross-validation groups for the discriminant equation involving the
129
PREDICTING JOB TERMINATION TABLE 4
Comparison of Differential and Total Hit Rates (Percent Accurate Predictions) for the Seven Discriminant Function Equations with Hit Rates Based Solely on Base Rates Development group
Cross-validation group
Prediction
Prediction
Source of Prediction
Leave
Stay
Total
Base rates only
20%
80%
69% 75% 27 MSQ scales 53% 0% 27 MSQ scales 49% tenure 0% tenure 52%
Discriminant Equations 21 MSQ scales 11 Biographical items 11 Biographical items plus 4 Biographical items 4 Biographical items plus 4 Biographical items plus 5 Biographical items plus plus 27 MSQ scales
Leave Stay
Total
80%
20%
80%
80%
84% 81% 88% 80% 86% 80%
83% 81% 82% 80% 80% 80%
58% 17% 35% 0% 40% 0%
82% 80% 84% 80% 83% 80%
81% 79% 75% 80% 78% 80%
89%
81%
41%
84%
17%
27 MSQ scales only. “Stay” hit rates were above base rates in both development and cross-validation only for the four discriminant equations which included the 27 MSQ scales; equations involving only biographical items did not consistently improve over base rates for predictions of “stay” or for the total group. Results for the prediction of “leave” reflected the major improvement over the base rate predictions. While only 20% of those predicted to leave would have actually left using base rate predictions, hit rates for the “leave” prediction using discriminant function equations ranged from 49% to 75% for the development group where any workers were predicted to leave. On crossvalidation, only the four discriminant functions using the MSQ continued to exceed the base rate for the “leave” prediction. Predictions from the MSQ alone showed the highest cross-validation hit rate for the “leave” prediction (58%). In an attempt to ascertain some utility for biographical data in predictions relating to turnover, biographical data were considered as variables related to mis-prediction of tenure outcomes. For this analysis the development and cross-validation groups were combined and a one-way analysis of variance was performed on the biographical data using the four cells of the hit-miss table based on the discriminant analysis using only the 27 MSQ scales as the independent variable. Table 5 presents the results of this analysis. As Table 5 shows, three variables-tenure in months at the beginning of the study, year of birth and sex-showed significant differences between the hits and misses. These are the same three varilables that had been statistically
130
TAYLOR AND WEISS TABLE 5 Means on Biographical Items for Groups Cross-Classified by Predicted and Actual Tenure Outcomes Predicted leave
Biographical Item 1. 2. 3. 4. 5. 6. 7. 8. 9.
Tenure in months Hours worked per week Year of birth Sex (l=male, 2=female) Minutes to work from home Number of dependents Years of schooling Another job? (l=yes, 2=no) Portion of income earned on job (l=all, to 6=less than %) 10. Family financial condition (l=low, to 6=high) 11. Present financial condition (l=better than most people 2=same, 3=worse)
Predicted stay
Left (N=18)
Stayed (N=lO)
Left (N=74)
Stayed (N=365)
18.56* 36.82 41.23* 1.50* 12.50 2.08 12.44 1.89
20.40 36.50 41.20 1.40 14.00 3.25 12.40 1.67
22.01 35.86 37.14 1.68 13.92 2.58 12.24 1.86
30.65 35.48 30.60 1.80 13.61 2.16 11.93 1.90
3.53
3.44
4.19
4.42
2.53
2.60
2.54
2.56
2.22
2.00
2.04
1.98
*Group mean differences statistically significant at the .05 level.
significant in prediction of staying and leaving. Comparison of these variables within the “predicted stay” groups show significant differences on these three variables between those who stayed and those who left. A discriminant function was computed using the eleven biographical items to predict the accuracy of the “stay” prediction but the discriminant equation was not statistically significant.
SUMMARY AND CONCLUSIONS In the present study, satisfaction data alone were the most stable predictors of termination. Satisfaction also showed the highest “total” and “leave” hit rates in both the development and cross-validation groups. Based on the number of predictors usedthe biographical items would be expected to hold up better in cross-validation than the satisfaction data, but in these analysesthe opposite result was obtained. One possibleexplanation for the failure of the biographical items to be stable predictors of tenure outcomes might be in the variablesincluded in this study. Some of the factors England (1969) has found to be often effective in
PREDICTING
JOB TERMINATION
131
weighted application blank prediction, such as data on previous jobs, club memberships and location of residence, were not included in this study. The expected relationship between job satisfaction and termination was confirmed. Use of the MSQ data in conjunction with the results of the discrirninant function analysis permits the organization to make predictions about individuals that it would be unable to make without it. By using the discriminant function information with MSQ data, the organization can predict that the individuals with scores above -1.49 will leave and have a hit rate for this group of 64% (both samples combined). Neither base rate information nor demographic information provide this degree of accuracy for identifying potential terminators. The practical value of the prediction of “leave” would be in identifying those individuals who might remain on the job if satisfaction could be increased. Manipulation of the job environment or a change in the job reward system might increase an individual’s satisfaction with a consequent accrual of benefits to the organization and the individual simultaneously. Examination of the satisfaction scales which showed a significant difference between stayers and leavers indicates that a majority of them are intrinsic to the work itself. Of these ten MSQ scales, all but two-Security and Company Prestige-had their heaviest loading on the Intrinsic satisfaction factor in a factor analysis of the MSQ (Weiss ef al., 1967). The Theory of Work Adjustment (Dawis et al., 1968) suggests two possible remedies in this situation. One would be provision of increased intrinsic rewards, which would probably involve job redesign. The second would involve selection of those with low need for the intrinsic reinforcers which differed between stayers and leavers in this study. Despite the fact that the biographical information did not increase predictive accuracy in this study, it does seem to have some value. The analysis of the mis-predictions indicates greater confidence might be placed in a prediction of “stay” for an older female worker who has been with the company for some time. This finding appears to be consistent with the hypothesis of Vroom (1964), who points out that the probability of termination is dependent on satisfaction-attraction to the job and those forces pulling the individual elsewhere. Probably the most important force pulling elsewhere would be alternative employment. The young male worker would obviously be at an advantage in this area and would be expected to be less likely to stay due to this factor. The above may suggest a strong relationship between satisfaction and the two variables of sex and age. Analysis of the intercorrelations of the predictors in the development group points to a lack of such relationship. The correlations of the sex variable with the 27 satisfaction scales showed no correlations over 0.20. For the year of birth variable the only correlation over 0.20 was with Social Status (-0.22); those who are older being more satisfied
132
TAYLOR AND WEISS
with their social status. With a larger sample use of sex and age as moderator variables might have been profitable. The results of this study suggest that prediction of job termination from measured job satisfaction is likely to be more fruitful than the use of biographical data for the same prediction; how the individual feels about his job is a better predictor of termination than “who he is.” These data are consistent with theories of motivation in industry and suggest the use of environmental manipulation to increase job satisfaction in order to maintain low rates of job termination. REFERENCES Blum, M. L. & Naylor, J. C. Industrial Psychology: Its theoretical and socinl foundations. New York: Harper and Row, 1968. Brayfield, A. H. & Crockett, W. H. Employee attitudes and employee performance. Psychological Bulletin, 1955, 52, 396-424. Dawis, R. V., Lofquist, L. H., & Weiss, D. J. A theory of work adjustment (a revision). Minnesota Studies in Vocational Rehabilitation, 1968, 23. England, G. W. Development and use of weighted application blanks. Minneapolis: Industrial Relations Center, University of Minnesota, 1969. Fournet, G. P., DiStefano, M. K., & Pryer, M. W. Job satisfaction: issues and problems. Personnel Psychology, 1966, 19, 165-184. Herzberg, F., Mausner, B., Peterson, R. O., & Capwell, D. Job attitudes: Review of research and opinion. Pittsburgh: Psychological Service, 1957. Hulin, C. L. Job satisfaction and turnover in a female clerical population. Journal of Applied Psychology, 1966, 50, 280-285. Hulln, C. L. Effects of changes in job satisfaction levels on employee turnover. Journal of Applied Psychology, 1968, 52, 122-126. Like& R. New Patterns of Management. New York: McGraw-Hill, 1961. Lopez, F. M. Jr. Personnel interviewing: Theory and practice. New York: McGraw-Hill, 1965. Rulon, P. J., Tiedeman, D. V., Tatsuoka, M. M., & Langmuir, C. R. Multivariate statistics for personnel classification. New York: Wiley, 1967. Schuh, A. J. Application blank items and intelligence as predictors of turnover. Personnel Psychology, 1967, 20, 59-63. (a). Schuh, A. J. The predictability of employee tenure: A review of the literature. Personnel Psychology, 1967,20, 133-152. (b). Scott, R. D. & Johnson, R. W. Use of the weighted application blank in selecting unskilled employees. Journal of Applied Psychology, 1967, 51, 393-395. Vroom, V. H. Work and motivation. New York: Wiley, 1964. Vroom, V. H. A comparison of static and dynamic correlational methods in the study of organizations. Organizational Behavior and Human Performance, 1966, 1, 55-70. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. Manual for the Minnesota Satisfaction Questionnaire. Minnesota Studies in Vocational Rehabilitation, 1967, 22. Received: March 18, 1971