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6
CHANGES IN REGISTERED TIME
TO THE DOCTORATE, TIME PRIOR
TO GRADUATE ENTRANCE, AND TIME NOT
ENROLLED IN THE UNIVERSITY
. .
This chapter uses the common and unique variables models defined in
Chapter 5 to explain changes in registered time to degree (RTD) and the common
variables model to explain changes in time prior to graduate entrance CAGE) and
time not enrolled at the university (ADIEU). As discussed in Chapter 1, TTD and
RTD have a similar time trend, and increases in RTD are largely responsible for
increases in l ID.
Registered Time to the Doctorate
RTD in the Common Variables Model
Using Linear and Log Linear Equations
Regression coefficients for each field, using both linear and log linear
estimating equations, appear in Appendix Tables 6 and 6A. A summary of the
findings for each variable in each model is given in Tables 6.1 and 6.2. As was
true for TTD, a comparison of the results for the linear and log estimates
suggests that the results are different depending on the model used. While the
importance of certain variables such as teaching assistantships, foreign
baccalaureate, and salary does not change across specifications, the role of others
such as age, federal support, and unemployment are affected. In most cases, the
signs of the statistically significant variables do not change, and the log linear
model explains the variation in the data no better than the linear model does.
RTD in the Unique Variables Model
Table 6.3 (pp. 82-83) summarizes the results of using a unique model
for each of the 11 fields. Age is no longer an important variable in all fields,
and no other variable has a significant impact on RTD in every field.
79
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TABLE 6.1: Summary of Common Linear Model Regression Results for RTD,
by Field
Variable
Fielders)
Statistically +/
Significant
Female Social Sciences yes +
Age Chemistry yes +
Mathematics yes +
Earth Atmospheric, yes +
- & Marine Sciences
Social Sciences yes +
Federal Support Earn, Atmospheric, yes
tic Marine Sciences
Biosciences yes
Teaching Assistantship Biosciences yes +
Research Assistantship no
Baccalaureate from Foreign Social Sciences yes +
Institution
Baccalaureate from Category I Chemistry yes
Research School Agricultural Sciences yes +
Graduate Degree from Category I no
Research School
Number of Faculty Earth, Atmospheric, yes +
& Marine Sciences
Biosciences yes
Salary Ratio: New Ph.D.s no
to Ph.D.s 10 yrs. after Degree
Unemployment Rate
of College-Educated
Per Capita Doctorates
in United States
Chemistry
Earth, Atmospheric,
& Marine Sciences
Social Sciences yes
yes
yes
no
80
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TABLE 6.2: Summary of Common Log-Linear Model Regression Results. for
RTD, by Field
Vanable Fielders) Statistically +/
Sign~ficant
Female . no
Age . Earth, Atmosphenc, yes
& Manne Sciences
Biosciences . yes
Federal Support Biosciences yes
Teaching Assistantship
Research Assistantship
Baccalaureate from Foreign
Institution
Baccalaureate from Category I
Research School
Graduate Degree from Category I
Research School
Number of Faculty
Salary Ratio: New Ph.D.s
to Ph.D.s 10 yrs. after Degree
Unemployment Rate
of College-Educated
Biosciences yes
no
Social Sciences yes
Agncultural Sciences yes
Agncultural Sciences yes
Earth, Aunosphenc, yes
& Marine Sciences
Biosciences yes
no
Earth, Atmospheric, yes
dc Manne Sciences
Per Capita Doctorates Earth, Atmosphenc, yes
In United States & Marine Sciences
+
+
+
+
Evaluation of the Results
A number of observations can be made about Table 6.4 (p. 84), which
shows the number of fields in which a particular independent variable was
statistically significant. For example, no one variable explains the widely
observed increases in RID across fields. Instead, the combinations of variables
81
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TABLE 6.3: Summary of Unique Variables Model Regression Results for RTD,
Field Variable~s)
Correlation Comment
Chemistry Age
Baccalaureate from +
Foreign Institution
Salary Ratio: New
Ph.D.s to Ph.D.s
10 yrs. after Degree
These three variables
accounted for 91 percent of
the variation in RTD. A
. .
One-year Increase In age
boosted RTD by 1.5 years.
A 1 percent increase in
doctorates with degrees from
r
Iorelgn Institutions increases
RTD by about a week.
Physics and Marital Status - These three variables
Astronomy Graduate Degree from - accounted for 91 percent of
Category I Research variation in RTD. A 1
School percent increase in married
Teaching Asst. + students lowered RTD by
nearly two weeks. A similar
increase in percentage of
students from Category I
school decreased RTD by a
little over two weeks.
~ Earth, Marital Status - These four variables
Atmospheric, Baccalaureate from - explained 89 percent of the
& Marine Category I Research variation in RTD.
Sciences School
Temp. U.S. Residents +
Receiving Ph.D.s
Baccalaureate from +
Top-20 School
Mathematics Female - + The two variables explained
& Computer Salary Ratio: New - 97 percent of the variation
Sciences Ph.D.s to Ph.D.s in RTD.
10 yrs. after Degree
EngineeringBaccalaureate from + These three variables
Foreign Institution explained 93 percent of the
Undergraduate Degree - variation in RTD.
in Same Field
Definite Employment
82
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by Field
Field Variable~s)
Correlation Comment
( I)
Agricultural Teaching Asst. + These four variables
Sciences Baccalaureate from + accounted for 82 percent of
Foreign Institution the variation in RTD.
Definite Employment
Salary Ratio: New
Ph.D.s to Ph.D.s
10 yrs. after Degree
Biological Research Asst. + These Tree variables
Sciences Percent Cohort + explained 95 percent of the
Seeking Emp variation in RTD. The
Salary Ratio: New - Durbin-Watson statistic for
Ph.D.s to Ph.D.s this regression is in the
10 yrs. after Degree indeterminate range.
Health Federal Support - These three variables
Sciences Salary Ratio: - explained 85 percent of the
Doctorates to variation in RTD. A 1
Baccalaureates percent rise in federal
Temp. U.S. Residents + support decreased RTD by
Receiving Ph.D.s about two weeks.
Psychology Federal Support
Salary Ratio: New
Ph.D.s to Ph.D.s
10 yrs. after Degree
Temp. U.S. Residents +
Receiving Ph.D.s
Economics Private Support
Baccalaureate from +
Foreign Institution
Temp. U.S. Residents +
Receiving Ph.D.s
These three variables
accounted for 96 percent of
the variation in RTD.
These three variables
explained 95 percent of the
variation in RTD. A 1
percent increase in those
with baccalaureate from
foreign institution lowered
RTD by nearly a month.
Social Private Support - These three variables
Sciences Salary Ratio: New - explained 99 percent of the
Ph.D.s to Ph.D.s variation in RTD. A 1
10 yrs. after Degree percent jump in private
Temp. U.S. Residents + support increased RTD by
Receiving Ph.D.s about a month.
83
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TABLE 6.4: Number of Fields in Which Variable Has Statistically Significant
Effect on AD
. . . .
MODEL
C O MM ON
UNIQUE
Linear Log Linear
. Variable POS NEG POS NAG POS BEG
WOMEN 1 0 0 , 0 1 0
AGE 4 0 2 0 1 0
SUPPED O
SUPTA
2
o
0 1
0 2
1 0 1 0 2 0
SUPRA O O O 0 1 O'
FORBACC 1 O 1 0 4 0
BCARN1ST 1 1 1 0 0
PCARN1ST 0 0 0 1 0
FACULTY 1 1 1 1 0 0
UNEMP4YR 0 3 0 1 O O
PERPOP 0 0 0 1 0 0
MARRED
TEMP
SAMEFLD -
SUPPRIV
BTOP20
SDRSAL10
SALRAT1
SAIRATIO
SEEK
DEFIN
0 2
5 0
o
O
2
I O
o
O
o
o
1
I O
NOTES: (1) "Pos" indicates a positive regression coefficient. "Neg" indicates a
negative regression coefficient. (2) Variables below the dotted line were not
entered in the common variables models. (3) Acronyms are defined in Appendix
B. pp. 175-177.
84
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with statistically significant effects on RTD vary by field. In both the linear
common variables model and the unique variables model, female gender was
significant and positive in just one field. In the log-linear model, gender was not
significant in any field. In those equations where age is statistically significant,
it tends to have a large impact on RTD, suggesting that as more older students
enroll in doctoral programs, RTD will increase. However, as noted earlier, age
may act as a proxy for cohort differences rather than for physiological or other
effects of aging. This possibility deserves more study before conclusive
statements can be made. The role of financial support in affecting RTD is
mixed. In a number of fields, financial variables did not enter the equation at all
and, in a few, they had a positive partial correlation, contrary to intuitive
expectations. This finding suggests that the effects of financial aid are field-
specific and the type of aid provided influences whether students complete the
doctorate more or less rapidly. The data do not allow firm conclusions about the
effects of increasing financial aid as the primary source of support. The analysis
suggests that in some fields increases in the number of foreign students or in the
percentage of students with foreign baccalaureates have led to increased RTD.
Finally, analysis supports the belief that changes in market variables
unemployment rate, salaries, and salary ratios affect RTD.
The results of this inquiry are best viewed as suggestive rather than
conclusive. Problems of multicollinearity, aggregation, and limited data suggest
the need for study of these issues in a cross-seciion and/or pooled time-series
cross-section framework. Further research is needed to affirm the role of age, to
elaborate on the role of financial aid, and to provide greater insight into the role
of student ability (see Chapter 7~.
Time Spent Prior
to Graduate School Entrance (TPGE)
The results summarized in Table 6.5 were obtained using the linear
common variables model to explain changes in TPGE (see Appendix Table 7~.
The implicit assumption in the use of these variables is that students have prior
knowledge of how their cohort is likely to fare in terms of receiving financial aid
and entering the labor market.
The R2 for the individual field equations are lower for TPGE than for
1-1 D or RTD and, for three fields, the equations themselves are not statistically
significant. In part, this results because decisions made at the time of
undergraduate graduation are more likely to be based on family background and
undergraduate performance factors not contained in the model (see Chapter 2~. It
may also be that new variables are needed to adequately capture conditions at the
/
85
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TABLE 6.5: Summary of Common Linear Model Regression Results for TPGE,
by Variable
Variable
Fielded Statistically Correlation
Significant
Female
no
Age Chemistry yes
Mathematics yes
Engineering yes
Biosciences yes
Health Sciences yes
Social Sciences yes
Federal Support no
Teaching Assistantship Social Sciences yes
Research Assistantship Chemistry
Baccalaureate from Foreign Mathematics
Institution
Baccalaureate from Category I
Research School
Graduate Degree from Category I
Research School
Number of Faculty
Salary Ratio: New Ph.D.s
to Ph.D.s 10 yrs. after Degree
Unemployment Rate
of College-Educated
Percent Population
with Doctorates
yes
yes
no
no
no
Mathematics yes
Mathematics yes
Mathematics yes
+
+
+
+
+
;
+
+
NOTE: No variables were significant for the following fields: earth, atmospheric
and marine sciences; agricultural sciences; and economics.
86
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time the decision to enter graduate school was made. For example, the relevant
financial variable may be the percentage of the prior year's entering class with
financial aid and the relevant market variable may be the percentage of doctorates
who found jobs in the year in which the person decided to enter graduate school.
Analysis of these issues may explain why fewer variables are statistically
significant in the TPGE equations than in the RID equations. It's interesting to
note that in math, biosciences, psychology, and social sciences, the equations
explained better than 90 percent of the variation in the data.
As was true for the linear analysis, in the log-linear analysis (Table
6.6), the equations for earth, atmospheric, and marine sciences; agricultural
sciences; and economics were not statistically significant. Also, the R2s were
generally lower on these equations than for TTD and RID.
Several points can be made about the determinants of TPGE based on
the findings in this section. First, in most of the fields, the variables that
explained most of the change in TPGE were demographic and economic in
nature. With rare exceptions, institutional factors did not affect the TPGE.
However, in the log equations the unemployment rate and salary variables were
statistically significant determinants of TPGE. Second, the financial aid
variables did affect TPGE in some fields, although not always in the expected
direction. TPGE in chemistry and physics and astronomy was consistently
affected by financial aid. Finally, in most fields neither the percentage of women
nor the percentage of students with foreign baccalaureates had a statistically
significant effect on IPGE.
Time Not Enrolled
in the University (TNEU)
TNEU, time the student spends away from his or her studies after
registering for graduate school, is affected by such factors as illness or financial
exigency, frustration with the doctoral program, and the need to take a break
from dissertation work (see Appendix Table 8~. Since the variables in the
common variables model do not specifically address these concerns, this model is
not expected to explain as much of the variation in TNEU as it did for other
dependent variables. Tables 6.7 and 6.8 summarize the results from the linear
and non-linear regression equations.
The analysis shows no one variable consistently explained changes in
TNEU in all fields. Compared to TPGE, unemployment and salary variables do
not appear to have a strong effect on TNEU. This is surprising. One would
expect student decisions to leave graduate school to be more affected by market
conditions. And, as with TPGE, factors such as gender and percent with foreign
baccalaureates do not appear to exert a strong influence on TNEU.
87
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TABLE 6.6: Summary of Common Log-Linear Model Regression Results for
TPGE, by Variable
Variable
Fielders) Statistically Correlation
Significant
Female Biosciences yes +
Age Chemistry yes +
Physics & yes +
Astronomy
E· .
ngmeenng
Biosciences
Health Sciences
Psychology
Social Sciences
Federal Support
Teaching Assistantship Physics &
Astronomy
Research Assistantship Chemistry
Baccalaureate from Foreign Mathematics
Institution
Baccalaureate from Category I
Research School
Graduate Degree from Category I
Research School
Number of Faculty
no
yes
yes
yes
no
no
no
yes
yes +
yes
yes
yes
+
+
Salary Ratio: New Ph.D.s Physics & yes
to Ph.D.s 10 yrs. after Degree Astronomy
Unemployment Rate Physics dc yes +
of College-Educated Astronomy
Psychology yes +
Mathematics yes
Percent Population no
with Doctorates
NOTE: No variables were significant for the following fields:
and marine sciences; agricultural sciences; and economics.
88
earth, atmospheric
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TABLE 6.7: Summary of Common Linear Model Regression Results for TNEU,
by Vanable
Vanable
Fielders)
Statistically Correlation
significant (+/-)
Female
Age
Federal Support
Teaching Assistantship
Biosciences
Research Assistantship Biosciences
Baccalaureate from Foreign Biosciences
Institution
Baccalaureate from Category I
Research School
Graduate Degree from Biosciences
Category I Research School Psychology
Number of Faculty
Salary Ratio: New Ph.D.s
to Ph.D.s 10 yrs. after Degree
Unemployment Rate
of College-Educated
Percent Population
with Doctorates
no
no
yes
no
yes
yes
Psychology yes
yes
yes
no
no
no
Biosciences yes
Psychology
yes
Summary of the Findings
The common variables model appears to be more effective for
understanding changes in RTD than for interpreting changes in TPGE and
TNEU. No one variable is responsible for the increase in RTD over time,
although in fields in which it is statistically significant, age has a relatively
large effect. Moreover, the mix of variables that affect RTD is different among
fields, although all five vectors described in Chapter 3 come into play.
89
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TABLE 6.8: Summary of Common Log-Linear Model Regression Results for
TNEU, by Field
Variable Fielders) Statistically Correlation
Female
no
Age Health Sciences yes +
Federal Support Chemistry yes +
Physics & yes +
Astronomy
Biosciences yes +
Teaching Assistantship no
Research Assistantship no
Baccalaureate from Foreign Biosciences
Institution
Baccalaureate from Category I
Research School
Graduate Degree from
Category I Research School
Number of Faculty
Salary Ratio: New Ph.D.s
to Ph.D.s 10 yrs. after Degree
Unemployment Rate
of College-Educated
Percent Population
with Doctorates
yes
no
no
Chemistry yes
no
no
Biosciences yes
NOTE: No variables were significant for the following fields: mathematics;
engineering; and agricultural sciences. Only biosciences and economics had R2s
greater than 90 percent.
90
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The linear model suggests that age has the largest impact on RTD; the
percentage of students with foreign baccalaureates and who are female also
consistently increases RTD. These results are field-specific and are not
generalizable to all 11 fields, however. The role of financial aid is ambiguous,
and different types of aid affect RTD differently.
The models explain less of the variance in TPGE and TNEU than in
TTD and RTD. In some fields, the models do not produce statistically
significant results. While generalizing across fields is difficult, the equations for
IMAGE and INEU have fewer statistically significant variables than those for
RTD and TTD. Interestingly, market variables explain time spent prior to
entering graduate school while, for the most part, they are not statistically
significant in He TNEU equations.
Additional work is needed to understand the factors that cause changes in
TPGE and TNEU.16 It is likely that institutional and psychological factors
beyond those captured in this common variables model affect the decision to
postpone entry to graduate school and/or to delay completion of the doctorate.
16 Knowledge of the determinants of TPGE would be useful, since it tells us
how long students take to move from undergraduate to graduate school. TNEU
is important because substantial differences exist across fields and we have little
understanding of Be underlying reasons: it may be that market opportunities for
ABDs are substantially different among fields or that some field work is useful
before obtaining the doctorate.
91
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Representative terms from entire chapter:
sciences yes
