A policy capturing approach was used to examine the advising variables that contribute to student satisfaction. Students (N = 468) rated 48 scenarios in which advising approach, relationship, advisor gender, emotional nature of the relationship, and type of advisor were manipulated. Results show that being known to the advisor, having a professional advisor, and receiving warmth and support from the advisor were important factors to advisee satisfaction. Ratings differed by student gender, advising experience, and age. Relational variables can exist across multiple advising approaches, and satisfaction likely depends more on the advisor’s interpersonal skills and style than advising approach.

1. What do Students Want in Advising? A Policy Capturing Study
Karen E. Mottarella, University of Central Florida
Barbara A. Fritzsche, University of Central Florida
Kara C. Cerabino, University of Central Florida
A policy capturing approach was used to exam- course selection, explaining degree requirements
ine the advising variables that contribute to student and registration procedures, and making referrals
satisfaction. Students (N = 468) rated 48 scenar- to other resources on campus, are associated with
ios in which advising approach, relationship, advi- the prescriptive approach (Fielstein, 1989). Advisors
sor gender, emotional nature of the relationship, and limit prescriptive-advising activities to academic
type of advisor were manipulated. Results show matters (Winston & Sandor, 1984a).
that being known to the advisor, having a profes- The two prevailing advising approaches do not
sional advisor, and receiving warmth and support differ solely on the types of task associated with
from the advisor were important factors to advisee them. Many interpersonal and relationship factors
satisfaction. Ratings differed by student gender, are woven into and subsumed under the develop-
advising experience, and age. Relational variables mental approach, and these may be the factors that
can exist across multiple advising approaches, and lead to student satisfaction. Relationship variables
satisfaction likely depends more on the advisor’s such as support, warmth, and respect (Winston &
interpersonal skills and style than advising Sandor, 1984b) are associated with the develop-
approach. mental approach and may be mutually exclusive of
the prescriptive approach. The contribution of spe-
cific elements, either those associated with the
KEY WORDS: advising approaches, developmental
advising-advisee relationship or the advising activ-
advising, prescriptive advising, student satisfac-
ities, to student satisfaction with advising is unclear.
tion with advising
Research is needed to unbundle and examine the
Since Beal and Noel (1980) published their variables in current advising approaches that are
landmark report in which they found academic important to students. In fact, students may appre-
advising to be one of three major areas promoting ciate either a prescriptive or a developmental advis-
student satisfaction and retention across 947 insti- ing approach as long as the advisor demonstrates
tutions of higher education, the importance of aca- certain key relational elements within the advising
demic advising within universities has increased session.
(Bedford & Durkee, 1989; Carstensen & Silberhorn, Since the 1960s, those conducting psychother-
1979; Pascarella & Terenzini, 1980; Steele, apy research have consistently argued that the
Kennedy, & Gordon, 1993; Tinto, 1998; Trombley nature of the therapeutic relationship rather than any
& Holmes, 1981). The developmental advising specific approach or technique may contribute the
approach, specifically, has gained increased cred- most to client satisfaction with the psychotherapy
ibility and has been referred to as the ideal approach they receive (Beutler, Machado, & Neufeldt, 1994;
for advising university students (Gordon, 1994). Rogers, 1957/1992; Truax & Carkhuff, 1967). In
Authors of numerous studies have found that the research studies, as long as the relational variables
developmental approach results in student satis- were present in the therapy, client satisfaction was
faction with advising (Alexitch, 1997; Broadbridge, reported regardless of whether the therapist was
1996), and some have suggested that the develop- humanistic and nondirective or was quite direc-
mental approach is preferred by students (Fielstein, tive, behavioral, and even therapeutically con-
1989; Herndon, Kaiser, & Creamer, 1996; Winston frontative (Bergin & Suinn, 1975). A parallel
& Sandor, 1984b). situation may exist in academic advising.
Certain advising activities described in the lit- Certain relational variables, such as the advisor
erature as “growth-oriented,” such as exploring the establishment of a relationship with the student
student’s values and how they relate to career choice and his or her conveyance of warmth and support,
as well as helping the student with interpersonal may be of critical importance in producing stu-
problems or with improving interpersonal skills dent satisfaction. Fielstein (1987), for example,
(Winston & Sandor, 1984b, p. 8), are associated found that 28.9% of students interviewed about
with the developmental approach. In contrast, con- advising reported that it was a high priority that their
crete task-oriented activities, such as discussing advisor be personally acquainted with them. Perhaps
48 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

2. Policy Capturing Study
once these relational variables are in place and Andrews (1987) found that students who are emo-
maintained, the advisor can adopt advising activi- tionally expressive have a high need for information
ties associated with multiple advising approaches, (rather than personal support) from advisors, and
including prescriptive or developmental tasks, and those who are socially sensitive and emotionally
alternate approaches as needed. For example, the expressive are more likely to seek academic advis-
advisor could assume a more hierarchical, expert, ing. Based on these findings, Andrews (1987, p. 64)
prescriptive role and directly instruct students on concluded, “The traditional ‘all for one and one for
tasks they need to complete to best prepare her or all’ approach to advising should be reexamined.”
him for certain career or graduate school goals. In More recently, the five-factor model of person-
other circumstances, the advisor could operate ality has gained prominence as a useful description
from a more open, egalitarian, nondirective of fundamental personality traits (Goldberg, 1993),
approach to facilitate exploration of students’ val- and these traits have been linked to a variety of out-
ues and attitudes as a means of promoting resolu- comes including effective use of coping strategies
tion of an undecided major. (Costa & McCrae, 1992), well-being (Costa &
In addition to relational variables, the impact of McCrae, 1992), job performance (Schneider,
other advising dynamics on student satisfaction Hough, & Dunnette, 1996), and success in college
needs to be investigated. Other variables may play (Fritzsche, McIntire, & Powell Yost, 2002). Because
a less obvious role than do relational elements in researchers have linked personality to advising
student satisfaction with advising. Unlike the rela- preferences and because explanations of the five-
tional advising variables, certain fixed character- factor model have contributed to the accumulation
istics, including advisor gender and type (faculty, and integration of knowledge about personality
peer, professional), may contribute to student sat- (Goldberg, 1993), we used the five-factor model to
isfaction. When studying a situation similar to examine the advising preferences of students with
advising, researchers have found that student eval- different personality traits.
uations of satisfaction with their instructors are Sedlacek (1991) urged advisors who want to
influenced by the instructor’s gender. For example, gain a deeper understanding of advisees to assess
Baslow (1987) found that female instructors were them on a number of sociocultural variables. For
not rated as highly as were male instructors. As is example, differences may exist among minority,
the case with unconscious bias in many situations, nontraditional, and first-generation college students.
students may not be aware of having a gender bias To better meet the advising needs of all students,
that relates to their satisfaction with advising. advisors need to determine if certain types of col-
Type of advisor is another advisor characteris- lege students have unique advising preferences.
tic that may impact students’ reported satisfaction Through this study, we empirically investigated
with advising. Advisors can be faculty advisors, pro- the factors that students value in the advising they
fessional advisors at centralized advising units, or receive and examined if differences exist between
peer advisors. Researchers have suggested that stu- the preferences of non-White, first-generation, and
dents are pleased with their peer advisors (Frisz & nontraditional-aged (age 25 or older) and White,
Lane, 1987; Privette & Delawder 1982) and with non-first-generation, and traditional-aged students.
available and accessible faculty advisors (Alexitch, To compare students’ advising preferences to their
1997). advising experiences, we used an established mea-
Preferences in advisor type may differ across sure of students’ prior advising experiences: the
types of students. Using the Myers-Briggs Type Academic Advising Inventory (AAI) (Winston &
Indicator (Briggs & Myers, 1983), Crockett and Sandor, 1984a). The AAI has been cited as the
Crawford (1989) found that certain personality most widely used research instrument in the inves-
styles are related to students’ preferences for certain tigation of advising (Daller, Creamer, & Creamer,
types of advising. Specifically, students who were 1997) and was developed to assess students’ per-
more intuitive and feeling were relatively more ceptions and preferences for the advising activities
interested in developmental advising than in the they reported to have received (Winston & Sandor,
practical activities of course selection. In contrast, 1986).
the more sensing and thinking students were more We specifically investigated student preferences
interested in the advising activities often associ- across five advising dimensions. First, we looked
ated with the prescriptive approach. Using a differ- at advising approach. We compared a prescriptive
ent personality measure (the Personal Attributes approach, in which the advisor instructs the student
Questionnaire) (Spence & Helmreich, 1978), on degree requirements and graduate school prepa-
NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004 49

3. Mottarella, Fritzsche, & Cerabino
ration, to a developmental approach, in which the of satisfaction. This methodology allowed us to
advisor and advisee discuss the student’s values explore how students weigh information from advis-
and preferences and work together to tailor plans to ing scenarios containing all variables to form sat-
the student. Students advised with a developmen- isfaction judgments.
tal advising approach also explore the academic and In addition to using the policy capturing method,
personal stressors facing them. we collected measures of individual differences in
Second, we investigated the emotional nature of personality, demographic information, and advis-
the advising relationship. We asked, “Do students ing experiences. Thus, we examined differences
seem to have any preference for businesslike and in advising experiences and preferences between
efficient versus warm and supportive advising rela- groups of students (as defined by their gender, per-
tionships?” sonality, age, and ethnicity).
Third, we examined the depth of the advising
relationship. We inquired, “Does it matter if the rela- Participants
tionship is established or nearly anonymous?” Participants were 468 students (161 males, 305
Finally, we looked at the impact of advisor vari- females, and 2 who did not report gender) enrolled
ables. We investigated whether the type of advisor in undergraduate psychology courses at a large
(peer, faculty, or professional) or advisor gender southeastern university. Participants received course
impact advising satisfaction. credit for their participation. To receive course
credit, participants either completed the research
Method instruments in their general psychology classrooms
In this study, we used the policy capturing method or within a psychology lab. All participants were
(Hammond, Stewart, Brehmer, & Steinmann, 1986). able to complete the study in 90 minutes or less.
Through this technique, the researcher uses multi- Participants had a median age of 18 years, and the
ple regression to model how individuals or groups sample was 71.8% White.
of individuals weigh and combine information to
make a judgment. Judgment profiles are created Measures
by manipulating a set of variables (called “cues”) that The advising scenarios. We developed scenarios
are believed to influence the judgment of interest. of advising session in which five cues were manip-
Participants make judgments about each profile, ulated. Cues were based on literature on the impor-
and a within-subject multiple regression is calculated tant factors that influence satisfaction with advising
in which the participant’s judgments are regressed and the definitions of and distinctions between
onto the cue values. A policy is “captured” when prescriptive and developmental advising models.
judgments can be modeled reliably with a linear The five cues and the levels of measure, as
model (i.e., when R2 is high). Policy capturing has depicted by advisor characteristic or action within
been widely and effectively used in studies of clin- the advising scenario, included
ical judgments, personnel selection, financial deci-
sion making, and social policy judgments (Fritzsche • advisor gender. The advisor was either male
& Brannick, 2002; Fritzsche, Finkelstein, & Penner, or female.
2000; Stevenson, Busemeyer, & Naylor, 1990). • advisor type. The advisor was a peer, faculty,
We applied policy capturing to judgments of or professional advisor.
student-advising scenarios. Five dimensions of the • depth of advising relationship. The advisor
advising relationship were systematically manipu- asked for the advisee’s name and asked how
lated in a 2 × 3 × 2 × 2 × 2 within-subjects facto- the semester is going, or the advisor knew the
rial design (48 scenarios). Each participant rated his advisee (by name) and inquired about a par-
or her satisfaction, as if an advisee receiving the ticular class that had been stressful to the
advising depicted in the vignette, on each of the 48 advisee.
advising scenarios. For each participant, we used • type of advising approach. The advisor
multiple regression analysis in which satisfaction printed the advisee’s degree audit, checked to
ratings were regressed onto each cue. We used the see if the student had completed necessary
squared multiple correlation coefficient (R2) to prerequisite course work, and then formulated
indicate the amount of variance in satisfaction rat- an academic course plan (without feedback
ings captured with the cues. The standardized beta from the advisee) and made specific recom-
weights for each cue indicate the relative importance mendations to the student to insure that the
of each dimension in influencing the student’s level academic goals would be satisfactorily ful-
50 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

4. Policy Capturing Study
filled. In the alternate scenario, the advisor personal concerns, goal setting, and identification
printed out the advisee’s degree audit and and utilization of resources on the campus” (Winston
discussed the advisee’s career interests. The & Sandor, 1984a, p. 11). The Academic Decision
advisor encouraged the student to select the Making subscale is used to focus “on the process of
best possible electives to meet his or her academic decision-making and the responsibilities
goals, discussed the student’s values and how for making and implementing those decisions”
they relate to psychology and a career path, (Winston & Sandor, 1984a, p. 11), such as assess-
and also discussed the student’s ongoing per- ing academic progress, student aptitude, and degree
sonal and academic stressors. of assistance with course registration. The Selecting
• the emotional nature of the advising rela- Courses subscale is used to assess the extent of
tionship. The advisor was helpful, efficient, advisor involvement with the student in course plan-
and businesslike, or the advisor was help- ning. Internal-consistency reliability estimates for
ful, warm, and supportive during the advis- the Developmental-Prescriptive Advising scale and
ing session. subscales were reported by Winston and Sandor
(1984a) as .78 for Developmental-Prescriptive
Demographic form. Participants provided infor- Advising, .81 for Personalizing Education, .66 for
mation about their age, gender, ethnic background, Academic Decision Making, and .42 for Selecting
class standing, residency, student status (i.e., tra- Courses. In assessing the validity of the AAI,
ditional-aged vs. nontraditional-aged, transfer vs. Winston and Sandor (1984a) found that freshmen
nontransfer, and first-generation vs. non-first-gen- with marginal academic preparation and freshmen
eration).They also provided data on their majors and who were admitted with regular academic prepara-
certainty of major choice, career plans, and advis- tion demonstrated significant differences on their
ing experiences at the university. Developmental-Prescriptive Advising and Person-
Five-factor inventory. We used the NEO Five- alizing Education scale scores.
Factor Inventory (NEO-FFI) (Costa & McCrae, Advisor-advisee activities are assessed by use of
1992) to measure the five basic personality traits. part two of the AAI, through which frequency of stu-
The NEO-FFI is a 60-item version of the Revised dent experience with particular advising activities
NEO Personality Inventory (Costa & McCrae, are reported. Activities such as declaring a major
1992). The NEO scales have gained widespread and providing general college-policy information
popularity as a research tool, and personality psy- are measured by the Exploring Institutional Policies
chologists agree that this model has empirically scale. The Providing Information scale is used to
determined five fundamental dimensions of per- assess activities such as discussing degree require-
sonality (Anastasi & Urbina, 1997). Reported inter- ments, financial aid information, and job placement
nal-consistency reliability estimates for the opportunities. The frequency with which advising
NEO-FFI subscales were as follows: neuroticism, activities are undertaken related to a student’s future,
.90; extraversion, .78; openness to experience, .76; college experiences in general, and personal expe-
agreeableness, .86; and conscientiousness, .90. riences are measured with the Personal Develop-
Academic advising inventory. The AAI (Winston ment and Interpersonal Relationships scale. The
& Sandor, 1984a) allows for investigation of the Registration and Class Scheduling scale is used to
importance and potential impact of academic advis- describe the frequency of advising experiences
ing in higher education. The AAI is a 64-item involving the mechanics of course registration and
measure of three aspects of academic advising scheduling. The Teaching Personal Skills scale con-
experiences. veys the number of experiences students have had
Part one of the AAI is used to assess a student’s in which advisors taught them “about study skills,
advising experiences on the developmental-pre- time management, and personal goal-setting”
scriptive continuum. Three subscales are used to rate (Winston & Sandor, 1984a, p.14).
this aspect of advising and are scored separately Part three of the AAI is used to measure satis-
before the data are totaled to form an overall faction with advising experiences during the cur-
Developmental-Prescriptive Advising score. High rent academic year. Students report “(1) overall
scores indicate that the respondent has relatively satisfaction, (2) accuracy of information provided,
more developmental advising experiences. The (3) adequacy of notice about important deadlines,
Personalizing Education subscale “reflects a concern (4) availability of advising when desired, and (5)
for the student’s total education, including amount of time available during advising sessions”
career/vocational planning, extracurricular activities, (Winston & Sandor, 1984a, p.14).
NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004 51

5. Mottarella, Fritzsche, & Cerabino
Procedure ricular activities and short-and long-term plans.
In a 90-minute session, participants in our study Moreover, non-White students (M = 61; SD = 14.48)
completed the advising-scenario policy capturing scored significantly lower than did White students
task, a demographic form, the NEO Five-Factor (M = 70.65; SD = 16.50) on Developmental-
Inventory, and the AAI. All participants completed Prescriptive Advising, t(124) = –2.801, p = 0.006,
the policy capturing task before completing the d = .60. According to the mean scores (Winston and
other measures. The 48 policy-capturing scenarios Sandor, 1984a), both Whites and non-Whites expe-
were randomly ordered in each packet as were the rienced more developmental than prescriptive advis-
individual differences measures. During the policy ing. Nevertheless, non-White students reported less
capturing task, participants read each of the 48 developmental advising experiences than did White
scenarios. For each scenario, each participant indi- students.
cated the degree to which he or she, as the advisee,
would be satisfied with the particular advising Capturing Advising-Preference Policies
experience. The scale ranged from 1 (extremely In pursuing the primary purpose of this study, we
dissatisfied) to 7 (extremely satisfied). examined how individuals weigh various dimen-
sions of the advising relationship in judging their
Results satisfaction with advising. We determined the rel-
Table 1 presents means, standard deviations, reli- ative contribution of each of the five dimensions,
ability estimates, and intercorrelations for the mea- or cues, via a within-subjects regression analysis.
sures used in this study. Internal-consistency Each participant’s 48 satisfaction-with-advising
reliability estimates were moderate to high for all judgments were regressed onto the 6 advising cues.1
except three scales: AAI Selecting Courses scale The resulting regression equation represents the
(α = .45), the AAI Academic Decision Making scale participant’s policy. A policy was considered to be
(α = .65), and the NEO Openness to Experience captured if the R2 for the regression equation was
scale (α = .66). Most AAI scales had small but sta- equal to or greater than 0.50. That is, the linear com-
tistically significant correlations with the NEO-FFI bination of the six cues could explain at least 50%
Extraversion scale: significant Pearson’s correlation of the variance in a participant’s satisfaction-with-
coefficients (r) ranged from .10 to .15. In addition, advising judgment. This decision rule is consis-
several AAI scales correlated significantly with the tent with that used in other policy-capturing studies
NEO-FFI Agreeableness and Conscientiousness with a similar number of cues and judgments
scale. All AAI scales correlated significantly with sat- (Cooksey, 1996).
isfaction with prior advising experiences: r values The R2 for the entire sample ranged from 0.02
ranged from .21 for the Selecting Courses scale to to 1.00 (M = .42; SD = .26). Based on the R2 = 0.50
.42 for the Developmental/Prescriptive Advising criterion, policies were captured for 180 out of 468
scale. Satisfaction with prior advising experiences (39%) participants. The R2 for the resulting sample
also correlated with extraversion (r = .10) and con- ranged from 0.50 to 1.00 (M = .70; SD = .13). For
scientiousness (r = .19). these participants, on average, 70% of the vari-
We used the AAI to measure advising experi- ance in satisfaction with advising, as measured by
ences. To control the family-wise error rate, we the six manipulated cues, was captured.
used a Bonferroni correction for the examination of Several possible reasons can explain why the lin-
group differences in AAI scores. Thus, alpha was ear regression model did not capture the remaining
set at .006. We found no differences between males participants’ policies. First, many participants rated
and females on any of the AAI scales, and thus we the advising scenarios uniformly high. Mean sat-
found no evidence that differences in gender affected isfaction across all 468 participants and across all
prior advising experiences. Nontraditional-aged advising scenarios was 5.5 (SD = 1.20) on a 7-
students (M = 15.57; SD = 2.15) scored significantly point scale, which suggests that some participants
lower on Personal Development and Interpersonal did not show a distinct preference for certain advis-
Skills than did traditional-aged students (M = 19.04; ing scenarios that were presented to them. Instead,
SD = 8.75), t(18.43) = –3.206, p = 0.005, d = 1.24), they reported that they would be satisfied regard-
suggesting that nontraditional-aged students are less of the manipulation. In other words, lack of vari-
less likely to discuss with their advisor various col- ance in the criterion could help explain why more
lege experiences such as classroom and extracur- policies could not be captured. Second, some par-
1
The title of the advisor cue is a categorical variable with three levels (i.e., peer, faculty, or professional advi-
sor). Thus, this cue was transformed into two variables and dummy coded.
52 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

6. NACADA Journal
Table 1 Descriptive statistics, internal-consistency reliability estimates, and intercorrelation data for NEO-FFI and AAI
Subscale N M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
NEO-FFI
1. Neuroticism 466 21.84 7.83 .83 –.25* .00 –.21* –.23* –.07 –.10 –.02 –.05 –.02 .04 .03 –.02 .04 –.02
2. Extraversion 468 30.56 6.31 .79 .00 .29* .18* .15* .14* .15* .12* .15* .02 .00 .10* .14* .10*
3. Openness to experience 468 28.26 6.39 .66 –.02 –.03 –.05 –.05 –.04 –.02 .03 .02 .01 .01 .10* .07
Volume 24 (1 & 2)
4. Agreeableness 468 30.60 6.09 .74 .24* .12* .03 .15* .17* –.09 –.10* –.08 –.04 –.06 .05
5. Conscientiousness 468 31.10 6.46 .82 .12* .08 .11* .15* .01 .02 .00 .00 .03 .19*
AAI
6. Developmental-prescriptive
advising 351 67.23 14.84 .78 .87* .69* .59* .41* .23* .15* .40* .33* .42*
7. Personalizing education 364 35.29 10.66 .75 .27* .21* .50* .31* .19* .44* .36* .39*
8. Academic decision making 393 21.57 5.26 .65 .58* .11* .03 .06 .17* .17* .24*
Spring & Fall 2004
9. Selecting courses 390 10.13 3.37 .45 .07 .01 .01 .12* .09 .21*
10. Personal development and
interpersonal relationships 414 18.81 8.55 .93 .65* .55* .77* .76* .33*
11. Exploring institutional policies 429 7.82 3.08 .72 .51* .58* .66* .25*
12. Registration and class scheduling 427 7.64 3.07 .73 .49* .67* .32*
13. Teaching personal skills 428 5.23 2.67 .76 .69* .29*
14. Academic majors and courses 425 12.06 5.13 .85 .39*
15. Satisfaction with advising 426 13.83 3.38 .88
Note. Internal-consistency reliability estimates are provided on the diagonal. * Indicates that figures are statistically significant at p < 0.05.
53
Policy Capturing Study

7. Mottarella, Fritzsche, & Cerabino
ticipants may have been inconsistent in their appli- istics of the sample. A higher percentage of women
cation of a policy, or their policies may have were included in the final sample than in the sam-
changed as they worked through the task. Boredom ple of participants for whom policies could not be
or lack of involvement in the task could have captured, χ2(1) = 5.627, p = 0.018. We found no
resulted in randomly selected responses. Regardless other demographic differences between the cap-
of the reasons why the policies of some participants tured- and noncaptured-policy populations.
could not be captured, only participants with cap- To examine the relative weights of the cues
tured policies were used in further analyses. across participants, we averaged satisfaction scores
The sample for analysis included 50 men and for each profile and then regressed the data onto the
129 women who ranged in age from 17 to 38 years. cues. By this procedure, we removed individual
See Table 2 for additional demographic character- differences in judgments from the error term: R2 =
Table 2 Differences in demographic characteristics between groups in which policy was captured versus
those in which it was not captured
Percentage of Participants in Each Group
Policy Policy
Not Captured Captured
Participant Characteristics (R2 < .495) (R2 ≥ .495) χ2 p
Ethnic Background 1.992 0.158
Minority 29.3 23.2
Nonminority 70.7 76.8
Gender 5.627 0.018
Male 38.7 27.9
Female 61.3 72.1
Year in College 5.196 0.268
Freshman 64.1 63.7
Sophomore 14.6 11.7
Junior 11.1 17.3
Senior 9.8 7.3
Nondegree seeking 0.3 0.0
First Generation Status .025 0.874
First generation 32.2 31.5
Not first generation 67.8 68.5
Nontraditional Student Status 1.140 0.286
Nontraditional 3.5 5.6
Traditional 96.5 94.4
Age Status 3.073 0.080
Younger than 25 years 98.9 96.6
25 years or older 1.1 3.4
Transfer Status .489 0.485
Transfer student 13.2 15.6
Nontransfer student 86.8 84.4
First Time in College (FTIC) Advising .043 0.835
No FTIC advising 47.9 46.9
Some FTIC advising 52.1 53.1
Faculty/Professional Advising .001 0.970
No faculty/professional advising 48.8 48.6
Some faculty/professional advising 51.2 51.4
Peer Advising .361 0.548
No peer advising 68.9 71.5
Some peer advising 31.1 28.5
Any Advising .667 0.414
No advising 27.9 24.4
Some advising 72.1 75.6
54 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

8. Policy Capturing Study
0.98; F (6, 41) = 377.90, p < 0.000. All of the beta alpha level was set at 0.008. Moreover, we computed
weights were statistically significant. The largest correlations to examine the degree of covariation in
regression weight was for depth of the advising regression weights across groups. A significant
relationship (β = .77). Having a more established Spearman’s rank correlation coefficient (rs) suggests
relationship with the advisor was the most impor- that the rank order of the six cues was similar
tant cue in participants’ satisfaction judgments. across the groups. In other words, the relative
The second most important cue was the faculty importance of the six cues was similar across
advisor variable (β = –.57). Participants preferred groups.
a nonfaculty advisor over a faculty advisor. We found no differences in the relative weights
The third most important cue was the emotional assigned to the advising variables across participant
nature of the advising relationship (β = .34). ethnic background or year in school. As seen in
Participants were more satisfied when the rela- Table 3, we found differences for gender, amount of
tionship was warm and supportive rather than busi- advising experience, and age. Specifically, the emo-
nesslike and efficient. tional nature of the advising relationship was found
The fourth most important cue was the peer to be a more important cue for women (mean β =
advisor variable (β = –.32). Participants preferred 0.18; SD = 0.29) than for men (mean β = 0.01; SD
a nonpeer advisor over a peer advisor. Taken = 0.28), t(177) = –3.575, p < 0.001, d = –.60.
together, the ratings on faculty and peer advisor vari- Women preferred a warm advising relationship
ables suggest that participants preferred a profes- more than did men. Moreover, we found no statis-
sional advisor more then a faculty advisor, and tically significant correlation between the rank
they least preferred a peer advisor. order of the six standardized beta weights between
The type of advising approach was the fifth the gender dichotomy (rs = .71; p = 0.111), sug-
most important cue (β = –.12). Participants preferred gesting a difference in relative cue usage.
a prescriptive rather than a developmental approach Participants who had some advising experience
to advising. with faculty or professional advisors preferred a
Finally, the gender of the advisor was the least female advisor (mean β = .08; SD = .17) more than
important cue (β = .10). Participants indicated a did participants without any prior advising experi-
preference for a female rather than a male advisor. ence (mean β = .01; SD = .16), t(177) = –2.723, p
The policy captured for all participants indicate = 0.007, d = –.41. However, we found a substantial
advisee preference for an established relationship degree of covariation in regression weights across
with a warm and supportive professional (top pref- ratings of students with and students without advis-
erence) or faculty advisor. The captured policy ing experiences with faculty or professional advi-
data reveal to a lesser degree advisee preference for sors (rs = .89; p = 0.019). Finally, participants under
a prescriptive approach in terms of tasks addressed 25 years indicated that they prefer that the advisor
and for a female advisor. know them by name (mean β = .34; SD = 0.34) more
than did participants age 25 years or older (mean β
Do Advising Preferences Vary Among Different = .07; SD = 0.14), t(7.194) = 4.283, p = 0.003, d
Groups of Students? =1.78. We found no statistically significant degree
To address the second purpose of our study, we of covariation in regression weights across age (rs
examined potential differences in advising prefer- = –.26; p = 0.623), suggesting a different pattern of
ences across different types of students. For this cue usage across age groups.
analysis, we used individual judgment policies in
which data on each participant’s satisfaction with Do Advising Preferences Vary Based on Personality
advising judgments were regressed onto the six or Prior Advising Experiences?
advising cues. Participants’ policies were then We examined potential differences in policies
grouped according to self-reported sex, age, ethnic across participants with different personality traits.
background, year in school, and amount of advis- Because prior advising experience could influence
ing experience. We used t tests of independent preferences for advising, we also examined poten-
samples to compare the standardized beta weights tial differences in advising preferences across par-
derived from the individual policies across groups. ticipants with different prior advising experiences.
A significant difference suggests that the satisfac- To complete these analyses, we first grouped par-
tion of one group was more heavily influenced by ticipants’ individual policies according to their
a cue than it was for the other group. For these anal- NEO-FFI and AAI subscale scores. For the NEO-
yses, we applied a Bonferroni correction, and the FFI scales, we used the normative tables provided
NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004 55

9. Mottarella, Fritzsche, & Cerabino
Table 3 Beta weight comparisons for demographic characteristics and advising experiences
Peer Faculty
Participant Characteristics Gender Title Title Depth Type Nature
Sex
Male .06 –.19 –.09 .35 –.11 .01 a
Female .04 –.20 –.13 .32 –.02 .18 b
Age Status
Younger than 25 years .05 –.19 –.11 .34 a –.02 .13
25 years or older .05 –.19 –.16 .07 b –.35 .18
Faculty/Professional Advising
No faculty/professional
advising .01 a –.14 –.12 .37 –.03 .17
Some faculty/professional
advising .08 b –.24 –.10 .28 –.06 .10
Developmental-Prescriptive Advising
Prescriptive .04 –.16 –.13 .38 –.13 .03 a
Developmental .06 –.20 –.09 .29 .03 .17 b
Personalizing Education Advising
Prescriptive .06 –.18 –.13 .34 –.13 a .06
Developmental .04 –.19 –.09 .29 .06 b .19
Registration and Class Scheduling
Less than median .06 –.12 –.04 a .37 –.02 .09
Equal to or greater than median .04 –.24 –.16 b .30 –.04 .16
Note. Beta weights within comparison groups that significantly differ at p < 0.008 have different
subscripts.
by Costa and McCrae (1992) to transform partici- ipants grouped by personality dimensions. We found
pants’ scores into linear T scores. Then, we formed two differences for participants grouped by prior
high, average, and low groups as follows: If a par- advising experiences. A difference was found on
ticipant had a T score of 44 or lower, he or she was the Personalizing Education subscale. Specifically,
categorized in the low group; if an individual’s T those who reported more prescriptive experiences
score was 45 to 55, she or he was considered part also preferred the prescriptive advising scenarios
of the average group; a student with a T score of 56 (mean β = –.13; SD = .41), and those who reported
or higher was placed in the high group. For AAI having more developmental advising experiences
scales, we used a median split except in instances preferred the developmental advising scenarios
in which Winston and Sandor’s (1984a) recom- (mean β = .06; SD = .37), F(1, 140) = 8.373,
mendations applied. For example, Winston and p = 0.004. However, we found a substantial degree
Sandor (1984a) suggested score ranges for classi- of covariation in regression weights across groups
fying participants according to the amount of pre- (rs = .83; p = 0.042).
scriptive or developmental advising experiences In addition, we found a significant difference
they had received, as rated by the respondent on the among responses to the Registration and Class
Developmental/Prescriptive Advising scale, and Scheduling scale. Those who reported more expe-
we used their developmental-prescriptive classifi- rience in advising related to registration and class
cations in our analysis. scheduling preferred a nonfaculty advisor over a fac-
After the groups were formed, we used inde- ulty advisor (mean β = –.16; SD = .33) more than
pendent sample t tests or one-way analysis of vari- did those who had less registration and class
ance (ANOVA) measures to compare the scheduling experience (mean β = –.04; SD = .21),
standardized beta weights derived from the indi- t(157) = 2.926, p = 0.004. Moreover, a statistically
vidual policies across groups. For these analyses, significant correlation exists between the rank order
a Bonferroni correction was applied, and thus the of the six standardized beta weights across regis-
alpha level was set at .008. Spearman rank corre- tration and class scheduling experience (rs = .71;
lation coefficients were also computed. p = 0.111), which suggests a difference in relative
In an interesting result, we found no significant cue usage by those with varying levels of advising
differences in beta weights across any of the partic- experience.
56 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

10. Policy Capturing Study
Discussion Thus, operational definitions for the different
Advisor’s Approach is More Important than Advising approaches need to be clearly defined, and the rela-
Approach tional assumptions that confound these approaches
Notably important to all participants is the depth must be removed from the definitions.
of the advising relationship. Participants also value Advising approaches may be better defined solely
the type of advisor and the emotional nature of the by the advising tasks conducted than by the theory
advising relationship. Most interesting, the advis- on which they are based. In their study on nontra-
ing approach was shown to be less important than ditional students, Fielstein, Scoles, and Webb (1992)
other variables. These results are consistent with the delineated the tasks they had associated with each
psychotherapy literature and suggest that regardless advising approach. For example, under develop-
of the particular tasks to be addressed in the advis- mental advising tasks, they included conversations
ing session, an advisor needs to give specific care on topics other than academics, including students’
to establish a relationship with the advisee and personal problems; self-esteem/interpersonal
convey warmth and support in this relationship. skills/study skills; student values, beliefs, and atti-
The advising literature supports the idea that only tudes and the conflicts between them. According to
advisors who use the developmental approach are the definition put forth by Fielstein et al. (1992), the
warm and know their students. For example, in tables prescriptive tasks involve assistance with course
in which prescriptive and developmental advising selection, schedule planning and registration; refer-
activities are listed and compared, Fielstein (1989, rals to other student support services; explanation
p. 36) associated “Advisor be personally acquainted of degree requirements and review of student’s sta-
with the student” with the developmental approach. tus in relation to them.
In their table comparing developmental and pre- The findings show that relational variables can
scriptive approaches, Winston and Sandor (1984b, exist across multiple approaches and their effect
p. 8) listed “Advisor is concerned about personal, likely depends more on the advisor’s interpersonal
social and academic life of student” and “relation- skills and style rather than the approach itself. If the
ship is based on trust and respect” under the devel- descriptions of various advising approaches, such
opmental approach. Thus, developmental advising is as prescriptive, developmental, and others, include
consistently described as the approach through which warmth and supportive relational dynamics, then the
a warm and caring advisor takes the time to get to exploration of contingencies (i.e., the consideration
know the student personally. In their discussion of of when to use which approach) can expand along
implementing developmental advising theory, with the practice of the approach.
Creamer and Creamer (1994, p. 17) stated, “A sup- The importance of relational variables in aca-
portive, or developmental, orientation is clearly demic advising is illuminated in the finding that the
favored by advisors over an information-sharing, or female students found warmth in the advising rela-
prescriptive, orientation to advising.” Clearly, the tionship to be of more importance than did male
authors of articles on advising have the idea that an respondents. Because females are socialized to be
advisor’s warmth and support is indicative of the more relationship oriented (Eagly, 1987), these
developmental approach, and thus the authors and findings are not surprising and may explain why the
readers have assumed that an advisor cannot be both advising relationship is relatively more important
warm and supportive and also prescriptive, directive, to female students than it is to male students.
instructive, information-sharing, or hierarchical. We In another interesting finding, all students who had
challenge this conclusion noting that, as among psy- prior advising experience with professional and fac-
chology practitioners (Brunink & Schroeder, 1979), ulty advisors expressed a preference for female advi-
the establishment of a solid, warm, and supportive sors. Both male and female students who had
relationship can be the foundation of advising regard- advising experience with professional and faculty
less of the specific approach and advising tasks to be advisors indicated that advisor gender is more impor-
accomplished. An advisor can warmly and support- tant than did students who had no advising experi-
ively instruct a student on the precise tasks the stu- ence with professional or faculty advisors. These
dent needs to accomplish to prepare for graduate results are consistent also with the finding that under-
school; advisors can convey warmth and support graduate students find female advisors to be more
even when directly addressing a student’s course of empathic and are more likely to recommend female
action for recovering from academic probation. advisors to their friends (Nadler & Nadler, 1993).
Being directive and prescriptive need not mean that They may indicate that students seek out advisors
the advisor is uncaring, unsupportive, and cold. who offer positive advising relationships and that stu-
NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004 57

11. Mottarella, Fritzsche, & Cerabino
dents perceive female advisors as being more warm need than nontraditional-aged students for per-
and supportive. The results of our study are also sonal development advising as they struggle to
consistent with the related finding that female fac- adopt the role of college student as well as negoti-
ulty are viewed by students as more accessible, and ate the developmental milestones that directly relate
in fact, female faculty devote more time to advising to major and career choices. The results of this
than do the male faculty (Bennett, 1982). study, as measured by the AAI Scale, imply that tra-
The preference for female advisors was not ditional-aged students both desire and receive more
apparent in students’ perceptions of peer advising. personal development advising. Advisors do not
Students are not looking for the same depth or have sole responsibility for meeting this need.
quality of advising from their peer advisors, who are Because they are looking for an interpersonal con-
typically trained to handle only the routine, basic nection, traditional-aged students are probably
academic-advising issues, as they seek from faculty seeking out and engaging advisors.
or professional advisors (Privette & Delawder, In this study, nontraditional-aged and non-White
1982; Seegmiller, 2003). Peer advisors may be best students perceive that they had been receiving less
characterized as “curriculum advisors” (Gordon, interpersonal support from their advisors than did the
1984, p. 456). Students are, therefore, unlikely to White and traditional-aged students. Although all stu-
expect or desire a deeper advising relationship with dents reported equivalent satisfaction with their
peer advisors. advising, nontraditional-aged students scored sig-
nificantly lower on the AAI Personal Development
Advisee Personality Affects Advising Experiences and Interpersonal Skills scale than did traditional-
and Preferences aged students; this result suggests that the nontradi-
The results of this study suggest that personal- tional-aged students were less likely to discuss with
ity differences among students make little impact their advisors various college experiences, including
on their advising preferences; in general, students classroom and extracurricular activities and short-and
value warmth and depth in advising relationships. long-term plans. Non-White students scored signif-
However, we found that personality also relates to icantly lower than did White students on amount of
prior advising experience. Extraverted, agreeable, developmental advising received. Advisor training
and conscientious students reported that they receive may be an effective way to increase advisors’ aware-
better quality and more satisfying advising expe- ness of non-White students’ perceptions that they
riences than did more intraverted, less agreeable, and receive less developmental advising than White stu-
less conscientious students. Keeping in mind that dents perceive receiving. Academic advisors can be
all students, regardless of their personality type, reminded of the crucial impact they can have with
want established, warm, and supportive relationships all students; academic advising has been cited as the
with them, advisors may need to challenge them- most critical supportive resource to assist in the
selves to build such relationships even with students retention, satisfaction, and positive integration of
who are not extraverted, agreeable, or conscientious. minority students (King, 1993).
These results emphasize the importance of training We found two other interesting differences when
in the area of interpersonal, relationship-building, participants were grouped by past advising expe-
and counseling skills that will allow advisors to be riences. First, those students who reported more pre-
successful in meeting the needs of students with var- scriptive experiences preferred the prescriptive
ious but common personality types. advising scenarios, and those who reported having
We found some that student characteristics relate more developmental advising experiences preferred
to the students’ advising preferences as well as the developmental advising scenarios. This finding
their experiences. The policies of the traditional- suggests that students will prefer the advising that
aged students suggest that they desire depth in an they are accustomed to receiving. Advisors have the
advising relationship, and the results on the Personal ability to influence the expectations and desires of
Development and Interpersonal Skills scale of the their advisees. They should pay careful attention to
AAI suggest that advisors are meeting this need. the type of advising students are receiving because
Compared to older students, the traditional-aged stu- their experiences generate and influence their future
dents in our study had a more significant need for expectations. This finding also has particular impli-
the advisor to know them. cations for students who transfer from one advisor
Because the sample size of nontraditional-aged or advising resource to another. In her or his
students was small, in the future, researchers should approach, an advisor may want to take into con-
examine if traditional-aged students have a greater sideration the type of advising the student has
58 NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004

12. Policy Capturing Study
received in the past. and supportive advising relationships regardless
Second, those students who reported more reg- of the objectives of the advising.
istration and class scheduling experience prefer a The results of this study also suggest that despite
nonfaculty advisor significantly more than did those personality differences among them, students seek
who had less registration and class scheduling expe- out advisors who are warm and supportive.
rience. This finding suggests that professional and Therefore, extra effort on the part of an advisor to
peer advisors are either doing a particularly good job build deeper and warmer connections with students
in meeting registration and class-scheduling needs who are relatively less extraverted, agreeable, or
or that students feel more comfortable dealing with conscientious will benefit the advisee-advisor rela-
professionals and peers rather than faculty advi- tionship. The nontraditional-aged students in this
sors for these particular needs. study were less likely to be self-disclosing about their
educational experiences. If this trend is confirmed
Study Limitations in future research, advisors may wish to put forth
The primary limitation of this study is use of extra effort to get to know these students well and
hypothetical advising scenarios. We studied simu- build warm advising relationships with them.
lated, rather than actual, judgments. Participants’ This study highlights the importance of an advi-
responses to real advising scenario may differ from sor establishing a relationship with an advisee and
those expressed in this study. We examined “cue uti- conveying warmth and support in this relationship.
lization” (Brunswik, 1943) (i.e., how cues relate to By understanding the specific elements of the cur-
satisfaction judgments). “Ecological validity” (i.e., rent advising approaches found to be valued by
how judgments relate to satisfaction in actual advis- students, advisors are free to adopt the advising tasks
ing experiences) still needs to be examined associated with various approaches as needed.
(Hammond et al., 1986). Based on this study, we emphasize the need for
Also specific to the policy capturing method, the clearly defined, operational definitions of the dif-
task that students were required to complete was ferent approaches, such as prescriptive and devel-
fairly lengthy. Fatigue or boredom with the task may opmental, based on the advising tasks conducted.
have affected the results and is a potential reason We also advocate for the removal of the relational
why all of the participants’ policies were not cap- assumptions that currently confound the effective-
tured. According to Cooksey (1996), the trade-off ness of the approaches.
between the participant’s ability to handle the
demands of the task and the researcher’s need to References
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14. Policy Capturing Study
advising communication. Journal on Excellence Student Development Associates.
in College Teaching, 4, 119–30. Winston, R. B., & Sandor, J. A. (1984b).
Pascarella, E. T., & Terenzini, P. T. (1980). Predicting Developmental academic advising: What do stu-
freshman persistence and voluntary drop out dents want? NACADA Journal, 4(1), 5–13.
decisions from a theoretical model. Journal of Winston, R. B., & Sandor, J. A. (1986). Evaluating
Higher Education, 51, 60–75. academic advising update: Addendum to the
Privette, G., & Delawder, J. E. (1982). Academic peer manual for the Academic Advising Inventory.
counseling: Advising with a personal touch. Athens, GA: Student Development Associates.
International Journal for the Advancement of
Counseling, 5, 109–14. Authors’ Note
Rogers, C. R. (1992). The necessary and sufficient This research was supported by a grant from
conditions of therapeutic personality change. NACADA awarded in 2003.
Journal of Consulting and Clinical Psychology,
60, 827–32. (Original work published 1957) Karen E. Mottarella joined the University of Central
Schneider, R. J., Hough, L. M., & Dunnette, M. D. Florida (UCF) in 1998 as the Coordinator of
(1996). Broadsided by broad traits: How to sink Undergraduate Advising for the Psychology Depart-
science in five dimensions or less. Journal of ment. She is currently instructor of psychology and
Organizational Behavior, 17, 639–55. a licensed psychologist in Florida. Dr. Mottarella
Sedlacek, W. E. (1991). Using noncognitive variables received the 2002 University Award for Excellence
in advising nontraditional students. NACADA in Faculty Advising at UCF and she has published
,
Journal, 11(1), 75–82. papers in journals such as Psychology Teaching
Seegmiller, B. R. (2003). A peer advising course and Learning, the International Journal of Reality
for undergraduate psychology majors. Teaching Therapy, and the American Journal of Family
of Psychology, 30, 51–53. Therapy. Her research interests include academic
Spence, J. T., & Helmreich, R. L. (1978). Work and advising, in-home counseling and outreach models,
Family Orientation Questionnaire: An objective psychological assessment, and Web-based training.
instrument to assess components of achievement
motivation and attitudes toward family and career. Barbara A. Fritzsche is associate professor and
Catalog of Selected Documents in Psychology, 8, Director of the PhD Program in Industrial and
35. Organizational Psychology at the University of
Steele, G. E., Kennedy, G. J., & Gordon, V N. (1993).
. Central Florida. Dr. Fritzsche’s research interests
The retention of major changers: A longitudinal include diversity issues, predictors of performance,
study. Journal of College Student Development, and Web-based training. She has published papers
34, 58–62. in journals such as Journal of Personality and
Stevenson, M. K., Busemeyer, J. R., & Naylor, J. C. Social Psychology, Journal of Occupational and
(1990). Judgment and decision-making theory. In Organizational Psychology, Journal of Vocational
M. D. Dunnette & L. M. Hough (Eds.), Handbook Behavior, Personality and Individual Differences,
of industrial and organizational psychology: Vol. Human Resource Management, Journal of Applied
1(2nd ed.) (pp. 283–374). Palo Alto, CA: Social Psychology, Psychology Teaching, and
Consulting Psychologists Press. Learning, and Social Behavior and Personality.
Tinto, V (1998). Colleges as communities: Taking
.
research on student persistence seriously. Review Kara C. Cerabino is a doctoral candidate in indus-
of Higher Education, 21, 167–77. trial and organizational psychology at the University
Trombley, T. T., & Holmes, D. (1981). Defining the of Central Florida. Her primary research interests
role of academic advising in the industrial setting: include work-family conflict and diversity issues.
The next phase. NACADA Journal, 1(2), 1–8. Kara has published in Personality and Individual
Truax, C. B., & Carkhuff, R. R. (1967). Toward Differences and was a recipient of the Lyman Porter
effective counseling and psychotherapy. Chicago: Award for Best Graduate Student Paper presented at
Aldine Atherton. the 2003 Academy of Management conference.
Webb, E. M. (1987). Retention and excellence
through student involvement: A leadership role for Correspondence concerning this article should
student affairs. NASPA Journal, 24, 6–11. be sent to: Dr. Karen Mottarella, kmottare@
Winston, R. B., & Sandor, J. A. (1984a). The mail.ucf.edu, 305 Bunker Street, Melbourne, Florida
Academic Advising Inventory. Athens, GA: 32901.
NACADA Journal Volume 24 (1 & 2) Spring & Fall 2004 61