1. 30
Performance Improvement, vol. 54, no. 10, November/December 2015
©2015 International Society for Performance Improvement
Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/pfi.21533
BARRIERS AND ENABLERS TO DATA-DRIVEN
DECISION MAKING BY HIGH SCHOOL
COUNSELORS AND ADVISORS
Carlos Antonio Viera, PhD, SPHR Kevin Freer, PhD
BACKGROUND
Over the last decade, both national and state level legisla-
tion have targeted improved outcomes for all students.
Achieving critical success factors benefits a data-driven
decision-making approach among the educators leading
and supporting these high schools to attain and maintain
an acceptable grade for the school. In the public school
district where this study was conducted, the need for
effective performance improvement solutions in data-
driven decision making has gained interest and attention.
Improved performance and outcomes are critical to the
district’s continued progress toward achieving its strate-
gic goals, such as increasing the number of students who
develop a postsecondary plan, achieve college readiness,
graduate, and are admitted into a college or university,
as well as who persist toward completion of a four-year
degree. Unfortunately, the implementation of strategies
designed to meet such benchmarks as recommended by
human performance improvement (HPI) researchers and
practitioners have been more the exception than the rule
(Van Tiem, Moseley, & Dessinger, 2012).
The intent of the research was to assist in the develop-
ment of commitment to change by facilitating the deriva-
tion of meaning and engagement, so that people would
act in new ways, feel engaged, and believe change is
possible in alignment with HPI (International Society for
Performance Improvement, 2011). Specifically, the school
business results indicators related to graduation rates and
college readiness are of particular concern in large urban
school districts, where minorities are quickly becoming
the majority and often lagging behind their white peers
on college entrance exam results (McKinsey & Company,
2009). It was postulated that there is a significant linkage
between the performance of school counselors, the busi-
ness results of their schools, and, in turn, the organiza-
tional results of their school systems.
LITERATURE REVIEW
The review of literature begins with a discussion of
Binder’s (1998, 2009, 2011) six boxes model, which is the
conceptual framework that underpins this study. Binder’s
This article presents a study that had as its purpose to assist a large urban school district’s
leadership in systematically supporting school counselors and advisors conducting data-driven
decision making. Binder’s Six Boxes® model served as the conceptual framework to collect and
analyze information pertaining to barriers and enablers across environmental and behavioral
factors. Barriers included lack of clear expectations and feedback, an overabundance of
technology without time to practice, and clerical duties. Enablers included supportive leadership,
knowledgeable counselors willing to participate, and leadership with positive perceptions.

2. Performance Improvement • Volume 54 • Number 10 • DOI: 10.1002/pfi 31
six boxes model evolved from the work of his predeces-
sors and contemporaries, Gilbert’s (1996, 2007) behav-
ior engineering model and Chevalier’s (2003) profiling
behavior (PROBE) questions, which relied on Vroom’s
(1964, 1990) valence instrumentality expectancy theory
and House’s (1971) path goal theory.
Binder’s Six Boxes Model
Binder’s six boxes (1998, 2009, 2011) model (see Figure 1)
is based on the initial framework of the behavior engi-
neering model (Gilbert, 1978). The six boxes model is
another way of organizing the six variables originally cre-
ated by Gilbert (1978) to facilitate ease of understanding
by frontline managers or nontechnical employees and
emphasizes performance over behavior.
Binder’s (1998, 2009, 2011) six boxes model was used
in this study to identify enablers and barriers to improved
performance by high school counselors and advisors in
data-driven decision making. The top row of boxes, as
displayed in Figure 1, pertain to environmental factors:
data, resources, and incentives. The bottom row of boxes
pertains to individual factors: knowledge, capacity, and
motives. Data collection and analysis flow from top to
bottom and right to left across the six boxes.
The field of HPI can inform practices and approaches
for school counselors and advisors by using the six boxes
model. The six boxes support organizations in “creating
a common language for understanding, communicating,
and optimizing all the variables that influence successful
interventions and continuous performance improve-
ment” (Czeropski, 2012, p. 14). This common language
can facilitate communication with clients who are unfa-
miliar with Gilbert’s behavior engineering concepts; it
draws from B. F. Skinner’s theory of operant condition-
ing, but uses language that is more accessible to managers
and nontechnical staff (Binder, 2011). Binder minimized
instead of omittted references to Skinner or operant
conditioning while adjusting the language to address
performance over behavior and conveying a numeric and
visual order of the steps involved in analyzing the perfor-
mance factors. The six boxes model includes individual
motives and preferences, such as perceived self-efficacy.
Binder first defined the performance chain to define per-
formance, then identifying milestones or work outputs
that are needed in achieving the targeted business results
during the implementation. Developing, supporting, and
encouraging the desired behavior is then thought to be
relatively direct (Binder, 2011).
The six boxes model is useful in designing a roadmap
to aid in identifying and documenting environmental
support and behavioral repertory variables related to
increasing self-efficacy in personnel who are developing
data-driven decision-making practices. The six boxes
can be used to guide the development of the needs
assessment for determining root causes affecting this
process. The environmental and individual behavioral
repertory enablers and barriers to data-driven decision
making experienced by participants may also support
the development of professional learning communities
or communities of practice that are organized to improve
performance in this essential professional practice.
Chevalier’s PROBE Questions
Chevalier (2001, 2003) developed the PROBE questions,
andtheyarealsobasedonandinsupportofGilbert’s(1978,
Over the last decade, both
national and state level
legislation have targeted
improved outcomes for all
students. Achieving critical
success factors benefit
a data-driven decision
making approach among
the educators leading and
supporting these high schools
to attain and maintain an
acceptable school grade.
FIGURE 1. BINDER’S (1998, 2009, 2011) SIX
BOXES™ MODEL
Note: Reprinted from “The six boxes: A Descendent of Gilbert’s Behavior
Engineering Model,” by C. Binder, 1998, Performance Improvement, 37,
p. 48–52. Reprinted with permission.

3. 32 www.ispi.org • DOI: 10.1002/pfi • NOVEMBER/DECEMBER 2015
1996, 2007) update of the behavior engineering model.
The questions can be used to assess the accomplishments
for any job in any work situation. The 36 PROBE ques-
tions are categorized across the six behavior engineering
model categorical factors: information, resources, and
incentives addressing environmental factors; and motives,
capacity, and knowledge and skills addressing behavioral
factors. Behavioral engineering model categories are
addressed with a set of direct questions designed to initi-
ate a conversation using the language of HPI with clients,
followed by open-ended questions designed to keep the
clients from becoming defensive in reaction to the direct
questions (Chevalier, 2001, 2003).
Chevalier’s (2001, 2003) PROBE questions serve as a
high-level template for designing and framing interview
or questionnaire items based on the behavior engineer-
ing model or Binder’s six boxes model for assessing the
accomplishment of any job in any situation. The collec-
tion of qualitative data from individuals from similar
work groups is also facilitated by using the PROBE
questions in an open-ended format. Using this standard
set of Chevalier’s PROBE questions as a foundation for
conducting assessments, based on the six boxes model,
the consistency of question structure and content are
maintained, along with the validity and reliability of par-
ticipants’ responses.
PURPOSE OF THE STUDY
The purpose of this study was to ascertain how the field
of HPI can inform performance improvement approaches
by district leadership for school counselors and advisors
serving in urban high schools. Binder’s six boxes model
was utilized to help design a roadmap for the researcher
identifying and documenting environmental support
and behavioral repertory variables needed for improved
effectiveness of student services personnel in data-driven
decision making.
RESEARCH QUESTIONS
This study focused on the following research question
with two sub-questions:
1. How does district leadership employ each of the fac-
tors in Binder’s (1998, 2009, 2011) six boxes model
when supporting student services personnel in using
data-driven decision-making practices?
a. What are the perceived barriers reported by
district leadership and student services personnel
that influence the use of data-driven decision
making in the study sample?
b. What are the perceived enablers reported by
district leadership and student services person-
nel that influence the use of data-driven decision
making in the study sample?
METHODOLOGY
A survey research design was used to address the research
questions for the study. Two data collection instruments
were administered with open-ended items related to
environmental and behavioral factors and an extension
of Chevalier’s (2001, 2003) PROBE questions. A purpo-
sive sample (Patton, 2002) was used and consisted of 25
high school counselors and 25 college assistance program
advisors who worked within traditional, non-charter high
schools where the use of data-driven decision making is
perceived to add value to the schools’ business results.
The posed questions were related to the individual beliefs
and perceptions of the subjects when conducting data-
driven decision making, as well as their understanding
of how these items relate to established professional
practices.
A focus group (Stewart, Shamdasani, & Rook, 2007)
was also conducted with key members of the district’s
leadership and representatives from the student services
personnel group. The data collected from the focus group
aided in completion of the categorical analysis and devel-
opment of recommendations based on guidelines from
the conceptual framework. For the purposes of this study,
the term district’s leadership refers to those individuals
within the target organization that have essential roles as
stakeholders in data-driven decision making performed
by student services personnel. Focus group responses
were used to verify and clarify the responses collected
from the two surveys, as well as to generate recommenda-
tions used to either reduce identified barriers or enhance
enablers related to relevant environmental and behavioral
factors. The results of the focus group interviews were tri-
angulated with those of the two questionnaires and used
to develop recommendations for minimizing barriers and
enhancing enablers.
KEY FINDINGS
Select items from the two questionnaires targeted the
indicators from each of Binder’s (1998, 2009, 2011) six
boxes that may be acting as barriers or enablers to effec-
tive data-driven decision making by senior high school
counselors and advisors. The contents of Table 1 display
the perceived barriers identified side by side by both
district leadership and counselors and advisors. The con-
tents of Table 2 display the perceived enablers identified

4. Performance Improvement • Volume 54 • Number 10 • DOI: 10.1002/pfi 33
side by side by both the district leadership as well as coun-
selors and advisors.
Focus Group Findings
Central to the development of the recommendations
that emerged from the study were those intended to
reduce barriers and enhance enablers to data-driven
decision making by counselors and advisors. Focus
group participants included representatives from dis-
trict leadership as well as high school counselors and
advisors. Table 3 identifies areas of consensus reached
by the participants.
DISCUSSION OF THE FINDINGS
The relationship between the findings and the six boxes
theoretical framework is discussed followed by an exami-
nation of the findings as they relate to the relevant
literature.
Relationship Between the Findings and the
Conceptual Framework
The use of the six boxes model as a framework was a
novel approach to systematically identifying barriers
and enablers to the professional practice of data-driven
decision making by high school counselors and advisors.
The study’s design intentionally used open-ended ques-
tions to collect perceptions and opinions from counselors
and advisors, as well as district leaders whose job roles
interface with and depend upon the effective perfor-
mance of the employees.
The sequential responses to open-ended items from
two questionnaires, as well as a cross-functional focus
group, helped to identify several barriers and enablers
to data-driven decision making by counselors and advi-
sors. Use of the six boxes model would entail address-
ing each of these findings one by one in the order in
which they are presented by the six categorizations.
This systematic approach could require several weeks to
months to implement, as well as to evaluate the impact
of each progressive step and intervention strategy. This
necessitated that the development of recommenda-
tions, though presented sequentially and in accordance
with the six boxes, be pragmatic, targeting multiple
barriers, as well as enhancing any identified enablers.
In fact, several of the recommendations that emerged
from the focus group after considering the compiled
responses to the two questionnaires, often overlapped
TABLE 1
PERCEIVED BARRIERS IDENTIFIED BY DISTRICT LEADERSHIP AND COUNSELORS
AND ADVISORS
BOXES DISTRICT LEADERSHIP COUNSELORS AND ADVISORS
Box #1:
Expectations & Feedback
❖ Need for consistency and continuity
regarding the establishment and
deployment of clear expectations and
feedback
❖ Limited connections to performance
management system
❖ Lack of clear guidance and direction for conducting
systematic approaches central to data-driven decision
making and continuous improvement
Box #2:
Tools & Resources
❖ Need time and training for technology
tools
❖ Lack of time during the school day
Box #3:
Consequences & Incentives
❖ No references to performance pay were
reported
❖ Performance pay system that does not align well with
the desired role and practices of counselors and advi-
sors with data-driven decision making
Box #4:
Knowledge & Skills
❖ Need for training in accessing and
interpreting data and in decision making
based on data analysis
❖ Need for additional training in accessing data and in
decision making based on data analysis
Box #5:
Selection & Assignment—
“Capacity”
❖ Lack of contact with counselors and
advisors or not being able to answer the
questionnaire item
❖ Need for additional training in accessing data and in
decision making based on data analysis
Box #6:
Motives & Preferences—
“Attitude”
❖ Nothing reported ❖ Nothing reported

5. 34 www.ispi.org • DOI: 10.1002/pfi • NOVEMBER/DECEMBER 2015
across categories. Sometimes these recommendations
also presented combined strategies that cut across mul-
tiple boxes. Whether the strategies used to address the
identified enablers and barriers really need to be imple-
mented one at a time or if a combined approach is just as
effective remains to be seen and presents opportunities
for further research.
DISCUSSION OF THE FINDINGS IN
RELATION TO THE LITERATURE
The findings from this exploratory study were reviewed
and compared to the findings in the literature as related to
the categories of the six boxes model. The findings dem-
onstrate how the transfer of knowledge from a learning
organization intervention can contribute to the sustain-
ability of informing district leaders. Using the six boxes to
increase data-driven decision making by student services
personnel can result in an increase in effectiveness and
the promotion of performance improvement. The valence
instrumentality expectancy theory (Vroom, 1964, 1990)
served as antecedent research to most HPI methodologies
in the leadership and management literature. The theory
is based on what an employee believes to be true about
both the value of a goal and the likelihood of obtaining
that goal (Vroom, 1964). At the core of Vroom’s (1964)
theory is that an employee’s actions are mediated by their
perception of the likelihood that an event will occur. The
path goal theory (House, 1971) is another antecedent to
most HPI methodologies in the leadership and manage-
ment literature. House (1971) emphasized the leader’s
effect on subordinates and on their ability to reach the
set goals, the associated rewards for reaching these goals,
the importance of the goals, and four types of leadership
styles: directive, supportive, participative, and achieve-
ment-oriented. This theory supports several variables
from the six boxes model, including the environment in
which the individual employee must complete a specific
assignment or task, including providing high expecta-
tions and offering feedback, tools and resources, and
consequences and incentives.
Expectations and Relevant Feedback
The first of the six boxes in the model, expectations and
feedback, emphasizes the importance of how performance
expectations are clearly communicated to employees.
Also of importance under box #1 is that employees under-
stand the various aspects of their roles and the priorities
for performing these tasks. Baker (2010) emphasized
the importance of providing opportunities for employee
feedback as part of the human performance system.
TABLE 2
PERCEIVED ENABLERS IDENTIFIED BY DISTRICT LEADERSHIP AND COUNSELORS AND
ADVISORS
BOXES DISTRICT LEADERSHIP COUNSELORS AND ADVISORS
Box #1:
Expectations & Feedback
❖ Broad range of expectations for counselors’ use of
data-driven decision making
❖ Broad range of positive behaviors and expecta-
tions from supervisors
Box #2:
Tools & Resources
❖ Abundance of technology tools are available to
support data-driven decision making
❖ Abundance of technology tools are available to
support data-driven decision making
Box #3:
Consequences & Incentives
❖ Successful student outcomes were repeatedly
reported as serving as the primary incentive
❖ Willingness to conduct data-driven decision making
for the available incentives
❖ Successful student outcomes were repeatedly
reported as serving as the primary incentive
Box #4:
Knowledge & Skills
❖ Nothing reported ❖ Perceptions of feeling knowledgeable about
data-driven decision making
Box #5:
Selection & Assignment—
“Capacity”
❖ Nothing reported ❖ Identified as an enabler for counselors and
advisors
Box #6:
Motives & Preferences—
“Attitude”
❖ Willingness of counselors and advisors to partici-
pate in professional learning communities or com-
munities of practice
❖ Positive attitudes with references to intrinsic
reinforcement for conducting data-driven
decision making

6. Performance Improvement • Volume 54 • Number 10 • DOI: 10.1002/pfi 35
Inconsistencies and contradictions between recommen-
dations from professional organizations for school coun-
selors and the reality of job assignments are occurring
within the educational system.
The role that counselors play in the educational sys-
tem has been an under-researched and underleveraged
resource (College Board Advocacy and Policy Center,
2012). However, substantial research has been conducted
in certain aspects of the counseling field, such as indi-
vidual and group counseling, crisis counseling, student
welfare, and other subjects linked to psychology and
mental health counseling. College admissions have also
received some attention by researchers in recent years
(College Board Advocacy and Policy Center, 2012).
Paisley and McMahon (2001) identified the debate over
role definition for school counselors as their most sig-
nificant challenge. Schimmel (2008) also concluded that
trends in research reflect that school counseling’s history
represents a profession searching for its identity. The
National Center for Transforming School Counseling
(The Education Trust, 1997) and the American School
Counselor Association (2005) have both developed
extensive lists intended to refocus the school counselor’s
role and guide the use of school counselors by school
administrators and leaders. The National Association for
College Admission Counseling (2009) identified postsec-
ondary admission counseling, the choice and scheduling
of courses, personal needs counseling, academic testing,
occupational counseling and job placement, teaching,
and other nonguidance activities as day-to-day job tasks
of counselors.
A lack of well-deployed expectations and feedback as
related to professional practice B: data-driven decision
making: Analyzes multiple sources of qualitative and
quantitative data to inform decision-making was identified
through the six boxes model to address the adequacy of
TABLE 3 FOCUS GROUP CONSENSUS
BOXES FINDINGS
Box #1:
Expectations & Feedback
Consensus was reached by the focus group participants in selecting Student Services Professional Practice
B: Analyzes multiple sources of qualitative and quantitative data to inform decision making (Florida
Department of Education, 2011).
Box #2:
Tools & Resources
Expanding the utilization of specific technology tools and resources for data-driven decision making was
addressed next by the focus group. Central to the comments and recommendations offered was that “Less
is more! Regarding number of tools available,” which referenced the need to allow and support the associ-
ated learning curve when learning to effectively use the myriad tools available.
Box #3:
Consequences & Incentives
Recommendations offered regarding the expansion of incentives and/or benefits for data-driven decision
making were somewhat surprising, in that the group focused on student success as the “biggest and best
incentive.” The focus group also offered the need for strengthening the connection between incentives and
the value-added services provided by counselors and advisors to their students. Last, it was suggested
that the opportunities and incentives developed should connect to the need for additional time and other
resources.
Box #4:
Knowledge & Skills
Comments offered by the focus group regarding whether most high school counselors and advisors pos-
sess the necessary knowledge and skills to conduct data-driven decision making, referred to as “Too many
changes, too fast!” and “Constantly reacting.” The focus group then emphasized the importance of being
“involved with professional organizations to support advocacy and legislative decision making.” Though
this recommendation would fall on individual counselors and advisors to pursue, the district could help in
emphasizing this involvement as supportive of the district’s needs.
Box #5:
Selection & Assignment—
“Capacity”
Comments and recommendations were offered by the focus group as to whether most counselors and
advisors have the capacity of conducting data-driven decision making. Several comments offered echoed
the need for time and resources, along with the need to reduce conflicts with clerical duties. However, the
majority of responses expressed confidence in the capacity of counselors and advisors. Recommendations
were made for providing opportunities for counselors and advisors to share lessons learned and best
practices.
Box #6:
Motives & Preferences—
“Attitude”
Last, the focus group offered comments that supported counselors and advisors being willing to invest their
time and energy to conduct data-driven decision making. Recommendations were also offered for increas-
ing their willingness to invest their time and energy in conducting data-driven decision making.

7. 36 www.ispi.org • DOI: 10.1002/pfi • NOVEMBER/DECEMBER 2015
expectations and relevant feedback. Performance expec-
tations for this indicator require analysis of data that
may be perceived as demanding high levels of expertise
with more sophisticated research and problem-solving
skills. The performances of these inappropriate tasks also
has direct implications for findings under the six boxes
model’s box #2: tools and resources.
Tools and Resources
The resources available to counselors and advisors for
conducting data-driven decision making are under
box #2 of the six boxes model. Specifically, “What do
employees need to perform successfully?” Also of rel-
evance to the findings was whether “employees have
the time they need to do their jobs.” The findings
for this category from the six boxes model reflected
an overabundance of technology tools that provide
access to data, which could be typically perceived as a
strength under “having the equipment to do their jobs.”
However, several reports by counselors and advisors
regarding the need for time and training to learn how
to effectively utilize these tools offer conflicting findings
regarding the adequacy of these resources. Two of the
most under-researched topics are the use of technology
in the counseling field (College Board Advocacy and
Policy Center, 2012) and the potential technology may
have on the work of school counselors (Van Horn &
Myrick, 2001).
Access to numerous technology resources has also been
identified as a challenge due to the sheer amount avail-
able, as well as to a lack of training support. Assignments
of clerical duties such as data entry were also identified as
challenges. The American School Counselor Association
(2005) has also identified several job tasks as inappropri-
ate for school counselors, including registering and sched-
uling students, coordinating academics tests, maintaining
student records, and preparing individual education plans.
These findings are also of interest when compared to the
responses provided by counselors and advisors to an item
that asked: “How many hours would you say you typically
work beyond the established workweek (37.5 hours)?”
which reported that the majority of these participants
were working significant number of hours above the
regular workweek. School counselors serving in public
schools reported spending an average of 14.7% of their
time conducting academic testing, 4.8% teaching, and
another 5% of their time on other nonguidance activities
(National Association for College Admission Counseling,
2009). The focus group mirrored these findings and
offered that the “use of data tools requires time to learn
deeply” and to “strengthen the line of sight between the
counselor’s role and data use and needs.”
Consequences and Incentives
Responses collected from counselors and advisors regard-
ing available consequences and incentives for conducting
data-driven decision making repeatedly reflected posi-
tive student outcomes as a primary incentive. This find-
ing supported effective data-driven decision making by
counselors, who have been shown to play an important
role in implementing Response to Intervention and other
individualized academic and behavioral interventions
(Snobarger & Kempson, 2009), which rely on data-driven
decision making. A weak connection between incentives
and value-added services also presented an environmen-
tal barrier to effective data-driven decision making by
high school counselors and advisors. However, Holcomb-
McCoy, Gonzalez, and Johnston’s (2009) work in self-
efficacy somewhat reduces the emphasis this finding
may play on the performance of school counselors. The
American School Counselor Association (2005) offers
a research-supported model for developing a school
counseling program that incorporates data collection and
accountability. However, lack of implemented models
for study and evaluation continue to be a challenge for
further research (College Board Advocacy and Policy
Center, 2012).
Knowledge and Skills
“Counselors are increasingly encouraged and prepared
to leverage data in their work” (College Board Advocacy
and Policy Center, 2012, p. 32). However, in the field of
counseling, the largest problems and unfilled promises
continue to involve the effective use and availability of
data. Dahir & Stone (2012) emphasized the use of data
to inform practice and respond to the needs of students
and schools. The six boxes model, under box #3, helped
to identify significant individual behavioral barriers
related to needed knowledge and skills for data-driven
decision making. Findings included the pace of change,
perceived as too much, too soon, as well a lack of time for
reflection; although the majority of respondents reported
feeling knowledgeable, limited examples were cited. A
few reported feeling somewhat knowledgeable or need
training.
Selection and Assignment: Capacity
Individual attitudes toward conducting data-driven
decision making were identified as challenges. As iden-
tified under box #4 of the six boxes model, barriers to
building capacity were also identified as a lack of time and
resources related to the performance of conflicting clerical
duties. Janson (2010) recommended that the school coun-
selor’s role in staff development should involve organizing
and planning these activities with other leaders from both

8. Performance Improvement • Volume 54 • Number 10 • DOI: 10.1002/pfi 37
within and outside their school. This approach should
help counselors benefit from the skills and knowledge
available across the larger school community. Janson’s
recommendations regarding the effectiveness of problem
solving, also supported by McGannon (2005), were also
relative to the context of delivery, such as a professional
learning community. The establishments of professional
learning communities could help not only to build capac-
ity, but also to create a shared vision for success among
their participants (Sagor, 2010).
Motives and Preferences: Attitudes
Most respondents’ attitudes indicated that counselors and
advisors are either very willing or willing to change their
data-driven decision-making practices. However, qualifi-
ers to this willingness were offered, including the impor-
tance that data-driven decision making support student
success and add value to their work, such as improved
service delivery. A strong connection emerged from the
findings between counselors’ willingness to learn about
and conduct data-driven decision making and the suc-
cess of their students. Counselors’ willingness to support
student success is essential.
As research has demonstrated, interventions led by
school counselors can have a positive impact on student
achievement and behavior in both the middle and sec-
ondary grades (Brigman & Campbell, 2003). Concerns
raised by counselors and advisors regarding the lack of
time available during the school day emphasize the need
to make any training relevant to their students’ success.
School counselor self-efficacy and general self-efficacy
have been found to be the most predictive school coun-
selor dispositions related to data usage (Holcomb-McCoy
et al., 2009). These findings are also supported by Katz’s
(1993) work, which reported that individuals are able to
acquire knowledge and skills, but dispositions are what
lead them to either use or not use what they have learned.
Holcomb-McCoy and colleagues’ (2009) descriptive sta-
tistics study also reflected fairly low data usage by school
counselors, from rarely to some of the time. Differences
were not found among the participants’ ethnicity or
school level. The results of these studies also indicated
that self-efficacy “could be the determining dispositions
of whether a school counselor uses data” (Holcomb-
McCoy et al., 2009, p. 348).
IMPLICATIONS FOR PRACTICE
The preliminary nature of this study was in identifying
barriers and enablers affecting data-driven decision
making by counselors and advisors. The research
generated a number of implications for alignment of
the findings with district priorities, as well as general
practice.
Alignment of Findings With District Priorities
Several of the identified barriers conflicted with the basic
assumptions for effectively implementing the school dis-
trict’s Comprehensive Student Services Program for Pre-K
through Adult, shared during conversations with district
leadership. Primary to these assumptions is awareness
by school administrators, staff, students, and parents of
the comprehensive services provided by student services
professionals. Also central to these assumptions was
that professional personnel are spending 100% of their
time in program delivery and support with appropriate
clerical assistance. Though district leaders were included
in the study as participants, the absence of school site
administrators presented a void in the data collected. The
perceptions and opinions of these leaders, who directly
supervise school counselors and advisors in conducting
data-driven decision making, were unknown at the time
of the study.
Though responses from most district leaders also
reflected a willingness by counselors and advisors to
change their practices, a few district leaders repeatedly
reported a lack of contact with counselors or a simple
don’t know as their responses. Because all leaders were
selected to participate due to their roles and responsi-
bilities having some relevance to the work of counselors,
this finding was of particular concern. In alignment
with district priorities, efforts of leadership should be
informed and aligned regarding relevant accountability
measures.
Alignment of Findings With General Practice
The findings of this study provided recommendations for
several general practices related to managing and sup-
porting school counselors and advisors. The importance
of providing clear and explicit expectations for perfor-
mance, as well as feedback regarding this performance,
was emphasized by the six boxes model. The effec-
tive implementation of professional practices related to
data-driven decision making was examined and explored,
which identified the need for knowledgeable leadership
to encourage and support data-driven decision making.
School site administrators should be included in regular
follow-up surveys and focus groups to add greater credit-
ability to future studies.
Achieving time and cost savings by working with the
district’s information technology services department to
streamline online tools toward a single point of access
should be encouraged. Regular and ongoing oppor-
tunities to provide training, follow-up, and technical

9. 38 www.ispi.org • DOI: 10.1002/pfi • NOVEMBER/DECEMBER 2015
assistance for the technology tools should also be made
available.
FUTURE RESEARCH CONSIDERATIONS
Several opportunities for further research emerged. Note
the following with recommendations:
Quantitative Follow-Up Surveys
The design and development of ongoing shorter surveys,
designed using a Likert-type scale, to gauge responses to
PROBE questions from counselors at all levels, as well
as other student services personnel should be pursued.
Follow-up annual quantitative surveys for school coun-
selors and advisors could be used to gain deeper insights
into the barriers and enablers to data-driven decision
making or other professional practices. The use of the
PROBE questions to identify barriers and enablers in
data-driven decision making by other service providers
may also benefit from further research. A combination of
quantitative and qualitative surveys, as well as interviews
with other student services personnel could also help to
identify barriers and enablers to the practices of other
service providers. Surveying school site administrators
should also help expand the knowledge base regarding
these stakeholders’ opinions and perceptions regard-
ing the student services personnel in their building.
Identifying barriers and enablers related toward improv-
ing the desired outcomes for other job-related duties may
also prove to be beneficial and helpful.
Time Management Study or Guidelines
Lack of time, as a necessary resource, was repeatedly
identified as a significant barrier to effective data-driven
decision making. This recurring theme of time as a
resource should be explored further; several guidelines
for the effective use of counselors’ time have been
developed and published (American School Counselor
Association, 2005). A time management study related
to the job tasks of counselors and advisors may help to
identify further opportunities for improvement related to
the professional practices of data-driven decision making.
The current state of how counselors are spending their
day versus best practices may prove helpful in identifying
daily activities that are value added (American School
Counselor Association, 2005).
Training Needs Assessment and Delivery Models
The need for how to best train and build capacity among
counselorsandadvisorsoffersseveralopportunitiesforfur-
ther research. Further training needs have been identified
as a repeated barrier to conducting effective data-driven
decision making by high school counselors and advisors.
The effectiveness of different delivery models for train-
ing should be explored. The skills and knowledge related
to developing and implementing professional learning
communities and communities of practice are also an
essential part of effective data-driven decision making.
The design and development of effective professional
learning communities and communities of practice in
relation to conducting data-driven decision making could
be examined using mixed methods approaches (Creswell,
2003). Collaborative action research designs related to the
development of professional learning communities have
been proposed by Sagor (2010), though these have typi-
cally involved the work of the classroom teacher.
Effectiveness of Recommended Interventions
Testing the effectiveness of each of the recommenda-
tions that emerged was not within the original intended
scope of this study; however, it does present several
opportunities for further research. Follow-up research
could also be pursued as to whether the identified strate-
gies can be implemented one at a time or a combined
approach is just as effective or more so.
CONCLUSIONS
This initial exploratory study attempted to lay a ground-
work in how Binder’s (1998, 2009, 2011) six boxes model
might support leadership’s efforts in supporting the
implementation of data-driven decision making by high
school counselors and advisors. The systematic iden-
tification of enablers and barriers to this professional
In times of economic
cutbacks, as well as increased
federal and state mandates,
it is increasingly important
that district leadership be
equipped with approaches
that allow for timely
and helpful responses to
overcoming adversities
identified in the field.

10. Performance Improvement • Volume 54 • Number 10 • DOI: 10.1002/pfi 39
practice for counselors also has implications for manage-
ment practices in school districts with limited time and
resources to solve complex challenges. Furthermore, the
development of recommendations based on a systematic
approach also allowed for directly targeting opportuni-
ties for improvement, as well as enhancing the identified
strengths of the organization and its employees. In times
of economic cutbacks, as well as increased federal and
state mandates, it is increasingly important that district
leadership be equipped with approaches that allow for
timely and helpful responses to overcoming adversities
identified in the field.
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CARLOS ANTONIO VIERA, PhD, SPHR, is a seasoned educator with many years of diverse pro-
fessional experiences, including his service as the district director for the Office of Performance
Improvement, a direct report to the chief of accountability and system-wide performance in a large
urban school district. He has recently been recognized by the International Society for Performance
Improvement (ISPI) with the Distinguished Dissertation Award—Second Place. He has also provided
independent consulting services for several private and nonprofit organizations including Inside the
School, College Summit, CASEL, National Academic Educational Partners (NAEP), and Performance
Associates. He is currently serving as director, policy and planning for Miami Dade College. He may
be reached at carlos.viera@live.com.
KEVIN FREER, PhD, serves as core faculty, training and performance improvement, in the School of
Education at Capella University. He may be reached at Kevin.Freer@capella.edu.