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Educational Research and Evaluation
An International Journal on Theory and Practice
ISSN: 1380-3611 (Print) 1744-4187 (Online) Journal homepage: http://www.tandfonline.com/loi/nere20
A validation study of the Teacher Collaboration
Assessment Survey
Rebecca Woodland, Minji Kang Lee & Jennifer Randall
To cite this article: Rebecca Woodland, Minji Kang Lee & Jennifer Randall (2013) A validation
study of the Teacher Collaboration Assessment Survey, Educational Research and Evaluation,
19:5, 442-460, DOI: 10.1080/13803611.2013.795118
To link to this article: https://doi.org/10.1080/13803611.2013.795118
Published online: 18 Jun 2013.
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A validation study of the Teacher Collaboration Assessment Survey
Rebecca Woodland*, Minji Kang Lee and Jennifer Randall
Educational Policy, Research, and Administration, University of Massachusetts, Amherst, MA, USA
(Received 19 October 2012; final version received 12 March 2013)
Although teacher collaboration is a school improvement imperative, it persists as an
under-empiricized construct that has proven difficult to establish and assess with
certainty. In this article, the authors present a validation study of the Teacher
Collaboration Assessment Survey (TCAS). The TCAS operationalizes and measures
4 key domains of teacher collaboration: dialogue, decision making, action, and
evaluation, and has been used to examine the quality of teacher teaming in district-
wide comprehensive school reform efforts in the Northeastern and Mid-Atlantic
regions of the United States. Five sources of validity evidence recommended by
Standards for Educational and Psychological Testing (AERA, APA, & NCME, 1999)
are explicated, which establish a strong argument in support of the instruments’
validity. The authors discuss how educational leaders and researchers can use the
TCAS for leveraging teacher collaboration for instructional innovation and student
achievement, and to systematically examine teacher teaming and its relationship to
other educational outcomes.
Keywords: teacher collaboration; validity testing; teacher teams; teacher collaboration
survey
Introduction
The purpose of school is to see to it that all of our students learn at high levels, and the future of
our students depends on our success. We must work collaboratively to achieve that purpose,
because it is impossible to accomplish if we work in isolation. (Dufour, Dufour, & Eaker,
2005, pp. 232–233)
The significance of teacher collaboration for improving instructional quality and increasing
student achievement has been suggested by many educational reform studies and embraced
by educational policy makers around the world (Gable & Manning, 1997; Moolenaar, Daly,
& Sleegers, 2011). High-quality teacher teaming/collaboration is theoretically and empiri-
cally linked with increases in teacher knowledge and skills, instructional quality, and
student learning (Garet, Porter, Desimone, Birman, & Yoon, 2001). Teacher collaboration
has been found to account for as much variance in math and science achievement as student
background (Wenglinsky, 2000), and dramatic decreases in dropout rates and increases in
student achievement have been achieved in low-income urban schools where strong
collaborative relationships exist that support targeted instructional improvement
© 2013 Taylor & Francis
*Corresponding author. Email: rebeccahwoodland@gmail.com
Educational Research and Evaluation, 2013
Vol. 19, No. 5, 442–460, http://dx.doi.org/10.1080/13803611.2013.795118
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(Darling-Hammond, Ancess, & Ort, 2002; Wasley et al., 2000). Goddard, Goddard, and
Tschannen-Moran (2007) reported that teacher collaboration served as a statistically signifi-
cant, positive predictor of variation among schools with respect to student achievement in
both mathematics and reading. They found that a one-standard-deviation increase in teacher
collaboration was associated with increases of .08 SD in mathematics achievement and .07
SD in reading achievement at the school level.
Although teacher collaboration has emerged as nothing less than a contemporary zeit-
geist of school reform, its definition is elusive, inconsistent, and often theoretical. The term
“collaboration”is used to signify just about any type of relationship between people. Rela-
tively few can say with certainty what teacher collaboration looks and feels like, how to
determine if the structural, procedural, and inter-professional relationships within teacher
teams are healthy, or how to make them better (Woodland [nee Gajda] & Koliba, 2007,
2008). Therefore, one of the most important actions that educational leaders and researchers
interested in evaluating the process and effects of teacher collaboration must take is to oper-
ationalize the construct. Operationalization, whereby we descend the “ladder of abstrac-
tion”by describing reality through theory, is a central component of all empirical
evaluation research. Developing a specific and explicit understanding of the desirable
and high leverage elements and attributes of teacher collaboration is a necessary prerequi-
site for designing instruments and methods for evaluating it.
In this article, we articulate dialogue, decision making, action taking, and evaluation –
the four key attributes present in high-functioning forms of teacher collaboration. We
present a validation study of the Teacher Collaboration Assessment Survey (TCAS),
which has been used since 2008 by university-based researchers, school district superinten-
dents, building level principals, and teacher leaders to better understand and improve
capacity for teacher collaboration in multiple school districts in the Northeast and Mid-
Atlantic regions of the United States. The purpose of the validation study was to
examine the extent to which the TCAS demonstrated validity based on the Standards for
Educational and Psychological Testing (American Educational Research Association
[AERA], American Psychological Association [APA], & National Council on Measure-
ment in Education [NCME], 1999). Findings suggest the TCAS is a valid instrument for
evaluating teacher collaboration. The article concludes with a discussion of how the
TCAS could be used in educational evaluation and research.
Operationalizing teacher collaboration
Teacher collaboration is generally understood as teachers working together, and engaging in
reflective dialogue, with the common goal of improving practice and increasing student learn-
ing. Effective teacher teaming entails on-going teacher collaboration focused on improving
students’achievement of clear learning goals and opportunities to observe these in action
and to reflect on the reasons for their effectiveness (Hiebert, 1999). More specifically,
high-quality teacher collaboration entails teachers working closely with colleagues during
the workday to examine student-learning data and solve problems of instructional practice
through a continuous cycle of dialogue, decision making, action taking, and evaluation
(Goodlad, Mantle-Bromley, & Goodlad, 2004; Koliba & Woodland [nee Gajda], 2009;
Woodland [nee Gajda] & Koliba, 2007, 2008). It is this cycle of dialogue,decision
making,action taking, and evaluation (DDAE) around shared problems of practice directly
related to the “instructional core”that builds the capacity of teachers to make substantive,
positive changes in their instructional practice and produce significant increases in student
achievement (City, Elmore, Fiarman, & Teitel, 2009; Darling-Hammond et al., 2002;
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Dufour et al., 2005; Pounder, 1998; Stevens & Kahne, 2006; Wasley et al., 2000; Zito, 2011).
As surmised by McLaughlin and Talbert (2006), teacher teaming entails “teachers working
collaboratively to reflect on their practice, examine evidence about the relationship
between practice and student outcomes, and making changes that improve teaching and learn-
ing for the particular students in their classes”(p. 4). The four inter-related elements of the
teacher collaboration cycle of inquiry are depicted in Figure 1.
Dialogue
Dialogue is one key component of an effective cycle of collaborative inquiry. While low-
functioning and non-rigorous forms of teaming tend to foster dialogue that confirms present
teaching practices without determining its worth (Little, 1990), high-functioning teams will
surface disagreements and recognize, address, and resolve their differences (Hord, 2004).
Highly developed teacher teams will engage in collective dialogue about student learning,
the effects of instruction on student achievement, and how to provide an appropriate level of
challenge and support to every student. Lower functioning teacher teams may find them-
selves conversing about such topics as grouping, curriculum pacing and alignment, test-
taking strategies, field trip planning, scheduling and dividing tasks, allocation of materials,
configuring bulletin board displays, discipline, and coordinating learning activities
(Pappano, 2007; Troen & Boles, 2012). By systematically evaluating teacher dialogue,
school leaders can help teachers to avoid “making nice”, whereby practitioners confuse
mere congeniality and imprecise conversation with the serious professional reflective dia-
logue vital to school improvement (Barth, 1990; Little, 1990; Pappano, 2007; Schön, 1983).
Decision making
Decision making is a key aspect of a teacher team cycle of inquiry. As Schmoker (2005)
asserts, “[School] improvement demands an overt acknowledgement that some teaching
had a greater impact on learning”(p. 142). Bacharach (1981) identified five areas of
decision-making authority for teachers: (a) allocation decisions (budget, scheduling,
Figure 1. Collaborative teacher team cycle of inquiry.
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personnel); (b) security decisions (including class safety, attendance and discipline
procedures); (c) boundary decisions (such as union activities); (d) evaluation decisions
(determining the merit worth of student and/or teacher performance; and (e) instructional
decisions (including what to teach and how to teach it). Of the five, the most important
decisions that teacher teams can make are those that deal with the quality and merit of
their individual and collective instructional practices and their affects on student learning
(Little, 1990; Valli & Buese, 2007). Agreeing to implement general instructional strategies,
choosing textbooks, or crafting discipline procedures do not lead to targeted improvements
in practice or increases in student learning. Teachers must work together to uncover and
determine relative differences in instructional quality and make decisions about what and
how to improve practice.
Action taking
By itself,a decision or a plan to actdoes not produce results, hence action taking is a key element
of a teacher team cycle of inquiry. Pfeffer and Sutton (2000) observed, “existing research on the
effectiveness of formal planning is clear …planning is essentially unrelated to organizational
performance”(p. 42). If teachers do nottake actions as a result of their team decisions, the cycle
of inquiry ceases to move forward and continuous improvement falters (McLaughlin & Talbert,
2006; Woodland [nee Gajda] & Koliba, 2008). Actions must be directlyrelated to the improve-
ment of practice and entail a degree of sophistication. If left unexamined, teacher team action
taking mayhave a tendency to be somewhat “shallow”or “superficial”and less than adequate to
the complexities of teaching (Little, 1987; Maeroff, 1993; Zahorik, 1987). Teachers, in order to
prevent conflict, may avoid issues of pedagogical and philosophical importance –resulting in
the entrenchment of instructional practices (Pounder, 1998).
Evaluation
Evaluation of practice is a crucial component of a fully developed teacher team cycle of
inquiry. School improvement experts urge educators to continually assess their effectiveness
on the basis of tangible evidence that students are acquiring essential knowledge, skills, and
dispositions (Earl & Katz, 2006; Goldring & Berends, 2009, Stiggins, 2005). The extent to
which the actions of a teacher team and changes made to practice have merit or worth is deter-
mined through evaluation and action research: the systematic collection, analysis, and use of
data (Gay, Mills, & Airasian, 2005; Patton, 2008). High-quality teacher collaboration entails
the collection and analysis of data about student learning and instructional quality. Teachers
in high-functioning teams will systematically collect and analyse both quantitative infor-
mation (such as scores on formative and summative assessments of student learning) and
qualitative information (such as notes taken during a classroom observation of a colleague
and student-written work), whereas less effective teacher teams tend to rely on anecdotes,
hearsay, and general recollections to inform their dialogue and decision making.
Teacher Collaboration Assessment Survey (TCAS)
The TCAS was designed to operationalize DDAE, the four main attributes of teacher col-
laboration present in effective teacher teams. It has been developed over time through an
iterative process involving university-based subject-matter experts (SME), school district
leaders, and teachers and piloted in multiple school districts in the Northeast and Mid-
Atlantic regions of the United States. For example, the TCAS has been administered in
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“District A”in Connecticut on an annual basis since 2008, and since 2010 in “District B”in
Massachusetts. The survey is composed of questions and Likert-type items measuring the
components of DDAE (see Appendix 1). Data generated via the TCAS have been used by
university-based researchers, superintendents, building level principals, and teacher leaders
to better understand and improve district capacity for teacher collaboration and the affects
of teacher collaboration on instructional improvement and student learning. The purpose of
this study was to systematically examine the extent to which the TCAS is a valid instrument
for measuring teacher collaboration that can be recommended for generalizable use in the
field of educational research and evaluation.
Validation of the Teacher Collaboration Assessment Survey
The Standards for Educational and Psychological Testing (AERA, APA, & NCME, 1999)
noted that validity is the most fundamental consideration in developing and evaluating tests,
in that it is the degree to which theory and evidence support the interpretation of test scores
entailed by the proposed uses of tests. Validation is a process of developing a scientifically
sound argument and accumulating evidence to provide a basis for the argument. The
decisions about what types of evidence are important are clarified by developing a set of
propositions that support the proposed interpretation for the use of the testing, in this
case the TCAS. The validation process evolves as these propositions are articulated and evi-
dence is gathered to evaluate their soundness. The sources of validity evidence described by
the Standards include: (a) evidence based on test content, (b) evidence based on response
processes, (c) evidence based on internal structure, (d) evidence based on relations to other
variables, and (e) convergent and discriminant evidence. In this study, the authors gathered
and examined validity data about the TCAS from these five sources.
Method
Sample
The 2012 TCAS data from District A and District B were systematically investigated for
validity and reliability. There were 294 respondents in District A (138 elementary school
staff members, 42 middle school staff, and 114 high school staff) and 297 respondents in
District B (160 elementary school staff, 51 middle school staff, 73 high school staff, 11
staff at special education schools, and 2 staff members at a central office). District A had
208 general education teachers, 42 special education teachers or clinicians (i.e., school
counsellor, school psychologist, therapist), one instructional assistant, 10 leaders (i.e.,
department heads, deans, assistant principals, or curriculum leaders), and 33 teachers in
special subjects (i.e., physical education, art, music, librarian, or English language learn-
ing). District B had 128 general education teachers, 62 special education teachers, 32 clin-
icians (i.e., school counsellor, school psychologist, speech therapist), 29 teachers of special
subjects (i.e., physical education, art, music, librarian, English language learning), 23
leaders (i.e., principal, assistant principal, dean, team leader), 21 instructional assistants,
and 2 central office administrative staff members.
Measure
The TCAS includes Likert-type items that measure the four components of a teacher team
cycle of inquiry: DDAE (see Appendix 1). Within each component are statements related to
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each construct: 11 statements for Dialogue, Action Taking, and Evaluation and 10 state-
ments for Decision Making. Respondents are asked to rate their agreement along a
6-point Likert scale ranging from Strongly Disagree to Strongly Agree.
Data analysis
The validation of the TCAS was carried out by providing all five sources of evidence
suggested by the Standards for Educational and Psychological Testing: evidence based
on content, evidence based on response processes, evidence based on internal structure, evi-
dence based on relations to other variables, and convergent and discriminant evidence
(AERA, APA, & NCME, 1999).
Evidence based on content
Evidence based on content came from the judgements of subject-matter experts about the
alignment between collaboration theory and the items on the survey, and documentations of
the process through which items were developed or added. Evidence also came from a
formal group interview and piloting of the survey with 12 school-based personnel that
took place in March of 2012. The purpose of the group interview was to (a) obtain evidence
of content validity of the survey, (b) assess how clear and comprehensive the survey ques-
tions are to teachers, and (c) consider and incorporate their suggestions for improving the
quality of the survey. The group was asked to judge the representativeness of the chosen set
of items, the ease of understanding questions, the format of the items, and wording. Align-
ment between the construct and the items were also gathered from the focus group on a
Likert-type scale of 1 (not at all)to5(very well).
Evidence based on response processes
Evidence based upon response processes is the determination of whether those who took the
survey understand what they are being asked and respond accordingly to the construct
being measured. This type of evidence was generated via pre- and post-survey adminis-
tration interviews with school leaders and teachers, as well as the focus group interview
described in the preceding paragraph. Evidence in this domain speaks to the extent to
which respondents viewed and understood the TCAS items and its instructions in the
way that the tool was intended.
Evidence based on internal structure
The evidence based on internal structure was evaluated by examining the five requirements
of accurate measurement. These five requirements include:
(1) Have we succeeded in defining a discernible line of increasing intensity?
(2) Is item placement along this line reasonable?
(3) Do the items work together to define a single variable? (consistency)
(4) Have we succeeded in separating persons along the line defined by the items?
(5) How valid is each person’s measure? (Wright & Masters, 1982, pp. 90–91).
The first three questions allow for an evaluation of the instrument’s items’ability to work
together in defining a meaningful variable. Questions 4 and 5 address the extent to which
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the individuals are separated along the same line and the meaningfulness/reasonableness
(e.g., person fit) of their individual measures. To determine where both items and teachers
are located on the latent trait continuum, and consequently, if this placement is reasonable, a
logit scale is used. A logit scale is simply an interval scale in which the unit intervals
between the locations of persons and items have a uniform value or meaning. The logit
scale, theoretically, ranges between –∞to ∞logits and mirrors the underlying latent con-
struct. For example, in the construct of teacher collaboration, –∞logits represents the
lowest quality teacher collaboration, and ∞logits represents the highest quality teacher
collaboration.
To determine if the items and persons are adequately separated along the logit scale, we
examine the reliability of separation, which provides a measure of the degree to which the
“elements”within a variable or a facet (i.e., the individual respondents or items) are separ-
ated. Essentially, this ratio represents the ratio of true score variance to observed score
variance (Wright & Masters, 1982). In addition, to address both item fit and person fit
(consistency), we refer to the fit statistics such as the outfit mean statistics, the unweighted
mean square residual differences between observed values and expected values (Wright &
Masters, 1982). Outfit statistics are useful for diagnosing potential item misfit to the
measurement model. Outfit mean square statistics greater than 1.2 may indicate inconsistent
responses by respondents or items. Outfit statistics greater than 2.0 indicate a great deal of
unexplained variance providing more misinformation than information. FACETS 3.62
(Linacre, 2007), a Rasch Measurement software programme, was used to estimate all
parameters.
Data analysis based on the Rasch model (1980) (as opposed to more classical method-
ologies such as factor analysis) was chosen for several reasons: (a) Unlike confirmatory
factor analysis, the Rasch model allows for person and item parameters to be estimated
independently of each other allowing for invariance across persons and items; (b) Rasch
analysis is a hierarchical implication model, in which difficult items are endorsed by respon-
dents who possess a greater amount of the trait, and respondents with a small amount of the
trait endorse only the easy items. Thus, the Rasch analysis enables one to design a question-
naire that employs items with a range of difficulty that matches the range of person
measures in the target audience. Factor analysis, on the other hand, is a correlational
model, and items that are difficult to endorse may not correlate strongly with items that
are easy to endorse, even if these items measure the same trait. Because easy and difficult
items may not load together, factor analysis tends to favour items that fall within a narrow
range of difficulty; and (c) with factor analytic methods, items that are redundant or lack
item independence tend to perform very well because they load strongly on common
factors. The high correlations tend to artificially increase reliability, leading scale develo-
pers to believe the items are measuring more accurately than they truly are. In a Rasch
analysis, however, such redundant items become candidates for deletion because they
provide no unique information about the respondents.
Rasch analysis rests on the assumption/requirement that a set of items is intended to
measure one underlying construct, in this case Teacher Collaboration. To insure the uni-
dimensionality of the entire scale, we first conducted a principal components analysis
which included all items across the four subcomponents of Teacher Collaboration.
Reckase (1979) has suggested that unidimensionality is supported when the first
component accounts for at least 20% of the variance. In this case, as illustrated in the
scree plot in Figure 2, the first component accounted for 46.80% of the variance, while
the second component 7.73% suggesting that Teacher Collaboration is, in fact, a
unidimensional construct.
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Evidence based on relation to other variables
Analyses of the relationships of scale scores to external variables provide an important
source of validity evidence. The external variables can include measures of a criterion
that the scale is expected to predict, or other tests measuring the same construct or
related or different constructs. According to the Standards, this evidence addresses the
degree to which these relationships are consistent with the construct underlying the pro-
posed test interpretations (AERA, APA, & NCME, 1999). For this evidence, we considered
the work of Zito (2011). Zito (2011) examined (a) the relationship between teacher collab-
oration (measured by the survey) and student achievement outcomes, (b) the relationship
between administrative support and student achievement, (c) the relationship of the inter-
action of the teacher collaboration and administrative support to student achievement,
and (d) the relationship between the quality of collaboration and changes in teachers’
instructional practice.
Convergent and discriminant evidence
The convergent and discriminant evidence can be evaluated by examining the correlations
between the variables known to be related to the construct of teacher collaboration. Conver-
gent evidence is provided by studying the relationships between scale scores and other
measures intended to assess similar constructs, and discriminant evidence by studying
the relationships between test scores and measures of different constructs. To that end, Pear-
son’s product-moment correlations were computed to investigate the relationship among
the scale scores. Specifically, the ratings on each scale of the cycle of inquiry were
summed within their respective scales and correlated with other variables for which we
had information such as (a) the percentage of one’s teacher team time spent on instructional
Figure 2. Scree plot of the principal components.
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practice and the improvement of student learning, (b) the extent to which the respondents
perceived their instructional practice to have been improved as a result of participating in
his/her team, (c) whether one’s teacher team has documented evidence of improved
student learning as a result of the work of the group, (d) the extent to which the respondent
shares the belief that high-quality teacher collaboration brings about improvement in
instructional practice and increases in student learning, and (e) the extent to which one’s
team influenced the choices she/he made about their instructional practice and how to
improve student learning. Based on the definition of the construct, we can predict that all
the variables mentioned above would have positive relationships with one another; the
four components in the cycle of inquiry (DDAE), in particular, will be more strongly
correlated with one another than the rest of the variables.
Results
Evidence based on content
The attributes of high-quality dialogue, decision making, action taking, and evaluation were
operationalized based on an SME’s extensive knowledge about the construct of teacher col-
laboration, the theoretical and empirical literature on collaboration, and consultation with
peers. Teachers and administrators from two secondary-school improvement initiatives
(i.e., High Schools on the Move and Teaching All Secondary Students) contributed directly
to the original development, refinement, and piloting of the teacher collaboration assess-
ment rubric, on which the survey items are based (Woodland [nee Gajda] & Koliba,
2008). This process gives a strong support for content validity of this survey. In addition,
the original survey was modified based on the findings of Lee and Randall (2011), Colvin,
Crotts, Li, and Randall (2011), and Cook, Foster, and Randall (2011), to include a greater
number of items that would capture a range of teacher/team functioning in the cycle of
inquiry.
Results of the focus group ratings about the extent to which there was alignment
between the TCAS content and the construct it is intended to measure revealed high align-
ment ratings (mean rating 4.5). The items on the cycle of inquiry garnered mean ratings
ranging from 4.2 to 4.6, and the open-ended items acquired the mean ratings ranging
from 4 to 5. The focus group strongly agreed that TCAS items accurately measure each
theme and are aligned with the overall purpose of the survey.
Evidence based on response processes
Evidence based upon response processes was also investigated. Pre- and post-TCAS
administration interviews with school leaders and teachers have been conducted each
year since 2008 by District A and for the past 2 years in District B. School stakeholders
were asked to comment about how clear they found the survey questions to be, to
explain how they went about answering the questions, what they thought the purpose of
the survey was, and how long it took them to complete the survey. In addition, a focus
group was conducted whereby participants took the survey and were subsequently asked
questions about how they viewed and understood what the instrument was asking and
how their answers matched those understandings. Average scores from the ratings gener-
ated via focus group and the qualitative data provided pre- and post-administration of the
survey by school-based stakeholders confirm that respondents viewed and understood
the TCAS items and its instructions in the way that the tool was intended. This validity
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evidence overlaps with content validity (discussed previously) because it is concerned with
how and why individuals respond the way they do. Our analysis of evidence based on
response processes suggested that individuals read and interpreted TCAS items in a
similar manner, and attempted to respond to the items using a framework that aligns
with what the scales were designed to measure.
Evidence based on internal structure
The five requirements of measurement related to internal structure were investigated. The
five requirements are reiterated here:
(1) Have we succeeded in defining a discernible line of increasing intensity?
(2) Is item placement along this line reasonable?
(3) Do the items work together to define a single variable? (consistency)
(4) Have we succeeded in separating persons along the line defined by the items?
(5) How valid is each person’s measure?
The first three questions help in evaluating the ability of the scales’items to work
together to define a meaningful variable. This evaluation can be aided by examining the
item reliability of separation, a variable map, and outfit statistics for items. As seen in
Table 1, the item reliability of separation was 0.98, which indicated that the scale defines
a discernible line of increasing intensity quite well.
Moreover, the overall outfit mean square of 1.07 suggested that, generally speaking, the
items in the scale are working well together to define their construct. There were no nega-
tively discriminating items in any of the scales. Two items –Dialogue (C) and Evaluation
(J) –exhibited outfit values greater than 2.0, suggesting the information from these two
items may be of little value. The chi-square test of the fixed effect hypothesis suggested
that the items in each scale have a range of difficulty (i.e., the statements bring out different
levels of agreement). Items were spread from –0.71 to 2.03 logits. As an illustration, the
placement of the items on the scale is graphically presented on the variable map in
Figure 3.
Table 1. Item measurement results.
DDAE
Measures
Mean 1.27
SD 0.72
N40
OUTFIT
Mean 1.07
SD 0.40
> 2 (count) 2
PBIS
1
(Counts)
< .00 0
0–0.2 0
Reliability of Separation .98
Chi-Square Statistic 3064.8
Degrees of Freedom 39
1
Point-biserial correlation is a measure of item discrimination.
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The last two requirements of measurement address the extent to which the teachers are
separated along the same line, and reasonableness of the individual answers. This evalu-
ation is aided by examining the person reliability of separation and outfit mean square
for persons. The reliability of separation for persons, seen in Table 2, was 0.74 including
extreme observations, and from .83 excluding them. These values imply that the scale
reasonably separates persons along the scales (e.g., low to high level of collaboration).
In addition, the mean outfit mean square for persons of 1.05 suggested that, overall, the
scale allows for a valid measure of each person.
Evidence based on relation to other variables
The relationship of the TCAS scale scores to instructional improvement, and student
achievement outcomes and administrative support were investigated by Zito (2011) to
test the following hypotheses.
(H1) Higher quality teacher collaboration is associated with greater changes in teachers’
instructional practice.
(H2) Higher quality teacher collaboration is associated with higher levels of student
achievement.
Zito examined TCAS results, specifically by summing the individual teacher responses to
the DDAE scales (shown in Appendix 1) and by aggregating and then averaging these
ratings for each individual teacher team in the district. Student achievement outcomes
came from the scaled scores of an annual state assessment, which was a standardized, cri-
terion-referenced assessment with sub-tests in math, reading, and writing for Grades 3
through 8, and science in Grades 5 and 8.
Zito’s (2011) first hypothesis was not supported in that there was no statistically signifi-
cant relationship between either of the separate TCAS measures of the quality of teacher
collaboration related to DDAE and student achievement outcomes on any of the sub-
tests. He explained that this lack of significant relationship may relate to the ceiling
effect that occurs when the range of difficulty of test items is limited, thereby restricting
the scores at the higher end of the possible score continuum. However, a secondary analysis
revealed a statistically significant positive relationship between collaboration and perceived
Table 2. Respondent’s measurement results.
Respondents
Measures
Mean 0.18
SD 1.37
N516
OUTFIT
Mean 1.05
SD 0.77
> 2 (count) 46
Reliability of Separation
1
.83 (.83)
Chi-Square Statistic 3612.0
Degrees of Freedom 515
1
The numbers in parentheses are the reliabilities computed by excluding ill-
fitting examinees.
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increases in student learning (r= .48, p< .01), which suggested that teachers who reported
higher levels of collaboration observed evidence of increased student learning in ways that
were not measured by the standardized assessments. Zito also addressed the relationship
between teacher collaboration and reported changes in instructional practice: A strong
Figure 3. Variable map for the entire scale measuring DDAE.
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Table 3. Correlation Matrix.
1
Dia Dec Act Eval Lead Time Impro Belief InfTeam Eviden Expect
Dia 1
Dec .77*** 1
Act .72*** .83*** 1
Eval .58*** .60*** .61*** 1
Lead .55*** .46*** .49*** .53*** 1
Time .55*** .58*** .55*** .44*** .37*** 1
Impro .41*** .46*** .46*** .43*** .35*** .36*** 1
Belief .14*** .12** .07 .08 .18** .05 .13** 1
InfTeam .48*** .51*** .48*** .45*** .35*** .31*** .44*** .13*** 1
Eviden .29*** .31*** .30*** .41*** .32*** .32*** .29*** .12* .22*** 1
Expect .14*** .09* .09 .16*** .26*** .13** .11* .05 .13*** .00 1
Notes:
1
Dia refers to Dialogue, Dec to Decision making, Act to Action, Eval to Evaluation, Lead to the Role of Leadership, Time to the percentage of one’s primary team time spent on
instructional practice and the improvement of student learning, Impro to the extent to which the respondent’s instructional practice improved as a result of participating in his/her primary
team, Belief to the extent to which the respondent shares the belief that high-quality teacher collaboration brings about improvement in instructional practice and increases in student
learning, InfTeam to the extent to which one’s team influenced the choices she/he made about their instructional practice and how to improve student learning, Evidence to whether one’s
primary team has documented evidence of improved student learning as a result of the work of the group, Expect to the extent to which the respondent experienced an increase in an
overall expectation to collaborate in the current school year as compared to previous years.
*p<.05, **p<.01, and *** p<.001.
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and statistically significant relationship (r= .513, p< .01) was found between these two
variables.
The relationship between teacher collaboration and changes in instructional practice
was also investigated using the most recent dataset in this validation study. Table 3
reports moderate and statistically significant correlations between perceived
improvement in instructional practice and teachers’dialogue (r= .41, p< .001), decision
making (r= .46, p< .001), action taking (r= .46, p< .001), and evaluation practices
(r= .43, p< .001). The findings described establish evidence of the validity of the
TCAS in relation to other related variables.
Convergent and discriminant evidence
As explained earlier in this article, strong conceptual links exist between the four com-
ponents of a fully developed cycle of inquiry: dialogue, decision making, action taking,
and evaluation. In addition to these conceptual links, we investigated the empirical corre-
lation values within the construct of the cycle of inquiry (DDAE). The relationships
between Dialogue, Decision making, and Action taking were statistically significantly
strong, ranging from .72 to .83 (Table 3), and the correlations between these variables
and the Evaluation scale ranged from .58 to .61, which provides convergent and discrimi-
nant evidence of TCAS validity.
The percentage of time teachers spent discussing teaching practices and student learning
moderately correlated with DDAE, ranging from .44 to .58. The extent to which the respon-
dents believed their instructional practice improved as a result of participating in his/her
teacher team correlated moderately with the cycle of inquiry, ranging from .41 to .46.
The extent to which one’s teacher team influenced the choices she/he made about their
instructional practice to improve student learning also correlated moderately strongly
with the four major scale scores, ranging from .45 to .51. Whether one’s primary team
has documented evidence of improved student learning as a result of the work of the
group was somewhat weaker, ranging from .30 to .41. All the correlations just described
were significant, p< .001.
Discussion
In this study, the authors investigated the extent to which the Teacher Collaboration Assess-
ment Survey is a valid measure of teacher collaboration. Evidence for TCAS validity was
provided in all five categories suggested by the Standards for Educational and Psychologi-
cal Testing: evidence based on content, evidence based on response processes, evidence
based on internal structure, evidence based on relations to other variables, and convergent
and discriminant evidence (AERA, APA, & NCME, 1999). The evidence based on content
was provided by describing the close connection between theory and survey constructs;
findings indicate close alignment between items and their respective scales as well as
between the scales and the overall purpose of the survey. In addition, our analysis of evi-
dence based on response processes suggested that individuals read and interpreted TCAS
items in a similar manner, and attempted to respond to the items using a framework that
aligns with what the scales were designed to measure. The four components within the
cycle of inquiry were analysed with respect to the requirements for appropriate measure-
ment. The scale was able to reliably measure respondents’scores along a logit scale with
increasing intensity, although data suggest that the instrument could be further improved
by adding more items that measure extreme ends of the scales, especially those items
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that are less likely to be agreed. The relationship of teacher collaboration to student achieve-
ment and administrative support as well as improvement in instructional practices provided
evidence that the construct measured by the survey has the expected relationship with
these other variables. Finally, the extent to which the scales support the construct of
teacher collaboration was evaluated by examining the correlations among the variables
of interest. The pattern of correlations supported the convergent and discriminant validity.
That is, the scale scores within the cycle of inquiry were more highly correlated with one
another than other related variables. The evidence based on content, requirements for
appropriate measurement, and the relationships with other variables support the proposed
use of the TCAS. Future studies could investigate the predictive validity of teacher collab-
oration on improved instructional practices and student learning.
The TCAS has been used over time to examine the quality of teacher collaboration in
several school districts in the Northeast and Mid-Atlantic regions of the United States. It
was originally created by university-based subject-matter experts and piloted and revised
through state-level school reform efforts (Woodland [nee Gajda] & Koliba, 2008). To
increase validity and generalizability of use, the instrument has been revised to narrow its
focus on DDAE, discard redundant items, and change the order and format of some items
for better flow and accessibility. Educational researchers and evaluators could use the
entire TCAS, or its subscales to measure aspects of teacher collaboration. Such measures
can be correlated with other variables of importance to school improvement stakeholders
such as instructional improvement, teacher retention, school climate, and student learning.
School leaders are increasingly employing techniques for tracking and evaluating the
quality of teacher collaboration through such means as requiring and reviewing team
agendas, collecting minutes, and observing teacher teams in action (Pappano, 2007). Edu-
cational researchers and leaders with whom the authors have worked confirm that the evalu-
ation of teacher collaboration can be greatly enhanced through the use of a measurement
instrument such as the TCAS, which operationalizes fundamental elements of teacher
teaming in detail. Data generated from the administration of the TCAS can be used by
administrators and practitioners to determine where and how teacher collaboration can
be celebrated, replicated, corrected, and improved. Principals have observed teacher team
meetings and reviewed archival data (such as meeting agendas, minutes, and products)
to evaluate and score the quality of team functioning using the TCAS, and have then
used the results to engage in conversation with teachers about how to improve the work
of the team. Individual teachers can use the TCAS to assess the attributes of their own
team’s cycle of inquiry; individual TCAS ratings from multiple members can then be dis-
cussed and analysed by the team as a whole. Such a process will surface varying percep-
tions about the quality of a teams’inquiry process and stimulate discussion for how to
improve. TCAS findings and the specific content and language of the survey items
provide principals and teachers with direction about how to make targeted and evi-
denced-based improvements in the quality of teacher team dialogue, decision making,
action, and/or evaluation. Educational evaluators and researchers can use the tool to empiri-
cally investigate teacher teaming/collaboration as an independent variable and its affect or
relationship to important dependent variables such as teacher knowledge and skill, instruc-
tional quality, and student learning.
Conclusion
Consensus exists that teacher collaboration is one of the essential requisites, if not the most
essential, for achieving substantive school improvement and critical student learning
456 R. Woodland et al.
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outcomes. As Dufour et al. (2005) attest, “In both education and industry, there has been a
prolonged, collective cry for such collaborative communities for more than a generation
now. Such communities hold out immense, unprecedented hope for schools and the
improvement of teaching”(p. 128). Teacher collaboration has been shown to have signifi-
cant positive effects on the quality of teachers’knowledge and skills and changes in class-
room practice –the primary factors attributed to improvements in student learning. Teacher
collaboration/teaming is a high-leverage school improvement strategy within the control of
school improvement stakeholders.School district administrators set and enact policy that
directly affects quality of teacher collaboration, strategically plan human capital develop-
ment, schedule teacher meeting times, help set agendas for teacher meetings, provide
vision for the work and direction of teacher teams, and monitor the implementation and
effects of teacher collaboration (Croft, Coggshall, Dolan, & Powers, 2010; Woodland
[nee Gajda] & Koliba, 2008). Efforts to enable, evaluate, and improve collaboration
among teachers will be rewarded with improved student achievement (Vescio, Ross, &
Adams, 2008). Educational leaders and researchers can use the TCAS as a tool for lever-
aging teacher collaboration for instructional innovation and student achievement, and to
systematically examine teacher teaming and its relationship to other educational outcomes.
Notes on contributors
Rebecca H. Woodland, Associate Professor of Educational Leadership in the Department of
Educational Policy, Research and Administration at the University of Massachusetts Amherst,
specializes in the examination of inter-professional collaboration in K-12 educational settings and
its effects on instructional practice and student learning.
Minji K. Lee is a doctoral candidate in the Psychometric Methods, Educational Statistics, and
Research Methods program and works for the Center for Educational Assessment at the University
of Massachusetts Amherst.
Jennifer Randall is an assistant professor of education at the University of Massachusetts Amherst.
Her research interests include the assessment and grading practices/philosophies of classroom
teachers, the utility and appropriateness of test accommodations for special populations, as well as
scale development for difficult to measure constructs.
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Appendix 1. Teacher Collaboration Assessment Survey DDAE scale items
1. Dialogue
a. The purpose of our collaboration is to systematically improve instruction to increase student
learning.
b. The membership configuration of my primary teacher team is appropriate –the right people
are members of the group.
c. Team meetings are consistently attended by ALL members.
d. Agenda for team dialogue is pre-planned, written, and accessible to all in advance of
meeting.
e. Team meetings are purposefully facilitated and employ the use of protocols to structure and
guide dialogue.
f. A thoughtful, thorough and accurate account of team dialogue, decisions and intended
actions is recorded.
g. Every member has access to running records of team dialogue, decisions and subsequent
actions to be taken.
h. Inter-professional disagreements occur regularly –these disagreements are welcomed,
openly addressed and lead to new shared understandings.
i. Team members participate equally in group dialogue; there are no “dominators”or “hiber-
nators”in the group.
j. Our dialogue is consistently focused on examination of evidence related to performance and
the attainment of goals.
k. The topic of the dialogue is focused on our instructional practices and not other issues (e.g.,
school schedules, textbook purchases, fund raising, discipline, students’family issues,
chaperoning).
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2. Decision making
a. My team regularly makes decisions about what instructional practices to initiate, maintain,
develop, or discontinue.
b. All of our decisions are informed by group dialogue.
c. The process for making any decision is transparent and adhered to –everyone knows what
the decisions are/were and how and why they were made.
d. The decisions we make are clearly and directly related to the improvement of instructional
practice and the improvement of student learning.
e. The team uses a specific process for every decision it makes (e.g., consensus, majority or
some other decision-making structure).
f. Team members regularly identify specific instructional practices that they will initiate or
maintain to increase student learning.
g. Team members regularly identify strategies they will change or discontinue.
h. Our group regularly determines what information about instructional practice and student
learning needs to be obtained.
3. Action
a. Each group member takes actions related to individual/team learning as a result of team
decision making.
b. As a result of group decision making, each one of us makes meaningful (pedagogically
complex) adjustments to our instructional practice.
c. Actions are directly related to student learning.
d. Each member knows what actions (related to learning) to take next at the end of the meeting.
e. Team member actions are coordinated and interdependent.
f. Each individual teacher employs specific instructional strategies that will increase student
learning.
g. Each individual teacher discontinues less effective strategies.
h. Actions that are taken after or between meetings are distributed equitably among team
members (i.e., every member takes steps to improve individual or team learning).
i. Each member can name some aspect of instruction that we have stopped/started or changed
as a result of the group decision making.
j. Each member of the team commits to carrying out team actions.
4. Evaluation
a. As a group we regularly collect and analyze quantitative data (e.g., numbers, statistics,
scores) about member teaching practices.
b. As a group we regularly collect and analyze qualitative data (e.g., open-ended responses,
interviews, comments) about member teaching practices.
c. As a group we regularly collect and analyze quantitative data (e.g., numbers, statistics,
scores) about student learning.
d. As a group we regularly collect and analyze qualitative data (e.g., numbers, statistics, scores)
about student learning.
e. We observe the classroom instruction of our colleagues.
f. We collect information on the quality of the instruction during our observation.
g. We analyze data collected through peer observation of classroom instruction.
h. We use student performance data to evaluate the merit of our instructional practices.
i. We regularly share evaluation data on the effect of our instruction in our primary team.
j. The accomplishments of our team are publicly recognized.
k. Our team can accurately and thoroughly articulate and substantiate its accomplishment
related to student learning over time
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