Hamman stevens1

NATIONAL FORUM OF APPLIED EDUCATIONAL RESEARCH JOURNAL
VOLUME 20, NUMBER 2, 2006--2007
DEVELOPING THE LEARNING TO TEACH
QUESTIONNAIRE: MEASURING
INTERACTION BETWEEN COOPERATING
AND STUDENT TEACHERS
Doug Hamman
Arturo Olivárez, Jr.
Tara Stevens
Texas Tech University
ABSTRACT
Interaction between cooperating and student teachers during the teaching
practicum may have important effects on the preparation of new teachers. In
order to examine possible outcomes associated with differing levels of
cooperating and student teacher interaction, we developed the Learning to
Teach Questionnaire (LTQ) using a dyadic interaction analysis framework
(Grannot, 1993). A sample of 274 student teachers was randomly split into two
samples to conduct, first an exploratory factor analysis, and then a
confirmatory analysis of the factor structure of the instrument. The two factors
that emerged in the exploratory factor analysis (Imitation and Guidance) were
confirmed with structural equation modeling techniques in the second phase of
the study. Usefulness of the instrument to teacher education is discussed.
earning to teach involves a change in the knowledge and skill
of the teacher candidate (e.g., Jones & Vesilind, 1996). Many
important program features, persons, and situations have been
identified as possible contributors to this change (Wideen, Mayer-
Smith, & Moon, 1998). Cooperating teachers play a pivotal role in
teacher education during the teaching practicum (Hollingsworth,
1989). Cooperating teachers are believed to influence new teachers’
feelings of career satisfaction, perception of their professional role,
philosophies of teaching (e.g., Goodfellow & Sumsion, 2000;
L
4

5 NATIONAL FORUM OF APPLIED EDUCATIONAL RESEARCH JOURNAL
Kelchtermans & Ballet, 2002; Kremer-Hayon & Wubbels, 1993), their
efficacy beliefs about teaching (Darling-Hammond, Chung, & Frelow,
2002) and even their instructional behaviors (Moskowitz, 1967;
Seperson, & Joyce, 1973). The focus of the study reported herein is
the development of an instrument intended to measure one important
mechanism through which change is affected -- interaction between
cooperating and student teachers.
Interaction with the Cooperating Teacher
Interaction between cooperating and student teachers is
typically poor. The manner of interaction between cooperating and
student teachers may offer some clue as to how cooperating teachers
communicate important aspects of working in schools and classrooms.
Guyton and McIntyre’s (1990) review of research related to
conferences between cooperating and student teachers suggests,
however, that the content of these conferences may be less than
optimal for assisting new teachers in learning to teach. Guyton and
McIntyre summarized research findings on conferences as involving
“low levels of thinking where descriptions and direction-giving
interactions predominate. Analysis and reflection on teaching are not
common; the substantive issues of conferences tend to focus on
teaching techniques, classroom management, and pupil
characteristics” (p. 525). In addition, cooperating teachers dominate
most speaking during conferences, and student teachers tend to adopt a
passive role during interactions (O’Neal & Edwards, 1983;
Tabachnick, Popewitz, & Zeichner, 1979; both cited in Guyton &
McIntyre).
More recently, Borko and Mayfield (1995) also examined
characteristics of conferences between a small number of cooperating
and student teachers. The researchers found that the content, duration,
and depth of discussions during the conferences varied greatly. In
most conferences, however, cooperating teachers made specific
suggestions about student teachers’ lessons and classroom

Doug Hamman, Arturo Olivárez, Jr., & Tara Stevens 6
management, discussed the behavior of specific students, and offered
suggestions for content-specific teaching strategies.
Similar to the findings discussed above, Borko and Mayfield
(1995) characterized the overall quality of the interactions as routine
rather than reflective, but noted two distinct patterns of interaction
during conferences. One group of cooperating teachers, who believed
they should be actively involved in their student teachers’ learning,
held regularly scheduled conferences that tended to be longer in
duration. Student teachers’ perceptions of the influence of these
cooperating teachers was positive and extended to a wide range of
teaching activities, including planning and teaching in their specific
content area. In contrast, the second group of cooperating teachers,
who seemed to believe they should not be as involved in their student
teachers’ learning, held few conferences most of which lasted for
shorter periods of time, and student teachers were much less positive
about how the cooperating teacher contributed to their learning to
teach.
Some interaction is better than none. Although researchers
have been critical of the types and quality of interaction that occur
between cooperating and student teachers, there is some evidence that,
overall, even this lower-level of interaction may bring about positive
outcomes for new teachers. For example, Darling-Hammond, Chung,
and Frelow (2002) compared new teachers who had completed their
certification requirements via a traditional route, including a student-
teaching practicum, to new teachers who had completed their
certification via an alternative route with no teaching practicum.
Several interesting differences were found, including that teachers (a)
felt more prepared to assume their teaching duties, (b) had a higher
sense of teacher efficacy, (c) felt a greater sense of responsibility for
student learning, and (d) were more likely to have plans for remaining
in the teaching field. These findings suggest that interaction with a
cooperating teacher during the teaching practicum may have an
important effect on new teachers’ development.

Teacher Development, Interaction, and Cognitive Change
The characteristics of effective teacher-development activities
are strikingly similar to those identified by developmentalists as being
associated with cognitive change among children and adolescents.
These similarities may offer some insight into exactly what role
interaction between cooperating and student teachers may play in the
development of new teachers.
Characteristics of effective teacher development. In a recent
study, Garet, et al. (2001) identified four features of professional
development experiences that were associated with positive change in
teacher development. Specifically, Garet et al. found that
professional-development experiences were significantly associated
with teachers’ self-report of knowledge acquisition and change in
instructional practice when they: (a) were perceived as useful in the
teachers’ instructional context; (b) were focused on teachers’ specific
subject matter; (c) were able to actively engage participants in
learning; and (d) were student teaching for a longer time. The
structure of the professional-development event (traditional workshop
vs. study-group or mentoring) was also modestly associated with
changes in knowledge and practice. In contrast, however,
professional-development experiences that were decontextualized,
concerned solely with general teaching, or were of shorter duration
were not associated with teacher change.
Other researchers have also identified similar characteristics of
effective development experiences specifically for new teachers. For
example, a case study by Bolin (1988) described the benefits to a
student teacher of using an on-going reflective journal with which to
enter into dialogue with the university supervisor. Hollingsworth
(1992) described how conversations between a teacher educator and
new teachers fostered awareness of and perspectives on literacy
instruction, classroom relationships, professionalism, and diversity.
Sullivan-Brown (2002) found that new teachers whose mentors
engaged them in reflective dialogues about educational and social
change were more likely to report transformative and empowering

outcomes. The contrast between these focused, active and extended
experiences parallel in some ways the types of interaction Borko and
Mayfield (1995) identified.
Interaction as a causal factor in cognitive development.
Interaction between members of dyads has been extensively studied in
relation to cognitive development. Developmentalists have examined
the effects of interaction between peers and between expert and novice
members. A majority of these studies used a research methodology
requiring dyads to collaborate on some form of problem-solving
activity, such as solving a concrete physics task, planning efficient
routes of travel, or resolving social dilemmas. Many of the findings
from this research seem to have important implications for examining
interactions between cooperating and student teachers.
Findings from these studies suggest that interaction between
peers, overall, enhances problem-solving capabilities (e.g., Doise,
Mugny, & Perret-Clermont, 1975). Interaction may be even more
effective at improving performance when the task at hand is ill-defined
such as a moral dilemma, versus a task that is more concrete or one
that has a clear, correct answer such as a balance-beam problem
(Phelps & Damon, 1989). When members of dyads possess differing
levels of expertise, the subsequent independent performance of
novices seems to be positively affected (Duvan & Gauvain, 1983).
Experts may affect the performance of novices by their demonstration
of superior capability, which may help bring about a shift in the
novice’s cognitive structure as opposed to simply imitating the
behavior of the expert (Berkowitz, Gibbs, & Broughton, 1980).
One mechanism for effecting this change may be the expert’s
use of transactive discussions (Berkowitz & Gibbs, 1983).
Transactive discussions are defined as “reasoning that operates on the
reasoning of another” (Berkowitz & Gibbs, p. 402). In a study
examining improvement in moral reasoning, Berkowitz and Gibbs
demonstrated that higher rates of transactive statements were
associated with improved reasoning of novices. In particular, change

in novices’ reasoning was greatest when experts’ statements provided
clarifications, refinements, contradictions, and critiques of reasoning.
Novices working with a more knowledgeable partner, who also used a
greater amount of transactive discussion, showed positive gains in
moral reasoning capability. The use of transactive statements to bring
about cognitive change is, in many respects, similar to characteristics
Garet et al., (2001) identified as leading to improvement for teachers.
Implications for examining interaction between cooperating
and student teachers. There are several noteworthy differences among
(a) participation in professional development, (b) the dyads in
developmental studies, and (c) the dyads composed of a cooperating
teacher and a student teacher (e.g., context for working together,
duration, objectives). And, even though research in these diverse
areas do not typically share a common theoretical perspective beyond
a loosely formulated constructivist approach, these studies seem to
have intriguing overlap with the teaching practicum. For example,
active engagement is best. In the professional development setting,
teachers who have a role beyond passive participation learn more and
apply their new knowledge to their instructional practice. During
conferences, teachers who believed their role was to be actively
involved spent greater amounts of time and had more sophisticated
conversations with their student teacher. Similarly, in dyads, when
expert members utilized transactive statements, novice members
improved in their understanding and performance. Second, content-
focused experiences are valued. In the professional development
setting, teachers also learned more and changed their teaching in
response to content-focused experiences. During conferences with
involved cooperating teachers, student teachers reported learning more
about teaching their particular content area. In problem-solving dyads,
experts are able to challenge novices’ thinking by using transactive
discussion and helping bring about change to the structure of novice’s
knowledge.
One model that may be useful for integrating these findings
and examining interaction between cooperating and student teachers is

the dyadic interaction framework described by Grannot (1993). This
model describes interaction between an expert and novice along a
continuum of collaboration ranging from independent activity, where
the novice simply imitates the actions of the expert, to highly
collaborative, where the expert guides and even scaffolds the learning
of the novice. Grannot’s theory, the basis upon which we constructed
our instrument, will be discussed in greater detail in the following
section.
Methodology
A split-sample was used for the development and validation of
this new scale (DeVellis, 1991, Jöreskog & Sörbom, 2002). Data were
collect from student teachers at the mid-point of the semester-long
teaching practicum. Student teachers were randomly assigned to one
of two subsamples for the study’s psychometric analyses.
Participants
Participants in this study included 274 student teachers who
were completing their practicum requirement through a large
university in the Southwest. Participants were working at all grade
levels of the K-12 system (early childhood = 9%; elementary = 44%;
middle-level = 24%; and high school = 23%). Both genders (female =
53%; male = 47%) were approximately equal in representation, but
student teachers were predominantly Caucasian (African American = .
7%; Asian American = .4%; Caucasian = 89%; Hispanic = 8.4%;
Other = 1.5%).
Instrument Development
Theoretical framework. The Learning to Teach Questionnaire
(LTQ) was constructed to examine interaction about instructional
matters that might occur between cooperating and student teachers.
The interaction types were derived from Grannot’s (1993) framework
of dyadic collaboration.

Grannot (1993) postulated a framework for classifying and analyzing
interactions of dyads based on the cognitive change theories of both
Piaget and Vygotsky. This framework consisted of two continua
along which interactions may be classified. The first continuum is
concerned with the degree of collaboration. Grannot described this
continuum as ranging from isolated work with only limited interaction,
to instances where dyad members shared goals and actively
collaborated. The second continuum is concerned with the relative
expertise of the two actors. Expertise may range from symmetric
expertise, meaning both members of the dyad have approximately
equal knowledge, to an asymmetric condition where one clearly has
more expertise than the other.
In the present study, we assumed that interactions regarding
instruction between cooperating and student teachers would most
accurately be categorized as an asymmetric (expert-novice) condition.
Within the asymmetric condition, Grannot (1993) identified three
types of interactions that might occur depending on the degree of
collaboration. We adapted these types to describe interactions that
might occur between a cooperating and student teacher.
A case where there is a low level of collaboration between the
cooperating and student teacher may be described as imitation. This
classification primarily describes a situation where the cooperating
teacher provides little help to the student teacher. During imitation
interactions, the cooperating teacher functions in a manner that does
not directly acknowledge the needs of the student teacher, but rather
continues on with “business as usual” leaving the student teacher to
figure things out on her or his own. The student teacher, left to her or
his own devices must learn to teach simply by observing and imitating
the cooperating teacher. Such a situation seems parallel to the
cooperating teachers Borko and Mayfield (1995) identified as not
actively participating in the learning of the student teacher.
The next level of collaboration is characterized by the cooperating
teacher’s guiding the student teacher, or treating her or him as an
apprentice. In such a situation, the cooperating teacher engages in

periods of active directing of the student teachers’ learning. The
cooperating teacher might observe and then evaluate activities of the
student teacher, or demonstrate actions and procedures for the student
teacher. In this type of situation, the cooperating teacher dominates
the interaction by having definite goals and standards for the student
teacher and using interaction to help her or him approximate the
desired outcomes. Cooperating teachers who engage in guidance-
types of interaction are taking an active role in the student teachers’
learning.
Finally, the highest level of collaboration is characterized by
the cooperating teacher scaffolding the learning of the student teacher.
This type of interaction is characterized by the cooperating teacher’s
support for the goal-directed activity of the student teacher. Goals
may be selected by the student teacher or cooperating and student
teacher might share a common goal and assist one another in
achieving some outcome. Cooperating teachers might also help
student teachers clarify goals and then provide support on an as-
needed basis. Cooperating teachers who engage in scaffolding-type
interactions are also taking an active role in the student teacher’s
learning, but the degree to which cooperating teachers control the
direction or goal selection is less than in guidance situations.
Scale development
In developing the LTQ, we followed the recommendations of
Netemeyer, Bearden, & Sharma (2003), who specified a series of steps
in scale development, including the definition of the theoretical
construct and the content domain, generation and judgment of scale
items, design and conduct of preliminary pilot studies to refine the
scale, and pilot study the final scale. For the LTQ, a pool of 30 items
was originally constructed with approximately 10 items each intended
to reflect imitation, guidance, and scaffolded interactions between
cooperating and student teachers.

Content analysis was initially conducted with a panel of 5
university graduate faculty members and 15 cooperating teachers.
Judgments were made about revising, editing or eliminating items.
This process resulted in the elimination of 7 of the original items
because they exhibited features that made them indistinguishable on
the continuum of interaction described by Grannot (1993).
Additionally, the panel of cooperating teachers indicated that items
reflecting scaffolded interactions, though desirable and important to
the study of dyadic interactions, were contrived or rarely occurred in
the context of student teaching (Seperson & Joyce, 1973). This
prompted the elimination of 6 items reflecting the scaffolding
component for this asymmetric relationship, in part due to the low
frequency with which they were likely to occur, and in part, to avoid
confusion with guidance-oriented items which also described active
participation of cooperating teachers. Thus, in the present scale, only
the imitation and guidance components of Grannot’s original
framework were retained for further examination.
The final form of the questionnaire required student teachers to
respond to items using a 6-point verbal frequency scale, where 1
indicated that the interaction behavior “never” occurred, and 6
indicated that the interaction behavior “always” occurred. Statements
were written to describe imitation interactions (e.g., When I’m
teaching, I try to use the same words and phrases that my cooperating
teacher uses), and guidance interactions (My cooperating teacher gives
me feedback that promotes self-reflection about my instruction). A
total of 17 items were retained for further analysis of their construct
dimensionality. The items for each of the two theoretical domains in
the questionnaire form were presented in a random order.
Statistical Analyses
The first phase of our analysis involved exploratory factor
analysis using principal components factor analysis and promax
rotations with one-half of the study random sample (calibration
sample, n = 137). Item-total correlations were used in conjunction

with factor loadings (orthogonal analysis) to examine the
characteristics of the items after each iteration. Rules for retaining
items in each interaction included use of item loadings/pattern
coefficients, which had to exceed .30 on just one factor, and for items
whose loading exceeded .30 on more than one factor, we required a
minimum gap of .1 between loadings or pattern coefficients
(Nunnally, 1978). The criteria utilized for determining the number of
factors were (a) latent root, or Eigenvalue test, (b) a priori
conceptualization of Grannot’s (1993) interaction analysis framework,
(c) percentage of variance, and (d) scree test (Hair, Anderson, Tatham,
& Black, 1998).
The second phase involved conducting a confirmatory factor
analysis (CFA) with a maximum likelihood estimation to assess the
adequacy of the proposed two-factor model of the LTQ. For measures
of fit, we used the root mean square error of approximations
(RMSEA), chi-square (and associated degrees of freedom), Bentler’s
(1990) comparative fit index (BCFI), goodness-of-fit index (GFI), and
adjusted goodness-of-fit index (AGFI) (Thompson & Daniels, 1996).
We looked for consistency across the subscales as an indication of
theoretically sound factors. Examination of standardized residuals and
modification indices were employed to refine the scale’s factor
structure on a validation sample (n = 135).
Results
Examination of Scale Dimensionality
Preliminary examination of the data indicated problems with
several variables exhibiting extreme skewness and kurtosis. Using the
SAS system for Window version 8 UNIVARIATE procedure (SAS,
Institute, 2002), two participants were found to be producing extreme
values and were discarded from the data set. Both of these offending
cases were found in the validation sample.

In order to determine the factorial structure of the LTQ scale, a
series of exploratory factor analyses were conducted on the calibration
sample using principal component extraction with orthogonal and
oblique rotation methods. There were no restrictions as to the number
of factors to be extracted in this initial examination of item-to-
construct clustering. In each of the exploratory analyses, rotation
converged typically within 3 iterations. Table 1 presents descriptive
statistics for the 17 items that remained after the content validity study.
The intercorrelation indices for these items ranged from -0.06 to 0.85
with the large majority of these coefficients being positive and
midrange in magnitude. The results from the initial principal
component factor analysis produced three factors with eigenvalues
greater than one.
Approximately 72% of the variance was explained by this
three-factor solution. Three items were identified as problematic.
Two items had factor loadings on more than one factor, and item 20
represented a single-item factor. The decision was made to eliminate
item 20, and the result was that item 2 became uniquely associated
with factor 2, and item 7 became uniquely associated with factor 1.

Table 1
Means and Standard Deviations for the17-item LTQ Scale on the
calibration sample (n = 137)
Items Mean SD
V1 4.18 1.32
V2 4.11 1.03
V4 4.67 1.07
V6 5.16 1.08
V7 4.21 1.13
V9 4.47 0.99
V10 4.47 1.33
V11 3.82 1.49
V13 4.28 1.07
V14 4.26 1.44
V16 4.42 1.40
V18 4.85 1.15
V19 4.23 1.44
V21 4.07 1.09
V22 4.69 0.87
V24 4.73 1.40
Table 2 reports the outcome of the factor structure for the
revised model. The results from this analysis produced two clear
factors with 9 scale items clustering in factor 1, and 7 scale items
clustering in factor 2, and explaining approximately 69% of the total
variance. Examination of the rotated pattern indicated that the
majority of items designed to measure their specific subscale did so.
Two items seemed to load on both factors simultaneously, and had
factor scores greater than 0.3. Further examinations of the item
content led the researchers to eliminate rather than revise these items.

Table 2
Initial Factor Analysis of the LTQ Scales Using Principal Component
Extraction Followed by a Promax Rotation
Scale item a
Components
1 2 h2
V19. .936 -.144 .772
V14 .935 -.095 .802
V16 .935 -.013 .863
V1 .923 -.176 .733
V10 .871 .080 .829
V24 .871 .020 .775
V11 .793 .059 .676
V7 .663 .147 .552
V18 .571 .368 .655
V21 -.322 .892 .635
V9 .024 .886 .805
V22 -.119 .803 .571
V6 -.025 .746 .540
V4 .189 .714 .670
V13 .203 .675 .624
V2. .201 .600 .512
Eigenvalues 8.282 2.732 ---
% σ2
explained 51.76 17.07 ---
Alpha 0.95 0.89 0.94b
a
See complete description of item in appendix
b
Total scale reliability

The final set of items was subjected, once more, to item
analysis using principal component analysis. Table 3 presents the item
clustering and the magnitude of the factor loadings for this final
version of the scale. Seventy-one percent of the variance was clearly
explained by the extraction of these two factors. Inspection of the
individual item clustering indicated appropriate item-to-scale
memberships. In other words, items that the developers intended to
load with the scales did, in fact, cluster accordingly. The first factor
now comprised of 7 items with factor loading ranging from .78 to .93,
and the second factor also comprised 7 items with factor loadings
ranging from .61 to .89. The two extracted factors were moderately
correlated with each other (r = 0.43). Items that constituted the first
factor were closely related to guidance –type of interactions between
cooperating and student teachers. The items loading on the second
factor closely resembled the imitation-type interactions.
In addition to the use of factor analytical procedures to perform
item analysis of the scale, the researchers conducted item-to-total
correlation analyses using reliability procedures. For each of the
iterations presented above, the items that were found problematic at
each step of the analyses were inspected as to their degree of
association to the intended subscale. The final scale composition
yielded more than acceptable levels of internal consistency (Whole
scale α = .93; Guidance α = .95 ; Imitation α = .89). Based on the
results of the initial exploratory factor analyses, we proceeded to
conduct a confirmatory factor analysis on the validation sample in
order to determine the adequacy of fit for the proposed 2-factor model.

Table 3
Revised Factor Analysis of the LTQ Scales Using Principal Component
Extraction Followed by a Promax Rotation
Scale itema
Component
1 2 h2
V1 .926 -.138 .767
V19 .925 -.109 .782
V16. .924 .022 .871
V14 .922 -.058 .808
V10 .862 .116 .842
V24
.847 .051 .757
V11
.780 .090 .676
V9
.029 .885 .807
V21.
-.313 .876 .632
V22
-.108 .792 .565
V6
-.019 .748 .548
V4
.196 .723 .682
V13
.209 .683 .632
V2 .214 .607 .524
Eigenvalues 7.18 2.71 ---
% of σ2
explained
subscale
51.28 19.39 ---
Alpha
0.95 0.89 0.92b
a
See complete description of item in appendix b
Total Scale reliability

Confirmatory Factor Analyses
Theoretical measurement model . We used a confirmatory
factor analysis procedure with the CALIS procedure (SAS Institute,
2002) on the validation sample (n = 135), to derive the final forms of
the guidance and imitation scales. For the first iteration, there was a
common pool of 14 items related to guidance and imitation. These
items were specific to a correlated two-factor confirmatory model --
the two factors reflecting the guidance and imitation constructs.
On the basis of suggestions found in the scale development
literature (Netemeyer, et al., 2003), items were deleted that (a) through
inspection of modification indices (i.e., Lagrange multipliers); and (b)
consistently resulted in within-factor correlated measurement error,
across-factor correlated measurement error, or both (i.e., items with a
standardized residual greater than 2.58 with other items). These
procedures were applied while maintaining the 2-factor model based
on Grannot’s (1993) theory.
The initial measurement model was estimated using the
maximum likelihood method, and the chi-square value for the model
was statistically significant, χ2(76, n = 135) = 270.3, p < .0001. This
indicates that the data did not fit the initial model adequately.
However, extant literature in this area warns users of the sensitive
nature of this statistic due primarily to sample size (Fan, Thompson, &
Wang, 1999; James, Mulaik, & Brett, 1982; Jöreskog & Sörbom,
1989). In addition, a series of other results, including the incremental
fit values, clearly corroborated the poor fit of the data to the proposed
model (Table 4). Hatcher (1994) suggests that model modification be
done by examining changes to the model’s fit one variable at a time.
Thus, several iterations were performed between the initial model and
the final model. The pattern of large normalized residuals, parameter
estimates significant tests, and modification procedures such as
Lagrange multiplier and Wald tests indicated problems with two
manifest indicators (V21 and V22). These variables yielded values that
affected their expected construct membership. Similar analyses were

conducted applying the above rules for further item-trimming. After
the first iteration, 5 items for the guidance factor and 7 items for the
imitation factor were retained for the next iteration.
Table 4
Goodness of Fit Indices for the Interaction Model for Validity Sample Phase
of the Study
Combined Model
Model χ2
df AGFI RMSR RMSEA CFI NFI NNFI
Null Model 1579.0 91 - - - - - -
One factor
model 414.0 77 .46 .16 .18 .77 .73 .73
Uncorrelated
Model 122.4 35 .78 .48 .14 .91 .88 .89
Initial
2-Factor
Model
270.3 76 .70 .15 .14 .87 .93 .84
Revised
Model 1 113.3 53 .82 .08 .09 .95 .91 .94
Revised
Model 2 86.9 43 .84 .07 .08 .96 .93 .95
Final 2-Factor
Measurement
Model
61.9 34 .86 .06 .07 .97 .94 .96
Note: n = 135. Analyses for the uncorrelated model were obtained from the
final measurement model.

A slightly different set of procedures was applied for the
second iteration. Items were deleted that (a) still exhibited correlated
measurement errors; and (b) had completely standardized factor
loadings less < .60. These efforts led to the elimination of two
additional manifest indictors from the model (v10 and v11). After
several iterations, the final iteration, 5 items for the guidance factor
and 5 items for the imitation factor were retained. The final form of
the 10-item questionnaire is displayed in the appendix. Table 4
presents the revisions made to the proposed measurement model.
The final revised measurement model. For each of the
aforementioned iterations, the researchers adhered closely to the
criteria set forth in their search for, not simply an empirically tenable
model, but a meaningful model that reflected Grannot’s (1993)
theoretical perspective. Goodness of fit models for the revised
measurement models 1 and 2 are presented in Table 4. The adjusted
goodness-of-fit index (AGFI) ranged from .70 to .89 and the root
mean square error of approximation (RMSEA) produced fit values that
ranged from .14 to .06. The RMSEA is a stand-alone measure
designed to correct for the tendencies of the chi-square statistic to
reject a model due primarily to issues of sample size. Advocates of
this measure have proposed that the RMSEA must exhibit values less
than .08 (Browne & Cudeck, 1993; Hu & Bentler, 1998). In as much
as AGFI may suffer from inconsistencies from sampling
characteristics, Bentler’s (1990) comparative fit index was also used
and exhibited values that ranged from .87 to .98. Likewise, the values
for normed fit index (NFI), and non-normed fit index (NNFI). The
PNFI measure is used here to determine the improvement in fit of one
model over another. Therefore, the last measurement model was
tentatively accepted as the study’s “final” measurement model and a
number of tests were performed to assess its reliability and validity.
Table 6 provides means, standard deviations, and inter-item
correlation estimates of the indicator variables for the final
measurement.

Dimensionality and internal consistency. Confirmatory factor
analysis was used to assess the scale dimensionality and internal
consistency of the final form of the subscales (Netemeyer et al., 2003).
We estimated two models (a) a two-factor model (i.e., two correlated
first-order factors) representing the hypothesized factor structure in
which the individual items were permitted to load only on the
hypothesized factors, with no cross-loadings or correlated
measurement errors, and (b) a one-factor model in which all items
were specified to a single factor. The one-factor model was used for
comparison purposes. The results from the validity sample present
overwhelming evidence for the hypothesis of more than one latent
factor. Table 4 displays unacceptable estimates of goodness-of-fit for
the single factor model.
Table 5 presents evidence of the composite reliability estimates
for the two-factor model. Hair et al. (1998) advocated a threshold
value around 0.70 whereas Bagozzi and Yi (1988) suggested threshold
values around .60 for this measure. Composite reliability coefficients
for the present study yielded values well beyond those suggested
values (Guidance, 0.93 and Imitation, 0.89). These results provide
clear evidence of internal consistency for these items to their
respective scales. Table 5 also provides results dealing with average
variance extracted estimate (AVE), another internal consistency-based
diagnostic tool. The AVE is a measure of the amount of variance
captured by a construct, relative to the variance due to random
measurement error (Fornell & Larcker, 1981). Both constructs
demonstrated variance extracted estimates in excess of .50, the level
recommended by Fornell and Larcker (1981).
Convergent and discriminant validity. In addition, evidence
that these scales exhibits convergent validity is made clear by close
examination of the individual ttest for the standardized factor loadings
results presented in Table 5. For this study, all the ttest results were
found to be significant, indicating that all indicator variables are
effectively measuring the same construct (Anderson & Gerbing,
1988). The completely standardized between-factor item loadings

range from .65 to .92. All these estimates are well above
recommended levels.
Table 5
Composite Reliability and Model Estimates for the Final
Measurement Model
Indicators
Standardized
Factor Loadings ta
Reliability
Estimates
Variance
extracted
Estimates
F1: Guidance .93b
.73
V1 .821 11.50 .674 .326
V14 .821 11.50 .674 .326
V16 .922 13.88 .849 .151
V19 .881 12.85 .776 .224
V24 .825 11.58 .681 .219
F2: Imitation .89 .60
V2 .775 10.34 .601 .399
V4 .885 10.92 .648 .353
V6 .651 8.14 .423 .577
V9 .861 12.11 .742 .258
V13 .774 10.31 .600 .400
r12 = 0.65c
(0.06) 95% C.I [0.53,0.77]
a
All t-tests were significant at the p < .001
b
Denotes composite reliability.
c
Denotes intercorrelation of the final measurement factors.

Evidence of discriminant validity was obtained by using
procedures (the chi-square difference test and the confidence interval
test) suggested by Anderson & Gerbing (1988) and a procedure (the
variance extracted test) suggested by Fornell and Larcker (1981). To
provide evidence of discriminant validity, we applied the chi-square
difference test on the validation sample (See table 4). The one-factor
model was compared with the hypothesized two-factor model. This
comparison provided evidence of discriminant validity because the
difference in chi-square between the models was statistically
significant [χ2
(1, N = 135) = 147.2, p < .001]. The confidence
interval test also produced similar results as the above test (See table
4). Fornell and Larcker’s (1981) test, the average variance extracted
test, again provided evidence of discriminant validity. The R2
between
the scales was .42 while the AVE estimates for each of the scales were
.73 and .60. Since both AVE estimates exceeded the squared
correlation, discriminant validity between the constructs was
demonstrated by the test results.

Table 6
Inter-Item Correlations, Means and Standard Deviations for the Final Form
of the 10-item LTQ Measurement Scale on the Validation Sample (n = 135)
v1 v2 v4 v6 V9 V13 v14 v16 v19 v24
v1 1.0
0
v2 .49 1.00
v4 .50 .62 1.0
0
v6 .32 .51 .50 1.00
v9 .49 .68 .71 .53 1.00
v13 .42 .54 .61 .59 .68 1.00
v14 .65 .47 .41 .44 .35 .47 1.00
v16 .78 .54 .46 .33 .45 .48 .77 1.00
v19 .69 .50 .48 .33 .42 .43 .74 .81 1.00
v24 .68 .51 .48 .32 .49 .42 .65 .74 .77 1.00
M 4.3
8
4.30 4.6
3
4.54 5.19 4.16 4.38 4.49 4.39 4.71
SD 1.3
2
1.05 1.1
7
1.13 1.02 1.22 1.44 1.38 1.44 1.45

Discussion
This study describes the development and construct validation
of a measure of interaction between cooperating and student teachers
during the teaching practicum. This measure is based on Grannot’s
(1993) dyadic interaction framework, originally intended to classify
and analyze interaction behaviors of dyads engaged in problem
solving. Two factors, imitation and guidance, are measured by five
items each.
Adequacy of the Measure
The scales exhibited adequate levels of internal consistency,
dimensionality, and convergent and divergent validity for the
validation sample. The results from the two-phase process provided
evidence that supports the reliability and validity of the scale. Internal
consistency, as measured by composite reliability procedures yielded
more than adequate values on each of the two subscales. Additionally,
for most of the guidance and imitation items, significant zero-order
correlations with the overall LTQ guidance and imitation scale were
detected as evidence of construct validity. Exploratory psychometric
procedures were used to examine the viability of the two-factor
solution. This factorial structure was corroborated in the confirmatory
phase with an independent sample. Convergent and discriminant
validity evidence lend credence to assumptions about the type of
interaction each scale purports to measure, as well as to their unique
contribution in assessing different aspects of interaction between
cooperating and student teachers. The moderately high correlation
found between the imitation and guidance subscales appeared
plausible, given that the teaching activities faced by student teachers
are not as discrete as the more controlled dyadic interaction research.
In fact, the correlation between the two factors – connoting distinct
types of interaction – seems to suggest a more dynamic pattern of
interaction than Grannot initially imagined.

Limitations and Future Research
This measure is a marked improvement over previous attempts
to examine the contribution of interaction to new teacher development;
however there are some limitations to the present effort. First, the
sample size in the current study may be considered smaller than
typically recommended for these types of analyses; however, an item
to participant ratio of 1:10 was maintained for all the analyses. This
suggests that an adequate level of stability for each of the observed
parameter estimates was maintained. Second, the results of these
analyses were based solely on student teachers’ perceptions of their
interaction with their cooperating teacher. These perceptions, though
valuable, provide only one source of information about how
interaction unfolds over the course of the teaching practicum.
Despite these limitations, the final model of the subscales is
much less cumbersome than those described by Kremor-Haydon and
Wubbles (1993), and they provide a viable theoretical underpinning
lacking in both the personality-based measures and the descriptions of
effective teacher development. The moderate correlation between the
subscales suggests that, although Grannot’s framework may provide a
useful tool for describing isolated interactions, it seems to need some
modification in order to capture the interaction-over-time aspect that is
a salient feature of the student-teaching practicum.
The examination of interaction types, over time, suggests many
additional questions that are relevant to teacher educators. For
example, are interaction types stable over time in a dyadic relationship
such as cooperating and student teacher, or is there a transformation of
interaction associated with time? Likewise, what is the ideal pattern of
interaction between cooperating and student teachers that will yield
the greatest benefit for new teacher development?
Finally, further research with other populations would also be
informative. The current study sample consisted of student teachers at
the mid-point of the teaching practicum. It would be valuable to

determine the extent to which these findings generalize to other
populations of educators working in mentoring or coaching situations,
such as new-teacher induction programs, or teacher internships apart
from the traditional teaching practicum. Moreover, the perspective of
both members of dyads, in the present case cooperating teachers, and
student teachers would be of interest. The LTQ may be a useful
instrument for examining the extent to which interaction between
cooperating and student teachers contributes to desirable outcomes for
new teachers.

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Appendix
Initial Item Description and Numbering
1. My cooperating teacher offers suggestions to improve my instruction
2. I teach in a way that is similar to my cooperating teacher
4. I watch what my cooperating teacher does during instruction and then I try it
myself
6. When I teach, I use the same materials as my cooperating teacher
7. My cooperating teacher works with me when I am faced with new situations in
the classroom
9. When I teach, I replicate my cooperating teacher's instructional methods
10. My cooperating teacher offers good suggestions that improve my instruction
11. My cooperating teacher states her/his instructional goals for me
13. When I am using new materials I do what my cooperating teacher does
14. My cooperating teacher gives me feedback after watching me teach
16. My cooperating teacher offers me guidance to improve my teaching
18. My cooperating teacher works with me when I'm faced with new teaching
materials
19. My cooperating teacher gives me feedback that promotes self-reflection about
my instruction
21. When I'm teaching, I try to use the same words and phrases that my
cooperating teacher uses
22. I follow my cooperating teacher's directions when I'm teaching a similar lesson
24. My cooperating teacher and I have worked together to improve my instruction
this semester

Final Items by Subscales for the Learning to Teach Questionnaire
Guidance
v1 My cooperating teacher offers suggestions to
improve my instruction.
v14 My cooperating teacher gives me feedback after
watching me teach.
v16 My cooperating teacher offers me guidance to
improve my teaching.
v19 My cooperating teacher gives me feedback that
promotes self-reflection about my instruction.
v24 My cooperating teacher and I have worked
together to improve my instruction this semester.
Imitatio
n
v2 I teach in a way that is similar to my cooperating
teacher.
v4 I watch what my cooperating teacher does during
instruction and then try it myself.
v6 When I teach, I use the same materials as my
cooperating teacher.
v9 When I teach, I replicate my cooperating
teacher’s instructional methods.
v13 When I’m using new materials, I do what my
cooperating teacher does.

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