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NATIONAL FORUM OF APPLIED EDUCATIONAL RESEARCH JOURNAL

VOLUME 20, NUMBER 2, 2006--2007

DEVELOPING THE LEARNING TO TEACH

QUESTIONNAIRE: MEASURING

INTERACTION BETWEEN COOPERATINGAND 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 skillof 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 importantmechanism 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 teacherscommunicate 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-givinginteractions predominate. Analysis and reflection on teaching are not

common; the substantive issues of conferences tend to focus on

teaching techniques, classroom management, and pupilcharacteristics” (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 examinedcharacteristics 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. Inmost 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 routinerather than reflective, but noted two distinct patterns of interaction

during conferences. One group of cooperating teachers, who believedthey 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 thesecooperating 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 positiveabout how the cooperating teacher contributed to their learning to

teach.

Some interaction is better than none. Although researchershave 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 positiveoutcomes 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 acooperating 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 professionaldevelopment 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 ininstructional 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 inlearning; 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 withchanges in knowledge and practice. In contrast, however,

professional-development experiences that were decontextualized,

concerned solely with general teaching, or were of shorter durationwere 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 examinedthe 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-solvingactivity, 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 examininginteractions 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 onethat 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’suse 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 studyexamining 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 agreater amount of transactive discussion, showed positive gains in

moral reasoning capability. The use of transactive statements to bringabout 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 tohave 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. Duringconferences, 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, whenexpert 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 inresponse 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, wherethe novice simply imitates the actions of the expert, to highly

collaborative, where the expert guides and even scaffolds the learningof the novice. Grannot’s theory, the basis upon which we constructed

our instrument, will be discussed in greater detail in the followingsection.

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 gradelevels 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 continuaalong which interactions may be classified. The first continuum is

concerned with the degree of collaboration. Grannot described thiscontinuum as ranging from isolated work with only limited interaction,

to instances where dyad members shared goals and activelycollaborated. 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 approximatelyequal 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. Thisclassification 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 imitatingthe 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 studentteacher. In this type of situation, the cooperating teacher dominates

the interaction by having definite goals and standards for the studentteacher 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’ssupport 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 inachieving 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’slearning, 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 stepsin 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 thescale, 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 betweencooperating 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 onthe continuum of interaction described by Grannot (1993).

Additionally, the panel of cooperating teachers indicated that itemsreflecting 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 avoidconfusion 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 originalframework 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 1indicated 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’mteaching, 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 constructdimensionality. 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/patterncoefficients, which had to exceed .30 on just one factor, and for items

whose loading exceeded .30 on more than one factor, we required aminimum 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 theSAS 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 offendingcases 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 andoblique 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 descriptivestatistics 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 andmidrange 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 thecalibration sample (n = 137)

Items Mean SD

V1 4.18 1.32V2 4.11 1.03V4 4.67 1.07V6 5.16 1.08V7 4.21 1.13V9 4.47 0.99V10 4.47 1.33V11 3.82 1.49V13 4.28 1.07V14 4.26 1.44V16 4.42 1.40V18 4.85 1.15V19 4.23 1.44V21 4.07 1.09V22 4.69 0.87V24 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 themajority 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 itemcontent 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 .829V24 .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 0.94 b

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 itemclustering and the magnitude of the factor loadings for this final

version of the scale. Seventy-one percent of the variance was clearlyexplained by the extraction of these two factors. Inspection of the

individual item clustering indicated appropriate item-to-scalememberships. 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 firstfactor 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 theiterations 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 compositionyielded 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 inorder to determine the adequacy of fit for the proposed 2-factor model.




Table 3Revised Factor Analysis of the LTQ Scales Using Principal ComponentExtraction 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 explained51.28 19.39 ---

Alpha0.95 0.89 0.92 b

a See complete description of item in appendix b Total Scale reliability




Confirmatory Factor Analyses

Theoretical measurement model . We used a confirmatoryfactor 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 acommon 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) throughinspection 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. Thisindicates 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 incrementalfit values, clearly corroborated the poor fit of the data to the proposed

model (Table 4). Hatcher (1994) suggests that model modification bedone 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 tenablemodel, 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 adjustedgoodness-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 toreject 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 muchas 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). ThePNFI 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 anumber 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 finalmeasurement.




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 inwhich the individual items were permitted to load only on the

hypothesized factors, with no cross-loadings or correlatedmeasurement 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 presentoverwhelming 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 thresholdvalue 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 provideclear 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-baseddiagnostic 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 levelrecommended by Fornell and Larcker (1981).

Convergent and discriminant validity. In addition, evidencethat these scales exhibits convergent validity is made clear by close

examination of the individual t test for the standardized factor loadings

results presented in Table 5. For this study, all the t test results werefound 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 FinalMeasurement Model

Indicators

Standardized

Factor Loadings ta

Reliability

Estimates

Variance

extracted

Estimates

F1: Guidance .93 b .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 .60V2 .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

r 12 = 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 (thevariance extracted test) suggested by Fornell and Larcker (1981). To

provide evidence of discriminant validity, we applied the chi-squaredifference test on the validation sample (See table 4). The one-factor

model was compared with the hypothesized two-factor model. Thiscomparison 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 R 2 betweenthe 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 wasdemonstrated 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

0v6 .32 .51 .50 1.00

v9 .49 .68 .71 .53 1.00

v13 .42 .54 .61 .59 .68 1.00v14 .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 validationof 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 problemsolving. 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. Internalconsistency, 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 weredetected 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 betweencooperating and student teachers. The moderately high correlation

found between the imitation and guidance subscales appeared

plausible, given that the teaching activities faced by student teachersare 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 attemptsto examine the contribution of interaction to new teacher development;

however there are some limitations to the present effort. First, thesample size in the current study may be considered smaller than

typically recommended for these types of analyses; however, an itemto 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 theseanalyses 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 howinteraction unfolds over the course of the teaching practicum.

Despite these limitations, the final model of the subscales ismuch 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 thesubscales 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 isa 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 apartfrom the traditional teaching practicum. Moreover, the perspective of

both members of dyads, in the present case cooperating teachers, andstudent teachers would be of interest. The LTQ may be a useful

instrument for examining the extent to which interaction betweencooperating 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

materials19. 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

Guidancev1 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

nv2 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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