revisiting the trust effect

REVISITING THE TRUST EFFECT IN

URBAN ELEMENTARY SCHOOLS

Curt M. AdamsPatrick B. Forsythuniversity ofoklahoma

abstractMore than a decade after Goddard, Tschannen-Moran,and Hoy (2001) found that collective faculty trust in cli-ents predicts student achievement in urban elementaryschools, we sought to identify a plausible link for thisrelationship. Our purpose in revisiting the trust effectwas twofold: (1) to test the main effect of collective fac-ulty trust on student achievement after controlling forfree and reduced-price lunch and prior achievement,and (2) to determine if self-regulated learning mediatesthe collective trust-achievement relationship. Data werecollected from 1,039 teachers and 1,648 students in 56urban elementary schools. Results confirmed the hy-pothesized main effect of collective faculty trust and thehypothesized mediating effect of self-regulated learning.Mean math and reading achievement were higher inschools with a stronger culture of collective faculty trust.Schools with a stronger culture of trust also had studentswith more self-regulated learning.

CO L L E C T I V E trust is a malleable school property that practitioners andscholars recognize as a vital resource for effective performance. Bryk andSchneider (2002) credited faculty trust for the improved performance ofschools in Chicago. Seashore Louis (2007) found that trust facilitated con-

tinuous improvement efforts in five high schools undergoing reform. Meier (2002)argued that teacher trust is the glue that keeps learning communities together. For-syth, Barnes, and Adams (2006) found high faculty trust in colleagues combined with

the elementary school journal volume 114 , number 1© 2013 by The University of Chicago. All rights reserved. 0013-5984/2013/11401-0001 $10.00

high parent trust in schools enhanced school effectiveness. In general, trust contrib-utes to a positive performance culture in schools, with each distinct form of trustshaping the teaching and learning context uniquely. For example, faculty trust incolleagues supports information exchange and knowledge development amongschool professionals (Tschannen-Moran, 2009), and faculty trust in the principalunites the school community around a shared vision for improvement (Tschannen-Moran, 2004).

Most forms of faculty trust have an indirect effect on student performance bycreating conditions supportive of teaching effectiveness. Faculty trust in clients (stu-dents and teachers) is the only form of collective trust found thus far to have anindependent and direct effect on school-level achievement differences (see Goddard,Salloum, & Berebitsky, 2009; Goddard, Tschannen-Moran, & Hoy, 2001). Goddardand colleagues first identified the trust effect over a decade ago in a sample of 47elementary schools in a large Midwestern urban district. More recently, they found asimilar achievement effect in a larger, more representative sample of public schoolsin the state of Michigan (Goddard et al., 2009). Unaddressed by Goddard and col-leagues’ studies was a theoretical explanation for why a culture of faculty trust inclients predicts student achievement.

In revisiting the trust effect in urban elementary schools more than a decade afterGoddard and colleagues’ original study, our purpose was to understand how collec-tive faculty trust in clients supports student achievement. With the largest portion ofachievement variation existing among students, not schools or classrooms (see Bor-man & Kimball, 2004; Heck, 2009; Nye, Konstantopoulos, & Hedges, 2004), it wasimportant to focus on how a culture of trust may or may not trigger in students aninternal agency for learning. We did this by testing a multilevel mediation model thatspecified collective faculty trust in clients (hereafter referred to as collective facultytrust) as a school-level predictor of achievement, and self-regulated learning as astudent-level mediator of the collective trust-achievement relationship. Our modelscontrolled for free and reduced-price lunch (FRL) at the individual and school levelsas well as school-level prior math and reading achievement.

Theoretical Framework

Much has been written elsewhere about the conceptual properties of trust and thebenefits of trust for organizational performance (Forsyth, Adams, & Hoy, 2011; seealso Adams, 2008; Bryk & Schneider, 2002; Das & Teng, 1998; Hoy & Tschannen-Moran, 1999; Tschannen-Moran, 2004). Rather than repeat this discussion in thisarticle, we chose to address gaps in the trust literature. Specifically, we conceptualizecollective trust as a normative property of school groups and use self-determinationtheory as a framework to explain how collective faculty trust shapes student achieve-ment.

Collective Trust as a Group Property

Schools are social systems defined in part by their relational networks, coordinat-ing structures, interdependencies, vulnerabilities, and behavioral expectations.Trust, in a social context like schools, emerges from interactions among organiza-tional actors rather than being determined by innate psychological dispositions of

2 � the elementary school journal september 2013

individuals (Lewis & Weigert, 1985). Recognition of the social nature of schools andthe influence of interdependent actions on the work of teachers led Bryk and Schnei-der (2002) to argue that relational trust is the form of trust characteristic of schoolorganizations. From a relational framework, trust between two individuals inschools grows out of repeated social exchanges that conform to expected behavior forthe role group (i.e., teachers, parents, administrators, students). To illustrate, when ateacher judges the behaviors and intentions of a parent to be respectful, competent,caring, and honest, her trust in the parent will be stronger (Bryk & Schneider, 2002).

Although collective trust and relational trust result from actions and interactionsof school members, they are distinct conditions. Relational trust is an individualemotional state of school members that may or may not be shared by others in theschool. Collective trust is a norm that forms within school role groups. The differ-ence between a collection of individual beliefs and a group norm can be understoodby the distinction between organizational climate and culture. Climate reflects com-mon perceptions and behavior of individuals within a group, but these perceptionsand behaviors remain a property of individuals, not a normative belief of the group(Glisson & James, 2002). Similarly, relational trust forms through individual discern-ments of another’s trustworthiness and can change relatively quickly depending oncircumstances, situations, and/or a trustee’s behavior (Bryk & Schneider, 2002).High relational trust represents the sum of individual beliefs, not necessarily a prop-erty of the school culture.

Organizational culture, in contrast to climate, is based on normative conditionsand shared expectations for how work gets carried out within organizations (Glisson& James, 2002). Culture reflects a deep understanding and internalization of theshared values, expectations, and assumptions that guide social action (Van Houtte &Van Maele, 2011). Likewise, collective trust is a normative condition that is sociallyconstructed by group members. As a norm, collective trust is part of the schoolculture and tends to be stable unless there are gross trust violations or dramaticchanges in group characteristics. Once formed, normative trust beliefs behave likeother group norms, whose acceptance and embrace by new members is a conditionof their integration and membership (Forsyth et al., 2011). We specifically definecollective trust as “a stable group property rooted in the shared perceptions and affectabout the trustworthiness of another group or individual that emerges over time outof multiple social exchanges within the group” (Forsyth et al., 2011, p.22). Facultytrust in clients is a form of collective trust rooted in shared teacher beliefs that parentsand students are trustworthy; that is, teachers perceive students and parents as open,honest, reliable, competent, and benevolent (Hoy & Tschannen-Moran, 1999).

Collective trust formation. The distinction between relational and collective trustmay seem trivial, but conceptualizing trust as a collective property of role groups hasimplications for the formation process. Collective trust is as much a product ofinteractions among role group members as it is a product of direct interactions withtrustees (Forsyth et al., 2011). Figure 1 models the formation process (Forsyth et al.,2011). Social construction occurs among role group members as they compare ob-served behavior of a trustee group against what is expected of these individuals.Comparisons are based on the facets of trustworthiness: openness, honesty, benev-olence, reliability, and competence. For example, when an elementary teacher ob-serves a parent encouraging and nurturing his child, such behavior is compared toexpectations of parent “benevolent” behavior. This event becomes part of the trust

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evidence about the parent group when the teacher shares the example with otherteachers through specific mention of it or through the teacher’s enhanced positiveaffect toward parents. As in the above example, perceived trustworthiness is higherwhen actual behavior matches expectations.

Conditions in the internal, external, and task contexts affect collective trust for-mation. The external context includes factors beyond the control of schools thatshape the behavior of those who attend and work in schools. For example, schoolscannot control past experiences that influence the value and belief systems of parentsor teachers. Similarly, schools have little to no control over external policies, theinfluence of the mass media, and the role of interest groups in education. The inter-nal context consists of organizational structures and conditions that affect socialinteractions in schools. Schools do control the internal context. Finally, task contextdefines the unique constraints and features of the teaching and learning process inschools, for example, the extent to which an instructional intervention scripts teach-ing. Once established, collective trust regulates beliefs, affect, decisions, and behaviorof group members. Newcomers to the group gain acceptance only through embrac-ing or pretending to embrace existing norms. Nonconformity with norms of trust,like other group norms, may be sanctioned by group members.

Many factors contribute to the formation of collective faculty trust. Two impor-tant characteristics of the internal and task contexts include the social network ofteachers and the behavior of students and parents. Relationships and connectionsamong teachers create the social infrastructure for trust-producing information tospread among group members. Weak or tenuous connections among teachers in aschool would impede the formation of shared beliefs by reducing opportunities toexchange information with other teachers. Patterns of student and parent behaviorprovide evidence under which a shared trust belief forms. Collective trust emergesfrom actions of the trustee group that align with expectations of the trustor (Forsythet al., 2011). In brief, cohesive teacher networks combined with responsible student/parent behavior can engender a shared belief among faculty that students and par-ents are trustworthy.

Self-Determination Theory and Quality Performance

Achievement differences attributed to economic conditions continue to be a pri-mary target of education policy and research. Since the Coleman Report (Coleman etal., 1966), evidence has shown that schools and teachers can counter the adverseeffects of poverty on student performance (Bryk & Raudenbush, 1988; Heck, 2009;Ma & Klinger, 2000; Nye et al., 2004), but closing achievement gaps on a large scaleremains elusive. School-effects research has established conditions like leadership,instructional program coherence, academic expectations, and teaching effectivenessas determinants of school performance, but, remarkably, much of the literature hasneglected theoretical explanations for how social conditions affect the academic per-formance of low-income students. Theoretical explanation can help policy makersand school leaders understand if, how, or why proposed policies and practices forurban schools can work.

Psychologists for some time have attributed strong school performance to studentmotivation and self-regulated learning (Ryan, Connell, & Grolnick, 1992). Cognitiveabilities aside, motivated students who identify with school and take responsibility


for their learning are likely to perform better than other students (Zimmerman &Schunk, 2008). External behavioral controls, such as grades or other incentives, canfacilitate self-regulation and internal motivation, but quality performance deterio-rates over time if internal motives do not supplant external contingencies as thedriving force behind behavior (Reeve, Ryan, Deci, & Jang, 2008). The problem is thatlow-income urban students disproportionately attend schools where behavioralcontrol is largely achieved through formal mechanisms rather than psychosocialconditions that elicit identification with school and internalization of academic suc-cess (Noguera, 2009).

Understanding human behavior and performance requires a framework that ex-plains the complex and dynamic interaction of psychological, social, and cognitivefactors. Self-determination theory integrates principles of human growth, motiva-tion, and personality needs to explain why some individuals are naturally engaged,stimulated, and motivated by an activity, while others rely, to a greater or lesserextent, on social forces to inspire their actions (Ryan & Deci, 2000). For example,some children gravitate to reading, while others need inducements by parents, teach-ers, or another external stimulus. Children in this latter group, although influencedby external conditions, are not necessarily less self-regulated or less proficient readersthan children intrinsically drawn to reading.

To explain the differential effects of external motives on self-regulation, Deci andRyan (1985) constructed a continuum of regulation types, their consequences forintrinsic motivation, and their relationship to external motives. The first type, exter-nal regulation, addresses the effect of extrinsic contingencies, like rewards or pun-ishments, on an individual’s actions and behavior. Underperformance and amoti-vation result from overreliance on external regulation (Grolnick & Ryan, 1987;Grolnick, Ryan, & Deci, 1991). Introjected regulation is the second type. Here, indi-viduals internalize some activity, but their motivation to perform the activity comesfrom satisfying feelings of self-worth or to avoid guilt associated with poor perfor-mance.

Identification and integration are the third and fourth types, and the most prox-imate to self-regulation and intrinsic motivation. These regulatory mechanismsform from an internalized value for an activity rather than external contingencies(Ryan & Deci, 2000). Identification occurs when personal importance and respon-sibility have been attached to an activity, whereas integration emerges when iden-tified activities have been fully integrated with one’s self-identity. Identified andintegrated regulation underpin autonomous, motivated, and confident learners(deCharms, 1976; Harter, 1982; Lorion, Cowen, & Cladwell, 1975).

Psychological need is also key to understanding self-determined behavior. Just asunmet physiological needs limit personal growth and fulfillment, unmet psycholog-ical needs adversely affect student learning and development (Reeve et al., 2008).Needs for autonomy, competence, and relatedness must be met for individuals toperform at their peak. Autonomy manifests itself as a perception that individualshave control over their own action; they are volitional (Reeve, Nix, & Hamm, 2003).Relatedness refers to our psychological dependence on attachment figures (Bowlby,1980), as well as our need to identify with a goal or activity (Reeve et al., 2008). Inschools, relatedness can describe children’s feelings of emotional security with otherchildren and teachers. Competence relates to efficacy beliefs (Bandura, 1997). Com-petent individuals persevere to achieve outcomes, while diminished competence


impedes initiative and performance. For students, competence includes knowingwhat it takes to do well and believing they have the skills to succeed.

Intrinsic motivation and self-regulation are greatest when the social environmentnurtures all three psychological needs (Reeve et al., 2008). Conversely, personalgrowth and maximum performance diminish when one or more of the three basicpsychological needs is insufficiently met (Ryan & Deci, 2000). A school environmentcapable of supporting volitional academic beliefs and behavior is the theoretical linkthat connects collective faculty trust and student achievement. For learning to occur,students need to commit to a task, engage in an inquiry process, and regulate theiractions. Schools can enhance these student behaviors through positive student-teacher interactions that engage students in learning, or they can thwart themthrough control mechanisms that make task performance contingent on an externalreward or threat (Reeve et al., 2008). Teachers must perceive students and parents astrustworthy before they are willing to use instructional strategies capable of support-ing volitional academic behavior. Collective faculty trust encourages teachers to reg-ulate student performance through social controls that are more student centered,engaging, and learning oriented. Social controls, however, lose their effectivenesswhen trust is low (Das & Teng, 1998; Forsyth et al., 2011). In the absence of trust,teachers attempt to control student behavior with threats or incentives that ulti-mately undermine internal regulation and identification.

Rationale and Hypotheses

First, we consider evidence supporting the collective faculty trust and achievementrelationship. Previous research indicates that school differences in faculty trust ex-plain more variation in student achievement than contextual conditions. Goddard etal. (2001) found that faculty trust was a stronger predictor of math and readingachievement than prior achievement and school socioeconomic status in a sample of47 urban elementary schools. In a larger, more representative sample, Goddard et al.(2009) found that faculty trust predicted math and reading achievement after con-trolling for the social composition of schools. Hoy (2002) discovered a trust-achievement relationship in a diverse sample of high schools. Finally, Bryk andSchneider (2002) found that achievement gains over 3 years were higher in Chicagoschools with greater faculty trust in students and parents.

Achievement effects in existing trust research have been attributed to the positiverelationships and cooperative interactions facilitated by trust. As argued, trust en-ables interdependent role groups to share information openly and to carry out theirresponsibilities for academic goals in a cooperative manner. Distrust in turn con-strains cooperation and limits critical information exchange among parties whoshare responsibility for student learning (Hoy, Tarter, & Woolfolk Hoy, 2006). In-structional climates defined by high collective faculty trust increase teacher willing-ness to experiment with student-centered instructional strategies, to facilitate infor-mation exchange, to build supportive relationships with students, and to workcooperatively with parents (Tschannen-Moran, 2004). Low teacher expectations forstudent performance sets the stage for impersonal and rigid instructional practicesthat undermine social support for effective learning (Bryk & Schneider, 2002; God-dard et al., 2001; Tschannen-Moran, 2004). Therefore, we hypothesize that collective


faculty trust in clients is positively related to math and reading achievement in urbanelementary schools.

Cooperative action is indeed critical to student learning and development, butexplanations for achievement differences based on cooperation alone do not suffi-ciently explain the dynamic social-psychological process that drives academic behav-ior in students. We argue that collective faculty trust has a psychological effect onstudents that has not been tested in existing studies. Our argument for a social-psychological interaction comes from self-determination theory. As suggested byself-determination theory, the difference between students who thrive when con-fronted with challenges and uncertainty and students who become alienated, unmo-tivated, and disengaged often comes down to differences in self-regulation (Deci &Ryan, 2000, 2008; Ryan & Deci, 2000, 2002). Zimmerman (1990) defined self-regulation as students who are metacognitively, motivationally, and behaviorallyactive learners. Such students act volitionally toward academic goals and possess theagency to control academic efforts (Reeve et al., 2008).

Self-regulated beliefs and behaviors are not fixed traits. All individuals have innateand natural orientations toward personal growth and goal attainment (Ryan & Deci,2002; Soenens & Vansteenkiste, 2005). However, the difference between internallymotivated and unmotivated academic behavior often depends on whether or not anexternal social environment supports or suppresses psychological determinants ofwell-being and adaptive behavior (Ryan & Deci, 2000). Achievement outcomesmade contingent on external inducements transfer the locus of causality for aca-demic outcomes to external motives instead of the internal regulatory mechanismsof students. Student academic performance benefits from a healthy relational net-work that transfers control over desired academic outcomes from external contin-gencies to internal capacities of students (Ryan & Deci, 2000, 2002).

Practices used by teachers and schools have both immediate and long-term con-sequences for how students monitor and regulate their academic behavior. Student-teacher relationships that promote student agency, establish attachments to adultsand peers, and build student competence in their academic capabilities can enhancethe capacity of students to control their learning (Jang, Reeve, & Deci, 2010). Withgreater collective faculty trust, teachers can rely on shared influence, cooperation,and relationships to deliver learning. In less predictable environments, where stu-dents and parents are perceived as untrustworthy, teachers are more likely to regulatestudent behavior through rigid, impersonal structures at the expense of leveraginginternal regulation. Contingencies may change behavior in the short term, but theytend to undermine self-regulated academic behavior in the long term if students failto internalize and value learning (Amabile, 1996; Grolnick & Ryan, 1987).

The presence of collective faculty trust does not lead automatically to studentautonomy and competence, but it does signal an instructional climate whereteacher-student relationships are more conducive to self-regulated academic beliefsand behavior. Without collective faculty trust, school and classroom environmentsare likely to be littered with controls that make academic achievement dependent onrewards, threats, punishments, or other inducements that reliably undermine inter-nal regulation (Ryan & Deci, 2000, 2002). Nonthreatening language, choices forinstructional activities, opportunities for self-directed student work, and relationalsupport enhance self-regulated academic beliefs and behavior (Assor, Kaplan, &Roth, 2002). Self-regulated learning is more likely to flourish in urban schools where


instructional practices are guided by trust in students and parents. Thus, we hypoth-esize that self-regulated learning mediates the relationship between collective facultytrust in clients and achievement in urban elementary schools.

Method

Data Source

Data were collected in the spring of 2011 from teachers and students in 56 urbanelementary schools from the same district in a Southwestern state. Researchers ad-ministered electronic surveys through Qualtrics to teachers in the 56 schools. Teach-ers were stratified by school and then randomly assigned to one of two surveys.Usable responses were received from 1,039 teachers across the district, resulting in aresponse rate of 68%. Student surveys were collected during the school day by des-ignated school liaisons. Thirty students were randomly sampled from grade 5. Usableresponses were received from 1,646 students, a 98% return rate. School achievementand demographic data came from district offices and the state department of educa-tion.

Measures

A subset of the Omnibus Trust Scale (Hoy & Tschannen-Moran, 1999) was used tomeasure faculty trust in clients. The scale parallels the theoretical properties of col-lective trust in that it captures teachers’ shared perceptions of the openness, honesty,benevolence, reliability, and competence of the student and parent groups. Ten itemswith a 6-point Likert response set ranging from “strongly disagree” to “stronglyagree” make up the scale. Sample items include “parents in this school are reliable intheir commitments,” and “students in this school can be counted on to do theirwork.” Use of the scale in multiple studies on faculty trust in clients supports itsstrong validity and reliability (Forsyth et al., 2011; Goddard et al., 2001, 2009; Hoy etal., 2006). That stated, we tested the internal structure validity and reliability of thescale with these data to determine how the internal structure of the items held up inour sample of urban elementary schools. Extraction was fixed to one factor sinceprevious evidence supported the one-factor structure of the measure. Results re-ported in Table 1 indicated that 59% of the variance was explained by one factor;factor loadings ranged from .62 to .87; communalities ranged from .40 to .67; andCronbach’s alpha was .87.

We measured the metacognitive dimension of self-regulated learning with theSelf-Efficacy for Self-Regulated Learning Scale (Bandura, 2006; Zimmerman, 1990;Zimmerman & Schunk, 2008). Seven items with a 4-point response set ranging from“strongly disagree” to “strongly agree” were used. An exploratory factor analysis ofthe seven items showed strong factor loadings, item correlations, and high interitemconsistency (see Table 2). Sample items include, “I remember well information pre-sented in class and textbooks,” “I arrange a place to study without distractions,” and“I get myself to study when there are other interesting things to do.”

Student math and reading achievement were operationalized with scale scoresfrom the state-mandated achievement tests. The tests are criterion-referenced,


scored on a standard scale, and aligned with the state’s curricular standards. Schoolaverage math and reading scores from grade 3 state curricular tests in 2008 –2009were used for prior achievement. These scores represent school averages of grade 3performance in 2008 –2009, the year the current grade 5 students were in grade 3. Ourmeasure of prior achievement is not a sample average since we did not have individ-ual grade 3 test data linked to students from our grade 5 sample. Rather, priorachievement is measured as school averages for math and reading achievement dur-ing the 2008 –2009 school year.

Student-level covariates of economic status and minority status were operation-alized using the school district’s demographic records. FRL students were coded as 1;non-FRL students, 0. Minority students were coded as 1; nonminority students, 0.This coding technique allowed us to calculate the poverty and minority achievementgaps.

Table 1. Exploratory Factor Analysis Results of the Faculty Trust in Clients Scale

FactorLoadings Communalities Factor Eigenvalue

% ofVariance

Teachers in this school trust their students .71 .54 1 7.67 59Teachers in this school trust the parents .65 .45Students in this school care about each other .76 .58Parents in this school are reliable in their

commitments .62 .40Students in this school can be counted on to do

their work .62 .40Teachers can count on parent support .77 .56Teachers believe students are competent learners .83 .71Teachers think that most of the parents do a

good job .82 .65Teachers can believe what parents tell them .78 .63Students here are secretive .76 .61

Note.—Cronbach’s alpha � .875.

Table 2. Exploratory Factor Analysis Results of the Self-Efficacy for Self-RegulatedLearning Scale

FactorLoadings Communalities Factor Eigenvalue

% ofVariance

I am able to finish my homework on time .71 .51 1 4.02 57I am able to study when there are other interesting

things to do .67 .46I am able to concentrate on my homework .70 .49I am able to remember information presented in

class and in my schoolbooks .71 .50I am able to arrange a place to study at home

where I won’t get distracted .74 .54I am able to motivate myself to do schoolwork .69 .48I am able to participate in class discussions .73 .54

Note.—Cronbach’s alpha � .89.


Analytical Technique

First, we empirically tested the collective nature of faculty trust in clients andself-regulated learning by calculating an Intraclass Correlation Coefficient (2)(ICC-2) with a one-way analysis of variance ANOVA (Mean Square Between �Mean Square Within/Mean Square Between) and an ICC-1 with a hierarchical linearmodel (HLM) unconditional model. ICC-2 is a measure of within-group homoge-neity and assesses the degree to which individual teacher responses cluster aroundthe school mean (Glisson & James, 2002). It is different than the HLM-derived ICCin that the HLM model estimates between-group differences in faculty trust, not thecohesiveness of teacher trust perceptions within schools. Both estimates are neces-sary to justify aggregation of school-level variables because within-group consistencyand between-group variability occur independent of each other (Glisson & James,2002).

We used a model-building process in HLM 6.08 to test our two hypotheses. Wefirst fitted a null model to decompose math and reading achievement variance intoindividual and school effects. Next, we used a random coefficient regression to testthe effect of student FRL and minority status on reading and math achievement. FRLand minority status were grand-mean centered and set to vary randomly acrossschools. Grand-mean centering has a computational advantage over no centering orgroup centering in that it reduces potential multicollinearity problems between in-tercepts and slopes across group estimations, and it isolates the net effect of school-level variables on an outcome by partialling out level I effects (Raudenbush & Bryk,2002).

Correlation coefficients produced by the random coefficient regression showedstrong multicollinearity between FRL and minority status (r � .89). To addresspotential problems with using both student-level covariates in the model, we decidedto remove minority status as a control. This decision was based on larger achieve-ment gaps between FRL and non-FRL students than between minority and nonmi-nority students.

We tested our first hypothesis, the main effect of collective faculty trust on mathand reading achievement, by specifying a one-way random-effects ANCOVA withFRL as a student-level covariate and FRL rate and prior achievement as school-levelcovariates. Student FRL qualification was constrained to have a common varianceacross all schools and grand-mean centered. School-level FRL and prior achievementwere also grand-mean centered. Grand-mean centering allowed us to estimate thenet effect of collective faculty trust on math and reading achievement (Luke, 2004;Raudenbush & Bryk, 2002).

We tested our second hypothesis, the mediating effect of self-regulated learning,with a 2-1-1 multilevel mediation model. A 2-1-1 mediation model is used when theantecedent variable is measured at the school level and the mediator and outcomevariables are measured at the individual level (Krull & MacKinnon, 2001). Multilevelmediation analysis follows a similar process advanced by Baron and Kenny (1986) inthat the first model tests the direct effect of the school-level predictor on the outcomevariable. The second step is to test the relationship between the antecedent, collectivetrust in this case, and the mediator. The third step tests the relationship between theantecedent and outcome variable with the mediator included (Krull & MacKinnon,2001; Zhang, Zyphur, & Preacher, 2009).

collective trust � 1 1

Model fit was assessed by comparing the deviance estimate of the null models formath and reading achievement against the deviance estimates for the main effectsand mediation tests. Deviance accounts for the lack of fit between sample data andthe model and is best used when comparing the fit between two or more models(Luke, 2004). For our purposes, we were interested in fit improvement (i.e., reduc-tion in deviance) between the main effect model and the mediation tests with self-regulated learning entered as a level I predictor. We tested the variance-covariancestructure of two models with the hypothesis-testing function in HLM 6.08. This testcompares the difference in model deviance with the chi-square distribution (Luke,2004). A significant chi-square suggests that one model fits the data better than theother. Using math achievement as an example, level I and level II equations arepresented for our models:

Results

Descriptive data are provided first (Table 3). We had valid math achievement data for1,482 students and valid reading achievement data for 1,412 students. Of these stu-dents, 81% qualified for the federal lunch subsidy. The average scale scores were 725for math and 716 for reading. Because math and reading exams were administered ondifferent days, there was a small difference in the number of valid scores. The schoolfree or reduced-price lunch rate ranged from 16% to 100%, with a mean of 86%. Thesocial composition of students and schools in our sample was slightly different thanthe sample of urban elementary schools in Goddard et al. (2001). Sixty-seven percent

Random Effects ANOVA (Null Model)

Level I: Math achievement � �0 � rLevel II: �0 � �00 � uRandom Coefficient Regression (Model 2)Level I: Math achievement � �0 � �1(FRL) � rLevel II: �0 � �00 � u0

�1 � �10One-Way Random-Effects ANCOVA (Model 3)Level I: Math achievement � �0 � �1(FRL) � rLevel II: �0 � �00 � �01 (FTC) � �02 (school FRL rate) � �03 (prior achievement) � u0

�1 � �10Mediation Analysis (Model 1)Level I: SRLearning � �0 � �1(FRL) � rLevel II: �0 � �00 � �01 (FTC) � �02 (school FRL rate) � �03 (prior achievement) � u0

�1 � �10Mediation Analysis (Model 2)Level I: Math achievement � �0 � �1(FRL) � �2(SRLearning) � rLevel II: �0 � �00 � �01 (FTC) � �02 (school FRL rate) � �03 (prior achievement) � u0

�1 � �10�2 � �10

�0 � school mean for math achievement�1 � achievement effect of FRL�2 � achievement effect of self-regulated learning�00 � grand mean for math achievement�01 � effect of collective faculty trust on math achievement�02 � effect of FRL rate on math achievement�03 � effect of prior math achievement


of students in their sample qualified for the subsidized lunch program, and theaverage FRL rate was 62%.

Tests of within-group homogeneity and between-school variability in facultytrust in clients confirm the collective nature of the variable. An ICC-2 estimate of .91exceeded the .70 threshold set by Cohen, Doveh, and Eick (2001) as indicative ofreliable group means. A robust ICC-2 indicates strong agreement among faculty as tothe trustworthiness of students and parents. Between-school variability in collectivefaculty trust was also significant, with school membership accounting for 45% of thevariance (Table 4). Intraclass coefficients for self-regulated learning indicate weakerwithin-group agreement among students within a school (ICC-2 � .61), but signif-icant variance at the school level (ICC-1� .11), suggesting that self-regulated learningis an individual orientation that is partly shaped by schools. School differences ac-counted for 19% of the variance in both math achievement and reading achievement.

The one-way random-effects ANCOVA tested our hypothesized main effect ofcollective faculty trust on mean achievement after controlling for FRL at the studentlevel and FRL rate and prior achievement at the school level. Results (see Table 5)indicate a main effect on math achievement (�01 � .20, p � .01) and reading achieve-ment (�01 � .18, p � .01). With all variables standardized to a mean of 0 and a

Table 3. Descriptive Statistics for Student- and School-Level Variables

Mean SD Min Max

Student level:Reading (n � 1,412) 716 83 400 990Math (n � 1,482) 725 90 400 990Free/reduced-price lunch .81 .42 0 1Minority status .69 .46 0 1Self-regulated learning 0 1 �3 2

School level (I � 56):Free/reduced-price lunch rate 86 23 16 100Faculty trust in clients 0 1 �2 2.1Prior math achievement 720 43 631 851Prior reading achievement 719 41 632 826

Note.—Free/reduced-price lunch reports the percentage of students in the sample who qualified

for the federal lunch subsidy. Minority status reports the percentage of students in the sample classified

as minority. Self-regulated learning and faculty trust in clients are reported as standardized values.

Achievement scores are reported as scale scores for the state exams. Free/reduced-price lunch rate rep-

resents the percentage of students in a school who qualified for the federal lunch subsidy. Free/reduced-

price lunch rate and achievement scores were standardized to a mean of 0 and standard deviation of 1 in

the multilevel analysis.

Table 4. Intraclass Correlation Coefficients: Estimates of GroupDependence and Within-Group Agreement

Variable ICC(1) Chi-Square ICC(2) F-Ratio

Faculty trust in clients .45 381.11 ** .75 4.67 **Self-regulated learning .1 117.36 ** .61 1.7Reading achievement .19 213.06 **Math achievement .19 236.75 **

** p � .01.


standard deviation of 1, a 1 point increase in collective faculty trust was associatedwith a .20 SD increase in math achievement and .18 SD increase in reading achieve-ment. Collective trust had the largest net effect on mean achievement of all thevariables in the model. Individually, collective trust accounted for around 4% of theunique variance in math and reading achievement. Figure 2 shows the moderatingeffect of collective faculty trust on the achievement of FRL students. The average FRLstudent in a high-trust school scored slightly below the average non-FRL student ina low-trust school and significantly better than their FRL peers in low-trust schools.

Variance components from the random-effects ANCOVA showed a reduction inthe average math variance from .19 to .02 between the null and main effects models.This suggests that approximately 89% of the school-level variability in average mathachievement was accounted for by the school predictors, with trust accounting formost of the variance. For average reading achievement, the intercept variancechanged from .19 to .04, indicating that school predictors accounted for about 79% ofthe between-school variability, with trust having the most explanatory power.

Three criteria were used to test the hypothesized mediation effect of self-regulatedlearning. Our first criterion, evidence of the relationship between faculty trust andachievement, was satisfied. As indicated in the previous paragraphs, faculty trust hada significant and positive effect on math and reading achievement. Our second cri-terion, a change in the relationship between faculty trust and achievement withself-regulated learning entered in the model, was also met. With self-regulated learn-ing in the model, the faculty trust effect decreased from .20 to nearly one-tenth of astandard deviation. In reading, the estimated effect decreased from .18 to .06. For

Table 5. Random-Effects ANCOVA Results for Math Achievement, Reading Achievement, andSelf-Regulated Learning

Math Achievement Reading AchievementSelf-Regulated

Learning

Fixed Effects Model 1 Model 2 Model 1 Model 2Mediation

Model

School predictors:FRL rate �.14 (.06) * �.08 (.06) �.09 (.06) �.04 (.06) .02 (.05)Prior achievement .09 (.06) .09 (.06) .13 (.06) * .11 (.06) � .00 (.05)FTC .20 (.07) ** .09 (.06) .18 (.04) ** .06 (.05) .45 (.04) * **

FRLunch slope �.50 (.12) ** �.47 (.12) ** �.54 (.12) ** �.51 (.12) ** .05 (.11)SRLearning slope .28 (.04) * ** .29 (.04) **Deviance (�2 log

likelihood) 1,478 1,421 1,470 1,423 1,447� Deviance �59 ** �57 ** �73 ** �47 ** �75 **Explained between-school

variability (%) 89 79

Note.—N � 1,482 students for math achievement; N � 1,412 students for reading achievement; N � 56 elementary schools; FRL

rate is the percentage of students in a school who qualify for the federal lunch subsidy. FTC is the abbreviation for Collective Faculty

Trust in Clients. FRLunch slope is the achievement gap between free/reduced-price lunch students and non-free/reduced-price lunch

students. SRLearning slope is the effect of self-regulated learning and achievement. School-level predictors were standardized to a

mean of 0 and a standard deviation of 1 for the multilevel analysis. Using a common metric enabled comparison of the relative

achievement effect for faculty trust, prior school achievement, and FRL rate. � Deviance for model 1 presents the difference from the

null model to model 1. � Deviance for model 2 presents the difference from model 1 to model 2.� p � .10.

* p � .05.

** p � .01.


both math and reading, collective trust went from having a moderate and significanteffect on achievement to a small and nonsignificant effect, suggesting a relativelystrong relationship between collective trust and self-regulated learning.

Our final criterion, a relationship between faculty trust and self-regulated learn-ing, was also confirmed. Collective trust had a strong, significant effect on studentself-regulated learning even after controlling for the social composition of the school(�1 � .45, p � .01). A 1 SD increase in collective trust resulted in a .45 SD increase inself-regulation. Stated differently, nearly 21% of the variation in self-regulated learn-ing was explained by collective faculty trust. Figure 3 illustrates the trust effect onself-regulation. Self-regulation was higher for both FRL and non-FRL students inschools with a strong culture of trust.

We also assessed changes in model fit across our analysis of math and readingachievement. Model fit improved for both math and reading achievement with self-regulation specified as a student-level predictor and faculty trust as a school-levelantecedent. There was a significant reduction in deviance between models 1 and 2 forboth math (�57) and reading (�47) achievement. In short, analyses with facultytrust and self-regulated learning were the best-fitting models and explained the mostvariance in achievement.

Discussion

Achievement disparity associated with student background characteristics remainsan unresolved social problem. Even though research has identified controllableschool conditions associated with high achievement in urban contexts (i.e., academicoptimism, social capital, collective trust), explanations for how normative charac-

Figure 2. Bar graph depicting the relationship between collective faculty trust, math achievement,

and FRL status. High-collective-trust schools represent schools at or above 1 SD from the sample

mean. Low collective trust schools represent schools at or below �1 SD from the sample mean.


teristics shape student achievement are limited. Our findings, when considered in thecontext of self-determination theory, offer a social-psychological explanation forachievement effects attributed to collective faculty trust.

Effects of Collective Faculty Trust

Both hypotheses guiding the empirical investigation were confirmed. Collectivefaculty trust explained variation in student math and reading achievement over andabove the FRL rate and prior school achievement. Further, students’ self-reported,self-regulated learning mediated the trust-achievement relationship. To be specific,students in high-trust schools were more likely to believe they can control theirlearning compared to students in low-trust schools. They also outscored peers instate reading and math exams. Collective faculty trust and self-regulated learning,not FRL rate or prior achievement, were the primary reason for achievement differ-ences across elementary schools in our sample.

We were not surprised by the smaller achievement effects of prior achievementand FRL rate. Goddard and colleagues also found that trusting environments had astronger association with achievement than the social composition of schools (God-dard et al., 2001). Achievement is certainly not impervious to the influence of con-textual conditions, but harmful effects of poverty and other environmental risks canbe offset by interactions between teachers and students that promote self-regulatedacademic beliefs and behaviors. Although schools have little control over poverty,and they cannot change past outcomes, they can control their responses to the ex-ternal environment. Actions that support collective faculty trust would have greaterpotential to improve student achievement than structures and processes that add

Figure 3. Bar graph depicting the relationship between collective faculty trust, self-regulated learn-

ing, and FRL status. High-collective-trust schools represent schools at or above 1 SD from the

sample mean. Low collective trust schools represent schools at or below �1 SD from the sample

mean.


constraints to the instructional systems. It is unknown from our results why somehigh-poverty schools in our sample were more effective at fostering a trusting envi-ronment than others.

We were also not surprised by the large amount of between-school variance inmath and reading achievement explained by the full models. For one, we controlledfor two variables (FRL rate and prior achievement) that often explain considerablevariability in student achievement. Further, we sampled schools within the sameurban district. Schools within the same district tend to have similar structures, use acommon curriculum, and follow standardized policies, thereby reducing the likeli-hood that achievement variance would be attributed to structural features. Schoolclimate is often what sets schools within the same district apart from other schools.We conjecture that a norm of collective trust, along with patterns of self-regulatedstudent behavior, are potent drivers of effective teaching and learning, and thusaccount for considerable school-level variance in achievement.

Even though we predicted a mediation effect of self-regulated learning, we did notexpect to find as large an effect as was estimated. We expected FRL rate and priorachievement to influence self-regulation, but these conditions had almost no rela-tionship. We were also surprised by the large drop in the effect of collective facultytrust on math and reading achievement when self-regulated learning was included inthe models. Collective faculty trust went from being statistically significant in model1 to nonsignificant in model 2. Results of the full models for achievement and themediation test underscore the importance of collective faculty trust for student ac-ademic beliefs and behavior.

Theoretical Explanation

The influence of collective faculty trust on self-regulated learning and achieve-ment is an intriguing process to understand. Unlike student trust in teachers, facultytrust in students and parents is not a direct source of student motivation; it operatesthrough the teaching and learning environment to influence student beliefs andbehavior. Many explanations for the performance benefits of faculty trust addresshow cooperation and open relationships contribute to effective teaching. For exam-ple, Hoy et al. (2006) stated, “Trust in parents and teachers liberates teachers toinnovate without fear of retribution if things do not go as planned, and it encouragescooperation and support between parents and teachers” (p. 440). Goddard et al. (2001)asserted, “trust makes schools better places for students to learn, perhaps by enabling andempowering productive connections between families and schools” (p. 14).

Our findings point to a psychological link that connects faculty trust and studentachievement. Collective faculty trust appeared to operate through self-regulatedlearning to influence math and reading achievement. That is, students in high-trustschools had on average stronger self-regulatory beliefs than students in low-trustschools. In fact, faculty trust was the only significant predictor of school differencesin self-regulated learning, explaining nearly 21% of the variance. Unresolved fromthe empirical investigation is an understanding of how collective faculty trust canfoster self-regulation. Absent rich empirical data addressing this relationship, weturn to self-determination theory for a plausible explanation.

Self-determination theory attributes volitional behavior and goal attainment tothe fulfillment of psychological needs. When psychological needs of autonomy, re-


latedness, and competence are satisfied by the social environment, self-regulated andself-determined behavior can flourish. Unsatisfied needs, in turn, suppress agentiveand volitional action (Ryan & Deci, 2000, 2002). It is important to point out that wedid not measure student autonomy, competence, or relatedness. Therefore, we donot know if collective faculty trust satisfies student psychological needs. What we doknow is that student-reported self-regulated learning and achievement were greaterin schools with stronger collective faculty trust. This leads us to speculate that col-lective trust functions as a social support for student beliefs and behaviors that con-tribute to academic achievement.

We argue that collective faculty trust enables teachers to use student-centeredinstructional practices that build student self-regulation and that foster engagementin learning activities. Low trust, in contrast, is likely to fuel teacher-centered instruc-tional practices that have the effect of controlling student behavior through externalmeans. Students trusted by their teachers are more likely to engage in learning, takeresponsibility for schoolwork, and regulate their performance (Goddard et al., 2001).Trust also facilitates an open exchange between teachers and students that can en-hance achievement. Further, trust leads to relationships and attachments that arecritical elements of a social environment supportive of self-regulated behavior andquality academic performance (Bowlby, 1973, 1980; Rotter, 1967; Webb, 1992). Bryk,Sebring, Allensworth, Luppescu, and Easton (2010) found this to be true in Chicagopublic schools where stronger and cohesive parent, school, and community tiesexisted in schools that made the greatest academic improvements.

Conclusion

Our findings, when considered with evidence from other studies, indicate that col-lective faculty trust is a vital social resource for urban students. Of concern, however,is evidence on the extensive variability in trust across schools in the same district.Although not the primary object of this study, we found that 45% of variance incollective faculty trust existed between schools. This finding is consistent with that ofGoddard et al. (2001), who found that 30% of variance in collective faculty trust wasat the school level. Large between-school variance suggests that instructional systemswithin the same urban district differ in their capacity to produce strong relationshipsamong teachers, parents, and schools. With potentially large differences in collectivetrust across schools, we see a need and an opportunity to explore how policies andpractices can better support trust formation as a means to urban school improve-ment.

From a policy lens, it is necessary to identify policy tools that support the devel-opment of trust in schools. Even though education policies are not directly related tocollective trust, they shape the formal structures and informal processes that regulateactions and interactions in schools. We envision the purpose of policy in collectivetrust formation as setting the contextual stage for teachers to develop trusting rela-tionships with students and parents. Rather than seeking to control school outcomesthrough external interventions, policies can support local capacity building in urbandistricts and schools. Policies that target capacity building do so by enabling schoolprofessionals to generate knowledge from practice and to adapt structures, pro-cesses, and actions to better meet the needs of students and families (Darling-Hammond, 2005). Such policies would appear on the surface to work through col-


lective trust to improve academic outcomes. Very few states and districts, however,have designed policies capable of building capacity and supporting sustainable re-form (Fullan, 2010). More research is needed on how policy processes and policiesthemselves can work to facilitate interactions and relationships within and across alleducational levels.

From a school improvement lens, we see value in establishing a science on howpositive normative conditions like collective faculty trust counteract developmentalchallenges associated with poverty and other environmental risks. Evidence on con-ditions that give life to effective teaching and learning can lead to improvementefforts that target the social and psychological sources of quality performance. Thisstudy was a modest step toward building a knowledge base on school attributes thatenable urban students to learn and grow. Future research can explore how collectivefaculty trust translates into teacher and school behaviors that effectively address thepsychological needs of learners. Additionally, there are other variables that likelymediate the trust-achievement relationship in urban schools. These variables can bespecified in theoretical models and tested to determine their mediating effects. Inshort, while trust studies in schools date back over 30 years, there is still more to learnabout how and why this resource facilitates quality teaching and learning.

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