HELPING STUDENTS TRANSITION TO MIDDLE SCHOOL: …

The Pennsylvania State University

The Graduate School

Department of Educational Psychology, Counseling, and Special Education

HELPING STUDENTS TRANSITION TO MIDDLE SCHOOL:

EFFICACY OF AN ATTRIBUTIONAL INTERVENTION

A Dissertation in

School Psychology

by

Gordon Emmett Hall

© 2019 Gordon Emmett Hall

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

May 2019

The dissertation of Gordon Emmett Hall was reviewed and approved* by the following:

James C. DiPerna Professor of Education Professor in Charge for Graduate Programs in School Psychology Dissertation Advisor Chair of Committee

Karen Bierman Evan Pugh University Professor Professor of Psychology and Human Development and Family Studies

Barbara A Schaefer Associate Professor of Education, Educational and School Psychology

Scott Gest Professor of Human Services at the University of Virginia

*Signatures are on file in the Graduate School

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ABSTRACT

Although students’ causal attributions yield divergent trajectories in achievement and well-being

(Blackwell, Trzesniewski, & Dweck, 2007; Yeager & Walton, 2011; Yeager et al., 2014), few

attribution intervention studies to date have focused on the transition to middle school, during

which time students often experience losses in achievement and sense of social belonging

(Eccles, 2004; Kingery, Erdley, & Marshall, 2011). As such, the purpose of the current study was

to develop and test the efficacy of an attributional intervention on students’ attributions,

motivation, social belonging, and achievement during the transition to middle school. The sample

consisted of 133 fifth and sixth grade students enrolled at two public middle schools. Students

were randomly assigned to treatment and control modules, which were modeled on previous

attributional interventions. Both modules consisted of information regarding the process of

transitioning to middle school. Measures of attribution, academic motivation, social belonging,

and academic achievement were collected immediately following implementation and at short-

and long-term follow-up.

Hierarchical linear regression was used to test if the intervention positively changed

students’ attribution, motivation, social belonging, and academic achievement. Results indicated

that the intervention was not successful in shifting attributions or impacting motivation, social

belonging, and achievement. There was a significant interaction effect between treatment status

and identification with minority status in predicting academic achievement at one time point. In

exploring the interaction, there is some evidence to suggest the treatment was successful for a

local minority population, which is a group that is a minority within the specific context of the

school. Implications for future research are discussed.

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TABLE OF CONTENTS

LIST OF FIGURES ................................................................................................................. vi

LIST OF TABLES ................................................................................................................... vii

ACKNOWLEDGEMENTS ..................................................................................................... viii

Chapter 1 Introduction ............................................................................................................. 1

Review of Literature ........................................................................................................ 3Attributional Theory ................................................................................................. 3Attributional Interventions in Education .................................................................. 6Characteristics of Successful Attributional Interventions ........................................ 15 Adapting Attributional Interventions for the Middle School Transition .................. 17 Effects of Mind-set Interventions ............................................................................. 20 Theory of Change ..................................................................................................... 21 Rationale, Purpose, and Hypotheses ........................................................................ 24

Chapter 2 Method ................................................................................................................... 27

Participants ....................................................................................................................... 26Measures .......................................................................................................................... 28

Attribution (Proximal Outcome) .............................................................................. 28Motivation (Medial Outcome) ................................................................................. 29Social belonging (Medial Outcome) ........................................................................ 29Academic achievement (Distal Outcome) ................................................................ 30

Procedures ........................................................................................................................ 30 Intervention development ......................................................................................... 30Intervention implementation .................................................................................... 31Data collection and timeline ..................................................................................... 32

Data Analysis ................................................................................................................... 33 Missing Data .................................................................................................................... 34

Chapter 3 Results .................................................................................................................... 36

Assumptions ..................................................................................................................... 35Proximal Outcome ........................................................................................................... 37

Attribution (Hypothesis 1) ....................................................................................... 37Medial Outcomes ............................................................................................................. 38

Motivation (Hypothesis 2) ....................................................................................... 38Social belonging (Hypothesis 3) .............................................................................. 39

Distal Outcome ................................................................................................................. 40 Achievement (Hypothesis 4) .................................................................................... 40

Follow-up Analyses ......................................................................................................... 43

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Chapter 4 Discussion .............................................................................................................. 48

Post-treatment Effects ...................................................................................................... 47Proximal outcomes ................................................................................................... 47Medial outcomes ...................................................................................................... 48Distal outcomes ........................................................................................................ 48Interpretation of key post-treatment findings ........................................................... 48

Follow-up Effects ............................................................................................................. 49 Medial outcomes ...................................................................................................... 49Distal outcomes ........................................................................................................ 49Interpretation of key findings at follow-up .............................................................. 49

Limitations ....................................................................................................................... 52 Implications and Future Research .................................................................................... 56 Conclusions ...................................................................................................................... 57

References ................................................................................................................................ 60

Appendix .................................................................................................................................. 71

Example survey questions and responses ........................................................................ 71 Example screen shot of attribution measure developed in the present study ................... 74 Two example screen shots of a welcome page and content pages from Yeager,

Paunesku, Walton, and Dweck (2013) ..................................................................... 75 Estimated marginal means of attribution by race/ethnicity at post-treatment .................. 76 Estimated marginal means of motivation by race/ethnicity at short-term follow-up ....... 77 Estimated marginal means of motivation by race/ethnicity at long-term follow-up ....... 78 Estimated marginal means of social belonging by race/ethnicity at short-term

follow-up .................................................................................................................. 79 Estimated marginal means of social belonging by race/ethnicity at long-term follow-

up .............................................................................................................................. 80

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LIST OF FIGURES

Figure 1: Theory of Change. .................................................................................................... 23

Figure 2: Flow of Participants Through Study Protocol. ......................................................... 28

Figure 3: Estimated Marginal Means of Achievement at Short-term Follow-up. ................... 43

Figure 4: Estimated Marginal Means of Achievement by Race/Ethnicity at Post-treatment. .......................................................................................................................... 45

Figure 5: Estimated Marginal Means of Achievement by Race/Ethnicity at Short-term Follow-up. ........................................................................................................................ 46

Figure 6: Estimated Marginal Means of Achievement Race/Ethnicity at Long-term Follow-up. ........................................................................................................................ 47

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LIST OF TABLES

Table 1: Summary of Effect Size Data in Attributional Intervention Research by Sample Type. ................................................................................................................................. 8

Table 2: Demographic Variables for Treatment and Control. ................................................. 29

Table 3: Time Points for Collection of Primary Outcome Variables. ..................................... 34

Table 4: Descriptive Statistics for the Primary Outcome Variables by Time and Condition. ......................................................................................................................... 37

Table 5: Intervention Effects on Attributions at Post-treatment. ............................................. 39

Table 6: Intervention Effects on Student Motivation at Post-treatment, Short-term, and Long-term Follow-up. ...................................................................................................... 40

Table 7: Intervention Effects on Social Belonging at Post-treatment, Short-term, and Long-term Follow-up. ...................................................................................................... 41

Table 8: Intervention Effects on Achievement at Post-treatment, Short-term, and Long-term Follow-up. ................................................................................................................ 42

viii

ACKNOWLEDGEMENTS

My wife has been a pillar of support through this entire process. This dissertation has

been a whole-family effort, and that help means more than I can put on this page. I love you,

Molly, and thank you for everything. Thank you, also, Mom and Dad, for the encouragement and

the willingness to pitch in: watching Ellie; buying meals; being there. In lifelong learning, I am

inspired by your example, and I love you both. Thank you, Dr. DiPerna for the advice about the

dissertation and your professional mentorship. I have certainly doubted myself, but I always left

my meetings with you feeling inspired and ready to finish. Thank you to my committee members,

Dr. Bierman, Dr. Gest, and Dr. Schaefer. Your time and expertise are deeply appreciated. Thanks

to all of the students who put in time to participate in my research. Lastly, thank you to the

teachers and administrators who made this research possible, specifically, Jonathan Myler and

Shradha Patel, two individuals who bent over backward to help me for no other benefit than

helping a fellow educator.

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Chapter 1

Introduction

Student attributions about academic and social outcomes can have a profound impact on

later behavior. Causal attributions are beliefs about the perceived causes of successes or failures.

Interventions aimed at student attributions have been associated with altered trajectories for

academic achievement and motivation (Yeager & Walton, 2011). Attributional interventions also

have been linked with changes in a student’s sense of social belonging, stress, shame, aggressive

retaliation, and even health outcomes (Blackwell, Trzesniewski, & Dweck, 2007; Yeager et al.,

2014; Yeager et al., 2016; Yeager, Miu, Powers, & Dweck, 2013). Attributions play a powerful

role in shaping students’ experience of the school environment. Although many attributional

intervention studies have targeted the transition to college (e.g. Aronson, Fried, & Good, 2002;

Menec et al., 2004; Walton & Cohen, 2011; Wilson & Linville, 1985), less research has been

focused on the transition to middle school.

Students experience changes in school structure and student motivation at the onset of

middle school. Transitioning to middle school is often associated with moving from a small

elementary school with self-contained classrooms to a larger middle school with subject-based

classrooms (Kingery, Erdley, & Marshall, 2011). Peer relationships take on greater importance

as youth begin to seek approval from peers and independence from adults (Farmer, Hamm,

Leung, Lambert, & Gravelle, 2011). Students’ connection to school and feelings of social

belonging decline after the transition (Eccles, Lord, & Midgley, 1991; Witherspoon & Ennett,

2011), and middle school is marked by a period of achievement loss (Alspaugh, 1998; Eccles,

Lord, & Midgley, 1991).

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Student attributions influence motivation. Building on the earlier work of Dweck and

Leggett (1988), Blackwell, Trzesniewski, and Dweck (2007) hypothesized that differing implicit

theories of intelligence, or attributions about the causes of achievement, result in distinct

motivational patterns. Specifically, students with a malleable theory of intelligence (i.e. students

who believe that intelligence can be changed) are less susceptible to frustration or

discouragement in the face of adversity. In a longitudinal study, Blackwell et al. (2007) found

that students who identified with a malleable theory of intelligence were associated with a growth

trajectory for mathematics achievement over a 2-year period, and students who identified with a

fixed theory of intelligence were associated with a gradually declining trajectory of mathematics

achievement (Blackwell, Trzesniewski, & Dweck, 2007).

Attributional interventions in education target students’ attributions about school and the

social world. These interventions are typically brief and do not replace high quality instruction

and evidence-based curricula. Rather, attributional interventions seek to alter self- and social

perceptions, such as a student’s sense of social belonging or theory of intelligence, in order to

promote positive behavior within the environment. The intervention tested in the present study is

based on studies of social-psychological interventions targeting attributions by Wilson and

Linville (1985), Blackwell et al. (2007), and Walton and Cohen (2011). Wilson and Linville

(1985) designed an intervention to disrupt negative attributions about adversity experienced

during the transition to college, and Walton and Cohen (2011) designed an intervention targeting

negative attributions about social belonging during the transition to college.

The results from these, and other intervention studies (e.g., Martens, Johns, Greenberg, &

Schimel, 2006), have demonstrated that attributions play a powerful role in shaping student

motivation and achievement, especially during periods of academic and social transition.

Students are at great risk for making negative attributions during the transition to middle school

because of the myriad changes associated with adolescence and middle school (Eccles, 2004). As

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such, an intervention was developed for the present study addressing student fears regarding

academic achievement and social belonging during the transition to middle school. The core

intervention strategy was an adaptation of the intervention protocol developed by Wilson and

Linville (1985) and implemented immediately following the start of middle school. The purpose

of this study was to test the effectiveness of this attributional intervention on students’

motivation, achievement, and social belonging during the transition to middle school.

Review of Literature

The literature review begins with an overview of attributional theory and the evolution of

attributional interventions. Next, recent attributional interventions in education are described.

The subsequent two sections highlight the adverse experiences that are possible during the

transition to middle school: the structural, social, and pedagogical differences between

elementary and middle schools and the social and developmental changes that occur during the

onset of puberty.

Attributional Theory

The attributional interventions featured in the present study have been informed by

attributional research. Early attribution theorists (e.g. Heider, 1958; Jones & Davis, 1965; Kelley,

1967) argued that human behavior can best be predicted by first understanding how people

explain to themselves the reasons for their own and others’ behavior. Attributions are the

perceived causes for success or failure. Once an individual identifies a cause to an outcome, then

behaviors that led to success can be repeated and behaviors that led to failure can be altered.

Thus, the attribution serves as a basis for how an individual reacts to the event, as well as for

expectations regarding future events (Weiner, 1985).

One of the first theorists to focus attributional theory on the area of academic

achievement was Bernard Weiner (Wilson, Damiani, & Shelton, 2002). Previous theorists had

focused on the locus dimension of attributions (e.g., Heider, 1958; Jones & Davis, 1965; Kelley,

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1967). The locus dimension reflects whether the perceived cause of the success or failure is an

internal quality (i.e., a factor occurring within the person), or an external quality (i.e., a factor

occurring within the environment). Weiner (1985) proposed that stability and controllability

offered a more fruitful avenue to intervention. Stability describes whether the perceived cause is

changeable, and controllability describes the degree to which the individual is able to influence

the cause. Weiner hypothesized that shifting attributions for poor performance from stable causes

to unstable ones would lead to greater effort and higher performance (Weiner, 1986).

Specifically, when a student receives a poor grade and attributes the poor grade to a lack of

ability, which is a stable and uncontrollable cause, they are likely to be demoralized. Shifting the

attribution to an unstable cause, such as bad luck or low effort, would increase the perceived

likelihood of future high performance for the student.

Carol Dweck (1975) was one of the first researchers to demonstrate that shifting poor

performance attributions to unstable causes could improve motivation, effort, and subsequent

academic performance for students. A study was conducted with children exhibiting signs of

learned helplessness. Learned helplessness occurs when children, who have experienced a

failure, do not perform a subsequent task despite having the skills necessary to complete the task.

Dweck demonstrated that an attributional retraining intervention increased academic performance

despite prior failure. The gains were significantly different from a success only treatment where

students were provided a series of academic tasks that were designed to be completed without

error by the students.

Dweck and Leggett (1988) proposed that individuals hold implicit theories about

intelligence that guide attributions and establish a goal framework. Their work expanded on

Weiner’s (1972) attribution theory and Seligman, Maier, and Solomon’s (1971) research in the

area of learned helplessness. Dweck and Leggett developed a model to explain why certain

students respond to adverse experience with “helpless” behavior and others respond with

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“mastery-oriented” behavior. A “helpless” response is one in which the individual’s performance

and motivation declines in the face of challenge; whereas a “mastery-oriented” response

maintains or increases performance and motivation in the face of failure. Students with an

implicit theory of intelligence as an entity, or fixed and stable quality, attribute academic failure

to a lack of intelligence. As a result, these students avoid academic tasks because they have

learned to anticipate failure despite effort. By contrast, students with an implicit theory of

intelligence as malleable, or a changeable and unstable quality, attribute academic failure to a

lack of effort. Consequently, these students pursue academic challenges with even greater

motivation because these students have learned that experiencing failure does not necessarily

predict failure in future performances.

In addition, Dweck and Leggett proposed an alternative to the notion from Weiner’s

attribution theory that certain factors were, by definition, stable and uncontrollable (Weiner,

1985). Specifically, in Weiner’s (1974) theoretical framework, intelligence was viewed as an

inherently stable factor. In contrast, Dweck proposed that individuals hold implicit theories about

all factors and individuals make attributions based on the implicit theory. Individuals who view

abilities as malleable are incremental theorists, and those who view abilities as fixed are entity

theorists. Incremental and entity theorists may blame the same factor, but one will view it as

controllable and unstable, just as the other views it as uncontrollable and stable. Thus, it is up to

each individual to determine whether or not a particular cause is controllable. In Dweck’s model,

an individual’s implicit theory determines whether an individual makes an attribution to a stable

or unstable factor (Dweck & Leggett, 1988).

Self-efficacy theory, by Albert Bandura (1997), builds on attribution theory and has

implications for attributional interventions in education. According to Bandura, self-efficacy is

an individual’s belief in his or her ability to successfully complete a given task. Bandura argued

that attributional interventions are successful inasmuch as they are able to shift an individual’s

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sense of self-efficacy. Only when an individual believes that they are capable of successfully

completing a given task, will they commit to completing the task. Similarly, Dweck’s entity

theorists are successful because the implicit theory allows for greater self-efficacy.

One final theory relevant to the development of attributional interventions is field theory

(Lewin, 1951). Field theory was developed as way to describe the social environment. Behavior,

according to Lewin, is a function of the person and the environment. Behavior exists within a

complex field of forces. Some forces promote a behavior and other forces restrain that same

behavior, such that there are two ways to promote behavior change. The first is to increase forces

that promote a certain behavior, for example by rewarding the behavior. An oft-overlooked route

to behavior change is to consider which forces are restraining a behavior. When students worry

about whether or not an assessment determines their intelligence, it creates a force that restrains

success. Restraining forces, such as an entity theory of intelligence or stereotype threat, can

disrupt student success (Lewin, 1951; Yeager & Walton, 2011). Attributional interventions seek

to remove forces that restrain student success in the school environment.

Attributional Interventions in Education

Attribution theory is relevant to school practitioners because student attributions

regarding success and failure at school impact achievement and motivation (Yeager & Walton,

2011). Attribution theory explains how one student can present with a motivated response to

adversity, and a second student of similar ability can present with a helpless response. In fact, as

Carol Dweck (1975) demonstrated, students often possess the skills necessary to complete

required tasks, but fail to do so because they do not believe that their effort will be rewarded.

Attributional interventions target thoughts about causal relationships to shift attributions from

stable factors like ability to unstable factors like effort and experience and to aid the student in

building an implicit theory that views effort as a controllable factor.

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The studies presented in the Table 1 (N = 25) took place in a school setting and targeted

attributions regarding achievement, motivation, and sense of social belonging. Collectively, these

studies demonstrate the success of attributional interventions in raising student achievement

across age ranges and subject matter with effect sizes that range from small (d = .17) to large (d =

1.50). Although academic outcomes were measured immediately following intervention

implementation in some studies (e.g., Menec et al., 1994) and several months or even years after

the intervention had been implemented in others (e.g., Ruthig, Perry, Hall, & Hladkyj, 2004;

Walton & Cohen, 2011), the results are positive and consistent with theory.

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Table 1 Summary of Effect Size Data in Attributional Intervention Research by Sample Type

Study Purpose

Sample

Effect Sizes

Achievement Motivation Social

Belonging Post-Secondary

Wilson & Linville (1982, 1985) (combined results)

Test the efficacy of an attributional intervention targeting concerns about academic performance

First-year (n = 776)

d = .27

ns

--

Noel, Forysth, & Kelley (1987)


Psychology Students (n = 36)

d = .81 -- --

Van Overwalle, Segebarth, & Goldchstein (1989)


First-year (n = 130)

d = .43

-- --

Van Overwalle & De Metsenaere (1990)


First-year (n = 124) d = .52 -- --

Perry & Penner (1990) Test the efficacy of an attributional intervention targeting concerns about academic performance

First-year (n = 198) d = .37 -- --

Menec et al. (1994) (Study 2) Test the efficacy of an attributional intervention with low-expressive instructor on high-risk and low-risk students


d = .41 -- --

Aronson, Fried, & Good (2002)

Test the effect of an attributional intervention targeting implicit theories of intelligence on

Undergraduate Students (n = 79)

d = .53 -- --

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stereotype threat

Martens, Johns, Greenberg, & Schimel (2006)

Test the efficacy of an attributional intervention on women’s stereotype threat


d = .44 -- --

Miyake et al. (2010) Test the efficacy of an attributional intervention targeting the effects of stereotype threat for students in a physics class

Undergraduate Students (n = 399)

d = .31 -- --

Perry et al. (2010) Test the efficacy of an attributional intervention targeting the controllability of unsatisfactory performance in academic settings

First-year Psychology Students (n = 357)

d = .73 -- --

Haynes et al. (2011) Test the efficacy of an attributional intervention targeting concerns about academic performance

First-year (n = 661) d = .78 -- --

Ruthig, Perry, Hall, & Hladkyj (2004)

Test the longitudinal effects of an attributional retraining intervention targeting test anxiety and achievement

First-year (n = 256) d = .28 -- --

Harackiewicz et al. (2014) Test the efficacy of a values affirmation intervention targeting attributions of first-generation students

Biology Students (n = 798)

d = .17 -- --

Struthers &Perry (1996)

Test the longitudinal effects of an attributional retraining intervention on motivation and achievement


d = .22 d = .23 --

Hall, Hladkyj, Perry, & Ruthig (2004)

Test the efficacy of an attributional retraining and elaborative learning intervention on the motivation and achievement attributions

First-year (n = 150) d = .35 d = .51

d = .46 d = .41

--

10

Stephens, Hamedani, & Destin (2014)

Test the efficacy of an attributional intervention targeting attributions regarding academic performance

First-generation Students (n = 168)

d = .46 -- d = .26

Walton & Cohen (2011) Test the efficacy of an attributional intervention targeting doubts about social belonging

First-year (n = 37) -- d = .85 d = .20

Haynes et al. (2015) Test the efficacy of an attributional retraining intervention targeting motivation and achievement attributions

First-year (n = 336) -- d = .38 d = .22

--

High School

Yeager et al. (2014) (Study 2) Test the efficacy of an attributional intervention targeting incremental theory of personality on stress, health, and achievement

Ninth-grade Students (n = 78)

d = .34 -- --

Middle School

Good, Aronson, & Inzlicht (2003)

Test the effect of an attributional intervention targeting middle school’s implicit theories of intelligence on stereotype threat (using three treatment conditions)

Seventh-grade Students (n = 138)

d = 1.13 d = 1.50 d = 1.30

-- --

Ziegler & Heller (2000) Test the efficacy of an attributional retraining intervention targeting task motivation in physics

Eighth-grade Students (n = 164)

d = .35 d = .35 --

Blackwell, Trzesniewski, & Dweck (2007)

Test the efficacy of an attributional intervention on implicit theories of intelligence (Study 2)


d = .62 r = .23 --

Cohen et al. (2009) Test the efficacy of an attributional intervention targeting the effects of stereotype threat

Three Cohorts of Seventh-grade Students (n = 133,

d = .57 d = .61 --

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149, 134) Sherman et al. (2013) Test the efficacy of a values affirmation and

attributional retraining intervention targeting stereotype threat

Sixth-, Seventh-, and Eighth-grade Students (n = 199)

d = .34 -- d = .36

Cook, Purdie-Vaughns, Garcia, & Cohen (2011)

Test the efficacy of a values affirmation exercise targeting student sense of belonging


-- --

d = .30

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As highlighted in Table 1, the majority of the extant attribution intervention research has

focused on the transition to college and examined how attributional interventions impact

academic achievement. Although a number of the studies have targeted adolescence, none of the

attributional interventions have explicitly targeted the middle school transition. All of the

identified studies feature interventions that directly attempted to re-frame the perceived causes of

success or failure. Three studies, however, directly informed the development of the intervention

tested in the current study. These studies (Blackwell, Trzesniewski, & Dweck, 2007; Walton &

Cohen, 2011; Wilson & Linville, 1985) feature attribution interventions that are brief, easily

implemented in schools, target a transition period, and could be adapted to the middle school

transition. These studies also included three outcomes of interest in the current study:

achievement, motivation, and social belonging.

Wilson and Linville (1985) conducted a study testing the efficacy of a brief social-

psychological intervention for first-year college students. They hypothesized that freshman

college students were more susceptible to negative attributions because the transition to college

often involves a greater amount of studying and planning than high school academic work. The

academic problems students experience may serve as confirmation of fears about a lack of ability

or low intelligence. Wilson and Linville designed an intervention to disrupt these beliefs by

providing information demonstrating that many people experience problems early in college but

find success in subsequent semesters.

Specifically, individuals were eligible to participate in the study if they had a GPA below

the median (3.0) and indicated that they worried about their academic performance. Participants

were informed that they were going to be part of a large-scale survey of students about college

experiences and that they would become familiar with the survey process by reviewing the results

of a survey of upperclassmen. It was made clear that the participants would need to attend to the

information because questions about it would be asked later. Participants were shown edited

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results from an actual survey of upperclassmen at Duke University. The results conveyed that

many students experience academic struggles during their first year of college but these problems

improve over time. Then, participants were shown videotaped interviews in which

upperclassmen described how they received low grades during freshman year but steadily

improved each year following freshman year. The purpose of the videotaped interviews was to

demonstrate that the upper-year students also initially struggled but ultimately found academic

success, thereby disrupting the belief that personal characteristics are causing academic struggles.

Finally, participants were asked about their general impressions regarding the survey. The

authors found that 1 year following the intervention students in the treatment group earned higher

GPAs (d = .27) and were less likely to drop out of college than the students in the control group

(Wilson & Linville, 1985).

Blackwell et al. (2007) conducted an intervention study regarding the relationship

between student theory of intelligence and mathematics achievement with junior high school

students. The authors hypothesized that differing theories of intelligence cause distinct

motivational patterns, wherein students with a malleable theory of intelligence are less

susceptible to frustration or discouragement in the face of adversity. In contrast, students with a

fixed theory of intelligence believe that individuals have a certain amount of ability and that

challenging tasks will reveal the limits of intelligence. As a result, students with a fixed theory

avoid challenging tasks and experience anxiety during assessments. In the Blackwell et al. study,

students who identified with a malleable theory of intelligence were found to be associated with a

growth trajectory for mathematics achievement over 2 years, and students who identified with a

fixed theory of intelligence were found to be associated with a gradually declining trajectory of

mathematics achievement.

Blackwell et al. (2007) then designed an intervention to teach students about a malleable

theory of intelligence. The intervention was delivered in the form of one 25-minute instructional

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period per week for 8 weeks. Lessons included information about the structure and function of

the brain, the incremental theory of intelligence, and study skills. The overarching message was

that learning alters the brain. The intervention was based upon experimental materials developed

in previous lab studies (Chiu, Hong, & Dweck, 1997). Results from the study demonstrated that

teaching students about a malleable theory of intelligence had a significant impact on a student’s

implicit theory of intelligence. The intervention group also demonstrated an association between

a belief in the malleable theory of intelligence and a growth trajectory in math achievement (d =

.62; Blackwell, Trzesniewski, & Dweck, 2007).

Walton and Cohen (2011) tested the efficacy of a brief intervention targeting college

freshman’s sense of social belonging. The authors hypothesized that African Americans, Latino

Americans, and other non-Asian ethnic minorities may be at greater risk for concern about social

belonging at school and that these concerns lead to higher levels of frustration and anxiety.

Similar to the Wilson and Linville (1985) intervention, participants were shown survey results

demonstrating that senior students at their school had initially worried about social belonging but

grew confident over time. Based on previous research indicating that people tend to endorse

messages they have advocated (Aronson, Fried, & Good, 2002), participants were asked to write

an essay describing how their own experiences in college matched the results of the survey.

Results indicated that the GPAs of African American students in the treatment condition showed

a significant increase over time (d = .20), whereas the GPAs of African American students in the

control condition showed no change. In addition, African American students in the treatment

condition reported a significantly higher sense of social belonging than African American

students in the control condition, following intervention (Walton & Cohen, 2011).

Each intervention targets specific cognitions during transition periods, and each of the

cognitions targeted are relevant to the middle school transition. The intervention developed by

Wilson and Linville targeted fears about capability during the transition to college. Blackwell et

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al. (2007) developed an intervention targeting implicit theories about the malleability of

intelligence, and Walton and Cohen’s (2011) intervention targeted the sense of social belonging

experienced by college freshman. The interventions were successful due not only to the

cognitions targeted but also the structure of intervention delivery.

Characteristics of Successful Attributional Interventions

Yeager and Walton (2011) published a review of research on attributional interventions

in education, and described the qualities of successful attributional interventions. Successful

attributional interventions in education directly reflect the lived experience of students, target

critical transition periods, enlist students in the creation of intervention materials, utilize elements

of the psychology of persuasion to deliver the treatment message, and deliver the treatment

message in a way that Yeager and Walton (2011) characterized as “stealthy” (p. 284).

Successful attributional interventions in education begin with a precise understanding of

students’ subjective experience in the school environment, not just as it appears to teachers,

parents, or researchers. The intervention can only be successful inasmuch as it speaks directly to

how the school is perceived by the student (Yeager & Walton, 2011). Interventions can achieve

these effects by enlisting students in the production of the intervention materials, themselves. For

example, Wilson and Linville (1985) implemented an intervention conveying that students

struggle during the first year of college but gradually improve their grades. Rather than providing

direct instruction of this material, the researchers videotaped older students describing their

transition to college.

The Wilson and Linville (1985) example is instructive because it also includes elements

of both subtlety and persuasion. For example, students in the Wilson and Linville study were not

initially informed that they were participating in an intervention. Rather, participants were told

they were needed to help interpret the results of a study of upper-year students. The intervention

employed a form of mild deception to communicate the treatment message. Yeager and Walton

16

(2011) suggested that direct instruction of a treatment message can actually be counterproductive

to the goals of the intervention. Participants may feel stigmatized by direct appeals because they

signal to the students that they are in need of intervention. In addition, the intervention can be

considered “stealthy” because students may not be aware that the intervention has shifted

attributions, and may not be capable of actively recalling the treatment message of the

intervention. Lastly, students take greater ownership of subsequent success and feel less

controlled by the message when they attribute success to self-derived thought and motivation

(Yeager & Walton, 2011).

Participants in successful attributional interventions in education are often asked to

advocate the treatment message to a fictional audience, such as younger students. Walton and

Cohen (2011) asked participants to write an essay, making connections to their own lives,

describing the results of the survey and what it might mean for students just starting college. This

written portion was included because, as noted by Walton and Cohen (2011), individuals are

more likely to endorse messages that they have freely advocated (Aronson, Fried, & Good, 2002).

Participants are more likely to internalize and believe in the treatment message of the intervention

if they repeat the message in their own words. Thus, the mechanism for shifting attributions is

inherent in the process of rephrasing the survey results.

Attributional interventions purportedly generate long-lasting benefits by setting into

motion recursive psychological processes when implemented during a transition period (Yeager

& Walton, 2011). Storms and McCaul (1976) described negative attributions as the start of an

exacerbation cycle. In this cycle, people experience an adverse event, and make a negative

attribution about the cause of the adverse event. Then, the negative attribution causes the

individual to experience anxiety, which makes performance of the desired behavior even more

difficult (Storms & McCaul, 1976). The converse is also possible wherein an individual

experiences a positive event, makes an attribution about high-effort behaviors that led to the

17

success, high-effort behaviors are reinforced, and the individual continues to experience success.

Thus, shifting attributions to effortful behavior and experiencing small gains will open the door to

further success.

The timing of attributional interventions is crucial because they aid students in

interpreting new experiences. As such, Yeager and Walton (2011) suggested it may be necessary

to implement attributional interventions during educational transitions. Many studies have

focused on the transition to college (e.g., Aronson et al., 2002; Menec et al., 2004; Nelum-Hart et

al., 1999; Walton & Cohen, 2011; Wilson & Linville, 1985) because it represents a period during

which students are expected to assume new responsibilities and achieve at a higher level of

academic rigor. The transition to middle school represents a similar period of increased

responsibility and academic rigor for students, but fewer attributional interventions have

addressed the fears associated with middle school and adolescence.

Adapting Attributional Interventions for the Middle School Transition

Adolescent students making the transition to middle school are particularly susceptible to

negative attributions because this transition period involves many changes for students as well as

aspects of their experiences in school. Adolescence is a period of rapid physical maturation.

Adolescents typically experience significant weight gain, growth in height, and changes to the

body associated with sexual maturation (Santrock, 2009). The brain also undergoes several

structural changes during adolescence. The connective tissue between the right and left

hemispheres of the brain, known as the corpus callosum, is strengthened (Gied et al., 2006). The

amygdala, a structure within the brain linked with emotional response, matures during

adolescence (Santrock, 2009).

Beyond the physical changes to the body, there are changes to the structure of the school

environment (Simmons & Blyth, 1987), changes in academic expectations (Eccles, 2004), and

changes in social experience (Farmer, Hamm, Leung, Lambert, & Gravelle, 2011). Mistakes are

18

a natural and common aspect of adapting to the new expectations placed on students in middle

school. However, when problems first occur, students are at risk of attributing their cause to

internal and unchangeable traits or personality characteristics. Successful attributional

interventions in education begin with a clear understanding of the student’s experience of the

school environment.

Students making the transition to middle school will typically move from several,

smaller-population elementary schools to a single, larger-population middle school. Students in

elementary school classrooms spend the majority of the day in a single room learning different

subjects with one teacher, whereas middle school students move between classrooms throughout

the day, interact with several subject-based teachers, and independently manage their belongings

with the use of a locker (Simmons & Blyth, 1987). In addition, elementary school classrooms

eschew ability tracking, favoring within-classroom ability grouping for reading and math. A

single elementary school classroom is typically heterogeneous for ability levels. Beginning in

middle school, it is more common to find between-class tracking, such that students will begin to

take classes in separate tracks during high school (Eccles, 2004).

The start of middle school often entails a change in academic expectations. When

grading student academic work, middle school teachers are stricter and employ more social

comparison-based standards than elementary school teachers (Midgley, Anderman, & Hicks,

1995). Teacher control and discipline are given greater emphasis by middle school teachers, and

the typical middle school classroom offers fewer opportunities for student decision making,

choice, and self-management in comparison to late elementary school classrooms (Eccles, 2004).

Middle school teachers are less likely than their elementary counterparts to view responsibility

for students’ mental health concerns as a part of their role (Roeser & Midgley, 1997). At a

school-level, it is more common for middle schools to emphasize relative ability, competition,

and social comparison through the use of public honor rolls, assemblies for the highest achieving

19

students, class rankings, and even ability-level tracking of classes (Eccles, 2004). These

practices promote a focus on ability and can favor an entity view over an incremental view of

intelligence (Wilson & Buttrick, 2016).

When students believe that school promotes an ability-focus over mastery-focus,

achievement and self-esteem decline whereas anger, depressive symptoms, and school truancy

increase (Roeser & Eccles, 1998). In contrast, when students believe that school promotes a

mastery-focus, students’ and teachers’ sense of personal efficacy is increased, achievement

increases, and teachers are viewed by students as friendly, caring, and respectful (Eccles, 2004;

Midgley, Anderman, & Hicks, 1995). The features that are common to most middle schools

(Eccles, 2004) promote an ability-focus. These results dovetail with the work conducted by

Dweck and Leggett (1988).

The transition to middle school coincides with changes in social experience. As noted in

the previous paragraphs, the relationships between teachers and students are affected by the

transition to middle school (Midgley, Anderman, & Hicks, 1995; Roeser & Midgley, 1997). Peer

relationships are also altered during the transition. Adolescents begin to seek approval from peers

above teachers and parents, and peer relationships are given greater emphasis by middle school

students (Farmer, Hamm, Leung, Lambert, & Gravelle, 2011). Witherspoon and Ennett (2011)

found, in a longitudinal study, that adolescents’ sense of belonging declined during the first year

of middle school. In similar fashion, middle school students have demonstrated lower levels of

connection to school than their elementary school counterparts (Eccles, Lord, & Midgley, 1991).

In an international comparison, students in U.S. middle schools, on the whole, considered the

peer culture of the school to be unkind and unsupportive (Juvonen et al., 2004).

Negative social and academic outcomes associated with transitioning to middle school

are believed to be products of the constructed environment. For example, scores on standardized

achievement tests do not show the same marked decline during the transition to middle school

20

which may suggest that declines in grades are a product of changes in grading policies (Eccles,

2004). In contrast to the goals of all school professionals involved, the middle school

environment may actually contribute to negative recursive processes, setting many students up to

believe that they will fail regardless of effort and setting others up to believe that they will

succeed only by ability. As a consequence, students who believe they will fail in school may

choose not to try at all, and students who believe they have ability will choose only those tasks

that affirm their ability. However, a brief attributional intervention targeting the experience of

elementary school students transitioning to middle school may disrupt the negative recursive

processes and set students up for success during middle and high school.

Effects of Mind-set Interventions

Sisk, Burgoyne, Sun, Butler, and Macnamara (2018) completed two meta-analyses to

determine the size of the relationship between growth mind-sets and achievement and mind-set

interventions and achievement. The first meta-analysis focused on examining the relationship

between growth mind-set and academic achievement, and the second focused on the relationship

between growth mind-set interventions and academic achievement. The results from the first

meta-analysis (n = 129 studies) indicated the relationship between growth mind-sets and

academic achievement is weak (r = .1). The authors then examined the relationship between

growth mind-set interventions and academic achievement. Results of this second meta-analysis (n

= 29 studies) indicated that mind-set interventions did not yield significant gains in academic

achievement for adolescents, typical students, and students facing situational challenges, such as

those transitioning from one school to another.

Despite these findings, Sisk et al. noted that growth mind-set interventions might still be

effective for certain subgroups. Specifically, they found that growth mind-set interventions may

benefit students with high risk for academic failure or those from families with fewer economic

resources. Interventions containing interactive components, such as reading about growth mind-

21

set and writing a reflection also were more effective than passive interventions where students

simply read about growth mind-set.

Lastly, the Sisk et al. meta-analyses restricted their focus to the relationship between

mind-set interventions, a subset of the broader attributional research, and a single student

outcome variable, academic achievement. The studies outlined in Table 1, however, feature many

types of attributional interventions. In addition, results from these studies demonstrate that

attributional interventions have been associated with a variety of outcomes, such as reductions in

stress, shame, aggressive retaliation, and negative health outcomes. These types of interventions

also have been associated with improvements in sense of social belonging, motivation, and

academic achievement (Blackwell, Trzesniewski, & Dweck, 2007; Yeager et al., 2014; Yeager et

al., 2016; Yeager, Miu, Powers, & Dweck, 2013).

Theory of Change

The present study is rooted in the field of attribution theory. Figure 1, adapted from the

work of Kelley and Michela (1980), provides a helpful framework for understanding the

relationship between attributions, outcomes, and the target of the intervention. The foci of

attribution theory can be divided into four main parts: antecedents, implicit theories, attributions,

and consequences. Antecedents are all of the elements that lead an individual to attribute one

cause to an event. Implicit theories, described by Dweck and Leggett (1988) as an “implicit

conception about the nature of ability” (p. 262), appear between antecedents and attributions.

Implicit theories provide a quick framework for interpreting the causes for success and failure.

Consequences are the experiences of the individual after making a particular attribution. In

Dweck and Leggett’s (1988) model, a low grade on an assignment in school would be considered

an antecedent, the student’s implicit theory about intelligence would guide the attribution, the

actual perceived cause for the low grade is the attribution, and any subsequent behavior or

emotional state prompted by the attribution are the consequences. Notably, a distinction exists

22

between “attribution” research, which focuses on how people make attributions, and

“attributional” research, which focuses on how attributions shape consequences (Kelley &

Michela, 1980).

23 Figure 1

Prior

Experience

Beliefs

Ability

Personality

Perceived Causes

of Success and

Failure

Behavior

Affect

Expectancy

Antecedents Implicit Theories Attributions Consequences

Self-perpetuating Processes

Point of Intervention

Attribution

Attributional

24

The attributional process can be recursive. The arrow labeled self-perpetuating processes

acknowledges this fact. When students have antecedents that result in an implicit theory of fixed

intelligence, a low grade is attributed to fixed intelligence. Within the implicit theory of fixed

intelligence effort is viewed as useless because the outcome is predetermined by the fixed

intelligence. The low grade is attributed to ability not effort. Students facing a new academic

challenge after a low grade have decreased motivation and engagement resulting in a greater

likelihood for a second low grade. The second low grade serves as further evidence of low, fixed

intelligence; it is now the antecedent for the next academic task. Thus, the process repeats itself.

Recursive processes can work for and against a student. In the previous example, the

antecedents leading to an implicit theory of fixed intelligence set in motion a negative recursive

process that harmed the student’s achievement. However, antecedents yielding to implicit theory

of malleable intelligence would lead a student to attribute a low grade to lack of effort. The

student facing a new academic challenge after a low grade would likely have higher motivation to

study and a greater likelihood for a higher grade. The higher grade affirms the attribution to

effort, and the positive recursive process repeats itself. It is important to distinguish between the

positive and negative recursive processes because the goal of the intervention is to set in motion a

positive recursive process.

The present study is “attributional” research in that the goal of the intervention is to

change attributions to achieve a series of desired outcomes in school. The previous section

detailed the many ways students can experience adversity during the transition to middle school.

The challenges of the middle school environment, along with the previous experiences and beliefs

of each student, make up the antecedents, as seen in Figure 1, for the current study. The students’

implicit theories about personality formation and intelligence are based on the antecedents. The

student’s sense of social belonging, academic motivation, and achievement are the consequences

of interest. The current intervention is intended to direct student attributions during a critical time

25

period in order to establish a positive recursive process. The intervention will provide a

framework for students to interpret the challenges posed by the middle school environment, such

that failures can be attributed to transient and changeable factors and success can be attributed to

individual effort.

Rationale, Purpose, and Hypotheses

The transition to middle school is often characterized by turmoil. It is a time associated

with achievement loss, increases in teacher control, and decreases in the quality of student-

teacher relationships (Alspaugh, 1998; Eccles, Lord, & Midgley, 1991). It is also a period of

remarkable physical, mental, and social growth. Given previous studies have indicated that

attribution interventions can alter trajectories of growth, particularly during periods of transition,

the purpose of the present study was to develop and test an intervention targeting student

attributions regarding adversity experienced during the transition to middle school.

The intervention developed as part of the present study focused on the attributions

students make regarding achievement loss and feelings of social alienation during the transition to

middle school. Specifically, the present study addressed the following research question, “Does

an attributional intervention targeting social belonging and achievement loss during the transition

to middle school improve academic achievement, motivation, and sense of social belonging?” To

answer this question, I developed an intervention targeting student attributions and tested

hypotheses regarding proximal (attribution), medial (belonging & motivation), and distal

(academic achievement) outcomes for middle school students.

Specifically, hypothesis for the proximal outcome was that exposure to the attribution

intervention changes student attributions about social belonging and achievement. This

hypothesis was based on the findings of Aronson et al. (2002), Blackwell et al., (2007), and Perry,

Stupnisky, Hall, Chipperfield, and Weiner (2010). The second hypothesis was that exposure to

the attribution intervention increases academic motivation, and this hypothesis was based on the

26

work of Dweck and Leggett (1988), Dweck (1975), and Mueller and Dweck (1998). Based on

the findings of Miu and Yeager (2014), Walton and Cohen (2007), and Yeager et al. (2014), the

third hypothesis was that the attribution intervention increases students’ sense of social belonging

during the first year of middle school. The fourth hypothesis was that the attribution intervention

improves academic achievement during the first year of middle school (Blackwell et al., (2007),

Martens, Johns, Greenberg, and Schimel (2006), Mueller and Dweck (1998), Wilson and Linville,

1985). A final set of hypotheses was tested. The attribution intervention was hypothesized to

yield larger gains for students with low prior achievement (Blackwell et al., 2007; Wilson &

Linville, 1985), students of minority status (Walton & Cohen, 2011; Yeager & Walton, 2011),

and female students (Martens et al., 2006).

27

Chapter 2

Method

Participants

Participants were drawn from a public middle school and a charter middle school, part of

the Uncommon Schools, Inc. (USI) in the mid-Atlantic U.S. Power was calculated based upon a

post hoc achieved power analysis of a fixed model, linear multiple regression using the G*Power

software package. Based upon these analyses the achieved power was 0.24.

As mentioned previously, 550 students from three middle schools were invited to

participate. However, one school dropped out owing to technological difficulties associated with

the treatment and control modules host website. In total, 460 students were invited to participate

from the two remaining schools. The sample consisted of 129 fifth- and sixth-grade (81 5th and

52 6th) students.

Figure 2 shows the flow of participants through the present study. Figure 2 is adapted

from the CONSORT 2010 Flow Diagram (Moher et al., 2010).

28

Figure 2. Flow of participants through study protocol

Assessed for eligibility (n = 550)

Excluded (n = 417) ♦ One middle school dropped out

owing to technological difficulty in implementation (n = 93)

♦ Did not complete module (n = 23) ♦ Did not return an informed consent

/ declined to participate (n = 304)

• Lost to follow-up (students left school; n = 2) • Did not return paper forms for motivation and

social belonging (student nonresponse; n = 30)

• Did not return paper forms for motivation and social belonging (one school site did not distribute forms; n = 38)

• Allocated to intervention (n = 66) • Did not receive allocated intervention (n =

0)


social belonging (one school site did not distribute forms; n = 34)

• Allocated to control (n = 67) • Did not receive allocated intervention (n =

0)


social belonging (student nonresponse; n = 32)

Long-termFollow-up

Short-TermFollow-Up

Randomized (n = 133)

Enrollment

Treatment Control

• Analysed (achievement data was not impacted by student nonresponse; n = 64)

• Multiple imputation was used with missing data

• Analysed (achievement data was not impacted by student nonresponse; n = 65)

• Multiple imputation was used with missing data

AnalyzedSample

29

Demographic characteristics by condition are reported in Table 2. Participants were 133

(73 female, 56 male, and 4 individuals for whom no gender information was available). The

treatment and control groups contained similar percentages of female and male students, students

of each racial/ethnic group, and students from each school site.

Table 2 Demographic Variables for Treatment and Control Treatment Control Variable % (N = 64) %(N = 65) Gender Female 53 60 Male 47 40 Race/Ethnicity Black/African American

44 49

White/Caucasian 42 38 Hispanic 8 14 Asian 0 2 Other 0 2 Site School 1 41 40 School 2 64 62

Measures

Multiple measures were used to assess the hypothesized proximal (attributions), medial

(social belonging & academic motivation) and distal (academic achievement) outcomes.

Attribution (Proximal outcome). Attribution was measured using a questionnaire based

on a measure developed by Blackwell et al. (2007). Students read a brief hypothetical scenario

wherein they are asked to imagine the first week at a new school and several experiences that

could prompt feelings of social rejection and academic failure. Students were then asked to rate

their likely response to items on a 6-point Likert-type scale from 1 (Agree Strongly) to 6

(Disagree Strongly). Items included positive attributions, such as, “The first week at this school

was hard, but it was only one week. I’ll give this school a chance,” and negative attributions, such

30

as, “I don’t fit in at this school.” Internal consistency was calculated based on the present sample.

The attribution measure consisted of 6 items (α = .708).

Motivation (Medial outcome). Motivation and engagement were measured using two

scales from the student version of the Academic Competence Evaluation Scales (ACES; DiPerna

& Elliott, 1999). The scores between the two scales were found to have correlations ranging from

.52 - .71 at each of the three time points in the present study. The two measures were combined

into a single score for the present study. Internal consistency was calculated based on the present

sample. The engagement subscale consisted of 8 items (α = .763). The motivation subscale

consisted of 9 items (α = .827).

The self-report was selected despite being slightly below the intended age range, because

self-report measures will give greater insight regarding student self-perceptions. Exploratory

factor analyses supported the hypothesized five-factor structure of academic competence, and

reliability estimates (internal consistency) are high (>.92). Convergent validity was demonstrated

through moderate correlation of the ACES teacher ratings with the Iowa Test of Basic Skills

(ITBS), ranging from .31 - .84, and the Academic Competence scale of the Social Skills Rating

System – Teacher Form (SSRS-T), ranging from .43 - .87 (DiPerna & Elliott, 1999). Cronbach’s

alpha for the two scales was .785

Social belonging (Medial outcome). Social belonging was assessed using the Child and

Adolescent Social Support Scale (CASSS), a 60-item measure divided into six subscales

(Malecki, Demaray, & Elliott, 2014). The CASSS is intended for children from third through

twelfth grade. Two subscales of the CASSS were used: Social Belonging to People at School and

Social Belonging to Classmates. The correlations between the two subscales ranged from .63 to

.68 at each of the three time points in the present study. Similar to the score for motivation, the

two measures were combined into a single score for social belonging for the current study.

Internal consistency was calculated based on the present sample. The Social Belonging to

31

Classmates subscale consisted of 12 items (α = .949). The Social Belonging to People at School

subscale consisted of 12 items (α = .967).

Internal consistency estimates for the CASSS are adequate (>.87). The Cronbach’s

coefficient alpha for the Level 1 scale was .94 and ranged from .87 to .93 on the four subscales

(Malecki & Demaray, 2002). Factor analyses conducted by Malecki and Demaray (2002) and

Rueger, Malecki, and Demaray (2010) supported a Source-Based Model of social support.

Convergent validity was demonstrated by the correlation of total scores on the CASSS with the

Social Support Scale for Children (SSSC; Rueger, Malecki, & Demaray, 2010). Correlation

between the total scale scores was .70. Validity was further supported via moderate to high

intercorrelations among the subscales, ranging from .20 to .54 (Malecki & Demaray, 2002).

Academic achievement (Distal outcome). Academic achievement was measured via

grades. Student grades are summarized and reviewed quarterly throughout the year at School 1

and School 2. Grades were calculated at each quarter based on student achievement in Math,

English Language Arts, History, and Science classes. A grade point average system ranging from

0.0 to 4.0 (A = 4.0, A- = 3.7, B+ = 3.3, B = 3.0, B- = 2.7, C+ = 2.3, C = 2.0, C- = 1.7, D+ = 1.3, D

= 1.0) was used to calculate a cumulative GPA for each quarter.

Procedures

Intervention development. Intervention materials were based on previous research

(Blackwell, Trzesniewski, & Dweck, 2007; Walton & Cohen, 2011; Wilson & Linville, 1985)

and informed by a focus group of seventh- and eighth-grade students. Focus group questions

were modeled after those in the Wilson and Linville (1985) survey of upper-year students,

although the questions were modified to ensure an age-appropriate reading level and middle

school transition content. Specifically, participants responded to questions regarding

achievement and social belonging during the initial transition to middle school. In addition,

participants were asked to describe how perceptions of achievement and social belonging

32

changed during their middle school experience. The results and videotaped interviews were

edited to highlight the treatment message, which is that most students experience academic

struggles and a decrease in sense of social belonging during the transition to middle school, but

these feelings are temporary and most students make friends and improve grades. A copy of the

questions used with the focus group of seventh- and eighth-grade students is included in the

appendix. These questions were informed by the survey questions utilized by Wilson and

Linville (1985) in their intervention targeting college student fears about academic achievement.

Intervention implementation. As shown in Figure 2, fifth-grade students with parental

permission were randomly assigned to the treatment and control conditions. Both the web-based

treatment and control computer modules took approximately 30 minutes to complete. A laptop

cart was brought to each classroom, and each student was given a laptop and headphones. The

researcher presented brief instructions about how to sign in to the module. In addition, all

students were told that the computer module would present information about adjusting to middle

school, and each student’s task was to communicate these results to future students. Once

students completed the login to the computer module, they were routed to the treatment or control

condition. The module presented information via slides, audio dictation, and brief video clips of

upper-year students. The instructions, login, and presentation of information took approximately

15 minutes to complete. Students were given an additional 15-20 minutes to respond to the brief

writing prompt and complete the Time 1 surveys. The responses were stored on the server, and

students then returned to school activity. Examples of screenshots from web-based treatment

conditions from model interventions can be found in the appendix.

Students in the treatment condition were presented with a screen welcoming students to

middle school and informing them that they would now receive information about what it means

to be a middle school student. Students were presented with the edited results of a focus group

with upper-year students, and students were instructed to aid future students in understanding the

33

results. The results of the survey were presented visually and orally over the course of several

screens. Three brief video clips comprised the final three screens. The clips displayed upper-

year students (1 male and 2 females) communicating the treatment message regarding academic

motivation, sense of social belonging, and achievement. The brief writing prompt asked students

in the treatment condition to describe the results of the survey and what it means for students just

starting middle school in a two- or three-paragraph essay. Students were asked to make

connections to their own lives. The writing activity was included in the intervention protocol

because, as noted by Walton and Cohen (2011), individuals are more likely to endorse messages

that they have freely advocated (Aronson, Fried, & Good, 2002). Participants are more likely to

internalize and believe in the treatment message of the intervention if they repeat the message in

their own words. Thus, the mechanism for shifting attributions is inherent in the process of

rephrasing the survey results.

Students in the control condition were also presented with the same initial screen

welcoming students to middle school and informing them that they were about to receive

information about what it means to be a middle school student. Students were then presented

with information about the academic demands of the middle school environment and study

strategies to accommodate those demands. The information was presented visually and audibly

over the course of several screens. The brief writing prompt asked students in the control

condition to describe the study skills necessary for success in the middle school environment.

Data collection and timeline. Data were collected at four time points during the year

(pre-treatment, post-treatment, short-term follow-up, and long-term follow-up). Table 3 shows

when the primary outcome variables were collected. The planned initiation of treatment and

control modules was after the first quarter of the year so that students would have already

received their initial report cards. Both school sites completed baseline data collection,

intervention implementation and the immediate post-treatment data collection over a 4-week

34

period in late fall (owing to difficulties stemming from the schools’ firewalls blocking the hosting

website). Short-term follow-up achievement was collected 10 weeks following baseline data

collection, and short-term follow-up academic motivation and social belonging measures were

collected 13 weeks following intervention implementation. Long-term follow-up measures were

completed just prior to the end of the school year at both schools. These measures were collected

approximately 10 weeks after short-term follow-up measures were collected (the school year

ended on different dates and there was slight variation in data collection as a result).

Table 3

Time Points for Collection of Primary Outcome Variables

Variables Pre-treatment Post-treatment Short-term Follow-up

Long-term Follow-up

Attribution √ √ -- --

Motivation √ √ √ Social Belonging

√ √ √

Academic Achievement

√ √ √ √

Data Analyses

Hierarchical linear regression was used to test the hypotheses related to intervention

effects on attribution, motivation, social belonging, and academic achievement. The following is

a Level 1 regression equation for each student, represented by the subscript j, to predict academic

achievement.

Υij = β0 + β1j(prior achievement) + β2(gender) + β3(race) + β4j(intervention) +

β5j(interaction) + eij

where Υ represents each of the three outcome variables; β1j represents the main effect of prior

achievement on the outcome variable, β2j represents the main effect of gender status on the

outcome variable, β3j represents the main effect of race/ethnicity status on the outcome variable,

35

β4j represents the main effect of intervention status on the outcome variable, and β5j represents an

interaction term. Interaction terms were entered into the model to test the hypotheses that the

treatment would be more effective for female students, students with low prior achievement, and

students of minority status. These interaction terms were tested separately given the size of the

model. No prior measure or baseline was available for the measures of social belonging and

motivation. As such, the equation was the same for these measures, but the pretest for the

measure was not included as a predictor.

Missing Data

Percentage of missing values ranged from 0 for some baseline measures (e.g.,

achievement) to 54.9% for the measure of motivation at short-term follow-up. 15.34% of all

values in the study were missing. Missingness was largely confined to the measures of motivation

and social belonging at the two follow-up time points. Data were missing at short-term follow-up

because of administrator failure to distribute the rating scales to students at School 1 within the

time specified, and data were missing at long-term follow-up primarily because of student

nonresponse. As such, a multiple imputation (MI) procedure (Manly & Wells, 2013) was used to

address missing data. Specifically, MI was used to address the missing social belonging and

motivation data at short-term and long-term follow-up. The problem of missing data is addressed

using the MI technique including all analysis variables under the assumption that missing values

are missing at random (Schafer & Graham, 2002). SPSS was used to generate 40 imputed

datasets, and visual inspection of imputation convergence led to the choice of 100 burn-in

iterations. Analyses from each dataset were pooled according to Rubin’s (1987) guidelines.

Pooling was completed using SPSS. Results using listwise deletion are similar to MI; so imputed

results are presented for the social belonging and motivation measures at short-term and long-

term follow-up.

36

Chapter 3

Results

Assumptions

Table 4 presents descriptive statistics for the key outcome variables. Prior to running the

primary analyses, data were examined to determine if they met assumptions for each of the

analyses conducted. Linearity was tested by plotting the residuals against the independent

variables in each of the analyses. The lowess fit lines were close to the regression lines for each

of the independent variables, indicating no departure from linearity. Boxplots of the residuals,

clustered by school site, were examined to test the assumption of independence of errors.

Variability was evident by school site in achievement, motivation, and sense of social belonging.

As such, school site was initially included as a covariate. However, given the fact that students of

minority status were grouped almost entirely within one school site, the school site covariate was

dropped. Normality of the residuals were examined via the histograms and p-p plots. Although

the histograms showed some heteroscedasticity, the p-p plots show straight lines. As a result, the

assumption of independence of errors was not violated.

37

Table 4 Descriptive Statistics for the Primary Outcome Variables by Time and Condition Treatment Control Predictor Variable n M (SD) Skew Kurtosis n M (SD) Skew Kurtosis Attribution

Pre-treatment 66 7.08 (5.01) .956 .854 67 6.76 (5.63) 1.154 .806

Post-treatment 63 7.29 (5.25) .279 -.788 62 7.05 (5.97) .721 -.013

Achievement

Baseline 66 3.03 (.694) -.547 -.576 67 2.99 (0.85) -.368 -1.128

Post-treatment 66 3.07 (.64) -.712 -.057 66 3.04 (.745) -.437 -.901

Short-term 66 3.09 (.64) -.655 -.256 65 3.09 (.714) -.548 -.744

Long-term 64 3.08 (.76) -1.27 .978 64 3.06 (.891) -.952 -.299

Motivation

Post-treatment 66 32.6 (5.89) -.534 -.236 66 32.31 (5.23) -.757 .595

Short-term 28 34.55 (5.55) -.953 .259 32 33.5 (6.02) -.230 -.980

Long-term 35 30.74 (7.09) -.624 .635 34 34.6 (5.86) -.723 .060

Social Belonging Post-treatment 59 47.94 (14.63) -.136 -1.01 64 48.87 (15.45) -.368 -.669

Short-term 30 50.82 (14.92) -.585 -.448 34 50.33 (17.1) -.619 -.363

Long-term 36 43.82 (16.34) .058 -.623 33 48.26 (15.72) -.392 -.692

Note. Smaller sample sizes for short- and long-term follow-up for social belonging and motivation due to technological difficulties.

38

Skew and kurtosis were analyzed to determine if data met assumptions of normality. A

significance level of p < .01 was selected based on recommendations from Field (2009). Several

of the outcome variables had statistically significant values of skewness (i.e., achievement at

post-intervention, short-term and long-term follow-up, attribution at baseline, and motivation at

post-intervention). The values of skewness indicate moderate skewness. The significant values do

not indicate a concern. Field (2009) recommends visual inspection of histograms. As noted

above, data met assumptions of normality. None of the outcome variables had significant values

for kurtosis at p < .01. Data met assumptions of normality for kurtosis. All variables also were

examined for outliers, and none were identified. In addition, the variables were examined by

ethnic/racial groups and treatment status to determine if there were outliers within groups. One

potential outlier was identified at a single time point; however, dropping this outlier did not alter

the findings, and the individual’s scores from other time points were not outliers. As such, the

score was included in the analyses.

Myers (1990) indicated when any VIF > 10 there is cause for concern regarding

multicollinearity. Bowerman and O’Connell (1990) suggested that if the average VIF is much

greater than 1 there may also be a problem with multicollinearity. Neither condition was met in

the current data. Menard (2002) provides guidelines indicating that tolerance values below 0.2

indicate potential problems. All tolerance values were > 0.2. The suggested criteria indicate the

data did not demonstrate multicollinearity. Thus, the data met assumptions of normality and were

appropriate for analysis and interpretation.

Proximal Outcome

Attribution (Hypothesis 1). Table 5 presents results of the multiple regression analyses

of treatment status predicting a post-treatment measure of attribution. Baseline measure of

attribution was a positive predictor of post-intervention measure of attribution indicating that

students with a higher score on the baseline measure of attribution scored higher on the post-

39

intervention measure of attribution. The relationships between the remaining predictor variables

and post-intervention measure of attribution were not statistically significant.

Each of the interaction terms was tested separately. The only interaction term that was

found to have a significant relationship was the interaction between treatment status and minority

status. As a result, this model is the one presented in the tables. Other models were run with the

treatment status by gender interaction and the treatment status by prior achievement interaction.

These models are not presented in the tables because the interaction terms were not significant.

Table 5 Intervention Effects on Attributions at Post-treatment

Predictor Variable β p ∆R2

Baseline .690 .000 .503 Female -.020 .760 .000 Minority Status .064 .338 .004 Treatment .024 .723 .000 Treatment X Minority Status

-.089 .189 .007

Note. N=125; nTreatment= 63, ncontrol= 62

Medial Outcomes

Motivation (Hypothesis 2). Table 6 presents results of the multiple regression analyses

of treatment status predicting student-rated motivation at post-treatment, short-term follow-up,

and long-term follow-up. None of the relationships were significant at post-treatment or short-

term follow-up. Treatment status was a negative predictor of motivation at long-term follow-up.

The interactions between treatment and achievement, treatment and race/ethnicity, and treatment

and gender were tested to determine if the intervention was more effective for students of

minority status, students with low prior achievement, and for female students. None of the

interaction terms were significant at post-treatment, short-term follow-up, or long-term follow-up.

40

Table 6 Intervention Effects on Student Motivation at Post-treatment, Short-term, and Long-term Follow-up Predictor Variable β p ∆R2

Post-treatment Female -.02 .833 .000 Minority Status -.052 .581 .002 Treatment .017 .864 .000 Treatment x Minority Status

-.032 .744 .001

Short-Term Female -.011 .902 .000 - .011 Minority Status .212 .465 .000 - .121 Treatment -.126 .328 .000 - .209 Treatment x Minority Status

.182 .274 .000 - .319

Long-Term Female .055 .585 .000 - .022 Minority Status .057 .55 .000 - .025 Treatment -.217 .008 .025 - .197 Treatment x Minority Status

.061 .723 .000 - .037

Note. N= 133

Social belonging (Hypothesis 3). Table 7 presents results of the multiple regression

analyses of treatment status predicting student-rated social belonging at post-treatment, short-

term follow-up, and long-term follow-up. The results for social belonging follow a similar pattern

to the relationships between treatment status and motivation. None of the relationships were

statistically significant, and none of the interaction terms were significant at all time points.

41

Table 7 Intervention Effects on Social Belonging at Post-treatment, Short-term, and Long-term Follow-up Predictor Variable β p ∆R2

Post-treatment Female -.147 .105 .022 Minority Status .037 .677 .001 Treatment .014 .879 .000 Treatment X Minority Status

.025 .788 .001

Short-Term Female .071 .444 .000 - .014 Minority Status .003 .99 .000 - .18 Treatment .116 .344 .000 – 264 Treatment X Minority Status

-.122 .372 .000 - .316

Long-Term Female -.01 .914 .000 - .03 Minority Status .101 .297 .000 - .116 Treatment -.052 .522 .000 - .081 Treatment X Minority Status

.102 .463 .000 - .049

Note. N= 133

Distal Outcome

Achievement (Hypothesis 4). Table 8 presents results of the multiple regression analyses

of treatment status predicting achievement at post-treatment, short-term follow-up, and long-term

follow-up. After controlling for baseline achievement, minority status had a significant

relationship with academic achievement at short-term follow-up (∆R2 = .02), whereby students

identified as belonging to a racial minority group exhibited greater achievement. None of the

other main effects were significant at any time point.

The interaction between treatment status and minority status was significant at short-term

follow-up (∆R2=.012), indicating that students in a racial minority in the treatment group had

greater achievement than their peers in the control group at short-term follow-up. In addition,

students who identify as Caucasian in the treatment group had lower achievement than their peers

42

in the control group. Figure 3 presents a plot of the interaction between treatment status and

minority status. The interaction between treatment status and minority status was not significant

at either post-treatment or long-term follow-up. The interactions between treatment and

achievement and treatment and gender were tested to determine if the intervention was more

effective for students with low prior achievement and for female students. None of the interaction

terms were statistically significant at post-treatment, short-term follow-up, or long-term follow-

up.

Table 8 Intervention Effects on Achievement at Post-treatment, Short-term, and Long-term Follow-up Predictor Variable β p ∆R2

Post-Treatment Baseline .929 .000 .808 Female .036 .371 .001 Minority Status .062 .246 .003 Treatment -.007 .859 .000 Treatment X Minority Status

.053 .195 .003

Short-Term Baseline .905 .000 .661 Female .072 .156 .006 Minority Status .174 .012 .020 Treatment -.031 .541 .000 Treatment X Minority Status

.113 .030 .012

Long-Term Baseline .737 .000 .587 Female .048 .416 .002 Minority Status -.020 .801 .000 Treatment -.020 .732 .000

Treatment X Minority Status

.066 .271 .004

Note. N= 126.

43

Figure 3 Estimated marginal means of achievement at short-term follow-up

44

Follow-up Analyses

The significant interaction effect between minority status and intervention on academic

achievement was explored in a follow-up analysis. Specifically, a six-group orthogonal planned

comparison using multiple regression was run to examine if the intervention was more effective

for students who identify as Black or students who identify as Hispanic. The results were not

statistically significant at p < .05, but two of the time points (i.e., post-intervention and long-term

follow-up) met a less stringent threshold of p < .10.

Given these findings and the exploratory nature of these analyses, the estimated marginal

means of the three main ethnic/racial groups (i.e., African American, Hispanic, & Caucasian)

were plotted to explore the interaction between treatment status and minority status. Figures 4, 5,

and 6 present plots of the estimated marginal means of the ethnic/racial groups by treatment

status at post-intervention, short-term follow-up, and long-term follow-up, respectively. As

shown in the figures, students who identify as Caucasian in the treatment group had slightly lower

achievement than their peers in the control group at all time points. Students who identify as

African American in the treatment group had slightly better achievement than their peers in the

control group at all time points, and students who identify as Hispanic in the treatment group had

higher achievement than their peers in the control group at all three time points. In addition, the

difference between the treatment and control group widened at each time point for the Hispanic

students.

45

Figure 4 Estimated marginal means of achievement at post-treatment

46

Figure 5. Estimated marginal means of achievement by race/ethnicity at short-term follow-up

47

Figure 6 Estimated marginal means of achievement by race/ethnicity at long-term follow-up

48

Chapter 4

Discussion

The purpose of this study was to examine the efficacy of an intervention targeting student

attributions about academic achievement and social belonging during the transition to middle

school. The intervention was patterned after social-psychological interventions that have been

used to target student attributions during the transition to junior high school (e.g., Blackwell,

Trzesniewski, & Dweck, 2007) and college (e.g., Wilson & Linville, 1985). The primary

hypothesis was that the treatment message would change students’ perceptions regarding the

causes for academic failure and feelings of social belonging. This change in attribution was then

hypothesized to yield improvements in academic motivation, students’ sense of social belonging,

and academic achievement (i.e., student’s GPA). To test these hypotheses students in the study

were randomly assigned to participate in treatment or control modules during their first semester

of middle school. Grades were collected over the course of four marking periods, and surveys of

motivation and social belonging were collected at three time points.

Post-treatment Effects

Proximal outcomes. Contrary to predictions, there was no significant relationship

between treatment status and attributions regarding academic achievement. The only significant

relationship was between baseline attributions and post-intervention attributions (∆R2 = .503).

The treatment was hypothesized to be most beneficial to students with lower initial achievement,

female students, and students of color. Interaction terms were entered into the model to test each

of these hypotheses, and none were statistically significant. As such, these hypotheses were not

supported.

49

Medial outcomes. Contrary to predictions, no immediate significant positive relationship

emerged between treatment status and motivation. Similarly, no statistically significant

relationship existed between treatment status and social belonging. Interaction terms were entered

in to both models, and none of the interaction terms were significant. As such, the hypotheses

regarding targeted benefits for students with lower prior achievement, female students, and

students of minority status were not supported.

Distal outcomes. Students in the treatment group were expected to demonstrate greater

gains in academic achievement relative to their peers in the control group based on the

assumption that attributions had shifted. Consistent with the results for attribution, motivation,

and social belonging, the academic achievement hypothesis was not supported. In addition,

students with low prior achievement, female students, and students of color did not demonstrate

greater benefit from the treatment message.

Interpretation of key post-treatment findings. The immediate post-treatment outcomes

indicate that the treatment did not have the hypothesized effects. Previous research (e.g., Perry &

Penner, 1990; Yeager & Walton, 2011), however, has indicated that social-psychological

interventions in education do not always yield immediate shifts in students’ outward expression

of attributions. Yeager and Walton (2011) argued that attributional interventions act as self-

reinforcing recursive processes over time. The self-reinforcing aspect of the treatment message

causes subsequent improvements in motivation and achievement. Walton and Cohen (2011)

found that participants in a 3-year follow-up study did not always remember the treatment

message - or even participating in the intervention – yet members of the treatment group

demonstrated significant changes in achievement and health outcomes.

In the present study, the proximal outcome (attributions) did not immediately change

after the intervention message. Following this pattern, the medial (motivation and social

belonging) and distal outcome (achievement) demonstrated no significant difference between the

50

treatment and control groups at post-treatment. Unfortunately, no follow-up measure of

attributions was collected so it cannot be determined if the treatment message in the current study

acted as a recursive process of building relationships and experiencing academic success and

failure in middle school over time. However, follow-up measures of motivation, social

belonging, and achievement were collected and are reviewed next.

Follow-up Effects

Medial outcomes. Contrary to predictions, there was no significant positive relationship

between the treatment status and motivation at short-term (13-17-week) follow-up. In addition,

there was no significant relationship between treatment status and student ratings of social

belonging at either short-term or long-term follow-up. There was a significant relationship

between treatment status and motivation at long-term follow-up. However, the relationship was

opposite of the expected direction, whereby students in the treatment group reported lower ratings

of motivation in comparison to their peers in the control group (∆R2 = .025 - .197). Interaction

terms for students with low prior achievement, female students, and students of minority status

were entered in to the model at both time points, and none of the interaction terms were

significant.

Distal outcomes. The results also did not support the hypothesis that students in the

treatment group experienced long-term gains in academic achievement relative to their peers in

the control group, and no interactions were observed based on gender or prior achievement. A

significant interaction effect was found between identification as a member of a racial/ethnic

minority and treatment condition at short-term follow-up but not long-term follow-up. These

findings indicate that students in the treatment group who identify as a member of racial/ethnic

minority exhibited greater academic achievement than their peers in the control group.

Interpretation of key findings at follow-up. The follow-up findings demonstrate the

treatment message was ineffective as a universal intervention. One potential explanation for this

51

finding is that the treatment materials, including the videos of upper-year students, were

developed in a middle school environment that is different from the two schools where they were

implemented. Stephens, Hamedani, and Destin (2014) concluded that treatment messages were

more effective when delivered by older students who emphasized similarities between themselves

and the individuals in the treatment group. As such, differences between the individuals

presenting the treatment message and the receivers of that message, as was the case in the present

study, may reduce the efficacy of the treatment message.

Specifically, all of the students filmed for the video interviews in the present study were

youth of color attending a middle school in a major metropolitan area. These student

demographic characteristics and school context are different from both of the participating school

sites but particularly so for School 1, which is located in a rural area, and its student population is

primarily White, Non-Hispanic (93%). School 2 is located in a smaller city in the mid-Atlantic,

and the student population is primarily comprised of individuals who identify as racial/ethnic

minorities in the U.S. As such, the students at School 1, and possibly at School 2, may not have

identified with the struggles presented in the video, and they may not have identified with the

students presenting the information, resulting in no change in attributions.

Alternatively, universal social psychological interventions in education to address

transition difficulties may not be as powerful as previously suggested in several studies (e.g.,

Berkely et al., 2011; Horner & Gaither, 2004; Morris, 2013). As noted previously, Sisk et al.’s

(2018) recent meta-analyses indicated that mind-set interventions were statistically and

practically non-significant for adolescents, typical students, and students facing a situational

challenge (e.g., transitioning to a new school). The current results are consistent with these

findings and suggest that attributional interventions may not be effective as a universal

intervention for early adolescents entering a new school.

52

Notably, a significant interaction emerged between treatment status and identification as

a member of a racial/ethnic minority in predicting academic achievement. This result lends some

support to the hypothesis that students of minority status may yield greater benefit from the

treatment message than peers who identify as White. However, results from the present study

indicated that students who identify as Hispanic yielded greater benefit than any other group. One

explanation for this finding is that students who identify as Hispanic occupy a unique position

within the two participating schools. Although both Black and Hispanic students are recognized

as members of a minority population within the U.S., the majority of the student population at

School 1 was comprised of White students (93%), and the majority of the student population at

School 2 was comprised of Black students (80%). As such, Hispanic students were a local

minority at both sites, making up less than 1% of the student population at School 1 and 16% at

School 2.

Social psychological interventions in education are hypothesized to work best for

students who are at greater risk for making negative attributions (Sisk et al., 2018; Walton &

Cohen, 2011; Yeager & Walton, 2011). The interaction effect was greatest for Hispanic students

in the treatment group. In addition, the effect is apparent immediately following the treatment and

became stronger over time, as demonstrated in Figures 5, 6, and 7. These results indicate that

Hispanic students experienced the greatest benefit from the treatment. In contrast, Black students,

though members of a minority group within the U.S., represented the racial majority at School 2.

The perception of belonging to the majority may have given students who identify as Black the

opportunity to make mistakes without attributing those mistakes to inherent qualities within

themselves. This could explain the difference in results between students who identify as

Hispanic and students who identify as Black.

Though not statistically significant, observed differences between the racial/ethnic groups

on the attribution measure paralleled the observed differences in achievement between groups.

53

Both Black and Hispanic students in the treatment group had lower ratings of helpless attributions

than their peers in the control group. The Hispanic students had the greatest difference in

attributions between treatment and control at post-treatment. Similarly, this group also

demonstrated increases in motivation and social belonging at short-term follow-up and in social

belonging at long-term follow-up. Plots of these marginal means have been included in the

appendix.

The group of students who identify as Hispanic represented a small portion of the total

sample (11%). The present study has limited statistical power owing to the small overall sample

size and the even smaller size of the subgroups. Further study appears warranted, though, to

confirm if a local minority population experiences a greater benefit from social psychological

interventions. Given the small sample size and lack of replication, it would be inappropriate to

draw firm conclusions; however, the current results suggest that the local minority (Hispanic

students) in the treatment group may have internalized the treatment message and made fewer

helpless attributions. These students then may have experienced a greater sense of social

belonging at the school and greater academic motivation, yielding higher academic achievement

than their peers in the control group.

Limitations

This study has several limitations that must be considered. First, one of the recruited

middle schools dropped out after initially agreeing to participate due to concerns about the

inability of their technology to deliver the intervention. As a result, the sample size was smaller

than anticipated. Second, there were significant missing data (15%). Multiple imputation allows

for statistically valid inferences despite the missing data. However, multiple imputation cannot

recreate the dataset that would have existed had the data been obtained.

A third limitation is that no baseline data were able to be collected for the motivation and

social belonging measures. The motivation and social belonging baseline measures were initially

54

going to be collected prior to intervention implementation; however, schools experienced

difficulty in accessing the website due to difficulties stemming from the schools’ firewalls

blocking the hosting website (i.e., Penn State’s Qualtrics account). Consequently, these measures

were not completed at baseline. Similarly, follow-up measures of attribution were unable to be

collected, and these same problems contributed to the missing data at short-term and long-term

follow-up. The lack of a baseline data point for motivation and social belonging means that the

results of the measures of motivation and social belonging could simply be attributable to

systematic initial differences between the two groups. Given the randomized design, baseline

differences should have been minimal; however, this cannot be confirmed given the lack of a

baseline measure. The lack of short-term and long-term follow-up measures of attribution make it

difficult to know if the treatment message became more powerful over time. Medial and distal

outcomes suggest that this is unlikely, though, with the possible exception for local minority

(Hispanic) students.

A fourth limitation, which is shared by many of the prior studies in this area as well, is a

lack of a standardized, psychometrically validated measure of attributions. Without such a

measure, it is difficult to determine if the results of the current study are due to a failure of the

treatment message or limitations of the measurement tool. Several of the prior school-based

attributional intervention studies did not collect any information about attributions (e.g., Miyake

et al., 2010). More commonly the research reviewed for the present study used a measure specific

to the negative attributions being targeted (e.g., Aronson, Fried, & Good, 2002). Some of the

studies provided psychometric information for the attribution measures (e.g. Blackwell et al.,

2007); whereas others did not (e.g., Perry, Stupnisky, Hall, Chipperfield, & Weiner, 2010). When

no measure of attribution was included, data from distal outcomes often have been used to infer

that attributions have been impacted. The most common indicator of success in shifting

attributions for each of the studies with no measure of attribution was a change in academic

55

achievement (Haynes et al., 2008; Martens et al., 2006; Miyake et al., 2010; Ruthig et al., 2004;

Van Overwalle & De Metsenaere, 1990).

Without validity data, it is unclear that the instruments are actually measuring

attributions, and without information about reliability, changes in the measures of attribution over

time may not correspond to actual changes in the construct. The lack of studies with a

psychometrically-sound attribution measure limits the evidence-base regarding the theory of

attributional interventions. One limitation to the broader field of attributional research is that

social-psychological interventions require targeting specific attributions, making the development

of a universal measure of attributions impractical. As such, further exploration is needed

regarding the process by which student achievement and motivation is affected by attributional

interventions.

A fifth potential limitation is that the treatment materials were developed at one school

site and then implemented at two other school sites. The students who were surveyed and

participated in the creation of the treatment module were drawn from an urban charter middle

school in the Northeastern United States. This charter school was in the same charter school

network and same state as School 2, and, the teachers and staff used similar curricula, disciplinary

practices, and even class schedules. However, School 2 is in a much smaller city, and the specific

demographic make up of the school population is different from the site where the treatment

materials were developed. Students from School 1 attended a rural public middle school in a

different state altogether, and the student population at School 1 was comprised primarily of

White students who transitioned to middle school in sixth grade.

In similar fashion, the messaging of the schools, as well as staff and school culture, could

have masked the efficacy of the treatment. School 1 is a public middle school, and the teachers at

the school did not engage in any form of consistent messaging from classroom to classroom

regarding behavioral expectations, school wide goals, or the purpose of the school. By contrast,

56

School 2, a charter middle school with a mission for all students to enter, succeed in, and graduate

from a four-year college or university, used consistent messaging across classrooms regarding the

mission of the school, behavioral expectations, and disciplinary practices. Perhaps most

importantly, staff members at School 2 receive professional development regarding the “growth

mindset,” and the research conducted by Blackwell et al. (2007). Teachers are encouraged to

teach lessons regarding the malleability of intelligence and challenge the idea of “fixed traits.” It

is possible that the students in both the treatment and control groups at School 2, having received

explicit instruction in the “growth mindset,” were already experiencing the benefit of the

treatment message.

There is some data to suggest that this could be the case. Table 2 shows the descriptive

data for the primary variables of the study. The mean achievement score for students at School 1,

a public middle school, declines over the course of the year, following the national trend of

declining achievement in middle school, whereas the mean achievement scores at School 2 rise

over the course of the year.

Data from the study suggest that the treatment was ineffective at both schools. For

several reasons, students from School 2 were hypothesized to have received greater benefit from

treatment. Students at School 1 were recruited during their computer class, and they were

individually responsible for bringing the informed consent paperwork to their homes and then

returning the paperwork to the computer classroom teacher. By contrast, students at School 2

were recruited during homeroom, and a folder for communicating with parents was used to send

the informed consent paperwork to the home. As a result, the student sample from School 1, a

small portion of the total student population, likely represented a more responsible subset of the

students in the school, whereas the student sample from School 2, comprising almost the entirety

of the fifth grade, represented a more typical population. Baseline differences in achievement

across the school sites bear this out (Initial GPA at School 1 = 3.9; Initial GPA at School 2 = 2.6).

57

The very population for whom the treatment message was hypothesized to be most powerful may

have already been receiving some aspects of the treatment message. In addition, the only students

for whom the treatment would be beneficial would be those who would potentially feel like

outsiders.

Implications and Future Research

Funding mind-set intervention research has been deemed a “national education priority”

(Rattan, Savani, Chugh, & Dweck, 2015, p. 723). Results from the present study indicate that a

mind-set intervention, however, may not be an effective universal intervention. The results are

consistent with a recent meta-analysis suggesting that social psychological interventions in

education provide a mild benefit for academically at-risk and economically disadvantaged

students but little benefit for other students (Sisk et al., 2018).

Many aspects of the attributional theory warrant further investigation, starting with the

theory of change outlined in Figure 1. Attributions are theorized to influence behavior, affect, and

expectancy. These variables, in turn, affect motivation. Motivation influences implicit perceptions

of ability and personality. Future studies are necessary to examine each component in this

theoretical model. Future research must systematically determine if changes in behavior are

directly attributable to changes in attribution. One challenge, though, is the lack of a validated

measure capable of detecting changes in attribution over time.

To address this need, another important future research direction is the development of

psychometrically sound measures of attribution. Doing so would enhance further study of the

theory underlying social psychological interventions. The meta-analysis by Sisk et al. (2018)

suggested that successful social psychological interventions may not be attributable to the

students’ mind-sets or attributions. Psychometrically-sound measures of attributions would allow

for examination of the theory supporting the social psychological interventions.

58

Dosage is another area in need of further exploration. Many of the interventions upon

which the present study was based were brief (i.e., ~45 minutes). However, Blackwell et al.

(2007) conducted their intervention over a period of several weeks. Notably, the students in the

Blackwell et al. study were in junior high school. It may be the case that younger students require

a greater amount of exposure to the treatment message. Further research is necessary to determine

the appropriate amount of exposure at each age level.

Lastly, the results from this study may indicate that an attributional intervention will yield

particular benefit to a local minority population. Future studies ought to confirm this result, as

well as expand the local minorities targeted for intervention. In this study, the local minority

population (i.e., students who identify as Hispanic) also happened to be a population that is a

minority group within the U.S. Future studies could explore if attributional interventions provide

benefit to individuals who are members of the national majority population (i.e., students who

identify as White/Caucasian) and are also members of a local minority population, such as

students with disabilities or students who identify as LGBTQ+.

Conclusions

The results of this study indicate that the attributional intervention for students making

the transition to middle school was not effective as a universal intervention. The treatment

message did not demonstrate the expected effect on the reported attributions of the students, and

no relationship was observed between the treatment message and motivation, social belonging, or

academic achievement. This study lends further evidence to the recent finding that attributional

interventions have little to no benefit as a universal intervention (Sisk et al., 2018). There is some

indication, though, that attribution interventions may provide some benefit to individuals who are

a local minority within their middle school. The present study, however, is a single study with a

small sample size, and the local minority in this study makes up an even smaller subset of the

sample population. Researchers interested in developing social psychological interventions in

59

education targeting attributions would do well to explore the preliminary finding that local

minority populations derive benefit from attributional interventions.

60

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Appendix

Example Survey Questions and Responses

Survey Results

Questions •  During the first year of middle

school, did you ever feel like the work you did wasn’t good enough?

•  During the first year of middle

school, did you ever feel like you weren’t smart enough?


school, did you ever feel like you didn’t belong in school?

Responses •  Half (50%) of the middle

school students felt like their work wasn’t good enough

•  More than half (66%) of

middle school students felt like they weren’t smart enough for middle school.

•  Most (83%) middle school students felt like they didn’t belong at school

Next =>

72

Survey Results

Questions •  During the first year of middle

school, were you satisfied with your grades?


school, did you ever get any bad grades?

•  Were the grades you received

in the first year of middle school above or below what you expected?

Responses •  Most (83%) middle school

students were dissatisfied with their grades

•  Most (83%) middle school

students had at least one bad grade in their first year.

•  More than half (66%) of middle school students felt their grades were below what they expected

Next =>

73

Survey Results

Questions •  Have your grades improved or

declined since the start of middle school?

•  Have your feelings of belonging improved or declined since the start of middle school?

•  Do you feel like you know what you’re doing in middle school now?

Responses •  Most (83%) middle school

students’ grades improved from the first year of middle school.

•  More than half (66%) of the middle school students felt like their feelings of belonging improved.

•  Every student (100%) we spoke with said they felt like they knew what they were doing in middle school by 7th and 8th grade.

Next =>

74

Example Screen Shot of Attribution Measure Developed in the Present Study

75

Two Example Screen Shots of a Welcome Page and Content Pages from Yeager, Paunesku,

Walton, and Dweck (2013)

76

Estimated Marginal Means of Attribution by Race/Ethnicity at Post-treatment

77

Estimated Marginal Means of Motivation by Race/Ethnicity at Short-term Follow-up

27

28

29

30

31

32

33

34

35

Control Intervention Control Intervention Control Intervention

Black Hispanic White

Series1

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Estimated Marginal Means of Motivation by Race/Ethnicity at Long-term Follow-up

27

28

29

30

31

32

33

34

35



Series1

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Estimated Marginal Means of Social Belonging by Race/Ethnicity at Short-term Follow-up

0

10

20

30

40

50

60



Series1

80

Estimated Marginal Means of Social Belonging by Race/Ethnicity at Long-term Follow-up

0

10

20

30

40

50

60



Series1

81

VITA GORDON EMMETT HALL

2321 Abington Circle, State College, PA 16801 | (717) 682 4820 | [email protected]

EDUCATION Pennsylvania State University, State College, Pennsylvania Ph.D, candidate in School Psychology, Certificate in College Teaching 2016 Certificate in Online Instruction 2016 M.Ed. in School Psychology, 2013 University of Pennsylvania, Philadelphia, Pennsylvania M.S. in Urban Education 2008 Cornell University, Ithaca, New York B.A. in Anthropology 2002

ACADEMIC AWARDS Training Interdisciplinary Educational Scientists Fellowship, Pennsylvania State University 2011 – 2015 Americorps VISTA Award, University of Pennsylvania 2006 - 2008 Cornell Tradition Fellowship, Cornell University 2002 – 2006 Hadden Scholarship, Cornell University 2002 – 2006

PREVIOUS WORK EXPERIENCE School Psychologist Mifflin County School District, Lewistown, PA 2015 – Present

5th and 6th Grade Science Teacher / Academy Director Excellence Boys Charter School, Uncommon Schools, Inc., Brooklyn, New York 2008 –2011

7th and 8th Grade Science Teacher, Teach For America Corps Member Barratt Middle School, School District of Philadelphia 2006 –2008

PUBLICATIONS – MANUSCRIPTS SUBMITTED FOR REVIEW Hall, G. E., & DiPerna, J. C. (2016). Childhood social skills as predictors of middle school academic

adjustment. The Journal of Early Adolescence, doi:0272431615624566. Nelson, P. M., Hall, G., & Christ, T. J. (2016). The Stability of Student Ratings of the Class

Environment. Journal of Applied School Psychology, 32(3), 254-267.doi: 10.1080/15377903.2016.1183543 Hall, G., & Woika, S. (2017) The fight to keep evolution out of schools, the law, and classroom instruction.

Manuscript accepted for publication at American Biology Teacher

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