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The Pennsylvania State University
The Graduate School
Department of Educational Psychology, Counseling, and Special Education
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
Procedures ........................................................................................................................ 30 Intervention development ......................................................................................... 30Intervention implementation .................................................................................... 31Data collection and timeline ..................................................................................... 32
Data Analysis ................................................................................................................... 33 Missing Data .................................................................................................................... 34
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
vi
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
vii
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.
1
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).
2
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
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.
7
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.
8
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)
Test the efficacy of an attributional intervention targeting concerns about academic performance
Psychology Students (n = 36)
d = .81 -- --
Van Overwalle, Segebarth, & Goldchstein (1989)
Test the efficacy of an attributional intervention targeting concerns about academic performance
First-year (n = 130)
d = .43
-- --
Van Overwalle & De Metsenaere (1990)
Test the efficacy of an attributional intervention targeting concerns about academic performance
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
Psychology Students (n = 120)
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 -- --
9
stereotype threat
Martens, Johns, Greenberg, & Schimel (2006)
Test the efficacy of an attributional intervention on women’s stereotype threat
Psychology Students (n = 100)
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
Psychology Students (n = 257)
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)
Seventh-grade Students (n = 91)
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 --
11
149, 134) Sherman et al. (2013) Test the efficacy of a values affirmation and
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)
• Lost to follow-up (students left school; n = 1) • Did not return paper forms for motivation and
social belonging (one school site did not distribute forms; n = 34)
• Allocated to control (n = 67) • Did not receive allocated intervention (n =
0)
• Lost to follow-up (students left school; n = 1) • Did not return paper forms for motivation and
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
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
Ziegler, A., & Heller, K. A. (2000). Effects of an attribution retraining with female students gifted
in physics. Journal for the Education of the Gifted, 23(2), 217–243. doi: 10.4219/jeg-
2000-572
71
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?
• During the first year of middle
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?
• During the first year of middle
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
78
Estimated Marginal Means of Motivation by Race/Ethnicity at Long-term Follow-up
27
28
29
30
31
32
33
34
35
Control Intervention Control Intervention Control Intervention
Black Hispanic White
Series1
79
Estimated Marginal Means of Social Belonging by Race/Ethnicity at Short-term Follow-up
0
10
20
30
40
50
60
Control Intervention Control Intervention Control Intervention
Black Hispanic White
Series1
80
Estimated Marginal Means of Social Belonging by Race/Ethnicity at Long-term Follow-up
0
10
20
30
40
50
60
Control Intervention Control Intervention Control Intervention
Black Hispanic White
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