Title: Social and Character Development in Elementary School: The Effectiveness of the Making Choices Program
1Social and Character Development in Elementary
School The Effectiveness of the Making Choices
Program
Preliminary Findings
- Mark W. Fraser, PI, School of Social Work,
UNC-Chapel Hill - Steven H. Day, School of Social Work
- Shenyang Guo, School of Social Work
- Alan Ellis, Sheps Center and School of Social
Work - Roderick A. Rose, School of Social Work
- Maeda J. Galinsky, School of Social Work
- Kim Dadisman, Co-PI, Center for Developmental
Science, UNC-CH - Dylan Robertson, Center for Developmental
Science - Tom Farmer, School of Education, Pennsylvania
State University
This presentation was given at the School of
Social Work, University of Maryland, Baltimore,
MD, on April 9, 2009. Portions of this report
were presented at the annual meeting of SACD
Project grantees on June 13, 2008 in Washington,
DC
2Agenda
- Theoretical Bases and programs
- Design and challenges
- Analytic strategies
- Analytic methods (skim see slides)
- Findings
3Acknowledgments
- This project was support by a cooperative
agreement (R305L030162) with the Institute of
Education Sciences at the U.S. Department of
Education (US DOE). Funding for the project was
appropriated by the US DOE and the Centers for
Disease Control and Prevention. - We thank Paul Rosenbaum (U Penn), Ben Hansen (U
of Michigan), and Matthias Schonlau (Rand Corp)
for their consultation on methodological issues
related to this presentation.
4Teachers Talk about Making Choices
- Changes in Classroom Atmosphere
- Observable Differences
- in Student Behaviors
- Measurable Academic Achievement
5Classroom Atmosphere
- I noticed that the classroom started working
more as one big group instead of individuals. - Gr.5 Sandy Grove Elementary,
- Hoke County
6Observable Behaviors
- The students tend to be less critical of each
other and more understanding of each others
differences. - Gr. 5 Sandy Grove Elementary,
- Hoke County
7Academic Achievement
- The program uses excellent books to support
the goals of being a good friend and not hurting
others. I use them during Language Arts time.
- Gr. 4 Tommys Road Elementary, Wayne County
8Observable Behaviors
- It provided a way for students to put their
feelings into words. -
-
-
- Gr.
2, Bunn Elementary, -
Franklin County
I am feeling really mad!
9Academic Achievement
- My students spend more time on task. They
seem less distracted by annoying behavior.
- Gr. 5 Scurlock Elementary, Hoke
County
10Make the right choice!
- Children are actually stopping and thinking about
making the right choices, and I have heard a lot
of children say to themselves, - Make the right choice.
- It is great to hear.
- Kdg. Bunn Elementary, Franklin County
11It made a difference with teaching children how
to deal with their feelings using better methods
rather than having tantrums or hitting.Kdg.
Bunn Elementary, Franklin County
Oh, boy! I need that Making Choices program.
12Classroom Atmosphere
- This program provided a foundation on which we
could build a classroom community. - Gr. 1 North Drive,
- Wayne County
13Children in my school need
social skills to make friends and deal with
interpersonal problems.
lessons that teach children respect toward
others and responsibility for their own actions.
a program designed to reduce disruptive
behavior and promote academic achievement.
Making Choices
14Does social and character education work?
(i.e., is Making Choices effective?)
15Intervention Research Perspective The Design and
Development Approach
- Specify the problem and develop a program theory
- Create and revise program materials
- Refine and confirm program components (sequential
experimentation perspective) - Assess effectiveness in a variety of practice
circumstances and settings - Disseminate findings and program materials
Source Fraser, M. W., Richman, J. M., Galinsky,
M. J., Day, S. H. (2009). Intervention
research Developing social programs. New York,
NY Oxford University Press.
16PROBLEM THEORYPerspectives on Conduct Problems
and Academic Achievement in Elementary School
Social and Character Development in Childhood A
Risk and Resilience Orientation
- Developmental risk perspective
- Ecological theory
- Social information processing theory
17Eco-Developmental Risk Cascade
POINT Risk factors for poor developmental
outcomes vary over time. Lacking effective
intervention, the potential for poor outcomes
increases and cascades as function of complex
bio-social processes. To promote positive
outcomes, we must disrupt malleable risk
mechanisms.
18Cognitive Mediation Model(in Developmental
Sciences)
Biological Predisposition
Biological Predisposition
- Parenting
- Monitoring
- Bonding
- Mental Processes
- Social knowledge
- Scripts
- Schema/skills
- Conduct Problems
- Conduct disorder
- Fighting
- Drug use
- Peers
- Deviancy training
- Contagion effect
- False consensus
- effect
- Sociocultural Context
- Stress/poverty
- Racism
- Street codes
- Acute/chronic stress
- Sociocultural Context
- Stress/poverty
- Racism
- Street codes
- Acute/chronic stress
Adapted from Dodge, K. A., Pettit, G. S.
(2003, p. 351). A biopsychosocial model of the
development of chronic conduct problems in
adolescence. Developmental Psychology, 39(2),
349-371.
19Social Information Processing Theory SIP Skills
and Emotional Regulation as Malleable Mediators?
State the problem
Interpret social cues
Set goal(s)
Generate potential solutions
Encode social cues
Assess outcomes
Evaluate potential solutions
Select enact the best solution(s)
Social Knowledge Life experiences producing
scripts, schemata, skills, and beliefs
20PROGRAM THEORY (specifies how a program is to
work)
21Intervention Program Structure Such as Targeting
Unit Classroom Entire school Other (after
school, family) Curriculum Structure
Distinct activities Embedded in
curriculum Activities to address SACD Goals Such
as Character education Violence prevention/peace
promotion Social and emotional development Toleran
ce and diversity Risk prevention and health
promotion Behavior management
Social and Character Development Prevention Model
Behavior Positive Behavior Responsible
behavior Prosocial behavior Self-regulation Cooper
ation Negative behavior Aggression Minor
delinquency Disruptive classroom behavior
Social - Emotional Competence (mediator) Attitudes
about aggression Self-efficacy Empathy
School Climate (mediator) School
connectedness Victimization Feelings of safety at
school Parent involvement
Academics Academic competence School
engagement Grades Standardized test scores
Moderating Factors Child Family Community Gend
er Parenting practices Community risk
factors Socioeconomic status Home
atmosphere Social capital Race/ethnicity Risk
status Program School Prior test
scores/grades Fidelity Activities to promote
social and character development Intensity and
dosage Organizational structure
22Social Development Model Perspective
23PROGRAMS
24The Competence Support Program
Social Dynamics Training for teachers
Social Skills Training for students
Group randomization Cohort 1 Hoke and Wayne
Counties (10 schools randomized to 5
intervention 5 control) Cohort 2 Franklin
County (4 schools randomized to 2 intervention 2
control)
Classroom Behavior Management Training and
Consultation for teachers
Developed by the program investigators, the
intervention simultaneously focuses on the
characteristics of children and on the classrooms
in which they learn. The intervention combines
three components.
25Program Elements
- Making Choices Skills Training curriculum for
students in elementary school. In-service
training introduced teachers to the risks of peer
rejection and social isolation, including poor
academic outcomes and conduct problems.
Throughout the school year, teachers received
consultation and support (2 times per month) in
providing lessons designed to enhance childrens
social information processing and other skills.
As a part of the Standard Course of Study, the
program was integrated into routine class
instruction. - Classroom Behavior Management provided teacher
consultation on classroom management strategies
designed to strengthen engagement in
instructional activities. - Social Dynamics Training provided teacher
consultation on classroom contexts, social
groupings, and interactional patterns that can be
used to reinforce academic achievement and
prosocial behavior.
26Theory of Change Making Choices
Core 1
Core 2
Core 3
Core 4
Core 5
Random Assignment
Application of Making Choices by Teacher or
Counselor
SIP skills of the Children in the School
Impact on Social Engage-ment and Peer Rejection
Training the Teacher or Counselor
Impact on Disruptive Behavior and Academics
- Test the degree to which the intervention is
delivered as intended, e.g., specific activities
- Assess implementation of training
- Assess if teacher acquires skills from
training/supervision
Characteristics of the Teacher or Counselor
Characteristics of the Children and the Classroom
Note. In a randomized trial, you must figure out
a way to measure each of the core elements.
Treatment as Usual Control Condition
27Make Program Manuals
- From risk mechanisms, mediators, and logic models
to the design of a program - Specifying program activities that target the
malleable mediators and have cultural congruence - Example Making Choices
For a discussion of issues in the development and
use of treatment manuals, see Galinsky, M. J.,
Terzian, M. A., Fraser, M. W. (2006). The art
of group work practice with manualized curricula.
Social Work with Groups, 29(1), 11-26.
28Warning It is easy to under estimate the
difficulty of developing a program manual.
29If you start in the wrong place, it usually does
not help to dig deeper!
Source Don Moyer, Harvard Business Review
(October, 2004, p. 160)
30Start with theory and research, plus practice
experience
How to begin in the right place
- Develop a template for each lesson or session
31Grade 2
Lesson 2
Overview
Standard Course of Study
Prep Materials
Activity 1
Review
Prop
Answers
Process Tip
32Avoid labeling
Scenarios
Activity 2 Write About It!
33Develop all worksheets and artwork
34(No Transcript)
35(No Transcript)
36Pete the Penguin Poster for Grade 2
37- Sample Lesson
- Activities
- from
- Making Choices
38Gr. 3 Lesson - Intentions
39Intentions Mean or Friendly?
40GOAL SETTING
- GOAL Something a person wants or something a
person wants to see happen. - RELATIONSHIP GOAL Goals that involve wanting to
get along with another person. - Grade 4 Lesson 6
41Are these Relationship Goals?
(thumbs up or thumbs down)
- I want to make an A on my math test.
- I want to play more often with my friend.
- I want a new video game for my birthday.
- I want to eat out at a restaurant for dinner.
- I want to become friends with the new student.
- I want to join in the basketball game at recess.
- I want to sit with Jose on the bus.
- I want to be in the class play this fall.
- I want to stop getting upset when friends ignore
me.
42GOAL SETTING
- Set a relationship goal for these situations
- I was playing basketball at recess with some
friends. Terrell, who is not very good at
basketball, asked if he could play with us.
43Set a Relationship Goal
- Denise just made me really upset. She tried to
pick a fight with me by saying things that are
not true. I am feeling angry with her right now.
44Set a Relationship Goal
- Yesterday, my mom gave me a really cool pen that
writes in all different colors. When I brought
it to school this morning, Stacey asked me if she
could borrow it. Last time I let Stacey borrow
something she lost it, but if I say no she might
get angry with me.
45EVALUATION DESIGN Cluster Randomized Trial with
Ten Schools Randomly Assigned to Treatment (j5)
and Control (j5) Conditions Cohort Design
Intervention provided in grades 3, 4, and 5
- Prior Studies
- Single-group qualitative trial of MC intervention
(8th grade girls) - Two-group cluster randomized trial at classroom
level in one middle school (6th grade only) - Two-group cluster randomized trial at classroom
level in one school (3rd grade) - Two-group, MCSF intervention randomized trial
(11 sites, 3rd 4th grade) - Cohort sequential study by classroom in two
schools (3rd grade) - (Current) Two-group cluster randomized trail at
14 elementary schools
46SAMPLE
47Two Overlapping Samples
Grade 3 n571
Grade 4 n557
- Grade 3-4 Sample
- 3rd and 4th graders
- 10 schools
- Any consented students on a 3rd or 4th grade
roster - Changeentrants-leavers
Grade 5 n433
n414
n370
- Grade 3-4-5 Sample
- 3rd, 4th, and 5th graders
- 9 schools
- Only consented students on a 5th grade roster
- Changeaddition of entrants
One treatment school was reorganized into a
different building and dropped the program
between 4th and 5th grade students from that
school were excluded from the 3-4-5 sample.
48Equivalence of Intervention and Control Groups on
Selected Child, Family, and School Attributes
Grade 3 Cohort 1
49Difference in School-Level Academic Performance
Percentage at Grade Level
Test results for 2005-06 and 2006-07 are based on
a revised accountability model and are not
comparable to those from previous years.
50Sample sizes vary because pretest measures were
collected from different respondents (teachers,
students) at different times. SLA and Peer
assessment pretest were collected from students
at the end of 2nd grade. CCC and ICST were
collected from teachers at the beginning of 3rd
grade. SLASkill Level Assessment (SIP skill
HOME Scale adaptation by Dodge, 1980).
CCCCarolina Child Checklist (Macgowan et al.
2002 Research on Social Work Practice).
ICST-Interpersonal Competency Scale Teacher
(Xie et al., 2002, Social Development)
51(No Transcript)
52Causation in Research Design Randomization Is
Supposed to Produce the Counterfactual
Note. We let the control group serve as evidence
for what would have happened counter to the fact
of participation in intervention (the stat
class). Randomization is supposed to create
equivalence or balance between the intervention
(taking the stat class) and control (not taking
the stat class) groups. But it didnt. On several
observed and an unknown number of unobserved
measures, the intervention and control group
schools differ.
53Four evaluation challenges
- Selection Bias Covariates are not balanced
between treated and control groups - Missing Data No baseline data on enterers and
lost data on leavers constant churning of
sample - Rater Effects Outcome ratings were made by the
same teachers within grades, but different
teachers over grades 3, 4, and 5 - Piecewise analyses change scores within grade
level - Treatment Contamination/History High
intervention content in control schools
Note. Student Citizen Act (SL 2001-363) was
passed into law by the Legislature in 2001. The
Act required local boards of education to develop
and incorporate character education instruction
into standard curricula. Local boards of
education began implementation in the 2002-2003
school year.
54MEASURES
55Site-Specific Outcomes
- Skill Level Assessment Activity (SLA) Based on
the Dodge Home Scale (1980), the SLA uses
students responses to questions about
hypothetical social situations. After viewing
picture scenarios, students answer questions
measuring different aspects of social information
processing skill encoding (a.78), goal
formulation (a.76), and response decision making
(a.80). - Carolina Child Checklist (CCC) The CCC is a 35
item teacher questionnaire that yields factor
scores on childrens behavior including social
contact (a.90), cognitive concentration (a.97),
social competence (a.90), and social aggression
(a.91). - Interpersonal Competence Scale-Teacher (ICST)
The ICST is an 18-item teacher questionnaire that
yields factor scores on childrens behavior
including aggression (a.84), academic competence
(a.74), and popularity (a.78). - Peer interpersonal assessments Peer
interpersonal assessments were used to examine
classmates perceptions of participants social
and behavioral characteristics including
aggression (a.92), prosocial skills (a.84 ),
and internalizing behavior (a.67 ).
56Summary of Data Collection Occasions
57Minutes of Skills Training Instruction in 3rd and
4th Grades by Student
Below benchmark 19 Above benchmark
81 (overall n571)
58ANALYTIC PROCEDURES
59Analytic Procedures Flow Chart for Use of
Bias-Correcting Statistical Methods
Multiple Imputation of Missing Data (The
imputation models employed both predictor and
outcome variables, but the analysis models
employed imputed missing values for predictor
variables only). 50 imputations for each outcome
variable
Estimation of propensity scores using
Generalized Boosted Modeling (gbm) -- aims to
optimize balance on observed covariates between
treated and control groups
Optimal pair matching using propensity scores
estimated by gbm
Optimal full matching using propensity scores
estimated by gbm
Piecewise change score HLM analysis using
propensity score weighting (propensity scores
estimated by gbm)
Heckman sample selection Model (Predictors of the
selection equation are similar to the input of
gbm)
Post-pair-matching with regression adjustment
Post-full-matching with Hodges-Lehmann aligned
rank test
Dose (efficacy subset) analysis) using Abadie et
al. Matching estimator
60Procedures for multiple imputation of missing data
- Test for MCAR (Little, 1988) confirms models are
not MCAR. - Assumption of MCAR not required if imputation
model is informed (i.e., data may be missing at
random) - A diagnostic stage identified models that
resulted in 99 relative efficiency for all
analysis variables. - 50 simulations (copies of the raw data set)
generated using MI. - DVs and predictors both used in imputation
imputed DVs discarded after imputation (MID
procedure von Hippel, 2007).
61Missing Data Diagnostics Proportion without
Missing Data and Proportion of Missing Data Points
62General Procedure for Propensity Score Analysis
- Step 2Analysis using propensity scores
- Analysis of weighted mean
- differences using kernel or local
- linear regression (difference-in-
- differences model of Heckman
- et al.)
- Step 1 Logistic regression
- Dependent variable log
- odds of receiving treatment
- Search an appropriate set of
- conditioning variables
- (boosted regression, etc.)
- Estimated propensity scores
- predicted probability (p) or
- log(1-p)/p.
- Step 2 Analysis using propensity scores
- Multivariate analysis using
- propensity scores as weights
- Step 3 Post-matching analysis
- Multivariate analysis
- based on matched
- sample
- Step 2 Matching
- Greedy match (nearest neighbor
- with or without calipers)
- Mahalanobis with or without
- propensity scores
- Optimal match (pair matching,
- matching with a variable number
- of controls, full matching)
- Step 3 Post-matching analysis
- Stratification
- (subclassification)
- based on matched
- sample
63Estimating propensity scores
- Need relevant conditioning variables
- Obtain best logistic regression (i.e., best
functional forms) however, no way to know - Used Multiple Additive Regression Trees (MART) to
run logistic regression. Rand Generalized Boosted
Modeling (gbm) Aims for best balance on observed
covariates between treated and controlled groups.
Iteration stops when the sample average
standardized absolute mean difference (ASAM) is
minimized.
64Example of gbm output Does gbm reduce the
difference between treated and control schools?
Point After using gbm propensity score weights,
all pretreatment differences are ns.
STRtreatment group LTRcontrol group ASAM
average standardized absolute mean difference
between treatment and control cases pretreatment
covariates red solid diamonds p-values before
use of gbm weights (if below line then
significant) black diamond outline p-values
after weights applied
65Predictors of the propensity score model
Note. Predictors vary by outcome variable.
Following the convention of propensity score
analysis, we did not include predictors that are
highly correlated with the outcome variable.
66Propensity score weighting
- When estimating the treatment effect, can use
propensity scores as sampling weights.(Hirano
Imbens, 2001 McCaffrey et al., 2004 Rosenbaum,
1987) - Suppose p is the propensity score of receiving
treatment. Then - Average treatment effect for the treated (ATT)
- control weight p/(1-p)
- treatment weight 1
- Average treatment effect for the population
(ATE) - control weight 1/(1-p)
- treatment weight 1/p
67Post-optimal-matching analysis
- For the matched sample created by optimal pair
matching, regress pairwise differences in Y
between treated and control cases on pairwise
differences in X vector between treated and
control cases (Rubin, 1979). In doing so, use the
intercept of the regression to estimate the
treatment effect and its p-value as a
significance test. - For matched sample created by optimal full
matching or optimal variable matching, use the
signed-rank test of Hodges and Lehmann (1962) to
estimate the average treatment effects.
68Dose analysis using Matching estimator
- The dose analysis evaluates the outcome
difference between a dose group (i.e., low,
benchmark, or high) and a comparison group using
Matching estimator developed by Abadie et al.
(2004). - Under the exogeneity assumption, this method
imputes the missing potential outcome by using
average outcomes for individuals with similar
values on observed covariates. - The estimator uses the vector norm (i.e.,
xv(xVx)1/2 with positive definite matrix V)
to calculate distances between one treated case
and each of the matched multiple nontreated
cases, and chooses the outcome of the nontreated
case whose distance is the shortest among all as
the predicted outcome for the treated case.
69Comparing model features
Note. Regression models include covariates age at
baseline, female, black, white, latino, primary
caregiver education, income-to-poverty ratio,
primary caregiver fulltime employment, father in
household, and midyear change in teacher.
70Comparing model features
71FINDINGS
72Findings Treatment effects measured by changes
in the 3rd Grade (g34)
73Findings Treatment effects measured by changes
in the 4th Grade (g34)
74Findings Treatment effects measured by changes
in the 3rd Grade (g345)
75Findings Treatment effects measured by changes
in the 4th Grade (g345)
76Findings Treatment effects measured by changes
in the 5th Grade (g345)
77Findings Growth curve and dose models (g345)
Low Confidence Do Not Cite
78Findings of Efficacy Subset Analysis Treatment
effects measured by changes in the 3rd and 4th
Grades (g34)
79Findings of Efficacy Subset Analysis Treatment
effects measured by changes in the 3rd, 4th, and
5th Grades (g345)
80Findings of Efficacy Subset Analysis Treatment
effects measured by changes in the 4th Grade
using subsets of Grade 3 exposure (g34)
81Findings of Efficacy Subset Analysis Treatment
effects measured by changes in the 4th 5th
Grades using subsets of Grade 3 exposure (g345)
82Summary
- From different methods of analysis, a pattern of
small, cumulative program effects emerges across
grades 3, 4, and 5. These analyses exclude one
poorly performing that was dissolved in third
year of the study. - Positive cumulative effects on
- Social competence including
- Prosocial behavior and
- Skill in regulating emotions
- Internalizing behavior
- Relational aggression
- By HLM and efficacy subsets, promising effects
observed on - Academic competence
- Aggression
83Focuses on Program Development and Steps in
Intervention Research
Focuses on (Selection) Bias-Correction
Statistical Methods
For a description of Making Choices and copies of
sample lessons, see http//ssw.unc.edu/jif/makingc
hoices/
84References
Abadie, A., Drukker, D., Herr, J. L., Imbens,
G. W. (2004). Implementing matching estimators
for average treatment effects in Stata. The Stata
Journal 4(3), 290-311. Fraser, M. W., Day, S.
H., Galinsky, M. J., Hodges, V. G., Smokowski,
P. R. (2004). Conduct problems and peer
rejection in childhood A randomized trial of
the Making Choices and Strong Families programs.
Research on Social Work Practice, 14(5),
313-324. Fraser, M. W., Galinsky, M. J.,
Smokowski, P. R., Day, S. H., Terzian, M. A.,
Rose, R. A., Guo, S. (2005).Social
information-processing skills training to promote
social competence and prevent aggressive behavior
in third grade. Journal of Consulting and
Clinical Psychology, 73(6), 1045-1055. Fraser,
M. W., Nash, J. K., Galinsky, M. J., Darwin, K.
E. (2000). Making choices Social
problem-solving skills for children. Washington,
DC NASW Press. Fraser, M. W., Richman, J. M.,
Galinsky, M. J., Day, S. H. (2009).
Intervention research Developing social
programs. New York, NY Oxford University
Press. Galinsky, M. J., Terzian, M. A.,
Fraser, M. W. (2006). The art of group work
practice with manualized curricula. Social Work
with Groups, 29(1), 11-26. Guo, S., Fraser, M.
W. (in press). Propensity score analysis
Statistical methods and applications. Thousand
Oaks, CA Sage Press. Hansen, B. B. (2007).
Optmatch Flexible, optimal matching for
observational studies. R News, 7,
18-24. Heckman, J. J. (1979). Sample selection
bias as a specification error. Econometrica, 47,
153-161. Heckman, J. J. (2005). The scientific
model of causality. Sociological Methodology, 35,
1-97.
85Hirano, K., Imbens, G. (2001). Estimation of
causal effects using propensity score weighting
An application to data on right heart
catheterization. Health Services and Outcomes
Research Methodology, 2, 259-278. Hodges, J.
L., Lehmann, E. L. (1962). Rank methods for
combination of independent experiments in the
analysis of variances. Annals of Mathematical
Statistics, 33, 482-497. McCaffrey, D. F.,
Ridgeway, G., Morral, A. R. (2004). Propensity
score estimation with boosted regression for
evaluating causal effects in observational
studies. Psychological Methods, 9,
403-425. Rosenbaum, P. (1987). Model-based
direct adjustment. Journal of the American
Statistical Association, 82, 387-394. Rosenbaum,
P. (2002). Observational studies (2nd ed.). New
York Springer-Verlag. Rubin, D. B. (2008). For
objective causal inference, design trumps
analysis. The Annals of Applied Statistics,
2(3), 808-840. Rubin, D. B. (1979). Using
multivariate matched sampling and regression
adjustment to control bias in observational
studies. Journal of the American Statistical
Association,74(366), 318-328. Rubin, D. B.
(1979). Using multivariate matched sampling and
regression adjustment to control bias in
observational studies. Journal of the American
Statistical Association,74(366), 318-328. Von
Hippel, P. T. (2007). Regression with missing Ys
An improved strategy for analyzing multiply
imputed data. Sociological Methodology, 37(1),
83-117.
Note. CCCCarolina Child Checklist ICST
Interpersonal Competency Scale - Teacher
86Source Fraser, M. W., Richman, J. M., Galinsky,
M. J., Day, S. H. (2009). Intervention
research Developing social programs. New York,
NY Oxford University Press.