Where are we going?

• What to do if no RCT, RD, ITS or sophisticated matching is possible?

• We describe and analyze principle of pattern matching to improve the basic workhorse design by a feature other than high quality matching

• Illustrate what you might do if there is no pretest at all and so not even a work-horse design is possible

• Before that, a bit of a summary

Yesterday 1

• Do not match from extremes unless forced to• Then match using a reliable set of measures,

moving to propensity score framework, latent variables, or statistical reliability-adjustments to handle unreliability

• But you will still have problem of possible specification error!

Yesterday 2

• In selecting non-equivalent control group:• Use local focal matching to reduce the degree of

initial non-comparability though you cannot expect total non-comparability

• Sometimes the control group so formed will not differ from what would have been achieved with random assignment, at least on observables

• At other times, the initial group non-equivalence will be reduced for when you come to do statistical analysis to “control” initial differences

Yesterday 3• When there is initial non-equivalence on observables:• Theory and empiricism about the constructs (covariates)

in the “true”selection model helps, as does careful measurement thereof

• Measures in multiple other domains helps too,• Unclear whether ANCOVA or propensity scores do

better--Shadish et al, Glazerman et al.--though demographic variables alone and Heckman IV models do not fare well. Propensity scores preferred on theory alone.

• You can never be sure of the final causal conclusion, though

Implication

• In designing research you do well to avoid the workhorse if you can, though it is modal in current educational practice

• Can you add prior pretest waves?• Can you add any of the other design elements, some

mentioned in our ITS discussion and others we discuss today

• How can you design your way out of reliance on a simple design with non-equivalent groups and pretest and posttest?

When there is no Pretest on the same scale as Outcome

• Do randomized experiment--Abcedarian, Perry-Preschool, Head Start, Early Head Start, CLIO, Even Start and Sesame Street

• If not possible, do everything possible to make control group focally local

• Add design elements to rule out alternative explanations of a possible causal relationship

• Here’s an example

Minton’s Dissertation

• Object: Evaluate Sesame St in 1st year• Problem 1: Program already launched• Problem 2: No pretest possible• Problem 3: No money for original data collection• Setting: One kindergarten in NJ that built SS into its

day and that has records on children and their families plus annual PPV assessment

Question 1: What control group is possible?

• What control group to find, given program was very popular in its first year.

• Why is popularity a problem?• Neighborhood kids who did not view• Next-door kids of same age who not view?• Older siblings in general• Older sibs attending same kindergarten within last N

years• Older sibs attending same kindergarten last 2 years

Older control siblings

Younger Sesame Street

siblings

Ach

ieve

men

t sco

res

Minton (1975) Sesame Street Study - 1

Older control siblings

Younger Sesame Street

siblings

Ach

ieve

men

t sco

res

Minton (1975) Sesame Street Study - 2

Letter skills

Non-letter PPVT skills

Older control siblings

Younger Sesame Street

siblings

Ach

ieve

men

t sco

res

Minton (1975) Sesame Street Study - 3

Letter skills – high viewers

Non-letter skills–low viewers

Non-letter skills–high viewers

Letter skills – low viewers

What has happened here?

• Single causal hypothesis of SS effective made to have multiple data implications

• These are meant to rule out alternative hypotheses and not to recreate same bias

• These implications here in the form of a difference in difference in differences

• Collect data and test hypothesis

Another Example

• How the Introduction of TV affected Library Circulation

1945

1975

Lib

rary

ci

rcul

atio

n pe

r ca

pita

Parker et al. (1966) Effects of TV - 1

1949

1953

Short interrupted time series

Fiction book circulation

1945

1975

Lib

rary

ci

rcul

atio

n pe

r ca

pita

Parker et al. (1966) Effects of TV - 2

1949

1953

Short interrupted time series with control

Early TV communities

Late TV communities

1945

1975

Lib

rary

ci

rcul

atio

n pe

r ca

pita

Parker et al. (1966) Effects of TV - 3

1949

1953

ITS with switching replication

1949 interruption

1953 interruption

1945

1975

Lib

rary

ci

rcul

atio

n pe

r ca

pita

Parker et al. (1966) Effects of TV - 4

1949

1953

ITS with switching replication and control

Fiction

Fact

Fact

Fiction

What has happened here?

Combine an ITS with non-equivalent DVs and switching replicationsWhat alternative interpretations can you come up with?How plausible are these?

• Have we seen this before with RD and ITS? • One general causal hypothesis has multiple

implications in the data• Predicted hypothesis as multiple differences of

differences; as higher order interactions

Reynolds and West’s (1987) “Ask for the Sale” Experiment

From all stores selling lottery tickets, some stores volunteered (or not) to post a sign reading “Did we ask you if you want a Lottery ticket? If not, you get one free”. So this is a basic nonequivalent control group design, with the control matched on zip code, store chain, and pretest ticket sales.

NR O1 X O2-----------------------------

NR O1 O2

The Outcome of the Basic Design

But there might be many reasons besides treatment that caused treatment group sales to rise.

Adding a Nonequivalent DV

• They added three nonequivalent dependent variables, showing that the intervention increased ticket sales but not sales of gas, cigarettes, or grocery items.

Adding Multiple Baselines - recasting as ITS Design

• They located some stores in which the treatment was initiated later than in other stores, or initiated and then removed, and found that the outcome tracked the introduction of treatment over time while sales in the matched controls remained unchanged

Adding Multiple Pretests and Posttests

• They added multiple pretests and posttests by examining mean weekly ticket sales for four weeks before and four weeks after the treatment started.

The Point is:

• To use the choice of additional design elements to rule out more alternative interpretations, hopefully all that can be currently identified

• The goal is ruling out plausible alternative interpretations, and it can also be reached via keeping the pattern of results constant but varying the number and type of comparisons involved

Main NAEP 4th grade math scores by year and proficiency standards

D & J Results: 4th Grade Math

Main NAEP 4th grade math scores by year: Public and Catholic schools

Main NAEP 4th grade math scores by year: Public and Other Private schools

Trend NAEP 4th grade math scores by year: Public and Catholic schools

Student Enrollment

Catholic Other Private Public

1994 5.73 4.72 89.55

1996 5.67 4.74 89.60

1998 5.58 4.87 89.56

2000 5.38 4.81 89.81

2002 5.26 5.13 89.61

2004 4.88 4.93 90.18

2006 4.56 5.07 90.37

Warning!

• This pattern matching strategy requires: • Clear causal hypothesis - relevance to

discontinuity• Careful measurement - reliability and ceiling

or floor effects• Large samples (or large effects) because

hypothesis is of a complex statistical interaction

• How lucky Minton was!

Examples from you of the Basic Work-Horse Design

• Let us take some from you and see if they can be improved by adding design elements.

Design Elements to be combined: Assignment

• Random Assignment• Cutoff-Based Assignment• Researcher-controlled Matching -- of many

kinds in econometric literature

Design Elements to be combined:Treatments

• Switching Replications• Reversed Treatments• Removed Treatments• Repeated Treatments

Design Elements to be combined: Measurement

• Single Pretest• Pretest Time Series• Proxy Pretests• Retrospective Pretests• Moderators with predicted Interactions• Measuring Threats to Validity

Design Elements to be combined: Comparison Groups

• Single Non-Equivalent Groups• Multiple Non-Equivalent Groups• Twins/Siblings• Cohorts• Other Focal, Local Comparison Groups

Golden Rules (1)

• You can’t put right through statistics what you have done wrong by design

• Statistical adjustments work better the less non-equivalence there is to adjust away in the first place

• Since the work horse is so prevalent but so problematic, how can we complexify the design through adding design elements

Golden Rules (2)

• First, Do an experiment; if not• Do Regression-discontinuity study. If not,• Do ITS with some sort of a comparison series.

If not• Do study combining multiple design element,

preferably with focal local intact controls, case matching on many covariates, reintroduction of treatment at new time, non-equivalent DVs, etc.

Golden Rules (3)

Don’t be bamboozled by fancy models in Greek clothing. Always translate them into structural design elements before evaluating their likely validity. That will reveal what you have got

• Remember you only control for the reliably measured part of any construct, not the construct itself.

Evaluation, formativeOn a scale from 1 to 6, with 6 being high, please rate the

following and then indicate how you would improve what we did.

Contact with Valerie about the workshop AccommodationsFoodCurriculum ContentCurriculum Relevance to your current or anticipated workQuality of InstructionAny other Suggestions for Improvements?

Grant Opportunities at the Institute of Education Sciences

Allen Ruby, Ph.D.

Associate Commissioner

Policy & Systems Division

National Center for Education Research

Take Away• IES has a small number of education grant programs

– Matrix of topics/goals within grant programs– Range of work: exploration, development, evaluation– Focus is on improving student outcomes– Competitions are held twice a year (6/24/10 and 9/16/10)– Competitive peer review using an absolute standard

• Take advantage of available information– http://ies.ed.gov (Request For Applications, abstracts of

projects, webinars, resources for researchers webpage)

– Program officer (listed in RFA)

IES StructureOffice of the

Director

National Board for Education

Sciences

National Center for Education Research

National Center for Education Evaluation

National Center for Education Statistics

National Center for Special Ed Research

Standards & Review

Overall Research Objectives

• Develop or identify education interventions (practices, programs, policies and approaches) that enhance academic achievement and that can be widely deployed

• Identify what does not work and thereby encourage innovation and further research

• Understand the processes that underlie variations in the effectiveness of education interventions

Final Outcomes of Interest are for StudentsPreschool• School readiness• Developmental outcomes for infants and toddlers with disabilities

Kindergarten through Grade 12• Academic outcomes in reading, writing, math and science• Behaviors, interactions, and social skills that support learning in

school and successful transitions to post-school opportunities• High school graduation• Functional skills for independent living of students with disabilities Postsecondary: enrollment, persistence, and completion

Adult Education: reading, writing, and math (basic, secondary, and EL)

Key Dates

Application Deadline

Letter of Intentiesreview.ed.gov

Application Package

www.grants.gov

Start Dates

6/24/10 4/29/10 4/29/10 3/1/11 to

9/1/11

9/16/10 7/19/10 7/19/10 7/1/11to

9/1/11

Research and Research Training Grant Programs

• Education Research Grant Programs • Special Education Research Grant Programs

• Statistical and Research Methodology in Education• Evaluation of State and Local Education Programs

and Policies• Postdoctoral Research Training Grant Programs• National Research and Development Centers

Education Research Grants Programs (84.305A)• Reading and Writing• Mathematics and Science Education• Cognition and Student Learning• Teacher Quality (Reading & Writing; Math & Science)• Social and Behavioral Context for Academic Learning• Education Leadership• Education Policy, Finance, and Systems• Postsecondary Education• English Learners• Early Learning Programs and Policies• Education Technology• Adult Education• Organization and Management of Schools and Districts• Analysis of Longitudinal Data to Support State & Local

Education Reform

Special Education Research Programs (84.324A)• Early Intervention and Early Childhood Special

Education• Reading, Writing, and Language Development• Mathematics and Science Education• Social and Behavioral Outcomes to Support Learning• Cognition and Student Learning in Special Education• Professional Development for Teachers and Related

Service Providers• Special Education Policy, Finance, and Systems• Transition Outcomes for Special Education

Secondary Students• Autism Spectrum Disorders

5 Research Goals1. Exploration: examine relationship between

malleable factors and education outcomes

2. Development and Innovation: iteratively develop new or improved education interventions

3. Efficacy & Replication: evaluate the efficacy and feasibility of an intervention

– Efficacy Follow-up Studies

4. Scale-up Evaluation: evaluate the impact & feasibility of interventions at scale

– Scale-up Follow-up Studies

5. Measurement: develop and validate assessments

Exploration• Explore malleable factors that are associated

with better student learning and education outcomes– Malleable factor: something that can changed by

the education system - student, teacher, or school characteristics, or an education program or policy

• Explore factors that mediate or moderate the relationship between malleable factors and student outcomes

• Small primary data studies, secondary analyses, and meta-analyses

Development and Innovation

• Develop new interventions (e.g., instructional practices, curricula, teacher professional development, policies)

• Demonstrate the feasibility of the intervention for implementation in an authentic education delivery setting

• Collect pilot data on promise of intervention to achieve intended outcomes

Efficacy and Replication• Test whether or not fully developed

interventions are effective under specified conditions and with specific types of students

• Take place under supportive conditions , e.g. homogenous sample, high assistance

• Studies using random assignment to intervention and comparison conditions are preferred where feasible

• New this year: Efficacy follow-up studies

Scale-up Evaluation

• Test whether interventions are effective when implemented under typical conditions.

• As implemented by practitioners and with sufficiently diverse samples to support generalizability.

• Studies using randomized assignment to treatment and comparison conditions are preferred whenever they are feasible.

• Added this year: Scale-up follow-up studies

Measurement• Develop and validate assessments or other

measurement tools– Typically to be used by practitioners (e.g.,

screening, progress monitoring, and outcome assessment) but can also be for researcher use

– Validation of non-student measures must involve student outcomes (e.g. Teacher Quality)

– Program specific, e.g., cost-accounting under Education Policy, Finance, and Systems

• Not for evaluating an assessment used as an intervention

• The measure is the primary product

Maximum Grant Duration and Typical Size

Grants include both direct and indirect costs

• Exploration: 2 years, $100,000 to $350,000 per yr 10%– Up to 4 years & $400,000 for primary data collection

• Development: 3 years, $150,000 to $500,000 per yr 50%• Efficacy: 4 years, $250,000 to $750,000 per yr 26%

– Follow-up: 3 years, $150,000 to $400,000 per yr

• Scale-up: 5 years, $500,000 to 1.2 million per yr 2%– Follow-up: 3 years, $250,000 to $600,000 per yr

• Measurement: 4 years, $150,000 to $400,000 per yr 12%

Information Sources• IES website: http://ies.ed.gov• Obtain IES Request for Applications

– Describes type of research that can be done and substance of what to include in application

– http://ies.ed.gov/funding• IES Grants.Gov Application Submission Guide

– Step by step instructions on how to apply– http://ies.ed.gov/funding

• Obtain Application from and Submit Application to– www.grants.gov– 84.305A (regular education) & 84.324A (special education)

• Initial IES Contact (feel free to contact)– Allen.Ruby@ed.gov

• Handout includes information covered in this presentation