Appendices A-C NAEP 2021 Supplemental Documents

^{NATIONAL CENTER} FOR EDUCATION STATISTICS

NATIONAL ASSESSMENT OF EDUCATIONAL PROGRESS

National Assessment of Educational Progress (NAEP) 2021

Appendices A-C

Appendix A: External Advisory Committees

Appendix B: NAEP 2013 Weighting Procedures

Appendix C: NAEP 2021 Sampling Memo

OMB# 1850-0928 v.18

November 2019

Table of Contents

Appendix A: External Advisory Committees … 3

Appendix B: NAEP 2013 Weighting Procedures (No changes since v.10) …15

The 2013 Weighting Procedures documentation is the most current version available to the public. At this time, there is not a timeline for when the details for later assessment years will be publicly available.

Appendix C: NAEP 2019 Sampling Memo (No changes since v.10) …………………………………….65

Appendix C provides a sample from 2019. The 2021 Sampling Memo is scheduled to be completed and submitted to OMB in Amendment #1 by April 2020.

NATIONAL CENTER FOR EDUCATION STATISTICS NATIONAL ASSESSMENT OF EDUCATIONAL PROGRESS

National Assessment of Educational Progress (NAEP) 2021

Appendix A

External Advisory Committees

OMB# 1850-0928 v.18

October 2019

Appendix A-1: NAEP Design and Analysis Committee

Name	Affiliation
Betsy Becker	Florida State University, FL
Peter Behuniak	University of Connecticut, CT
Dan Bolt	University of Wisconsin, Madison, WI
Lloyd Bond	University of North Carolina, Greensboro, NC (Emeritus)/Carnegie Foundation (retired)
Derek Briggs	University of Colorado, CO
Richard Duran	University of California, Santa Barbara, CA
Steve Elliott	Arizona State University, AZ
Ben Hansen	University of Michigan, MI
Brian Junker	Carnegie Mellon University, PA
David Kaplan	University of Wisconsin-Madison, WI
Kenneth Koedinger	Carnegie Mellon University, PA
Sophia Rabe-Hesketh	University of California, Berkeley, CA
Michael Rodriguez	University of Minnesota, MN
S.Lynne Stokes	Southern Methodist University, TX
Chun Wang	University of Minnesota, MN

Appendix A-2: NAEP Validity Studies Panel

Name	Affiliation
Peter Behuniak	University of Connecticut, CT
Jack Buckley	American Institutes for Research, Washington, DC
Jim Chromy	RTI International (Emeritus Fellow), Raleigh, NC
Phil Daro	Strategic Education Research (SERP)
Richard Duran	University of California, Berkeley, CA
David Grissmer	University of Virginia, VA
Larry Hedges	Northwestern University, IL
Gerunda Hughes	Howard University, Washington, DC
Ina Mullis	Boston College, MA
Scott Norton	Council of Chief State School Officers, Washington, DC
Jim Pellegrino	University of Illinois at Chicago/Learning Sciences Research Institute, IL
Gary Phillips	American Institutes for Research, Washington, DC
Lorrie Shepard	University of Colorado at Boulder, CO
David Thissen	University of North Carolina at Chapel Hill, NC
Gerald Tindal	University of Oregon, Eugene, OR
Sheila Valencia	University of Washington, WA
Ting Zhang	American Institutes for Research, Washington, DC

Appendix A-3: NAEP Quality Assurance Technical Panel

Name	Affiliation
Jamal Abedi	University of California, Davis, CA
Chuck Cowan	Analytic Focus LLC, San Antonio, TX
Gail Goldberg	Gail Goldberg Consulting, Ellicott City, MD
Brian Gong	National Center for the Improvement of Educational Assessment, Dover, NH
Richard Luecht	University of North Carolina-Greensboro, NC
Jim Pellegrino	University of Illinois at Chicago/Learning Sciences Research Institute, IL
Mark Reckase	Michigan State University, MI
Michael (Mike) Russell	Boston College, MA
Phoebe Winter	Consultant, Chesterfield, VA
Richard Wolfe	University of Toronto (Emeritus), Ontario, Canada

Appendix A-4: NAEP National Indian Education Study Technical Review Panel

Name	Affiliation
Doreen E. Brown	ASD Education Center, Anchorage, AK
Robert B.Cook	Native American Initiative/Teach for America, Summerset, SD
Steve Andrew Culpepper	University of Illinois at Urbana-Champaign, IL
Susan C. Faircloth	University of North Carolina Wilmington, NC
Jeremy MacDonald	Rocky Boy Elementary, Box, Elder, MT
Holly Jonel Mackey	University of Oklahoma, OK
Jeannette Muskett Miller	Tohatchi High School, Tohatchi, NM
Sedelta Oosahwee	National Education Association, DC
Debora Norris	Salt River Pima-Maicopa Indian Community
Martin Reinhardt	Northern Michigan University, MI
Tarajean Yazzie-Mintz	Wakanyeja ECE Initative/American Indian College Fund, Denver, CO

Appendix A-5: NAEP Mathematics Standing Committee

Name	Affiliation
Scott Baldridge	Louisiana State University, LA
Carl Cowen	Indiana University–Purdue University, IN
Kathleen Heid	Pennsylvania State University, PA
Mark Howell	Gonzaga College High School, Washington, DC
Carolyn Maher	Rutgers University, NJ
Michele Mailhot	Maine Department of Education, Augusta, ME
Matthew Owens	Spring Valley High School, Columbia, SC
Carole Philip	Alice Deal Middle School, Washington, DC
Kayonna Pitchford	University of North Carolina, NC
Melisa M. Ramos Trinidad	Educación Bilingüe Luis Muñoz Iglesias, Cidra, PR
Allan Rossman	College of Science and Mathematics-CalPoly, CA
Carolyn Sessions	Louisiana Department of Education, LA
Lya Snell	Georgia Department of Education, GA
Ann Trescott	Stella Maris Academy, La Jolla, CA
Vivian Valencia	Espanola Public Schools, NM

Appendix A-6: NAEP Reading Standing Committee

Name	Affiliation
Patricia Alexander	University of Maryland, MD
Alison Bailey Jensa Bushey	University of California, LA, CA Shelburne Community Schools, Shelburne, VT
Julie Coiro	University of Rhode Island, RI
Bridget Dalton Christy Howard	University of Colorado Boulder, CO East Carolina University, Greenville, NC
Jeanette Mancilla-Martinez	Vanderbilt University, TN
Pamela Mason	Harvard Graduate School of Education, MA
P. David Pearson	University of California, Berkeley, CA
Frank Serafini	Arizona State University, AZ
Kris Shaw Ana Taboada Barber	Kansas State Department of Education, KS University of Maryland, MD
Diana Townsend Brandon Wallace	University of Nevada, Reno, NV National Office of Urban Teachers, Baltimore, MD
Victoria Young	Texas Education Agency, Austin, TX

Appendix A-7: NAEP Science Standing Committee

Name	Affiliation
Alicia Cristina Alonzo	Michigan State University, MI
George Deboer	American Association for the Advancement of Science, Washington, DC
Alex Decaria	Millersville University, PA
Crystal Edwards	Lawrence Township Public Schools, Lawrenceville, NJ
Ibari Igwe	Shrewd Learning, Elkridge, MD
Michele Lombard	Kenmore Middle School, Arlington, VA
Emily Miller	Consultant, WI
Blessing Mupanduki	Department of Defense, Washington, DC
Amy Pearlmutter	Littlebrook Elementary School, Princeton, NJ
Brian Reiser	Northwestern University, Evanston, IL
Michal Robinson	Alabama Department of Education, Montgomery, AL
Gloria Schmidt	Darby Junior High School, Fort Smith, AR
Steve Semken	Arizona State University, Tempe, AZ
Roberta Tanner	Board of Science Education, Longmont, CO
David White	Lamoille North Supervisory Union School District, Hyde Park, VT

Appendix A-8: NAEP Survey Questionnaire Standing Committee

Name	Affiliation
Angela Duckworth	University of Pennsylvania, PA
Hunter Gehlbach	Harvard University, MA
Camille Farrington	University of Chicago, Chicago, IL
Gerunda Hughes	Howard University, DC
David Kaplan	University of Wisconsin-Madison, WI
Henry Levin	Teachers College, Columbia University, NY
Stanley Presser	University of Maryland, MD
Augustina Reyes	University of Houston, Houston, TX
Leslie Rutkowski	Indiana University Bloomington, IN
Jonathon Stout	Lock Haven University, PA
Roger Tourangeau	Westat, Rockville, MD
Akane Zusho	Fordham University, NY

Appendix A-9: NAEP Mathematics Translation Review Committee

Name	Affiliation
Mayra Aviles	Puerto Rico Department of Education, PR
David Feliciano	P.S./M.S 29, The Melrose School, Bronx, NY
Yvonne Fuentes	Author and Spanish Linguist, Carrollton, GA
Marco Martinez-Leandro	Sandia High School, NM
Jose Antonio (Tony) Paulino	Nathan Straus Preparatory School, NY
Evelisse Rosado Rivera	Teacher, PMB 35 HC, PR
Myrna Rosado-Rasmussen	Austin Independent School District, TX
Gloria Rosado Vazquez	Teacher, HC-02, PR
Enid Valle	Kalamazoo College, Kalamazoo, MI

Appendix A-10: NAEP Science Translation Committee

Name	Affiliation
Daniel Berdugo	Teacher, PS 30X Wilton, NY
Yvonne Fuentes	Author and Spanish Linguist, Carrollton, GA
Myrna Rosado- Rasmussen	Austin Independent School District, Austin, TX
Enid Valle	Kalamazoo College, Kalamazoo, MI

Appendix A-11: NAEP Grade 8 Social Science Translation Review Committee

Name	Affiliation
Yvonne Fuentes	Author and Spanish Linguist, Carrollton, GA
Jose Antonio Paulino	Middle School Teacher, Nathan Strauss Preparatory School, NY
Dagoberto Eli Ramierz	Bilingual Education Expert, Palmhurst, TX
Enid Valle	Kalamazoo College, Kalamazoo, MI

Appendix A-12: NAEP Grade 4 and 8 Survey Questionnaires and eNAEP DBA System Translation Committee

Name	Affiliation
Daniel Berdugo	PS 30X Wilton, Bronx, NY
Yvonne Fuentes	Carrollton, GA
Marco Martinea-Leandro	Sandia High School. Albuquerque, NM
Jose Antonio (Tony) Paulino	Nathan Straus Preparatory School, New York, NY
Evelisse Rosado Rivera	PMB 36 HC 72, Naranjito, PR
Myrna Rosado-Rasmussen	Austin Independent School District, Austin, TX
Gloria M. Rosado Vazquez	HC – 02 Barranquitas, PR
Enid Valle	Kalamazoo College, Kalamazoo, MI

Appendix A-13: NAEP Principals’ Panel Standing Committee

Name	Affiliation
David Atherton	Clear Creek Middle School, Gresham, OR
Ardith Bates	Gladden Middle School, Chatsworth, GA
Williams Carozza	Harold Martin Elementary School, Hopkinton, NH
Diane Cooper	St. Joseph’s Academy, Clayton, MO
Brenda Creel	Alta Vista Elementary School, Cheyenne, WY
Rita Graves	Pin Oak Middle School, Bellaire, TX
Don Hoover	Lincoln Junior High School, Springdale, AR
Stephen Jackson	(Formerly with) Paul Laurence Dunbar High School, Washington, DC
Anthony Lockhart	Lake Shore Middle School, Belle Glade, FL
Susan Martin	Berrendo Middle School, Roswell, NM
Lillie McMillan	Porter Elementary School, San Diego, CA
Kourtney Miller	Chavez Prep Middle School, Washington, DC
Jason Mix	Howard Lake–Waverly–Winsted High School, Howard Lake, MN
Leon Oo-Sah-We	Ch’ooshgai Community School, Tohatchi, NM
Sylvia Rodriguez Vargas	Atlanta Girls’ School, Atlanta Georgia, GA

NATIONAL CENTER FOR EDUCATION STATISTICS NATIONAL ASSESSMENT OF EDUCATIONAL PROGRESS

National Assessment of Educational Progress (NAEP)

2021

Appendix B

NAEP 2013 Weighting Procedures

OMB# 1850-0928 v.18

September 2018

No changes since v.10

The 2013 Weighting Procedures documentation is the most current version available to the public. At this time, there is not a timeline for when the details for later assessment years will be publicly available.

NAEP Technical Documentation Website

NAEP Technical Documentation Weighting Procedures for the 2013 Assessment

NAEP assessments use complex sample designs to create student samples that generate population and subpopulation estimates with reasonably high precision. Student sampling weights ensure valid inferences from the student samples to their respective populations. In 2013, weights were developed for students sampled at grades 4, 8, and 12 for assessments in mathematics and reading.

Computation of Full-Sample Weights

Computation of Replicate Weights for Variance Estimation

Quality Control on Weighting Procedures

Each student was assigned a weight to be used for making inferences about students in the target population. This weight is known as the final full-sample student weight and contains the following major components:

the student base weight;

school nonresponse adjustments; student nonresponse adjustments; school weight trimming adjustments;

student weight trimming adjustments; and student raking adjustment.

The student base weight is the inverse of the overall probability of selecting a student and assigning that student to a particular assessment. The sample design that determines the base weights is discussed in the NAEP 2013 sample design section.

The student base weight is adjusted for two sources of nonparticipation: school level and student level. These weighting adjustments seek to reduce the potential for bias from such nonparticipation by

increasing the weights of students from participating schools similar to those schools not participating; and

increasing the weights of participating students similar to those students from within participating schools who did not attend the assessment session (or makeup session) as scheduled.

Furthermore, the final weights reflect the trimming of extremely large weights at both the school and student level. These weighting adjustments seek to reduce variances of survey estimates.

An additional weighting adjustment was implemented in the state and Trial Urban District Assessment (TUDA) samples so that estimates for key student-level characteristics were in agreement across assessments in reading and mathematics. This adjustment was implemented using a raking procedure.

In addition to the final full-sample weight, a set of replicate weights was provided for each student. These replicate weights are used to calculate the variances of survey estimates using the jackknife repeated replication method. The methods used to derive these weights were aimed at reflecting the features of the sample design, so that when the jackknife variance estimation procedure is implemented, approximately unbiased estimates of sampling variance are obtained. In addition, the various weighting procedures were repeated on each set of replicate weights to appropriately reflect the impact of the weighting adjustments on the sampling variance of a survey estimate. A finite population correction (fpc) factor was incorporated into the replication scheme so that it could be reflected in the variance estimates for the reading and mathematics assessments. See Computation of Replicate Weights for Variance Estimation for details.

Quality control checks were carried out throughout the weighting process to ensure the accuracy of the full-sample and replicate weights. See Quality Control for Weighting Procedures for the various checks implemented and main findings of interest.

In the linked pages that follow, please note that Vocabulary, Reading Vocabulary, and Meaning Vocabulary refer to the same reporting scale and are interchangeable.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/naep_assessment_weighting_procedures.aspx

NAEP Technical Documentation Computation of Full- Sample W eights for the 2013 Assessment

The full-sample or final student weight is the sampling weight used to derive NAEP student estimates of population and subpopulation characteristics for a specified grade (4, 8, or 12) and assessment subject (reading or mathematics). The full-sample student weight reflects the number of students that the sampled student represents in the population for purposes of estimation. The summation of the final student weights over a particular student group provides an estimate of the total number of students in that group within the population.

The full-sample weight, which is used to produce survey estimates, is

Computation of Base Weights

School and Student Nonresponse Weight Adjustments

School and Student Weight Trimming Adjustments

Student Weight Raking Adjustment

distinct from a replicate weight that is used to estimate variances of survey estimates. The full-sample weight is assigned to participating students and reflects the student base weight after the application of the various weighting adjustments. The full-sample weight for student k from school s in stratum j (FSTUWGT_jsk) can be expressed as follows:

where

STU_BWT_jsk is the student base weight;

SCH_NRAF_js is the school-level nonresponse adjustment factor; STU_NRAF_jsk is the student-level nonresponse adjustment factor; SCH_TRIM_js is the school-level weight trimming adjustment factor; STU_TRIM_jsk is the student-level weight trimming adjustment factor; and STU_RAKE_jsk is the student-level raking adjustment factor.

School sampling strata for a given assessment vary by school type and grade. See the links below for descriptions of the school strata for the various assessments.

Public schools at grades 4 and 8 Public schools at grade 12

Private schools at grades 4, 8 and 12

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/computation_of_full_sample_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation Computation of Base Weights for the 2013 Assessment

Every sampled school and student received a base weight equal to the reciprocal of its probability of selection. Computation of a school base weight varies by

type of sampled school (original or substitute); and sampling frame (new school frame or not).

Computation of a student base weight reflects

School Base Weights Student Base Weights

the student's overall probability of selection accounting for school and student sampling; assignment to session type at the school- and student-level; and

the student's assignment to the reading or mathematics assessment.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/computation_of_base_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation School Base Weights for the 2013 Assessment

The school base weight for a sampled school is equal to the inverse of its overall probability of selection. The overall selection probability of a sampled school differs by

type of sampled school (original or substitute); sampling frame (new school frame or not).

The overall selection probability of an originally selected school in a reading or mathematics sample is equal to its probability of selection from the NAEP public/private school frame.

The overall selection probability of a school from the new school frame in a reading or mathematics sample is the product of two quantities:

the probability of selection of the school's district into the new-school district sample, and the probability of selection of the school into the new school sample.

Substitute schools are preassigned to original schools and take the place of original schools if they refuse to participate. For weighting purposes, they are treated as if they were the original schools that they replaced; so substitute schools are assigned the school base weight of the original schools.

Learn more about substitute schools for the 2013 private school national assessment and substitute schools for the 2013 twelfth grade public school assessment.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/school_base_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation Student Base Weights for the 2013 Assessment

Every sampled student received a student base weight, whether or not the student participated in the assessment. The student base weight is the reciprocal of the probability that the student was sampled to participate in the assessment for a specified subject. The student base weight for student k from school s in stratum j (STU_BWT_jsk) is the product of seven weighting components and can be expressed as follows:

where

SCH_BWT_js is the school base weight;

SCHSsessionassignmentESWT_js is the school-level session assignment weight that reflects the conditional probability, given the school, that the particular session type was assigned to the school;

WINSCHWT_js is the within-school student weight that reflects the conditional probability, given the school, that the student was selected for the NAEP assessment;

STUSESWT_jsk is Stu_bookmarkthe student-level session assignment weight that reflects the conditional probability, given that the particular session type was assigned to the school, that the student was assigned to the session type;

SUBJFACsubjfac_jsk is the subject spiral adjustment factor that reflects the conditional probability, given that the student was assigned to a particular session type, that the student was assigned the specified subject;

SUBADJ_js is the substitution adjustment factor to account for the difference in enrollment size between the substitute and original school; and

YRRND_AF_js is the year-round adjustment factor to account for students in year- round schools on scheduled break at the time of the NAEP assessment and thus not available to be included in the sample.

The within-school student weight (WINSCHWT_js) is the inverse of the student sampling rate in the school.

The subject spiral adjustment factor (SUBJFAC_jsk) adjusts the student weight to account for the spiral pattern used in distributing reading or mathematics booklets to the students. The subject factor varies by grade, subject, and school type (public or private), and it is equal to the inverse of

the booklet proportions (reading or mathematics) in the overall spiral for a specific sample.

For cooperating substitutes of nonresponding original sampled schools, the substitution adjustment factor (SUBADJ_js) is equal to the ratio of the estimated grade enrollment for the original sampled school to the estimated grade enrollment for the substitute school. The student sample from the substitute school then "represents" the set of grade-eligible students from the original sampled school.

The year-round adjustment factor (YRRND_AF_js) adjusts the student weight for students in year- round schools who do not attend school during the time of the assessment. This situation typically arises in overcrowded schools. School administrators in year-round schools randomly assign students to portions of the year in which they attend school and portions of the year in which they do not attend. At the time of assessment, a certain percentage of students (designated as OFF _js) do not attend school and thus cannot be assessed. The YRRND_AF_js for a school is calculated as 1/(1- OFF _js/100).

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/student_base_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation School and Student

Nonresponse Weight Adjustments for the 2013 Assessment

Nonresponse is unavoidable in any voluntary survey of a human population. Nonresponse leads to the loss of sample data that must be compensated for in the weights of the responding sample members. This differs from ineligibility, for which no adjustments are necessary. The purpose of the nonresponse adjustments is to reduce the mean square error of survey estimates. While the nonresponse adjustment reduces the bias from the loss of sample, it also increases variability among the survey weights leading to increased variances of the sample estimates. However, it is presumed that the reduction in bias more than compensates for the increase in

School Nonresponse Weight Adjustment

Student Nonresponse Weight Adjustment

the variance, thereby reducing the mean square error and thus improving the accuracy of survey estimates. Nonresponse adjustments are made in the NAEP surveys at both the school and the student levels: the responding (original and substitute) schools receive a weighting adjustment to compensate for nonresponding schools, and responding students receive a weighting adjustment to compensate for nonresponding students.

The paradigm used for nonresponse adjustment in NAEP is the quasi-randomization approach (Oh and Scheuren 1983). In this approach, school response cells are based on characteristics of schools known to be related to both response propensity and achievement level, such as the locale type (e.g., large principal city of a metropolitan area) of the school. Likewise, student response cells are based on characteristics of the schools containing the students and student characteristics, which are known to be related to both response propensity and achievement level, such as student race/ethnicity, gender, and age.

Under this approach, sample members are assigned to mutually exclusive and exhaustive response cells based on predetermined characteristics. A nonresponse adjustment factor is calculated for each cell as the ratio of the sum of adjusted base weights for all eligible units to the sum of adjusted base weights for all responding units. The nonresponse adjustment factor is then applied to the base weight of each responding unit. In this way, the weights of responding units in the cell are "weighted up" to represent the full set of responding and nonresponding units in the response cell.

The quasi-randomization paradigm views nonresponse as another stage of sampling. Within each nonresponse cell, the paradigm assumes that the responding sample units are a simple random sample from the total set of all sample units. If this model is valid, then the use of the quasi-randomization weighting adjustment will eliminate any nonresponse bias. Even if this model is not valid, the weighting adjustments will eliminate bias if the achievement scores are homogeneous within the response cells (i.e., bias is eliminated if there is homogeneity either in response propensity or in achievement levels). See, for example, chapter 4 of Little and Rubin (1987).

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/school_and_student_nonresponse_weight_adjustments_for_the_2013_assessment.aspx

NAEP Technical Documentation School Nonresponse Weight Adjustment

The school nonresponse adjustment procedure inflates the weights of cooperating schools to account for eligible noncooperating schools for which no substitute schools participated. The adjustments are computed within nonresponse cells and are based on the assumption that the cooperating and noncooperating schools within the same cell are more similar to each other than to schools from different cells. School nonresponse adjustments were carried out separately by sample; that is, by

sample level (state, national), school type (public, private), and grade (4, 8, 12).

Development of Initial School Nonresponse Cells

Development of Final School Nonresponse Cells

School Nonresponse Adjustment Factor Calculation

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/school_nonresponse_weight_adjustment_for_the_2013_assessment.aspx

NAEP Technical Documentation Development of Initial School Nonresponse Cells

The cells for nonresponse adjustments are generally functions of the school sampling strata for the individual samples. School sampling strata usually differ by assessment subject, grade, and school type (public or private). Assessment subjects that are administered together by way of spiraling have the same school samples and stratification schemes. Subjects that are not spiraled with any other subjects have their own separate school sample. In NAEP 2015, all operational assessments were spiraled together.

The initial nonresponse cells for the various NAEP 2015 samples are described below.

Public School Samples for Reading and Mathematics at Grades 4 and 8

For these samples, initial weighting cells were formed within each jurisdiction using the following nesting cell structure:

Trial Urban District Assessment (TUDA) district vs. the balance of the state for states with TUDA districts, urbanicity (urban-centric locale) stratum; and

race/ethnicity classification stratum, or achievement level, or median income, or grade enrollment.

In general, the nonresponse cell structure used race/ethnicity classification stratum as the lowest level variable. However, where there was only one race/ethnicity classification stratum within a particular urbanicity stratum, categorized achievement, median income, or enrollment data were used instead.

Public School Sample at Grade 12

The initial weighting cells for this sample were formed using the following nesting cell structure: census division stratum,

urbanicity stratum (urban-centric locale), and race/ethnicity classification stratum.

Private School Samples at Grades 4, 8 and 12

The initial weighting cells for these samples were formed within each grade using the following nesting cell structure: affiliation,

census division stratum,

urbanicity stratum (urban-centric locale), and race/ethnicity classification stratum.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/development_of_initial_school_nonresponse_cells_for_the_2013_assessment.aspx

NAEP Technical Documentation Development of Final School Nonresponse Cells

Limits were placed on the magnitude of cell sizes and adjustment factors to prevent unstable nonresponse adjustments

and unacceptably large nonresponse factors. All initial weighting cells with fewer than six cooperating schools or adjustment factors greater than 3.0 for the full sample weight were collapsed with suitable adjacent cells. Simultaneously, all initial weighting cells for any replicate with fewer than four cooperating schools or adjustment factors greater than the maximum of

0. or two times the full sample nonresponse adjustment factor were collapsed with suitable adjacent cells. Initial weighting cells were generally collapsed in reverse order of the cell structure; that is, starting at the bottom of the nesting structure and working up toward the top level of the nesting structure.

Public School Samples at Grades 4 and 8

For the grade 4 and 8 public school samples, cells with the most similar race/ethnicity classification within a

given jurisdiction/Trial Urban District Assessment (TUDA) district and urbanicity (urban-centric locale) stratum were collapsed first. If further collapsing was required after all levels of race/ethnicity strata were collapsed, cells with the most similar urbanicity strata were combined next. Cells were never permitted to be collapsed across jurisdictions or TUDA districts.

Public School Sample at Grades 12

For the grade 12 public school sample, race/ethnicity classification cells within a given census division stratum and urbanicity stratum were collapsed first. If further collapsing was required after all levels of race/ethnicity classification were collapsed, cells with the most similar urbanicity strata were combined next. Any further collapsing occurred across census division strata but never across census regions.

Private School Samples at Grades 4, 8, and 12

For the private school samples, cells with the most similar race/ethnicity classification within a given affiliation, census division, and urbanicity stratum were collapsed first. If further collapsing was required after all levels of race/ethnicity strata were collapsed, cells with the most similar urbanicity classification were combined. Any further collapsing occurred across census division strata but never across affiliations.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/development_of_final_school_nonresponse_cells_for_the_2013_assessment.aspx

NAEP Technical Documentation School Nonresponse Adjustment Factor Calculation

In each final school nonresponse adjustment cell c, the school nonresponse adjustment factor SCH_NRAF_c was computed as follows:

where

S_c is the set of all eligible sampled schools (cooperating original and substitute schools and refusing original schools with noncooperating or no assigned substitute) in cell c,

R_c is the set of all cooperating schools within S_c, SCH_BWT_s is the school base weight,

SCH_TRIM_s is the school-level weight trimming factor, SCHSESWT_s is the school-level session assignment weight, and

X_s is the estimated grade enrollment corresponding to the original sampled school.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/school_nonresponse_adjustment_factor_calculation_for_the_2013_assessment.aspx

NAEP Technical Documentation Student Nonresponse Weight Adjustment

The student nonresponse adjustment procedure inflates the weights of assessed students to account for eligible sampled students who did not participate in the assessment. These inflation factors offset the loss of data associated with absent students. The adjustments are computed within nonresponse cells and are based on the assumption that the assessed and absent students within the same cell are more similar to one another than to students from different cells. Like its counterpart at the school level, the student nonresponse adjustment is

Development of Initial Student Nonresponse Cells

Development of Final Student Nonresponse Cells

Student Nonresponse Adjustment Factor Calculation

intended to reduce the mean square error and thus improve the accuracy of NAEP assessment estimates. Also, like its counterpart at the school level, student nonresponse adjustments were carried out separately by sample; that is, by

grade (4, 8, 12),

school type (public, private), and

assessment subject (mathematics, reading, science, meaning vocabulary).

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/student_nonresponse_weight_adjustment_for_the_2013_assessment.aspx

NAEP Technical Documentation Development of Initial Student Nonresponse Cells for the 2013 Assessment

Initial student nonresponse cells are generally created within each sample as defined by grade, school type (public, private), and assessment subject. However, when subjects are administered together by way of spiraling, the initial student nonresponse cells are created across the subjects in the same spiral. The rationale behind this decision is that spiraled subjects are in the same schools and the likelihood of whether an eligible student participates in an assessment is more related to its school than the subject of the assessment booklet. In NAEP 2013, there was only one spiral, with the reading and mathematics assessments spiraled together. The initial student nonresponse cells for the various NAEP 2013 samples are described below.

Nonresponse adjustment procedures are not applied to excluded students because they are not required to complete an assessment.

Public School Samples for Reading and Mathematics at Grades 4 and 8

The initial student nonresponse cells for these samples were defined within grade, jurisdiction, and Trial Urban District Assessment (TUDA) district using the following nesting cell structure:

students with disabilities (SD)/English language learners (ELL) by subject, school nonresponse cell,

age (classified into "older"¹ student and "modal age or younger" student), gender, and

race/ethnicity.

The highest level variable in the cell structure separates students who were classified either as having disabilities (SD) or as English language learners (ELL) from those who are neither, since SD or ELL students tend to score lower on assessment tests than non-SD/non-ELL students. In addition, the students in the SD or ELL groups are further broken down by subject, since rules for excluding students from the assessment differ by subject. Non-SD and non-ELL students are not broken down by subject, since the exclusion rules do not apply to them.

Public School Samples for Reading and Mathematics at Grade 12

The initial weighting cells for these samples were formed hierarchically within state for the state-reportable samples and the balance of the country for remaining states as follows:

SD/ELL,

school nonresponse cell,

age (classified into "older"¹ student and "modal age or younger" student), gender, and

race/ethnicity.

Private School Samples for Reading and Mathematics at Grades 4, 8, and 12

The initial weighting cells for these private school samples were formed hierarchically within grade as follows: SD/ELL,

school nonresponse cell,

age (classified into "older"¹ student and "modal age or younger" student), gender, and

race/ethnicity.

Although exclusion rules differ by subject, there were not enough SD or ELL private school students to break out by subject as was done for the public schools.

¹Older students are those born before October 1, 2002, for grade 4; October 1, 1998, for grade 8; and October 1, 1994, for

grade 12.

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NAEP Technical Documentation Development of Final Student Nonresponse Cells for the 2013 Assessment

Similar to the school nonresponse adjustment, cell and adjustment factor size constraints are in place to prevent unstable nonresponse adjustments or unacceptably large adjustment factors. All initial weighting cells with either fewer than 20 participating students or adjustment factors greater than 2.0 for the full sample weight were collapsed with suitable adjacent cells. Simultaneously, all initial weighting cells for any replicate with either fewer than 15 participating students or an adjustment factor greater than the maximum of 2.0 or 1.5 times the full sample nonresponse adjustment factor were collapsed with suitable adjacent cells.

Initial weighting cells were generally collapsed in reverse order of the cell structure; that is, starting at the bottom of the nesting structure and working up toward the top level of the nesting structure. Race/ethnicity cells within SD/ELL groups, school nonresponse cell, age, and gender classes were collapsed first. If further collapsing was required after collapsing all race/ethnicity classes, cells were next combined across gender, then age, and finally school nonresponse cells. Cells are never collapsed across SD and ELL groups for any sample.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/development_of_final_student_nonresponse_cells_for_the_2013_assessment.aspx

NAEP Technical Documentation Student Nonresponse Adjustment Factor Calculation

In each final student nonresponse adjustment cell c for a given sample, the student nonresponse adjustment factor STU_NRAF_c was computed as follows:

where

S_c is the set of all eligible sampled students in cell c for a given sample, R_c is the set of all assessed students within S_c,

STU_BWT_k is the student base weight for a given student k,

SCH_TRIM_k is the school-level weight trimming factor for the school associated with student k, SCH_NRAF_k is the school-level nonresponse adjustment factor for the school associated with student k, and SUBJFAC_k is the subject factor for a given student k.

The student weight used in the calculation above is the adjusted student base weight, without regard to subject, adjusted for school weight trimming and school nonresponse.

Nonresponse adjustment procedures are not applied to excluded students because they are not required to complete an assessment. In effect, excluded students were placed in a separate nonresponse cell by themselves and all received an adjustment factor of 1. While excluded students are not included in the analysis of the NAEP scores, weights are provided for excluded students in order to estimate the size of this group and its population characteristics.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/student_nonresponse_adjustment_factor_calculation_for_the_2013_assessment.aspx

NAEP Technical Documentation School and Student Weight Trimming Adjustments for the 2013 Assessment

Weight trimming is an adjustment procedure that involves detecting and reducing extremely large weights. "Extremely large weights" generally refer to large sampling weights that were not anticipated in the design of the sample. Unusually large weights are likely to produce large sampling variances for statistics of interest, especially when the large weights are associated with sample cases reflective of rare or atypical characteristics. To reduce the impact of these large weights on variances, weight reduction methods are typically employed. The goal of employing weight reduction methods is to reduce the mean square error of survey estimates. While the

Trimming of School Base Weights

Trimming of Student Weights

trimming of large weights reduces variances, it also introduces some bias. However, it is presumed that the reduction in the variances more than compensates for the increase in the bias, thereby reducing the mean square error and thus improving the accuracy of survey estimates (Potter 1988). NAEP employs weight trimming at both the school and student levels.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/school_and_student_weight_trimming_adjustments_for_the_2013_assessment.aspx

NAEP Technical Documentation Trimming of School Base Weights

Large school weights can occur for schools selected from the NAEP new-school sampling frame and for private schools. New schools that are eligible for weight trimming are schools with a disproportionately large student enrollment in a particular grade from a school district that was selected with a low probability of selection. The school base weights for such schools may be large relative to what they would have been if they had been selected as part of the original sample.

To detect extremely large weights among new schools, a comparison was made between a new school's school base weight and its ideal weight (i.e., the weight that would have resulted had the school been selected from the original school sampling frame). If the school base weight was more than three times the ideal weight, a trimming factor was calculated for that school that scaled the base weight back to three times the ideal weight. The calculation of the school-level trimming factor for a new school s is expressed in the following formula:

where

EXP_WT_s is the ideal base weight the school would have received if it had been on the NAEP public school sampling frame, and

SCH_BWT_s is the actual school base weight the school received as a sampled school from the new school frame.

Thirty-seven (37) schools out of 377 selected from the new-school sampling frame had their weights trimmed: eight at grade 4, 29 at grade 8, and zero at grade 12.

Private schools eligible for weight trimming were Private School Universe Survey (PSS) nonrespondents who were found subsequently to have either larger enrollments than assumed at the time of sampling, or an atypical probability of selection given their affiliation, the latter being unknown at the time of sampling. For private school s, the formula for computing the school-level weight trimming factor SCH_TRIM_s is identical to that used for new schools. For private schools,

EXP_WT_s is the ideal base weight the school would have received if it had been on the NAEP private school sampling frame with accurate enrollment and known affiliation, and

SCH_BWT_s is the actual school base weight the school received as a sampled private school.

No private schools had their weights trimmed.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/trimming_of_school_base_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation Trimming of Student Weights

Large student weights generally come from compounding nonresponse adjustments at the school and student levels with artificially low school selection probabilities, which can result from inaccurate enrollment data on the school frame used to define the school size measure. Even though measures are in place to limit the number and size of excessively large weights—such as the implementation of adjustment factor size constraints in both the school and student nonresponse procedures and the use of the school trimming procedure—large student weights can occur due to compounding effects of the various weighting components.

The student weight trimming procedure uses a multiple median rule to detect excessively large student weights. Any student weight within a given trimming group greater than a specified multiple of the median weight value of the given trimming group has its weight scaled back to that threshold. Student weight trimming was implemented separately by grade, school type (public or private), and subject. The multiples used were 3.5 for public school trimming groups and 4.5 for private school trimming groups. Trimming groups were defined by jurisdiction and Trial Urban District Assessment (TUDA) districts for the public school samples at grades 4 and 8; by dichotomy of low/high percentage of Black and Hispanic students (15 percent and below, above 15 percent) for the public school sample at grade 12; and by affiliation (Catholic, Non-Catholic) for private school samples at grades 4, 8 and 12.

The procedure computes the median of the nonresponse-adjusted student weights in the trimming group g for a given grade and subject sample. Any student k with a weight more than M times the median received a trimming factor calculated as follows:

where

M is the trimming multiple,

MEDIAN_g is the median of nonresponse-adjusted student weights in trimming group g, and STUWGT_gk is the weight after student nonresponse adjustment for student k in trimming group g.

In the 2013 assessment, relatively few students had weights considered excessively large. Out of the approximately 840,000 students included in the combined 2013 assessment samples, 226 students had their weights trimmed.

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NAEP Technical Documentation Student Weight Raking Adjustment for the 2013 Assessment

Weighted estimates of population totals for student-level subgroups for a given grade will vary across subjects even though the student samples for each subject generally come from the same schools. These differences are the result of sampling error associated with the random assignment of subjects to students through a process known as spiraling. For state assessments in particular, any

Development of Final Raking Dimensions Raking Adjustment Control Totals

Raking Adjustment Factor Calculation

difference in demographic estimates between subjects, no matter how small, may raise concerns about data quality. To remove these random differences and potential data quality concerns, a new step was added to the NAEP weighting procedure starting in 2009. This step adjusts the student weights in such a way that the weighted sums of population totals for specific subgroups are the same across all subjects. It was implemented using a raking procedure and applied only to state-level assessments.

Raking is a weighting procedure based on the iterative proportional fitting process developed by Deming and Stephan (1940) and involves simultaneous ratio adjustments to two or more marginal distributions of population totals. Each set of marginal population totals is known as a dimension, and each population total in a dimension is referred to as a control total. Raking is carried out in a sequence of adjustments. Sampling weights are adjusted to one marginal distribution and then to the second marginal distribution, and so on. One cycle of sequential adjustments to the marginal distributions is called an iteration. The procedure is repeated until convergence is achieved. The criterion for convergence can be specified either as the maximum number of iterations or an absolute difference (or relative absolute difference) from the marginal population totals. More discussion on raking can be found in Oh and Scheuren (1987).

For NAEP 2013, the student raking adjustment was carried out separately in each state for the reading

and mathematics public school samples at grades 4 and 8, and in the 13 states with state-reportable samples for the reading and mathematics public school samples at grade 12. The dimensions used in the raking process were National School Lunch Program (NSLP) eligibility, race/ethnicity, SD/ELL status, and gender. The control totals for these dimensions were obtained from the NAEP student sample weights of the reading

and mathematics samples combined.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/student_weight_raking_adjustment_for_the_2013_assessment.aspx

NAEP Technical Documentation Development of Final Raking Dimensions

The raking procedure involved four dimensions. The variables used to define the dimensions are listed below along with the categories making up the initial raking cells for each dimension.

National School Lunch Program (NSLP) eligibility

1. Eligible for free or reduced-price lunch

2. Otherwise Race/Ethnicity

1. White, not Hispanic

2. Black, not Hispanic

3. Hispanic

4. Asian

5. American Indian/Alaska Native

6. Native Hawaiian/Pacific Islander

7. Two or More Races SD/ELL status

1. SD, but not ELL

2. ELL, but not SD

3. SD and ELL

4. Neither SD nor ELL Gender

1. Male

2. Female

In states containing districts that participated in Trial Urban District Assessments (TUDA) districts at grades 4 and 8, the initial cells were created separately for each TUDA district and the balance of the state. Similar to the procedure used for school and student nonresponse adjustments, limits were placed on the magnitude of the cell sizes and adjustment factors to prevent unstable raking adjustments that could have resulted in unacceptably large or small adjustment factors. Levels of a dimension were combined whenever there were fewer than 30 assessed or excluded students (20 for any of the replicates) in a category, if the smallest adjustment was less than 0.5, or if the largest adjustment was greater than 2 for the full sample or for any replicate.

If collapsing was necessary for the race/ethnicity dimension, the following groups were combined first: American Indian/Alaska Native with Black, not Hispanic; Hawaiian/Pacific Islander with Black, not Hispanic; Two or More Races with White, not Hispanic; Asian with White, not Hispanic; and Black, not Hispanic with Hispanic. If further collapsing was necessary, the five categories American Indian/Alaska Native; Two or More Races; Asian; Native Hawaiian/Pacific Islander; and White, not Hispanic were combined. In some instances, all seven categories had to be collapsed.

If collapsing was necessary for the SD/ELL dimension, the SD/not ELL and SD/ELL categories were combined first, followed by ELL/not SD if further collapsing was necessary. In some instances, all four categories had to be collapsed.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/development_of_final_raking_dimensions_for_the_2013_assessment.aspx

NAEP Technical Documentation Raking Adjustment Control Totals for the 2013 Assessment

The control totals used in the raking procedure for NAEP 2013 grades 4, 8, and 12 were estimates of the student population derived from the set of assessed and excluded students pooled across subjects. The control totals for category c within dimension d were computed as follows:

where

R_c(d) is the set of all assessed students in category c of dimension d, E_c(d) is the set of all excluded students in category c of dimension d, STU_BWT_k is the student base weight for a given student k,

SCH_TRIM_k is the school-level weight trimming factor for the school associated with student k, SCH_NRAF_k is the school-level nonresponse adjustment factor for the school associated with student k, STU_NRAF_k is the student-level nonresponse adjustment factor for student k, and

SUBJFAC_k is the subject factor for student k.

The student weight used in the calculation of the control totals above is the adjusted student base weight, without regard to subject, adjusted for school weight trimming, school nonresponse, and student nonresponse. Control totals were computed for the full sample and for each replicate independently.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/raking_adjustment_control_totals_for_the_2013_assessment.aspx

NAEP Technical Documentation Raking Adjustment Factor Calculation for the 2013 Assessment

For assessed and excluded students in a given subject, the raking adjustment factor STU_RAKE_k was computed as follows:

First, the weight for student k was initialized as follows:

where

STU_BWT_k is the student base weight for a given student k,

SCH_TRIM_k is the school-level weight trimming factor for the school associated with student k, SCH_NRAF_k is the school-level nonresponse adjustment factor for the school associated with student k, STU_NRAF_k is the student-level nonresponse adjustment factor for student k, and

SUBJFAC_k is the subject factor for student k.

Then, the sequence of weights for the first iteration was calculated as follows for student k in category c of dimension d:

For dimension 1:

For dimension 2:

For dimension 3:

For dimension 4:

where

R_c(d) is the set of all assessed students in category c of dimension d, E_c(d) is the set of all excluded students in category c of dimension d, and Total_c(d) is the control total for category c of dimension d.

k

The process is said to converge if the maximum difference between the sum of adjusted weights and the control totals is 1.0 for each category in each dimension. If after the sequence of adjustments the maximum difference was greater than 1.0, the process continues to the next iteration, cycling back to the first dimension with the initial weight for student k equaling STUSAWT ^adj(4) from the previous iteration. The process continued until

convergence was reached.

Once the process converged, the adjustment factor was computed as follows:

where STUSAWT_k is the weight for student k after convergence.

The process was done independently for the full sample and for each replicate.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/raking_adjustment_factor_calculation_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Computation of Replicate Weights for the 2013 Assessment

In addition to the full-sample weight, a set of 62 replicate weights was provided for each student. These replicate weights are used in calculating the sampling variance of estimates obtained from the data, using the jackknife repeated replication method. The method of deriving these weights was aimed at reflecting the features of the sample design appropriately for each sample, so that when the jackknife variance estimation procedure is implemented, approximately unbiased estimates of sampling variance are

obtained. This section gives the specifics for generating the

Defining Variance Strata and Forming Replicates

Computing School-Level Replicate Factors

Computing Student-Level Replicate Factors

Replicate Variance Estimation

replicate weights for the 2013 assessment samples. The theory that underlies the jackknife variance estimators used in NAEP studies is discussed in the section Replicate Variance Estimation.

In general, the process of creating jackknife replicate weights takes place at both the school and student level. The precise implementation differs between those samples that involve the selection of Primary Sampling Units (PSUs) and those where the school is the first stage of sampling. The procedure for this second kind of sample also differed starting in 2011 from all previous NAEP assessments. The change that was implemented permitted the introduction of a finite population correction factor at the school sampling stage, developed by Rizzo and Rust (2011). In assessments prior to 2011, this adjustment factor has always been implicitly assumed equal to 1.0, resulting in some overestimation of the sampling variance.

For each sample, the calculation of replicate weighting factors at the school level was conducted in a series of steps. First, each school was assigned to one of 62 variance estimation strata. Then, a random subset of schools in each variance estimation stratum was assigned a replicate factor of between 0 and 1. Next, the remaining subset of schools in the same variance stratum was assigned a complementary replicate factor greater than 1. All schools in the other variance estimation strata were assigned a replicate factor of exactly 1. This process was repeated for each of the 62 variance estimation strata so that 62 distinct replicate factors were assigned to each school in the sample.

This process was then repeated at the student level. Here, each individual sampled student was assigned to one of 62 variance estimation strata, and 62 replicate factors with values either between 0 and 1, greater than 1, or exactly equal to 1 were assigned to each student.

For example, consider a single hypothetical student. For replicate 37, that student’s student replicate factor might be 0.8, while for the school to which the student belongs, for replicate 37, the school replicate factor might be 1.6. Of course, for a given student, for most replicates, either the student replicate factor, the school replicate factor, or (usually) both, is equal to 1.0.

A replicate weight was calculated for each student, for each of the 62 replicates, using weighting procedures similar to those used for the full-sample weight. Each replicate weight contains the school and student replicate factors described above. By repeating the various weighting procedures on each set of replicates, the impact of these procedures on the sampling variance of an estimate is appropriately reflected in the variance estimate.

Each of the 62 replicate weights for student k in school s in stratum j can be expressed as follows:

where

STU_BWT_jsk is the student base weight;

SCH_REPFAC_js(r) is the school-level replicate factor for replicate r; SCH_NRAF_js(r) is the school-level nonresponse adjustment factor for replicate r; STU_REPFAC_jsk(r) is the student-level replicate factor for replicate r; STU_NRAF_jsk(r) is the student-level nonresponse adjustment factor for replicate r; SCH_TRIM_js is the school-level weight trimming adjustment factor; STU_TRIM_jsk is the student-level weight trimming adjustment factor; and STU_RAKE_jsk(r) is the student-level raking adjustment factor for replicate r.

Specific school and student nonresponse and student-level raking adjustment factors were calculated separately for each replicate, thus the use of the index (r), and applied to the replicate student base weights. Computing separate nonresponse and raking adjustment factors for each replicate allows resulting variances from the use of the final student replicate weights to reflect components of variance due to these various weight adjustments.

School and student weight trimming adjustments were not replicated, that is, not calculated separately for each replicate. Instead, each replicate used the school and student trimming adjustment factors derived for the full sample. Statistical theory for replicating trimming adjustments under the jackknife approach has not been developed in the literature. Due to the absence of a statistical framework, and since relatively few school and student weights in NAEP require trimming, the weight trimming adjustments were not replicated.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/computation_of_replicate_weights_for_the_2013_assessment.aspx

NAEP Technical Documentation Defining Variance Strata and Forming Replicates for the 2013 Assessment

In the NAEP 2013 assessment, replicates were formed separately for each sample indicated by grade (4, 8, 12), school type (public, private), and assessment subject (mathematics, reading). To reflect the school-level finite population corrections in the variance estimators for the two-stage samples used for the mathematics and reading assessments, replication was carried out at both the school and student levels.

The first step in forming replicates was to create preliminary variance strata in each primary stratum. This was done by sorting the appropriate sampling unit (school or student) in the order of its selection within the primary stratum and then pair off adjacent sampling units into preliminary variance strata. Sorting sample units by their order of sample selection reflects the implicit stratification and systematic sampling features of the sample design. Within each primary stratum with an even number of sampling units, all of the preliminary variance strata consisted of pairs of sampling units. However, within primary strata with an odd number of sampling units, all but one variance strata consisted of pairs of sampling units, while the last one consisted of three sampling units.

The next step is to form the final variance strata by combining preliminary strata if appropriate. If there were more than 62 preliminary variance strata within a primary stratum, the preliminary variance strata were grouped to form 62 final variance strata. This grouping effectively maximized the distance in the sort order between grouped preliminary variance strata. The first 62 preliminary variance strata, for example, were assigned to 62 different final variance strata in order (1 through 62), with the next 62 preliminary variance strata assigned to final variance strata 1 through 62, so that, for example, preliminary variance stratum 1, preliminary variance stratum 63, preliminary variance stratum 125 (if in fact there were that many), etc., were all assigned to the first final variance stratum.

If, on the other hand, there were fewer than 62 preliminary variance strata within a primary stratum, then the number of final variance strata was set equal to the number of preliminary variance strata. For example, consider a primary stratum with 111 sampled units sorted in their order of selection. The first two units were in the first preliminary variance stratum; the next two units were in the second preliminary variance stratum, and so on, resulting in 54 preliminary variance strata with two sample units each (doublets). The last three sample units were in the 55th preliminary variance stratum (triplet). Since there are no more than 62 preliminary variance strata, these were also the final variance strata.

Within each preliminary variance stratum containing a pair of sampling units, one sampling unit was randomly assigned as the first variance unit and the other as the second variance unit. Within each preliminary variance stratum containing three sampling units, the three first-stage units were randomly assigned variance units 1 through 3.

Reading and Mathematics Assessments

At the school-level for these samples, formation of preliminary variance strata did not pertain to certainty schools, since they are not subject to sampling variability, but only to noncertainty schools. The primary stratum for noncertainty schools was the highest school-level sampling stratum variable listed below, and the order of selection was defined by sort order on the school sampling frame.

Trial Urban District Assessment (TUDA) districts, remainder of states (for states with TUDAs), or entire states for the public school samples at grades 4, 8, and 12; and

Private school affiliation (Catholic, non-Catholic) for the private school samples at grades 4, 8, and 12.

At the student-level, all students were assigned to variance strata. The primary stratum was school, and the order of selection was defined by session number and position on the administration schedule.

Within each pair of preliminary variance strata, one first-stage unit, designated at random, was assigned as the first variance unit and the other first-stage unit as the second variance unit. Within each triplet preliminary variance stratum, the three schools were randomly assigned variance units 1 through 3.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/defining_variance_strata_and_forming_replicates_for_the_2013_assessment.aspx

NAEP Technical Documentation Computing School- Level Replicate Factors for the 2013 Assessment

The replicate variance estimation approach for the mathematics and reading assessments involved finite population corrections at the school level. The calculation of school-level replicate factors for these assessments depended upon whether or not a school was selected with certainty. For certainty schools, the school-level replicate factors for all replicates are set to unity – this is true regardless of whether or not the variance replication method uses finite population corrections – since certainty schools are not subject to sampling variability. Alternatively, one can view the finite population correction factor for such schools as being equal to zero. Thus, for each certainty school in a given assessment, the school-level replicate factor for each of the 62 replicates (r = 1, ..., 62) was assigned as follows:

where SCH_REPFAC_js(r) is the school-level replicate factor for school s in primary stratum j for the r-th replicate.

For noncertainty schools, where preliminary variance strata were formed by grouping schools into pairs or triplets, school-level replicate factors were calculated for each of the 62 replicates based on this grouping. For schools in variance strata comprising pairs of schools, the school-level replicate factors,SCH_REPFAC_js(r),r = 1,..., 62, were calculated as follows:

where

min(π_j1, π_j2) is the smallest school probability between the two schools comprising R_jr,

R_jr is the set of schools within the r-th variance stratum for primary stratum j, and U_js is the variance unit (1 or 2) for school s in primary stratum j.

For noncertainty schools in preliminary variance strata comprising three schools, the school-level replicate factors SCH_REPFAC_js(r), r = 1,..., 62 were calculated as follows:

For school s from primary stratum j, variance stratum r,

while for r' = r + 31 (mod 62):

and for all other r* other than r and r' :

where

min(π_j1, π_j2,π_j3) is the smallest school probability among the three schools comprising R_jr,

R_jr is the set of schools within the r-th variance stratum for primary stratum j, and U_js is the variance unit (1, 2, or 3) for school s in primary stratum j.

In primary strata with fewer than 62 variance strata, the replicate weights for the “unused” variance strata (the remaining ones up to 62) for these schools were set equal to the school base weight (so that those replicates contribute nothing to the variance estimate).

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/computing_school_level_replicate_factors_for_the_2013_assessment_.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Computing Student- Level Replicate Factors for the 2013 Assessment

For the mathematics and reading assessments, which involved school-level finite population corrections, the student- level replication factors were calculated the same way regardless of whether or not the student was in

a certainty school.

For students in student-level variance strata comprising pairs of students, the student-level replicate factors, STU_REPFAC_jsk(r), r = 1,..., 62, were calculated as follows:

where

π_s is the probability of selection for school s,

R_jsr is the set of students within the r-th variance stratum for school s in primary stratum j, and U_jsk is the variance unit (1 or 2) for student k in school s in stratum j.

For students in variance strata comprising three students, the student-level replicate factors STU_REPFAC_jsk(r), r = 1,..., 62, were calculated as follows:

while for r' = r + 31 (mod 62):

and for all other r* other than r and r' :

where

π_s is the probability of selection for school s,

R_jsr is the set of students within the r-th replicate stratum for school s in stratum j, and U_jsk is the variance unit (1, 2, or 3) for student k in school s in stratum j.

Note, for students in certainty schools, where π_s = 1, the student replicate factors are 2 and 0 in the case of pairs, and 1.5, 1.5, and 0 in the case of triples.

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NAEP Technical Documentation Website

NAEP Technical Documentation Replicate Variance Estimation for the 2013 Assessment

Variances for NAEP assessment estimates are computed using the paired jackknife replicate variance procedure. This technique is applicable for common statistics, such as means and ratios, and differences between these for different subgroups, as well as for more complex statistics such as linear or logistic regression coefficients.

In general, the paired jackknife replicate variance procedure involves initially pairing clusters of first-stage sampling units to form H variance strata (h = 1, 2, 3, ...,H) with two units per stratum. The first replicate is formed by assigning, to one unit at random from the first variance stratum, a replicate weighting factor of less than 1.0, while assigning the remaining unit a complementary replicate factor greater than 1.0, and assigning all other units from the other (H - 1) strata a replicate factor of 1.0. This procedure is carried out for each variance stratum resulting in H replicates, each of which provides an estimate of the population total.

In general, this process is repeated for subsequent levels of sampling. In practice, this is not practicable for a design with three or more stages of sampling, and the marginal improvement in precision of the variance estimates would be negligible in all such cases in the NAEP setting. Thus in NAEP, when a two-stage design is used – sampling schools and then students – beginning in 2011 replication is carried out at both stages. (See Rizzo and Rust (2011) for a description of the methodology.) When a three-stage design is used, involving the selection of geographic Primary Sampling Units (PSUs), then schools, and then students, the replication procedure is only carried out at the first stage of sampling (the PSU stage for noncertainty PSUs, and the school stage within certainty PSUs). In this situation, the school and student variance components are correctly estimated, and the overstatement of the between-PSU variance component is relatively very small.

The jackknife estimate of the variance for any given statistic is given by the following formula:

where

represents the full sample estimate of the given statistic, and represents the corresponding estimate for replicate h.

Each replicate undergoes the same weighting procedure as the full sample so that the jackknife variance estimator reflects the contributions to or reductions in variance resulting from the various weighting adjustments.

The NAEP jackknife variance estimator is based on 62 variance strata resulting in a set of 62 replicate weights assigned to each school and student.

The basic idea of the paired jackknife variance estimator is to create the replicate weights so that use of the jackknife procedure results in an unbiased variance estimator for simple totals and means, which is also reasonably efficient (i.e., has a low variance as a variance estimator). The jackknife variance estimator will then produce a consistent (but not fully unbiased) estimate of variance for (sufficiently smooth) nonlinear functions of total and mean estimates such as ratios, regression coefficients, and so forth (Shao and Tu, 1995).

The development below shows why the NAEP jackknife variance estimator returns an unbiased variance estimator for totals and means, which is the cornerstone to the asymptotic results for nonlinear estimators. See for example Rust (1985). This paper also discusses why this variance estimator is generally efficient (i.e., more reliable than alternative approaches requiring similar computational resources).

The development is done for an estimate of a mean based on a simplified sample design that closely approximates the sample design for first-stage units used in the NAEP studies. The sample design is a stratified random sample with H strata with population weights W_h, stratum sample sizes n_h, and stratum

sample means . The population estimator and standard unbiased variance estimator are:

with

The paired jackknife replicate variance estimator assigns one replicate h=1,…, H to each stratum, so that the number of replicates equals H. In NAEP, the replicates correspond generally to pairs and triplets (with the latter only being used if there are an odd number of sample units within a particular primary stratum generating replicate strata). For pairs, the process of generating replicates can be viewed as taking a simple random sample (J) of size n_h/2 within the replicate stratum, and assigning an increased weight to the sampled elements, and a decreased weight to the unsampled elements. In certain applications, the increased weight is double the full sample weight, while the decreased weight is in fact equal to zero. In this

simplified case, this assignment reduces to replacing with , the latter being the sample mean of the sampled n_h/2 units. Then the replicate estimator corresponding to stratum r is

The r-th term in the sum of squares for is thus:

In stratified random sampling, when a sample of size n_r/2 is drawn without replacement from a population of size n_r,, the sampling variance is

See for example Cochran (1977), Theorem 5.3, using n_r, as the “population size,” n_r/2 as the “sample size,” and s_r² as the “population variance” in the given formula. Thus,

Taking the expectation over all of these stratified samples of size n_r/2, it is found that

In this sense, the jackknife variance estimator “gives back” the sample variance estimator for means and totals as desired under the theory.

In cases where, rather than doubling the weight of one half of one variance stratum and assigning a zero weight to the other, the weight of one unit is multiplied by a replicate factor of (1+δ), while the other is multiplied by (1- δ), the result is that

In this way, by setting δ equal to the square root of the finite population correction factor, the jackknife variance estimator is able to incorporate a finite population correction factor into the variance estimator.

In practice, variance strata are also grouped to make sure that the number of replicates is not too large (the total number of variance strata is usually 62 for NAEP). The randomization from the original sample distribution guarantees that the sum of squares contributed by each replicate will be close to the target expected value.

For triples, the replicate factors are perturbed to something other than 1.0 for two different replicate factors, rather than just one as in the case of pairs. Again in the simple case where replicate factors that are less than 1 are all set to 0, with the replicate weight factors calculated as follows.

For unit i in variance stratum r

where weight w_i is the full sample base weight.

Furthermore, for r' = r + 31 (mod 62):

And for all other values r*, other than r and r´,w_i(r*) = 1.

In the case of stratified random sampling, this formula reduces to replacing with for replicate r, where is the sample mean from a “2/3” sample of 2n_r/3 units from the n_r sample units

in the replicate stratum, and replacing with for replicate r', where is the sample mean from another overlapping “2/3” sample of 2n_r/3 units from the n_r sample units in the replicate stratum.

The r-th and r´-th replicates can be written as:

From these formulas, expressions for the r-th and r´-th components of the jackknife variance estimator are obtained (ignoring other sums of squares from other grouped components attached to those replicates):

These sums of squares have expectations as follows, using the general formula for sampling variances:

Thus,

as desired again.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/replicate_variance_estimation_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Quality Control on Weighting Procedures for the 2013 Assessment

Given the complexity of the weighting procedures utilized in NAEP, a range of quality control (QC) checks was conducted throughout the weighting process to identify potential problems with collected student-level demographic data or with specific weighting procedures. The QC processes included

checks performed within each step of the weighting process; checks performed across adjacent steps of the weighting process; review of participation, exclusion, and accommodation rates; checking demographic data of individual schools;

comparisons with 2011 demographic data; and nonresponse bias analyses.

Final Participation, Exclusion, and Accommodation Rates

Nonresponse Bias Analyses

To validate the weighting process, extensive tabulations of various school and student characteristics at different stages of the process were conducted. The school-level characteristics included in the tabulations were minority

enrollment, median income (based on the school ZIP code area), and urban-centric locale. At the student level, the tabulations included race/ethnicity, gender, relative age, students with disability (SD) status, English language learners (ELL) status, and participation status in National School Lunch Program (NSLP).

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/quality_control_on_weighting_procedures_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Final Participation, Exclusion, and Accommodation Rates for the 2013 Assessment

Final participation, exclusion, and accommodation rates are presented in quality control tables for each grade and subject by geographic domain and school type. School-level

participation rates have been calculated according to National Center for Education Statistics (NCES) standards as they have been for previous assessments.

School-level participation rates were below 85 percent for private schools at all three grades (4, 8, and 12). Student-level participation rates were also below 85 percent for grade 12 public school student sample overall and in specific states: Connecticut, Florida, Illinois, Iowa, Massachusetts, New Hampshire, New Jersey, and West Virginia. As required by NCES standards, nonresponse bias analyses were conducted on each reporting group falling below the 85 percent participation threshold.

Grade 4 Mathematics

Grade 4 Reading

Grade 8 Mathematics

Grade 8 Reading

Grade 12 Mathematics

Grade 12 Reading

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/final_participation_exclusion_and_accommodation_rates_for_the_2013_assessment.aspx

National all1

8,590

97.27

90.32

209,800

1.41

94.57

13.44

National public

8,060

99.69

99.54

202,700

1.52

94.49

14.22

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 4 Mathematics for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 4 mathematics assessment by school type and jurisdiction. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

Participation, exclusion, and accommodation rates, grade 4 mathematics assessment, by school type and jurisdiction: 2013

		School	School
		participation	participation
		rates	rates
	Number	(percent)	(percent)			Weighted
	of	before	before			student
	schools	substitution	substitution	Number	Weighted	participation
	in	(weighted	(weighted	of	percent	rates	Weighted
School type	original	by base	by base	students	of	(percent)	percent of
and	sample,	weight and	weight	sampled,	students	after	students
jurisdiction	rounded	enrollment)	only)	rounded	excluded	makeups	accommodated
All	8,760	97.30	90.45	214,900	1.40	94.57	13.55
Northeast all	1,480	95.63	85.22	34,500	1.29	93.85	15.68
Midwest all	2,190	97.27	88.80	47,300	1.32	94.84	12.87
South all	2,740	98.20	93.44	73,600	1.37	94.71	14.38
West all	2,120	96.86	91.04	51,800	1.62	94.57	10.98
Alabama	120	100.00	100.00	3,200	1.10	94.82	5.15
Alaska	200	99.48	96.56	3,100	1.14	93.18	21.85
Arizona	120	100.00	100.00	3,400	1.20	95.07	12.97
Arkansas	120	100.00	100.00	3,400	1.24	94.66	15.16
California	300	99.17	98.75	9,000	1.93	94.79	8.78
Colorado	120	100.00	100.00	3,400	1.15	92.34	12.11
Connecticut	120	97.22	97.25	3,200	1.36	93.85	15.52
Delaware	100	100.00	100.00	3,400	2.10	94.36	13.58
District of Columbia	140	100.00	100.00	2,300	1.37	95.09	17.59
Florida	240	100.00	100.00	6,900	1.84	94.11	20.24
Georgia	170	100.00	100.00	5,300	1.43	94.18	11.22
Hawaii	120	100.00	100.00	3,500	1.25	94.70	10.64
Idaho	130	100.00	100.00	3,500	1.29	95.24	9.58
Illinois	200	97.98	98.40	5,100	1.00	94.40	15.44
Indiana	120	100.00	100.00	3,300	1.52	95.18	17.03
Iowa	140	100.00	100.00	3,100	0.70	95.16	14.50
Kansas	150	100.00	100.00	3,400	1.62	94.79	15.16
Kentucky	160	100.00	100.00	4,700	1.45	94.67	11.30
Louisiana	130	100.00	100.00	3,300	1.08	94.49	18.38
Maine	160	100.00	100.00	3,400	2.11	93.95	17.44

Maryland	170	100.00	100.00	4,700	0.99	94.22	17.30
Massachusetts	190	100.00	100.00	5,200	2.03	93.74	17.18
Michigan	190	100.00	100.00	4,600	1.96	94.14	11.02
Minnesota	130	100.00	100.00	3,500	1.37	94.85	10.62
Mississippi	120	100.00	100.00	3,300	0.76	95.44	6.73
Missouri	130	100.00	100.00	3,600	1.41	95.42	11.20
Montana	200	99.85	98.28	3,400	1.68	93.92	8.56
Nebraska	170	100.00	100.00	3,500	1.72	95.37	14.37
Nevada	120	100.00	100.00	3,500	1.41	95.75	22.90
New Hampshire	130	100.00	100.00	3,400	1.22	93.74	14.78
New Jersey	120	100.00	100.00	3,300	1.17	94.85	16.62
New Mexico	150	99.69	99.48	4,200	1.22	95.06	16.90
New York	160	98.84	96.79	4,500	1.23	92.27	20.02
North Carolina	160	100.00	100.00	4,800	1.24	94.19	14.17
North Dakota	270	99.86	99.19	3,700	2.56	95.57	9.78
Ohio	210	100.00	100.00	4,700	1.33	94.29	13.52
Oklahoma	140	100.00	100.00	3,600	1.85	94.35	13.95
Oregon	130	100.00	100.00	3,500	2.12	94.18	15.23
Pennsylvania	170	100.00	100.00	4,500	1.64	94.30	12.95
Rhode Island	120	100.00	100.00	3,400	1.12	94.98	15.17
South Carolina	120	100.00	100.00	3,200	1.08	96.08	11.87
South Dakota	190	100.00	100.00	3,400	1.42	95.36	10.56
Tennessee	120	100.00	100.00	3,400	1.34	94.21	13.54
Texas	310	100.00	100.00	9,200	1.65	95.36	17.92
Utah	120	99.08	99.32	3,600	1.25	94.79	12.66
Vermont	220	100.00	100.00	3,000	1.37	95.04	15.72
Virginia	110	100.00	100.00	3,300	1.51	94.35	13.07
Washington	120	99.09	99.35	3,600	2.17	93.50	14.12
West Virginia	150	100.00	100.00	3,200	1.71	94.77	10.03
Wisconsin	190	100.00	100.00	4,400	1.79	95.42	16.21
Wyoming	200	100.00	100.00	3,500	1.01	94.65	12.76
DoDEA2	120	99.23	98.08	3,700	1.66	95.05	12.20
Trial Urban (TUDA) Districts and Other Jurisdictions
Albuquerque	50	100.00	100.00	1,700	1.15	94.71	20.47
Atlanta	60	100.00	100.00	2,000	0.98	95.42	9.76
Austin	60	100.00	100.00	1,700	2.04	93.69	30.80
Baltimore City	70	100.00	100.00	1,600	1.59	94.32	19.27
Boston	80	100.00	100.00	2,000	3.69	93.72	19.59
Charlotte	50	100.00	100.00	1,700	1.19	94.18	12.81
Chicago	100	100.00	100.00	2,500	1.07	94.85	19.30
Cleveland	90	100.00	100.00	1,500	4.26	93.62	22.29
Dallas	50	100.00	100.00	1,700	2.33	95.79	35.42
Detroit	70	100.00	100.00	1,300	4.88	90.92	14.80
Fresno	50	100.00	100.00	1,800	0.90	93.58	7.51
Hillsborough	60	100.00	100.00	1,700	1.17	95.74	23.30
Houston	80	100.00	100.00	2,600	1.88	96.62	27.25

Jefferson County, KY

50 100.00 100.00 1,700 1.74 94.66 11.61

Los Angeles 80 100.00 100.00 2,500 1.96 95.80 9.83

Miami 90 100.00 100.00 2,300 2.35 95.07 28.05

Milwaukee 70 100.00 100.00 1,500 3.40 94.68 26.55

New York City

80 100.00 100.00 2,500 1.33 91.74 27.56

Philadelphia 60 100.00 100.00 1,600 3.45 94.71 15.82

San Diego 50 100.00 100.00 1,500 1.48 95.18 11.80

National private

410

71.19

64.52

3,300

0.08

95.61

4.38

District of Columbia (TUDA)

90 100.00 100.00 1,500 1.97 95.52 18.06

Catholic 130 88.65 89.70 1,700 0.06 95.60 4.95

Non-Catholic private

280 56.94 52.97 1,600 0.11 95.62 3.92

Puerto Rico	170	100.00	100.00	5,100	0.24	94.47	27.19
Includes national public, national private, and Bureau of Indian Education schools located in the United States and all Department of Defense Education Activity schools, but not schools in Puerto Rico. Department of Defense Education Activity schools. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Mathematics Assessment.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_4_mathematics_for_the_2013_assessment.aspx

National all1

8,590

97.27

90.32

216,400

2.52

94.78

12.17

National public

8,060

99.69

99.54

209,100

2.69

94.70

12.87

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 4 Reading for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 4 reading assessment by school type and jurisdiction. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

Participation, exclusion, and accommodation rates, grade 4 r eading assessment, by school type and jurisdiction: 2013

		School	School
		participation	participation
		rates	rates
	Number	(percent)	(percent)			Weighted
	of	before	before			student
	schools	substitution	substitution	Number	Weighted	participation
	in	(weighted	(weighted	of	percent	rates	Weighted
School type	original	by base	by base	students	of	(percent)	percent of
and	sample,	weight and	weight	sampled,	students	after	students
jurisdiction	rounded	enrollment)	only)	rounded	excluded	makeups	accommodated
All	8,590	97.27	90.32	216,400	2.52	94.78	12.17
Northeast all	1,480	95.63	85.22	35,600	1.72	93.97	15.30
Midwest all	2,190	97.27	88.80	48,700	2.01	95.04	12.22
South all	2,740	98.20	93.44	76,000	3.39	95.00	12.25
West all	2,120	96.86	91.04	53,500	2.13	94.71	9.92
Alabama	120	100.00	100.00	3,400	1.14	95.49	5.39
Alaska	200	99.48	96.56	3,300	1.45	93.65	20.65
Arizona	120	100.00	100.00	3,500	1.08	95.46	13.24
Arkansas	120	100.00	100.00	3,600	1.11	95.16	15.34
California	300	99.17	98.75	9,300	2.50	94.88	7.73
Colorado	120	100.00	100.00	3,500	1.52	93.66	12.61
Connecticut	120	97.22	97.25	3,400	1.58	94.29	15.33
Delaware	100	100.00	100.00	3,500	4.70	94.34	10.38
District of Columbia	140	100.00	100.00	2,400	1.65	94.46	17.41
Florida	240	100.00	100.00	7,100	2.96	93.98	19.02
Georgia	170	100.00	100.00	5,400	4.90	95.34	8.13
Hawaii	120	100.00	100.00	3,600	1.80	93.97	10.48
Idaho	130	100.00	100.00	3,600	1.49	94.99	9.32
Illinois	200	97.98	98.40	5,200	1.24	95.13	14.76
Indiana	120	100.00	100.00	3,500	2.43	94.40	16.31
Iowa	140	100.00	100.00	3,200	1.08	95.11	14.42
Kansas	150	100.00	100.00	3,500	1.82	95.07	13.41
Kentucky	160	100.00	100.00	4,800	2.99	94.97	9.74
Louisiana	130	100.00	100.00	3,400	1.16	94.73	18.61
Maine	160	100.00	100.00	3,500	1.69	93.65	17.87
Maryland	170	100.00	100.00	4,900	12.86	94.40	5.70
Massachusetts	190	100.00	100.00	5,300	2.66	93.77	15.53
Michigan	190	100.00	100.00	4,800	3.81	94.64	9.66
Minnesota	130	100.00	100.00	3,600	2.71	94.93	9.61

Mississippi	120	100.00	100.00	3,400	0.53	94.99	6.85
Missouri	130	100.00	100.00	3,700	1.23	95.26	11.16
Montana	200	99.85	98.28	3,500	2.86	94.40	7.33
Nebraska	170	100.00	100.00	3,600	3.57	95.83	14.26
Nevada	120	100.00	100.00	3,700	1.50	95.10	22.73
New Hampshire	130	100.00	100.00	3,500	2.56	93.45	13.48
New Jersey	120	100.00	100.00	3,400	1.72	94.87	15.27
New Mexico	150	99.69	99.48	4,300	1.02	94.55	15.04
New York	160	98.84	96.79	4,600	1.35	93.06	20.15
North Carolina	160	100.00	100.00	5,000	1.80	94.88	13.06
North Dakota	270	99.86	99.19	3,800	4.06	96.28	8.73
Ohio	210	100.00	100.00	4,800	2.61	94.58	12.80
Oklahoma	140	100.00	100.00	3,700	1.72	94.58	14.35
Oregon	130	100.00	100.00	3,700	2.49	93.98	12.20
Pennsylvania	170	100.00	100.00	4,600	2.29	94.42	12.53
Rhode Island	120	100.00	100.00	3,500	1.34	94.78	14.43
South Carolina	120	100.00	100.00	3,300	1.73	94.64	9.74
South Dakota	190	100.00	100.00	3,500	2.22	95.69	9.26
Tennessee	120	100.00	100.00	3,500	3.10	95.34	12.29
Texas	310	100.00	100.00	9,500	4.90	95.50	14.40
Utah	120	99.08	99.32	3,700	3.05	93.71	10.29
Vermont	220	100.00	100.00	3,100	1.17	95.05	15.65
Virginia	110	100.00	100.00	3,400	1.54	94.93	12.21
Washington	120	99.09	99.35	3,700	2.81	93.71	12.45
West Virginia	150	100.00	100.00	3,300	1.78	93.62	8.89
Wisconsin	190	100.00	100.00	4,500	1.61	94.97	16.63
Wyoming	200	100.00	100.00	3,600	1.25	94.38	13.00
DoDEA2	120	99.23	98.08	3,800	5.95	95.48	7.39
Trial Urban (TUDA) Districts and Other Jurisdictions
Albuquerque	50	100.00	100.00	1,800	0.74	93.43	17.51
Atlanta	60	100.00	100.00	2,000	1.12	95.96	9.39
Austin	60	100.00	100.00	1,700	3.90	94.12	27.06
Baltimore City	70	100.00	100.00	1,700	15.85	93.62	4.33
Boston	80	100.00	100.00	2,000	4.33	94.03	17.64
Charlotte	50	100.00	100.00	1,700	0.90	94.49	11.72
Chicago	100	100.00	100.00	2,600	1.45	94.58	18.56
Cleveland	90	100.00	100.00	1,500	4.70	94.08	22.22
Dallas	50	100.00	100.00	1,700	17.11	96.08	24.30
Detroit	70	100.00	100.00	1,300	5.51	92.09	13.44
Fresno	50	100.00	100.00	1,800	2.36	94.94	6.04
Hillsborough	60	100.00	100.00	1,800	1.07	94.92	23.00
Houston	80	100.00	100.00	2,700	6.41	96.63	23.90
Jefferson County, KY	50	100.00	100.00	1,800	5.28	95.03	7.56
Los Angeles	80	100.00	100.00	2,500	2.10	94.63	10.75
Miami	90	100.00	100.00	2,400	4.51	95.37	26.36
Milwaukee	70	100.00	100.00	1,500	4.08	93.65	25.71
New York City	80	100.00	100.00	2,500	1.62	92.44	27.13

Philadelphia	60	100.00	100.00	1,600	3.83	94.61	15.31
San Diego	50	100.00	100.00	1,600	2.32	94.74	10.45
District of Columbia (TUDA)	90	100.00	100.00	1,600	2.26	94.50	17.21
Catholic	130	88.65	89.70	1,700	0.23	95.75	3.84
Non-Catholic private	280	56.94	52.97	1,600	0.79	95.96	4.22
Includes national public, national private, and Bureau of Indian Education schools located in the United States and all Department of Defense Education Activity schools, but not schools in Puerto Rico. Department of Defense Education Activity schools. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Reading Assessment.

National private

410

71.19

64.52

3,400

0.53

95.85

4.05

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_4_reading_for_the_2013_assessment.aspx

National all1

7,240

96.94

84.59

195,600

1.48

93.15

11.79

National public

6,760

99.48

99.61

189,400

1.59

93.02

12.25

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 8 Mathematics for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 8 mathematics assessment by school type and jurisdiction. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

Participation, exclusion, and accommodation rates, grade 8 mathematics assessment, by school type and jurisdiction: 2013

		School	School
		participation	participation
		rates	rates
	Number	(percent)	(percent)			Weighted
	of	before	before			student
	schools	substitution	substitution	Number	Weighted	participation
	in	(weighted	(weighted	of	percent	rates	Weighted
School type	original	by base	by base	students	of	(percent)	percent of
and	sample,	weight and	weight	sampled,	students	after	students
jurisdiction	rounded	enrollment)	only)	rounded	excluded	makeups	accommodated
All	7,370	96.97	84.74	201,500	1.47	93.14	11.88
Northeast all	1,160	93.53	75.06	32,700	1.60	92.00	15.85
Midwest all	1,920	97.62	85.21	44,100	1.42	93.69	11.78
South all	2,380	97.75	86.70	68,800	1.51	93.24	11.59
West all	1,720	97.42	89.08	48,000	1.41	93.28	9.25
Alabama	110	100.00	100.00	3,000	1.04	94.23	5.14
Alaska	150	99.91	98.79	3,000	1.08	91.72	18.75
Arizona	120	99.03	99.16	3,200	1.30	93.42	10.71
Arkansas	110	100.00	100.00	3,200	1.93	95.00	13.92
California	260	100.00	100.00	8,400	1.49	93.59	7.91
Colorado	120	100.00	100.00	3,100	1.12	93.47	11.50
Connecticut	110	98.00	97.87	3,100	2.05	92.44	13.92
Delaware	70	100.00	100.00	3,200	1.31	90.65	14.90
District of Columbia	90	100.00	100.00	2,100	0.96	91.26	20.71
Florida	230	100.00	100.00	6,400	1.70	91.06	15.32
Georgia	130	100.00	100.00	4,800	1.55	93.38	9.82
Hawaii	60	100.00	100.00	3,200	1.67	90.26	12.28
Idaho	100	100.00	100.00	3,100	1.06	94.15	8.42
Illinois	190	100.00	100.00	4,800	1.01	94.48	13.83
Indiana	110	97.06	96.65	3,000	1.64	92.49	13.95
Iowa	120	100.00	100.00	3,100	0.77	93.74	13.28
Kansas	130	100.00	100.00	3,300	1.67	93.94	11.23
Kentucky	140	99.04	99.21	4,300	2.08	94.54	10.09
Louisiana	150	100.00	100.00	3,200	1.06	94.14	14.26
Maine	120	100.00	100.00	2,900	1.33	92.79	15.99
Maryland	160	100.00	100.00	4,400	1.74	92.08	13.33
Massachusetts	140	100.00	100.00	4,800	2.01	91.98	16.11
Michigan	170	100.00	100.00	4,200	2.46	92.93	10.55

Minnesota	130	98.99	99.67	2,900	1.70	91.58	9.16
Mississippi	110	100.00	100.00	3,200	0.80	93.80	6.51
Missouri	130	100.00	100.00	3,100	1.28	94.25	10.57
Montana	150	99.80	98.82	3,200	1.44	92.28	9.20
Nebraska	130	100.00	100.00	3,100	1.85	93.41	12.02
Nevada	90	100.00	100.00	3,300	1.04	92.80	11.91
New Hampshire	90	100.00	100.00	3,200	1.06	91.60	15.99
New Jersey	110	100.00	100.00	3,100	1.64	92.26	16.38
New Mexico	120	99.68	99.02	4,000	1.57	93.07	12.00
New York	160	93.08	95.81	4,300	1.90	91.15	19.38
North Carolina	140	100.00	100.00	4,500	1.29	92.95	13.74
North Dakota	190	99.92	99.44	3,700	2.93	94.98	11.44
Ohio	200	100.00	100.00	4,500	1.51	93.07	13.54
Oklahoma	130	100.00	100.00	3,100	1.63	92.97	14.09
Oregon	130	100.00	100.00	3,100	1.47	92.91	10.88
Pennsylvania	160	100.00	100.00	4,300	1.70	92.17	14.66
Rhode Island	60	100.00	100.00	3,200	1.11	93.93	15.92
South Carolina	110	100.00	100.00	3,200	1.33	94.19	9.86
South Dakota	150	100.00	100.00	3,200	1.30	94.44	8.66
Tennessee	110	100.00	100.00	3,200	1.77	92.81	9.81
Texas	230	100.00	100.00	8,800	1.92	93.82	12.13
Utah	120	100.00	100.00	3,300	1.53	92.07	10.15
Vermont	120	100.00	100.00	3,000	0.83	93.91	15.36
Virginia	110	100.00	100.00	3,200	1.05	93.39	12.18
Washington	120	100.00	100.00	3,100	2.03	90.87	11.47
West Virginia	110	100.00	100.00	3,200	1.69	92.62	9.02
Wisconsin	170	100.00	100.00	4,300	1.51	94.25	14.73
Wyoming	100	100.00	100.00	3,300	1.50	93.66	12.51
DoDEA2	70	99.40	96.83	2,600	1.15	94.47	9.23
Trial Urban (TUDA) Districts and Other Jurisdictions
Albuquerque	30	100.00	100.00	1,400	1.53	90.76	14.44
Atlanta	30	100.00	100.00	1,600	0.72	91.57	11.10
Austin	30	100.00	100.00	1,600	1.88	90.97	20.60
Baltimore City	60	100.00	100.00	1,300	1.70	89.54	19.73
Boston	40	100.00	100.00	1,800	2.55	91.61	20.88
Charlotte	40	100.00	100.00	1,500	1.29	90.94	10.11
Chicago	100	100.00	100.00	2,300	1.28	94.80	17.19
Cleveland	90	100.00	100.00	1,500	2.62	91.57	28.48
Dallas	40	100.00	100.00	1,600	2.44	93.81	18.35
Detroit	50	100.00	100.00	1,100	4.29	91.58	15.07
Fresno	20	100.00	100.00	1,400	1.74	92.52	7.06
Hillsborough	50	100.00	100.00	1,600	1.35	93.78	20.46
Houston	50	100.00	100.00	2,400	2.21	92.37	14.67
Jefferson County, KY	40	100.00	100.00	1,600	1.65	93.37	12.72
Los Angeles	70	100.00	100.00	2,200	1.54	94.39	10.83
Miami	80	100.00	100.00	2,300	2.25	92.63	18.78
Milwaukee	60	100.00	100.00	1,500	4.10	91.60	25.55

New York City

90 99.00 97.58 2,400 1.72 91.78 26.10

Philadelphia 50 100.00 100.00 1,400 3.74 92.67 20.69

San Diego 30 100.00 100.00 1,300 2.32 92.60 11.81

National private

400

69.63

60.45

3,400

0.26

94.74

6.54

District of Columbia (TUDA)

40 100.00 100.00 1,100 1.69 90.15 22.20

Catholic 130 87.18 84.76 1,800 0.26 95.73 5.50

Non-Catholic private

270 53.51 48.11 1,600 0.26 93.50 7.51

Puerto Rico	130	100.00	100.00	5,900	0.03	92.75	23.05
Includes national public, national private, and Bureau of Indian Education schools located in the United States and all Department of Defense Education Activity schools, but not schools in Puerto Rico. Department of Defense Education Activity schools. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Mathematics Assessment.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_8_mathematics_for_the_2013_assessment.aspx

National all1

7,240

96.94

84.59

199,100

2.15

93.11

10.76

National public

6,760

99.48

99.61

192,900

2.32

92.93

11.16

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 8 Reading for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 8 reading assessment by school type and jurisdiction. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

Participation, exclusion, and accommodation rates, grade 8 r eading assessment, by school type and jurisdiction: 2013

		School	School
		participation	participation
		rates	rates
	Number	(percent)	(percent)			Weighted
	of	before	before			student
	schools	substitution	substitution	Number	Weighted	participation
	in	(weighted	(weighted	of	percent	rates	Weighted
School type	original	by base	by base	students	of	(percent)	percent of
and	sample,	weight and	weight	sampled,	students	after	students
jurisdiction	rounded	enrollment)	only)	rounded	excluded	makeups	accommodated
All	7,240	96.94	84.59	199,100	2.15	93.11	10.76
Northeast all	1,160	93.53	75.06	33,300	1.55	91.80	15.53
Midwest all	1,920	97.62	85.21	45,100	1.93	93.48	11.08
South all	2,380	97.75	86.70	69,900	2.60	93.39	9.99
West all	1,720	97.42	89.08	48,900	2.08	93.21	8.32
Alabama	110	100.00	100.00	3,100	1.14	94.26	4.83
Alaska	150	99.91	98.79	3,100	1.40	91.91	18.39
Arizona	120	99.03	99.16	3,300	1.47	93.67	9.67
Arkansas	110	100.00	100.00	3,200	1.96	93.21	13.36
California	260	100.00	100.00	8,500	2.52	93.42	6.74
Colorado	120	100.00	100.00	3,200	1.15	93.46	10.89
Connecticut	110	98.00	97.87	3,100	2.13	91.38	13.88
Delaware	70	100.00	100.00	3,200	3.49	91.59	12.23
District of Columbia	90	100.00	100.00	2,100	1.82	91.33	19.57
Florida	230	100.00	100.00	6,500	1.86	91.72	15.15
Georgia	130	100.00	100.00	4,900	3.80	93.67	8.18
Hawaii	60	100.00	100.00	3,300	1.93	90.58	12.33
Idaho	100	100.00	100.00	3,200	1.61	93.64	7.76
Illinois	190	100.00	100.00	4,900	1.44	93.76	12.94
Indiana	110	97.06	96.65	3,100	1.90	93.12	13.75
Iowa	120	100.00	100.00	3,100	1.27	93.44	12.16
Kansas	130	100.00	100.00	3,300	1.72	93.42	11.72
Kentucky	140	99.04	99.21	4,300	3.28	93.93	8.47
Louisiana	150	100.00	100.00	3,300	1.24	93.78	14.15
Maine	120	100.00	100.00	3,000	1.55	92.34	15.16
Maryland	160	100.00	100.00	4,400	9.41	93.77	5.45
Massachusetts	140	100.00	100.00	4,900	2.15	91.82	15.04
Michigan	170	100.00	100.00	4,300	3.53	93.66	9.68
Minnesota	130	98.99	99.67	3,000	2.33	91.30	8.43

Mississippi	110	100.00	100.00	3,200	0.70	93.72	6.55
Missouri	130	100.00	100.00	3,100	1.02	92.55	10.62
Montana	150	99.80	98.82	3,200	2.29	91.61	7.51
Nebraska	130	100.00	100.00	3,200	2.99	92.32	10.14
Nevada	90	100.00	100.00	3,400	1.00	92.19	10.91
New Hampshire	90	100.00	100.00	3,200	2.93	91.46	14.28
New Jersey	110	100.00	100.00	3,200	2.64	92.01	14.78
New Mexico	120	99.68	99.02	4,000	1.70	93.39	10.00
New York	160	93.08	95.81	4,400	0.96	90.46	20.03
North Carolina	140	100.00	100.00	4,600	1.72	92.51	12.29
North Dakota	190	99.92	99.44	3,800	4.30	94.07	9.52
Ohio	200	100.00	100.00	4,600	2.22	93.08	13.08
Oklahoma	130	100.00	100.00	3,200	1.39	93.43	12.42
Oregon	130	100.00	100.00	3,200	1.45	92.62	11.30
Pennsylvania	160	100.00	100.00	4,300	1.78	91.94	14.51
Rhode Island	60	100.00	100.00	3,300	1.37	92.96	15.18
South Carolina	110	100.00	100.00	3,200	1.88	94.03	7.48
South Dakota	150	100.00	100.00	3,300	2.95	95.01	6.02
Tennessee	110	100.00	100.00	3,200	3.13	93.54	7.75
Texas	230	100.00	100.00	8,900	3.51	93.78	10.05
Utah	120	100.00	100.00	3,400	3.05	93.00	8.36
Vermont	120	100.00	100.00	3,100	0.92	92.93	15.08
Virginia	110	100.00	100.00	3,300	1.40	92.97	10.56
Washington	120	100.00	100.00	3,200	2.46	91.22	9.78
West Virginia	110	100.00	100.00	3,200	1.82	93.10	7.60
Wisconsin	170	100.00	100.00	4,400	1.61	94.11	14.45
Wyoming	100	100.00	100.00	3,400	1.14	93.15	12.27
DoDEA2	70	99.40	96.83	2,600	3.84	94.13	7.11
Trial Urban (TUDA) Districts and Other Jurisdictions
Albuquerque	30	100.00	100.00	1,400	2.04	93.46	11.79
Atlanta	30	100.00	100.00	1,700	1.02	92.20	10.98
Austin	30	100.00	100.00	1,600	3.35	88.54	18.36
Baltimore City	60	100.00	100.00	1,300	16.39	89.73	5.14
Boston	40	100.00	100.00	1,800	3.41	93.05	18.94
Charlotte	40	100.00	100.00	1,500	1.68	92.20	9.90
Chicago	100	100.00	100.00	2,300	1.60	94.72	16.76
Cleveland	90	100.00	100.00	1,500	3.52	91.90	27.75
Dallas	40	100.00	100.00	1,600	3.51	93.98	15.20
Detroit	50	100.00	100.00	1,100	5.74	91.37	12.53
Fresno	20	100.00	100.00	1,500	3.10	93.27	5.86
Hillsborough	50	100.00	100.00	1,600	1.94	91.85	19.74
Houston	50	100.00	100.00	2,400	3.80	93.58	12.29
Jefferson County, KY	40	100.00	100.00	1,600	4.30	94.71	9.49
Los Angeles	70	100.00	100.00	2,300	2.70	94.30	9.97
Miami	80	100.00	100.00	2,400	2.88	94.21	18.45
Milwaukee	60	100.00	100.00	1,500	4.06	93.15	25.08
New York City	90	99.00	97.58	2,400	1.46	91.17	26.00

Philadelphia	50	100.00	100.00	1,400	3.79	91.35	20.91
San Diego	30	100.00	100.00	1,300	2.58	93.78	10.58
District of Columbia (TUDA)	40	100.00	100.00	1,100	2.53	90.18	22.13
Catholic	130	87.18	84.76	1,900	0.21	96.07	4.96
Non-Catholic private	270	53.51	48.11	1,600	0.39	94.67	7.56
Includes national public, national private, and Bureau of Indian Education schools located in the United States and all Department of Defense Education Activity schools, but not schools in Puerto Rico. Department of Defense Education Activity schools. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Reading Assessment.

National private

400

69.63

60.45

3,500

0.30

95.45

6.32

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_8_reading_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 12 Mathematics for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 12 mathematics assessment. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

National all1

2,200

89.51

82.66

62,200

2.16

84.33

8.65

National public

2,030

92.95

93.31

60,400

2.31

84.17

8.77

Participation, exclusion, and accommodation rates, grade 12 mathematics assessment, by school type and geographic r egion: 2013

			School
		School	participation
		participation	rates			Weighted
	Number	rates (percent)	(percent)			student
	of	before	before			participation
	schools	substitution	substitution	Number	Weighted	rates	Weighted
	in	(weighted by	(weighted by	of	percentage	(percent)	percentage of
School type and geographic	original	base weight	base weight	students	of students	after	students
region	sample	and enrollment)	only)	sampled	excluded	makeups	accommodated
All	2,200	89.51	82.66	62,200	2.16	84.33	8.65
Northeast all	510	89.05	81.63	16,200	2.29	81.79	11.95
Midwest all	650	87.14	83.20	16,600	1.65	83.87	8.61
South all	710	89.42	85.99	20,300	2.31	86.52	7.98
West all	330	92.21	77.24	9,100	2.32	83.37	7.15
Arkansas	100	100.00	100.00	2,900	2.78	92.09	8.61
Connecticut	110	98.93	99.45	3,200	1.76	81.22	8.71
Florida	120	99.05	99.30	3,300	3.21	77.25	12.67
Idaho	100	100.00	100.00	3,000	1.65	89.17	6.72
Illinois	130	90.38	93.98	3,300	1.85	85.16	9.79
Iowa	120	100.00	100.00	3,300	1.13	83.05	10.78
Massachusetts	110	99.04	99.45	3,200	2.21	81.71	11.13
Michigan	140	100.00	100.00	4,000	1.90	86.94	8.78
New Hampshire	80	100.00	100.00	4,100	1.61	76.64	11.22
New Jersey	110	98.14	98.57	3,300	1.89	84.10	14.28
South Dakota	140	99.74	99.07	3,100	1.51	87.48	5.78
Tennessee	130	100.00	100.00	4,100	2.51	88.15	7.84
West Virginia	90	100.00	100.00	3,300	2.00	83.68	7.01
Remaining jurisdictions2	570	91.16	90.91	16,200	2.26	84.41	10.55
Catholic	40	68.06	79.95	1,000	0.83	85.53	5.46
Non-Catholic private	120	38.52	50.25	800	0.42	87.96	9.28
Includes national public, national private, Bureau of Indian Education, and Department of Defense Education Activity schools located in the United States. Includes national public schools not part of the state assessment. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Mathematics Assessment.

National private

160

53.34

55.43

1,800

0.63

86.51

7.32

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_12_mathematics_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Participation, Exclusion, and Accommodation Rates for Grade 12 Reading for the 2013 Assessment

The following table displays the school- and student-level response, exclusion, and accommodation rates for the grade 12 reading assessment. Various weights were used in the calculation of the rates, as indicated in the column headings of the table.

The participation rates reflect the participation of the original sample schools only and do not reflect any effect of substitution. The rates weighted by the base weight and enrollment show the approximate proportion of the student population in the jurisdiction that is represented by the responding schools in the sample. The rates weighted by just the base weight show the proportion of the school population that is represented by the responding schools in the sample. These rates differ because schools differ in size.

National public

2,030

92.95

93.31

60,400

2.56

83.77

8.73

Participation, exclusion, and accommodation rates, grade 12 r eading assessment, by school type and geographic r egion: 2013

		School				Weighted
	Number	participation rates	School			student
	of	(percent) before	participation rates			participation
	schools	substitution	(percent) before	Number	Weighted	rates	Weighted
	in	(weighted by base	substitution	of	percentage	(percent)	percentage of
School type and	original	weight and	(weighted by	students	of students	after	students
geographic region	sample	enrollment)	base weight only)	sampled	excluded	makeups	accommodated
All	2,200	89.51	82.66	62,300	2.41	83.89	8.55
National all1	2,200	89.51	82.66	62,300	2.41	83.89	8.55
Northeast all	510	89.05	81.63	16,500	2.16	80.91	12.89
Midwest all	650	87.14	83.20	16,700	2.05	84.05	8.75
South all	710	89.42	85.99	20,000	2.87	85.51	7.18
West all	330	92.21	77.24	9,000	2.24	83.58	7.14
Arkansas	100	100.00	100.00	3,000	2.56	90.21	8.24
Connecticut	110	98.93	99.45	3,400	2.34	79.77	8.70
Florida	120	99.05	99.30	3,300	3.55	77.34	12.14
Idaho	100	100.00	100.00	3,200	1.66	88.68	6.42
Illinois	130	90.38	93.98	3,400	2.29	83.72	9.92
Iowa	120	100.00	100.00	3,500	1.51	84.26	10.62
Massachusetts	110	99.04	99.45	3,200	1.87	79.84	11.31
Michigan	140	100.00	100.00	3,900	4.01	87.21	6.17
New Hampshire	80	100.00	100.00	4,300	2.55	76.91	10.25
New Jersey	110	98.14	98.57	3,300	1.80	84.67	14.78
South Dakota	140	99.74	99.07	3,300	1.60	86.17	5.16
Tennessee	130	100.00	100.00	3,900	2.88	88.82	7.13
West Virginia	90	100.00	100.00	3,400	2.37	84.28	6.89
Remaining jurisdictions2	570	91.16	90.91	15,200	2.77	83.98	10.05
Catholic	40	68.06	79.95	1,100	0.92	84.67	4.01
Non-Catholic	120	38.52	50.25	800	0.75	86.75	9.41
Includes national public, national private, Bureau of Indian Education, and Department of Defense Education Activity schools located in the United States. Includes national public schools not part of the state assessment. NOTE: Numbers of schools are rounded to nearest ten, and numbers of students are rounded to nearest hundred. Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2013 Reading Assessment.

National private

160

53.34

55.43

1,900

0.84

85.52

6.67

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/participation_exclusion_and_accommodation_rates_for_grade_12_reading_for_the_2013_assessment.aspx

NAEP Technical Documentation Website

NAEP Technical Documentation Nonresponse Bias Analyses for the 2013 Assessment

NCES statistical standards call for a nonresponse bias analysis to be conducted for a sample with a response rate below 85 percent at any stage of sampling. Weighted school response rates for the 2013 assessment indicated a need for school nonresponse bias analyses for private school samples in grades 4, 8, and 12 (operational subjects). Student nonresponse bias analyses were necessary for the grade 12 public school student sample overall and in specific states, for both reading and mathematics: Connecticut, Florida, Iowa, Massachusetts, New Hampshire, and West Virginia. Additionally, a student nonresponse bias analysis was required for the grade 12 public school student sample in Illinois based on the weighted response rate for reading, while such an analysis was required for grade 12 public school student sample in New Jersey based on the weighted response rate for mathematics. Thus, three separate school-level analyses and nine separate student-level analyses were conducted.

The procedures and results from these analyses are summarized briefly below. The analyses conducted consider only certain characteristics of schools and students. They do not directly consider the effects of the nonresponse on student achievement, the primary focus of NAEP. Thus, these analyses cannot be conclusive of either the existence or absence of nonresponse bias for student achievement. For more details, please see the NAEP 2013 NRBA report (657.56 KB).

Each school-level analysis was conducted in three parts. The first part of the analysis looked

for potential nonresponse bias that was introduced through school nonresponse. The second part of the analysis examined the remaining potential for nonresponse bias after accounting for the mitigating effects of substitution. The third part of the analysis examined the remaining potential for nonresponse bias after accounting for the mitigating effects of both school substitution and school-level nonresponse weight adjustments. The characteristics examined were Census region, reporting subgroup (private school type), urban-centric locale, size of school (categorical), and race/ethnicity percentages (mean).

Based on the school characteristics available, for the private school samples at grade 4, there does not appear to be evidence of substantial potential bias resulting from school substitution or school nonresponse. However, the analyses suggest that a potential for nonresponse bias remains for the grade 8 and 12 private school samples. For grade 8, this result is evidently related to the fact that, among non- Catholic schools, larger schools were less likely to respond. Thus, when making adjustments to address the underrepresentation of non-Catholic schools among the respondents, the result is to over

represent smaller schools at the expense of larger ones. The limited school sample sizes involved means that it is not possible to make adjustments that account fully for all school characteristics. For grade 12, the analyses suggested potential bias for percentage Asian and percentage Two or more races. Please see the full report for more details.

Each student-level analysis was conducted in two parts. The first part of the analysis examined the potential for nonresponse bias that was introduced through student nonresponse. The second part of the analysis examined the potential for bias after accounting for the effects of nonresponse weight adjustments. The characteristics examined were gender, race/ethnicity, relative age, National School Lunch Program eligibility, student disability (SD) status, and English language learner (ELL) status.

Based on the student characteristics available, there does not appear to be evidence of substantial potential bias resulting from student nonresponse. Please see the full report for more details.

http://nces.ed.gov/nationsreportcard/tdw/weighting/2013/nonresponse_bias_analyses_for_the_2013_assessment.aspx

NATIONAL CENTER FOR EDUCATION STATISTICS NATIONAL ASSESSMENT OF EDUCATIONAL PROGRESS

National Assessment of Educational Progress (NAEP)

2021

Appendix C

2019 Sampling Memo

OMB# 1850-0928 v.18

September 2018

No changes since v.10

Appendix C provides a sample from 2019. The 2021 Sampling Memo is scheduled to be completed and submitted to OMB in Amendment #1 by April 2020.

Date:	February 28, 2018	Memo: 2019- 1.1A/1.1B/1.1D/1.1E
To:	William Ward, NCES	Chris Averett
	Ed Kulick, ETS	Kavemuii Murangi
	David Freund, ETS	Erin Wiley
	Amy Dresher, ETS	Dwight Brock
	Cathy White, Pearson	David Hubble
	Saira Brenner, Fulcrum	Yiting Dai
	Dianne Walsh	Jing Kang
	Lauren Byrne	Sabrina Zhang
	Lisa Rodriguez	Leslie Wallace
	Rick Rogers	Natalia Weil
	Rob Dymowski	Greg Binzer
	William Wall
From:	Amy Lin, John Burke, and Lloyd Hicks
Reviewer:	Keith Rust
Subject:	Sample Design for 2019 NAEP - DRAFT

1. Introduction

For 2019, the NAEP assessment involves the following components:

1. National assessments in reading, mathematics, and science at grades 4, 8, and 12;

2. State-by-state and Trial Urban District Assessment (TUDA) assessments in reading and mathematics for public schools at grades 4 and 8;

3. An assessment of mathematics in Puerto Rico at grades 4 and 8;

4. Pilot tests in reading, mathematics, and vocabulary at grades 4 and 8.

Below is a summary list of the features of the 2019 sample design.

1. The alpha samples for grades 4 and 8 public, and the delta samples for private schools at grades 4 and 8, will be used for the operational assessments in reading and mathematics.

2. The beta public school samples and the epsilon private school samples at grades 4 and 8 will be used for the national science assessments and the various pilot tests. The beta and epsilon samples at grade 12 will be used for the operational reading, mathematics, and science assessments.

3. As in recent NAEP studies, each Trial Urban District Assessment (TUDA) sample will form part of the corresponding state sample, and each state sample will form part of the national sample. There are twenty-seven Trial Urban District Assessment (TUDA) participants. These are the same districts that participated in 2017.

4. Schools in the alpha and delta samples will be assessed using DBA with tablets. Schools in the beta and epsilon samples will receive a mixture of DBA assessments, using tablets, and pencil and paper (PBA) assessments.

5. All BIE schools and students will be included in the operational samples at grades 4 and

8. This is because, after a hiatus in 2017, the National Indian Education Study (NIES) is resuming. Having all BIE students in sample is designed to provide detailed national results for American Indian and Alaskan Native (AIAN) students in reading and mathematics, as part of the NIES.

6. There will be no samples in territories other than for Puerto Rico at grades 4 and 8.

7. As in 2017, the Department of Defense Schools are expected to be reported as a single jurisdiction (DoDEA).

8. At grade 12, there will be no state-level samples.

9. Oversampling of private schools at grades 4 and 8 will be done at the same level as 2017. Response rates permitting, this will allow separate reporting for reading and mathematics for Catholic and non-Catholic schools at grades 4 and 8, but no further breakdowns by private school type.

10. The sample sizes of assessed students for these various components are shown in Table 1 (which also shows the approximate numbers of participating schools).

11. In the beta samples, there will be moderate oversampling of schools with moderate to high proportions of Black, Hispanic, and American Indian and Alaska Native students.

	Spiral	Jurisdictions		Students		Total
	Spiral Indic.	States (incl. DC, DoDEA)	Urban districts	Public school students	Private school students
Grade 4
Nat’l/state reading (DBA)	DS	52	27	176,000	3,700	179,000
Nat’l/state math (DBA)	DS	52	27	144,000	3,000	147,000
Puerto Rico (DBA)	DP	1		3,000	0	3,000
Total - alpha				323,000		323,000
Total- delta					6,700	6,700
Typical max. no. students/school				50	50
Average assessed students/school				40	25
Total schools - alpha, delta				8,075	268	8,343
Science (DBA)	DA			17,100	1,900	19,000
Science (PBA)	PA			8,100	900	9,000
Math Pilot	DA			10,350	1,150	11,500
Reading Pilot	DA			4,050	450	4,500
Vocabulary initial-Pilot	DA			1,980	220	2,200
Total - beta				41,580		41,580
Total - epsilon					4,620	4,620
Typical max. no. students/school				62	62
Average assessed students/school				50	25
Total schools - beta, epsilon				832	185	1,017
Total number of students grade 4				364,580	11,320	375,900
Total number of schools grade 4				8,907	453	9,360

	Spiral	Jurisdictions		Students		Total
	Spiral Indic.	States (incl. DC, DoDEA)	Urban districts	Public school students	Private school students
Grade 8
Nat’l/state reading (DBA)	DS, DT	52	27	176,000	3,700	179,000
Nat’l/state math (DBA)	DS, DT	52	27	144,000	3,000	147,000
Puerto Rico (DBA)	DP	1		3,000	0	3,000
Total - alpha				323,000		323,000
Total- delta					6,700	6,700
Typical max. no. students/school				50	50
Average assessed students/school				47	25
Total schools - alpha, delta				6,870	268	7,138
Science (DBA)	DA			17,100	1,900	19,000
Science (PBA)	PA			9,000	1,000	10,000
Math Pilot	DA			10,350	1,150	11,500
Reading pilot	DA			4,050	450	4,500
Vocabulary initial-Pilot	DA			1,980	220	2,200
Total – beta				42,480		42,480
Total – epsilon					4,720	4,720
Typical max. no. students/school				63	63
Average assessed students/school				52	25
Total schools - beta, epsilon				817	189	1,006
Total number of students grade 8				365,480	11,420	376,900
Total number of schools grade 8				7,687	457	8,144

Table 1. Target sample sizes of assessed students, and expected number of participating schools, for 2019 NAEP (Continued)

	Spiral	Jurisdictions		Students		Total
	Spiral Indic.	States (incl. DC, DoDEA)	Urban districts	Public School students	Private school students
Grade 12
Reading (DBA)	DA			13,500	1,500	15,000
Reading (PBA)	PA			11,700	1,300	13,000
Math (DBA)	DA			12,600	1,400	14,000
Math (PBA)	PA			12,600	1,400	14,000
Science (DBA)	DA			17,100	1,900	19,000
Science (PBA)	PA			9,900	1,100	11,000
Total - beta				77,400		77,400
Total- epsilon					8,600	8,600
Typical max. no. students/school				68	68
Average assessed students/school				50	40
Total schools – beta, epsilon				1,548	215	1,763
Total number of students grade 12				77,400	8,600	86,000
Total number of schools grade 12				1,548	215	1,763
GRAND TOTAL STUDENTS				807,460	31,340	838,800
GRAND TOTAL SCHOOLS				18,142	1,125	19,267

2. Assessment Types

The assessment spiral types are shown in Table 2. Four different spirals will be used at grades 4 and 8, and two at grade 12. Session IDs contain six characters, traditionally. The first two characters identify the assessment “type” (subjects and type of spiral in a general way). Grade is contained in the second pair of characters, and the session sequential number (within schools) in the last two characters. For example, session DS0401 denotes the first grade 4 reading and mathematics operational DBA assessment in a given school.

Table 2. NAEP 2019 assessment types and IDs

ID	Type	Subjects	Grades	Schools	Comments
DS	Operational DBA	Reading, math (22:27)	4, 8	Public, Private	All schools in the alpha (except Puerto Rico) and delta samples.
DA	Operational, and pilot DBA	Science, reading, math, vocabulary (190:45:115:22)	4, 8	Public, Private	All schools in the beta and epsilon samples.
PA	Operational	Science	4, 8	Public, Private	All schools in the beta and epsilon samples.
DA	Operational	Reading, math, science, (15:14:19)	12	Public, Private	All schools in the beta and epsilon samples.
PA	Operational	Reading, math, science (13:14:11)	12	Public, Private	All schools in the beta and epsilon samples.
DP	Operational	Mathematics	4, 8	Public	Puerto Rico alpha samples

3. Sample Types and Sizes

In similar fashion to past years (but somewhat different), we will identify four different types of school samples: Alpha, Beta, Delta, and Epsilon. These distinguish sets of schools that will be conducting distinct portions of the assessment.

1. Alpha Samples at Grades 4 and 8

These are public school samples for grades 4 and 8. They will be used for the operational state-by- state assessments in reading and mathematics, and contribute to the national samples for these subjects as well. There will be alpha samples for each state, DC, DoDEA, BIE, and Puerto Rico.

The details of the target student sample sizes for the alpha samples are as follows:

1. At each grade, the target student sample size is 5,700 per state. The goal in each state (before considering the contribution of TUDA districts) is to roughly assess 2,700 student for math and 2,200 students for reading. The DS session type will be used.

2. There will be samples for twenty-seven TUDA districts. For the six large TUDA districts (New York, Los Angeles, Chicago, Miami-Dade, Clark Co., and Houston) the assessed student target sample sizes are three-quarters the size of a state sample (3,675). The target sample size after considering attrition is 4,275.

3. For the remaining 21 TUDA districts, the assessed student target sample sizes are half the size of a state sample (2,450). The target sample size after inflation to account for attrition is 2,850.

4. Note that, above, there is a conflict between sample size requirements at the state level, and the TUDA district level. This will be resolved as in previous years: the districts will have the target samples indicated in B and C, and reflected in Table 3. For the states that contain one or more of these districts, the target sample size indicated in A (and shown in Table 3) will be used to determine a school sampling rate for the state, which will be applied to the balance of the state outside the TUDA district(s). Thus the target student sample sizes, shown in Table 3, for states that contain a TUDA district, are only ‘design targets’, and are smaller than the final total sample size for the state, but larger than the sample for the balance of the state, exclusive of its TUDA districts. In the case of the District of Columbia, the state sample size requirement is that all schools and students be included. This renders moot any requirements for the DC TUDA sample, which by default consists of all schools operated by the DCPS district (but excludes charter schools in DC, even though those are all included in the state sample, as these are not operated by DCPS).

5. In Puerto Rico, the target sample size is 4,000 per grade (grades 4 and 8), with the goal of assessing 3,000 students. Under normal circumstances this target would be set at 3,500, but because of the rapid and substantial shifts in the school population in Puerto Rico, this has been increased to provide some insurance against attrition due to closed schools and declining enrollments.

As in past state-by-state assessments, schools with fewer than 20 students in the grade in question will be sampled at a moderately lower rate than other schools (at least half, and often higher, depending upon the size of the school). This is in implicit recognition of the greater cost and burden associated with surveying these schools.

As mentioned above, the NAEP 2019 design includes an oversample of high proportion American Indian schools in certain states (as part of the NIES design). These schools will be sampled at higher rates than the other schools. The NIES oversample will take place in Arizona, Minnesota, North Carolina, Oregon, Utah, Washington, and Wisconsin. Schools with relatively large percentages of American Indian students will be separately stratified, as explained below, and oversampled by factors ranging from 3 to 6 based on state and grade. Table 3 below shows the thresholds used to define the NIES oversampling strata along with their corresponding oversampling factors.

Table 3. Percent American Indian thresholds and oversampling factors for the NIES school oversample by state and grade

State	Grade 4		Grade 8
	Percent American Indian thresholds	Oversampling factor	Percent American Indian thresholds	Oversampling factor
Arizona	50	4	50	3
Minnesota	10	5	10	5
North Carolina	10	6	10	6
Oregon	10	6	10	6
Utah	5	6	5	6
Washington	10	6	10	6
Wisconsin	10	6	10	6

Table 4 shows the target student sample sizes, and the approximate counts of schools to be selected in the alpha samples, along with the school and student frame counts, by state and TUDA districts for grades 4 and 8. The table also identifies the jurisdictions where we take all schools and where we take all students. Note that the additional sample that will result from NIES oversampling is not included in this table.

Table 5 consolidates the target student (and resulting school) sample size numbers, to show the total target sample sizes in each state, combining the TUDA targets with those for the balance of the state.

Jurisdiction	Grade 4					Grade 8
	Schools in frame	Schools in sample	Students in frame	Overall target student sample size		Schools in frame	Schools in sample	Students in frame	Overall target student sample size
Alabama	709	120	57,548	5,700		456	118	55,820	5,700
Alaska	352	185	9,361	5,700		270	131	9,019	5,700
Arizona	1,193	123	86,472	5,700		793	122	83,469	5,700
Arkansas	480	121	36,937	5,700		303	114	36,503	5,700
Bureau of Indian Education	137	137	3,357	3,357	**	113	113	2,936	2,936	**
California	5,979	119	471,633	5,700		2,933	120	455,487	5,700
Colorado	1,054	123	67,814	5,700		567	121	65,088	5,700
Connecticut	602	121	39,544	5,700		339	118	40,679	5,700
Delaware	119	99	10,393	5,700		61	61	10,105	5,700	*
District of Columbia	119	119	5,536	5,536	**	69	69	4,520	4,520	**
DoDEA Schools	110	95	7,547	5,700		65	65	5,629	5,629	**
Florida	2,225	118	212,520	5,700		1,219	119	202,235	5,700
Georgia	1,248	115	133,243	5,700		562	115	129,475	5,700
Hawaii	205	118	15,494	5,700		83	62	13,314	5,700
Idaho	381	128	22,864	5,700		209	100	22,319	5,700
Illinois	2,205	124	149,235	5,700		1,561	123	151,830	5,700
Indiana	1,050	119	78,837	5,700		489	116	79,653	5,700
Iowa	638	128	37,147	5,700		368	118	35,691	5,700
Kansas	704	132	37,202	5,700		393	125	36,033	5,700
Kentucky	721	120	52,221	5,700		417	121	50,755	5,700
Louisiana	760	121	55,735	5,700		488	120	51,981	5,700
Maine	320	147	13,444	5,700		202	112	13,473	5,700
Maryland	903	119	67,399	5,700		373	117	61,983	5,700
Massachusetts	958	120	70,968	5,700		485	116	71,662	5,700
Michigan	1,711	123	111,240	5,700		1,083	123	114,211	5,700
Minnesota	956	126	65,262	5,700		712	128	63,732	5,700
Mississippi	423	118	38,316	5,700		287	112	36,486	5,700
Missouri	1,166	129	69,574	5,700		709	127	67,833	5,700

Jurisdiction	Grade 4					Grade 8
	Schools in frame	Schools in sample	Students in frame	Overall target student sample size		Schools in frame	Schools in sample	Students in frame	Overall target student sample size
Montana	392	174	11,534	5,700		271	136	10,811	5,700
Nebraska	532	146	23,315	5,700		294	114	22,561	5,700
Nevada	394	119	35,875	5,700		171	91	34,346	5,700
New Hampshire	270	135	13,734	5,700		142	89	14,078	5,700
New Jersey	1,371	120	99,697	5,700		765	118	99,117	5,700
New Mexico	444	128	26,208	5,700		232	110	25,079	5,700
New York	2,471	118	201,226	5,700		1,498	117	196,197	5,700
North Carolina	1,457	119	118,118	5,700		728	117	117,176	5,700
North Dakota	261	166	8,471	5,700		184	142	7,789	5,700
Ohio	1,740	121	129,087	5,700		1,093	119	131,562	5,700
Oklahoma	869	132	50,988	5,700		583	127	48,784	5,700
Oregon	746	128	43,589	5,700		428	124	42,824	5,700
Pennsylvania	1,607	118	130,442	5,700		888	116	131,525	5,700
Puerto Rico	931	169	31,308	4,000		398	161	30,211	4,000
Rhode Island	164	111	10,777	5,700		60	60	10,720	5,700	*
South Carolina	643	118	57,878	5,700		306	115	54,617	5,700
South Dakota	312	163	10,517	5,700		246	135	9,657	5,700
Tennessee	995	120	77,202	5,700		584	119	73,441	5,700
Texas	4,431	118	399,283	5,700		2,251	119	383,849	5,700
Utah	621	118	50,010	5,700		256	113	47,320	5,700
Vermont	216	216	6,204	6,204	**	121	121	5,999	5,999	**
Virginia	1,109	117	97,550	5,700		379	114	95,187	5,700
Washington	1,231	122	81,904	5,700		609	122	79,084	5,700
West Virginia	417	138	20,578	5,700		190	110	20,464	5,700
Wisconsin	1,099	128	61,686	5,700		649	123	61,152	5,700
Wyoming	192	137	7,639	5,700		89	89	7,042	5,700	*

Jurisdiction	Grade 4					Grade 8
	Schools in frame	Schools in sample	Students in frame	Overall target student sample size		Schools in frame	Schools in sample	Students in frame	Overall target student sample size
Albuquerque	95	57	7,412	2,850		40	40	6,691	2,850	*
Atlanta	55	55	4,285	2,850	*	23	23	3,554	3,554	**
Austin	80	56	6,867	2,850		22	22	5,427	2,850	*
Baltimore City	128	64	6,716	2,850		96	62	5,504	2,850
Boston	72	57	4,086	2,850		43	43	3,667	3,667	**
Charlotte	105	57	11,696	2,850		46	35	11,007	2,850
Chicago	433	93	27,360	4,275		434	93	27,895	4,275
Clark County, NV	226	87	25,311	4,275		80	58	24,676	4,275
Cleveland	71	71	2,754	2,754	**	70	70	2,685	2,685	**
Dallas	151	58	13,325	2,850		41	41	10,873	2,850	*
Denver	102	59	7,108	2,850		60	47	6,060	2,850
Detroit	65	55	3,889	2,850		49	49	2,963	2,963	**
Duval County, FL	119	58	10,313	2,850		50	35	8,873	2,850
Fresno	68	55	5,788	2,850		19	19	5,147	2,850	*
Fort Worth	85	57	7,073	2,850		32	32	5,977	2,850	*
Guilford County, NC	74	56	5,492	2,850		29	29	5,339	2,850	*
Hillsborough County, FL	176	58	16,522	2,850		87	50	15,096	2,850
Houston	174	86	17,729	4,275		61	49	13,063	4,275
Jefferson County, KY	100	59	7,718	2,850		43	29	7,306	2,850
Los Angeles	496	87	45,361	4,275		122	75	36,142	4,275
Miami	285	88	26,690	4,275		177	82	26,957	4,275
Milwaukee	111	65	5,668	2,850		83	56	4,977	2,850
New York City	788	88	73,248	4,275		524	88	66,513	4,275
Philadelphia	148	58	11,227	2,850		112	54	8,849	2,850
San Diego	120	59	9,125	2,850		38	38	7,433	2,850	*
Shelby County, TN	120	59	9,250	2,850		61	44	8,277	2,850
District of Columbia PS	76	76	3,584	3,584	**	32	32	2,394	2,394	**

Counts for states do not reflect the oversampling for their constituent TUDA districts, nor the impact of oversampling for NIES.

Target student sample sizes reflect sample sizes prior to attrition due to exclusion, ineligibility, and nonresponse.

* identifies jurisdictions where all schools (but not all students) for the given grade are included in the NAEP sample.

** identifies jurisdictions where all students for the given grade are included in the NAEP sample.

Jurisdiction	Grade 4					Grade 8
	Schools in frame	Schools in sample	Students in frame	Overall target student sample size		Schools in frame	Schools in sample	Students in frame	Overall target student sample size
Alabama	709	120	57,548	5,700		456	117	55,820	5,700
Alaska	352	184	9,361	5,700		270	131	9,019	5,700
Arizona	1,193	123	86,472	5,700		793	123	83,469	5,700
Arkansas	480	121	36,937	5,700		303	114	36,503	5,700
Bureau Of Indian Education	137	137	3,357	3,357	**	113	113	2,936	2,936	**
California	5,979	305	471,633	14,945		2,933	240	455,487	15,064
Colorado	1,054	169	67,814	7,950		567	157	65,088	8,018
Connecticut	602	121	39,544	5,700		339	118	40,679	5,700
Delaware	119	99	10,393	5,700		61	61	10,105	5,700	*
District Of Columbia	119	119	5,536	5,536	**	69	69	4,520	4,520	**
DoDEA Schools	110	95	7,547	5,700		65	65	5,629	5,629	**
Florida	2,225	293	212,520	14,238		1,219	256	202,235	14,238
Georgia	1,248	166	133,243	8,367		562	135	129,475	9,098
Hawaii	205	118	15,494	5,700		83	61	13,314	5,700
Idaho	381	128	22,864	5,700		209	100	22,319	5,700
Illinois	2,205	194	149,235	8,927		1,561	194	151,830	8,924
Indiana	1,050	119	78,837	5,700		489	116	79,653	5,700
Iowa	638	128	37,147	5,700		368	118	35,691	5,700
Kansas	704	132	37,202	5,700		393	125	36,033	5,700
Kentucky	721	162	52,221	7,709		417	133	50,755	7,730
Louisiana	760	121	55,735	5,700		488	120	51,981	5,700
Maine	320	147	13,444	5,700		202	112	13,473	5,700
Maryland	903	169	67,399	7,983		373	167	61,983	8,044
Massachusetts	958	170	70,968	8,222		485	153	71,662	9,076
Michigan	1,711	174	111,240	8,350		1,083	168	114,211	8,515
Minnesota	956	126	65,262	5,700		712	128	63,732	5,700
Mississippi	423	118	38,316	5,700		287	112	36,486	5,700
Missouri	1,166	129	69,574	5,700		709	127	67,833	5,700
Montana	392	174	11,534	5,700		271	136	10,811	5,700

Jurisdiction	Grade 4					Grade 8
	Schools in frame	Schools in sample	Students in frame	Overall target student sample size		Schools in frame	Schools in sample	Students in frame	Overall target student sample size
Nebraska	532	145	23,315	5,700		294	114	22,561	5,700
Nevada	394	124	35,875	5,945		171	91	34,346	5,874
New Hampshire	270	135	13,734	5,700		142	89	14,078	5,700
New Jersey	1,371	120	99,697	5,700		765	118	99,117	5,700
New Mexico	444	152	26,208	6,923		232	123	25,079	7,021
New York	2,471	164	201,226	7,899		1,498	165	196,197	8,042
North Carolina	1,457	215	118,118	10,570		728	165	117,176	10,604
North Dakota	261	166	8,471	5,700		184	142	7,789	5,700
Ohio	1,740	189	129,087	8,332		1,093	186	131,562	8,269
Oklahoma	869	132	50,988	5,700		583	127	48,784	5,700
Oregon	746	128	43,589	5,700		428	124	42,824	5,700
Pennsylvania	1,607	166	130,442	8,059		888	162	131,525	8,167
Puerto Rico	931	169	31,308	4,000		398	161	30,211	4,000
Rhode Island	164	111	10,777	5,700		60	60	10,720	5,700	*
South Carolina	643	118	57,878	5,700		306	115	54,617	5,700
South Dakota	312	163	10,517	5,700		246	135	9,657	5,700
Tennessee	995	165	77,202	7,866		584	149	73,441	7,907
Texas	4,431	361	399,283	17,881		2,251	251	383,849	17,999
Utah	621	118	50,010	5,700		256	113	47,320	5,700
Vermont	216	216	6,204	6,204	**	121	121	5,999	5,999	**
Virginia	1,109	117	97,550	5,700		379	114	95,187	5,700
Washington	1,231	122	81,904	5,700		609	122	79,084	5,700
West Virginia	417	138	20,578	5,700		190	110	20,464	5,700
Wisconsin	1,099	181	61,686	8,024		649	168	61,152	8,085
Wyoming	192	137	7,639	5,700		89	89	7,042	5,700	*
Total	52,343	8,314	3,831,663	369,705		29,024	7,082	3,732,513	370,482

Sample sizes for each state do reflect the samples in the TUDA districts within the state, but do not reflect the impact of NIES oversampling.

* identifies jurisdictions where all schools (but not all students) for the given grade are included in the NAEP sample.

** identifies jurisdictions where all students for the given grade are included in the NAEP sample.

Stratification

Each state and grade will be stratified separately but using a common approach in all cases. TUDA districts will be separated from the balance of their state, and each part stratified separately. The first level of stratification will be based on urban-centered type of location. This variable has 12 levels (some of which may not be present in a given state or TUDA district), and these will be collapsed so that each of the resulting location categories contains at least 9 percent of the student population (12 percent for large TUDA districts and 18 percent for small TUDA districts). In those states with school oversampling for NIES, the schools to be oversampled will be placed in a separate stratum, apart from the location strata used for other schools.

Within each of the resulting location categories (with the exception of the NIES oversampling strata), schools will be assigned a minority enrollment status. This is based on the two race/ethnic groups that are the second and third most prevalent within the location category. If these groups are both low in percentage terms, no minority classification will be used. Otherwise three (or occasionally four) equal-sized groups (generally high, medium, and low minority) will be formed based on the distribution across schools of the two minority groups.

Within the resulting location and minority group classes (of which there are likely to be from three to fifteen, depending upon the jurisdiction), and the NIES oversampling stratum in states where this is applicable, schools will be sorted by a measure derived from school level results from the most recent available state achievement tests at the relevant grade. In general, mathematics test results will be used, but where these are not available, reading results will be used. In the few states that do not have math or reading tests at grades 4 and 8 (or where we are unable to match the results to the NAEP school frame), instead of achievement data, schools will be sorted using a measure of socio- economic status. This is the median household income of the 5-digit ZIP Code area where the school is located, based on the 2016 ACS (5-year) data. For BIE and DoDEA schools neither achievement data nor income data are available, and so grade enrollment is used in these cases.

Once the schools are sorted by location class, minority enrollment class, and achievement data (or household income), a systematic sample of schools will be selected using a random start. Schools will be sampled with probability proportional to size. The exact details of this process are described in the individual sampling specification memos.

2. Beta Sample

The beta sample comprises the national public school samples at grades 4, 8, and 12. At grades 4 and 8 the beta samples will be used for the national science assessments (PBA and DBA) and for pilot tests of reading, math, and vocabulary (DBA-only). At grade 12 the beta sample will be used for the operational reading, mathematics, and science assessments (PBA and DBA). Each of these samples will be nationally representative, selected to have minimal overlap with the alpha sample schools at the same grade. The number of students targeted per school will be 62 at grade 4, 63 at grade 8, and 68 at grade 12.

In order to increase the likelihood that the results for American Indian/Alaskan Native (AIAN) students can be reported for the operational samples, we will oversample high-AIAN public schools. That is, a public school with more than 5 AIAN students and greater than 5 percent AIAN enrollment will be given four times the chance of selection of a public school of the same size with a lower AIAN percentage. For all other schools, whenever there are more than 10 Black or Hispanic students enrolled and the combined Black and Hispanic enrollment exceeds 15 percent, the school will be given twice the chance of selection of a public school of the same size with a lower percentage of these two groups. This approach is effective in increasing the sample sizes of AIAN, Black, and Hispanic students without inducing undesirably large design effects on the sample, either overall, or for particular subgroups.

Stratification

The Beta samples will have an implicit stratification, using a hierarchy of stratifiers and a serpentine sort. The highest level of the hierarchy is Census division (9 implicit strata). The next stratifier in the hierarchy is type of location, which has twelve categories. Many of the type of location strata nested within Census divisions will be collapsed with neighboring type of location cells (this will occur if the expected school sample size within the cell is less than 4.0). These geographic strata will be subdivided into three substrata: 1) schools being oversampled for AIAN, 2) schools being oversampled for Blacks and Hispanics, and 3) low-minority schools not being oversampled. If the expected sample size in an oversampled substratum is less than 8.0, it will be left as is. If the expected sample size is greater than 8.0, then it will be subdivided into up to four substrata (two for expected sample size up to 12.0, three for expected sample size up to 16.0, and four for expected sample size greater than 16.0). For the oversampling strata, the subdivision will be by percentage AIAN or percentage Black and Hispanic, as appropriate. For the low-minority sampling strata, the subdivision will be by state or groups of contiguous states. Within these substrata, the schools are to

be sorted by school type (public, BIE, DoDEA) and median household income from the 2016 5- year ACS (using a serpentine sort within the school type substrata).

3. Delta Samples

These are the private school samples at grades 4 and 8 for conducting the operational assessments in reading and mathematics. The sample sizes are large enough to report results by Catholic and non- Catholic at grades 4 and 8. Approximately half the sample at each grade will be from Catholic schools. The number of students targeted per school will be 50 at each grade.

Stratification

The private schools are to be explicitly stratified by private school type (Catholic/Other). Within each private school type, stratification will be by Census region (4 categories), type of location (12 categories), race/ethnicity composition, and enrollment size. In general, where there are few or no schools in a given stratum, categories will be collapsed together, always preserving the private school type.

4. Epsilon Sample

With regard to subjects and grades assessed, this sample is analogous to the beta sample, but for private schools. However, in contrast to the beta sample, there will be no oversampling of high minority schools. The same stratification variables will be used as for the delta samples. The epsilon sample schools will have minimum overlap with the delta sample schools which, given the respective sample sizes, means that no schools will be selected for both the delta and epsilon samples at the same grade. The number of students targeted per school will be 62 at grade 4, 63 at grade 8, and 68 at grade 12.

To compensate for the fact that files used to create the NAEP school sampling frames are at least two years out of date at the time of frame construction, we will supplement the Alpha, Beta, Delta, and Epsilon samples with new school samples at each grade.

The new school samples will be drawn using a two-stage design. At the first stage, a minimum of ten school districts (in states with at least ten districts) will be selected from each state for public schools, and ten Catholic dioceses will be selected nationally for the private schools. The sampled districts and dioceses will be asked to review lists of their respective schools and identify new schools. Frames of new schools will be constructed from these updates, and new schools will be drawn with probability proportional to size using the same sample rates as their corresponding original school samples.

The school sample sizes in the above tables do not reflect new school samples.

V. Substitute Samples

Substitute samples will be selected for each of the Beta, Delta and Epsilon samples. The substitute school for each original will be the next “available” school on the sorted sampling frame, with the following exceptions:

1. Schools selected for any NAEP samples will not be used as substitutes.

2. Private schools whose school affiliation is unknown will not be used as substitutes. Also, unknown affiliated private schools in the original samples will not get substitutes.

3. A school can be a substitute for one and only one sample. (If a school is selected as a substitute school for grade 8, for example, it cannot be used as a substitute for grade 4.)

4. A public school substitute will always be in the same state as its original school.

5. A catholic school substitute will always be a Catholic school, and the same for non-Catholic schools.

The districts that are taking part in the TUDA program are volunteers. Thus it is possible that at some point over the next few months, a given district might choose to opt out of the TUDA program for 2019. However, it is not acceptable for all schools in such a district to decline NAEP, as then the state estimates will be adversely affected. Thus to deal with this possibility, in each TUDA district, subsamples of the alpha sample schools will be identified as contingency samples. In the event that the district withdraws from the TUDA program prior to the selection of the student sample, all alpha sampled schools from that district will be dropped from the sample, with the exception of those selected in the contingency sample. The contingency sample will provide a proportional representation of the district, within the aggregate state sample. Student sampling in those schools will then proceed in the same way as for the other schools within the same state.

7. Student Sampling

Students within the sampled schools will be selected with equal probability. The student sampling parameters vary by sample type (Alpha, Beta, Delta, and Epsilon) and grade, as described below.

Alpha Sample, Grades 4 and 8 Schools (Except Puerto Rico)

1. All students, up to 52, will be selected.

2. If the school has more than 52 students, a systematic sample of 50 students will be selected. In some schools, the school may be assigned more than one ‘hit’ in sampling. In these schools we will select a sample of size 50 times the number of hits, taking all students if this target is greater than or equal to 50/52 of the total enrollment.

Alpha Sample, Puerto Rico Grades 4 and 8

1. All students, up to 26, will be selected.

2. If the school has more than 26 students, a systematic sample of 25 students will be selected.

Delta Samples, Grades 4 and 8

1. All students, up to 52, will be selected.

2. If the school has more than 52 students, a systematic sample of 50 students will be selected.

1. At grade 4 all students will be selected, up to 70. If the school has more than 70 students, 62 will be selected. Of these students, 50 will be assigned to DBA and the rest to PBA. In schools with fewer than 21 students, all will be assigned to DBA or all to PBA. In schools with 32 to 37 students, 25 will be assigned to DBA and the rest to PBA. In all other schools, 25/31 of the students will be assigned to DBA with the rest to PBA.

2. At grade 8 all students will be selected, up to 70. If the school has more than 70 students, 63 will be selected. Of these students, 50 will be assigned to DBA and the rest to PBA. In schools with fewer than 21 students, all will be assigned to DBA or all to PBA. In schools with 31 to 37 students, 25 will be assigned to DBA and the rest to PBA. In all other schools, 50/63 of the students will be assigned to DBA with the rest to PBA.

3. At grade 12 all students will be selected, up to 75. If the school has more than 75 students, 68 will be selected. Of these students, 38 will be assigned to DBA and the rest to PBA. In schools with fewer than 20 students, all will be assigned to DBA or all to PBA. In schools with 32 to 36 students, 19 will be assigned to DBA and the rest to PBA. In all other schools, 19/34 of the students will be assigned to DBA with the rest to PBA.

7. Weighting Requirements

The Operational Reading and Mathematics Assessments, Grade 4 and 8

The sample weights will reflect probabilities of selection, school and student nonresponse, any trimming, and the random assignment to the particular subject. There will be separate replication schemes by grade and public/private. Weights will also be derived for the Puerto Rico KaSA assessment at grades 4 and 8.

The Operational Reading and Mathematics Assessments, Grade 12, and Science Assessment, Grades 4, 8, and 12

The exact weighting requirements for these samples have yet to be determined. One possibility is that three sets of weights will be required – for DBA alone, PBA alone, and DBA/PBA combined. The sample weights will reflect probabilities of selection, school and student nonresponse, any trimming, and the random assignment to the particular subject. There will be a separate replication scheme by grade and public/private.

As is standard practice, only preliminary weights will be provided for these assessments. The sample weights will reflect probabilities of selection, and the random assignment to the particular subject (necessary because these assessments are spiraled in with other assessment components).