UFEF OMB2 Supp Stmt PtB 12-1-20 (1)

December 1, 2020

Supporting Statement for OMB Clearance Request

Part B: Collection of Information Employing Statistical Methods

Study of District and School Uses of Federal Education Funds

Submitted to:

Stephanie Stullich

National Center for Education Evaluation

Institute of Education Sciences

U.S. Department of Education

550 12th Street, SW

Washington, DC 20202

Prepared by:

SRI International

Ashley Campbell

Julie Harris

Deborah Jonas

Jaunelle Pratt-Williams

Augenblick, Palaich & Associates

Bob Palaich

Robert Reichardt

Contract GS-10F-0554N/BPA Order ED-PEP-16-A-0005/91990019F0407 (Task 4.11)

Content

s

B. Collections of information employing statistical methods 1

1. Respondent universe and selection methods 1

2. Procedures for the collection of information 3

State extant data and documents (previous OMB package) 3

District- and school-level data collection (current OMB package) 3

3. Methods to maximize response rates and to deal with issues of nonresponse 3

4. Tests of procedures or methods to be undertaken to minimize burden and improve utility 4

5. Names and telephone numbers of individuals consulted on statistical aspects of the design and the names of the contractors who will actually collect or analyze the information for the agency 4

Exhibits

Exhibit 1. Universe of respondents and sample selection 1

Exhibit 2. Preliminary sampling framework for districts that receive federal funds 2

Exhibit 3. Staff responsible for collecting and analyzing study data 4

Introduction

The U.S. Department of Education, through its Institute of Education Sciences (IES), is requesting clearance for a new data collection to examine how the distribution of federal funds varies in relation to program goals and student needs.

This information clearance request is for a study to examine targeting and resource allocation for five major federal education programs: Part A of Titles I, II, III, and IV of the Elementary and Secondary Education Act (ESEA) — including school improvement grants provided under Section 1003 of Title I, Part A — as well as Title I, Part B of the Individuals with Disabilities Education Act (IDEA). The study will also collect information on the distribution and uses of funds provided to school districts through the Coronavirus Aid, Relief, and Economic Security Act (CARES Act).

This package is the second of two OMB clearance requests for this study. The previous package requested approval for selection and recruitment of the study sample and was approved by OMB on June 24, 2020.

B. Collections of information employing statistical methods

1. Respondent universe and selection methods

The study will select a sample of districts and schools that is representative of the population of interest, which includes all districts, and schools that receive funds from Part A of Title programs I, II, III, IV, and/or Title I, Part B of the Individuals with Disabilities Education Act. Exhibit 1 provides information about the universe of potential respondents, sample size (where applicable), and expected response rates.

Exhibit 1. Universe of respondents and sample selection

Data collection activity	Universe of respondents	Sample selection	Expected response rate
Extant data and documents	All states and the District of Columbia	All states and the District of Columbia	100 percent
Fiscal and personnel data	17,554 districts 99,785 schools1	400 districts 2,8002	> 80 percent

¹The estimated number of districts and schools in the universe of respondents for the resource allocation data came from the NCES Common Core of Data (2018-19 school year). The number of districts includes all regular public school and independent charter districts, which excludes regional education service agencies and supervisory union administrative centers, state-operated agencies, and federally operated agencies. The number of schools includes all public schools (including all types of charter schools as well).

²Estimated number of schools assuming an average of 7 schools per district. The sample will include all schools from within the sampled 400 districts.

District-level sampling criteria

The sample will be determined by first selecting 400 districts, stratifying based on district size (number of students), predominant locale (urban, rural, or suburban), region of the country, and poverty rate. Districts will have equal probabilities of selection within these strata, with the exception that we will include extremely large districts (defined as those in the top 0.1 percent of student count) with certainty. Stratifying based on these variables will allow us to ensure adequate representation of districts with important characteristics that may be excluded from the random sample. Random selection within the identified strata allows us to increase the generalizability of the results within each subgroup.

Exhibit 2 summarizes the district sampling framework.

Exhibit 2. Preliminary sampling framework for districts that receive federal funds

Region	Locale type	Number of students	Poverty rate	Number of sample districts
Northeast	City	Large	High	13
			Low	13
	Urban fringe/town	Large	High	13
			Low	13
		Small	High	12
			Low	12
	Rural	Small	High	12
			Low	12
Midwest	City	Large	High	13
			Low	13
	Urban fringe/town	Large	High	13
			Low	13
		Small	High	12
			Low	12
	Rural	Small	High	12
			Low	12
South	City	Large	High	13
			Low	13
	Urban fringe/town	Large	High	13
			Low	13
		Small	High	12
			Low	12
	Rural	Small	High	12
			Low	12
West	City	Large	High	13
			Low	13
	Urban fringe/town	Large	High	13
			Low	13
		Small	High	12
			Low	12
	Rural	Small	High	12
			Low	12

Note: The study team will specify operational definitions of each stratum after examining relevant district- and school-level data, likely using a natural cut point in the distribution to define large/small and high/low. We may decide to implement separate strata definitions for each locale type if we find sufficient variation within locales. We may decide to include additional strata if, for example, we find there are many districts in rural communities that have a large number of students. District poverty rates will be based on the most recent data available from the Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program. Data for other sampling strata will be obtained from the NCES Common Core of Data (CCD).

School-level sampling criteria

The sample will include all schools within sampled districts. Districts will be asked to provide fiscal and personnel data for all schools within their district.

2. Procedures for the collection of information

State extant data (previous OMB package)

State-level extant data will be collected in two phases. Based on OMB’s prior approval for the study design and the collection of preliminary state-level information (1850-0951), all 50 states and the District of Columbia will receive a letter by email requesting lists of subgrantees and suballocation amounts for each program, the state chart of accounts, and a cross-walk from F-33 survey revenue and expenditure data reporting codes to the state chart of accounts.

In the second phase of extant data collection from states (after OMB approval for the fiscal data collection instruments), we will collect the school-level expenditure data that states are required to make publicly available through state and district report cards. We will first seek to harvest machine-readable data from SEA websites and, in states where these data are not readily accessible in a machine-readable format, we will ask the states to provide such data electronically.

District- and school-level data collection (current OMB package)

The district-and school-level data collection will be a collection of fiscal data through exported accounting files or resource allocation workbooks from the nationally representative sample of 400 districts and sampled schools within those districts.

• Fiscal and personnel data. The study will collect detailed fiscal data on the uses of federal education funds, including program revenues, expenditures, and personnel data,¹ from the nationally representative sample of school districts. District staff will be asked to provide these data for both the district at large and for all schools within the district. Data will be collected via Excel files, that are typically exported from district accounting systems. If this is not possible for a distrct, Excel workbooks that have been customized to the accounting codes and conventions used in each state are available for participants. Districts will be given the option to submit the data in a format of the respondent’s choosing.

3. Methods to maximize response rates and to deal with issues of nonresponse

To minimize respondent burden and to facilitate collection of valid and reliable data, respondents will receive a webinar that provides an overview of data collection instruments (i.e., details of requested expenditure data), operational definitions for easy reference, and a regularly updated frequently asked questions (FAQ) guide. In addition, project staff with will be available to respond to email or phone questions within 24 business hours of receiving a question. Team members will be assigned to regions so that participating districts have a consistent, personal point of contact to answer their questions and support their data submission. Respondents’ ongoing questions will receive one-on-one video or phone meetings to discuss their individual needs.

A week after receiving the resource allocation data workbooks, respondents will receive a follow-up email that includes a reminder of the due date and invites them to contact the data collection administrator with any questions or concerns. Follow up with nonrespondents will continue via email approximately once a week for three weeks. Persistent nonrespondents will receive additional follow up by telephone. Similar approaches in past data collection activities have yielded very high response rates, but bias due to nonresponse is still a possibility. To mitigate this potential for bias, SRI will fit a logistic regression to model the probability of responding as a function of district characteristics. Each respondent’s initial weight (described above) will be modified using the estimated probability of response (i.e., multiplying the initial weight by the inverse of the probability of response) to generate a final weight. Statistical analyses will then be weighted by the final weight to obtain conclusions that are representative of the universe of eligible districts.

4. Tests of procedures or methods to be undertaken to minimize burden and improve utility

The resource allocation data collection processes will be piloted with up to nine individual respondents. These pilot tests help researchers understand how instruments can be improved by providing information about clarity of questions, specificity of measures, and the overall user-friendliness of the instruments. Follow-up phone calls with pilot respondents will help the study team learn more about the respondents’ understanding of data collection format, data entry procedures, and definitions of key terms. This feedback will be incorporated into revisions of the instruments.

5. Names and telephone numbers of individuals consulted on statistical aspects of the design and the names of the contractors who will actually collect or analyze the information for the agency

Exhibit 3. Staff responsible for collecting and analyzing study data

Name	Project role	Organization	Phone number
Ashley Campbell	Project director	SRI	720-389-5906
Julie Harris	Study design and quantitative research expert	SRI	703-247-8619
Rebecca Schmidt	Senior advisor	SRI	703-247-8491
Robert (Bob) Palaich	Deputy project director	APA	720-227-0072
Mark Fermanich	Data collection oversight	APA	720-227-0101
Robert Reichardt	Design, instrumentation, and analysis contributor	APA	702-227-0098
Justin Silverstein	Design, instrumentation, and analysis contributor	APA	720-227-0075

1 Personnel data are public information but typically are not readily accessible online.