1810-0618 Supporting Statement Part B Title II-A LEA Use of Funds Survey 9.24.24

SUPPORTING STATEMENT

FOR PAPERWORK REDUCTION ACT SUBMISSION

OMB Number: 1810-0618

B. Collection of Information Employing Statistical Methods

The agency should be prepared to justify its decision not to use statistical methods in any case where such methods might reduce burden or improve accuracy of results. The following documentation should be provided with the Supporting Statement Part A to the extent that it applies to the methods proposed. For further information, please obtain a copy of the FAQs for statistical surveys by the Office of Management and Budget via this link. The standards and guidelines are available from ICCD’s SharePoint site here.

1. Describe the potential respondent universe (including a numerical estimate) and any sampling or other respondent selection method to be used. Data on the number of entities (e.g., establishments, state and local government units, households, or persons) in the universe covered by the collection and in the corresponding sample are to be provided in tabular form for the universe as a whole and for each of the strata in the proposed sample. Indicate expected response rates for the proposed sample. Indicate expected response rates for the collection as a whole. If the collection had been conducted previously, include the actual response rate achieved during the last collection.

The target respondent universe for the Use of Funds District Survey is Title II-A subgrant recipients. For the 2025 survey administration, we will draw the sample from a list sampling frame constructed from a pre-release version of the 2023–24 National Center for Education Statistics (NCES) Common Core of Data (CCD) Public Elementary and Secondary Agency Universe File, which is expected to be available in summer 2024. Our approach for 2025 will follow the sampling method used in previous years. The sample frame for the 2024 survey administration (based on the 2022–23 CCD) included approximately 17,300 traditional school districts and charter school districts, of which about 13,100 (or 76%) were classified as traditional school districts and about 4,200 (24%) as charter school local educational agencies (LEAs) (i.e., school districts).¹ To be eligible for inclusion in the frame, school districts must be operational during the school year, have students enrolled, and be located in one of the 50 states, the District of Columbia (DC), or Puerto Rico (collectively referred to as states).² Because the poverty measure aligns well with the measure used to assign Title II-A funds, the sample design will use child poverty data from the U.S. Census Bureau Small Area Income and Poverty Estimates (SAIPE) Program. Specifically, we will use the estimated percentage of children ages 5 to 17 years old living in poverty. LEAs with missing SAIPE estimates, including charter LEAs and some traditional LEAs, will utilize Census tract child poverty estimates from the American Community Survey. For the remaining LEAs missing both estimates, a beta regression model with CCD Free and Reduced Lunch data as the predictor will be used to estimate child poverty rates for those districts. Subsequently, for any remaining LEAs still lacking estimates, another beta regression model with the School Neighborhood Poverty Estimates as the predictor will be used to estimate child poverty rates for those districts. No districts will be excluded from the frame based on child poverty rates. To ensure the sample is representative of Title II-A recipients, we will stratify by poverty status (higher/lower)³ to sample both higher and lower poverty districts.

The sample approach incorporates a target response rate of at least 80 percent, aligning with past survey administrations’ achieved response rates. Considering these estimates and the specified response rate target, we anticipate sampling around 5,000 traditional LEAs, covering all 50 states, the District of Columbia, and Puerto Rico. Additionally, a nationally representative sample of approximately 500 charter LEAs will be included, constituting roughly one third of all traditional and charter LEAs. This sampling strategy of 5,500 LEAs strikes a balance by minimizing respondent burden while enabling the Department to generate reliable descriptive statistics at both state and national levels, as demonstrated in previous cycles.

Traditional School District Sample

For the traditional school districts,⁴ we will stratify the frame by state to produce state-level estimates with the desired precision. We will use a precision-based power analysis (a precision level of a 10 percent coefficient of variation or twice a standard error of 5 percent for state-level estimates) to determine an appropriate sample size allocation for the traditional districts.

Each state with at least 60 targeted sampled traditional LEAs will have a minimum of eight strata (two size strata crossed by two urbanicity strata crossed by two poverty strata). ⁵ To avoid stratum fragmentation, each stratum must encompass at least seven to eight sampled districts. States with at least 120 targeted sampled traditional LEAs will have sixteen strata (two size strata crossed by four urbanicity strata crossed by two poverty strata) instead of eight.⁶ To ensure adequate representation and minimize sampling error for each state, even with potential nonresponse, states with fewer than 60 traditional LEAs will include a census of all districts in the sample. Additionally, to enhance estimates of Title II-A dollar amounts, we will sample with certainty those districts that are disproportionately larger than the next largest district in their state.

For the stratification by size within each state, we will employ a sampling method that balances the significance of incorporating large LEAs for more efficient estimation of size-related factors, while also including a reasonable number of small districts for more efficient estimation of proportions. This method will utilize proportional allocation based on the square root of the district student enrollment. The square root allocation provides a balanced approach, compromising between proportional to count allocation (e.g., the number of LEAs) and proportional to size allocation (e.g., the number of enrolled students within an LEA). This compromise allocation is preferred, as the survey results can offer estimates not only of the amounts of funds used but also of the proportions of districts with specific attributes that utilize funds differently. Following the proportional allocation of the state sample to size strata based on the sums of the size measures, a systematic sample with equal probability will be selected from each stratum, utilizing the zip code as the sorting variable. This approach aims to achieve a geographical spread of districts in the sample. The strata allocation for traditional LEAs is illustrated in Table 1.

Table 1. Strata Allocation for Traditional LEAs

Target Sample Size	Total Number of Strata	Number of Size Strata	Number of Urbanicity Strata	Number of Poverty Strata
< 60	1	1	1	1
60–120	8	2	2	2
> 120	16	2	4	2

To establish the sample size within states for traditional districts, our design aims to generate reliably precise estimates of the percentage of LEAs with specific characteristics and aggregate measures (e.g., total dollar amounts allocated for allowable activities). When determining the target total sample size and specific target sample sizes for each state, the objective is to achieve the most precise estimates at both national and state levels, ensuring similar reliability across states, as previously outlined. The sample allocation per state is detailed in Table 2, presenting the frame size based on the 2022–23 NCES CCD provisional data, the target sample size (i.e., the actual sample size aligned with the overall objective of a sample of about 5,000 traditional LEAs), and target completed surveys (i.e., the desired number of completed surveys given the overall objective of a sample of about 5,000 traditional LEAs with an estimated 80 percent response rate). Please note that total sample sizes may not precisely match target sample sizes due to rounding. For the 2024–25 sample, we will update the sample allocation per state using the 2023–24 CCD data.

Table 2. Frame Size and Sample Allocation for Traditional LEAs, by State (2023–24 sample)

State	Frame Size	Target Sample Size	Target Completed Surveys
Alabama	139	96	77
Alaska	54	54	43
Arizona	216	113	90
Arkansas	233	108	86
California	986	180	144
Colorado	178	107	86
Connecticut	169	114	91
Delaware	19	19	15
District Of Columbia	1	1	1
Florida	67	60	48
Georgia	180	99	79
Hawaii	1	1	1
Idaho	115	91	73
Illinois	853	162	130
Indiana	290	115	92
Iowa	327	128	102
Kansas	286	117	94
Kentucky	171	101	81
Louisiana	71	64	51
Maine	221	118	94
Maryland	24	24	19
Massachusetts	321	128	102
Michigan	537	148	118
Minnesota	327	125	100
Mississippi	138	91	73
Missouri	517	136	109
Montana	396	152	122
Nebraska	244	113	90
Nevada	18	18	14
New Hampshire	167	95	76
New Jersey	544	144	115
New Mexico	89	72	58
New York	685	156	125
North Carolina	125	82	66
North Dakota	168	136	109
Ohio	616	142	114
Oklahoma	509	138	110
Oregon	173	114	91
Pennsylvania	499	135	108
Puerto Rico	1	1	1
Rhode Island	36	36	29
South Carolina	76	74	59
South Dakota	149	110	88
Tennessee	140	94	75
Texas	1021	152	122
Utah	41	41	33
Vermont	51	51	41
Virginia	131	85	68
Washington	297	133	106
West Virginia	55	55	44
Wisconsin	420	130	104
Wyoming	48	48	38
Total	13,140	5,007	4,005

Note. Detail may not sum to totals because of rounding.

Source. National Center for Education Statistics, Common Core of Data, Local Education Agency (School District) Universe Survey Data, 2022–23 provisional file.

Charter School District Sample

In the case of the sample of charter LEAs,⁷ the frame will be stratified by enrollment size, urbanicity, and poverty estimates, aiming for an approximately 8 percent coefficient of variation (two times a standard error of 4 percent) at the national level. As in the approach for traditional LEAs, the square root of the district size of student enrollment will be utilized for charter LEA sampling, striking a balance between the inclusion of large districts and a reasonable number of small districts within each size stratum. The sample will aim for a minimum of 12 charter LEAs per stratum, with a total target sample of about 500 charter LEAs distributed across 40 strata formed by crossing five size strata with four urbanicity strata with two poverty strata.⁸ As in the traditional LEA sample, an equal probability sample will be chosen from each stratum. The frame size and sample allocation for charter LEAs is detailed in Table 3.

Table 3. Frame Size and Sample Allocation for Charter LEAs, by Stratum (2023–24 sample)

Strata: Size	Strata: Urbanicity	Strata: Poverty	Frame Size	Target Sample Size	Target Completed Surveys
Quintile 1	Urban	Lower	170	9	7
Quintile 1	Urban	Higher	280	16	13
Quintile 1	Suburban	Lower	94	5	4
Quintile 1	Suburban	Higher	75	4	3
Quintile 1	Town	Lower	47	3	2
Quintile 1	Town	Higher	35	2	2
Quintile 1	Rural	Lower	92	5	4
Quintile 1	Rural	Higher	47	2	2
Quintile 2	Urban	Lower	207	18	14
Quintile 2	Urban	Higher	301	25	20
Quintile 2	Suburban	Lower	118	10	8
Quintile 2	Suburban	Higher	76	6	5
Quintile 2	Town	Lower	34	3	2
Quintile 2	Town	Higher	25	2	2
Quintile 2	Rural	Lower	56	5	4
Quintile 2	Rural	Higher	27	2	2
Quintile 3	Urban	Lower	244	26	21
Quintile 3	Urban	Higher	325	35	28
Quintile 3	Suburban	Lower	120	13	10
Quintile 3	Suburban	Higher	68	7	6
Quintile 3	Town	Lower	12	1	1
Quintile 3	Town	Higher	10	1	1
Quintile 3	Rural	Lower	42	5	4
Quintile 3	Rural	Higher	12	1	1
Quintile 4	Urban	Lower	185	25	20
Quintile 4	Urban	Higher	312	41	33
Quintile 4	Suburban	Lower	157	21	17
Quintile 4	Suburban	Higher	81	11	9
Quintile 4	Town	Lower	12	2	2
Quintile 4	Town	Higher	17	2	2
Quintile 4	Rural	Lower	41	6	5
Quintile 4	Rural	Higher	34	4	3
Quintile 5	Urban	Lower	229	48	38
Quintile 5	Urban	Higher	250	54	43
Quintile 5	Suburban	Lower	163	36	29
Quintile 5	Suburban	Higher	88	21	17
Quintile 5	Town	Lower	14	3	2
Quintile 5	Town	Higher	14	3	2
Quintile 5	Rural	Lower	61	13	10
Quintile 5	Rural	Higher	20	4	3
Total			4,195	500	401

Note. Some strata will be collapsed to achieve minimum sample size. Detail may not sum to totals because of rounding.

Source. National Center for Education Statistics, Common Core of Data, Local Education Agency (School District) Universe Survey Data, 2022-23 provisional file.

2. Describe the procedures for the collection of information, including:

• Statistical methodology for stratification and sample selection.

• Estimation procedure.

• Degree of accuracy needed for the purpose described in the justification.

• Unusual problems requiring specialized sampling procedures, and

• Any use of periodic (less frequent than annual) data collection cycles to reduce burden.

Notification of the Sample

We will send a notification letter via email to the district contact for Title II, Part A identified by the state education agency (SEA) Title II-A coordinator (see Appendix C) explaining the study and emphasizing the importance of the district’s response to this data collection. The notification letter also will include login information. School districts receiving educational funds are obligated to participate in Department evaluations (Educational Department General Administrative Regulations (EDGAR) (34 CFR § 76.591)). The district letter will note the mandatory nature of their response.

The notification email will be sent to district contacts beginning in April 2025 and will include a URL to the web-based data collection form and reference an invitation email they will receive shortly after the letter is mailed. The district survey URL will include embedded login information to reduce the number of communications from the study team to securely provide login information separate from the survey URL. This method of providing login information also reduces the burden of sharing access to the survey within the district if a different respondent is identified as the best person to complete the survey.

We will monitor completion rates, review the survey responses for completeness through the field period, and follow-up by email and telephone as needed to answer questions and encourage completion.

Statistical Methodology for Stratification and Sample Selection

The study will include a nationally- and state-representative sample of traditional school districts and a nationally-representative same of charter school districts. Based on experience, the study design assumes that 80 percent of school districts will respond. Therefore, the study team plans to construct weights to account for district-level nonresponse.

Estimation Procedures

We will develop survey weights for the LEA survey, starting with the base weight (i.e., the inverse of the sampling probability), and adjusting for unit nonresponse. The nonresponse weighting adjustment will use the sampling strata (i.e., size, urbanicity, and poverty) as nonresponse cells, as past work in this study has indicated not much difference in response propensity within strata. We will use the jackknife variance estimator to estimate the variance of LEA survey estimates by creating variance strata and primary sampling units within the strata. This is important for unbiased variance estimation for complex survey designs such as this study. The sample design and weighting procedures described here were established starting in the 2018–19 data collection and have been applied successfully in the past rounds of data collection.

Degree of Accuracy Needed

For the sample of traditional school districts, we will calculate an effective sample size using a design effect of 1.3 to account for variable sampling weights and a standard error of 5 percent. We expect to achieve a precision level of 10 percent coefficient of variation (two times the standard error) for state-level estimates for traditional school districts, so that the total of all states is 5,000 sampled districts.

For the sample of charter school districts, we will stratify the frame by enrollment size, urbanicity, and poverty estimates with the desired precision of about 8 percent coefficient of variation (two times the standard error) at the national level, so that the total is 500 charter districts.

Universal Problems Requiring Specialized Sample Procedures

There are no unusual problems requiring specialized sampling procedures.

Use of Periodic (less than annual) Data Collection to Reduce Burden

Section 2104(b) of ESEA requires school districts to describe how Title II, Part A funds are used. To understand how school districts are using Title II, Part A funds and if school districts are improving equitable access to teachers for low-income and minority students as outlined under Section 2104(b), the Department requires information on an annual basis.

3. Describe methods to maximize response and to deal with issues of non-response. The accuracy and reliability of information collected must be shown to be adequate for intended uses. For collections based on sampling, a special justification must be provided for any collection that will not yield “reliable” data that can be generalized to the universe studied.

The study team will work with school districts to explain the importance of this data collection effort and to make it as easy as possible to comply. We will provide the Office of Management and Budget clearance information along with a clear description of the study and its importance. We will be courteous, yet persistent in following up with participants who do not respond in a timely manner to our attempts. We also will be very flexible gathering the data, allowing different people to respond if necessary. Project staff will monitor completion rates and follow up by email and telephone as needed to answer questions and encourage completion. Throughout the data collection period, project staff will review data provided for completeness and follow up with respondents with any questions about data and provide respondents with an opportunity to update data if needed.

4. Describe any tests of procedures or methods to be undertaken. Testing is encouraged as an effective means of refining collections of information to minimize burden and improve utility. Tests must be approved if they call for answers to identical questions from 10 or more respondents. A proposed test or set of tests may be submitted for approval separately or in combination with the main collection of information.

The previous study team conducted a pilot test of the district survey with seven respondents and held debriefing sessions during October and November 2021 to ensure that questions are clear and that the average completion time is within expectations. Based on the feedback from these districts, the study team added or revised text in the survey instructions and response options for several survey questions to improve clarity. Based on the respondents’ reported time to complete the survey, the study team determined the estimated average time to complete the survey is approximately 120 minutes at maximum. We will use the same set of questions in the 2024–25 survey.

5. Provide the name and telephone number of individuals consulted on statistical aspects of the design and the name of the agency unit, contractor(s), grantee(s), or other persons who will actually collect and/or analyze the information for the agency.

Cong Ye, Principal Researcher at AIR ([email protected]), was consulted on the statistical aspects of the survey sample design.

1 Traditional LEAs function as the local government administrative authority overseeing the education system at a designated local level, representing the interests of the public and the state. Within the Common Core of Data, these are categorized as local educational agency types 1 and 2, except in the instances of New York City and Vermont, where they fall under type 3. In these specific cases, financial data from component districts under supervisory unions were unavailable, leading to the inclusion of supervisory unions in the sampling frame. Charter school LEAs are educational entities established under state charter legislation; these districts exclusively operate charter schools and operate autonomously without administrative control from another LEA. Within the CCD, these are categorized as LEA type 7.

2School districts excluded from the respondent universe are those outside of the 50 states; DC; or Puerto Rico (e.g., American Samoa, BIE, DoDEA, Guam, Northern Mariana Island, Virgin Islands); those that are neither regular school districts nor charter school districts; those that were closed or otherwise not operational during the school year; and those with missing enrollment or an enrollment of 0 students.

3Poverty strata were defined by the estimated percentage of children ages 5 to 17 years old in the district who are in poverty. Each state's median district poverty percentage was used to create two poverty strata (above and below the median).

4Traditional school districts are the local government administrative authority that governs the education system at a specified local level on behalf of the public and the state. Within the CCD data, these are districts types 1 and 2. However, in the case of New York City and Vermont, they are type 3. Based on past experience, New York City and Vermont will sample the supervisory union because the component districts under supervisory unions are unable to respond to the Use of Funds District Survey.

5 The two size strata will be established by calculating the median of the square root of the district size of student enrollment within the state. For the two urbanicity strata, the four high-level locale categories (city, suburban, town, and rural) will be grouped into two categories (city/suburban vs. town/rural). The two poverty strata will be determined using the median district percentage of poor students across all traditional districts in the state.

6 States with a targeted sample size of at least 120 districts will be allocated 16 strata, while states with a target sample size ranging from 60 to 120 districts will be assigned 8 strata.

7 Charter school LEAs are educational entities established under state charter legislation; these districts exclusively operate charter schools and operate autonomously without administrative control from another Local Educational Agency (LEA). Within the Common Core of Data (CCD), these are categorized as LEA type 7.

8 The five size strata will be established by utilizing quintiles of the square root of the district size of student enrollment. The four urbanicity strata will be created based on the four high-level locale categories (city, suburban, town, and rural). The two poverty strata will be determined by utilizing the median district percentage of impoverished students across all charter LEAs in the nation. Based on historical data, an anticipated two percent of the sampled districts are projected to be ineligible.