|
||||||||||||||||
|
||||||||||||||||
The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy. 5
|
||||||||||||||||
 |
 |
Page 103 |
 |
 |
 |
|
| ||||||||||||||||||||||||||||||||
|
|
|||||||||||||||||||||||||||||||
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 103
5
Comparison of American Community Survey Estimates and State Counts
The previous chapters have described various aspects of the American Community Survey (ACS) and have documented state policies, practices, and criteria that affect the counts of English language learner (ELL) students that are reported by state education agencies. It is readily apparent that these two allowable sources of data for use in allocating Title III funds to states have distinct strengths and weaknesses. In this chapter, we first briefly discuss the concepts and methods that underlie the two counts. We then consider in depth the differences between and the ACS estimates and state-provided counts on several dimensions.
CONCEPTUAL DIFFERENCES IN THE TWO SOURCES
The ACS estimates and state-provided counts of ELL students are two very different mechanisms for determining the number of school-age children in a state likely to have difficulty with English. As shown in Table 5-1, they differ along a number of dimensions.
The ACS is an indirect and subjective measure in that a parent or other adult household member provides an assessment for each child in the home. Since the question only asks about spoken English, it focuses on a single modality, and no context for English use is specified. The respondent may be considering the child’s proficiency with English in any number of settings (i.e., family life, community, social, academic), and the child may have different levels of proficiency in different settings. However, the ACS questions and criteria are consistent across states.
In contrast, the state-provided counts are based on direct, relatively objective measures of students’ English language proficiency. The counts are based on comprehensive processes established by state and local education agencies that consider
OCR for page 104
TABLE 5-1 Differences Between the ACS Estimates and State-Provided Counts of ELL Students
Type of Difference
ACS Estimate
State-Provided Count
Age Range
5-21 years of age
Not specified (elementary and secondary school-aged population is usually defined as 5-18 years of age)
School Enrollment
Enrollment status not specified (i.e., includes public and private schools)
Newly and continually enrolled in elementary and secondary schools for which Consolidated State
Performance Reports are submitted by state education agencies (i.e., public schools including charter schools)
Assessment Method
Single question regarding spoken English ability
Comprehensive assessment that incorporates information from multiple sources
Mode of Response
Indirect and subjective measure, based on the response of a parent (or other adult in the household) to a single question
Direct evaluation based on a student’s performance in acquiring English proficiency
Modality(ies) Assessed
Speaking
Speaking, listening, reading, and writing
Context Assessed
Not specified: likely to be community and family setting
Classroom setting
Basis for Distinguishing Proficient from Not Proficient
Single national cut score
State- or local-determined criteria
Comparability Across States
Item is identically presented across the nation; estimates based on a uniform methodology across the states
States use different assessments, procedures, cut scores, and criteria; estimates based on different methodologies
language proficiency across multiple modalities (listening, speaking, reading, and writing). The measures of language proficiency explicitly address both academic and social contexts. Unlike the ACS estimates, the information from the states varies because the policies, practices, and criteria used by the states are not uniform.
CONCLUSION 5-1 The criteria used by the states for counts of English language learner students are more conceptually sound than the criteria on which American Community Survey (ACS) estimates are based. However, the policies, practices, and criteria used by the states differ from state to
OCR for page 105
state, while the ACS provides estimates on the basis of a uniform methodology across the country.
Despite their differences, the ACS estimates and state counts represent conceptually similar entities—both measure the number of school-age children in the state that have not mastered English. Thus, some level of correspondence between the two measures would be expected.
We conducted a series of analyses to evaluate the consistency of the ACS and state-provided percentages of ELL students. In order to facilitate comparisons between the ACS estimates and state-provided counts we limit the ACS population to those aged 5-18 and only to those enrolled in public school. It is important to point out that by limiting the ACS estimate to this comparison group, we have created an ACS-based variable that is more limited than the legal definition of ELL students used by the U.S. Department of Education (DoEd).
As detailed in Chapters 2 and 4, there are two ways to calculate the percentages: the number of ELL children in the state as a percentage of the total number of ELL children in the country, which is the state’s share of ELL children; and the proportion that ELL students constitute of the total number of enrolled students, which is the rate of ELL students. We also conducted a series of multiple regression analyses to evaluate the correspondence between the ACS and state estimates. In these analyses, we focus on rates, rather than shares, in order to assess the degree of consistency of the two data sources in a manner that is relatively independent of state population. That is, analyses that focus on state counts or shares are dominated by the agreement between the ACS and state-provided numbers for some states, suggesting only that certain states (notably, California and Texas) are large and others are small, a trivial finding that provides little information about how well the two measures agree on estimation of ELL students.
COMPARISON OF SHARES OF ELL STUDENTS
In this section we compare the state shares (of Title III funding) based on ACS estimates with those based on state-provided counts. Since the funding allocations are based on each state’s share of ELL students in the country, this analysis allows us to evaluate how the allocations would be affected on the basis of which measure was used, as well as the ways that the measures would result in different funding decisions. We compare the shares in three ways: (1) the percentage shares themselves, (2) the ratio of the shares, and (3) the absolute differences in the shares across the states.
State Percentage Shares
Table 5-2 shows each state’s share of ELL students based on the two data sources. The first four columns on the left-hand side of the table show the shares based on the ACS estimates. Included are 1-year estimates for 2006, 2007, and 2008
OCR for page 106
TABLE 5-2 Shares of ELL Students Based on ACS and State-Provided Counts (in percentage)
State-Provided Count
ACS Estimate
All ELLa
Tested Not, Proficientb
State
2006
2007
2008
2006-2008
2006-2007
2007-2008
2008-2009
2007-2008
2008-2009
Alabama
0.43
0.40
0.40
0.40
0.43
0.46
0.43
0.53
0.40
Alaska
0.17
0.17
0.15
0.17
0.48
0.37
0.27
0.46
0.44
Arizona
3.66
3.90
3.69
3.74
3.56
3.31
2.79
4.15
2.86
Arkansas
0.45
0.48
0.37
0.44
0.55
0.57
0.61
0.77
0.80
Californiac
29.12
28.31
27.25
28.12
36.35
34.32
33.68
29.37
28.63c
Colorado
1.70
1.74
1.66
1.70
2.10
1.89
1.98
1.75
2.70
Connecticut
0.88
0.65
0.58
0.71
0.61
0.66
0.66
0.61
0.54
Delaware
0.17
0.16
0.15
0.16
0.15
0.16
0.16
0.10
0.16
District of Columbia
0.08
0.06
0.07
0.06
0.11
0.11
0.13
0.15
0.15
Florida
5.54
5.51
5.25
5.38
5.47
5.11
5.03
5.42
5.16
Georgia
2.10
2.20
1.97
2.09
1.73
1.77
1.80
2.05
2.01
Hawaii
0.30
0.22
0.38
0.29
0.37
0.41
0.41
0.49
0.50
Idaho
0.28
0.25
0.32
0.29
0.39
0.37
0.39
0.46
0.34
Illinois
4.52
4.81
4.74
4.67
4.03
4.20
4.55
3.56
3.24
Indiana
0.91
0.81
0.89
0.88
0.99
1.02
1.02
1.26
1.33
Iowa
0.44
0.40
0.40
0.43
0.42
0.43
0.45
0.47
0.45
Kansas
0.51
0.49
0.54
0.52
0.67
0.70
0.76
0.93
0.86
Kentucky
0.31
0.39
0.45
0.41
0.25
0.28
0.32
0.38
0.41
Louisiana
0.26
0.38
0.39
0.36
0.20
0.25
0.28
0.38
0.33
Maine
0.14
0.11
0.08
0.12
0.09
0.09
0.09
0.10
0.12
Maryland
0.96
1.10
0.98
1.03
0.80
0.89
0.89
0.65
1.07
Massachusetts
1.71
1.57
1.61
1.64
1.26
1.23
1.09
0.86
1.16
Michigan
1.56
1.51
1.40
1.49
1.63
1.14
1.35
1.86
1.23
Minnesota
1.18
1.22
1.35
1.27
1.49
1.35
1.37
1.17
1.67
Mississippi
0.18
0.16
0.16
0.20
0.12
0.12
0.15
0.04
0.18
OCR for page 107
Missouri
0.56
0.69
0.60
0.64
0.52
0.42
0.36
0.40
0.52
Montana
0.06
0.05
0.04
0.06
0.16
0.15
0.10
0.05
0.03
Nebraska
0.41
0.37
0.39
0.39
0.42
0.42
0.41
0.40
0.38
Nevada
1.02
1.15
1.38
1.19
1.64
1.04
1.69
2.08
2.12
New Hampshire
0.06
0.11
0.09
0.09
0.07
0.07
0.09
0.09
0.11
New Jersey
2.55
2.29
2.57
2.48
1.27
1.20
1.20
1.28
1.30
New Mexico
0.97
0.84
0.74
0.83
1.42
1.35
1.20
1.47
1.40
New York
6.63
6.36
6.80
6.61
4.57
4.65
4.09
5.59
5.31
North Carolina
2.06
1.88
2.25
2.04
2.05
2.82
2.53
3.33
2.97
North Dakota
0.04
0.07
0.06
0.05
0.06
0.10
0.09
0.14
0.06
Ohio
1.03
0.93
1.02
1.00
0.68
0.77
0.81
0.97
1.00
Oklahoma
0.45
0.53
0.47
0.50
0.89
0.83
0.85
0.97
0.91
Oregon
1.25
1.23
1.14
1.21
1.44
1.37
1.40
1.81
1.77
Pennsylvania
1.48
1.52
1.60
1.53
1.06
1.02
1.06
1.18
1.02
Rhode Island
0.19
0.29
0.27
0.25
0.23
0.16
0.16
0.19
0.21
South Carolina
0.51
0.57
0.51
0.56
0.70
0.63
0.70
0.86
0.89
South Dakota
0.05
0.05
0.09
0.07
0.08
0.09
0.08
0.09
0.09
Tennessee
0.61
0.67
0.66
0.68
0.54
0.57
0.61
0.63
0.59
Texas
16.98
17.75
18.55
17.63
11.69
15.31
15.97
15.09
15.25
Utah
0.75
0.77
0.71
0.75
1.13
1.03
0.99
0.91
0.88
Vermont
0.04
0.03
0.03
0.03
0.04
0.03
0.03
0.04
0.04
Virginia
1.57
1.22
1.45
1.42
2.01
1.86
1.93
1.73
2.67
Washington
2.04
2.31
2.35
2.22
1.98
1.78
1.84
2.21
2.22
West Virginia
0.12
0.09
0.09
0.11
0.05
0.05
0.04
0.04
0.03
Wisconsin
0.96
1.18
0.86
1.01
0.96
0.97
1.06
0.42
1.43
Wyoming
0.04
0.03
0.05
0.04
0.07
0.05
0.05
0.06
0.06
Total Count in the U.S.
2,491,160
2,482,420
2,433,845
2,462,330
4,289,046
4,525,892
4,499,072
3,052,983
3,131,685
aThe total number of ELL students was obtained from the EDEN database.
bThe number of tested, not proficient students was computed for each state from the state Consolidated State Performance Reports by subtracting the number of all LEP students proficient or above on state annual ELP assessments (1.6.3.1.2) from the number of all LEP students tested on state annual ELP assessments (1.6.3.1.1).
cCounts for California for 2008-2009 were unavailable, percentage is based on the 2007-2008 count.
OCR for page 108
and the 3-year estimate across these years. The table includes two types of shares calculated from the state-provided counts. Three of the columns show the state-provided counts of all ELL students for the 2006-2007, 2007-2008, and 2008-2009 school years. The other two columns show the shares based on the state-provided counts of ELL students who were determined to be not proficient on the English language proficiency (ELP) test for the 2007-2008 and 2008-2009 school years.
Ratios of the Shares
To help compare the percentages from the two measures, we calculated the ratio of the share based on the ACS estimate to the share based on the state-provided counts. These ratios are shown in Table 5-3. The tables include the ratios of the ACS 1-year estimate to the state counts for each school year, as well as the ratios of the ACS 3-year estimate to the most recent state school year data.
Ratios higher than 1.00 indicate that the share based on the ACS estimate was higher than the share based on the state-provided count, and ratios that are less than 1.00 indicate that the state-provided count was higher than the ACS estimate. Scanning the ratios across the time spans and the type of state-provided counts reveals considerable consistency. That is, for a given state, the ratios were generally consistently above 1.00 (ACS estimate higher than state-provided count) or consistently below 1.00 (state-provided count higher than ACS estimate). It is difficult to discern any explanatory factors from this comparison. No patterns appear to be evident due to region of the country or type of proficiency test used. For example, about half of the states that used the ACCESS for ELLs test developed by the World-Class Instructional Design and Assessment Consortium (see Table 4-1 in Chapter 4) had ACS rates higher than the state rates and half were lower.
Absolute Differences in the Shares
To quantify the potential effects of the differences between the two data sources in terms of the distribution of Title III funds, we calculated the total absolute differences between the shares based on ACS estimates and those based on state-provided counts. The differences are shown in Table 5-4. The left-hand side of the table shows the values for the differences between ACS estimates and the state-provided counts of all ELL students, for both 1-year and 3-year ACS estimates; the right-hand side of the table shows the values of the differences between ACS estimates and the state-provided counts of tested, not proficient students.
This quantity varies from about 20 percent to 26 percent of the total allocation, depending on the years considered. Because every dollar moved is counted twice in this total (once when it is taken from a state with a reduced share and once when added to one with an increased share), it means that from 10 percent to 13 percent of the total dollars would be moved by switching from one allocation to another, a substantial change in allocations.
OCR for page 109
TABLE 5-3 Ratio of State Shares Based on ACS Estimate to Shares Based on State-Provided Counts
All ELL Studentsa
Tested, Not Proficient Studentsb
State
ACS 2006 to State 2006-2007
ACS 2007 to State 2007-2008
ACS 2008 to State 2008-2009
ACS 2006-2008 to State 2008-2009
ACS 2007 to State 2007-2008
ACS 2008 to State 2008-2009
ACS 2006-2008 to State 2008-2009
Alabama
1.00
0.87
0.91
0.91
0.76
0.99
0.99
Alaska
0.35
0.47
0.56
0.65
0.37
0.34
0.39
Arizona
1.03
1.18
1.32
1.34
0.94
1.29
1.31
Arkansas
0.81
0.84
0.61
0.72
0.62
0.47
0.55
Californiac
0.80
0.82
0.81
0.83
0.96
0.95
0.98c
Colorado
0.81
0.93
0.84
0.86
1.00
0.61
0.63
Connecticut
1.43
0.98
0.87
1.08
1.07
1.07
1.33
Delaware
1.10
1.01
0.93
1.02
1.60
0.93
1.02
District of Columbia
0.71
0.56
0.51
0.46
0.41
0.45
0.40
Florida
1.01
1.08
1.05
1.07
1.02
1.02
1.04
Georgia
1.21
1.25
1.10
1.16
1.07
0.98
1.04
Hawaii
0.83
0.54
0.92
0.71
0.45
0.76
0.58
Idaho
0.72
0.68
0.80
0.74
0.54
0.94
0.87
Illinois
1.12
1.15
1.04
1.03
1.35
1.46
1.44
Indiana
0.92
0.79
0.88
0.87
0.65
0.67
0.66
Iowa
1.04
0.93
0.89
0.96
0.86
0.89
0.96
Kansas
0.76
0.69
0.71
0.69
0.52
0.62
0.61
Kentucky
1.22
1.38
1.40
1.27
1.04
1.11
1.01
Louisiana
1.27
1.52
1.41
1.30
1.02
1.20
1.11
Maine
1.68
1.19
0.88
1.30
1.03
0.68
1.01
Maryland
1.20
1.23
1.10
1.16
1.70
0.92
0.96
Massachusetts
1.36
1.28
1.48
1.50
1.83
1.39
1.41
Michigan
0.96
1.32
1.04
1.10
0.81
1.15
1.22
Minnesota
0.79
0.90
0.99
0.93
1.04
0.81
0.76
Mississippi
1.53
1.34
1.07
1.37
4.24
0.86
1.11
Missouri
1.08
1.63
1.66
1.77
1.72
1.16
1.23
Montana
0.35
0.34
0.39
0.61
0.99
1.19
1.84
OCR for page 110
All ELL Studentsa
Tested, Not Proficient Studentsb
State
ACS 2006 to State 2006-2007
ACS 2007 to State 2007-2008
ACS 2008 to State 2008-2009
ACS 2006-2008 to State 2008-2009
ACS 2007 to State 2007-2008
ACS 2008 to State 2008-2009
ACS 2006-2008 to State 2008-2009
Nebraska
0.96
0.87
0.96
0.96
0.92
1.02
1.02
Nevada
0.62
1.10
0.82
0.71
0.55
0.65
0.56
New Hampshire
0.78
1.55
0.98
0.99
1.18
0.83
0.84
New Jersey
2.01
1.90
2.13
2.06
1.79
1.98
1.91
New Mexico
0.68
0.62
0.62
0.69
0.57
0.53
0.59
New York
1.45
1.37
1.66
1.62
1.14
1.28
1.25
North Carolina
1.01
0.67
0.89
0.81
0.56
0.76
0.69
North Dakota
0.69
0.66
0.63
0.56
0.49
0.92
0.82
Ohio
1.51
1.20
1.27
1.23
0.96
1.03
1.00
Oklahoma
0.51
0.64
0.56
0.59
0.55
0.52
0.55
Oregon
0.86
0.90
0.82
0.87
0.68
0.65
0.69
Pennsylvania
1.39
1.48
1.51
1.45
1.29
1.58
1.50
Rhode Island
0.82
1.85
1.71
1.59
1.55
1.31
1.22
South Carolina
0.73
0.91
0.73
0.81
0.66
0.57
0.63
South Dakota
0.71
0.56
1.12
0.91
0.56
1.00
0.81
Tennessee
1.14
1.19
1.09
1.11
1.06
1.12
1.14
Texas
1.45
1.16
1.16
1.10
1.18
1.22
1.16
Utah
0.66
0.75
0.72
0.76
0.85
0.81
0.85
Vermont
0.96
0.88
0.94
0.98
0.72
0.81
0.84
Virginia
0.78
0.65
0.75
0.73
0.70
0.54
0.53
Washington
1.03
1.30
1.28
1.21
1.05
1.06
1.00
West Virginia
2.35
1.83
2.46
3.03
2.51
3.24
4.01
Wisconsin
1.00
1.21
0.81
0.95
2.79
0.60
0.71
Wyoming
0.56
0.54
0.95
0.83
0.47
0.81
0.71
aThe total number of ELL students was obtained from the Education Data Exchange Network (EDEN) database.
bThe number of tested, not proficient students was computed for each state from the state Consolidated State Performance Reports by subtracting the number of all LEP (ELL) students proficient or above on state annual ELP assessments (1.6.3.1.2) from the number of all LEP (ELL) students tested on state annual ELP assessments (1.6.3.1.1).
cCounts for California for 2008-2009 were unavailable; the percentage is based on the 2007-2008 count.
OCR for page 111
TABLE 5-4 Total Absolute Difference Between Shares Based on ACS Estimates and Shares Based on State-Provided Counts
ALL ELL Students
Tested, Not Proficient Students
Type of ACS Estimate
ACS 2006 and State 2006-2007
ACS 2007 and State 2007-2008
ACS 2008 and State 2008-2009
ACS 2006-2008 and State 2006-2007
ACS 2006-08 and State 2007-2008
ACS 2006-2008 and State 2008-2009
1-year
23.68
21.13
21.57
N/A
18.21
20.55
3-year
25.94
20.44
19.73
N/A
17.94
18.66
COMPARISON OF RATES OF ELL STUDENTS
As noted above, comparison of state rates removes the simple effect of size from the analyses and thereby focuses attention on differences in measurement.
State Rates
Table 5-5 shows each state’s rate of ELL students based on the two data sources. The first four columns on the left-hand side of the table show the rates based on the ACS estimates, including 1-year estimates for 2006, 2007, and 2008 and the 3-year estimate across these years. For the state-provided counts, two types of rates calculated are shown: the rates based on state-provided counts of all ELL students for the 2006-2007, 2007-2008, and 2008-2009 school years and the rates based on the state-provided counts of tested, not proficient students for the 2007-2008 and 2008-2009 school years.
The rates derived from the ACS were lower than the rates derived from state-provided counts in all but two states (New Jersey and West Virginia), when state-provided counts were based on all ELL students. When the state-provided count was based on the number of tested, not proficient students, the ACS estimates were consistently lower in five states (Illinois, Massachusetts, New Jersey, Pennsylvania, and West Virginia). In the most recent period, the average percentage of ELL students for the nation was about 5 percent for the ACS and about 9 percent for the state-provided rates; for the state-provided rate of tested, not proficient students, the rate was 6 percent.
Ratio of the Rates
To compare the percentages from the two measures, we calculated the ratio of the rate based on the ACS estimate to the rate based on each of the state-provided counts. These ratios are shown in Table 5-6. The left-hand side of the table shows
OCR for page 112
TABLE 5-5 Rate of ELL Students by State Based on ACS Estimates and State-Provided Counts (in percentage)
State-Provided Count
ACS Estimate
All ELLa
Tested, Not Proficientb
State
2006
2007
2008
2006-2008
2006-2007
2007-2008
2008-2009
2007-2008
2008-2009
Alabama
1.44
1.36
1.31
1.33
2.47
2.81
2.62
2.16
1.68
Alaska
3.34
3.56
3.09
3.53
15.66
12.84
9.21
10.82
10.61
Arizona
8.50
8.82
8.01
8.40
14.30
13.77
11.55
11.65
8.23
Arkansas
2.40
2.54
1.94
2.32
4.96
5.41
5.77
4.93
5.24
Californiac
11.34
11.13
10.54
11.00
24.34
24.48
24.23
14.13
14.34c
Colorado
5.55
5.53
5.14
5.37
11.32
10.64
10.86
6.65
10.34
Connecticut
3.90
2.91
2.55
3.16
4.58
5.26
5.25
3.25
2.98
Delaware
3.51
3.20
2.93
3.26
5.44
5.92
5.73
2.52
3.99
District of Columbia
2.91
2.60
2.57
2.34
6.47
6.54
8.52
5.94
6.79
Florida
5.35
5.33
4.99
5.16
8.78
8.68
8.59
6.20
6.15
Georgia
3.24
3.31
2.89
3.15
4.55
4.85
4.89
3.79
3.80
Hawaii
4.40
3.40
5.60
4.36
8.66
10.38
10.34
8.39
8.72
Idaho
2.69
2.30
2.84
2.70
6.25
6.13
6.42
5.20
3.83
Illinois
5.41
5.72
5.57
5.54
8.16
8.99
9.66
5.15
4.79
Indiana
2.24
1.98
2.12
2.13
4.07
4.42
4.37
3.66
3.97
Iowa
2.24
2.07
2.03
2.22
3.75
4.01
4.17
2.93
2.91
Kansas
2.75
2.60
2.85
2.81
6.16
6.78
7.24
6.08
5.73
Kentucky
1.18
1.48
1.67
1.54
1.58
1.94
2.18
1.73
1.91
Louisiana
0.96
1.44
1.40
1.29
1.28
1.68
1.82
1.68
1.49
Maine
1.81
1.35
1.05
1.55
1.90
2.06
2.19
1.60
1.97
Maryland
2.85
3.25
2.87
3.02
4.03
4.78
4.75
2.33
3.97
Massachusetts
4.51
4.16
4.15
4.27
5.58
5.79
5.12
2.72
3.79
Michigan
2.27
2.23
2.06
2.18
4.05
3.04
3.67
3.36
2.31
Minnesota
3.52
3.67
4.07
3.81
7.60
7.31
7.35
4.28
6.27
Mississippi
0.87
0.79
0.74
0.98
1.01
1.10
1.33
0.23
1.15
OCR for page 113
Missouri
1.57
1.88
1.63
1.75
2.43
2.08
1.78
1.33
1.78
Montana
0.94
0.86
0.66
1.02
4.84
4.71
3.21
1.10
0.74
Nebraska
3.56
3.22
3.40
3.42
6.33
6.57
6.29
4.20
4.12
Nevada
5.99
6.45
7.62
6.79
16.61
10.96
17.53
14.82
15.31
New Hampshire
0.70
1.35
1.09
1.09
1.55
1.59
2.06
1.41
1.69
New Jersey
4.68
4.26
4.65
4.56
3.92
3.94
3.92
2.82
2.94
New Mexico
7.03
6.26
5.43
6.14
18.50
18.60
16.34
13.64
13.27
New York
5.72
5.59
5.87
5.72
6.98
7.61
6.71
6.17
6.06
North Carolina
3.54
3.19
3.73
3.47
6.07
8.56
7.65
6.82
6.26
North Dakota
0.95
1.70
1.44
1.27
2.48
4.89
4.29
4.48
2.03
Ohio
1.43
1.29
1.42
1.38
1.59
1.92
2.00
1.62
1.72
Oklahoma
1.84
2.17
1.89
2.02
5.96
5.88
5.90
4.59
4.41
Oregon
5.47
5.40
4.90
5.28
11.01
10.98
11.19
9.79
9.82
Pennsylvania
2.05
2.11
2.22
2.13
2.43
2.57
2.69
2.00
1.80
Rhode Island
3.11
4.71
4.52
4.12
6.62
4.84
6.47
3.89
4.48
South Carolina
1.84
2.03
1.77
1.99
4.26
3.98
4.38
3.67
3.89
South Dakota
1.06
0.99
1.66
1.38
2.72
3.47
2.84
2.34
2.23
Tennessee
1.62
1.76
1.71
1.76
2.35
2.66
2.82
2.01
1.91
Texas
9.69
10.02
10.07
9.89
10.90
14.82
15.12
9.85
10.05
Utah
3.54
3.52
3.15
3.42
9.25
8.12
7.94
4.81
4.94
Vermont
1.03
0.77
0.85
0.87
1.83
1.55
1.62
1.29
1.31
Virginia
3.28
2.51
2.94
2.92
7.08
6.85
7.04
4.30
6.76
Washington
4.94
5.64
5.60
5.38
8.26
7.83
7.98
6.54
6.71
West Virginia
1.13
0.87
0.79
0.99
0.80
0.83
0.57
0.41
0.30
Wisconsin
2.82
3.39
2.45
2.91
4.71
5.01
5.48
1.47
5.12
Wyoming
1.17
0.82
1.34
1.20
3.53
2.77
2.61
2.17
2.13
United States
5.10
5.10
5.00
5.10
8.70
9.20
9.10
6.19
6.37
aThe total number of ELL students was obtained from the EDEN database.
bThe total number of tested, not proficient was computed for each state from the state Consolidated State Performance Reports by subtracting the number of all LEP students proficient or above on state annual ELP assessments (1.6.3.1.2) from the number of all LEP students tested on state annual ELP assessments (1.6.3.1.1).
cCounts for California for 2008-2009 were unavailable; percentage is based on the 2007-2008 count.
OCR for page 122
TABLE 5-8 Analysis of Using ACS 3-Year Estimate and Other Variables to Predict State-Provided Rate of Tested, Not Proficient ELL Students
2007-2008
2008-2009
Model 1
Adjusted R2
.66**
.66**
Regression Coefficients (Standard Error):
ACS 2006-2008
1.300** (.1300)
1.2871 (.1293)**
Intercept
.0034 (.0052)
.0057 (.0051)
Model 2
Adjusted R2
.66**
.66**
Regression Coefficients (Standard Error):
ACS 2006-2008
1.320 (.1773)**
1.2752 (.1763)**
Percent immigrants
–0.1566 (.920)
.0918 (.9152)
Intercept
.0039 (.0060)
.0052 (.0062)
Model 3
Adjusted R2
.67**
.66*
Regression Coefficients (Standard Error):
ACS 2006-2008
0.8173 (.4597)
1.0598 (.4614)*
Percent immigrants in poverty
1.612 (1.4722)
.7589 (1.4778)
Intercept
.0040 (.0052)
.0060 (.0053)
Model 4
Adjusted R2
.67**
.66*
Regression Coefficients (Standard Error):
ACS 2006-2008
0.9594 (.2950)**
0.9758(.2941)**
Percent unauthorized immigrants
0.5396 (.4201)
.4930 (.4188)
Intercept
.0049 (.0053)
.0071(.0053)
Model 5
Adjusted R2
.71**
.68**
Regression Coefficients (Standard Error):
ACS 2006-2008
0.8221 (.2102)**
0.9773 (.2185)**
Percent unauthorized Mexicans
1.1009 (.3943)**
.7134 (.4099)
Intercept
.0090 (.0053)*
.0094 (.0055)
Model 6
Adjusted R2
.70**
.67**
Regression Coefficients (Standard Error):
ACS 2006-2008
0.9105 (.1862)**
1.0807 (.1954)**
States with high percent unauthorized
.0387 (.0140)**
.0205 (.0146)
Intercept
.0127 (.0059)*
.0107 (.0062)*
Model 7
Adjusted R2
.71**
.67**
Regression Coefficients (Standard Error):
ACS 2006-2008
0.7279 (.2154)**
0.9395 (.2290)**
Percent unauthorized Mexican
.7306 (.4537)
.5649 (.4822)
States with high percent unauthorized
.0253 (.0160)
.0102 (.0170)
Intercept
.0132 (.0058)*
.0110 (.0062)
Model 8
Adjusted R2
.66**
.66**
Regression Coefficients (Standard Error):
ACS 2006-2008
1.2327(.1449)**
1.2208 (.1444)**
ACCESS user
–.0080(.0068)
–.0067 (.0067)
ELDA user
–.0056 (.0095)
–.0074 (.0094)
Intercept
.0093 (.0074)
.0114 (.0074)
OCR for page 123
Allocating Federal Funds for State Programs for English Language Learners
2007-2008
2008-2009
Model 9
Adjusted R2
.65**
.64**
Regression Coefficients (Standard Error):
ACS 2006-2008
0.7988 (.4795)
1.0263 (.4833)*
Percent immigrants
–.3598 (.9523)
–.1165 (.9598)
Percent immigrants in poverty
1.5989 (1.5016)
.6992 (1.5134)
ACCESS user
–.0083 (.0070)
–.0068 (.0070)
ELDA user
–.0057 (.0098)
–.0074 (.0099)
Intercept
.0111 (.0084)
.0120 (.0084)
NOTES:
*p < .05;
**p < .01. All R2 values were adjusted for the sample size (n = 51).
on the ACS language item appears to best approximate school assessments of English language learner status.
It should be noted that the small number of units (50 states and the District of Columbia) limited the number of variables that could be considered. Furthermore, the concentration of ELL populations in relatively few states further limited our ability to infer systematic correlates of the discrepancies between the data sources.
Within-State Analyses
We also conducted a series of regression analyses to examine the relationships between ACS estimates and state-provided counts for school districts (local education agencies or LEAs) within each state. The purpose of this analysis was to assess how well the ACS and state-provided numbers tracked each other under a consistent set of procedures, criteria, and tests, that is, those of a single state. We obtained the 3-year (2006-2008) ACS estimates and the state-provided (2007-2008) counts of all ELL students for each unified school district for which they were available, which limited us to school districts with total populations of at least 20,000 (due to ACS release restrictions for small areas). Also excluded were several states for which LEA-level data were unavailable (California, New Jersey, and South Dakota). This analysis could be conducted only with rates based on state counts of all ELL students, since LEA counts of tested, not proficient students were not available. We formed the rate for each district (that is, we divided each of the counts by the number of K-12 students enrolled in public schools in the state).
For smaller units of analyses, such as most school districts, the sampling variability of ACS estimates of rates is generally greater than for states. Simple sample correlations would be attenuated by this error, underestimating the strength of the underlying relationship between the ACS and state-provided measures. We therefore used hierarchical models that adjust for the sampling variability of the ACS data to estimate this relationship. For these analyses, the dependent variable was the ACS
OCR for page 124
estimate of the school district’s ELL rate, and the explanatory variable was the state-provided estimate of the district rate. (Making the ACS rate the dependent variable facilitated specification of a hierarchical model in which the difference between the ACS estimate and its linear prediction from the state-reported rate is modeled as the sum of two random effects, one for ACS sampling error with known variance, and one for the discrepancy between the ACS and the state rate with variance to be estimated.)
Table 5-9 shows descriptive information for each of the states included in the analysis. The first two columns show the school enrollment and the number of unified districts in the state. The third column shows the overall rate of ELL students in the state based on state-provided information. The next three columns provide distributional information about the LEA rates within the state (based on the state-provided information): the average rate across the districts and the 20th and 80th percentiles of the LEA rates in the state. The seventh column shows the overall rate of ELL students in the state based on the ACS information. The eighth column presents the ratio of the ACS rate to the state-provided rate. The final column shows the sample correlation of the rates based on ACS estimates and state-provided counts for the unified school districts within a state. For instance, the correlation between the two sets of rates for the 58 unified school districts in Alabama was 0.697. The correlation is labeled “unadjusted” because it has not been corrected for sampling error associated with the ACS estimates.
Table 5-10 presents the results of the within-state regressions in states with at least 10 eligible LEA units, incorporating a correction for sampling error in the ACS estimates. The first four columns show the results from regressions that include the intercept in the model. The first two columns show, respectively, the regression coefficients for the intercept and for the rate based on the state-provided estimate. The third column shows the root mean square residual error (RMSE) of the model, which quantifies the amount by which the ACS estimates by LEA vary around the regression line. The fourth column shows the correlations after adjustment for sampling error. The median of these estimated correlation coefficients is 0.949, and the coefficient exceeds 0.90 in 30 of 41 states, although there are also a few states for which these LEA-level correlations are relatively low.
The fifth and sixth columns show parallel results (regression coefficients and RMSE) from the regressions that did not include the intercept in the model. The final column is the ratio of the errors from the two models (with and without intercepts). This ratio is usually not far from 1.0 (except in a few states where the denominator is very small due to an extremely good model fit), suggesting that the no-intercept (proportional) model fits the data almost as well as the unconstrained linear model. As noted previously, the proportional model implies that ACS-based and state-data-based allocations would be equivalent.
In general, the results suggest very good consistency between the ACS and state-provided numbers within states. This greater consistency, relative to similar models fitted at the state level, might be attributed to two features of the within-
OCR for page 125
TABLE 5-9 Descriptive Summaries of LEA-Level Data on Rate of ELL Students, by State
State Counts
State ELL Rates
ACS Overall Rate (%)
Ratio of ACS/State
Unadjusted Correlation
State
School Enrollment
Number of LEAs
Overall Rate (%)
Mean of LEAs (%)
20%-tile (%)
80%-tile (%)
Alabama
579,913
58
3.0
2.8
0.4
4.7
1.3
0.44
0.697
Alaska
93,838
5
7.4
5.8
2.4
10.6
2.6
0.34
0.207
Arizona
872,395
72
14.2
14.5
3.7
26.2
9.6
0.68
0.798
Arkansas
227,292
30
8.7
5.9
0.6
7.3
3.1
0.36
0.918
Colorado
684,657
35
11.3
11.9
2.5
21.4
5.8
0.51
0.920
Connecticut
378,744
56
7.3
5.3
1.4
10.1
3.9
0.54
0.877
Delaware
102,396
13
6.7
5.8
2.1
9.0
3.3
0.49
0.529
District of Columbia
57,877
1
7.1
7.1
7.1
7.1
2.3
0.33
NA
Florida
2,619,362
54
8.8
5.2
0.9
9.4
5.2
0.59
0.682
Georgia
1,487,247
97
5.2
3.5
0.7
5.4
3.3
0.64
0.885
Hawaii
179,897
1
10.4
10.4
10.4
10.4
4.4
0.42
NA
Idaho
180,200
20
5.2
5.5
0.3
11.3
2.4
0.46
0.904
Illinois
1,519,448
202
11.4
7.2
0.8
10.6
6.4
0.56
0.769
Indiana
705,862
87
5.5
4.9
1.0
7.5
2.4
0.44
0.776
Iowa
220,538
29
6.0
4.8
0.8
7.2
2.5
0.42
0.646
Kansas
272,573
28
9.6
9.2
1.6
12.9
4.1
0.42
0.907
Kentucky
463,556
54
2.4
1.4
0.2
1.8
1.6
0.67
0.647
Louisiana
606,547
49
1.8
1.2
0.1
1.6
1.4
0.76
0.418
Maine
45,917
12
5.8
4.3
0.4
6.3
3.7
0.63
0.797
Maryland
843,426
23
4.8
2.6
0.6
3.4
3.0
0.63
0.862
Massachusetts
639,309
110
8.1
4.8
0.6
9.3
5.3
0.65
0.819
Michigan
830,996
103
4.6
3.5
0.4
5.1
2.6
0.55
0.718
Minnesota
537,291
60
9.2
6.5
1.3
10.1
4.7
0.51
0.779
Mississippi
300,235
44
1.4
1.4
0.3
2.1
1.0
0.71
0.069
OCR for page 126
State Counts
State ELL Rates
ACS Overall Rate (%)
Ratio of ACS/State
Unadjusted Correlation
State
School Enrollment
Number of LEAs
Overall Rate (%)
Mean of LEAs (%)
20%-tile (%)
80%-tile (%)
Missouri
551,434
65
2.7
2.2
0.5
2.9
2.1
0.79
0.671
Montana
58,413
15
2.3
1.9
0.2
3.5
1.3
0.54
0.290
Nebraska
169,074
15
8.6
7.2
1.4
11.8
4.0
0.47
0.781
Nevada
419,488
8
10.9
12.5
10.3
13.9
6.9
0.63
–0.550
New Hampshire
86,972
17
2.8
1.7
0.3
2.6
1.6
0.56
0.407
New Mexico
270,081
22
18.8
18.3
6.0
33.8
6.6
0.35
0.658
New York
1,063,809
175
4.9
4.2
0.8
7.1
3.1
0.64
0.746
North Carolina
1,373,592
89
9.1
8.0
2.9
12.4
3.5
0.39
0.826
North Dakota
44,279
6
4.2
3.6
0.3
7.0
1.9
0.45
0.782
Ohio
1,026,648
151
2.9
2.1
0.3
2.8
1.6
0.56
0.726
Oklahoma
325,060
30
8.4
5.6
2.3
8.1
2.7
0.32
0.717
Oregon
430,730
45
12.2
11.4
2.2
18.7
5.9
0.48
0.921
Pennsylvania
107,022
18
4.5
2.6
0.2
5.3
3.1
0.70
0.841
Rhode Island
112,975
19
5.3
3.0
0.6
3.7
4.9
0.92
0.801
South Carolina
649,424
53
4.2
3.5
1.2
5.1
2.1
0.50
0.685
Tennessee
848,210
70
2.9
1.8
0.3
3.0
1.9
0.65
0.733
Texas
3,770,908
207
16.4
12.3
4.4
19.0
11.0
0.67
0.901
Utah
513,430
19
8.7
9.2
3.1
16.1
3.4
0.39
0.950
Virginia
1,125,896
77
7.2
4.3
0.5
5.7
3.0
0.42
0.738
Washington
837,945
78
7.5
6.4
1.5
9.9
5.4
0.72
0.859
West Virginia
239,577
32
0.9
0.6
0.0
0.7
1.0
1.06
–0.163
Wisconsin
498,915
66
7.3
4.6
0.7
7.1
3.8
0.52
0.729
Wyoming
43,609
6
1.8
2.2
0.5
4.5
1.3
0.73
0.951
NOTE: Data include only eligible LEAs, as described in the text.
OCR for page 127
TABLE 5-10 Results of Within-State Regressions
Model with Intercept, State Data Rate
No-Intercept Model
Number of LEAs
Ratio of RMSE Estimates
Intercept Coefficient
State Coefficient
RMSE
Adjusted Correlation
State Coefficient
RMSE
Alabama
0.0026
0.2508
0.0036
0.9040
0.3084
0.0033
58
0.93
Arizona
0.0216
0.3852
0.0316
0.8433
0.4765
0.0346
72
1.09
Arkansas
0.0049
0.2747
0.0003
0.9999
0.3099
0.0003
30
1.11
Colorado
0.0029
0.4477
0.0004
1.0000
0.4693
0.0013
35
3.20
Connecticut
0.0043
0.4038
0.0065
0.9596
0.4602
0.0067
56
1.03
Delaware
0.0171
0.1587
0.0027
0.8904
0.3816
0.0069
13
2.56
Florida
0.0073
0.4762
0.0127
0.8737
0.5540
0.0134
54
1.06
Georgia
0.0044
0.5006
0.0043
0.9814
0.5723
0.0048
97
1.13
Idaho
0.0053
0.3292
0.0005
0.9997
0.3958
0.0036
20
7.73
Illinois
0.0139
0.3808
0.0189
0.8834
0.4739
0.0220
202
1.17
Indiana
0.0034
0.2750
0.0035
0.9778
0.3224
0.0033
87
0.92
Iowa
0.0095
0.1688
0.0066
0.8514
0.2623
0.0096
29
1.46
Kansas
0.0017
0.3394
0.0102
0.9675
0.3508
0.0102
28
1.00
Kentucky
0.0046
0.4069
0.0033
0.9360
0.5251
0.0021
54
0.65
Louisiana
0.0061
0.2929
0.0020
0.9192
0.4801
0.0047
49
2.38
Maine
0.0037
0.3091
0.0054
0.9590
0.3769
0.0043
12
0.79
Maryland
0.0049
0.4777
0.0067
0.8872
0.5681
0.0074
23
1.11
Massachusetts
0.0064
0.4551
0.0042
0.9891
0.5173
0.0047
110
1.11
Michigan
0.0075
0.2692
0.0052
0.9318
0.3665
0.0068
103
1.31
Minnesota
0.0092
0.3170
0.0107
0.9038
0.3984
0.0123
60
1.16
Mississippi
0.0051
0.0874
0.0024
0.6004
0.2673
0.0001
44
0.04
Missouri
0.0068
0.3750
0.0019
0.9820
0.5429
0.0038
65
2.04
Montana
0.0045
0.1244
0.0013
0.8678
0.2576
0.0038
15
2.91
Nebraska
0.0081
0.3081
0.0006
0.9996
0.3758
0.0063
15
10.44
OCR for page 128
Model with Intercept, State Data Rate
No-Intercept Model
Number of LEAs
Ratio of RMSE Estimates
Intercept Coefficient
State Coefficient
RMSE
Adjusted Correlation
State Coefficient
RMSE
New Hampshire
0.0060
0.3166
0.0004
0.9976
0.4780
0.0003
17
0.80
New Mexico
0.0118
0.1972
0.0271
0.7143
0.2418
0.0284
22
1.05
New York
0.0086
0.3293
0.0058
0.9533
0.4390
0.0078
175
1.33
North Carolina
0.0002
0.3369
0.0056
0.9625
0.3393
0.0056
89
1.00
Ohio
0.0057
0.2022
0.0028
0.9620
0.3289
0.0039
151
1.42
Oklahoma
–0.0014
0.2894
0.0045
0.9483
0.2735
0.0048
30
1.05
Oregon
0.0026
0.4207
0.0040
0.9963
0.4425
0.0021
45
0.52
Pennsylvania
0.0021
0.5318
0.0058
0.9485
0.5606
0.0058
18
1.00
Rhode Island
0.0019
0.7080
0.0022
0.9960
0.7493
0.0030
19
1.36
South Carolina
0.0047
0.2788
0.0035
0.9254
0.3800
0.0035
53
0.99
Tennessee
0.0030
0.3389
0.0037
0.8840
0.4274
0.0042
70
1.14
Texas
0.0018
0.6058
0.0197
0.9603
0.6152
0.0197
207
1.00
Utah
0.0020
0.3175
0.0077
0.9591
0.3352
0.0079
19
1.02
Virginia
0.0082
0.2299
0.0059
0.9420
0.2931
0.0084
77
1.42
Washington
0.0024
0.5805
0.0097
0.9715
0.6075
0.0095
78
0.98
West Virginia
0.0069
–0.0525
0.0020
0.2609
0.2488
0.0036
32
1.79
Wisconsin
0.0100
0.2911
0.0061
0.9139
0.4134
0.0070
66
1.15
NOTE: Data include only eligible LEAs, as described in the text.
OCR for page 129
state comparison: (1) the use of consistent procedures and criteria within most states but different ones in different states, and (2) the possibly greater similarity among immigrant populations within the same state than those in different states. The first of these reasons points to the difficulties in making present state-provided data comparable across states, while the second indicates possible difficulties in interstate comparability for ACS data. Nonetheless, the high degree of within-state consistency does give some reason for optimism that better consistency is achievable.
CONCLUSION 5-3 In the absence of other factors, such as the legislated minimum allocation, the American Community Survey and state-provided data would yield broadly similar allocations to most states. However, the differences in allocations to a few states are substantial and not readily explainable by such factors as region of the country, demographic characteristics of the English language learner population, or the proficiency test used by the state.
Temporal Variation
Another criterion for comparison of the ACS estimates and state counts is the degree of variation over time of the estimates for each state. There are conflicting values in consideration of such variation. Responsiveness refers to the tendency of a set of estimates to respond quickly to changes in conditions, such as rapid growth of the population of immigrant children in a state from one year to the next. This term suggests a positive value in that resources will be more rapidly directed to states with growing needs if a more responsive measure is used. Volatility refers to the tendency of estimates to vary or fluctuate from year to year. It suggests a negative value since such funding fluctuations make it more difficult to plan and maintain program continuity. Responsiveness contributes directly to volatility when populations are changing, but there are additional sources of volatility particular to each data source. Sampling variation contributes to purely random volatility in the ACS estimates. State data could become volatile when a state changes its tests, standards, or procedures from one year to the next or when there is an error or change in the mechanisms for reporting ELL counts from school districts to states to the DoEd.
Table 5-11 summarizes the volatility of ACS and state-provided estimates of ELL counts in two ways (parallel to those used in the sensitivity analyses in Chapter 2). The first is the sum of absolute changes in state shares, equivalent to twice the portion of the total allocation that would be moved from one state to another in consecutive years. The second is the mean absolute value of relative changes in shares, which summarizes the amount by which allocations in each state change relative to the size of its allocation. As expected, the single-year ACS changes are about equal in the two pairs of years (2006 to 2007 and 2007 to 2008). As explained in Chapter 2, the 3-year ACS estimates are much more stable, both because of the greater reliability of 3 years of data and because only one out of the years changes in overlapping
OCR for page 130
TABLE 5-11 Comparison of Volatility in ACS Estimates and State-Provided Counts (in percentage)
Sum of Absolute Difference in State Shares
Mean Absolute Relative Difference in State Shares
ACS 2006 to ACS 2007
All
6.07
14.84
Large
3.02
5.77
Medium
2.27
10.99
Small
0.71
21.16
Minimum
0.07
26.11
ACS 2007 to ACS 2008
All
6.47
12.84
Large
3.38
5.64
Medium
2.46
11.02
Small
0.54
14.17
Minimum
0.09
26.72
ACS 2005-2007 to ACS 2006-2008
All
3.64
4.83
Large
2.36
2.46
Medium
1.04
4.34
Small
0.21
5.99
Minimum
0.02
7.18
State 2006-2007 to State 2007-2008
All
10.55
11.90
Large
6.81
7.79
Medium
3.21
11.80
Small
0.42
9.83
Minimum
0.11
23.57
State 2007-2008 to State 2008-2009
All
5.53
9.98
Large
2.91
5.61
Medium
2.11
8.38
Small
0.42
13.01
Minimum
0.09
14.58
NOTES:
Large States: Arizona, California, Florida, Georgia, Illinois, New Jersey, New York, Texas, Washington.
Medium States: Colorado, Connecticut, Indiana, Kansas, Maryland, Massachusetts, Michigan, Minnesota, Missouri, North Carolina, New Mexico, Nevada, Pennsylvania, Oregon, Ohio, Oklahoma, South Carolina, Tennessee, Utah, Virginia, Wisconsin.
Small States: Alabama, Alaska, Arkansas, Delaware, Hawaii, Idaho, Iowa, Kentucky, Louisiana, Maine, Mississippi, Nebraska, New Hampshire, Rhode Island, West Virginia.
Minimum States: District of Columbia, Montana, North Dakota, South Dakota, Vermont, Wyoming.
OCR for page 131
3-year periods. (For the same reason, these estimates are also the least responsive.) Interestingly, the between-year changes in state-provided shares are much larger in 2006-2007 than in 2007-2008. We do not have enough detailed information about changes in state practices to identify specific reasons for the changes that might cause this variation and predict whether results would be similar in future years.
The more detailed information by state share grouping sheds more light on patterns of volatility. In absolute terms, the largest part of annual changes in share occurs in the states with relatively large shares; as noted above, these encompass about 74 percent of allocations. However in relative terms, these states show the least volatility by any measure. Since volatility in ACS estimates is largely driven by sampling variation, it is consistently larger in relative terms for each group of successively smaller states. The pattern is less consistent in the state-provided estimates, although generally the larger states tend to have more stable numbers. This stability may reflect the greater effects on smaller states of rapid changes in ELL population in a few local areas, or it may reflect changes in reporting. Overall, the 3-year ACS estimates appear to be the most stable, at the cost of some loss of responsiveness. And as discussed in Chapter 2, 1-year ACS estimates do not capture year-to-year changes with acceptable precision. The evidence is ambiguous on comparative stability of single-year ACS estimates and state-based estimates.
CONCLUSION 5-4 The superior precision and stability of the 3-year American Community Survey (ACS) estimates outweigh their slower responsiveness to changes and make them superior to the ACS 1-year estimates as a basis for allocations.
OCR for page 132
This page intentionally left blank.