Note: SES refers to socioeconomic status. The gaps are standard deviation scores for high-SES children relative to low-SES children after adjusting for all family and child characteristics, pre-K schooling, and enrichment activities with parents, and parental expectations for children’s educational attainment. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Tables 3 and 4, Model 4.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 1.29 | |
Math | 1.46 | -0.15 |
Self-control (by teachers) | 0.32 | -0.10 |
Approaches to learning (by teachers) | 0.64 | -0.24 |
Self-control (by parents) | 0.47 | -0.14 |
Approaches to learning (by parents) | 0.66 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have mothers in the top quintile of the education distribution and low-SES children have mothers in bottom quintile of the education distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 7, Model 1.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 1.09 | -0.13 |
Math | 1.31 | -0.23 |
Self-control (by teachers) | 0.42 | |
Approaches to learning (by teachers) | 0.60 | -0.13 |
Self-control (by parents) | 0.44 | |
Approaches to learning (by parents) | 0.44 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children are in households with incomes in the top quintile of the income distribution and low-SES children are in households with incomes in bottom quintile of the income distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 8, Model 1.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 0.74 | 0.08 |
Math | 0.97 | |
Self-control (by teachers) | 0.32 | |
Approaches to learning (by teachers) | 0.46 | |
Self-control (by parents) | 0.28 | |
Approaches to learning (by parents) | 0.58 | 0.09 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have a number of books in the home in the top quintile of the books-in-the-home distribution and low-SES children have a number of books in the home in the bottom quintile of the books-in-the-home distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 9, Model 1.
Reading | Mathematics | Self-control (by teachers) | Approaches to learning (by teachers) | Self-control (by parents) | Approaches to learning (by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | |
Gap in 2010–2011 | 1.169*** | 0.944*** | 1.250*** | 0.911*** | 0.386*** | 0.363*** | 0.513*** | 0.562*** | 0.391*** | 0.326*** | 0.563*** | 0.460*** |
(0.024) | (0.036) | (0.024) | (0.034) | (0.029) | (0.041) | (0.027) | (0.041) | (0.028) | (0.041) | (0.028) | (0.044) | |
Controls | ||||||||||||
Demographics | No | No | No | No | No | No | No | No | No | No | No | No |
Education and engagement | No | No | No | No | No | No | No | No | No | No | No | No |
Parental expectations | No | No | No | No | No | No | No | No | No | No | No | No |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 14,090 | 14,090 | 14,040 | 14,040 | 12,180 | 12,180 | 13,280 | 13,280 | 12,890 | 12,890 | 12,900 | 12,900 |
Adjusted R2 | 0.165 | 0.281 | 0.190 | 0.276 | 0.021 | 0.114 | 0.034 | 0.105 | 0.018 | 0.028 | 0.037 | 0.118 |
Note: Using the full sample. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. Sizes may differ from those inferred from Tables 3–6, and from those in García 2015, due to differences in the sample sizes or to rounding.
Source: EPI analysis of ECLS-K, kindergarten class of 2010–2011 (National Center for Education Statistics)
1998–1999 | Low-SES (quintile 1) | Low-middle SES (quintile 2) | Middle SES (quintile 3) | High-middle SES (quintile 4) | High-SES (quintile 5) | All quintiles | |
---|---|---|---|---|---|---|---|
Child and family characteristics and main developmental activities | |||||||
Race/ethnicity | White | 26.40% | 53.70% | 61.20% | 68.10% | 78.80% | 57.70% |
Black | 26.20% | 17.80% | 15.50% | 12.00% | 6.40% | 15.60% | |
Hispanic | 39.80% | 21.20% | 15.80% | 12.70% | 6.80% | 19.20% | |
Hispanic English language learner (ELL) | 28.40% | 9.50% | 4.80% | 3.10% | 1.40% | 9.40% | |
Hispanic English speaker | 11.50% | 11.70% | 10.90% | 9.60% | 5.40% | 9.80% | |
Asian | 2.30% | 1.70% | 2.30% | 2.70% | 4.70% | 2.70% | |
Other | 5.30% | 5.60% | 5.30% | 4.40% | 3.40% | 4.80% | |
Poverty status | Lives in poverty | 71.30% | 22.30% | 10.60% | 4.20% | 1.10% | 21.80% |
Language | Child’s language at home is not English | 31.20% | 12.00% | 7.00% | 6.10% | 5.30% | 12.30% |
Family composition | Not living with two parents | 45.60% | 30.50% | 23.80% | 15.80% | 11.10% | 25.10% |
Number of family members | 4.84 | 4.55 | 4.42 | 4.36 | 4.40 | 4.51 | |
First- or second-generation immigrant | 30.30% | 15.10% | 12.80% | 13.10% | 15.40% | 17.30% | |
Pre-K care arrangements | Pre-K care | 64.20% | 70.90% | 76.50% | 81.00% | 87.80% | 76.20% |
Pre-K care, center-based | 43.70% | 45.00% | 50.20% | 55.40% | 65.80% | 52.20% | |
Parental care | 30.50% | 22.60% | 17.20% | 15.40% | 9.90% | 18.90% | |
Care by relative | 15.90% | 18.30% | 16.20% | 11.80% | 6.60% | 13.70% | |
Care by nonrelative | 5.30% | 8.20% | 10.90% | 11.60% | 13.70% | 10.00% | |
Care by multiple sources | 4.60% | 5.90% | 5.50% | 5.80% | 3.90% | 5.20% | |
Activities indices | Literacy/reading | -0.221 | -0.059 | -0.010 | 0.070 | 0.193 | -0.003 |
Other educational and engagement activities | -0.114 | -0.011 | 0.014 | 0.042 | 0.071 | 0.002 | |
Number of books | Average number | 32.4 | 58.1 | 74.3 | 87.9 | 107.3 | 72.5 |
Number of books, grouped by least to most | 0–25 | 61.70% | 31.60% | 20.20% | 11.30% | 5.00% | 25.50% |
26–50 | 23.10% | 34.80% | 30.80% | 30.60% | 21.40% | 28.20% | |
51–100 | 11.30% | 23.40% | 32.90% | 36.00% | 41.00% | 29.10% | |
101–199 | 1.80% | 4.00% | 5.70% | 6.60% | 9.50% | 5.60% | |
More than 200 | 2.10% | 6.20% | 10.30% | 15.50% | 23.00% | 11.50% | |
Parents’ expectations for their children’s educational attainment | |||||||
Highest education level expected | High school or less | 24.10% | 15.20% | 7.70% | 3.70% | 1.20% | 10.20% |
Two or more years of college, vocational school | 16.40% | 21.80% | 21.40% | 11.60% | 3.80% | 14.90% | |
Bachelor’s degree | 33.20% | 38.70% | 46.70% | 58.80% | 57.20% | 47.10% | |
Master’s degree | 9.20% | 9.40% | 10.30% | 13.60% | 22.80% | 13.10% | |
Ph.D. or M.D. | 17.10% | 15.00% | 13.90% | 12.30% | 15.00% | 14.60% | |
2010–2011 | Low-SES (quintile 1) | Low-middle SES (quintile 2) | Middle SES (quintile 3) | High-middle SES (quintile 4) | High-SES (quintile 5) | All quintiles | |
Child and family characteristics, and main developmental activities | |||||||
Race/ethnicity | White | 23.10% | 45.50% | 56.80% | 69.00% | 71.30% | 52.90% |
Black | 19.60% | 17.00% | 13.40% | 9.40% | 5.80% | 13.20% | |
Hispanic | 50.40% | 28.30% | 19.70% | 12.20% | 8.60% | 24.10% | |
Hispanic English language learner (ELL) | 36.10% | 11.90% | 5.20% | 2.10% | 0.90% | 11.40% | |
Hispanic English speaker | 14.30% | 16.30% | 14.40% | 10.10% | 7.70% | 12.60% | |
Asian | 2.50% | 2.80% | 3.20% | 4.40% | 8.70% | 4.20% | |
Others | 4.40% | 6.40% | 7.00% | 4.90% | 5.60% | 5.70% | |
Poverty status | Lives in poverty | 84.60% | 35.70% | 10.90% | 3.10% | 0.60% | 25.50% |
Language | Child’s language at home is not English | 40.30% | 15.60% | 8.00% | 5.00% | 7.00% | 15.30% |
Family composition | Not living with two parents | 54.90% | 41.70% | 34.10% | 19.30% | 9.60% | 31.80% |
Number of family members | 4.81 | 4.62 | 4.53 | 4.44 | 4.46 | 4.57 | |
First- or second-generation immigrant | 49.80% | 25.70% | 18.90% | 17.20% | 21.60% | 26.10% | |
Pre-K care arrangements | Pre-K care | 66.60% | 75.60% | 81.60% | 85.00% | 88.30% | 79.30% |
Pre-K care, center-based | 44.30% | 47.00% | 53.10% | 61.60% | 69.90% | 55.10% | |
Parental care | 34.90% | 25.40% | 19.10% | 15.40% | 12.00% | 21.40% | |
Care by relative | 16.00% | 19.70% | 17.40% | 12.70% | 8.60% | 14.90% | |
Care by nonrelative | 3.30% | 5.50% | 7.40% | 7.30% | 6.90% | 6.10% | |
Care by multiple sources | 1.50% | 2.40% | 3.10% | 2.90% | 2.70% | 2.50% | |
Activities indices | Literacy/reading | -0.231 | -0.038 | 0.033 | 0.094 | 0.171 | 0.008 |
Other educational and engagement activities | -0.049 | 0.022 | 0.029 | 0.026 | 0.001 | 0.006 | |
Number of books | Average number | 35.2 | 57.6 | 74.1 | 90.8 | 106.3 | 73.1 |
Number of books, grouped by least to most | 0–25 | 59.30% | 33.60% | 19.40% | 11.50% | 5.00% | 25.50% |
26–50 | 24.70% | 31.70% | 32.50% | 26.90% | 22.40% | 27.70% | |
51–100 | 11.20% | 24.80% | 32.30% | 39.00% | 41.70% | 30.00% | |
101–199 | 1.70% | 3.10% | 5.50% | 6.50% | 7.70% | 4.90% | |
More than 200 | 3.10% | 6.80% | 10.30% | 16.20% | 23.20% | 12.00% | |
Parents’ expectations for their children’s educational attainment | |||||||
Highest education level expected | High school or less | 11.40% | 6.20% | 5.00% | 2.40% | 1.00% | 5.20% |
Two or more years of college, vocational school | 16.70% | 25.00% | 17.20% | 9.80% | 3.20% | 14.40% | |
Bachelor’s degree | 34.80% | 39.10% | 47.00% | 57.10% | 53.10% | 46.30% | |
Master’s degree | 10.70% | 12.30% | 14.60% | 16.80% | 26.60% | 16.20% | |
Ph.D. or M.D. | 26.40% | 17.30% | 16.20% | 13.90% | 16.10% | 17.90% |
Note: SES refers to socioeconomic status.
Reading models | Mathematics models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 1.071*** | 0.846*** | 0.641*** | 0.596*** | 1.258*** | 0.932*** | 0.668*** | 0.610*** |
(0.024) | (0.032) | (0.031) | (0.031) | (0.022) | (0.033) | (0.030) | (0.031) | |
Change in gap by 2010 | 0.098*** | 0.122*** | 0.096* | 0.080 | -0.008 | 0.025 | 0.053 | 0.051 |
(0.033) | (0.046) | (0.051) | (0.052) | (0.032) | (0.045) | (0.047) | (0.048) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 30,950 | 30,950 | 26,050 | 26,050 | 31,850 | 31,850 | 26,890 | 26,890 |
Adjusted R2 | 0.152 | 0.243 | 0.289 | 0.293 | 0.189 | 0.265 | 0.331 | 0.336 |
Notes: Models 1 and 2 use the full sample; Models 3 and 4 use the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. SES refers to socioeconomic status.
Self-control (reported by teachers) models | Approaches to learning (reported by teachers) models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 0.394*** | 0.304*** | 0.217*** | 0.182*** | 0.630*** | 0.630*** | 0.493*** | 0.435*** |
(0.025) | (0.037) | (0.037) | (0.038) | (0.024) | (0.035) | (0.036) | (0.037) | |
Change in gap by 2010 | -0.009 | 0.065 | 0.078 | 0.085 | -0.117*** | -0.066 | -0.042 | -0.043 |
(0.037) | (0.054) | (0.060) | (0.061) | (0.035) | (0.053) | (0.057) | (0.057) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 29,500 | 29,500 | 25,080 | 25,080 | 31,260 | 31,260 | 26,460 | 26,460 |
Adjusted R2 | 0.019 | 0.117 | 0.173 | 0.175 | 0.040 | 0.117 | 0.199 | 0.204 |
Self-control (reported by parents) models | Approaches to learning (reported by parents) models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 0.467*** | 0.424*** | 0.357*** | 0.291*** | 0.539*** | 0.479*** | 0.215*** | 0.132*** |
(0.025) | (0.036) | (0.039) | (0.040) | (0.025) | (0.032) | (0.033) | (0.033) | |
Change in gap by 2010 | -0.076** | -0.084 | -0.032 | 0.001 | 0.024 | -0.024 | 0.096* | 0.112** |
(0.037) | (0.054) | (0.060) | (0.061) | (0.036) | (0.053) | (0.055) | (0.056) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 30,400 | 30,400 | 27,220 | 27,220 | 30,420 | 30,420 | 27,240 | 27,240 |
Adjusted R2 | 0.022 | 0.037 | 0.075 | 0.079 | 0.035 | 0.057 | 0.218 | 0.228 |
Year | Reduction | Change in reduction from 1998 to 2010 (in percentage points) | |
---|---|---|---|
Reading | 1998 | 45.5% | |
2010 | 42.9% | -2.6 | |
Math | 1998 | 52.6% | |
2010 | 48.6% | -4.1 | |
Self-control (reported by teachers) | 1998 | 50.8% | |
2010 | 32.6% | -18.1 | |
Approaches to learning (reported by teachers) | 1998 | 28.3% | |
2010 | 20.3% | -8 | |
Self-control (reported by parents) | 1998 | 35.3% | |
2010 | 34.3% | -1.1 | |
Approaches to learning (reported by parents) | 1998 | 73.5% | |
2010 | 56.0% | -17.5 |
Note: SES refers to socioeconomic status. Declining values from 1998 to 2010 indicate that factors such as early literacy activities and other controls were not as effective at shrinking SES-based gaps in 2010 as they were in 1998.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |
---|---|---|---|---|---|---|
Correlations between selected practices and skills measured at kindergarten entry in 1998 | ||||||
Center-based pre-K | 0.106*** | 0.097*** | -0.125*** | -0.001 | -0.006 | 0.018 |
(0.016) | (0.015) | (0.018) | (0.018) | (0.019) | (0.016) | |
Number of books | 0.012*** | 0.016*** | 0.004** | 0.008*** | 0.002 | 0.006*** |
(0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
Reading/literacy | 0.166*** | 0.068*** | 0.010 | 0.030* | 0.143*** | 0.315*** |
(0.016) | (0.015) | (0.018) | (0.016) | (0.018) | (0.017) | |
Other activities | -0.115*** | -0.036*** | 0.047*** | 0.033** | 0.046*** | 0.292*** |
(0.015) | (0.014) | (0.017) | (0.016) | (0.017) | (0.016) | |
Correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry in 1998 | ||||||
Two or more years of college/vocational school | 0.029 | 0.066** | 0.072* | 0.115*** | 0.180*** | 0.136*** |
(0.025) | (0.026) | (0.042) | (0.037) | (0.038) | (0.033) | |
Bachelor’s degree | 0.114*** | 0.172*** | 0.141*** | 0.211*** | 0.272*** | 0.228*** |
(0.023) | (0.023) | (0.036) | (0.032) | (0.036) | (0.030) | |
Master’s degree or more | 0.160*** | 0.220*** | 0.120*** | 0.219*** | 0.254*** | 0.377*** |
(0.026) | (0.025) | (0.039) | (0.034) | (0.036) | (0.033) | |
Changes from 1998 to 2010 in the correlations between selected practices and skills measured at kindergarten entry | ||||||
Center-based pre-K | -0.005 | -0.036 | 0.060* | -0.010 | -0.020 | 0.010 |
(0.025) | (0.025) | (0.032) | (0.031) | (0.031) | (0.026) | |
Number of books | 0.002 | -0.001 | 0.001 | 0.002 | -0.002 | 0.004 |
(0.003) | (0.002) | (0.003) | (0.003) | (0.003) | (0.002) | |
Reading/literacy | 0.018 | 0.008 | 0.015 | 0.014 | -0.079*** | -0.173*** |
(0.025) | (0.024) | (0.031) | (0.028) | (0.030) | (0.027) | |
Other activities | -0.008 | -0.016 | 0.031 | 0.020 | 0.218*** | 0.265*** |
(0.025) | (0.024) | (0.029) | (0.028) | (0.029) | (0.025) | |
Changes from 1998 to 2010 in the correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry | ||||||
Two or more years of college/vocational school | 0.121** | 0.106* | 0.201** | 0.204*** | -0.030 | 0.151** |
(0.055) | (0.059) | (0.081) | (0.072) | (0.084) | (0.066) | |
Bachelor’s degree | 0.139*** | 0.103** | 0.136* | 0.174*** | -0.084 | 0.100 |
(0.048) | (0.051) | (0.070) | (0.063) | (0.078) | (0.061) | |
Master’s degree or more | 0.186*** | 0.117** | 0.140* | 0.189*** | -0.041 | 0.076 |
(0.052) | (0.054) | (0.074) | (0.066) | (0.081) | (0.063) | |
Observations | 26,050 | 26,890 | 25,080 | 26,460 | 27,220 | 27,240 |
Adj.R2 | 0.293 | 0.336 | 0.175 | 0.204 | 0.079 | 0.228 |
Notes: The robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 1.294*** | 0.696*** | 1.457*** | 0.681*** | 0.317*** | 0.076 | 0.638*** | 0.409*** | 0.471*** | 0.254*** | 0.655*** | 0.221*** |
(0.038) | (0.058) | (0.036) | (0.050) | (0.039) | (0.048) | (0.038) | (0.042) | (0.039) | (0.049) | (0.039) | (0.045) | |
Change in gap by 2010 | -0.020 | -0.075 | -0.154*** | -0.119* | -0.099* | 0.046 | -0.237*** | -0.141* | -0.136** | -0.093 | -0.084 | -0.004 |
(0.051) | (0.082) | (0.049) | (0.070) | (0.055) | (0.081) | (0.053) | (0.074) | (0.053) | (0.080) | (0.053) | (0.070) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 26,660 | 23,880 | 27,570 | 24,710 | 25,790 | 23,170 | 27,200 | 24,380 | 27,280 | 25,040 | 27,290 | 25,050 |
Adjusted R2 | 0.134 | 0.282 | 0.166 | 0.328 | 0.009 | 0.172 | 0.029 | 0.199 | 0.017 | 0.079 | 0.032 | 0.223 |
Notes: Model 1 uses the full sample; Model 4 uses the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 1.090*** | 0.384*** | 1.308*** | 0.443*** | 0.419*** | 0.119** | 0.603*** | 0.325*** | 0.443*** | 0.272*** | 0.436*** | 0.073 |
(0.042) | (0.058) | (0.041) | (0.060) | (0.045) | (0.050) | (0.044) | (0.049) | (0.045) | (0.051) | (0.044) | (0.052) | |
Change in gap by 2010 | -0.127** | -0.006 | -0.230*** | -0.060 | 0.049 | 0.228*** | -0.128** | 0.008 | 0.044 | 0.106 | 0.032 | 0.051 |
(0.060) | (0.084) | (0.059) | (0.082) | (0.066) | (0.081) | (0.064) | (0.079) | (0.065) | (0.084) | (0.064) | (0.080) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 28,650 | 26,050 | 29,560 | 26,890 | 27,550 | 25,080 | 29,110 | 26,460 | 28,170 | 27,220 | 28,190 | 27,240 |
Adjusted R2 | 0.103 | 0.276 | 0.143 | 0.321 | 0.023 | 0.174 | 0.036 | 0.199 | 0.019 | 0.079 | 0.019 | 0.226 |
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 0.736*** | 0.347*** | 0.966*** | 0.424*** | 0.324*** | 0.105*** | 0.455*** | 0.241*** | 0.283*** | 0.117*** | 0.583*** | 0.136*** |
(0.028) | (0.034) | (0.027) | (0.031) | (0.029) | (0.035) | (0.028) | (0.033) | (0.029) | (0.037) | (0.028) | (0.033) | |
Change in gap by 2010 | 0.083** | -0.540*** | -0.019 | -0.818*** | -0.068 | -0.126 | -0.058 | -0.244 | -0.044 | -0.248 | 0.085** | -0.026 |
(0.039) | (0.184) | (0.038) | (0.188) | (0.042) | (0.225) | (0.041) | (0.184) | (0.041) | (0.216) | (0.039) | (0.178) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 29,060 | 26,050 | 29,920 | 26,890 | 27,730 | 25,080 | 29,350 | 26,460 | 30,200 | 27,220 | 30,220 | 27,240 |
Adjusted R2 | 0.080 | 0.270 | 0.120 | 0.314 | 0.012 | 0.172 | 0.024 | 0.194 | 0.009 | 0.075 | 0.047 | 0.226 |
Part of school district | Entire school district | Across multiple school districts |
---|---|---|
Austin, Texas | Joplin, Missouri | Eastern Kentucky* |
Boston, Massachusetts | Kalamazoo, Michigan | |
Durham, North Carolina (East Durham) | Montgomery County, Maryland* | |
Minneapolis, Minnesota (North Minneapolis) | Pea Ridge, Arkansas | |
New York, New York | Vancouver, Washington** | |
Orange County, Florida (Tangelo Park) |
*Indicates that while the initiative covers the entire county or region, a portion of the county or region receives more intensive services. **Indicates that the initiative will cover the entire school district under plans to expand.
Source: Case studies published on the Broader, Bolder Approach to Education website (www.boldapproach.org/case-studies)
1. Values are in 2008 dollars.
2. Early investments in education strongly predict adolescent and adult development (Cunha and Heckman 2007; Heckman 2008; Heckman and Kautz 2012). For instance, students with higher levels of behavioral skills learn more in school than peers whose attitudinal skills are less developed (Jennings and DiPrete 2010). In general, as Heckman asserted, “skills beget skills,” meaning that creating basic, foundational knowledge makes it easier to acquire skills in the future (Heckman 2008). Conversely, children who fail to acquire this early foundational knowledge may experience some permanent loss of opportunities to achieve to their full potential. Indeed, scholars have documented a correlation between lack of kindergarten readiness and not reading well at third grade, which is a key point at which failing to read well greatly reduces a child’s odds of completing high school (Fiester 2010; Hernandez 2011).
3. Research by Reardon (2011) had found systematic increases in income gaps among generations. Recent studies by Bassok and Latham (2016) and Reardon and Portilla (2016), however, show narrower achievement gaps at kindergarten entry between a recent cohort and the previous one, and thus a possible discontinuation or interruption of that trend. (Bassok et al. [2016] use an SES construct to compare relative teacher assessments of cognitive and behavioral skills among low-SES children versus all children, adjusted by various other characteristics; Reardon and Portilla [2016] look at relative performance of children in the 90th and 10th income percentiles, and use age-adjusted, standardized, outcome scores.) Research by Carnoy and García (2017) shows persistent social-class gaps, but no solid evidence regarding trends: their findings for students in the fourth and eighth grades, in math and reading, show that achievement gaps neither shrink nor grow consistently (they are a function of the social-class indicator, the grade level, or the subject).
4. Clustering takes into account the fact that children are not randomly distributed, but tend to be concentrated in schools or classrooms with children of the same race, social class, etc. These estimates offer an estimate of gaps within schools. See Appendix B for more details.
5. Results available upon request. See García 2015 for results for all SES-quintiles (the baseline or unadjusted gaps in that report correspond with Model 2 in this paper).
6. The Early Childhood Longitudinal Study asks both parents and teachers to rate children’s abilities across a range of these skills. The specific skills measured may vary between the home and classroom setting. Teachers likely evaluate their students’ skills levels relative to those of other children they teach. Parents, on the other hand, may be basing their expectations on family, community, culture, or other factors.
7. See García 2015 for a discussion of which factors in children’s early lives and their individual and family characteristics (in addition to social class) drive the gaps among children of the 2010 kindergarten class.
8. Note that the SES quintiles are constructed using each year’s distribution, and that changes in the overall and relative distribution may affect the characteristics of children in the different quintiles each year (i.e., there may be some groups who are relatively overrepresented in one or another quintile if changes in the SES components changed over time).
9. The detailed frequency with which parents develop or practice some activities with their children at home and others is available upon request.
10. Literature on expectations and on parental behaviors in the home find that they positively correlate with children’s cognitive development and outcomes (Simpkins, Davis-Kean, and Eccles 2005; Wentzel, Russell, and Baker 2016). This literature acknowledges the multiple pathways through which expectations and behaviors influence educational outcomes, as well as the importance of race, social class, and other factors as moderators of such associations (Davis-Kean 2005; Redd et al. 2004; Wentzel, Russell, and Baker 2016; Yamamoto and Holloway 2010).
11. This may be affected by the fact that the highest number of reported books in 1998 was “more than 200,” while in 2010 parents could choose from more categories, up to “more than 1,000.” We had to use 200 as our cap in order to compare data for the two kindergarten classes.
12. Evidence also points to many other factors that affect children’s school readiness, and these, too, likely changed over this time period. For example, access to prenatal care, health screenings, and nutritional programs could all have affected children’s development differently across these two cohorts, but we do not have access to these data and thus cannot control for them in our study. For links between school readiness, children’s health, and poverty, see AAP COCP 2016; Currie 2009; U.S. HHS and U.S. ED 2016.
13. Models include all quintiles in their specification. Tables that offer a comparison for all quintiles relative to the first quintile are available upon request. We focus the discussion on the gap between the top and bottom.
14. As a result, sample sizes become smaller (see Appendix Table C1). Assuming “missingness” (observations without full information) is completely at random, the findings are representative of the original sample and of the populations they represent. Analytic samples once missingness is accounted for are called the complete case samples. We tested to see whether the unadjusted gaps estimated above with the full sample remained the same when using the complete case samples. For Model 1, we found an average difference of 0.01 sd in the estimates of 1998 SES gaps, and an average difference of 0.02 sd in the estimates of the change in the gaps. For Model 2, the differences were 0.01 sd for the gaps’ estimates and 0.04 for changes in the gaps’ estimates. In terms of statistical significance, there are no significant changes in the estimates associated with the 1998 gaps, but there are two changes in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011, and one change in the magnitude of the coefficient. The first change in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011 is the change in the gap in approaches to learning as reported by parents, which is statistically significant when using the restricted sample (0.07 sd, at the 10 percent significance level, Model 1); and the second is the change in the gap in math which also becomes statistically significant when using the restricted sample (0.09, at the 10 percent significance level, Model 2). Finally, the one change in the magnitude of the coefficient, in this model, is the estimate of the change in the gap in reading, which increases when using the restricted sample (from 0.12 sd to 0.18 sd). Results are available upon request.
15. These interactions between inputs and time test for whether the influence of inputs in 2010 is smaller than, the same as, or larger than the influence of inputs in 1998. Also, although only the fully specified results are shown, as noted in Appendix B, these sets of controls are entered parsimoniously in order to determine how sensitive gaps and changes in gaps over time are to the inclusion of family characteristics only, to the added inclusion of family investments, and, finally, to the inclusion of parental expectations (for the inclusion of parental expectations, we incorporated interactions of the covariates with time parsimoniously as well). For all outcomes, and focusing on the models without interactions between covariates and time, we find that all gaps in 1998 continuously shrink as we add more controls. For example, in reading, adding family characteristics reduces the gap in 1998 by 11 percent, adding investments further reduces it by 15 percent, and adding expectations further reduces it by 9 percent. In math, these changes equal to 16 percent, 13 percent, and 10 percent. For changes in the gap by 2010–2011, for both reading and math, adding family characteristics and investments shrink the changes in the gaps, but adding expectations slightly increases the estimated coefficients (which are statistically significant for reading, but not for math in these models. For self-control (as reported by teachers) and approaches to learning (by parents), which are the only two noncognitive skills for which the change in the gap is statistically significant, adding family characteristics reduces the change in the “gap [by 2010–2011” coefficient], but adding investments increases it, and adding expectations further increases the changes in the gaps by 2010–2011. These results are not shown in the appendices, but are available upon request.
16. The interactions between parental expectations of children’s educational attainment and the time variable test for whether the influence of expectations in 2010 is smaller, the same, or larger, than the influence of expectations in 1998.
17. The change in the skills gaps by SES in 2010 due to the inclusion of the controls is not directly visible in the tables in this report. To see this, see the comparison of estimates of models MS1–MS3 in García 2015. The change in the skills gaps by SES in 1998 is directly observable in Tables 3 and 4 and is discussed below.
18. The numbers in the “Reduction” column in Table 5 (showing the shares of the SES-based skills gaps that are accounted for by controls) are always higher for 1998 than for 2010.
19. Please note that until this point in the report we have been concerned with SES gaps and not with performance directly (though SES gaps are the result of the influence of SES on performance, which leads to differential performance of children by SES and hence to a performance gap). The paragraphs above emphasize how controls mediate or explain some of the skills gaps by SES, so, in a way, controls inform our analysis of gaps because they reveal how changes in gaps may have been affected by changes in various factors’ capacity to influence performance. Now the focus is on exploring the independent effect of the covariates of interest on performance. In this report, because we address whether the education and selected practices affect outcomes, the main effect is measured for the 1998 cohort, and we measure how it changed between 1998 and 2010. The detailed discussion for the correlation between covariates and outcomes in 2010 is provided in Table 3 in García 2015.
20. This variable indicates whether the child was cared for in a center-based setting during the year prior to the kindergarten year, compared with other options (as explained in García 2015, these alternatives include no nonparental care arrangements; being looked after by a relative, a nonrelative, at home or outside; or a combination of options. Any finding associated with this variable may be interpreted as the association between attending prekindergarten programs, compared with other options, but must be interpreted with caution. In other words, the child may have attended a high-quality prekindergarten program, which could have been either private or public, or a low-quality one, which would have different impacts. He or she might have been placed in (noneducational) child care, either private or public, of high or low quality, for few or many hours per day, with very different implications for his or her development (Barnett 2008; Barnett 2011; Magnuson et al. 2004; Magnuson, Ruhm, and Waldfogel 2007; Nores and Barnett 2010). For the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010, and for a meta-analysis of results, see Duncan and Magnuson 2013. Thus, more detailed information on the characteristics of the nonparental care arrangements (type, quality, and quantity) would help researchers further disentangle the importance of this variable. This additional information would provide a much clearer picture of the effects of early childhood education on the different educational outcomes.
21. Because these associations seemed counterintuitive, we tested whether they were sensitive to the composition of the index. We removed one component of the index at a time and created five alternative measures of other enrichment activities that parents do with their children. The results indicate that the negative association between the index and reading is not sensitive to the components of the index (the coefficients for the main effect, i.e., for the effect in 1998 range between -0.14 and -0.09, are all statistically significant). For math, the associations lose some precision, but retain the negative sign (negative association) in four out of the five cases (minimum coefficient is -0.06). As a caveat, these components do not reflect whether the activities are undertaken by the child or guided by the adult, the time devoted to them, or how much they involve the use of vocabulary or math concepts. The associations could indicate that time spent on nonacademic activities detracts from parents’ time to spend on activities that are intended to boost their reading and math skills, among other possible explanations. These results are available upon request.
22. Note that in this section, “social class” and “socioeconomic status” (SES) are treated as equivalent terms; in the rest of the report, we refer to SES as a construct that is one measure of social class. See Appendices C and D for discussions of two other sensitivity analyses, one based on imputation of missing values for the main analysis in this paper, and the other on the utilization of various metrics of the cognitive variables. Overall, our findings were not sensitive to various multiple imputation tests. In terms of the utilization of different metrics for the cognitive variables, some sensitivity of the point estimates was detected.
23. With certain activities that are already so provided to high-SES children, there may be little room for doing more for them. For example, there are only 24 hours per day to read to your child, so there is a cap on reading from a cap on time. But perhaps there is still room to improve the influence of reading, if, for example, the way reading is done changes.
24. Eight of the 12 districts explored in this paper are the subjects of published case studies. Case studies for the other four are in progress and will be published later this year. When citing information from the published case studies, we cite the specific published study. For the four that are not yet published, we refer to the original sources being used to develop the case studies.
25. Missing or incomplete cells in the table indicate that data were not available on that aspect of student demographics or other characteristics. As per the source note, most data came either from the districts’ websites or from NCES.
26. In the country as a whole, poverty rates, which had been rising prior to 2007, sped up rapidly during the recession and in its aftermath (through 2011–2012), and minority students (mainly Hispanic and Asian) grew as a share of the U.S. public school student body. Between 2000 and 2013, even with a decline in the proportion of black students, the share of the student body that is minority (of black or Hispanic origin) increased from 30.0 percent to 40.5 percent, and the proportion of low-income students (those eligible for free or reduced-price lunch) also increased, up from 38.3 percent of all public school students in 2000 to 52.0 percent in 2013 (Carnoy and García 2017). The Southern Education Foundation revealed a troubling tipping point in 2013: for the first time since such data have been collected, over half of all public school students (51 percent) qualified for free or reduced-priced meals (i.e., over half of students were living in households at or below 185 percent of the federal poverty line). Across the South, shares were much higher, with the highest percentage, 71 percent—or nearly three in four students—in Mississippi (Southern Education Foundation 2015).
27. A full cross-cutting analysis of why and how these districts have employed whole-child/comprehensive educational approaches will be published as part of a book that draws on these case studies.
28. The federal Early Head Start (EHS) program includes both a home visiting and a center-based component, with many of the low-income infants and toddlers served benefiting from a combination of the two. Studies of EHS find improved cognitive, behavioral, and emotional skills for children as well as enhanced parenting behaviors.
29. According to one important source for data on access to and quality of state pre-K programs, the State of Preschool yearbook produced annually by the National Institute for Early Education Research (NIEER) at Rutgers University, as of 2015, 42 states and the District of Columbia were funding 57 programs. Moreover, programs continued to recover from cuts made during the Great Recession; enrollment, quality, and per-pupil spending were all up, on average, compared with the year before, albeit with the important caveat that two major states—Texas and Florida—lost ground, and that “[f]or the nation as a whole,…access to a high-quality preschool program remained highly unequal, and this situation is unlikely to change in the foreseeable future unless many more states follow the leaders” (NIEER 2016).
30. Elaine Weiss interview with Joshua Starr, June 2017.
31. Murnane and Levy 1996; Elaine Weiss interview with Joshua Starr, June 2017.
32. In recent years, a growing number of reports have emerged that some charter schools—which are technically public schools and often tout their successes in serving disadvantaged students—keep out students unlikely to succeed through complex application processes, fees, parent participation contracts, and other mechanisms, and then further winnow the student body of such students by pushing them out when they struggle academically or behaviorally. For more on this topic, see Burris 2017, PBS NewsHour 2015, and Simon 2013.
33. See AIR 2011 and Sparks 2017. The federal school improvement models, in order of severity (from lightest to most stringent) are termed “transformation,” “turnaround,” “restart,” and “closure” (AIR 2011, 3).
34. While the cut score on any given assessment/test needed for a student to be considered “proficient” is an arbitrary one, and, in Minnesota and many other states, changes from year to year and from one assessment to another, these gains are a helpful indicator of program effectiveness, as they are comparable over the time period described.
35. Joplin statistics are from internal data produced for the superintendent at that time that are no longer available.
36. Attendance Works , a national campaign to reduce chronic absence, points to a range of studies that document and explain the connections between chronic absenteeism, student physical and mental health, and student achievement. Areas of research include elementary school absenteeism, middle and high school absenteeism, health issues, and state and local data on how these problems play out, among others.
37. Elaine Weiss interview with C.J. Huff, June 2016.
38. See Appendix D for a discussion of results using other metrics for reading and math achievement. Results are not meaningfully different across metrics, though the point estimates differ slightly.
39. This last feature will be explored in a companion paper to this one, as soon as the necessary information is released by NCES. (As Tourangeau et al. [2013] note, the assessment scores for the 2010–2011 cohort are not directly comparable with those for the 1998–1999 cohort. We are waiting on the availability of this data to conduct a companion study that allows us to learn whether starting levels of knowledge rose over these years, and what the relative gains were for different demographic groups.)
40. We acknowledge that there are multiple noneducation public policy and economic policy areas to be called upon to address the problems studied in this report, namely, all the ones that ensure other factors that correlate with low-SES are attended, and, obviously, the ones that lead to fewer low-SES children. These other policies could help ensure that more children grow up in contexts with sufficient resources and healthy surroundings, or would leave fewer children without built-in supports at home that need to be compensated for afterwards. We made these points in two early studies, and in the policy brief companion to this study (García 2015; García and Weiss 2015; García and Weiss 2017). A similar comprehensive approach in terms of policy recommendations was used by Putnam (2015).
AAP Council on Community Pediatrics (AAP COCP). 2016. “Poverty and Child Health in the United States.” Pediatrics vol. 137, no. 4. pii:e20160339.
Adamson, Frank, and Linda Darling-Hammond. 2012. “Funding Disparities and the Inequitable Distribution of Teachers: Evaluating Sources and Solutions.” Education Policy Analysis Archives vol. 20 (November), 37.
Alvarez, Lizette. 2015. “ One Man’s Millions Turn a Community in Florida Around .” New York Times , May 25.
American Institutes for Research (AIR). 2011. School Turnaround: A Pocket Guide .
Austin Independent School District (AISD). 2017. “ Pre-K 4 ” (section on the AISD website).
Baker, Bruce D., and Sean P. Corcoran. 2012. The Stealth Inequities of School Funding . The Center for American Progress.
Barbarin, O.A., J. Downer, E. Odom, and D. Head. 2010. “Home–School Differences in Beliefs, Support, and Control during Public Pre-Kindergarten and Their Link to Children’s Kindergarten Readiness.” Early Childhood Research Quarterly vol. 25, no. 3, 358–72.
Barnett, W. Steven. 2008. Preschool Education and Its Lasting Effects: Research and Policy Implications . Great Lakes Center for Education Research and Practice.
Barnett, W. Steven. 2011. “Effectiveness of Early Educational Intervention.” Science vol. 333, no. 6045, 975–78. doi:10.1126/science.1204534.
Barnett, W. Steven, Elizabeth Votruba-Drzal, Eric Dearing, and Megan E. Carolan. 2017. “Publicly Supported Early Care and Education Programs.” In The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies , Elizabeth Votruba-Drzal and Eric Dearing, eds. Malden, Mass., and Oxford: John Wiley.
Bassok, Daphna, Jenna E. Finch, RaeHyuck Lee, Sean F. Reardon, and Jane Waldfogel. 2016. “Socioeconomic Gaps in Early Childhood Experiences: 1998 to 2010.” AERA Open vol. 2, no. 3.
Bassok, Daphna, and Scott Latham. 2016. “ Kids Today: Changes in School-Readiness in an Early Childhood Era .” EdPolicyWorks Working Paper Series no. 35.
Berea College. 2013. “ U.S. Secretary of Education Visits First Rural Promise Neighborhood ” (news release). November 12.
Bradbury, Bruce, Miles Corak, Jane Waldfogel, and Elizabeth Washbrook. 2015. Too Many Children Left Behind: The U.S. Achievement Gap in Comparative Perspective. New York: Russell Sage Foundation.
Brooks-Gunn, Jeanne, and Lisa Markman. 2005. “The Contribution of Parenting to Ethnic and Racial Gaps in School Readiness.” Future of Children vol. 15, no. 1, 139–68.
Bivens, Josh. 2016. Progressive Redistribution without Guilt. Using Policy to Shift Economic Power and Make U.S. Incomes Grow Fairer and Faster . Economic Policy Institute.
Bivens, Josh, Emma García, Elise Gould, Elaine Weiss, and Valerie Wilson. 2016. It’s Time for an Ambitious National Investment in America’s Children: Investments in Early Childhood Care and Education Would Have Enormous Benefits for Children, Families, Society, and the Economy . Economic Policy Institute.
Boston College Center for Optimized Student Support. 2012. The Impact of City Connects: Progress Report 2012 .
Boston College Center for Optimized Student Support. 2014. The Impact of City Connects: Progress Report 2014 .
Burris, Carol. 2017. “ What the Public Isn’t Told about High Performing Charter Schools in Arizona .” Washington Post Answer Sheet blog, March 30.
Camilli, Gregory, Sadako Vargas, Sharon Ryan, and W. Steven Barnett. 2010. “Meta-Analysis of the Effects of Early Education Interventions on Cognitive and Social Development.” Teachers College Record vol. 112, no. 3, 579–620.
Carnoy, Martin, and Emma García. 2017. Five Key Trends in U.S. Student Performance. Progress by Blacks and Hispanics, the Takeoff of Asians, the Stall of Non-English Speakers, the Persistence of Socioeconomic Gaps, and the Damaging Effect of Highly Segregated Schools . Economic Policy Institute.
Carter, Prudence L., and Kevin G. Welner, eds. 2013. Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance . New York: Oxford Univ. Press.
Caspe, Margaret, and Joy Lorenzo Kennedy. 2014. Sustained Success: The Long-Term Benefits of High Quality Early Childhood Education. New York: Children’s Aid Society.
Chaudry, Ajay, Taryn Morrissey, Christina Weiland, and Hirokazu Yoshikawa. 2017. Cradle to Kindergarten: A New Plan to Combat Inequality . New York: Russell Sage Foundation.
Chetty, Raj, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. 2016. “ The Fading American Dream: Trends in Absolute Income Mobility since 1940 .” NBER Working Paper no. 22910.
Child Trends. 2014. Making the Grade: Assessing the Evidence for Integrated Student Supports .
Clark, H., et al. 2009. Study Comparing Children’s Aid Society Community Schools to Other New York City Public Schools (All Schools and Peer Schools ). ActKnowledge.
Coleman, J.S., E. Campbell, C. Hobson, J. McPartland, A. Mood, F. Weinfeld, and R. York. 1966. Equality of Educational Opportunity . Washington, D.C.: U.S. Office of Education.
Collaborative for Academic, Social, and Emotional Learning (CASEL). 2017. “ Partner Districts: Austin ” (webpage). Accessed August 31, 2017.
Cook-Harvey, C.M., L. Darling-Hammond, L. Lam, C. Mercer, and M. Roc. 2016. Equity and ESSA: Leveraging Educational Opportunity Through the Every Student Succeeds Act . Palo Alto, Calif.: Learning Policy Institute.
Cunha, Flavio, and James J. Heckman. 2007. “The Technology of Skill Formation.” American Economic Review vol. 97, no. 2, 31–47.
Currie, Janet. 2009. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” Journal of Economic Literature vol. 47, no. 1, 87–122.
Davis-Kean, Pamela E. 2005. “The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment.” Journal of Family Psychology vol. 19, no. 2 (June 2005), 294–304. doi:10.1037/0893-3200.19.2.294.
Duncan, Greg J., Chantelle J. Dowsett, Amy Claessens, Katherine A. Magnuson, Aletha C. Huston, Pamela Klebanov, Linda S. Pagani, Leon Feinstein, Mimi Engel, and Jeanne Brooks-Gunn. 2007. “School Readiness and Later Achievement.” Developmental Psychology vol. 43, no. 6, 1428–46.
Duncan, Greg J., and Katherine A. Magnuson. 2011. “The Nature and Impact of Early Achievement Skills, Attention Skills, and Behavior Problems.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds., 47–69. New York: Russell Sage Foundation.
Duncan, Greg J., and Katherine Magnuson. 2013. “Investing in Preschool Programs.” Journal of Economic Perspectives vol. 27, no. 2, 109–32.
Duncan, Greg J., Pamela A. Morris, and Chris Rodrigues. 2011. “Does Money Really Matter? Estimating Impacts of Family Income on Young Children’s Achievement with Data from Random-Assignment Experiments.” Developmental Psychology vol. 47, no. 5, 1263–79. doi:10.1037/a0023875.
Duncan, Greg J., and Richard Murnane. 2011. “Introduction: The American Dream, Then and Now.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.
Economic Policy Institute (EPI). 2012. “ The Great Recession .” State of Working America feature.
Economic Policy Institute (EPI). 2013. “ Inequality.is ” (interactive website).
Elmore, Richard, David Thomas, and Tonika Cheek Clayton. 2006. Differentiated Treatment in Montgomery County Public Schools . Public Education Leadership Project at Harvard University.
Fiester, Leila. 2010. Early Warning! Why Reading by the End of Third Grade Matters. KIDS COUNT Special Report . Annie E. Casey Foundation.
García, Emma. 2015. Inequalities at the Starting Gate: Cognitive and Noncognitive Skills Gaps between 2010–2011 Kindergarten Classmates . Economic Policy Institute.
García, Emma, and Elaine Weiss. 2015. Early Education Gaps by Social Class and Race Start U.S. Children Out on Unequal Footing. A Summary of the Major Findings in Inequalities at the Starting Gate . Economic Policy Institute.
García, Emma, and Elaine Weiss. 2016. Making Whole-Child Education the Norm: How Research and Policy Initiatives Can Make Social and Emotional Skills a Focal Point of Children’s Education . Economic Policy Institute.
García, Emma, and Elaine Weiss. 2017. Key Findings from the Report “Education Inequalities at the School Starting Gate” . Economic Policy Institute.
Hart, Betty, and Todd R. Risley. 1995. Meaningful Differences in the Everyday Experience of Young American Children . Baltimore, Md.: Brookes.
Heckman, James J. 2008. “Schools, Skills, and Synapses.” Economic Inquiry vol. 46, no. 3, 289–324.
Heckman, James J., and Tim Kautz. 2012. “Hard Evidence on Soft Skills.” Labour Economics vol. 19, no. 4, 451–64.
Henderson, Anne T. 2010. Community Organizing to Build Partnerships in Schools: The Alliance Schools Movement in Austin . Annenberg Institute for School Reform.
Hernandez, Donald J. 2011. Double Jeopardy: How Third-Grade Reading Skills and Poverty Influence High School Graduation . Annie E. Casey Foundation.
Gizriel, Sarah. 2016. “ Bright Futures Looking to Expand to Schools across Shenandoah Valley .” localDVM.com , December 9.
Jennings, J.L., and T.A. DiPrete. 2010. “Teacher Effects on Social and Behavioral Skills in Early Elementary School.” Sociology of Education vol. 83, no. 2, 135.
Kalamazoo Public Schools (KPS). 2017. “ PEEP Information and Applications ” (webpage). Accessed August 31, 2017.
Lee, Valerie E., and David T. Burkam. 2002. Inequality at the Starting Gate . Washington, D.C.: Economic Policy Institute.
Levin, Henry M. 2012a. “More Than Just Test Scores.” Prospects vol. 42, no. 3, 269–84.
Levin, Henry M. 2012b. “The Utility and Need for Incorporating Noncognitive Skills into Large-scale Educational Assessments.” In The Role of International Large-Scale Assessments: Perspectives from Technology, Economy, and Educational Research , Matthias von Davier et al., eds. Springer.
Magnuson, Katherine, and Greg J. Duncan. 2016. “Can Early Childhood Interventions Decrease Inequality of Economic Opportunity?” RSF: The Russell Sage Foundation Journal of the Social Sciences vol. 2, no. 2, 123–41.
Magnuson, Katherine A., M.K. Meyers, C.J. Ruhm, and Jane Waldfogel. 2004. “Inequality in Preschool Education and School Readiness.” American Educational Research Journal vol. 41, no. 1, 115–57.
Magnuson, Katherine A., Christopher Ruhm, and Jane Waldfogel. 2007. “Does Prekindergarten Improve School Preparation and Performance?” Economics of Education Review vol. 26, no. 1, 33–51.
Marietta, Geoff. 2010. Lessons for PreK-3rd from Montgomery County Public Schools: An FCD Case Study . Foundation for Child Development.
Maryland State Department of Education. 2017. “ Judy Centers ” (webpage). Accessed August 31, 2017.
Miller-Adams, Michelle. 2015. Promise Nation: Transforming Communities through Place-Based Scholarships . Kalamazoo, Mich.: W.E. Upjohn Institute for Employment Research.
Mishel, Lawrence. 2015. “ The Opportunity Dodge .” American Prospect , April 9.
Mishel, Lawrence, Josh Bivens, Elise Gould, and Heidi Shierholz. 2012. The State of Working America, 12th Edition , An Economic Policy Institute Book. Ithaca, N.Y.: Cornell Univ. Press.
Mishel, Lawrence, and Jessica Schieder. 2016. Stock Market Headwinds Meant Less Generous Year for Some CEOs . Economic Policy Institute.
Montgomery County Public Schools (MCPS). 2015. Graduation Rate Rises, Gap Narrows for MCPS Class of 2014 (public announcement). January 27.
Montgomery County Public Schools (MCPS). 2016. Linkages to Learning (brochure).
Montgomery County Public Schools (MCPS). 2017. “ Maryland Meals for Achievement ” (webpage). Accessed August 31, 2017.
Morsy, Leila, and Richard Rothstein. 2015. Five Social Disadvantages That Depress Student Performance: Why Schools Alone Can’t Close Achievement Gaps . Economic Policy Institute.
Murnane, Richard J., and Frank Levy. 1996. Teaching the New Basic Skills: Principles for Educating Children to Thrive in a Changing Economy . New York: The Free Press.
Murnane, Richard J., John B. Willett, Kristen L. Bub, and Kathleen McCartney. 2006. “Understanding Trends in the Black-White Achievement Gaps during the First Years of School.” Brookings-Wharton Papers on Urban Affairs.
Najarian, M., K. Tourangeau, C. Nord, K. Wallner-Allen, and J. Leggitt. Forthcoming. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), First-Grade and Second-Grade Psychometric Report . Washington, D.C.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
National Center for Education Statistics (NCES) (U.S. Department of Education). Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K 1998–1999) .
National Center for Education Statistics (NCES) (U.S. Department of Education). Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K 2010–2011) .
National Institute for Early Education Research (NIEER). 2016. The State of Preschool 2015: State Preschool Yearbook .
New York Times /CBS News. 2015. “ Americans’ Views on Income Inequality and Workers’ Rights ” (poll results). June 3.
Nores, Milagros, and W. Steven Barnett. 2010. “Benefits of Early Childhood Interventions across the World: (Under) Investing in the Very Young.” Economics of Education Review vol. 29, no. 2, 271–82.
Nores, Milagros, and W. Steven Barnett. 2014. Access to High Quality Early Care and Education: Readiness and Opportunity Gaps in America . New Brunswick, N.J.: Center on Enhancing Early Learning Outcomes.
Nores, Milagros, and Emma García. 2014. “Language, Immigration and Hispanics. Understanding Achievement Gaps in the Early Years.” Paper presented at the Association for Public Policy Analysis and Management Fall Research Conference, November 6–8, Albuquerque, N.M.
Oakes, Jeannie, Anna Maier, and Julia Daniel. 2017. Community Schools: An Evidence-Based Strategy for Equitable School Improvement , Learning Policy Institute, June 5.
PBS NewsHour . 2015. “ In Reforming New Orleans, Have Charter Schools Left Some Students Out? ” (news segment).
Peterson, T.K., ed. 2013. Expanding Minds and Opportunities: Leveraging the Power of Afterschool and Summer Learning for Student Success . Washington, D.C.: Collaborative Communications Group.
Phillips, Meredith. 2011. “Parenting, Time Use, and Disparities in Academic Outcomes.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.
Proctor, Bernadette D., Jessica L. Semega, and Melissa A. Kollar. 2016. Income and Poverty in the United States: 2015 . U.S. Census Bureau, Current Population Reports, P60-256(RV).
Putnam, Robert. 2015. Our Kids: The American Dream in Crisis . New York: Simon and Schuster.
Ready, Douglas D. 2010. “Socioeconomic Disadvantage, School Attendance, and Early Cognitive Development.” Sociology of Education vol. 83, no. 4, 271–86.
Reardon, Sean F. 2007. “ Thirteen Ways of Looking at the Black-White Test Score Gap .” Working paper.
Reardon, Sean F. 2011. “The Widening Academic Achievement Gap between the Rich and the Poor: New Evidence and Possible Explanations.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.
Reardon, Sean F., and Ximena A. Portilla. 2016. “Recent Trends in Income, Racial, and Ethnic School Readiness Gaps at Kindergarten Entry.” AERA Open vol. 2, no. 3, 1–18. doi: 10.1177/2332858416657343.
Redd, Z., L. Guzman, L. Lippman, L. Scott, and G. Matthews. 2004. Parental Expectations for Children’s Educational Attainment: A Review of the Literature . Prepared by Child Trends for the National Center for Education Statistics.
Rolnick, Art, and Rob Grunewald. 2003. “Early Childhood Development: Economic Development with a High Public Return.” The Region vol. 17, no. 4, 6–12.
Rothstein, Richard. 2004. Class and Schools: Using Social, Economic, and Educational Reform to Close the Achievement Gap . Washington, D.C.: Economic Policy Institute; New York: Columbia University Teachers College.
Rothstein, Richard. 2010. “Family Environment in the Production of Schooling.” In International Encyclopedia of Education , Dominic J. Brewer, Patrick J. McEwan, eds. Oxford: Elsevier. doi: 10.1016/B978-0-08-044894-7.01233-1.
Saez, Emmanuel. 2016. Striking It Richer: The Evolution of Top Incomes in the United States (Updated with 2015 Preliminary Estimates) .
Schanzenbach, Diane, Megan Mumford, Ryan Nunn, and Lauren Bauer. 2016. Money Lightens the Load . The Hamilton Project, Brookings Institute.
Selzer, Michael H., Ken A. Frank, and Anthony S. Bryk. 1994. “The Metric Matters: The Sensitivity of Conclusions about Growth in Student Achievement to Choice of Metric.” Educational Evaluation and Policy Analysis vol. 16, 41–49.
Sharkey, Patrick. 2013. Stuck in Place: Urban Neighborhoods and the End of Progress toward Racial Equality . Chicago, Ill.: Univ. of Chicago Press.
Simon, Stephanie. 2013. “ Class Struggle: How Charter Schools Get Students They Want .” Reuters , February 15.
Simpkins, Sandra D., Pamela E. Davis-Kean, and Jacquelynne S. Eccles. 2005. “Parents’ Socializing Behavior and Children’s Participation in Math, Science, and Computer Out-of-School Activities.” Applied Developmental Science vol. 9, no. 1, 14–30. doi:10.1207/s1532480xads0901_3.
Southern Education Foundation. 2015. A New Majority: Low-Income Students Now a Majority in the Nation’s Public Schools . January.
Sparks, Sarah D. 2017. “Billions in School Improvement Spending but Not Much Student Improvement.” EdWeek , January 19.
StataCorp. 2015. Stata: Release 14 [statistical software]. College Station, Texas: StataCorp LP.
Stringhini, Silvia, et al. 2017. “Socioeconomic Status and the 25×25 Risk Factors as Determinants of Premature Mortality: A Multicohort Study and Meta-Analysis of 1.7 Million Men and Women.” The Lancet . Published online January 31, 2017. doi:10.1016/S0140-6736(16)32380-7.
Tourangeau, K., C. Nord, T. Lê, A.G. Sorongon, and M. Najarian. 2009. Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K): Combined User’s Manual for the ECLS-K Eighth-Grade and K–8 Full Sample Data Files and Electronic Codebooks (NCES 2009-004) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.
Tourangeau, K., C. Nord, T. Lê, A.G. Sorongon, M.C. Hagedorn, P. Daly, and M. Najarian. 2013. Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), User’s Manual for the ECLS-K:2011 Kindergarten Data File and Electronic Codebook (NCES 2013-061). U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.
Tourangeau, K., C. Nord, T. Lê, K. Wallner-Allen, M.C. Hagedorn, J. Leggitt, and M. Najarian. 2015. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), User’s Manual for the ECLS-K:2011 Kindergarten–First Grade Data File and Electronic Codebook, Public Version (NCES 2015-078) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.
Tourangeau, K., C. Nord, T. Lê, K. Wallner-Allen, N. Vaden-Kiernan, L. Blaker, and M. Najarian. 2017. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011) User’s Manual for the ECLS-K:2011 Kindergarten–Second Grade Data File and Electronic Codebook, Public Version (NCES 2017-285) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.
U.S. Department of Education (U.S. ED). 2015. A Matter of Equity: Preschool in America .
U.S. Department of Health and Human Services (U.S. HHS) and U.S. Department of Education (U.S. ED). 2016. Policy Statement to Support the Alignment of Health and Early Learning Systems .
Van Voorhis, F.L., M.F. Maier, J.L. Epstein, C.M. Lloyd, and T. Leung. 2013. The Impact of Family Involvement on the Education of Children Ages 3 to 8: A Focus on Literacy and Math Achievement Outcomes and Social-Emotional Skills . MDRC.
Waldfogel, Jane. 2006. “What Do Children Need?” Public Policy Research vol. 13, no. 1, 26–34.
Weiss, Elaine. 2016a. Bright Futures in Joplin, Missouri . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016b. Vancouver Public Schools (Vancouver, WA) . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016c. Partners for Education at Berea College, Berea, Kentucky . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016d. Northside Achievement Zone (North Minneapolis, MN) . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016e. East Durham Children’s Initiative (East Durham, NC). A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016f. Bright Futures (Pea Ridge, AR) . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016g. City Connects (Boston, MA) . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016h. The Children’s Aid Society Community Schools (New York, NY) . A Broader, Bolder Approach to Education.
Weiss, Elaine. 2016i. A Broader, Bolder Education Policy Framework . A Broader, Bolder Approach to Education.
Wentzel, Kathryn R., Shannon Russell, and Sandra Baker. 2016. “Emotional Support and Expectations from Parents, Teachers, and Peers Predict Adolescent Competence at School.” Journal of Educational Psychology vol. 108, no. 2, 242–255.
Yamamoto, Yoko, and Susan D. Holloway. 2010. “Parental Expectations and Children’s Academic Performance in Sociocultural Context.” Educational Psychology Review vol. 22, no. 3, 189–214. doi:10.1007/s10648-010-9121-z.
Introduction.
Our research benefits from the existence of two companion studies conducted by the National Center for Education Statistics (NCES), the Early Childhood Longitudinal Study of the Kindergarten Class of 1998–1999 and the Early Childhood Longitudinal Study of the Kindergarten Class of 2010–2011 (hereafter, ECLS-K 1998–1999 and ECLS-K 2010–2011). The data from these studies come with multiple advantages and a few disadvantages.
The studies follow two nationally representative samples of children starting in their kindergarten year and continuing through their elementary school years (eighth grade for 1998–1999 cohort and fifth grade for the 2010–2011 cohort). The tracking of students over time is one of the most valuable features of the data. The studies include assessments of the children’s cognitive performance and knowledge as well as skills that belong in the category of noncognitive, or social and emotional, skills. The studies also include information on teachers and schools (provided by teachers and administrators) and interviews with parents.
Another valuable feature of the data is the availability of two ECLS-K studies (ECLS-K 1998–1999 and ECLS-K 2010–2011), which allows for cross-comparisons “of two nationally representative kindergarten classes experiencing different policy, educational, and demographic environments” (Tourangeau et al. 2013). The two studies are 12 years apart, or a full school cycle apart: when the 2010–2011 kindergarten class was starting school, the 1998–1999 class was starting the grade leading to their graduation. A comparison of the studies thus offers insightful information about the consequences of changes in the system that may have occurred during an entire cohort’s school life. For the 2010 study, the sample included 18,174 children in 968 schools. i The 1998 study sample included 21,409 children in 903 schools. ii
This existence of data from two cohorts is also a limitation to the current study, as explained by Tourangeau et al. (2013), who note that the assessment scores for the 2010–2011 class are not directly comparable with those developed for the class of 1998–1999. Although the IRT (Item Response Theory) procedures used in the analysis of data were similar across the two studies, each study incorporated different items, which means that the resulting scales are different. Tourangeau et al. (2013) state that “a subsequent release of the ECLS-K: 2010–2011 data will include IRT scores that are comparable with the ECLS-K 1998 cohort.” Up to the point of publication of the current study, this information had not yet been released, and we use standardized scores, instead of raw scores, for the outcomes examined. We can assess changes in the relative position in a distribution (i.e., how far apart high- and low-SES children are in 1998 and how far apart high- and low-SES children are in 2010), but not overall changes in their performance (i.e., it is not possible to ascertain whether performance has improved overall, or if gaps are smaller or larger due to an improvement in performance of children at the low end (specifically the lowest fifth) of the distribution or due to a decrease in the performance of children at the high end (highest fifth) of the distribution, etc.). A full comparison remains to be produced, upon data availability.
We use data for the first wave of each study, corresponding with fall kindergarten (or school entry).
For the analyses, we use the by-year standardized scores corresponding to the fall semester. (The 1998 IRT scale scores for reading and mathematics achievement and assessments of noncognitive skills are standardized using the 1998 distribution and its mean and sd; for 2010, we use the mean and sd of the 2010 distribution.)
Cognitive skills are assessed with instruments that measure each child’s:
We use the term “principal” to identify a set of noncognitive skills that are measured by both the ECLS-K 1998–1999 and 2010–2011 surveys, and that have been relatively extensively used in research.
Teachers are asked to assess each child’s:
Parents are asked to assess their child’s:
For the analyses, we use the following set of covariates. The definitions, and the coding used for the covariates, by year, are shown in Appendix Table A1 .
Gaps by socioeconomic status.
The expressions below show the specifications used to estimate the socioeconomic status–based (SES-based) performance gaps. For any achievement outcome A , we estimate four models:
These estimates build on all the available observations (i.e., only those children who have missing values in the outcome variables are eliminated from the analysis).
Because of lack of response in some of the covariates used as predictors of performance, we construct a common sample with observations with no missing information in any of the variables of interest (see information about missing data for each variable in Appendix Table C1 ). We estimate two more models: iii
The equation below shows the equation we estimate for Models 1 through 4.
Following standard approaches in this field, we use multiple imputation to impute missing values in both the independent and dependent variables, for the analysis of skills gaps and changes in them from 1998 to 2010 by socioeconomic status (main analysis). See share of missing data by variable in Appendix Table C1 . We use the mi commands in Stata 14, using chained equations, which jointly model all functional terms. The number of iterations was set up equal to 20. Imputation is performed by year.
Our functional form of the imputation model is specified using SES, gender, race, disability, age, type of family, number of books, educational activities, and parental expectations, as well as the original cognitive and noncognitive variables, as variables to be imputed. We use various specifications, combining different sets of auxiliary variables, mi impute methods, and other parameters, to capture any sensitivity of the results to the characteristics of the model. For example, income, family size, and ELL status are set as auxiliary variables and used in several of the imputation models. Another imputation option that was altered across models is the use of weights, as we ran out of imputation models using weights and not using them.
In the imputation model, in order to impute categorical variables’ missingness, we use the option augment, to prevent the large number of categorical variables to be imputed from causing problems of perfect prediction (StataCorp. 2015). The rest of the variables are first imputed as continuous variables. In a second exercise, we also impute SES and educational expectations as ordinal variables (also using the option augment).
In order to calculate the standardized dependent variables, we use the variables derived from the imputation variables (also known as passive imputation). This “fills in only the underlying imputation variables and computes the respective functional terms from the imputed variables” (StataCorp. 2015). In one case, we imputed the dependent variables directly as continuous variables (though we anticipated that the distribution of the scores imputed this way would not necessarily have a mean of 0 and a standard deviation of 1).
Using the imputed data, we estimate Models 1 through 4 following the specifications explained above (from no regressors to fully specified models).
The main findings of our analysis are not sensitive to missing data imputation. The estimates of the gaps in 1998 and the changes in the gaps from 1998 to 2010 are consistent across models in terms of statistical significance. There are some minor changes in the sizes of the estimated coefficients, especially those associated with the changes in the gaps (though all are statistically not different from 0, as discussed in the report using the results from the analysis with the complete cases). There are also some minor changes in the standard errors, though they are small enough to widen the coefficients’ statistical bandwidth to not include the 0.
Children’s reading and mathematics skills are measured using several different metrics in ECLS-K. Among these, the best-known or more commonly used metrics in research are the IRT-based theta scores and the IRT-based scale scores (IRT stands for Item Response Theory). NCES provides data users with definitions of these metrics and recommendations on how to appropriately choose among the different metrics. NCES explains that both theta and IRT-based scale scores are valid indicators of ability. This makes them suitable for research purposes, even though each is expressed in its own unit of measurement. NCES recommends that analysts “consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience” when choosing the appropriate score for analysis (see Tourangeau et al. 2013).
Although nothing would indicate that this could be the case, our work noted that results of analyses such as the one developed in this study are in some ways sensitive to the metrics used as dependent variables. v Thus, the purpose of this appendix is to illustrate the differences in the results associated with different analytic decisions in terms of the metrics used. As we will see, in essence, point estimates depend on the metric used, but the results do not change in a meaningful way and conclusions and implications remain unchanged. That is, although caution is required when interpreting the results obtained using different combinations of metrics, procedures (including standardization), and data waves, it is important to state that the main conclusions of this study— that social-class gaps in cognitive and noncognitive skills are large and have persisted over time — hold . So do the policy recommendations derived from those findings: sufficient, integrated, and sustained over-time efforts to tackle early gaps in a more effective manner.
NCES makes the following recommendations for researchers who are choosing among scales (see Tourangeau et al. 2013): vi
When choosing scores to use in analysis, researchers should consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience. […] The IRT-based scale scores […] are overall measures of achievement. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or in different rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] Results expressed in terms of scale score points, scale score gains, or an average scale score may be more easily interpretable by a wider audience than results based on the theta scores. The IRT-based theta scores are overall measures of ability. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or across rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] The theta scores may be more desirable than the scale scores for use in a multivariate analysis because generally their distribution tends to be more normal than the distribution of the scale scores. However, for a broader audience of readers unfamiliar with IRT modeling techniques, the metric of the theta scores (from -6 to 6) may be less readily interpretable. […]
The two scores are defined as follows (see Tourangeau et al. 2013, section “3.1 Direct Cognitive Assessment: Reading, Mathematics, Science”):
The IRT-based scale score is an estimate of the number of items a child would have answered correctly in each data collection round if he or she had been administered all of the questions for that domain that were included in the kindergarten and first-grade assessments. To calculate the IRT-based overall scale score for each domain, a child’s theta is used to predict a probability for each assessment item that the child would have gotten that item correct. Then, the probabilities for all the items fielded as part of the domain in every round are summed to create the overall scale score. Because the computed scale scores are sums of probabilities, the scores are not integers. The IRT-based theta score is an estimate of a child’s ability in a particular domain (e.g., reading, mathematics, science, or SERS) based on his or her performance on the items he or she was actually administered. […] The theta scores are reported on a metric ranging from -6 to 6, with lower scores indicating lower ability and higher scores indicating higher ability. Theta scores tend to be normally distributed because they represent a child’s latent ability and are not dependent on the difficulty of the items included within a specific test.
Reardon (2007) describes the calculation of the theta scores in the following manner: vii
For each test [math and reading], a three-parameter IRT model was used to estimate each student’s latent ability…at each wave…. The IRT model assumes that each student’s probability of answering a given test item correctly is a function of the student’s ability and the characteristics [discrimination, difficulty, and guessability] of the item…. Given the pattern of students’ responses to the items on the test that they are given, the IRT model provides estimates of both the person-specific latent abilities at each wave… and the item parameters. (Reardon 2007, 10) viii
He also notes that “[b]ecause the ECLS-K tests contain many more ‘difficult’ items than ‘easy’ items, the relationship between theta and scale scores is not linear (a unit difference in theta corresponds to a larger difference in scale scores at theta=1 than at theta=-1, for example). The scale scores are difficult to interpret as an interval-scale metric (or are an interval-scaled metric only with respect to the specific set of items on the ECLS-K tests),” while he shows that the “theta scores are interval-scale metrics, in a behaviorally-meaningful sense” (Reardon 2007, 11, 13). ix
For the analyses, both the scale and the theta scores need to be standardized by year (the original variables are not directly comparable because they rely on different instruments, as explained by NCES, and the resulting standardized variables have mean 0 and standard deviation 1). This is a common practice in the education field, as it allows researchers to use data that come from different studies and would not have a common scale otherwise. We need to take into consideration that the underlying units of measurement for each variable are different, but after standardization, the metrics are common, expressed in standard deviations and represent the population’s distribution of abilities.
The distributions of the scale and theta scores are shown in Appendix Figures D1 and D2 . In each figure, the plots reflect a more normally distributed pattern for the theta scores (right panel) than for the scale scores (left panel). The companion table, Appendix Table D1 , shows the range of variation for the four outcomes (mean and standard deviations are 0 and 1 as per construction).
We next offer a comparison of the results obtained when using the scale scores versus using the theta scores ( Appendix Table D2 ). We highlight the following main similarities and differences between the results obtained using the scale scores and the results using the theta scores.
In Appendix Table D3 , we compare the results obtained using the different scales and the different proxies of socioeconomic status (our composite SES index, mother’s education, number of books, and household income).
There are two other significant pieces of information affecting the cognitive scores in more recent documentation released by NCES. In 2015, NCES announced in its ECLS-K User’s Manual that a
change in methodology required a re-calibration and re-reporting of the kindergarten reading scores since the release of the base-year file. Therefore, the kindergarten reading theta scores included in the K-1 data file are calculated differently than the previously released kindergarten theta scores and replace the kindergarten reading theta scores included in the base-year data file. The modeling approach stayed the same for mathematics and science, so the recalculation of kindergarten mathematics and science theta scores was not needed. (Tourangeau et al. 2015)
Following up on this, the most recent (2017) data user’s manual explains that
The method used to compute the theta scores allows for the calculation of theta for a given round that will not change based on later administrations of the assessments (which is not true for the scale scores, as described in the next section). Therefore, for any given child, the kindergarten, first-grade, and second-grade theta scores provided in subsequent data files will be the same as theta scores released in earlier data files , with one exception: the reading thetas provided in the base-year data file . After the kindergarten-year data collection, the methodology used to calibrate and compute reading scores changed; therefore, the reading thetas reported in the base-year file are not the same as the kindergarten reading thetas provided in the files with later-round data [emphasis added]. Any analysis involving kindergarten reading theta scores and reading theta scores from later rounds, for example an analysis looking at growth in reading knowledge and skills between the spring of kindergarten and the spring of first grade, should use the kindergarten reading theta scores from a data file released after the base year. The reading theta scores released in the kindergarten-year data file are appropriate for analyses involving only the kindergarten round data; analyses conducted with only data released in the base-year file are not incorrect, since those analyses do not compare kindergarten scores to scores in later rounds that were computed differently. However, now that the recomputed kindergarten theta scores are available in the kindergarten through first-grade and kindergarten through second-grade data files, it is recommended that researchers conduct any new analyses with the recomputed kindergarten reading theta scores. For more information on the methods used to calculate theta scores, see the ECLS-K: 2011 First-Grade and Second-Grade Psychometric Report (Najarian et al. forthcoming). (Tourangeau et al. 2017)
Therefore, because of these changes in NCES methodology and reporting, and in light of the comparisons in this appendix, one could expect additional slight changes in the estimates using the IRT-theta scores for reading for kindergarten if using rounds of data posterior to the first round (and probably if using the IRT-scale scores as well, as these values are derived from the theta scores), relative to the first data file of ECLS-K: 2010-2011 released by NCES in 2013. We would not necessarily expect, though, any changes when using the standardized transformation of those scores, because NCES’s documentation does not mention changes to the distribution of the scores, only to their values. We will explore these issues further upon the release of the scores that are comparable across the two ECLS-K studies without any transformation.
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The needs of children in Austin Independent School District (AISD) schools with the highest concentrations of poor, immigrant, and non-English-speaking families are supported through a combination of parent-organizing (schools with parent-organizing programs, led by the nonprofit Austin Interfaith, form a network of “Alliance Schools”), intensive embedding of social and emotional learning (SEL) in all aspects of school policy and practice, and the transformation of schools into “community schools” (i.e., schools that are hubs for the provision of academic, health, and social services).
The City Connects program provides targeted academic, social, emotional, and health supports to every child in 20 of the city’s schools with the highest shares of low-income, black, Hispanic, and immigrant students.
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The Northside Achievement Zone (NAZ) is a Promise Neighborhood, a designation awarded by the U.S. Department of Education Promise Neighborhoods program to some of the most distressed neighborhoods in the nation. Through the program, children and families who live in the 13-by-18 block NAZ receive individualized supports.
Through a collaboration between The Children’s Aid Society and the New York City Department of Education, 16 community schools in some of the most disadvantaged neighborhoods in three of the city’s five boroughs provide wraparound health, nutrition, mental health, and other services to students along with enriching in-and-out-of-school experiences, amplified by extensive parental and community engagement.
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Joplin’s Bright Futures initiative (which has spawned dozens of other Bright Futures affiliate districts under a Bright Futures USA umbrella since it launched in 2010) has a rapid response component that addresses children’s basic needs (within 24 hours of a need being reported), while strong school–community partnerships help meet students’ longer-term needs. Bright Futures also provides meaningful service learning opportunities in every school.
The “Kalamazoo Promise,” a guarantee by a group of anonymous local philanthropists to provide full college scholarships in perpetuity for graduates of the district’s public high schools brought Kalamazoo Public Schools (KPS), the city, and the community together to develop a set of comprehensive supports that enable more students to use the scholarships.
All students in Montgomery County Public Schools (MCPS) benefit from zoning laws that advance integration and strong union–district collaboration on an enriching, equity-oriented curriculum. These efforts are bolstered by extra funding and wraparound supports for high-needs schools and communities.
The Pea Ridge School District, a small suburban–rural district outside Fayetteville, Arkansas, is among the newer affiliates of Bright Futures USA, a national umbrella group that grew out of Bright Futures Joplin. As a Bright Futures affiliate, Pea Ridge is making good progress toward identifying and meeting students’ basic needs, engaging the community to meet longer-term needs, and making service learning a core component of school policy and practice.
Family and Community Resource Centers (FCRCs) currently serve 16 of the highest-needs Vancouver Public Schools (VPS) district schools, with mobile and lighter-touch support in other schools and plans to expand districtwide by 2020.
Eastern (appalachian) kentucky.
A federal Promise Neighborhood grant helps Berea College’s Partners for Education provide intensive supports for students and their families in four counties in the Eastern (Appalachian) region of Kentucky and provide lighter-touch supports in an additional 23 surrounding counties. (Berea College, which was established in 1855 by abolitionist education advocates, is unique among U.S. higher-education institutions. It admits only economically disadvantaged, academically promising students, most of whom are the first in their families to obtain postsecondary education, and it charges no tuition, so every student admitted can afford to enroll and graduates debt-free.)
Covariates from these models : ecls-k 1998--1999 and 2010--2011.
ECLS-K 1998–1999 | ECLS-K 2010–2011 |
---|---|
The SES is a composite variable reflecting the socioeconomic status of the household at the time of data collection. SES was created using components such as father/male guardian’s education and occupation; mother/female guardian’s education and occupation; and household income (see Tourangeau et al. 2009, 7-23–7-30). We use five SES quintiles dummies that are available. We use the following labels in the tables and figures: “Low SES” indicates the first or lowest socioeconomic quintile, “Middle-low SES” indicates the second-lowest quintile, “Middle SES” is the third quintile, “High-middle SES” indicates the fourth quintile, and “High SES” represents the highest or fifth quintile. | The construct is based on three different components (five total variables), including the educational attainment of parents or guardians, occupational prestige (determined by a score), and household income (see more details in Tourangeau et al. 2013, 7-56–7-60). We use the quintile indicators based on the continuous SES variable (we construct them). |
Information about whether the child’s household lives in poverty is obtained from a household-level poverty variable. The household’s income is compared with census poverty thresholds for 2006 (which vary by household size) and the household is considered to be in poverty if total household income is below the poverty threshold determined by the U.S. Census Bureau poverty threshold (Tourangeau et al. 2009, 7-24 and 7-25). | Information about whether the child’s household lives in poverty is obtained from a household-level poverty variable. This variable indicates whether the household income is below 200 percent of the U.S. Census Bureau poverty threshold. More details are provided in Tourangeau et al. 2013 (7-53 and 7-54). |
A variable indicates whether the student is a girl or a boy. | A dummy indicator represents whether the child is a boy or a girl. |
A variable indicates the race/ethnicity of the student—whether the child is white, black, Hispanic, Asian, or another ethnicity. Hispanic children are divided into two groups, those whose families speak English at home and those whose families do not. (This latter decomposition was first described and utilized by Nores and Barnett [2014] and Nores and García [2014]). | Our analysis includes dummy indicators of whether the race/ethnicity of the child is white, black, Hispanic, Asian, or “other.” Hispanic children are divided into two groups, those whose families speak English at home and those whose families do not. |
Age of the student calculated in months. | Age of the student is calculated in months. |
A variable indicates whether the language the student speaks at home is a language other than English. | Our analysis includes a dummy indicator that represents whether the language spoken in the child’s home is a language other than English (we call a child in this setting an English language learner, or ELL), versus whether the language spoken at home is English or English and other language(s). |
A variable indicates whether the child has a disability that has been diagnosed by a professional (composite variable). Questions in the parents’ interview about disabilities ask about the child’s ability to pay attention and learn, overall activity level, overall behavior and relationships to adults, overall emotional behavior (such as behaviors indicating anxiety or depression), ability to communicate, difficulty in hearing and understanding speech, and eyesight (Tourangeau et al. 2009, 7-17). | A dummy indicator represents whether the child has been diagnosed with a disability. |
A variable indicates whether the child is living with two parents, or with one parent or in another family structure. | A variable indicates whether the child lives with two parents versus living with one parent or in another family composition. |
A dummy indicator represents whether the child was cared for in a center-based setting or attended Head Start during the year prior to the kindergarten year, compared with other options. These alternatives include no nonparental care arrangements and care provided through other means (by a relative or a nonrelative, at home or outside the home, or a combination of options). | Our analysis includes a dummy indicator of whether the child was cared for in a center-based setting (including Head Start) during the year prior to the kindergarten year, compared with other options. These alternatives include no nonparental care arrangements and care provided through other means (by a relative or a nonrelative, at home or outside the home, or a combination of options). Any finding associated with this variable may be interpreted as the association between attending prekindergarten (pre-K) programs, compared with other options, but must be interpreted with caution. These coefficients should not be interpreted as the impact of pre-K schooling because the variable’s information is limited and the model uses it as a control-only variable. For a review of the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010. |
This index captures the variance on a wide set of family early literacy practices. Using an index of activities instead of the underlying questions the index is composed of overcomes potential problems of multicolinearity and therefore improves the properties of our specifications. (This has an alpha of 0.6716). In particular, parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: reading books; telling stories; singing songs; and talking about nature or doing science projects. Parents are also asked how often the child reads picture books outside of school, and reads to or pretends to read to himself or to others outside of school. | This index captures the variance on a wide set of family early literacy practices. Using an index of activities instead of the underlying questions the index is composed of overcomes potential problems of multicolinearity and therefore improves the properties of our specifications. (This has an alpha of 0.6948.) In particular, parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: reading books; telling stories; singing songs; and talking about nature or doing science projects. Parents are also asked how often the child reads picture books outside of school, and reads to or pretends to read to himself or to others outside of school. |
Parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: playing games or doing puzzles; playing sports; building something or playing with construction toys; doing arts and crafts; or doing science projects. (This has an alpha of 0.5972.) | Parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: playing games or doing puzzles; playing sports; building something or playing with construction toys; doing arts and crafts; or doing science projects. (This has an alpha of 0.5527.) |
This is coded as “below high school (8th–12th grades); high school graduate or equivalent; vocational/technical program/some college; bachelor’s degree/graduate or professional school with no degree; and graduate (master’s, doctorate, or professional) degree.” | This is coded as “below high-school (8th–12th grades); high school graduate or equivalent; vocational/technical program/some college; bachelor’s degree/graduate or professional school with no degree; and graduate (master’s, doctorate, or professional) degree”. |
We adjust the income brackets in 2010 for inflation. We use the continuous variable to construct the 18 categories to make it comparable to the variable in 2010. We calculate a continuous income variable using the midpoint between the minimum and maximum for each category (equal to the values in 2010 adjusted by inflation). We calculate the income quintiles using this variable. | The original income variable comes in 18 categories. We calculate a continuous income variable using the midpoint between the minimum and maximum for each category. We calculate the income quintiles using this variable. |
This is coded as “HS or less; 2 or more years of college; BA; MA; PHD or MD.” Parents are asked, “How far in school do you expect your child to go? Would you say you expect {him/her} to {attend or complete a certain level}?” | This is coded as “HS or less; 2 or more years of college/attend a vocational or technical school; BA; MA; PHD or MD.” |
This is represented by a continuous variable (0–200) and a categorical variable coded as “0 to 25; 26 to 50; 51 to 100; 101 to 199; more than 200.” For the regression analysis, the variable is divided by 10. Parents are asked, “About how many children’s books {does {CHILD} have/are} in your home now, including library books? Please only include books that are for children.” | This is represented by a continuous variable (0–200) and a categorical variable coded as “0 to 25; 26 to 50; 51 to 100; 101 to 199; more than 200.” For the regression analysis, the variable is divided by 10. |
Source: ECLS-K, kindergarten classes of 1998–1999 and 2010–2011 (National Center for Education Statistics)
1998 | 2010 | |
---|---|---|
Variable | Percent missing | Percent missing |
Race/ethnicity | ||
White | 0.2 | 0.5 |
Black | 0.2 | 0.5 |
Hispanic | 0.2 | 0.5 |
Hispanic English language learner (ELL) | 6.6 | 11.8 |
Hispanic English speaker | 6.6 | 11.8 |
Asian | 0.2 | 0.5 |
Others | 0.2 | 0.5 |
Socioeconomic status | 5.9 | 11.9 |
Family composition: Not living with two parents | 15.5 | 26.3 |
Mother’s education | 7.5 | 42.8 |
Pre-K care, center-based | 16.8 | 17.4 |
“Literacy/reading activities” index | 15.6 | 26.4 |
“Other activities” index | 15.6 | 26.5 |
Parents’ expectations for children’s educational attainment | 16.1 | 26.5 |
Number of books | 16.3 | 26.7 |
Outcomes | ||
Reading | 17.7 | 13.8 |
Math | 13.0 | 14.2 |
Self-control (by teachers) | 13.8 | 25.4 |
Approaches to learning (by teachers) | 10.4 | 18.7 |
Self-control (by parents) | 15.8 | 27.3 |
Approaches to learning (by parents) | 15.8 | 27.3 |
Note: For detailed information about the construction of these variables, see Appendix Table A1.
Scale scores, 1998 (left) and 2010 (right).
1998 | 2010 | |||||||
---|---|---|---|---|---|---|---|---|
N | (Mean, sd) | Min | Max | N | (Mean, sd) | Min | Max | |
Scale score–reading | 17,620 | (0,1) | -1.39 | 10.13 | 15,670 | (0,1) | -2.4 | 4.06 |
Theta score–reading | 17,620 | (0,1) | -2.72 | 4.30 | 15,670 | (0,1) | -3.47 | 5.01 |
Scale score–math | 18,640 | (0,1) | -1.69 | 9.86 | 15,600 | (0,1) | -2.22 | 4.23 |
Theta score–math | 18,640 | (0,1) | -3.13 | 4.48 | 15,600 | (0,1) | -5.78 | 6.28 |
Note: N is rounded to the nearest multiple of 10.
Model 1 (unadjusted) | Model 4 (fully adjusted) | |||||||
---|---|---|---|---|---|---|---|---|
Full sample | Restricted sample | |||||||
Scale scores | Theta scores | Scale scores | Theta scores | |||||
Reading | Math | Reading | Math | Reading | Math | Reading | Math | |
Gap in 1998 | 1.071*** | 1.258*** | 1.233*** | 1.330*** | 0.596*** | 0.610*** | 0.684*** | 0.632*** |
(0.024) | (0.022) | (0.024) | (0.022) | (0.031) | (0.031) | (0.032) | (0.031) | |
Change in gap by 2010 | 0.098*** | -0.008 | -0.052 | -0.078** | 0.080 | 0.051 | -0.016 | -0.002 |
(0.033) | (0.032) | (0.033) | (0.032) | (0.052) | (0.048) | (0.054) | (0.050) | |
N | 30,950 | 31,850 | 30,950 | 31,850 | 26,050 | 26,890 | 26,050 | 26,890 |
Adj.R2 | 0.152 | 0.189 | 0.170 | 0.197 | 0.293 | 0.336 | 0.336 | 0.353 |
Notes: Standard errors are in the parentheses. N is rounded to the nearest multiple of 10. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: ECLS-K, kindergarten classes of 1998-1999 and 2010–2011 (National Center for Education Statistics)
Model 1 (unadjusted) | Model 4 (fully adjusted) | ||||||||
---|---|---|---|---|---|---|---|---|---|
Full sample | Restricted sample | ||||||||
Scale scores | Theta scores | Scale scores | Theta scores | ||||||
Reading | Math | Reading | Math | Reading | Math | Reading | Math | ||
By SES | Gap in 1998 | 1.071*** | 1.258*** | 1.233*** | 1.330*** | 0.596*** | 0.610*** | 0.684*** | 0.632*** |
(0.024) | (0.022) | (0.024) | (0.022) | (0.031) | (0.031) | (0.032) | (0.031) | ||
Change in gap by 2010 | 0.098*** | -0.008 | -0.052 | -0.078** | 0.080 | 0.051 | -0.016 | -0.002 | |
(0.033) | (0.032) | (0.033) | (0.032) | (0.052) | (0.048) | (0.054) | (0.050) | ||
By mother’s education | Gap in 1998 | 1.294*** | 1.457*** | 1.412*** | 1.502*** | 0.696*** | 0.681*** | 0.739*** | 0.685*** |
(0.038) | (0.036) | (0.038) | (0.035) | (0.058) | (0.050) | (0.048) | (0.044) | ||
Change in gap by 2010 | -0.020 | -0.154*** | -0.135*** | -0.218*** | -0.075 | -0.119* | -0.135* | -0.182*** | |
(0.051) | (0.049) | (0.051) | (0.048) | (0.082) | (0.070) | (0.075) | (0.067) | ||
By number of books | Gap in 1998 | 0.736*** | 0.966*** | 0.847*** | 1.032*** | 0.347*** | 0.424*** | 0.388*** | 0.438*** |
(0.028) | (0.027) | (0.028) | (0.026) | (0.034) | (0.031) | (0.033) | (0.031) | ||
Change in gap by 2010 | 0.083** | -0.019 | -0.015 | -0.088** | -0.540*** | -0.818*** | -0.594*** | -0.829*** | |
(0.039) | (0.038) | (0.039) | (0.038) | (0.184) | (0.188) | (0.181) | (0.174) | ||
By household income | Gap in 1998 | 1.090*** | 1.308*** | 1.214*** | 1.320*** | 0.384*** | 0.443*** | 0.429*** | 0.439*** |
(0.042) | (0.041) | (0.042) | (0.041) | (0.058) | (0.060) | (0.049) | (0.050) | ||
Change in gap by 2010 | -0.127** | -0.230*** | -0.247*** | -0.292*** | -0.006 | -0.060 | -0.058 | -0.099 | |
(0.060) | (0.059) | (0.060) | (0.059) | (0.084) | (0.082) | (0.076) | (0.072) |
Notes: Standard errors are in parentheses. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.
i. The sample design used to select the individuals in the study was a three-stage process that involved using primary sampling units and schools with probabilities proportional to the number of children and the selection of a fixed number of children per school. In the last stage, children enrolled in kindergarten or ungraded schools were selected within each sampled school. A clustered design was used to limit the number of geographic areas and to minimize the number of schools and the costs of the study (Tourangeau et al. 2013, 4-1).
ii. The dataset in the first year followed a stratified design structure (Ready 2010, 274), in which the primary sampling units were geographic areas consisting of counties or groups of counties. About 1,000 schools — 903 for 1998 and 968 for 2010—were selected, and about 24 children per school were surveyed. Assessment of the children was performed by trained evaluators, while parents were surveyed over the telephone. Teachers and school administrators completed the questionnaires in their schools.
iii. As a sensitivity check, we estimate Models 1 and 2 using Models 1’s and Model 2’s specifications but using the restricted sample (these results are not shown here, but are available upon request).
iv. As a sensitivity check, we estimate Model 3 parsimoniously, by including family characteristics only, and then adding family investments (prekindergarten care arrangements, early literacy practices at home, and number of books the child has), and then adding parental expectations (with and without interactions with time); results of the sensitivity check are not shown, but are available upon request).
v. We refer to the fact that we are using the same data and that the scale and theta scores are based on the same instruments and are not independent from each other. Advice on this possibility is found in Reardon (2007), who cites work by Murnane et al. (2006) and Selzer, Frank, and Bryk (1994) that also warn about this option.
vi. From NCES: “IRT uses the pattern of right and wrong responses to the items actually administered in an assessment and the difficulty, discriminating ability, and guess-ability of each item to estimate each child’s ability on the same continuous scale. IRT has several advantages over raw number-right scoring. By using the overall pattern of right and wrong responses and the characteristics of each item to estimate ability, IRT can adjust for the possibility of a low-ability child guessing several difficult items correctly. If answers on several easy items are wrong, the probability of a correct answer on a difficult item would be quite low. Omitted items are also less likely to cause distortion of scores, as long as enough items have been answered to establish a consistent pattern of right and wrong answers. Unlike raw number-right scoring, which treats omitted items as if they had been answered incorrectly, IRT procedures use the pattern of responses to estimate the probability of a child providing a correct response for each assessment question” (Tourangeau et al. 2017, 3-2).
vii. The quoted text is abridged to remove variables and formulas specific to Reardon’s study and not central here.
viii. Also, “the estimated scale score is the estimated number of questions the student would have gotten correct if he or she had been asked all of the items on the test. The estimated scale score is obtained by summing the predicted probabilities of a correct response over all items, given the student’s estimated theta score and the estimated item parameters” (Reardon 2007, 11).
ix. They are equally spaced units along the scale without a predefined zero point.
See related work on Student achievement | Education | Educational inequity | Children | Economic inequality | Inequality and Poverty | Early childhood
See more work by Emma García and Elaine Weiss
Case-based learning.
Case-based learning (CBL) is an established approach used across disciplines where students apply their knowledge to real-world scenarios, promoting higher levels of cognition (see Bloom’s Taxonomy ). In CBL classrooms, students typically work in groups on case studies, stories involving one or more characters and/or scenarios. The cases present a disciplinary problem or problems for which students devise solutions under the guidance of the instructor. CBL has a strong history of successful implementation in medical, law, and business schools, and is increasingly used within undergraduate education, particularly within pre-professional majors and the sciences (Herreid, 1994). This method involves guided inquiry and is grounded in constructivism whereby students form new meanings by interacting with their knowledge and the environment (Lee, 2012).
There are a number of benefits to using CBL in the classroom. In a review of the literature, Williams (2005) describes how CBL: utilizes collaborative learning, facilitates the integration of learning, develops students’ intrinsic and extrinsic motivation to learn, encourages learner self-reflection and critical reflection, allows for scientific inquiry, integrates knowledge and practice, and supports the development of a variety of learning skills.
CBL has several defining characteristics, including versatility, storytelling power, and efficient self-guided learning. In a systematic analysis of 104 articles in health professions education, CBL was found to be utilized in courses with less than 50 to over 1000 students (Thistlethwaite et al., 2012). In these classrooms, group sizes ranged from 1 to 30, with most consisting of 2 to 15 students. Instructors varied in the proportion of time they implemented CBL in the classroom, ranging from one case spanning two hours of classroom time, to year-long case-based courses. These findings demonstrate that instructors use CBL in a variety of ways in their classrooms.
The stories that comprise the framework of case studies are also a key component to CBL’s effectiveness. Jonassen and Hernandez-Serrano (2002, p.66) describe how storytelling:
Is a method of negotiating and renegotiating meanings that allows us to enter into other’s realms of meaning through messages they utter in their stories,
Helps us find our place in a culture,
Allows us to explicate and to interpret, and
Facilitates the attainment of vicarious experience by helping us to distinguish the positive models to emulate from the negative model.
Neurochemically, listening to stories can activate oxytocin, a hormone that increases one’s sensitivity to social cues, resulting in more empathy, generosity, compassion and trustworthiness (Zak, 2013; Kosfeld et al., 2005). The stories within case studies serve as a means by which learners form new understandings through characters and/or scenarios.
CBL is often described in conjunction or in comparison with problem-based learning (PBL). While the lines are often confusingly blurred within the literature, in the most conservative of definitions, the features distinguishing the two approaches include that PBL involves open rather than guided inquiry, is less structured, and the instructor plays a more passive role. In PBL multiple solutions to the problem may exit, but the problem is often initially not well-defined. PBL also has a stronger emphasis on developing self-directed learning. The choice between implementing CBL versus PBL is highly dependent on the goals and context of the instruction. For example, in a comparison of PBL and CBL approaches during a curricular shift at two medical schools, students and faculty preferred CBL to PBL (Srinivasan et al., 2007). Students perceived CBL to be a more efficient process and more clinically applicable. However, in another context, PBL might be the favored approach.
In a review of the effectiveness of CBL in health profession education, Thistlethwaite et al. (2012), found several benefits:
Students enjoyed the method and thought it enhanced their learning,
Instructors liked how CBL engaged students in learning,
CBL seemed to facilitate small group learning, but the authors could not distinguish between whether it was the case itself or the small group learning that occurred as facilitated by the case.
Other studies have also reported on the effectiveness of CBL in achieving learning outcomes (Bonney, 2015; Breslin, 2008; Herreid, 2013; Krain, 2016). These findings suggest that CBL is a vehicle of engagement for instruction, and facilitates an environment whereby students can construct knowledge.
Science – Students are given a scenario to which they apply their basic science knowledge and problem-solving skills to help them solve the case. One example within the biological sciences is two brothers who have a family history of a genetic illness. They each have mutations within a particular sequence in their DNA. Students work through the case and draw conclusions about the biological impacts of these mutations using basic science. Sample cases: You are Not the Mother of Your Children ; Organic Chemisty and Your Cellphone: Organic Light-Emitting Diodes ; A Light on Physics: F-Number and Exposure Time
Medicine – Medical or pre-health students read about a patient presenting with specific symptoms. Students decide which questions are important to ask the patient in their medical history, how long they have experienced such symptoms, etc. The case unfolds and students use clinical reasoning, propose relevant tests, develop a differential diagnoses and a plan of treatment. Sample cases: The Case of the Crying Baby: Surgical vs. Medical Management ; The Plan: Ethics and Physician Assisted Suicide ; The Haemophilus Vaccine: A Victory for Immunologic Engineering
Public Health – A case study describes a pandemic of a deadly infectious disease. Students work through the case to identify Patient Zero, the person who was the first to spread the disease, and how that individual became infected. Sample cases: The Protective Parent ; The Elusive Tuberculosis Case: The CDC and Andrew Speaker ; Credible Voice: WHO-Beijing and the SARS Crisis
Law – A case study presents a legal dilemma for which students use problem solving to decide the best way to advise and defend a client. Students are presented information that changes during the case. Sample cases: Mortgage Crisis Call (abstract) ; The Case of the Unpaid Interns (abstract) ; Police-Community Dialogue (abstract)
Business – Students work on a case study that presents the history of a business success or failure. They apply business principles learned in the classroom and assess why the venture was successful or not. Sample cases: SELCO-Determining a path forward ; Project Masiluleke: Texting and Testing to Fight HIV/AIDS in South Africa ; Mayo Clinic: Design Thinking in Healthcare
Humanities - Students consider a case that presents a theater facing financial and management difficulties. They apply business and theater principles learned in the classroom to the case, working together to create solutions for the theater. Sample cases: David Geffen School of Drama
Finding and Writing Cases
Consider utilizing or adapting open access cases - The availability of open resources and databases containing cases that instructors can download makes this approach even more accessible in the classroom. Two examples of open databases are the Case Center on Public Leadership and Harvard Kennedy School (HKS) Case Program , which focus on government, leadership and public policy case studies.
Implementing Cases
Take baby steps if new to CBL - While entire courses and curricula may involve case-based learning, instructors who desire to implement on a smaller-scale can integrate a single case into their class, and increase the number of cases utilized over time as desired.
Use cases in classes that are small, medium or large - Cases can be scaled to any course size. In large classes with stadium seating, students can work with peers nearby, while in small classes with more flexible seating arrangements, teams can move their chairs closer together. CBL can introduce more noise (and energy) in the classroom to which an instructor often quickly becomes accustomed. Further, students can be asked to work on cases outside of class, and wrap up discussion during the next class meeting.
Encourage collaborative work - Cases present an opportunity for students to work together to solve cases which the historical literature supports as beneficial to student learning (Bruffee, 1993). Allow students to work in groups to answer case questions.
Form diverse teams as feasible - When students work within diverse teams they can be exposed to a variety of perspectives that can help them solve the case. Depending on the context of the course, priorities, and the background information gathered about the students enrolled in the class, instructors may choose to organize student groups to allow for diversity in factors such as current course grades, gender, race/ethnicity, personality, among other items.
Use stable teams as appropriate - If CBL is a large component of the course, a research-supported practice is to keep teams together long enough to go through the stages of group development: forming, storming, norming, performing and adjourning (Tuckman, 1965).
Walk around to guide groups - In CBL instructors serve as facilitators of student learning. Walking around allows the instructor to monitor student progress as well as identify and support any groups that may be struggling. Teaching assistants can also play a valuable role in supporting groups.
Interrupt strategically - Only every so often, for conversation in large group discussion of the case, especially when students appear confused on key concepts. An effective practice to help students meet case learning goals is to guide them as a whole group when the class is ready. This may include selecting a few student groups to present answers to discussion questions to the entire class, asking the class a question relevant to the case using polling software, and/or performing a mini-lesson on an area that appears to be confusing among students.
Assess student learning in multiple ways - Students can be assessed informally by asking groups to report back answers to various case questions. This practice also helps students stay on task, and keeps them accountable. Cases can also be included on exams using related scenarios where students are asked to apply their knowledge.
Barrows HS. (1996). Problem-based learning in medicine and beyond: a brief overview. New Directions for Teaching and Learning, 68, 3-12.
Bonney KM. (2015). Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains. Journal of Microbiology and Biology Education, 16(1): 21-28.
Breslin M, Buchanan, R. (2008) On the Case Study Method of Research and Teaching in Design. Design Issues, 24(1), 36-40.
Bruffee KS. (1993). Collaborative learning: Higher education, interdependence, and authority of knowledge. Johns Hopkins University Press, Baltimore, MD.
Herreid CF. (2013). Start with a Story: The Case Study Method of Teaching College Science, edited by Clyde Freeman Herreid. Originally published in 2006 by the National Science Teachers Association (NSTA); reprinted by the National Center for Case Study Teaching in Science (NCCSTS) in 2013.
Herreid CH. (1994). Case studies in science: A novel method of science education. Journal of Research in Science Teaching, 23(4), 221–229.
Jonassen DH and Hernandez-Serrano J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology, Research and Development, 50(2), 65-77.
Kosfeld M, Heinrichs M, Zak PJ, Fischbacher U, Fehr E. (2005). Oxytocin increases trust in humans. Nature, 435, 673-676.
Krain M. (2016) Putting the learning in case learning? The effects of case-based approaches on student knowledge, attitudes, and engagement. Journal on Excellence in College Teaching, 27(2), 131-153.
Lee V. (2012). What is Inquiry-Guided Learning? New Directions for Learning, 129:5-14.
Nkhoma M, Sriratanaviriyakul N. (2017). Using case method to enrich students’ learning outcomes. Active Learning in Higher Education, 18(1):37-50.
Srinivasan et al. (2007). Comparing problem-based learning with case-based learning: Effects of a major curricular shift at two institutions. Academic Medicine, 82(1): 74-82.
Thistlethwaite JE et al. (2012). The effectiveness of case-based learning in health professional education. A BEME systematic review: BEME Guide No. 23. Medical Teacher, 34, e421-e444.
Tuckman B. (1965). Development sequence in small groups. Psychological Bulletin, 63(6), 384-99.
Williams B. (2005). Case-based learning - a review of the literature: is there scope for this educational paradigm in prehospital education? Emerg Med, 22, 577-581.
Zak, PJ (2013). How Stories Change the Brain. Retrieved from: https://greatergood.berkeley.edu/article/item/how_stories_change_brain
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This chapter makes the case that case study research is making a comeback in educational research because it allows researchers a broad range of methodological tools to suit the needs of answering questions of “how” and “why” within a particular real-world context. As Stake (1995) suggests, case study is often a preferred method of research because case studies may be epistemologically in harmony with the reader’s experience and thus to that person a natural basis for generalization.
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Bassey, M. (1999). Case study research in educational settings . Buckingham, England: Open University Press.
Google Scholar
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report , 13(4), 544–559.
Becker, H. S. (2000). Generalizing from case studies. In E. W. Eisner & A. Peshkin (Eds.), Qualitative inquiry in education: The continuing debate (pp. 233–242). New York, NY: Teachers College Press.
Corcoran, P. B., Walker, K. E., & Wals, A. E. (2004). Case studies, make-your-case studies, and case stories: A critique of case-study methodology in sustainability in higher education. Environmental Education Research , 10(1), 7–21.
Article Google Scholar
Creswell, J. (2002). Research design: Qualitative, quantitative and mixed method approaches . London, England: Sage.
Grauer, K. (1998). Beliefs of preservice teachers in art education. Studies in Art Education, 39(4) , 350–370.
Grauer, K., Irwin R. L., de Cosson, A., & Wilson, S. (2001). Images for understanding: Snapshots of learning through the arts. International Journal of Education & the Arts , 2(9). Retrieved from http://www.ijea.org/v2n9.
Guba, E. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries. Educational Resources Information Center Annual Review Paper, 29 , 75–91.
Hancock, D. R., & Algozzine, B. (2006). Doing case study research: A practical guide for beginning researchers . New York, NY: Teachers College Press.
Henderson, J. (2001). Reflective teaching: Professional artistry through inquiry (3rd ed.). Upper Saddle River, NJ: Merrill/Prentice Hall.
Klein, S. (Ed.). (2003). Teaching art in context: Case studies for art education. Reston, VA: National Art Education Association.
Lather, P. (1992). Critical frames in educational research: Feminist and poststructural perspectives. Theory into Practice , 31(2), 87–99.
Lincoln, Y. S., & Guba, E. A. (1985). Naturalistic inquiry . Beverly Hills, CA: Sage.
Merriam, S. B. (1998). Case study research and case study applications in education . San Francisco, CA: Jossey-Bass.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded source book (2nd ed.). Thousand Oaks, CA: Sage.
Patton, M. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.
Richards, L., & Richards, T. (1994). From filing cabinet to computer. In A. Bryman & R. G. Burgess (Eds.), Analysing qualitative data (pp. 146–172). London, England: Routledge.
Chapter Google Scholar
Richards, T. J., & Richards, L. (1998). Using computers in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Collecting and interpreting qualita¬tive materials (pp. 445–462). London, England: Sage.
Stake, R. E. (1995). The art of case study research . Thousand Oaks, CA: Sage.
VanWynsberghe, R., & Khan, S. (2007). Redefining case study. International Journal of Qualitative Methods , 6(2), 80–94. Retrieved from http://ejournals.library.ualberta.ca/index.php/IJQM/article/view/542.
Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage.
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Grauer, K. (2012). A Case for Case Study Research in Education. In: Klein, S.R. (eds) Action Research Methods. Palgrave Macmillan, New York. https://doi.org/10.1057/9781137046635_4
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Case Studies in International Education (CSIE) is an open access, online, peer-reviewed publication that utilizes case studies for the development and advancement of the field. CSIE seeks to promote further professional development through engaging case study discussion. We offer publication opportunities for scholar-practitioners, including working professionals, faculty, and graduate students. Each case study provides an opportunity to integrate theory and practice through interdisciplinary analysis/insight for the benefit of the field’s future growth. Names, position titles, organizations, specific places, events, and all other identifying details have been changed for privacy. Each has been designated by the reviewers as relevent to the field of international education.
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Seven meta-skills that stick even if the cases fade from memory.
It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.
During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”
Teaching excellence & educational innovation, case studies, what are case studies.
Case studies are stories. They present realistic, complex, and contextually rich situations and often involve a dilemma, conflict, or problem that one or more of the characters in the case must negotiate.
A good case study, according to Professor Paul Lawrence is:
“the vehicle by which a chunk of reality is brought into the classroom to be worked over by the class and the instructor. A good case keeps the class discussion grounded upon some of the stubborn facts that must be faced in real life situations.”
(quoted in Christensen, 1981)
Although they have been used most extensively in the teaching of medicine, law and business, case studies can be an effective teaching tool in any number of disciplines. As an instructional strategy, case studies have a number of virtues. They “bridge the gap between theory and practice and between the academy and the workplace” (Barkley, Cross, and Major 2005, p.182). They also give students practice identifying the parameters of a problem, recognizing and articulating positions, evaluating courses of action, and arguing different points of view.
Case studies vary in length and detail, and can be used in a number of ways, depending on the case itself and on the instructor’s goals.
It is possible to write your own case studies, although it is not a simple task. The material for a case study can be drawn from your own professional experiences (e.g., negotiating a labor dispute at a local corporation or navigating the rocky shoals of a political campaign), from current events (e.g., a high-profile medical ethics case or a diplomatic conundrum), from historical sources (e.g., a legal debate or military predicament), etc. It is also possible to find published cases from books and on-line case study collections. Whatever the source, an effective case study is one that, according to Davis (1993):
How you use case studies will depend on the goals, as well as on the format, of your course. If it is a large lecture course, for example, you might use a case study to illustrate and enrich the lecture material. (An instructor lecturing on principles of marketing, for example, might use the case of a particular company or product to explore marketing issues and dilemmas in a real-life context.) Also in a large class you might consider breaking the class into small groups or pairs to discuss a relevant case. If your class is a smaller, discussion-format course, you will be able to use more detailed and complex cases, to explore the perspectives introduced in the case in greater depth, and perhaps integrate other instructional strategies, such as role playing or debate. Regardless of the format in which you employ case studies, it is important that you, as the instructor, know all the issues involved in the case, prepare questions and prompts in advance, and anticipate where students might run into problems. Finally, consider who your students are and how you might productively draw on their backgrounds, experiences, personalities, etc., to enhance the discussion. While there are many variations in how case studies can be used, these six steps provide a general framework for how to lead a case-based discussion:
Some variations on this general method include having students do outside research (individually or in groups) to bring to bear on the case in question, and comparing the actual outcome of a real-life dilemma to the solutions generated in class.
Barkley, E. F, Cross, K. P. & Major, C. H. (2005) Collaborative Learning Techniques: A Handbook for College Faculty. San-Francisco: Jossey-Bass.
Christensen, C. R. (1981) Teaching By the Case Method. Boston: Harvard Business School.
Davis, B. G. (1993) Tools for Teaching. San Francisco: Jossey-Bass.
Ethical dilemmas abound in education. Should middle school teachers let a failing eighth-grade student graduate, knowing that if she’s held back, she’ll likely drop out? Should a private school principal condone inflated grades? Should an urban district pander to white, middle-class families — at the expense of poor, minority families — in order to boost the achievement of all schools?
Teachers, principals, superintendents, and education policymakers face questions such as these every day. And for many, amid the tangle of conflicting needs, disparate perspectives, and frustration over circumstances, lies the worry that discussing an ethical dilemma with colleagues will implicate you as not knowing how to make the right choice — or as already having made the wrong one.
Educational philosopher Meira Levinson and doctoral student Jacob Fay take up these challenges in the new book Dilemmas of Educational Ethics: Cases and Commentaries . In detailing the moral predicaments that arise in schools, the researchers also provide a framework for educators to discuss their own dilemmas with colleagues, opening the door to making these conversations more common.
The book presents six detailed case studies of common educational dilemmas, each accompanied by commentaries of varying viewpoints. Written by a range of practitioners — from classroom teachers to district leaders to African American Studies professors to philosophers — these commentaries each dissect the cases differently, introducing new solutions and new ways to consider what is “right.”
In the first case study, middle schools teachers debate whether to allow a failing eighth grade student to graduate, knowing that she’s both unprepared for ninth-grade coursework but also likely to drop out if she’s held back. Despite having lived in three different foster homes in the past year and having her brother die from a gunshot wound, the student, Ada, put forth enormous amounts of effort to raise her grades — until recently, when she grew discouraged. While the district provides an alternative school for struggling students, the teachers rule it out immediately; it’s known as a flat-out school-to-prison pipeline.
The commentaries on this case, and on the other five, range from providing concrete solutions to proposing total reconsiderations of the situation to suggesting that the whole system change. Classroom teacher Melissa Aguirre, for instance, says that the school should retain Ada in order to uphold its standards, but she also comments that this case shows why it’s necessary to make “competency-based” education, and not just “age-based,” a norm for all. Sigal Ben-Porath , an education and political science professor, notes that high-poverty schools are more likely to define students solely by academic standards, and disregarding noncognitive skills. She writes that Ada should be recognized as a complex person and consulted in the decision on whether she should matriculate to ninth grade.
Others provide more abstract interpretations. Willie "J.R." Fleming, a human rights advocate, explains that the circumstances Ada is living under could be defined as an armed conflict or a war zone. As a response to Ada’s dilemma, the writer imagines appropriate alternative schooling that will allow Ada to heal and thrive. Deputy superintendent Toby Romer, explains that the teachers in this case are focused on “worse-case scenarios”; by dismissing the alternative school as too dangerous, he explains, they have ruled-out any possibility of it working for diligent students like her. Ideally, he says, the teachers would make decisions on how the system is supposed to work, rather than on how it does.
Ada’s story does not lend itself to one solution; instead, it provokes a whirlwind of feelings and reactions. So how can this case, and the five others in the book, assist teachers in considering their own ethical dilemmas — and in reaching viable solutions?
Case studies offer a safe way for educators to begin recognizing and discussing ethical dilemmas they may face in their own work, since no real person is implicated. “We hope that by reading and talking about the cases and commentaries, professional communities can become more practiced and comfortable in having these sorts of discussions, so that when their own particular dilemmas arise, they have the cases and a language to be able to speak about what it is they’re struggling with in their own practice,” says Fay.
The cases also give educators a chance to consider diverse perspectives. “Right now, our conversation in the United States about education policy and practice is so polarized, and so dismissive of the other side,” explains Levinson. “Both wrap themselves up in the mantle of social justice, and they refuse to recognize that in fact, both sides may really care deeply about equity, opportunity, and social justice, and just have different ways to try to achieve those goals.” Because the cases, and especially the commentaries, delve into different viewpoints, they may allow educators to better understand where the other side is coming from — and how to work with them.
Along the same lines, says Levinson, “the commentaries also provide some guidance for how you can think through the cases. They model that you can have disparate views among people of good intent, and they model that that might happen because you are coming at it from a different experiential perspective.”
Eventually, Levinson envisions the discussion of ethical dilemmas as common professional development in schools. If teachers and principals have enough practice discussing case studies of morally unclear situations, they might become more prepared to discuss their own. “You can imagine that, over time, educators themselves being able to say to their colleagues, ‘Here’s my case, here’s my dilemma, I would really appreciate hearing you talk through it.’”
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Connecting education research to practice — with timely insights for educators, families, and communities
Looking to the past to make sense of today, engage students and encourage critical thinking.
Gale Case Studies addresses contemporary social issues, providing expert commentary and historical context to promote critical thinking around timely topics organized into course ready modules centered around primary source documents.
Six topically focused modules prepare students for future careers and promote the growth of transferrable skills by encouraging them to think analytically about topics related to LGBTQ rights, public health concerns, political extremism, women’s studies and race.
Higher education prepares students before they enter a challenging, unpredictable world. Universities and colleges can provide students with educational resources that will help them become better analytical thinkers and problem solvers outside of the classroom. Many leading educators see teaching case studies as an effective way for students to practice their critical thinking skills. Academically, a case study is a thorough examination of a specific subject or problem, and this method of learning is popular in the fields of health administration, political science, and social work among others. Teaching case studies can help students put theories into practice and is often useful in identifying problems not revealed through a more traditional approach.
Gale Case Studies was created by university faculty and developed specifically for the classroom. This new higher education tool gives undergraduate students the chance to sharpen their critical-thinking skills by using historical content to evaluate and discuss contemporary social issues within the educational context of a case study. Gale Case Studies not only promotes the development of transferable skills; it also encourages students to think analytically as they explore crucial topics, such as lesbian, gay, bisexual, transgender and queer (LGBTQ) rights; gender identity development and sexual orientation; systemic racism; public health; urban development; political extremism; and diversity in the workplace. In addition, this resource allows university faculty and administration to expose students to a process of study not typically seen in the classroom.
Each module of Gale Case Studies is organized by topic and supported by a curated collection of primary sources. Students and faculty can access case studies, related discussion questions, and links to curated content. Teaching case studies complements traditional higher education practices and it also makes remote and online learning more accessible to all students. The Learning Management Systems (LMS) integration adds to the accessibility of this resource. Learning materials can be seamlessly embedded into the academic workflow without the need for a separate application. Students can prepare for informed, course-related discussions; develop problem-solving skills; and grow more confident using primary sources for classroom projects and research papers.
Intersectional LGBTQ Issues
Public Health Issues
Political Extremism
Race and Civil Rights
Refugees and Migration
Women's Issues
LMS Integration
Enables instructors to easily access and link to pertinent content directly within their LMS. Plus, persistent URLs give users added confidence in knowing the content will never disappear.
Curated Content
Topics and sources are carefully chosen by an editor-in-chief and academic instructor and/or faculty member for quality of content, accuracy, and learning approach.
Discussion Questions
Discussion questions are provided at the end of the workflow to create prompts for students to analyze case-based primary sources and teaching topics.
Guided Experience
Instructors and students are guided through the workflow by an intuitive experience that fosters learning through both primary sources and discussion questions that promote further research.
Image Viewer
Users can look at the primary source content, such as images, handwritten letters, or newspaper articles as they were originally published to better understand the historical context.
Cross-Search Capability
Students and instructors can search across content from complementary modules in one intuitive environment and make new research connections.
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Sample of case studies found in Intersectional LGBTQ Issues module
Sample case study found in Intersectional LGBTQ Issues module
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“A set of case studies like Gale Case Studies and its archival material can help scholars to keep framing why the humanities are relevant—they are relevant for social change, they are relevant for understanding and analyzing public policy and the impact on people’s lives . . . this is the kind of thing that can help us show students how relevant this is and why it’s important to be engaged in humanities research.” - Danielle DeMuth, Ph.D. Associate Professor, Grand Valley State University
A discussion-based case study is an education tool to facilitate learning about, and analysis of, a real-world situation.
A case study provides a well-researched and compelling narrative about an individual, or a group of people, that needs to make a decision in an organisational setting.
The case study narrative includes relevant information about the situation, and gives multiple perspectives on the problem or decision that needs to be taken, but does not provide analysis, conclusions, or a solution.
How does a case study work in education, top tips for writing a case study, what is the difference between teaching cases and research focused cases.
Which publication would suit my case study.
Read about getting ready to publish and visit the Emerald Cases Hub for courses and guides on writing case studies and teaching notes.
Teaching cases expose students to real-world business dilemmas in different cultural contexts.
Students are expected to read the case study and prepare an argument about the most appropriate course of action or recommendation, which can be debated in a facilitated case study class session, or documented in a case study assignment or examination.
A case teaching note, containing recent and relevant theoretical and managerial frameworks, will be published alongside the teaching case, and can be used to demonstrate the links between course content and the case situation to support teaching of the case method.
Teaching case studies have a distinctive literary style: they are written in the third person, in the past tense, and establish an objectivity of core dilemmas in the case.
We have gathered some top tips for you to think about as your write your case study.
Collect information
Cases can be based on primary or secondary data; however, carrying out interviews with the protagonist and others in the organisation, where possible, often results in a better and more balanced case study.
Make sure that you have all the materials you will need before you start the writing process. This will speed up the actual process. Most case studies have a mixture of primary and secondary sources to help capture the spirit of the protagonist.
Structure the narrative
Tell the story in chronological order and in the past tense. Identify and establish the central protagonist and their dilemma in the first paragraph and summarise the dilemma again at the end of the case.
Develop the protagonist
Ensure the protagonist is a well-developed character and that students can identify with their motivations throughout the case.
Get permission
When you submit your case study and teaching note, you must include signed permission from the relevant protagonist or company featured in the case and for any material for which you don’t own the copyright.
Be clear on your teaching objective
The case method offers a variety of class participation methods, such as discussion, role-play, presentation, or examination. Decide which method best suits the case you want to write.
Identify case lead author
You might want to consider writing your case study in partnership with colleagues. However, if you are writing a case with other people you need to make sure that the case reads as one voice.
You do not have to share the work evenly. Instead, play to your individual strengths: one author might be better at data analysis, one a better writer. Agree and clarify the order of appearance of authors. This is very important since this cannot be changed after publication.
Write a thorough teaching note
A well-written case study needs an equally well-written teaching note to allow instructors to adopt the case without the need for additional research. The standard teaching note provides key materials such as learning objectives, sample questions and answers, and more. See 'What to include in your teaching note' to produce effective teaching note for your case.
Writing a teaching case requires a distinctive literary style; it should be written in the third person, in the past tense, and establish objectivity of the core dilemmas in the case.
To begin with, a case has to have a hook: an overriding issue that pulls various parts together, a managerial issue or decision that requires urgent attention.
The trick is to present the story so that the hook is not immediately apparent but ‘discovered’ by students putting the relevant pieces together. More importantly, the hook must be linked to a particular concept, theory, or methodology.
A teaching case reflects the ambiguity of the situation and need not have a single outcome, as the intent is to create a dialogue with students, encourage critical thinking and research, and evaluate recommendations.
Research cases are a methodology used to support research findings and add to the body of theoretical knowledge, and as such are more academically-focused and evidence-based.
How to write & structure a case
The opening paragraph should make clear:
The body of the case should:
The concluding paragraph should:
Before you start, choose where to publish your case study and familiarise yourself with the style and formatting requirements.
Get ready to publish
Case synopsis.
Provide a brief summary (approximately 150-200 words) describing the case setting and key issues. Include:
Clearly identify the appropriate audience for the case (e.g., undergraduate, graduate, or both). Consider:
If there are multiple target audiences, discuss different teaching strategies.
Top tip: remember that the deciding factor for most instructors looking to find a case for their classroom is relevancy. Working with a specific audience in mind and sharing guidance on case usage helps develop the applicability of your case.
Set a minimum of one objective for a compact case study and three to four for a longer case. Your objectives should be specific and reflective of the courses you suggest your case be taught in. Make it clear what students can expect to learn from reading the case.
Top tip: Good learning objectives should cover not only basic understanding of the context and issues presented in the case, but also include a few more advanced goals such as analysis and evaluation of the case dilemma.
Outline the types of data used to develop the case, how this data was gathered, and whether any names/details/etc. within the case have been disguised. Please note that you will need to obtain consent from the case protagonist/organisation if primary data has been used. Cases based on secondary data (i.e., any information that is publicly available) are not required to obtain consent.
Provide a breakdown of the classroom discussion time into sections. Include a brief description of the opening and closing 10-15 minutes, as well as challenging case discussion questions with comprehensive sample answers.
Provide instructors a detailed breakdown of how you would teach the case in 90 minutes. Include:
Include a set of challenging assignment questions that align with the teaching objectives and relate to the dilemma being faced in the case.
Successful cases will provide:
Successful sample answers should:
Supporting materials can include any additional information or resources that supplement the experience of using your case. Examples of these materials include such as worksheets, videos, reading lists, reference materials, etc. If you are including classroom activities as part of your teaching note, please provide detailed instructions on how to direct these activities.
When you have finished writing your case study and teaching note, test them!
Try them out in class to see if students have enough information to thoughtfully address the case dilemma, if the teaching note supports an engaged class discussion, and if the teaching note assignments/lesson plan timing are appropriate. Revise as needed based on the class experience before submitting.
Our short PDF guide will give you advice on writing your teaching note, what you should include and our top tips to creating an effective teaching note.
Download our guide
What makes a great teaching case?
Common review feedback comments
What makes a good teaching note?
Register on the Emerald Cases Hub to access free resources designed by case-writing experts to help you write and publish a quality case study. Develop your skills and knowledge with a course on writing a case study and teaching note, view sample cases, or explore modules on teaching/leaning through the case method.
Visit the Emerald Cases Hub
A key factor in boosting the chances of your case study being published is making sure it is submitted to the most suitable outlet. Emerald is delighted to offer two key options:
EMCS welcomes well-researched, instructive, and multimedia online cases about the most interesting companies in complex emerging market contexts, to be used by faculty to develop effective managers globally.
Cases must be factual and be developed from multiple sources, including primary data sourced and signed-off by the company involved.
Find out more about EMCS
TCJ is the official journal of The CASE Association, the leading online, double-blind, peer-reviewed journal featuring factual teaching cases and case exercises spanning the full spectrum of business and management disciplines.
TCJ invites submissions of cases designed for classroom use.
Find out more about TCJ
Understand the journal and case study peer review process and read our tips for revising your submission.
Submit your case through your chosen channel’s online submission site, find author support and understand your next steps to publish your case study.
We partner with a range of organisations to offer case writing competitions. Applying for an award opens the door to the possibility of you receiving international recognition and a cash prize.
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Explore around-the-world case studies on UNICEF's education programme in various countries and regions. Learn how UNICEF supports education systems, policies, programmes and practices to achieve quality, equity and inclusion for all children and adolescents.
Case studies are real-world problems that students can solve using reflection and critical thinking. Learn how to identify, contextualize, and assess case studies in your teaching practice with examples and resources.
Learn how to use case studies as a teaching tool to apply theories or concepts to real situations. Find out the features of good cases, how to create your own, and where to find existing cases in various disciplines.
The NCCSTS Case Collection, created and curated by the National Center for Case Study Teaching in Science, on behalf of the University at Buffalo, contains over a thousand peer-reviewed case studies on a variety of topics in all areas of science. Cases (only) are freely accessible; subscription is required for access to teaching notes and ...
Case study research in education: A qualitative approach. San Francisco: Jossey-Bass. This is Merriam's initial text on case study and is eminently accessible. The author establishes and reinforces various key features of case study; demonstrates support for positioning the case within a subject domain, e.g., psychology, sociology, etc.; and ...
Advantages to the use of case studies in class. A major advantage of teaching with case studies is that the students are actively engaged in figuring out the principles by abstracting from the examples. This develops their skills in: Problem solving. Analytical tools, quantitative and/or qualitative, depending on the case.
The Stanford Graduate School of Education Case Library is a repository for teaching materials that concern real world situations in education, non-profits, government agencies and their reform efforts.The case materials are presented in a way that allows readers to consider multiple forms of explanation, design, and management, and thereby enhance the learning experience of students.
Higher Education | K-12; Case Studies. Kansas State University. July 20, 2023. The Kansas Board of Regents demands that faculty be evaluated even after the conferral of tenure. In the face of strong faculty resistance, a special task force must decide how…
Case studies offer a student-centered approach to learning that asks students to identify, explore, and provide solutions to real-world problems by focusing on case-specific examples (Wiek, Xiong, Brundiers, van der Leeuw, 2014, p 434). This approach simulates real life practice in sustainability education in that it illuminates the ongoing complexity of the problems being addressed.
This book provides an accessible introduction to using case studies. It makes sense of literature in this area, and shows how to generate collaborations and communicate findings. The authors bring together the practical and the theoretical, enabling readers to build expertise on the principles and practice of case study research, as well as ...
This article introduces a unifying framework for studying panel experiments with population interference, in which a treatment assigned to one experimental unit affects another experimental unit's outcome. Findings have implications for fields as diverse as education, economics, and public health. 1. 2. ….
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This section of the report draws on a set of case studies published by the Broader, Bolder Approach to Education (BBA), a national campaign that advances evidence-based strategies to mitigate the impacts of poverty-related disadvantages on teaching and learning.24 The case studies feature school districts that have employed comprehensive ...
Case-Based Learning. Case-based learning (CBL) is an established approach used across disciplines where students apply their knowledge to real-world scenarios, promoting higher levels of cognition (see Bloom's Taxonomy ). In CBL classrooms, students typically work in groups on case studies, stories involving one or more characters and/or ...
This chapter makes the case that case study research is making a comeback in educational research because it allows researchers a broad range of methodological tools to suit the needs of answering questions of "how" and "why" within a particular real-world context. As Stake (1995) suggests, case study is often a preferred method of ...
s of the Foundational Learning Study. This second study involves one-on-one testing of Grade 3 students on foundational numeracy and literacy and is slated t. take place at the end of March 2022. UNICEF is a technical partner. for both learning assessment studies.The Government has invited UNICEF, as a trusted partner, to provide technical advice.
Case Studies in International Education (CSIE) is an open access, online, peer-reviewed publication that utilizes case studies for the development and advancement of the field. CSIE seeks to promote further professional development through engaging case study discussion. We offer publication opportunities for scholar-practitioners, including ...
The chief. purpose of his book is the explication of a set of interpretive orientations towards case study. which include "naturalistic, holistic, ethnographic, phenomenological, and biographic ...
What the Case Study Method Really Teaches. Summary. It's been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study ...
Background. Cases were initially associated with business and medical education, but subsequently expanded across other subjects (Belt, Citation 2001; Bonney, Citation 2015).Case studies are widely understood as "stories that are used as a teaching tool to show the application of a theory or concept to real situations" (Vanderbilt University, Citation 2022), although in different ...
Case studies vary in length and detail, and can be used in a number of ways, depending on the case itself and on the instructor's goals. They can be short (a few paragraphs) or long (e.g. 20+ pages). They can be used in lecture-based or discussion-based classes. They can be real, with all the detail drawn from actual people and circumstances ...
The Case of the Failing Eighth Grader. The book presents six detailed case studies of common educational dilemmas, each accompanied by commentaries of varying viewpoints. Written by a range of practitioners — from classroom teachers to district leaders to African American Studies professors to philosophers — these commentaries each dissect ...
The case study seems to be a methodological approach well suited for the development of such theories. Two examples from Sweden, one from the school level and one from the classroom level, are used to illustrate the potential of case-studies to develop theory in this area of research. ... In studying inclusive education at the system level, a ...
This study has investigated the use of case studies, applied by Master´s students in Educational Sciences. Given the increasing use of case study in educational research, key aspects of its ...
Teaching case studies can help students put theories into practice and is often useful in identifying problems not revealed through a more traditional approach. Gale Case Studies was created by university faculty and developed specifically for the classroom. This new higher education tool gives undergraduate students the chance to sharpen their ...
UNICEF EDUCATION Education Case Study INDIA India is one of the countries most vulnerable to the adverse impacts of climate change, ranking 26 out of 163 countries in the UNICEF children's climate risk index of 2021. Fast-onset hazards such as flooding, landslides and cyclones have repeatedly caused destruction to schools.
he case studies in this chapter address the needs of students with the exceptionalities most often observed in classrooms. To prepare for the analysis of the studies, review your philosophy of education that you devel-oped in the last chapter to connect your strategies for helping students to your belief system about teaching. Remember, the ...
What is a teaching case study? A discussion-based case study is an education tool to facilitate learning about, and analysis of, a real-world situation. A case study provides a well-researched and compelling narrative about an individual, or a group of people, that needs to make a decision in an organisational setting.