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Mercer, and M. Palo Alto, Calif. Cunha, Flavio, and James J. Currie, Janet. Davis-Kean, Pamela E. Duncan, Greg J. Dowsett, Amy Claessens, Katherine A. Magnuson, Aletha C. Huston, Pamela Klebanov, Linda S. Duncan and Richard Murnane, eds. Morris, and Chris Rodrigues. Fiester, Leila. Early Warning! Annie E. Casey Foundation. Children Out on Unequal Footing. Hart, Betty, and Todd R. Baltimore, Md. Heckman, James J. Henderson, Anne T. Annenberg Institute for School Reform. Hernandez, Donald J. Gizriel, Sarah. Jennings, J.

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Lee, Valerie E. Inequality at the Starting Gate. Levin, Henry M. Magnuson, Katherine, and Greg J. Magnuson, Katherine A. Meyers, C. Ruhm, and Jane Waldfogel. Marietta, Geoff. Foundation for Child Development.

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Maryland State Department of Education. Miller-Adams, Michelle. Kalamazoo, Mich. Upjohn Institute for Employment Research. Mishel, Lawrence. Ithaca, N. Mishel, Lawrence, and Jessica Schieder. January Morsy, Leila, and Richard Rothstein. Murnane, Richard J. New York: The Free Press. Willett, Kristen L. Bub, and Kathleen McCartney. Najarian, M. Tourangeau, C. Nord, K. Wallner-Allen, and J. Department of Education. June 3. Nores, Milagros, and W. New Brunswick, N. Understanding Achievement Gaps in the Early Years.

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PBS NewsHour. Peterson, T. Phillips, Meredith. Proctor, Bernadette D. Semega, and Melissa A. Income and Poverty in the United States: Putnam, Robert. New York: Simon and Schuster. Ready, Douglas D.

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Reardon, Sean F. Redd, Z. Guzman, L. Lippman, L. Scott, and G. Rolnick, Art, and Rob Grunewald. Rothstein, Richard. Brewer, Patrick J. McEwan, eds. Oxford: Elsevier. Saez, Emmanuel. Money Lightens the Load. The Hamilton Project, Brookings Institute. Selzer, Michael H. Frank, and Anthony S. Sharkey, Patrick. Chicago, Ill.

Simon, Stephanie. Simpkins, Sandra D. Davis-Kean, and Jacquelynne S. Southern Education Foundation. Sparks, Sarah D. Stringhini, Silvia, et al. Published online January 31, Tourangeau, K. Nord, T. Sorongon, and M. Sorongon, M. Hagedorn, P. Daly, and M. Wallner-Allen, M.

Hagedorn, J. Leggitt, and M. Wallner-Allen, N. Vaden-Kiernan, L. Blaker, and M. Department of Health and Human Services U. Department of Education U. Van Voorhis, F. Maier, J. Epstein, C. Lloyd, and T. Waldfogel, Jane. Weiss, Elaine. Bright Futures in Joplin, Missouri. A Broader, Bolder Approach to Education.

City Connects Boston, MA. Wentzel, Kathryn R. Yamamoto, Yoko, and Susan D. 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 — cohort and fifth grade for the — cohort. The tracking of students over time is one of the most valuable features of the data. The studies also include information on teachers and schools provided by teachers and administrators and interviews with parents.

The two studies are 12 years apart, or a full school cycle apart: when the — kindergarten class was starting school, the — class was starting the grade leading to their graduation. For the study, the sample included 18, children in schools. This existence of data from two cohorts is also a limitation to the current study, as explained by Tourangeau et al. 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. We can assess changes in the relative position in a distribution i. 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 IRT scale scores for reading and mathematics achievement and assessments of noncognitive skills are standardized using the distribution and its mean and sd; for , we use the mean and sd of the distribution. 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.

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. 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 main parameters of interest are and : These show the performance of low-SES children in , the gap between high- and low-SES children in , the change in the scores of low-SES children from to and the change in the gap between high- and low-SES children from to 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 to 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 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. 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. 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 and the changes in the gaps from to 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.

NCES provides data users with definitions of these metrics and recommendations on how to appropriately choose among the different metrics. This makes them suitable for research purposes, even though each is expressed in its own unit of measurement. 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. 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. 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. The IRT-based theta scores are overall measures of ability.

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. 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. 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.

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 theta scores are reported on a metric ranging from -6 to 6, with lower scores indicating lower ability and higher scores indicating higher ability. Reardon describes the calculation of the theta scores in the following manner: vii. Reardon , 10 viii. 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. 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. There are two other significant pieces of information affecting the cognitive scores in more recent documentation released by NCES. 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. 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. 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: released by NCES in We will explore these issues further upon the release of the scores that are comparable across the two ECLS-K studies without any transformation.

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 by block NAZ receive individualized supports. Bright Futures also provides meaningful service learning opportunities in every school. 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.

Berea College, which was established in by abolitionist education advocates, is unique among U. 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. Note: For detailed information about the construction of these variables, see Appendix Table A1.

N is rounded to the nearest multiple of Notes: Standard errors are in parentheses. 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. The dataset in the first year followed a stratified design structure Ready , , in which the primary sampling units were geographic areas consisting of counties or groups of counties.

About 1, schools — for and for —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. 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.

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 , who cites work by Murnane et al. 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. They are equally spaced units along the scale without a predefined zero point. See related work on Education , Student achievement , Educational inequity , Children , Economic inequality , Inequality and Poverty , and Early childhood. Download PDF Press release. Figure A. Chart Data Download data The data below can be saved or copied directly into Excel.

The data underlying the figure. Share on Facebook Tweet this chart. Copy the code below to embed this chart on your website. Figure B.

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Figure C. Gap between top and bottom quintiles in Change in gap from to Reading 1. Figure D. Figure E.

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Gap between top and bottom quintiles in Change in gap from to Reading 0. Table 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 — 1. Table 2. Table 3. Reading models Mathematics models 1 unadjusted 2 3 4 fully adjusted 1 unadjusted 2 3 4 fully adjusted Gap in 1.

Table 4. 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 0. Table 5. Year Reduction Change in reduction from to in percentage points Reading Table 6. Table 7. Table 8. Table 9. Table Appendix Table A1. The SES is a composite variable reflecting the socioeconomic status of the household at the time of data collection. We use five SES quintiles dummies that are available. Socioeconomic status SES. 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.

We use the quintile indicators based on the continuous SES variable we construct them. Child living in poverty. Census Bureau poverty threshold Tourangeau et al. This variable indicates whether the household income is below percent of the U. Census Bureau poverty threshold. More details are provided in Tourangeau et al. 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. Hispanic children are divided into two groups, those whose families speak English at home and those whose families do not.

Age of student. Age of the student calculated in months. Age of the student is calculated in months. Language at home is not English. A variable indicates whether the language the student speaks at home is a language other than English. Language spoken at home. A variable indicates whether the child has a disability that has been diagnosed by a professional composite variable.

A dummy indicator represents whether the child has been diagnosed with a disability. Win amazing prizes with our monthly contest! Add to Cart. Tell me more about this Deal. Product Description. No limit. Order as many as you want. VAT included. No refund is allowed seven 7 days after date of receipt of item. The Guptas, however, have denied the allegations. Bloomberg reported on Wednesday that a number of companies forming part of the Guptas-controlled Oakbay Group were suing Bank of Baroda. The banks cited the need for compliance with international banking rules. Atul started the family's first business in South Africa, Sahara Computers, in Today, the Gupta family's businesses in South Africa include coal mines, computers, newspapers and a news channel.

Duduzile Zuma, his daughter, was a director at Sahara Computers. His son, Duduzane Zuma, was a director of a few Gupta-owned companies but stepped down last year after public furore. In , the Gupta brothers ran into a controversy when a chartered plane carrying guests for the wedding of their sister's daughter landed at the Waterkloof Air Base near Pretoria. The base is reserved for visiting heads of state and diplomatic delegations. According to a newspaper report, the Guptas had even applied for diplomatic passports because they often travelled with Zuma on international trips promoting South Africa.


The application was rejected, according to a report by The Sunday Times. The Gupta brothers rose dramatically from a humble business background in India. Their father Shiv Kumar Gupta ran a company at Saharanpur that distributed soapstone powder. The Gupta brothers came to South Africa when the Apartheid was ending in early Nineties after exploring possibilities of a computer hardware business in China.

Last year, the Gupta family announced they would quit their own company amid increasing calls for a probe into their alleged influence in government through their links with President Zuma. They had come under immense pressure after several high-ranking members of the ruling African National Congress, including the current Deputy Minister of Finance, claimed that the family had offered them government positions, including appointments as ministers.

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