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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.
Therefore, it is important to be sensitive when teaching the topic because students may have different experiences with access to education and academic resources. On the other hand, in classrooms where the majority students are white and from wealthier communities, students may not be aware of educational inequality because they are more privileged in this area.
In these classrooms, educators may need to focus more of their time on teaching students about the root causes of educational inequality and how this type of inequality impacts communities. United 4SC [email protected]. How do education inequalities develop beyond the school years? Richard Breen University of Oxford Read more. Simon Field Skills Policy Read more. Related content. Background reading.
One country which achieved high growth and low and steady rates of inequality throughout the period of its economic development is South Korea. But, as the chart below shows, it also retained low levels of income inequality compared to Brazil.
There are a number of factors which may help to explain this. Quality primary education for all children had an equalizing effect, putting the majority of the population in a position to benefit from increased prosperity. In the aftermath of the Korean War, the country placed great importance on universal primary education with a focus on literacy and numeracy. There was a focus on universal learning of core skills with a focus on getting all schools to a decent standard. It was only when employers started demanding different skills — like ship building in the s and s— that compulsory education was expanded from 6 to 9 years.
The chart below shows primary education spending as a priority for many years after higher levels of economic growth started. As the debates about the post development framework gather pace, there is growing debate about inequality and why it matters for development. But even if the argument that inequality matters is won, this will beg the question: what can policy makers actually do about it?
How can more low income countries successfully grow with equity? There are no easy answers, but as a minimum, creating broad-based and equitable education systems with all children learning during primary school must be a central part of the response. Without education systems which ensure that all children are learning, shared economic growth is likely to remain elusive.
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