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The and analyses used the more inclusive person level poverty rate. When the DFGs were developed, exploratory analysis suggested that this variable was no longer a useful indicator of SES.
Therefore, it was dropped. The report noted that over time, this has become a less reliable indicator for SES as people became increasingly likely to relocate to pursue better career opportunities.
This variable has not been utilized since the DFG report. While a detailed explanation of this procedure is beyond the scope of this report, a general description will provide better insight into how the DFGs are determined.
PCA is a technique designed to express the information contained in a group of highly correlated variables in a smaller number of variables.
For example, assume a situation in which an analyst has collected height and weight data for a population. PCA could be used to calculate a new variable called a principal component that captures the same information, but with the use of only one variable instead of two.
One could view this combination of the height and weight data as a more generic size measure. This description is very simplified.
In fact, the PCA process will not produce just one principal component. Rather, it will create as many principal components as there are variables in the original analysis.
One would not use all of the principal components, however, because that would be inconsistent with the objective of reducing the number of variables included in the analysis.
This is a reasonable approach if the variables included in the analysis impact the first principal component in a manner consistent with expectations for example, if the results show higher income decreases the first principal component, it is likely that the first principal component is not measuring SES.
Once the PCA analysis has been implemented and the first principal component has defined a numeric measure of relative SES, the districts must be grouped into the DFG classes.
The first two DFG reports utilized a simple method. The districts were grouped into deciles ten groups containing an approximately equal number of districts based on their SES score the first principal component discussed above.
The report noted that this grouping method, while straightforward, was flawed. The process of classifying districts into equally sized deciles did not account for the magnitude of the difference in the SES scores across districts.
This represented a particular problem in the middle of the distribution, where a large number of districts had similar SES scores. One result of this problem was that in some cases, average test scores were higher in lower DFGs.
The analysis classified districts based on the range of SES scores. These groupings became the eight DFG categories currently used.
Given the expanded use of the DFG classification, particularly the lowest and highest categories, efforts were made to preserve the underlying meaning of these groups.
In determining the DFGs using the Decennial Census data, the overarching goal was to continue refining the methodology in ways that will make the calculation more accurate while simultaneously preserving the basic meaning of the DFG classifications particularly the two lowest and two highest categories.
To this end, the department began the process by obtaining feedback from districts regarding modifications that may be required.
Through various means of communication, the department received a significant number of comments. The most common concerns can be classified into one of four categories:.
It should be noted that questions were not raised regarding the statistical technique used to determine the SES scores and the method for grouping districts into DFG classes.
Given the previous and future uses of the DFGs, one key objective is to preserve the underlying meaning of the groupings, particularly at the low and high ends.
In the absence of any compelling reason to modify these methods, the decision was made to continue the same quantitative analysis technique and grouping method used in the development of the DFGs.
The four subject areas raised during various discussions were explored at length in developing the DFGs. The process is discussed and the final decisions made are explained here.
In reviewing the previous DFG analyses and discussing the measure with representatives from school districts, a number of questions were raised with regards to variables that may improve the DFG calculation.
The previous inclusion of one variable, population density, was called into question. When determining whether such variables should be added to the model, several factors were considered:.
Empirical Results: After experimenting with various models, variables that do a poor job of defining SES should be dropped from the final analysis.
In updating the DFGs, six changes in the model specification were tested with the above four considerations in mind.
The empirical analysis is straightforward. The first model was a baseline version that included the same seven variables as the DFGs.
Each additional option made one change to allow a clear comparison to the baseline version. Each variable used is discussed below. Table 2 summarizes the results of the PCA models.
A review of literature on SES does not reveal frequent use of this measure. Furthermore, a table in the DFG report suggests that this variable was substantially weaker than the other six in terms of explaining SES.
The share of explained variance increases by nearly 10 percentage points or 14 percent. However, the percent of students classified as LEP is not an appropriate measure for this analysis as it is at least partly determined by district policy and practice.
The census data provides two variables that could be used to measure this phenomenon: 1 the percent of people between the ages of 5 and 17 who do not speak English well and 2 the percent of households that are "linguistically isolated" households in which no one over the age of 14 speaks English well.
It should be noted that some analysis was done with the first variable when the DFGs were developed. However, the report concluded that this was not a reasonable measure of SES.
The empirical analysis here corroborates those results. Including the percent of individuals who do not speak English well decreases the explained variance by 6.
Including linguistic isolation yields a similarly sized decrease 5. While it appears that further analysis is warranted, it should be noted that the DFG analysis explored using this variable as an alternative to the poverty measure.
It was determined that poverty was a more appropriate variable. In this analysis, the percent of families with children is explored as a supplement to the other variables.
However, the results show a slight decrease in the percent of variable explained 1. As noted in the report, this does not provide information on how poor these individuals are.
While the inclusion of this variable seems intuitive, it caused a small decrease in the percent of variance explained 0. This idea raises two concerns.
First, similar to the percent of students classified as LEP, it is a measure that partly depends on district level decisions.
Second, there appears to be nothing in the research literature on this topic that link disability status to SES.
To explore this linkage, census data are used to estimate the percent of people between the ages of 5 and 20 who have some disability this measure has the benefit of not being affected by district level decision-making.
The explained variance decreases 4. The census data do not include variables that may be used as a proxy for student mobility. As an alternative, data from the School Report Card were aggregated to the school district level to estimate the mobility rate.
Given the above discussion, it appears that the best model should include six variables: percent of adults with no high school diploma, percent of adults with some college education, occupational status, median family income, poverty rate, and unemployment rate.
A considerable number of school districts are engaged in sending-receiving relationships whereby a district educates students from another community on a tuition basis.
There may be situations in which a district receives students from a community with substantially different demographics. As designed in the past, the DFGs were based on the characteristics of the community in which the district is located, not the communities in which the enrolled students live.
This may lead to a district being classified in an inappropriate DFG. When submitting the Application for State School Aid ASSA data, districts involved in sending-receiving relationships provide information on the community from which their students originate.
It should be noted that this method prevents the assignment of a DFG to non-operating school districts, as these districts do not operate school buildings.
The characteristics of students in these communities will be accounted for in the district where the student actually attends school.
The census data used to calculate the DFGs provide information on the characteristics of the community in which the school districts are located.
In general, this provides a reasonable approximation of the demographics of students served by the public schools.
However, some district representatives raised concerns that the demographics of the community are not representative of the students served by the schools.
This situation may occur, for example, in communities where the more privileged children in a community attend non-public schools.
In attempting to address this concern, one needs a data source that provides a broad range of data on demographic characteristics specifically for the students enrolled in public schools.
This data set aggregates information from the Decennial Census at the school district rather than municipal level.
More importantly, it also provides information specifically for parents who have children enrolled in public schools. In theory, these data should be useful in addressing the concern that was raised.
Upon release of the data, the department developed estimates of the DFGs based on the characteristics of parents with children enrolled in the public schools.
Detailed analysis of these data suggested that it would not be a suitable replacement for the data used in the past. These data raised two concerns.
First, there were a significant number of school districts in which there were fewer than 70 parents included in the sample.
With all survey data, it is necessary to have a sufficient sample size to ensure the sample is representative of the population in question.
While there is not a specific requirement, the Census Bureau uses a sample size of 70 for reporting purposes when writing reports based on other data collections.
Second, using this data would require omitting the unemployment rate from the analysis. As will be discussed in Appendix B, there was a problem with the unemployment rate as estimated using the Decennial Census data.
The Bureau on Labor Statistics BLS provides an alternative, more accurate measure of the unemployment rate at the municipal level.
There is no source that will provide this information specifically for the parents of children enrolled in public schools.
Some have recommended using the demographic data collected to develop the School Report Card to determine the SES of districts. These data raise two concerns, however.
First, the data do not contain the wider range of variables that are most strongly associated with SES. While the data do include information on income level the percent of students who are eligible for free or reduced lunch there is no information on other key indicators.
Second, the department reviewed independently conducted analysis that classified districts using these data defining SES by race and percent of students eligible for free and reduced lunch.
The results demonstrated the limitations of this data source. The districts were divided into five SES groups, with more than half of all school districts being classified in the highest SES category.
The lack of variation observed diminishes the utility of such a classification mechanism. In the absence of a more suitable data source, the Decennial Census data are used.
To avoid classifying school districts in an inappropriate DFG, two limitations are imposed. First, no SES score is calculated for a community in which there were fewer than 70 respondents to the Decennial Census "long form" the questionnaire delivered to one in six households containing more detailed questions.
Second, a school district will not have a DFG classification if more than half of the school-aged children in the community attend nonpublic schools.
Both limitations were also used in the DFG analysis. In the past, county vocational districts were not included in the DFG classification process.
It has been suggested that this process creates a comparison of county vocational districts to each other, even though they may serve students of dissimilar demographic backgrounds.
It was recommended that county vocational districts receive a DFG classification based on the district of origin of the students they serve.
While this recommendation is intuitive on a certain level, its appropriateness rests on the assumption that the students who choose to attend the county vocational schools are demographically similar to their counterparts who do not.
Given the self-selection process involved and the fact that a relatively small share of students from any given district attends county vocational schools, it is unlikely that this is a reasonable assumption.
As such, vocational districts will continue to not be included in the DFG calculations. Based on the above considerations, the DFGs are devised using a process that includes the following steps:.
The SES score is determined by applying principal components analysis to the six variables previously discussed. In most cases, schools receive students from the community in which it is situated.
However, there are some districts that receive a significant share of students from other communities. To preserve the underlying meaning of each DFG classification relative to the current measure, the same grouping method is used in this version.
Table 3 summarizes the impact each of the six variables has on the final SES score that was calculated for each municipality.
The results indicate that the three parameters that have the largest impact on SES are related to education attainment and occupation. Through implementation of the PCA, each municipality has an SES score calculated based on its values of the six variables listed in Table 3.
Table 4 provides a hypothetical example of school district in which the students enrolled in its schools originate from three different municipalities.
The district serves Municipality 1, but also receives students on a tuition basis from two other communities. The municipal level SES scores indicate that Municipalities 1 and 2 have slightly higher and lower than average SES characteristics, respectively.
Municipality 3 has SES characteristics substantively greater than average. This figure is only slightly higher than the SES score for Municipality 1 because only a small fraction of the students enrolled in District 1 resides in Municipality 3.
The school district level SES scores range from As noted in the DFG report, these scores have little meaning to a non-statistical observer.
To ensure that the underlying meaning of each DFG class does not substantively change given the multiple uses of the DFGs , the same method used in the analysis to divide the districts into discrete groups is replicated here.
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