Wednesday, November 26, 2014

Factors Influencing Student Outcomes and How They Interact - 15 - Family Income

In response to the feedback received on "Educational Funding and Student Outcomes: The Relationship as Evidenced by State-Level Data," the KASB Research Department is working on a "Part II" which will dig further into other factors that impact student outcomes, and how funding impacts when these other factors are taken into consideration.

In this series of blog posts, I will describe the preliminary correlation analysis comparing these factors (independent variables) with each other in an attempt to show how closely tied to each other they are.  

Today's topic is Family Income

The Bureau of Economic Analysis provides an average per capita personal income by year and state.  The US Census Bureau provides a median household income by year and state.  Both measures were used to indicate family income; as actual dollar amounts, amounts adjusted for inflation over time using the Bureau of Labor Statistics' Consumer Price Index, and amounts adjusted for state cost of living using the Bureau of Economic Analysis' Regional Price Parity calculation.

In terms of other independent variables, I will list the interactions in terms of the strength of the highest correlation between variables observed; Strong (+/- 1.0 to 0.5), Moderate (+/- 0.5 to 0.3), Weak (+/- .03 to 0.1), and None or Very Weak (+/- 0.1 to 0.0).

Strong

  • There is a strong positive correlation between RPP and personal and household income.  This correlation remains strong when adjusted for inflation (CPI2014), and becomes weak when adjusted for RPP; which means that even when the dollar amounts are adjusted for state cost of living, the relationship remains statistically significant between RPP and personal and household income.
  • There is a moderate to strong negative correlation between percent of students eligible for free or reduced-price lunch and both personal and household income.  This holds true when income is adjusted for inflation (CPI2014) and state cost of living (RPP).  
  • There is a moderate to strong positive correlation between teacher salaries and both per capita and household income; indicating that states with higher teacher salaries also have higher per capita and household incomes.
  • There is a weak to strong positive correlation between school spending and per capita or household income; indicating states that spend more per pupil have higher average individual and family income.  
  • There is a moderate to strong positive correlation between educational attainment and per capita and household income; indicating states with higher education levels also have higher income levels.
  • There is a moderate to strong negative correlation between poverty and personal or family income; with higher percents of poverty tied to lower income.  This holds true when the income amounts are adjusted both for inflation (CPI2014) and for regional cost of living (RPP).
  • There is a weak to strong positive correlation between population per square mile and family income; indicating that incomes are lower in less densely populated states. 

Moderate

  • There is a moderate positive correlation between period and per capita personal income; indicating that individual incomes are increasing over time.  This relationship becomes non-significant when controlled for inflation (CPI2014), but becomes a strong positive correlation when adjusted for cost of living by state (RPP). There is a weak positive correlation between period and household income; indicating that family incomes are not increasing as noticeably over time as personal incomes.  This relationship becomes a weak negative relationship when controlled for inflation (CPI2014); implying that family incomes are actually decreasing over time, and increases slightly when adjusted for cost of living by state (CPI2014). 
  • There is a moderate negative correlation between percent taking the ACT and personal or household income, and a moderate positive correlation between percent taking the SAT and personal or household income.  This is consistent when adjusting for inflation (CPI2014) and cost of living (RPP); meaning students from states with lower average income are more likely to take the ACT and less likely to take the SAT. 
  • There is a weak negative to no correlation between percent of white students and personal or household income, a weak to moderate negative correlation between percent of black students and personal or household income, a weak positive to no correlation between percent of Hispanic students and personal or household income, and no correlation between percent of American Indian or Alaska native and personal or household income when looking at actual dollar amounts and amounts adjusted for inflation (CPI2014).  When adjusting for state cost-of-living (RPP), there is a weak positive correlation between the percent of white students and personal or household income, a weak to moderate negative correlation between percent of black students and personal or household income, and no correlation between the percent of Hispanic and American Indian or Alaska native students and personal or household income.  This suggests that, beyond regional cost differences, states with higher percents of white students and lower percents of black students have higher personal or household incomes.
  • There is a weak to moderate negative correlation between student teacher ratio and per capita personal income but no correlation between student teacher ratio and median household income; suggesting states with more students per teacher are also likely to have lower per capita personal income.  

Weak

  • There is no correlation between percent of students served under IDEA and the median household income (actual, CPI2014, and RPP), but there is a weak positive correlation between the percent of special education students and the per capita personal income (CPI2014 and RPP, but not actual); indicating that states with higher percents of special education students have higher per capita personal incomes when adjusted for inflation or cost of living.
  • There is no correlation between percent of ELL students and per capita personal income when looking at actual dollars, a weak positive correlation when looking at personal income adjusted for inflation (CPI2014), and a weak negative correlation when looking at personal income adjusted for state cost-of-living (RPP).  There is a weak positive correlation between between percent of ELL students and household income when looking at actual dollars and dollars adjusted for inflation (CPI2014), and no correlation when looking at household income adjusted for state cost-of-living (RPP).  This suggests that states with higher percents of ELL students have higher incomes except when taking state cost-of-living into account, though this relationship is weak.
  • There is a weak positive correlation between instruction as a percent of current spending and per capita or household income, and no correlation between instruction as a percent of total revenue and per capita or household income; indicating that states spending more on expenses categorized as "instruction" also have higher income levels. 

None or Very Weak

  • There were no correlations related to educational attainment yielding very weak or non-significant results.

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