Education

by Theodore Fuller

In this module, we will be looking at educational issues, particularly children who are having problems with the educational process.

1. Use the 2002 Kids Count Data Book Online and look at how the "Percent of teens who are high school dropouts" (called "dropouts" below) varies across the states.

   
   Looking at the map, how does Virginia compare to other states in terms of the percent of high school dropouts?
   [MAP: dropouts]

   How much variation is there in the percent of dropouts?
   [RANKINGS: dropouts]

   Which state has the lowest percent of dropouts, and what is the percent in that state? Which state has the highest percent of dropouts, and what is the percent in that state?

   	State	Rate
   Lowest Dropout Rate	___________________	______%
   Highest Dropout Rate	___________________	_____%

   Next, look at the trend in high school dropouts over the decade of the 1990s. For this part, we will not look at the trend for each of the 50 states. Instead, we will look at only Virginia, the two states you already identified as having the highest and lowest percent of dropouts, and the United States as a whole. To do this, you will need to select the appropriate geographic areas, indicator, and time-period.
   [GRAPH]

   Did these three states and the United States overall follow the same trend over the 1990s? How would you summarize these trends?

2. Becoming a high school dropout is not a random event that "comes out of the blue." Often students who become high school dropouts had problems earlier in their educational career. To get a sense of earlier problems, let's look at the percent of 4^th grade students who scored below basic reading level. These data are not available on the Kids Count web site accessed above, but they are available on the SSDAN KidsCount web site, under "Data Resources and Analysis Tools." The file you will need is called "tool_us.xls".

Open the file "tool_us.xls" (click on "Enable Macros" ­ SSDAN is a reliable source) and click on "Chart". Then, make "Percent of 4^th Graders Scoring below the basic reading level" the X variable and make "Percent of teens who are high school drop outs" (DROPOUTS) the Y variable. (Do this by clicking on the X variable box and Y variable box, respectively, and scrolling down to select the specified variable.) For both variables, select the most recent data available; for 4^th grade reading level the most recent data are from 1998 and for DROPOUTS the most recent data are from 1999.

After you select the two variables and the years, "tool_us.xls" will automatically display a scatter plot showing the relationship between the two variables. A scatter plot is a simple graph that shows the relationship between two variables by showing a point for each case on a two-dimensional graph (in this situation, each case is a state or the District of Columbia). Looking at a scatter plot, we can get an idea about whether the two variables are related and, if so, how strong the relationship is.

I would expect that those geographic areas that have a higher percent of 4^th graders who score below the basic reading level will have more teenagers who drop out of high school. (If more kids are having reading problems in 4^th grade, more will drop out of high school.)

One measure of the strength of the relationship between two variables is called a "correlation coefficient". The correlation coefficient can vary from ­1.0 to 1.0. 1.0 means the two variables are perfectly related to each other in a positive direction; in other words, if one variable increases, the other one increases by a corresponding amount. ­1.0 also means the two variables are perfectly related to each other, but in a negative direction; if one variable increases, the other one decreases by a corresponding amount. 0.0 means that the two variables are not related; a change is one variable is not predictably related to a change in the other variable. In practice, correlations are usually not close to 1.0 or ­1.0. A correlation of .2 is usually considered a weak relationship; a correlation of .6 is strong; a correlation of .8 is extremely strong.

"tool_us.xls" automatically reports the correlation between the two variables in the scatter plot. What is the correlation between the percent of 4^th graders who have reading problems and the percent of teenagers who drop out of high school?

There are several "outliers". "Outliers" are points that are not close to the other points. (Each point corresponds to a state or the District of Columbia.) Several outliers have a very low percent of 4^th graders with low reading scores, compared to the other states. One outlier has a much higher percent of 4^th graders who have low reading scores. What geographic area is the outlier that has a much higher percent of 4^th graders with low reading scores? (You can find out by putting the cursor on top of a point. After a second, the name of the geographic area will appear, as well as the X and Y value for that area.)

In general, we see from these data that the more 4^th graders have reading problems, the more teenagers are likely to drop out of high school. It is not a perfectly predictable relationship, but it is fairly strong.

3. Becoming a high school dropout has obvious consequences. In the long run, one's social and economic status is likely to be severely restricted if one drops out of high school. Even in the short run, while those who do graduate from high school usually get a job or go to college, those who drop out of high school might not do either, at least for a while. Teenagers who are not going to school and not working are sometimes called "idle". To get a sense of how many teenagers are idle and how that varies across states, we will go back to the "tool_us.xls" file and do some more analysis.

Make "Dropouts" the X variables and make "Percent of teens not attending school and not working" ("Idle") the Y variable. (Select "1999" for both variables.) I would expect that states that have a larger percent of drop outs have a larger percent of teens who are "idle". What is the correlation between these two variables?

The results in the scatter plot are consistent with the expectation that "dropouts" is related to "idle". In fact, the relationship is fairly strong.

4. Thinking about states, why do you think 4^th graders in some states have better reading scores than 4^th graders in other states? Why are teenagers more likely to drop out of high school in some states than in other states? Why are teenagers more likely to be "idle" in some states than in others?

   Finally, thinking about individual children, why do some children read better in 4^th grade than others do? Why do some teenagers drop out of high school, while others graduate from high school? Why are some teenagers idle while others remain in school or get a job?