This module focuses on education and child well-being.
Use the Kids Count Data Book Online and examine how "Percent of teens who are high school dropouts(ages 16-19)" varies across the
states.
Eyeballing the map, which region or regions of the country have a higher percentage of high school
dropouts?
[MAP:
dropouts]
Use the ranking tool to find the variation in the percent of high school dropouts.
[RANKINGS: dropouts]
Which state has the lowest(best) percent of high school dropouts, and what is the
percent in that state? Which state has the highest(worst) percent of high school dropouts, and
what is the percent in that state? Also, what is the range of variation between these percents?
| State Ranking |
Percent of teens who are high school dropouts |
Best
___________________ |
______% |
Worst
___________________ |
_____% |
| Variation (high-low percent) |
_____% |
Examine "percent of teens who are high school dropouts" as a trend over time. Choose the United States,
Minnesota, and one other state for your line graph. Consider choosing one of the worst of best-ranked states as a
comparison. Choose "percent teens who are high school dropouts" as your indicator. Graph the data from 1990-1999.
[GRAPH]
Describe the overall national trend. Was there an increase, decrease, or did it stay the same. Put
another way, nationally are we experiencing more or less teen dropouts?
Describe the trend for Minnesota and the other state chosen. Do these state trends differ from the
national trend? Describe any similarities and differences.
Conflict theorists argue that education reproduces the class system stacking the deck against the lower
class. Specifically they argue that funding local schools with local property taxes means areas where people are richer
(hence higher property values) results in higher property taxes and better school funding. Poor neighborhoods provide fewer
dollars from local property taxes and thus are not as well funded. Poorly funded schools provide a discouraging environment
for children with less materials for books, computers, poorly maintained physical space, and most importantly, are not as
able to compete for talented and committed teachers. Children may give up on these schools believing they have nothing to
provide them.
Write a hypothesis stating the relationship you predict between poverty and teen dropouts.
To test the relationship between the percent of children living in poverty and
the dropout rate, open the excel file called "tool_us.xls".
Make a scatter plot by using the pull down menu. Let x be Poverty ("percent of children living in poverty 1999") and
y be Dropouts ("percent of teens who are high school dropouts 1999".) Cut and paste the scatter plot into a Word file and record the
correlation coefficient. (An explanation of the correlation coefficient can be found below)
Write an interpretation of the correlation coefficient.
Are there any data points that seem to stand out --not part of the cluster of data points? These are
called outliers. Click on an outlier to see which state is represented.
Was the hypothesis confirmed? Explain your answer.
Explanation of Correlation Coefficient
The correlation coefficient or Pearson's r is a measure of the degree of linear association existing between two
variables. We want to pay close attention to both the direction and strength of the association. A positive correlation
is indicated by the absence of a negative sign and means that variables are changing in the same direction. An increase
or decrease in one variable corresponds to the same change in another variable. For example, we would expect that the
more time a students studies for an exam (x) the higher the exam score (y). A negative relationship is indicated by a
minus sign and means that as one variable increases there is a corresponding decrease in another variable. The strength
of a relationship is indicated by the numeric value of the coefficient. Coefficients range from 1.0 to -1.0. These values
are examples of perfect correlations. In reality most values are found in between 1.0 and -1.0. Correlations of .30 or
less (either + or -) are considered weak, .31 - .70 (either + or -) are deemed moderate and .71 and above (either + or -)
considered strong. These are not absolute rules but should be used as a guide in interpretation. Note that the higher the
correlation coefficient (either positive or negative), the more closely clustered the data points are in the shape of a
diagonal line.
Health issues also impact educational outcomes. We know that low birth weight (defined as babies
weighing less than 2,500 grams or about 5.5 pounds) is linked to developmental delays in children which impacts school
performance. To some extent this is also linked to family economics but other factors are also relevant such as the increase
in multiple births (twins and triplets weigh less than babies in single births) due to fertility drugs.
Develop a hypothesis accessing the relationship between low birth rate babies (x) and percent of fourth
graders scoring below basic math level (y).
Test this relationship using the "tool_us.xls" file.
Make a scatter plot using the pull down menu. Let x be LowBirthWeight ("percent low weight babies 1991") and y be
%4thGradersScoringBelowBasicMathLevel ("Percent 4th graders scoring
below basic math level 2000.") Children born before September 1st
1991 would have been 4th graders in 2000. While we know that not all low
birth babies recorded in a state in 1991 ended up in those state's
public schools in 2000, some of those children did stay and may have impacted math performance. Cut and paste the scatter plot into a
word file and record the correlation coefficient.
Interpret your findings.
Economics may not only impact teen drop out rate, but may also impact child learning.
Develop a hypothesis accessing the relationship between "percent children living in poverty" (x) and
Percent 4th graders scoring below basic reading level.
Test this relationship using the "tools_us.xls" file.
Make a scatter plot using the pull down menu. Let x be Poverty ("Percent of children living in poverty 1998") and y
be %4thGradersScoringBelowBasicReadingLevel ("Percent 4th graders scoring below basic reading level 1998.") Cut and paste the scatter plot
into a word file and record the correlation coefficient.
Interpret your findings.
