This module focuses on factors related to juvenile delinquency.
Use the Kids Count Data Book Online and examine how "percent of teens not attending school and not
working (ages 16-19)" varies across the states. Think of this variable as a measure of
teen idleness, since these teenagers are neither working nor going to school.
Eyeballing the map, which region or regions of the country have a higher rate of teen idleness?
[MAP: idle
teens]
How much variation is there in the rate of teen idleness?
[RANKINGS: idle teens]
Which state has the lowest(best) rate of teen idleness, and what is the
percent in that state? Which state has the highest(worst) rate of
teen idleness, and what is the percent in that state? Also, what is the range of variation between these percents?
| State Ranking |
Percent of teens not attending school/working |
Best
___________________ |
______% |
Worst
___________________ |
_____% |
| Variation (high-low percent) |
_____% |
Examine teen idleness 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 not
attending school/working" 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 idleness?
Describe the trend for Minnesota and the other state chosen. Do these state trends differ from the
national trend? Describe any similarities and differences.
Merton's strain theory of deviance suggests that deviance occurs when legitimate or institutionalized
means (getting a good education and job) of achieving cultural goals (becoming wealthy or achieving high status) are
blocked. We might expect that teens not attending school or working might experience strain. Letıs examine the
relationship between teen idleness (percent of teens not attending school and not working, ages 16-19) and two
juvenile justice indicators, Juvenile violent crime arrest rate (arrests per
100,000 youths age 10-17, 1998) and Juvenile property crime arrest rate (arrests per 100,000 youths age 10-17,
1998).
Write a hypothesis consistent with Merton's strain theory.
To test the relationship between teen idleness (percent teens not attending school/working) and
juvenile violent crime rate (arrests per 100,000 youths age 10-17, 1998), open the excel file called "tool_us.xls".
Make a scatter plot by using the pull down menu. Let x be "idle" (percent teens not attending school/working)
1998 and y be "juvenile violent crime arrest rate 1998". 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)
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.
Repeat these steps, substituting juvenile property crime arrest rate as the new y variable.
To test the relationship between teen idleness (percent teens not attending school/working) and
juvenile property crime rate (arrests per 100,000 youths age 10-17, 1998), open the excel file called "tool_us.xls".
Make a scatter plot by using the pull down menu. Let x be "idle" (percent teens not attending school/working)
1998 and y be "juvenile property crime arrest rate 1998". Cut and paste the scatter plot into a Word file and record
the correlation coefficient.
Write an interpretation of the correlation coefficient for the scatter plot (x = percent teens not attending
school/working 1998), y = juvenile property crime arrest rate 1998). See below for an explanation 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.
Should we assume that Merton's theory is invalid? Asssuming Merton is correct, what might be a better
measure of juvenile justice than arrest rates?
Not all behavior classified as deviant is criminal activity. Becoming a teen mother is still considered
to be deviant. While the stigma has lessened since the 1950s, teen pregnancy is considered a social problem because many of
these children and their mothers live in poverty. While the causes of teen pregnancy are complicated, becoming a parent
through early childbearing may for some be a route to adult status when other routes are blocked.
Using Merton's Strain theory, develop a hypothesis between teen idleness, or idle (x) and teen birthrate (births
per 1,000 females aged 15-17).
To test the relationship between teen idleness (percent teens not attending school/working) and Teen
birthrate, use the "tool_us.xls" file.
Make a scatter plot using the pull down menu. Let x be "percent teens not attending
school/working 1998" and y be "births per 1000 females aged 15-17 1999." Since we are hypothesizing that idleness can
result in a pregnancy and a birth nine months later, it makes sense to have y measured in a later year. While data analysis
over time is much more complicated than this with many pitfalls to avoid, this analysis allows us to emphasize the logic of
time order and theoretical concepts. Cut and paste scatter plot into word file and record the correlation coefficient.
Interpret your findings.
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 your hypothesis confirmed? Explain your answer.
