Research Methods for the Behavioral Sciences. Pay attention to the descriptions and examples of linear and nonlinear relationships, positive and negative linear relationships, and curvilinear relationships.
Consider how these relationships are determined and what impact each type of relationship may have on a researcher’s ability to make predictions.
Using the Walden Library, select and review two or three articles on criminal recidivism, violent crime, or domestic violence in which the variables have positive and negative linear relationships.
Consider the implications if the variables had a curvilinear relationship instead.
With these thoughts in mind:
Post by Day 4 a description of two variables that have a positive linear relationship and two variables that have a negative linear relationship in the research articles you reviewed. Then, explain the implications on the studies if each of those variables had a curvilinear relationship instead.
Full Answer Section
Negative linear relationships:
- Education level and recidivism: Research has shown that offenders with higher levels of education are less likely to recidivate than offenders with lower levels of education. This is likely due to the fact that education provides offenders with the skills and knowledge they need to find and maintain employment, which can help them to avoid re-offending.
- Social support and domestic violence: Research has shown that victims of domestic violence with strong social support networks are less likely to remain in abusive relationships. This is likely due to the fact that social support can provide victims with the resources and emotional strength they need to leave their abusers.
Implications of curvilinear relationships:
If the relationship between age and recidivism were curvilinear, it would mean that the risk of recidivism would increase at a faster rate for younger offenders, but then peak and start to decline at a certain age. This could be due to a number of factors, such as the fact that younger offenders are more likely to mature and become more responsible over time.
If the relationship between substance abuse and violent crime were curvilinear, it would mean that the risk of violent crime would increase as substance abuse increases, but then peak and start to decline at a certain level of substance abuse. This could be due to a number of factors, such as the fact that people at higher levels of substance abuse are more likely to be hospitalized or incarcerated, which would reduce their opportunities to commit violent crimes.
If the relationship between education level and recidivism were curvilinear, it would mean that the risk of recidivism would decrease as education level increases, but then start to increase again at higher levels of education. This could be due to a number of factors, such as the fact that offenders with higher levels of education may be more likely to commit white-collar crimes, which have a lower recidivism rate.
If the relationship between social support and domestic violence were curvilinear, it would mean that the risk of remaining in an abusive relationship would decrease as social support increases, but then start to increase again at higher levels of social support. This could be due to a number of factors, such as the fact that victims with very high levels of social support may feel guilty about leaving their abusers because they know that they have a lot of people who depend on them.
It is important to note that these are just examples, and the specific implications of a curvilinear relationship would depend on the specific variables involved. However, it is clear that curvilinear relationships can be more difficult to understand and predict than linear relationships.
Conclusion
By understanding the different types of relationships between variables, researchers can better understand the phenomena they are studying and make more accurate predictions. It is important to note that curvilinear relationships can exist even when linear relationships are observed in the data. This is because curvilinear relationships can be masked by noise in the data or by the fact that the data is only collected for a limited range of values.
Researchers should always consider the possibility of curvilinear relationships when analyzing data, and they should use statistical methods that are able to detect curvilinear relationships.