Respond to a colleague’s post who has selected a different value. Describe whether or not your colleague’s post swayed your point of view. Does your opinion change when on reporting in different contexts? Why or why not?
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2 days ago
Week 4 Main Post/Sandy Starch
As described by Dr. Jones (Laureate Education, 2017), logistic regression is a unique method to identify and report conditional ratio, probabilities, and the odds from a given set of data. Also noted by Dr. Jones, the purpose of using logistical regression rather than correlation values depends on the research question posed and who the identified readers will be.
Logistic regression is a typical research format in the medical field, such as the odds of developing a given disease (i.e., Coronary Artery Disease) based on patient characteristics (i.e., age, sex, weight, blood tests, etc.). However, some patients may develop such a disease, while others do not. Therefore, using logistical regression allows one to identify the presence of risk factors as increasing the odds of a given outcome (Osbourne, 2015).
In the field of forensic psychology, specifically in the correctional systems, the question exists as to the possibility of a released inmate conducting another crime, thus returning to incarceration. Factors influencing recidivism may be the environment to which the inmate returns, substance abuse, support networks, financial and personal stress, and mental health disorders. To study the influence of these independent variables (IV) on the dependent variable of recidivism (DV), the logit model would be the most appropriate and the strongest research model to answer posed research questions. In addition, the use of probability reports could best state the possibility of recidivism (DV) occurring given the strength and the values of the IV’s. Therefore, application of probability is the selected option in reporting the study analysis.
Laureate Education (Producer). (2017). Creating a contingency table in Microsoft Excel [Video file]. Baltimore, MD: Author.
Osborne, J. W. (2015). Best practices in logistic regression. Thousand Oaks, CA: SAGE Publications.