Instructor's Slides Independent Project Slides June 2020.pptx Independent Project Slides June 2020.pptx - Alternative Formats DUE DATE: End of Course20 points Independent Project Data - Excel (137 KB) (this file contains the data and descriptions of the study) Independent Project Data Raw Data to Upload to StatCrunch - Excel (133 KB) (upload this "clean" data file to StatCrunch)Using the “Independent Project Data” set file supplied above, perform an analysis in StatCrunch for the following using the variable(s) of your choice:1. Frequency distribution of a variable and bar graph of the same variable2. Descriptives of a continuous variable: mean, median, mode, skewness, kurtosis, standard deviation and graph of that variable3. Cross tabulation of two variables with the appropriate statistical test4. Comparison of two groups (single variable) on a single continuous variable with the appropriate statistical test5. Comparison of the effect of three or more groups (single variable) on a single continuous variable with the appropriate statistical test6. Scatterplot and correlation between the two continuous variables with the appropriate statistical test Think carefully about what kind of variables to choose for the given tasks. A short descriptive statement should accompany each of the above including a description of the variables used and any meaning that may be attached to the results. Write up the project in a WORD document for submission. Grading on this project is as follows:3 points for each task 1-6: 1 point each for variable choice, appropriate display/test, description of result.2 points for overall format/readability/construct (the writing and graphs should be formal and of publishable quality as you would see in a journal article).A sample project writeup on another data set is located in "Course Resources" to guide you in constructing the project.

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?Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.REPLY QUOTE 2 days agoSandra Starch Week 4 Main Post/Sandy StarchCOLLAPSEAs 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.ReferencesLaureate 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.

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