SPSS Stacked and Clustered Bar Graphs for Therapy Data and Age and Program of Enrollment

Therapy Data and Home Care vs Program

1. A researcher has collected data for 148 patients who received therapy from Hospital ABC (Excel file: 2300FTassignment3.xls; worksheet: therapy).  The data includes the unique resident identifier, sex (female = 1; male = 2), age in years, program (physical therapy = 1; occupational therapy = 2; recreational therapy = 3; combination therapy = 4), hours of week spent in therapy, prior home care (yes = 1; no = 0), test score (scalar variable) and responses to three likert scale survey statements about care:  S1(I understand the goals of my therapy), S2(Care team addresses my questions and concerns), and S3(The therapy addresses my needs).  The likert scale responses to all 3 statements include the following options: 1 = very well; 2 = fairly well; 3 = not very well; 4 = not at all well.  

a. Generate a stacked bar graph such that each bar represents a therapy program and the stacks represent the percentage that had or did not have prior home care for each program.  Add data labels.  Copy and paste the generated SPSS stacked bar graph.

b. Generate a clustered bar graph of the median test scores by sex, clustered by program.  Add data labels.  Copy and paste the generated SPSS clustered bar graph.

c. Generate a stacked bar graph such that each bar represents a different likert scale statement (S1-S3) and the stacks represent the percent of each response category (1 = very well; 2 = fairly well; 3 = not very well; 4 = not at all well).  Add data labels (0 decimal places with trailing % sign).  Note that very narrow stacks will not be able to show the data labels – this is okay.  Copy and paste the generated SPSS stacked bar graph.

2. The following table shows the numbers of students classified according to their age category and program of enrollment.  

If a student is selected at random (leave all answers in fractional form),

a. What is the probability that are enrolled in the informatics program?

b. What is the probability of selecting a policy student that is 20 years or younger?

c. What is the probability that they are enrolled in the management program and between 21-24 years of age?

d. What is the probability that they are enrolled in the management program given that they are 21-24 years of age?

e. What percentage of management students are at least 21 years of age or older?    

f. What is the probability of selecting a 25 year or older policy student?  

g. What is the probability of selecting a 25 year or older student from among the policy students?  

h. Is program of enrollment and age category independent?  Show your calculations.  

3. Imagine a screening questionnaire for COVID-19, administered on 500 persons with symptoms of COVID-19.  The actual presence or absence of COVID-19 was diagnosed from a COVID-19 polymerase chain reaction (PCR) assay.  The results are shown in the table.

Note: Leave all answers in fractional form.

a. Compute the sensitivity of the test.  

b. What proportion of diagnosed COVID-19 cases were screened negative with the questionnaire?  

c. If COVID-19 is absent, what is the probability that the questionnaire will be positive?  

d. The probability calculated in part c) is also known as what?

e. What is the negative predictive value of the questionnaire?  

f. If the questionnaire comes out positive, what is the probability that the patient will actually have COVID-19?  

g. The probability calculated in part f) is also known as what?

h. If the questionnaire comes out negative, what is the probability that the patient will be healthy (ie COVID-19 absent)?  

i. Compute the false positive fraction of the questionnaire.  

4. Ontario has collected data for COVID-19 cases in long-term care (LTC) homes (Excel file: 2300FTassignment3.xls; worksheet: covid).  The worksheet includes the daily count (for a full month) of the number of confirmed active resident cases of COVID-19 (Confirmed_Active_LTC_Resident_Cases) and the number of confirmed active home care worker cases of COVID-19 (Confirmed_Active_LTC_HCW_Cases).  

Generate a scatterplot between active LTC home care worker cases (x-axis) and