What was the exact likelihood of obtaining the r value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true?

Description

Homework 3 involves survey data from the Centers for Disease Control (CDC) that focused on residential care facilities in the United States in 2010 (CDC, 2010). Residential care facilities primarily consist of persons in assisted living communities who receive housing and supportive services because they cannot live independently but generally do not require the skilled level of care provided by nursing homes. The focus of the first national study of acute care use in residential care facilities by Kahveci and Cipher (2014) was on acute care use (emergency department visits and hospitalizations) among persons in various disease categories, and information regarding the residents’ length of stay and visitor frequency was also collected.

Length of stay in the facility was coded as 1 =0 to 3 months; 2 =3 to 6 months; 3= 6 months to a year; 4= 1 to 3 years; 5 =3 to 5 years; 6 =More than 5 years. Visitor frequency was coded as 1 =Every day; 2 =At least several times a week; 3 =About once a week; 4 =Several times in past month; 5 =At least once in past month; 6 =Not at all in the last 30 days. Therefore, higher values of the length of stay variable represent longer stays in the facility. Higher values of visitor frequency represent fewer visitor frequencies, which are considered inverse scoring.

The null hypothesis is “There is no correlation between length of stay and visitor frequency among residents of residential care facilities.” The data are presented in HW3.sav. A subset of 15 residents was randomly selected for this example so that the computations would be small and manageable. In actuality, studies involving Pearson correlations need to be adequately powered (Aberson, 2010; Cohen, 1988) and in the case of CDC survey data, adjusted for complex sampling.

Download the file: HW3.sav

Open in SPSS. Use the dataset to answer the following questions.

Calculate the Pearson r between LOS and visitor frequency, using the formula presented in Exercise 28. List the SOLVED numerator and the denominator. See attached for starter calculations.

Compute the Pearson r with SPSS. Paste the correlation matrix table into your homework.

What was the exact likelihood of obtaining the r value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true? (this question is asking you for the exact p value, converted to a percentage.)

How would you characterize the magnitude of the effect between length of stay and visitor frequency? Refer to Table 28-1 for the size of the effect. State your rationale.

Write your interpretation of the results, as you would in an APA-formatted journal. Remember that visitor frequency is REVERSE coded, with larger values represented FEWER visitors.

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