Bivariate Regression

Write-Up: Bivariate Regression Template

Instructions: For this graded assignment, you will complete the write-up below after completing the corresponding tutorial to this assignment. You will delete figures and tables where appropriate and then insert correct figures and tables. Also delete and then insert correct answers where there is RED text

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Scenario: The purpose of this study was to see if the number of nurses employed at a hospital could predict patient satisfaction. Researchers examined 20 hospitals similar in size and location. The amount of nurses working full-time for the hospitals were gathered from HR and patient satisfaction was averaged from surveys gathered over the past year. The patient satisfaction survey ranges from 1 to 10 with 1 being not satisfied and 10 being extremely satisfied.

To begin, select the “Data View” tab, then cut and paste the data set below into SPSS (or you can type in the data manually).  Do not copy the header row when you paste into SPSS.

 Code Number of Nurses Patient Satisfaction 1 200 7.1 2 187 6.3 3 254 8.8 4 210 7.3 5 200 6.9 6 190 6.2 7 160 5.4 8 214 7.8 9 198 6.6 10 186 6.4 11 270 9.1 12 215 7.0 13 175 5.6 14 300 9.3 15 250 8.1 16 155 5.0 17 198 7.0 18 212 7.4 19 215 7.2 20 195 6.9

FINDINGS

Overview

The purpose of this study was to see if the number of nurses employed could predict patient satisfaction. The independent variable was the number of nurses employed. The dependent variable was patient satisfaction. A bivariate regression was used to test the hypothesis.  The Findings section includes the research question, null hypothesis, data screening, descriptive statistics, assumption testing, and results.

Research Question

RQ: Is there a significant predictive relationship between the dependent variable (patient satisfaction) and the independent variable (number of nurses employed) for a group of similar hospitals.

Null Hypothesis

H0: There is no significant relationship between the dependent variable (patient satisfaction) and the independent variable (number of nurses employed) for a group of similar hospitals.

Data Screening

The researcher sorted the data and scanned for inconsistencies on each variable. No data errors or inconsistencies were identified. A scatter plot was used to detect bivariate outliers between the independent variable and the dependent variable. No bivariate outliers where identified. See Figure 1 for the scatter plots.

Figure 1

Simple Scatter Plot

Descriptive Statistics

Descriptive statistics were obtained on each of the variables. The sample consisted of 00 participants. The number of nurses at each hospital was obtained through HR. Patient satisfaction scores were obtained through averaging surveys obtained throughout the past year. Patient satisfaction scores could range from 1 (not satisfied at all) to 10 (extremely satisfied). Descriptive statistics can be found in Table 1.

Table 1

Descriptive Statistics

Assumption Testing

Assumption of Linearity

The multiple regression requires that the assumption of linearity be met. Linearity was examined using a scatter plot. The assumption of linearity was met/not met. See Figure 1 for the bivariate scatter plot.

Assumption of Bivariate Normal Distribution

The bivariate regression requires that the assumption of bivariate normal distribution be met.  The assumption of bivariate normal distribution was examined using a scatter plot. The assumption of bivariate normal distribution was met/not met. See Figure 1 for scatter plot.

Results

A bivariate regression was conducted to see if the number of nurses employed at a hospital could predict patient satisfaction. The independent variable was the number of nurses employed. The dependent variable was patient satisfaction scores. The researcher rejected/failed to reject the null hypothesis at the 95% confidence level where F(0, 00) = 00.00, p = .00.  There was/was not a statistical relationship between the independent variable (SAT exam scores) and the dependent variable Praxis 1 exam score). See Table 3 for regression model results.

Table 3

Regression Model

The model’s effect size was small/medium/large/extremely large where R = .000. Furthermore, R2 = .000 indicating that approximately 00% of the variance of dependent variable can be explained by the independent variable. See Table 5 for model summary.

Table 5

Model Summary

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