## Understanding Simple Linear Regression

Exercise 14
Understanding Simple Linear Regression
Statistical Technique in Review
In nursing practice, the ability to predict future events or outcomes is crucial, and researchers calculate and report linear regression results as a basis for making these predictions. Linear regression provides a means to estimate or predict the value of a dependent variable based on the value of one or more independent variables. The regression equation is a mathematical expression of a causal proposition emerging from a theoretical framework. The linkage between the theoretical statement and the equation is made prior to data collection and analysis. Linear regression is a statistical method of estimating the expected value of one variable, y, given the value of another variable, x. The focus of this exercise is simple linear regression, which involves the use of one independent variable, x, to predict one dependent variable, y.

The regression line developed from simple linear regression is usually plotted on a graph, with the horizontal axis representing x (the independent or predictor variable) and the vertical axis representing the y (the dependent or predicted variable; see Figure 14-1). The value represented by the letter a is referred to as the y intercept, or the point where the regression line crosses or intercepts the y-axis. At this point on the regression line, x = 0. The value represented by the letter b is referred to as the slope, or the coefficient of x. The slope determines the direction and angle of the regression line within the graph. The slope expresses the extent to which y changes for every one-unit change in x. The score on variable y (dependent variable) is predicted from the subject’s known score on variable x (independent variable). The predicted score or estimate is referred to as Ŷ (expressed as y-hat) (Cohen, 1988; Grove, Burns, & Gray, 2013; Zar, 2010).

image
FIGURE 14-1 GRAPH OF A SIMPLE LINEAR REGRESSION LINE
140
Simple linear regression is an effort to explain the dynamics within a scatterplot (see Exercise 11) by drawing a straight line through the plotted scores. No single regression line can be used to predict, with complete accuracy, every y value from every x value. However, the purpose of the regression equation is to develop the line to allow the highest degree of prediction possible, the line of best fit. The procedure for developing the line of best fit is the method of least squares. If the data were perfectly correlated, all data points would fall along the straight line or line of best fit. However, not all data points fall on the line of best fit in studies, but the line of best fit provides the best equation for the values of y to be predicted by locating the intersection of points on the line for any given value of x.

The algebraic equation for the regression line of best fit is y = bx + a, where:

y=dependentvariable(outcome)

image
x=independentvariable(predictor)

image
b=slopeoftheline(beta,orwhattheincreaseinvalueisalongthex-axisforeveryunitofincreaseintheyvalue),alsocalledtheregressioncoefficient.

image
a=y−intercept(thepointwheretheregressionlineintersectsthe y-axis),alsocalledtheregressionconstant(Zar,2010).

image
In Figure 14-2, the x-axis represents Gestational Age in weeks and the y-axis represents Birth Weight in grams. As gestational age increases from 20 weeks to 34 weeks, birth weight also increases. In other words, the slope of the line is positive. This line of best fit can be used to predict the birth weight (dependent variable) for an infant based on his or her gestational age in weeks (independent variable). Figure 14-2 is an example of a line of best fit that was not developed from research data. In addition, the x-axis was started at 22 weeks rather than 0, which is the usual start in a regression figure. Using the formula y = bx + a, the birth weight of a baby born at 28 weeks of gestation is calculated below.

Formula:y=bx+a

image
Inthisexample,a=500,b=20,andx=28weeks

image
y=20(28)+500=560+500=1,060grams

image
image
FIGURE 14-2 EXAMPLE LINE OF BEST FIT FOR GESTATIONAL AGE AND BIRTH WEIGHT
141
The regression line represents y for any given value of x. As you can see, some data points fall above the line, and some fall below the line. If we substitute any x value in the regression equation and solve for y, we will obtain a ŷ that will be somewhat different from the actual values. The distance between the ŷ and the actual value of y is called residual, and this represents the degree of error in the regression line. The regression line or the line of best fit for the data points is the unique line that will minimize error and yield the smallest residual (Zar, 2010). The step-by-step process for calculating simple linear regression in a study is presented in Exercise 29.

Research Article
Source
Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), 927–931.

Introduction
Medications and other therapies often necessitate knowing a patient’s weight. However, a child may be admitted to a pediatric intensive care unit (PICU) without a known weight, and instability and on-going resuscitation may prevent obtaining this needed weight. Clinicians would benefit from a tool that could accurately estimate a patient’s weight when such information is unavailable. Thus Flannigan et al. (2014) conducted a retrospective observational study for the purpose of determining “if the revised APLS UK [Advanced Paediatric Life Support United Kingdom] formulae for estimating weight are appropriate for use in the paediatric care population in the United Kingdom” (Flannigan et al., 2014, p. 927). The sample included 10,081 children (5,622 males and 4,459 females), who ranged from term-corrected age to 15 years of age, admitted to the PICU during a 5-year period. Because this was a retrospective study, no geographic location, race, and ethnicity data were collected for the sample. A paired samples t-test was used to compare mean sample weights with the APLS UK formula weight. The “APLS UK formula ‘weight = (0.05 × age in months) + 4’ significantly overestimates the mean weight of children under 1 year admitted to PICU by between 10% [and] 25.4%” (Flannigan et al., 2014, p. 928). Therefore, the researchers concluded that the APLS UK formulas were not appropriate for estimating the weight of children admitted to the PICU.

Relevant Study Results
“Simple linear regression was used to produce novel formulae for the prediction of the mean weight specifically for the PICU population” (Flannigan et al., 2014, p. 927). The three novel formulas are presented in Figures 1, 2, and 3, respectively. The new formulas calculations are more complex than the APLS UK formulas. “Although a good estimate of mean weight can be obtained by our newly derived formula, reliance on mean weight alone will still result in significant error as the weights of children admitted to PICU in each age and sex [gender] group have a large standard deviation . . . Therefore as soon as possible after admission a weight should be obtained, e.g., using a weight bed” (Flannigan et al., 2014, p. 929).

image
FIGURE 1 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (0.5 × age in months) + 4” and novel formula “Weight in kg = (0.502 × age in months) + 3.161” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.
image
FIGURE 2 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (2 × age in years) + 8” and novel formula “Weight in kg = (0.176 × age in months) + 7.241” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.
image
FIGURE 3 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (3 × age in years) + 7” and novel formula “Weight in kg = (0.331 × age in months) − 6.868” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 929.

144
Study Questions
1. What are the variables on the x- and y-axes in Figure 1 from the Flannigan et al. (2014) study?

2. What is the name of the type of variable represented by x and y in Figure 1? Is x or y the score to be predicted?

3. What is the purpose of simple linear regression analysis and the regression equation?

4. What is the point where the regression line meets the y-axis called? Is there more than one term for this point and what is the value of x at that point?

5. In the formula y = bx + a, is a or b the slope? What does the slope represent in regression analysis?

6. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 5 months of age? Show your calculations.

145
7. What are the variables on the x-axis and the y-axis in Figures 2 and 3? Describe these variables and how they might be entered into the regression novel formulas identified in Figures 2 and 3.

8. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child at 4 years of age? Show your calculations.

9. Does Figure 1 have a positive or negative slope? Provide a rationale for your answer. Discuss the meaning of the slope of Figure 1.

10. According to the study narrative, why are estimated child weights important in a pediatric intensive care (PICU) setting? What are the implications of these findings for practice?

146
1. The x variable is age in months, and the y variable is weight in kilograms in Figure 1.

2. x is the independent or predictor variable. y is the dependent variable or the variable that is to be predicted by the independent variable, x.

3. Simple linear regression is conducted to estimate or predict the values of one dependent variable based on the values of one independent variable. Regression analysis is used to calculate a line of best fit based on the relationship between the independent variable x and the dependent variable y. The formula developed with regression analysis can be used to predict the dependent variable (y) values based on values of the independent variable x.

4. The point where the regression line meets the y-axis is called the y intercept and is also represented by a (see Figure 14-1). a is also called the regression constant. At the y intercept, x = 0.

5. b is the slope of the line of best fit (see Figure 14-1). The slope of the line indicates the amount of change in y for each one unit of change in x. b is also called the regression coefficient.

6. Use the following formula to calculate your answer: y = bx + a
y = 0.502 (5) + 3.161 = 2.51 + 3.161 = 5.671 kilograms
Note: Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.502 × age in months) + 3.161 in the title of Figure 1.

7. Age in years is displayed on the x-axis and is used for the APLS UK formulas in Figures 2 and 3. Figure 2 includes children 1 to 5 years of age, and Figure 3 includes children 6 to 12 years of age. However, the novel formulas developed by simple linear regression are calculated with age in months. Therefore, the age in years must be converted to age in months before calculating the y values with the novel formulas provided for Figures 2 and 3. For example, a child who is 2 years old would be converted to 24 months (2 × 12 mos./year = 24 mos.). Then the formulas in Figures 2 and 3 could be used to predict y (weight in kilograms) for the different aged children. The y-axis on both Figures 2 and 3 is weight in kilograms (kg).

8. First calculate the child’s age in months, which is 4 × 12 months/year = 48 months.
y = bx + a = 0.176 (48) + 7.241 = 8.448 + 7.241 = 15.689 kilograms
Note the x value needs to be in age in months and Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.176 × age in months) + 7.241.

147
9. Figure 1 has a positive slope since the line extends from the lower left corner to the upper right corner and shows a positive relationship. This line shows that the increase in x (independent variable) is associated with an increase in y (dependent variable). In the Flannigan et al. (2014) study, the independent variable age in months is used to predict the dependent variable of weight in kilograms. As the age in months increases, the weight in kilograms also increases, which is the positive relationship illustrated in Figure 1.

149
EXERCISE 14 Questions to Be Graded

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the study narrative and Figure 1 in the Flannigan et al. (2014) study, does the APLS UK formula under- or overestimate the weight of children younger than 1 year of age? Provide a rationale for your answer.

2. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms (kg) for a child at 9 months of age? Show your calculations.

3. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 2 months of age? Show your calculations.

4. In Figure 2, the formula for calculating y (weight in kg) is Weight in kg = (0.176 × Age in months) + 7.241. Identify the y intercept and the slope in this formula.

150
5. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 3 years of age? Show your calculations.

6. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 5 years of age? Show your calculations.

7. In Figure 3, some of the actual mean weights represented by blue line with squares are above the dotted straight line for the novel formula, but others are below the straight line. Is this an expected finding? Provide a rationale for your answer.

8. In Figure 3, the novel formula is (weight in kilograms = (0.331 × Age in months) − 6.868. What is the predicted weight in kilograms for a child 10 years old? Show your calculations.

9. Was the sample size of this study adequate for conducting simple linear regression? Provide a rationale for your answer.

10. Describe one potential clinical advantage and one potential clinical problem with using the three novel formulas presented in Figures 1, 2, and 3 in a PICU setting.

(Grove 139-150)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Exercise 19
Understanding Pearson Chi-Square
Statistical Technique in Review
The Pearson Chi-square (χ2 ) is an inferential statistical test calculated to examine differences among groups with variables measured at the nominal level. There are different types of χ2 tests and the Pearson chi-square is commonly reported in nursing studies. The Pearson χ2 test compares the frequencies that are observed with the frequencies that were expected. The assumptions for the χ2 test are as follows:

1. The data are nominal-level or frequency data.

2. The sample size is adequate.

3. The measures are independent of each other or that a subject’s data only fit into one category (Plichta & Kelvin, 2013).

The χ2 values calculated are compared with the critical values in the χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text). If the result is greater than or equal to the value in the table, significant differences exist. If the values are statistically significant, the null hypothesis is rejected (Grove, Burns, & Gray, 2013). These results indicate that the differences are probably an actual reflection of reality and not just due to random sampling error or chance.

In addition to the χ2 value, researchers often report the degrees of freedom (df). This mathematically complex statistical concept is important for calculating and determining levels of significance. The standard formula for df is sample size (N) minus 1, or df = N − 1; however, this formula is adjusted based on the analysis technique performed (Plichta & Kelvin, 2013). The df formula for the χ2 test varies based on the number of categories examined in the analysis. The formula for df for the two-way χ2 test is df = (R − 1) (C − 1), where R is number of rows and C is the number of columns in a χ2 table. For example, in a 2 × 2 χ2 table, df = (2 − 1) (2 − 1) = 1. Therefore, the df is equal to 1. Table 19-1 includes a 2 × 2 chi-square contingency table based on the findings of An et al. (2014) study. In Table 19-1, the rows represent the two nominal categories of alcohol 192use and alcohol nonuse and the two columns represent the two nominal categories of smokers and nonsmokers. The df = (2 − 1) (2 − 1) = (1) (1) = 1, and the study results were as follows: χ2 (1, N = 799) = 63.1; p < 0.0001. It is important to note that the df can also be reported without the sample size, as in χ2(1) = 63.1, p < 0.0001.

TABLE 19-1

CONTINGENCY TABLE BASED ON THE RESULTS OF AN ET AL. (2014) STUDY

Nonsmokers n = 742 Smokers n = 57*
No alcohol use 551 14
Alcohol use† 191 43
*Smokers defined as “smoking at least 1 cigarette daily during the past month.”

†Alcohol use “defined as at least 1 alcoholic beverage per month during the past year.”

An, F. R., Xiang, Y. T., Yu., L., Ding, Y. M., Ungvari, G. S., Chan, S. W. C., et al. (2014). Prevalence of nurses’ smoking habits in psychiatric and general hospitals in China. Archives of Psychiatric Nursing, 28(2), 120.

If more than two groups are being examined, χ2 does not determine where the differences lie; it only determines that a statistically significant difference exists. A post hoc analysis will determine the location of the difference. χ2 is one of the weaker statistical tests used, and results are usually only reported if statistically significant values are found. The step-by-step process for calculating the Pearson chi-square test is presented in Exercise 35.

Research Article
Source
Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), 217–226.

Introduction
Darling-Fisher and colleagues (2014, p. 219) conducted a mixed-methods descriptive study to evaluate the clinical usefulness of the Rapid Assessment for Adolescent Preventative Services (RAAPS) screening tool “by surveying healthcare providers from a wide variety of clinical settings and geographic locations.” The study participants were recruited from the RAAPS website to complete an online survey. The RAAPS risk-screening tool “was developed to identify the risk behaviors contributing most to adolescent morbidity, mortality, and social problems, and to provide a more streamlined assessment to help providers address key adolescent risk behaviors in a time-efficient and user-friendly format” (Darling-Fisher et al., 2014, p. 218). The RAAPS is an established 21-item questionnaire with evidence of reliability and validity that can be completed by adolescents in 5–7 minutes.

“Quantitative and qualitative analyses indicated the RAAPS facilitated identification of risk behaviors and risk discussions and provided efficient and consistent assessments; 86% of providers believed that the RAAPS positively influenced their practice” (Darling-Fisher et al., 2014, p. 217). The researchers concluded the use of RAAPS by healthcare providers could improve the assessment and identification of adolescents at risk and lead to the delivery of more effective adolescent preventive services.

Relevant Study Results
In the Darling-Fisher et al. (2014, p. 220) mixed-methods study, the participants (N = 201) were “providers from 26 U.S. states and three foreign countries (Canada, Korea, and Ireland).” More than half of the participants (n = 111; 55%) reported they were using the RAAPS in their clinical practices. “When asked if they would recommend the RAAPS to other providers, 86 responded, and 98% (n = 84) stated they would recommend RAAPS. The two most common reasons cited for their recommendation were for screening (n = 76, 92%) and identification of risk behaviors (n = 75, 90%). Improved communication (n = 52, 63%) and improved documentation (n = 46, 55%) and increased patient understanding of their risk behaviors (n = 48, 58%) were also cited by respondents as reasons to recommend the RAAPS” (Darling-Fisher et al., 2014, p. 222).

193
“Respondents who were not using the RAAPS (n = 90; 45%), had a variety of reasons for not using it. Most reasons were related to constraints of their health system or practice site; other reasons were satisfaction with their current method of assessment . . . and that they were interested in the RAAPS for academic or research purposes rather than clinical use” (Darling-Fisher et al., 2014, p. 220).

Chi-square analysis was calculated to determine if any statistically significant differences existed between the characteristics of the RAAPS users and nonusers. Darling-Fisher et al. (2014) did not provide a level of significance or α for their study, but the standard for nursing studies is α = 0.05. “Statistically significant differences were noted between RAAPS users and nonusers with respect to provider types, practice setting, percent of adolescent patients, years in practice, and practice region. No statistically significant demographic differences were found between RAAPS users and nonusers with respect to race, age” (Darling-Fisher et al., 2014, p. 221). The χ2 results are presented in Table 2.

TABLE 2

DEMOGRAPHIC COMPARISONS BETWEEN RAPID ASSESSMENT FOR ADOLESCENT PREVENTIVE SERVICE USERS AND NONUSERS

Current user Yes (%) No (%) χ2 p
Provider type (n = 161) 12.7652, df = 2 < .00
Health care provider 64 (75.3) 55 (72.4)
Mental health provider 13 (15.3) 2 (2.6)
Other 8 (9.4) 19 (25.0)
Practice setting (n = 152) 12.7652, df = 1 < .00
Outpatient health clinic 20 (24.1) 36 (52.2)
School-based health clinic 63 (75.9) 33 (47.8)
% Adolescent patients (n = 154) 7.3780, df = 1 .01
≤50% 26 (30.6) 36 (52.2)
>50% 59 (69.4) 33 (47.8)
Years in practice (n = 157) 6.2597, df = 1 .01
≤5 years 44 (51.8) 23 (31.9)
>5 years 41 (48.2) 49 (68.1)
U.S. practice region (n = 151) 29.68, df = 3 < .00
Northeastern United States 13 (15.3) 15 (22.7)
Southern United States 11 (12.9) 22 (33.3)
Midwestern United States 57 (67.1) 16 (24.2)
Western United States 4 (4.7) 13 (19.7)
Race (n = 201) 1.2865, df = 2 .53
Black/African American 11 (9.9) 5 (5.6)
White/Caucasian 66 (59.5) 56 (62.2)
Other 34 (30.6) 29 (32.2)
Provider age in years (n = 145) 4.00, df = 2 .14
20–39 years 21 (25.6) 8 (12.7)
40–49 years 24 (29.3) 19 (30.2)
50+ years 37 (45.1) 36 (57.1)
image

χ2, Chi-square statistic.

df, degrees of freedom.

Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), p. 221.

194
Study Questions
1. What is the sample size for the Darling-Fisher et al. (2014) study? How many study participants (percentage) are RAAPS users and how many are RAAPS nonusers?

2. What is the chi-square (χ2) value and degrees of freedom (df) for provider type?

3. What is the p value for provider type? Is the χ2 value for provider type statistically significant? Provide a rationale for your answer.

4. Does a statistically significant χ2 value provide evidence of causation between the variables? Provide a rationale for your answer.

5. What is the χ2 value for race? Is the χ2 value statistically significant? Provide a rationale for your answer.

6. Is there a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percentage adolescent patients? In your own opinion is this an expected finding? Document your answer.

195
7. What is the df for U.S. practice region? Complete the df formula for U.S. practice region to visualize how Darling-Fisher et al. (2014) determined the appropriate df for that region.

8. State the null hypothesis for the years in practice variable for RAAPS users and RAAPS nonusers.

9. Should the null hypothesis for years in practice developed for Question 8 be accepted or rejected? Provide a rationale for your answer.

10. How many null hypotheses were accepted by Darling-Fisher et al. (2014) in Table 2? Provide a rationale for your answer.

196
1. The sample size is N = 201 with n = 111 (55%) RAAPS users and n = 90 (45%) RAAPS nonusers as indicated in the narrative results.

2. The χ2 = 12.7652 and df = 2 for provider type as presented in Table 2.

3. The p = < .00 for the provider type. Yes, the χ2 = 12.7652 for provider type is statistically significant as indicated by the p value presented in Table 2. The specific χ2 value obtained could be compared against the critical value in a χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text) to determine the significance for the specific degrees of freedom (df), but readers of research reports usually rely on the p value provided by the researcher(s) to determine significance. Most nurse researchers set the level of significance or alpha (α) = 0.05. Since the p value is less than alpha, the result is statistically significant. You need to note that p values never equal zero as they appear in this study. The p values would not be zero if carried out more decimal places.

4. No, a statistically significant χ2 value does not provide evidence of causation. A statistically significant χ2 value indicates a significant difference between groups exists but does not provide a causal link (Grove et al., 2013; Plichta & Kelvin, 2013).

5. The χ2 = 1.2865 for race. Since p = .53 for race, the χ2 value is not statistically significant. The level of significance is set at α = 0.05 and the p value is larger than alpha, so the result is nonsignificant.

6. Yes, there is a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percent of adolescent patients. The chi-square value = 7.3780 with a p = .01.You might expect that nurses caring for more adolescents might have higher RAAPS use as indicated in Table 2. However, nurses need to be knowledgeable of assessment and care needs of populations and subpopulations in their practice even if not frequently encountered. Two valuable sources for adolescent care include the Centers for Disease Control and Prevention (CDC) Adolescent and School Health at http://www.cdc.gov/HealthyYouth/idex.htm and the World Health Organization (WHO) adolescent health at http://www.who.int/topics/adolescent_health/en/.

7. The df = 3 for U.S. practice region is provided in Table 2. The df formula, df = (R − 1) (C − 1) is used. There are four “R” rows, Northeastern United States, Southern United States, Midwestern United States, and Western United States. There are two “C” columns, RAAPS users and RAAPS nonusers. df = (4 − 1)(2 − 1) = (3)(1) = 3.

8. The null hypothesis: There is no difference between RAAPS users and RAAPS nonusers for providers with ≤5 years of practice and those with >5 years of practice.

197
9. The null hypothesis for years in practice stated in Questions 8 should be rejected. The χ2 = 6.2597 for years in practice is statistically significant, p = .01. A statistically significant χ2 indicates a significant difference exists between the users and nonusers of RAAPS for years in practice; therefore, the null hypothesis should be rejected.

10. Two null hypotheses were accepted since two χ2 values (race and provider age) were not statistically significant (p > 0.05), as indicated in Table 2. Nonsignificant results indicate that the null hypotheses are supported or accepted as an accurate reflection of the results of the study.

199
EXERCISE 19 Questions to Be Graded

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the relevant study results section of the Darling-Fisher et al. (2014) study, what categories are reported to be statistically significant?

2. What level of measurement is appropriate for calculating the χ2 statistic? Give two examples from Table 2 of demographic variables measured at the level appropriate for χ2.

3. What is the χ2 for U.S. practice region? Is the χ2 value statistically significant? Provide a rationale for your answer.

4. What is the df for provider type? Provide a rationale for why the df for provider type presented in Table 2 is correct.

200
5. Is there a statistically significant difference for practice setting between the Rapid Assessment for Adolescent Preventive Services (RAAPS) users and nonusers? Provide a rationale for your answer.

6. State the null hypothesis for provider age in years for RAAPS users and RAAPS nonusers.

7. Should the null hypothesis for provider age in years developed for Question 6 be accepted or rejected? Provide a rationale for your answer.

8. Describe at least one clinical advantage and one clinical challenge of using RAAPS as described by Darling-Fisher et al. (2014).

9. How many null hypotheses are rejected in the Darling-Fisher et al. (2014) study for the results presented in Table 2? Provide a rationale for your answer.

10. A statistically significant difference is present between RAAPS users and RAAPS nonusers for U.S. practice region, χ2 = 29.68. Does the χ2 result provide the location of the difference? Provide a rationale for your answer

(Grove 191-200)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Exercise 29
Calculating Simple Linear Regression
Simple linear regression is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (Cohen & Cohen, 1983). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in Exercise 14, Figure 14-1 illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.

A regression equation can be generated with a data set containing subjects’ x and y values. Once this equation is generated, it can be used to predict future subjects’ y values, given only their x values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable y (dependent variable, or outcome) is predicted from the same subject’s known score on variable x (independent variable, or predictor).

Research Designs Appropriate for Simple Linear Regression
Research designs that may utilize simple linear regression include any associational design (Gliner et al., 2009). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.

Statistical Formula and Assumptions
Use of simple linear regression involves the following assumptions (Zar, 2010):

1. Normal distribution of the dependent (y) variable

2. Linear relationship between x and y

3. Independent observations

4. No (or little) multicollinearity

5. Homoscedasticity

320
Data that are homoscedastic are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of x, the distribution of y values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (Cohen & Cohen, 1983; Zar, 2010). Visual examples of homoscedasticity and heteroscedasticity are presented in Exercise 30.

In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters a and b are computed to obtain a regression equation. To understand the mathematical process, recall the algebraic equation for a straight line:

y=bx+a

image
where

y=the dependent variable(outcome)

image
x=the independent variable(predictor)

image
b=the slope of the line

image
a=y-intercept(the point where the regression line intersects the y-axis)

image
No single regression line can be used to predict with complete accuracy every y value from every x value. In fact, you could draw an infinite number of lines through the scattered paired values (Zar, 2010). However, the purpose of the regression equa­tion is to develop the line to allow the highest degree of prediction possible—the line of best fit. The procedure for developing the line of best fit is the method of least squares. The formulas for the beta (β) and slope (α) of the regression equation are computed as follows. Note that once the β is calculated, that value is inserted into the formula for α.

β=n∑xy−∑x∑yn∑x 2 −(∑x) 2

image
α=∑y−b∑xn

image
Hand Calculations
This example uses data collected from a study of students enrolled in a registered nurse to bachelor of science in nursing (RN to BSN) program (Mancini, Ashwill, & Cipher, 2014). The predictor in this example is number of academic degrees obtained by the student prior to enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is “Number of degrees does not predict the number of months until completion of an RN to BSN program.”

The data are presented in Table 29-1. A simulated subset of 20 students was selected for this example so that the computations would be small and manageable. In actuality, studies involving linear regression need to be adequately powered (Aberson, 2010; Cohen, 1988). Observe that the data in Table 29-1 are arranged in columns that correspond to 321the elements of the formula. The summed values in the last row of Table 29-1 are inserted into the appropriate place in the formula for b.

TABLE 29-1

ENROLLMENT GPA AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Number of Degrees) (Months to Completion)
1 1 17 1 17
2 2 9 4 18
3 0 17 0 0
4 1 9 1 9
5 0 16 0 0
6 1 11 1 11
7 0 15 0 0
8 0 12 0 0
9 1 15 1 15
10 1 12 1 12
11 1 14 1 14
12 1 10 1 10
13 1 17 1 17
14 0 20 0 0
15 2 9 4 18
16 2 12 4 24
17 1 14 1 14
18 2 10 4 20
19 1 17 1 17
20 2 11 4 22
sum Σ 20 267 30 238
image

The computations for the b and α are as follows:

Step 1: Calculate b.
From the values in Table 29-1, we know that n = 20, Σx = 20, Σy = 267, Σx2 = 30, and Σxy = 238. These values are inserted into the formula for b, as follows:

b=20(238)−(20)(267)20(30)−20 2

image

b=−2.9

image

Step 2: Calculate α.
From Step 1, we now know that b = −2.9, and we plug this value into the formula for α.

α=267−(−2.9)(20)20

image

α=16.25

image

Step 3: Write the new regression equation:

y=−2.9x+16.25

image

322
Step 4: Calculate R.
The multiple R is defined as the correlation between the actual y values and the predicted y values using the new regression equation. The predicted y value using the new equation is represented by the symbol ŷ to differentiate from y, which represents the actual y values in the data set. We can use our new regression equation from Step 3 to compute predicted program completion time in months for each student, using their number of academic degrees prior to enrollment in the RN to BSN Program. For example, Student #1 had earned 1 academic degree prior to enrollment, and the predicted months to completion for Student 1 is calculated as:

y ̂ =−2.9(1)+16.25

image

y ̂ =13.35

image

Thus, the predicted ŷ is 13.35 months. This procedure would be continued for the rest of the students, and the Pearson correlation between the actual months to completion (y) and the predicted months to completion (ŷ) would yield the multiple R value. In this example, the R = 0.638. The higher the R, the more likely that the new regression equation accurately predicts y, because the higher the correlation, the closer the actual y values are to the predicted ŷ values. Figure 29-1 displays the regression line where the x axis represents possible numbers of degrees, and the y axis represents the predicted months to program completion (ŷ values).

image
FIGURE 29-1 REGRESSION LINE REPRESENTED BY NEW REGRESSION EQUATION.
Step 5: Determine whether the predictor significantly predicts y.

t=Rn−21−R 2 ‾ ‾ ‾ ‾ √

image

To know whether the predictor significantly predicts y, the beta must be tested against zero. In simple regression, this is most easily accomplished by using the R value from Step 4:

t=.638200−21−.407 ‾ ‾ ‾ ‾ ‾ √

image

t=3.52

image

323

The t value is then compared to the t probability distribution table (see Appendix A). The df for this t statistic is n − 2. The critical t value at alpha (α) = 0.05, df = 18 is 2.10 for a two-tailed test. Our obtained t was 3.52, which exceeds the critical value in the table, thereby indicating a significant association between the predictor (x) and outcome (y).

Step 6: Calculate R2.
After establishing the statistical significance of the R value, it must subsequently be examined for clinical importance. This is accomplished by obtaining the coefficient of determination for regression—which simply involves squaring the R value. The R2 represents the percentage of variance explained in y by the predictor. Cohen describes R2 values of 0.02 as small, 0.15 as moderate, and 0.26 or higher as large effect sizes (Cohen, 1988). In our example, the R was 0.638, and, therefore, the R2 was 0.407. Multiplying 0.407 × 100% indicates that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at admission (Cohen & Cohen, 1983).
The R2 can be very helpful in testing more than one predictor in a regression model. Unlike R, the R2 for one regression model can be compared with another regression model that contains additional predictors (Cohen & Cohen, 1983). The R2 is discussed further in Exercise 30.
The standardized beta (β) is another statistic that represents the magnitude of the association between x and y. β has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. This value can be calculated by hand but is best computed with statistical software. The standardized beta (β) is calculated by converting the x and y values to z scores and then correlating the x and y value using the Pearson r formula. The standardized beta (β) is often reported in literature instead of the unstandardized b, because b does not have lower or upper limits and therefore the magnitude of b cannot be judged. β, on the other hand, is interpreted as a Pearson r and the descriptions of the magnitude of β can be applied, as recommended by Cohen (1988). In this example, the standardized beta (β) is −0.638. Thus, the magnitude of the association between x and y in this example is considered a large predictive association (Cohen, 1988).

324
SPSS Computations
This is how our data set looks in SPSS.

image

Step 1: From the “Analyze” menu, choose “Regression” and “Linear.”

Step 2: Move the predictor, Number of Degrees, to the space labeled “Independent(s).” Move the dependent variable, Number of Months to Completion, to the space labeled “Dependent.” Click “OK.”

image

325
Interpretation of SPSS Output
The following tables are generated from SPSS. The first table contains the multiple R and the R2 values. The multiple R is 0.638, indicating that the correlation between the actual y values and the predicted y values using the new regression equation is 0.638. The R2 is 0.407, indicating that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at enrollment.

Regression
image
The second table contains the ANOVA table. As presented in Exercises 18 and 33, the ANOVA is usually performed to test for differences between group means. However, ANOVA can also be performed for regression, where the null hypothesis is that “knowing the value of x explains no information about y”. This table indicates that knowing the value of x explains a significant amount of variance in y. The contents of the ANOVA table are rarely reported in published manuscripts, because the significance of each predictor is presented in the last SPSS table titled “Coefficients” (see below).

image
The third table contains the b and a values, standardized beta (β), t, and exact p value. The a is listed in the first row, next to the label “Constant.” The β is listed in the second row, next to the name of the predictor. The remaining information that is important to extract when interpreting regression results can be found in the second row. The standardized beta (β) is −0.638. This value has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. The t value is −3.516, and the exact p value is 0.002.

image
326
Final Interpretation in American Psychological Association (APA) Format
The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). Simple linear regression was performed with number of earned academic degrees as the predictor and months to program completion as the dependent variable. The student’s number of degrees significantly predicted months to completion among students in an RN to BSN program, β = −0.638, p = 0.002, and R2 = 40.7%. Higher numbers of earned academic degrees significantly predicted shorter program completion time.

327
Study Questions
1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for months to completion (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis for the example where number of degrees was used to predict time to BSN program completion.

3. In the formula y = bx + a, what does “b” represent?

4. In the formula y = bx + a, what does “a” represent?

5. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 0. Show your calculations.

6. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 2. Show your calculations.

328
7. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

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

9. How much variance in months to completion is explained by knowing the student’s number of earned degrees?

10. How would you characterize the magnitude of the R2 in the example? Provide a rationale for your answer.

329
1. The Shapiro-Wilk p value for months to RN to BSN program completion was 0.16, indicating that the frequency distribution did not significantly deviate from normality. Moreover, visual inspection of the frequency distribution indicates that months to completion is approximately normally distributed. See SPSS output below for the histograms of the distribution:

image

2. The null hypothesis is: “The number of earned academic degrees does not predict the number of months until completion of an RN to BSN program.”

3. In the formula y = bx + a, “b” represents the slope of the regression line.

4. In the formula y = bx + a, “a” represents the y-intercept, or the point at which the regression line intersects the y-axis.

5. The predicted months to program completion if a student’s number of academic degrees is 0 is calculated as: ŷ = −2.9(0) + 16.25 = 16.25 months.

6. The predicted months to program completion if a student’s number of academic degrees is 2 is calculated as: ŷ = −2.9(2) + 16.25 = 10.45 months.

7. The correlation between the actual y values and the predicted y values using the new regression equation in the example, also known as the multiple R, is 0.638.

8. The exact likelihood of obtaining a t value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 0.2%. This value was obtained by looking at the SPSS output table titled “Coefficients” in the last value of the column labeled “Sig.”

9. 40.7% of the variance in months to completion is explained by knowing the student’s number of earned academic degrees at enrollment.

10. The magnitude of the R2 in this example, 0.407, would be considered a large effect according to the effect size tables in Exercises 24 and 25.

330
Using the example from Mancini and colleagues (2014), students enrolled in an RN to BSN program were assessed for demographics at enrollment. The predictor in this example is age at program enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is: “Student age at enrollment does not predict the number of months until completion of an RN to BSN program.” The data are presented in Table 29-2. A simulated subset of 20 students was randomly selected for this example so that the computations would be small and manageable.

TABLE 29-2

AGE AT ENROLLMENT AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Student Age) (Months to Completion)
1 23 17 529 391
2 24 9 576 216
3 24 17 576 408
4 26 9 676 234
5 31 16 961 496
6 31 11 961 341
7 32 15 1,024 480
8 33 12 1,089 396
9 33 15 1,089 495
10 34 12 1,156 408
11 34 14 1,156 476
12 35 10 1,225 350
13 35 17 1,225 595
14 39 20 1,521 780
15 40 9 1,600 360
16 42 12 1,764 504
17 42 14 1,764 588
18 44 10 1,936 440
19 51 17 2,601 867
20 24 11 576 264
sum Σ 677 267 24,005 9,089
image

331
EXERCISE 29 Questions to Be Graded
Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.

3. What is b as computed by hand (or using SPSS)?

4. What is a as computed by hand (or using SPSS)?

332
5. Write the new regression equation.

6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.

7. How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Given the results of your analyses, would you use the calculated regression equation to predict future students’ program completion time by using enrollment age as x? Provide

(Grove 319-332)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

https://eazyweezyhomeworks.com/benefits-of-using-tablets-in-schools/

Exercise 35
Calculating Pearson Chi-Square
The Pearson chi-square test (χ2) compares differences between groups on variables measured at the nominal level. The χ2 compares the frequencies that are observed with the frequencies that are expected. When a study requires that researchers compare proportions (percentages) in one category versus another category, the χ2 is a statistic that will reveal if the difference in proportion is statistically improbable.

A one-way χ2 is a statistic that compares different levels of one variable only. For example, a researcher may collect information on gender and compare the proportions of males to females. If the one-way χ2 is statistically significant, it would indicate that proportions of one gender are significantly higher than proportions of the other gender than what would be expected by chance (Daniel, 2000). If more than two groups are being examined, the χ2 does not determine where the differences lie; it only determines that a significant difference exists. Further testing on pairs of groups with the χ2 would then be warranted to identify the significant differences.

A two-way χ2 is a statistic that tests whether proportions in levels of one nominal variable are significantly different from proportions of the second nominal variable. For example, the presence of advanced colon polyps was studied in three groups of patients: those having a normal body mass index (BMI), those who were overweight, and those who were obese (Siddiqui, Mahgoub, Pandove, Cipher, & Spechler, 2009). The research question tested was: “Is there a difference between the three groups (normal weight, overweight, and obese) on the presence of advanced colon polyps?” The results of the χ2 test indicated that a larger proportion of obese patients fell into the category of having advanced colon polyps compared to normal weight and overweight patients, suggesting that obesity may be a risk factor for developing advanced colon polyps. Further examples of two-way χ2 tests are reviewed in Exercise 19.

Research Designs Appropriate for the Pearson χ2
Research designs that may utilize the Pearson χ2 include the randomized experimental, quasi-experimental, and comparative designs (Gliner, Morgan, & Leech, 2009). The variables may be active, attributional, or a combination of both. An active variable refers to an intervention, treatment, or program. An attributional variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. Regardless of the whether the variables are active or attributional, all variables submitted to χ2 calculations must be measured at the nominal level.

410
Statistical Formula and Assumptions
Use of the Pearson χ2 involves the following assumptions (Daniel, 2000):

1. Only one datum entry is made for each subject in the sample. Therefore, if repeated measures from the same subject are being used for analysis, such as pretests and posttests, χ2 is not an appropriate test.

2. The variables must be categorical (nominal), either inherently or transformed to categorical from quantitative values.

3. For each variable, the categories are mutually exclusive and exhaustive. No cells may have an expected frequency of zero. In the actual data, the observed cell frequency may be zero. However, the Pearson χ2 test is sensitive to small sample sizes, and other tests, such as the Fisher’s exact test, are more appropriate when testing very small samples (Daniel, 2000; Yates, 1934).

The test is distribution-free, or nonparametric, which means that no assumption has been made for a normal distribution of values in the population from which the sample was taken (Daniel, 2000).

The formula for a two-way χ2 is:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image
The contingency table is labeled as follows. A contingency table is a table that displays the relationship between two or more categorical variables (Daniel, 2000):

A B
C D
With any χ2 analysis, the degrees of freedom (df) must be calculated to determine the significance of the value of the statistic. The following formula is used for this calculation:

df=(R−1)(C−1)

image
where

R=Number of rows

image
C=Number of columns

image
Hand Calculations
A retrospective comparative study examined whether longer antibiotic treatment courses were associated with increased antimicrobial resistance in patients with spinal cord injury (Lee et al., 2014). Using urine cultures from a sample of spinal cord–injured veterans, two groups were created: those with evidence of antibiotic resistance and those with no evidence of antibiotic resistance. Each veteran was also divided into two groups based on having had a history of recent (in the past 6 months) antibiotic use for more than 2 weeks or no history of recent antibiotic use.

411
The data are presented in Table 35-1. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

TABLE 35-1

ANTIBIOTIC RESISTANCE BY ANTIBIOTIC USE

Antibiotic Use No Recent Use
Resistant 8 7
Not resistant 6 21
The computations for the Pearson χ2 test are as follows:

Step 1: Create a contingency table of the two nominal variables:

Used Antibiotics No Recent Use Totals
Resistant 8 7 15
Not resistant 6 21 27
Totals 14 28 42 ←Total n
image

Step 2: Fit the cells into the formula:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image

χ 2 =42[(8)(21)−(7)(6)] 2 (8+7)(6+21)(8+6)(7+21)

image

χ 2 =666,792158,760

image

χ 2 =4.20

image

Step 3: Compute the degrees of freedom:

df=(2−1)(2−1)=1

image

Step 4: Locate the critical χ2 value in the χ2 distribution table (Appendix D) and compare it to the obtained χ2 value.

The obtained χ2 value is compared with the tabled χ2 values in Appendix D. The table includes the critical values of χ2 for specific degrees of freedom at selected levels of significance. If the value of the statistic is equal to or greater than the value identified in the χ2 table, the difference between the two variables is statistically significant. The critical χ2 for df = 1 is 3.84, and our obtained χ2 is 4.20, thereby exceeding the critical value and indicating a significant difference between antibiotic users and non-users on the presence of antibiotic resistance.

Furthermore, we can compute the rates of antibiotic resistance among antibiotic users and non-users by using the numbers in the contingency table from Step 1. The antibiotic resistance rate among the antibiotic users can be calculated as 8 ÷ 14 = 0.571 × 100% = 57.1%. The antibiotic resistance rate among the non-antibiotic users can be calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

412
SPSS Computations
The following screenshot is a replica of what your SPSS window will look like. The data for subjects 24 through 42 are viewable by scrolling down in the SPSS screen.

image

413
Step 1: From the “Analyze” menu, choose “Descriptive Statistics” and “Crosstabs.” Move the two variables to the right, where either variable can be in the “Row” or “Column” space.

image

Step 2: Click “Statistics” and check the box next to “Chi-square.” Click “Continue” and “OK.”

image

414
Interpretation of SPSS Output
The following tables are generated from SPSS. The first table contains the contingency table, similar to Table 35-1 above. The second table contains the χ2 results.

Crosstabs
image

image

The last table contains the χ2 value in addition to other statistics that test associations between nominal variables. The Pearson χ2 test is located in the first row of the table, which contains the χ2 value, df, and p value.

Final Interpretation in American Psychological Association (APA) Format
The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). A Pearson χ2 analysis indicated that antibiotic users had significantly higher rates of antibiotic resistance than those who did not use antibiotics, χ2(1) = 4.20, p = 0.04 (57.1% versus 25%, respectively). This finding suggests that extended antibiotic use may be a risk factor for developing resistance, and further research is needed to investigate resistance as a direct effect of antibiotics.

415
Study Questions
1. Do the example data meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. What is the null hypothesis in the example?

3. What was the exact likelihood of obtaining a χ2 value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true?

4. Using the numbers in the contingency table, calculate the percentage of antibiotic users who were resistant.

5. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who were resistant.

6. Using the numbers in the contingency table, calculate the percentage of resistant veterans who used antibiotics for more than 2 weeks.

416
7. Using the numbers in the contingency table, calculate the percentage of resistant veterans who had no history of antibiotic use.

8. What kind of design was used in the example?

9. What result would have been obtained if the variables in the SPSS Crosstabs window had been switched, with Antibiotic Use being placed in the “Row” and Resistance being placed in the “Column”?

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

417
1. Yes, the data meet the assumptions of the Pearson χ2:

a. Only one datum per participant was entered into the contingency table, and no participant was counted twice.

b. Both antibiotic use and resistance are categorical (nominal-level data).

c. For each variable, the categories are mutually exclusive and exhaustive. It was not possible for a participant to belong to both groups, and the two categories (recent antibiotic user and non-user) included all study participants.

2. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

3. The exact likelihood of obtaining a χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 4.0%.

4. The percentage of antibiotic users who were resistant is calculated as 8 ÷ 14 = 0.5714 × 100% = 57.14% = 57.1%.

5. The percentage of non-antibiotic users who were resistant is calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

6. The percentage of antibiotic-resistant veterans who used antibiotics for more than 2 weeks is calculated as 8 ÷ 15 = 0.533 × 100% = 53.3%.

7. The percentage of resistant veterans who had no history of antibiotic use is calculated as 6 ÷ 27 = 0.222 × 100% = 22.2%.

8. The study design in the example was a retrospective comparative design (Gliner et al., 2009).

9. Switching the variables in the SPSS Crosstabs window would have resulted in the exact same χ2 result.

10. The sample size was adequate to detect differences between the two groups, because a significant difference was found, p = 0.04, which is smaller than alpha = 0.05.

418
A retrospective comparative study examining the presence of candiduria (presence of Candida species in the urine) among 97 adults with a spinal cord injury is presented as an additional example. The differences in the use of antibiotics were investigated with the Pearson χ2 test (Goetz, Howard, Cipher, & Revankar, 2010). These data are presented in Table 35-2 as a contingency table.

TABLE 35-2

CANDIDURIA AND ANTIBIOTIC USE IN ADULTS WITH SPINAL CORD INJURIES

Candiduria No Candiduria Totals
Antibiotic use 15 43 58
No antibiotic use 0 39 39
Totals 15 82 97
image

419
EXERCISE 35 Questions to Be Graded
Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. Compute the χ2 test. What is the χ2 value?

3. Is the χ2 significant at α = 0.05? Specify how you arrived at your answer.

4. If using SPSS, what is the exact likelihood of obtaining the χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

420
5. Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.

6. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.

7. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.

8. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

(Grove 409-420)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Need answers of questions to be graded at the end of each exercise.

## Apply Nightingale’s Environmental Theory to an area of your nursing practice, what patient population would benefit from this approach?

Create your Assignment submission and be sure to cite your sources if needed, use APA style as required, and check your spelling.

Assignment:

Case Study:

Mrs. Adams, a 68-year-old widow who was referred to case management upon discharge from the hospital based on her physician’s recommendation that she is not able to care for herself independently. Her diagnosis is diabetes, hypertension, and breast cancer. She is 5 days’ post-op from a right sided mastectomy.   Mrs. Adams apartment is located in a low income area of the city where crime is prevalent.  Upon assessment by the Community Health Nurse, Mrs. Adams apartment was in disorder with minimal airflow or light. Her cloths appeared unchanged and she had no food in the apartment.  The small apartment also housed 3 cats and a small dog who Mrs. Adams considers family since the death of her husband 1 year ago. Mrs. Adams complains of pain and draining from her surgical site and a broken air conditioner.

• Using Nightingales Environmental Theory, what actions would the nurse take upon the first assessment?
• What are the five essential components of Nightingales Theory?
• Prioritize an appropriate care plan for Mrs. Adams?
• Apply Nightingale’s Environmental Theory to an area of your nursing practice, what patient population would benefit from this approach?  Support this practice change with at least one evidenced-based article (this means current EBP of 5 years of less for the article.)

The assignment should be completed in APA format, as an essay of between 1000 and 1500 words. The paper should include at least 2 outside references and the textbook. The paper should be in APA format with a title page, level headings, and reference page, please see the Shell that I have included in this module. 1st person is not acceptable in APA papers, make sure to keep this is 3rd person.

## Final Exam questions

Question

Question 1

More than half of all cardiac arrhythmias involve the atria.

True

False

Question 2.

What are the most common symptoms caused by tachyarrhythmias?

Sweating

Thirst

Palpitations

Question 3.

For women with known CAD and diabetes, which is most appropriate to assess CAD risk?

ETT

Coronary bypass surgery

Coronary catheterization

ETT with imaging

Question 4.

Of the following, which is the best answer when asked for an advantage of echocardiogram exercise testing over thallium stress testing?

Does not depend on operator experience

Costs are the same

Results are available more quickly

Doesn’t matter because there are no advantages

Question 5.

Your patient has uncomplicated pyelonephritis. In deciding your recommended treatment, you consider the most common pathogenic reason for this diagnosis. What pathogen accounts for the majority of pyelonephritis?

E. Coli

Gardnerella Vaginalis

Mycoplasma Hominis

Chlamydia

Question 6.

What purpose does the principle of fidelity serve in the provider/patient relationship?

Ensures that providers honor their commitments to the patient

Obligates the provider to a one-on-one relationship with the individual

Ensures that patients receive whatever they want

Maintains costs in the healthcare arena

Question 7.

In CAD, after both systolic and diastolic dysfunction have occurred, the typical pattern of chest pain and related EKG changes occur. During an EKG, you should expect to see ST-segment and T-wave changes that are central to demonstration of ischemia occurring relatively late in the ischemic cascade. Is this true or false?

True

False

Question 8.

The leads on the ECG showing ischemic changes during or immediately after an ETT can correlate roughly to the culprit artery or arteries with significant CAD. Is this true or false?

True

False

Question 9.

Skin cancer is the most common malignant neoplasm in males in the US. What is the second leading cause of cancer deaths in men greater than 50 years of age?

Prostate cancer

Lung cancer

Lymphoma

Lupus

Question 10.

What ECG changes can reduce the specificity of the ETT?

Exercise induced bundle branch blocks

Paced rhythm and resting bundle branch block

Paced rhythm and exercise induced bundle branch blocks

Low voltage up sloping of the ST-segment

Question 11.

You have confirmed that your patient does indeed have an abdominal aortic aneurysm. In teaching your patient about symptoms to report immediately to the vascular surgeon, you instruct the patient to report which of the following?

Newly diagnosed diabetes

Back pain or flank pain

Visual disturbances

Question 12.

What is one of the common causes of a Saccular Abdominal Aneurysm?

Poor kidney functioning

Age

Drugs: illicit and prescribed

Trauma

Question 13.

The diagnostic accuracy of stress testing is decreased among women compared to men for what reasons?

Women having thinner ventricular and septal muscles

Women usually have single vessel or non-obstructive disease

Women cannot exercise as vigorously as men

Women typically have multiple vessel disease

Question 14.

Population disease management is a term used to describe:

High specificity disease states

Low specificity diseases states

Low prevalence specific diseases

High prevalence specific diseases

Question 15.

You receive a report back on the suspected abdominal aortic aneurysm for your patient. It confirms your suspicion of AAA. The report describes the aneurysm as a symmetric weakness of the entire circumference of the aorta. You know that this form of aneurysm is referred to as what kind of aneurysm?

Thoracic aneurysm

Budging sac aneurysm

Saccular aneurysm

Fusiform aneurysm

Question 16.

Your practice partner just ordered an exercise echocardiography 2DE for a patient with suspected cardiovascular risk. This patient has known resting wall motion abnormalities.Why would this not be the best test to assess this patient’s cardiac risk?

Sensitivity is increased

Sensitivity is decreased

Specificity is increased

Specificity is decreased

Question 17.

Your 60-year old male patient arrives for his appointment. He complains of general malaise and fever over the past several days with low back pain. He also states that he is getting up at night more often to urinate and never feels his bladder is completely empty.What differential diagnosis should you consider in this patient?

Acute viral prostatitis

Stomach virus

Acute bacterial prostatitis

BPH only

Question 18.

We all know that collaboration is integral to becoming a successful nurse practitioner. Among collaborations, however, only one can be considered as the most important. While each example below is important, which is the most important collaboration? The one that occurs:

Between the nurse practitioner and their physician mentor

Between two healthcare providers about a single patient

Between the patient and their family

Between the patient and the nurse practitioner

Question 19.

The sensitivity of a routine ETT is effort dependent. What physiological changes occur during effort in the routine ETT?

Rapid heart rates and coronary artery narrowing

Decrease in coronary blood flow

Decreased heart rate and increased systolic blood pressure

Increased coronary flow and increased systolic blood pressure

Question 20.

A 47-year old female with general complaints of fatigue and shortness of breath shows up in your clinic as a referral from another nurse practitioner. Several blood tests and chest x-rays have been completed without any diagnosis or outstanding abnormalities.You decide to order an ETT despite the fact that the recent ECG does not show any abnormalities. From the answers below, which would be the best answer to support your decision?

You are out of other options

CAD in women is under diagnosed

Women present with the same pattern of CAD as do males

Question 21.

Your patient underwent an exercise stress test for CAD. There is significant elevation of the ST-segment.What do you need to know about these changes to manage your patient’s care?

: This patient needs to see someone more experienced in treatment of CAD

These changes are predictive of myocardial infarction

These changes have minimal predictive value for CAD

These changes predict dire outcomes

Question 22.

When there is a consequential loss of structural integrity of the abdominal aorta, the resulting issue is what condition?

Bloated stomach

Kidney failure

Bleeding ulcers

Abdominal aortic aneurysm

Question 23.

You see a 60-year old African American male in your clinic with a recent diagnosis of hypertension. He asks you what he should restrict in his diet, and is particularly interested in limiting his sodium intake. What amount of sodium intake would you recommend on a daily basis for this patient?

1.5 g/day

3.0 g/day

2.3 g/day

Question 24.

Why would inability to exercise reduce the specificity of the routine ETT?

Produces QRS changes that cannot be interpreted

Produces persistent ST-segmental changes and T-wave abnormalities

Causes ST-segment changes and P-wave abnormalities

Will not produce any changes in ECG

Question 25.

By standard criteria, how is a positive stress test defined?

Development of a horizontal or down sloping ST-segment depression of 1mm

Down sloping of the ST-segment at the J point of the QRS

Development of a horizontal or down sloping ST-segment depression of 10mm

Upward sloping ST-segment measured at the J point of the QRS

Question 26.

What are the two types of bradycardia recognized by the American Heart Association?

Relative and absolute

Absolute and pending

Refractory and non-refractory

Relative and dynamic

Question 27.

You see a 75-year old female in your clinic today complaining of urinary incontinence. She is otherwise healthy based upon her last visit. She states that her mother told her this would happen someday because it happens to every woman at some age. What would you tell this patient?

This happens to all women as they age

No need to worry. This is normal. Your mother was correct.

This is not an expected condition related to aging.

This happens to men as well and most women before your age.

Question 28.

What do you know regarding ischemia that is confined to only the posterior and or lateral segments of the left ventricle?

ETT cannot be used for detection

Difficult to detect by ETT

Requires both for detection of changes by ETT

Easier to detect by ETT

Question 29.

What three conditions definitely alter the results of echocardiography in determining CAD?

Obesity, rapid heart rate and lung disease

Diabetes, kidney disease and tooth decay

Obesity, slow heart rates and hypertension

Previous MI, hypotension and diabetes

Question 30.

Specifically, when is an ETT considered to be negative?

Patient has ST-segmental changes with down sloping of greater than 1 mm at 50% of age-predicted maximum heart rate

Patient exercises to 85% of age predicted maximum heart rate without evidence of induced ischemia

Patient exercises to 20% maximum age-predicted heart rate without induced ischemia

Patient exercises until tired without evidence of induced ischemia

Question 31.

All patients, even is asymptomatic, require risk stratification according to the Farmingham risk score. At present, ACC/AHA guidelines, however, do not normally support stress tests for asymptomatic patients without addiitonal justification. From the list below, what could be used to justify a ETT in an asymptomatic patient?

A smoker of 3 weeks

A member of congress

Sedentary and wishes to begin aggressive exercise

Developmentally challenged

Question 32.

BPH is not a risk factor for Prostate cancer. Is this statement true or false?

True

Question 33.

Spread of genital herpes only occurs during the time period with active lesions. Is this statement true or false?

True

False(not confirm)

Question 34.

Abdominal aortic aneurysms are often asymptomatic. What percent of AAA’s are discovered in asymptomatic patients?

40%

20%

10%

75%

Question 35.

Improvements in the delivery and management of healthcare are necessary if we are to improve the overall health of this nation’s population. Which of the following are identified in your readings as strategic in the movement to improve the healthcare system?

President and Congress

Population management and healthcare practice

Socialized medicine and governmental controls

Monetary savings and limited disruption in healthcare delivery

Question 36.

What are the two main types of heart failure?

Systolic and diastolic

Hopeless and severe

Left sided and main

End diastolic and pressure related

Question 37.

The majority of all strokes are non-ischemic. Is this statement true or false?

True

False

Question 38.

When a murmur is first heard, it is important to determine if it is due to a pathological condition or benign. For an experienced practitioner, it is always easy to determine the cause of a murmur merely by listening to the sound. Is this statement true or false?

True

False

Question 39.

Maintenance of an Isometric ST-segment during exercise is the response of?

A normal heart

Hypo profusion

An abnormal heart

Question 40.

The goal of self-management is to specifically do what?

Engage patients in their own care

Engage insurance providers in patient care

Engage providers in patient care

Engage government in greater involvement in patient care

Question 41.

Your 56-year old patient presents with bradycardia with a rate of 55 and first degree AV block. The patient is hemodynamically stable and is not experiencing any syncope or chest pain. History includes previous myocardial infarction. Home medications include beta blockers, daily aspirin. Lab work is non-significant for electrolyte imbalance. You decided to treat this patient for the arrhythmia to prevent future destabilization. From the choices below, which might be the appropriate first measure to consider?

Add digitalis to control the heart rate

Consult cardiologist immediately for guidance

Discontinue Beta Blocker and replace with another therapy if necessary

Atropine injections

Question 42.

Your patient is morbidly obese and cannot sit on a bicycle or walk a treadmill. She also has marked and severe emphysema. You need to make an assessment of the risk of significant CAD and your patient’s family says that their relative had their diagnosis based on an ultrasound echocardiography. What facts would influence your decision regarding the family request for echo assessment?

Sensitivity would be increased because of lung disease

Specificity would be increased because of obesity

Sensitivity would be reduced because of obesity and lung disease

Specificity would be reduced because of obesity and lung disease

Question 43.

You are in the clinic with your mentor observing the Echocardiogram exercise test of a 45-year old male that has been experiencing slight chest pressure almost daily during exercise.While observing your patient, your mentor points out that the left ventricle wall is thinning and there is some hyperkinesias of the ventricular wall. From your time in the clinic, you know that this test will be considered to be what type of result?

Negative

Impossible

Positive

Question 44.

Your patient is newly diagnosed with persistent Atria Fibrillation. You consider electrocardioversion. Before undergoing this procedure you should order the following examination to assess thrombus risk.

X-Ray of chest

Tranesophageal echocardiography

Ultrasound of chest

CT Scan

Question 45.

Tachyarrhythmias cause a drop in commonly blood pressure, cardiac output, syncope, shortness of breath, and chest pain. What phenomenon most often occurs during these arrhythmias to cause these symptoms?

Shortened diastole

Lengthened diastole

Lengthened systole

Shortened systole

Question 46.

At what age is atria fibrillation most common?

In childhood

60 years or older

30 years

45 years

Question 47.

Automaticity is a property common to all cardiac cells. Is this statement true or false?

True

False

Question 48.

Your patient has a maximum age-predicted heart rate of 180. During the exercise he reaches a heart rate of 140 and then states he can no longer exercise. You see evidence of ischemic changes on the ECG. This would be predictive of what condition?

Stroke

Impending death

Question 49.

You tell a patient that he has a murmur. He says he has been told this before, but wonders what causes the unique sounds of a murmur. Which of the following would be your best option?

Turbulent flow of blood

High pressures caused from HTN

There is no reason, it just happens

Almost always from a sclerotic valve

Question 50.

What is the treatment of choice for uncomplicated community-acquired cystitis?

TMP-SMZ

Any antibiotic will treat this diagnosis

Amoxicillin

Penicillin

Question 51.

Any patient presenting with symptomatic bradycardia should be referred

to a cardiologist for management. Is this statement true or false?

True

False

Question 52.

Encouragement of patients to take effective actions in their own healthcare refers to the concept of:

Self-management support

Interprofessional support

Physician or provider-driven care

Family care givers

Question 53.

You are considering adding an adjunctive form of testing to detect wall motion abnormalities during the ETT.You select Echocardiography as the added testing. You choose this test because you know that echocardiography does what when added to a standard ETT?

Enhances sensitivity and specificity of CAD detection

Enhances sensitivity while reducing specificity of CAD detection

Enhances specificity while not changing sensitivity of detection for CAD

You like pretty pictures of wall motion

Question 54.

Medicaid is mandated to be provided by each state through federal codes. Each state must offer Medicaid exactly as the federal government prescribes. True or false?

True

False

Question 55.

What sexually transmitted disease is most widespread in the USA today?

Chlamydia

Gonorrhea

Syphilis

HIV/AIDS

Question 56.

Your preceptor decides to add Doppler Flow studies to the echocardiogram exercise test for a patient with a recent history of a holistic murmur best auscultated at the left steral boarder. The patient has no history of cardiac surgeries. He asks you what might be the main advantages of adding Doppler Flow for this particular patient. You know from your readings that there are several reasons to add Doppler Flow and below are listed more than one correct reason. Your best response for this specific case, however, would be that Doppler Flow studies would be of what additive value during the echocardiogram study?

Detect and evaluate blood shunting from a septal defect

No advantage is seen for this patient

Gives better screen shots of wall abnormalities

Provides assessment of prosthetic valve function

Question 57.

Sexual partners of a patient with a diagnosed STI should always be examined and treated. Is this statement true or false?

True

False

Question 58.

Your patient presents with tachycardia. The QRS is measured at 0.10 seconds. Which of the following tachycardias would be an appropriate conclusion based on this information alone?

Ventricular tachycardias

Premature junctional contractions

Atria fibrillation

Ventricular fibrillation

Question 59.

A 65-year old white male arrives in your clinic with general complaints of slight abdominal discomfort. He has a known history of smoking two packs per day for 40 years and hypertension. He also has COPD and has been treated numerous times with oral steroids. You consider several optional diagnoses. Of the ones listed below, which should be included as a potential top suspect in your choice of diagnosis?

Chronic bowel obstruction

Meglacolon

Appendicitis

Abdominal aortic aneurysm

Question 60.

What are the most common mechanisms to produce cardiac arrhythmias?

Decreased automaticity, triggered activity or reentry

Reentry, electrical dysfunction or activity

Stress, hard work or swimming

Enhanced automaticity, triggered activity or reentry

## Legislation Comparison Grid And Testimony/Advocacy Statement

Post an explanation for how you think the cost-benefit analysis in the statement from page 27 of Feldstein (2006) affected efforts to repeal/replace the ACA. Then, explain how analyses such as the one portrayed by the Feldstein statement may affect decisions by legislative leaders in recommending or positioning national policies (e.g., Congress’ decisions impacting Medicare or Medicaid).

Based on the health-related bill you selected, complete the Legislation Comparison Grid Template. Be sure to address the following:

Determine the legislative intent of the bill you have reviewed.
Identify the proponents/opponents of the bill.
Identify the target populations addressed by the bill.
Where in the process is the bill currently? Is it in hearings or committees?
Is it receiving press coverage?

Based on the health-related bill you selected, develop a 1- to 2-page Legislation Testimony/Advocacy Statement that addresses the following:

Advocate a position for the bill you selected and write testimony in support of your position.
Describe how you would address the opponent to your position. Be specific and provide examples.
Recommend at least one amendment to the bill in support of your position.

## Clearly identify and accurately describe in detail at least three academic resources or strategies that can be applied to the Masters Nursing MSN program.  2. Clearly identify and accurately describe in detail at least three professional resources that can be applied to success in the nursing practice in general or in a specialty.

1. Clearly identify and accurately describe in detail at least three academic resources or strategies that can be applied to the Masters Nursing MSN program.  2. Clearly identify and accurately describe in detail at least three professional resources that can be applied to success in the nursing practice in general or in a specialty.                                                                                                                                                                   3. Clearly and thoroughly explain in detail how you intend to use these resources, and how they might benefit you academically and professionally.

Remember to include an introduction paragraph which contains a clear and comprehensive purpose statement which delineates all required criteria, and end the assignment Part with a conclusion paragraph.

Professional Resource/Strategy 1

Professional Resource/Strategy 2

Professional Resource/Strategy 3

## Discussion: Building a Health History

Discussion: Building a Health History

Effective communication is vital to constructing  an accurate and detailed patient history. A patient’s health or illness  is influenced by many factors, including age, gender, ethnicity, and  environmental setting. As an advanced practice nurse, you must be aware  of these factors and tailor your communication techniques accordingly.  Doing so will not only help you establish rapport with your patients,  but it will also enable you to more effectively gather the information  needed to assess your patients’ health risks.

For this Discussion, you will take on the role  of a clinician who is building a health history for a particular new  patient assigned by your Instructor.

To prepare:

By Day 1 of this week, you will be assigned a new patient profile by your Instructor for this Discussion. (new patient profile):  14-year-old biracial male living with his grandmother in a high-density public housing complex

How would your communication and interview techniques for building a health history differ with each patient?

How might you target your questions for building a health history based on the patient’s social determinants of health?

What  risk assessment instruments would be appropriate to use with each  patient, or what questions would you ask each patient to assess his or  her health risks?

Identify  any potential health-related risks based upon the patient’s age,  gender, ethnicity, or environmental setting that should be taken into  consideration.

Select one of the risk assessment instruments presented in Chapter 1 or Chapter 5 of the Seidel’s Guide to Physical Examination text, or another tool with which you are familiar, related to your selected patient.
Develop at least five targeted questions you would ask your selected patient to assess his or her health risks and begin building a health history.

Post a summary of the interview and a  description of the communication techniques you would use with your  assigned patient. Explain why you would use these techniques. Identify  the risk assessment instrument you selected, and justify why it would be  applicable to the selected patient. Provide at least five targeted  questions you would ask the patient.

## Pharmacokinetics And Pharmacodynamics

Pharmacokinetics describes what the body does to the drug through absorption, distribution, metabolism, and excretion, whereas pharmacodynamics describes what the drug does to the body.

Photo Credit: Getty Images/Ingram Publishing

When selecting drugs and determining dosages for patients, it is essential to consider individual patient factors that might impact the patient’s pharmacokinetic and pharmacodynamic processes. These patient factors include genetics, gender, ethnicity, age, behavior (i.e., diet, nutrition, smoking, alcohol, illicit drug abuse), and/or pathophysiological changes due to disease.

For this Discussion, you reflect on a case from your past clinical experiences and consider how a patient’s pharmacokinetic and pharmacodynamic processes may alter his or her response to a drug.

##### To Prepare
• Review the Resources for this module and consider the principles of pharmacokinetics and pharmacodynamics.
• Reflect on your experiences, observations, and/or clinical practices from the last 5 years and think about how pharmacokinetic and pharmacodynamic factors altered his or her anticipated response to a drug.
• Consider factors that might have influenced the patient’s pharmacokinetic and pharmacodynamic processes, such as genetics (including pharmacogenetics), gender, ethnicity, age, behavior, and/or possible pathophysiological changes due to disease.
• Think about a personalized plan of care based on these influencing factors and patient history in your case study.
##### By Day 3 of Week 1

Post a description of the patient case from your experiences, observations, and/or clinical practice from the last 5 years. Then, describe factors that might have influenced pharmacokinetic and pharmacodynamic processes of the patient you identified. Finally, explain details of the personalized plan of care that you would develop based on influencing factors and patient history in your case. Be specific and provide examples.

Four  references not more than 5years

Rosenthal, L. D., & Burchum, J. R. (2018). Lehne’s pharmacotherapeutics for advanced practice providers. St. Louis, MO: Elsevier.

• Chapter 1, “Prescriptive Authority” (pp. 1–3)
• Chapter 2, “Rational Drug Selection and Prescription Writing” (pp. 5–9)
• Chapter 3, “Promoting Positive Outcomes of Drug Therapy” (pp. 11–16)
• Chapter 4, “Pharmacokinetics, Pharmacodynamics, and Drug Interactions” (pp. 17–40)
• Chapter 5, “Adverse Drug Reactions and Medical Errors” (pp. 41–49)
• Chapter 6, “Individual Variation in Drug Response” (pp. 51–56)

## Evidence-Based Practice Change Process

### Purpose

The purpose of this assignment is:

• To apply a change process using the ACE Star Model of Knowledge Transformation and a systematic review after identifying a clinical topic of concern and related nursing practice issue.
• The information from the ‘Illustration’ part of our lessons in Weeks 1-6 will mentor you through this process. Your change process is to be set up as a pilot project.

### Course Outcomes

This assignment enables the student to meet the following course outcomes:

• CO2: Proposes leadership and collaboration strategies for use with consumers and other healthcare providers in managing care and/or delegating responsibilities for health promotion, illness prevention, health restoration and maintenance, and rehabilitative activities. (PO#2)
• CO8: Selects evidence for best practices when planning professional nursing care involving systems, processes, and devices for individuals, families, aggregates and communities. (PO#8)

### Directions

Please do not use any of the Nurse Daniel information for your own topic, nursing intervention, or change project. Nurse Daniel serves as an example only to illustrate the change process.

1. Please review the infographic as way to guide you in getting started with your assignment: Developing an Assignment with Integrity (Links to an external site.)
2. View a short tutorial with tips for completing this assignment: Evidence-Based Practice Change Process Assignment Tutorial (Links to an external site.) or by reading the transcript (Links to an external site.).
3. Download the EBP Change Process form (Links to an external site.) during Week 1. The use of this specific form is REQUIRED
4. Identify a clinical topic and related nursing practice issue you think needs to be changed.
5. Locate a systematic review on your topic from the CCN Library databases. Be sure this involves nursing actions.
6. Work through each step of the ACE Star Model as outlined on the assignment form (Star Points 1-5: Discovery, Summary, Translation, Implementation, and Evaluation). Respond to the instructions provided on the form.
7. Follow the activities and thinking of Nurse Daniel in Weeks 1-6 in the ‘Illustration’ part of each lesson. He will be working through a clinical topic and nursing practice issue to demonstrate a change (ACE Star Model and systematic review).
8. Work on a portion of the process each week, as the illustration unfolds.

### Best Practices

• Please reach out to your instructor for feedback or assistance with your PICOT question as needed.
• Required and Additional Background Reading in Weeks 1 and 2 under Readings is available for more information on the ACE Star Model and the use of systematic reviews.

### Scholarly Sources and Citations

• Please cite any references (in APA format) of your systematic review or other scholarly document (optional) as needed.
• Paraphrasing information, rather than quoting, is expected. No quotes for this assignment please!

Chamberlain College of Nursing values honesty and integrity. All students should be aware of the Academic Integrity policy and follow it in all discussions and assignments.

By submitting this assignment, I pledge on my honor that all content contained is my own original work except as quoted and cited appropriately. I have not received any unauthorized assistance on this assignment.

## Rubric

Week 6: EBP Change Process Assignment Grading RubricWeek 6: EBP Change Process Assignment Grading RubricCriteriaRatingsPts. This criterion is linked to a Learning OutcomeSelected Systematic ReviewA systematic review from the CCN Library databases was selected, identified, and was appropriate for the selected nursing change process.25.0 pts

One systematic review from the CCN Library databases was identified and was clearly appropriate.22.0 pts

A systematic review was selected from the CCN Library databases and was mostly appropriate for a nursing change process.20.0 ptsA systematic review was selected from the CCN Library databases and was fairly appropriate for a nursing change process.10.0 ptsA systematic review was selected, but not from the CCN Library databases and/or was not appropriate for this assignment.0.0 ptsNo systematic review selected or used.25.0 pts
This criterion is linked to a Learning OutcomeStar Point 1 (Discovery)The topic, nursing practice issue, rationale and scope of the problem were clearly identified and described.25.0 ptsStar Point 1 elements in the first column were thoroughly addressed.22.0 ptsStar Point 1 elements in the first column were mostly well addressed.20.0 ptsStar Point 1 was missing one element in the first column or one lacked detail.10.0 ptsStar Point 1 was missing more than one element in the first column and others lacked detail.0.0 ptsThe ACE Star Model Star Point 1 was not completed.25.0 pts
This criterion is linked to a Learning OutcomeStar Point 2 (Summary)The NURSING practice problem, NURSING related PICOT question, a systematic review from any database in the Chamberlain Library, and other optional references, evidence summary, strength, and solutions, are listed and described.35.0 ptsStar Point 2 elements in the first column were thoroughly addressed.31.0 ptsStar Point 2 elements in the first column were mostly well addressed.28.0 ptsStar Point 2 was missing one element in the first column or one lacked detail.13.0 ptsStar Point 2 was missing more than one element in the first column and others lacked detail. or were inappropriate.0.0 ptsThe ACE Star Model Star Point 2 was not completed.35.0 pts
This criterion is linked to a Learning OutcomeStar Point 3 (Translation)Care standards, practice guidelines, or protocols; stakeholders and their roles and responsibilities; the nursing role; rationale for including certain stakeholders, and cost analysis plan are addressed.35.0 ptsStar Point 3 elements in the first column were thoroughly addressed.31.0 ptsStar Point 3 elements in the first column were mostly well addressed.28.0 ptsStar Point 3. was missing one element in the first column or one lacked detail.13.0 ptsStar Point 3 was missing more than one element in the first column and others lacked detail.0.0 ptsThe ACE Star Model Star Point 3 was not completed.35.0 pts
This criterion is linked to a Learning OutcomeStar Point 4 (Implementation)Permission process, education plan, timeline, measurable outcomes, forms, resources, and stakeholder meetings, are addressed.35.0 ptsStar Point 4 elements in the first column were thoroughly addressed.31.0 ptsStar Point 4 elements in the first column were mostly well addressed28.0 ptsStar Point 4 was missing one element in the first column or one lacked detail.13.0 ptsStar Point 4 was missing more than one element in the first column and others lacked detail.0.0 ptsThe ACE Star Model Star Point 4 was not completed.35.0 pts
This criterion is linked to a Learning OutcomeStar Point 5 (Evaluation)Reporting results, process and next steps are addressed.35.0 ptsStar Point 5 elements in the first column were thoroughly addressed.31.0 ptsStar Point 5 elements in the first column were mostly well addressed28.0 ptsStar Point 5 was missing one element in the first column or one lacked detail.13.0 ptsStar Point 5 was missing more than one element in the first column and others lacked detail.0.0 ptsThe ACE Star Model Star Point 5 was not completed.35.0 pts
This criterion is linked to a Learning OutcomePresentationInformation was presented clearly and thoughts were well organized and logical.20.0 ptsInformation was presented clearly and thoughts were well organized and logical throughout.18.0 ptsInformation was presented clearly and thoughts were mainly organized and logical throughout.16.0 ptsInformation was presented clearly and thoughts were somewhat organized and logical throughout.8.0 ptsInformation was not consistently clear and/or was not consistently organized and logical.0.0 ptsInformation was disorganized and difficult to understand.20.0 pts
This criterion is linked to a Learning OutcomeMechanics/APAThe systematic review and any other scholarly resources were properly listed in APA format.
The writing includes error free grammar and spelling, and complete sentence structure.15.0 ptsExcellent mechanics and APA formatting with minimal errors in grammar, spelling, and sentence structure.13.0 ptsGood mechanics and formatting considering the elements listed in the first column12.0 ptsFair mechanics and formatting considering the elements listed in the first column6.0 ptsPoor mechanics and formatting considering the elements listed in the first column0.0 ptsVery poor mechanics and formatting such that information is difficult to read.15.0 pts
This criterion is linked to a Learning OutcomeAssignment Form Used0.0 pts0 points deductedCorrect assignment form used0.0 pts22.5 points (10%) deductedIncorrect form used resulting in point deduction0.0 pts
This criterion is linked to a Learning OutcomeLate Deduction0.0 pts0 points deductedSubmitted on time0.0 ptsNot submitted on time – Point deduction1 day late =11.25 deduction; 2 days=22.5 deduction; 3 days=33.75 deduction; 4 days =45 deduction; 5 days = 56.25 deduction; 6 days =67.5 deduction; 7 days =78.75 deduction; Score of 0 if more than 7 days late0.0 pts

## Heath Care Management Case

With the strong possibility of changes to the Affordable Care Act, list strategies to improve healthcare and reduce disparities within your community, city or town. What are some cost saving policies you could implement. Type 2 pages, double space using your text and any other references to support your answers

text book name

introduction to health care management third edition

Chapter 3

Management and Motivation

Case study 2- High Employee Turnover at Hillcrest Memorial Hospital, page 399, complete all 3 questions.

## In A Reflection Of 450-600 Words, Explain How You See Yourself Fitting Into The Following IOM Future Of Nursing Recommendations

In a reflection of 450-600 words, explain how you see yourself fitting into the following IOM Future of Nursing recommendations:

1. Recommendation 4: Increase the proportion of nurses with a baccalaureate degree to 80% by 2020.

2. Recommendation 5: Double the number of nurses with a doctorate by 2020.

3. Recommendation 6: Ensure that nurses engage in lifelong learning.

Identify your options in the job market based on your educational level. (I have Associate degree in nursing (ADN) and this a Bachelor science in nursing program, (BSN) so you can have based it on BSN, because I will be done soon)

1. How will increasing your level of education affect how you compete in the current job market?

2. How will increasing your level of education affect your role in the future of nursing?

A minimum of Four scholarly references are required for this assignment. solid academic writing is expected, and in-text citations and references should be presented using APA documentation guidelines, an abstract is not required, CITE WEBSITE SOURCE.