Exercise 26
Details:
Use MS Word to complete "Questions to be Graded: Exercise 26" in Statistics for Nursing Research: A Workbook for EvidenceBased Practice. Submit your work in SPSS by copying the output and pasting into the Word document. In addition to the SPSS
output, please include explanations of the results where appropriate.
1. How many of the relationships presented in Table 3 of the Li et al. (2014) study are statistically signiﬁcant at p <
0.05? Provide a rationale for your answer.
2. What two variables from Table 3 have the strongest positive correlation? Provide a rationale for your answer.
3. What do the results in Question 2 mean? How might this information be used clinically in caring for patients on
hemodialysis?
4. What two variables from Table 3 have the strongest negative relationship?
5. What do the results in Question 4 mean? How might this information be used clinically in caring for patients on
hemodialysis?
6. Is the relationship between age and total self-management signiﬁcant? Provide a rationale for your answer.
7. Is there a stronger relationship between depression and partnership or subjective support and partnership?
Provide a rationale for your answer.
8. Describe the relationship between availability of social support and emotional management. What is the strength
of the relationship, is it positive or negative, and is it statistically signiﬁcant?
9. Identify one relationship in Table 3 for which the direction of the relationship (positive or nega-tive) in your
opinion makes sense clinically. Provide a rationale for your answer.
10. Calculate the Spearman rho value for the evaluations of four nurses ’ patient care by two manag-ers, with 1
indicating the highest quality of care and 4 indicating the lowest quality of care. Discuss the meaning of the result
Nurses
Manager # 1 Rankings
Manager # 2 Rankings
M. Jones
3
4
C. Smith
2
3
T. Robert
1
1
S. Hart
4
2
Understanding Spearman Rank-Order Correlation Coefﬁcient
STATISTICAL TECHNIQUE IN REVIEW
The Spearman rank-order correlation coefﬁ cient , or Spearman rho , is a nonparamet-ric test used to
identify relationships or associations between two variables. The Spearman analysis technique is an
adaptation of the Pearson product-moment correlation (see Exercises 13 and 28 ) and is calculated when
the assumptions of the Pearson analysis cannot be met. Thus the Spearman rho statistical technique is
used to analyze data that are ordinal level of measurement or scores measured at the interval or ratio
levels that are skewed or not normally distributed. Each subject included in the analysis must have a score
(or value) on each of two vari-ables, variable x and variable y . Scores must be ranked to conduct this
analysis. The scores on each variable are ranked separately ( Plichta & Kelvin, 2013 ). Calculation of
Spearman rho is based on difference scores between a subject ’ s ranking on the ﬁ rst (variable x ) and
second (variable y ) sets of scores. The formula for difference scores is D = x − y . Because results with
negative scores cancel out positive scores, results are squared for use in the analysis. The formula for
calculation of Spearman rho is:
The Spearman rank-order correlation coefﬁ cient values range from − 1 to + 1, where a positive value
indicates a positive relationship and a negative value indicates a negative or inverse relationship.
Numbers closest to + 1 or − 1 indicate the strongest relationships. In comparison to the Pearson
correlation coefﬁ cient ( r ), the Spearman rho has a statistical power of 91%, which means the Spearman
rho has a 9% smaller probability of detecting a relationship or association if one exists. If the study
sample size is greater than 50 partici-pants, the power of the Spearman rho is approximately equal to the
Pearson r in detecting a relationship in a study. The strength of rho values are as follows: < 0.3 or < − 0.3
are weak relationships, 0.3 to 0.5 or − 0.5 to − 0.3 are moderate relationships, and > 0.5 or < − 0.5 are
strong relationships. A Spearman rho of 0 indicates no relationship between the two variables; the closer
the rho value is to 0, the weaker the relationship ( Cohen, 1988 ; Grove, Burns, & Gray, 2013 ). The
signiﬁ cance of rho is determined by comparing the calculated value with the critical value in a Spearman
rho table. The Spearman rho is calculated using the following hypothetical example, where ﬁ ve students ’
intramuscular (IM) injection techniques were ranked by two instructors from a high score of 1 to a low
score of 5. The data for this example are presented in Table 20-1 . The purpose of this example is to
examine the relationship between the two instructors ’ rankings of the ﬁ ve students ’ IM injection
techniques.
RESEARCH ARTICLE
Introduction Li, Jiang, and Lin (2014 ) conducted a descriptive correlational study to determine the
factors associated with self-management in people undergoing hemodialysis. A conve-nience sampling
method was used to recruit 216 hemodialysis patients from three Beijing hospitals; 198 of these patients
completed the data collection process. Data were collected using the 20-item Hemodialysis SelfManagement Instrument (HDSMI), which is orga-nized into four subscales of problem-solving, emotional
management, self-care, and part-nership. Each item on the HDSMI is measured using a four-point scale,
with scores ranging from 0 to 80 with “the higher scores indicating higher levels of self-management” (
Li et al., 2014 , p. 210). The total HDSMI score for the sample had a mean = 56.01 ( SD = 10.75).
Li and colleagues (2014 ) concluded that self-management was suboptimal among study participants. Key
self-management factors for participants were knowledge, self-efﬁ cacy, the availability of social support,
and depression. Future research is needed to explore self-efﬁ cacy training or other interventions aimed at
increasing self-management in hemodialysis patients.
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