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By Elissa Torres

Application of Analysis

Essential Questions

What are the essential elements in evaluating prior research?

How does the analysis of quantitative versus qualitative studies differ?

How are results communicated from data collection and analysis?

Introduction

The use of statistics and statistical analysis is part of the clinical practitioner’s role. This may appear

in different ways from reviewing existing clinical research to participating in a study. There are

some critical questions when understanding statistics and the role of clinician in health care:

Why is it important to keep up-to-date on clinical research?

Why is it important for health care facilities to conduct ongoing studies?

What type of studies are important?

Previous chapters focused on understanding the elements of statistics and research, including how

to select and conduct hypothesis testing based upon the type of data collected. This chapter

focuses on the application of prior information to understand information written in prior research

studies and set up statistical tests and interpret results both statistically and clinically.

Academic Research Study Extraction

In the evaluation of research articles, it is important that key areas can be identified for

interpretation and understanding. In the review of both qualitative and quantitative research, it can

be daunting to extract the relevant information to determine the primary goals and outcomes of the

study. For clinical studies, this also means addressing the epidemiology.

The simplest way to extract relevant information is to first start with those key areas.

1. Topic: What is the broad topic research area/title?

2. Problem statement: What is the problem that the research is attempting to address? In many

studies, authors identify a lack of research in a specific area or population.

3. Purpose statement: Why did the author complete the study? In some studies, this often

appears in a sentence containing the phrase, “the focus of this study … ”

4. Research questions: What specific questions does the author need to address? In many

articles, this is not explicitly written but can be derived.

5. Hypothesis, variables, or phenomena: What are the variables the author has identified to

address the research goal (quantitative)? How is the phenomena described that the author

seeks to better understand (qualitative)?

6. Sample and location: What was the sample used, and where did the study take place?

7. Methodology: Was the research quantitative or qualitative? Did the author provide any more

details, such as quantitative correlational or qualitative case study?

8. Data collection: How did the author approach data collection? For example, did the author

use surveys, interviews, or clinical studies?

9. Data analysis: What approach did the author use to analyze the data? Did the author

mention statistical tests? What type of statistical data was provided? What type of

information is provided with qualitative studies?

10. Results: What were the results of the study? Did the author find anything significant? Did the

study address epidemiology?

These 10 questions for article evaluation are useful to perform a quick review of the study’s key

elements; however, it is important to start the process by first reading the full article. The format in

which information is displayed in Table 5.1 can be used as a template to organize information found

for each of these article elements. In some studies, information can be easily located in the abstract

and in clearly organized sections; however, this is not always the case.

Table 5.1

Quantitative Article Evaluation

Article Citation

Aljohani, A. H., Alrubyyi, M. A., Alharbi, A. B., Alomair, A. M.,

Alomair, A. A., Aldossari, N. A., & … Tallab, O. M. (2018).

The relation between diabetes type II and anemia. The

Egyptian Journal of Hospital Medicine, 70(4), 526.

doi:10.12816/0043795

Point

Description

Broad Topic Area/Title

The Relation Between Diabetes Type II and Anemia

Problem Statement

“There is consequently a need for more studies on the

incidence and prevalence of anemia among patients with

diabetes mainly those with renal malfunction” (p. 527).

Purpose Statement

“This study consequently purposed to determine the

pervasiveness of anemia due to renal insu[ciency among

patients with type 2 diabetes” (p. 526, 527).

Research Questions

Is there a relationship between patients with anemia and

patients with type II diabetes?

De^ne Variables/

Categorical variable: Gender

Hypotheses

Continuous variables: Age, Hb, Ferritin, MCV, TIBC, FBG,

Erythroietin, eGFR, Urea, Na, K, CA, and HbA1c

(found on pages 528 and 529)

Sample

50 participants

Case group: 25 participants with diabetes (8 male/17

female)

Control group: 25 participants without diabetes (7 male/18

female (p. 528)

Methodology

Quantitative, case-control study (p. 527)

How was Data Collected?

Medical records for the patients were examined from

physical examinations (p. 528)

How was Data Analyzed?

SPSS; descriptive statistics for categorical; summary

statistics, independent t-test; and ANOVA test; Pearson

correlation for Hb and HG for both male and female (p.

528)

What Were the Results?

The study indicated the following were statistically

signi^cant (low p-values) between the case group and

control group.

Hb Male and Hb Female

Ferritin Male and Ferritin Female

MCV

TIBC

Of the biochemical parameters, the following were

signi^cant:

FBG, Erthropoietin, eGFR, Urea, K, C1, Ca, HbA1c

Creatinine was not signi^cant

In the correlation test, HB and HG (female) was signi^cant,

but

HB and HG (male) was not signi^cant.

(pp. 528-529)

Clinical implications:

The study did ^nd a higher occurrence of anemia in

patients with diabetes (87.5% males, 82.3% female). The

study also concluded that the presence of anemia may

increase the likelihood of poorly controlled diabetes (p.

529).

Check for Understanding

1. Would there be any additional evaluation of the article?

2. Did the researchers appear to follow ethical guidelines?

3. What were the assumptions and limitations of the study?

Table 5.2

Qualitative Article Evaluation

Article Citation

Jangland, E., Nyberg, B., & Yngman-Uhlin, P. (2017). It’s a

matter of patient safety: Understanding challenges in

everyday clinical practice for achieving good care on the

surgical ward – a qualitative study. Scandinavian Journal

of Caring Sciences, 31(2), 323-331.

doi:10.1111/scs.12350

Point

Description

Broad Topic Area/Title

Identify the challenges and barriers linked to quality care

and patient safety in the surgical ward.

Problem Statement

“Identify the challenges and barriers linked to quality of

care and patient safety in the surgical ward” (p. 324). Study

addresses gap where there were only a few studies that

looked at both the nurses’ and leaders’ perspective.

Purpose Statement

“The aim of this study was to explore, from the

perspectives of care leaders, the situations and processes

that support or hinder good and safe care on the surgical

ward” (p. 324).

Research Questions

What are the perspectives of leaders on the processes that

support good quality care in the surgical ward?

What are the perspectives of leaders on processes that

hinder good quality care in the surgical ward?

How do leaders’ experiences inform improvement in

clinical practice?

Describe Phenomena

Categorical variable: Gender

The two evaluations above provide a roadmap for reviewing prior research. Much of the research

completed in the clinical setting may not be as comprehensive; however, it is important to

understand the process. In a clinical setting, there may be opportunities to reduce cycle time,

increase quality, or participate in studies that influence health outcomes. Understanding the

process, knowing how to evaluate the data, and communicating the results enables contribution to

the organization.

Application of Statistics to Scenario

A medical office has noticed an increase in patient dissatisfaction and as well as an increase in

usage of urgent care facility services rather than seeing their primary care physicians (PCPs). To

increase understanding of the patient perception, the office surveyed the patients and received 81

Continuous variables: Age, Hb, Ferritin, MCV, TIBC, FBG,

Erythroietin, eGFR, Urea, Na, K, CA, and HbA1c

(found on pages 528 and 529)

Sample

“10 leaders in surgery departments (four department

leaders and six nursing managers) from 1 university

hospital and 2 county hospitals in different regions in

Sweden” (pp. 324-325).

Methodology

Qualitative-descriptive design

How was Data Collected?

Repeated renective interviews using semistructured

interviews

How was Data Analyzed?

Systematic text condensation

What Were the Results?

Study identi^ed four major themes (pp. 326-328):

1. Constant demands for increased e[ciency and

production

2. Continual nursing turnover and loss of competence

3. A traditional hierarchical culture

4. Vague goals and responsibilities in the development

of nursing care

Clinical implications:

Based upon the study, which has limitations as it was

performed in one country (Sweden), organizational

changes are required to ensure higher levels of

competence of staff and resources available to surgical

ward nurses to ensure higher quality care (p. 330).

responses. The survey includes a total of eight questions. The first five questions capture

satisfaction and urgent care utilization responses, and the last three questions capture data on

education, gender, and age group.

Q1: You meet with your Primary Care Physician greater than one time per year. Responses

Strongly Disagree to Strongly Agree.

Q2: You spend more than 10 minutes with your Primary Care Physician discussing health

concerns. Responses Strongly Disagree to Strongly Agree.

Q3: You are more likely to go to urgent care versus your Primary Care Physician. Responses

Strongly Disagree to Strongly Agree.

Q4: What is the number of times you went to urgent care in the past 12 months? Numerical

response requested.

Q5: Rate your overall satisfaction with the medical office. Responses Strongly Disagree to

Strongly Agree.

Q6: What is the highest level of education you completed?

Q7: What is your gender?

Q8: What is your age?

To review the responses from the data collected in the scenario, click on the button below.

Table 5.3

Patient Dissatisfaction Application Scenario

Point

Description

Broad Topic Area/Title

Understand the relationship between patient satisfaction

and usage of services at urgent care facilities.

Problem Statement

Recent indicator identi^ed lower patient satisfaction and

higher incidence of using services at urgent care facilities.

There is a need to understand the perception of patient

satisfaction for the XYZ medical o[ce and decrease

usage of urgent care.

Research Questions

What is the patient perception of satisfaction with the

medical o[ce?

Do patients use urgent care as an alternative to the

primary care physician (PCP)?

Is there a relationship between patient satisfaction and

usage of urgent care facilities?

Hypothesis

H10: There is no relationship between the perception for

number of visits and perception of time spent with PCP.

H1A: There is a relationship between the perception for

number of visits and perception of time spent with PCP.

H20: There is no relationship between the perception for

number of visits and the likelihood to go to urgent care.

H2A: There is a relationship between the perception for

number of visits and the likelihood to go to urgent care.

H30: There is no relationship between the perception for

number of visits and the overall satisfaction.

H3A: There is a relationship between the perception for

number of visits and the overall satisfaction.

H40: There is no relationship between the perception time

spent with PCP and likelihood to go to urgent care.

H4A: There is a relationship between the perception of

time spent with PCP and likelihood to go to urgent care.

H50: There is no relationship between the perception of

time spent with PCP and overall satisfaction.

H5A: There is a relationship between the perception of

time spent with PCP and overall satisfaction.

H50: There is no relationship between the number of visits

to urgent care in past 12 months and overall satisfaction.

H5A: There is no relationship between the number of times

went to urgent care in past 12 months and overall

satisfaction.

Describe Phenomena

(qualitative) or De^ne

Variables/ Hypotheses

(quantitative)

Nominal: education, gender, age group

Ordinal: Survey Questions 1-3 and 5

Continuous: Survey Question 4: Number of visits to urgent

care in last 12 months

Sample

80 patients from XYZ medical o[ce

How is Data Being

Collected?

Sent electronic survey to 300 patients, and received 80

responses.

How Will Data be Analyzed

Descriptive statistics

Correlation analysis

Communicating Results

The data can be sorted for communication based upon summary and descriptive statistics for some

of the variables prior to the hypothesis tests. As an example, to describe the sample respondents by

age group and gender, the data can be converted in Excel to percentages (see Table 5.4). These

percentages can be written out or included in a table.

Even though the responses to the survey questions were ordinal as they were translated from

Strongly Disagree (1) to Strongly Agree (5), with larger samples, responses can be treated as

continuous. Frequently, the three most common forms of descriptive statistics are displayed in a

chart. These include the mean, median, and standard deviation (see Table 5.5).

What Were the Results?

Statistical relationships were identi^ed. The null

hypothesis would be rejected and the alternative

hypothesis would be accepted in all cases.

From a practical perspective, while the results indicated

higher scores for the likelihood to go to urgent care versus

the PCP, the actual descriptive statistics for urgent care

visits do not support this.

Table 5.4

Converting Frequency to Percentage Example

Age

Group

Female

Percent Female

Male

Percent Male

Total

Percent Total by Age Group

< 20

9

18.0%

2

6.7%

11

13.8%

20-25

7

14.0%

4

13.3%

11

13.8%

23-31

10

20.0%

5

16.7%

15

18.8%

32-37

8

16.0%

4

13.3%

12

15.0%

38-43

6

12.0%

4

13.3%

10

12.5%

> 44

10

20.0%

11

36.7%

21

26.3%

Total

50

30

80

Table 5.5

Example of Descriptive Statistics

Beyond addressing some information on descriptive statistics, the hypothesis tests need to be

addressed. Prior to conducting statistical testing, the data needs to be assessed for normality.

When assessing for normality, a statistical program, such as SPSS, determines if the data meets

the conditions of a normal distribution. Often, when data is derived from survey data responses

with ranges from strongly disagree to strongly agree, the data is not normally distributed unless the

samples are very large. In this case, the sample received was only 80. Table 5.6 displays the

normality tests for the variables that will be tested. Because the sample size is lower, the ShapiroWilk results should be used. The Kolmogorov-Smirnov test is most applicable for samples of more

than 2,000 data points. Based upon a 0.05 level of significance, a researcher would reject the null

hypothesis, which stated that the data was normally distributed.

Question

n

M

Mdn

SD

Q1

80

1.93

2.00

1.11

Q2

80

2.15

2.00

1.29

Q3

80

3.31

4.00

1.41

Q4

80

1.41

1.00

1.37

Q5

80

3.13

3.00

1.31

Table 5.6

Test for Normality

Because the test results identified that the data was not normally distributed, a nonparametric test

would be used to conduct the hypothesis testing for correlation. The correlation test to use in this

scenario is the Spearman Rho test. If the data was normally distributed, the commonly used

Pearson Product Moment test would be used. Table 5.7 demonstrates the SPSS output for the

Spearman Rho correlation test between survey Questions 1 and 2. Correlation coefficients are

reviewed on a scale of -1 to +1. The relationship is stronger if the calculated coefficient is closer to

either -1 or +1. In this case, there is a strong relationship between meeting with the PCP more than

one time per year and spending more than 10 minutes with the PCP discussing health concerns.

Another statistic to review in the output is the p value. If the p-value is less than the level of

significance identified in the study, the null hypothesis would be rejected and the alternative

hypothesis would be accepted.

Tests of Normality

Kolmogoroz-Smirnova

Statistic df Sig.

Shapiro-Wilk

Statistic df Sig.

Q1

.247

80

.000

.771

80

.000

Q2

.250

80

.000

.810

80

.000

Q3

.237

80

.000

.866

80

.000

Q4

.256

80

.000

.801

80

.000

Q5

.211

80

.000

.895

80

.000

a. Lilliefors Signi^cance Correction

Table 5.7

Test for Correlation Q1&Q2

Correlation coefficients are reviewed on a scale of -1 to +1. The relationship is stronger if the

calculated coefficient is closer to either -1 or +1. If the correlation coefficient is positive, then the

two variables are moved upward in the same direction. If the statistic is negative, then one variable

increases as the other variable results decrease (Levine, Krehbiel, Berenson, 2013). In this case,

there is a strong relationship between meeting with PCP more than one time per year and spending

more than 10 minutes with the PCP discussing health concerns. Another statistic to review in the

output is the p-value. If the p-value is less than the level of significance identified in the study, the

null hypothesis would be rejected and the alternative hypothesis would be accepted. Table 5.8

displays the remaining correlation coefficients depicted in the table as r and the corresponding pvalues for the test.

Spearman’s rho Q1 Correlation Coe[cient 1.000

Sig. (2-tailed) .

N 80

.777**

.000

80

Q2 Correlation Coe[cient .777**

Sig. (2-tailed) .000

N 80

1.000

.

80

Table 5.8

Correlation tests from Example

Variable

n

r’s

p-value

Q1&Q2

80

.777

.000*

Q1&Q3

80

.566

.000*

Q1&Q5

80

-.313

.005*

Q2&Q3

80

.419

.000*

Q2&Q5

80

-.348

.002*

Q4&Q5

80

-.212

.060*

Table 5.8 demonstrates that there is a statistical correlation between all variables tested at a 0.05

level significance except Q4 (number of times visited urgent care in the last 12 months) and Q5

(overall satisfaction with the medical office). The data output requires analysis to the original

hypothesis questions in the study.

Reflective Summary

This chapter reviewed the application of statistics to research, how to identify data, select the

appropriate tests, and apply this to data sets. The chapter also explored how to review articles or

studies for the key elements for understanding. This understanding was further applied to a

practical scenario including analysis of data collected. The statistical and practical analysis of results

for communication are essential in the roles of a clinician and the tools learned in this course

provided the framework for increased understanding.

Key Terms

Hypothesis: A testable statement of a relationship; an epidemiologic hypothesis is the relationship

is between the exposure (person, time, and/or place) and the occurrence of a disease or condition.

M: Table notation for statistical mean of data array.

Mdn: Table notation for statistical median of data array.

N: Table notation representing the sample size.

P values: The probability that there is enough evidence to make conclusions resulting from the data

collected in the study.

r: Table notation representing the coefficient of correlation.

SD: Table notation representing the standard deviation of the data array.

Variable: A data item such as characteristics, numbers, properties, or quantities that can be

measured or counted. The value of the data item can vary or be manipulated from one entity to

another. There are three different types of variables—dependent, independent, and extraneous.

References

Aljohani, A. H., Alrubyyi, M. A., Alharbi, A. B., Alomair, A. M., Alomair, A. A., Aldossari, N. A., & …

Tallab, O. M. (2018). The relation between diabetes type II and anemia. The Egyptian Journal of

Hospital Medicine, 70(4), 526. doi:10.12816/0043795

Levine, D. M., Krehbiel, T. C., & Berenson, M. L. (2013). Business statistics: A first course (6th ed.).

Upper Saddle River, NJ: Pearson.

Jangland, E., Nyberg, B., & Yngman-Uhlin, P. (2017). It’s a matter of patient safety: Understanding

challenges in everyday clinical practice for achieving good care on the surgical ward – a

qualitative study. Scandinavian Journal of Caring Sciences, 31(2), 323-331.

doi:10.1111/scs.12350

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