Workbook & Answer Keys – Statistics for Nursing Research: A Workbook for Evidence-Based Practice (4th Edition, Susan Grove)

WORKBOOK & ANSWER KEYS




WORKBOOK

,  Contents
PART 1: Understanding Statistical Methods
1. Identifying Levels of Measurement: Nominal, Ordinal, Interval, and Ratio
2. Identifying Probability and Nonprobability Sampling Methods in Studies
3. Understanding the Sampling Section of a Research Report:

Population, Sampling Criteria, Sample Size, Refusal Rate, and
Attrition Rate
4. Understanding Reliability of Measurement Methods
5. Understanding Validity of Measurement Methods
6. Understanding Frequencies and Percentages
7. Interpreting Line Graphs
8. Measures of Central Tendency: Mean, Median, and Mode
9. Measures of Dispersion: Range and Standard Deviation
10. Description of a Study Sample
11. Interpreting Scatterplots
12. Algorithm for Determining the Appropriateness of Inferential Statistical
Techniques
13. Understanding Pearson Product-Moment Correlation Coefficient
14. Understanding Simple Linear Regression
15. Understanding Multiple Linear Regression
16. Understanding Independent Samples t-test
17. Understanding Paired or Dependent Samples t-test
18. Understanding Analysis of Variance (ANOVA) and Post Hoc Analyses
19. Understanding Pearson Chi Square
20. Understanding Spearman Rank-Order Correlation Coefficient
21. Understanding Mann-Whitney U Test
22. Understanding Wilcoxon Signed-Rank Test
PART 2: Conducting and Interpreting Statistical Analyses
23. Selecting Appropriate Analysis Techniques for Studies
24. Describing the Elements of Power Analysis: Power, Effect Size, Alpha, and
Sample

, Size
25. Conducting Power Analysis
26. Determining the Normality of a Distribution
27. Calculating Descriptive Statistics
28. Handling Missing Data NEW!
29. Calculating Pearson Product-Moment Correlation Coefficient
30. Calculating Simple Linear Regression
31. Calculating Multiple Linear Regression
32. Calculating t-tests for Independent Samples
33. Calculating t-tests for Paired (Dependent) Samples
34. Calculating the Mann-Whitney U Test NEW!
35. Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses

Following ANOVA
36. Calculating Sensitivity and Specificity
37. Calculating Pearson Chi-Square
38. Calculating Odds Ratio and 95% Confidence Intervals
References
Appendices
Appendix A: Critical Values for Student's t Distribution
Appendix B: Critical Values of r for Pearson Product Moment Correlation
Coefficient Appendix C: Critical Values of F for α = 0.05 and α = 0.01
Appendix D: Critical Values of the χ2 Distribution
Index
ANSWER KEYS

,PA R T 1 :
Understanding Statistical
Methods
OUTLINE

1. Identifying levels of measurement:
Nominal, ordinal, interval, and ratio
2. Identifying probability and nonprobability
sampling methods in studies
3. Understanding the sampling section of a
research report: Population, sampling criteria,
sample size, refusal rate, and a rition rate
4. Understanding reliability of measurement
methods
5. Understanding validity of measurement
methods
6. Understanding frequencies and
percentages
7. Interpreting line graphs
8. Measures of central tendency:
Mean, median, and mode
9. Measures of dispersion: Range
and standard deviation
10. Description of a study sample
11. Interpreting sca erplots
12. Algorithm for determining the
appropriateness of inferential statistical



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,techniques
13. Understanding Pearson product-moment
correlation coefficient
14. Understanding simple linear regression
15. Understanding multiple linear regression
16. Understanding independent samples
17. Understanding paired or dependent
samples
18. Understanding analysis of variance
(ANOVA) and post hoc analyses
19. Understanding Pearson chi-square
20. Understanding Spearman rank-order
correlation coefficient
21. Understanding Mann-Whitney
22. Understanding Wilcoxon signed-rank test




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,EXERCISE 1




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,Identifying levels of
measurement: Nominal,
ordinal, interval, and ratio




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,Statistical technique in review
The levels of measurement were identified in 1946 by Stevens,
who organized the rules for assigning numbers to objects so that a
hierarchy of measurement was established. The levels of
measurement, from lowest to highest, are nominal, ordinal,
interval, and ratio. Figure 1-1 summarizes the rules for the four
levels of measurement that are described in the following sections.




FIGURE 1-1 ■ SUMMARY OF THE RULES FOR THE
LEVELS OF MEASUREMENT.


Nominal and ordinal levels of measurement
Variables measured at the nominal level of measurement are at
the lowest level and must conform to the following two rules: (1)
the data categories must be exclusive (each datum will fit into
only one category) and (2) the data categories must be
exhaustive
(each datum will fit into at least one category). The data categories
are developed for the purpose of naming or labeling the variables
for a study (Gray, Grove, & Sutherland, 2017; Wal , Strickland, &
Lenz, 2017). For example, the variable medical diagnosis of heart
failure (HF) is measured at the nominal level and includes two
categories, yes has HF or no HF. Variables measured at the
nominal level that are frequently described in studies include
gender, race/ethnicity, marital status, and medical diagnoses. For
some nominal variables, such as medical diagnoses, some study




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,participants might check more than one category because they
have more than one medical diagnosis.
Ordinal level of measurement includes categories that can be
rank ordered and, like nominal-level measurement, the categories
are exhaustive and mutually exclusive (see Figure 1-1). In ranking
categories of a variable, each category must be recognized as
higher or lower or be er or worse than another category.
However, with ordinal level of measurement, you do not know
exactly how much higher or lower one subject’s value on a
variable is in relation to another subject’s value. Thus, variables
measured at the ordinal level do not have a continuum of values
with equal distance between them like variables measured at the
interval and ratio levels (Grove & Gray, 2019). For example, you
could have subjects identify their levels of acute pain as no pain,
mild pain, moderate pain, or severe pain. Pain is measured at the
ordinal level in this example because the categories can be
ranked from a low of no pain to a high of severe pain; however,
even though the subjects’ levels of pain can be ranked, you do
not know the differences between the levels of pain. The
difference between no pain and mild pain might be less than that
between moderate and severe pain. Thus, ordinal-level data have
unknown, unequal intervals between the categories, such as
between the levels of pain (Wal et al., 2017).
Nonparametric or distribution-free analysis techniques are
conducted to analyze nominal and ordinal levels of data to
describe variables, examine relationships among variables, and
determine differences between groups in distribution-free or non-
normally distributed samples. The measure of central tendency,
which is conducted to describe variables measured at the nominal
level, is the mode or the most frequently occurring value in the
data set. The median or middle value in a data set is calculated to
describe variables measured at the ordinal level (see Exercise 8).
Descriptive statistical analyses, such as frequencies and
percentages, are often calculated to describe demographic
variables measured at the nominal and ordinal levels in a study
(see Exercise 6). Range is calculated to determine the dispersion
or spread of values of a variable measured at the ordinal level
(see Exercise 9).
Chi-square analysis is calculated to examine differences in
variables measured at the nominal level (see Exercise 19). The


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