Statistics for Nursing Research A Workbook for Evidence-Based Practice 3rd Edition pdf

Table of Contents

Cover image

Title page

Copyright

Dedication

Preface

Acknowledgments

Part 1: Understanding Statistical Methods

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

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 1 Questions to be graded

2. Identifying probability and nonprobability sampling methods in studies

6

Statistical technique in review

Study questions

Answers to study questions

Exercise 2 Questions to be graded

3. Understanding the sampling section of a research report:

Population, sampling criteria, sample size, refusal rate, and a rition rate

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 3 Questions to be graded

4. Understanding reliability of measurement methods

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 4 Questions to be graded

5. Understanding validity of measurement methods

Statistical technique in review

Research article

7

Study questions

Answers to study questions

Exercise 5 Questions to be graded

6. Understanding frequencies and percentages

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 6 Questions to be graded

7. Interpreting line graphs

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 7 Questions to be graded

8. Measures of central tendency: Mean, median, and mode

Statistical technique in review

Research article

Study questions

Answers to study questions

8

Exercise 8 Questions to be graded

9. Measures of dispersion: Range and standard deviation

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 9 Questions to be graded

10. Description of a study sample

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 10 Questions to be graded

11. Interpreting sca erplots

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 11 Questions to be graded

9

12. Algorithm for determining the appropriateness of inferential

statistical techniques

Statistical technique in review

Study questions

Answers to study questions

Exercise 12 Questions to be graded

13. Understanding Pearson product-moment correlation coefficient

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 13 Questions to be graded

14. Understanding simple linear regression

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 14 Questions to be graded

15. Understanding multiple linear regression

Statistical technique in review

10

Research article

Study questions

Answers to study questions

Exercise 15 Questions to be graded

16. Understanding independent samples t-test

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 16 Questions to be graded

17. Understanding paired or dependent samples t-test

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 17 Questions to be graded

18. Understanding analysis of variance (ANOVA) and post hoc

analyses

Statistical technique in review

Research article

Study questions

11

Answers to study questions

Exercise 18 Questions to be graded

19. Understanding Pearson chi-square

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 19 Questions to be graded

20. Understanding Spearman rank-order correlation coefficient

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 20 Questions to be graded

21. Understanding Mann-Whitney U test

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 21 Questions to be graded

12

22. Understanding Wilcoxon signed-rank test

Statistical technique in review

Research article

Study questions

Answers to study questions

Exercise 22 Questions to be graded

Part 2: Conducting and Interpreting Statistical Analyses

23. Selecting appropriate analysis techniques for studies

Statistics to address basic difference research questions and hypotheses

Statistics to address basic associational/correlational research questions and hypotheses

Applying inferential statistics to population data

Study questions

Answers to study questions

Exercise 23 Questions to be graded

24. Describing the elements of power analysis: Power, effect size,

alpha, and sample size

Effect size

Study questions

Answers to study questions

13

Exercise 24 Questions to be graded

25. Conducting power analysis

Power analysis 1: Cohen’s d

Power analysis 2: Cohen’s f

Power analysis 3: Pearson r

Power analysis 4: R2

Power analysis 5: Odds ratio

Power analysis 6: d

Power analysis 7: Cohen’s dz

Study questions

Answers to study questions

Exercise 25 Questions to be graded

26. Determining the normality of a distribution

The normal curve

Skewness

Kurtosis

Tests of normality

SPSS computation

Interpretation of SPSS output

Study questions

Answers to study questions

14

Data for additional computational practice for Questions to be graded

Exercise 26 Questions to be graded

27. Calculating descriptive statistics

Measures of central tendency

Measures of dispersion

Sampling error

SPSS computations

Interpretation of SPSS output

Study questions

Answers to study questions

Exercise 27 Questions to be graded

28. Calculating Pearson product-moment correlation coefficient

Research designs appropriate for the Pearson r

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Effect size

Study questions

15

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 28 Questions to be graded

29. Calculating simple linear regression

Research designs appropriate for simple linear regression

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Data for additional computational practice for the Questions

to be graded

Exercise 29 Questions to be graded

30. Calculating multiple linear regression

Research designs appropriate for multiple linear regression

Statistical formula and assumptions

SPSS computations

Interpretation of SPSS output

16

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Exercise 30 Questions to be graded

31. Calculating t-tests for independent samples

Research designs appropriate for the independent samples ttest

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 31 Questions to be graded

32. Calculating t-tests for paired (dependent) samples

Research designs appropriate for the paired samples t-test

Statistical formula and assumptions

Hand calculations

17

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Answers to study questions

Data for additional computational practice for Questions to be

graded

Exercise 32 Questions to be graded

33. Calculating analysis of variance (ANOVA) and post hoc analyses following ANOVA

Research designs appropriate for the one-way ANOVA

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 33 Questions to be graded

34. Calculating sensitivity and specificity

18

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Study questions

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 34 Questions to be graded

35. Calculating Pearson chi-square

Research designs appropriate for the Pearson χ2

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 35 Questions to be graded

36. Calculating odds ratio and 95% confidence intervals

Research designs appropriate for the odds ratio

Statistical formula and assumptions

Hand calculations

SPSS computations

Interpretation of SPSS output

Final interpretation in American Psychological Association (APA) format

Study questions

Answers to study questions

Data for additional computational practice for Questions to be graded

Exercise 36 Questions to be graded

References

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 χ

2 distribution Index

20

Inside back cover

Contents

PART 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 37 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

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 41 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 distributionfree or nonnormally 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 42 Spearman Rank-Order Correlation Coefficient is calculated to examine relationships among variables measured at the ordinal

level (see Exercise 20). The Mann-Whitney U and Wilcoxon Signed-Ranks tests can be conducted to determine differences among groups when study data are measured at the ordinal level (see Exercises 21 and 22). Nonparametric analyses are also conducted when interval- and ratio-level data are not normally distributed. The process for determining normality of a distribution is presented in Exercise 26. More details about conducting nonparametric analyses are presented in selected exercises of Part 2 of this text (Kim & Mallory, 2017; Pe , 2016).

Interval and ratio levels of measurement

With interval level of measurement, the distances between intervals of the scale are numerically equal. However, there is no absolute zero, which means the score of zero does not indicate the

property being measured is absent. Temperature is an example of a variable that is measured at the interval level, because the intervals between the different temperatures on either Fahrenheit or centigrade temperature scales are numerically equal. In addition, zero temperature is not the absence of temperature but only indicates it is very cold or freezing. Subjects’ scores obtained from multi-item scales are usually considered interval-level measurement. Likert scales are an example of multi-item scales commonly used to collect data about abstract concepts such as anxiety, perception of pain, quality of life, and depression. Each item on the scale has a response set for subjects to mark, which might include 1—strongly disagree to 4— strongly agree. The number and type of response options vary

based on the scale. Usually the values obtained from each item in the scale are summed to obtain a single score for each subject. Although the values of each item are technically ordinal-level data, the summed score is often analyzed as intervallevel data (Gray et al., 2017; Wal et al., 2017). The Center for Epidemiological Studies Depression Scale is an example of a 20item, 4-point Likert scale that is used to measure depression in nursing studies, and the data from the scale is considered interval level for analysis.