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# Interpretation and Use of Statistics in Nursing Research

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A working understanding of the major fundamentals of statistical analysis is required to incorporate the findings of empirical research into nursing practice. The primary focus of this article is to describe common statistical terms, present some common statistical tests, and explain the interpretation of results from inferential statistics in nursing research. An overview of major concepts in statistics, including the distinction between parametric and nonparametric statistics, different types of data, and the interpretation of statistical significance, is reviewed. Examples of some of the most common statistical techniques used in nursing research, such as the Student independent t test, analysis of variance, and regression, are also discussed. Nursing knowledge based on empirical research plays a fundamental role in the development of evidence-based nursing practice. The ability to interpret and use quantitative findings from nursing research is an essential skill for advanced practice nurses to ensure provision of the best care possible for our patients.
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Interpretation and Use of Statistics
in Nursing Research
Volume 19, Number 2, pp.211–222
Karen K. Giuliano, PhD, RN, FAAN
Michelle Polanowicz, MSN, RN
A working understanding of the major funda-
mentals of statistical analysis is required to
incorporate the findings of empirical
research into nursing practice. The primary
statistical terms, present some common sta-
tistical tests, and explain the interpretation of
results from inferential statistics in nursing
research. An overview of major concepts in
statistics, including the distinction between
parametric and nonparametric statistics, dif-
ferent types of data, and the interpretation of
statistical significance, is reviewed. Exam-
ples of some of the most common statistical
techniques used in nursing research, such as
the Student independent ttest, analysis of
variance, and regression, are also discussed.
Nursing knowledge based on empirical
research plays a fundamental role in the
development of evidence-based nursing
practice. The ability to interpret and use
quantitative findings from nursing research
is an essential skill for advanced practice
nurses to ensure provision of the best care
possible for our patients.
Keywords: nonparametric statistics, para-
metric, statistical significance, statistical
tests
ABSTRACT
Over the past decade, the use of evidence-
based medical practice has increased
dramatically and become the standard for
healthcare decision making. Its popularity has
given rise to a proliferation of evidence-based
articles, conferences, and tools related to
healthcare delivery. French1reported that a
frequency analysis of the key word evidence-
based in the healthcare literature yielded cita-
tions for almost 6000 articles, with the majority
published since 1995. The evidence-based
movement has elicited strong interest among
healthcare professionals as one of the key ele-
ments for optimal clinical decision making
and provision of quality healthcare. It is likely
that some aspects of healthcare reimburse-
ment will be tied to the use of the best avail-
able evidence within the next decade as
suggested by the current debates about pay
for performance.2
Scholarly inquiry to develop and apply evi-
dence-based practice in nursing can take variety
Karen K. Giuliano is Principal Scientist, Philips Medical Sys-
tems, 3000 Minuteman Rd, MS 500, Andover, MA 01810
(Karen.giuliano@philips.com); and Associate Research Pro-
fessor, Boston College, William F. Connell School of Nursing,
Chestnut Hill, Massachusetts.
Michelle Polanowicz is Senior Product Manager, Philips
Medical Systems, Andover, Massachusetts.
of forms, and there is much evidence to support
the premise that nursing knowledge develops
from multiple perspectives and lenses.3In nurs-
ing, there has been an explosion in knowledge
during the past 20 years, thus providing the
discipline with diverse and multifaceted theo-
Although there is widespread agreement that
the development of nursing knowledge should
not support an epistemology in which empiri-
cal knowledge is the pinnacle or “criterion
standard,” the contribution of empirical
inquiry to the science of nursing must be rec-
ognized. The philosophy of Aristotle and his
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impact on the process of empirical scientific
inquiry continue to be a major force in the
building of evidence-based practice para-
approaches will continue to exert an important
influence on the development of knowledge
through research. As a result, it is imperative
for nurses to strive for continual improvement
in their understanding of quantitative research
approaches including research design, data
analysis, and interpretation of results. Such
understanding is an absolute requirement for
the clinical application of research evidence, a
more complete understanding of individual
patient responses to the provision of health-
care, and the conduct of new research. The
overview of some of the most common statis-
tical terms and analytic methods found in
nursing research and describe how statistical
results can inform the process of clinical care
provided by nurses.
Descriptive and
Inferential Statistics
Statistics uses well-defined mathematical proce-
dures to collect, analyze, interpret, and present
data. The major purpose of these quantitative
procedures is to summarize and reduce data
into parts that are small enough to interpret
within the context of a predetermined theoreti-
cal background. Two major forms of statistics
are available: descriptive and inferential.5–8
Rather than collecting data from an entire
population, researchers usually study a care-
fully selected subset of the population, which
is known as the study sample. Descriptive sta-
tistics organize and describe the characteristics
of the data in a particular study sample. The
main purpose of descriptive statistics is to pro-
vide as much detail as possible about the char-
acteristics of the study sample, which helps
determine whether it is appropriate to apply
research findings from that study sample to
populations similar to the one that has been
included in the sample. Descriptive statistics
are commonly used in many aspects of daily
life and are primarily used to measure central
tendency and variance, which are described
below. For example, descriptive statistics are
used when the average body temperature,
weight, or age is reported for a group of
patients or study subjects.5–8
Descriptive statistics are used to describe a
study sample, whereas inferential statistics
use information or data collected about the
study sample to make inferences about a
larger population. Inferential statistics allow
researchers and clinicians to make predic-
tions about a specific population on the basis
of information obtained from a sample that is
representative of that population. The pri-
interpretation of inferential statistics in nurs-
ing research because knowledge based on
results of inferential statistical analysis plays
a critical role in the development of evidence-
based nursing practice.5–8
Types of Data
A fundamental tenet of the interpretation of
data involves understanding the type of data
that are to be analyzed. Data can be catego-
rized into 2 types: categorical or continuous.
Categorical data are based on counts that
simply put variables into categories that have
no meaningful numerical or quantitative rela-
tionship. A common example of a categorical
variable is gender. A statistical analysis soft-
ware program such as the Statistical Package
for the Social Sciences assigns values such as
1 for male and 2 for female to categorize gen-
der for a study sample. The numbers assigned
to the 2 gender categories allow the statistical
software program to calculate the frequency
of males and females in a given sample. The
number assigned to each gender category is
arbitrary and has no actual numerical signifi-
cance.5–8
In contrast, continuous data provide infor-
mation that can be measured on a continuum
or a scale. Continuous data are based on a
measurement scale that can be used mathe-
matically to calculate additional values. These
data have a potentially infinite number of val-
ues along a measurement continuum.5–8 Com-
mon examples of continuous healthcare data
include height, weight, cholesterol level, waist
circumference, and temperature.
Measures of Central Tendency
Statistics are based on the idea of a normal dis-
tribution of data or what is more commonly
referred to as a bell curve. Because most data
are clustered around the center of the distribu-
tion, measures of central tendency are a funda-
mental component of statistics. The 3 main
measures of central tendency are mean, median,
and mode. Each measure provides a slightly dif-
ferent view of the distribution of the data for a
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sample.5–8 The mean is the most common meas-
ure of central tendency. It is the sum of all the
values in a group, divided by the number of val-
ues in that group. The result is referred to as the
mean or, more commonly, the average. The
median defines the midpoint in data set, which
is the point at which half the data fall above and
half fall below the midway mark. The mode is
the most general measure of central tendency,
but it still plays an important role in under-
standing the characteristics of the data. The
mode is the value that occurs most frequently in
the sample. It can be easily ascertained visually
by arranging the data set in order and reviewing
it if the data set is small or by using a statistical
analysis package for a large data set.5–8
The other concept fundamental to the use of
descriptive data is variance, which measures the
dispersion of values collected from a sample
around the measure of central tendency. It is not
possible to fully interpret measures of central
tendency without knowing the variance or
degree to which scores vary from the measure
of central tendency. The most commonly used
measure of variance in descriptive statistics is
the standard deviation, which describes how
widely spread the values in a data set are from
the mean.5–8 For example, let us look at 3 study
groups, each with 3 men in the sample with a
mean group weight of 160 lb. In study group 1,
each of the 3 men weighs exactly 160 lb, which
means there is no variance and the SD would be
0. In study group 2, the 3 men weigh 150, 160,
and 170 lb, respectively, again with a mean
group weight of 160 lb. This constitutes a small
amount of variance, which would be reflected
by a small standard deviation value. In group 3,
1 man weighs 100 lb, another weighs 120 lb,
and the third weighs 260 lb. Although the mean
weight for group 3 is still 160 lb, the weights
among the 3 individuals vary much more than
the first 2 groups, which means that the vari-
ance would be greater with a large standard
deviation. Because the variance is so different in
the 3 study groups, it is unlikely that research
findings on weight dosing for a new medication
could reliably be applied to all 3 groups with
the same results, although the group means are
identical.
Data from a study sample are often pre-
sented graphically as a standard normal dis-
tribution. Figure 1 displays a standard normal
distribution curve with a mean of 0 and an SD
of 1. One standard deviation above and below
the midpoint includes 68% of the values in a
sample that are normally distributed and
about 95% of the values fall within 2 SDs
of the mean.5–8
Hypothesis Testing and
Significance Levels
Medical researchers and clinicians ask many
different types of questions about relation-
ships between interventions (such as a medica-
tion) and patient outcomes. An essential
component of the research process is forming
and evaluating hypotheses, which are empiri-
cally testable statements about a relationship
between 2 or more variables. Hypothesis test-
ing allows researchers to determine whether
the variance between 2 or more study groups
can be explained either by chance or by the
intervention, such as administration of a med-
ication to lower systolic blood pressure (SBP).
Every clinical study requires a statement of a
null hypothesis and an alternate hypothesis.
The null hypothesis asserts that there is no
relationship either between the variables being
studied or between or among groups. The
alternate hypothesis states that there is a true
relationship between the variables of interest
that is not attributable to chance. When asking
any research question, the null hypothesis is
assumed to be true unless another hypothesis
is supported by the research findings.5–8
The significance level is another important
concept in statistical analysis because it affects
the likelihood that null hypotheses will be
accepted or rejected. No research study is per-
fect and all research findings are subject to
potential error due to either study design or
chance. Thus, levels of statistical significance
must be established before the conduct of any
research or statistical analysis in terms of a
probability or a Pvalue. The significance level
indicates the probability (ie, the chance) of
rejecting the null hypothesis when it is actually
Figure 1: Standard normal distribution.
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true. Although significance levels can be set at
any value, the most common Pvalues in nurs-
ing research are .05 (5%) or .01 (1%). This
means that there is either a 5% or a 1%
chance of rejecting the null hypothesis when,
in fact, it is true. Rejecting the null hypothesis
when it is true is known as a type I error or a
false-positive result, and it occurs when an
observed difference between study groups is
actually due to chance. A type I error is also
known as an alpha error denoted by the Greek
letter . The lower the significance level or P
value, the less likely it is that a researcher will
make a type I or false-positive error.5–8
Type II errors, or errors, can also occur
when a researcher is evaluating the statistical
significance of study results. A type II error
occurs when researchers fail to reject the null
hypothesis and when the alternative hypothe-
sis is actually true. This is referred to as a
false-negative error and means that the
researchers attributed the study results to
chance, rather than a true difference between
the study groups.5–8
Sensitivity and Specificity
Screening tests are often used to determine
whether a patient does or does not have a dis-
ease. Two common ways to evaluate the accu-
racy of a screening test are sensitivity and
specificity. Sensitivity refers to the likelihood
that an instrument, measurement, or medical
test will correctly identify those individuals who
have a particular attribute, such as a disease.
This is often referred to as a true positive. Con-
versely, the term specificity refers to the proba-
bility that an instrument, measurement, or test
will correctly identify the absence of a disease in
patients who do not have the disease, which is
referred to as a true negative. Tests or measures
with a high level of specificity minimize the
number of times a healthy patient is diagnosed
with a disease or a condition that he or she does
not have.5–8
Table 1 shows the results when the Surviv-
ing Sepsis Campaign practice recommenda-
tions were applied to a group of critically ill
patients at risk for sepsis.9According to Sepsis
Campaign recommendations, a total of 232
individuals were predicted not to have sepsis
compared with an observed number of 157
individuals without sepsis and 191 who had
sepsis. With respect to prediction of sepsis, the
guidelines estimated that 471 individuals
would have the condition. However, there
were 280 observed or confirmed cases of sep-
sis and 75 cases of predicted nonsepsis accord-
ing to the Sepsis Campaign recommendations.
Analysis of these data reveals that the recom-
mendations were more accurate for the predic-
tion of sepsis in individuals who actually had
the condition, and 78.9% of the cases were
correctly classified by the recommendations,
which is the sensitivity of the test. The formula
for calculation of sensitivity is number of true
positives divided by the number of true posi-
tives plus false negatives (true positives/[true
positives false positives]). In the sepsis study
example, the sensitivity is calculated as
280/(280 75), which equals 78.9%. In con-
trast, the guidelines correctly predicted only
45.1% of the individuals who did not have
sepsis, which is a measure of the specificity of
the recommendations for prediction of sepsis.
Specificity is calculated by dividing the total
number of true negatives by the total number
of false positives plus the true negatives. In this
example, specificity is calculated as 157/(157
191), which equals 45.1%, representing a
low specificity value.9
Positive predictive value (PPV) provides a
measure of the predictive value of an instru-
ment or test and is based on the proportion of
individuals tested who are truly positive. The
Table 1: Classification Table for Sepsisa
Predicted
Observed Nonsepsis Sepsis % Correct
Nonsepsis 157 191 45.1
Sepsis 75 280 78.9
Overall % 62.2
aUsed with permission from Giuliano KK. Physiologic monitoring for critically ill patients: testing a predictive model for the early detection
of sepsis. Am J Crit Care. 2007;16:122–130.10
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formula to calculate PPV is the total of true
positives divided by true positives plus false
positives (true positives/[true positive false
positives]). In the sepsis study example, the
PPV is calculated as 280 observed cases of
sepsis (true positives)/(191 [total number of
true positives] 280 [total number of false
positives]), which equals 59.4%. Finally, the
negative predictive value (NPV) can be calcu-
lated by totaling the number of true negatives
and dividing this value by the total of true
negatives plus false negatives. In the sepsis
study, the NPV is 67.7% on the basis of the
calculation of 157 true negatives/(157 [true
negatives] 75 [false negatives]).9
The weakness of the measure of sepsis used
in this study is that whereas most patients with
sepsis would be accurately identified as having
sepsis (true positives), a fairly high number of
patients without sepsis who were equally ill
would also be screened positive for sepsis (false
positives). This means that a fairly high num-
ber of patients without sepsis would be pre-
dicted to indeed have sepsis. In other words,
although the measure of sepsis model per-
formed reasonably well for screening in patients
who had sepsis, it did a poor job of identifying
those patients who did not have sepsis.9
Confidence Intervals
A confidence interval (CI) provides a range of
values associated with the probability that a
variable will fall within that range. The larger
the CI, the less precise is the measurement of
that variable. Confidence intervals of 95%
and 99% are most common in medical
research. For example, if we wanted to know
the average SBP among a sample of 100
women treated with a medication to lower
SBP and the CI was set to 95%, we could
expect that 95% of the women in the sample
would have an SBP that fell between the
upper and lower limits of the range. If we had
calculated a 99% CI, we would be more pre-
cise in estimating the average SBP among
women receiving the treatment to lower blood
pressure. However, a CI does not mean that
95% or 99% of each woman in the group has
an SBP that equals the mean SBP for the entire
sample. Rather, the CI suggests that 95% or
99% of the women will have SBP that falls
between the lower and upper limits of the
range of blood pressures. This concept is
important for understanding the correct inter-
pretation of research findings.5–8
Common Statistical Techniques
Used in Nursing Research
Many research questions are asked to deter-
mine whether a difference exists between 2
groups on a variable or a group of variables and
whether that difference is due to chance. Before
deciding on the most appropriate statistical test
to be used, a few questions must be answered
about the individual variables and data set to
be evaluated. First, the variable must be identi-
fied as either categorical or continuous. If the
variable is a categorical one, the most fre-
quently used statistical technique is the 2test,
which evaluates statistically significant differ-
ences between frequencies or proportions for 2
or more groups. The 2test compares the
actual frequency with the expected frequency of
each variable measured in each group.5–8 For
example, in the study of patients with sepsis, 2
descriptive variables were survival to hospital
discharge (yes or no) and gender (male or
female). A 2analysis was used to determine
whether either of these variables was signifi-
cantly different between the septic and nonseptic
patient groups.9
Using a significance value of .05, the results
in Table 2 show that there is a significant dif-
ference in survival to hospital discharge (P
.008) in patients with sepsis compared with
those who are not affected by sepsis. How-
ever, the 2test for differences in frequency of
sepsis attributable to gender was not signifi-
cant (P.455), and we would conclude that
the probability of having sepsis was not asso-
ciated with gender.10
Assessment of group differences for contin-
uous variables requires the researcher to first
determine whether the variables are normally
distributed (eg, conform to the standard nor-
mal distribution or bell curve) or whether the
distribution of the variables is skewed, which
means the data are not normally distributed.
Statistical tests for skewness include the Fisher
measure of skewness. Using this test, any val-
ues that exceed 1.96 SDs from the mean
would be considered significantly skewed,
because in a normal distribution, 95% of values
for a variable fall between 1.96 SDs of the
mean.5–8 In the example data set for the patients
with sepsis, all variables were significantly ske-
wed as shown in Table 3.10
Because the variables were skewed and not
normally distributed, it is necessary to calculate
a nonparametric statistical test to determine
whether group differences were statistically
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significant. Nonparametric statistical tests
allow interpretation of skewed data collected
from a sample, which often occurs when data
do not have a strong numerical relationship.
Nonparametric statistical models rely on
distribution-free statistical methods and make
fewer assumptions than parametric statistics.
Examples of nonparametric statistical tests
include the 2test, the Cochran Qtest, the
Fisher exact test, and the Mann-Whitney U
test or Wilcoxon rank sum test. In contrast,
parametric statistical tests are based on the
assumption that the study variables are contin-
uous, normally distributed, and have equal
variance (referred to as homogeneity of vari-
ance). One of the major differences between a
parametric statistical test and a nonparametric
statistical test is that nonparametric tests are
not used to estimate population parameters,
because they do not have normally distributed
data. Examples of parametric statistical tests
include the ttest, 1-way analysis of variance
(ANOVA), repeated-measures ANOVA, Pear-
son correlation, simple linear and nonlinear
regressions, and multivariate linear and non-
linear regressions.5–8 Table 4 shows the data
and significance levels for the Mann-Whitney
Utests completed for each of the variables in
the sepsis study.9The mean and the standard
deviation are shown only as a point of refer-
ence but were not used in the analyses.
Upon review of all of the variables meas-
ured in the sepsis study, it was determined that
the lowest respiratory rate (RR) was, in fact,
normally distributed. Therefore, a parametric
ttest shown in Table 5 was completed to
determine whether lowest RRs differed
significantly between the groups of patients
Table 2: Chi-Square Test for Dichotomous Variables Among Demographic and Study
Variables for Septic and Nonseptic Patient Groupsa
Sepsis Frequency, % Nonsepsis Frequency, % Total Frequency, % P
Survivalb
Survivor 202 (55.6) 235 (64.7) 437 (60.2) .008
Nonsurvivor 161 (44.4) 128 (35.3) 289 (39.8)
Total 363 363 726
Gender
Male 191 (52.69) 189 (51.9) 380 (52.3) .455
Female 172 (47.4) 175 (48.1) 347 (47.7) NS
Total 363 364 727
Abbreviation: NS, not significant.
aUsed with permission from Giuliano KK. Physiologic monitoring for critically ill patients: testing a predictive model for the early detection of
sepsis. Am J Crit Care. 2007;16:122–130.10
bSurvival to hospital discharge.
Table 3: Skewness for Study Variables
(N 727)a
Variable Skewnessb
Age –7.38
SAPS II 6.26
24-h urine output 21.50
Hospital LOS 40.91
ICU LOS 54.10
24-h highest HR, beats/min 2.76
24-h highest RR, breaths/min 12.70
24-h lowest SBP –4.04
24-h lowest MAP, mm Hg –3.20
24-h lowest temperature, C –10.45
24-h highest temperature, C 6.97
Abbreviations: HR, heart rate; LOS; length of stay; MAP, mean
arterial pressure; RR, respiratory rate; SAPS II, Simplified Acute
Physiology Score; SBP, systolic blood pressure.
aUsed with permission from Giuliano KK. Physiologic Monitoring
for Critically Ill Patients: Testing a Predictive Model for the Early
Detection of Sepsis [dissertation]. Chestnut Hill, MA: Boston
College; 2005.9
bValues 1.96 are significantly skewed at the .05 level.
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Table 4: Mann-Whitney UTest of Demographic and Study Variables for Septic and
Nonseptic Patient Groupsa
Variable Total (n727) Septic (n363) Nonseptic (n364) P
Patient age, y
Mean (SD) 65.13 63.43 66.82 .001
Range 18–90 18–90 18–90
Mean rank 339.02 388.91
SAPS II score
Mean (SD) 52.38 (19.18) 52.61 (19.33) 52.16 (19.04) .755
Range 20–113 20–113 20–110
Mean rank 354 48 349.69
Urine output,bmL
Mean (SD) 1572.84 (1457.68) 1422.11 (1341.12) 1721.05 (1551.44) .003
Range 0–12 450 0–8275 0–12 450
Mean rank 333.51 379.11
Hospital LOS, d
Mean (SD) 21.63 (26.48) 25.45 (30.58) 17.82 (20.98) .001
Range 1–232 1–232 1–232
Mean rank 401.94 326.17
ICU LOS, d
Mean (SD) 7.15 (9.59) 9.04 (11.78) 5.26 (6.18) .001
Range 0.4–126 0.4–126 0.4–37.8
Mean rank 411.36 316.77
Highest HR,bbeats/min
Mean (SD) 117.91 (25.52) 120.62 (23.87) 115.21 (26.83) .002
Range 59–231 70–231 59–204
Mean rank 405.08 355.92
Highest RR,bbreaths/min
Mean (SD) 27.79 (9.07) 28.77 (8.49) 26.80 (9.53) .001
Range 8–80 8–62 10–80
Mean rank 394.84 332.16
Lowest SBP,bmm Hg
Mean (SD) 83.22 (24.99) 78.62 (22.20) 87.81 (26.74) .001
Range 0–180 0–180 0–163
Mean rank 320.28 407.60
(continues)
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with and without sepsis.5The results indicate
that the mean lowest RR for patients who did
not have sepsis was significantly lower at
13.25 than the mean lowest RR of 13.98
observed in patients with sepsis (P.000).9
Analysis of data from the sepsis study
required multiple comparisons between vari-
ables. Consequently, it was necessary to calcu-
late the Bonferroni correction test for all
continuous variables. This test is done to
reduce the chance of a type I error, which
would assume that a true difference exists
between groups when that difference is actually
due to chance. Applying the Bonferroni correc-
tion to the Mann-Whitney Utests completed
for the sepsis study, a P.004 was required
for a difference to be considered statistically
significant and not due to chance. Upon review
of Table 4, the only variable that was not sig-
nificantly different between the groups was the
Simplified Acute Physiology Score (SAPS II),
which provides a measure of illness acuity. The
mean SAPS II score was 52.61 for patients who
developed sepsis compared with 52.16 in
Table 4: Mann-Whitney UTest of Demographic and Study Variables for Septic and
Nonseptic Patient Groupsa(Continued)
Variable Total (n727) Septic (n363) Nonseptic (n364) P
Lowest MAP,bmm Hg
Mean (SD) 56.74 (17.08) 52.21 (15.15) 60.30 (18.17) .001
Range 0–120 0–110 0–120
Mean rank 317.62 404.74
Lowest temperature,bC
Mean (SD) 36.21 (0.93) 36.31 (0.96) 36.11 (0.88) .001
Range 31.1–39.3 31.1–38.9 31.9–39.3
Mean rank 389.50 338.57
Highest temperature,bC
Mean (SD) 37.85 (1.03) 38.05 (1.08) 37.64 (0.93) .001
Range 33.3–42.2 34.8–42.2 35.3–41.9
Mean rank 405.89 322.23
Abbreviations: HR, heart rate; LOS, length of stay; MAP, mean arterial pressure; RR, respiratory rate; SAPS II, Simplified Acute Physiology
Score; SBP, systolic blood pressure.
aUsed with permission from Giuliano KK. Physiologic monitoring for critically ill patients: testing a predictive model for the early detection of
sepsis. Am J Crit Care. 2007;16:122–130.10
bDuring the first 24 hours of ICU admission.
Table 5: t-Test of Demographic and Study Variables for Septic and Nonseptic
Patient Groupsa
Lowest RR,bbreaths/min
Mean (SD) Range P
Total (n760) 13.98 (4.88) 0–30 .000
Septic (n363) 14.72 (4.99) 0–30 .000
Nonseptic (n364) 13.25 (4.65) 0–30 .000
Abbreviation: RR, respiratory rate.
aUsed with permission from Giuliano KK. Physiologic monitoring for critically ill patients: testing a predictive model for the early detection of
sepsis. Am J Crit Care. 2007;16:122–130.10
bDuring the first 24 hours of ICU admission.
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patients not affected by sepsis. The Pvalue for
this difference was .755 and did not meet the
Bonferroni correction value of 0.004 or less.
This can be interpreted as an indication of no
significant difference in illness acuity between
the septic and nonseptic patient groups, which
was expected because the two groups were
matched for acuity.
Analysis of Variance
An important concept to understand when
interpreting results from any research study is
the difference between independent and
dependent variables. Independent variables
are deliberately manipulated as part of a study
design (such as medication vs placebo, or
dietary intervention vs regular diet) to achieve
a desired outcome such as lower blood pres-
sure and are also referred to as predictor or
explanatory variables. Dependent variables
are those that change in response to exposure
to the different independent variable groups
and may also be called response variables or
outcome variables. For example, one could
ask the following research question: “Does
treatment with an angiotensin-converting
enzyme (ACE) inhibitor or placebo for hyper-
tension cause changes in measures of blood
pressure (where the dependent variable is
blood pressure values of patients enrolled in
the study)?” Notice that the independent vari-
able (treatment with either an ACE inhibitor
or placebo medication) is a categorical vari-
able and the dependent variable of blood pres-
sure is a continuous variable.
Analysis of variance is a statistical method
that allows simultaneous comparisons between
2 or more groups (independent variables) on a
dependent variable to determine whether any
observed differences in the dependent variable
are statistically significant or due to chance. If
they are due to chance, the researcher will
accept the null hypothesis. However, if the
ANOVA results reveal statistically significant
differences between groups, the researcher can
assume that the alternate hypothesis has been
supported. Several different ANOVAs are
available, but the most commonly used types of
ANOVA in nursing research include 1-way
ANOVA, 2-way ANOVA, and repeated-meas-
ures ANOVA. A 1-way ANOVA is used to test
for differences on 1 independent variable with
2 or more levels. A 2-way ANOVA includes
more than 1 independent variable. A repeated-
measures ANOVA is performed when multiple
measurements of the dependent variable are
collected from each of the subjects in the study
samples. In the example of a study comparing
the effect of an ACE inhibitor with placebo
on blood pressure, multiple measurements of
blood pressure over time would require analy-
sis with repeated-measures ANOVA. Another
more complicated form of ANOVA is a multi-
variate ANOVA, also known as MANOVA. A
MANOVA is conducted when there is more
than 1 dependent variable, such as diastolic
and systolic blood pressure levels.5–8
Repeated-Measures ANOVA
The following describes a statistical analysis
with repeated-measures ANOVA for a study
that was conducted to compare the effect of
varying degrees of backrest elevation on car-
diac output measurements in critically ill
patients. The research question for this study
was “what is the effect of backrest elevation
and time on CO measurement in critically ill
patients using the CCO method?”11(p244) This
study included 2 independent variables: back-
rest elevation at angles of 0,30, and 45
(3 levels) and duration of elevation for 0, 5,
and 10 minutes (3 levels) after each change in
backrest elevation. The dependent variables
were cardiac output assessed by 4 measures
including continuous cardiac index, stroke vol-
ume (SV), heart rate (HR), and mean arterial
pressure (MAP). Each dependent variable was
a continuous measure. Analysis of study results
involved a single-group, repeated-measures
ANOVA with time (0, 5, and 10 minutes) and
backrest elevation (0, 30, and 45). A
repeated-measures ANOVA was appropriate
for this study because a total of 9 measure-
ments of cardiac index were obtained for each
patient at each time point and level of backrest
elevation including 3 backrest elevations for 3
different time intervals.11
The overall results of the repeated-measures
ANOVA are displayed in Table 6. No signifi-
cant differences were reported in any of the 4
dependent measures of cardiac index across
the 9 repeated measurements for either time or
degree of backrest elevation. In addition, the
results of the statistical analysis revealed no
interaction between the 2 independent vari-
ables of elevation and time.11 An ANOVA is a
particularly informative statistical test
because it evaluates the independent effects of
the predictor or independent variables and
also takes into account any significant
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GIULIANO AND POLANOWICZ AACN Advanced Critical Care
220
interactions between these variables. Interac-
tion is a statistical analysis term that means
that 1 variable is affected by the values of 1 or
more other variables. In this study, an interac-
tion would be present if 1 or more of the 4
measures of cardiac index was affected by a
specific combination of the degree of backrest
elevation and amount of time spent at the dif-
ferent degrees of elevation. Table 6 indicates
that there are no statistically significant inde-
pendent effects of backrest elevation or dura-
tion of time elevated. Nor is the interaction of
backrest elevation with duration of time ele-
vated associated with statistically significant
changes in any of the 4 measures of cardiac
index. In short, the null hypothesis must be
accepted and it be assumed that backrest
position, duration of time elevated, and the
interaction of these 2 variables had no signifi-
cant effect on the cardiac index in the criti-
cally ill patients of this study.11
In this research, the variables of HR, SV,
and MAP were included because they are
physiologically related to cardiac output and
cardiac index, which is the cardiac output
ses were done in order to confirm that the
lack of significant differences in cardiac index
values was due to a real absence of change,
not just the occurrence of physiologic com-
pensation resulting from changes in either
HR or SV. Because the values of SV and HR
did not change across the 9 repeated meas-
urements, the results of this research can be
generalized with greater confidence to the
clinical setting.11
Regression
Regression is a statistical technique that pro-
vides an evaluation of the relationship
between a dependent variable with specific
independent variables. Regression analysis
uses the degree of relationship or correlation
between the dependent and independent vari-
ables to develop a regression equation that can
be used for prediction. It is assumed that the
dependent variable is representative of a stan-
dard normal distribution. Regression is partic-
ularly useful as a statistical method to explain
interrelationships among a set of variables,
which is why it is so widely used in nursing
research. For example, regression analysis
might be performed to evaluate results from a
study that examined the effects of 3 increasing
dosing regimens (continuous variable) of 2 dif-
ferent types of medications or a placebo (cate-
gorical variable) intended to lower blood
pressure or a placebo on patients’ blood pres-
sure levels (continuous variable) following the
administration of 3 different medications at 3
doses. The independent variables would be the
different doses of the 2 blood pressure medica-
tions or a placebo, whereas the dependent
Several different types of regression are
commonly used in nursing research including
linear regression, multivariate regression, and
logistic regression. To develop the regression
equation, linear regression assesses the degree
of correlation between 2 variables whereas
multivariate regression evaluates correlations
among several variables. One of the limita-
tions of both linear and multiple regressions is
that they must use dependent variables that
are continuous measures.
Binary or binomial logistic regression uses
only 2 dependent variable groups based on cat-
egorical data that are mutually exclusive. It has
become increasingly common in nursing
research.12 An assumption of logistic regression
Table 6: Results of Within-Subjects Repeated-Measures ANOVAa
CCI, L/min SV, mL HR, beats/min MAP, mm Hg
Source df FPdf FPdf FPdf FP
Time 2 0.71 .472 2 0.103 .843 2 1.6 .212 2 0.779 .413
Backrest 2 1.51 .234 2 0.928 .383 2 0.043 .916 2 3.47 .051
elevation
Interaction 4 0.367 .715 4 0.470 .614 4 1.23 .289 4 1.45 .246
Abbreviations: CCI, continuous cardiac index; df, degrees of freedom; HR, heart rate; MAP, mean arterial pressure; SV, stroke volume.
aUsed with permission from Giuliano KK, Scott SS, Brown V, Olson M. Backrest angle and cardiac output measurement in critically ill patients.
Nurs Res. 2003:52(4):242–248.11
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221
is that the model correctly contains all of the
relevant predictors and no irrelevant predictors
(such as eye color, which is unlikely to have
anything to do with a clinical outcome such as
septic or not septic). In clinical practice, how-
ever, this assumption is rarely met because it is
virtually impossible to identify and control all
irrelevant predictors. Fortunately, logistic
regression is a robust statistical method that is
able to withstand violations such as inclusion
of some irrelevant predictors.12
Binomial logistic regression was used to
analyze data from the sepsis research study,
which was intended to answer the research
question: “Can a combination of the physio-
logic parameters of heart rate, mean arterial
blood pressure, body temperature, and RR
measured during the first 24 hours of the criti-
cal care admission distinguish between criti-
cally ill adult patients who develop sepsis and
those who are not diagnosed with sepsis?”10(p123)
In this study, the independent variables were
recoded as dichotomous variables using the
cutoff values recommended by the current clini-
cal practice standards of the Surviving Sepsis
Campaign.13 These dichotomous variables were
then entered into a binomial logistic regression
model to predict whether or not patients devel-
oped sepsis. The final logistic regression model
used the 4 dependent variables of HR, MAP,
body temperature, and RR and their values cor-
respond with results from the binomial logistic
regression analysis presented in Table 7.
Two of the 4 independent variables were
significantly and independently associated
with being septic: MAP (P.001) and tem-
perature (P.001). Patients who have a MAP
of 69 mm Hg or less and a temperature of
38C or higher during the first 24 hours of
intensive care unit (ICU) admission were more
likely to develop sepsis than those who did not
have blood pressure or temperatures that met
these cutoff values. The 2 other variables in
the model, RR and HR, were not significant
predictors of sepsis diagnosis with Pvalues of
.207 and .433, respectively.10
An indication of the strength of the associa-
tion between the independent variable and the
dependent variable is the odds ratio (OR). The
results of the logistic regression provide evi-
dence that patients with a temperature of 38C
or higher during the first 24 hours of ICU
admission were approximately twice as likely
to be septic than patients with a temperature
below 38C. The other significant predictor,
MAP, had an OR of 3.874, which can be inter-
preted to mean that a MAP of 69 mm Hg or
less increased the odds of developing sepsis
almost 4-fold. Because a low MAP is one of
the most salient physiologic features of sepsis,
a high OR for this variable was expected. As
these data have shown, septic patients tend to
have both clinically and statistically signifi-
cantly lower blood pressures than equally sick
patients with normal MAP. The remaining 2
predictor variables, RR and HR, were not sta-
tistically significant, meaning that they were
not significant predictors for differentiating
between septic and nonseptic patients.10
Conclusions
Although both qualitative and quantitative
methods are important research techniques to
reviewed some of the key concepts related to
the use of quantitative research in nursing. The
conduct and interpretation of findings from
quantitative nursing research represents a
thoughtful and scholarly strategy that can
yield answers and knowledge to inform the
practice of nursing care and promote applica-
tion of evidence-based research to ultimately
Table 7: Significance and Odds Ratios for Final Logistic Regression Modela
Variable SE Wald POdds ratio CI (95%)
RR, breaths/min 0.253 0.200 1.596 .207 1.288 0.870–1.906
MAP, mm Hg 1.354 0.227 35.577 .001 3.874 2.483–6.046
Temperature, C 0.754 0.164 21.190 .001 2.126 1.542–2.930
HR, beats/min 0.184 0.235 0.616 .433 1.202 0.759–1.904
Abbreviations: CI, confidence interval; HR, heart rate; MAP, mean arterial pressure; RR, respiratory rate.
aUsed with permission from Giuliano KK. Physiologic monitoring for critically ill patients: testing a predictive model for the early detection of
sepsis. Am J Crit Care. 2007;16:122–130.10
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GIULIANO AND POLANOWICZ AACN Advanced Critical Care
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enhance the care of patients. Common quanti-
tative approaches are useful for the study of
nursing research issues that have made and
will continue to make important contributions
to the science of nursing. Although it is not
completely unbiased, the quantitative
approach provides a level of objectivity that
increases our confidence in the conclusions we
draw with regard to scientific facts and exist-
ing scientific principles. To use these types of
data most effectively, a sound understanding
of major research principles and statistical
methods is required.
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