RESEARCH SERIES
STATISTICS
FOR
EMERGENCY NURSES
Authors: Kathy M. Baker, PhD, RN, NE-BC, Kathleen E. Zavotsky, MS, RN, CEN, ACNS-BC, CCRN, Lisa A. Wolf, PhD, RN, CEN, FAEN, Margaret J. Carman, DNP, ACNP-BC, CEN, Paul R. Clark, PhD, MA, RN, Kevin Langkeit, MSN, RN, Gail Lenehan, EdD, MSN, FAEN, FAAN, and Michael Moon, PhD, MSN, RN, CEN, CNS-CC, FAEN, Richmond, VA, Brunswick, NJ, Des Plaines, IL, Durham, NC, Louisville, KY, Bountiful, UT, Boston, MA, San Antonio, TX Section Editor: Lisa A. Wolf, PhD, RN, CEN, FAEN
Earn Up to 9.5 CE Hours. See page 294. ou are reading a study that suggests best practice. How do you know that the findings of the study are valid and reliable or generalizable? In other words, how do you know that the conclusions the researchers are making are true? Nursing research, evidence-based practice (EBP), and performance improvement (PI) projects are all essential in helping to develop the science of emergency nursing. Nursing research requires that the researcher apply statistical concepts to determine whether the results are generally applicable enough to change or maintain practice. EBP and PI projects require the nurse to determine the level of evidence; to do so, an understanding of the usefulness of the results is necessary. The appropriateness of the method of statistical analysis and the size of the sample are both important components, but they are not well understood by many emergency nurses. The purpose of this article is to
Y
Kathy M. Baker, Member, Central Virginia Chapter, is Nursing Director, Virginia Commonwealth University Health System, Richmond, VA. Kathleen E. Zavotsky, Member, West Central New Jersey Chapter, is Director, Nursing Research, Advanced Practice and Education, Robert Wood Johnson University Hospital, New Brunswick, NJ. Lisa A. Wolf, Member, Pioneer Valley Chapter, is Director, Institute for Emergency Nursing Research, Emergency Nurses Association, Des Plaines, IL. Margaret J. Carman, Member, Cardinal Chapter, is Assistant Professor, Duke University School of Nursing, Durham, NC. Paul R. Clark, Member, Kentuckiana Chapter, is System Educator, Norton Healthcare Institute for Nursing, Louisville, KY. Kevin Langkeit, Member, Timpanogos Chapter, is Emergency Department Director, Intermountain Medical Center, Bountiful, UT. Gail Lenehan, Member, Mayflower Chapter, is Clinical Nurse Specialist, Massachusetts General Hospital, Boston, MA. Michael Moon, Member, San Antonio Chapter, is Associate Professor, School of Nursing, University of the Incarnate Word, San Antonio, TX. For correspondence, write: Lisa A. Wolf, PhD, RN, CEN, FAEN, Institute for Emergency Nursing Research, Emergency Nurses Association, 915 Lee St, Des Plaines, IL 60016; E-mail: [email protected].
provide an overview of the fundamental statistical principles to facilitate understanding of and participation in nursing research, EBP, and PI projects. Overview of Statistical Concepts
In most scientific projects, both descriptive and inferential statistics are used. Descriptive statistics are necessary to provide an overview of the study population. The purpose of descriptive statistics is just that, to describe the characteristics of the study participants (eg, age, sex, education level, work role). Descriptive statistics help to organize and summarize information about a population or actual observation and provide details regarding characteristics of the study population, such as an analysis of the percentage of male versus female study participants or the percentage of emergency departments that are part of academic teaching hospitals versus those that are part of community hospitals. Descriptive statistics that describe the study population are necessary to help evaluate whether the results of the scientific inquiry should be applied to a different population. For example, if a research study examining critical decision making in the ED setting contained a high percentage of novice nurses, the results of that study may not be applicable to a population with a high percentage of emergency nurses with more than 10 years of experience. Inferential statistics, on the other hand, are not used to describe but, rather, are used to examine whether there is a relationship between variables (eg, triage level and length of stay) and whether that relationship is statistically significant, which helps to make valid conclusions. For inferential statistics to be used appropriately, there must be some form of randomization and enough subjects in the study population for the mathematical calculations to detect a significant relationship between the variables if one exists— this is referred to as statistical power.
J Emerg Nurs 2014;40:286-8. 0099-1767/$36.00 Copyright © 2014 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jen.2014.02.005
286
JOURNAL OF EMERGENCY NURSING
P VALUE
The mathematical calculation that expresses whether the relationship between the study variables is significant is the
VOLUME 40 • ISSUE 3
May 2014
Baker et al/RESEARCH SERIES
P value. Hypothesis tests are used to test the validity of a claim that is made about a population. This is called the null hypothesis. 1 This is usually understood to mean that there is no relationship between X and Y. The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue (ie, there is a relationship between X and Y). All hypothesis tests ultimately use a P value to weigh the strength of the evidence (what the data are telling you about the variables). The P value is a number between 0 and 1 and is interpreted in the following way: A small P value (typically ≤ .05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large P value (N .05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. 1 What this means, then, is that you have not shown a strong relationship between the variables you are studying. Depending on the nature and types of relationships that may exist between the study variables, there are a variety of statistical tests that can be used in research studies. Each of these statistical tests examines the relationship between the variables using a P value. Examples of the most common statistical tests that may be used in emergency nursing research include a chisquare (χ 2) test (proportions of cases that fall into different categories), a t test (which tests the differences between the mean scores of 2 groups), and correlation tests (which test the differences between 2 groups and can suggest cause and effect), such as the Pearson r value. 2
manner in which it is expected to occur. This is referred to as goodness of fit. The prevalence of a specific phenomenon in a population is examined in relation to the expected prevalence of that phenomenon. For example, we want to know whether the incidence of pulmonary complications from chest trauma affects one group of people more than another. The χ 2 test can be used to compare the incidence of complications in a specific group (eg, persons aged N 70 years who have chest trauma) with that in the general population (eg, everyone who has chest trauma). The χ 2 test would detect whether there was a significantly different percentage of patients aged greater than 70 years who had pulmonary complications after chest trauma when compared with the general population. There are several types of χ 2 tests that can be used, but most often in health care, the Pearson χ 2 statistic is used.
DEPENDENT AND INDEPENDENT VARIABLES
Correlation testing can tell us whether the relationship between two variables is significant. It tells us that “if X is present, then chances are better that Y will be present” but does not actually tell us that X causes Y. A correlation is often used when the variables in the research study are measured according to a ranking and when multiple independent or predictor variables are used, but it must be interpreted with caution because a correlation does not imply causality. Nurses often use correlation studies when examining factors that influence patient satisfaction. A P value is still used to determine whether there is a significant relationship, but an r value is also used to determine the strength of the relationship. A perfect correction returns an r value of 1.0, and generally, an r value of 0.8 to 1.0 is considered very high. In health care it is unusual to see a correlation that high because there are many influences on patients and practice and the ability to control for the variables is difficult. 2 An r value of between 0.4 and 0.8 is considered a strong correlation, and an r value below 0.4 is considered weak. 3
The study variables fall into 1 of 2 categories— independent or dependent variables. Independent variables are what influence the dependent variable. 3 Independent variables are sometimes called predictor variables. For example, in a study examining the relationships between drug use in adolescents and teen dating violence, the researcher is interested in knowing whether drug use can predict the prevalence of teen dating violence. Drug use represents the factor that influences the outcome, or the independent variable, and teen dating violence represents the outcome measure, or the dependent variable. CORRELATIONS
χ 2 Test In a χ 2 statistical test, the researcher is examining whether a phenomenon occurs in a population in the
May 2014
VOLUME 40 • ISSUE 3
t Test A t test is used when you are comparing the means (averages) between two populations. For example, a t test would be used if a researcher were examining the average weight loss between study groups when different types of exercise regimens were used. Different types of t tests can be used when comparing population means in multiple different ways, including measuring changes in populations longitudinally over time. Correlation (Pearson r, Spearman rho)
WWW.JENONLINE.ORG
287
RESEARCH SERIES/Baker et al
Conclusions
Understanding statistics is an important skill set for interpreting the validity and usefulness of research in emergency nursing practice. Although statistics can seem complicated and challenging, understanding basic statistical principles gives emergency nurses the foundation to actively participate in nursing research, EBP, and PI projects.
288
JOURNAL OF EMERGENCY NURSING
REFERENCES 1. Rumsey D. Statistics for Dummies. 2nd ed. Hoboken, NJ: J. Wiley & Sons; 2011. 2. Polit D, Beck CT. Nursing Research: Generating and Assessing Evidence for Nursing Practice. 8th ed. Philadelphia, PA: Wolters Kluwer Health, Lippincott, Williams & Wilkins; 2008. 3. Witte R, Witte J. Statistics. 9th ed. Hoboken, NJ: J. Wiley & Sons; 2010.
VOLUME 40 • ISSUE 3
May 2014
STATISTICS
FOR
EMERGENCY NURSES
Authors: Kathy M. Baker, PhD, RN, NE-BC, Kathleen E. Zavotsky, MS, RN, CEN, ACNS-BC, CCRN, Lisa A. Wolf, PhD, RN, CEN, FAEN, Margaret J. Carman, DNP, ACNP-BC, CEN, Paul R. Clark, PhD, MA, RN, Kevin Langkeit, MSN, RN, Gail Lenehan, EdD, MSN, FAEN, FAAN, and Michael Moon, PhD, MSN, RN, CEN, CNS-CC, FAEN, Richmond, VA, Brunswick, NJ, Des Plaines, IL, Durham, NC, Louisville, KY, Bountiful, UT, Boston, MA, San Antonio, TX Section Editor: Lisa A. Wolf, PhD, RN, CEN, FAEN
Earn Up to 9.5 CE Hours. See page 294. ou are reading a study that suggests best practice. How do you know that the findings of the study are valid and reliable or generalizable? In other words, how do you know that the conclusions the researchers are making are true? Nursing research, evidence-based practice (EBP), and performance improvement (PI) projects are all essential in helping to develop the science of emergency nursing. Nursing research requires that the researcher apply statistical concepts to determine whether the results are generally applicable enough to change or maintain practice. EBP and PI projects require the nurse to determine the level of evidence; to do so, an understanding of the usefulness of the results is necessary. The appropriateness of the method of statistical analysis and the size of the sample are both important components, but they are not well understood by many emergency nurses. The purpose of this article is to
Y
Kathy M. Baker, Member, Central Virginia Chapter, is Nursing Director, Virginia Commonwealth University Health System, Richmond, VA. Kathleen E. Zavotsky, Member, West Central New Jersey Chapter, is Director, Nursing Research, Advanced Practice and Education, Robert Wood Johnson University Hospital, New Brunswick, NJ. Lisa A. Wolf, Member, Pioneer Valley Chapter, is Director, Institute for Emergency Nursing Research, Emergency Nurses Association, Des Plaines, IL. Margaret J. Carman, Member, Cardinal Chapter, is Assistant Professor, Duke University School of Nursing, Durham, NC. Paul R. Clark, Member, Kentuckiana Chapter, is System Educator, Norton Healthcare Institute for Nursing, Louisville, KY. Kevin Langkeit, Member, Timpanogos Chapter, is Emergency Department Director, Intermountain Medical Center, Bountiful, UT. Gail Lenehan, Member, Mayflower Chapter, is Clinical Nurse Specialist, Massachusetts General Hospital, Boston, MA. Michael Moon, Member, San Antonio Chapter, is Associate Professor, School of Nursing, University of the Incarnate Word, San Antonio, TX. For correspondence, write: Lisa A. Wolf, PhD, RN, CEN, FAEN, Institute for Emergency Nursing Research, Emergency Nurses Association, 915 Lee St, Des Plaines, IL 60016; E-mail: [email protected].
provide an overview of the fundamental statistical principles to facilitate understanding of and participation in nursing research, EBP, and PI projects. Overview of Statistical Concepts
In most scientific projects, both descriptive and inferential statistics are used. Descriptive statistics are necessary to provide an overview of the study population. The purpose of descriptive statistics is just that, to describe the characteristics of the study participants (eg, age, sex, education level, work role). Descriptive statistics help to organize and summarize information about a population or actual observation and provide details regarding characteristics of the study population, such as an analysis of the percentage of male versus female study participants or the percentage of emergency departments that are part of academic teaching hospitals versus those that are part of community hospitals. Descriptive statistics that describe the study population are necessary to help evaluate whether the results of the scientific inquiry should be applied to a different population. For example, if a research study examining critical decision making in the ED setting contained a high percentage of novice nurses, the results of that study may not be applicable to a population with a high percentage of emergency nurses with more than 10 years of experience. Inferential statistics, on the other hand, are not used to describe but, rather, are used to examine whether there is a relationship between variables (eg, triage level and length of stay) and whether that relationship is statistically significant, which helps to make valid conclusions. For inferential statistics to be used appropriately, there must be some form of randomization and enough subjects in the study population for the mathematical calculations to detect a significant relationship between the variables if one exists— this is referred to as statistical power.
J Emerg Nurs 2014;40:286-8. 0099-1767/$36.00 Copyright © 2014 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jen.2014.02.005
286
JOURNAL OF EMERGENCY NURSING
P VALUE
The mathematical calculation that expresses whether the relationship between the study variables is significant is the
VOLUME 40 • ISSUE 3
May 2014
Baker et al/RESEARCH SERIES
P value. Hypothesis tests are used to test the validity of a claim that is made about a population. This is called the null hypothesis. 1 This is usually understood to mean that there is no relationship between X and Y. The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue (ie, there is a relationship between X and Y). All hypothesis tests ultimately use a P value to weigh the strength of the evidence (what the data are telling you about the variables). The P value is a number between 0 and 1 and is interpreted in the following way: A small P value (typically ≤ .05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large P value (N .05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. 1 What this means, then, is that you have not shown a strong relationship between the variables you are studying. Depending on the nature and types of relationships that may exist between the study variables, there are a variety of statistical tests that can be used in research studies. Each of these statistical tests examines the relationship between the variables using a P value. Examples of the most common statistical tests that may be used in emergency nursing research include a chisquare (χ 2) test (proportions of cases that fall into different categories), a t test (which tests the differences between the mean scores of 2 groups), and correlation tests (which test the differences between 2 groups and can suggest cause and effect), such as the Pearson r value. 2
manner in which it is expected to occur. This is referred to as goodness of fit. The prevalence of a specific phenomenon in a population is examined in relation to the expected prevalence of that phenomenon. For example, we want to know whether the incidence of pulmonary complications from chest trauma affects one group of people more than another. The χ 2 test can be used to compare the incidence of complications in a specific group (eg, persons aged N 70 years who have chest trauma) with that in the general population (eg, everyone who has chest trauma). The χ 2 test would detect whether there was a significantly different percentage of patients aged greater than 70 years who had pulmonary complications after chest trauma when compared with the general population. There are several types of χ 2 tests that can be used, but most often in health care, the Pearson χ 2 statistic is used.
DEPENDENT AND INDEPENDENT VARIABLES
Correlation testing can tell us whether the relationship between two variables is significant. It tells us that “if X is present, then chances are better that Y will be present” but does not actually tell us that X causes Y. A correlation is often used when the variables in the research study are measured according to a ranking and when multiple independent or predictor variables are used, but it must be interpreted with caution because a correlation does not imply causality. Nurses often use correlation studies when examining factors that influence patient satisfaction. A P value is still used to determine whether there is a significant relationship, but an r value is also used to determine the strength of the relationship. A perfect correction returns an r value of 1.0, and generally, an r value of 0.8 to 1.0 is considered very high. In health care it is unusual to see a correlation that high because there are many influences on patients and practice and the ability to control for the variables is difficult. 2 An r value of between 0.4 and 0.8 is considered a strong correlation, and an r value below 0.4 is considered weak. 3
The study variables fall into 1 of 2 categories— independent or dependent variables. Independent variables are what influence the dependent variable. 3 Independent variables are sometimes called predictor variables. For example, in a study examining the relationships between drug use in adolescents and teen dating violence, the researcher is interested in knowing whether drug use can predict the prevalence of teen dating violence. Drug use represents the factor that influences the outcome, or the independent variable, and teen dating violence represents the outcome measure, or the dependent variable. CORRELATIONS
χ 2 Test In a χ 2 statistical test, the researcher is examining whether a phenomenon occurs in a population in the
May 2014
VOLUME 40 • ISSUE 3
t Test A t test is used when you are comparing the means (averages) between two populations. For example, a t test would be used if a researcher were examining the average weight loss between study groups when different types of exercise regimens were used. Different types of t tests can be used when comparing population means in multiple different ways, including measuring changes in populations longitudinally over time. Correlation (Pearson r, Spearman rho)
WWW.JENONLINE.ORG
287
RESEARCH SERIES/Baker et al
Conclusions
Understanding statistics is an important skill set for interpreting the validity and usefulness of research in emergency nursing practice. Although statistics can seem complicated and challenging, understanding basic statistical principles gives emergency nurses the foundation to actively participate in nursing research, EBP, and PI projects.
288
JOURNAL OF EMERGENCY NURSING
REFERENCES 1. Rumsey D. Statistics for Dummies. 2nd ed. Hoboken, NJ: J. Wiley & Sons; 2011. 2. Polit D, Beck CT. Nursing Research: Generating and Assessing Evidence for Nursing Practice. 8th ed. Philadelphia, PA: Wolters Kluwer Health, Lippincott, Williams & Wilkins; 2008. 3. Witte R, Witte J. Statistics. 9th ed. Hoboken, NJ: J. Wiley & Sons; 2010.
VOLUME 40 • ISSUE 3
May 2014
