Define new terms presented in the chapter and distinguish terms associated with quantitative and qualitative research
Distinguish experimental and nonexperimental research
Identify the three main disciplinary traditions for qualitative nursing research
Describe the flow and sequence of activities in quantitative and qualitative research and discuss why they differ
Cause-and-effect (causal) relationship
THE BUILDING BLOCKS OF RESEARCH
Research, like any discipline, has its own language—its own jargon—and that jargon can sometimes be intimidating. We readily admit that the jargon is abundant and can be confusing. Some research jargon used in nursing research has its roots in the social sciences but, sometimes, different terms are used in medical research. Also, some terms are used by both quantitative and qualitative researchers, but others are used mainly by one or the other group. Please bear with us as we cover key terms that you will likely encounter in the research literature.
When researchers answer a question through disciplined research, they are doing a study (or an investigation). Studies with humans involve two sets of people: those who do the research and those who provide the information. In a quantitative study, the people being studied are called subjects or study participants, as shown in Table 3.1. In a qualitative study, the people cooperating in the study are called study participants or informants. The person who conducts the research is the researcher or investigator. Studies are often undertaken by a research team rather than by a single researcher.
HOW-TO-TELL TIP How can you tell if an article appearing in a nursing journal is a study? In journals that specialize in research (e.g., the journal Nursing Research), most articles are original research reports, but in specialty journals, there is usually a mix of research and nonresearch articles. Sometimes you can tell by the title, but sometimes you cannot. You can tell, however, by looking at the major headings of an article. If there is no heading called “Method” or “Research Design” (the section that describes what a researcher did) and no heading called “Findings” or “Results” (the section that describes what a researcher learned), then it is probably not a study.
Research can be undertaken in a variety of settings (the types of place where information is gathered), like in hospitals, homes, or other community settings. A site is the specific location for the research—it could be an entire community (e.g., a Haitian neighborhood in Miami) or an institution (e.g., a clinic in Seattle). Researchers sometimes do multisite studies because the use of multiple sites offers a larger and often more diverse group of participants.
Concepts, Constructs, and Theories
Research involves real-world problems, but studies are conceptualized in abstract terms. For example, pain, fatigue, and obesity are abstractions of human characteristics. These abstractions are called phenomena (especially in qualitative studies) or concepts.
Researchers sometimes use the term construct, which also refers to an abstraction, but often one that is deliberately invented (or constructed). For example, self-care in Orem’s model of health maintenance is a construct. The terms construct and concept are sometimes used interchangeably, but a construct often refers to a more complex abstraction than a concept.
A theory is an explanation of some aspect of reality. In a theory, concepts are knitted together into a coherent system to describe or explain some aspect of the world. Theories play a role in both quantitative and qualitative research. In a quantitative study, researchers often start with a theory and, using deductive reasoning, make predictions about how phenomena would behave in the real world if the theory were valid. The specific predictions are then tested. In qualitative studies, theory often is the product of the research: The investigators use information from study participants inductively to develop a theory rooted in the participants’ experiences.
TIP The reasoning process of deduction is associated with quantitative research, and induction is associated with qualitative research. The supplement for Chapter 3 on website explains and illustrates the distinction.
In quantitative studies, concepts are usually called variables. A variable, as the name implies, is something that varies. Weight, anxiety, and fatigue are all variables—they vary from one person to another. Most human characteristics are variables. If everyone weighed 150 pounds, weight would not be a variable; it would be a constant. But it is precisely because people and conditions do vary that most research is conducted. Quantitative researchers seek to understand how or why things vary and to learn how differences in one variable relate to differences in another. For example, in lung cancer research, lung cancer is a variable because not everybody has this disease. Researchers have studied factors that might be linked to lung cancer, such as cigarette smoking. Smoking is also a variable because not everyone smokes. A variable, then, is any quality of a person, group, or situation that varies or takes on different values. Variables are the central building blocks of quantitative studies.
TIP Every study focuses on one or more phenomena, concepts, or variables, but these terms per se are not necessarily used in research reports. For example, a report might say, “The purpose of this study is to examine the effect of nurses’ workload on hand hygiene compliance.” Although the researcher did not explicitly label anything a variable, the variables under study are workload and hand hygiene compliance. Key concepts or variables are often indicated in the study title.
Characteristics of Variables
Variables are often inherent human traits, such as age or weight, but sometimes researchers create a variable. For example, if a researcher tests the effectiveness of patient-controlled analgesia compared to intramuscular analgesia in relieving pain after surgery, some patients would be given one type of analgesia, and some would receive the other. In the context of this study, method of pain management is a variable because different patients are given different analgesic methods.
Some variables take on a wide range of values that can be represented on a continuum (e.g., a person’s age or weight). Other variables take on only a few values; sometimes such variables convey quantitative information (e.g., number of children), but others simply involve placing people into categories (e.g., male, female, other; or blood type A, B, AB, or O).
Dependent and Independent Variables
As noted in Chapter 1, many studies seek to understand causes of phenomena. Does a nursing intervention cause improvements in patient outcomes? Does smoking cause lung cancer? The presumed cause is the independent variable, and the presumed effect is the dependent or outcome variable. The dependent variable is the outcome that researchers want to understand, explain, or predict. In terms of the PICO scheme discussed in Chapter 2, the dependent variable corresponds to the “O” (outcome). The independent variable corresponds to the “I” (the intervention, influence, or exposure), plus the “C” (the comparison).
TIP In searching for evidence, a nurse might want to learn about the effects of an intervention or influence (I), compared to any alternative, on a designated outcome. In a cause-probing study, however, researchers always specify what the comparative intervention or influence (the “C”) is.
The terms independent variable and dependent variable also can be used to indicate direction of influence rather than cause and effect. For example, suppose we compared levels of depression among men and women diagnosed with pancreatic cancer and found men to be more depressed. We could not conclude that depression was caused by gender. Yet the direction of influence clearly runs from gender to depression: It makes no sense to suggest that patient’s depression influenced their gender. In this situation, it is appropriate to consider depression as the outcome variable and gender as the independent variable.
TIP Few research reports explicitly label variables as dependent and independent. Moreover, variables (especially independent variables) are sometimes not fully spelled out. Take the following research question: What is the effect of exercise on heart rate? In this example, heart rate is the dependent variable. Exercise, however, is not in itself a variable. Rather, exercise versus something else (e.g., no exercise) is a variable; “something else” is implied rather than stated in the research question.
Many outcomes have multiple causes or influences. If we were studying factors that influence people’s body mass index, the independent variables might be height, physical activity, and diet. And, two or more outcome variables may be of interest. For example, a researcher may compare two alternative dietary interventions in terms of participants’ weight, lipid profile, and self-esteem. It is common to design studies with multiple independent and dependent variables.
Variables are not inherently dependent or independent. A dependent variable in one study could be an independent variable in another. For example, a study might examine the effect of an exercise intervention (the independent variable) on osteoporosis (the dependent variable) to answer a therapy question. Another study might investigate the effect of osteoporosis (the independent variable) on bone fracture incidence (the dependent variable) to address a prognosis question. In short, whether a variable is independent or dependent is a function of the role that it plays in a particular study.
Example of independent and dependent variables
Research question (Etiology/Harm question): Among heart failure patients, is reduced gray matter volume (as measured through magnetic resonance imagery) associated with poorer performance in instrumental activities of daily living? (Alosco et al., 2016).
Independent variable: Volume of gray matter in the brain
Dependent variable: Performance in instrumental activities of daily living
Conceptual and Operational Definitions
The concepts of interest to researchers are abstractions, and researchers’ worldviews shape how those concepts are defined. A conceptual definition is the theoretical meaning of a concept. Researchers need to conceptually define even seemingly straightforward terms. A classic example is the concept of caring. Morse and colleagues (1990) examined how researchers and theorists defined caring and identified five categories of conceptual definitions: as a human trait, a moral imperative, an affect, an interpersonal relationship, and a therapeutic intervention. Researchers undertaking studies of caring need to clarify how they conceptualized it.
In qualitative studies, conceptual definitions of key phenomena may be a major end product, reflecting an intent to have the meaning of concepts defined by those being studied. In quantitative studies, however, researchers must define concepts at the outset because they must decide how the variables will be measured. An operational definition indicates what the researchers specifically must do to measure the concept and collect needed information.
Readers of research articles may not agree with how researchers conceptualized and operationalized variables. However, definitional precision is important in communicating what concepts mean within the context of the study.
Example of conceptual and operational definitions
Stoddard and colleagues (2015) studied the relationship between young adolescents’ hopeful future expectations on the one hand and bullying on the other. The researchers defined bullying conceptually as “intentional aggressive behaviors that are repetitive and impose a power imbalance between students who bully and students who are victimized” (p. 422). They operationalized bullying behavior by asking a set of 12 questions. One question asked how often in the past month did the study participant “say things about another student to make others laugh?” (p. 426). Participants were asked to respond on a scale from 0 (never) to 5 (five or more times).
Research data (singular, datum) are the pieces of information gathered in a study. In quantitative studies, researchers identify and define their variables and then collect relevant data from subjects. The actual values of the study variables constitute the data. Quantitative researchers collect primarily quantitative data—information in numeric form. For example, if we conducted a quantitative study in which a key variable was depression, we would need to measure how depressed participants were. We might ask, “Thinking about the past week, how depressed would you say you have been on a scale from 0 to 10, where 0 means ‘not at all’ and 10 means ‘the most possible’?” Box 3.1 presents quantitative data for three fictitious people. The subjects provided a number along the 0 to 10 continuum corresponding to their degree of depression—9 for subject 1 (a high level of depression), 0 for subject 2 (no depression), and 4 for subject 3 (little depression).
Box 3.1 Example of Quantitative Data
Question: Tell me about how you’ve been feeling lately—have you felt sad or depressed at all, or have you generally been in good spirits?
Data: 9 (Subject 1)
0 (Subject 2)
4 (Subject 3)
In qualitative studies, researchers collect primarily qualitative data, that is, narrative descriptions. Narrative data can be obtained by conversing with participants, by making notes about their behavior in naturalistic settings, or by obtaining narrative records, such as diaries. Suppose we were studying depression qualitatively. Box 3.2 presents qualitative data for three participants responding conversationally to the question “Tell me about how you’ve been feeling lately—have you felt sad or depressed at all, or have you generally been in good spirits?” Here, the data consist of rich narrative descriptions of participants’ emotional state. In reports on qualitative studies, researchers include excerpts from their narrative data to support their interpretations.
Box 3.2 Example of Qualitative Data
Question: Tell me about how you’ve been feeling lately—have you felt sad or depressed at all, or have you generally been in good spirits?
Data: “Well, actually, I’ve been pretty depressed lately, to tell you the truth. I wake up each morning and I can’t seem to think of anything to look forward to. I mope around the house all day, kind of in despair. I just can’t seem to shake the blues and I’ve begun to think I need to go see a shrink.” (Participant 1)
“I can’t remember ever feeling better in my life. I just got promoted to a new job that makes me feel like I can really get ahead in my company. And I’ve just gotten engaged to a really great guy who is very special.” (Participant 2)
“I’ve had a few ups and downs the past week but basically things are on a pretty even keel. I don’t have too many complaints.” (Participant 3)
Researchers usually study phenomena in relation to other phenomena—they examine relationships. A relationship is a connection between phenomena; for example, researchers repeatedly have found that there is a relationship between frequency of turning bedridden patients and the incidence of pressure ulcers. Quantitative and qualitative studies examine relationships in different ways.
In quantitative studies, researchers are interested in the relationship between independent variables and outcomes. Relationships are often explicitly expressed in quantitative terms, such as more than or less than. For example, consider a person’s weight as our outcome variable. What variables are related to (associated with) a person’s weight? Some possibilities include height, caloric intake, and exercise. For each independent variable, we can make a prediction about its relationship to the outcome:
Height: Tall people will weigh more than short people.
Caloric intake: People with high caloric intake will be heavier than those with low caloric intake.
Exercise: The lower the amount of exercise, the greater will be the person’s weight.
Each statement expresses a predicted relationship between weight (the outcome) and a measurable independent variable. Most quantitative research is conducted to assess whether relationships exist among variables and to measure how strong the relationship is.
TIP Relationships are expressed in two basic forms. First, relationships can be expressed as “if more of Variable X, then more of (or less of) Variable Y.” For example, there is a relationship between height and weight: With greater height, there tends to be greater weight, i.e., tall people tend to weigh more than short people. The second form involves relationships expressed as group differences. For example, there is a relationship between gender and height: Men tend to be taller than women.
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