PROBLEM AND PURPOSE STATEMENTS.
RESEARCH QUESTIONS AND HYPOTHESES
Overview Of Research Problems
Studies begin as problems that researchers want to solve or as questions they want to answer. This chapter discusses the formulation and development of research problems. We begin by clarifying some relevant terms.
Basic Terminology. At the most general level, a researcher selects a topic or a phenomenon on which to focus.
Examples of research topics are adolescent smoking, patient compliance, coping with disability, and pain management. Within each of these broad topics are many potential research problems. In this section, we illustrate various terms using the topic side effects of chemotherapy.
A research problem is an enigmatic, perplexing, or troubling condition. Both qualitative and quantitative researchers identify a research problem within a broad topic area of interest. The purpose of research is to “solve” the problem — or to contribute to its solution — by accumulating relevant information. A problem statement articulates the problem to be addressed and indicates the need for a study. Table 4-1 presents a problem statement related to the topic of side effects of chemotherapy.
Research questions are the specific queries researchers want to answer in addressing the research problem. Research questions guide the types of data to be collected in a study. Researchers who make specific predictions regarding answers to the research question pose hypotheses that are tested empirically.
Many reports include a statement of purpose (or purpose statement), which is the researcher’s summary of the overall goal of a study. A researcher might also identify several research aims or objectives—the specific accomplishments the researcher hopes to achieve by conducting the study. The objectives include obtaining answers to research questions or testing research hypotheses but may also encompass some broader aims (e.g., developing recommendations for changes to nursing practice based on the study results).
These terms are not always consistently defined in research methods textbooks, and differences between the terms are often subtle. Table 4-1 illustrates the interrelationships among terms as we define them.
Research Problems and Paradigms. Some research problems are better suited for studies using qualitative versus quantitative methods.
Quantitative studies usually involve concepts that are fairly well developed, about which there is an existing body of literature, and for which reliable methods of measurement have been developed. For example, a quantitative study might be undertaken to determine if postpartum depression is higher among women who are employed 6 months after delivery than among those who stay home with their babies. There are relatively accurate measures of postpartum depression that would yield quantitative information about the level of depression in a sample of employed and nonemployed postpartum women.
Qualitative studies are often undertaken because some aspect of a phenomenon is poorly understood, and the researcher wants to develop a rich, comprehensive, and context-bound understanding of it. Qualitative studies are usually initiated to heighten awareness and create a dialogue about a phenomenon. In the example of postpartum depression, qualitative methods would not be well suited to comparing levels of depression among the two groups of women, but they would be ideal for exploring, for example, the meaning of postpartum depression among new mothers. Thus, the nature of the research question is closely allied to paradigms and research traditions within paradigms.
Sources Of Research Problems
Students are sometimes puzzled about the origins of research
problems. Where do ideas for research problems come from? How do esearchers select
topic areas and develop research questions? At the most basic level, research
topics originate with researchers’ interests. Because research is a
time-consuming enterprise, curiosity about and interest in a topic are essential
to a project’s success.
Explicit sources that might fuel researchers’ curiosity include experience, the nursing literature, social issues, theories, and ideas from others.
Experience and Clinical Fieldwork. The nurse’s everyday clinical experience is a rich source of ideas for research problems. As you are performing your nursing functions, you are bound to find a wealth of research ideas if you are curious about why things are the way they are or about how things could be improved if something were to change. You may be well along the way to developing a research idea if you have ever asked the following kinds of questions:Why are things done this way? What information would help to solve this problem? What is the process by which this situation arose? What would happen if ... ? For beginning researchers in particular, clinical experience (or clinical coursework) is often the most compelling source for topics. Immediate problems that need a solution or that excite the curiosity are relevant and interesting and, thus, may generate more enthusiasm than abstract and distant problems inferred from a theory. Clinical fieldwork before a study may also help to identify clinical problems.
TIP: Personal experiences in clinical settings are a provocative source of research ideas. Here are some hints on how to proceed:
• Watch for recurring problems and see if you can discern a pattern in situations that lead to the problem.
Example: Why do many patients complain of being tired after being transferred from a coronary care unit to a progressive care unit?
• Think about aspects of your work that are irksome, frustrating, or do not result in the intended outcome — then try to identify factors contributing to the problem that could be changed.
Example: Why is suppertime so frustrating in a nursing home?
• Critically examine some decisions you make in performing your functions. Are these decisions based on tradition, or are they based on systematic evidence that supports their efficacy? Many practices in nursing that have become custom might be challenged.
Example: What would happen if visiting hours in the intensive care unit were changed from 10 minutes every hour to the regularly scheduled hours existing in the rest of the hospital?
Nursing Literature. Ideas for research projects often come from reading the nursing literature. Beginning nurse researchers can profit from regularly reading nursing journals, either clinical specialty journals or research journals such as Nursing Research or the Western Journal of Nursing Research. Nonresearch articles can be helpful in alerting researchers to clinical trends and issues of importance in clinical settings. Published research reports may suggest problem areas indirectly by stimulating the imagination and directly by specifying further areas in need of investigation.
Example of a direct suggestion for further research:
Stranahan (2001) studied the relationship between nurse practitioners’ attitudes about spiritual care and their spiritual care practices. She made several recommendations for further research in her report, such as the following: “Studies should be conducted to determine reasons why nurse practitioners do not practice spiritual care in the primary care setting” (p. 87).
Inconsistencies in the findings reported in nursing literature sometimes generate ideas for studies. For example, there are inconsistencies regarding which type of tactile stimulation or touch (e.g., gentle touch, stroking, rubbing) has the most beneficial physiologic and behavioral effects on preterm infants. Such discrepancies can lead to the design of a study to resolve the matter.
Researchers may also wonder whether a study similar to one reported in a journal article would yield comparable results if applied in a different setting or with a different population. Replications are needed to establish the validity and generalizability of previous findings.
In summary, a familiarity with existing research, or with problematic and controversial nursing issues that have yet to be understood and investigated systematically, is an important route to developing a research topic. Students who are actively seeking a problem to study will find it useful to read widely in areas of interest.
TIP: In a pinch, do not hesitate to replicate a study that is reported in the research literature. Replications are a valuable learning experience and can make important contributions if they corroborate (or even if they challenge) earlier findings.
Social Issues. Sometimes, topics are suggested by more global contemporary social or political issues of relevance to the health care community. For example, the feminist movement has raised questions about such topics as sexual harassment, domestic violence, and gender equity in health care and in research. The civil rights movement has led to research on minority health problems, access to health care, and culturally sensitive interventions. Thus, an idea for a study may stem from a familiarity with social concerns or controversial social problems.
Theory. The fourth major source of research problems lies in the theories and conceptual schemes that have been developed in nursing and related disciplines. To be useful in nursing practice, theories must be tested through research for their applicability to hospital units, clinics, classrooms, and other nursing environments.
When researchers decide to base a study on an existing theory, deductions from the theory must be developed. Essentially, researchers must ask the following questions: If this theory is correct, what kind of behavior would I expect to find in certain situations or under certain conditions? What kind of evidence would support this theory? This process would eventually result in a specific problem that could be subjected to systematic investigation.
Ideas From External Sources. External sources can sometimes provide the impetus for a research idea. In some cases, a research topic may be given as a direct suggestion. For example, a faculty member may give students a list of topics from which to choose or may actually assign a specific topic to be studied. Organizations that sponsor funded research, such as government agencies, often identify topics on which research proposals are encouraged. Ideas for research are also being noted on various websites on the internet (see, for example, Duffy, 2001).
Research ideas sometimes represent a response to priorities that are established within the nursing profession. Priorities for nursing research have been established by many nursing specialty practices. Priority lists can often serve as a useful starting point for exploring research topics.
Often, ideas for studies emerge as a result of a brainstorming session. By discussing possible research topics with peers, advisers or mentors, or researchers with advanced skills, ideas often become clarified and sharpened or enriched and more fully developed. Professional conferences often provide an excellent opportunity for such discussions.
Development And Refinement Of Research Problems
Unless a research problem is developed on the basis of theory or an explicit suggestion from an external source, the actual procedures for developing a research topic are difficult to describe. The process is rarely a smooth and orderly one; there are likely to be false starts, inspirations, and setbacks in the process of developing a research problem statement. The few suggestions offered here are not intended to imply that there are techniques for making this first step easy but rather to encourage beginning researchers to persevere in the absence of instant success.
Selecting a Topic. The development of a research problem is a creative process that depends on imagination and ingenuity. In the early stages, when research ideas are being generated, it is wise not to be critical of them immediately. It is better to begin by relaxing and jotting down general areas of interest as they come to mind. At this point, it matters little if the terms used to remind you of your ideas are abstract or concrete, broad or specific, technical, or colloquial—the important point is to put some ideas on paper. Examples of some broad topics that may come to mind include nurse—patient communication, pain in patients with cancer, and postoperative loss of orientation.
After this first step, the ideas can be sorted in terms of interest, knowledge about the topics, and the perceived feasibility of turning the topics into a research project. When the most fruitful idea has been selected, the rest of the list should not be discarded; it may be necessary to return to it.
Narrowing the Topic. Once researchers have identified a topic of interest, they need to ask questions that lead to a researchable problem. Examples of question stems that may help to focus an inquiry include the following:
• What is going on with ...?
• What is the process by which ...?
• What is the meaning of ...?
• Why do ...?
• When do ...?
• How do ...?
• What can be done to solve ...?
• What is the extent of ...?
• How intense are ...?
• What influences ...?
• What causes ...?
• What characteristics are associated with ...?
• What differences exist between ...?
• What are the consequences of ...?
• What is the relationship between ...?
• What factors contribute to ...?
• What conditions prevail before ...?
• How effective is ...?
Here again, early criticism of ideas is often counterproductive. Try not to jump to the conclusion that an idea sounds trivial or uninspired without giving it more careful consideration or without exploring it with advisers or colleagues.
Beginning researchers often develop problems that are too broad in scope or too complex and unwieldy for their level of methodologic expertise. The transformation of the general topic into a workable problem is typically accomplished in a number of uneven steps, involving a series of successive approximations. Each step should result in progress toward the goals of narrowing the scope of the problem and sharpening and defining the concepts.
As researchers move from general topics to more specific researchable problems, more than one potential problem area can emerge. Let us consider the following example. Suppose you were working on a medical unit and were puzzled by that fact that some patients always complained about having to wait for pain medication when certain nurses were assigned to them and, yet, these same patients offered no complaints with other nurses. The general problem area is discrepancy in complaints from patients regarding pain medications administered by different nurses. You might ask the following: What accounts for this discrepancy? How can I improve the situation? Such questions are not actual research questions; they are too broad and vague. They may, however, lead you to ask other questions, such as the following: How do the two groups of nurses differ? What characteristics are unique to each group of nurses? What characteristics do the group of complaining patients share? At this point, you may observe that the ethnic background of the patients and nurses appears to be a relevant factor. This may direct you to a review of the literature for studies concerning ethnicity in relation to nursing care, or it may provoke you to discuss the observations with others. The result of these efforts may be several researchable questions, such as the following:
• What is the essence of patient complaints among patients of different ethnic backgrounds?
• What is the patient’s experience of waiting for pain medication?
• How do complaints by patients of different ethnic backgrounds get expressed by patients and perceived by nurses?
• Is the ethnic background of nurses related to the frequency with which they dispense pain medication?
• Is the ethnic background of patients related to the frequency and intensity of complaints when waiting for pain medication?
• Does the number of patient complaints increase when patients are of dissimilar ethnic backgrounds as opposed to when they are of the same ethnic background as nurses?
• Do nurses’ dispensing behaviors change as a function of the similarity between their own ethnic background and that of patients?
All these questions stem from the same general problem, yet each would be studied differently—for example, some suggest a qualitative approach and others suggest a quantitative one. A quantitative researcher might become curious about nurses’ dispensing behaviors, based on some interesting evidence in the literature regarding ethnic differences.
Both ethnicity and nurses’ dispensing behaviors are variables that can be measured in a straightforward and reliable manner. A qualitative researcher who noticed differences in patient complaints would likely be more interested in understanding the essence of the complaints, the patients’ experience of frustration, the process by which the problem got resolved, or the full nature of the nurse—patient interactions regarding the dispensing of medications. These are aspects of the research problem that would be difficult to quantify.
Researchers choose the final problem to be studied based on several factors, including its inherent interest to them and its compatibility with a paradigm of preference. In addition, tentative problems usually vary in their feasibility and worth. It is at this point that a critical evaluation of ideas is appropriate.
Evaluating Research Problems. There are no rules for making a final selection of a research problem. Some criteria, however, should be kept in mind in the decision-making process. The four most important considerations are the significance, researchability, and feasibility of the problem, and its interest to the researcher.
Significance of the Problem. A crucial factor in selecting a problem to be studied is its significance to nursing—especially to nursing practice. Evidence from the study should have the potential of contributing meaningfully to nursing knowledge. Researchers should pose the following kinds of questions: Is the problem an important one? Will patients, nurses, or the broader health care community or society benefit from the evidence that will be produced? Will the results lead to practical applications? Will the results have theoretical relevance? Will the findings challenge (or lend support to) untested assumptions? Will the study help to formulate or alter nursing practices or policies? If the answer to all these questions is “no,” then the problem should be abandoned.
Researchability of the Problem. Not all problems are amenable to study through scientific investigation. Problems or questions of a moral or ethical nature, although provocative, are incapable of being researched. Take, for example, the following: Should assisted suicide be legalized?
The answer to such a question is based on a person’s values. There are no right or wrong answers, only points of view. The problem is suitable to debate, not to research. To be sure, it is possible to ask related questions that could be researched. For instance, each of the following questions could be investigated in a research project:
• What are nurses’ attitudes toward assisted suicide?
• Do oncology nurses hold more favorable opinions of assisted suicide than other nurses?
• What moral dilemmas are perceived by nurses who might be involved in assisted suicide?
• What are the attitudes of terminally ill patients toward assisted suicide?
• Do terminally ill patients living with a high level of pain hold more favorable attitudes toward assisted suicide than those with less pain?
• How do family members experience the loss of a loved one through assisted suicide?
The findings from these hypothetical projects would have no bearing, of course, on whether assisted suicide should be legalized, but the information could be useful in developing a better understanding of the issues.
In quantitative studies, researchable problems are ones involving variables that can be precisely defined and measured. For example, suppose a researcher is trying to determine what effect early discharge has on patient well-being. Well-being is too vague a concept for a study. The researcher would have to sharpen and define the concept so that it could be observed and measured. That is, the researcher would have to establish criteria against which patients’ progress toward well-being could be assessed.
When a new area of inquiry is being pursued, however, it may be impossible to define the concepts of interest in precise terms. In such cases, it may be appropriate to address the problem using in-depth qualitative research. The problem may then be stated in fairly broad terms to permit full exploration of the concept of interest.
Feasibility of
Addressing the Problem. A problem that is both significant and researchable may
still be inappropriate if a study designed to address it is not feasible. The
issue of feasibility encompasses various considerations. Not all of the
following factors are relevant for every problem, but they should be kept in
mind in making a final decision.
Time and Timing. Most studies have deadlines or at least goals for completion. Therefore, the problem must be one that can be adequately studied within the time allotted. This means that the scope of the problem should be sufficiently restricted that enough time will be available for the various steps and activities reviewed in Chapter 3. It is wise to be conservative in estimating time for various tasks because research activities often require more time to accomplish than anticipated. Qualitative studies may be especially time-consuming.
A related consideration is the timing of the project. Some of the research steps — especially data collection — may be more readily performed at certain times of the day, week, or year than at other times. For example, if the problem focused on patients with peptic ulcers, the research might be more easily conducted in the fall and spring because of the increase in the number of patients with peptic ulcers during these seasons. When the timing requirements of the tasks do not match the time available for their performance, the feasibility of the project may be jeopardized.
Availability of Study Participants. In any study involving humans, researchers need to consider whether individuals with the desired characteristics will be available and willing to cooperate. Securing people’s cooperation may in some cases be easy (e.g., getting nursing students to complete a questionnaire in a classroom), but other situations may pose more difficulties. Some people may not have the time, others may have no interest in a study that has little personal benefit, and others may not feel well enough to participate. Fortunately, people usually are willing to cooperate if research demands are minimal. Researchers may need to exert extra effort in recruiting participants—or may have to offer a monetary incentive—if the research is time-consuming or demanding. An additional problem may be that of identifying and locating people with needed characteristics.
For example, if we were interested in studying the coping strategies of people who had lost a family member through suicide, we would have to develop a plan for identifying prospective participants from this distinct and inconspicuous population.
Cooperation of Others. Often, it is insufficient to obtain the cooperation of prospective study participants alone. If the sample includes children, mentally incompetent people, or senile individuals, it would be necessary to secure the permission of parents or guardians. In institutional or organizational settings (e.g., hospitals), access to clients, members, personnel, or records usually requires administrative authorization. Many health care facilities require that any project be presented to a panel of reviewers for approval. Critical requirement in many qualitative studies is gaining entrée into an appropriate community, setting, or group, and developing the trust of gatekeepers.
Facilities and Equipment. All studies have resource requirements, although in some cases, needs may be modest. It is prudent to consider what facilities and equipment will be needed and whether they will be available before embarking on a project to avoid disappointment and frustration. The following is a partial list of considerations:
• Will assistants be needed, and are such assistants available?
• If technical equipment and apparatus are needed, can they be secured, and are they functioning properly? Will audiotaping or videotaping equipment be required, and is it of sufficient sensitivity for the research conditions? Will laboratory facilities be required, and are they available?
• Will space be required, and can it be obtained?
• Will telephones, office equipment, or other supplies be required?
• Are duplicating or printing services available, and are they reliable?
• Will transportation needs pose any difficulties? Money. Monetary requirements for research projects vary widely, ranging from $10 to $20 for small student projects to hundreds of thousands (or even millions) of dollars for large-scale, government-sponsored research. The investigator on a limited budget should think carefully about projected expenses before making the final selecion of a problem. Some major categories of research-related expenditures are the following:
• Literature costs—computerized literature search and retrieval service charges, Internet access charges, reproduction costs, index cards, books and journals
• Personnel costs—payments to individuals hired to help with the data collection (e.g., for conducting interviews, coding, data entry, transcribing, word processing)
• Study participant costs—payment to participants as an incentive for their cooperation or to offset their own expenses (e.g., transportation or baby-sitting costs)
• Supplies—paper, envelopes, computer disks, postage, audiotapes, and so forth
• Printing and duplication costs—expenditures for printing forms, questionnaires, participant recruitment notices, and so on
• Equipment—laboratory apparatus, audio- or video-recorders, calculators, and the like
• Computer-related expenses (e.g., purchasing software)
• Laboratory fees for the analysis of biophysiologic data
• Transportation costs
Experience of the Researcher. The problem should be chosen from a field about which investigators have some prior knowledge or experience.
Researchers have difficulty adequately developing a study on a topic that is totally new and unfamiliar—although clinical fieldwork before launching the study may make up for certain deficiencies. In addiion to substantive knowledge, the issue of technicalexpertise should not be overlooked. Beginning researchers with limited methodologic skills should avoid research problems that might require the development of sophisticated measuring instruments or that involve complex data analyses.
Ethical Considerations. A research problem may not be feasible because the investigation of the problem would pose unfair or unethical demands on participants. The ethical responsibilities of researchers should not be taken lightly. People engaged in research activities should be thoroughly knowledgeable about the rights of human or animal subjects.
Interest to the Researcher. Even if the tentative problem is researchable, significant, and feasible, there is one more criterion: the researcher’s own interest in the problem. Genuine interest in and curiosity about the chosen research problem are critical prerequisites to a successful study. A great deal of time and energy are expended in a study; there is little sense devoting these personal resources to a project that does not generate enthusiasm.
TIP: Beginning researchers often seek out suggestions on topic areas, and such assistance may be helpful in getting started. Nevertheless, it is rarely wise to be talked into a topic toward which you are not personally inclined. If you do not find a problem attractive or stimulating during the beginning phases of a study—when opportunities for creativity and intellectual enjoyment are at their highest—then you are bound to regret your choice later.
Communicating The Research Problem
It is clear that a study cannot progress without the choice of a problem; it is less clear, but nonetheless true, that the problem and research questions should be carefully stated in writing before proceeding with the design of the study or with field work. Putting one’s ideas in writing is often sufficient to illuminate ambiguities and uncertainties.
This section discusses the wording of problem statements, statements of purpose, and research questions, and the following major section discusses hypotheses.
Problem Statements. A problem statement is an expression of the dilemma or disturbing situation that needs investigation for the purposes of providing understanding and direction. A problem statement identifies the nature of the problem that is being addressed in the study and, typically, its context and significance.
In general, the problem statement should be broad enough to include central concerns, but narrow enough in scope to serve as a guide to study design.
Example of a problem statement from a quantitative study:
Women account for an increasing percentage of adults with human immunodeficiency virus (HIV).... Most of these HIV-infected women are in their childbearing years. As a result, approximately 7,000 infants are exposed prenatally each year.... All infants exposed to HIV prenatally are at risk for developmental problems.... Little is known about the quality of parental caregiving for infants of mothers with HIV, because few studies have examined parenting in this vulnerable group.... The purpose of this report is to describe the development of infants of mothers with HIV and to determine the extent to which child characteristics, parental caregiver characteristics, family characteristics, and parenting quality influence development (Holditch-Davis, Miles, Burchinal, O’Donnell, McKinney, & Lim, 2001, pp. 5–6).
In this example, the general topic could be described as infant development among at-risk children. The investigators’ more specific focus is on four factors that influence infant development among children exposed to HIV prenatally. The problem statement asserts the nature of the problem (these children are at risk of developmental problems) and indicates its breadth (7000 infants annually). It also provides a justification for conducting a new study: the dearth of existing studies on parenting in this population.
The problem statement for a qualitative study similarly expresses the nature of the problem, its context, and its significance, as in the following example:
Example of a problem statement from a qualitative study:
Members of cultural minority groups may find themselves surrounded
by people whose values, beliefs, and interpretations differ from their own
during hospitalization. This is often the case for
As in the previous example, these qualitative researchers clearly articulated the nature of the problem and the justification for a new study.
Qualitative studies that are embedded in a particular research tradition usually incorporate terms and concepts in their problem statements that foreshadow their tradition of inquiry (Creswell, 1998). For example, the problem statement in a grounded theory study might refer to the need to generate a theory relating to social processes. A problem statement for a phenomenological study might note the need to know more about people’s experiences (as in the preceding example) or the meanings they attribute to those experiences. And an ethnographer might indicate the desire to describe how cultural forces affect people’s behavior.
Problem statements usually appear early in a research report and are often interwoven with a review of the literature, which provides context by documenting knowledge gaps.
Statements of Purpose. Many researchers first articulate their research goals formally as a statement of purpose, worded in the declarative form. The statement captures—usually in one or two clear sentences—the essence of the study.
The purpose statement establishes the general direction of the inquiry. The words purpose or goal usually appear in a purpose statement (e.g., The purpose of this study was..., or, The goal of this study was...), but sometimes the words intent, aim,or objective are used instead. Unfortunately, some research reports leave the statement of purpose implicit, placing an unnecessary burden on readers to make inferences about the goals.
In a quantitative study, a statement of purpose identifies the key study variables and their possible interrelationships, as well as the nature of the population of interest.
Example of a statement of purpose from a quantitative study:
“The purpose of this study was to determine whether viewing a video of an actual pediatric inhalation induction would reduce the level of parental anxiety” (Zuwala & Barber, 2001, p. 21).
This statement identifies the population of interest (parents whose child required inhalation induction), the independent variable (viewing a video of such an induction, versus not viewing the video), and the dependent variable (parental anxiety).
In qualitative studies, the statement of purpose indicates the nature of the inquiry, the key concept or phenomenon, and the group, community, or setting under study.
Example of a statement of purpose from a qualitative study:
Gallagher and Pierce (2002) designed their qualitative study for the following two purposes: “to gain the family caregivers’ perspective of dealing with UI [urinary incontinence] for the care recipient who lives in a home setting, and to gain care recipients’ perspective on the UI care given by family caregivers in the home setting” (p. 25).
This statement indicates that the central phenomenon of interest is perspectives on caregiving and that the groups under study are UI patients in home settings and the family caregivers caring for them.
Often, the statement of purpose specifically mentions the underlying research tradition, if this is relevant.
Example of a statement of purpose from a grounded theory study:
The purpose is “to generate a grounded substantive theory of the process of forgiveness in patients with cancer” (Mickley and Cowles, 2001, p. 31).
The statement of purpose communicates more than just the nature of the problem. Through researchers’ selection of verbs, a statement of purpose suggests the manner in which they sought to solve the problem, or the state of knowledge on the topic. That is, a study whose purpose is to explore or describe some phenomenon is likely to be an investigation of a little-researched topic, often involving a qualitative approach such as a phenomenology or ethnography. A statement of purpose for a qualitative study—especially a grounded theory study—may also use verbs such as understand, discover, develop, or generate. Creswell (1998) notes that the statements of purpose in qualitative studies often “encode” the tradition of inquiry not only through the researcher’s choice of verbs but also through the use of certain terms or “buzz words” associated with those traditions, as follows:
• Grounded theory: Processes; social structures; social interactions
• Phenomenological studies: Experience; lived experience; meaning; essence
• Ethnographic studies: Culture; roles; myths; cultural behavior
Quantitative researchers also suggest the nature of the inquiry through their selection of verbs. A purpose statement indicating that the study purpose is to test or determine or evaluate the effectiveness of an intervention suggests an experimental design, for example. A study whose purpose is to examine or assess the relationship between two variables is more likely to refer to a nonexperimental quantitative design. In some cases, the verb is ambiguous: a purpose statement indicating that the researcher’s intent is to compare could be referring to a comparison of alternative treatments (using an experimental approach) or a comparison of two preexisting groups (using a nonexperimental approach). In any event, verbs such as test, evaluate, and compare suggest an existing knowledge base, quantifiable variables, and designs with tight scientific controls.
Note that the choice of verbs in a statement of purpose should connote objectivity. A statement of purpose indicating that the intent of the study was to prove, demonstrate,or showsomething suggests a bias.
TIP: In wording your research questions or statement of purpose, look at published research reports for models. You may find, however, that some reports fail to state unambiguously the study purpose or specific research questions. Thus, in some studies, you may have to infer the research problem from several sources, such as the title of the report. In other reports, the purpose or questions are clearly stated but may be difficult to find. Researchers most often state their purpose or questions at the end of the introductory section of the report.
Research Questions. Research questions are, in some cases, direct rewordings of statements of purpose, phrased interrogatively rather than declaratively, as in the following example:
• The purpose of this study is to assess the relationship between the dependency level of renal transplant recipients and their rate of recovery.
• What is the relationship between the dependency level of renal transplant recipients and their rate of recovery?
The question form has the advantage of simplicity and directness. Questions invite an answer and help to focus attention on the kinds of data that would have to be collected to provide that answer. Some research reports thus omit a statement of purpose and state only research questions. Other researchers use a set of research questions to clarify or lend greater specificity to the purpose statement.
Example of research questions clarifying a statement of purpose:
Statement of Purpose: The purpose of this study was to explore the relationship between method of pain management during labor and specific labor and birth outcomes.
Research Questions: (1) Are nonepidural and epidural methods of pain relief associated with augmentation during the first stage of labor? (2) Is the length of second stage labor associated with epidural and nonepidural methods of pain relief? (3) Are newborn Apgar scores at 1 minute and 5 minutes associated with method of pain relief? (4) Does epidural anesthesia affect maternal temperature? (Walker & O’Brien, 1999)
In this example, the statement of purpose provides a global message about the researchers’ goal to explore relationships among several variables.
The research questions identified the two methods of pain management (the independent variable) and the specific labor and birth outcomes of interest (the dependent variables).
Research Questions in Quantitative Studies. In quantitative studies, research questions identify the key variables (especially the independent and dependent variables), the relationships among them, and the population under study. The variables are all measurable concepts, and the questions suggest quantification. For example, a descriptive research question might ask about the frequency or prevalence of variables, or their average values (What percentage of women breastfeed their infants? or What is the average interstitial fluid volume at 60 minutes after intravenous infiltration following treatment with cold applications?).
Most quantitative studies, however, ask questions about relationships between variables. These can be illustrated with an example of women’s emotional responses to miscarriage:
1. Existence of relationship: Is there a relationship between miscarriage and depression—that is, are there differences in depression levels of pregnant women who miscarry compared with those who do not?
2. Direction of relationship: Do women who miscarry exhibit higher (or lower) levels of depression than pregnant women who do not?
3. Strength of relationship: How strong is the risk of depression among women who miscarry?
4. Nature of relationship: Does having a miscarriage contribute to depression? Does depression contribute to a miscarriage? Or does some other factor influence both?
5. Moderated relationship: Are levels of depression among women who miscarry moderated by whether the woman has previously given birth? (i.e., Is the relationship between depression and miscarriage different for primiparas and multiparas?)
6. Mediated relationship: Does a miscarriage directly affect depression or does depression occur because the miscarriage had a negative effect on marital relations?
The last two research questions involve mediator and moderator variables, which are variables of interest to the researcher (i.e., that are not extraneous) and that affect the relationship between the independent and dependent variables. A moderator variable is a variable that affects the strength or direction of an association between the independent and dependent variable. The independent variable is said to interact with the moderator: the independent variable’s relationship with the dependent variable is stronger or weaker for different values of the moderator variable (Bennett, 2000). In the preceding example, it might be that the risk of depression after a miscarriage is low among women who had previously given birth (i.e., when the moderating variable parity is greater than 0), but high among women who do not have children (i.e., when parity equals 0). When all women are considered together without taking parity into account, the relationship between experiencing a miscarriage (the independent variable) and levels of depression (the dependent variable) might appear moderate. Therefore, identifying parity as a key moderator is important in understanding when to expect a relationship between miscarriage and depression, and this understanding has clinical relevance.
Research questions that involve mediator variables concern the identification of causal pathways. A mediator variable is a variable that intervenes between the independent and dependent variable and helps to explain why the relationship exists. In our hypothetical example, we are asking whether depression levels among women who have experienced a miscarriage are influenced by the negative effect of the miscarriage on marital relations. In research questions involving mediators, researchers are typically more interested in the mediators than in the independent variable, because the mediators are key explanatory mechanisms.
In summary, except for questions of a purely descriptive nature, research questions in quantitative research focus on unraveling relationships among variables.
Example of a research question from a quantitative study:
Watt-Watson, Garfinkel, Gallop, Stevens, and Streiner (2000) conducted a study about acute care nurses’ empathy and its effects on patients. Their primary research question was about the existence and direction of a relationship:
Do nurses with greater empathy have patients experiencing less pain and receiving adequate analgesia than those with less empathy?
Research Questions in Qualitative Studies. Researchers in the various qualitative traditions vary in their conceptualization of what types of questions are important. Grounded theory researchers are likely to ask process questions, phenomenologists tend to ask meaning questions, and ethnographers generally ask descriptive questions about cultures.
The terms associated with the various traditions, discussed previously in connection with purpose statements, are likely to be incorporated into the research questions.
Example of a research question from a phenomenological study:
What is the lived experience of caring for a family member with Alzheimer’s disease at home? (Butcher, Holkup, & Buckwalter, 2001)
It is important to note, however, that not all qualitative studies are rooted in a specific research tradition. Many researchers use naturalistic methods to describe or explore phenomena without focusing on cultures, meaning, or social processes.
Example of a research question from a qualitative study:
Wilson and Williams (2000) undertook a qualitative study that explored the potential effects of visualism (a prejudice in favor of the seen) on the perceived legitimacy of telephone work in community nursing. Among the specific research questions that guided their in-depth interviews with community nurses were the following:
Is telephone consultation considered real work? Is it considered real communication? Can telephone consultation bring the community and its nursing services into close relationship?
In qualitative studies, research questions sometimes evolve over the course of the study. The researcher begins with a focus that defines the general boundaries of the inquiry. However, the boundaries are not cast in stone; the boundaries “can be altered and, in the typical naturalistic inquiry, will be” (Lincoln & Guba, 1985, p. 228). The naturalist begins with a research question that provides a general starting point but does not prohibit discovery; qualitative researchers are often sufficiently flexible that the question can be modified as new information makes it relevant to do so.
Research Hypotheses
A hypothesis is a prediction about the relationship between two or more variables. A hypothesis thus translates a quantitative research question into a precise prediction of expected outcomes. In qualitative studies, researchers do not begin with a hypothesis, in part because there is usually too little known about the topic to justify a hypothesis, and in part because qualitative researchers want the inquiry to be guided by participants’ viewpoints rather than by their own. Thus, this discussion focuses on hypotheses used to guide quantitative inquiries (some of which are generated within qualitative studies).
Function of Hypotheses in Quantitative Research. Research questions, as we have seen, are usually queries about relationships between variables.
Hypotheses are proposed solutions or answers to these queries. For instance, the research question might ask: Does history of sexual abuse in childhood affect the development of irritable bowel syndrome in women? The researcher might predict the following: Women who were sexually abused in childhood have a higher incidence of irritable bowel syndrome than women who were not.
Hypotheses sometimes follow directly from a theoretical framework. Scientists reason from theories to hypotheses and test those hypotheses in the real world. The validity of a theory is never examined directly. Rather, it is through hypothesis testing that the worth of a theory can be evaluated. Let us take as an example the theory of reinforcement. This theory maintains that behavior that is positively reinforced (rewarded) tends to be learned or repeated.
The theory itself is too abstract to be put to an empirical test, but if the theory is valid, it should be possible to make predictions about certain kinds of behavior. For example, the following hypotheses have been deduced from reinforcement theory:
• Elderly patients who are praised (reinforced) by nursing personnel for self-feeding require less assistance in feeding than patients who are not praised.
• Pediatric patients who are given a reward (e.g., a balloon or permission to watch television) when they cooperate during nursing procedures tend to be more obliging during those procedures than nonrewarded peers.
Both of these propositions can be put to a test in the real world. The theory gains support if the hypotheses are confirmed.
Not all hypotheses are derived from theory. Even in the absence of a theory, well-conceived hypotheses offer direction and suggest explanations. Perhaps an example will clarify this point.
Suppose we hypothesized that nurses who have received a baccalaureate education are more likely to experience stress in their first nursing job than are nurses with a diploma-school education. We could justify our speculation based on theory (e.g., role conflict theory, cognitive dissonance theory), earlier studies, personal observations, or on the basis of some combination of these.
The development of predictions in and of itself forces researchers to think logically, to exercise critical judgment, and to tie together earlier research findings.
Now let us suppose the preceding hypothesis is not confirmed by the evidence collected; that is, we find that baccalaureate and diploma nurses demonstrate comparable stress in their first job.
The failure of data to support a prediction forces researchers to analyze theory or previous research critically, to carefully review the limitations of the study’s methods, and to explore alternative explanations for the findings.
The use of hypotheses in quantitative studies tends to induce critical thinking and to facilitate understanding and interpretation of the data.
To illustrate further the utility of hypotheses, suppose we conducted the study guided only by the research question, Is there a relationship between nurses’ basic preparation and the degree of stress experienced on the first job? The investigator without a hypothesis is, apparently, prepared to accept any results. The problem is that it is almost always possible to explain something superficially after the fact, no matter what the findings are. Hypotheses guard against superficiality and minimize the possibility that spurious results will be misconstrued.
Characteristics of Testable Hypotheses. Testable research hypotheses state expected relationships between the independent variable (the presumed cause or antecedent) and the dependent variable (the presumed effect or outcome) within a population.
Example of a research hypothesis:
Cardiac patients receiving an intervention involving “vicarious experience” through support from former patients have (1) less anxiety; (2) higher self-efficacy expectation; and (3) higher self-reported activity than other patients (Parent & Fortin, 2000).
In this example, the independent variable is receipt versus nonreceipt of the intervention, and the dependent variables are anxiety, self-efficacy expectation, and activity. The hypothesis predicts better outcomes among patients who receive the intervention.
Unfortunately, researchers occasionally present hypotheses that fail to make a relational statement. For example, the following prediction is not an acceptable research hypothesis:
Pregnant women who receive prenatal instruction regarding postpartum experiences are not likely to experience postpartum depression.
This statement expresses no anticipated relationship; in fact, there is only one variable (postpartum depression), and a relationship by definition requires at least two variables.
When a prediction does not express an anticipated relationship, it cannot be tested. In our example, how would we know whether the hypothesis was supported—what absolute standard could be used to decide whether to accept or reject the hypothesis? To illustrate the problem more concretely, suppose we asked a group of mothers who had been given instruction on postpartum experiences the following question 1 month after delivery: On the whole, how depressed have you been since you gave birth? Would you say (1) extremely depressed, (2) moderately depressed, (3) somewhat depressed, or (4) not at all depressed?
Based on responses to this question, how could we compare the actual outcome with the predicted outcome? Would all the women have to say they were “not at all depressed?” Would the prediction be supported if 51% of the women said they were “not at all depressed” or “somewhat depressed?” There is no adequate way of testing the accuracy of the prediction.
A test is simple, however, if we modify the prediction to the following: Pregnant women who receive prenatal instruction are less likely to experience postpartum depression than those with no prenatal instruction. Here, the dependent variable is the women’s depression, and the independent variable is their receipt versus nonreceipt of prenatal instruction. The relational aspect of the prediction is embodied in the phrase less than. If a hypothesis lacks a phrase such as more than, less than, greater than, different from, related to, associated with, or something similar, it is not amenable to testing in a quantitative study. To test this revised hypothesis, we could ask two groups of women with different prenatal instruction experiences to respond to the question on depression and then compare the groups’ responses. The absolute degree of depression of either group would not be at issue.
Hypotheses, ideally, should be based on sound, justifiable rationales. The most defensible hypotheses follow from previous research findings or are deduced from a theory. When a relatively new area is being investigated, the researcher may have to turn to logical reasoning or personal experience to justify the predictions. There are, however, few problems for which research evidence is totally lacking.
The Derivation of Hypotheses. Many students ask the question, How do I go about developing hypotheses? Two basic processes—induction and deduction—constitute the intellectual machinery involved in deriving hypotheses.
An inductive hypothesis is a generalization based on observed relationships. Researchers observe certain patterns, trends, or associations among phenomena and then use the observations as a basis for predictions. Related literature should be examined to learn what is already known on a topic, but an important source for inductive hypotheses is personal experiences, combined with intuition and critical analysis. For example, a nurse might notice that presurgical patients who ask a lot of questions relating to pain or who express many pain-related apprehensions have a more difficult time in learning appropriate postoperative procedures. The nurse could then formulate a hypothesis, such as the following, that could be tested through more rigorous procedures: Patients who are stressed by fears of pain will have more difficulty in deep breathing and coughing after their surgery than patients who are not stressed. Qualitative studies are an important source of inspiration for inductive hypotheses.
Example of deriving an inductive hypothesis:
In Beck’s (1998) qualitative study of postpartum-onset panic disorder, one of her findings was a theme relating to self-esteem: “As a result of recurring panic attacks, negative changes in women’s lifestyles ensued—lowering their self-esteem and leaving them to bear the burden of disappointing not only themselves but also their families” (p. 134). A hypothesis that can be derived from this qualitative finding might be as follows:
Women who experience postpartum onset panic disorder have lower self-esteem than women who do not experience this disorder.
The other mechanism for deriving hypotheses is through deduction. Theories of how phenomena behave and interrelate cannot be tested directly.
Through deductive reasoning, a researcher can develop hypotheses based on general theoretical principles. Inductive hypotheses begin with specific observations and move toward generalizations; deductive hypotheses have as a starting point theories that are applied to particular situations. The following syllogism illustrates the reasoning process involved:
• All human beings have red and white blood cells.
• John Doe is a human being.
• Therefore, John Doe has red and white blood cells.
In this simple example, the hypothesis is that John Doe does, in fact, have red and white blood cells, a deduction that could be verified.
Theories thus can serve as a valuable point of departure for hypothesis development. Researchers must ask: If this theory is valid, what are the implications for a phenomenon of interest? In other words, researchers deduce that if the general theory is true, then certain outcomes or consequences can be expected. Specific predictions derived from general principles must then be subjected to testing through the collection of empirical data. If these data are congruent with hypothesized outcomes, then the theory is strengthened.
The advancement of nursing knowledge depends on both inductive and deductive hypotheses. Ideally, a cyclical process is set in motion wherein observations are made (e.g., in a qualitative study); inductive hypotheses are formulated; systematic and controlled observations are made to test the hypotheses; theoretical systems are developed on the basis of the results; deductive hypotheses are formulated from the theory; new data are gathered; theories are modified, and so forth.
Researchers need to be organizers of concepts (think inductively), logicians (think deductively), and, above all, critics and skeptics of resulting formulations, constantly demanding evidence.
Wording of Hypotheses. A good hypothesis is worded in simple, clear, and concise language. Although it is cumbersome to include conceptual or operational definitions of terms directly in the hypothesis statement, it should be specific enough so that readers understand what the variables are and whom researchers will be studying.
Simple Versus Complex Hypotheses. For the purpose of this book, we define a simple hypothesis as a hypothesis that expresses an expected relationship between one independent and one dependent variable. A complex hypothesis is a prediction of a relationship between two (or more) independent variables and/or two (or more) dependent variables. Complex hypotheses sometimes are referred to as multivariate hypotheses because they involve multiple variables.
We give some concrete examples of both types of hypotheses, but let us first explain the differences in abstract terms. Simple hypotheses state a relationship between a single independent variable, which we will call X, and a single dependent variable, which we will label Y. Y is the predicted effect, outcome, or consequence of X, which is the presumed cause, antecedent, or precondition. The nature of this relationship is presented in Figure 4-1A. The hatched area of the circles, which represent variables X and Y, signifies the strength of the relationship between them. If there were a one-to-one correspondence between variables X and Y, the two circles would completely overlap, and the entire area would be hatched. If the variables were totally unrelated, the circles would not overlap at all.
Example of a simple hypothesis:
Patients receiving a warmed solution for body cavity irrigation during surgical procedures [X] will maintain a higher core body temperature [Y] than patients receiving a room temperature solution (Kelly, Doughty, Hasselbeck, & Vacchiano, 2000).
Most phenomena are the result not of one variable but of a complex array of variables. A person’s weight, for example, is affected simultaneously by such factors as the person’s height, diet, bone structure, activity level, and metabolism. If Y in Figure 4-1A was weight, and X was a person’s caloric intake, we would not be able to explain or understand individual variation in weight completely.
For example, knowing that Dave Harper’s daily caloric intake averaged 2500 calories would not allow us a perfect prediction of his weight.
Knowledge of other factors, such as his height, would improve the accuracy with which his weight could be predicted.
Figure 4-1B presents a schematic representation of the effect of two independent variables on one dependent variable. The complex hypothesis would state the nature of the relationship between Y on the one hand and X1 and X2 on the other. To pursue the preceding example, the hypothesis might be: Taller people (X1) and people with higher caloric intake (X2) weigh more (Y) than shorter people and those with lower caloric intake. As the figure shows, a larger proportion of the area of Y is hatched when there are two independent variables than when there is only one. This means that caloric intake and height do a better job in helping us explaining variations in weight (Y) than caloric intake alone. Complex hypotheses have the advantage of allowing researchers to capture some of the complexity of the real world. It is not always possible to design a study with complex hypotheses.
Practical considerations (e.g., researchers’ technical skills and resources) may make it difficult to test complex hypotheses. An important goal of research, however, is to explain the dependent variable as thoroughly as possible, and two or more independent variables are typically more successful than one alone.
Example of a complex hypothesis—multiple independent variables:
Among breast cancer survivors, emotional well-being [Y] is influenced by the women’s self-esteem [X1], their resourcefulness [X2] and their degree of social support [X3] (Dirksen, 2000).
Just as a phenomenon can result from more than one independent variable,
so a single independent variable can have an effect on, or be antecedent to,
more than one phenomenon. Figure 4-
Example of a complex hypothesis—multiple dependent variables:
The implementation of an evidence-based protocol for urinary incontinence [X] will result in decreased frequency of urinary incontinence episodes (Y1), decreased urine loss per episode [Y2], and decreased avoidance of activities [Y3] among women in ambulatory care settings (Sampselle et al., 2000).
Finally, a more complex type of hypothesis, which links two or more independent variables to two or more dependent variables, is shown in Figure 4-1D. An example might be a hypothesis that smoking and the consumption of alcohol during pregnancy might lead to lower birth weights and lower Apgar scores in infants.
Hypotheses are also complex if mediator or moderator variables are included in the prediction. For example, it might be hypothesized that the effect of caloric intake (X) on weight (Y) is moderated by gender (Z)—that is, the relationship between height and weight is different for men and women.
Example of a complex hypothesis with mediator:
The quality of life of a family [Y] during the survivor phase after cancer diagnosis is affected by family resources [X1] and illness survival stressors such as fear of recurrence [X2], through the mediating variable, the family meaning of the illness [Z] (Mellon & Northouse, 2001).
In general, hypotheses should be worded in the present tense. Researchers make predictions about relationships that exist in the population, and not just about a relationship that will be revealed in a particular sample. Hypotheses can be stated in various ways as long as the researcher specifies or implies the relationship to be tested. Here are examples:
1. Older patients are more at risk of experiencing a fall than younger patients.
2. There is a relationship between the age of a patient and the risk of falling.
3. The older the patient, the greater the risk that she or he will fall.
4. Older patients differ from younger ones with respect to their risk of falling.
5. Younger patients tend to be less at risk of a fall than older patients.
6. The risk of falling increases with the age of the patient.
Other variations are also possible. The important point to remember is that the hypothesis must specify the independent variable (here, patients’ age) and the dependent variables (here, risk of falling) and the anticipated relationship between them.
Directional Versus Nondirectional Hypotheses. Sometimes hypotheses are described as being either directional or nondirectional. A directional hypothesis is one that specifies not only the existence but the expected direction of the relationship between variables. In the six versions of the hypothesis in the preceding list, versions 1, 3, 5, and 6 are directional because there is an explicit prediction that older patients are at greater risk of falling than younger ones.
A nondirectional hypothesis, by contrast, does
not stipulate the direction of the relationship. Versions 2 and
Hypotheses derived from theory are almost always directional because theories explain phenomena, thus providing a rationale for expecting variables to be related in certain ways. Existing studies also offer a basis for directional hypotheses. When there is no theory or related research, when the findings of related studies are contradictory, or when researchers’ own experience leads to ambivalence, nondirectional hypotheses may be appropriate. Some people argue, in fact, that nondirectional hypotheses are preferable because they connote a degree of impartiality. Directional hypotheses, it is said, imply that researchers are intellectually committed to certain outcomes, and such a commitment might lead to bias. This argument fails to recognize that researchers typically do have hunches about outcomes, whether they state those expectations explicitly or not. We prefer directional hypotheses—when there is a reasonable basis for them—because they clarify the study’s framework and demonstrate that researchers have thought critically about the phenomena under study. Directional hypotheses may also permit a more sensitive statistical test through the use of a one-tailed test.
Research Versus Null Hypotheses. Hypotheses are sometimes classified as being either research hypotheses or null hypotheses.
Research hypotheses (also referred to as substantive, declarative,or scientific hypotheses) are statements of expected relationships between variables. All the hypotheses presented thus far are research hypotheses that indicate researchers’ actual expectations.
The logic of statistical inference operates on principles that are somewhat confusing to many beginning students. This logic requires that hypotheses be expressed such that no relationship is expected. Null hypotheses (or statistical hypotheses) state that there is no relationship between the independent and dependent variables.
The null form of the hypothesis used in our preceding examples would be a statement such as:
“Patients’ age is unrelated to their risk of falling” or “Older patients are just as likely as younger patients to fall.” The null hypothesis might be compared with the assumption of innocence of an accused criminal in our system of justice: the variables are assumed to be “innocent” of any relationship until they can be shown “guilty” through appropriate statistical procedures. The null hypothesis represents the formal statement of this assumption of innocence.
TIP: If you formulate hypotheses, avoid stating them in null form. When statistical tests are performed, the underlying null hypothesis is assumed without being explicitly stated. Stating hypotheses in the null form gives an amateurish impression.
Hypothesis Testing. Hypotheses are formally tested through statistical procedures; researchers seek to determine through statistics whether their hypotheses have a high probability of being correct. However, hypotheses are never proved through hypothesis testing; rather, they are accepted or supported. Findings are always tentative. Certainly, if the same results are replicated in numerous investigations, then greater confidence can be placed in the conclusions. Hypotheses come to be increasingly supported with mounting evidence.
Let us look more closely at why this is so.
Suppose we hypothesized that height and weight are related. We predict
that, on average, tall people weigh more than short people. We then obtain
height and weight measurements from a sample and analyze the data. Now suppose
we happened by chance to choose a sample that consisted of short, heavy people,
and tall, thin people. Our results might indicate that there is no relationship
between a person’s height and weight. Would we then be justified in
stating that this study proved or demonstrated that height and weight in humans are
unrelated?
As another example, suppose we hypothesized that tall nurses are more effective than short ones.
This hypothesis is used here only to illustrate a point because, in reality, we would expect no relationship between height and a nurse’s job performance. Now suppose that, by chance again, we drew a sample of nurses in which tall nurses received better job evaluations than short ones.
Could we conclude definitively that height is related to a nurse’s performance? These two examples illustrate the difficulty of using observations from a sample to generalize to a population. Other issues, such as the accuracy of the measures, the effects of uncontrolled extraneous variables, and the validity of underlying assumptions prevent researchers from concluding with finality that hypotheses are proved.
TIP: If a researcher uses any statistical tests (as is true in most quantitative studies), it means that there are underlying hypotheses—regardless of whether the researcher explicitly states them—because statistical tests are designed to test hypotheses. In planning a quantitative study of your own, do not be afraid to make a prediction, that is, to state a hypothesis.
Research Examples
This section describes how the research problem and research questions were communicated in two nursing studies, one quantitative and one qualitative.
Research Example of a Quantitative Study. Van Servellen, Aguirre, Sarna, and Brecht (2002) studied emotional distress in HIV-infected men and women. The researchers noted that, despite the fact that AIDS rates have been dropping for men but increasing for women, few studies have described the health experiences of HIV-infected women or compared them with those of men. This situation was viewed as especially troubling because of certain evidence indicating that, once HIV infected, women may be at greater risk than men for illness-related morbidity and adverse outcomes.
As stated by the researchers, the purpose of their study was “to describe and compare patterns of emotional distress in men and women with symptomatic HIV seeking care in community-based treatment centers” (p. 50). The researchers went on to note that understanding gender differences and similarities in relation to sociodemographic characteristics, health status, and stress-resistant resources could “provide important information in designing gender-specific programs to improve quality of life and reduce emotional distress in clients affected by HIV” (p. 50).
The conceptual framework for the study was attribution theory, which offers explanations of links between life stressors and emotional distress. This framework guided the development of the four study hypotheses, which were as follows:
Hypothesis 1: Sociodemographic vulnerability (less than high school education, etc.) will be associated with emotional distress in both men and women.
Hypothesis 2: Poor physical and functional health status will be associated with emotional distress in both men and women.
Hypothesis 3: Optimism and social support will be associated with positive mental health outcomes ... in both men and women.
Hypothesis 4: Women will have higher levels of emotional distress than men (pp. 53–54).
Data for the study were collected from 82 men and 44 women with HIV
disease in
However, the first hypothesis was not supported in this low-income sample: there were no significant relationships between any sociodemographic vulnerability indicators and the subjects’ level of anxiety or depression.
Research Example of a Qualitative Study. Beery, Sommers, and Hall (2002) studied the experiences of women with permanent cardiac pacemakers.
The researchers stated that biotechnical devices such as pacemakers are increasingly being implanted into people to manage an array of disorders, yet relatively little research has examined the emotional impact of such an experience. They further noted that women may have distinctive responses to implanted devices because of cultural messages about the masculinity of technology, but little was known about women’s unique responses to permanent cardiac pacemakers.
The purpose of Beery and colleagues’ study was to explore women’s responses to pacemaker implementation, using in-depth interviews to solicit the women’s life stories. The researchers identified two specific research questions for their study: “What is the experience of women living with permanent cardiac pacemakers?” and “How do women incorporate permanent cardiac pacemakers into their lives and bodies?” (p. 8).
A sample of 11 women who were patients at the cardiology service of a large hospital participated in the study. During interviews, the women were asked a series of questions regarding life events that led up to, and occurred during and after, their pacemaker’s implantation. Each woman participated in two interviews. An example of the questions asked in the initial interview is: “What has living with a pacemaker been like for you?” (p. 12). In the follow-up interviews, more specific questions were asked, such as, “How often do you think about the pacemaker?” and “When might you be reminded of it?” (p. 12).
The researchers’ analysis revealed eight themes that emerged from the interview data: relinquishing care, owning the pacemaker, experiencing fears and resistance, imaging their body, normalizing, positioning as caregivers, finding resilience, and sensing omnipotence.
SUMMARY POINTS
•A research problem is a perplexing or enigmatic situation that a researcher wants to address through disciplined inquiry.
• Researchers usually identify a broad topic, narrow the scope of the problem, and then identify questions consistent with a paradigm of choice.
• The most common sources of ideas for nursing research problems are experience, relevant literature, social issues, theory, and external sources.
• Various criteria should be considered in assessing the value of a research problem. The problem should be clinically significant; researchable (questions of a moral or ethical nature are inappropriate); feasible; and of personal interest.
• Feasibility involves the issues of time, cooperation of study participants and other people, availability of facilities and equipment, researcher experience, and ethical considerations.
• Researchers communicate their aims in research reports as problem statements, statements of purpose, research questions, or hypotheses. The problem statement articulates the nature, context, and significance of a problem to be studied.
•A statement of purpose summarizes the overall study goal; in both qualitative and quantitative studies, the purpose statement identifies the key concepts (variables) and the study group or population.
• Purpose statements often communicate, through the use of verbs and other key terms, the underlying research tradition of qualitative studies, or whether study is experimental or nonexperimental in quantitative ones.
•A research question is the specific query researchers want to answer in addressing the research problem. In quantitative studies, research questions usually are about the existence, nature, strength, and direction of relationships.
• Some research questions are about moderating variables that affect the strength or direction of a relationship between the independent and dependent variables; others are about mediating variables that intervene between the independent and dependent variable and help to explain why the relationship exists.
• In quantitative studies, a hypothesis is a statement of predicted relationships between two or more variables. A testable hypothesis states the anticipated association between one or more independent and one or more dependent variables.
• Simple hypotheses express a predicted relationship between one independent variable and one dependent variable, whereas complex hypotheses state an anticipated relationship between two or more independent variables and two or more dependent variables (or state predictions about mediating or moderating variables).
• Directional hypotheses predict the direction of a relationship; nondirectional hypotheses predict the existence of relationships, not their direction.
• Research hypotheses predict the existence of relationships; statistical or null hypotheses express the absence of a relationship.
• Hypotheses are never proved or disproved in an ultimate sense—they are accepted or rejected, supported or not supported by the data.
DEVELOPING HYPOTHESIS AND RESEARCH QUESTIONS
Introduction
■ Processes involved before formulating the hypotheses.
■ Definition
■ Nature of Hypothesis
■ Types
■ How to formulate a Hypotheses in
Quantitative Research
Qualitative Research
■ Testing and Errors in Hypotheses
■ Summary
The research structure helps us create research that is:
Quantifiable Verifiable Replicable Defensible
Corollaries among the model common sense & paper format
|
Model |
Common Sense |
Paper Format |
|
Model |
Why |
Intro |
|
Research Question |
Your Answer |
Intro |
|
Develop a Theory |
How |
Method |
|
Identify Variables (if applicable) |
Expectations |
Method |
|
Identify hypotheses |
Collect/Analyze data |
Results |
|
Test the hypotheses |
What it Means |
Conclusion |
|
Evaluate the Results |
What it doesn’t Mean |
Conclusion |
|
Critical Review |
Why |
Intro |
Most research projects share the same general structure, which could be represented in the shape of an hourglass.
The “Hourglass” notion of research
· Begin with broad questions
· Narrow down, focus in
· Operationalize
· Observe
· Analyze data
· Reach conclusions
· Generalize back to questions
SOME OF THE METHODS THAT ARE INCLUDED FOR RESEARCH FORMULATION ARE
■ Where does the problem origination or discovery begin?
Previous Experience
Triggered Interest
Potential problem fields
■ Criteria of problems and problem statement
■ Goals & Planning
■ Search, Explore & Gather the Evidence
■ Generate creative and logical alternative solutions
Making the educated guess- the hypothesis!
Definitions of hypothesis
■ “Hypotheses are single tentative guesses, good hunches - assumed for use in devising theory or planning experiments intended to be given a direct experimental test when possible”. (Eric Rogers, 1966)
■ “A hypothesis is a conjectural statement of the relation between two or more variables”. (Kerlinger, 1956)
■ “Hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable.”(Creswell, 1994)
■ “A research question is essentially a hypothesis asked in the form of a question.”
■ “It is a tentative prediction about the nature of the relationship between two or more variables.”
■ “A hypothesis can be defined as a tentative explanation of the research problem, a possible outcome of the research, or an educated guess about the research outcome.” (Sarantakos, 1993: 1991)
■ “Hypotheses are always in declarative sentence form, an they relate, either generally or specifically , variables to variables.”
■ “An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved.” (Macleod Clark J and Hockey L 1981)
Nature of Hypothesis
■ The hypothesis is a clear statement of what is intended to be investigated. It should be specified before research is conducted and openly stated in reporting the results. This allows to:
Identify the research objectives
Identify the key abstract concepts involved in the research
Identify its relationship to both the problem statement and the literature review
■ A problem cannot be scientifically solved unless it is reduced to hypothesis form
■ It is a powerful tool of advancement of knowledge, consistent with existing knowledge and conducive to further enquiry
■ It can be tested - verifiable or falsifiable
■ Hypotheses are not moral or ethical questions
■ It is neither too specific nor to general
■ It is a prediction of consequences
■ It is considered valuable even if proven false
An Example...
Imagine the following situation:
You are a nutritionist working in a zoo, and one of your responsibilities is to develop a menu plan for the group of monkeys. In order to get all the vitamins they need, the monkeys have to be given fresh leaves as part of their diet. Choices you consider include leaves of the following species: (a) A (b) B (c) C (d) D and (e) E. You know that in the wild the monkeys eat mainly B leaves, but you suspect that this could be because they are safe whilst feeding in B trees, whereas eating any of the other species would make them vulnerable to predation. You design an experiment to find out which type of leaf the monkeys actually like best: You offer the monkeys all five types of leaves in equal quantities, and observe what they eat.
There are many different experimental hypotheses you could formulate for the monkey study. For example:
When offered all five types of leaves, the monkeys will preferentially feed on B leaves.
This statement satisfies both criteria for experimental hypotheses. It is a
Prediction: It predicts the anticipated outcome of the experiment
Testable: Once you have collected and evaluated your data (i.e. observations of what the monkeys eat when all five types of leaves are offered), you know whether or not they ate more B leaves than the other types.
Incorrect hypotheses would include:
When offered all five types of leaves, the monkeys will preferentially eat the type they like best.
This statement certainly sounds predictive, but it does not satisfy the second criterion: there is no way you can test whether it is true once you have the results of your study. Your data will show you whether the monkeys preferred one type of leaf, but not why they preferred it (i.e., they like it best). I would, in fact, regard the above statement as an assumption that is inherent in the design of this experiment, rather than as a hypothesis.
When offered all five types of leaves, the monkeys will preferentially eat B leaves because they can eat these safely in their natural habitat.
This statement is problematic because its second part ('because they can eat these safely in their natural habitat') also fails to satisfy the criterion of testability. You can tell whether the monkeys preferentially eat baobab leaves, but the results of this experiment cannot tell you why.
In their natural habitat, howler monkeys that feed in B trees are less vulnerable to predation than monkeys that feed on A, C, D, or E.
This is a perfectly good experimental hypothesis, but not for the experiment described in the question. You could use this hypothesis if you did a study in the wild looking at how many monkeys get killed by predators whilst feeding on the leaves of A, B etc. However, for the experimental feeding study in the zoo it is neither a prediction nor testable.
When offered all five types of leaves, which type will the monkeys eat preferentially?
This is a question, and questions fail to satisfy criterion #1: They are not predictive statements. Hence, a question is not a hypothesis.
Types
of Hypotheses
The null hypothesis represents a theory that has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved.
■ Has serious outcome if incorrect decision is made!
The alternative hypothesis is a statement of what a hypothesis test is set up to establish.
■ Opposite of Null Hypothesis.
■ Only reached if Ho is rejected.
■ Frequently “alternative” is actual desired conclusion of the researcher!
EXAMPLE
In a clinical trial of a new drug, the null hypothesis might be that the new drug is no better, on average, than the current drug.
We would write Ho: there is no difference between the two drugs on average.
The alternative hypothesis might be that:
the new drug has a different effect, on average, compared to that of the current drug.
We would write Hi: the two drugs have different effects, on average.
The new drug is better, on average, than the current drug.
We would write H1: the new drug is better than the current drug, on average.
We give special consideration to the null hypothesis...
■ This is due to the fact that the null hypothesis relates to the statement being tested, whereas the alternative hypothesis relates to the statement to be accepted if / when the null is rejected.
■ The final conclusion, once the test has been carried out, is always given in terms of the null hypothesis. We either 'reject Ho in favor of Hi' or 'do not reject Ho'; we never conclude 'reject Hi', or even 'accept Hi'.
■ If we conclude 'do not reject Ho', this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against Ho in favor of Hi; rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.
The formulation of the hypothesis basically varies with the kind of research project conducted:
…is important to narrow a question down to one that can reasonably be studied in a research project.
QUALITATIVE QUANTITATIVE
Can also be divided into:
Qualitative Approach
The use of Research Questions as opposed to objectives or hypothesis, is more frequent.
Characteristics
■ Use of words- what or how.
Specify whether the study: discovers, seeks to understand, explores or describes the experiences.
■ Use of non-directional wording in the question.
These questions describe, rather than relate variables or compare groups.
■ The questions are under continual review and reformulation-will evolve and change during study.
■ The questions are usually open-ended, without reference to the literature or theory.
■ Use of a single focus.
The rules of Qualitative research
Kleining offers four rules for a scientific and qualitative process of approaching understanding to reality.
Rule 1 (refers to subject / researcher)
"Prior understandings of the phenomenon to be researched should be seen as provisional and should be transcended with [the discovery of] new information with which they are not consistent." (1982: 231)
Rule 2 (refers to the object of study)
'The object is provisional; it is only fully known after the successful completion of the process of discovery." (1982: 233)
Rule 3 (refers to action in relation to the subject of research, hence to data collection)
'The object should be approached from "all" sides; rule of the maximum variation of perspectives." (1982: 234)
Rule 4 (refers to the evaluation of information gathered, hence to data analysis)
"Analysis of the data for common elements." (1982: 237)
Quantitative Approach
In survey projects the use of research questions and objectives is more frequent
In experiments the use of hypotheses are more frequent
Represent ►comparison between variables
► relationship between variables
Characteristics
■ The testable proposition to be deduced from theory.
■ Independent and dependent variables to be separated and measured separately.
■ To be either writing-questions, or objectives or hypotheses, but not a combination.
■ Consider the alternative forms for writing and make a choice based on the audience for the research
Generation of Research Hypothesis
Problem statements become research hypotheses when constructs are operationalized
Consider the example of a simple association between two variables, Y and X.
1. Y and X are associated (or, there is an association between Y and X).
2. Y is related to X (or, Y is dependent on X).
3. As X increases, Y decreases (or, increases in values of X appear to effect reduction in values of Y).
■ The first hypothesis provides a simple statement of association between Y and X. Nothing is indicated about the association that would allow the researcher to determine which variable, Y or X, would tend to cause the other variable to change in value.
■ The second hypothesis is also a simple statement of association between Y and X, but this time it may be inferred that values of Y are in some way contingent upon the condition of the X variable.
■ The third hypothesis is the most specific of the three. Not only does it say that Y and X are related and that Y is dependent on X for its value, but it also reveals something more about the nature of the association between the two variables.
Testing & Challenging
The degree of challenge to the hypothesis will depend on the type of problem and its importance. It can range from just seeking “a good enough” solution to a much more rigorous challenge.
The term “challenging” may include:
■ Verification
■ Justification
■ Refutability
■ Validity
■ Rectification
■ Repeatability
■ Falsification
There are two possibilities
Nothing Happened the Null Hypothesis - H0
Something Happened the Alternative Hypothesis - H1
Hypothesis testing is a four-step procedure:
1. Stating the hypothesis (Null or Alternative)
2. Setting the criteria for a decision
3. Collecting data
4. Evaluate the Null hypothesis
Errors in Hypotheses
Two types of mistakes are possible while testing the hypotheses.
Type I
Type II
Type I Error:
■ A type I error occurs when the null hypothesis (Ho) is wrongly rejected.
For example, A type I error would occur if we concluded that the two drugs produced different effects when in fact there was no difference between them.
■ A type II error occurs when the null hypothesis Ho, is not rejected when it is in fact false.
For example: A type II error would occur if it were concluded that the two drugs produced the same effect, that is, there is no difference between the two drugs on average, when in fact they produced different ones.
■ A type I error is often considered to be more serious, and therefore more important to avoid, than a type II error.
Summary
“Research questions and hypotheses become “signposts” for explaining the purpose of the study & guiding the research...”, Creswell
A hypothesis is an explanation, tentative and unsure of itself, for specific phenomena about which you have questions.
A well-crafted hypothesis very often suggests the best way to perform the research and gives you clues as to your research design.
There are different types of hypotheses.
· deductive
· inductive
Research Hypothesis can either be non-directional or directional. There exists a hypothesis that is opposite of the positively stated one, i.e. the null hypothesis
Thus to conclude it would be fitting to say “hypothesis is perhaps the most powerful tool, man has invented to achieve dependable knowledge” - Fred Kerlinger...
Research Questions and Hypotheses
Ivestigators place signposts to carry the reader through a plan for a study. The first signpost is the purpose statement, which establishes the central direction for the study. From the broad, general purpose statement, the researcher narrows the focus to specific questions to be answered or predictions based on hypotheses to be tested. This chapter begins by advancing several principles in designing and scripts for writing qualitative research questions; quantitative research questions, objectives, and hypotheses; and mixed methods research questions.
QUALITATIVE RESEARCH QUESTIONS
In a qualitative study, inquirers state research questions, not objectives (i.e., specific goals for the research) or hypotheses (i.e., predictions that involve variables and statistical tests). These research questions assume two forms: a central question and associated subquestions.
The central question is a broad question that asks for an exploration of the central phenomenon or concept in a study. The inquirer poses this question, consistent with the emerging methodology of qualitative research, as a general issue so as to not limit the inquiry. To arrive at this question, ask, “What is the broadest question that I can ask in the study?” Beginning researchers trained in quantitative research might struggle with this approach because they are accustomed to the reverse approach: identifying specific, narrow questions or hypotheses based on a few variables. In qualitative research, the intent is to explore the complex set of factors surrounding the central phenomenon and present the varied perspectives or meanings that participants hold. The following are guidelines for writing broad, qualitative research questions:
● Ask one or two central questions followed by no more than five to seven subquestions.
Several subquestions follow each general central question; the subquestions narrow the focus of the study but leave open the questioning. This approach is well within the limits set by Miles and Huberman (1994), who recommended that researchers write no more than a dozen qualitative research questions in all (central and subquestions). The subquestions, in turn, can become specific questions used during interviews (or in observing or when looking at documents). In developing an interview protocol or guide, the researcher might ask an ice breaker question at the beginning, for example, followed by five or so subquestions in the study (see Chapter 9). The interview would then end with an additional wrap-up or summary question or ask, as I did in one of my qualitative case studies, “Who should I turn to, to learn more about this topic?” (Asmussen & Creswell, 1995).
● Relate the central question to the specific qualitative strategy of inquiry.
For example, the specificity of the questions in ethnography at this stage of the design differs from that in other qualitative strategies. In ethnographic research, Spradley (1980) advanced a taxonomy of ethnographic questions that included a mini-tour of the culture-sharing group, their experiences, use of native language, contrasts with other cultural groups, and questions to verify the accuracy of the data. In critical ethnography, the research questions may build on a body of existing literature. These questions become working guidelines rather than truths to be proven (Thomas, 1993, p. 35). Alternatively, in phenomenology, the questions might be broadly stated without specific reference to the existing literature or a typology of questions. Moustakas (1994) talks about asking what the participants experienced and the contexts or situations in which they experienced it. A phenomenological example is, “What is it like for a mother to live with a teenage child who is dying of cancer?” (Nieswiadomy, 1993, p. 151). In grounded theory, the questions may be directed toward generating a theory of some process, such as the exploration of a process as to how caregivers and patients interact in a hospital setting. In a qualitative case study, the questions may address a description of the case and the themes that emerge from studying it.
● Begin the research questions with the words what or how to convey an open and emerging design.
The word why often implies that the researcher is trying to explain why something occurs, and this suggests to me a causeand- effect type of thinking that I associate with quantitative research instead of the more open and emerging stance of qualitative research.
● Focus on a single phenomenon or concept. As a study develops over time, factors will emerge that may influence this single phenomenon, but begin a study with a single focus to explore in great detail.
● Use exploratory verbs that convey the language of emerging design. These verbs tell the reader that the study will • Discover (e.g., grounded theory) • Seek to understand (e.g., ethnography) Designing Research Research Questions and Hypotheses • Explore a process (e.g., case study) • Describe the experiences (e.g., phenomenology) • Report the stories (e.g., narrative research)
● Use these more exploratory verbs that are nondirectional rather than directional words that suggest quantitative research, such as “affect,” “influence,” “impact,” “determine,” “cause,” and “relate.”
● Expect the research questions to evolve and change during the study in a manner consistent with the assumptions of an emerging design. Often in qualitative studies, the questions are under continual review and reformulation (as in a grounded theory study). This approach may be problematic for individuals accustomed to quantitative designs, in which the research questions remain fixed throughout the study.
● Use open-ended questions without reference to the literature or theory unless otherwise indicated by a qualitative strategy of inquiry.
● Specify the participants and the research site for the study, if the information has not yet been given. Here is a script for a qualitative central question: _________ (How or what) is the _________ (“story for” for narrative research; “meaning of” the phenomenon for phenomenology; “theory that explains the process of ” for grounded theory; “culture-sharing pattern” for ethnography; “issue” in the “case” for case study) of _________ (central phenomenon) for _________ (participants) at _________ (research site). The following are examples of qualitative research questions drawn from several types of strategies.
Example 7.1
A Qualitative Central Question From an Ethnography Finders (1996) used ethnographic procedures to document the reading of teen magazines by middle-class European American seventh-grade girls. By examining the reading of teen zines (magazines), the researcher explored how the girls perceive and construct their social roles and relationships as they enter junior high school. She asked one guiding central question in her study: How do early adolescent females read literature that falls outside the realm of fiction? These three central questions all begin with the word how; they include open-ended verbs, such as “describe,” and they focus on three aspects of the doctoral experience—returning to school, reentering, and changing. They also mention the participants as women in a doctoral program at a Midwestern research university.
QUANTITATIVE RESEARCH QUESTIONS AND HYPOTHESES
In quantitative studies, investigators use quantitative research questions and hypotheses, and sometimes objectives, to shape and specifically focus the purpose of the study.
Quantitative research questions inquire about the relationships among variables that the investigator seeks to know. They are used frequently in social science research and especially in survey studies.
Quantitative hypotheses, on the other hand, are predictions the researcher makes about the expected relationships among variables. They are numeric estimates of population values based on data collected from samples. Testing of hypotheses employs statistical procedures in which the investigator draws inferences about the population Finders’s (1996) central question begins with how; it uses an openended verb, read; it focuses on a single concept, the literature or teen magazines; and it mentions the participants, adolescent females, as the culture-sharing group. Notice how the author crafted a concise, single question that needed to be answered in the study. It is a broad question stated to permit participants to share diverse perspectives about reading the literature.
Designing Research Example 7.2
Qualitative Central Questions From a Case Study Padula and Miller (1999) conducted a multiple case study that described the experiences of women who went back to school, after a time away, in a psychology doctoral program at a major Midwestern research university. The intent was to document the women’s experiences, providing a gendered and feminist perspective for women in the literature. The authors asked three central questions that guided the inquiry: (a) How do women in a psychology doctoral program describe their decision to return to school? (b) How do women in a psychology doctoral program describe their reentry experiences? And (c) How does returning to graduate school change these women’s lives?
Research Questions and Hypotheses from a study sample. Hypotheses are used often in experiments in which investigators compare groups. Advisers often recommend their use in a formal research project, such as a dissertation or thesis, as a means of stating the direction a study will take. Objectives, on the other hand, indicate the goals or objectives for a study. They often appear in proposals for funding, but tend to be used with less frequency in social and health science research today. Because of this, the focus here will be on research questions and hypotheses. Here is an example of a script for a quantitative research question: Does _________ (name the theory) explain the relationship between _________ (independent variable) and _________ (dependent variable), controlling for the effects of _________ (control variable)? Alternatively, a script for a quantitative null hypothesis might be as follows: There is no significant difference between _________ (the control and experimental groups on the independent variable) on _________ (dependent variable). Guidelines for writing good quantitative research questions and hypotheses include the following.
● The use of variables in research questions or hypotheses is typically limited to three basic approaches. The researcher may compare groups on an independent variable to see its impact on a dependent variable. Alternatively, the investigator may relate one or more independent variables to one or more dependent variables. Third, the researcher may describe responses to the independent, mediating, or dependent variables. Most quantitative research falls into one or more of these three categories.
● The most rigorous form of quantitative research follows from a test of a theory (see Chapter 3) and the specification of research questions or hypotheses that are included in the theory.
● The independent and dependent variables must be measured separately. This procedure reinforces the cause-and-effect logic of quantitative research.
● To eliminate redundancy, write only research questions or hypotheses, not both, unless the hypotheses build on the research questions (discussion follows). Choose the form based on tradition, recommendations from an adviser or faculty committee, or whether past research indicates a prediction about outcomes.
● If hypotheses are used, there are two forms: null and alternative. A null hypothesis represents the traditional approach: it makes a prediction that in the general population, no relationship or no significant difference exists between groups on a variable. The wording is, “There is no difference (or relationship)” between the groups. The following example illustrates a null hypothesis. Designing Research
Example 7.3
A Null Hypothesis An investigator might examine three types of reinforcement for children with autism: verbal cues, a reward, and no reinforcement. The investigator collects behavioral measures assessing social interaction of the children with their siblings. A null hypothesis might read, There is no significant difference between the effects of verbal cues, rewards, and no reinforcement in terms of social interaction for children with autism and their siblings.
● The second form, popular in journal articles, is the alternative or directional hypothesis. The investigator makes a prediction about the expected outcome, basing this prediction on prior literature and studies on the topic that suggest a potential outcome. For example, the researcher may predict that “Scores will be higher for Group A than for Group B” on the dependent variable or that “Group A will change more than Group B” on the outcome. These examples illustrate a directional hypothesis because an expected prediction (e.g., higher, more change) is made. The following illustrates a directional hypothesis.
Example 7.4
Directional Hypotheses Mascarenhas (1989) studied the differences between types of ownership (state-owned, publicly traded, and private) of firms in the offshore drilling industry. Specifically, the study explored such differences as domestic market dominance, international presence, and customer orientation. The study was a controlled field study using quasi-experimental procedures. Hypothesis 1: Publicly traded firms will have higher growth rates than privately held firms. Hypothesis 2: Publicly traded enterprises will have a larger international scope than state-owned and privately held firms. 07-Creswell (RD)-45593:07-Creswell (RD)-45593.qxd 6/20/2008 4:37 PM Page 134 Research Questions and Hypotheses
● Another type of alternative hypothesis is nondirectional—a prediction is made, but the exact form of differences (e.g., higher, lower, more, less) is not specified because the researcher does not know what can be predicted from past literature. Thus, the investigator might write, “There is a difference” between the two groups. An example follows which incorporates both types of hypotheses: 135 Hypothesis 3: State-owned firms will have a greater share of the domestic market than publicly traded or privately held firms. Hypothesis 4: Publicly traded firms will have broader product lines than stateowned and privately held firms. Hypothesis 5: State-owned firms are more likely to have state-owned enterprises as customers overseas. Hypothesis 6: State-owned firms will have a higher customer-base stability than privately held firms. Hypothesis 7:
In less visible contexts, publicly traded firms will employ more advanced technology than state-owned and privately held firms. (Mascarenhas, 1989, pp. 585–588)
Example 7.5
Nondirectional and Directional Hypotheses Sometimes
directional hypotheses are created to examine the relationship among
variables rather than to compare groups. For example,
● Unless the study intentionally employs demographic variables as predictors, use nondemographic variables (i.e., attitudes or behaviors) as independent and dependent variables. Because quantitative studies attempt to verify theories, demographic variables (e.g., age, income level, educational level, and so forth) typically enter these models as intervening (or mediating or moderating) variables instead of major independent variables.
● Use the same pattern of word order in the questions or hypotheses to enable a reader to easily identify the major variables. This calls for repeating key phrases and positioning the variables with the independent first and concluding with the dependent in left-to-right order (as discussed in Chapter 6 on good purpose statements). An example of word order with independent variables stated first in the phrase follows.
Designing Research A Model for Descriptive Questions and Hypotheses
Consider a model for writing questions or hypotheses based on writing descriptive questions (describing something) followed by inferential questions or hypotheses (drawing inferences from a sample to a population). These questions or hypotheses include both independent and dependent variables. In this model, the writer specifies descriptive questions for each independent and dependent variable and important intervening or moderating variables. Inferential questions (or hypotheses) that relate variables or compare groups follow these descriptive questions. A final set of questions may add inferential questions or hypotheses in which variables are controlled.
Example 7.6
Standard Use of Language in Hypotheses 1. There is no relationship between utilization of ancillary support services and academic persistence for non-traditional-aged women college students. 2. There is no relationship between family support systems and academic persistence for non-traditional-aged college women. 3. There is no relationship between ancillary support services and family support systems for non-traditional-aged college women.
Example 7.7
Descriptive and Inferential Questions To illustrate this approach, a researcher wants to examine the relationship of critical thinking skills (an independent variable measured on an instrument)
Research Questions and Hypotheses
This example illustrates how to organize all the research questions into descriptive and inferential questions. In another example, a researcher may want to compare groups, and the language may change to reflect this comparison in the inferential questions. In other studies, many more independent and dependent variables may be present in the model being tested, and a longer list of descriptive and inferential questions would result. I recommend this descriptive–inferential model. This example also illustrates the use of variables to describe as well as relate. It specifies the independent variables in the first position in the questions, the dependent in the second, and the control variables in the third. It employs demographics as controls rather than central variables in the questions, and a reader needs to assume that the questions flow from a theoretical model. to student achievement (a dependent variable measured by grades) in science classes for eighth-grade students in a large metropolitan school district. The researcher controls for the intervening effects of prior grades in science classes and parents’ educational attainment. Following the proposed model, the research questions might be written as follows: Descriptive Questions 1. How do the students rate on critical thinking skills? (A descriptive question focused on the independent variable) 2. What are the student’s achievement levels (or grades) in science classes? (A descriptive question focused on the dependent variable) 3. What are the student’s prior grades in science classes? (A descriptive question focused on the control variable of prior grades) 4. What is the educational attainment of the parents of the eighthgraders? (A descriptive question focused on another control variable, educational attainment of parents) Inferential Questions 1. Does critical thinking ability relate to student achievement? (An inferential question relating the independent and the dependent variables) 2. Does critical thinking ability relate to student achievement, controlling for the effects of prior grades in science and the educational attainment of the eighth-graders’ parents? (An inferential question relating the independent and the dependent variables, controlling for the effects of the two controlled variables)
MIXED METHODS RESEARCH QUESTIONS AND HYPOTHESES
In discussions about methods, researchers typically do not see specific questions or hypotheses especially tailored to mixed methods research. However, discussion has begun concerning the use of mixed methods questions in studies and also how to design them (see Creswell & Plano Clark, 2007; Tashakkori & Creswell, 2007). A strong mixed methods study should start with a mixed methods research question, to shape the methods and the overall design of a study. Because a mixed methods study relies on neither quantitative or qualitative research alone, some combination of the two provides the best information for the research questions and hypotheses. To be considered are what types of questions should be presented and when and what information is most needed to convey the nature of the study:
● Both qualitative and quantitative research questions (or hypotheses) need to be advanced in a mixed methods study in order to narrow and focus the purpose statement. These questions or hypotheses can be advanced at the beginning or when they emerge during a later phase of the research. For example, if the study begins with a quantitative phase, the investigator might introduce hypotheses. Later in the study, when the qualitative phase is addressed, the qualitative research questions appear.
● When writing these questions or hypotheses, follow the guidelines in this chapter for scripting good questions or hypotheses.
● Some attention should be given to the order of the research questions and hypotheses. In a two-phase project, the first-phase questions would come first, followed by the second-phase questions so that readers see them in the order in which they will be addressed in the proposed study. In a single-phase strategy of inquiry, the questions might be ordered according to the method that is given the most weight in the design.
● Include a mixed methods research question that directly addresses the mixing of the quantitative and qualitative strands of the research. This is the question that will be answered in the study based on the mixing (see Creswell & Plano Clark, 2007). This is a new form of question in research methods, and Tashakkori and Creswell (2007, p. 208) call it a “hybrid” or “integrated” question. This question could either be written at the beginning or when it emerges; for instance, in a two-phase study in which one phase builds on the other, the mixed methods questions might be placed in a discussion between the two phases. This can assume one of two forms. The first is to write it in a way that conveys the methods or procedures in a study (e.g., Does the qualitative data help explain the results from the initial quantitative phase of the study?
The second form is to write it in a way that conveys the content of the study (e.g., Does the theme of social support help to explain why some students become bullies in schools? (see Tashakkori & Creswell, 2007.)
● Consider several different ways that all types of research questions (i.e., quantitative, qualitative, and mixed) can be written into a mixed methods study: • Write separate quantitative questions or hypotheses and qualitative questions. These could be written at the beginning of a study or when they appear in the project if the study unfolds in stages or phases. With this approach, the emphasis is placed on the two approaches and not on the mixed methods or integrative component of the study. • Write separate quantitative questions or hypotheses and qualitative questions and follow them with a mixed methods question. This highlights the importance of both the qualitative and quantitative phases of the study as well as their combined strength, and thus is probably the ideal approach. • Write only a mixed methods question that reflects the procedures or the content (or write the mixed methods question in both a procedural and a content approach), and do not include separate quantitative and qualitative questions. This approach would enhance the viewpoint that the study intends to lead to some integration or connection between the quantitative and qualitative phases of the study (i.e., the sum of both parts is greater than each part).
Example 7.8
Hypotheses and Research Questions in a Mixed Methods Study Houtz (1995) provides an example of a two-phase study with the separate quantitative and qualitative research hypotheses and questions stated in sections introducing each phase. She did not use a separate, distinct mixed methods research question. Her study investigated the differences between middle-school (nontraditional) and junior high (traditional) instructional strategies for seventh-grade and eighth-grade students and their attitudes toward science and their science achievement. Her study was conducted at a point when many schools were moving away from the two-year junior high concept to the three-year middle school (including sixth grade) approach to education. In this two-phase study, the first phase involved assessing pre-test (Continued)
Designing Research (Continued) and post-test attitudes and achievement using scales and examination scores. Houtz then followed the quantitative results with qualitative interviews with science teachers, the school principal, and consultants. This second phase helped to explain differences and similarities in the two instructional approaches obtained in the first phase. With a first-phase quantitative study, Houtz (1995, p. 630) mentioned the hypotheses guiding her research: It was hypothesized that there would be no significant difference between students in the middle school and those in the junior high in attitude toward science as a school subject. It was also hypothesized that there would be no significant difference between students in the middle school and those in the junior high in achievement in science. These hypotheses appeared at the beginning of the study as an introduction to the quantitative phase. Prior to the qualitative phase, Houtz raised questions to explore the quantitative results in more depth. Focusing in on the achievement test results, she interviewed science teachers, the principal, and the university consultants and asked three questions: What differences currently exist between the middle school instructional strategy and the junior high instructional strategy at this school in transition? How has this transition period impacted science attitude and achievement of your students? How do teachers feel about this change process? (Houtz, 1995, p. 649) Examining this mixed methods study shows that the author included both quantitative and qualitative questions, specified them at the beginning of each phase of her study, and used good elements for writing both quantitative hypotheses and qualitative research questions. Had Houtz (1995) developed a mixed methods question, it might have been stated from a procedural perspective: How do the interviews with teachers, the principal, and university consultants help to explain any quantitative differences in achievement for middleschool and junior high students? Alternatively, the mixed methods question might have been written from a content orientation, such as: How do the themes mentioned by the teachers help to explain why middleschool children score lower than the junior high students?
Research Questions and Hypotheses
This is a good example of a mixed methods question focused on the intent of mixing, to integrate the qualitative interviews and the quantitative data, the relationship of scores and student performance. This question emphasized what the integration was attempting to accomplish—a comprehensive and nuanced understanding—and at the end of the article, the authors presented evidence answering this question.
SUMMARY
Research questions and hypotheses narrow the purpose statement and become major signposts for readers. Qualitative researchers ask at least one central question and several subquestions. They begin the questions with words such as how or what and use exploratory verbs, such as explore or describe. They pose broad, general questions to allow the participants to explain their ideas. They also focus initially on one central phenomenon of interest. The questions may also mention the participants and the site for the research. Quantitative researchers write either research questions or hypotheses. Both forms include variables that are described, related, categorized into groups for comparison, and the independent and dependent variables are measured separately. In many quantitative proposals, writers use research questions; however, a more formal statement of research employs hypotheses. These hypotheses are predictions about the outcomes of the results, and they may be written as alternative hypotheses specifying the exact results to be expected (more or less, higher or lower of something). They also may be stated in the null form, indicating no expected difference or no relationship between groups on a dependent variable. Typically, the researcher writes the independent variable(s) first, followed by the dependent variable(s). One model for ordering the questions in a quantitative proposal is to begin with descriptive questions followed by the inferential questions that relate variables or compare groups.
Example 7.9
A Mixed Methods Question Written in Terms of Mixing Procedures To what extent and in what ways do qualitative interviews with students and faculty members serve to contribute to a more comprehensive and nuanced understanding of this predicting relationship between CEEPT scores and student academic performance, via integrative mixed methods analysis? (Lee & Greene, 2007) I encourage mixed methods researchers to construct separate mixed methods questions in their studies. This question might be written to emphasize the procedures or the content of the study, and it might be placed at different points. By writing this question, the researcher conveys the importance of integrating or combining the quantitative and qualitative elements. Several models exist for writing mixed methods questions into studies: writing only quantitative questions or hypotheses and qualitative questions, or writing both quantitative questions or hypotheses and qualitative questions followed by a mixed methods question, or writing only a mixed methods question.
Designing Research Writing
Exercises 1. For a qualitative study, write one or two central questions followed by five to seven subquestions. 2. For a quantitative study, write two sets of questions. The first set should be descriptive questions about the independent and dependent variables in the study. The second set should pose questions that relate (or compare) the independent variable(s) with the dependent variable(s). This follows the model presented in this chapter for combining descriptive and inferential questions. 3. Write a mixed methods research question. Write it first as a question incorporating the procedures of your mixed methods study and then rewrite it to incorporate the content. Comment on which approach works best for you.