Typical Descriptive Study Designs

Figure 11-1 presents the commonly used descriptive study design that examines characteristics of a single sample. The design identifies a phenomenon of interest and the variables within the phenomenon, develops conceptual and operational definitions of the variables, and describes the variables. The description of the variables leads to an interpretation of the theoretical meaning of the findings and provides knowledge of the variables and the study population that can be used for future research in the area.

Most studies contain descriptive components; however, the methodology of some studies is confined to the typical descriptive design. This is a critically important design for acquiring knowledge in an area in which little research has been conducted. Peterson, Schwab, van Oostrom, Gravenstein, and Caruso (2010) implemented a descriptive study design to examine the effects of various patient positions on the development of pressure in common areas such as the sacrum and hips. Minimizing skin–support surface interface pressure is important in preventing and controlling pressure ulcers, but the effects of standard patient repositioning on skin interface pressure has not been clearly described. The study design and key results are presented in the abstract, which is reprinted in full:

A descriptive research design is appropriate when the current practice (that is, routine turning of patients) does not seem sufficient to alleviate the problem (risk for pressure ulcers). To discover why traditional nursing interventions have failed to eliminate the pressure ulcers on three at-risk areas of the body, Peterson et al. (2010) decided to explore the currently used techniques (pillows versus wedges) and objectively measure and describe the pressures that participants experienced using digital pressure sensors. This study involved no treatment and had one study group of participants who were measured for pressures in the at-risk areas.

Some descriptive studies use questionnaires (surveys) to describe an identified area of concern. For example, Strand and Lindgren (2010) used a descriptive design to study nurses’ knowledge, attitude, and barriers regarding preventing pressure ulcers in patients in intensive care units (ICUs). The following excerpt from the abstract describes the design of their study:

The results of this descriptive study indicated that nurses are lacking in comprehensive knowledge of pressure ulcers; additionally, time, acuity of the patient’s condition, and the availability of supportive equipment were barriers to the prevention of pressure ulcers. The study focused on surveying nurses’ knowledge of, attitudes toward, and perceived barriers to opportunities for pressure ulcer prevention using a questionnaire. A descriptive design was appropriate for identifying variables that might influence nursing care of patients at risk for pressure ulcers in ICU settings.

Some descriptive studies obtain data from retrospective chart review. For example, Kline and Edwards (2007) conducted a chart review to describe the effectiveness of intrapartum intravenous (IV) insulin on antepartum and intrapartum control of the mother’s diabetes and on the occurrence and severity of hypoglycemia in the neonate. Researchers measured and described the variables of intrapartum IV insulin, antepartum diabetic control, intrapartum diabetic control, and hypoglycemia in the neonate.

It is not uncommon for researchers using a descriptive design to combine quantitative descriptive methods and qualitative methods (mixed methods) (Creswell, 2009). Chapter 10 includes a discussion of different types of mixed-methods approaches. To use this strategy, consult with a researcher experienced in using qualitative methods or include this person as a research partner to appropriately collect, analyze, and interpret qualitative data.

Meghani and Keane (2007) used quantitative and qualitative methods in their study of preference for analgesic treatment in African-American cancer patients. The investigators used demographic data, the Brief Pain Inventory, and in-depth semi-structured interviews. Their sample of 35 patients was taken from three outpatient oncology clinics. Their study identified the major sources of anxiety described by this sample. The goal of this study was to improve our understanding of patients’ needs and assist in the development of specific interventions that might alleviate their problems.

Comparative Descriptive Designs

The comparative descriptive design (Figure 11-2) examines and describes differences in variables in two or more groups that occur naturally in the setting. Descriptive statistics and inferential statistical analyses may be used to examine differences between or among groups. Commonly, the results obtained from these analyses are not generalized to a population because the description is for a very specific sample and would not necessarily apply to a larger population. An example of this design is the study by Cramer, Chen, Roberts, and Clute (2007) of the social and economic impact of community-based prenatal care. The abstract for this study, which is reprinted in full, describes the focus, design, and major findings:

Using a comparative descriptive design, Cramer and colleagues (2007) described the variables incidence of case management, home visits, screening, referral, transportation, and health education, as well as the outcomes low birth weight, infant mortality, adequacy of care, trimester of care, and costs of care in three groups (OHS birth mothers, non-OHS birth mothers, and Douglas County birth mothers) yearly for 3 years. The results of the study were comparisons across the 3 years and among the three groups.

Time-Dimensional Designs

Time-dimensional designs were developed within the discipline of epidemiology, a field that studies the occurrence and distribution of disease among populations. These designs examine trends over time, growth, or sequences and patterns of change. The dimension of time, then, becomes an important factor. Within the field of epidemiology, the samples in time-dimensional studies are called cohorts. Originally, cohorts were age categories; however, the concept has been expanded to apply to groups distinguished by many other variables. Other means of classifying populations that have relevance in relation to time are time of diagnosis, point of entry into a treatment protocol, point of entry into a new lifestyle, and age at which the subject started smoking. An understanding of temporal sequencing is an important prerequisite to examining causality between variables. Thus, the results of these designs lead to description of trends, processes, patterns, and changes over time as well as the development of hypotheses, and are often forerunners of experimental designs (Fawcett & Garity, 2009).

Epidemiological studies that use time-dimensional designs determine the risk factors or causal factors of illness states. Cause determined in this manner is called inferred causality (Kerlinger & Lee, 2000). These studies also examine trends, patterns, and changes over time. The best-known studies in this area are those on smoking and cancer. Because of the strength of studies that have undergone multiple repetitions, the causal link is strong. The strategy is not as powerful as experimental designs in supporting causality; however, in this situation, as in many nursing contexts, one can never ethically conduct a true experiment. A true experiment requires that there be an experimental group (who would not smoke) and a control group (who smoke). The participants must be randomly assigned to one of these groups. Therefore, without being provided a choice, some individuals would be required to smoke and others would be required to abstain from smoking over a long period.

Epidemiologists use two strategies to examine changes over time: retrospective studies and prospective studies. The norm in epidemiological studies is to use the word cohorts to refer to groups of subjects in prospective studies, but the term is generally not used in retrospective studies. In retrospective studies, both the proposed cause and the proposed effect have already occurred. For example, the subjects could have a specific type of cancer, and the researcher could be searching for commonalities among subjects that may have led to the development of that type of cancer. In a prospective cohort study, causes may have occurred, but the proposed effect has not.

The Framingham study is the best-known example of a prospective study (U.S. Department of Health and Human Services, 1968). In this study, researchers monitored members of a community for 20 years and examined variables such as dietary patterns, exercise, weight, and blood lipid levels. As the subjects experienced illnesses, such as heart disease, hypertension, and lung disease, their illnesses could be related to previously identified variables.

Prospective studies are considered more powerful than retrospective studies in inferring causality, because the researcher can demonstrate that the risk factors occurred before the illness and are positively related to the illness. Both designs are important for use in nursing studies, because a person’s responses to health situations are patterns that developed long before the health situation occurred. These patterns then influence the person’s responses to nursing interventions. Several designs are used to conduct time-dimensional studies: longitudinal, cross-sectional, trend, and event or treatment partitioning. These designs are discussed in the following sections.

Longitudinal Designs

Longitudinal designs examine changes in the same subjects over time. They are sometimes called panel designs (Figure 11-3). Longitudinal designs are expensive and require a long period of researcher and subject commitment. The area to be studied, the variables, and their measurement must be clearly identified before data collection begins. Measurement must be carefully planned and implemented because the measures will be used repeatedly over time. If children are being studied, the measures must be valid for all the ages being studied. To use this design, researchers must be familiar with how the construct being measured changes and its patterns and trends over time. In addition, they need to provide a clear rationale for the points of time they have selected for measurement. There is often a bias in selection of subjects because of the requirement for a long-term commitment. Individuals participating in a study conducted over long periods might differ in some important ways from the target population. In addition, attrition or loss of subjects from the study can be high and can decrease the validity of findings.

The sample size calculated with power analysis needs to take into consideration the potential attrition rate when determining the final number of subjects to recruit. As a researcher, you must invest considerable energy in developing effective strategies to maintain the sample (see Chapter 15). The period during which subjects will be recruited into the study must be carefully planned, and a timeline depicting data collection points for each subject must be developed to enable planning for the numbers and availability of data collectors. If this issue is not carefully thought out, data collectors may be confronted with the need to recruit new subjects while they are attempting to collect data scheduled for subjects recruited earlier. You must also decide whether you will use a single data collector to obtain all data from a particular subject or whether you will use a different data collector at each point to ensure that data are collected blindly.

Because of the large volumes of data acquired in a longitudinal study, you must give careful attention to strategies for managing the data. The repetition of measures requires that data analysis be carefully thought through. Analyses commonly used are repeated measures analyses of variance, multivariate analyses of variance (MANOVA), regression analysis, cluster analysis, and time-series analysis (see Chapters 24 and 25) (Corty, 2007; Munro, 2005).

Lee, Chaboyer, and Wallis (2010) conducted a descriptive study using a longitudinal cohort design. This study was conducted to describe the perceptions and physical manifestations of injury and illness of patients with traumatic injury and to examine the changes they experienced over time. The following abstract from the study demonstrates the background, longitudinal design, key results, conclusions, and implications for practice:

“Background: Traumatic injury has attracted global concern because it is the major reason for death and disability in people under 45 years old. One model, the Common Sense Model of Illness Representation (CSMIR), has the potential to help individuals adjust to changes in health status such as traumatic injury.

Design: Longitudinal study design.

Methods: This study was conducted using data collected prior to hospital discharge and at three and six months after hospital discharge. One individual question form and the Chinese Illness Perception Questionnaire Revised (IPQ-R) (Trauma) were used to collect demographic data, clinical data, and illness representations.

Results: A total of 114 participants completed the survey three times. The overall response rate was 79.7%. Six subscales of the Chinese IPQ-R (Trauma)… identity, emotional representations, consequences, controllability, illness coherence, and causes… changed significantly over time. Two subscales, Timeline (acute/chronic) and Timeline Cyclical, did not change significantly.

Conclusions: Based on these findings, there may be a window of opportunity to provide appropriate interventions to individuals with traumatic injury at each time point. The results of this study have implications for nursing practice and further nursing research.

Relevance to Clinical Practice: Understanding illness representation in patients with traumatic injury may help nurses to provide anticipatory guidance and to design nursing interventions before and after hospital discharge, ultimately to improve health outcomes of those patients.” (Lee et al., 2010, p. 556)

Cross-Sectional Designs

Cross-sectional designs examine groups of subjects in various stages of development, trends, patterns, and changes simultaneously with the intent to describe changes in the phenomenon across stages (see Figure 11-4). The assumption is that the stages are part of a process that will progress over time. Selecting subjects at various points in the process provides important information about the totality of the process, even though the same subjects are not monitored through the entire process. The processes of development selected for the study might be related to age, position in an educational system, growth pattern, or stages of maturation or personal growth (if they could be clearly enough defined that criteria could be developed for inclusion within differentiated groups or disease stages). Subjects are then categorized by group, and data on the selected variables are collected at a single point in time.

For example, suppose you wish to study grief reactions at various periods after the death of a spouse. With a cross-sectional design, you could study a group of individuals whose spouses had died 1 week ago, another group composed of individuals whose losses occurred 6 months ago, and other groups whose losses occurred 1 year, 2 years, and 5 years ago (see Figure 11-4). You could study all of these groups during one period of time, but you could describe a pattern of grief reactions over a 5-year period. The design is not as strong as the longitudinal design, in which the same participants continue in the study over time, thus eliminating some variance, but it allows some understanding of the phenomenon over time when time allowed for the study is limited.

Sidani et al. (2007) conducted a cross-sectional study titled “Outcomes of Nurse Practitioners in Acute Care: An Exploration.” The following abstract describes the design of their study:

“The purpose of this study was to compare the outcomes achieved by adult patients who did (n = 78) and did not (n = 45) receive care by acute care nurse practitioners (ACNPs), within one week following discharge. A comparative, cross-sectional design was used. Consenting patients completed the outcome measures within one week following discharge. The outcomes included satisfaction with care, functional status, symptom resolution, and sense of well-being, which were measured with established instruments. The two groups of patients were equivalent in terms of their demographic profile and severity of condition. The results indicated that patients who received ACNP care, as compared to those who did not, reported higher levels of satisfaction with care and of physical, psychological, and social functioning. These findings provide preliminary evidence supporting the contribution of ACNPs to high quality care. However, the small sample size limits the generalizability of the study findings.” (Sidani et al., 2007, p. 1)

Trend Designs

Trend designs examine changes in the general population in relation to a particular phenomenon (see Figure 11-5). The researchers select different samples of subjects from the same population at preset intervals of time, and at each selected time, they collect data from that particular sample. Researchers need to be able to justify generalizing from the samples to the population under study. Analysis involves strategies to predict future trends by examining past trends. Harris, Gordon-Larsen, Chantala, and Udry (2006, p. 74) used a trend design to describe “longitudinal trends in race/ethnic disparities in 20 leading health indicators from Healthy People 2010 [U.S. Department of Health and Human Services, 2000] across multiple domains from adolescence to young adulthood.” These researchers examined the study trends in an ethnically diverse, national database, and their study design is described in the following excerpt.

“Design, Setting, and Participants: Nationally representative data for more than 14,000 adolescents enrolled in wave I (1994-1995) or wave II (1996) of the National Longitudinal Study of Adolescent Health (Add Health) were followed up into adulthood (wave III; 2001-2002). We fit longitudinal regression models to assess and contrast the trend in health indicators among racial/ethnic groups of adolescents as they transition into adulthood….

Results: Diet, inactivity, obesity, health care access, substance use, and reproductive health worsened with age. Perceived health, mental health, and exposure to violence improved with age. On most health indicators, white and Asian subjects were at the lowest risk and Native American subjects at the highest risk. Although white subjects had more favorable health in adolescence, they experienced the greatest declines by young adulthood. No single race/ethnic group consistently leads or falters in health across all indicators.” (Harris et al., 2006, p. 74)

Harris et al. (2006) found the trend design to be an effective way to examine health indicators of Americans over time. They noted for 15 of 20 indicators that the health risk increased and access to health care decreased from teen to adult years for most Americans. The health indicators varied over time by gender and race/ethnicity, causing the health disparities to fluctuate over time.

Event-Partitioning Designs

A merger of the cross-sectional or longitudinal and trend designs, the event-partitioning design, is used in some cases to enlarge sample size and to avoid the effects of history on the validity of findings. Cook and Campbell (1979) referred to these as cohort designs with treatment partitioning. Figure 11-6 shows a model of the cross-sectional study design with treatment partitioning, and Figure 11-7 provides the model of a longitudinal design with treatment partitioning. The term treatment is used loosely here to mean a key event that is thought to lead to change. In a descriptive study, the researcher would not cause or manipulate the key event but rather would clearly define it so that when it occurred naturally, it would be recognized.

For example, you could use the event-partitioning design to study subjects who have completed programs to stop smoking. Smoking behaviors and incidence of smoking-related diseases might be measured at intervals of 1 year for a 5-year period. However, the number of subjects available at one time might be insufficient for you to adequately analyze findings. Therefore, you could use subjects from several programs offered at different times. You would examine the data in terms of the relative time since the subjects’ completion of the stop-smoking program, not the absolute length of time. Data would be assumed to be comparable, and a larger sample size would be available for analysis of changes over time.

Sutton’s (2007) dissertation described a study to examine the relationship of preoperative education for the aging adult and anxiety. Most individuals experience anxiety in anticipation of surgery, and they expect education and support to help alleviate their anxiety prior to entering the operating room. Because a growing number of surgical procedures are being conducted on an outpatient basis (day surgery), there is limited time for healthcare personnel to provide psychological preparation of patients for the perioperative process. Sutton used an event-partitioning design and correlational analysis to examine the relationship between education and the state anxiety experienced by aging adults in the preoperative setting. Quantitative data were collected using the Visual Analog Scale (VAS) to measure state anxiety in a pre-test and post-test format, with treatment consisting of a scripted preoperative educational presentation.

Patients scheduled for general anesthesia for a surgical procedure in two acute care facilities in southern West Virginia were asked to participate in this study over a 4-week time frame. The sample included 52 pre-surgical patients, ages 65 to 94 years, who were asked to participate in the study. Participants were asked to score their state anxiety on the VAS prior to the scripted educational presentation to establish a baseline anxiety level. State anxiety is the emotion a person experiences in a particular situation, versus trait anxiety, which is the innate anxiety of a person. Upon completion of this presentation, the participants were given an opportunity to ask questions and receive answers. They were then asked to indicate their state anxiety on the VAS. The study results indicated that 75% of the study participants reported a decrease in their state anxiety levels following the preoperative educational presentation. This intervention requires further testing using a quasi-experimental study design but has the potential to reduce state anxiety in individuals experiencing day surgery (Sutton, 2007).

Case Study Designs

The case study design involves an intensive exploration of a single unit of study, such as a person, family, group, community, or institution, or of a small number of subjects. Although the number of subjects tends to be small, the number of variables involved is usually large. In fact, it is important to examine all variables that might have an impact on the situation being studied.

Case studies were commonly used in nursing research in the 1970s. Their use then declined, but they are beginning to appear in the literature more frequently today. Well-designed case studies are good sources of descriptive information and can be used as evidence for or against theories. Case studies can use a mixed-methods approach, incorporating both quantitative and qualitative methods (Creswell, 2009; Fawcett & Garity, 2009). Sterling and McNally (1992) recommended single-subject case studies for examining process-based nursing practice. This strategy allows the researcher to investigate daily observations and interventions that are a common aspect of nursing practice.

Case studies also can demonstrate the effectiveness of specific therapeutic techniques. In fact, by reporting a case study, the researcher introduces the technique to other practitioners. The case study design also has potential for revealing important findings that can generate new hypotheses for testing. Thus, the case study can lead to the design of large sample studies to examine factors identified through the case study. For example, Sprague and Chang (2011) used a single subject case study design to examine the effect of acupuncture on the treatment of chronic pain. Their study abstract identifies the key elements of their study:

Sprague and Chang (2011) noted that this patient had dramatic improvements in depression, disability, and pain scores over the 6 months of the study. Thus, this case study justifies conducting further clinical studies to determine the effectiveness of acupuncture in the management of patients with CRPS. They recommended conducting studies with larger samples and randomized controlled treatment designs.

How you design a case study depends on the circumstances of the case but usually includes an element of time. History and previous behavior patterns are usually explored in detail. As the case study proceeds, you may become aware of components important to the phenomenon being examined that were not originally built into the study. A case study is likely to have both quantitative and qualitative elements; and if the study incorporates both of these components, the study design must clearly present this fact (Creswell, 2009). Methods used to analyze and interpret qualitative data need to be carefully planned. Consultation with a qualitative researcher can strengthen the study. Large volumes of data are generally obtained during a case study. Organizing the findings of a case study into a coherent whole is a difficult but critical component of the study (Fawcett & Garity, 2009). Generalizing study findings in the statistical sense is not appropriate; however, generalizing the findings to theory is appropriate and important (Crombie & Davies, 1996; Gray, 1998; Sandelowski, 1996).

Not all case studies are research. Many of the articles referring to case studies are clinical practice articles, in which a clinical situation is reported for the purpose of illustrating clinical practice, problems in clinical practice, or changes that need to be made in clinical practice. These articles do not use research methods but, rather, describe events out of the patient record or the writer’s personal experience.

Descriptive Correlational Designs

A descriptive correlational design examines the relationships that exist in a situation. Using this design facilitates the identification of many interrelationships in a situation in a short time. Although the descriptive design discussed earlier may reveal relationships among variables, the descriptive correlational design focuses specifically on relationships among study variables. Descriptive correlational studies may lead to hypotheses for later studies. Figure 11-8 provides a model of a typical descriptive correlational design for examining a relationship between two research variables. This design can be expanded to include examination of relationships among several study variables.

A descriptive correlational study may examine variables in a situation that has already occurred or is currently occurring. No attempt is made to control or manipulate the situation. As with descriptive studies, variables must be clearly identified and defined. An example of a descriptive correlational design is the study by Bailey, Sabbagh, Loiselle, Boileau, and McVey (2010, p. 114) titled “Supporting families in the ICU: A descriptive correlational study of informational support, anxiety, and satisfaction with care.” The researchers conducted this study to describe family members’ perceptions of informational support, anxiety, and satisfaction with care and to examine the relationships among these variables. The design for this study is described in the following excerpt from its abstract:

By implementing a descriptive, correlational research design, Bailey et al. (2010) were able to describe their study variables and determine relationships among them. The ultimate objective of the researchers was to further refine a local informational support initiative for families with members in the intensive care unit. Thus, this research provided the basis for development of an intervention that might be tested in future quasi-experimental or experimental studies.

This study had a descriptive correlational design, as evidenced by the single study group, the absence of treatment, and the use of descriptive and correlational statistical techniques to analyze study data. The study variables informational support, anxiety, and satisfaction with care were described with means, standard deviations, and ranges (see Chapter 22). The relationships among these three variables were determined using Pearson’s product moment correlational coefficient (see Chapter 23).

Predictive Designs

Predictive designs are used to predict the value of one variable on the basis of values obtained from another variable or variables. Prediction is one approach you can use to examine causal relationships between or among variables. Because causal phenomena are being examined, the terms dependent and independent are used to describe the variables. One variable (the one to be predicted) is classified as the dependent variable, and all other variables (those that are predictors) are classified as independent variables.

The aim of a predictive design is to predict the level of the dependent variable from the independent variables. Figure 11-9 is a model of a predictive design with two independent variables used to predict the dependent variable. Independent variables most effective in prediction are highly correlated with the dependent variable but not highly correlated with other independent variables used in the study. Predictive designs require you to develop a theory-based mathematical hypothesis proposing the independent variables that are expected to predict the dependent variable effectively. You can then test the hypothesis using regression analysis (see Chapter 24) (Corty, 2007; Munro, 2005). Predictive studies are also used to establish the predictive validity of measurement scales.

Mancuso (2010) conducted a correlational study with a cross-sectional predictive correlational design to examine the impact of health literacy and patient trust on glycemic control in diabetic adults. The independent variables of patient trust, health literacy, knowledge of diabetes, performance of self-care activities, and depression were used to predict the dependent variable of glycosylated hemoglobin concentration (HbA1c). Regression analysis was conducted to determine how effective the independent variables were in predicting the dependent variable of HbAlc. The following except from the abstract describes the study design, results, and conclusions:

Mancuso (2010) reported that patient trust and depression were important factors that significantly predicted HbA1c (R² = 0.320; R²_adj = 0.285; F(5, 96) = 9.047; p < 0.05). However, knowledge of diabetes, health literacy, and self-care activities was not a significant predictor of glycemic control. She recommended further research examining depression and patient trust as predictors of glycemic control and additional studies to explore other influences on or barriers to glycemic control. Mancuso’s implementation of a predictive correlational design and conduct of regression analysis were appropriate to address the study purpose and research questions.

Model-Testing Designs

Some studies are designed specifically to test the accuracy of a hypothesized causal model. The model-testing design requires that all variables relevant to the model be measured. A large, heterogeneous sample is required. All the paths expressing relationships between concepts are identified, and a conceptual map is developed (see Figure 11-10). The analysis determines whether or not the data are consistent with the model. For some studies, you might set aside data from half of the subjects and not include them in the initial path analysis. You might use these data from the second half of the subjects to test the fit of the paths defined by the initial analysis.

Variables are classified into three categories: exogenous variables, endogenous variables, and residual variables. Exogenous variables are within the theoretical model but are caused by factors outside this model. Endogenous variables are those whose variation is explained within the theoretical model. Exogenous variables influence the variation of endogenous variables. Residual variables indicate the effect of unmeasured variables not included in the model. These variables explain some of the variance found in the data but not the variance within the model (Mason-Hawkes & Holm, 1989; Norris, 2005a).

In Figure 11-10, the illustration of a model-testing design, paths are drawn to demonstrate directions of cause and effect. The arrows (paths) from the exogenous variables 1, 2, and 3 lead to the endogenous variable 4, indicating that variable 4 is theoretically proposed to be caused by variables 1, 2, and 3. The arrow (path) from endogenous variable 4 to endogenous variable 5 indicates that variable 4 theoretically causes variable 5.

To measure exogenous and endogenous variables, you would collect data from the subjects and analyze the accuracy of the proposed paths. Historically, these analysis procedures were performed with a series of regression analyses. Researchers now conduct statistical procedures that have been developed specifically for path analysis using the computer programs LISREL and EQS (Norris, 2005a). Structural equation modeling is a commonly used statistical procedure (Norris, 2005b). Path coefficients are calculated that indicate the effect that one variable has on another. The amount of variance explained by the model, as well as the fit between the path coefficients and the theoretical model, indicates the accuracy of the theory. Variance that is not accounted for in the statistical analysis is attributed to residual variables (variables a and b) not included in the analyses (Mason-Hawkes & Holm, 1989; Norris, 2005a).

An example of this design is the Cummings, Estabrooks, Midodzi, Wallin, and Hayduk (2007) study testing a model of the influence of organizational characteristics and context on research utilization in nursing. The following study abstract identifies the study purpose, design, results, and conclusions:

The Nursing Interventions Classification

The Nursing Interventions Classification (NIC) is a standardized language used to describe interventions or treatments performed by nurses in research and practice. Each intervention consists of a label, a definition, and a set of activities performed by nurses carrying out the intervention. The NIC was initiated by the University of Iowa in Iowa City, IA, in 1987 (NIC, 2011). The intervention labels developed over the last 20 years were derived from nursing education and practice. The research to develop the NIC was initiated in 1987 and progressed through four phases that overlapped in time: “Phase I: Construction of the Classification (1987-1992); Phase II: Construction of the Taxonomy (1990-1995); Phase III: Clinical Testing and Refinement (1993-1997); and Phase IV: Use and Maintenance (1996-ongoing)” (Bulechek, Butcher, & Dochterman, 2008, p. 5). The research methods used to develop the classification included content analysis, surveys, focus groups, similarity analysis, and hierarchical clustering. The NIC Taxonomy contained seven domains: Domain 1: Physiological: Basic; Domain 2: Physiological: Complex; Domain 3: Behavioral; Domain 4: Safety; Domain 5: Family; Domain 6: Health System; and Domain 7: Community. There are a total of 30 classes under the seven domains (Bowles & Naylor, 1996; Bulechek et al., 2008).

Tripp-Reimer, Woodworth, McCloskey, and Bulechek (1996), in their analysis of the structure of the NIC interventions, identified three dimensions: intensity of care, focus of care, and complexity of care. A high intensity of care is associated with the physiological illness level of the patient and the emergency nature of the illness. The dimension of intensity of care includes indicators of (1) intensity (or acuity) and (2) whether the care is typical or novel. The dimension of focus of care addresses (1) the target of the intervention, ranging from the individual to the system; (2) whether the care action is direct or on behalf of the patient; and (3) the continuum of practice from independent to collaborative actions. The dimension of complexity of care encompasses a range of knowledge, skill, and urgency of the interventions (Bulechek et al., 2008).

The interventions in the NIC have been subjected to multiple studies examining the effects on different populations and the effects of varying degrees of intensity. The 5th edition of the Nursing Interventions Classification, developed by faculty at the University of Iowa, included 542 research-based interventions (Bulechek et al., 2008). NIC development continues through the Center for Nursing Classification & Clinical Effectiveness located at the University of Iowa, and you can email them with questions (classification-center@uIowa.edu/; see Chapter 14 for more details on NIC).

Currently, studies are being conducted to determine the outcomes of each intervention and to establish links between the intervention and outcomes at varying points in time after the intervention has been implemented. Outcomes that occur immediately following the intervention are easiest to determine. However, the most important outcomes may be those that occur after a client has been discharged or several weeks or months after the intervention. Table 11-1 provides some of the most current examples of the research related to the NIC and the Nursing Outcomes Classification (NOC) being conducted nationally and internationally. This information is critical for ensuring the quality of care provided by nurses and justifying nursing actions in a cost-conscious market (Doran, 2011). For a more extensive discussion of the importance of linking interventions with outcomes measures, see Chapter 13.