Professional nurses make many important decisions daily. In fact, some authors argue effective clinical decision making (CDM) is one of the principal skills that distinguishes professional nursing from technical nursing (Hughes & Young, 1990). Bakalis and Watson (2005) and White (2003) noted that nurses who make effective clinical decisions provide safer, more competent nursing care and influence patient outcomes.
Because CDM is an important acquired skill for nurses, the process of learning it must be introduced and practiced throughout prelicensure nursing education programs. Several influences impact the learning and adeptness of CDM. Self-confidence and anxiety are affective influences (termed emotional barriers) to consider when teaching and learning the process of CDM (Baxter & Rideout, 2006; Haffer & Raingruber, 1998). If nurse educators are more fully aware of the CDM process in students and what affective states influence CDM, they can foster students’ confidence and lessen their anxiety (Itano, 1989).
Problem and Purpose
After an extensive review of the literature, no instrument was found that measures the perceived levels of self-confidence and anxiety in nursing students as they progress through the process of CDM. Although a surfeit of research related to CDM has been conducted using qualitative (Baxter & Boblin, 2008) and quantitative (Bakalis & Watson, 2005) methods, the instruments used for quantitative inquiry most often have had limited psychometric testing.
Because the development of the process of CDM is imperative for novice nurses (Baxter & Boblin, 2008; O’Neill, Dluhy, & Chin, 2005) and because the emotional barriers of low self-confidence and high anxiety have been shown to affect decision making processes (Haffer & Raingruber, 1998), the purpose of this methodological research study was to test, validate, and establish psychometric properties for the Nursing Anxiety and Self-Confidence with Clinical Decision Making (NASC-CDM) scale. The scale is a 6-point, Likert-type, norm-referenced, self-report instrument (Polit & Beck, 2008; Waltz, Strickland, & Lenz, 2010), designed to measure the level of self-confidence and level of anxiety experienced by undergraduate nursing students as they progress through the CDM process.
Two concepts of psychometric theory essential to instrument development are reliability and validity (Rust & Golombok, 2009). Three research questions were addressed in the study: two related to the assessment of the scale’s validity and one related to the scale’s reliability.
A domain-referenced approach (Gable & Wolf, 1993) was used to examine CDM literature, to ensure content validity, and to create an inclusive item pool for the NASC-CDM scale. Literature was also reviewed to understand the relationship between self-confidence and anxiety and CDM. The extensive literature review resulted in the establishment of four content domains of CDM. Items on the initial draft of the scale were generated from these domains.
Content Domain One: Investigating Information and Cues
Essential to this domain of the decision making process are such factors as attending to available patient cues and recognizing problematic elements from these cues (Elstein, Kagan, Shulman, Hilliard, & Loupe, 1972; Kelly, 1964). Other important factors such as patient chart information and a basic knowledge base (called pre-encounter data) are foundational to CDM (Benner, Sutphen, Leonard, & Day, 2010; Standing, 2007). Active listening, knowing the patients, connecting with them, and observing nonverbal cues (Baxter & Boblin, 2008; White, 2003) are identified as crucial to CDM. Finally, intuition is validated as important in the CDM process (Rew, 2000; Tanner, 2006).
Content Domain Two: Interpreting Information and Meanings
Interpreting information and determining the relevancy or irrelevancy of information can be difficult for novice clinicians (Benner et al., 2010; O’Neill, Dluhy, Hansen, & Ryan, 2006). Elstein, Shulman, and Sprafka (1978) found that medical students gather excessive data but then overinterpret, underinterpret, or misinterpret the information, whereas Kelly (1964) stated that inexperienced nurses often ignore highly relevant cues. Knowledge and experience are also leading influences on CDM (Benner, 2001; Itano, 1989). However, novice clinicians lack extensive nursing knowledge and widespread clinical experiences. Students in a simulated learning environment admitted to anxiousness during the activity but also admitted to gaining experience about clinical judgment by observing their peers (Lasater, 2007). Increased exposure to clinical situations offers novice practitioners a chance to gain nursing knowledge and experience.
Content Domain Three: Integrating Findings and Illuminating Options
Novice clinicians tend to be rule driven; they often have difficulty comprehending the full clinical picture and seeing patterns among cues (Benner, 2001; Lauri & Salanterä, 1995). The accuracy of decision making improves when cues are clustered to see the complete clinical picture (Elstein et al., 1978; O’Neill et al., 2006). The assessment of the risks and benefits of decision options is important to CDM. O’Neill et al. (2005), as well as Baxter and Boblin (2008), indicated that nurses rank the degree of risk of patient problems and interventions, then implement interventions to decrease the likelihood of the worst risk occurring. Using appropriate resources to aid the CDM process is crucial for novice clinicians. Such resources are described as staff nurses (Baxter & Boblin, 2008), clinical faculty members (Benner et al., 2010), and evidence-based literature (Lauri et al., 2001).
Content Domain Four: Intervening and Reflecting on the Decision Process
Action or intervention is elemental to CDM (Bakalis & Watson, 2005; Tschikota, 1993). Reflective practice is essential for gaining knowledge, improving clinical reasoning skills (Tanner, 2006), and improving confidence with decision making skills (Hoffman & Elwin, 2004). Professional accountability for decisions made within one’s own clinical practice is also important (Muir, 2004). Seldomridge (1997) and Benner et al. (2010) argued that taking responsibility for ones’ decisions is stressful. Although students often seek support while making decisions, they need to be prepared to be accountable for their decisions (Baxter & Boblin, 2008).
Emotional Barriers of Self-Confidence and Anxiety
Expert and novice nurses differ regarding the cognitive processes of CDM, but there are also affective influences that affect CDM in the novice nurse. O’Neill (1996) found that the more confident the nurse, the better the ability to consider plausible decision options. White (2003) indicated that when self-confidence was stronger, students were better able to focus on the patient, but when self-confidence was diminished, students focused on their own anxiety.
Most nurse educators would agree that prelicensure nursing students often experience anxiety and frequently lack confidence. Empirical research cannot firmly conclude whether lesser amounts of anxiety promote self-confidence or whether higher amounts of self-confidence curb anxiety. Various authors have argued that each is the case (Mellalieu, Neil, & Hanton, 2006; White, 2009). Regardless, the reality is that emotional barriers strongly affect novice clinicians. Novice decision makers need a safe, supportive environment in which to practice this skill.
Research that advances the science of nursing is underpinned by theory (Gall, Gall, & Borg, 2007). The tenets of one learning theory and two embedded theoretical nursing models were foundational to the development, testing, and validation of the NASC-CDM scale.
Social Cognitive Theory
Social cognitive theory (SCT) comprises the concepts of cognition, regulation, reinforcement, self-efficacy, and emotional arousal (Bandura, 1977b, 1997). Self-reinforcement is a means of regulating behavior and allows a person to self-correct as necessary (Bandura, 1977b) based on considerations of effort expenditure and expectancy. Efficacy-expectancy (i.e., self-efficacy) refers to the belief that people can produce the effects they desire by their own actions (Bandura, 1997). To be effective decision makers, nursing students must believe they can be successful with the skill.
Bandura (1977a) described emotional arousal as a source of self-efficacy. Emotional arousal equates to the level of anxiety or physiological arousal a person experiences when confronted with threatening situations (Bandura, 1997). Control over anxiety is crucial. Nursing students must be able to realize and curb their level of emotional arousal to engage fully in the CDM process.
Clinical Decision Making and Novice Clinical Reasoning Models
Two embedded models reveal the relationship between the emotional barriers and the development of CDM in novice clinicians (O’Neill et al., 2005). The first model highlights the multidimensional CDM process utilized by experienced nurses and notes the importance of working nursing knowledge. Novice clinicians have limited working nursing knowledge. Thus, O’Neill et al. conceived the second model, the novice clinical reasoning model (NCRM). This model highlights managing emotional barriers (i.e., high anxiety and diminished confidence) and experiencing positive clinical situations as imperative to foster CDM (O’Neill et al., 2005).
Many authors have supported that more experiences promote more confidence (Lindsey & Kleiner, 2005), successful outcomes enhance confidence (Savitsky, Medvec, Charlton, & Gilovich, 1998), and higher confidence promotes increased performance (Schunk & Pajares, 2005)—all of which diminish anxiety arousal. Hence, the design of the NASC-CDM scale’s items, as well as its primary intent, is fundamentally similar to the principals of the theoretical frameworks that grounded the current study.
Early Instrument Development
Numerous a priori decisions are imperative when designing a new scale. During the early construction phase of the NASC-CDM scale, choices were made based on literature from instrument development experts.
Initial Design, Item Pool, and Content Validity
First, a comprehensive concept analysis of self-confidence was conducted (White, 2009). Second, the NASC-CDM scale was conceived as a hybrid scale because it examines the cognitive process of clinical decision making, but its ultimate purpose is to assess the affective domain (Gable & Wolf, 1993; Polit & Beck, 2008). Third, responses to items on the scale were designed as rank-ordered, Likert type, but summated raw scores are calculated. Therefore, the scale is considered as interval level for purposes of data analysis (Gall et al., 2007).
A preliminary appraisal of content validity of the 82-item first draft was performed by five internationally known CDM experts to assess the scale for relevancy, clarity, and comprehensiveness (DeVellis, 2012). Both item content validity and scale content validity indices were calculated (Polit, Beck, & Owen, 2007). Based on feedback from the expert panel and the literature related to instrument development, a 6-point, forced-choice response option format, ranging from 0 = not at all to 6 = totally, was chosen. Six anchor points allow a wider array of responses and better discrimination (DeVellis, 2012); a 5-point to 7-point scale tends to be more reliable and ensures increased stability during factor analytic procedures (Comrey, 1988; Gable & Wolf, 1993), and forced-choice formats tend to avoid ambiguity or neutrality and are able to gather more useful data (Coombs & Coombs, 1976).
Item Reduction, Face Validity, and Pilot Version
Items were reduced or revised based on expert panelist feedback and content validity indices. The second draft of the NASC-CDM scale was critiqued by RNs and undergraduate nursing students, including many with English as a second language, to ensure item clarity and readability and to ensure face validity (DeVellis, 2012). After further revision and reduction of items, the scale was finalized into the 41-item draft used for pilot testing.
Instrument Testing Phases
Two phases of testing were completed. To maintain constant conditions, similar sampling, recruitment, and data collection procedures were used during both phases of instrument testing (Rust & Golombok, 2009).
A convenience sampling framework was used for the study. Institutional review board approval was obtained from the university where the author was affiliated and from 54 institutions of higher education (27 associate and 27 baccalaureate degree nursing programs that met the inclusion criteria) from four states in the northeast portion of the United States. One half of the 54 eligible programs were randomly chosen to participate in the pilot-testing phase during a fall academic semester. To minimize sampling bias, the remaining programs were invited to participate in the main-testing phase during the subsequent spring academic semester (Polit & Beck, 2008). After institutional review board approval and Dean/Program Director of Nursing permission was secured, prelicensure nursing students within their final two clinical semesters of completing their program were recruited for the study.
Data Collection Procedures
Nursing faculty member liaisons from each program e-mailed information sent from the researcher (i.e., via recruitment flyer, survey package, and reminders) and forwarded that information onto students. The researcher established a rapport with the faculty member liaisons, made numerous face-to-face recruitment visits to nursing classes (Gable & Wolf, 1993), and sent two e-mail reminders about the study to students through their faculty liaison (Crawford, Couper, & Lamias, 2001) to maximize the response rate.
A secure encrypted electronic survey platform was used to deploy the survey package. Student participants voluntarily completed the online survey package, which included the informed consent, general directions, demographic questions, the NASC-CDM scale, a brief general self-efficacy scale, and a brief general anxiety scale. Completion time took 15 to 20 minutes. The General Perceived Self-Efficacy (GSE) scale (Schwarzer, Mueller, & Greenglass, 1999) and the Generalized Anxiety Disorder-7 (GAD-7) scale (Kroenke, Spitzer, Williams, Monahan, & Löwe, 2007) were used with permission and were completed by all student participants to assess convergent validity.
Steps were taken to minimize social desirability bias (Rust & Golombok, 2009), which may be high in a student population. Survey directions informed participants to answer honestly and choose the most accurate response because answers were neither right nor wrong. Students could skip a question and exit the survey at any time. Confidentiality and anonymity of all participants was maintained throughout the study.
Pilot testing of the tool (41 items) was completed to preliminarily assess its reliability and validity (DeVon et al., 2007). The scale was revised and items were reduced based on data analysis from this sample. Main testing of the revised tool (32 items) was completed by a second sample to assess its reliability and validity.
Data Analysis and Results
SPSS® version 17.0.0 with GradPack software was used for all data analysis procedures. Similar data analytic procedures were used for both first and second sample data to make appropriate comparisons. The self-confidence and anxiety subscales are two similar but separate scales; therefore, data analysis was completed independently for each. Statistical assumptions were tested prior to data analysis.
Approximately 9.5% of data from each sample were noted as missing-not-at-random after analysis by SPSS Missing Values Analysis software (i.e., participants may have completed demographic information only). These participants were excluded from data analysis. Subsequently, the use of modal replacement for missing values (Munro, 2005) replaced less than 1% of survey package data. Univariate outliers were replaced with the largest data value, which was not an outlier. Multivariate outliers were identified using linear regression and locating the maximum value for Mahalanobis distance; this determined the number of cases to be excluded from factor analytic procedures (Tabachnick & Fidell, 2007).
Six associate and six baccalaureate nursing programs participated in the pilot-testing phase (n = 303), for a response rate of 30.6%. Six associate and eight baccalaureate nursing programs participated in the main-testing phase (n = 242), for a response rate of 24.5%. Response rates exceeded the estimated 20% cited as usual for electronic surveys (Cantrell & Lupinacci, 2007).
The two samples differed statistically on most demographic variables. Groups were statistically similar on gender and ethnicity. Table 1 presents a comparison of the two samples. Despite the two samples being statistically different on several demographic variables data analysis, results were strikingly similar among both samples. Therefore, the statistical results presented are reported from data analysis from the second sample only (main-testing phase).
Demographics From Pilot and Main Samples (N = 545)
Measures of central tendency and variability were calculated for the composite scores on the NASC-CDM subscales, the GSE scale, and the GAD-7 scale. Scores on both subscales of the NASC-CDM scale (a 6-point Likert scale) ranged from 32 to 192. The mean score on the NASC-CDM self-confidence subscale was 126.88 (SD ± 27.40); the mean score on the NASC-CDM anxiety subscale was 78.48 (SD ± 23.01). The GSE is a 10-item, 4-point Likert scale (range, 10 to 40). The mean score on the GSE was 31.70 (SD ± 3.48). The GAD-7 is a 7-item, 4-point Likert scale (range, 0 to 21). The mean score on the GAD-7 was 8.13 (SD ± 5.31).
Exploratory factor analysis (EFA) is commonly used during instrument development (Comrey, 1988). Sample sizes achieved during the pilot-testing (n = 303) and main-testing (n = 242) phases of the study were consistent with recommendations of instrument development experts, from 100 participants (Sapnas & Zeller, 2002) to 500 participants (Comrey & Lee, 1992).
Principal component analysis with varimax (orthogonal) rotation was used for the initial factor analysis run for both subscales (Comrey & Lee, 1992; Tabachnick & Fidell, 2007). Using Kaiser’s criterion (Kaiser, 1958) three factors for the NASC-CDM self-confidence subscale and the NASC-CDM anxiety subscale achieved eigenvalues exceeding 1 and explained 69.51% and 63.39% of total variance, respectively.
Inter-item and item-total correlations were conducted to reveal the relationship of items within the scale. The well-accepted criterion of item correlations between 0.30 and 0.70 was used to review and reduce several items that were weak or redundant (Gable & Wolf, 1993). Factor loadings were examined using the cut-off value of less than 0.40 to warrant review, reduction, or revision of items (Ellenbecker & Byleckie, 2005). Because of the considerable overlap among factor loadings, intermingled points on the factor plots, and the fluid process of decision making, items within the factor solutions were determined to be related. Alpha factoring maximizes the alpha reliability of factors and is appropriate during the process of scale development; oblique rotation is suitable when factor solutions are interrelated (Tabachnick & Fidell, 2007).
Therefore, the final run used alpha factoring with promax (oblique) rotation. A stable three-factor solution was achieved for both subscales. Table 2 provides the alpha factoring with promax rotation results from both subscales. Item analysis was again conducted using <0.30 and >0.70 as indicators for weak or redundant items, respectively. Items with secondary loadings were reviewed.
Alpha Factoring with Promax Rotation for the 32-Item Self-Confidence and Anxiety Subscales
Nine items were reduced from the 41-item pilot version (first sample data) of the scale based on EFA and item analysis results. An additional four items were rephrased slightly for grammatical and clarification purposes only. Five items were reduced from the 32-item revised version (second sample data) of the scale based on EFA and item analysis results.
The researcher and a panel of five doctorally prepared nurse educators independently reviewed factor structures and assigned labels. Final labels that thematically summarized each factor were ultimately assigned by the researcher: Factor I, using resources to gather information and listening fully (13 items); Factor II, using information to see the big picture (7 items); and Factor III, knowing and acting (7 items). The factor structure flows similarly to the steps of the nursing process. Table 3 presents sample items and describes the factors.
Factor Description and Sample Items from the Nursing Anxiety and Self-Confidence with Clinical Decision Making Scale
A correlation of scores on the psychometrically sound GSE and GAD-7 scales with scores on the respective NASC-CDM subscales provided an assessment of convergent validity (DeVon et al., 2007). A positive Pearson r correlation coefficient of approximately 0.50 is acceptable for a newly designed scale (Waltz et al., 2010). A statistically significant, moderate-positive correlation was noted between the variables NASC-CDM self-confidence subscale and GSE (r = 0.62, p < 0.001, n = 242), indicating a stronger relationship than was found with the first sample (r = 0.54). A statistically significant, low-positive correlation was noted between the variables NASC-CDM anxiety subscale and GAD-7 (r = 0.38, p < 0.001, n = 241), indicating a weaker relationship than was found with the first sample (r = 0.52). Reliability coefficients were computed for the two comparison scales (GSE: α = 0.84, n = 242; GAD-7: α = 0.91, n = 241).
Commonly, an inverse relationship exists between self-confidence and anxiety. Therefore, Pearson r was examined based on scores on the subscales. A statistically significant, strong-negative correlation was noted between the variables NASC-CDM self-confidence subscale and NASC-CDM anxiety subscale (r = −0.75, p < 0.001, n = 242), indicating a stronger negative relationship than was found during the pilot phase of the study (r = −0.67). Results indicated that undergraduate nursing students with higher levels of self-confidence during the process of CDM had lower levels of anxiety during the process and vice versa.
Cronbach’s alpha internal consistency reliability coefficient (Cronbach, 1951) was used to compute the reliability for the self-confidence and anxiety subscales of the 32-item NASC-CDM scale. An alpha of 0.70 is respectable for a newly designed affective scale (DeVellis, 2012; Rust & Golombok, 2009). Results indicated the NASC-CDM self-confidence subscale α = 0.98 and the NASC-CDM anxiety subscale α = 0.97. Examination of the item-total statistics for both subscales revealed no substantial influence on alpha if any item was deleted.
Conclusions Related to the Research Questions and Theoretical Frameworks
The purpose of this study was to develop and test a newly designed quantitative self-report tool, the NASC-CDM scale. Despite similar sampling, recruitment, and data collection techniques, the two samples were statistically different on most demographic variables. Data analysis results regarding scale validity and reliability were comparable across both testing phases; this may enhance the strength of the study and increase generalizability.
Each of three research questions was asked and answered by the study. Research question one addressed construct validity. The 41-item pilot version of the scale structured content domains in sequential fashion from the acquisition of cues, through intervention, and, finally, to reflection. A primary tenet of factor analytic procedures is that items should correlate strongly with similar ones and weakly with those that are dissimilar. Therefore, the resultant factors emerged as thematic rather than sequential (Comrey, 1988; Munro, 2005). Factor analytic results indicated the NASC-CDM scale is a stable multidimensional, three-factor scale. Items on the NASC-CDM subscales did not remain in their original content domains after stable factor structures were achieved; this was not entirely unexpected. Comparable EFA results between two heterogeneous samples and stable factor solutions over two testing semesters provided evidential support that the two NASC-CDM subscales are construct valid.
Research question two addressed convergent validity. Students with higher amounts of self-confidence on the NASC-CDM self-confidence subscale showed higher levels of general self-confidence on the GSE scale. This was an anticipated finding and provided evidence to support the convergent validity of the self-confidence subscale. The relationship between the NASC-CDM anxiety subscale and the GAD-7 scale revealed a weaker, less positive correlation from the first to the second sample. Pearson r was, nonetheless, statistically significant and did flow in a positive direction. Although the decline in correlation was not substantial, it was not anticipated. Reasons for the correlation decline were considered carefully without considerable success. Because the word anxious can have two meanings (uneasy or eager), participants may have been confused by the phrasing of items, ultimately influencing the results. This lower correlation was an unexpected finding and provided incomplete support for the convergent validity of the anxiety subscale.
Research question three examined the new scale’s reliability. High alpha coefficients for both subscales indicated variance in scores was attributed to the measurement of true score and not the measurement of error (Rust & Golombok, 2009). Such findings lend support to a high degree of internal consistency and suggest the two subscales do in fact measure the constructs of self-confidence and anxiety during the process of CDM. It is acknowledged that a large sample size and large number of items on the scale may inflate the alpha (Gall et al., 2007).
Both theoretical frameworks that undergirded the study (Bandura, 1997; O’Neill et al., 2005) suggest an inverse relationship between self-confidence and anxiety arousal; such was the case with the results of this study. However, the fact that students perceived higher levels of self-confidence and lower levels of anxiety during the process of CDM was an unanticipated finding. CDM literature related to novice practitioners supports that the opposite is true (Bakalis & Watson, 2005; O’Neill et al., 2005; Standing, 2007). Explanations for the unanticipated results may include that respondents have been provided a safe nurturing clinical environment in which to practice their CDM skills (Baxter & Rideout, 2006), that they have effectively utilized a variety of resources to assist their CDM skills (Lauri & Salanterä, 2002), or that these students have experienced enough real-life patient encounters to foster their CDM skills (Benner et al., 2010; O’Neill, Dluhy, Fortier, & Michel, 2004).
Levels of self-confidence and anxiety varied among demographic variables, although results were not statistically significant. Male participants had higher levels of confidence and less anxiety with CDM. Associate degree students had higher levels of confidence and less anxiety with CDM than their baccalaureate counterparts. Age was not a determinant in scores on the two subscales. The only statistically significant comparison was that those students who participated in an extern program had higher levels of confidence and lower levels of anxiety.
Limitations are intrinsic in any empirical study. The use of convenience sampling created selection bias and inherently limits the generalizability of findings. Although numerous nursing classes were visited for recruitment purposes, not all of the nursing classes were visited. The ambiguity in the word anxious might have confused participants. Students self-selected their participation in the study. Response set bias occurred if respondents provided socially acceptable or extreme response answers to items (Gable & Wolf, 1993; Polit & Beck, 2008).
Implications for Nursing Education
The NASC-CDM scale was designed for a number of intended uses. It was written deliberately in a generic manner to allow for use among different program types, different levels of students, and varied clinical situations. The scale may be useful to evaluate changes in self-confidence and anxiety with CDM when used longitudinally. It could be used in a formative or summative fashion around real-life or simulated patient encounters. The NASC-CDM scale may also have utility in a pretest and posttest design surrounding clinical experiences. Although the purpose of the scale relates to a variety of uses, confirmation of its merit will come only from its actual usage in these situations. Results of studies that use the NASC-CDM scale will indicate its performance in a variety of situations across a variety of populations.
Making strong confident clinical decisions is a cornerstone skill for professional nurses. The NASC-CDM scale is important to nursing. When nurse educators can successfully evaluate where students’ levels of self-confidence and anxiety lie, they can intervene with appropriate teaching–learning strategies. The NASC-CDM scale is merely in its infancy of development and establishment of sound psychometrics. Currently, the scale is being used in three quantitative doctoral dissertation studies related to nursing education in the United States and abroad.
- Bakalis, N.A. & Watson, R. (2005). Nurses’ decision-making in clinical practice. Nursing Standard, 19(23), 33–39. doi:10.7748/ns2005.02.19.23.33.c3805 [CrossRef]
- Bandura, A. (1977a). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. doi:10.1037/0033-295X.84.2.191 [CrossRef]
- Bandura, A. (1977b). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: WH Freeman/Times Books/Henry Holt & Co.
- Baxter, P. & Rideout, E. (2006). Second-year baccalaureate nursing students’ decision making in the clinical setting. Journal of Nursing Education, 45, 121–127.
- Baxter, P.E. & Boblin, S. (2008). Decision making by baccalaureate nursing students in the clinical setting. Journal of Nursing Education, 47, 345–350. doi:10.3928/01484834-20080801-02 [CrossRef]
- Benner, P. (2001). From novice to expert: Excellence and power in clinical nursing practice (Commemorative ed.). Upper Saddle River, NJ: Prentice Hall.
- Benner, P., Sutphen, M., Leonard, V. & Day, L. (2010). Educating nurses: A call for radical transformation. San Francisco, CA: Jossey-Bass.
- Cantrell, M.A. & Lupinacci, P. (2007). Methodological issues in online data collection. Journal of Advanced Nursing, 60, 544–549. doi:10.1111/j.1365-2648.2007.04448.x [CrossRef]
- Comrey, A.L. (1988). Factor-analytic methods of scale development in personality and clinical psychology. Journal of Consulting and Clinical Psychology, 56, 754–761. doi:10.1037/0022-006X.56.5.754 [CrossRef]
- Comrey, A.L. & Lee, H.B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
- Coombs, C.H. & Coombs, L.C. (1976). “Don’t know”: Item ambiguity or respondent uncertainty?The Public Opinion Quarterly, 40, 497–514. doi:10.1086/268336 [CrossRef]
- Crawford, S.D., Couper, M.P. & Lamias, M.J. (2001). Web surveys: Perceptions of burden. Social Science Computer Review, 19, 146–162. doi:10.1177/089443930101900202 [CrossRef]
- Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. doi:10.1007/BF02310555 [CrossRef]
- DeVellis, R.F. (2012). Scale development: Theory and applications (3rd ed.). Newbury, CA: Sage.
- DeVon, H.A., Block, M.E., Moyle-Wright, P., Ernst, D.M., Hayden, S.J., Lazzara, D.J. & Kostas-Polton, E. (2007). A psychometric toolbox for testing validity and reliability. Journal of Nursing Scholarship, 39, 155–164. doi:10.1111/j.1547-5069.2007.00161.x [CrossRef]
- Ellenbecker, C.H. & Byleckie, J.J. (2005). Home healthcare nurses’ job satisfaction scale: Refinement and psychometric testing. Journal of Advanced Nursing, 52, 70–78. doi:10.1111/j.1365-2648.2005.03556.x [CrossRef]
- Elstein, A.S., Kagan, N., Shulman, L.S., Hilliard, J. & Loupe, M.J. (1972). Methods and theory in the study of medical inquiry. Journal of Medical Education, 47, 85–92.
- Elstein, A.S., Shulman, L.S. & Sprafka, S.A. (1978). Medical problem solving: An analysis of clinical reasoning. Cambridge, MA: Harvard University Press.
- Gable, R.K. & Wolf, M.B. (1993). Instrument development in the affective domain: Measuring attitudes and values in corporate and school settings (2nd ed.). Boston, MA: Kluwer Academic. doi:10.1007/978-94-011-1400-4 [CrossRef]
- Gall, M.D., Gall, J.P. & Borg, W.R. (2007). Educational research: An introduction (8th ed.). Boston, MA: Pearson Education.
- Haffer, A.G. & Raingruber, B.J. (1998). Discovering confidence in clinical reasoning and critical thinking development in baccalaureate nursing students. Journal of Nursing Education, 37, 61–70.
- Hoffman, K. & Elwin, C.J. (2004). The relationship between critical thinking and confidence in decision-making. Australian Journal of Advanced Nursing, 22(1), 8–12.
- Hughes, K.K. & Young, W.B. (1990). The relationship between task complexity and decision-making consistency. Research in Nursing & Health, 13, 189–197. doi:10.1002/nur.4770130308 [CrossRef]
- Itano, J.K. (1989). A comparison of the clinical judgment process in experienced registered nurses and student nurses. Journal of Nursing Education, 28, 120–126.
- Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200. doi:10.1007/BF02289233 [CrossRef]
- Kelly, K. (1964). An approach to the study of clinical inference in nursing: Part I. Introduction to the study of clinical inference in nursing. Nursing Research, 13, 314–315. doi:10.1097/00006199-196413040-00006 [CrossRef]
- Kroenke, K., Spitzer, R.L., Williams, J.B., Monahan, P.O. & Löwe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146, 317–325. doi:10.7326/0003-4819-146-5-200703060-00004 [CrossRef]
- Lasater, K. (2007). High-fidelity simulation and the development of clinical judgment: Students’ experiences. Journal of Nursing Education, 46, 269–276.
- Lauri, S. & Salanterä, S. (1995). Decision-making models of Finnish nurses and public health nurses. Journal of Advanced Nursing, 21, 520–527. doi:10.1111/j.1365-2648.1995.tb02736.x [CrossRef]
- Lauri, S. & Salanterä, S. (2002). Developing an instrument to measure and describe clinical decision making in different nursing fields. Journal of Professional Nursing, 18, 93–100. doi:10.1053/jpnu.2002.32344 [CrossRef]
- Lauri, S., Salanterä, S., Chalmers, K., Ekman, S.-L., Kim, S., Käppeli, S. & MacLeod, M. (2001). An exploratory study of clinical decision-making in five countries. Journal of Nursing Scholarship, 33, 83–90. doi:10.1111/j.1547-5069.2001.00083.x [CrossRef]
- Lindsey, G. & Kleiner, B. (2005). Nurse residency program: An effective tool for recruitment and retention. Journal of Health Care Finance, 31(3), 25–32.
- Mellalieu, S.D., Neil, R. & Hanton, S. (2006). Self-confidence as a mediator of the relationship between competitive anxiety intensity and interpretation. Research Quarterly for Exercise and Sport, 77, 263–270.
- Muir, N. (2004). Clinical decision-making: Theory and practice. Nursing Standard, 18(36), 47–52. doi:10.7748/ns2004.05.18.36.47.c3614 [CrossRef]
- Munro, B.H. (2005). Statistical methods for health care research (5th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
- O’Neill, E. (1996). An exploratory study of clinical decision making in home healthcare nursing. Home Healthcare Nurse, 14, 362–368. doi:10.1097/00004045-199605000-00006 [CrossRef]
- O’Neill, E.S., Dluhy, N.M. & Chin, E. (2005). Modelling novice clinical reasoning for a computerized decision support system. Journal of Advanced Nursing, 49, 68–77. doi:10.1111/j.1365-2648.2004.03265.x [CrossRef]
- O’Neill, E.S., Dluhy, N.M., Fortier, P.J. & Michel, H. (2004). The N-CODES project: The first year. Computers Informatics, Nursing, 22, 345–350. doi:10.1097/00024665-200411000-00010 [CrossRef]
- O’Neill, E.S., Dluhy, N.M., Hansen, A.S. & Ryan, J.R. (2006). Coupling the N-CODES system with actual nurse decision-making. Computers, Informatics, Nursing, 24, 28–34. doi:10.1097/00024665-200601000-00008 [CrossRef]
- Polit, D.F. & Beck, C.T. (2008). Nursing research: Generating and assessing evidence for nursing practice (8th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
- Polit, D.F., Beck, C.T. & Owen, S.V. (2007). Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30, 459–467. doi:10.1002/nur.20199 [CrossRef]
- Rew, L. (2000). Acknowledging intuition in clinical decision making. Journal of Holistic Nursing, 18, 94–113. doi:10.1177/089801010001800202 [CrossRef]
- Rust, J. & Golombok, S. (2009). Modern psychometrics: The science of psychological assessment (3rd ed.). London, England: Routledge.
- Sapnas, K.G. & Zeller, R.A. (2002). Minimizing sample size when using exploratory factor analysis for measurement. Journal of Nursing Measurement, 10, 135–154. doi:10.1891/jnum.10.2.135.52552 [CrossRef]
- Savitsky, K., Medvec, V.H., Charlton, A.E. & Gilovich, T. (1998). ‘What, me worry?’ Arousal, misattribution and the effect of temporal distance on confidence. Personality and Social Psychology Bulletin, 24, 529–536. doi:10.1177/0146167298245008 [CrossRef]
- Schunk, D.H. & Pajares, F. (2005). Competence perceptions and academic functioning. In Elliot, A.J. & Dweck, C.S. (Eds.), Handbook of competence and motivation (pp. 85–104). New York, NY: Guilford Press.
- Schwarzer, R., Mueller, J. & Greenglass, E. (1999). Assessment of perceived general self-efficacy on the internet: Data collection in cyber-space. Anxiety, Stress & Coping, 12, 145–161. doi:10.1080/10615809908248327 [CrossRef]
- Seldomridge, E.A. (1997). Faculty and student confidence in their clinical judgment. Nurse Educator, 22(5), 6–8. doi:10.1097/00006223-199709000-00007 [CrossRef]
- Standing, M. (2007). Clinical decision-making skills on the developmental journey from student to registered nurse: A longitudinal inquiry. Journal of Advanced Nursing, 60, 257–269.
- Tabachnick, B.G. & Fidell, L.S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education.
- Tanner, C.A. (2006). Thinking like a nurse: A research-based model of clinical judgment in nursing. Journal of Nursing Education, 45, 204–211.
- Tschikota, S. (1993). The clinical decision-making processes of student nurses. Journal of Nursing Education, 32, 389–398.
- Waltz, C.F., Strickland, O.L. & Lenz, E.R. (2010). Measurement in nursing and health research (4th ed.). New York, NY: Springer.
- White, A.H. (2003). Clinical decision making among fourth-year nursing students: An interpretive study. Journal of Nursing Education, 42, 113–120.
- White, K.A. (2009). Self-confidence: A concept analysis. Nursing Forum, 44, 103–114. doi:10.1111/j.1744-6198.2009.00133.x [CrossRef]
Demographics From Pilot and Main Samples (N = 545)
|Demographic Question||Pilot Sample (n = 303 [%])||Main Sample (n = 242 [%])||Statistic|
|Gender||χ2 = 0, p = 1|
| Female||283 (93.4)||226 (93.4)|
| Male||20 (6.6)||16 (6.6)|
|Age (mean ± SD)||29.16 ± 7.5||25.19 ± 5.67||t = 6.71*|
|Ethnicitya||LR = 11.64, p = 0.07|
| African American||13 (4.3)||18 (7.4)|
| American Indian||1 (0.3)||1 (0.4)|
| Asian||13 (4.3)||7 (2.9)|
| Caucasian||257 (84.8)||207 (85.5)|
| East Indian||0||1 (0.4)|
| Hispanic||13 (4.4)||5 (2.1)|
| Other||3 (2.6)||3 (1.2)|
|Program type||χ2 = 57.89*|
| Associate degree||192 (63.4)||74 (30.6)|
| Baccalaureate degree||111 (36.6)||168 (69.4)|
|Program formata||χ2 = 128.28*|
| Accelerated||18 (6)||13 (5.4)|
| Evening/weekend||66 (21.8)||5 (2.1)|
| Traditional, 2 semesters per academic year||141 (46.5)||219 (90.5)|
| Year round, 3 semesters per academic year||77 (25.4)||5 (2.1)|
|Currently working as nursing assistanta||χ2 = 19.67*|
| No||207 (68.3)||120 (49.6)|
| Yes||95 (31.4)||121 (50)|
|College experience before nursing schoola||χ2 = 35.89*|
| I started my nursing program right out of high school||45 (14.9)||74 (30.6)|
| 1 to 2 semesters||30 (10)||36 (14.9)|
| 3 to 4 semesters||53 (17.5)||51 (21.1)|
| > 4 semesters||79 (26.1)||31 (12.8)|
| I completed a college degree before starting my nursing program||92 (30.4)||49 (20.2)|
Alpha Factoring with Promax Rotation for the 32-Item Self-Confidence and Anxiety Subscales
|Question on the NASC-CDM Scale||Factor Loadings for Self-Confidence Subscalea,b,c||Factor Loadings for Anxiety Subscalea,b,c|
|Q1d||0.40||0.39||0.01||No loading ⩾0.40 on any factor|
|Q27d||No loading ⩾0.40 on any factor||0.46||0.44||−0.05|
|Q40d||No loading ⩾0.40 on any factor||No loading ⩾0.40 on any factor|
|Q6d||No loading ⩾0.40 on any factor||0.51||0.33||−0.01|
|Rotation sums of squared loadingse||16.40||16.55||15.97||14.77||13.07||14.28|
Factor Description and Sample Items from the Nursing Anxiety and Self-Confidence with Clinical Decision Making Scale
|Factor Number and Label||Factor Description||Sample Items|
|I: Using resources to gather information and listening fully (13 items on full scale)||Items include: using instructor, family, shift report, protocols, and literature as resources for information gathering; listening actively; assessing nonverbal cues; and focusing assessment to gathering more information||I am __ self-confident and __ anxious in my ability to recognize the need to talk with my clinical nursing instructor to help sort out client assessment findings.
I am __ self-confident and __ anxious in my ability to assess the client’s nonverbal cues.|
|II: Using information to see the big picture (7 items on full scale)||Items include: seeing patterns and relevance of information; recalling past information learned (i.e., labs, anatomy and physiology) to help interpret information; seeing the full clinical picture||I am __ self-confident and __ anxious in my ability to identify which pieces of clinical information I gathered are related to the client’s current problem.
I am __ self-confident and __ anxious in my ability to easily see important patterns in the information I gathered from the client.|
|III: Knowing and acting (7 items on full scale)||Items include: analyzing risks versus benefits of decision options; implementing the “best” option for the situation; using intuition for decision making||I am __ self-confident and __ anxious in my ability to act on at least one intervention I considered based on my gut feeling or intuition.
I am __ self-confident and __ anxious in my ability to INDEPENDENTLY make a clinical decision to solve the client’s problem.|