Journal of Nursing Education

Major Article 

Integrative Review of Admission Factors Related to Associate Degree Nursing Program Success

Jeanette M. Olsen, PhD, RN

Abstract

Background:

High attrition in associate degree nursing (ADN) programs contributes to the nursing shortage and causes hardship for students, families, faculty, colleges, and taxpayers. The purpose of this integrative literature review is to identify admission criteria related to ADN program success to inform evidence-based admission policies and reduce attrition.

Method:

Integrative review methodology, suggested by Whittemore and Knafl, was used. A systematic search of existing professional literature was conducted using five databases and key word searches. The final sample included 26 documents that were analyzed and synthesized with the matrix method.

Results:

Five categories of admission criteria and factors related to success in ADN programs were revealed from the analysis of findings: academic aptitude, demographic factors, psychological hardiness, specialty skills and experience, and socioeconomic support.

Conclusion:

ADN programs with a goal of decreasing attrition may want to implement admission selection guidelines that consider applicant criteria and attributes across all five dimensions. [J Nurs Educ. 2017;56(2):85–93.]

Abstract

Background:

High attrition in associate degree nursing (ADN) programs contributes to the nursing shortage and causes hardship for students, families, faculty, colleges, and taxpayers. The purpose of this integrative literature review is to identify admission criteria related to ADN program success to inform evidence-based admission policies and reduce attrition.

Method:

Integrative review methodology, suggested by Whittemore and Knafl, was used. A systematic search of existing professional literature was conducted using five databases and key word searches. The final sample included 26 documents that were analyzed and synthesized with the matrix method.

Results:

Five categories of admission criteria and factors related to success in ADN programs were revealed from the analysis of findings: academic aptitude, demographic factors, psychological hardiness, specialty skills and experience, and socioeconomic support.

Conclusion:

ADN programs with a goal of decreasing attrition may want to implement admission selection guidelines that consider applicant criteria and attributes across all five dimensions. [J Nurs Educ. 2017;56(2):85–93.]

The threat of a nursing shortage in the coming years remains a significant concern (American Association of Colleges of Nursing, 2014). Meeting the care needs of an aging population in the face of Baby Boomer nurse retirements will necessitate 1.05 million more nurses by 2022 (Bureau of Labor Statistics, 2013). To accomplish this, more nursing program graduates are needed. However, more than 30% of qualified applicants are turned away each year, primarily due to lack of faculty and clinical sites (National League for Nursing [NLN], 2016). This problem is further complicated by high attrition rates among the students who do enter into nursing programs. First-year retention rates are reportedly 87% for bachelor of science in nursing (BSN) programs and 80% for associate degree nursing (ADN) programs (NLN, 2016). In addition, graduation rates of 67% have been reported by some schools and others have reported on-time graduation rates as low as 3% (Work, 2008). Such high attrition is problematic for multiple reasons. In addition to contributing to the nursing shortage, it may reduce tuition revenue for nursing schools, ineffectively use financial aid resources, and cause student, family member, and faculty distress. Therefore, it is critical that nursing programs ensure that admitted students are well positioned for success. Establishing effective admission criteria can help achieve this goal (Wolkowitz & Kelley, 2010).

Despite recent initiatives to increase the number of BSN-prepared nurses, 61% of prelicensure nursing graduates are from ADN programs (NLN, 2016). Currently, ADN graduates are essential for meeting the needed numbers and desired diversity in the nursing workforce (Fulcher & Mullin, 2011). However, the majority of research on admission criteria and factors associated with success in nursing programs has focused on BSN students (Czubatyj, 2010). For example, a meta analysis of studies reported associations between program completion and academic variables, including entrance examination scores with the strongest evidence in support of preprogram grade point average (GPA), specifically GPAs in science courses (Campell & Dickson, 1996). Demographic factors that predicted success were parents' education level and age (Campell & Dickson, 1996). In addition, theoretical frameworks specific to this topic have been proposed. Wells' (2007) theory of nursing student retention suggests social and external environments, as well as academic factors contribute toward attrition. Similarly, Jeffreys' (2004) Nursing Universal Retention and Success model (NURS) demonstrates the relationship between program success and factors from multiple dimensions, including academics, the environment, student affect, psychological outcomes, demographic characteristics, and professional integration. However, lacking in the current literature is a systematic review of findings specific to ADN students. This is concerning because ADN students are different than other groups of nursing students. In general, they are less academically prepared and more likely to have family or work obligations, limited financial resources (O'Gara, Karp, & Hughes, 2009), and diverse ethnic backgrounds (Fulcher & Mullin, 2011). Accordingly, the purpose of this integrative review of literature is to identify admission factors and criteria related to success in ADN programs. The goal is to provide information that ADN programs can use to establish evidence-based program admission policies that maximize program completion rates.

Method

This integrative review was guided by Whittemore's and Knafl's (2005) five-stage methodology. In summary, a research problem was identified, a well-defined literature search was conducted, the literature was evaluated for relevance and quality, data in applicable studies were analyzed, and conclusions were synthesized.

A systematic search of professional literature on admission factors associated with success in ADN programs was conducted in May 2016 using several computerized databases: Academic Search™ Premier, CINAHL®, ERIC™, Health Source®, Nursing/Academic Edition, and MEDLINE®. The following search terms were used:

  • Admission factors (and) associate degree nursing programs.
  • Admission factors (and) associate degree nursing programs (and) graduation.
  • Associate degree nursing programs (and) predictors of success (and) admission.
  • Associate degree nursing programs (and) admission predictors (and) graduation.
  • Associate degree nursing programs (and) admission criteria.
  • Admission criteria (and) associate degree nursing programs (and) retention.
  • Admission criteria (and) associate degree nursing programs (and) attrition.

The search was replicated in July 2016 to capture any new publications. Due to the dynamic nature of nursing curricula with frequent changes to reflect current practice and modifications to the NCLEX-RN® test plan, results were limited to documents published since January 2005. Additional relevant studies were identified by searching reference lists of retrieved articles.

Consistent with integrative review methodology, research studies with diverse designs that examined admission factors or criteria associated with success among students in U.S. ADN programs were included. Definitions of program success included passing specific courses or semesters, as well as program completion or graduation. All article titles and abstracts acquired through the search process were reviewed. Those that did not meet the inclusion criteria were screened out. Articles reviewed in full were rescreened for relevance and inclusion criteria along with the following exclusion criteria: studies that examined postadmission retention interventions and those that examined only factors associated with NCLEX-RN success. Several studies examined admission factors associated with both program and NCLEX-RN success. Those studies were retained, but only the results specific to program success were analyzed. The final sample for this review included 26 documents. The Figure illustrates the flow of information through the phases of the search and selection process.


Flow of information through phases of the review. Note. ADN = associate degree nursing.

Figure.

Flow of information through phases of the review. Note. ADN = associate degree nursing.

Each study was analyzed according to purpose, sample, design, variables, analytic strategies, and findings using the matrix method (Garrard, 2007; Table A, available in the online version of this article). Each study was also evaluated for methodological quality based on five criteria and a summed scoring system. Table 1 is an explanation of the scoring system, and Table 2 shows the ratings of the reviewed studies. Findings were synthesized through comparison and identification of themes.


Methodological Quality Ratings Criteria and Scoring

Table 1:

Methodological Quality Ratings Criteria and Scoring


Methodological Quality Ratings of Included Studies

Table 2:

Methodological Quality Ratings of Included Studies

Results

The 26 studies included in this review used quantitative (n = 22), qualitative (n = 3), and mixed (n = 1) methods. Notably, almost 60% (n = 15) were doctoral dissertations. Descriptive correlational designs were used for all quantitative studies, as well as the quantitative part of the mixed-methods study. Several studies used only bivariate statistical analysis (n = 10) and others included multivariate analysis (n = 13). Methodologies used for the qualitative studies were the constant comparative method (n = 1), coding for themes (n = 2), and grounded theory (n = 1).

Most of the studies (n = 16) included students from only one ADN program. Two qualitative studies (Higgens, 2005; Rogers, 2010) included ADN faculty and directors, in addition to students. More than two thirds of the studies (n = 18) did not report how sample size was determined. A few used power analysis (n = 3), and several either sampled until saturation was reached or cited academic rationale to support the sample size analyzed (n = 5).

This review examined ADN program success as the dependent variable. This was defined as a passing grade in one or more specific courses or semesters (n = 10), program completion (n = 14), or both (n = 2). Although students' educational paths may include delays or interruptions and noncompletion can occur due to both academic and nonacademic issues, only two studies considered these alternatives when examining outcomes. In addition, although most studies were reportedly guided or informed by theory, only a few included variables that were clearly aligned with the selected theoretical constructs (n = 5) or included factors from multiple dimensions (n = 4) beyond just academic or demographic factors.

Five categories of admission criteria and factors related to success in ADN programs were revealed from the analysis of findings: academic aptitude, demographic factors, psychological hardiness, specialty skills and experience, and socioeconomic support. Table 3 provides a synthesis of results that quantifies and differentiates findings based on the outcome of interest.


Synthesis of Results

Table 3:

Synthesis of Results

Academic Aptitude

Almost all the review studies examined the relationship between academic factors and program success. Specific academic measures included grades in preprogram courses, GPA, entrance examination scores, and other factors.

Preprogram Course Grades. Nine studies reported on the relationship between preprogram grades and program success. Most reported a significant positive correlation (n = 8). This was most prevalent for science courses, including pathophysiology (Beery, 2014), microbiology (Higgens, 2005; Muecke, 2008), anatomy (Beery, 2014), and anatomy and physiology (Gilmore, 2008; Higgens, 2005; Payne, 2011), followed by math courses (Dolinar, 2010; Esper, 2009; Preston, 2007). Inconsistent results were reported for English grades, with one study showing a positive correlation (Esper, 2009) and another showing no significant relationship (Higgens, 2005). In addition, two studies reported contrasting science results. Jeffreys (2007) did not find a significant relationship between anatomy and physiology I grades and retention or attrition category. Similarly, Higgens (2005) found anatomy and physiology I and chemistry grades to be insignificant, although anatomy and physiology II and microbiology grades were associated with success.

GPA. Ten studies reported on the relationship between GPA and program success. Similar to course grades, most reported a significant positive relationship (n = 7). This included preprogram college GPA (Gilmore, 2008; Luna, 2014; Muecke, 2008; Payne, 2011; Preston, 2007; Shelton, 2012), science GPA (Rogers, 2009), and high school GPA or rank (Muecke, 2008). Notably, Luna (2014) reported that GPA was no longer significant in a model controlling for Test of Essential Academic Skills (TEAS) composite scores. In addition, Jackson (2010) and Beery (2014) did not find GPA to be significant. Jeffreys (2007) reported higher prenursing GPAs among program graduates, but the difference was not significant.

Entrance Examination Scores. Entrance examination scores were commonly studied (n = 14). They included TEAS (n = 3), Health Education Systems Incorporated Admission Assessment (HESI A2) (n = 5), American College Test (ACT) (n = 2), Nursing Entrance Test (NET) (n = 3) and unspecified (n = 1) examination scores. A mixture of composite and component scores was studied. Most reported a positive correlation between scores and success (n = 12). The strongest evidence was in support of composite scores (Bodman, 2012; Chen & Voyles, 2013; Czubatyj, 2010; Esper, 2009; Gilmore, 2008; Knauss & Willson, 2013; Luna, 2014; Muecke, 2008; Murray et al., 2008; Rogers, 2009), followed by science (n = 7) and English (n = 4) component scores. Relatively equal representation of the different entrance examinations was present in the literature; however, no studies compared two or more examinations for the ability to predict success. The absence of a significant relationship between examination scores and program success was reported by Hilke-Lampe (2014) for the HESI A2 composite and component scores and by Benn and Pacquiao (2010) for the NET math and reading scores. Bodman (2012) reported significant findings for HESI A2 composite, biology, and chemistry scores but inconsistent results for the reading and math components. Similarly, Czubatyj (2010) reported significant findings for NET composite and scientific reading scores but found that implementing the NET as an admission requirement did not significantly increase the percentage of students retained in the ADN program.

Other Academic Factors. Several additional academic factors were examined in the reviewed studies. Most were related to students' academic histories. For example, Muecke (2008) reported that significantly more preprogram college credits were completed by ADN graduates than those who did not graduate. Similarly, Smith (2013) found that retained students were more likely to have completed related courses and less likely to have taken remedial courses. Likewise, Shelton (2012) reported that students who persisted were more likely to have a prior degree than those who voluntarily withdrew. Sall (2009) found that successful students had taken more college preparatory courses in high school and full-time loads when completing their ADN prerequisites. Further, interviews with ADN program directors and faculty revealed the perception that preadmission requirements and prerequisites were important for lowering program attrition (Higgens, 2005). In contrast, Hilke-Lampe (2014) did not find a significant relationship between program success and students self-identifying as academically disadvantaged. Jeffreys (2007) reported a negative relationship between number of transfer credits and program success but no relationship for local credits.

Demographic Factors

Multiple studies examined student demographic data, such as race, age, and gender. Although some simply used this information to describe the study sample, others examined the direct relationships of the variables to program success or used them as control variables.

Race. Seven studies examined the relationship between race or ethnicity and student success. Three reported a positive correlation between being White and program success (Benn & Pacquiao, 2010; Pence, 2010; Smith, 2013). In addition, Jeffreys (2007) reported White students had the highest ideal retention rate (32.2%). In contrast, three studies did not find a significant relationship between program success and race (Jackson, 2010; Higgens, 2005; Hopkins, 2008). Further, Benn and Pacquiao (2010) reported that program graduation was not related to the primary spoken language or whether a student attended a foreign versus a domestic high school.

Age. Similar to race, findings related to age were contradictory. Two studies reported more program success among older or nontraditional students (Preston, 2007; Rogers, 2009), one among younger students (Jeffreys, 2007), and one among those near the mean sample age of 32 years (Dolinar, 2010). In addition, five studies reported no relationship (Higgens, 2005; Hopkins, 2008; Jackson, 2010; Pence, 2010; Shelton, 2012).

Gender. The predominance of female nursing students complicates efforts to statistically examine the relationship between gender and ADN program success. Four studies reported no relationship between these two variables (Higgens, 2005; Hopkins, 2008; Payne, 2011; Pence, 2010). However, Jeffreys (2007) reported more stopouts—breaks in continuous enrollment, often for non-academic reasons, such as pregnancy or care of family members—among female students (27%, compared with 16% for men).

Psychological Hardiness

Six studies addressed students' psychological characteristics for their influence on ADN program success. The three qualitative (Cook, 2010; Rogers, 2010; Sall, 2009) and one mixed-methods (Higgens, 2005) studies contributed considerably to this category, along with two quantitative studies (Pence, 2010; Smith, 2013). The results aligned with the subcategories of resilience, motivation, and self-efficacy.

Resilience. Results from several studies indicated that the ability to adapt and overcome challenges was common among successful ADN students. For example, Cook (2010) reported that students enter ADN programs with different levels of psychic strength that is weakened due to the challenges and stress of nursing school. Most students adapt and regain homeostasis. However, failure to adapt can result in feelings of powerlessness and anxiety, potentially leading to loss of courage to continue in school. Similarly, Sall (2010) reported that although successful students described nursing courses as challenging, they responded by studying more. However, unsuccessful students perceived classes and examinations as unnecessarily difficult, passing as beyond their control, and felt instructors wanted students to fail. Interviews with successful ADN graduates and faculty revealed the perception that an ability to manage life events and extreme stress is important (Rogers, 2010). Smith (2013) found that nonretained students held higher flexibility and autonomy career values, indicating that successful students may find it easier to adapt to the rigor and rules of the ADN program. Notably, no relationship was found between program success and locus of control as measured by Rotter's locus of control scales (Payne, 2011) or emotional intelligence as measured by the assessing emotions scale (Pence, 2010).

Motivation. Several studies indicated a relationship between motivation and student success. Higgens (2005) reported that new ADN graduates perceived student motivation to be important for decreasing program attrition. Similarly, Rogers (2010) noted that ADN graduates and professors thought motivation was a vital student quality. Pence (2010) reported a significant positive relationship between first-semester retention and several subscale scores, as measured by the Motivated Strategies for Learning instrument. The relationship was no longer significant when race and ADN college were controlled. Finally, Cook (2010) identified the importance of caring as a motivator for becoming a nurse in a grounded theory study.

Self-Efficacy. The third psychological characteristic in the literature was self-efficacy. Payne (2011) reported a significant positive relationship between nursing fundamentals grades and self-efficacy. However, this attribute was not significant to predict course success in a logistic regression model. Smith (2013) found that retained students were significantly more confident in their abilities than those who were not retained. Similarly, Shelton (2012) reported that students who persisted in the ADN program had significantly higher levels of expected educational attainment; however, academic efficacy expectations and academic outcome expectations were not significantly different between those who persisted and those who did not.

Specialty Skills and Experience

Six studies examined a variety of diverse factors for their association with success in ADN programs. They fell within the category of specialty skills and experiences.

Skills. Several distinct skill sets were examined by a limited number of researchers, providing unique insight for further study. For example, Payne (2011) found that each unit of increase in computer competence tripled the odds of success in the nursing fundamentals courses. Testing-taking skills were identified as important to success by both Higgens (2005) and Rogers (2010) in interviews with successful ADN graduates, faculty, and directors. Additional themes identified by Rogers (2010) were Study Skills, Organization, Prioritization of Roles and Responsibilities, and Critical Thinking. In contrast with these findings, critical thinking, as measured by the HESI A2, and test-taking skills, as measured by the NET examinations, were not significant to success in studies conducted by Bodman (2012) and Czubatyj (2010), respectively.

Experience. Two studies indicated that health care experience may support ADN program success. Rogers (2010) identified this as a theme from interviews with ADN faculty and graduates. Similarly, Preston (2007) reported noncompleters were more likely to have no health care experience than those who completed the program.

Socioeconomic Support

The relationship between socioeconomic factors and ADN program success was considered in six studies. Findings indicated that both finances and social support may be relevant.

Finances. Evidence in the reviewed studies supported a negative relationship between financial stress and ADN program success. Shelton (2012) reported that the students who failed had significantly more financial concerns than those who persisted. Similarly, Smith (2013) found that nonretained students were more than three times as likely to have family members financially dependent on them. Sall (2009) and Rogers (2010) found that successful students received more financial support from families. Working more than 20 hours per week, a factor that may indicate financial need, was negatively associated with success in a study conducted by Payne (2011). In contrast, Shelton (2012) did not find work hours to be significant to success. In addition, Hilke-Lampe (2014) did not find a difference in first-semester retention among students who self-identified as economically disadvantaged.

Social Support. A variety of social factors were considered in the reviewed studies. Several indicated that ADN program success was not influenced by family structure, including marital status (Payne, 2011), single parenting (Hilke-Lampe, 2014), or having family members under the age of 18 years (Shelton, 2012; Payne, 2011). However, both Smith (2013) and Rogers (2010) found that encouragement and support from family and friends were higher among successful students.

Synthesis of Findings

The reviewed literature indicate that five categories of admission factors and criteria may help identify which students are most likely to be successful in ADN programs: academic aptitude, demographic factors, psychological hardiness, specialty skills and experience, and socioeconomic support. The findings provide strong evidence for the benefit of academic aptitude measures, specifically science grades, preprogram admission GPA, entrance examination scores, and educational history. Composite, science, and English entrance examination scores have the most consistent positive correlation with success. However, clear support for one company's entrance examination is not evident. Educational history factors associated with program success include the number of completed credits, prior degrees, and the lack of remedial courses in one's academic history. The data on demographic factors are inconsistent, specifically for race and age. One noteworthy finding from the literature is the higher number of program stopouts among female students. Factors that aligned with the theme of Psychological Hardiness are present among successful ADN students. One is reliance, demonstrated by the ability to manage stress, adapt to changes and challenges, and take accountability for learning. Another is motivation, including the reason one is motivated to pursue a career as a nurse. The third, self-efficacy, has some inconsistent findings in the reviewed studies. Few studies indicate that several specialty skills and experiences are associated with ADN program success. They include experiences in health care and specialty skills such as computer competence, test-taking ability, time management, study skills, and critical thinking. Finally, the reviewed literature provides support for the association between socioeconomic factors and success. Students are less likely to be successful if they have financial concerns or family members financially dependent on them. In contrast, students with more social support and encouragement from family and friends are more likely to be successful.

Discussion

The aim of this integrative review of literature is to identify admission factors and criteria related to success in ADN programs to inform evidence-based admission policies. The results indicate ADN programs with a goal of increasing retention may want to implement admission selection guidelines that consider applicant criteria and attributes across multiple dimensions. Specific areas for consideration include academic aptitude, demographic factors, psychological hardiness, specialty skills and experience, and socioeconomic support.

Strong evidence exists in support of the association between academic aptitude measures and ADN program success. Therefore, evidence-based admission policies should give priority status to applicants with higher scores on one or more of the following: science grades, preprogram admission GPA, and entrance examination scores. In addition, factors such as prior degrees earned, total number of credits completed, and amount of needed remedial education should be considered. These results, in support of academic aptitude measures as a predictor of nursing program success, are consistent with previous studies of ADN (Phillips, Spurling, & Armstrong, 2002) and BSN (Campbell & Dickson, 1996) students. Future research to strengthen evidence in this area should use multivariate analysis methods that compare and control different academic aptitude measures, as well as variables from the other categories identified in this review, to determine which academic measures or specific examinations are most valuable.

According to these findings, consideration of applicant psychological hardiness during the admission selection process may help improve ADN student retention. Although findings related to self-efficacy were inconsistent in the reviewed literature, previous studies have supported the relevance of this concept (Jeffreys, 1998; McConville & Lane, 2006). More research testing different assessment methods and instruments is needed to strengthen evidence in this area. However, ADN programs may benefit from adding an admission essay or interview component that asks applicants to describe how they manage stress or challenging academic situations, as well as what motivated them to pursue a nursing career.

These findings provide preliminary support for recognizing work experience in health care when making ADN program admission decisions. More research is needed to strengthen the evidence and determine whether the amount or type of experience is significant. Future studies should also clarify the relationship between ADN program success and specialty skills, such as computer competence, test taking, study skills, time management, and critical thinking.

Two categories of findings—demographic factors, and socioeconomic support—should be considered in evidence-based admission policies from a supportive, rather than an exclusive, perspective. Although the reviewed studies do not indicate a clear relationship between ADN program success and race, age, or gender, minority graduation rate disparities are well documented in higher education (Diverse Issues in Higher Education, 2010). In addition, a notable finding from one study in this review (Jeffreys, 2007) is that female students have more stopouts, often due to family responsibilities or pregnancy. In light of this, ADN programs should consider demographic factors and screen for financial concerns and low levels of social support at the time of admission as a means of identifying at-risk students. The provision of appropriate resources, referrals, and tools on program admission, rather than waiting until students begin to struggle, may help to achieve equity, support the diversity goals of the profession, and improve retention. More research is needed on factors associated with the different reasons for ADN program noncompletion. In addition, the association of flexible program completion paths and program success among different demographic groups should be examined.

The reviewed studies contained a large number of dissertations that sampled students from one school, used primarily academic measures for independent variables, and analyzed data with bivariate statistical methods. Therefore, nurse educators who advise doctoral candidates interested in researching this topic should encourage studies that address gaps in knowledge and strengthen evidence by sampling students from multiple programs. Multivariate statistical analysis methods should be used to examine variables clearly aligned with theory, such as the NURS (Nursing Universal Retention and Success) model (Jeffreys, 2004). Independent variables should be selected from multiple dimensions and dependent variables should differentiate multiple program outcomes, including graduation, academic failure, and withdrawal for personal reasons. Research that samples students from programs using a state-wide curriculum may increase the validity of findings.

Limitations

Because multiple studies used only bivariate statistical analysis, and many examined academic or demographic factors only, the level of certainty that can be inferred from these findings is limited. In addition, it is possible relevant articles were missed in the search process. The reference lists of retrieved articles were reviewed to minimize this limitation.

Conclusion

Lowering ADN program attrition is an important strategy for ensuring an adequate nursing workforce and responsibly using available nursing education resources. Effective admission criteria may help achieve this goal. Measures from the categories of academic aptitude, psychological hardiness, specialty skills and experience, demographic factors, and socioeconomic support may help identify which students are most likely to succeed in ADN programs. All five dimensions should be considered in evidence-based admission policies for either applicant selection or the identification of at-risk students.

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Methodological Quality Ratings Criteria and Scoring

CriteriaScoring
Type of study1 = Best practice report
2 = Qualitative design
3 = Quantitative correlational design
4 = Mixed, with both qualitative and quantitative descriptive designs
5 = Quantitative experimental design
Sampling method1 = One-program convenience sampling
2 = One-program purposive sampling
3 = Multiple-program purposive sampling
4 = One-program random sampling or 100% of available population
5 = Multiple-program convenience sampling
6 = Multiple-program random sampling or 100% of available population
Sample size1 = No power analysis or rationale for sample size
2 = Rationale cited for sample size
3 = Power analysis
Variable selection1 = Academic scores only (for analysis)
2 = Academic scores and demographics (e.g., age, gender, race/ethnicity)
3 = Above plus one or more additional domains of influence (e.g., psychological variables, family or work demands)
4 = Above and guided by theory with clear alignment of constructs and variables
Analysis1 = Narrative
2 = Descriptive statistics
3 = Bivariate analyses
4 = Multivariate analyses

Methodological Quality Ratings of Included Studies

Author (Year)Type of StudySampling MethodSample SizeVariable SelectionAnalysisQuality Rating Score
Beery (2014)3621315
Benn & Pacquiao (2010)3414315
Bodman (2012)3411413
Chen & Voyles (2013)3411312
Cook (2010)252NA110
Czubatyj (2010)3421313
Dolinar (2010)3412414
Esper (2009)3412414
Gilmore (2008)3611415
Higgens (2005)45121 + 316
Hilke-Lampe (2014)3413314
Hopkins (2008)3413415
Jackson (2010)3612416
Jeffreys (2007)3412313
Knauss & Willson (2013)3411312
Luna (2014)3421414
Muecke (2008)3412414
Murray et al. (2008)3611314
Payne (2011)3534417
Pence (2010)3534417
Preston (2007)3423416
Rogers (2009)3433417
Rogers (2010)221NA16
Sall (2009)221NA16
Shelton (2012)3514316
Smith (2013)3514417

Synthesis of Results

CategoryAdmission Criteria and FactorSuccess MidprogramaGraduationaInconsistent Findingsa
Academic aptitudeGrades in preprogram courses+ Pathophysiology (1)+ A&P (2)X A&P (2)
+ Anatomy/A&P (2)+ Microbiology (2)X Chemistry (1)
+ Science (1)+ Science (1)X English (1)
+ Math (1)− Repeating science coursesX Psychology (1)
+ English (1)+ Math (2)
GPA+ Preprogram college (2)+ Preprogram college (4)X Science GPA (1)
+ HS GPA or rank (2)X Pre-program college (2)
+ Science GPA (1)
Entrance examination scores+ TEAS composite, English, Math, and Science (2)+ NET reasoning and analytic factors (1)+ Unspecified examinations (1)+ HESI A2 composite (1)+ ACT composite and Reading (2 each), English and Math (1 each)+ NET composite and Scientific reading (1 each)X HESI A2 composite and Vocabulary/general knowledge (1 each), Math and Reading comprehension (2 each)X NET Reading and Math (1 each)
Other+ Completion of related courses (1)− Remedial courses (1)+ Number of completed college credits or prior education (2)+ College preparatory in HS (1)+ Full-time load with prerequisites (1)− Transfer credits (1)X Academically disadvantaged (1)X Local credits (1)
Demographic factorsRace+ White (3)+ White (1)X Race (3)
X Language (1)
X Foreign HS (1)
Age+ Near age 32 (1)+ Age (2)X Age (5)
− Age (1)
Gender− Female (1)X Gender (4)
Psychological hardinessResilience− Value for flexibility (1)+ Accountability for success (2)X Locus of control (1)
− Value for autonomy (1)+ Stress management (1)X Emotional intelligence (1)
+ Adaptation (1)
+ Courage (1)
Motivation+ Motivation (1)+ Motivation (2)
Self-efficacy+ Self-efficacy or confidence (2)+ Expected educational level (1)X Self-efficacy (1)
Specialty skills and experienceSkills+ Computer competence (1)+ Test-taking skills (2)X Test-taking skills (1)
+ Time management (1)X Critical thinking (1)
+ Study skills (1)
+ Critical thinking (1)
+ Health care experience (2)
Socioeconomic supportExperience finances− Financially dependent family members (1)+ Financial support from family (2)X Economically disadvantaged (1)
X Hours of employment (1)
− Working over 20 hours per week (1)− Financial concerns (1)
Social support+ Family encouragement (1)+ Family support (1)X Dependent family members (2)
+ Support from friends (1)+ Support from friends (1)X Marital status (2)

Admission Factors Associated with Success in ADN Programs

Authors/YearPurpose and SampleDesign, Methods, and VariablesFindings
Beery (2014)

Examine relationship between prerequisite biological course grades and grades in the beginning and advanced medical-surgical nursing courses

ADN students from two programs in 2009 and 2010 (N = 200)

Records-based correlational study

Paired sample t tests and correlation

DVs: grades in beginning and advanced medical-surgical nursing courses and nursing GPA

IVs: anatomy grade; pathophysiology grade; pre-requisite science GPA

Grade in pathophysiology was positively correlated with grades in the beginning (p < .001) and advanced (p < .01) medical-surgical nursing courses.

Grade in anatomy was positively correlated with grades in the advanced (p < .01) medical-surgical nursing course.

No significant relationships were found between the pathophysiology grade, anatomy grade, or the pre-requisite science GPA and the nursing GPA.

Benn & Pacquiao (2010)

Examine relationship between demographic factors and academic background to retention and graduation among minority students in an ADN program

One cohort of ADN students from an urban program (N = 76)

Secondary quantitative analysis of student records

Correlation, chi-square, student t test, ANOVA

DVs: program success or failure; graduation GPA

IVs: US or foreign HS education; citizenship status; program completion time; number of courses repeated; race/ethnicity; language spoken; Nursing Entrance Test (NET) scores in reading and math, GPA in prerequisite courses

More white than non-white students graduated: X2 (1, N = 76) = 6.14, p=.013.

Language nor location of HS was significantly related to graduation.

NET math and reading scores did not differ significantly between those who did and did not complete the program.

A positive correlation was found between NET math score and graduation GPA (p < .01).

Bodman (2012)

Examine relationships between HESI A2 scores and program success

Three cohorts of ADN students admitted to a program between 2008 and 2010 (N = 263)

Retrospective correlational

Discriminant function analysis

DVs: success (grade of ≥76%) or failure in Nursing 1, 2, 3, 4

IVs: HESI A2 composite, biology, chemistry, math, reading comprehension, and critical thinking scores

Mean HESI A2 biology scores were over 10 points higher for those who passed Nursing 1.

HESI A2 composite, biology, and chemistry scores were positively correlated with final scores in Nursing 1 and 2 for most cohorts (p < .01). HESI A2 reading comprehension and math scores were inconsistently significant among cohorts.

HESI A2 composite and biology scores predicted those who passed or failed Nursing 1, 2, 3, and 4, properly categorizing 64.7% – 87.8% of students.

Chen & Vogles (2013)

Examine the relationship between HESI A2 scores and success in first semester nursing courses

Students admitted to one ADN program between 2008 and 2011 (N = 508)

Correlational curriculum evaluation

Correlation, t test

DVs: grades and success or failure in three first semester nursing courses

IVs: HESI A2 composite, basic math, reading comprehension, vocabulary and general knowledge, grammar, and anatomy and physiology scores

HESI A2 composite and all five component scores were significantly higher for those who completed all three first-semester courses (p < .01) than those who did not complete one or more courses.

Significant positive correlations (p < .01) were reported between HESI A2 composite and component scores and grades in all three first semester nursing courses with one exception: HESI reading comprehension score and Pharmacology grade.

Cook (2010)

Gain insight on attrition from the perspective of ADN students

Nurses who left their ADN program for one or more semesters and were successful when they returned (N = 7)

Qualitative

Grounded theory

Interview guide

Four axial categories identified: caring (as a motivator to be a nurse), courage, control, and adaptation. They formed the core category of psychic strength.

Students enter ADN programs with different levels of psychic strength which is weakened due to the challenges and stress of nursing school. Most adapt and regain homeostasis. Challenges and adaptation are cyclic and can lead to personal growth. Those not able to adapt felt powerless and anxious. This can lead to loss of courage to continue in school.

Czubatyj (2010)

Examine the relationship between various NET sections and sub-sections and student success

Students admitted to an ADN program between 2001 and 2007 (N = 305); half admitted before NET implementation (n = 150) and half after (n = 155)

Retrospective correlational

Chi-square tests, two sample independent t test, ANOVA, Z test

DVs: program success or failure

IVs: NET composite, math, reading, writing, test taking skills, and scientific reading comprehension scores; admission group (before or after NET)

Mean NET composite and scientific reading scores were significantly higher for those who passed the program (p = .04 and p = .03, respectively). The other scores were not significantly different between those who passed or failed.

Difference in the proportion of students who graduated before and after NET implementation was not significant (p = .42).

Dolinar (2010)

Identify ADN admission criteria that predict program completion

Students admitted to an urban ADN program starting August 2001 who had graduated, withdrawn, or failed by June 2009 (N = 1350)

Retrospective quantitative data analysis

Logistic regression, discriminant analysis, stepwise regression

DVs: program success or failure

IVs: grades in English, math, and science courses; HS diploma or GED; developmental courses taken (yes/no); HS GPA; college GPA; institution's weighted admission score; gender; ethnicity; age

Regression model of the independent variables was statistically significant for predicting program completion with 85% success (r2 = .1333; p < .0001).

Lower grades in math (p = .0006) and two of three science courses (p = .0006; p = .0001), as well as GED (p < .0058) and age greater or less than the mean age of 32 (p < .0004), were significant in predicting program non-completion.

Esper (2009)

Examine common admission indicators for ability to predict success in the first semester of an ADN program

Students who started an ADN program in 2008 (N = 120)

Exploratory action research

Correlation and forward stepwise regression

DVs: Nursing Fundamentals and Health Assessment course grades; medication/math exam score; first semester ATI Fundamentals score

IVs: TEAS overall and English scores; last earned grades in science, math, and English on admission; age; gender

Fundamentals grade was significantly correlated with admission English (r = .322; p = .001), math (r = .224; p = .024), and science (r = .251; p = .009) grades; and TEAS English (r = .312; p = .002), math (r = .258; p = .009), science (r = .241; p = .016), and overall (r = .320; p = .001) scores.

Health Assessment grade was significantly correlated with admission English (r = .199; p = .047) and science (r = .346; p = .000) grades; and TEAS English (r = .331; p = .001), math (r = .231; p = .020), and overall (r = .283; p = .005) scores.

Medication/math score was significantly correlated with age (r = .230; p = .013) and TEAS science score (r = .231; p = .017).

ATI Fundamentals score was significantly correlated with TEAS English (r = .360; p = .0000), math (r = .284; p = .004), reading (r = .220; p = .024), science (r = .245; p = .015), and overall (r = .429; p = .000) scores.

Regression models for each of the four dependent variables explained 17.9% to 21.3% of the variance in outcomes. TEAS science, English, and overall scores; age; and admission grades in English and science were significant variables in one or more of the models.

Gilmore (2008)

Identify predictor factors to help nursing programs determine admission criteria

ADN students from two programs admitted between 2001 and 2003 (N = 218)

Retrospective descriptive correlational

ANOVA, logistic regression

DVs: program success or failure; nursing GPA, and NCLEX-RN success

IVs: ACT composite score; ACT math, reading, English, and science subscores; admission GPA; grades and anatomy and physiology I and II

Students who completed the program had higher mean ACT composite scores than non-completers (19.70 versus 18.85). ACT reading and English scores and grades in the biological courses were also higher.

Admission GPA, grades in anatomy and physiology I and II, ACT composite score, and ACT reading and math sub-scores accounted for 20% of the variance in nursing GPA F(8, 216) = 6.59, p < .001, R2 = .196).

Higgens (2005)

Identify strategies to lower attrition and raise the NCLEX-RN pass rate in an ADN program

Quantitative: all students enrolled in an ADN program between 1999 and 2000 (N = 213);

Qualitative: 10 full-time faculty, 30 new graduates, and 45 directors of ADN programs

Mixed methods

Correlation; narrative data coding

DVs: program completion; NCLEX-RN pass rate

IVs: prerequisite course grades; preadmission test components; demographic variables; HESI Exit Examination scores; nursing skills laboratory scores

Grades in Anatomy and Physiology II (r = .152) and Microbiology (r = .191) were significantly related to completion of the program.

Prerequisite course grades in English, Anatomy and Physiology I, Chemistry, and Psychology, age, gender, and race were not significantly related to completion of the program.

Themes for lowering attrition were presented for ADN directors (preadmission requirements, campus counselors, remediation, and faculty); faculty (prerequisites for program admission, mentoring for students, faculty needs), and students (student motivation, test-taking skills, NCLEX-RN review books during program, test reviews, study groups, and faculty contact for at-risk students).

Hilke-Lampe (2014)

Determine if HESI composite and sub-scores predict grades of C or higher in first semester of an ADN program

ADN students from one program (N = 133)

Correlational

Chi-square, logistic regression

DVs: success or failure in first semester of the ADN program

IVs: HESI composite and subscores; age; gender; ethnicity; single parent; economic disadvantage; academic disadvantage

IVs were considered individually in statistical analyses. No significant relationships were found: HESI composite (χ2 (1, n = 86) = 1.09, p = .30), Reading Comprehension (χ2 (1, n = 131) = 0.52, p = .47), Vocabulary/General Knowledge (χ2 (1, n = 125) =.19, p = .66) Language (χ2 (1, n = 25) =.001, p = .97), Math (χ2 (1, n = 86) = 2.01, p = .16) scores; economic disadvantage (χ2 (1, n = 126) = .51, p = .48, phi = −.08); academic disadvantage (χ2 (1, n = 131) = .00, p = 1.00, phi = .03); single parent (χ2 (1, n = 123) = .03, p = .87, phi = .04).

Hopkins (2008)

Examine how cognitive and noncognitive variables related to success in the ADN Nursing Fundamentals course

First semester ADN students from one program between 2001 and 2004 (N = 383)

Correlational study

Logistic regression

DVs: success (grade of ≥80%) or failure in first semester Fundamentals course

IVs: college GPA, gender, race, age and five factors identified with factor analysis of multiple NET scores: reasoning, learning style, analytic, anxiety, commitment

Regression model was statistically significant X2(9, N = 383) = 33.10, p < .01), predicting student success with 99% accuracy but only 5.9% of failures.

All IVs were in regression model, but only the reasoning and analytic factors were individually significant (p < .01) with odds ratios of 1.58 and 1.80, respectively.

Jackson (2010)

Examine ability of cumulative and/or pre-requisite GPA admission criteria for predicting timely ADN graduation

Student records from three ADN programs between 2003 and 2006 (N = 437)

Retrospective archived record review

ANOVA, correlation, logistic regression

DVs: timely graduation (with original cohort)

IVs: pre-admission cumulative GPA; pre-requisite course GPA; age; race/ethnicity

Average timely graduation rate for the three colleges in the study was 44%. Neither cumulative nor pre-requisite GPA predicted timely graduation (p = .830 and p = .643, respectively). This remained true after controlling for colleges, race, and age. The only significant factor was college (p < .0001).

Jeffreys (2007)

Examine ADN student entry, progression, graduation, and licensure characteristics

ADN students entering their first clinical course during the 1997 1998 academic year (N = 112)

Retrospective evaluation

Correlation and t-tests

DVs: retention: ideal (graduation in four semesters), continuous (5+ semesters, no stopouts), and interim (5+ semesters, 1+ stopouts); Attrition: voluntary, involuntary, and first semester failure; Licensure: pass versus fail on first versus subsequent attempts

IVs: pre-nursing GPA, Anatomy and Physiology I grade, number of local credits, number of transfer credits, age, race/ethnicity, gender

Pre-nursing GPA, local credits, transfer credits, and Anatomy and Physiology I grades were not significantly different by retention or attrition category.

Program graduates were younger (t = 2.741; df = 110; p = .007) and had fewer transfer credits (t = 2.270; df = 110; p = .025) than nongraduates. Prenursing GPA was higher, although not significantly.

Women had more stopouts than men (27% versus 16%).

White students had the highest ideal retention rate (32.2%).

Knauss & Willson (2013)

Examine relationship between HESI A2 scores and grades in two first semester

ADN nursing courses ADN students entering one program in 2008 and 2009 (N = 157)

Correlational study

Correlation coefficients

DVs: grades in Nursing 1 and Nursing 2

IVs: HESI A2 composite and component scores for math, reading comprehension, vocabulary/general knowledge, and grammar

HESI A2 composite score was positively correlated with final course grades in Nursing 1 and Nursing 2 (p < .01), accounting for 27% and 21% of the variance, respectively.

All HESI component scores were positively correlated with final course grades in Nursing 1 and Nursing 2 (p < .01) with the exception of the math and Nursing 2 (p < .05). Scores accounted for 4% to 14% of grade variance.

Luna (2014)

Identify admission criteria that indicate high likelihood for success in the first semester of an ADN program

First semester ADN students starting one program in 2013 (N = 78)

Nonexperimental correlational

Logistic regression

DVs: grade in first semester nursing course; success or failure in first semester

IVs: TEAS composite, reading, science, English, and math scores; prenursing GPA, and grades in Anatomy & Physiology

Significant positive correlations were found between the first semester nursing course grade and the TEAS composite (r = .455; p < .001), English (r = .329; p = .007), math (r = .279; p = .023), and science (r = .447; p < .001) scores and the prenursing GPA (r = .264; p = .032).

The regression model used only the TEAS composite and pre-nursing GPA due to multi-collinearity issues. It explained 22% of the variance in first semester nursing course grades. Only the TEAS composite was significant (p = .001).

Muecke (2008)

Analyze pre- and postadmission criteria to predict ADN program and first-time NCLEX-RN success

Graduates and noncompleters from one ADN program between 1998 and 2005 (N = 404)

Quantitative nonexperimental

Stepwise backward linear regression, logistic regression, ANOVA

DVs: graduation or noncompletion; final ADN GPA; success or failure on first attempt at NCLEX-RN

IVs: preadmission criteria (HS GPA; HS rank; ACT composite and reading subject scores; number of applicable college credits and GPA from those classes); postadmission criteria (first-term nursing course GPA, second term grade nursing course GPA, and GPA for anatomy, physiology, and microbiology courses)

All pre- and post-admission criteria variables except number of previous college credits had a significant positive relationship to final ADN GPA at the p ≤ .001 level.

Significant differences were reported between those who graduated and passed the NCLEX-RN on the first attempt and those who never graduated for the following variables at the p < .005 level: HS GPA, HS rank, ACT composite, previous college credits, previous college GPA, and grade in Microbiology.

Regression models using pre- and postadmission criteria predicted 92% of the variance in final ADN program GPA. Previous college GPA was a significant pre-admission variable (p < .001).

Murray, Merriman, & Adamson. (2008)

Examine the ability of the HESI A2 examination for predicting ADN and BSN success

Students from one ADN (N = 217) and one BSN program (N = 69)

Longitudinal descriptive

Bivariate regression and t-test

DVs: program completion or noncompletion; Nursing course grades

IV: HESI A2 composite score

The A2 scores for ADN students were significantly positively correlated (r = 0.253 to 0.442; p = .05 to .01) with grades in eight of the nine nursing program courses. A2 scores for BSN students were positively correlated with grades in 10 of 20 courses.

Mean A2 scores of those who completed the ADN program were significantly higher than those who did not (75.98 and 70.44, respectively; p < .001).

Payne (2011)

Identify variables that predict ADN success in first semester Fundamentals of Nursing course

80% of ADN students from two programs (N = 117)

Retrospective correlational

Chi-square, logistic regression

DV: grade in Fundamentals course

IVs: self-rated scores on General Self-efficacy and J.B. Rotter's Locus of Control scales and competency with computers; demographic factors (age, gender, marital/family status, employment status, prenursing GPA) and grade in Anatomy and Physiology I

Success versus failure in Fundamentals did not vary by gender (p < .532), marital status (p < .889), or number of children under 18 (p < .058).

Significant positive correlations were found between Fundamentals grade and Anatomy and Physiology I grade (r =.637; p < .001), pre-nursing GPA (r = .657; p < .001), and self-efficacy (r = .219; p = .027).

Working 20 hours or more per week was negatively associated with success in Fundamentals (X2(1, N = 17) =6.11, p = .013).

Logistic regression was used to predict success in Fundamentals with the IVs of self-efficacy, locus of control, Anatomy and Physiology 1 grade, and computer competency. Anatomy and Physiology 1 grade (OR = 3.870, 95% CI (1.959, 7.647), p < .001) and computer competency (OR = 3.388, 95% CI (1.016, 11.296), p = .047) were significant.

Pence (2010)

Determine the relationship between EI, motivation, demographic variables, and nursing student retention

First-year students from nine ADN programs in fall 2009 (N = 390) in fall 2009; pregnant students excluded; participation rate of 67.6%

Quantitative, descriptive nonexperimental

Regression analysis and independent sample t tests

DV: first-semester retention

IVs: motivation (MSLQ score); EI (AES score); age; gender; race/ethnicity; ADN school; readmission status

The relationship between first semester retention and several MLSQ mean subscale scores was significant: extrinsic motivation (p = .043), task value (p = .048), time and study (p = .048), effort regulation (p = .040), control of learning beliefs (p = .060), test anxiety (p = .093), critical thinking (p = .077), and peer learning (p = .067).

Mean age did not differ significantly between those retained versus not retained (p = .915).

Hierarchal logistic regression indicated race/ethnicity (p < .001) and the ADN school (p = .022) were predictors of first semester retention. Mean AES and MSLQ were not significant predictors.

Preston (2007)

Identify admission factors associated with ADN program and NCLEX-RN success

Students from one multi-campus ADN program admitted between 1990 and 1995 (N = 572)

Retrospective quantitative

Logistic regression

DVs: success (graduation with original cohort and first-time NCLEX-RN success; program completion

IVs: 27 admission variables, categorized into three clusters: academic, policy, personal and experiential factors, and campus entry and exit locations

None of the 27 IVs were statistically significant in predicting success. Students who failed or left the program were not included in this sample.

Among noncompleters, first semester clinical had the highest attrition rate (651%), decreasing to 164% as semesters progressed.

Attrition among students exempted from meeting the math requirement was 219% higher than for those who met it.

Noncompleters were younger, had fewer years since HS, were more likely to have no health care experience and repeated science courses, and had lower admission GPAs and standardized admission test scores. They received admission requirement waivers four times the rate of completers.

Rogers (2009)

Examine preadmission academic factors for ability to predict ADN program completion and NCLEX-RN success

ADN students admitted to one program between 2005 and 2007 (N = 294)

Retrospective quantitative

Backward stepwise logistic regression

DVs: program completion, NCLEX-RN exam success

IVs: TEAS reading, math, science, and English scores; ACT reading, math, science, and English scores; cumulative admission GPA; prerequisite admission college GPA; HS GPA; support course credits taken at admission; general education, health-related, and science support course GPAs; LPN licensure; student type (traditional or nontraditional), gender, race/ethnicity, year of HS graduation

TEAS science score (p = .003), science GPA (p < .001), and student type (p = .023) were significant predictors of program completion.

Each unit increase in TEAS science score increased the probability of program completion by a factor of 1.06. For each unit increase in science GPA, students were almost five times as likely to complete the program. Nontraditional students were more likely to complete the program than traditional students.

Rogers (2010)

Explore factors that contribute to ADN program completion and NCLEX-RN success

Successful ADN graduates who passed the NCLEX-RN (N = 6) and professors (N = 3) from one ADN program

Qualitative

Thematic coding

Semi-structured interviews; document analysis

Three themes emerged:

○ Factors related to either student qualities and skill sets (motivation; academic abilities such as critical thinking, test taking, and study skills; organization; prioritization of roles and responsibilities; the ability to manage life events and extreme stress; and health care experience);

○ Collaboration with others (support systems such as religion, finance, family, and friends; communication with faculty members; level of faculty involvement with students); and

○ Nursing curriculum (innovative teaching methods and carefully constructed course examinations; practice questions).

Prenursing academic factors were not cited as important for success. This was clarified in the final interviews. Due to the competitive admission process, success was attributed to other factors.

Sall (2009)

Identify factors ADN students perceive as contributing to academic success or failure

Successful ADN students from one program with lower than average entrance GPA and TEAS score (N = 10); Unsuccessful students with above average entrance GPA and TEAS score (N = 10)

Qualitative

Modified constant comparative method

Interviews

Both groups received emotional support from their families, but successful students received more financial support.

Successful students took more college prep courses in high school and took full course loads when completing prerequisites.

Successful students described nursing courses as challenging and responded by studying more. Unsuccessful students perceived classes and exams as unnecessarily difficult, passing as beyond their control, and felt instructors wanted students to fail.

Shelton (2012)

Partially test the Model of Nursing Student Retention

Nontraditional students from nine ADN programs (N = 458); three groups: persistently enrolled without withdrawing (n = 300), formerly enrolled and voluntarily withdrew (n = 83), formerly enrolled and withdrew for academic failure (n = 75)

Survey return rate was 96% for currently enrolled students and 42% for formerly enrolled students

Quantitative correlational

Chi-square and ANOVA

DV: program status (persisted, withdrew voluntarily, failed academically)

IVs: background variables (age, gender, marital status, responsibility for dependent family members, adequacy of financial resources, number of hours of employment per week, prior education, expected level of education, parental education, HS GPA, and college GPA); internal psychological processes (academic self-efficacy measured as academic efficacy expectations with the Self-Efficacy for Self-Regulated Learning scale and academic outcome expectations with the Outcomes Expectations Questionnaire-ADN); and external supports (perceived faculty support measured with the Perceived Faculty Support scale

Background variables differed significantly between students who persisted and those who failed for financial concerns (p = .035), high school GPA (p = .005), and college GPA (p < .001). Differences between those who persisted and those who voluntarily withdrew were prior education (p = .010), expected education level (p = .018), and college GPA (p = .028).

Academic efficacy expectations, academic outcome expectations, age, dependents family members, and hours of employment per week were not significantly different among groups.

Students who persisted had significantly higher perceived faculty support than those who failed and those who voluntarily withdrew (p < .001 for both).

Smith (2013)

Examine the relationship between sociodemographic characteristics, dispositional factors, situational factors, and institutional factors on first semester ADN retention

ADN students from eight programs (N = 439)

Descriptive correlational

Chi-square, factor analysis, correlation, ANOVA, binary logistic regression

DV: success in first semester

IVs: Sociodemographic characteristics of ethnicity, gender, related courses completed, course load, developmental courses taken, highest degree earned, parents college attendance, relationship status, children financially dependent, other family members financially dependent, and receiving financial aid; Institutional constructs of faculty, peers, and diversity and overall experience; Career values of autonomy, flexibility, caring, work style, and job characteristics; Situational constructs of work issues and financial concerns; Dispositional construct of confidence in ability; and other items of family encouragement, support of friends, and missing class due to family obligations.

Retained students were more likely to be white (p = .022), have completed related courses (p = .009), and taken fewer developmental courses (p = .018). They were less likely to have other family members financially dependent on them (p = .003).

Nonretained students were more likely to value job autonomy (p = .018) and flexibility (p =.001). Retained students were more confident in their ability (p = .008), had more family encouragement (p = .03) and support from friends (p = .02), and rarely missed class for family obligations (p = .04).

A model of the significant factors above explained 10.6% to 16.7% of the variance in retention. Most notable, nonretained students were over three times as likely to have family members financially dependent on them, over twice as likely to have incomplete related courses and have taken two or more remedial courses.


Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs
Admission Factors Associated with Success in ADN Programs

Table A:

Admission Factors Associated with Success in ADN Programs

Authors

Dr. Olsen is Nursing Instructor, Wisconsin Indianhead Technical College, Rice Lake, Wisconsin.

The author has disclosed no potential conflicts of interest, financial or otherwise.

The author thanks Mary Alice Larson and staff for their assistance acquiring the articles and dissertations reviewed in this study, and Mary Jean Jergenson, MSN, RN, for her insightful feedback.

Address correspondence to Jeanette M. Olsen, PhD, RN, Nursing Instructor, Wisconsin Indianhead Technical College, 1900 College Drive, Rice Lake, WI 54868; e-mail: jeanette.olsen@witc.edu.

Received: August 14, 2016
Accepted: September 28, 2016

10.3928/01484834-20170123-05

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