Journal of Nursing Education

Major Article 

Predictors of Students’ Success in Community College Nursing Programs

Jean Ann Seago, PhD, RN, FAAN; Dennis Keane, MPH; Eric Chen, MPH; Joanne Spetz, PhD, FAAN; Kevin Grumbach, MD

Abstract

Many conceptual models have been applied in the investigation of college retention of nursing students. We tested a model that specifies four general constructs as predictors of student success in nursing education—dispositional factors, career value factors, situational factors, and institutional factors. The purpose of this article is to describe predictors of nursing students’ success, specifically: (a) What factors predict success of nursing students graduating from community colleges in California? and (b) What factors predict success of nursing students graduating on time from community colleges in California? The study design was correlational and descriptive in nature, with a convenience sample of six intervention colleges and six matched-pair control colleges. Results of the logistic regressions indicated on-time and any-time graduations were predicted by higher grade point averages in prenursing and science. Higher prenursing grades are positive predictors of graduation; improvements in performance prior to commencing nursing education should improve student success.

Dr. Seago is Professor Emerita, Department of Community Health Systems; Mr. Kean is Analyst, and Dr. Spetz is Adjunct Professor, Philip R. Lee Institute for Health Policy Studies; Mr. Chen is Research Assistant, Center for California Health Workforce Studies; and Dr. Grumbach is Professor and Chair, Department of Family and Community Medicine, University of California, San Francisco, California.

This study was funded by The California Endowment and the Center for California Health Workforce Studies.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Jean Ann Seago, PhD, RN, FAAN, 756 Gull Avenue, Foster City, CA 94404; e-mail: Jean.ann.seago@nursing.ucsf.edu.

Received: July 07, 2011
Accepted: April 18, 2012

Posted Online: July 30, 2012

Abstract

Many conceptual models have been applied in the investigation of college retention of nursing students. We tested a model that specifies four general constructs as predictors of student success in nursing education—dispositional factors, career value factors, situational factors, and institutional factors. The purpose of this article is to describe predictors of nursing students’ success, specifically: (a) What factors predict success of nursing students graduating from community colleges in California? and (b) What factors predict success of nursing students graduating on time from community colleges in California? The study design was correlational and descriptive in nature, with a convenience sample of six intervention colleges and six matched-pair control colleges. Results of the logistic regressions indicated on-time and any-time graduations were predicted by higher grade point averages in prenursing and science. Higher prenursing grades are positive predictors of graduation; improvements in performance prior to commencing nursing education should improve student success.

Dr. Seago is Professor Emerita, Department of Community Health Systems; Mr. Kean is Analyst, and Dr. Spetz is Adjunct Professor, Philip R. Lee Institute for Health Policy Studies; Mr. Chen is Research Assistant, Center for California Health Workforce Studies; and Dr. Grumbach is Professor and Chair, Department of Family and Community Medicine, University of California, San Francisco, California.

This study was funded by The California Endowment and the Center for California Health Workforce Studies.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Jean Ann Seago, PhD, RN, FAAN, 756 Gull Avenue, Foster City, CA 94404; e-mail: Jean.ann.seago@nursing.ucsf.edu.

Received: July 07, 2011
Accepted: April 18, 2012

Posted Online: July 30, 2012

Beginning in 1998, many factors contributed to the decade-long shortage of RNs in California. Because it was feared that the health of both the public and hospital industry would be profoundly affected by the RN shortage, government and industry developed multiple creative responses to alleviate the workforce deficit. One response to the shortage was to increase the number of RN graduates at all levels of education from the over 100 nursing programs in the state. Approximately three quarters of nursing education in California is provided by community colleges, which grant associate degrees in nursing. Because Californian community colleges are mandated to provide open access to California residents, some associate degree nursing programs had a high percentage of students who were not successful in completing the programs or who were not able to complete programs in the planned time span.

The California Endowment funded a 5-year project to increase the number of nursing program graduates, particularly underrepresented minority graduates, from four community college nursing programs and two baccalaureate nursing programs located in the Central Valley of California. Each college determined their needs and created different interventions to increase the number of nurse graduates from the six programs. The funded project, the Central Valley Nursing Diversity Initiative, also provided for a comprehensive evaluation of the success of the strategies used by the colleges to increase the number of graduates. Our research team was funded to perform both a formative evaluation and a summative evaluation. The purpose of this article is to describe the findings of part of the summative evaluation, specifically to answer the questions: (a) What factors predict success of nursing students graduating from community colleges in California? and (b) What factors predict success of nursing students graduating on time from community colleges in California?

Conceptual Model

Many theoretical and conceptual models have been proposed and applied to the investigation of college student retention (Anderson, 1985; Swail, Redd, & Perna, 2003; Tinto, 1975, 1982, 1988). Some models, such as those proposed by Tinto (1975, 1982, 1988), were specifically developed to understand the factors influencing minority student academic success. The Tinto model emphasizes a student’s academic and social integration into college, which includes dispositional factors (e.g., role models), situational factors (e.g., peer support), and institutional factors (e.g., supportive faculty).

After considerable review, we found that the work by Cross (1981), Pascarella (1982), and Pascarella and Terezini (1991) provided a broad framework that encompassed the constructs relevant to student retention in nursing education. Therefore, our theoretical and conceptual framework is based on research on adult students’ success by Cross (1981), Pascarella (1982), Pascarella and Terezini (1991), and Tinto (1975, 1982, 1988). Both the Cross and Pascarella models were applied to nursing education in a study by Phillips, Spurling, and Armstrong (2002), which explored the validation of prerequisite course grades as predictors of success in nursing programs in Californian community colleges. As mentioned in the Phillips et al. report, there has long been a concern about students’ success in associate degree nursing programs and simultaneously maintaining open access to those programs (Grzegorczyk, 1995; Lamoureux & Johannsen, 1977). Scientists have worked to understand the predictors of success in associate degree nursing programs while encouraging access to these programs by diverse student groups (Astin, 1975a, 1975b; Bello, Haber, & King, 1977; Brown, 1978; Lamoureux & Johannsen, 1977; Naron & Widlak, 1991; Petty & Todd, 1985). A constellation of predictors of success are likely (Astin & Oseguera, 2005; Stieren, 1981; Stronck, 1979), and these predictors are related to the student, family, and the college environment.

For our study, we tested a conceptual model that specifies four general constructs as predictors of students’ success in nursing education—dispositional factors, career value factors, situational factors, and institutional factors. The dispositional construct includes characteristics related to students’ underlying academic aptitude, motivation, emotional outlook, and physical well being that may affect educational achievement. The career values construct (which is closely related to the dispositional construct) can be defined as those job characteristics that are important to students when selecting a career and that may influence students’ determination to persist in a field of study. The situational construct relates to characteristics of students’ life circumstances, such as social support and financial status, which facilitate or impede achievement of educational goals. The institutional construct consists of those characteristics of the college or academic institution that may influence successful pursuit of students’ educational goals (National Center for Education Statistics, 1993–1994, 1998, 2000–2001). Based on predictors of success found in the Phillips et al. (2002) study, we also tested the grade point average (GPA) for science and prenursing (courses taken prior to beginning the nursing courses).

Although public education institutions in the state are not permitted to target students based on ethnicity (Connerly & Heriot, 1996), the colleges in our study implemented various interventions targeted to all potential or actual nursing students, which included increased financial aid, increased marketing via media, increased skills laboratory time, increased individual tutoring for students, the hiring of additional nursing and science faculty, and other activities that would increase student success and therefore increase numbers of graduates.

We hypothesized that interventions implemented by the study colleges positively affected the factors mediating students’ experiences in the educational pipeline, and thereby would affect the ultimate outcome of student success in the nursing programs. For example, we hypothesized that marketing of nursing careers would enhance the dispositional factor of college students’ attitudes about the attractiveness of nursing as a career and, in turn, would increase the likelihood of students applying to nursing programs and persisting in their nursing studies. Greater availability of financial aid would improve the situational aspects of pursuing a nursing education in the Central Valley, which would make it more likely for students to persist in prenursing courses, enter nursing programs, and successfully graduate from these programs. In addition, funding of academic support at the colleges would lead to improvements in the institutional environment and, in turn, reduce attrition in nursing programs.

The conceptual model is described in greater detail in previous articles (Seago, Alvarado, & Grumbach, 2007; Seago, Spetz, Alvarado, Keane, & Grumbach, 2006; Seago, Spetz, Keane, & Grumbach, 2006; Seago, Wong, Keane, & Grumbach, 2008; Wong, Seago, Keane, & Grumbach, 2008). The student questionnaires used in our study were subjected to rigorous validity and reliability testing, and this work is described previously (Seago et al., 2008). The questionnaires are lengthy; therefore, they are not provided herein, but they can be obtained from the corresponding author.

Design and Method

The study design was descriptive, tested for correlations, and used a convenience sample. As part of the Central Valley Nursing Diversity Initiative project, we surveyed students in 12 colleges: six intervention colleges (four associate degree and two baccalaureate degree) and six matched-pair control colleges (four associate degree and two baccalaureate degree). Control colleges were matched with the intervention colleges on characteristics, including size, rural or urban location, and ethnicity of the student population. After receiving human subjects study approval from the institutional review board, nursing students who agreed to participate were surveyed between the fall semester 2002 and the spring semester 2006. Students in nursing courses in the intervention colleges were surveyed each fall, and students in the control colleges were surveyed each spring (survey instruments are available on request). Some students were surveyed more than one time; however, that analysis is not included in this article. The students were surveyed in different semesters because we did not have sufficient resources to survey all 12 schools in one semester, and this may have affected our results. If a student had been surveyed more than once, we used the results from the last survey data.

The California Community College Chancellor’s Office (CCCCO) maintains an electronic record of every course taken and of every grade received by each student in the state. The California State University system does not maintain such a similar record. Therefore, we eliminated the four state universities from this analysis (two intervention and two control institutions), which reduced our analyses to the eight community colleges.

To create a database that included our variables of interest, we merged our (Central Valley Nursing Diversity Initiative) sample (1085 students) of first-year community college nursing students with the 2006 CCCCO database of courses. We matched the surveys completed by the students with the same students in the CCCCO database. The matches were based on last name, date of birth, and last four numbers of the students’ Social Security number. The merge yielded 738 matches with complete data. Using the CCCCO data, we were able to calculate each student’s GPA prior to entering the nursing program (prenursing GPA), each student’s science GPA, and each student’s overall GPA.

Our analyses included variable definitions (Table 1), descriptive statistics (Tables 2 and 3), bivariate correlations (Table 4), and logistic regressions (Table 5). The numerous scales, question scores, and descriptive information from the lengthy student survey were tested using bivariate correlations; however, the results are not shown herein. Any score that was not significantly related to either of the two outcome variables of interest in the bivariate correlations was omitted from the logistic regression. Because prenursing GPA and science GPA are highly correlated, we analyzed two models for each of the outcome variables of interest—one with prenursing GPA and one with science GPA. The logistic regression included outcome variables of interest (on-time graduation and any-time graduation) and predictor variables of interest (ethnicity, dispositional factors, situational factors, institutional factors, career value factors). Prenursing GPA or science GPA and control variables (descriptor variables we believed might affect graduation rate) included college, gender, whether the person was born in the United States, and whether the student’s parents went to college (Table 1).

Logistic Regression Study Variables and Definitions

Table 1: Logistic Regression Study Variables and Definitions

Description of Student Sample (N = 738)

Table 2: Description of Student Sample (N = 738)

Description of Student Sample (N = 738)

Table 3: Description of Student Sample (N = 738)

Bivariate Correlations of Any-Time Graduation and On-Time Graduation for Ethnicity and College

Table 4: Bivariate Correlations of Any-Time Graduation and On-Time Graduation for Ethnicity and College

Logistic Regression Models of Any-Time Graduation and On-Time Graduation (Outcome Variable)

Table 5: Logistic Regression Models of Any-Time Graduation and On-Time Graduation (Outcome Variable)

Results

Our sample was predominately White, with Hispanics being the second largest ethnic group of students. Most students were women, were married, had children, were U.S. born, and had parents who attended college. The average student age was 31 years, with an overall GPA average of 2.56. Most students said they had adequate financial support, but reported missing class often or sometimes because of work. Of the 738 students in the sample, 66 (9%) did not graduate. Of the 672 who graduated, all but 16 (2.4%) graduated on time (six terms including summers or two calendar years) (Tables 2 and 3).

The bivariate correlations indicated that being African-American was correlated with not graduating, but there was no other ethnic group that was correlated with either any-time graduation or on-time graduation (Table 4). Several colleges had higher shares of various ethnic groups of students (correlations not shown), but overall, the intervention colleges tended to have more non-White and more Hispanic students. In the bivariate correlations, there was no individual college (neither intervention nor control college) that was correlated with any-time graduation or on-time graduation (Table 4).

The logistic regression estimates indicated that both on-time and any-time graduations were predicted by higher prenursing GPA and science GPA. Ethnic group was not significantly related to either outcome variable, except that being Hispanic predicted higher rates of graduation in one of the models. Only Confident in my Ability positively predicted higher any-time and on-time graduation rates. No other demographic variables or the dispositional, situational, or institutional scale factors predicted either any-time graduation or on-time graduation. However, in the logistic regression, being a student at certain colleges positively predicted any-time graduation and on-time graduation.

Limitations of the study include lack of consistency among the interventions of the funded colleges (therefore, any effect [if it existed] was small; perhaps too small to identify), inability to collect data from all sites simultaneously (which may have affected the results), and making changes in some of the survey questions in that some questions were not asked over time (thus limiting our ability to analyze for changes over time).

Organizational evaluations are inherently difficult, and estimating the effects of interventions within organizations is even more difficult. However, our evaluation would have been stronger if we had a larger sample size of institutions and if we had performed a power calculation to determine the estimated number of student responses needed to show an effect.

Discussion

The most important findings from these analyses are consistent with other work (Phillips et al., 2002), which found that previous academic achievement, as measured by prenursing GPA and science GPA, was a positive predictor of any-time and on-time graduation. In addition, our study found that the self-report dispositional variable of academic self-confidence predicted higher any-time and on-time graduation rates. This dispositional variable may be influenced by academic success prior to commencing nursing education. Situational variables, such as cost, employment, family variables, and social support, were not significant predictors of any-time or on-time graduation, contrary to our hypothesis. Some significant correlations were noted across ethnicities in the bivariate correlations, but these correlations generally disappeared in the logistic regression. We also found considerable variation in graduation rates by individual schools in the models, even when holding constant all other predictors. This indicates that school environment is a predictor of graduation success, but it was not necessarily seen at the intervention schools. Therefore, the activities that were funded in the study were either not effective or had an effect too small to be captured by the graduation rates.

Future research should focus on three areas. First, how can students’ prenursing academic achievement be improved? Prenursing grades are positive predictors of graduation; thus, improvements in performance prior to commencing nursing education should improve student success. Second, what factors increase students’ academic confidence? Finally, what are the characteristics of specific schools that make students more likely to graduate (Seago & Spetz, 2003)?

References

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  • Seago, J.A. & Spetz, J. (2003). Admission policies and attrition rates in California community college nursing programs: A report to Senator Charles Poochigian and the California Post Secondary Education Commission (CA SB 664). Sacramento, CA: California Post Secondary Commission and The University of California Policy Research Center.
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Logistic Regression Study Variables and Definitions

VariableDefinition (Construct)
Science grade point average (GPA)GPA for science courses
Prenursing GPAGPA for courses taken prior to entering the nursing program
EthnicitySelf-described primary ethnic affiliation
  African-American
  White/Caucasian
  HispanicSpanish-speaking country of origin
  Other AsianChinese, South Asian Indian, Japanese, Korean
  Filipino
  Southeast AsianVietnamese, Thai, Laotian, Hmong, Mienh, Cambodian
  OtherAmerican Indian, Middle Eastern, European, other
Family encourages meYes or no (situational factor)
Friends support meYes or no (situational factor)
Missed class because of jobI have missed class due to work (situational factor)
Adequate financial supportI can afford the cost of college (situational factor)
Confident in my abilityI am confident in my ability to succeed (career values and dispositional factors)
Job security is a career valueJob security and income is important to me (career values and dispositional factors)
Flexibility is a career valueFlexibility of time off is important to me (career values and dispositional factors)
Autonomy is a career valueMaking decisions about my work is important to me (career values and dispositional factors)
Work style is a career valueWorking with people who are similar to me is important to me (career values and dispositional factors)
Community college
  InterventionColleges where study interventions occurred (institutional factor)
  ControlMatched-pair schools with no interventions (institutional factor)
FemaleFemale or not
U.S. bornU.S. born or not
Marital statusMarried or not
ChildrenHave children or not
Parents attended collegeParents attended college or not
Overall GPAGPA for all courses during college

Description of Student Sample (N = 738)

VariablenaPercentage
Ethnicity738100
  African-American304.1
  White/Caucasian43859.4
  Hispanic15821.4
  Other Asian223
  Filipino476.4
  Southeast Asian223
  Other212.9
Family encourages me712 of 73896.5
Friends support me708 of 73796.1
Missed class because of job (often or sometimes)692 of 73594.1
Adequate financial support576 of 73678.3
Community college738100
  Intervention39954.1
  Control33946
Female638 of 73486.5
U.S. born620 of 73784.1
Married473 of 73764.2
Have children402 of 73454.8
Parents attended college423 of 69960.5

Description of Student Sample (N = 738)

VariablenaMean (SD)Range
Overall GPA7382.56 (0.59)1 to 4
Prenursing GPA6712.76 (0.78)1 to 4
Science GPA6442.66 (0.86)1 to 4
Age (years)71431.1 (8.0)19.7 to 61.2
Confident in my ability73478.9 (7.1)0 to 100
Job security is a career value73477.4 (17.2)0 to 100
Flexibility is a career value73268.7 (22.5)0 to 100
Autonomy is a career value73449.4 (21.6)0 to 100
Work style is a career value72912.3 (20.9)0 to 100

Bivariate Correlations of Any-Time Graduation and On-Time Graduation for Ethnicity and College

VariableOn-Time GraduationAny-Time Graduation
Any-time graduation0.8864*1.0000
Ethnicity
  African-American−0.1019−0.1278*
  White/Caucasian0.05850.0403
  Hispanic−0.02570.0015
  Other Asian0.03660.0270
  Filipino−0.0314−0.0155
  Southeast Asian−0.0394−0.0288
  Other0.06050.0536
Community college
  Intervention college 1−0.0510−0.0124
  Intervention college 2−0.01020.0033
  Intervention college 30.06790.0701
  Intervention college 4−0.0071−0.0127
  Control college 10.03950.0151
  Control college 2−0.0337−0.0567
  Control college 3−0.0759−0.0717
  Control college 40.05710.0462

Logistic Regression Models of Any-Time Graduation and On-Time Graduation (Outcome Variable)

VariableAny-Time Graduation
On-Time Graduation
Odds Ratio (Robust SE)aOdds Ratio (Robust SE)bOdds Ratio (Robust SE)aOdds Ratio (Robust SE)b
Prenursing GPA1.994** (0.19)2.057** (0.21)
Science GPA2.188** (0.215)2.218** (0.31)
African-American0.594 (0.29)0.704 (0.371)0.667 (0.28)0.797 (0.32)
White/CaucasianReferentReferentReferentReferent
Hispanic1.448 (0.37)1.658* (0.395)1.141 (0.54)1.389 (0.60)
Other AsianDropped (co-linear)Dropped (co-linear)Dropped (co-linear)Dropped (co-linear)
Filipino1.577 (1.0)1.629 (1.06)1.170 (0.89)1.006 (0.82)
Southeast Asian1.180 (1.31)1.115 (1.29)0.890 (0.64)0.659 (0.58)
OtherDropped (co-linear)Dropped (co-linear)Dropped (co-linear)Dropped (co-linear)
Intervention college 11.126 (0.24)1.112 (0.226)0.741 (0.13)0.731 (0.127)
Intervention college 21.489* (0.27)1.751* (0.323)1.004 (0.13)1.233 (0.14)
Intervention college 32.372** (0.21)2.162** (0.153)2.319** (0.17)2.401** (0.21)
Intervention college 4ReferentReferentReferentReferent
Control college 11.054 (0.20)1.097 (0.194)1.186 (0.20)1.243 (0.21)
Control college 20.534** (0.06)0.534** (0.08)0.610** (0.06)0.625** (1.07)
Control college 30.829 (0.10)0.838 (0.103)0.726* (0.11)0.7445 (0.12)
Control college 42.760** (0.39)2.791** (0.426)3.620** (0.62)3.788** (0.61)
Confident in my ability1.024** (0.01)1.020* (0.008)1.015** (0.004)1.012* (0.005)
Family encourages me0.780 (0.25)0.787 (0.221)0.823 (0.23)0.820 (0.201)
Friends support me1.167 (0.46)1.127 (0.401)1.131 (0.35)1.055 (0.30)
Job security is a career value0.998 (0.004)0.999 (0.004)0.996 (0.005)0.996 (0.006)
Flexibility is a career value1.003 (0.007)1.002 (0.007)1.007 (0.006)1.005 (0.006)
Autonomy is a career value0.995 (0.008)0.996 (0.008)1.002 (0.005)1.002 (0.005)
Work style is a career value0.992 (0.008)0.994 (0.006)0.993 (0.008)0.998 (0.007)
Missed class because of job (often or sometime)0.991 (0.008)0.993 (0.008)0.000 (0.009)1.002 (0.009)
Adequate financial support1.003 (0.006)1.003 (0.006)1.006 (0.006)1.007 (0.006)
Female0.933 (0.319)0.748 (0.290)0.874 (0.29)0.655 (0.21)
U.S. born1.839 (0.67)1.658 (0.583)1.962* (0.60)1.524 (0.40
Parents attended college0.590 (0.32)0.644 (0.359)0.518 (0.22)0.528 (0.24)
Authors

Dr. Seago is Professor Emerita, Department of Community Health Systems; Mr. Kean is Analyst, and Dr. Spetz is Adjunct Professor, Philip R. Lee Institute for Health Policy Studies; Mr. Chen is Research Assistant, Center for California Health Workforce Studies; and Dr. Grumbach is Professor and Chair, Department of Family and Community Medicine, University of California, San Francisco, California.

This study was funded by The California Endowment and the Center for California Health Workforce Studies.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Jean Ann Seago, PhD, RN, FAAN, 756 Gull Avenue, Foster City, CA 94404; e-mail: Jean.ann.seago@nursing.ucsf.edu.

Received: July 07, 2011
Accepted: April 18, 2012

Posted Online: July 30, 2012

10.3928/01484834-20120730-03

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