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A correlational study of a reading comprehension program and attrition rates of ESL nursing students in Texas.

Abstract

AIM The purpose of this study was to examine the associations between English as a second language (ESL), a reading comprehension program, and attrition rates of nursing students.

BACKGROUND Higher attrition rates of ESL nursing students are an assumption, seemingly based on anecdotal evidence. Data reflecting ESL student attrition should be measured and analyzed so that students can be identified prior to attrition.

METHOD A secondary analysis of a large database of 27 initial licensure programs in Texas was completed. results Data analysis identified that ESL students who used a reading comprehension program were almost twice as likely to be off track or out of the program as ESL students who did not use the program.

CONCLUSION Nurse educators need to evaluate student profile characteristics in a comprehensive way when determining risk of attrition.

KEY WORDS

Nursing Students--Minorities--English as a Second Language--Student Attrition

**********

Once nursing applicants are accepted into a nursing program, issues with attrition and the ability to retain students become the focus for graduating new registered nurses. The National League for Nursing (NLN, 2010) reports that the graduation rates of RN programs are approximately 80 percent.

The consequences of attrition (delayed graduation or noncompletion) in a nursing program are not just deleterious to the individual student. For example, while students may experience financial loss and psychological difficulties as a result of attrition, colleges and universities experience financial loss in the form of federal and state monies based on enrollment data. In the United States in 2007, the cost for first-year higher education students not returning for a second year was $1.35 billion (Schneider, 2010). When a student leaves a nursing program, the program cannot admit another student to replace the student who left, and the college or university cannot regain the lost revenue caused by that student.

Attrition can also deny another qualified applicant admission into a program. The lost student garnered a place in the program only to lose it, but the other qualified applicant was never given an opportunity to succeed in the program. Within this context, the predictive value of admission requirements becomes highly significant. Nursing programs want to admit the students most likely to succeed.

A subgroup of the nursing student population includes those for whom English is not their first language. Although English might be a second, third, or fourth language, these students are usually identified as English-as-a-second-language (ESL) students. Various authors have stated that ESL nursing students experience a higher attrition rate than non-ESL students (Gilchrist & Rector, 2007; Guhde, 2003; Olson, 2012; Taxis, 2002). However, as no recent statistics support this statement, the higher attrition rate is an assumption that may be based on anecdotal evidence.

Researchers cannot afford to assume that ESL students have higher attrition rates than non-ESL students. Rather, actual statistics reflecting ESL nursing student attrition should be calculated so that students can be identified early and interventions started before attrition occurs. Barriers that contribute to ESL student attrition and interventions designed to reduce them should be studied because ESL nurses represent health care professionals who are valuable to the American population and will strengthen the abilities of the nursing profession.

THE RESEARCH STUDY

The purpose of this retrospective, longitudinal correlational study was to examine the associations among language, participation in a reading comprehension program, and attrition rates of pre-licensure nursing students in Texas. The study was a secondary analysis of the Texas Higher Education Coordinating Board's database from the Statewide At Risk Tracking and Interventions for Nurses (SATIN) study, which examined the association of selected variables representing concepts in the Nursing Undergraduate Retention and Success (NURS) model.

The NURS model was developed to illustrate the relationships among multifaceted variables that affect the retention and success of nursing students (Jeffreys, 2012). The model helps develop prescriptive strategies, suggests a positive view of student outcomes, and guides studies focused on interventions to prevent attrition. As the study tested the predictability of attrition rates based on ESL status when controlling for certain student profile characteristics, including age, ethnicity, race, sex, educational background, and whether participants were first-generation college students, the NURS model was modified to focus on selected concepts and their associated variables.

Two research questions were addressed: 1) Is there an association between ESL status and attrition rates in nursing students in initial licensure programs within Texas? 2) Is there an association between participating in a reading comprehension program and attrition rates in ESL nursing students in initial licensure programs within Texas?

METHOD

The research design was a secondary analysis using retrospective, longitudinal data in a correlational design to determine the relationships among variables (Field, 2009).

Sample

In early 2011, 27 initial RN licensure programs agreed to participate in the Nurse Innovative Grant Program sponsored by the Texas Higher Education Coordinating Board. Each program was responsible for administering the SATIN survey, providing interventions to students, and reporting on the interventions and each student's program status at the end of each semester. The lead university's project manager provided an access code for the Weaver reading program to students identified as being at risk due to reading comprehension scores. The two-year data collection period began June 1,2011 and continued through May 31, 2013.

Researchers at the lead university were responsible for developing the survey, interventions, and reporting forms as well as entering raw data into the database. The project statistician was responsible for maintaining the database, identifying students at risk of attrition, and disseminating the information to each program about which of their students were at risk. Project directors at each pre-licensure nursing program were responsible for notifying students of their at-risk status and making interventions available to them. The student was responsible for participating in group interventions, such as simulation, and completing individual interventions, such as a reading comprehension program.

Data were collected during the first year of the program (June 2011 to May 2012). Data from 3,305 students were obtained and entered into the database. The project statistician reduced the sample due to incomplete data regarding student status in the program. The final sample from the first year included 3,258 pre-licensure nursing students older than 18 years of age who attended one of the 27 programs in Texas. Of the 3,258 students, 2,611 were not ESL students and 529 were ESL students.

A power analysis using G*Power resulted in an estimated sample size of 360 for this study (Faul, Erdfelder, Buchner, & Lang, 2009), based on a two-tailed test, alpha = .05, power = .80, odds ratio (OR) = 1.5, and Pr(Y = 1|X = 1)[H.sub.0] = 0.16. The 0.16 was based on previous research using the same risk model for Texas nursing students in which 15.3 percent to 17.3 percent of the students were identified as off track or out of the program (Walker et al., 2011).

Setting

The survey was administered to students at the campuses of the participating programs. Students were typically taken as a group to a computer laboratory and directed to the survey website. Students were instructed that if they did not wish to participate in the study, they could use the computer to access the Internet for any reason. Students remained in the computer laboratory as a group until all students indicated they were finished.

Measurement Methods

The SATIN survey is composed of four separate surveys: a) the nursing student survey-1; b) the nursing student survey-2; c) the student perception appraisal-1; and d) a self-efficacy scale. The purpose of the SATIN survey was to collect information on each student regarding demographics, preadmission academic outcomes, perceptions of family and social support, and perceptions of personal qualities. Information from the survey was used to determine if a student should be considered at risk for attrition. Data concerning student status at the end of each semester were also collected from participating programs.

Administrator reports from the online Weaver Reading Intervention program were collected each semester from the company website (Weaver Instructional Systems, 2013). The program is divided into two sections, reading comprehension and vocabulary. Students begin the Weaver program by taking a diagnostic examination to identify their grade level; they then complete lessons and examinations before advancing to the next level. Student progress is tracked by the Weaver program and is available for instructors and administrators of the software.

The student profile characteristics, including age, sex, ethnicity, race, whether they were a first-generation college student, and whether they were an ESL student, were drawn from the SATIN survey. Use of the reading comprehension program was taken from the Weaver administrator reports. Student status in the program was gathered from information reported from the nursing schools.

Ethical Considerations

Written permission to conduct the original SATIN study was obtained from the institutional review board (IRB) at the lead university. Permission was obtained from the University of Texas at Arlington IRB for the secondary analysis. Informed consent was obtained from participants during the original data collection process.

Data Analysis

Statistical analysis was completed using SPSS version 21.0. Descriptive statistics were computed for student profile characteristics (Table 1), and missing values were analyzed to determine whether they should be omitted from analysis.

Logistic regression was computed to answer research question 1. The Hosmer and Lemeshow test was computed to determine whether the model was a good fit for the data (Field, 2009). The Wald test and its significance level were examined for the ESL variable to determine its predictive quality on attrition after controlling for age, sex, ethnicity, race, and first-generation college student status (Field). Unadjusted and adjusted ORs were evaluated to examine the relationship between ESL and attrition (Osborne, 2012).

A model that included data from ESL students alone identified the predictability of Weaver use on ESL student attrition after controlling for student profile characteristics. The Hosmer and Lemeshow test was computed to determine whether the model was a good fit for the data (Field, 2009). The Wald test and its significance level were examined for the Weaver use variable to determine its predictive quality on ESL student attrition (Field), and ORs were evaluated to examine the association of Weaver use and ESL student attrition after controlling for covariates (Osborne, 2012).

RESULTS

Sample Description

The sample size (N = 3,258) included nursing students in initial licensure programs in Texas whose status in the program was documented at the end of the first year of data collection. A total of 3,123 students reported race, and 3,245 students reported age. Missing data were casewise deleted from the analyses because the sample size was large enough that statistical power was not significantly affected by deleting the cases (Howell, 2007). Further description of the sample is shown in Table 1.

Research Question 1

The sample was divided according to the outcome variable, on track or off track/out of the program. Of the total number of students, 2,611 were on track; 647 were off track or out of the program. Of the students on track, 14.6 percent were ESL students and 85.4 percent were non-ESL students. Of the off track/out of the program students, 22.6 percent were ESL students and 77.4 percent were non-ESL students.

The assumption of linearity was violated by the continuous variable, age, so it was transformed into a nominal variable and coded as 18 to 25 years = 0 (reference category), 26 to 30 years = 1,31 to 35 years = 2,36 to 40 years = 3,41 to 45 years = 4,46 to 50 years = 5, and 51 years and older = 6. Unadjusted ORs were calculated for each variable. The data are shown in Table 2. Students off track or out of the program were more likely than on track students to be older. Black students were more than 1.5 times more likely to be off track or out of the program than Caucasian students, and male students were almost 1.5 times more likely to be off track or out of the program than female students. First-generation college students were 1.3 times more likely to be off track or out of the program than those who were not first-generation college students, and ESL students were more than 1.5 times more likely to be off track or out of the program than non-ESL students. Ethnicity was not a significant predictor of attrition.

The association of ESL to attrition when controlling for other baseline variables was evaluated using logistic regression; variables were simultaneously entered into the model. The overall predictive model was a good fit for the data as evidenced by the Hosmer and Lemeshow test ([chi square] = 3.110, df = 8, p = .927). ESL was not a significant predictor of attrition (OR = 1.287, 95 percent CI [.992, 1.671], p = .058) when controlling for age, ethnicity, race, sex, and first-generation college student status.

Research Question 2

The SATIN sample was divided into ESL students and non-ESL students. ESL students were divided according to the outcome variable and on track/off track/out of the program status. Of the total number of ESL students, 383 were on track and 146 were off track or out of the program. Of the ESL students who were on track, 9.7 percent used the Weaver reading program and 90.3 percent did not use Weaver. Of the off track/out of the program students, 17.1 percent used Weaver and 82.9 percent did not use Weaver.

Unadjusted ORs were calculated using logistic regression for each of the variables. Age was coded as in research question 1. ESL students aged 46 and older were almost 3.5 times more likely to be off track or out of the program than younger ESL students (OR = 3.396, 95 percent CI [1.540,7.491], p = .002). ESL students who used Weaver were almost twice as likely to be off track or out of the program than ESL students who did not use Weaver (Table 3). No significant differences were seen in ethnicity, race, sex, and first-generation college student status between the two groups.

Logistic regression was calculated to determine the association between Weaver use and attrition in ESL students when controlling for age, ethnicity, race, sex, and first-generation college student status. Variables were loaded into the regression model using forced entry. The model was a good fit for the data as indicated by the Hosmer and Lemeshow test ([chi square] = 3.216, df = 7, p = .864). Weaver use was a significant predictor of ESL student attrition (OR = 2.155, 95 percent CI [1.169, 3.975], p = .014) independent of age, ethnicity, race, sex, and first-generation college student status. Unadjusted and adjusted ORs for each variable are shown in Table 3.

DISCUSSION

This study included a larger (N = 3,258), more heterogeneous (27 initial licensure programs) sample of nursing students than other previously published research (Jeffreys, 2007; Salamonson, Everett, Koch, Andrew, & Davidson, 2008). Age and sex were similar to those reported in the literature (Jeffreys; Walker et al., 2011), and the racial and ethnic composition of the participants was consistent with the current Texas and US RN workforce (Buerhaus, Staiger, & Auerbach, 2009; Texas Board of Nursing, 2013). Based on this sample, the racial and ethnic disparities that exist between the RN and the general populations will continue, highlighting the differences between nursing student composition and the general population (US Census Bureau, 2013). This comparison supports the need for continued focus on the recruitment and retention of nursing students from diverse ethnic and racial backgrounds.

Initially, the analysis of the profile characteristics indicated significant unadjusted ORs between race, sex, age, ESL, and first-generation college student status and attrition. Compared with students who were on track, students off track or out of the program were more likely to be older, men, ESL, first-generation college students, and black, Asian, or Native Hawaiian/other Islander. This finding is consistent with previous reports in which several combinations of the effect of these variables on student attrition were evaluated (Salamonson et al., 2008, 2011).

In this study, profile characteristics were also analyzed as a group using logistic regression to determine significant predictors of attrition. Analysis of the model did not identify a significant association between student ESL status and attrition. The only significant predictors of attrition were older age ([greater than or equal to] 46 years), black or Native Hawaiian/Other Islander, men, or first-generation college student status. These results are important because no study has yet evaluated the synergist effects of all these student profile characteristics on attrition.

To answer research question 2, the database was divided into ESL students and non-ESL students. Use of the Weaver reading comprehension program was added as a variable. Analyses of the individual student profile characteristics for ESL students indicated significant correlations between age and attrition and between Weaver use and attrition. ESL students who were off track or out of the program were more likely to be 46 years of age or older and use the Weaver program than ESL students who were on track. No current literature has evaluated student profile characteristics and their relationship to attrition in ESL students.

Student profile characteristics were also analyzed as a group using logistic regression to determine significant predictors of attrition of ESL students. Use of the Weaver program and its association with attrition, independent of the other student profile characteristics, was of primary interest. Analysis of the model did identify a significant association between Weaver use and attrition in ESL students. The only other significant predictor of attrition was age 46 years or older.

Students using the Weaver reading program have been identified as being at risk for attrition based on reading comprehension scores. The literature has shown that the riskiest students are those with low reading comprehension scores (Walker et al., 2011); therefore, the Weaver program is not a viable intervention for decreasing student attrition. A possible explanation is that the students who used the Weaver program were at such a high risk for attrition that even use of the program could not sufficiently decrease their risk of attrition. The Weaver reading program was initiated in the first semester of nursing school. Students beginning a nursing program may typically underestimate the demands placed on them at that time (Jeffreys, 2007), so it is possible that students could not maximize their use of the Weaver reading program.

This study evaluated the effects of student profile characteristics on attrition. Findings support the research framework developed by the author (Donnell, 2013), in which student profile characteristics affect academic outcomes. The study findings also support the NURS model, indicating an association between student profile characteristics and academic outcomes. Consistent with the NURS model, the results from research question 2 supported the association among student profile characteristics, academic factors, and academic outcomes.

Study Limitations

Limitations of a secondary analysis include the inability of the researcher to control the questions asked. For example, the SATIN database included race and ethnicity as two separate categories, which follows the mandate of the Office of Management and Budget and is used by the US Census Bureau (2013). Comparisons of race and ethnicity in the SATIN database with those in other studies were also difficult due to differences in definitions (Loftin, Newman, Bond, Dumas, & Gilden, 2012). Pre-licensure nursing programs report attrition rates ranging from 25 percent to 33 percent (Jeffreys, 2007; Pryjmachuk, Easton, & Littlewood, 2008); however, the overall attrition rate for the sample of the current study was 19.9 percent and included 27 nursing programs.

Another limitation of the study was the implementation of the Weaver reading program during the first year of the SATIN grant, which may have affected the attrition rate of the sample. Walker et al. (2011) reported an attrition rate of 15 percent to 17 percent after their interventions were implemented to decrease attrition.

The SATIN database includes Texas nursing programs with administrators who applied for and received a grant focused on decreasing attrition rates. These programs were highly motivated to decrease attrition rates, which may have resulted in a biased sample. The sample of the study was large enough and had adequate power to answer the research questions.

Takeaway Points and Future Research

In analyzing student profile characteristics that contribute to nursing student attrition, study results support using multiple characteristics to predict attrition rates. The important finding was the synergy that occurred between the six variables evaluated in the model. These interactions support the complexity of student attrition in nursing programs and provide evidence supporting the NURS model (Jeffreys, 2012).

As previously identified, poor reading comprehension is a major barrier to the successful completion of initial licensure nursing programs by ESL students (Donnelly, McKiel, & Hwang, 2009). A reading comprehension program alone may not be able to surmount the synergistic effect of multiple student profile characteristics on rates of attrition among ESL students. Analysis of the model suggests that new interventions to improve the reading comprehension of ESL students are needed. These interventions must be based on theory, responsive to the individualized needs of ESL students, and validated with large, diverse samples.

Researchers evaluating the effects of race and ethnicity on nursing student attrition should come to a consensus on how these variables are defined. Until then, meta-analyses and generalizations of results will be problematic. Interventions to assist nursing students who are part of racial and ethnic minorities cannot begin until researchers know exactly who these students are.

CONCLUSION

These findings can have an important impact on nursing education, because they reveal the importance of evaluating a combination of factors when determining students at risk for attrition. The current study findings also highlight the possible need for early identification of students at risk for attrition due to reading comprehension scores so that interventions can begin prior to the start of nursing school. Nursing faculty must comprehensively evaluate student profile characteristics to determine specific interventions for each student. It has become clear that the prediction of students at risk for attrition is a complex process.

Replication of this study, which must use the at-risk prediction model and a comprehensive approach to evaluating student profile characteristics, is critical in order to generalize the results of the current study. Further research is also needed to evaluate interventions to improve the reading comprehension of ESL students. Research in nursing education would benefit from using consistent definitions for categories such as race and ethnicity, perhaps by using the US Census Bureau categories to define these variables. This type of consensus would permit future meta-analyses and collaboration among researchers.

doi: 10.5480/13-1212

REFERENCES

Buerhaus, P. I., Staiger, D. O., & Auerbach, D. I. (2009). The future of the nursing workforce in the United States: Data, trends, and implications. Sudbury, MA: Jones & Bartlett.

Donnell, W. M. (2013). A correlational study of a reading comprehension program and attrition rates of ESL nursing students in Texas. (Doctoral dissertation). Available from ProQuest Digital Dissertations. (AAT 3568841)

Donnelly, T. T., McKiel, E., & Hwang, J. J. (2009). Challenges and motivators influencing the academic performance of English as an additional language (EAL) nursing students: The perspectives of the students. Canadian journal of Nursing Research, 41,130-150.

Faul, R., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage.

Gilchrist, K. L., & Rector, C. (2007). Can you keep them? Strategies to attract and retain nursing students from diverse populations: Best practices in nursing education. Journal of Transcultural Nursing, 18(3), 277-285.

Guhde, J. (2003). English as a second language (ESL) nursing students: Strategies for building verbal and written language skills. Journal of Cultural Diversity, 10(4), 113-117.

Howell, D. (2007). The treatment of missing data, in W. Outhwaite & S. P. Turner (Eds.), The SAGE handbook for social science methodology (pp. 208-224). London, England: SAGE.

Jeffreys, M. R. (2007). Tracking students through program entry, progression, graduation, and licensure: Assessing undergraduate nursing student retention and success. Nurse Education Today, 27,406-419.

Jeffreys, M. R. (2012). Nursing student retention: Understanding the process and making a difference. New York, NY: Springer.

Loftin, C., Newman, S. D., Bond, M. L., Dumas, B. P., & Gilden, G. (2012). Diversity in Texas nursing programs: A study of the relationship between supportive characteristics and graduation of Hispanic and other underrepresented minority nursing students. Hispanic Health Care International, 10, 159-167.

National League for Nursing. (2010). 2010 NLN nurse educator shortage fact sheet. Retrieved from www.nln.org/governmentaffairs/pdf/nursefacultyshortage.pdf

Olson, M. A. (2012). English as a second language (ESL) nursing student success: A critical review of the literature. Journal of Cultural Diversity, 19, 26-32.

Osborne, J. W. (2012). Logits and tigers and bears, oh my! A brief look at the simple math of logistic regression and how it can improve dissemination of results. Practical Assessment, Research & Evaluation, 17,1-10. Retrieved from http://pareonline.net/getvn.asp?v=17&n=11

Pryjmachuk, S., Easton, K., & Littlewood, A. (2008). Nurse education: Factors associated with attrition. Journal of Advanced Nursing, 65, 149-160.

Salamonson, Y., Andrew, S., Clauson, J., Cleary, M., Jackson, D., & Jacobs, S. (2011). Linguistic diversity as sociodemographic predictor of nursing program progression and completion. Contemporary Nurse, 38, 84-93.

Salamonson, Y., Everett, B., Koch, J., Andrew, S., & Davidson, P. M. (2008). English-language acculturation predicts academic performance in nursing students who speak English as a second language. Research in Nursing and Health, 31, 86-94. doi:10.1002/nur.20224

Schneider, M. (2010). Finishing the first lap: The cost of first-year student attrition in America's four-year colleges and universities. Washington, DC: American Institutes for Research.

Taxis, J. C. (2002). The underrepresentation of Hispanics/Latinos in nursing education: A deafening silence. Research and Theory for Nursing Practice, 16(4), 249-262.

Texas Board of Nursing. (2013). RNs by race/ethnic group, age, and sex. Retrieved from www.bne.state.tx.us/reports_and_data_nursing_statistics.asp

US Census Bureau. (2013). State and county quickfacts. Retrieved from http:// quickfacts.census.gov/qfd/states/48000.html

Walker, G. C., Klotz, L., Martin, P., Miller, G. K., Missildine, K., Bishop, S., ... Glymph, D. (2011). A regional academic partnership for the early identification and retention of at-risk nursing students. Journal of Professional Nursing, 27, e8-c13.

Weaver Instructional Systems. (2013). Reading intervention. Retrieved from www. wisesoft.com/reading-intervention/

Wendy M. Donnell, PhD, RN, CHSE, is an assistant professor, Texas A&M International University Dr. F. M. Canseco School of Nursing, Laredo, Texas. For more information, contact Dr. Donnell at wendy.donnell@tamiu.edu.

Table 1: Description of Sample from the SATIN Database (N = 3,258)

Variable                            Frequency (%)

Sex
  Female                            2714(83.3)
  Male                              544(16.7)
  Missing                           0
  ESL
  Yes                               529 (16.2)
  No                                2729 (83.8)
  Missing                           0
  First-Generation
  College Student
  Yes                               921 (28.3)
  No                                2237 (71.7)
  Missing                           0
  Age
  Minimum                           18
  Maximum                           72
  Mean                              28.3
  Standard                          8.652
  Deviation
  Missing                           13
Ethnicity
  Hispanic/Latino                   736 (22.6)
  Non-Hispanic                      2522 (77.4)
  Missing                           0
Race
  White                             2231 (68.5)
  Black/African American            530 (16.3)
  Asian                             277 (8.5)
  American Indian/Alaskan           27 (0.8)
  Native
  Native Hawaiian/Other Islander    15 (0.5)
  Combination                       43 (1.2)
  Missing                           135 (4.1)

Table 2: Association Between Characteristics and Attrition of Total
Sample

                                      Odds Ratio
Variable                             (Unadjusted)   p Value

Age (years)                                         < .0001
  18-25                                 1.000
  26-30                                 1.063        .632
  31-35                                 1.222        .145
  36-40                                 1.367        .047
  41-45                                 1.566        .011
  [greater than or equal to] 46         2.581       < .0001
Ethnicity
  Non-Hispanic/ Non-Latino              1.000
  Hispanic/Latino                       1.066        .540
Race
  White                                 1.000
  Black                                 1.773       < .0001
  Asian                                 1.517        .006
  American Indian/ Alaskan               .583        .380
  Native Hawaiian/ Other Islander       3.108        .032
  Mixed                                  .613        .308
English as a First Language
  Yes                                   1.000
  No                                    1.695       < .0001
Sex
  Female                                1.000
  Male                                  1.455        .001
First-Generation College Student
  No                                    1.000
  Yes                                   1.307        .005

                                      Odds Ratio
Variable                              (Adjusted)    p Value

Age (years)                                         < .0001
  18-25                                 1.000
  26-30                                  .974        .845
  31-35                                 1.142        .349
  36-40                                 1.225        .215
  41-45                                 1.335        .117
  [greater than or equal to] 46         2.354       < .0001
Ethnicity
  Non-Hispanic/ Non-Latino              1.000
  Hispanic/Latino                       1.256        .068
Race
  White                                 1.000
  Black                                 1.759       <.0001
  Asian                                 1.362        .078
  American Indian/ Alaskan               .662        .443
  Native Hawaiian/ Other Islander       2.949        .045
  Mixed                                  .665        .397
English as a First Language
  Yes                                   1.000
  No                                    1.288        .058
Sex
  Female                                1.000
  Male                                  1.418        .003
First-Generation College Student
  No                                    1.000
  Yes                                   1.267        .019

Table 3: Association Between Characteristics and Attrition for ESL
Students

                                     Odds Ratio
Variable                            (Unadjusted)   p Value

Age (years)                                         .014
  18-25                                1.000
  26-30                                1.086        .770
  31-35                                1.291        .391
  36-40                                 .775        .492
  41-45                                2.231        .018
  -46                                  2.536        .063
Ethnicity
  Non-Hispanic/ Non-Latino             1.000
  Hispanic/Latino                       .683        .062
Race                                                .152
  White                                1.000
  Black                                1.805        .022
  Asian                                1.323        .263
  Other                                1.327        .423
Weaver Use
  No                                   1.000
  Yes                                  1.932        .019
Sex
  Female                               1.000
  Male                                 1.081        .738
First-Generation College Student
  No                                   1.000
  Yes                                   .995        .979

                                     Odds Ratio
Variable                             (Adjusted)    p Value

Age (years)                                         .025
  18-25                                1.000
  26-30                                1.010        .973
  31-35                                1.180        .593
  36-40                                 .736        .418
  41-45                                1.967        .054
  -46                                  3.236        .005
Ethnicity
  Non-Hispanic/ Non-Latino             1.000
  Hispanic/Latino                       .619        .278
Race                                                .618
  White                                1.000
  Black                                1.172        .726
  Asian                                 .964        .934
  Other                                1.493        .273
Weaver Use
  No                                   1.000
  Yes                                  2.290        .005
Sex
  Female                               1.000
  Male                                  .978        .929
First-Generation College Student
  No                                   1.000
  Yes                                  1.143        .534
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Author:Donnell, Wendy M.
Publication:Nursing Education Perspectives
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2015
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