Predictors of Success on the MLT and MLS Board of Certification Exam

Sample Description

Five-hundred ninety-nine students graduated with associates and/or bachelor’s degrees in MLS between August 15, 2015 and August 15, 2019. Excluded from the pool of participants were 133 students who had not taken the corresponding BOC exam at the time of data retrieval, 16 who took the BOC exam more than 1 year after graduation, and 5 who had no record of program start date. According to Laerd Statistics, multiple regression can be performed only on data that meet assumptions of homoscedasticity, multicollinearity, and leverage points and lack highly influential points.¹⁵ If assumptions are violated, corrections need to be made to the data, or another statistical test must be used.¹⁵ In the study, 8 participants demonstrated values that were flagged as highly influential during assumptions testing in SPSS. To facilitate regression analysis, the 8 participants were excluded. After exclusions, 437 graduates were included in the study, of which 205 were MLT graduates and 232 were MLS graduates. Students included in the study took the corresponding BOC exam (MLT or MLS) within 1 year of graduation.

Figure 1 shows a breakdown of each program by mode of instruction. Of the MLT graduate pool, 116 received the degree online and 89 were traditional face-to-face students. Of the MLS graduate pool, 81 were traditional students and 151 graduates received the degree online.

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Figure 1.

Participant count by program and instruction mode N = 437.

Students completing the program online were required to complete rotation face to face in their state of residence, usually in their place of employment or another affiliated clinical laboratory.

Demographics in Table 1 show student age range was broadest at the MLT level, with students ranging from 19 to 60 years old at the time of graduation. The median ages of students in the face- to-face and online MLT program were 23 and 31, respectively. The MLS program had a median age of 25 for face-to-face students and 34 for those online; the latter encompassed the oldest group of participants. Although Hispanic and Asian graduates were present in both programs and modes of instruction, most were Caucasian.

Data Collection and Analysis

The PA-GPA used in the study was the overall GPA on the student record at the time of program application for MLT or MLS. Years to completion were calculated per program, counting days to completion between the first MLS course and graduation. Days were converted into years in Microsoft Excel. Participants were categorized into online and face to face as recorded in graduation records. Age at graduation and sex were also recorded and added to the analysis. Age at graduation was calculated in Microsoft Excel by subtracting the a student’s date of birth from the date at graduation. Sex was recorded as male or female as reported by students and stored in university records.

To align with NAACLS accreditation benchmarks, first-year BOC scores were evaluated but not first-time scores. For participants who took the exam more than once, the BOC score of the last attempt within 1 year of graduation was recorded. Each BOC score was recorded as a numeric continuous variable. Statistical analysis was performed by program (MLT and MLS), as each degree has individual certification requirements. BOC scores were compared to 3 main predictors: PA-GPA, time to degree completion, and instruction mode (online vs face to face). Multiple stepwise linear regression using SPSS determined a model of predictors that best forecasted BOC scores. Pearson correlations further assessed relationships between BOC scores and each predictor alone. Because PA-GPA was found to be the strongest predictor, correlations for the remaining variables were conducted controlling for PA-GPA.

Preadmissions Grade Point Average

Regression and Pearson correlation results are displayed in Tables 2 and 3, respectively.

At the MLT level, PA-GPA demonstrated a statistically significant, positive correlation with BOC scores (r = .490, n = 205, p < .001). In the MLS program, a 2-step model also revealed a statistically significant, positive correlation (r = .313, n = 232, p < .001). Sex was included in the model as a significant predictor and showed a statistically significant positive correlation (r = .234, n = 232, p < .001) demonstrating higher scores in the male student population. As demonstrated by the R² change (Table 2), PA-GPA accounted for 24% of the variance in BOC scores for MLT and 9.8% of the variances of BOC scores for MLS.

Time to Degree Completion

In the MLT program, a negative correlation was found between years to graduation and BOC scores, but the correlation was not statistically significant (r = −.172, n = 205, p = .181). The MLS program also showed a negative, nonstatistically significant correlation (r = −.010, n = 232, p = .914).

Instruction Mode

At the MLT level, a positive correlation between instruction mode and BOC scores was seen in favor of face-to-face students. The correlation was not statistically significant (r = .308, n = 205, p = .068). A positive, not statistically significant correlation was also seen in the MLS program (r = .181, n = 232, p = .362).

Demographic Variables

PA-GPA

MLT and MLS programs are tasked with preparing students to enter the workforce and relieve the shortage of laboratory personnel in the health care field. At the same time, programs must meet rigorous accreditation standards, among which are BOC pass rates. The results of this analysis suggest that PA-GPA is the most valuable predictor of BOC scores at both the MLT and MLS levels. The influence of PA-GPA was expected, as similar results were seen in the nursing and MLS fields.^3,5 This finding provides a foundation on which to base criteria for admission.

Historically, the study institution’s MLS program has enrolled online students with lower GPAs (as low as 2.0) to expand the student pool and relieve the widespread shortage of laboratory professionals. Although it is not the norm to accept students with GPAs under 2.5 in the study institution, exceptions to this rule have historically allowed educational opportunities for students who work in entry-level laboratory positions and demonstrate ample experience and good work ethic. Such students may not be scholastically inclined and may not have previously considered a college education but are often loyal and come highly recommended by their employers who can offer full clinical support. Although it is rewarding to provide such educational opportunities through flexible preadmission criteria, it is also crucial to consider the requirement for these students to pass the BOC.

When applying the stepwise regression model in Table 2 to the study institution, minimum PA- GPAs of 2.8 for MLT and 3.6 for MLS guarantee a score of 450 or above on the BOC (passing score is 400 or above). However, it is important to consider that this is not a perfect model, as in this study PA-GPA accounted for 24% of the variance in BOC scores for MLT and 9.8% of the variances of BOC scores for MLS. Regardless, the statistically significant correlation between PA-GPA and BOC scores suggests the use of PA-GPA as a primary admission tool, as the correlation demonstrates that BOC scores increase as PA-GPA increases. In terms of pre admission criteria, incremental increases in PA-GPA requirements may be necessary, particularly in programs approaching substandard pass rates on the BOC. In fact, the findings of the current study prompted an increase of the PA-GPA requirement from 2.5 to 2.75 in both programs in the study institution.

Time to Degree Completion

A negative correlation was noted between years to degree completion and BOC scores. The lack of statistical significance may be attributed to the way years-to-graduation were documented in the study. Time to degree completion was underestimated in both programs, as each participant’s timeframe began with the enrollment date in the first MLT or MLS course, with no regard to courses taken prior to program admission, such as science courses. The timeframe limitation is especially noteworthy at the MLS level, as most students in the MLS program took science prerequisites during or prior to the MLT program. Nevertheless, limiting the timeframe to only MLT or MLS courses allowed for consistency given the diversity of students, locations, and timing of non-MLT/MLS courses in their education. Unfortunately, no current studies were found evaluating time to graduation and BOC pass rates. However, a faster trajectory to graduation is desirable, as passing BOC scores are more likely in graduates who take the exam no later than 90 days after graduation.⁹

Instruction Mode

Instruction mode and BOC scores demonstrated a nonsignificant correlation favoring traditional face-to-face education. These findings opposed some of the literature that found online students in MLS scored higher than their traditional counterparts.⁵ The results were attributed to online students’ increased ability to handle pressure because of maturity and experience level.⁵ In contrast, higher BOC scores noted in face-to-face students in the study institution may have resulted from stricter preadmission requirements currently in place in the campus program, particularly PA-GPA. In addition, MLS campus students seem to face fewer family, work, and life demands than online students. It is important to note that research suggests that online and traditional education are equally effective, even when teaching clinical skills.¹¹ Thus, university programs should continue to offer online instruction in MLS, as it is a way to relieve the personnel shortage and reach students in rural areas lacking MLS programs.

Demographics

The finding of male students obtaining significantly higher scores than females was unforeseen and contradicted the findings in the literature.^14,15 Historically, female students outnumber male students in the programs evaluated. This occurrence may be due to premedical students in the campus program who use MLS as a degree of choice prior to applying to medical school.

Traditionally, premedical students at this institution are high-achieving, predominantly male students in the MLS campus program.