Biostatistics—the application of statistics to the analysis of biological or medical data—is quickly becoming an integral component for the foundation of nursing practice. Increasing complexity in the provision of health care demands that clinicians understand epidemiological and statistical principles to provide safe, evidence-based, high-quality patient-centered care (West & Ficalora, 2007). The American Association of Colleges of Nursing (AACN) clearly differentiates the statistical competencies between baccalaureate-prepared and master’sprepared nurses. The baccalaureate-prepared nurse is required to attain a fundamental understanding of basic applied statistics to understand such principles as prevalence, risk factors, and determinants of health (AACN, 2008). The master’s-prepared nurse is required to further build on that fundamental understanding through the application of biostatistical analysis of outcome data to synthesize and evaluate those data to achieve optimal health outcomes (AACN, 2011). Moreover, the implementation of the Patient Protection and Affordable Care Act (PPACA) increases the demand for the provision of preventative and primary care. Meeting this demand will depend on master’s-prepared nurse clinicians to lead the implementation of evidence-based practice to improve the access to and the quality of patient care. Competency in improving patient outcomes requires a certain level of sophistication and understanding of biostatistics to make meaningful interpretations of the application of research findings.
The format and delivery of higher education is rapidly changing with advances in educational technologies. In particular, distance learning bypasses geographic boundaries imposed by traditional classroom-based learning, allowing for a wider reaching and a more broadly dispersed dissemination of knowledge and education. Distance learning has been identified as an essential movement in nursing education, namely to alleviate the nursing shortage (AACN, 2000; Mancuso-Murphy, 2007). The Institute of Medicine’s (IOM; 2010) landmark Future of Nursing report recommends that nurses achieve higher levels of education, further supporting the need for implementation of diverse technology for distance education. Although a 2002 survey of 162 accredited nursing programs revealed that nearly all programs offered or planned to offer distance learning (Hodson-Carlton, Siktberg, Flowers, & Scheibel, 2003), the AACN (2012b) more recently reported that despite these advances in distance education, more than 75,000 qualified applicants to professional nursing programs were turned away. This lack of access to nursing education, particularly graduate education, necessitates innovation in the delivery of education to a diverse nursing population.
Studies have shown that nursing students at all academic levels are generally satisfied with distance learning. Most notably, students enjoy the availability, accessibility, and flexibility of online education (Ali, Hodson-Carlton, & Ryan, 2004; Fearing & Riley, 2005). The convenience of distance learning is attractive to nursing students, especially those at the graduate level who may be employed full time or raising families and therefore require more flexibility in their method of pursuing higher learning (Heller, Oros, & Durney-Crowley, 2000). However, dissatisfactions, such as feelings of isolation (Ali et al., 2004), difficulty with applying concepts taught, and inadequate opportunities to establish peer and faculty support (Sit, Chung, Chow, and Wong, 2005), have also been reported. These barriers to effective distance learning may be magnified in certain courses, such as biostatistics, that require more practice and application.
Graduate students who have little mathematical training often find biostatistics challenging due to its quantitative nature (Boyd Enders & Diener-West, 2006). Research examining statistics anxiety reveals that these challenges stem from a lack of statistical skills and training, as well as from misperceptions about statistics and low self-efficacy in performing well in statistical courses (Pan & Tang, 2004). However, some strategies have reduced statistics anxiety and improved student performance. For example, Pan and Tang (2004) found a reduction in statistics anxiety by using application-oriented teaching methods and increasing instructor attentiveness. In addition, active learning strategies, such as group-based and Internet-based activities, improve graduate student performance in biostatistics course examinations, compared with didactic teaching alone (Boyd Enders & Diener-West, 2006). Taken together, these strategies are instrumental in the effective delivery of biostatistics material to graduate nursing students; although they often prefer distance learning, graduate nursing students also find biostatistics intimidating and difficult to apply in their daily nursing practice.
Little is known about the learning styles of graduate nursing students in online programs (Fearing & Riley, 2005), but some evidence suggests that they vary widely. Based on Kolb’s experimental model of learning, Smith (2010) found that all four learning styles (i.e., diverger, assimilator, converger, and accommodator) exist among RNs enrolled in online nursing programs. On one hand, some students prefer reflective observation (i.e., watching), whereas others prefer active experimentation (i.e., doing); on the other hand, some students prefer concrete experience (i.e., feeling), whereas others prefer abstract conceptualization (i.e., thinking). Using the same model to understand distance learners’ preferences for course delivery, Yoder (1994) found that nursing students who were reflective observers preferred linear video modules, whereas active experimenters preferred more interactive videos.
Given the diversity of learning styles among graduate nursing students and the specific challenges in delivering biostatistics content in traditional formats, it is vital to include different delivery formats to engage and meet the learning needs of graduate nursing students who take biostatistics courses online. As Fearing and Riley (2005) noted, nurses who pursue higher education to obtain advanced degrees are prepared to devote the time and effort to successfully complete the required courses. In light of this dedication to completing graduate programs, challenging content offered in a variety of ways to stimulate their interest in and understanding of the content will ultimately support these students in their successful completion of the graduate education.
This educational innovation article describes the pioneering approach of faculty in one graduate nursing program to implementing best practices for delivering an online biostatistics course. Course development is complete, with the first live delivery of the course having occurred in fall 2013. The outcomes of this course are for students to attain both statistical literacy and statistical communication skills, demonstrating biostatistics competency for the master’s-prepared nurse.
Determination of the graduate-level competencies to set the foundation for the course curriculum started with an evaluation of the AACN (2012a) Graduate-Level QSEN Competencies: Knowledge, Skills and Attitudes. This AACN report provides a description of the competencies and the knowledge, skills, and attitudes necessary for graduate nursing education to meet the standards for quality and safety in the delivery of health care. Evaluating the necessary statistical literacy and communication skills needed to achieve each of these competencies was the driving force behind determining which statistical tests should be introduced and the level at which they would be delivered in the graduate program. For example, the QSEN competency for informatics describes necessary knowledge as being able to identify critical and useful electronic data for providing high-quality, efficient patient care through the skill of searching, retrieving, and managing data to make clinical decisions with an attitude that appreciates the importance of valid, reliable, and significant data. Biostatistics directly relates to the development of these knowledge, skills, and attitudes, requiring an understanding of research design to manage data, correlations to interpret reliability, and inferential statistics to determine the significance of data.
In addition, a review of multiple graduate nursing program outcome competencies revealed common themes in three key areas: (a) applying the research process to both appraise and develop nursing knowledge; (b) using analytic methods to evaluate existing literature and determine gaps in implementing evidence-based practice; and (c) disseminating new nursing knowledge to health care professionals, patients and families, and the public. These general competencies supported the need for graduate nursing students to acquire an understanding of multiple biostatistical methodologies and guided the selection of the following topics for inclusion in each learning module of the course:
- Module #1: Levels of Measurement/Measures of Central Tendency.
- Module #2: Descriptive Statistics Versus Inferential Parametric and Nonparametric Statistics.
- Module #3: Research Design and Sampling Methods.
- Module #4: Hypothesis Testing, Statistical Significance and Inference, Type I and Type II Error.
- Module #5: Tests for Dichotomous Data: Chi-Square.
- Module #6: Tests for Comparing Means: t Tests and Analysis of Variance.
- Module #7: Correlation and Factor Analysis.
- Module #8: Predicting Future Events: Linear Regression.
- Module #9: Epidemiology, Rates, and Measuring Risk.
- Module #10: Measurement Reliability and Validity, Bias, Internal/External Validity.
- Module #11: Measurement Sensitivity and Specificity.
- Module #12: Sample Size Calculation, Power Analysis, Effect Size.
Preparation for Online Delivery
Students accepted to all master’s degree programs at this school of nursing are required to have an undergraduate statistics course as a prerequisite for admission into the program, providing the necessary foundational preparation in basic statistical analysis at the undergraduate level. Academic advisors recommend that students take this 3-credit course in the second semester of their master’s degree program, although it is offered each semester and they are able to take it at any time during their program.
Each learning module was developed with a standardized format consisting of three components: content delivery via recorded lectures, interactive activities, and discussion board assignments. Content delivery was created following the current recommendations for teaching statistics at the college level as outlined in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) report (Franklin & Garfield, 2006):
- Emphasize statistical literacy and develop statistical thinking. Introductory lecture provides real-world examples of the application of statistics in clinical practice. Interactive activities following the lecture include each student developing a research topic relevant to his or her clinical practice setting. Based on this topic, students produce a null hypothesis, produce a nondirectional research hypothesis, produce a directional research hypothesis, and identify the independent and dependent variables. Finally, they are instructed to identify the ways in which those variables might be measured and to define multiple levels of measurement for each variable.
- Use real data and technology for developing conceptual understanding and analyzing data. Throughout each recorded lecture and interactive activity session, actual data sets based on relevant clinical experiences are used. Although at this level students are not expected to calculate statistical analyses from data sets, the professor uses SPSS® software to run the analysis in the recorded lecture and provides students with the output. Students are then expected to interpret the analysis and post their interpretation of the findings in the assignment section of Blackboard™ at multiple times throughout the lecture.
- Stress conceptual understanding rather than mere knowledge of procedures. Following the analysis calculation in the recorded lecture and the students’ interpretation of the data output, students provide a manuscript-style write-up of the findings, offering their interpretation of the results and implications for the importance in clinical practice.
- Foster active learning. Discussion board strategies are incorporated throughout each of the modules. The first discussion board assignment involves the students posting his or her clinical question and hypotheses as determined in the first module. Then, throughout each module, the student posts examples of ways in which each module’s topic may apply to the individual’s clinical question. Other students are then expected to respond and offer other suggestions for ways in which the variables may be measured or statistically analyzed.
- Use assessments to improve and evaluate student learning. Online examinations are delivered to students through the use of Blackboard. Examinations are time limited, delivering questions randomly from a bank of test questions. This approach to online examination ensures that students will never receive the same questions in the same order to limit the possibility of unethical test-taking practices. In addition, the time-limited nature of examination administration also limits the potential for students to look up answers to every question they may encounter. Although the professor is not able to ensure complete freedom of test-taking aides, there is rigor in the expectation that the content is understood at some level to be able to successfully complete the examination in the time allotted. Students also sign an academic honor code prior to taking each test, which serves as a reminder of permissible test-tasking practices.
The implications of increasing biostatistics competency in the master’s-prepared nurse are more pressing than ever. Demonstrating statistical competency is critical to meeting the IOM’s recommendations outlined in the Future of Nursing (2010) report to achieve higher levels of education to become full partners with physicians and other health care providers in redesigning the delivery of health care through the provisions of the PPACA. Only by way of ensuring both an understanding of and dedication to incorporating biostatistics into every day clinical practice can nurses be prepared to practice to the full extent of their education and training. Data collection and analysis are vital to the workforce planning and policy-making needs of the future health care system. Master’s-prepared nurses are well-positioned to lead those efforts when equipped with the knowledge, skills, and attitudes to effectively evaluate outcomes in practice based on utilization of biostatistics competencies. Furthermore, demand for nurses prepared with these skills will continue to increase as the needs of health care organizations transition away from delivery of acute care in the inpatient setting to offering more holistic and preventative care in the out-patient and primary care settings.
Online learning has become a fundamental part of graduate nursing education and will increase in demand as more nurses seek advanced degrees. Teaching biostatistics rigorously in an online format may offer such an option with the greatest flexibility for many students. This evidence-based curriculum for the online biostatistics course will prepare master’s-level nurses with the necessary statistical knowledge and analytical skills to improve patient care and outcomes. An iterative evaluation of the course, captured by student outcomes, including final grades and evaluation of satisfaction, applicability, ease of access, and ability to meet course objectives, will enable course instructors to continue with further improvement of the relevance of the content and the mechanisms of online delivery to meet students’ needs.
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