Although statistics education is a core component of nursing education, no standardized competency guidelines, learning objectives, expected learning outcomes, or curriculum standards exist with regard to what should be taught, when in a program it should be taught, or how it should be taught. The topic of statistics as taught in nursing often lacks a connection to other coursework in the full nursing curriculum. Much of the age-old stigma about statistics as being a difficult topic and as a class to get through may potentially be addressed with inclusion and integration of newer approaches to online and classroom teaching and learning of statistics (Garfield & Ben-Zvi, 2008). In fact, studies have shown that statistics knowledge and, specifically, a base in statistical literacy and thinking are necessary for understanding published research (delMas, Garfield, Ooms, & Chance, 2007). Statistics is a topic deemed important enough that most academic nursing programs at all degree levels require successful completion of one or more courses on the topic. Yet, the topic has received little attention in nursing education.
The American Association of Colleges of Nursing (AACN) establishes standards for the academic nursing discipline. On its Web site ( http://www.aacn.nche.edu/faculty/curriculum-guidelines), the AACN includes the curriculum standards for academic nursing programs, which are aimed at providing “a framework for positioning baccalaureate and graduate-degree nursing programs to meet the health care challenges of a new century” (para. 1). Statistics education or training is not mentioned in the extensive list of AACN curriculum standards. The Essentials publications provided by the AACN (2006, 2008, 2010, 2011) include recommended guidelines for education and training of nursing students at all levels (baccalaureate nursing [BSN], Master of Science in Nursing [MSN], Doctor of Nursing Practice [DNP], and Doctor of Philosophy [PhD]). In each of these publications, statistics is only briefly mentioned and little to no guidance is provided with respect to curriculum development, course content, or learning objectives. The only statements directly related to statistics education in the Essentials publications are described in the Table; however, these brief mentions of statistics are general and vague. The AACN Essentials documents are not informative for faculty who are developing and teaching statistics courses in an academic nursing program.
Statements About Statistics Education in the American Association of Colleges of Nursing (AACN) Essentials Documents
Statistics is a stand-alone discipline (Nicholls, 2001). The statistics field is vast and includes many topics, focuses, specializations, and subdisciplines. Its scope and depth needs to be considered in making curriculum decisions about statistics education, including deciding who teaches, what is taught, the number of credit hours, pedagogy, assessment, and timing of coursework in the context of a full nursing curriculum. A significant focus of statistics education within the statistics in recent years has included a focus on the development of statistically literate students with an acquired ability to think and reason about statistical information (Garfield & Ben-Zvi, 2008). It has been well documented since the 1990s that traditional approaches to teaching statistics have many limitations and change is needed to adequately prepare students to reason or think statistically (Moore, 1997). The traditional approach to teaching statistics focused on statistical methods, computations, and procedures. Numerous study findings in recent years have propelled a shift toward a focus on statistical literacy and thinking. The statistics education literature is developed on these topics, and statistical literacy and thinking have been well defined and described (delMas, 2002).
The purpose of this article is to provide an introduction to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) report (American Statistical Association [ASA], 2005) recommendations and a description of strategies for integrating study findings in the statistics education discipline and extending the GAISE report to inform statistics curriculum development in nursing education. No known published or available standard competency guidelines exist on statistics education for nursing students.
The importance of this topic was illuminated by an expert panel of statistics experts, each teaching in an academic nursing program. A group of five statistics educators convened at the 2012 Joint Statistical Meetings, which is an annual conference hosted by the ASA and other international organizations, and held a panel discussion on the topic “Teaching Statistics to Nursing Students” (Hayat, Eckardt, Higgins, Kim, & Schmiege, 2013). Each panelist had an advanced degree in statistics and a faculty appointment in an academic nursing program. The panel discussed the need to plan studies and collect data to make informed, evidence-based curriculum decisions and set standards on statistics education. There was unanimous agreement that competency standards on statistics education are needed for each nursing degree.
The GAISE Report
Although no known standard competency guidelines exist for statistics education in nursing, much can be learned from the GAISE report (ASA, 2005). The GAISE was approved in 2005 by the Board of Directors of the ASA. The published report included a section for K-12 and a section for college-level instruction. Although the college-level GAISE report was intended for teaching an introductory college-level statistics course, its recommendations may be beneficial for faculty developing statistics courses for undergraduate and graduate nursing students. This article describes suggestions for integrating research findings and knowledge into teaching statistics and statistics education for nursing students. The GAISE report is an excellent resource that can be used to integrate statistics education research findings and knowledge about teaching statistics into statistics education for nursing students. The report provides a cohesive summary of core recommended changes to teaching statistics resulting from the statistics education reform movement.
The GAISE report recommendations were intended to encourage statistics instructors to modernize introductory statistics courses and make them more engaging and authentic (Zieffler, Park, Garfield, delMas, & Audbjorg, 2012). The six recommendations are:
Emphasize statistical literacy and develop statistical thinking.
Use real data.
Stress conceptual understanding, rather than mere knowledge of procedures.
Foster active learning in the classroom.
Use technology for developing conceptual understanding and analyzing data.
Use assessments to improve and evaluate student learning.
These recommendations hold great potential for improving statistics courses offered to nursing students at all degree levels. The following sections include suggestions for implementing the GAISE recommendations in statistics coursework for nursing students.
Emphasize Statistical Literacy and Develop Statistical Thinking
In today’s information age, there is an overwhelming personal and professional need for most citizens to be statistically literate (Utts, 2003, 2010). Nurses often need to devote significant time to correcting patient perceptions and misperceptions created from poor or unsubstantiated statistical reporting accessed on the Internet about a health condition or treatment. Daily news reports include an abundance of statistical reporting, such as poll results in political elections or statistics about a drug, health scare, and prevalence of a disease. Social media, such as Facebook® and Twitter™, appeal to the masses, and users often perpetuate media reports that include statistical findings. Reading and critically evaluating an evidence-based paper rarely necessitates calculations or formulas; instead, knowledge and skill is needed to understand how a study is conducted and how statistical results are reported and interpreted.
Several definitions for statistical literacy, reasoning, and thinking are included in the statistics education literature (Rumsey, 2002). The GAISE report (ASA, 2005) provides formal definitions for statistical literacy and thinking:
Statistical Literacy: We define statistical literacy as understanding the basic language of statistics (e.g., knowing what statistical terms and symbols mean and being able to read statistical graphs) and fundamental ideas of statistics.
Statistical Thinking: Statistical thinking has been defined as the type of thinking that statisticians use when approaching or solving statistical problems. Statistical thinking has been described as understanding the need for data, the importance of data production, the omnipresence of variability, and the quantification and explanation of variability (Cobb, 1992).
In lieu of the traditional statistics class content that focuses on statistical methods, a course in statistical literacy and thinking may be structured so students learn the language of statistics and acquire an ability to think and reason about data, distributions, concepts of center and spread, and visual displays of data and to develop an understanding of the core building blocks of statistical inference, including concepts such as samples, sampling variability, sampling error, effect size, covariation, and correlation (Garfield & Ben-Zvi, 2008). Readers interested in more details about statistical literacy may visit Milo Shield’s Statistical Literacy Project Web site ( http://web.augsburg.edu/~schield). This is an excellent resource that provides a comprehensive overview of statistical literacy.
Use Real Data
The relevance and applicability of teaching and learning of statistics should be made apparent to nursing students from the beginning of a statistics course. The use of real data is an important and easily implemented practice that can also function to maintain a focus on application while learning theory and concepts. Students begin firsthand to consider many questions, such as how and why data were produced and collected, as well as thinking through an analysis that would be informative and meaningful and understanding how data can be used to answer a well-formulated research question (Gould, 2010; Neumann, Neumann, & Hood, 2010).
A method used by the author to generate real data with graduate nursing students has been to administer a 23-item survey to students on the first day of class (Table A; available in the online version of this article). The survey is anonymous and intended to be fun and humorous. It is easy to administer and recommended for use in class sizes of at least 25 or more students to ensure anonymity. Questions are intended to produce results representing different levels of measurement and both discrete and continuous data. Open-ended questions producing continuous data include “How many minutes on average does it take you to get ready in the morning?” and “What is your average commute time (estimate, in minutes) from the time you exit your home to the time you walk through the front doors of the [school of nursing]?” Open-ended questions producing count data that may, for example, follow a Poisson distribution, include “How many hours have you spent on Facebook in the past 7 days?” and “How many text messages on average do you send per week?” A series of multiple choice items are also included that produce ordinal response data, as well as skewed and non-normal data. Analysis of correlated (clustered) data is a hot topic in nursing research (Hayat & Hedlin, 2012). The survey produces a pre–post response, with students recording their pulse before taking the survey and then after. Student feedback has suggested that this approach of collecting data from students and using the data to be analyzed in the classroom has been fun, interesting, and an effective tool for teaching and learning statistics. In addition, students are able to reconcile their own responses in the context of a database, with their individual responses composing a single row in a spreadsheet and 23 columns composed of the 23 survey items.
Another effective method is to use nurse faculty publications to highlight concepts and methods applied to real data and to couple this with guest lectures and visits from these faculty to discuss their work. The author has collaborated with nurse faculty to provide subsets of data from completed nursing research studies to the statistics class for instructional purposes. It is a great learning experience, as students can confirm results published in a paper, question the appropriateness of the methods applied, and consider other ways to analyze data and interpret results. It is especially effective to have study authors visit class and talk about the lifeline of a study—from the planning stages, through the data analysis, dissemination, and promoting discussion on impact and possible future work.
Stress Conceptual Understanding, Rather Than Mere Knowledge of Procedures
An understanding of statistical concepts is needed to grasp the meaning and application of a set of procedures (ASA, 2005). Traditional approaches to teaching statistics too often require students to memorize sets of procedures and statistical methods. When a collection of ideas are taught in this way—at a topical level and without integration with conceptual understanding—the knowledge is usually quickly forgotten. An advantage of focusing on conceptual understanding is that students build a foundation and graduate well prepared to study additional statistical techniques as needed.
Statistical significance is too often confused with clinical importance (Hayat, 2010). Students who are learning which statistical test to use with a given study design or set of data will not be adequately prepared to avoid this problematic confusion. Lang (2007) reported the most common statistical reporting errors in biomedical journals. He found the three most frequently occurring errors to be confusing statistical significance with clinical importance, describing the dispersion of skewed data with the standard deviation, and inappropriately reporting the standard error. For nurse investigators and other scientists to be adequately educated to avoid these statistical errors, conceptual understanding needs to be emphasized in statistics coursework.
The author has enjoyed many stimulating discussions with students in statistics classes focused on promoting statistical thinking and understanding. For example, it can be useful to ask students to consider how they may be using statistical thinking already in their nursing practice. Conversely, an open discussion and brainstorming session has been thought provoking and stimulating for the students in thinking about what kinds of studies and data are needed to provide an evidence basis for the hundreds of decisions made during a single work shift.
Another approach for stressing conceptual understanding has been to ask each student to choose an article in a peer-reviewed nursing journal in his or her area of practice or research interest and, with instructor guidance and suggestions on what to look for, find a statistical reporting error or incorrect use of a statistic. The student is then asked to consider the potential impact of this reporting error on a patient, nursing practice, and the profession. This exercise emphasizes the importance of statistical knowledge and conceptual understanding and promotes integrating this knowledge with his or her area of specialty or focus.
Foster Active Learning in the Classroom
Student engagement in the classroom can change students’ attitude toward and perception of statistics, as well as the way statistics is learned and taught (Cobb, 1991). Many different methods exist for evolving from the traditional lecture-based format. For example, throughout the semester, the author begins each weekly class with an interactive review of the previous week’s material. Students are engaged, come prepared with lingering questions, and often reveal areas or topics for which additional attention is needed. Another active learning approach has been to have students teach each other. Throughout class lectures, the author embeds multiple choice examination questions in the class slides. When arriving at such a slide, the students are asked to decide on an answer and then turn to a classmate and discuss it. This promotes the use of statistical language and encourages students to speak about concepts and verbally explain their thought process, statistical thinking, and reasoning process. In fact, this method has been formalized by Professor Erik Mazur, a physics professor at Harvard University, and described in his book Peer Instruction (Mazur, 1997). A similar active learning strategy is think-pair-share, a cooperative learning exercise that has students reflect on a question and then practice sharing and receiving responses with other students (Barkley, Cross, & Major, 2005).
Individual and group activities are another way to actively engage students in the classroom. For example, the author uses an in-class counting exercise with M&M candies to demonstrate the goodness-of-fit chi-square statistical test. In addition to the sugar rush and chance to indulge in chocolate, students enjoy comparing the color distributions of the candies in their bag to the manufacturer claims about expected proportions of each of the six colors. Students then discuss possible extensions of this exercise in thinking about expected standards with respect to health and medical instruments and equipment. An interesting group activity is to give a practice scenario to students and ask groups of two to three students to consider how they would design a study to generate data to inform practice, analyze the collected data, and interpret the results.
A new, active learning strategy being used by some statistics educators is the flipped, or inverted, classroom (Bergmann & Sams, 2012; Lage, Platt, & Treglia, 2000). This approach aims to avoid a lecture-based class format by having students prepare outside of class by studying material so classroom time can be used for questions and active learning. Another active learning approach used by the author has been to ask each student to select an article on his or her area of practice or research interest, and present a summary of it to the class. The student is asked to present a summary of the work, a thorough overview of the statistical analyses presented, and any reporting errors or assumption violations detected and lead a discussion about the results and interpretations of the statistical findings.
Use Technology for Developing Conceptual Understanding and Analyzing Data
Although statistical software and technology in general is used for computations and crunching numbers, the tools now available can and should be used to change how statistics is taught and learned (Chance, Ben-Zvi, Garfield, & Medina, 2007). For example, statistical tables such as the standard normal distribution table are no longer needed to determine a p value. As a result, more time can be spent on conceptual understanding of the normal distribution and meaning of a p value, rather than the time traditionally devoted to using a table at the back of a statistics textbook. Statistical software has made graphing and visual displays of data accessible and easy to create. This should be used to help students to visualize concepts and understand abstract ideas. In addition to statistical software, many other tools are readily and freely available on the Internet, such as Web applets, simulation tools, data analysis routines, online texts, and online graphing and computational calculators. Technology can also be used in the classroom to inform students about the accessibility of large, real-world datasets, government reports, and statistical summaries from various government agencies (e.g., the Centers for Disease Control and Prevention, National Institutes of Health, National Aeronautics and Space Administration). With the ease of use of screen-shot technology, the author routinely makes use of media headlines with obvious statistical errors, such as confusions between correlation and causality. Students’ understanding can be greatly enhanced with real-time visual displays of data, as well as real-time demonstrations of what-if data scenarios, such as removal of an outlier to assess impact on analysis results.
Use Assessments to Improve and Evaluate Student Learning
Assessments need to be aligned with a clearly stated set of learning goals and objectives (Garfield et al., 2011). The focus of assessments should be on the concepts and understanding of key ideas, rather than on skills, procedures, formulaic answers, and computations. The method of assessment can be with homework, quizzes and examinations, projects, activities, oral presentations, written reports, or article critiques. It is also useful to align assessments with active learning and class activities, instead of with knowledge gained through memorization of techniques, formulas, or computations. A variety of assessments ensures that students have ample opportunity to demonstrate knowledge and understanding. Statistical literacy can be assessed through presentation of publication abstracts, media headlines and articles, and graphical displays in news reports. The ARTIST Web site ( https://apps3.cehd.umn.edu/artist) is a useful resource for statistical literacy, reasoning, and thinking assessment tools. This Web site has been cited as the most complete current source for statistics education–related assessment items, instruments, and resources (Pearl et al., 2012).
Statistics Education Recommendations by Nursing Degree
Statistics course structure and content needs to be appropriate and designed to address student needs. The prevalent current approach to statistics education in most academic nursing programs is a methods-based statistics course for undergraduate nursing students, and one or more methods-based courses for graduate nursing students. This approach is a traditional and outdated paradigm for statistics education. Recommendations for each higher education nursing degree were provided in a study by Hayat et al. (2013) and correspond to the findings in the statistics education research literature. Suggestions for degree-specific education are provided below.
The needs of undergraduate students entail a foundation in critical thinking and an introduction to statistical literacy. Data analysis skills are not needed at the undergraduate level. A course in statistical literacy and reasoning is recommended for BSN students, with a strong emphasis on critical thinking.
Graduate nursing students need to be able to read the nursing and health care literature to understand evidence-based nursing practice. A strong foundation in statistical literacy and reasoning is needed to read scientific papers. A course in statistical literacy and reasoning is recommended for MSN students, with a strong emphasis on reading peer reviewed publications in nursing and health journals.
This practice-focused degree emphasizes evidence-based nursing practice. Students focus on program evaluation and quality improvement, and particular attention is given to clarifying the focus on practice. Science training is minimal, and research is not a focus for DNP student projects or training. Thus, a strong course in statistical literacy and reasoning, with an emphasis on reading peer-reviewed publications in nursing and health care journals, is recommended. Statistics education should also include an overview of the role and skills needed in a scientific investigation, accompanied by instruction on working with a statistician.
Research-focused graduate training necessitates an ability to analyze data, read the nursing and health care literature, utilize statistical software, and write reports with statistical findings. Statistics coursework for PhD students should include at least three statistics courses, including a course on statistical literacy and reasoning, a course on statistical inference, and a course on statistical modeling.
The potential impact of changing the way statistics is taught is significant. For example, one of the recommendations in the Institute of Medicine’s 2010 Future of Nursing report is to double the number of nurses with a doctorate degree by 2020. It is possible that the stigma, fear, and negative perceptions of statistics may serve as a deterrent to enrollment. For example, Hogg (1991) found that “Students often consider statistics as the ‘worst’ course they take while in college” (p. 342). Given that a typical requirement for admission into a nursing PhD program is an undergraduate statistics course, a negative experience in one’s first statistics course may affect a subsequent decision about whether to enroll in a graduate nursing program. The statistics education discipline has specifically focused on changing the stigma, fear, and negative perceptions of the topic of statistics. The GAISE report (ASA, 2005) recommendations emphasize making statistics material accessible and having its relevance become quickly apparent after drawing on-real world data and applications. Active learning and the use of technology transform the traditional classroom lecture approach to teaching and engage students in a manner that better aligns with the flow of daily living and activities.
Ideally, curriculum decisions should be evidence based and backed with meaningful and high-quality data. Currently, no known aggregate data are available to inform decisions about statistics coursework and curriculum for nursing students at any degree level. Studies are needed to assess the quality and content of statistics course offerings in academic nursing programs, the barriers perceived or encountered in studying this topic, and information on how statistics knowledge is applied in subsequent research and publications. An important challenge that also merits consideration in future research into statistics education for nurses is the student progression through the education system. For example, some students progress from BSN to MSN to DNP and complete a progression of statistics coursework during this process. Thought needs to be given to how to build on knowledge of statistics literacy, reasoning, and thinking as students move through the education continuum. Interprofessional education is “when students from two or more professions learn about, from and with each other to enable effective collaboration and improve health outcomes” (Interprofessional Education Collaborative Expert Panel, 2011, p. 8; World Health Organization, 2010). This education platform offers another opportunity to consider statistics education and the development of core competency guidelines for statistics education and training. Most importantly, whether referring to the AACN Essentials publications (2006, 2008, 2010, 2011) or the Interprofessional Education Collaborative Expert Panel’s Core Competencies report (2011), no known guidelines or competencies exist in statistics education for nursing students. Currently, no known funding agencies have made funds available to pursue rigorous investigation into the teaching and learning of statistics in academic nursing. Although several agencies fund nursing research projects (e.g., National Institute of Nursing Research and National Institutes of Health, Robert Wood Johnson Foundation), to date no monies have been made available for funding the needed studies described here.
In the past 3 years, a growing group of statisticians in nursing have been networking and communicating about the use of statistics in nursing education and research. An electronic mailing list for statisticians with a primary or secondary faculty appointment in an academic nursing program was established in August 2011. As of May 2013, the list had 27 members. Multiple panel, paper, and roundtable discussion sessions with groups of statisticians teaching and consulting in nursing colleges and schools had been organized and presented at each of the 2011, 2012, and 2013 Joint Statistical Meetings, the annual conference sponsored by the ASA. Each session was sponsored by an ASA section, including the Section on Statistical Education, Section on Statistical Consulting, and Section on Teaching Statistics in the Health Sciences. Another successful collaboration was a special focus issue on the use of statistics in nursing research in the journal Nursing Research (Hayat, 2012). These collaborative efforts and interdisciplinary collaborations between the disciplines of nursing and statistics are needed to apply science and increased rigor to the use of statistics in nursing education and research.
Statistical knowledge is needed when reading a journal article, clarifying patient misperceptions received through media reports with poor or misleading statistical reporting, and discerning appropriate conclusions and interpretations from studies conducted and analyzed by others. The statistics education reform movement within the statistics discipline over the past two decades has focused on making the topic more accessible and less daunting. The GAISE report (ASA, 2005) and its encompassing six recommendations are a good launching point for shifting statistics education in nursing toward a focus on statistical literacy, reasoning, and thinking. As a result of the emphasis in nursing and health care on evidence-based practice, students at all degree levels need to have an ability to critically read and understand the nursing and health care literature. Statistics education is an important and necessary element for adequately preparing students and helping them to acquire this ability. Although there are no known standard competency guidelines, extending the GAISE recommendations into nursing education can greatly influence student learning and interest in statistics. Increasing attention is being given to the use of statistics in nursing education and research, and its potential impact for meeting the recommendations in the Future of Nursing report (Institute of Medicine, 2010). Funding and studies are needed to build an evidence basis for making informed decisions about the core statistics education requirements in academic nursing programs.
- American Association of Colleges of Nursing. (2006). The essentials of doctoral education for advanced nursing practice. Retrieved from http://www.aacn.nche.edu/dnp/Essentials.pdf
- American Association of Colleges of Nursing. (2008). The essentials of baccalaureate education for professional nursing practice. Retrieved from http://www.aacn.nche.edu/education-resources/baccessentials08.pdf
- American Association of Colleges of Nursing. (2010). The research-focused doctoral program in nursing: Pathways to excellence. Retrieved from http://www.aacn.nche.edu/education-resources/PhDPosition.pdf
- American Association of Colleges of Nursing. (2011). The essentials of master’s education in nursing. Retrieved from http://www.aacn.nche.edu/education-resources/MastersEssentials11.pdf
- American Statistical Association. (2005). Guidelines for assessment and instruction in statistics education. Retrieved from http://www.amstat.org/education/gaise/GaiseCollege_Full.pdf
- Barkley, E.F., Cross, P.F. & Major, C.H. (2005). Collaborative learning techniques: A handbook for college faculty. San Francisco, CA: Jossey-Bass.
- Bergmann, J. & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Washington, DC: International Society for Technology in Education.
- Chance, B., Ben-Zvi, D., Garfield, J. & Medina, E. (2007). The role of technology in improving student learning of statistics. Technology Innovations in Statistics Education, 1(1). Retrieved from http://www.escholarship.org/uc/item/8sd2t4rr
- Cobb, G. (1992). Teaching statistics. In Steen, L.A. (Ed.), Heeding the call for change: Suggestions for curricular action (pp. 3–43). Washington, DC: The Mathematical Association of America.
- Cobb, G.W. (1991). Teaching statistics: More data, less lecturing. Amstat News, 1, 3–5.
- delMas, R., Garfield, J., Ooms, A. & Chance, B. (2007). Assessing students’ conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2), 28–58.
- delMas, R.C. (2002). Statistical literacy, reasoning, and learning: A commentary. Journal of Statistics Education, 10(3). Retrieved from http://www.amstat.org/publications/jse/v10n3/delmas_discussion.html
- Garfield, J. & Ben-Zvi, D. (2008). Developing students statistical reasoning: Connecting research and teaching practice. New York, NY: Springer.
- Garfield, J., Zieffler, A., Kaplan, D., Cobb, G., Chance, B. & Holcomb, J.P. (2011). Rethinking assessment of student learning in statistics courses. The American Statistician, 65, 1–10. doi:10.1198/tast.2011.08241 [CrossRef]
- Gould, R. (2010). Statistics and the modern student. International Statistical Review, 78, 297–315. doi:10.1111/j.1751-5823.2010.00117.x [CrossRef]
- Hayat, M.J. (2010). Understanding statistical significance. Nursing Research, 59, 219–223. doi:10.1097/NNR.0b013e3181dbb2cc [CrossRef]
- Hayat, M.J. (2012). Statistics in nursing research. Nursing Research, 61, 147–148. doi:10.1097/NNR.0b013e318257f5dc [CrossRef]
- Hayat, M.J., Eckardt, P., Higgins, M., Kim, M. & Schmiege, S.J. (2013). Teaching statistics to nursing students: An expert panel consensus. Journal of Nursing Education, 52, 330–334. doi:10.3928/01484834-20130430-01 [CrossRef]
- Hayat, M.J. & Hedlin, H. (2012). Modern statistical modeling approaches for analyzing repeated-measures data. Nursing Research, 61, 188–194. doi:10.1097/NNR.0b013e31824f5f58 [CrossRef]
- Hogg, R.V. (1991). Statistical education: Improvements are badly needed. The American Statistician, 45, 342–343.
- Institute of Medicine. (2010). The future of nursing: Leading change, advancing health. Washington, DC: National Academies Press.
- Interprofessional Education Collaborative Expert Panel. (2011). Core competencies for interprofessional collaborative practice: Report of an expert panel. Washington, DC: Interprofessional Education Collaborative.
- Lage, M.J., Platt, G.J. & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. Journal of Economic Education, 31, 30–43.
- Lang, T. (2007). The need for accurate statistical reporting. A commentary on “Guidelines for reporting statistics in journals published by the American Physiological Society: The sequel.”Advances in Physiology Education, 31, 299, 306–307. doi:10.1152/advan.00032.2007 [CrossRef]
- Mazur, E. (1997). Peer instruction: A user’s manual series in educational innovation. Upper Saddle River, NJ: Prentice Hall.
- Moore, D.S. (1997). New pedagogy and new content: The case of statistics. International Statistical Review, 65, 123–165.
- Neumann, D.L., Neumann, M.M. & Hood, M. (2010). The development and evaluation of a survey that makes use of student data to teach statistics. Journal of Statistics Education, 18(1). Retrieved from http://www.amstat.org/publications/jse/v18n1/neumann.pdf
- Nicholls, D. (2001). Future directions for the teaching and learning of statistics at the tertiary level. International Statistical Review, 69(1), 11–15. doi:10.2307/1403525 [CrossRef]
- Pearl, D.K., Garfield, J.B., delMas, R., Groth, R.E., Kaplan, J.J., McGowan, H. & Lee, H.S. (2012). Connecting research to practice in a culture of assessment for introductory college-level statistics. Retrieved from http://www.causeweb.org/research/guidelines/ResearchReport_Dec_2012.pdf
- Rumsey, D.J. (2002). Statistical literacy as a goal for introductory statistics courses. Journal of Statistics Education, 10(3). Retrieved from http://www.amstat.org/publications/jse/v10n3/rumsey2.html
- Utts, J. (2003). What educated citizens should know about statistics and probability?The American Statistician, 57(2), 74–79. doi:10.1198/0003130031630 [CrossRef]
- Utts, J. (2010). Unintentional lies in the media: Don’t blame journalists for what we don’t teach. Retrieved from http://iase-web.org/documents/papers/icots8/ICOTS8_1G2_UTTS.pdf
- World Health Organization. (2010). Framework for action on interprofessional education and collaborative practice. Geneva, Switzerland: Author. Retrieved from http://whqlibdoc.who.int/hq/2010/WHO_HRH_HPN_10.3_eng.pdf
- Zieffler, A., Park, J., Garfield, J., delMas, R. & Audbjorg, B. (2012). The statistics teaching inventory: A survey on statistics teachers’ classroom practices and beliefs. Journal of Statistics Education, 20(1), 1–29. Retrieved from http://www.amstat.org/publications/jse/v20n1/zieffler.pdf
Guidelines for Assessment and Instruction in Statistics Education (GAISE): Extending GAISE Into Nursing Education