Dr. DiMaria-Ghalili is 2007–2009 John A. Hartford Foundation/Atlantic Philanthropies Claire M. Fagin Fellow, School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania; Dr. Ostrow is Professor Emeritus, Department of Health Restoration, School of Nursing, West Virginia University, Morgantown, West Virginia. At the time this article was written, Dr. DiMaria-Ghalili was Associate Professor of Nursing, and Dr. Ostrow was Associate Professor and Chair, Department of Health Restoration, School of Nursing, West Virginia University, Morgan-town, West Virginia.
Address correspondence to Rose Ann DiMaria-Ghalili, PhD, RN, CNSN, School of Nursing, University of Pennsylvania, Hartford Center for Geriatric Nursing Excellence, Claire M. Fagin Hall, 418 Curie Boulevard, Philadelphia, PA 19104-6020; e-mail: email@example.com.
Research is one of the seven core competencies of all graduate nursing education programs (American Association of Colleges of Nursing [AACN], 1996). Master’sprepared nurses are taught to evaluate research findings and to develop and implement evidence-based practice guidelines (AACN, 2006). Development of basic statistical analysis skills is a component of the research core. According to the AACN (1996), the skills that graduate nursing students need to develop in a graduate program include:
use of computer hardware and appropriate software, and to understand statistics and research methods; and, [use of] information systems for the storage and retrieval of data, consistent with the particular population focus. (p. 7)
To achieve these skills, nursing faculty can choose from a variety of data management and statistical software packages. Examples of such specialized statistical software packages include SPSS or SAS. However, spreadsheet programs such as Microsoft Excel®
can provide a more efficient way to demonstrate the data analysis skills component of the research process. In addition, students learn informatics skills such as “utilizing information systems for storage and retrieval of data” (AACN, 1996
, p. 7). The purpose of this article is to discuss how Excel can be used to teach statistics to graduate advanced practice nursing students and to offer nursing educators suggestions for teaching statistics with Excel.
The research and systematic analysis course at West Virginia University’s School of Nursing is a 5-credit hour core course taught in the second 15-week semester of the first year of coursework for all master’s advanced nursing practice (ANP) students. Statistics and data analysis is one third of the research methods course and for many years SPSS version 14 was the statistical software package used to perform data analysis. Two factors influenced the School’s decision to switch from SPSS to Excel to teach statistics in the advanced practice MSN program.
First, a student theme commonly found in either course evaluations or end-of-program evaluations was the limited transferability of specialized statistical software packages beyond the research methods course. Many students commented that they would never use the SPSS program again. Students were also unhappy with the high cost of the software program. Students said the purchase of SPSS was for one course only and that, unlike course books, they would never use it again in practice. Faculty acknowledged that graduate school is expensive. Graduate nursing students need to pay for tuition, books, the purchase of a new computer and related software, and high-speed Internet access for online courses. There is also a reduction in hours worked per week for some students to meet the requirements of graduate study. Therefore, the graduate faculty were willing to support a proposal to explore a relatively inexpensive method to deliver the research and statistics core competency, as long as the overall outcome was the same.
The second factor supporting the change to Excel was, in part, graduate faculty reassessment of the goal of teaching statistics at the graduate level. Because so many clinical hours are required for graduate ANP students, many graduate nursing programs have stopped requiring a thesis, following the recommendation of AACN that a research thesis is not appropriate in professional master’s programs, such as nurse practitioner programs, and should be considered an option only if the student is preparing to enter a doctoral program (AACN, 1996).
In our school, the capstone experience for advanced practice nurses has evolved over the years from a research thesis to a guided research practicum, and now it is a 1-credit clinical scholarship course focused on the implementation and dissemination of an evidence-based practice project in the clinical setting. Thus, a statistical program for data analysis is not needed. Therefore, it seemed appropriate to switch to Excel, as the emphasis of research knowledge in our graduate program is critical analysis and application of research to develop and implement evidence-based practice, not generation of new knowledge.
Excel can provide a more efficient way to demonstrate the data analysis skills component of the research process. Choosing appropriate statistical tests and performing basic statistical analysis and interpretations output correctly are all possible with Excel. If a graduate student desires a research career and plans to obtain a research doctorate, the statistical skills learned in Excel will be an excellent foundation for programs such as SPSS and SAS.
Integration of Excel into the Research Course
Excel was first integrated into the research course in spring 2004 and during the past several years, the course has evolved based on teaching methods found to be effective by course faculty. The expected statistical learning outcomes of the course include:
- To critically evaluate the conceptual, ethical, methodological, analytical, and interpretive dimensions of the research process.
- To identify steps involved with developing a clinical database.
- To perform descriptive and inferential statistical procedures using Excel software on a personal computer.
Software requirements for admission to West Virginia University School of Nursing include the purchase of Microsoft Office®
, which includes Excel; therefore, students do not need to purchase another expensive software program. In addition, Excel is available on all computers within the university.
The two authors taught the research course as a team and had prior experience using Excel to organize data and perform simple statistical procedures, such as calculating student grades. Before integrating Excel into the research course, the first author (R.A.D.-G.) contacted a peer who had experience using Excel to teach business statistics for advice on teaching resources. The authors reviewed textbooks and decided which book to use as a reference for the course. The authors spent blocks of time during the fall semester working on problems in the textbook and the assignments typically used in the course. During, the first semester the authors taught the course, a text was adopted, Data Analysis with Microsoft Excel: Updated for Windows XP (Berk & Carey, 2004). As the authors became more proficient with Excel, the text was abandoned because several online resources were found that were more student friendly and met the course objectives more appropriately. For example, the faculty have developed five Microsoft PowerPoint® presentations with audio to provide students with detailed directions for all statistical procedures used in the course. These presentations were linked to the course’s home page in WebCT® Vista.
Course faculty used readily available databases, with variables that addressed clinical situations, for homework assignments to reinforce what was learned in class. To provide feedback to students about their homework assignments, a screen capture program (SnagIt®) was used to copy and annotate Excel procedures, which were then inserted into PowerPoint presentations with audio to provide detailed instruction on how to conduct specific analysis. These presentations were linked to the course’s home page.
Although the graduate program at West Virginia University School of Nursing is a distance learning program with synchronous classes via Web casting, students were required to come to campus for two 5-hour days in the computer laboratory. Faculty worked with students in the computer laboratory to demonstrate how to use the Excel program. Faculty also helped students ask questions of the data sets provided to them and helped students run and interpret the statistical analysis. Only a minority of students have had experience using Excel prior to their graduate program. Thus, the authors found these 2-day workshops vital for students to learn the software and for decision making about statistical tests and interpretation of results. Past experience demonstrated that most students became frustrated trying to learn the software on their own. The annotated Excel procedures in the PowerPoint presentations with audio provided excellent reinforcement of what was learned in the workshop. For many students, additional time was required to absorb some of the concepts taught that day and the prepared reviews were vital to them. Students evaluated these “on-campus” sessions highly and asked for an additional day. As this was the only time students and faculty met face to face, these 2-day statistics workshops enhanced student-faculty and student interaction.
Student Evaluative Data on the Use of Excel
Student evaluations gave us some initial feedback on the benefits of using Excel. Students were surveyed after the spring 2007 course, which had the largest enrollment at the time (n = 45). Of the 21 students responding to the survey, only 2 students had any experience with another data analysis program, and both stated that it was minimal exposure. Thus, it is not possible for these students to compare Excel with other statistical packages. But, 50% of the respondents had had some exposure to Excel either in a high school course, in their undergraduate statistics course, or in their job. Although most students said they did not have a working knowledge of Excel from this earlier exposure, their course did build on that initial exposure and may have contributed to students’ moderately high rating (x = 3.15) of the statement, “How comfortable do you feel using Excel on your own to develop a spreadsheet to enter your own data and perform simple descriptive analysis?” Student comments included:
- Much better than before I attended the class and better than after I have attended an outside Excel class. I think the reason is that research was specific to nursing.
- I feel comfortable enough to perform basic functions in Excel.
Students also gave high ratings (x = 3.98) to the question, “How helpful was it to you to perform statistical procedures in Excel to understand and interpret statistical data published in research articles?” Examples of student responses included:
- I feel that it was very helpful so now when I read a research article I have a better understanding of the results and how they were obtained.
- I think it helped me to understand the steps the researchers went through in order to come to their conclusion on the data. It also helped the different types of statistical data “stick” in my mind.
Although students were not able to compare Excel with other software programs, they positively evaluated the use of Excel in helping them to understand statistical analysis and to be able to comprehend the research literature more easily.
Program Evaluation Data on the Use of Excel
Teaching advanced practice nurses how to use a spreadsheet program enables them to directly apply these skills to create databases in their clinical practice. Advanced practice demands collection and organization of patient information, patient referrals, and outcomes. Clinical practice databases can be queried for research use questions, as well as for monitoring adherence to evidenced-based practice protocols. After students graduate and begin to practice in an advanced role, they will have the skills to develop a database and can teach support staff to enter data. As Excel integrates with other Office products, students can easily prepare simple charts and graphs to visually describe their data and import data directly into PowerPoint for professional presentations. Also, an Excel spreadsheet can be easily downloaded into a handheld personal digital assistance device to permit point of contact data entry in clinical practice.
We also think that the outcomes of use of Excel extend beyond the course and need to be evaluated in subsequent courses. The Graduate Curriculum Committee is discussing continued application of Excel in the students’ practicum courses. Evaluation measures related to the topics of database development and quantitative measures of patient outcomes are being incorporated into the clinical practicum courses. Questions about the applicability of Excel are also being incorporated into the end-of-program evaluation of the nurse practitioner program. It is expected that more long-term evaluation measures, such as those discussed in this article, will provide more comprehensive and meaningful analysis of the contribution of this software program to the graduate ANP student’s entire education.
Teaching Statistics with Excel
Tips for Becoming Self-Proficient
Nurse educators interested in teaching statistics with Excel need to become familiar first with the general features of Excel, as well as the advantages and disadvantages to using Excel (Table). Many university computer learning centers offer introductory and advanced classes and guides on Excel (The Learning Center, 2008a, 2008b). Faculty can often attend these workshops free of charge. The length of time it takes to become proficient in Excel can vary from person to person, from several weeks to a semester or longer.
Table: General Features, Advantages, and Disadvantages of Microsoft Excel®
Online resources are also available for faculty interested in learning about Excel’s data analysis potential. The Association of Statistics Specialists Using Microsoft Excel (2000) homepage hosts links to various Internet resources on statistical analysis with Excel. In addition, some universities have developed and opened several free online courses, including a statistics course using Excel, through their Open Learning Initiative (Carnegie-Mellon University Open Learning Initiative, n.d.). We found this course extremely helpful because of its emphasis on application of statistical concepts using data files that were often about clinical topics. In addition, the course had excellent graphics and digital media to explain concepts and many opportunities for reinforcement of learning. The content was presented in many different formats, including:
- Graphical slides with audio overlay.
- Interactive exercises.
- Intelligent tutoring system (StatTutor).
- Small inline tutors (Mini-tutors).
Textbooks such as that by Berk and Carey (2004) may serve as another valuable resource. Faculty may also find excellent colleagues at their university, especially at business schools, with which to consult and to share resources or helpful information about using Excel.
Tips for Transitioning to Excel
After nurse educators have learned the basics of Excel, they must begin to think about how to teach with Excel. The first step is to use a familiar data set. Unlike other specialized statistical software programs, Excel does not include any data sets on which to perform analysis. However, a faculty research data set that is de-identified, or a favorite data set that has been used over the years can easily be imported into Excel. It is helpful to use a familiar data set because the educator knows the depth of the data and what kinds of statistical procedures can be performed. Next, make a list of all the statistical procedures to be taught in the course. Then, determine how the statistical procedures are performed in Excel. Faculty need to remember that because Excel is primarily a business application, it does not offer all the features of a scientific statistical program or the intuitiveness of statistical software programs. However, the statistical procedures included in Excel’s Analysis ToolPak are sufficient to teach an introductory statistical course at the graduate level. Other educators, including Warner and Meehan (2001), also share the opinion that:
Most introductory courses will not cover many procedures beyond those included in Excel’s Analysis ToolPak. (p. 298)
Finally, decide which type of resources will be provided to the students, including textbooks, online resources, or instructor-developed resources.
An additional tip for faculty is to ensure that all students and faculty are using the most recent version of Excel to ensure compatibility between assignments in Excel given to students by the teacher. If students are using an older version of Excel, they may not be able to open an Excel file that was created in a newer version.
Coping with Frustration
Faculty who are familiar with SPSS or SAS may find learning to run the same procedures in Excel counterintuitive and similar to learning a new language. However, these issues will not be a disadvantage to novice graduate students learning to manipulate data with a software program for the first time. The advantages of direct application in clinical work is a compelling reason to use Excel in graduate ANP programs, which far outweighs the disadvantages.
The authors shared their journey of teaching basic statistical analysis to graduate ANP students. Statistical analysis is one objective of a larger 5-credit course entitled Research Methods: Statistical Analysis and Evaluation. Different software programs and textbooks have been used in this course, and feedback from students, as well as faculty experiences, are considered in the continual adjustments made to improve this course. The current method of using Excel and parts of online courses, such as described in the Carnegie-Mellon University Open Learning Initiative (n.d.), have achieved the goals of easy accessibility for students, applicability of these programs and skills beyond this one course, and a reduction in student course costs. The help that exists on various Web sites offers tremendous resources for both faculty and students. These suggested sites are user-friendly initial preparation activities for any faculty members contemplating the use of Excel in their graduate nursing programs.
- American Association of Colleges of Nursing. 1996. The essentials of master’s education for advance practice nursing. Washington, DC: Author.
- American Association of Colleges of Nursing. 2006. AACN position statement on nursing research. Washington, DC: Author.
- Association of Statistics Specialists Using Microsoft Excel. 2000. Retrieved July 30, 2007, from https://www.jiscmail.ac.uk/cgi-bin/filearea.cgi?LMGT1=ASSUME&a=get&f=/welcome.html
- Berk, KN & Carey, P. 2004. Data analysis with Microsoft Excel: Updated for Windows XP. Belmont, CA: Thomson Brook-Cole.
- Carnegie-Mellon University open learning initiative. (n.d.). Retrieved July 30, 2007 from http://www.cmu.edu/oli
- The Learning Center, Robert C. Byrd Health Sciences Center, West Virginia University. 2008a. Excel I, Office 2007. Retrieved December 29, 2008, from http://www.hsc.wvu.edu/its/LC/Documents/HowTo/MSOffice2007/ExcelI.pdf
- The Learning Center, Robert C. Byrd Health Sciences Center, West Virginia University. 2008b. Excel II, Office 2007. Retrieved December 29, 2008, from http://www.hsc.wvu.edu/its/LC/Documents/HowTo/MSOffice2007/ExcelII.pdf
- Sewell, JP2006. Getting the most from your software: Using Excel as the poor man’s database. CIN: Computers, Informatics, Nursing, 24, 13–17.
- Warner, CB & Meehan, AM2001. Microsoft Excel™ as a tool for teaching basic statistics. Teaching of Psychology, 28, 295–298. doi:10.1207/S15328023TOP2804_11 [CrossRef]
General Features, Advantages, and Disadvantages of Microsoft Excel®
|General Features of Excel|
|Spreadsheet program first developed for business to perform accounting functions (Sewell, 2006).|
|Ability to function as a database program because it can organize data and perform calculations (Sewell, 2006).|
|Statistical procedures performed with the Analysis ToolPak, the standard add-in component of Excel.|
|Simple to complex graphs created with Chart Wizard tool.|
|Easy file transferability among Microsoft Office® programs. For example, Excel files can be imported into Access® for tracking of data. Databases created in Access can be imported into Excel for data analysis. Graphs and charts created in Excel can be inserted into a PowerPoint® presentation or Word® document.|
|File transferability among specialized statistical programs. For example, Excel files can be imported into SPSS for more sophisticated data analysis.|
|Four Ways to Perform Statistical Procedures in Excel|
Charts and graphs: From the standard toolbar, select the Chart Wizard icon and follow the steps to select the type of chart.
From the standard tool bar, select the function (fx icon) procedure. From the drop-down menu, select statistical function category to perform procedures such as correlation, chi-square, and F test.
From the tools menu, select data analysis to perform procedures such as single-factor or two-factor ANOVA, correlation, descriptives, histogram, regression, paired t test and two-sample t test, assuming equal or unequal variance
From the data menu, select pivot table or pivot chart report to run frequency distributions.
|Advantages to Using Excel|
|Accessibility: easy access to Excel as Microsoft Office is one of the most common programs found on personal computers.|
|Applicability: performing statistical analysis with Excel transcends beyond the classroom to any desktop computer in a real-world setting to create clinical practice databases.|
|Economics: no need to purchase a separate statistical software package to perform data analysis.|
|Disadvantages to Using Excel|
|It does not offer full features of scientific statistical program, nor the intuitiveness, because it was developed primarily as a business program.|
|Data must be manipulated before certain statistical procedures are run. For example, to perform a t test on age by gender, the variables for age and gender need to be copied to a new worksheet. Then age is sorted by gender and two new variables are created (i.e., male age and female age) before the t test can be performed.|