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Original research article, effectiveness of in-class excel-based active learning activities for transportation engineering courses.

• Department of Civil and Mechanical Engineering, Purdue University Fort Wayne, Fort Wayne, IN, United States

Recently, transportation engineering industry members showed concern that students lacked the skills to solve real-world engineering problems using spreadsheet data analysis. In response to the circumstances shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class and helping them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools, including cell references, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that spreadsheet activities may increase student learning.

Industrial Advisory Board (IAB) members at the study university play a critical role in improving an engineering program by guiding the program’s direction and ensuring the needs of their workforce are met. Encouraged by accreditation standards, many departments conduct an annual survey to receive feedback from IAB members on improving their program so that students are ready to work in the industry upon graduation. Recently, civil engineering IAB members specialized in transportation engineering showed concern that is graduating students lack the spreadsheet skills needed for solving real-world engineering problems. In response to this concern, this study investigated how to help students learn Excel to solve transportation engineering problems as a part of the lecture.

The primary objective of this study is to help students learn Excel spreadsheets for solving transportation engineering problems. The in-class worksheets were reshaped to achieve the objective, incorporating Excel spreadsheets to solve example problems and hand calculations. For the Excel activity, students are provided the data in an Excel spreadsheet. Students use that data to solve the same worksheet problem using built-in Excel tools, including cell references, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc.

Literature review

Previous studies suggested that active learning is a standard method to foster students learning in engineering ( Nickels, 2000 ; Douglas and Chiu, 2009 ; McCloskey and Bussom, 2013 ; Anitha and Rao, 2014 ; Ssemakula et al., 2018 ). Different active learning techniques include think-pare-share (TPS), group assignments, reciprocal questioning, the pause procedure, the muddiest point technique, the devil’s advocate approach, group discussions, formative quizzes, and lecture summaries, etc. ( Nickels, 2000 ; Douglas and Chiu, 2009 ; McCloskey and Bussom, 2013 ; Anitha and Rao, 2014 ; Ssemakula et al., 2018 ). Among the techniques, TPS is the most used technique in many engineering classes where solving example problems are a vital component of the lecture ( Nickels, 2000 ). According to this technique, a specifiv topic is presented to the class, and students take notes. Then, an example problem related to the covered topic is ready to be solved. Sometimes, students are provided a handout known as an in-class worksheet listing the example problems that will be solved during class.

McCloskey and Bussom (2013) studied active learning in the business curriculum using an Excel spreadsheet. This study reviews how Excel spreadsheet was used in business classes to learn problem-solving techniques and the active use of spreadsheets. Several previous studies, from science to engineering to political science, investigated how to engage students better ( Smith et al., 2005 ; Heller, 2010 ; Marshall and Nykamp, 2010 ; Peters and Beeson, 2010 ; Popkess and McDaniel, 2011 ). McCloskey and Bussom (2011) studied students’ engagement in the learning process using an Excel spreadsheet. Uddin et al. (2017) learned how to use Excel to teach physics. In this study, Excel was used as a simulating tool. Several aspects of Excel were demonstrated in this article. All these studies suggest that Excel can be an excellent tool to enhance student learning. To the author’s knowledge, no studies were found investigating the effectiveness of Excel on student learning in engineering classes. This study investigated student learning in civil engineering classes, especially in the transportation area.

Several papers were studied related to how to use Excel spreadsheets for solving engineering problems and financial analysis efficiently ( Nickels, 2000 ; Douglas and Chiu, 2009 ; McCloskey and Bussom, 2013 ; Anitha and Rao, 2014 ). Microsoft Excel has many tools, including Visual Basic for Applications (VBA). VBA is developed explicitly for Excel and is currently used for many applications, including financial analysis, engineering problems, data management, etc. Sprego (Spreadsheet Lego) is a simplified version of VBA that helps to solve advanced engineering problems ( Nickels, 2000 ; Douglas and Chiu, 2009 ; McCloskey and Bussom, 2013 ). McCloskey and Bussom (2013) conducted a case study on the effectiveness of teaching spreadsheet management using Sprego. Results indicated that Sprego could be used for solving advanced problems easily efficiently. Also, S. Abramovich studied students’ ideas in the digital era ( Anitha and Rao, 2014 ). The primary projective of this study was to contribute to Technology-immune Technology-enabled (TITE) mathematics education research efforts by using Wolfram Alpha and Microsoft Excel spreadsheet.

Methodology

The primary requirement to execute the activities related to Excel is that students need a computer to work on the activities during class. If the classes are taught in a traditional classroom, students must to bring their laptops, or the course must be taught in a computer lab. For this study, the methodology has two main steps: (1) Develop the worksheets; (2) Evaluate the effectiveness of the learning strategy. The overall methodology can be seen in Figure 1 .

Figure 1. Study methodology.

Students are provided with handouts and in-class worksheets at the beginning of a typical lecture class. In-class worksheets include the description of example problems, and handouts provide the tables, figures, and equations needed to solve the problems. The class starts with discussing a topic; then, students try to solve a problem provided in the in-class worksheet. After that, the instructor solves the problem with the students’ help, followed by creating an excel spreadsheet to solve the same problem using built-in Excel tools. The spreadsheet should produce the outputs by only changing the inputs. At the end of the lecture, students must submit their in-class work, including a worksheet and an Excel spreadsheet. First, students are learn how to use Excel tools to solve engineering problems. Students’ evaluation shows that students enjoy solving the problems using Excel spreadsheets, and most importantly, they mentioned that it helped them understand the problem better. Also, it was noticed that Excel activities help energize students to be active in the middle of a class. Students need to bring their laptops to class. A Wireless internet connection is also beneficial for downloading the necessary files for the class.

In this study, two courses were considered, transportation planning and transportation engineering. Both courses are very similar in terms of course assessment. The following section discusses only the transportation planning course as an example. In the analysis section, the data from both courses are presented. The courses will be referred to as Transportation Planning and Transportation Engineering for discussion purposes. Both are junior/senior-level courses, respectively.

CE 401 transportation planning

This course was a small, technical elective class taught at a public university in the Midwest. As the prerequisite of this course is a junior-level transportation engineering course only senior-level students usually take this course, and is offered once a year in the fall semester. From 2018 to 2020, the enrollment was 6, 7, and 10 students, respectively. The course assessment included two exams (40%), weekly quizzes (5%), homework assignments (25%), course project (20%), and class participation (10%). Class participation comes from the in-class worksheets, and Excel spreadsheet analysis is a significant part of the in-class worksheets.

The CE 401 course structure included two 1 h and 20-min class periods per week and involved some active learning exercises in the form of calculation-based problems in every class. During a typical class, students learn a topic through lectures and then work on a relevant calculation-based example problem. Usually, the problems are iteration-based, which means the answer from each iteration will be close to the solution. As the number of iterations increases, the accuracy of final the answer increases. Because of the nature of the problems, it was observed that an Excel spreadsheet could be of great help in getting the final answer quickly and easily. In-class activities included hand calculations and then developing an Excel spreadsheet to get the same answer from hand calculation. At the end of the lecture, students must submit their in-class work. The whole in-class activities were graded out of 10%.

The instructor created the in-class activities involving Excel spreadsheet using real-world data on local transportation networks obtained from different transportation agencies, including city governments and metropolitan planning organizations. The activities were like the standard end-of-chapter problems regarding the steps and equations required. As the data used for the activities are on the local transportation network, students can relate to the problems better, as evidenced by the student’s comments from the student course evaluations. After the lecture, the solution to the Excel activities was posted on the course website.

Excel-based problems are also a significant part of homework assignments. Weekly homework assignments comprised of 3–5 homework problems. 1–3 problems were created by the instructor that requires an Excel spreadsheet, and those are very similar to the in-class problems.

In the exams, 70% of the exam grades are open books, and the remaining are closed books. The open book part consists of calculation-based problems like homework problems, but in a short version so that students can solve them during exam without an Excel spreadsheet. Students are not allowed to bring their laptops during exams to solve the problems, but they can use other course materials, including homework assignments, handouts, books, and lecture slides.

In transportation planning courses, sometimes example problems require several iterations to get the final answer. For instance, sample example problems can be seen in Figure 2 . The solution to the problems can be seen in Table 1 . In the solution matrix (see Table 1 ), 25 values (5 rows × 5 columns) needed to be calculated. The same set of equations is used to get each value of all 25. Using a spreadsheet, the solution can be obtained very quickly and easily.

Figure 2. Sample in-class activity from the transportation planning course.

Table 1. Solution to the in-class activity shown in Figure 2 .

The effectiveness of using Excel spreadsheets was divided into two categories: (1) instructor observations from a transportation planning course and (2) Before-after analysis of student data. These two categories are discussed and supported by the data below.

Instructor observations

The incorporation of Excel activities was seen to serve two benefits. First, students practiced learning to solve real-world problems from hands-on experience. Second, students expressed enthusiasm for working on Excel activities, as evidenced by in-class discussion, in-class engagement, and students’ course evaluations, which will be discussed later.

Overall, creating Excel-based in-class activities appeared to motivate students. The instructor plans to continue this active learning technique in future semesters.

Before-after analysis of student data

Two primary indicators were investigated to assess the effectiveness of incorporating Excel activities, including (1) student comments and (2) overall instructor ratings. In the following subsections, these two indicators were discussed.

Student comments

Every semester, the university conducts an anonymous student course evaluation for every course in the final quarter of the semester. After submitting the final grades, the instructor receives the results, so the survey respons cannot impact grades. In the course evaluation, students rate their instructor by answering 11 questions and writing comments on four topics. One of the four topics was “Please identify and explain aspects of the course that you encourage the instructor to maintain in the future.” For this study, the student’s responses were investigated to identify those related to the Excel activities. Table 2 shows the quantitative summary of the comments for CE 401. In the fall of 2018, Excel activities were introduced in five lectures on CE 401 Transportation Planning. From the student’s course evaluation, one student suggested adding more Excel activities in future semesters. The comment is: “Continue to incorporate the assignments and activities using Excel, solver, statistical analysis, and other practical applications. Reserve a computer lab once a week.” Based on this comment, the instructor incorporated Excel activities once a week (50% of total meetings) for Fall 2019. In fall of 2019, two comments were received, and both were related to Excel activities. One favors of continuing Excel activity, and the other suggested having these activities every other week. In 2020, four comments were received, three of them were related to Excel activities, and all were in great favor of continuing Excel activities. The comments can be seen in Table 3 . In 2021, Excel activities were also introduced in the CE 301 Transportation Engineering course. Only one comment on Excel activities was received (see Table 2 ). Overall, students liked the idea of the Excel activities building off the lecture and found this extremely helpful in enhancing their learning. As per the students’ suggestion, the instructor also realizes that a handout providing step by step explanation of using an Excel spreadsheet to solve the problem would be helpful for future reference.

Table 2. Summary of Students’ comments for CE 401.

Table 3. Students’ comments on the Excel spreadsheet.

Overall instructor ratings

Overall instructor ratings was investigated to assess the effect of incorporating Excel activities from 2018 to 2021 ( Appendix A1 ). Tables 4 , 5 present two courses’ enrollment data and overall student evaluation from 2018 to 2021. In the fall of 2018, the instructor started to teach CE 401 and incorporated Excel activities in only five lectures. Students’ course evaluations may also indicate their effectiveness. Students’ evaluations of CE 401 increased from 3.11/4.0 in 2018 to 3.6/4.0 (ratings on a Likert scale of Poor = 1 to Excellent = 4) in 2019, which may indicate the effectiveness of incorporating Excel activities during lectures. Similarly, for CE 301, student evaluations also increased from 2.89 to 3.9. It is important to note that no other major changes were made in instructing the course except the incorporation of Excel activities.

Table 4. Before-after comparison of implementing Excel spreadsheet, CE 401.

Table 5. Before-after comparison of implementing Excel spreadsheet, CE 301.

In this study, an active learning technique using Excel spreadsheet analysis was investigated in two transportation engineering courses to determine whether it fosters students’ learning. A worksheet was developed by listing calculation-based example problems for every lecture. Students are assigned to work on those example problems by hand calculation during class time. Then, students created an Excel spreadsheet to solve the same problem using different built-in tools, including solver, cell referencing, data analysis, conditional formatting, charts, built-in functions, etc. The effectiveness of this technique was investigated using students’ course evaluations and comments. Results concluded that the technique was very effective in fostering students learning.

Data availability statement

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by Purdue IRB. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : engineering, active learning, transportation, excel, spreadsheet

Citation: Saha P (2022) Effectiveness of in-class excel-based active learning activities for transportation engineering courses. Front. Educ. 7:879174. doi: 10.3389/feduc.2022.879174

Received: 18 February 2022; Accepted: 13 June 2022; Published: 15 December 2022.

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Copyright © 2022 Saha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Promothes Saha, [email protected]

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Current Perspectives on the Value, Teaching, Learning, and Assessment of Design in STEM Education

Systematic Literature Review Using Excel Software: A Case of the Visual Narratives in Education

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• Marina Mota   ORCID: orcid.org/0000-0002-1939-9854 19 ,
• Cristina Manuela Sá   ORCID: orcid.org/0000-0002-8768-661X 19 &
• Cecília Guerra   ORCID: orcid.org/0000-0002-2560-165X 19  

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This study does a systematic literature review on visual narratives, in the educational context. The research focuses on articles published in English in scientific journals, between 1999 and 2020, available in the Scopus and Web of Science databases. The goal was to understand how the scientific community has been working on this theme. After applying the inclusion and exclusion criteria, nineteen articles were selected from both databases. All articles should focus on the study of visual narratives to promote learning. Three different kinds of visual narratives were identified in the studies: comic-strip, comics, and animated cartoon. The objective of these studies was to verify if the students understood the information more effectively and if there was an engagement in that process. We concluded that visual narratives have the potential to explain complex concepts and to keep students interested in the learning process. Also, the importance of fully understanding the context of the application of these resources is emphasized to be successful in planning actions in the educational context.

This work is financially supported by National Funds through FCT – Fundação para a Ciência e Tecnologia, I.P. under the project UIDB/00194/2020.

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Mota, M., Sá, C.M., Guerra, C. (2021). Systematic Literature Review Using Excel Software: A Case of the Visual Narratives in Education. In: Costa, A.P., Reis, L.P., Moreira, A., Longo, L., Bryda, G. (eds) Computer Supported Qualitative Research. WCQR 2021. Advances in Intelligent Systems and Computing, vol 1345. Springer, Cham. https://doi.org/10.1007/978-3-030-70187-1_23

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Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis

• Jeruza L Neyeloff 1 ,
• Sandra C Fuchs 1 , 2 &
• Leila B Moreira 1 , 2  

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Meta-analyses are necessary to synthesize data obtained from primary research, and in many situations reviews of observational studies are the only available alternative. General purpose statistical packages can meta-analyze data, but usually require external macros or coding. Commercial specialist software is available, but may be expensive and focused in a particular type of primary data. Most available softwares have limitations in dealing with descriptive data, and the graphical display of summary statistics such as incidence and prevalence is unsatisfactory. Analyses can be conducted using Microsoft Excel, but there was no previous guide available.

We constructed a step-by-step guide to perform a meta-analysis in a Microsoft Excel spreadsheet, using either fixed-effect or random-effects models. We have also developed a second spreadsheet capable of producing customized forest plots.

Conclusions

It is possible to conduct a meta-analysis using only Microsoft Excel. More important, to our knowledge this is the first description of a method for producing a statistically adequate but graphically appealing forest plot summarizing descriptive data, using widely available software.

Meta-analyses and systematic reviews are necessary to synthesize the ever-growing data obtained from primary research. Performing a search on Pubmed limiting to the type of article, the Mesh term "meta-analysis" will wield 4223 results in 2010 only. Although reviews of interventional studies, especially clinical trials, provide the best evidence, there are several situations in which observational studies are the only alternative. Meta-analyses of these studies are becoming more common, particularly after publication of the MOOSE statement [ 1 ]. Some of the studies are not concerned with the assessment of relative risks or odds ratios, but are focused on a summary statistics of incidence or prevalence.

General purpose statistical packages such as SPSS, Stata, SAS, and R can be used to perform meta-analyses, but it is not their primary function and hence they all require external macros or coding. These can be downloaded, but are not always easy for the researcher to understand or customize. Additionally, the first three programs do not have free access, with prices ranging from $250 to over $30,000 depending on version and country. R is a very resourceful open source package, but its use in health is still limited, due mostly to the need of programming instead of a point-and-click interface.

There are some software packages specifically developed to conduct meta-analyses. RevMan [ 2 ] is a freeware program from the Cochrane Collaboration that requires the researcher to fill all steps of a systematic review. It only accepts effect sizes in traditional formats. Metawin [ 3 ] and Comprehensive Metanalysis (CMA) [ 4 ] are commercial software that have user friendly interfaces. The former only accepts three types of primary data, while the latter has a purchase cost, but accepts more types of data. It can perform advanced analyses, but there are still limitations regarding graphic display, particularly of descriptive data, since CMA does not allow customization of the forest plot produced. Finally, there is also Meta-Analysis Made Easy (MIX) [ 5 ], an add-on for Excel. It can be used for analysis of descriptive data selecting the input type to "continuous", but the free version does not allow for analysis of original data, only build in datasets. Some other options are no longer available, as FAST*PRO [ 6 ], and others are still currently under development, as Meta-Analyst [ 7 ].

Another option would be to analyze data using directly Microsoft Excel. Although it has a purchase cost, it is usually already installed in most computers, bundled with Microsoft Office package. Most researchers would be uncomfortable entering all the formulas themselves, since they may seem complex at first. However, if the calculations are done in steps, statistics like Q and I 2 can be computed with basic arithmetic operations. Borestein et al [ 8 ] cites the impossibility of producing forest plots as an important limitation, but we have developed a method to turn a scatter plot into a statistically correct forest plot, allowing the researcher to take advantage of all excel formatting tools. Our work is separated into two spreadsheets, so researchers can use both to conduct all calculations or simply the second one if they have already analyzed the data in any other software, but want an appealing graphical way of presenting it [Additional file 1 ].

Technical notes

The method described here was designed on a laptop with Intel Core Duo 2.2 GHz processor, 4 GB RAM, running Windows Seven 64 bit and Microsoft Office Excel 2007. The spreadsheets were later tested on Excel 2003, with no differences found in either the calculations or graphs.

The outcome of meta-analyses is the effect summary. However, some reviews may only aim in combining rates or prevalences; technically these cannot be called "effects", since there is nothing "causing" it, and the correct term would be single group summary. We will refer to both these estimates simply as "outcome" in order to avoid confusion, and maintain only the abbreviation as es to follow textbooks standard.

Since we have established that the limitation of the existing software packages is handling descriptive data, we will be using rates in our example so that the difference in the final forest plot is more overt. The data could be the prevalence of smoking in a country or the incidence of myocardial infarction in high risk patients. We chose to use theoretical numbers so we could openly distribute the spreadsheets, test particular formulas and compare results obtained with other software. All formulas are presented in traditional equations and also in excel format.

Steps 1 and 2 always require adjustments according to study type and outcome. Columns in light grey in spreadsheet 1 are the ones to be adapted, while columns in dark grey do not require any modification regardless of study type (this includes all further steps of the guide). The necessary adjustments can be easily found on methodological books [ 8 – 10 ].

Cell B14 should be filled with the number of studies being analyzed. There are annotations on the spreadsheet that pop up when the mouse pointer is upon selected cells, so the downloaded file can be used without constant consultation of the full article. The explanation for the formulas and detailing of steps are not present on the spreadsheet though. A recently published paper by Schriger et al [ 11 ] reviewed over 300 systematic reviews and highlighted important aspects of producing forest plots, which were considered in developing this approach.

Steps in analyzing data and producing a forest plot

Spreadsheet 1-analysis (figure 1 ).

1. Calculating the outcome (effect size, es)

In our example we have the number of events and the number of subjects in columns B and C, so we can simply compute the rate in column D as n e v e n t s n t o t a l or D 3 = B 3/ C 3 in Excel. It is the same from D3 to D12, and copy and paste will automatically adjust the cell numbers. This copying and pasting should be done for steps 1 through 6 and in step 9 B.1.

Spreadsheet 1 : Analysis This spreadsheet contains the calculations necessary for the analyses. Input in light gray columns must be adapted according to effect size type. Calculations in dark grey columns are the same for any effect size type.

2. Calculating Standard Error (SE)

All SE can be derived from the formula SE = ∑ ( x ̄ - μ ) 2 n , but there are simplified derived equations for different types of studies. Since we are using rates, we can use SE = es es*n or SE = events n , the same formula used in CMA. In excel this will be E 3 = D 3/SQRT( D 3* C 3).

3. Computing variance (Var)

This formula is simple: Var = SE 2 . In Excel, F 3 = E 3^2.

4. Computing individual study weights (w)

We must weight each study with the inverse of its variance, so w = 1 S E 2 or G 3 = 1/ F 3 in Excel.

5. Computing each weighted effect size (w*es)

This is computed multiplying each effect size by the study weight. If we are not using any corrections on the weight (meaning, single effect model) this equation will result again in the study size for some types of studies. In excel, this will be H 3 = G 3* D 3

6. Other necessary variables (w*es 2 and w 2 )

We will need two other variables in order to calculate the Q statistics (columns I and J of spreadsheet 1). In excel this will be I 3 = G 3*( D 3 ^ 2) and J 3 = G 3 ^ 2.

Now we need to sum all values of each variable. In our spreadsheet they are in line 14, labeled "Sums": G 14 = SUM ( G 3: G 12), H 14 = SUM ( H 3: H 12), I 14 = SUM ( I 3: I 12), J 14 = SUM ( J 3: J 12)

7. Calculating Q

The Q test measures heterogeneity among studies, and works like a t test. It is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method. Q is distributed as a chi-square statistic with k (number of studies) minus 1 degrees of freedom. Our null hypothesis is that all studies are equal. To test that, we need to calculate Q and compare it against a table of critical values. If our calculated Q is lower than that of the table's, than we fail to reject the null hypothesis (and hence the studies are similar).

The formula is Q = ∑ ( w*ES 2 ) − [ ∑ ( w*ES ) ] 2 ∑ w , but in our spreadsheet it will be simply B 17 = I 14 - (( H 14 ^ 2)/ G 14) since we already have all the sums.

8. Calculating I 2

The I 2 was proposed as a method to quantify heterogeneity, and it is expressed in percentage of the total variability in a set of effect sizes due to true heterogeneity, that is, to between-studies variability. The formula is I 2 = ( Q - df ) Q * 100 , where "df" stands for "degrees of freedom", simply the total number of studies (k) minus 1. In excel, B 18 = (( B 17 - B 15)/ B 17)*100.

9. Deciding on effect summary ( e ¯ s ¯ ) model.

If heterogeneity is low, we can use a fixed effect model, that assumes the effect size is the same in our parameter population, and differences in studies are just from sampling error. However, if we think our sample populations may differ from each other, we can use a random effects model. Many researchers will choose this model even if heterogeneity is low. In our example, Q is higher than 16.919, the critical value for 9 degrees of freedom found in a chi-square distribution, and I 2 is 49%, so we have moderate heterogeneity [ 12 ]. We must decide whether the data is possible to meta-analyze, and if so we may choose to proceed to a random effects models.

Fixed effects Model

Our effect summary is e ¯ s ¯ = ∑ ( w*es ) ∑ w , or B 20 = ( H 14/ G 14). The standard error is S E e ¯ s ¯ = 1 ∑ w , or B 21 = RAIZ (1/ G 14). With the S E e ¯ s ¯ we calculate the 95% Confidence Interval, as C I e ¯ s ¯ = e ¯ s ¯ ∓ 1 , 96 * S E . In Excel, B 22 = B 20 - (1.96* B 21) and C 22 = B 20 - (1.96* B 21). In our example we will not use these results.

Random effects model

Since we are assuming that variability is not only due to sampling error, but also to variability in the population of effects, in this model the weight of each study will be adjusted with a constant ( v ) that represents this.

B1. The formula is v = Q - ( k - 1 ) ∑ w - ∑ w 2 ∑ w . We have all these information, except for ∑ w 2 . We can compute w 2 in column J with J 3 = G 3 ^ 2, and then its sum with J14 = SOMA ( J 3: J 12). Now, applying the formula, M 16 = ( B 17 - B 15)/( G 14 - ( J 14/ G 14)).

B2. Once we have the constant, we can calculate new weight for each study, using w v = 1 ( S E 2 + v ) . In excel, L 3 = 1/(( E 3 ^ 2)+$ M $16). We need the $ to fix cell M16, or else it will change when we copy the equation to cells L4 to L12.

B3. Now we repeat steps 5 to 8, but using our new weight W v . The results are in columns M, N and O. Applying the Q and I 2 formulas we have now an acceptable Q and low heterogeneity. We calculate our effect summary as e ¯ s ¯ v = ∑ ( w v *ES ) ∑ w v , and standard error as S E e ¯ s ¯ v = 1 ∑ w v .

In excel: F 20 = M 14/ L 14, F 21 = SQRT (1/ L 14), F 22 = F 20 - (1.96* F 21) and G 22 = F 20+(1.96* F 21). The confidence intervals are broader than the ones calculated with fixed effect model, however, little change in the effect summary is expected.

Analyzing these numbers in CMA we achieved exactly the same results. - [Additional files 2 and 3 ].

Spreadsheet 2-forest plot (Figure 2 )

Spreadsheet 2 : Forest Plot This spreadsheet contains the final forest plot. Data must be manually entered, either after using spreadsheet 1 or any other analysis software.

Columns A-G have the studies information. The user can insert each study effect size and confidence interval directly into columns D, F and G if he has the data. In our example we copied the calculations from spreadsheet 1, and also the values of the random effects model effect summary.

1. Make sure the information is the way we want it displayed. In our example, we wanted the rates in percentages, so column I = column D*100.

2. We usually read the lower and upper confidence interval as a value, but excel understands it as a difference to the mean. This is key to obtain a proper forest plot. These values are J 2 = I 2 - (100* F 2) and K 2 = I 2 + (100* F 2). Again, we multiply by 100 to have it in percentage.

3. In order to have each study in a different line, we will assign ordinal numbers to the studies. Our effect summary must be number 1 if we want it in the bottom of the graph. This is done manually in column H of our spreadsheet.

4. We are ready to build the graph. Insert > Graph > Scatter Plot. X values will be column I, lines 2-12, and Y values column H, lines 2-12.

5. We must now add the error bars. In Excel 2007 this is done in the Layout tab, clicking the "Error Bar" button on the right side. In Excel 2003 we must right click on the data series (points on the graph) and click "format data series", then chose the "X error bar" tab. In this window we mark the option "personalized values", and then assign columns J and K, lines 2 to 12, to the lower and upper value.

6. To insert the line marking the summary effect value we will add another data series. First we manually build this data set in the spreadsheet. Then right click on the graph > Select Data. Click on "add", and chose X values as column C, lines 15 to 26, and Y values as columns B, lines 15 to 26. A new set of points will appear on the graph. Right-click on any of the new dots and select "format data series". Then we will choose "no marker" and "solid line" on the Marker Options and Line Color tabs.

7. We can now format the X axis, right-clicking on it. In our example we want it to begin on 10 and end on 28, interval of 2 units. It is not our case, but if the researcher is dealing with relative data, then "logarithmic scale" must be marked.

8. The graph is ready. The user can format colors, outlines, shadows and sizes. In our example we changed the summary effect to a diamond shape. This is done by selecting only one dot (double click) and then right clicking it.

9. For presentation we recommend copying and pasting the graph over a table with study information (Figure 3 ).

Comparison of Forest Plots Comparison of forest plots produced using our spreadsheet (left) and CMA (right) .

We have constructed a guide to aid researchers interested in meta-analyzing data using a spreadsheet. To the best of our knowledge there is no prior step-by-step approach, but it should be noted that all formulas and methodology were previously publicly available.

The main limitation of analyzing data in a spreadsheet is the potential for errors by typing incorrect formulas. We believe that a step-by-step approach as those presented in this article with all formulas already incorporated in the excel format can help minimize this possibility. The guide presented also does not handle advanced analyses such as multiple regression. However, this is not frequently used in summarizing descriptive data. All sensitivity analysis must be done manually, including and excluding each study of the effect summary calculations, but this limitation is also present in other softwares.

Microsoft Excel is part of the Microsoft Office Package, and therefore it is not free of costs. However, for those who already have the package, this use of Excel could amplify its utility offering an alternative for customizing the graphic presentation of the forest plot.

The main limitation of the forest plot is that all studies are represented by squares of the same size, instead of proportional to study weight. We did not feel this could overshadow all other formatting possibilities, since study weight can also be estimated by the confidence interval width.

In conclusion, it is possible to meta-analyze data using a Microsoft Excel spreadsheet, using either fixed effect or random effects model. The main advantages of this approach are the understanding of the complete process and formulas, and the use of widely available software. It is also possible and simple to make a forest plot using excel. Since displaying results in a graphically appealing but also statistically correct way is usually a problem to most researchers, we believe the method presented here could be of great use. Figure 3 compares the graph obtained with our method and with CMA software.

Availability and requirements

Project name: Meta-analyses and Forest Plots using a Microsoft Excel spreadsheet: step-by-step guide focusing on descriptive data analysis;

Project home page: none;

Operating systems: any OS supporting Microsoft Excel;

Programming language: not-applicable;

Other requirements: Microsoft Excel 2003 or higher;

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0);

Restrictions to use by non-academics: none

Availability of supporting data

The spreadsheets mentioned and the CMA files used for comparison of statistics are available as complementary material.

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Acknowledgements

This study was funded by Conselho Nacional de Pesquisas (CNPq) and Fundo de Incentivo à Pesquisa do Hospital de Clínicas de Porto Alegre (FIPE-HCPA).

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Jeruza L Neyeloff, Sandra C Fuchs & Leila B Moreira

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Authors' contributions

JLN conceived the article, designed the spreadsheets, and drafted the manuscript. LBM and SCF revised the manuscript and approved the final version.

Electronic supplementary material

13104_2011_1382_moesm1_esm.xlsx.

Additional file 1:Meta-analyses and forest plots in MS Excel. This file contains both spreadsheets developed. (XLSX 26 KB)

13104_2011_1382_MOESM2_ESM.PDF

Additional file 2:CMA calculations fixed effect. This is a portable document format (pdf) of the calculations performed by the software Comprehensive Meta-Analysis, when calculating the effect summary using fixed effect model. It is provided so readers may compare the calculations and results obtained using Microsoft Excel spreadsheet and the commercial software. (PDF 822 KB)

13104_2011_1382_MOESM3_ESM.PDF

Additional file 3:CMA calculations random effects. This is a portable document format (pdf) of the calculations performed by the software Comprehensive Meta-Analysis, when calculating the effect summary using random effects model. It is provided so readers may compare the calculations and results obtained using Microsoft Excel spreadsheet and the commercial software. (PDF 862 KB)

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Neyeloff, J.L., Fuchs, S.C. & Moreira, L.B. Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis. BMC Res Notes 5 , 52 (2012). https://doi.org/10.1186/1756-0500-5-52

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DOI : https://doi.org/10.1186/1756-0500-5-52

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Level of Proficiency in Using Microsoft Office Applications of Grade 12 Students in Baybay in National High School (Grade 7-12)

• Siefred Guino
• John Raymond Levita
• Edd Vincent Penaflorida
• Anthony Andal

INTRODUCTION

Education has challenged both teachers and students to process very large pieces of information and to present data in a concise manner. Microsoft Office is one of the most used office applications in the education sector. There are several Microsoft applications but the most widely used are Microsoft Word, Microsoft Excel, and Microsoft PowerPoint. This study aims to determine the level of proficiency of Grade 12 students in using Microsoft applications.

The descriptive method of research was used employing an adapted questionnaire in measuring the level of proficiency in using MS office applications wherein student's skills are categorized as beginner, intermediate, or advanced. The respondents of this study were 165 Grade 12 students. The data obtained were presented in tabulated forms, analyzed and interpreted by the researchers by using frequency, percentage, ranking, and chi-square.

Among the 165 respondents who participated in the study, 13.30% were from ICT, 25. 40% were from TVL-HE, 27.20% from HUMSS, 9.09% from STEM, and 24.80% from ABM. 75 students or 45.45 % of the population were male students while 90 students or 54.54 % were female. As for the age of the respondents, half or 51.52 % of the respondents are 18 years old. In terms of the skills in using Microsoft office excel, 67% of the respondents are beginners, 24% are intermediate, and only 9% have advanced skills. Meanwhile,in using Microsoft Office Word, 14.3% of the respondents are beginners, 76.86% are intermediate, and 8.01% are advanced. Lastly, 55% of the respondents are beginners, 36% are intermediates, and 9% are advanced in using Microsoft PowerPoint.

DISCUSSIONS

Results showed that 86% of Grade 12 students only have beginner and intermediate skills when it comes to using the three most basic Microsoft applications and only 14% have advanced skills. This result signified a need for an intervention. It is in this line of reasoning that the researchers designed a tutorial video that could be used to bridge the skills of students to the advanced level. However, since this tutorial video was designed by students, it is recommended that this video be submitted to authorities for evaluation as an instructional material.

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• Afr J Emerg Med
• v.10(Suppl 2); 2020

Research skills and the data spreadsheet: A research primer for low- and middle-income countries

David mcd. taylor.

a Department of Medicine, University of Melbourne, Parkville, Victoria, Australia

b Emergency Department, Austin Health, Heidelberg, Victoria, Australia

Peter W. Hodkinson

c Division of Emergency Medicine, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa

Abdus Salam Khan

d Shifa International Hospital, Islamabad, Pakistan

Erin L. Simon

e Cleveland Clinic Akron General, Department of Emergency Medicine, Akron, OH, United States of America

f Northeast Ohio Medical University, Rootstown, OH, United States of America

The specialty of Emergency Medicine continues to expand and mature worldwide. As a relatively new specialty, the body of research that underpins patient management in the emergency department (ED) setting needs to be expanded for optimum patient care. Research in the ED, however, is complicated by a number of issues including limited time and resources, urgency for some therapeutic investigations and interventions, and difficulties in obtaining truly informed patient consent. Notwithstanding these issues, many of the fundamental principles of medical research apply equally to ED research. In all medical disciplines, data needs to be collected, collated and stored for analysis and a data spreadsheet is employed for this purpose. Like other aspects of clinical research, the use of the data spreadsheet needs to be exacting and appropriate.

This research primer explores the choice of available spreadsheets and a range of principles for their best-practice use. It is deliberately, not an exhaustive review of the subject. However, we aim to explore basic principles and some of the most accessible and widely used data spreadsheets.

African relevance

• • Clinical research should be most relevant to the population where it is undertaken.
• • Research capacity should move forward as a country develops.
• • Generation and management of a spreadsheet is a fundamental research skill.

The International Federation for Emergency Medicine global health research primer

This paper forms part 10 of a series of how to papers, commissioned by the International Federation for Emergency Medicine. This research primer explores the choice of available spreadsheets and a range of principles for their best-practice use. It explores basic principles and some of the most accessible and widely used data spreadsheets.

As a relatively new specialty, research in emergency medicine is still developing. Only in the last three decades has substantial research been undertaken specifically in the emergency department (ED) setting. The importance of this lies in that ED patients differ in many ways from those in other settings. They are, by definition, undifferentiated and often affected by anxiety, pain, fear, and vulnerability. Hence, ED research must be undertaken in the ED setting and inferences from other settings are unacceptable.

One fundamental research skill is the generation and management of a data spreadsheet. Essentially, this is an electronic document where data on each enrolled patient or entity under investigation is stored in a systematic way. Spreadsheets can be used for organization, analysis and storage of data in tabular form. They also allow the manipulation of data and the generation of graphics and summary or simple statistics. Where complicated statistical analysis is required, that cannot be done using spreadsheet software, datasets can usually be imported into statistical packages and analyzed further.

Spreadsheets can also serve as data storage facilities. Subsequent access to the data may be required well after its original analysis and publication of the project's findings e.g. secondary data analysis, merger with data from similar projects and the sharing of data with other researchers (an increasing trend) [ 1 , 2 ].

Organizations ensure high quality, ethical research by utilizing research governance [ 3 ]. Governance is involved at all stages of research especially at project submission, during patient enrolment and at closure. Requirements also include data storage for many years and it may be audited to ensure good research practice [ 2 ].

Which spreadsheet to use

A wide variety of spreadsheets are now available including Microsoft Excel® [ 4 ], Libre Office® [ 5 ], Open Office® [ 6 ] and Google Sheets® [ 7 ]. In addition, there are database applications with considerable functionality behind the spreadsheet interface e.g. Access® [ 4 ] and RedCap® [ 8 ]. As each has its own characteristics and functionality, the researcher needs to consider the ways in which the spreadsheet will be used and this should be an informed decision. It is recommended, however, that the researcher becomes thoroughly familiar with one variety of spreadsheet.

For the novice researcher, Microsoft Excel® [ 4 ], Libre Office® [ 5 ] or Open Office® [ 6 ] will likely more than suffice. Microsoft Excel® [ 4 ] is relatively simple, inexpensive, widely accessible and has an increasing range of functionality. A free comprehensive instruction manual is also available on-line [ 9 ]. Given this, Microsoft Excel will be used throughout this primer where examples are provided.

Setting up the spreadsheet

Time and care in setting up the spreadsheet prior to data entry is important and should be done in concert with the data collection document. This will facilitate data entry, help to avoid confusion and mistakes and provide the most appropriate formatting for data analysis either within the spreadsheet or when exported to a statistical program. If a biostatistician will be undertaking the data analysis, it is highly recommended that he/she assists in setting up the spreadsheet. This may save considerable time and effort later on. A data collection and management practice instruction document is also recommended to assist in the efficient and accurate data procurement and process [ 10 ].

The most commonly used spreadsheet format for studies involving individual patients is to assign each patient to a single row of data cells ( Fig. 1 ). Row 1 is usually reserved for the column headings and patient data in the rows below. Next, the data from patient study identification (ID) number 1 is placed in row 2, data from patient number 2 in row 3 and so on.

Excel spreadsheet of sample data from a laceration repair study.

Each column is reserved for data on specific characteristics (variables) of the patients ( Fig. 1 ). Commonly, column A is reserved for the patient study ID number and all other data in the columns to the right. It is advisable to group the columns according to the type of data they contain e.g. columns B, C, D and E may contain patient demographics (e.g. age, sex weight, co-morbidities). Other data groups may include:

• • other patient characteristics (e.g. ethnicity, religion, employment status)
• • study details (e.g. group allocation, date of enrolment)
• • potential confounding and bias variables (e.g. triage score, pain scores, language fluency)
• • outcome variables (e.g. patient satisfaction, procedural outcome, adverse events)

Within each column, the data may be numerical (e.g. blood pressure), ordinal (e.g. age group) or categorical (e.g. ethnic group). For ease of data analysis, assign ordinal and categorical subgroup variables with a number and use these numbers to populate the cells. For example, in ‘laceration depth’ column, insert 1 if epidermal, 2 if dermal and so on ( Fig. 1 ). In anticipation of merging data with that of others, unambiguous (‘Pat. ID’ not ‘PTID’) or standard variable nomenclature (e.g. 1 = male, 2 = female) should be used.

Determining the number of variable subgroups will depend upon the nature of the study and the relevance of each variable. In general, approximately 5-6 subgroups are appropriate. Where possible, adjust the subgroups to ensure that each contains similar numbers of patients. For example, if the age subgroup of 80–100 years has few patients, that group could be adjusted to 70–100 years. Another approach may be to use actual data in the first data spreadsheet and then form the sub-groups. It is advisable to add parameters for the expected data item range in drop down boxes. This will prevent inaccurate data entry e.g. age of 201 entered instead of 101 years.

Once the spreadsheet has been set up, it should be trialed before definitive data entry is commenced. This involves entering data for a sample of patients (e.g. 50). At this stage, it is not uncommon to find errors in the way the spreadsheet has been set up or in the way data is to be entered. For example, country subgroups may comprise European, Asian, North American, South American, Australasian and ‘other’. However, if the ‘other’ subgroup is, unexpectedly, found to be large then one or more new subgroups could be added or a new column. Sometimes, there may be two items to be entered into the one cell. For example, pain management subgroups might be oral simple analgesia, parenteral simple analgesia, oral opioid, parenteral opioid etc. However, a patient might have received more than one of these medications. Entering ‘3,4’ in a cell to reflect the nature of the analgesia received, will make data analysis difficult. It is advisable to add additional columns – in this example, the first column will contain ‘3’ and the next will contain ‘4’.

Navigation around the spreadsheet

Data entry, cleaning and analysis involves moving up and down and across the spreadsheet. While this can become rather confusing, there are tricks to mitigate this. As discussed above, there may be a number of subgroups for each study variable. It is important to devise a way of finding out which subgroup classification a certain cell number represents. This could be on a separate electronic or hard copy form that states ‘column F, country of birth: 1 = European, 2 = Asian and so on’. However, this is cumbersome. It is recommended that the column headings have a ‘comment’ inserted. To do this, left click on the column heading cell (row 1), then right click on the same cell and choose ‘insert comment’ from the dropdown list. A small dialogue box will appear. Within this box, type the variable subgroups (e.g. 1 = European, 2 = Asian) with each on a separate line. Once done, left click on any other cell. You should then see a small red ‘flag’ appear on the top right of the cell you have just added the comment to ( Fig. 1 ). If you then move your cursor over that cell (do not click), the dialogue box will appear with all the subgroup details within. You can edit the comment by right clicking on the cell and then choosing ‘edit comment’.

Color coding of the column headings (or even the entire column) will help to identify particular data groups. For example, the column headings (in row 1) for the demographic, study detail and outcome variables could be colored yellow, pink and blue, respectively ( Fig. 1 ). This makes finding the column much easier e.g. simply cross to the columns of the appropriate color and search through those ones.

When an Excel® spreadsheet is opened for the first time, moving to the columns on the right may push column A out of view. Similarly, moving down the spreadsheet may push row 1 out of view. When this happens, it is easy to become disorientated if you cannot view the column heading or the patient ID numbers. To avoid this, it is advisable to use the ‘freeze panes’ function to freeze row 1 and column A in place so they are always visible. To do this, left click in cell B2, then the ‘view’ tab and then ‘freeze panes’ in the drop down list. This can be undone at any time and various numbers of columns and rows can be frozen depending upon which cell you first click on.

The transfer of data from its source to the study spreadsheet is commonly associated with mistakes. When done manually, the mistakes usually involve misinterpretation of a data item to be transferred (e.g. entering 456 instead of 654) or typographical errors (e.g. entering 653 instead of 654).

Ideally, data would be transferred electronically e.g. vitals signs transferred directly from ED computers into the spreadsheet [ 11 ]. However, this requires sophisticated computer systems and is usually not possible. A reasonable option is to enter data manually, from its source directly into the spreadsheet. In this option, data are extracted from the medical record (or other source) and typed directly into the spread sheet, often via portable devices such as computer tablets or laptops. The least favorable option is manual transfer of data from the source onto hard copy data collection forms and subsequently into the spreadsheet. The more transfer required, the greater the risk of mistakes.

To mitigate the risk of mistakes, data ‘double entry’ can be employed, where two persons enter the data separately. Upon completion, the two datasets are compared, inconsistencies between the data sets identified, those data are double-checked and reconciled, and the spreadsheet corrected if necessary. However, ‘double entry’ is resource intensive and this may preclude its use.

An alternative is to scan the data into tables using optical mark recognition (OMR) and optical character recognition (OCR) software. Machine readable forms can be created using this special software. When scanned or faxed, the handwritten information on these forms is read into the database. The advantage is that keyboard entry is eliminated. The disadvantage is that they are more difficult and costly to set up.

In all studies where a single person manually enters the data, a data quality assurance exercise should be undertaken. This involves a second person extracting at least 10% of all data as well (e.g. data from 10% of patients). Like ‘double entry’, the data from both extractors is compared. Differences (if any) are then reconciled. It may sometimes be necessary to check the entire dataset if more than the very occasional mistake is identified. It should be noted that many journals now require a description of the data quality assurance exercise to be included in the Methods section of the research paper [ 12 ]. Failure to undertake and report this exercise will likely result in rejection of the paper.

Data cleaning

Once all data entry is complete and the quality assurance exercise has been undertaken, the data needs to be ‘cleaned’. Data cleaning refers to identifying incomplete, inaccurate or irrelevant data and then replacing coarse data with clean entries in a methodical way [ 13 ]. In most cases, this involves identifying missing or incorrect data in the spreadsheet. Even if 10% of the data has been checked by a second person, there may be mistakes in the remaining 90% that should be sought.

Rather than scanning every spreadsheet cell to identify inconsistencies, ‘range checking’ can be undertaken. In Excel®, this technique involves highlighting an entire column of data and clicking on ‘sort smallest to largest’ (for numerical data) or ‘sort A to Z’ (for text data) on the ‘Sort & Filter’ dropdown box on the Home tab. Incorrect data items will be found at either the top or the bottom of the sorted data column. For example, a range check of sex (where 1 = male and 2 = female) may identify an errant ‘3’ ( Fig. 1 ). This value is ‘out of range’ for the study and needs to be corrected.

Once the range check of a column has been done, ‘undo’ the sorting before correcting any errors or moving on to the next column. To find the error once the sorting has been undone, highlight the column, click ‘Find’ from the ‘Find and Select’ dropdown box on the Home tab. Enter the incorrect value in the ‘find what’ box (e.g. 13 or 134 in the age example above) and click ‘Find Next’. The incorrect cell will be highlighted, the patient's study number determined and the data can be corrected.

If data are collected by several investigators or across different sites, then means and medians should be compared across investigators and sites. If there are substantial differences this can indicate systematic differences in measurement or data collection.

Version control

It is very easy to make mistakes or lose track of progress, especially during the data cleaning process and formatting for data analysis. In this regard, spreadsheet ‘version control’ is vital with the saving of all versions of the spreadsheet, appropriately named and dated. The importance of this lies in the possibility of mistakes being made in the cleaning or analysis (e.g. forgetting to ‘undo’ sorted columns, accidental deletion of data). If these mistakes cannot be corrected, then at least it is possible to go back to the immediately preceding version and start again.

It is recommended that the first version of the spreadsheet is saved as its own file before every major data manipulation. For example, once all data has been entered, that file could be named and saved as ‘raw data’. To progress with the data, the ‘raw data’ file should be opened and saved as the next version e.g. ‘raw data–cleaned’. Once cleaned and saved, the file is opened and saved as the next version e.g. ‘raw data–cleaned–formatted for the statistical software’.

It is recommended that version numbers and dates are also added to the file names e.g. ‘v2–raw data–cleaned–11062019’. Having the version number first has the advantage of having all the files stored in the correct version order. This could be lost if the file name comes first, or if it is changed for some reason in a subsequent version. The date is an additional means of tracking the versions if files names are written incorrectly.

Finally, like any electronic documentation, all files need to be backed up in the event of computer failure, theft, fire or other catastrophe. Many facilities have dedicated institutional hard drives on which files can be backed up. An alternative is to use an external computer hard drive. These are now relatively inexpensive, with some having the function of automatically backing up files on a regular basis (e.g. daily). Spreadsheets can also be stored on CD discs or ‘in the cloud’.

One consideration for important data spreadsheets (and other files), is to plan for the worst case scenario. Consider the consequences of a fire in your office that destroys your computer, your external hard drive and your storage discs. If these amounted to 3 years of PhD research then it may all be lost. Given such possibilities, it is recommended that important files be kept in at least one remote facility (e.g. your home computer, in the ‘cloud’).

Confidentiality

Personal data on research subjects must always be treated confidentially. Presently, Institutional Review Boards and Ethics Committees require a description of how the data will be stored confidentially and securely.

In regard to confidentiality, one sound principle is never to have patient identifying information (e.g. name, date of birth) on data collection forms or spreadsheets that also contain their personal data. We recommend setting up a unique identification number (“patient study ID”) for each study participant. Using a unique subject identifier that has no meaning external to the study database simplifies the process of “de-linking” study data from personal identifiers for purposes of maintaining subject confidentiality. A separate document called a ‘Master List’ should be developed. This document will contain and link the ID numbers with the patients' identities. This may be important if the original patient data source needs to be accessed again to check on data items. The Master List and all other files should be stored separately and should not be shared by all investigators. In the event that either the Master List or the spreadsheet is accessed by an unauthorized person, they will not be able to link patient identity with their data.

In regard to security, all hardcopy data collection sheets should be stored in a locked cabinet within an office that is locked when unattended. Similarly, electronic files, including the study spreadsheets, should be password protected and stored on password protected computers. Only authorized study investigators should be able to access these files.

Tips on this topic

• • Prior to data entry, perfect the design, trial and revise the spreadsheet. Invite your statistician to assist at this stage. There are many tutorials on line to assist with spreadsheet set up [ 14 , 15 ]
• • Train yourself on the type of spreadsheet that you plan to use. Make mock files and test interactivity, graphs and statistics functions.
• • Clean data thoroughly before analyses – this will save time and effort
• • Undertake a data quality assurance exercise prior to data analysis. This will ensure high quality data and is required by many journals.

Pitfalls to avoid

• • Do not forget the importance of version control and backup of your spreadsheet
• • Avoid the possibility of breaches of confidentiality and security of the data
• • Avoid multiple persons entering data into the spreadsheet. Also, minimize the number of times data needs to be transferred manually, wherever possible.

Authors' contribution

Authors contributed as follow to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content: DT contributed 70%; and PH, ASK and ES 10% each. All authors approved the version to be published and agreed to be accountable for all aspects of the work.

Annotated bibliography

Harvey G. (1) Excel 2016 All-in-one for Dummies. Available at: http://www.allitebooks.in/excel-2016-all-in-one-for-dummies/ (accessed December 11, 2019).

This book is free to download from the Internet. It is a comprehensive guide to the use of Excel. As such it can be somewhat heavy going. It is certainly a reference source when learning new skills but is not for a casual read. A 2019 edition is available.

Declaration of competing interest

The authors declared no conflicts of interest.

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Using Microsoft Excel for Social Research

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Providing step-by-step instructions for how to use Microsoft Excel for doing statistics, Charlotte Brookfield discusses different stages of the research process, from first planning to writing and presenting your research. With a focus on conducting robust data analysis, the book is reassuring, clear and straightforward, helping you to:  

·       Learn important data skills, such as preparing, cleaning and managing data

·       Reduce anxiety about approaching statistics and quantitative data

·       Boost your employability, showing you how to develop transferable skills, such as effective time management.

Whether you’re learning data skills for the first time or translating your statistics knowledge from other software, this book will help you successfully carry out social research in any setting with confidence, via an engaging pedagogy that includes: colour-coded chapters by difficulty, activities, 'Remember' boxes, further reading and skills checklists.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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This is an invaluable resource for people learning statistics. Brookfield provides a clear, accessible and engaging introduction to using Excel to explore, analyze and report quantitative data. 

It makes perfect sense to train our students in Microsoft Excel: not only does the programme have increased functionality for social research, but it is also a staple in many graduate workplaces. This text is distinct from other Excel help guides in that it is explicitly designed around the social research process and with undergraduate students in mind. Using real-life datasets and tools such as ‘reminder boxes’, it is a highly comprehensive, engaging and accessible resource for introductory quantitative research methods modules. 

This is the book I have been waiting for. We have learned that employers value Excel and that many small workplaces cannot afford SPSS licences. This covers everything we would do in SPSS (possibly excepting recoding variables). Sold.

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Preparing Data in Excel

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The members of CCORDA have expertise in design and planning of studies, including preparation of data collection forms and database creation. We encourage researchers to include CCORDA in all phases of the study from design to analysis and dissemination of results. Some researchers collect and enter their own data for analysis. Accurate data entry is critical for the success of the study. We have prepared some helpful hints for entering data into an Excel Workbook for ease in statistical analysis.  

Microsoft Excel can be a useful platform to enter and maintain research study data. Excel is fairly easy to learn and use. Researchers can use Excel's simple statistical and plotting functions to help gain insight into their data. However, most research projects require more extensive statistical techniques that can be most easily performed using additional statistical software packages such as SAS or SPSS software.

In order to have your data easily imported into a statistical software package we have developed these guidelines for data entry into Excel.

Here is a good example of data entry into an Excel file followed by guidelines for data entry.

Good Excel Data Sheet:

Guidelines:.

• Include a unique identifying number for each case.
• Be sure that each variable name is unique (no duplicate variable names).
• Variable names must start with a letter.
• Do not include special characters (#, !, ?, %, etc.) or spaces in your variable names.
• Choose readily recognizable names for variables - but not too long (<= 16 characters best).
• Don’t enter data such as "120/80" for blood pressure. Enter systolic blood pressure as one variable and diastolic blood pressure as another variable. Don't enter data as "A,C,D" or "BDF" if there are three possible answers to a question. Include a separate column for each answer.
• Two digit years can cause problems for statistical software when reading data from Excel files. The best format for dates is mm/dd/yyyy, where mm is a 2 digit month, dd is a 2 digit day and yyyy is a 4 digit year.
• Missing data can cause a multitude of problems. To enter a missing data value either enter a blank or an "impossible" numeric code (for numbers) or an easily recognizable single digit character code for character (trying to avoid mixing numeric and character data). Be sure, if you use a missing value code, that it cannot be confused with a "real" data value.
• When entering data keep the same format throughout.

  Bad Example

Notice in the Good Example above that the date variable has the same format (mm/dd/yyyy) and the sex variable is consistent throughout in both case and type (character variable). In the Bad Example the date variable is in different formats without a 4 digit year for all the observations. The sex variable is still a character variable, but statistical software will read this variable as having six different levels instead of two.

• If you decide to use multiple sheets for you data, follow the variable naming conventions for the tabs that name the sheets (keep the names simple and unique).
• For example, treated versus non-treated patients can be handled by column variable that has a code for Treated (yes/no).
• These features can be used on other separate "subset" or "analysis" spreadsheets that are for the investigator, but not the statistician or programmer.

Data Dictionary:

• Be sure the effort you are putting forth is necessary. The CCORDA member should be able to tell you precisely what form the data needs to be in to suit its conversion and analysis.

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Microsoft Excel Research Articles Showing Evidence that Excel is beneficial with medical calulations

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Hello, EricW7,

I can list several tutorials that may help.

Use Excel as your Calculator

https://support.office.com/en-us/article/Use-Excel-as-your-calculator-A1ABC057-ED11-443A-A635-68216555AD0A

Help creating a Medical Document

https://support.office.com/en-us/community?threadid=714dec02-7183-41ff-9488-fb74e6aa50af

Using Solver to determine the Optimal Product Mix

Dosage Calculations Formula

http://www.indianhills.edu/_myhills/courses/ADN841/documents/lu03_calculations.pdf

If needing further assistance, repost to Excel Tech Cente r.

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How-To Geek

Lied on your resume about excel expertise 8 concepts you need to learn right now.

A whistle-stop tour of Microsoft Excel's must-knows.

Quick Links

The excel interface, order of operations, cell references, key data analysis formulas, filtering and ordering ranges and tables, pivot tables, macros and vbas.

Can you explain what a ribbon is? Do you know the three types of cell references? Do you know how to convert a number to a currency? If not, then look no further, as we take you through some key Excel features you need to know to show your expertise.

First, let's take a look at what Excel looks like, what its key elements are called, and what each of those elements do.

• The ribbon and tabs —Excel has hundreds of different menus and drop-downs, and they're all accessible through the ribbon, which is the area above where you do your work. The ribbon choices labeled number 1 in the screenshot above—such as Home and Insert—are the tabs. Anytime you want to make things happen, you first need to go to the ribbon and make sure you have opened the correct tab. For example, for formatting, you'll need the Home tab on the ribbon, and if you want to add a chart, you'll need the Insert tab.
• The groups —Once you have opened a tab on the ribbon, you'll see the different groups in that tab. These groups are designed to clump together similar functions, making it easier for you to find the function that you're looking for. For example, in the Home tab on the ribbon, you can see the Font group, where you can embolden your text, change the typeface, or make your font larger.
• The name box —If you select a cell or range of cells, this box shows you the cell reference. In the screenshot above, the name box reads A1, because this is the cell we have selected in this worksheet. The name box is useful for jumping to a specific cell or range of cells you have named , especially handy if your workbook has lots of tabs.
• The formula bar —When you type a formula into a cell, the formula bar is where you'll see what you have typed and, if needed, edit your formula. Essentially, the cells show you the final product, and the formula bar shows you the formula you have used to create it.
• Columns and rows —The columns (A, B, C, and so on) run vertically in your sheet, while the rows (1, 2, 3, etc.) go across.

Remember your math lessons in school when you were told that you had to follow a certain order to make sure your equations worked correctly? Well, because Excel doubles up as an advanced calculator, it also has a particular order that it follows when dealing with your formulas, and it's important to know what this order is so that you know what to expect when performing calculations.

At its most basic level, Excel follows the PEMDAS order—parentheses, then exponents (also known as indices), then multiplication and division, and finally, addition and subtraction.

There are other elements in a formula that Excel has to follow (including cell references, percentages, and logical operators), but knowing PEMDAS will be enough to show you understand how Excel's calculation process works.

A cell reference is the name of a cell or range of cells in your Excel workbook, and you can use these in formulas. For example, if you click cell A1 and type

=SUM(A2+A3)

into the formula bar, you're telling Excel to reference cells A2 and A3, and add their values together.

There are three types of cell references in Excel— relative, absolute, and mixed .

A relative reference (the default reference type in Excel), refers to the relative position of the cell. If you type a formula in cell A1 that involves A2, you're referencing the cell that is one below where you're typing. If you were to copy the formula to another cell, the reference would automatically perform the same relative action as in its previous location.

An absolute reference does not change if you copy the formula into another cell. For example, if you type a formula that references cell A1 using an absolute reference, it will always reference cell A1, regardless of where you copy that formula to. To create an absolute reference, we place the dollar ($) symbol in front of the column and row reference: $A$1 (or press F4 after having typed the reference).

A mixed reference combines relative and absolute references at the same time. For example, $A1 is a mixed reference that tells Excel to continually refer to column A, but switch the row number relative to where the formula is located.

There are many different types of data that can be entered into an Excel cell, and the importance of having the right data type is that it helps Excel to perform the correct operation according to the type of data. Before you enter any data into a cell, the data type is set to General by default—this is Excel's way of telling you it's waiting for something to be entered into the cell so that it can then read what you enter and change the data type accordingly.

The data type can be seen in the Number group of the Home tab on the ribbon, and you can click the drop-down arrow to see the different data types available.

If you were to type $12 into a cell, Excel would see the dollar symbol and automatically change the data type to Currency. Likewise, if you typed Apr 14 , Excel would read this as a date. If Excel doesn't identify the correct data type, you can change it manually using this drop-down list.

On top of the most basic formulas, such as SUM and AVERAGE , some of Excel's formulas are particularly handy for data analysis. Here are some that you might need to know if asked about important Excel formulas:

• IF —The IF function returns different values depending on whether a condition is true or false.
• COUNT and COUNTIF —The COUNT lets you count the number of cells in a range that contain numbers, and COUNTIF counts all the cells in a range that match a condition or multiple conditions.
• VLOOKUP —This function searches a table by row to extract certain information.
• CONCATENATE —The CONCATENATE function lets you join two or more strings of data into one place, useful for keeping your key information in once place.

Knowing the many useful data-entry functions is essential if you want to show off your Excel knowledge.

Knowing how to handle data in Excel is crucial for anyone looking to manage the spreadsheet's numbers, and filtering and sorting the data is one way to do this. There are three ways to filter and order data in Excel—use the Filter icon, format a table, or right-click the data.

If you have an unformatted data set, you can add a filter and sort function by selecting the header row before clicking the Filter icon in the Data tab on the ribbon.

This will create a filter button at the top of each column in your data, which you can click to filter or sort your data.

Another way to filter and sort your data is by formatting your data into a table. Select all your data (including the header row), and click "Format As Table" in the Home tab on the ribbon.

Then, choose an appropriate table style, and make sure you check the "My Table Has Headers" box in the dialog box that appears. When you click "OK," your data will be formatted in a table with a filter button running across the header row of your table.

Finally, you can sort and filter a specific series of data by selecting and right-clicking your data. Then, choose between the Filter and Sort options in the menu that appears.

Even if you haven't used one in the past, anyone with Excel expertise will know the huge benefits of a pivot table . A pivot table is a tool that lets you quickly convert large amounts of data into a readable and useful table. You can use a pivot table to display specific criteria in a table, control your charts and graphs, and analyze numerical data in detail.

The pivot table option is accessed through the Insert Tab on the ribbon.

Once created, a sidebar appears to the right of your screen, where you can manipulate and choose the data you want to analyze in your table.

Macros and VBAs are ways to automate data in Excel, useful for data managers who want to save time when working with large volumes of data. They also help you to avoid errors that might have occurred had you completed the tasks manually.

As with pivot tables, knowing what they do is the first step to demonstrating Excel expertise—and you're even more likely to impress if you can demonstrate you know how to use them . Macros and VBAs are closely related:

• Macros —These are fantastic lines of code for automating actions you do repeatedly in Excel, saving you have to go through a series of clicks every time you want a certain action to occur. For example, if you want to turn a certain cell red and change the text color to white, you can create a button within your spreadsheet that lets you do this, especially useful if you have to go through this process repeatedly.
• VBA —Short for Visual Basic for Applications, VBA is the scripting language used to create macros.

If this is sounds a little too technical for you, the key thing to note is that macros and VBAs are useful tools in Excel to create automation.

As well as the tips listed in this article, you might also find knowing how to create a form , a checklist , or a dynamic chart helpful when convincing others of your above-average Excel knowledge.

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Molecular characterization of Escherichia coli virulence markers in neonatal and postweaning piglets from major pig-producing districts of Uganda

• Margaret Tusiime 1 ,
• Frank. N. Mwiine 2 ,
• Mathias Afayoa 3 ,
• Steven Arojjo 4 &
• Joseph Erume 5  

BMC Veterinary Research volume  20 , Article number:  230 ( 2024 ) Cite this article

323 Accesses

Metrics details

Piggery production is highly constrained by diseases, with diarrhoea in piglets being a major cause of economic losses to smallholder farmers in Uganda. Enterotoxigenic Escherichia coli (ETEC) is thought to be one of the major etiologies of this diarrhoea. A cross-sectional study was carried out in two high pig-producing districts of Uganda with the aim of determining the significance of piglet diarrhoea and the pathogenic determinants of causative E. coli .

Methodology

A total of 40 households with piglets were visited in each district for a questionnaire survey and faecal sample collection. The questionnaire-based data collected included; demographic data and pig management practices. E. coli were isolated from diarrheic (43) and non-diarrheic (172) piglets and were subjected to antimicrobial susceptibility testing against nine commonly used antimicrobial agents. The E. coli isolates were further screened for the presence of 11 enterotoxin and fimbrial virulence gene markers using multiplex polymerase chain reaction. Data entry, cleaning, verification and descriptive statistics were performed using Microsoft Excel. Statistical analysis to determine any association between the presence of virulence markers and diarrhea in piglets was done using SPSS software (Version 23), with a p  value of less than 0.05 taken as a statistically significant association.

Escherichia coli were recovered from 81.4% (175/215) of the faecal samples. All the isolates were resistant to erythromycin, and most showed high resistance to tetracycline (71%), ampicillin (49%), and trimethoprim sulfamethoxazole (45%). More than half of the isolates (58.3%) carried at least one of the 11 virulence gene markers tested. EAST1 was the most prevalent virulence marker detected (35.4%), followed by STb (14.8%). Expression of more than one virulence gene marker was observed in 6.2% of the isolates, with the EAST1/STa combination being the most prevalent. Three adhesins; F17 (0.6%), F18 (6.3%) and AIDA-I (0.6%) were detected, with F18 being the most encountered. There was a statistically significant association between the occurrence of piglet diarrhoea and the presence of the AIDA-1 ( p value  = 0.037) or EAST1 ( p value  = 0.011) gene marker among the isolates.

Conclusion and recommendation

The level of antimicrobial resistance among E. coli isolates expressing virulence markers were high in the sampled districts. The study established a significant association between presence of EAST1 and AIDA-I virulence markers and piglet diarrhea. Further studies should be carried out to elucidate the main adhesins borne by these organisms in Uganda and the actual role played by EAST1 in the pathogenesis of the infection since most isolates expressed this gene.

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Introduction

Pig production is a very important global economic activity that is driven by increased demand for animal source proteins resulting from exponential human population growth [ 1 , 2 ]. The relatively limited investment required in the local piggery enterprise coupled with the fast maturing and high prolificacy of the pigs make the enterprise ideal for most poor rural communities [ 2 , 3 ] The sector is, however, constrained by several factors, key among which are diseases that highly affect piglets [ 4 ]. These diseases affect the survivability and performance of piglets, which in turn negatively affects enterprises that are highly dependent on the performance of piglets [ 5 , 6 ]. One of the major clinical conditions in piglets is diarrhoea, which can be a result of several factors; however, Escherichia coli has been highly implicated in the diarrhoea observed in piglets [ 7 , 8 ].

Escherichia coli- induced diarrhoea, colibacillosis, is primarily caused by enterotoxigenic Escherichia coli pathotypes [ 8 ]. Worldwide, over two different diarrhoea syndromes are recognized: neonatal diarrhoea in piglets from 1 to 7 days of age and postweaning diarrhoea, which manifests approximately one week following weaning [ 9 ]. ETEC associated with piglet diarrhoea expresses one or more fimbrial adhesins (F4, F5, F6, F18 and F41) along with enterotoxins STa, STb, LT or their combinations [ 4 ]. In addition to the latter, a number of other adhesins have also been reported among these strains, including F42, F165 and AIDA-I [ 10 ]. The Uganda piggery system experiences more than 30% preweaning piglet mortality, most of which is attributed to infectious diarrhoeal diseases [ 11 ]. This has prompted research to generate evidence of the role of E. coli as an etiology of piglet diarrhoea/mortalities in Uganda. Preliminary research identified ETEC expressing enterotoxin genes (STa and STb) and F4 adhesins in preweaning piglets in Uganda [ 1 ]. The purpose of this study was to expand this effort to the major pig-producing districts of Uganda with the aim of elucidating virulence markers, husbandry practices in small-scale piggeries and antimicrobial resistance levels of these organisms toward sustainable mitigation of colibacillosis losses.

Materials and methods

Study area and study population.

The study targeted smallholder pig producers in two of the major pig-producing districts [ 12 ] of Uganda that is Masaka (0.4464 S, 31.9018 E) and Mukono (0.3549 N, 32.7520 E) districts located in central Uganda, Buganda subregion. These two districts are among the four high pig producing districts with a contribution to the pig population of 40.2% in central region and 16.5% in the whole country [ 13 ]. The study was performed in four sub counties in each of the selected districts. The study group was piglets under two months of age (new-borns and weaners) from selected farms.

Study design

This was a cross-sectional study carried out from December 2021 to October 2022 involving the characterization of virulence markers and assessment of the antimicrobial susceptibility of E. coli to selected drugs. The study entailed a questionnaire survey to determine the husbandry practices under which the piglets were raised and faecal sample collection from the animals. Visits were made to the Veterinary Offices in the study districts to introduce the project, and with the help of the District Veterinary Officers (DVOs), four Sub Counties in each district were selected to contribute animals for study. Subsequently, ten households/pig farmers were selected for study from each of the sub counties. These farmers were identified by the snowballing method [ 14 ] with the help of field guides (paraprofessionals and veterinarians) assigned by the DVOs.

Sample size determination

The sample size (n) for this study was determined based on an established formula (Thrusfield, 2005). Based on the findings of a previous study by Kallau et al. [ 15 ], the expected prevalence of E. coli in the faecal matter of pigs was 85.4% [ 15 ].

where n is the sample size, Z is the statistical value at the 95% confidence interval, d is the absolute precision level with a value of 5% and P  is the prevalence of Escherichia coli . With Z  = 1.96, P  = 0.854 and e  = 5%, this yields n  = 192 faecal samples. This was rounded to 200 samples, which is approximately 100 faecal samples per district.

Ethical approval

This study entailed questionnaire administration to the pig farmers as well as fecal sampling from piglets. Therefore, the study procedure was assessed and approved by the School of Biosecurity, Biotechnical and Laboratory Sciences (SBLS), College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Makerere University, Uganda on the 11th November 2021, reference number (SBLS.JE.2021) and by the Uganda National Council of Science and Technology (UNCST), approval number A190ES.

Field data collection

Questionnaire survey.

A pig herd level closed and semi closed questionnaire tool was developed for this study (Supplementary file_Questionnaire_English ) consisting of 41 questions was administered to 80 household heads from whose piglets’ faecal samples were obtained. The data obtained included demographic information, pig management and husbandry practices, and occurrence of piglet diarrhoea, among other data. With the help of field guides, 10 households with piglets were selected in each of the sub counties, and a questionnaire was administered. In total, 40 questionnaires were administered per district.

Faecal sample collection

Rectal swabs from two piglets, identified as either diarrhoeic or non-diarrhoeic, from each litter were obtained as aseptically as possible. Each swab was placed in a sterile Falcon tube containing sterile peptone water as the transport medium. The swabs were transported under cold chain in a cool box with ice packs to Makerere University microbiology Laboratory at the College of Veterinary Medicine, Animal Resources and Biosecurity (CoVAB), Makerere University for analysis. The samples were temporally stored at 4 °C until further processing and analysis.

Isolation and identification of E. Coli

Standard bacteriological procedures were followed for the isolation of E. coli . Briefly, faecal samples were enriched overnight in sterilized buffered peptone water at 37 °C, after which the samples were inoculated onto MacConkey agar. The suspected E. coli colonies were then identified using a combination of the following: colony characteristics, different standard biochemical tests such as the indole test, methyl red test, Voges Proskauer test and citrate test (IMViC tests) and typical morphology after Gram staining. The preservation of the isolates was performed using glycerol and brain heart infusion broth. The process involved suspending a pure colony of E. coli in 700 µl of brain heart infusion broth and culturing it overnight. This was then added to 30% (final concentration) sterilized glycerol in 1 ml cryovials. The mixture was vortexed to allow for uniform mixing and then preserved at -20 °C awaiting antimicrobial susceptibility testing and molecular characterization for the virulence markers.

Antimicrobial susceptibility profiles of E. Coli isolates

Antibiograms of all the isolates were determined using the Kirby-Bauer disc diffusion technique [ 16 ] employing Muller Hinton Agar. A total of nine antimicrobials, including nalidixic acid (NA), trimethoprim sulfamethoxazole (SxT), gentamicin (CN), chloramphenicol (C), ciprofloxacin (CIP), erythromycin (E), kanamycin (K), ampicillin (AM), and tetracycline (Te), were tested. The drug inhibition zones were measured, and the results were interpreted as resistant (R), intermediate (I) or susceptible (S) according to the Clinical and Laboratory Standards Institute guidelines, 2018 [ 17 ].

Molecular characterization of E. coli virulence markers

E. coli dna extraction.

The boiling method was used to extract DNA [ 18 ]. Briefly, 50 µl of the preserved isolate was placed in a 1.5 ml Eppendorf tube and centrifuged at 4000 rpm for 10 min. The supernatant was decanted, and 150 µl of 1x phosphate-buffered saline (PBS) solution was added to the pellet and vortexed. The mixture was then boiled in a water bath set at 70 °C for 10 min to lyse the cells and then cooled at -20 °C. The cooled mixture was centrifuged at 13,000 rpm for 5 min at 4 °C, and then the supernatant containing genomic DNA was aliquoted into a new tube and stored at -20 °C pending PCR amplification.

Detection of virulence markers using multiplex PCR

The genes targeted included 7 adhesins (F4, F5, F6, F17, F18, F41, AIDA-1) and 4 E. coli enterotoxins (STa, STb, LT and EAST1). All these virulence markers were detected by multiplex PCR using specific primers as presented in Table  1 . The sizes of the target amplicons for the different primers are reflected in Table  1 . PCR amplification was performed using One Taq DNA polymerase (BioLabs) in a reaction volume of 12.5 µl, comprising 0.25 µl of the forward and reverse primers, 6.25 µl of One Taq 2X Master Mix, 1.25 µl of nuclease-free water and 2 µl of the DNA template. PCR conditions comprised an initial denaturation at 94 °C for 30 s, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 1 min, and extension at 68 °C for 1 min and thereafter a final extension at 68 °C for 5 min [ 19 ]). DNA from the in-house E. coli strains 853/67; O149 (F4 + , F6 + , LT + , STa + , STb + and EAST1 + ), and Bd 60/84 I; O141(F18 + , VT2e + , STa + (NVI, Uppsala, Sweden) and a blank sample without DNA were used as positive and negative controls, respectively.

Agar gel electrophoresis

The PCR amplicons (4 µl) and DNA ladder were loaded and electrophoresed on a 2% (w/v) agar rose gel in 1x TAE buffer containing ethidium bromide (1.5 µg/ml) at 125 V for 45 min. Electrophoresed amplicons were visualized using ultraviolet transillumination and documented using an ENDURO GDS documentation system (Labnet International, USA, New Jersey).

Data analysis

The data obtained were entered into Excel spreadsheets, where they were verified and cleaned. The data were then exported and analyzed statistically using SPSS software (Version 23). Descriptive, graphical, and summary statistics were performed using Microsoft Excel. A chi-square test was used to determine any association between the presence of virulence markers and diarrhoea in piglets, with a p value of less than 0.05 taken as a statistically significant association.

Sociodemographic characteristics of the household heads

Eighty households with piglets of target ages were interviewed. Most of the respondents (75%) were males, and the majority (92.5%) had attained formal education. Most of the respondents (73.8%) were above the age of 40 years (Table  2 ). When the demographics of the two study districts were compared, Masaka had more respondents who had attained formal education than Mukono, and this difference was statistically significant ( p value  = 0.001). In both districts, most households owned up to 10 pigs, with exotic breeds being mainly kept (Table  3 ).

Isolation and identification of E. Coli from faecal samples

The overall prevalence of E. coli from faecal samples from piglets for the two districts (Masaka and Mukono) was 81.4% (175/215), as shown in Table  4 . There were 43 (20%) piglets presenting with signs of diarrhoea, 32 (74.4%) of which were confirmed to have E. coli , while 172 samples were from piglets without typical signs of diarrhoea, 83.1% of which possessed the E. coli bacterium.

The overall picture of the drug susceptibility of the 175 isolates from the two districts is depicted in Fig.  1 . All the isolates were found to be resistant to erythromycin, and 71% showed resistance to tetracycline. There was high susceptibility to drugs such as nalidixic acid (73%), chloramphenicol (85%), ciprofloxacin (90%), and gentamicin (86%). Comparison of the specific antimicrobial resistance of the isolates from the two districts revealed a similar trend in both districts (Fig.  2 ). Interestingly, multidrug resistance (resistance to three or more drug classes) was high ( n  = 98, 56%).

Shows the overall antimicrobial susceptibility of isolates from the two districts

Resistance level against specific antimicrobials by district

Molecular characterization of virulence markers of E. Coli isolates

Prevalence of virulence gene markers.

Out of the 175 isolates, 102 (58.3%) carried at least one of the E. coli virulence markers screened. A few (6.3%) of the isolates carried adhesin markers (F17, AIDA-I and F18). A total of 21.7% had heat-stable enterotoxin markers (STa and STb), whereas the EAST1 gene marker was detected in 35.4% of the isolates, as shown in Table  5 . A representative gel is shown in Fig.  3 . Several (17.7%) isolates expressed more than one virulence marker, with the EAST1 and STb combination being the most frequently expressed (Table  6 ).

Representative gel of the PCR detection of virulence markers. Lanes M- 100 bp ladder, PC- positive control, 1- negative control, Lanes 2 to 14 are the test E. coli isolates screened. The amplicons were run on a 2% agarose gel and detected by ultraviolet transillumination and documented

Association of E. Coli virulence markers with diarrhoea status of the sampled piglets

There was a significant association between the presence of the EAST1 gene in the E. coli isolates and the occurrence of diarrhea in the sampled piglets ( p value of 0.011). Similarly, the presence of the AIDA-I gene was significantly associated with diarrhea ( p value of 0.037). However, there was no significant association between the presence of F17, STa, STb and F18 and the occurrence of diarrhoea in piglets from the Mukono and Masaka districts, as shown in Table  7 .

This study detected and characterized virulence determinants of E. coli associated with the occurrence of diarrhoea in piglets in Ugandan settings. Overall, most of the respondents in the two study districts (75%) were males, which can be explained by the fact that most households in Uganda are headed by males, as also established by Yussif et al. [ 28 ]. The present study also revealed that most (92.5%) of the respondents had attained formal education, a key factor in better management practices. Notably, most of the household heads were peasant farmers, which is an indication that agriculture is the key source of income, with a few Ugandans under the formal employment system, while the majority are under small-scale agricultural production. Consistent with the findings of the studies by Ikwap et al. and Aliro et al. [ 4 , 29 ], most pig farms in Uganda are primarily smallholders, rearing an average of three to five adult pigs, and the majority (> 50%) of the farmers in the study districts owned fewer than 10 pigs. These farmers face a problem of infections, with 63.4% of the respondents in this study reporting encountering a challenge of diarrhoea among the different age groups. Consistent with the findings by Ikwap et al. and Aliro et al. [ 4 , 29 ] in Northern and Eastern Uganda, diarrhoea was reported by farmers as one of their major challenge. This in turn leads to rampant drug misuse as a way of curbing these infections, a key factor in the development of AMR [ 15 ].

When the demographics of the two study districts were compared, Masaka had more respondents who had attained formal education than Mukono, and this difference was statistically significant ( p value  = 0.001). This result could indicate that farmers who are most educated are most likely to have a better understanding of agricultural management principles and practices and hence less animal disease prevalence. This result agrees with a finding stated by Obala and colleagues in their study in Uganda [ 1 ]. When infections are minimal, there is minimized and thus more controlled antimicrobial drug use, a key factor in reducing AMR rates.

There was a high prevalence of E. coli (81.4%) recovery from faecal samples of piglets from both Masaka and Mukono districts. This result could be attributed to the fact that E. coli is a normal gut flora, and thus, its isolation frequency from faecal matter is expected to be high. This finding concurs with that obtained by Kallau et al. [ 15 ], who reported a slightly higher (85.4%) prevalence of E. coli isolates from pigs. There was also a high isolation frequency of the bacteria from both piglets with (74.4%) and without (83.1%) typical signs of diarrhoea. This could be explained by the fact that E. coli is a competitive pathogenic bacterium and thus can still be isolated from a diarrheic piglet sample where gut flora health is expected to be disturbed. Our findings are like those of a previous study by Vidal et al. [ 30 ], who also reported no significant difference in the isolation of this bacteria in diarrhoeic and non- diarrhoeic piglets.

Two out of every ten piglets sampled presented with typical signs of diarrhoea, which was probably a trigger for antimicrobial drug administration by farmers and veterinary practitioners. The majority (73.2%) of the farmers reported using antibiotics on their farms, and most of them (95.8%) sought professional veterinary care, which implies that there was prudent use of the antibiotics. However, as noted by Nohrborg et al. [ 31 ], selling antibiotics is a source of income to animal health practitioners, and thus, most often, they administer these drugs to obtain some money. Therefore, it was imperative for this study to determine the prevalence of E. coli antimicrobial resistance (AMR) against commonly used drugs in pig production. This was based on the fact that E. coli is a normal gut flora recommended by the World Organization for Animal Health (WOAH) as an indicator species useful in monitoring and surveillance of AMR [ 15 ].

All the E. coli isolates from piglets in Masaka and Mukono districts were resistant to erythromycin, as shown in Fig.  1 , which could be attributed to misuse of this drug in pig production. The estimates of frequency of antibiotics use are generally unavailable in Uganda. Much as the actual antimicrobial consumption in the farm, at country/district level, are largely unknown, a study by authors in [ 32 ], reported 82.8% of the farmers in Wakiso district, Uganda, used antibiotics for treatment of their animals in the previous month with a frequency of use of Erythromycin at 7.8%. This is reflective of high antimicrobial consumption in animal sector, thus, our findings of total resistance to erythromycin call for urgent studies to understand the molecular mechanisms behind this resistance and come up with mitigation measures to combat this great challenge. There was high resistance to tetracycline, ampicillin, and trimethoprim sulfamethoxazole, which can be explained by the fact that these drugs have been reported to be readily available and thus commonly used by farmers in ways that may be imprudent and thus trigger resistance development [ 7 , 15 ]. There was a high susceptibility of the E. coli isolates to gentamicin, chloramphenicol, ciprofloxacin, and nalidixic acid. Chloramphenicol is no longer used in food animal production and thus could explain the high susceptibility levels of the isolates to this drug. Ciprofloxacin and nalidixic acid are also mostly used in human medicine, and thus, the high susceptibility of E. coli isolates to the drugs, even though in this case the susceptibility was not up to 100%, may be suggestive of cross resistance and mutation. Gentamicin has been infrequently used in Uganda due to its high cost and thus is unavailable to most farmers [ 33 ] however, due to the high resistance against commonly used drugs, farmers have now resorted to the use of this drug, and thus, we see emerging resistance against the drug. Worryingly, the multidrug resistance level found in this study was high (56%), which calls for efforts to mitigate AMR challenge.

The prevalence of E. coli virulence gene markers was high, with at least 58.3% of the isolates expressing one of the virulence gene markers screened. Adhesin gene markers (F18, F17 and AIDA-I) were found in 7.5% of the isolates, with fimbrial F18 (6.3%) being the most prevalent adhesin, one isolate expressing F17 and another one having non fimbrial adhesin AIDA-I. More isolate expressed enterotoxin gene markers than adhesins, whereby enterotoxin gene markers (EAST1, STb and STa) were found in 57.1% of the isolates, with EAST1 (35.4%) being the most predominant, followed by STb (14.8%) and STa (6.9%). There was expression of more than one virulence gene marker, and 11 different combinations were obtained, with the enterotoxin STb and EAST1 combination being expressed more frequently. The E. coli virulence gene marker prevalence obtained in this study is much higher than that obtained by Obala et al. [ 1 ], which was 18.4% in the districts of Central Uganda. A study conducted by Abubakar et al. [ 7 ] in South Africa revealed a prevalence of 33%, which is also lower than the 58.3% obtained in this study. This clearly highlights the significance of E. coli pathotypes as causers of piglet diarrhoea and mortality in Uganda. The EAST1 gene was the most prevalent (35.4%), as reported by Ikwap et al. [ 34 ] and Abubakar et al [ 7 ] among South African piglets; thus, there is a need to further understand the precise role of this gene marker possessed by these bacteria in the pathogenesis of enteric colibacillosis. Similar to the results from a study by Ikwap et al. [ 4 ], genes encoding fimbriae F5, F6 and F41 were not detected in the current study. However, our study did not reveal any F4 gene marker that was detected by both Obala et al. [ 1 ] and Ikwap et al. [ 4 ]. The F18 gene was detected in E. coli isolates in this study, similar to a previous study performed on commercial pig farms in Central Uganda by Okello et al. [ 35 ].

The expression of more than one virulence gene marker is worrying development since it is an indication that the bacterium could evolve into a more virulent pathotype with severe detrimental effects on piglet performance and survival. There was a significant association between the occurrence of diarrhoea in piglets sampled and the presence of EAST1 and AIDA-I virulence gene markers among the E. coli isolates from the piglets. This result further highlights the role of the nonfimbrial adhesin AIDA-I in the etiology of piglet diarrhoea [ 7 ]. However, the association was nonsignificant for F17, F18, STb and STa, indicating that these genes are expressed by both diarrhoeic and the non-diarrhoeic piglets, which agrees with the findings of Ikwap et al. [ 4 ]

In conclusion, this study established a high prevalence of virulence markers among E. coli isolates from diarrhoeic piglets; hence, these virulence factors were associated with the cause of diarrhoea in piglets. The level of E. coli multidrug resistance against the commonly used antimicrobials in districts of Central Uganda was worryingly high. Many virulent E. coli isolates do not have known adhesion genes, and their mechanism of attachment to the host tissue remains obscure. Further studies should be done to elucidate key adhesins employed by this E. coli in establishing itself in the intestine of piglets, and this could be used to identify appropriate vaccines for mitigation of colibacillosis in piglets in Uganda. Additionally, urgent measures should be put in place to address rampant AMR.

Data availability

The datasets generated and analysed during our study are not publicly available as the work is part of an ongoing master’s course but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to the teams at the Microbiology and Molecular Laboratory, College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University for the valuable efforts toward the attainment of results and the Government of Uganda for the financial support.

This research was supported by the Makerere University Research and.

Innovation Fund (MaKRIF) from the Government of Uganda.

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Department of Biosecurity, Ecosystems and Veterinary Public Health, College of Veterinary Medicine, Animal Resource and Biosecurity, Makerere University, Kampala, Uganda

Margaret Tusiime

Department of Biomolecular Resources and Biolab Sciences, College of Veterinary Medicine, Animal Resource and Biosecurity, Makerere University, Kampala, Uganda

Frank. N. Mwiine

Department of Veterinary Pharmacy, Clinical and Comparative Medicine, College of Veterinary Medicine, Animal Resource and Biosecurity, Makerere University, Kampala, Uganda

Mathias Afayoa

Department of Sociology and Anthropology, College of Humanities and Social Sciences, Makerere University, Kampala, Uganda

Steven Arojjo

Department of Biotechnical and Diagnostic Sciences, College of Veterinary Medicine, Animal Resource and Biosecurity, Makerere University, Kampala, Uganda

Joseph Erume

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Contributions

EJ conceptualized the project and assisted with the research and writing of the manuscript. FM and TM participated in the research and analyzed the data and drafted the content of the manuscript. AM and AS assisted with the data collection, laboratory work, supervision, and writing of the manuscript. All the authors approved the final manuscript.

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Correspondence to Margaret Tusiime .

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Ethical approval for the research was first obtained from the Higher Degrees Research Committee of the School of Biosecurity, Biotechnical and Laboratory Sciences, College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Ref. No. SBLS.JE.2021.The research was thereafter approved by the Uganda National Council of Science and Technology under permit No. A190ES. Informed consent was obtained from pig farmers or their care workers for voluntary participation in the study and where agreement was not given, that farm was not included in the study. All methods for the research were carried out in accordance with the relevant guidelines and regulations. The methods followed are all presented in accordance with the ARRIVE guidelines ( https://arriveguidelines.org ) for the reporting of research on animals. Informed Consent was sought from the district veterinary officers to allow the study to be undertaken in the districts.

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Tusiime, M., Mwiine, F.N., Afayoa, M. et al. Molecular characterization of Escherichia coli virulence markers in neonatal and postweaning piglets from major pig-producing districts of Uganda. BMC Vet Res 20 , 230 (2024). https://doi.org/10.1186/s12917-024-04092-x

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Received : 07 September 2023

Accepted : 22 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12917-024-04092-x

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