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Research Methods ResourcesA spreadsheet for research data |
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Designing an MS Excel spreadsheet for research data |
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The recommended layout for experimental data in an MS Excel spreadsheet has 3 components: experiment details, design factors and measurement variables. There is also a row where we will put short but relevantly named column titles. This will make it easier for future exp ort to a statistical software. |
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Click here to see an example of a completed spreadsheet for experimental data.
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The recommended layout for survey data in an MS Excel spreadsheet is very similar to the one shown above. The 3 components are: survey details, survey descriptors and measurement variables. Again we include a row with short column titles. |
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Click here to see an example of a completed spreadsheet for survey data.
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Advantages of this format |
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Further resources on data entry and validation in an MS Excel spreadsheet |
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Designing a spreadsheet, data entry and data checking |
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Chapter 4.6 of The
Green Book: "Data management", written by Gerald W. Chege
and Peter K. Muraya.
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This PowerPoint presentation summarizes some further issues when designing spreadsheets for research data.
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This PowerPoint presentation by Wim Buysse shows you several ways of preventing yourself from entering mistakes in a spreadsheet. This is especially useful if you're working with several people on the same spreadsheet.
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This PowerPoint presentation by Wim Buysse shows you two ways for finding and correcting errors in lists with repeated elements such as factor levels.
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This PowerPoint presentation by Wim Buysse shows some tricks how to import field data that have been recorded using devices such as a GPS or data loggers.
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Watch this small movie file if you're sometimes confused between absolute and relative cell references when copying formulas in an MS Excel spreadsheet. The files are in 2 different formats. If the first one doesn't play properly on your computer, try the second one.
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SSC guides on data management |
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Following guides from the Statistical Services Centre of the University of Reading |
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Statistical Services Centre. 1998. Data Management Guidelines for Experimental Projects. Statistical Services Centre, The University of Reading. 19 pp.
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Statistical Services Centre. 2001. Disciplined Use of Spreadsheet Packages for Data Entry. Statistical Services Centre, The University of Reading. 27 pp.
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Statistical Services Centre. 2000. The Role of a Database Package for Research Projects. Statistical Services Centre, The University of Reading. 27 pp.
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Statistical Services Centre. 1998. Project Data Archiving – Lessons from a Case Study. Statistical Services Centre, The University of Reading. 11 pp.
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Murekezi, C., Abeyasekera, S., Mulumba, Y., Rwakatungu, A., Kubiriba, J. and Tushemereirwe, W.K. (2004). Guidelines and Procedures for Effective Data Management (with emphasis on banana research). National Banana Research Programme, Kawanda Agricultural Research Institute, Kampala, Uganda, 35 pp.
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Look also at the case study of "Good practice in data management" - based on a bilateral project in Malawi. |
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