![]() ![]() Despite these limitations Excel remains a very valuable tool for quantitative data analysis as you will see Making the choice Many basic analysis projects involving primarily data exploration, descriptive statistics and simple inferential statistics can be successfully completed using standard Excel. The extensive range of graph (chart) templates is also criticised for encouraging bad practice in data presentation through inappropriate use of colour, 3-D display, etc. There is also concern amongst some statisticians over the format of specific output in some functions. More surprisingly, it lacks some common tools (such as boxplots) that are widely taught in basic statistics. Inevitably it does not cover many of the more advanced statistical techniques that are used in research. It remains first and foremost a spreadsheet package. 2.2 Limitations of Excel Useful though it is, Excel does have limitations for statistical analysis. Finally, even if using bespoke statistical software, Excel can be helpful when preparing data for analysis in those packages. In addition, it provides all of the standard spreadsheet functionality, which makes it useful for other analysis and data manipulation tasks, including generating graphical and other presentation formats. 2.1 What you can do with Excel As a spreadsheet, Excel can be used for data entry, manipulation and presentation but it also offers a suite of statistical analysis functions and other tools that can be used to run descriptive statistics and to perform several different and useful inferential statistical tests that are widely used in business and management research. ![]() Excel also integrates easily into other Microsoft Office software products which can be helpful when preparing reports or presentations. Another benefit, particularly for those new to data analysis, is to remove the need to learn a software program as well as getting to grips with the analysis techniques. Management Research: Applying the Principles 2015 Susan Rose, Nigel Spinks & Ana Isabel Canhoto 1ΔΆ 2 Why use Excel? With so many specialist software packages available, why use Excel for statistical analysis? Convenience and cost are two important reasons: many of us have access to Excel on our own computers and do not need to source and invest in other software. There are also various websites, including Microsoft s Office Support area ( that offer advice to get you started. Alternatively, you may find that Excel training or support material is available in your institution. If you have never used Excel, there are many reasonably-priced textbooks to get you started. how to create worksheets, enter data, use formulae and functions, create charts (graphs), print and work, etc.). The guide assumes that you are already familiar with the basics of using Excel (e.g. The guide is not written for a specific version of Excel although it includes screenshots for Excel Most of the functionality referred to in the guide is also available in earlier and later versions, although the user interface has changed somewhat. Further guides give information on the use of Excel to apply various analysis techniques discussed in Chapter 13. It covers some of the key features of Excel that are particularly useful when doing a research project. 1 Contents An introduction to using Microsoft Excel for quantitative data analysis 1 Introduction Why use Excel? Quantitative data analysis tools in Excel Entering your data Preparing your data Appendix A: Using Excel statistical functions Appendix B: Loading and using the Data Analysis ToolPak Introduction The aim of this short guide is to provide an introduction to using Microsoft Excel (hereafter: Excel) for quantitative data analysis within the context of a business and management research project.
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