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- W3023102804 abstract "Chapter 2 is about knowing your data. The better you know your data, the more likely you are to answer your research questions clearly. The specific type of data you have will dictate what statistics you can (and can’t) perform. Nominal data are categorical, such as binary task success or males and females. Nominal data are usually expressed as frequencies or percentages. χ2 (chi square) tests can be used when you want to learn whether the frequency distribution is random or there is some underlying significance to the distribution pattern. Ordinal data are rank orders, such as a severity ranking of usability issues. Ordinal data are also analyzed using frequencies, and the distribution patterns can be analyzed with a χ2 test. Interval data are continuous data where the intervals between each point are meaningful but without a natural zero. Interval data can be described by means, standard deviations, and confidence intervals. Means can be compared to each other for the same set of users (paired samples t test) or across different users (independent samples t test). Analysis of variance (ANOVA) can be used to compare more than two sets of data. Relationships between variables can be examined through correlations. Ratio data are the same as interval but with a natural zero. One example is completion times. Essentially, the same statistics that apply to interval data also apply to ratio data. Any time you can calculate a mean, you can also calculate a confidence interval for that mean. Displaying confidence intervals on graphs of means helps the viewer understand the accuracy of the data and to see quickly any differences between means. When presenting your data graphically, use the appropriate types of graphs. Use bar graphs for categorical data and line graphs for continuous data. Use pie charts, donut charts, or stacked bar graphs when the data sum to 100%." @default.
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- W3023102804 date "2013-01-01" @default.
- W3023102804 modified "2023-09-23" @default.
- W3023102804 title "Background" @default.
- W3023102804 doi "https://doi.org/10.1016/b978-0-12-415781-1.00002-9" @default.
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