Steps in Secondary Data Analysis | Online Resources
Understanding variable types — Different data types demand discrete treatment, so it has important to be able to distinguish variables by both cause and effect dependent or independent , and their measurement scales nominal, ordinal, interval, and ratio. Run descriptive statistics — These are used to summarize the basic features of a data set through measures of central tendency mean, mode, and median , dispersion range, quartiles, variance, and standard deviation , and distribution skewness and kurtosis. Run appropriate inferential statistics — This allows researchers to assess their ability to draw conclusions that extend beyond the immediate data. For example, if a sample represents the population; if there are differences between two or more groups; if there are changes over time; or if there is a relationship between two or more variables.
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