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Before doing any kind of statistical testing or model building, you should always examine your data using summary statistics and graphs. This process is called exploratory data analysis, and it's a crucial part of every research project. Exploratory data analysis is about "getting to know" your data: which values are typical, which values are unusual; where is it centered, how spread out is it; what are its extremes. More importantly, it's an opportunity to identify and correct any problems in your data that would affect the conclusions you draw from your analysis.
How do we "get to know" our data? The answer is different depending on whether our variables are numeric or categorical. In this section, we'll demonstrate which statistics and SPSS procedures to use for both types of data.
When summarizing a quantitative (continuous/interval/ratio) variable, we are typically interested in things like:
In Part 1, we discuss how to explore quantitative (continuous/interval/ratio scale) data using the Descriptives, Compare Means, Explore, and Frequencies procedures. Each of these procedures offers different strengths for summarizing continuous variables. The Descriptives and Frequencies commands provide summary statistics for an entire sample, while the Explore and Compare Means commands can produce descriptive statistics for subsets of the sample.
When summarizing qualitative (nominal or ordinal) variables, we are typically interested in things like:
Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below: