![]() Mean – This is the arithmetic mean across the observations. Maximum – This is the maximum, or largest, value of the variable.Į. Minimum – This is the minimum, or smallest, value of the variable.ĭ. The total number of observations is the sum of N and the number of missingĬ. N – This is the number of valid observations for the variable. Valid N (listwise) – This is the number of non-missing values.ī. statistics = mean stddev variance min max semean kurtosis skewness.Ī. We will use the hsb2.sav data file for our You need just a few numbers, you may want to use the descriptives You are looking for, but can be overwhelming if you are not used to it. This can be very helpful if you know what You will find that the examine commandĪlways produces a lot of output. Have deleted unnecessary subcommands to make the syntax as short and We have added some options to each of these commands, and we We will show two: descriptives andĮxamine. ThereĪre several commands that you can use to get descriptive statistics for aĬontinuous variable. That you need to end the command (and all commands) with a period. In quotes, you need to specify where the data file is located In the syntax below, the get file command is used to load the data The variable female is a dichotomous variable coded 1 if the student was Scores on various tests, including science, math, reading and social studies ( socst). The data used in these examples were collected on 200 high schools students and are © W.This page shows examples of how to obtain descriptive statistics, with footnotes explaining the If in contrast you would like to appear first the values of all men (or women, depending on the way gender is coded in your data) by country and next those of all women (or men) by country, because you want to compare how the genders differ by country, the order of variables has to be reversed: EXAMINE var203 BY gender BY country ![]() That is, the plot that results will have several groups (the countries) and within each group the plots of women and men will appear side by side. The following example will first divide the data by country and then by gender. If you want to use more than one "by" variable please be sure to enumerate them in the right order. Provides 5, 10, 25, 50, 75, 90, 95 percentiles (by default other percentiles may be requestedīy specifying them after the perc keyword) Supresses output for entire sample if only results for subgroups are desired Suppresses stem-and-leaf plot (and provides box plot only) Suppresses box plot (and provides stem-and-leaf plot only)ĭisplays histogram in addition to stem-and-leaf plot Keywords to specify the required output or to suppress parts of it: keyword Sometimes not all of this output is desired, or more output is desired. The EXAMINE command without any additional keywords provides sample statistics, stem-and-leaf plots and box plots for the entire group as well as for any subgroups which result from grouping BY a variable (or perhaps even more variables after additional BY keywords). ![]() EDA procedures in SPSS also provide the most important sample statistics.Įxample for stem-and-leaf plots with sample statistics EXAMINE var203Įxample for comparing groups via box plots EXAMINE var203 BY gender The most important means of EDA are stem-and-leaf plots and box-and-whisker plots (henceforth box plots). Especially in the case of metric or continuous variables with many values, EDA is preferable to other procedures (such as frequency tables). EDA provides important first insights into the structure of your data.
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