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Using SPSS for Descriptive Statistics Using SPSS for Descriptive Statistics This tutorial will show you how to use SPSS version 12.0 to perform exploratory data analysis and descriptive statistics. You will use SPSS to create histograms, frequency distributions, stem and leaf plots, Tukey box plots, calculate the standard measures of central tendency (mean, median, and mode), calculate the standard measures of dispersion (range, semi-interquartile range, and standard deviation / variance), and calculate measures of kurtosis and skewness. This tutorial assumes that you have:. Downloaded the standard class (click on the link and save the data file).
Started SPSS (click on Start Programs SPSS for Windows SPSS 12.0 for Windows). Loaded the standard data set The Frequency Command The frequencies command can be used to determine quartiles, percentiles, measures of central tendency (mean, median, and mode), measures of dispersion (range, standard deviation, variance, minimum and maximum), measures of kurtosis and skewness, and create histograms. The command is found at Analyze Descriptive Statistics Frequencies (this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Descriptive Statistics from the drop down menu, and Frequencies from the pop up menu.): The frequencies dialog box will appear: Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the frequencies dialog box. Then click on the arrow button to move the variable into the Variables pane: Be sure to select 'Display frequency tables' if you want a frequency distribution. Specify which statistics you want to perform by clicking on the Statistics button.
The Statistics dialog box will appear: From the statistics dialog box, click on the desired statistics that you want to perform. To calculate a given percentile, click in the box to the left of percentile(s). Type in the desired percentile and click on the Add button. When you have selected all the desired statistics (e.g. Mean, median, mode, standard deviation, variance, ragne, etc.), click on the Continue button. Specify which chart you want to display by clicking on the Chart button.
The chart dialog box will appear: Click on the desired chart (usually Histogram) and click on the Continue button. Click on OK in the frequencies dialog box. The SPSS Output Viewer will appear. In the SPSS Output Viewer, you will see the requested statistics and chart. This is what the Statistics output looks like.
It lists the requested measures of central tendency, measures of dispersion, measures of skewness and kurtosis, and the quartiles and percentiles. The output has two columns. The left column names the statistic and the right column gives the value of the statistic.
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For example, the mean of this data is 1.26 (since your data set may be different, you may get a different value.) The skewness measure is greater than 0 when the distribution is skewed. The kurtosis measure is 0 for a normal distribution. Positive values imply a leptokurtic distribution, while negative values imply a platykurtic distribution. If you scroll down, you will see the frequency distributions.
If you scroll down, you will see the histogram (or whatever chart you requested.) The Descriptives Command The descriptives command can be used to determine measures of central tendency (mean), measures of dispersion (range, standard deviation, variance, minimum and maximum), and measures of kurtosis and skewness. The command is found at Analyze Descriptive Statistics Descriptives (this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Descriptive Statistics from the drop down menu, and Descriptives from the pop up menu.): The descriptives dialog box will appear: Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the descriptives dialog box. Then click on the arrow button to move the variable into the Variables pane: Specify which statistics you want to perform by clicking on the Options button.
The Options dialog box will appear: Select the statistics that you want by clicking on them (e.g. Mean, standard deviation, variance, range, minimum, etc.). Then click on the Continue button. Click on the OK button in the Descriptives dialog box. The SPSS Output Viewer will appear with your results in it.
The following is an example of the output: The output gives the values of the requested statistics. The Explore Command The explore command can be used to determine measures of central tendency (mean and median), measures of dispersion (range, interquartile range, standard deviation, variance, minimum and maximum), measures of kurtosis and skewness, and prepare histograms, stem and leaf plots, and Tukey box plots. The command is found at Analyze Descriptive Statistics Explore: The explore dialog box will appear: Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the explore dialog box. Then click on the top arrow button to move the variable into the Dependent List: Specify which plots you want to prepare by clicking on the Plots button. The Plots dialog box will appear: Select the plots that you want by clicking on them (e.g. Stem-and-leaf and histogram).
Then click on the Continue button. Click on the OK button in the Explore dialog box. The SPSS Output Viewer will appear with your results in it. The following is an example of the output for the descriptive statistics: The output gives the values of the requested statistics.
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I have a similar situation to the question asked. However, I don't want to list my 300 variable names in the var statement since they are all unique. Is there a way to use proc means or proc summary to output summary statistics for all the numeric variables in one data set? I've tried: proc means data=mydata min median max; output out=summarydata min=min median=median max=max; run; But this only outputs the summary statistics for the first variable. I have also tried with the help of ods trace: proc means data=mydata min median max; ods output Summary=summarydata; run; Which gives me the summary statistics for all the variables, but still in one row: VNameVAR1 VAR1Minimum VAR1Median VAR1Maximum VNameVAR2 VAR2Minimum etc. VAR1 3 3 3 VAR2 3 Where my VAR names are all unique.
Is there some other way to use proc means or proc summary to output summary statistics for all the numeric variables in one data set? UPDATE: When I removed min=min median=median max=max: proc means data=mydata min median max; output out=summarydata; run; The code then produces the output: Obs TYPE FREQ STAT VAR1 VAR2. Etc 1 0 91 N 91.00 91. Etc 2 0 91 MIN 2005.00 13. 3 0 91 MAX 2014.00 13.
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4 0 91 MEAN 2009.34 13. 5 0 91 STD 3.02 0 However, it still doesn't give me the MEDIAN. When I transpose the data before using proc means I get the desired output. If you're just after min / med / max then the following will work (such that you don't have to name the variables):- ods output quantiles = quantiles; proc univariate data = sashelp.cars; var numeric; proc sort; by varname; run; proc transpose data = quantiles out = quantran (drop=name rename=(100max = max 50median = median 0min = min)); by varname; var estimate; id quantile; where quantile in: ('100', '50', '0'); run; If you want other types of measures - mean, std, etc. proc univariate outputs them in separate datasets meaning you'd have merge tables and etc.
it turns into a pain again. The output datasets from SAS can really be puzzlingly bad with proc means, for me, being the most egregious example. UPDATED Here is a macro-based solution, with new step-by-step comments added. It uses metadata from the SAS dictionary.columns to discover all numeric variables in a dataset.
Basically, I take the MIN, MEDIAN, and MAX of all the numeric variables, outputting the results in three separate datasets. I then concatenate the datasets, using the IN variable to figure out where each row is coming from and thus labeling it with the appropriate statistic name. The output is then three rows and n columns. As the OP demonstrated in his answer, the whole macro / meta-data thing to get the numeric variables can all be replaced by simply using the special NUMERIC variable.
I will leave my current approach in place in case someone is interested in using it for other things. Furthermore, the OP's answer is a macro-free solution that uses PROC TRANSPOSE to get to the same place as this one, without any concatenation of separate result sets. I urge all readers to review it as it is more 'SAS-like'.%GLOBAL varnames dsntempmin dsntempmedian dsntempmax;%LET dsntempmin = minsummary;%LET dsntempmedian= medsummary;%LET dsntempmax= maxsummary; /. Identify dataset./%LET libname = WORK; /. change to your library./%LET dsn = mydata; /. Retrieve numeric variable names from SAS metadata and store in `varname`./ /.
macro variable. Library and dataset name must be upper-case since that is./ /. how they are stored in `dictionary.columns`./ /. UPDATE: this all can be avoided by just using the NUMERIC special variable./ /. but I am leaving this in here in case anyone is interested in querying./ /. meta-data for other purposes.