describe {psych}R Documentation

Basic descriptive statistics useful for psychometrics


There are many summary statistics available in R; this function provides the ones most useful for scale construction and item analysis in classic psychometrics. Range is most useful for the first pass in a data set, to check for coding errors.


describe(x, na.rm = TRUE, interp=FALSE,skew = TRUE, ranges = TRUE,trim=.1,



A data frame or matrix


The default is to delete missing data. na.rm=FALSE will delete the case.


Should the median be standard or interpolated


Should the skew and kurtosis be calculated?


Should the range be calculated?


trim=.1 – trim means by dropping the top and bottom trim fraction


Which estimate of skew and kurtosis should be used? (See details.)


Should we check for non-numeric variables? Slower but helpful.


show the first 1:head cases for each variable in describeData


Show the last nobs-tail cases for each variable in describeData


In basic data analysis it is vital to get basic descriptive statistics. Procedures such as summary and hmisc::describe do so. The describe function in the psych package is meant to produce the most frequently requested stats in psychometric and psychology studies, and to produce them in an easy to read data.frame. The results from describe can be used in graphics functions (e.g., error.crosses).

The range statistics (min, max, range) are most useful for data checking to detect coding errors, and should be found in early analyses of the data.

Although describe will work on data frames as well as matrices, it is important to realize that for data frames, descriptive statistics will be reported only for those variables where this makes sense (i.e., not for alphanumeric data). If the check option is TRUE, variables that are categorical or logical are converted to numeric and then described. These variables are marked with an * in the row name. This is somewhat slower.

In a typical study, one might read the data in from the clipboard (read.clipboard), show the splom plot of the correlations (pairs.panels), and then describe the data.

na.rm=FALSE is equivalent to describe(na.omit(x))

When finding the skew and the kurtosis, there are three different options available. These match the choices available in skewness and kurtosis found in the e1071 package (see Joanes and Gill (1998) for the advantages of each one).

If we define m_r = [sum(X- mx)^r]/n then

Type 1 finds skewness and kurtosis by g_1 = m_3/(m_2)^{3/2} and g_2 = m_4/(m_2)^2 -3.

Type 2 is G1 = g1 * √{n *(n-1)}/(n-2) and G2 = (n-1)*[(n+1)g2 +6]/((n-2)(n-3)).

Type 3 is b1 = [(n-1)/n]^{3/2} m_3/m_2^{3/2} and b2 = [(n-1)/n]^{3/2} m_4/m_2^2).

The additional helper function describeData just scans the data array and reports on whether the data are all numerical, logical/factorial, or categorical. This is a useful check to run if trying to get descriptive statistics on very large data sets where to improve the speed, the check option is FALSE.


A data.frame of the relevant statistics:
item name
item number
number of valid cases
standard deviation
trimmed mean (with trim defaulting to .1)
median (standard or interpolated
mad: median absolute deviation (from the median)
standard error


For very large data sets that are data.frames, describe can be rather slow. Converting the data to a matrix first is recommended. However, if the data are of different types, (factors or logical), this is not possible. If the data includes columns of character data, it is also not possible. Thus, a quick pass with describeData is recommended.

For the greatest speed, at the cost of losing information, do not ask for ranges or for skew and turn off check.


Maintainer: William Revelle


Joanes, D.N. and Gill, C.A (1998). Comparing measures of sample skewness and kurtosis. The Statistician, 47, 183-189.

See Also, skew, kurtosi interp.median, pairs.panels, read.clipboard, error.crosses




[Package psych version 1.4.5 Index]
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