describe {psych} | R Documentation |

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,type=3)

`x` |
A data frame or matrix |

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

`interp` |
Should the median be standard or interpolated |

`skew` |
Should the skew and kurtosis be calculated? |

`ranges` |
Should the range be calculated? |

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

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

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). Variables that are categorical or logical are converted to numeric and then described. These variables are marked with an * in the row name.

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)*.

A data.frame of the relevant statistics:

item name

item number

number of valid cases

mean

standard deviation

trimmed mean (with trim defaulting to .1)

median (standard or interpolated

mad: median absolute deviation (from the median)

minimum

maximum

skew

kurtosis

standard error

Describe uses either the mean or colMeans functions depending upon whether the data are a data.frame or a matrix. The mean function supplies means for the columns of a data.frame, but the overall mean for a matrix. Mean will throw a warning for non-numeric data, but colMeans stops with non-numeric data. Thus, the describe function uses either mean (for data frames) or colMeans (for matrices). This is true for skew and kurtosi as well.

http://personality-project.org/revelle.html

Maintainer: William Revelle revelle@northwestern.edu

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

`describe.by`

, `skew`

, `kurtosi`

`interp.median`

, `pairs.panels`

, `read.clipboard`

, `error.crosses`

data(sat.act) describe(sat.act) describe(sat.act,skew=FALSE)

[Package *psych* version 1.3.2 Index]