psych (version 1.0-17)

describe: Basic descriptive statistics useful for psychometrics

Description

There are many summary statistics available in R; this function provides the ones most useful for scale construction and item analysis in classic psychometrics. Although describe (optionally) will calculate skew, for large data sets this produces a noticable slowing. Range is most useful for the first pass in a data set, to check for coding errors.

Usage

describe(x, digits = 2, na.rm = TRUE, skew = FALSE, ranges = TRUE)

Arguments

x

A data frame or matrix

digits

How many significant digits to report

na.rm

The default is to delete missing data

skew

Should the skew be calculated?

ranges

Should the range be calculated.

Value

• A data.frame of the relevant statistics: item name item number number of valid cases mean standard deviation median mad: median absolute deviation (from the median) minimum maximum skew standard error

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.

The item skew is useful to know but will lead to somewhat slower processing times.

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.

See Also

describe.by, pairs.panels, read.clipboard, error.crosses

Examples

#describe(attitude)   #attitude is taken from R data sets

#           var  n  mean    sd median   mad min max range   se
#rating       1 30 64.63 12.17   65.5 10.38  40  85    45 2.22
#complaints   2 30 66.60 13.31   65.0 14.83  37  90    53 2.43
#privileges   3 30 53.13 12.24   51.5 10.38  30  83    53 2.23
#learning     4 30 56.37 11.74   56.5 14.83  34  75    41 2.14
#raises       5 30 64.63 10.40   63.5 11.12  43  88    45 1.90
#critical     6 30 74.77  9.89   77.5  7.41  49  92    43 1.81
#advance      7 30 42.93 10.29   41.0  8.90  25  72    47 1.88

#describe(attitude,skew=TRUE,range=TRUE)

#           var  n  mean    sd median   mad min max range    skew   se
#rating       1 30 64.63 12.17   65.5 10.38  40  85    45 -645.56 2.22
#complaints   2 30 66.60 13.31   65.0 14.83  37  90    53 -508.49 2.43
#privileges   3 30 53.13 12.24   51.5 10.38  30  83    53  694.44 2.23
#learning     4 30 56.37 11.74   56.5 14.83  34  75    41  -87.36 2.14
#raises       5 30 64.63 10.40   63.5 11.12  43  88    45  222.03 1.90
#critical     6 30 74.77  9.89   77.5  7.41  49  92    43 -838.77 1.81
#advance      7 30 42.93 10.29   41.0  8.90  25  72    47  926.20 1.88


## The function is currently defined as
function (x, digits = 2,na.rm=TRUE,skew=FALSE,range=TRUE)
#basic stats after dropping non-numeric data
#somewhat faster if we don't do skews
{    #first, define a local function
    valid <- function(x) {  sum(!is.na(x))}
   		 
    if (is.vector(x) )          #do it for vectors or 
    	{
    	    stats = matrix(rep(NA,6),ncol=6)    #create a temporary array
    
			stats[1, 1] <-  mean(x, na.rm=na.rm )
			stats[1, 2] <-  median(x,na.rm=na.rm  )
			stats[1, 3] <-  min(x, na.rm=na.rm )
			stats[1, 4] <-  max(x, na.rm=na.rm )
			stats[1, 5] <-  skew(x,na.rm=na.rm  )
			stats[1, 6] <-  valid(x )
        
    	}
    	
    	
    else  {len = dim(x)[2]     #do it for matrices or data.frames
    
    stats = matrix(rep(NA,len*6),ncol=6)    #create a temporary array
    for (i in 1:len) {
    	if (is.numeric(x[,i])) {   #just do this for numeric data
			stats[i, 1] <-  mean(x[,i], na.rm=na.rm )
		if (range) {
			stats[i, 2] <-  median(x[,i],na.rm=na.rm  )	
				stats[i, 3] <-  min(x[,i], na.rm=na.rm )
				stats[i, 4] <-  max(x[,i], na.rm=na.rm )}
		    if (skew) {stats[i, 5] <-  skew(x[,i],na.rm=na.rm  )}
			stats[i, 6] <-  valid(x[,i] )
        		}
    	}
    	}
    if (range)
    	{if(skew){temp <-  data.frame(var = seq(1:len),n = stats[,6],mean=stats[,1],
    	sd = sd(x,na.rm=TRUE), median = stats[, 
        2],min= stats[,3],max=stats[,4], range=stats[,4]-stats[,3],skew = stats[, 5])}
         
      	 else {temp <-  data.frame(var = seq(1:len),n = stats[,6],mean=stats[,1],
      	 sd = sd(x,na.rm=TRUE), median = stats[, 
        2],min= stats[,3],max=stats[,4], range=stats[,4]-stats[,3])}}
        
        else {if(skew){temp <-  data.frame(var = seq(1:len),n = stats[,6],mean=stats[,1], 
        sd = sd(x,na.rm=TRUE),skew = stats[, 5])}
       else {temp <-  data.frame(var = seq(1:len),n = stats[,6],mean=stats[,1],
       sd = sd(x,na.rm=TRUE))}}
                
    answer <-  round(data.frame(temp, se = temp$sd/sqrt(temp$n)),  digits)
     return(answer)
  }