descript: Descriptive Statistics

Description

Computes descriptive statistics for dichotomous and polytomous response matrices.

Usage

descript(data, n.print = 10, chi.squared = TRUE, B = 1000)

Arguments

data

a matrix or a data.frame containing the manifest variables as columns.

n.print

numeric indicating the number of pairwise associations with the highest \(p\)-values to be printed.

chi.squared

logical; if TRUE the chi-squared test for the pairwise associations between items is performed. See Details for more info.

an integer specifying the number of replicates used in the Monte Carlo test (i.e., this is the B argument of chisq.test()).

Value

descript() returns an object of class descript with components,

sample

a numeric vector of length 2, with elements the number of items and the number of sample units.

perc

a numeric matrix containing the percentages of negative and positive responses for each item. If data contains only dichotomous manifest variables the logit of the positive responses (i.e., second row) is also included.

items

a numeric matrix containing the frequencies for the total scores.

pw.ass

a matrix containing the \(p\)-values for the pairwise association between the items.

n.print

the value of the n.print argument.

name

the name of argument data.

missin

a numeric matrix containing the frequency and percentages of missing values for each item; returned only if any NA's exist in data.

bisCorr

a numeric vector containing sample estimates of the biserial correlation of dichotomous manifest variables with the total score.

ExBisCorr

a numeric vector containing sample estimates of the biserial correlation of dichotomous manifest variables with the total score, where the latter is computed by excluding the specific item.

data

a copy of the data.

alpha

a numeric matrix with one column containing the sample estimates of Cronbach's alpha, for all items and excluding each time one item.

Details

The following descriptive statistics are returned by descript():

(i): the proportions for all the possible response categories for each item. In case all items are dichotomous, the logit of the proportion for the positive responses is also included.
(ii): the frequencies of all possible total scores. The total score of a response pattern is simply its sum. For dichotomous items this is the number of positive responses, whereas for polytomous items this is the sum of the levels represented as numeric values (e.g., the response categories "very concerned", "slightly concerned", and "not very concerned" in Environment are represented as 1, 2, and 3).
(iii): Cronbach's alpha, for all items and excluding each time one of the items.
(iv): for dichotomous response matrices two versions of the point biserial correlation of each item with the total score are returned. In the first one the item is included in the computation of the total score, and in the second one is excluded.
(v): pairwise associations between items. Before an analysis with latent variable models, it is useful to inspect the data for evidence of positive correlations. In the case of binary or polytomous data, this ad hoc check is performed by constructing the \(2 \times 2\) contingency tables for all possible pairs of items and examine the Chi-squared \(p\)-values. In case any expected frequencies are smaller than 5, simulate.p.value is turned to TRUE in chisq.test(), using B resamples.

Examples

Run this code

# NOT RUN {
## Descriptives for LSAT data:
dsc <- descript(LSAT, 3)
dsc
plot(dsc, type = "b", lty = 1, pch = 1:5)
legend("topleft", names(LSAT), pch = 1:5, col = 1:5, lty = 1, bty = "n")

# }