paradom {LagSequential}

paradom

Description

Tests for parallel dominance in lag sequential data.

Usage

paradom(data, labels = NULL, lag = 1, adjacent = TRUE,
        tailed = 1, permtest = FALSE, nperms = 10)

Arguments

data

A one-column dataframe, or a vector of code sequences, or a square frequency transition matrix. If data is not a frequency transition matrix, then data must be either (a) a series of string (non-numeric) code values, or (b) a series of integer codes with values ranging from "1" to what ever value the user specifies in the "ncodes" argument. There should be no code values with zero frequencies. Missing values are not permitted.

labels

Optional argument for providing labels to the code values. Accepts a list of string variables. If unspecified, codes will be labeled "Code1", "Code2", etc.

lag

The lag number for the analyses.

adjacent

Can adjacent values be coded the same? Options are "TRUE" for yes or "FALSE" for no.

tailed

Specify whether significance tests are one-tailed or two-tailed. Options are "1" or "2".

permtest

Do you want to run permutation tests of significance? Options are "FALSE" for no, or "TRUE" for yes. Warning: these computations can be time consuming.

nperms

The number of permutations per block.

Details

Tests for parallel dominance or asymmetry in predictability, which is the difference in predictability between i to j and j to i (e.g., whether B's behavior is more predictable from A's behavior than vice versa), as described by Wampold (1984, 1989, 1992, 1995).

Value

Displays the transitional frequency matrix and matrices of expected frequencies, expected and observed parallel dominance frequencies, parallel dominance kappas, z values for the kappas, and significance levels. There are four possible cases, or kinds, of parallel dominance (see Wampold 1989, 1992, 1995), and the function returns a matrix indicating the kind of case for each cell in the transitional frequency matrix.

Returns a list with the following elements:

freqs

The transitional frequency matrix

expfreqs

The expected frequencies

domfreqs

The parallel dominance frequencies

expdomfreqs

The expected parallel dominance frequencies

domtypes

There are 4 sequential dominance case types described by Wampold (1989). These cases describe the direction of the effect for i on j and j on i. The four cases are: (1) i increases j, and j increases i, (2) i decreases j, and j decreases i, (3) i increases j, and j decreases i, and (4) i decreases j, and j increases i. Each cell of this matrix indicates the case that applies to the transition indicated by the cell.

kappas

The parallel dominance kappas

z

The z values for the kappas

pk

The p-values for the kappas

Author(s)

Zakary A. Draper & Brian P. O'Connor

References

O'Connor, B. P. (1999). Simple and flexible SAS and SPSS programs for analyzing lag-sequential categorical data. Behavior Research Methods, Instrumentation, and Computers, 31, 718-726.

Wampold, B. E. (1984). Tests of dominance in sequential categorical data. Psychological Bulletin, 96, 424-429.

Wampold, B. E. (1989). Kappa as a measure of pattern in sequential data. Quality & Quantity, 23, 171-187.

Wampold, B. E. (1992). The intensive examination of social interactions. In T. Kratochwill & J. Levin (Eds.), Single-case research design and analysis: New directions for psychology and education (pp. 93-131). Hillsdale, NJ: Erlbaum.

Wampold, B. E. (1995). Analysis of behavior sequences in psychotherapy. In J. Siegfried (Ed.), Therapeutic and everyday discourse as behavior change: Towards a micro-analysis in psychotherapy process research (pp. 189-214). Norwood, NJ: Ablex.

Examples

paradom(data_Wampold_1984, 
        labels = c('HPos','HNeu','HNeg','WPos','WNeu','WNeg'), 
        permtest = TRUE, nperms = 1000)