paradom: paradom

In LagSequential: Lag-Sequential Categorical Data Analysis

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)

LagSequential documentation built on May 16, 2019, 5:09 p.m.