Tests for parallel dominance in lag sequential data.
paradom(data, labels = NULL, lag = 1, adjacent = TRUE,
tailed = 1, permtest = FALSE, nperms = 10)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.
Optional argument for providing labels to the code values. Accepts
a list of string variables. If
unspecified, codes will be labeled "Code1", "Code2", etc.
The lag number for the analyses.
Can adjacent values be coded the same? Options are "TRUE" for yes or "FALSE" for no.
Specify whether significance tests are one-tailed or two-tailed.
Options are "1" or "2".
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.
The number of permutations per block.
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:
The transitional frequency matrix
The expected frequencies
The parallel dominance frequencies
The expected parallel dominance frequencies
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.
The parallel dominance kappas
The z values for the kappas
The p-values for the kappas
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).
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.
# NOT RUN {
paradom(data_Wampold_1984,
labels = c('HPos','HNeu','HNeg','WPos','WNeu','WNeg'),
permtest = TRUE, nperms = 1000)
# }
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