sequential: sequential
In LagSequential: Lag-Sequential Categorical Data Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
Computes a variety of lag sequential analysis statistics for one
series of codes.
Usage
1
2
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? Enter "FALSE" if adjacent events
can never be the same. Enter "TRUE" if any adjacent events can be the
same. If some adjacent events can, and others cannot, be the
same, then enter the appropriate onezero matrix for your data using the
onezero argument.
onezero
Optional argument for specifying the one-zero matrix for the data.
Accepts a square matrix of ones and zeros with length ncodes. A "1"
indicates that the expected frequency for a given cell is to be estimated,
whereas a "0" indicates that the expected frequency for the cell should
NOT be estimated, typically because it is a structural zero (codes that
cannot follow one another). By default, the matrix that is created by the
above commands has zeros along the main diagonal, and ones everywhere
else, which will be appropriate for most data sets. However, if your data
happen to involve structural zeros that occur in cells other than the
cells along the main diagonal, then you must create a onezero matrix with
ones and zeros that is appropriate for your data.
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 unidirectional dependence of states (codes). Specifically, this function
tests the hypothesis that state i (the antecedent) follows state
j (the consequence) with a greater than chance probability.
Computes a variety of statistics including two indices of effect size with
corresponding significance tests. The larger the effect the more like the
consequence is to follow the antecedent.
Value
Displays the transitional frequency matrix, expected frequencies, transitional
probabilities, adjusted residuals and significance levels, Yule's Q values,
transformed Kappas (Wampold, 1989, 1992, 1995), z values for the kappas,
and significance levels.
Returns a list with the following elements:
freqs
The transitional frequency matrix
expfreqs
The expected frequencies
probs
The transitional probabilities
chi
The overall chi-square test of the difference between the
observed and expected transitional frequencies
adjres
The adjusted residuals
p
The statistical significance levels
YulesQ
Yule's Q values, indicating the strength of the relationships
between the antecedent and the consequence transitions
kappas
The nonparallel 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. (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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
# data is a one-column dataframe of code sequences
sequential(data_sequential, permtest = TRUE, nperms = 100)
# in this case, data is the frequency transition matrix from
# Griffin, W. A., & Gottman, J. M. (1990). Statistical methods for analyzing family
# interaction. In G. R. Patterson (Ed.), Family social interaction: Content and methodology
# issues in the study of aggression and depression (p. 137). Hillsdale, NJ: Erlbaum.
freqs <- t(matrix(c(
0, 0, 0, 0, 2, 2,
0,10, 5, 5,60,20,
0, 9, 2, 1, 3, 0,
0, 3, 0, 1, 5, 0,
3,54, 6, 2,24, 8,
1,24, 2, 1, 3, 12 ), 6, 6) )
sequential(freqs, adjacent = 1,
labels = c('H+','Ho','H-','W+','Wo','W-'))
# Data from p 159 of Bakeman & Quera (2011), Sequential Analysis and Observational
# Methods for the Behavioral Sciences. Cambridge University Press.
data_BQ2011 <- t(matrix(c(
2,1,4,3,3,4,3,4,2,1,4,4,5,4,1,3,4,5,3,2,2,1,4,1,2,
5,2,1,2,3,3,1,4,4,1,4,1,3,3,3,1,5,2,1,1,3,1,4,1,2,
3,3,4,5,5,2,3,3,5,2,5,4,4,2,3,1,5,5,2,2,1,3,3,3,3 )) )
sequential(data_BQ2011, labels=c('Chat','Write','Read','Ask','Attentive'),
permtest = TRUE, nperms = 1000, tailed = 1)
Example output
Lag Sequential Analysis
The code frequencies:
data
1 2 3 4 5 6
4 21 36 11 9 41
Requested 'tail' (1 or 2) for Significance Tests = 2
Cell Frequencies, Row & Column Totals, & N
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6 Totals
Code 1 0 0 0 0 0 3 3
Code 2 0 0 1 1 2 17 21
Code 3 0 0 8 8 7 13 36
Code 4 1 4 3 2 0 1 11
Code 5 0 0 9 0 0 0 9
Code 6 3 17 14 0 0 7 41
Totals 4 21 35 11 9 41 121
Expected Values/Frequencies
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.10 0.52 0.87 0.27 0.22 1.02
Code 2 0.69 3.64 6.07 1.91 1.56 7.12
Code 3 1.19 6.25 10.41 3.27 2.68 12.20
Code 4 0.36 1.91 3.18 1.00 0.82 3.73
Code 5 0.30 1.56 2.60 0.82 0.67 3.05
Code 6 1.36 7.12 11.86 3.73 3.05 13.89
Transitional Probabilities
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.00 0.00 0.00 0.00 0.00 1.00
Code 2 0.00 0.00 0.05 0.05 0.10 0.81
Code 3 0.00 0.00 0.22 0.22 0.19 0.36
Code 4 0.09 0.36 0.27 0.18 0.00 0.09
Code 5 0.00 0.00 1.00 0.00 0.00 0.00
Code 6 0.07 0.41 0.34 0.00 0.00 0.17
Tablewise Likelihood Ratio (Chi-Square) test = 116.87, df = 25, p = 0
Adjusted Residuals
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -0.32 -0.80 -1.12 -0.55 -0.50 2.45
Code 2 -0.93 -2.31 -2.69 -0.76 0.40 5.01
Code 3 -1.32 -3.28 -1.06 3.27 3.28 0.34
Code 4 1.13 1.75 -0.13 1.10 -0.99 -1.82
Code 5 -0.58 -1.43 4.89 -0.99 -0.88 -2.23
Code 6 1.77 5.01 0.91 -2.49 -2.23 -2.80
Significance Levels for the Adjusted Residuals
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.7457 0.4215 0.2632 0.5791 0.6191 0.0143
Code 2 0.3513 0.0209 0.0072 0.4478 0.6886 0.0000
Code 3 0.1856 0.0010 0.2899 0.0011 0.0011 0.7363
Code 4 0.2604 0.0808 0.8991 0.2713 0.3241 0.0684
Code 5 0.5642 0.1530 0.0000 0.3241 0.3767 0.0256
Code 6 0.0773 0.0000 0.3646 0.0128 0.0256 0.0052
Yule's Q Values
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -1.00 -1.00 -1.00 -1.00 -1.00 1.00
Code 2 -1.00 -1.00 -0.82 -0.38 0.17 0.86
Code 3 -1.00 -1.00 -0.24 0.77 0.82 0.07
Code 4 0.56 0.52 -0.04 0.43 -1.00 -0.70
Code 5 -1.00 -1.00 1.00 -1.00 -1.00 -1.00
Code 6 0.72 0.86 0.19 -1.00 -1.00 -0.56
Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -1.00 -1.00 -1.00 -1.00 -1.00 0.62
Code 2 -1.00 -1.00 -0.84 -0.47 0.06 0.71
Code 3 -1.00 -1.00 -0.25 0.61 0.68 0.04
Code 4 0.18 0.23 -0.08 0.10 -1.00 -0.73
Code 5 -1.00 -1.00 1.00 -1.00 -1.00 -1.00
Code 6 0.62 0.71 0.08 -1.00 -1.00 -0.49
z values for the Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -0.37 -0.92 -1.31 -0.64 -0.57 1.77
Code 2 -0.92 -2.29 -2.72 -0.75 0.41 5.03
Code 3 -1.31 -3.25 -1.14 3.28 3.29 0.38
Code 4 1.13 1.76 -0.17 1.11 -0.98 -1.80
Code 5 -0.57 -1.42 4.80 -0.98 -0.88 -2.21
Code 6 1.77 5.03 0.80 -2.46 -2.21 -2.74
Significance Levels for the Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.7092 0.3558 0.1901 0.5238 0.5676 0.0759
Code 2 0.3558 0.0222 0.0065 0.4562 0.6804 0.0000
Code 3 0.1901 0.0012 0.2556 0.0010 0.0010 0.7059
Code 4 0.2584 0.0790 0.8652 0.2678 0.3284 0.0723
Code 5 0.5676 0.1569 0.0000 0.3284 0.3810 0.0272
Code 6 0.0759 0.0000 0.4261 0.0138 0.0272 0.0062
Data Permutation Significance Levels (number of permutations = 100)
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.91 0.42 0.21 0.73 0.79 0.08
Code 2 0.51 0.02 0.00 0.44 0.52 0.00
Code 3 0.27 0.00 0.22 0.01 0.00 0.46
Code 4 0.37 0.08 0.53 0.17 0.49 0.11
Code 5 0.69 0.10 0.00 0.38 0.54 0.03
Code 6 0.16 0.00 0.24 0.02 0.03 0.00
Lag Sequential Analysis
Requested 'tail' (1 or 2) for Significance Tests = 2
Cell Frequencies, Row & Column Totals, & N
H+ Ho H- W+ Wo W- Totals
H+ 0 0 0 0 2 2 4
Ho 0 10 5 5 60 20 100
H- 0 9 2 1 3 0 15
W+ 0 3 0 1 5 0 9
Wo 3 54 6 2 24 8 97
W- 1 24 2 1 3 12 43
Totals 4 100 15 10 97 42 268
Expected Values/Frequencies
H+ Ho H- W+ Wo W-
H+ 0.06 1.49 0.22 0.15 1.45 0.63
Ho 1.49 37.31 5.60 3.73 36.19 15.67
H- 0.22 5.60 0.84 0.56 5.43 2.35
W+ 0.13 3.36 0.50 0.34 3.26 1.41
Wo 1.45 36.19 5.43 3.62 35.11 15.20
W- 0.64 16.04 2.41 1.60 15.56 6.74
Transitional Probabilities
H+ Ho H- W+ Wo W-
H+ 0.00 0.00 0.00 0.00 0.50 0.50
Ho 0.00 0.10 0.05 0.05 0.60 0.20
H- 0.00 0.60 0.13 0.07 0.20 0.00
W+ 0.00 0.33 0.00 0.11 0.56 0.00
Wo 0.03 0.56 0.06 0.02 0.25 0.08
W- 0.02 0.56 0.05 0.02 0.07 0.28
Tablewise Likelihood Ratio (Chi-Square) test = 107.6, df = 25, p = 0
Adjusted Residuals
H+ Ho H- W+ Wo W-
H+ -0.25 -1.55 -0.49 -0.40 0.58 1.90
Ho -1.55 -7.13 -0.33 0.85 6.26 1.50
H- -0.49 1.87 1.34 0.62 -1.34 -1.72
W+ -0.38 -0.25 -0.74 1.19 1.23 -1.32
Wo 1.63 4.68 0.32 -1.09 -2.94 -2.52
W- 0.49 2.74 -0.29 -0.53 -4.35 2.41
Significance Levels for the Adjusted Residuals
H+ Ho H- W+ Wo W-
H+ 0.8041 0.1200 0.6237 0.6916 0.5626 0.0571
Ho 0.1200 0.0000 0.7429 0.3979 0.0000 0.1326
H- 0.6237 0.0615 0.1797 0.5370 0.1792 0.0857
W+ 0.7072 0.8017 0.4574 0.2347 0.2189 0.1883
Wo 0.1037 0.0000 0.7522 0.2774 0.0033 0.0118
W- 0.6229 0.0062 0.7684 0.5955 0.0000 0.0160
Yule's Q Values
H+ Ho H- W+ Wo W-
H+ -1.00 -1.00 -1.00 -1.00 0.28 0.70
Ho -1.00 -0.82 -0.09 0.26 0.68 0.25
H- -1.00 0.46 0.48 0.32 -0.41 -1.00
W+ -1.00 -0.09 -1.00 0.55 0.39 -1.00
Wo 0.69 0.55 0.09 -0.40 -0.39 -0.47
W- 0.28 0.42 -0.11 -0.27 -0.81 0.43
Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ -1.00 -1.00 -1.00 -1.00 0.22 0.40
Ho -1.00 -0.73 -0.10 0.29 0.39 0.15
H- -1.00 0.36 0.08 0.06 -0.45 -1.00
W+ -1.00 -0.10 -1.00 0.08 0.30 -1.00
Wo 0.61 0.29 0.06 -0.38 -0.31 -0.48
W- 0.11 0.30 -0.17 -0.30 -0.81 0.14
z values for the Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ -0.25 -1.55 -0.49 -0.37 0.58 1.87
Ho -1.55 -7.08 -0.32 1.16 6.28 1.38
H- -0.49 1.88 1.34 0.73 -1.33 -1.74
W+ -0.37 -0.24 -0.74 1.32 1.24 -1.33
Wo 1.63 4.71 0.33 -0.88 -2.90 -2.60
W- 0.49 2.75 -0.29 -0.41 -4.32 2.32
Significance Levels for the Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ 0.8048 0.1218 0.6250 0.7083 0.5593 0.0619
Ho 0.1218 0.0000 0.7518 0.2467 0.0000 0.1677
H- 0.6250 0.0602 0.1787 0.4625 0.1833 0.0827
W+ 0.7083 0.8087 0.4592 0.1884 0.2162 0.1840
Wo 0.1029 0.0000 0.7441 0.3801 0.0038 0.0094
W- 0.6208 0.0059 0.7734 0.6854 0.0000 0.0202
Lag Sequential Analysis
The code frequencies:
data
1 2 3 4 5
16 14 20 15 10
Requested 'tail' (1 or 2) for Significance Tests = 1
Cell Frequencies, Row & Column Totals, & N
Chat Write Read Ask Attentive Totals
Chat 1 3 4 6 2 16
Write 6 2 4 0 2 14
Read 4 1 9 4 1 19
Ask 5 2 2 3 3 15
Attentive 0 5 1 2 2 10
Totals 16 13 20 15 10 74
Expected Values/Frequencies
Chat Write Read Ask Attentive
Chat 3.46 2.81 4.32 3.24 2.16
Write 3.03 2.46 3.78 2.84 1.89
Read 4.11 3.34 5.14 3.85 2.57
Ask 3.24 2.64 4.05 3.04 2.03
Attentive 2.16 1.76 2.70 2.03 1.35
Transitional Probabilities
Chat Write Read Ask Attentive
Chat 0.06 0.19 0.25 0.38 0.12
Write 0.43 0.14 0.29 0.00 0.14
Read 0.21 0.05 0.47 0.21 0.05
Ask 0.33 0.13 0.13 0.20 0.20
Attentive 0.00 0.50 0.10 0.20 0.20
Tablewise Likelihood Ratio (Chi-Square) test = 30.95, df = 16, p = 0.01364
Adjusted Residuals
Chat Write Read Ask Attentive
Chat -1.69 0.14 -0.21 1.94 -0.13
Write 2.14 -0.36 0.14 -2.10 0.09
Read -0.07 -1.63 2.32 0.10 -1.22
Ask 1.23 -0.48 -1.34 -0.03 0.82
Attentive -1.79 2.90 -1.30 -0.02 0.65
Significance Levels for the Adjusted Residuals
Chat Write Read Ask Attentive
Chat 0.0458 0.4442 0.4183 0.0264 0.4467
Write 0.0160 0.3600 0.4426 0.0181 0.4626
Read 0.4721 0.0510 0.0103 0.4608 0.1112
Ask 0.1086 0.3147 0.0905 0.4884 0.2053
Attentive 0.0371 0.0019 0.0962 0.4909 0.2594
Yule's Q Values
Chat Write Read Ask Attentive
Chat -0.68 0.05 -0.07 0.53 -0.06
Write 0.58 -0.15 0.05 -1.00 0.04
Read -0.02 -0.67 0.57 0.03 -0.56
Ask 0.37 -0.20 -0.48 -0.01 0.30
Attentive -1.00 0.75 -0.58 -0.01 0.27
Unidirectional Kappas
Chat Write Read Ask Attentive
Chat -0.71 0.00 -0.06 0.24 -0.06
Write 0.27 -0.23 0.03 -1.00 0.02
Read -0.06 -0.73 0.25 0.00 -0.62
Ask 0.15 -0.29 -0.50 0.00 0.12
Attentive -1.00 0.39 -0.62 0.00 0.08
z values for the Unidirectional Kappas
Chat Write Read Ask Attentive
Chat -1.65 0.01 -0.17 1.96 -0.11
Write 2.17 -0.46 0.18 -2.06 0.12
Read -0.17 -1.82 2.15 0.00 -1.27
Ask 1.26 -0.59 -1.30 0.00 0.84
Attentive -1.76 2.71 -1.27 0.00 0.66
Significance Levels for the Unidirectional Kappas
Chat Write Read Ask Attentive
Chat 0.0495 0.4962 0.4330 0.0250 0.4563
Write 0.0152 0.3216 0.4296 0.0197 0.4540
Read 0.4330 0.0344 0.0158 0.5000 0.1017
Ask 0.1038 0.2780 0.0973 0.5000 0.1995
Attentive 0.0395 0.0033 0.1017 0.5000 0.2541
Data Permutation Significance Levels (number of permutations = 1000)
Chat Write Read Ask Attentive
Chat 0.106 0.616 0.570 0.067 0.623
Write 0.033 0.538 0.541 0.035 0.614
Read 0.586 0.076 0.035 0.633 0.177
Ask 0.171 0.416 0.158 0.683 0.267
Attentive 0.071 0.024 0.208 0.724 0.343
LagSequential documentation built on May 16, 2019, 5:09 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
Computes a variety of lag sequential analysis statistics for one series of codes.
Usage
1 2 |
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? Enter "FALSE" if adjacent events can never be the same. Enter "TRUE" if any adjacent events can be the same. If some adjacent events can, and others cannot, be the same, then enter the appropriate onezero matrix for your data using the onezero argument. |
onezero |
Optional argument for specifying the one-zero matrix for the data. Accepts a square matrix of ones and zeros with length ncodes. A "1" indicates that the expected frequency for a given cell is to be estimated, whereas a "0" indicates that the expected frequency for the cell should NOT be estimated, typically because it is a structural zero (codes that cannot follow one another). By default, the matrix that is created by the above commands has zeros along the main diagonal, and ones everywhere else, which will be appropriate for most data sets. However, if your data happen to involve structural zeros that occur in cells other than the cells along the main diagonal, then you must create a onezero matrix with ones and zeros that is appropriate for your data. |
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 unidirectional dependence of states (codes). Specifically, this function tests the hypothesis that state i (the antecedent) follows state j (the consequence) with a greater than chance probability. Computes a variety of statistics including two indices of effect size with corresponding significance tests. The larger the effect the more like the consequence is to follow the antecedent.
Value
Displays the transitional frequency matrix, expected frequencies, transitional probabilities, adjusted residuals and significance levels, Yule's Q values, transformed Kappas (Wampold, 1989, 1992, 1995), z values for the kappas, and significance levels.
Returns a list with the following elements:
freqs |
The transitional frequency matrix |
expfreqs |
The expected frequencies |
probs |
The transitional probabilities |
chi |
The overall chi-square test of the difference between the observed and expected transitional frequencies |
adjres |
The adjusted residuals |
p |
The statistical significance levels |
YulesQ |
Yule's Q values, indicating the strength of the relationships between the antecedent and the consequence transitions |
kappas |
The nonparallel 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. (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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # data is a one-column dataframe of code sequences
sequential(data_sequential, permtest = TRUE, nperms = 100)
# in this case, data is the frequency transition matrix from
# Griffin, W. A., & Gottman, J. M. (1990). Statistical methods for analyzing family
# interaction. In G. R. Patterson (Ed.), Family social interaction: Content and methodology
# issues in the study of aggression and depression (p. 137). Hillsdale, NJ: Erlbaum.
freqs <- t(matrix(c(
0, 0, 0, 0, 2, 2,
0,10, 5, 5,60,20,
0, 9, 2, 1, 3, 0,
0, 3, 0, 1, 5, 0,
3,54, 6, 2,24, 8,
1,24, 2, 1, 3, 12 ), 6, 6) )
sequential(freqs, adjacent = 1,
labels = c('H+','Ho','H-','W+','Wo','W-'))
# Data from p 159 of Bakeman & Quera (2011), Sequential Analysis and Observational
# Methods for the Behavioral Sciences. Cambridge University Press.
data_BQ2011 <- t(matrix(c(
2,1,4,3,3,4,3,4,2,1,4,4,5,4,1,3,4,5,3,2,2,1,4,1,2,
5,2,1,2,3,3,1,4,4,1,4,1,3,3,3,1,5,2,1,1,3,1,4,1,2,
3,3,4,5,5,2,3,3,5,2,5,4,4,2,3,1,5,5,2,2,1,3,3,3,3 )) )
sequential(data_BQ2011, labels=c('Chat','Write','Read','Ask','Attentive'),
permtest = TRUE, nperms = 1000, tailed = 1)
|
Example output
Lag Sequential Analysis
The code frequencies:
data
1 2 3 4 5 6
4 21 36 11 9 41
Requested 'tail' (1 or 2) for Significance Tests = 2
Cell Frequencies, Row & Column Totals, & N
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6 Totals
Code 1 0 0 0 0 0 3 3
Code 2 0 0 1 1 2 17 21
Code 3 0 0 8 8 7 13 36
Code 4 1 4 3 2 0 1 11
Code 5 0 0 9 0 0 0 9
Code 6 3 17 14 0 0 7 41
Totals 4 21 35 11 9 41 121
Expected Values/Frequencies
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.10 0.52 0.87 0.27 0.22 1.02
Code 2 0.69 3.64 6.07 1.91 1.56 7.12
Code 3 1.19 6.25 10.41 3.27 2.68 12.20
Code 4 0.36 1.91 3.18 1.00 0.82 3.73
Code 5 0.30 1.56 2.60 0.82 0.67 3.05
Code 6 1.36 7.12 11.86 3.73 3.05 13.89
Transitional Probabilities
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.00 0.00 0.00 0.00 0.00 1.00
Code 2 0.00 0.00 0.05 0.05 0.10 0.81
Code 3 0.00 0.00 0.22 0.22 0.19 0.36
Code 4 0.09 0.36 0.27 0.18 0.00 0.09
Code 5 0.00 0.00 1.00 0.00 0.00 0.00
Code 6 0.07 0.41 0.34 0.00 0.00 0.17
Tablewise Likelihood Ratio (Chi-Square) test = 116.87, df = 25, p = 0
Adjusted Residuals
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -0.32 -0.80 -1.12 -0.55 -0.50 2.45
Code 2 -0.93 -2.31 -2.69 -0.76 0.40 5.01
Code 3 -1.32 -3.28 -1.06 3.27 3.28 0.34
Code 4 1.13 1.75 -0.13 1.10 -0.99 -1.82
Code 5 -0.58 -1.43 4.89 -0.99 -0.88 -2.23
Code 6 1.77 5.01 0.91 -2.49 -2.23 -2.80
Significance Levels for the Adjusted Residuals
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.7457 0.4215 0.2632 0.5791 0.6191 0.0143
Code 2 0.3513 0.0209 0.0072 0.4478 0.6886 0.0000
Code 3 0.1856 0.0010 0.2899 0.0011 0.0011 0.7363
Code 4 0.2604 0.0808 0.8991 0.2713 0.3241 0.0684
Code 5 0.5642 0.1530 0.0000 0.3241 0.3767 0.0256
Code 6 0.0773 0.0000 0.3646 0.0128 0.0256 0.0052
Yule's Q Values
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -1.00 -1.00 -1.00 -1.00 -1.00 1.00
Code 2 -1.00 -1.00 -0.82 -0.38 0.17 0.86
Code 3 -1.00 -1.00 -0.24 0.77 0.82 0.07
Code 4 0.56 0.52 -0.04 0.43 -1.00 -0.70
Code 5 -1.00 -1.00 1.00 -1.00 -1.00 -1.00
Code 6 0.72 0.86 0.19 -1.00 -1.00 -0.56
Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -1.00 -1.00 -1.00 -1.00 -1.00 0.62
Code 2 -1.00 -1.00 -0.84 -0.47 0.06 0.71
Code 3 -1.00 -1.00 -0.25 0.61 0.68 0.04
Code 4 0.18 0.23 -0.08 0.10 -1.00 -0.73
Code 5 -1.00 -1.00 1.00 -1.00 -1.00 -1.00
Code 6 0.62 0.71 0.08 -1.00 -1.00 -0.49
z values for the Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 -0.37 -0.92 -1.31 -0.64 -0.57 1.77
Code 2 -0.92 -2.29 -2.72 -0.75 0.41 5.03
Code 3 -1.31 -3.25 -1.14 3.28 3.29 0.38
Code 4 1.13 1.76 -0.17 1.11 -0.98 -1.80
Code 5 -0.57 -1.42 4.80 -0.98 -0.88 -2.21
Code 6 1.77 5.03 0.80 -2.46 -2.21 -2.74
Significance Levels for the Unidirectional Kappas
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.7092 0.3558 0.1901 0.5238 0.5676 0.0759
Code 2 0.3558 0.0222 0.0065 0.4562 0.6804 0.0000
Code 3 0.1901 0.0012 0.2556 0.0010 0.0010 0.7059
Code 4 0.2584 0.0790 0.8652 0.2678 0.3284 0.0723
Code 5 0.5676 0.1569 0.0000 0.3284 0.3810 0.0272
Code 6 0.0759 0.0000 0.4261 0.0138 0.0272 0.0062
Data Permutation Significance Levels (number of permutations = 100)
Code 1 Code 2 Code 3 Code 4 Code 5 Code 6
Code 1 0.91 0.42 0.21 0.73 0.79 0.08
Code 2 0.51 0.02 0.00 0.44 0.52 0.00
Code 3 0.27 0.00 0.22 0.01 0.00 0.46
Code 4 0.37 0.08 0.53 0.17 0.49 0.11
Code 5 0.69 0.10 0.00 0.38 0.54 0.03
Code 6 0.16 0.00 0.24 0.02 0.03 0.00
Lag Sequential Analysis
Requested 'tail' (1 or 2) for Significance Tests = 2
Cell Frequencies, Row & Column Totals, & N
H+ Ho H- W+ Wo W- Totals
H+ 0 0 0 0 2 2 4
Ho 0 10 5 5 60 20 100
H- 0 9 2 1 3 0 15
W+ 0 3 0 1 5 0 9
Wo 3 54 6 2 24 8 97
W- 1 24 2 1 3 12 43
Totals 4 100 15 10 97 42 268
Expected Values/Frequencies
H+ Ho H- W+ Wo W-
H+ 0.06 1.49 0.22 0.15 1.45 0.63
Ho 1.49 37.31 5.60 3.73 36.19 15.67
H- 0.22 5.60 0.84 0.56 5.43 2.35
W+ 0.13 3.36 0.50 0.34 3.26 1.41
Wo 1.45 36.19 5.43 3.62 35.11 15.20
W- 0.64 16.04 2.41 1.60 15.56 6.74
Transitional Probabilities
H+ Ho H- W+ Wo W-
H+ 0.00 0.00 0.00 0.00 0.50 0.50
Ho 0.00 0.10 0.05 0.05 0.60 0.20
H- 0.00 0.60 0.13 0.07 0.20 0.00
W+ 0.00 0.33 0.00 0.11 0.56 0.00
Wo 0.03 0.56 0.06 0.02 0.25 0.08
W- 0.02 0.56 0.05 0.02 0.07 0.28
Tablewise Likelihood Ratio (Chi-Square) test = 107.6, df = 25, p = 0
Adjusted Residuals
H+ Ho H- W+ Wo W-
H+ -0.25 -1.55 -0.49 -0.40 0.58 1.90
Ho -1.55 -7.13 -0.33 0.85 6.26 1.50
H- -0.49 1.87 1.34 0.62 -1.34 -1.72
W+ -0.38 -0.25 -0.74 1.19 1.23 -1.32
Wo 1.63 4.68 0.32 -1.09 -2.94 -2.52
W- 0.49 2.74 -0.29 -0.53 -4.35 2.41
Significance Levels for the Adjusted Residuals
H+ Ho H- W+ Wo W-
H+ 0.8041 0.1200 0.6237 0.6916 0.5626 0.0571
Ho 0.1200 0.0000 0.7429 0.3979 0.0000 0.1326
H- 0.6237 0.0615 0.1797 0.5370 0.1792 0.0857
W+ 0.7072 0.8017 0.4574 0.2347 0.2189 0.1883
Wo 0.1037 0.0000 0.7522 0.2774 0.0033 0.0118
W- 0.6229 0.0062 0.7684 0.5955 0.0000 0.0160
Yule's Q Values
H+ Ho H- W+ Wo W-
H+ -1.00 -1.00 -1.00 -1.00 0.28 0.70
Ho -1.00 -0.82 -0.09 0.26 0.68 0.25
H- -1.00 0.46 0.48 0.32 -0.41 -1.00
W+ -1.00 -0.09 -1.00 0.55 0.39 -1.00
Wo 0.69 0.55 0.09 -0.40 -0.39 -0.47
W- 0.28 0.42 -0.11 -0.27 -0.81 0.43
Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ -1.00 -1.00 -1.00 -1.00 0.22 0.40
Ho -1.00 -0.73 -0.10 0.29 0.39 0.15
H- -1.00 0.36 0.08 0.06 -0.45 -1.00
W+ -1.00 -0.10 -1.00 0.08 0.30 -1.00
Wo 0.61 0.29 0.06 -0.38 -0.31 -0.48
W- 0.11 0.30 -0.17 -0.30 -0.81 0.14
z values for the Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ -0.25 -1.55 -0.49 -0.37 0.58 1.87
Ho -1.55 -7.08 -0.32 1.16 6.28 1.38
H- -0.49 1.88 1.34 0.73 -1.33 -1.74
W+ -0.37 -0.24 -0.74 1.32 1.24 -1.33
Wo 1.63 4.71 0.33 -0.88 -2.90 -2.60
W- 0.49 2.75 -0.29 -0.41 -4.32 2.32
Significance Levels for the Unidirectional Kappas
H+ Ho H- W+ Wo W-
H+ 0.8048 0.1218 0.6250 0.7083 0.5593 0.0619
Ho 0.1218 0.0000 0.7518 0.2467 0.0000 0.1677
H- 0.6250 0.0602 0.1787 0.4625 0.1833 0.0827
W+ 0.7083 0.8087 0.4592 0.1884 0.2162 0.1840
Wo 0.1029 0.0000 0.7441 0.3801 0.0038 0.0094
W- 0.6208 0.0059 0.7734 0.6854 0.0000 0.0202
Lag Sequential Analysis
The code frequencies:
data
1 2 3 4 5
16 14 20 15 10
Requested 'tail' (1 or 2) for Significance Tests = 1
Cell Frequencies, Row & Column Totals, & N
Chat Write Read Ask Attentive Totals
Chat 1 3 4 6 2 16
Write 6 2 4 0 2 14
Read 4 1 9 4 1 19
Ask 5 2 2 3 3 15
Attentive 0 5 1 2 2 10
Totals 16 13 20 15 10 74
Expected Values/Frequencies
Chat Write Read Ask Attentive
Chat 3.46 2.81 4.32 3.24 2.16
Write 3.03 2.46 3.78 2.84 1.89
Read 4.11 3.34 5.14 3.85 2.57
Ask 3.24 2.64 4.05 3.04 2.03
Attentive 2.16 1.76 2.70 2.03 1.35
Transitional Probabilities
Chat Write Read Ask Attentive
Chat 0.06 0.19 0.25 0.38 0.12
Write 0.43 0.14 0.29 0.00 0.14
Read 0.21 0.05 0.47 0.21 0.05
Ask 0.33 0.13 0.13 0.20 0.20
Attentive 0.00 0.50 0.10 0.20 0.20
Tablewise Likelihood Ratio (Chi-Square) test = 30.95, df = 16, p = 0.01364
Adjusted Residuals
Chat Write Read Ask Attentive
Chat -1.69 0.14 -0.21 1.94 -0.13
Write 2.14 -0.36 0.14 -2.10 0.09
Read -0.07 -1.63 2.32 0.10 -1.22
Ask 1.23 -0.48 -1.34 -0.03 0.82
Attentive -1.79 2.90 -1.30 -0.02 0.65
Significance Levels for the Adjusted Residuals
Chat Write Read Ask Attentive
Chat 0.0458 0.4442 0.4183 0.0264 0.4467
Write 0.0160 0.3600 0.4426 0.0181 0.4626
Read 0.4721 0.0510 0.0103 0.4608 0.1112
Ask 0.1086 0.3147 0.0905 0.4884 0.2053
Attentive 0.0371 0.0019 0.0962 0.4909 0.2594
Yule's Q Values
Chat Write Read Ask Attentive
Chat -0.68 0.05 -0.07 0.53 -0.06
Write 0.58 -0.15 0.05 -1.00 0.04
Read -0.02 -0.67 0.57 0.03 -0.56
Ask 0.37 -0.20 -0.48 -0.01 0.30
Attentive -1.00 0.75 -0.58 -0.01 0.27
Unidirectional Kappas
Chat Write Read Ask Attentive
Chat -0.71 0.00 -0.06 0.24 -0.06
Write 0.27 -0.23 0.03 -1.00 0.02
Read -0.06 -0.73 0.25 0.00 -0.62
Ask 0.15 -0.29 -0.50 0.00 0.12
Attentive -1.00 0.39 -0.62 0.00 0.08
z values for the Unidirectional Kappas
Chat Write Read Ask Attentive
Chat -1.65 0.01 -0.17 1.96 -0.11
Write 2.17 -0.46 0.18 -2.06 0.12
Read -0.17 -1.82 2.15 0.00 -1.27
Ask 1.26 -0.59 -1.30 0.00 0.84
Attentive -1.76 2.71 -1.27 0.00 0.66
Significance Levels for the Unidirectional Kappas
Chat Write Read Ask Attentive
Chat 0.0495 0.4962 0.4330 0.0250 0.4563
Write 0.0152 0.3216 0.4296 0.0197 0.4540
Read 0.4330 0.0344 0.0158 0.5000 0.1017
Ask 0.1038 0.2780 0.0973 0.5000 0.1995
Attentive 0.0395 0.0033 0.1017 0.5000 0.2541
Data Permutation Significance Levels (number of permutations = 1000)
Chat Write Read Ask Attentive
Chat 0.106 0.616 0.570 0.067 0.623
Write 0.033 0.538 0.541 0.035 0.614
Read 0.586 0.076 0.035 0.633 0.177
Ask 0.171 0.416 0.158 0.683 0.267
Attentive 0.071 0.024 0.208 0.724 0.343
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