SSRA {SSRA} | R Documentation |
Sakai Sequential Relation Analysis
Description
This function conducts Sakai Sequential Relation Analysis (SSRA) based on Sakai 2016.
Usage
SSRA(dat, r.crt = 0.3, mu.sq = 0, mu.eq = Inf, d.sq = 0.2, d.eq = 0.2,
pairwise = TRUE, method = c("pearson", "kendall", "spearman"), alpha = 0.05,
p.adjust.method = c("holm", "hochberg", "hommel",
"bonferroni", "BH", "BY", "fdr", "none"),
digits = 3, vnames = TRUE, order = c("no", "decreasing", "increasing"),
exclude = TRUE, output = TRUE)
Arguments
dat |
requires a data frame with polytomous data |
r.crt |
correlation coefficient criterion to be judged 'sequential' or 'equivalent |
mu.sq |
Absolute mean difference criterion to be judged 'sequential' |
mu.eq |
maximal absolute mean difference to be judged 'equivalent' |
d.sq |
effect size for mean difference criterion to be judged 'sequential' |
d.eq |
maximal effect size Cohen's d to be judged 'equivalent' |
pairwise |
pairwise deletion of missing data,
if |
method |
character string indicating which correlation coefficient to be used, 'pearson' = Pearson's product moment correlation coefficien 'spearman' = Spearman's rho statistic 'kendall' = Kendall's tau (default) |
alpha |
significance level |
p.adjust.method |
p-value correction method for multiple comparisons, see: ?p.adjust (default = holm) |
digits |
integer indicating the number of decimal places to be used |
vnames |
use variable names for labeling? |
order |
sort by item mean of j and k? |
exclude |
exclude paths with no relationship? |
output |
print result table? |
Details
In Sakai Sequential Relation Analysis (SSRA), a pair of items is judged 'sequential', if there is a higher correlation and a bigger mean difference than defined criterions between the two items. If there is a higher correlation and a smaller mean difference than defined criterions between the two items, the relation of the two items is judged 'equal'.
Value
Returns an object of class ssra
, to be used for the seqtable
function. The object is a list with
following entries: 'dat' (data frame), 'call" (function call), 'args' (specification of arguments),
'time' (time of analysis), 'R' (R version), 'package' (package version), and 'restab' (result table).
The 'restab' entry has following entries:
j | item j |
k | item k |
n | sample size |
j.mean | mean of item j |
j.sd | standard deviation of item j |
k.mean | mean of item k |
k.sd | standard deviation of item k |
r | correlation coefficient |
r.t | test statistic of the statistical significanc test for the correlation coefficient |
r.p | statistical significance value of the correlation |
r.sig | statistical significance of the correlation (0 = not significant / 1 = significant) |
r.crt | correlation criterion for judging 'sequential' or 'equal': 'r.p < alpha' and 'r > r.crt' (0 = no / 1 = yes) |
m.diff | mean difference |
sd.diff | standard deviation difference |
m.diff.eff | effect size Cohen's d for dependent samples |
m.t | test statistic of the statistical significanc test for mean difference |
m.p | statistical significance value of the mean difference |
m.sig | statistical significance of the mean difference (0 = not significant / 1 = significant) |
m.crt.sq | mean difference criteria for judging 'sequential': 'm.diff.p < alpha', 'm.diff > mu.sq' and 'm.diff.eff > d.sq' (0 = no / -1 = yes negative / 1 = yes postive) |
m.crt.eq | mean difference criteria for judging 'equivalence': statistical significant and 'm <= mu.eq' 'd <= d.sq' (0 = no 1 = yes) |
seq | sequential relation of item pairs ("+","-", "") |
eq | equivalence of item pairs ("=" or "") |
order | order structure of item pairs ("=", "+","-") |
Author(s)
Takuya Yanagida takuya.yanagida@univie.ac.at, Keiko Sakai keiko.sakai@oit.ac.jp
References
Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.
See Also
seqtable
, TSSA
, plot.ssra
, scatterplot
Examples
# Example data based on Takeya (1991)
# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
SSRA(exdat)