Nothing
resp_distributions()
,
if missing values are allowed. Now within respondent mean imputation is used.
Before missing values were turned to a value of 0, which is wrong in almost any
case and would have skewed the results of respondents with missing values.
Mean imputation is not an ideal solution, but it allows the observed data
to influence the value of the mahalanobis distance value under missing data.
Because within respondent mean imputation is not ideal, a new section
in the description is added to call for caution when interpreting the mahalanobis
distance values produced by resp_distributions()
if min_valid_responses
is
smaller than 1.flag_resp()
.resp_nondifferentiation
.resp_styles()
was used
on even numbered response scales.nep
from the GESIS panel.resp_patterns()
and resp_nondifferentiation()
as a new function.id
column to all outputs to make it easier to identify respondents or
merge function outputs to data frames. id
is either True
for an integer id,
False
for no id
column, or a vector of unique integer or character values
identifying each respondent.flag_resp()
function to quickly create and compare different flagging
strategies based on response quality indicators.resp_*()
and flag_resp()
functions.resp_*()
functions.flag_resp()
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