ddi_dataColl | R Documentation |
Information about the data collection methodology employed in the codebook. More information on these elements, especially their allowed attributes, can be found in the references.
ddi_dataColl(...) ddi_actMin(...) ddi_cleanOps(...) ddi_collectorTraining(...) ddi_collMode(...) ddi_collSitu(...) ddi_ConOps(...) ddi_dataCollector(...) ddi_deviat(...) ddi_frequenc(...) ddi_instrumentDevelopment(...) ddi_resInstru(...) ddi_sampProc(...) ddi_timeMeth(...) ddi_weight(...)
... |
Child nodes or attributes. |
Parent nodes
dataColl
is contained in method
.
dataColl specific child nodes
ddi_actMin()
is the summary of actions taken to minimize data loss.
Includes information on actions such as follow-up visits, supervisory
checks, historical matching, estimation, etc.
ddi_cleanOps()
are the methods used to "clean" the data collection,
e.g., consistency checking, wild code checking, etc. The "agency" attribute
permits specification of the agency doing the data cleaning.
ddi_collectorTraining()
describes the training provided to data
collectors including interviewer training, process testing, compliance with
standards etc. This is repeatable for language and to capture different
aspects of the training process. The type attribute allows specification of
the type of training being described.
ddi_collMode()
is the method used to collect the data; instrumentation
characteristics.
ddi_collSitu()
is the description of noteworthy aspects of the data
collection situation. Includes information on factors such as
cooperativeness of respondents, duration of interviews, number of
call-backs, etc.
ddi_ConOps()
are control operations. These are methods to facilitate
data control performed by the primary investigator or by the data archive.
Specify any special programs used for such operations. The "agency"
attribute maybe used to refer to the agency that performed the control
operation.
ddi_dataCollector()
is the entity (individual, agency, or institution)
responsible for administering the questionnaire or interview or compiling
the data. This refers to the entity collecting the data, not to the entity
producing the documentation.
ddi_deviat()
are major deviations from the sample design. This is
information indicating correspondence as well as discrepancies between the
sampled units (obtained) and available statistics for the population (age,
sex-ratio, marital status, etc.) as a whole.
ddi_frequenc()
is the frequency of data collection. It's for data
collected at more than one point in time.
ddi_instrumentDevelopment()
describes any development work on the data
collection instrument.
ddi_resInstru()
is the type of data collection instrument used.
ddi_sampProc()
is the type of sample and sample design used to select
the survey respondents to represent the population. May include reference
to the target sample size and the sampling fraction.
ddi_weight()
defines the weights used to produce accurate statistical
results within the sampling procedures. Describe here the criteria for
using weights in analysis of a collection. If a weighting formula or
coefficient was developed, provide this formula, define its elements, and
indicate how the formula is applied to data.
A ddi_node object.
ddi_sampleFrame()
ddi_sources()
ddi_targetSampleSize()
collectorTraining documentation
instrumentDevelopment documentation
ddi_dataColl() # Functions that need to be wrapped in ddi_dataColl() ddi_actMin("To minimize the number of unresolved cases and reduce the potential nonresponse bias, four follow-up contacts were made with agencies that had not responded by various stages of the data collection process.") ddi_cleanOps("Checks for undocumented codes were performed, and data were subsequently revised in consultation with the principal investigator.") ddi_collectorTraining(type = "interviewer training", "Describe research project, describe population and sample, suggest methods and language for approaching subjects, explain questions and key terms of survey instrument.") ddi_collMode("telephone interviews") ddi_collSitu("There were 1,194 respondents who answered questions in face-to-face interviews lasting approximately 75 minutes each.") ddi_ConOps(agency = "ICPSR", "Ten percent of data entry forms were reentered to check for accuracy.") ddi_dataCollector(abbr = "SRC", affiliation = "University of Michigan", role = "questionnaire administration", "Survey Research Center") ddi_deviat("The suitability of Ohio as a research site reflected its similarity to the United States as a whole. The evidence extended by Tuchfarber (1988) shows that Ohio is representative of the United States in several ways: percent urban and rural, percent of the population that is African American, median age, per capita income, percent living below the poverty level, and unemployment rate. Although results generated from an Ohio sample are not empirically generalizable to the United States, they may be suggestive of what might be expected nationally.") ddi_frequenc("monthly") ddi_instrumentDevelopment(type = "pretesting", "The questionnaire was pre-tested with split-panel tests, as well as an analysis of non-response rates for individual items, and response distributions.") ddi_resInstru("structured") ddi_sampProc("National multistage area probability sample") ddi_weight("The 1996 NES dataset includes two final person-level analysis weights which incorporate sampling, nonresponse, and post-stratification factors. One weight (variable #4) is for longitudinal micro-level analysis using the 1996 NES Panel. The other weight (variable #3) is for analysis of the 1996 NES combined sample (Panel component cases plus Cross-section supplement cases). In addition, a Time Series Weight (variable #5) which corrects for Panel attrition was constructed. This weight should be used in analyses which compare the 1996 NES to earlier unweighted National Election Study data collections.")
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