Data Quality Must Reads For Researchers

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Running head: DATA QUALITY IN QUESTIONNAIRE
DATA QUALITY IN QUESTIONNAIRES
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DATA QUALITY IN QUESTIONNAIRES
Data Quality (DQ) allude to twain the characteristics corresponding with and the
processes employed to assess or ameliorate quality of data [CITATION Red17 \l 2057 ]. Data Quality
commonly exhibits the following facets; accuracy, completeness, consistency, integrity,
reasonability, timeliness, uniqueness/ deduplication, validity and accessibility.
The Federal Committee on Statistical Methodology (FCSM)[CITATION Amr08 \l 1033 ] has
set major goals towards data quality that include; dissemination of information on the best
statistical practice, recommendation of the introduction of new technologies that improve data
quality and provision of a process for statisticians in different confederate agencies to meet and
exchange ideas.
The models for data collection reflects the errors present in most questionnaire and their
effects. The two major modes of include Telephone Data Collection and Computer Assisted
Survey Information Collection[CITATION ECu10 \l 1033 ]. Telephone use as a mode of data
collection is the most versatile due to the growing use of telephones in households which proves
to be cost effective and timely though full consideration should also be based on respondent
coordination and non-response prejudice, assessor aid to variance. On the other hand, computer
assisted survey proves more effective due to software systems can be multilevel in rostering and
well organized.
Some of the major data quality challenges and errors Redman (2013) are based on the
above models of data collection include; over time/idle time, cram of data quality efforts
concentrated to the control of comparative data in enterprises; unstructured data, concepts and
object data; the classical concerns of garbage in-garbage out (GIGO) which drives data quality
endeavors in computing with robotics/artificial intelligence; larger volume and higher speeds of