CRITERIA FOR DETERMINING GOOD DATA AND THE USE OF FIELD WORK, INTERVIEWS,
QUESTIONNAIRES, OBSERVATIONS AND FOCUSED GROUP TECHINQUES.
Introduction
Data in its simplest term is an organized and processed information. In fact, all is data (Glaser,
2001). It could further refer to sequences of symbols or continuous values that convey
information because they are formally interpreted. Data is a collection of facts, numbers,
observations and other useful information. Olson (2021) remarks that the components of data
used in qualitative research involve both the verbal and non-verbal elements. Be that as it may,
data from diverse researches reflect the researcher’s thought process or sense of truth.
The facts or information processed systematically in order to ascertain the originality and
authenticity of its claims also to gain valuable insights. This generally promotes better research
outcomes and aide the overall decision making and outcome of various researches. Data can be
classified as either good or bad it depends on several factors including mode of data collection,
documentation and recording of the data and then the analysis of data itself. Researchers are to
carefully study the patterns and logically separate the facts from otherwise the inconsequential
parts of the data. Gualandi, Pareschi and Peroni (2023) notes that currently in the European
Commission recognizes the principle of FAIR data in the leading maxim guiding evaluation of
data. FAIR here stands for: Findable, Accessible, Inter-operable and Reusable.
Good data and Criteria for Good Data/High Quality Data
Meaning of Good Data
Data quality refers to the degree of accuracy, consistency, completeness, reliability and
relevance of the data collected, stored and used within a specific context in research. The
capacity of deducing reality or facts o supposition rom misrepresentation and falsity is an
attribute of a seasoned researcher. A good data is characteristically obtained through clear and
logical reasoning. High quality data therefore marks the exact situation or condition of things or
phenomena. Theriault (2024) mentions that data is only useful if it is high quality data but bad
data can lead to mistakes in the research. It indicates a true representation of reality as at when
the data was collected. Good data contain all necessary information and at the same time free
1
, from contradictions. Evidently a good data is one that meets up with series of criteria of judging
good data. Essentially it leads the researcher towards making a more reliable and accurate
decision and analysis concerning the research. Solving correctly the research problem and
answering key research questions primarily hinges on the ability to extract high quality data.
High quality data further depends on data collection methods, data entry processes, data
storage and data integration.
Criteria for Good/High Quality Data
Completeness- The data set must set contain all necessary records without missing
values or aps. This gives room for more comprehensive analysis and decision making.
Accuracy- Accuracy implies that the data utilized must significantly represent real-world
values or events. Implementing efficiently data validation rules help to cushion errors
and inaccurate information.
Consistency- A good data must maintain data values that coherent and compatible
across different data set or system. Inconsistent and incorrect data can lead to wrong
conclusion and confusion among the users who may depend on the research to make
research decisions in the future. Utilizing standardization techniques however can
prevent inconsistency in the data.
Timeliness and Currency- High quality data is expected to up-to-date and relevant when
used for analysis or decision making as outdated information can lead to incorrect
conclusions.
Validity- Validity as a criterion maintains that the data collection method itself adheres
to predefined rules, formats or standards. The goal is to ensure that the data is
structured correctly and is also usable.
Uniqueness- This refers to the absence of duplicated records in a data set. Duplicate
records/entries can mar analysis by over representing specific data points. The
researcher however can carefully shield the research from concurrence using computer
automated deduplication tools.
2
QUESTIONNAIRES, OBSERVATIONS AND FOCUSED GROUP TECHINQUES.
Introduction
Data in its simplest term is an organized and processed information. In fact, all is data (Glaser,
2001). It could further refer to sequences of symbols or continuous values that convey
information because they are formally interpreted. Data is a collection of facts, numbers,
observations and other useful information. Olson (2021) remarks that the components of data
used in qualitative research involve both the verbal and non-verbal elements. Be that as it may,
data from diverse researches reflect the researcher’s thought process or sense of truth.
The facts or information processed systematically in order to ascertain the originality and
authenticity of its claims also to gain valuable insights. This generally promotes better research
outcomes and aide the overall decision making and outcome of various researches. Data can be
classified as either good or bad it depends on several factors including mode of data collection,
documentation and recording of the data and then the analysis of data itself. Researchers are to
carefully study the patterns and logically separate the facts from otherwise the inconsequential
parts of the data. Gualandi, Pareschi and Peroni (2023) notes that currently in the European
Commission recognizes the principle of FAIR data in the leading maxim guiding evaluation of
data. FAIR here stands for: Findable, Accessible, Inter-operable and Reusable.
Good data and Criteria for Good Data/High Quality Data
Meaning of Good Data
Data quality refers to the degree of accuracy, consistency, completeness, reliability and
relevance of the data collected, stored and used within a specific context in research. The
capacity of deducing reality or facts o supposition rom misrepresentation and falsity is an
attribute of a seasoned researcher. A good data is characteristically obtained through clear and
logical reasoning. High quality data therefore marks the exact situation or condition of things or
phenomena. Theriault (2024) mentions that data is only useful if it is high quality data but bad
data can lead to mistakes in the research. It indicates a true representation of reality as at when
the data was collected. Good data contain all necessary information and at the same time free
1
, from contradictions. Evidently a good data is one that meets up with series of criteria of judging
good data. Essentially it leads the researcher towards making a more reliable and accurate
decision and analysis concerning the research. Solving correctly the research problem and
answering key research questions primarily hinges on the ability to extract high quality data.
High quality data further depends on data collection methods, data entry processes, data
storage and data integration.
Criteria for Good/High Quality Data
Completeness- The data set must set contain all necessary records without missing
values or aps. This gives room for more comprehensive analysis and decision making.
Accuracy- Accuracy implies that the data utilized must significantly represent real-world
values or events. Implementing efficiently data validation rules help to cushion errors
and inaccurate information.
Consistency- A good data must maintain data values that coherent and compatible
across different data set or system. Inconsistent and incorrect data can lead to wrong
conclusion and confusion among the users who may depend on the research to make
research decisions in the future. Utilizing standardization techniques however can
prevent inconsistency in the data.
Timeliness and Currency- High quality data is expected to up-to-date and relevant when
used for analysis or decision making as outdated information can lead to incorrect
conclusions.
Validity- Validity as a criterion maintains that the data collection method itself adheres
to predefined rules, formats or standards. The goal is to ensure that the data is
structured correctly and is also usable.
Uniqueness- This refers to the absence of duplicated records in a data set. Duplicate
records/entries can mar analysis by over representing specific data points. The
researcher however can carefully shield the research from concurrence using computer
automated deduplication tools.
2
