Which characteristic best distinguishes quantitative data from qualitative data

The correct answer to the question “Which characteristic best distinguishes Quantitative data from Qualitative data” is, option ©. Its ability to be graphed. This is the most accurate answer to the question. And if you are eager to learn Data Science, then watch the following YouTube video on Data Science Tutorial. If you are interested in breaking into the industry, then check out the Data Science online courses from Intellipaat. This course offers you an industry-grade course with guided projects that will enhance your hands-on experience in the field.

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-Reading and rereading of the transcripts - this helps the researcher become familiar with each participant's version/explanation
Take notes on initial observations such as key phrases, contradictions, language use, etc that could be useful for analysis
Note comments on left-hand margin of the text (suggestion)
-Identification of emergent themes that seem to jump out of the text; should capture some essential idea about what is being said or studied
-Notes on right-hand margin: "Raw Data Themes"
-Researcher may possibly use psychological terms to describe observations at this point

-Structuring emergent themes - after listing the emergent themes, the researcher may see if they relate to each other somehow (clusters, hierarchies, etc).
-Labels given to individual clusters
-Can be in vivo terms used by participants, quotes, or descriptive labels
-Make sure that labels make sense, because these clusters are important to repeatedly refer back to in order to make sure that the interpretation is supported by the data

-Summary table of the structured themes and relevant quotations that illustrate each theme
-Should only include themes/categories that capture the essentials of the participant's perspective; exclude other themes
-Includes data under an organizational scheme with "subordinate theme labels" important quotes, and detailed references to the location of relevant excerpts in the interview transcript

While quantitative research is based on numbers and mathematical calculations (aka quantitative data), qualitative research is based on written or spoken narratives (or qualitative data). Qualitative and quantitative research techniques are used in marketing, sociology, psychology, public health and various other disciplines.

Comparison chart

	Qualitative	Quantitative
Purpose	The purpose is to explain and gain insight and understanding of phenomena through intensive collection of narrative data Generate hypothesis to be test , inductive.	The purpose is to explain, predict, and/or control phenomena through focused collection of numerical data. Test hypotheses, deductive.
Approach to Inquiry	subjective, holistic, process- oriented	Objective, focused, outcome- oriented
Hypotheses	Tentative, evolving, based on particular study	Specific, testable, stated prior to particular study
Research Setting	Controlled setting not as important	Controlled to the degree possible
Sampling	Purposive: Intent to select “small, ” not necessarily representative, sample in order to get in-depth understanding	Random: Intent to select “large, ” representative sample in order to generalize results to a population
Measurement	Non-standardized, narrative (written word), ongoing	Standardized, numerical (measurements, numbers), at the end
Design and Method	Flexible, specified only in general terms in advance of study Nonintervention, minimal disturbance All Descriptive— History, Biography, Ethnography, Phenomenology, Grounded Theory, Case Study, (hybrids of these) Consider many variable, small group	Structured, inflexible, specified in detail in advance of study Intervention, manipulation, and control Descriptive Correlation Causal-Comparative Experimental Consider few variables, large group
Data Collection Strategies	Document and artifact (something observed) that is collection (participant, non-participant). Interviews/Focus Groups (un-/structured, in-/formal). Administration of questionnaires (open ended). Taking of extensive, detailed field notes.	Observations (non-participant). Interviews and Focus Groups (semi-structured, formal). Administration of tests and questionnaires (close ended).
Data Analysis	Raw data are in words. Essentially ongoing, involves using the observations/comments to come to a conclusion.	Raw data are numbers Performed at end of study, involves statistics (using numbers to come to conclusions).
Data Interpretation	Conclusions are tentative (conclusions can change), reviewed on an ongoing basis, conclusions are generalizations. The validity of the inferences/generalizations are the reader’s responsibility.	Conclusions and generalizations formulated at end of study, stated with predetermined degree of certainty. Inferences/generalizations are the researcher’s responsibility. Never 100% certain of our findings.

Type of data

Qualitative research gathers data that is free-form and non-numerical, such as diaries, open-ended questionnaires, interviews and observations that are not coded using a numerical system.

On the other hand, quantitative research gathers data that can be coded in a numerical form. Examples of quantitative research include experiments or interviews/questionnaires that used closed questions or rating scales to collect information.

Applications of Quantitative and Qualitative Data

Qualitative data and research is used to study individual cases and to find out how people think or feel in detail. It is a major feature of case studies.

Quantitative data and research is used to study trends across large groups in a precise way. Examples include clinical trials or censuses.

When to use qualitative vs. quantitative research?

Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well. It is hard, for example, to conduct in-depth interviews with thousands of people or to analyze their responses to open-ended questions. But it is relatively easier to analyze survey responses from thousands of people if the questions are closed-ended and responses can be mathematically encoded in, say, rating scales or preference ranks.

Conversely, qualitative research shines when it is not possible to come up with closed-ended questions. For example, marketers often use focus groups of potential customers to try and gauge what influences brand perception, product purchase decisions, feelings and emotions. In such cases, researchers are usually at very early stages of forming their hypotheses and do not want to limit themselves to their initial understanding. Qualitative research often opens up new options and ideas that quantitative research cannot due to its closed-ended nature.

Analysis of data

Qualitative data can be difficult to analyze, especially at scale, as it cannot be reduced to numbers or used in calculations. Responses may be sorted into themes, and require an expert to analyze. Different researchers may draw different conclusions from the same qualitative material.

Quantitative data can be ranked or put into graphs and tables to make it easier to analyze.

Data Explosion

Data is being generated at an increasing rate because of the expansion in the number of computing devices and the growth of the Internet. Most of this data is quantitative and special tools and techniques are evolving to analyze this "big data".

Effects of Feedback

The following diagram illustrates the effects of positive and negative feedback on Qualitative vs Quantitative research:

References

• Qualitative Quantitative - Simply Psychology
• Qualitative and Quantitative Research - University of Oxford

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