Qualitative Data & Qualntitative Data

Data is a collection of facts, such as numbers, words, meassurements, observations or even just descriptions of things.

There are two types of data.

1.      Qualitative data is descriptive information about characteristics that are difficult to define or measure or cannot be expressed numerically.

2.      Quantitative data is numerical information that can be measured or counted.

What is Qualitative Data

v  Transcripts of individual interviews and focus groups or field notes, copies of documents, audio and video recordings from observation of certain activities.

v  Data that are related to concepts, opinions, values and behaviours of people in a social context.

v  Data that are not easily reduced to numbers. (www.socialresearchmethods.net/kb/qualdata.php)

Definition of Qualitative Data

Qualitative Data refers to the data that provides insights and understanding about a particular problem. It can be approximated but cannot be computed.

The nature of data is descriptive and so it is a bit difficult to analyze it. This type of data can be classified into categories, on the basis of physical attributes and properties of the object. The data is interpreted as spoken or written narratives rather than numbers. It is concerned with the data that is observable in terms of smell, appearance, taste, feel, texture, gender, nationality and so on.

Examples of Qualitative Data

v  feelings and emotions

v  texture

v  flavor

v  color (unless it can be written as a specific wavelength of light)

v  expressions of more/less, ugly/beautiful, fat/thin, healthy/sickly

Characterstics of Qualitative Data

The Design

·         Naturalistic -- refers to studying real-world situations as they unfold naturally; nonmanipulative and noncontrolling;

·         Emergent -- acceptance of adapting inquiry as understanding deepens and/or situations change; \

·         Purposeful -- cases for study are selected because they are “information rich” and illuminative.

The Collection of Data

·         Data -- observations yield a detailed, "thick description" [in-depth understanding];

·         Personal experience and engagement -- one has direct contact with and gets close to the people, situation, and phenomenon under investigation;

·         Empathic neutrality -- an empathic stance in working with study responents seeks vicarious understanding without judgment [neutrality] by showing openness, sensitivity, respect, awareness, and responsiveness;

·         Dynamic systems -- there is attention to process; assumes change is ongoing, whether the focus is on an individual, an organization, a community, or an entire culture.

The Analysis

·         Unique case orientation -- assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied;

·         Inductive analysis -- immersion in the details and specifics of the data to discover important patterns, themes, and inter-relationships;

·         Holistic perspective -- the whole phenomenon under study is understood as a complex system that is more than the sum of its parts;

·         Context sensitive -- places findings in a social, historical, and temporal context;

·         Voice, perspective, and reflexivity -- the qualitative methodologist owns and is reflective about her or his own voice and perspective;

Organisation of Qualitative Data

Data sources may be personal interviews (written or recorded), surveys, questionnaires, official documents or observation notes.To extricate and code data from multiple data sources can be difficult, but made much easier if the data is organized appropriately.

a.       Review the entire data set so that themes or patterns begin to emerge. Note these themes or patterns and assign letters, numbers or symbols to designate categories. Like responses on a particular topic can be grouped together, thereby making item analysis easier.

b.      Create a code table so that codes can be consistent and readily accessible for multiple researchers. When conducting qualitative research, it is preferable to use multiple researchers so that a variety of perspectives are considered in data analysis.

c.       Separate the data into the groups -- themes, patterns or other categories. Once the data set has been coded the data can be grouped according to the code. This will also make data analysis and discussion easier. The discussion and analysis can then focus on independent themes that are noted in the data.

d.      Organize survey data by question, respondent or sub-topic. It is important to organize survey data so that it can be easily analyzed. One method of organization is to separate the data according to the question, respondent category or sub-topic.

e.       Code transcribed data so that the source is readily evident. Researchers often use data that is obtained by transcribing recorded or written interviews notes. Since data will be generated from a variety of interviews or verbal recordings and grouped, it is important that source of the data is labeled.

Methodology

Data collection approaches for qualitative data usually involves:

a.       Direct interaction with individuals on a one to one basis

b.      Or direct interaction with individuals in a group setting

Qualitative data collection methods are time consuming, therefore data is usually collected from a smaller sample than would be the case for quantitative approaches - therefore this makes qualitative research more expensive.

The benefits of the qualitative approach is that the information is richer and has a deeper insight into the phenomenon under study

The main methods for collecting qualitative data are:

What is Qualitative Data Analysis

Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected, into some form of explanation, understanding or interpretation of the people and situations we are investigating. QDA is usually based on an interpretative philosophy. The idea is to examine the meaningful and symbolic content of qualitative data.

FOUR BASIC STEPS

All qualitative data analysis involves the same four essential steps:

}  Raw data management- ‘data cleaning’

}  Data reduction, I, II – ‘chunking’, ‘coding’

}  Data interpretation – ‘coding’, ‘clustering’

}  Data representation – ‘telling the story’, ‘making sense of the data for others’

DATA ANALYSIS SPIRAL #1

DATA ANALYSIS SPIRAL #2

Step I: Raw Data Management

}  What is raw data management?

◦      The process of preparing and organizing raw data into meaningful units of analysis:

– Text or audio data transformed into transcripts

– Image data transformed into videos, photos, charts

Step II: Data Reduction I

}  Get a sense of the data holistically, read several times (immersion)

}  Classify and categorize repeatedly, allowing for deeper immersion

}  Write notes in the margins (memoing)

}  Preliminary classification schemes emerge, categorize raw data into groupings (chunking

Step II: Data Reduction II

◦      The process of reducing data from chunks into clusters and codes to make meaning of that data:

– Chunks of data that are similar begin to lead to initial clusters and coding

– Clusters – assigning chunks of similarly labeled data into clusters and assigning preliminary codes

– Codes – refining, developing code books, labeling codes, creating codes through 2-3 cycles

Step III: Data Interpretation & Themes

}  ‘Chunks’ of related data that have similar meaning are coded in several cycles

}  Once coded, those ‘chunks’ become clustered in similar theme categories

}  Create meaning for those clusters with labels

}  Themes emerge from those clusters

}  Interpret themes to answer research questions

Step IV: Data Representation

}  Interpretation or analysis of qualitative data simultaneously occurs

}  Researchers interpret the data as they read and re-read the data, categorize and code the data and inductively develop a thematic analysis

}  Themes become the story or the narrative

1.   Quantitative → Quantities

Examples of Quantitative Data

Quantitative data can be expressed as numbers. If you can measure it, it can be expressed as a quantity.

v  height

v  weight

v  number of objects

v  volume

v  temperature

v  pressure

v  price

v  speed

v  percentages

Definition of Quantitative Data

Quantitative Data, as the name suggests is one which deals with quantity or numbers. It refers to the data which computes the values and counts and can be expressed in numerical terms is called quantitative data.

Quantitative data may be used in computation and statistical test. It is concerned with measurements like height, weight, volume, length, size, humidity, speed, age etc.The methods used for the collection of data are:

v  Surveys

v  Experiments

v  Observations and Interviews

Characterstics of Qualitative Data

Its main characteristics are:

·         The data is usually gathered using structured research instruments.

·         The results are based on larger sample sizes that are representative of the population.

·         The research study can usually be replicated or repeated, given its high reliability.

·         Researcher has a clearly defined research question to which objective answers are sought.

·         All aspects of the study are carefully designed before data is collected.

·         Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.

·         Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.

·         Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

Analysing Quantitative Data

There are a wide range of statistical techniques available to analyse quantitative data, from simple graphs to show the data through tests of correlations between two or more items, to statistical significance. Other techniques include cluster analysis, useful for identifying relationships between groups of subjects where there is no obvious hypothesis, and hypothesis testing, to identify whether there are genuine differences between groups.

Steps in Quantitative Data Analysis

Stepping Your Way through Effective Quantitative Data Analysis

1.      Data management – This involves familiarizing yourself with appropriate software; systematically logging in and screening your data: entering the data into a program; and finally, ‘cleaning’ your data.

2.      Understanding variable types – Different data types demand discrete treatment, so it has important to be able to distinguish variables by both cause and effect (dependent or independent), and their measurement scales (nominal, ordinal, interval, and ratio).

3.      Run descriptive statistics – These are used to summarize the basic features of a data set through measures of central tendency (mean, mode, and median), dispersion (range, quartiles, variance, and standard deviation), and distribution (skewness and kurtosis).

4.      Run appropriate inferential statistics – This allows researchers to assess their ability to draw conclusions that extend beyond the immediate data. For example, if a sample represents the population; if there are differences between two or more groups; if there are changes over time; or if there is a relationship between two or more variables.

5.      Make sure you selecting the right statistical test – This relies on knowing the nature of your variables; their scale of measurement; their distribution shape; and the types of question you want to ask.

6.      Look for statistical significance – This is generally captured through a ‘p-value’, which assesses the probability that your findings are more than coincidence. The lower the p-value, the more confident researchers can be that findings are genuine.

Methodology of Quantitative Data

Data can be readily quantified and generated into numerical form, which will then be converted and processed into useful information mathematically. The result is often in the form of statistics that is meaningful and, therefore, useful.

Quantitative Surveys

Unlike the open-ended questions asked in qualitative questionnaires, quantitative paper surveys pose closed questions, with the answer options provided. The respondents will only have to choose their answer among the choices provided on the questionnaire.

Interviews

Personal one-on-one interviews may also be used for gathering quantitative data. In collecting quantitative data, the interview is more structured than when gathering qualitative data, comprised of a prepared set of standard questions.

These interviews can take the following forms:

·         Face-to-face interviews: Much like when conducting interviews to gather qualitative data, this can also yield quantitative data when standard questions are asked.

·         Telephone and/or online, web-based interviews:. Rapidly rising to take the place of telephone interviews is the video interview via internet connection and web-based applications, such as Skype.

·         Computer-assisted interviews: This is called CAPI, or Computer-Assisted Personal Interviewing where, in a face-to-face interview, the data obtained from the interviewee will be entered directly into a database through the use of a computer.

Quantitative Observation

Data may be collected through systematic observation by, say, counting the number of users present and currently accessing services in a specific area, or the number of services being used within a designated vicinity.

Experiments

These methods involve manipulation of an independent variable, while maintaining varying degrees of control over other variables, most likely the dependent ones

·         Laboratory experiments. This is your typical scientific experiment setup, taking place within a confined, closed and controlled environment (the laboratory), with the data collector being able to have strict control over all the variables.

·         Field experiments: This takes place in a natural environment, “on field” where, although the data collector may not be in full control of the variables, he is still able to do so up to a certain extent

·         Natural experiments: This time, the data collector has no control over the independent variable whatsoever, which means it cannot be manipulated.

Key Differences Between Qualitative and Quantitative Data

The fundamental points of difference between qualitative and quantitative data are discussed below:

1.      The data type, in which the classification of objects is based on attributes (quality) is called qualitative data. The type of data which can be counted and expressed in numbers and values is called quantitative data.

2.      The research methodology is exploratory in qualitative data, i.e. to provide insights and understanding. On the other hand, quantitative data is conclusive in nature which aims at testing a specific hypothesis and examine the relationships.

3.      The approach to inquiry in the case of qualitative data is subjective and holistic whereas quantitative data has an objective and focused approach.

4.      When the data type is qualitative the analysis is non-statistical. As opposed to quantitative data which uses statistical analysis.

5.      In qualitative data, there is an unstructured gathering of data. As against this, data collection is structured in quantitative data.

6.      While qualitative data determines the depth of understanding, quantitative data ascertains the level of occurrence.

7.      Quantitative data is all about ‘How much or how many’. On the contrary, qualitative data asks ‘Why?’

8.      In qualitative data the sample size is small and that too is drawn from non-representative samples. Conversely, the sample size is large in quantitative data drawn from the representative sample.

9.      Qualitative data develops initial understanding, i.e. it defines the problem. Unlike quantitative data, which recommends the final course of action.

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.

	Qualitative	Quantitative
Purpose	The purpose is to explain and gain	The purpose is to explain, predict,
	insight and understanding of phenomena	and/or control phenomena through
	through intensive collection of narrative	focused collection of numerical data.
	data Generate hypothesis to be test ,	Test hypotheses, deductive.
	inductive.
Approach to	subjective, holistic, process- oriented	Objective, focused, outcome- oriented
Inquiry
Hypotheses	Tentative, evolving, based on particular	Specific, testable, stated prior to
	study	particular study
Research	Controlled setting not as important	Controlled to the degree possible
Setting

Sampling	Purposive: Intent to select “small, ” not	Random: Intent to select “large, ”
	necessarily representative, sample in	representative sample in order to
	order to get in-depth understanding	generalize results to a population
Measurement	Non-standardized, narrative (written	Standardized, numerical
	word), ongoing	(measurements, numbers), at the end
Design and	Flexible, specified only in general terms	Structured, inflexible, specified in
Method	in advance of study Nonintervention,	detail in advance of study
	minimal disturbance All Descriptive—	Intervention, manipulation, and
	History, Biography, Ethnography,	control Descriptive Correlation
	Phenomenology, Grounded Theory, Case	Causal-Comparative Experimental
	Study, (hybrids of these) Consider many	Consider few variables, large group
	variable, small group
Data	Document and artifact (something	Observations (non-participant).
Collection	observed) that is collection (participant,	Interviews and Focus Groups (semi-
Strategies	non-participant). Interviews/Focus	structured, formal). Administration of
	Groups (un-/structured, in-/formal).	tests and questionnaires (close ended).
	Administration of questionnaires (open
	ended). Taking of extensive, detailed
	field notes.
Data Analysis	Raw data are in words. Essentially	Raw data are numbers Performed at
	ongoing, involves using the	end of study, involves statistics (using
	observations/comments to come to a	numbers to come to conclusions).
	conclusion.
Data	Conclusions are tentative (conclusions	Conclusions and generalizations
Interpretation	can change), reviewed on an ongoing	formulated at end of study, stated
	basis, conclusions are generalizations.	with predetermined degree of
	The validity of the	certainty. Inferences/generalizations
	inferences/generalizations are the	are the researcher’s responsibility.
	reader’s responsibility.	Never 100% certain of our findings.

Conclusion

So, for the collection and measurement of data, any of the two methods discussed above can be used. Although both have its merits and demerits, i.e. while qualitative data lacks reliability, quantitative data lacks description. Both are used in conjunction so that the data gathered is free from any errors. Further, both can be acquired from the same data unit only their variables of interest are different, i.e. numerical in case of quantitative data and categorical in qualitative data.