QUANTITATIVE RESEARCH
VERSUS
QUALITATIVE RESEARCH
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Qualitative research is the process of collecting, analyzing, and
interpreting non-numerical data, such as language. Qualitative
research can be used to understand how an individual
subjectively perceives and gives meaning to their social reality.
Quantitative research is defined as a systematic investigation of
phenomena by gathering quantifiable data and performing statistical,
mathematical, or computational techniques. Quantitative research
collects information from existing and potential customers using sampling
methods and sending out online surveys, online polls, questionnaires,
etc., the results of which can be depicted in the form of numerical.
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Data collection methods
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When to use qualitative vs. quantitative research
A rule of thumb for deciding whether to use qualitative or
quantitative data is:
• Use quantitative research if you want to confirm or test
something (a theory or hypothesis)
• Use qualitative research if you want to understand
something (concepts, thoughts, experiences)
Quantitative research approach
You survey 300 students at your university and ask them questions
such as: “on a scale from 1-5, how satisfied are your with your
professors?”
You can perform statistical analysis on the data and draw conclusions
such as: “on average students rated their professors 4.4”.
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Qualitative research approach
You conduct in-depth interviews with 15 students and ask them
open-ended questions such as: “How satisfied are you with your
studies?”, “What is the most positive aspect of your study program?”
and “What can be done to improve the study program?”
Based on the answers you get you can ask follow-up questions to
clarify things. You transcribe all interviews using
transcription software and try to find commonalities and patterns.
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Mixed methods approach
You conduct interviews to find out how satisfied students are with
their studies. Through open-ended questions you learn things you
never thought about before and gain new insights. Later, you use
a survey to test these insights on a larger scale.
It’s also possible to start with a survey to find out the overall
trends, followed by interviews to better understand the reasons
behind the trends.
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How to analyze qualitative and quantitative data
Qualitative or quantitative data by itself can’t prove or
demonstrate anything, but has to be analyzed to show its
meaning in relation to the research questions. The method of
analysis differs for each type of data.
Analyzing quantitative data
Quantitative data is based on numbers. Simple math or more
advanced statistical analysis is used to discover commonalities or
patterns in the data. The results are often reported in graphs and
tables.
Applications such as Excel, SPSS, or R can be used to calculate things
like:
• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results
Analyzing qualitative data
Qualitative data is more difficult to analyze than quantitative data. It
consists of text, images or videos instead of numbers.
Some common approaches to analyzing qualitative data include:
• Qualitative content analysis: Tracking the occurrence, position and
meaning of words or phrases
• Thematic analysis: Closely examining the data to identify the main
themes and patterns
• Discourse analysis: Studying how communication works in social
contexts
VARIABLES
Variable Definition in Research
A variable is any quantity that you are able
to measure in some way. This could be
temperature, height, age, etc. Basically, a
variable is anything that contributes to the
outcome or result of your experiment in any
way.
Types of Variables
✗ A qualitative variable, also called a categorical variable, is a
variable that isn’t numerical. It describes data that fits into
categories.
✗ Categorical variables may again be described
as nominal and ordinal.
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✗ Ordinal variables are those ✗ Nominal variables are those
which can be logically ordered who can neither be ranked nor
or ranked higher or lower than logically ordered, such as
another but do not necessarily religion, sex, etc.
establish a numeric difference
between each category, such as
examination grades (A+, A,
B+, etc., clothing size (Extra
large, large, medium, small).
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Quantitative variables, also called numeric
variables, are those variables that are measured in
terms of numbers. A simple example of a
quantitative variable is a person’s age.
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Quantitative variables are again of two types: discrete and
continuous.
A discrete variable, restricted to certain values, usually (but not
necessarily) consists of whole numbers, such as the family size,
number of defective items in a box. They are often the results of
enumeration or counting.
A continuous variable is one that may take on an infinite number
of intermediate values along a specified interval.
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Dependent and Independent Variables
The variable that is used to describe or measure the factor that is
assumed to cause or at least to influence the problem or outcome is
called an independent variable.
The variable that is used to describe or measure the problem or
outcome under study is called a dependent variable.
Example:
1. You want to figure out which brand of
microwave popcorn pops the most
kernels so you can get the most value
for your money. You test different brands
of popcorn to see which bag pops the
most popcorn kernels.
Independent
variable:
Independent Variable: Brand of
popcorn bags
Dependent Variable: number of
kernels popped
Example:
2. You want to see which type of
fertilizer helps plant grow fastest, so
you add a different brand of
fertilizer to each plant and see how
tall they grow.
Independent variable: Type of
fertilizer given to the plant
Dependent variable: Plant height
Controlled Variable
A controlled variable is one which the scientist
holds constant (controls) during an
experiment. Thus we also know the controlled
variable as a constant variable or sometimes
as a “control” only.
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Research title
✗ Formulate 3 Research Title about your topic. Identify
the independent and dependent variable
✗ Independent Variable
✗ Dependent Variable
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