Q4-DATA ANALYSIS METHODS-WK4.pdf

DATA
DATA
ANALYSIS METHODS
ANALYSIS METHODS
Research Project
Finding the Answers to the Research Questions-Qualitative

Qualitative data analysis works a little differently from quantitative
data, primarily because qualitative data is made up of words,
observations, images, and even symbols. Deriving absolute meaning
from such data is nearly impossible; hence, it is mostly used for
exploratory research.
Analyzing Qualitative Data
Analyzing Qualitative Data

Data
Data
Preparation
Preparation
and Basic
and Basic
Data Analysis
Data Analysis

Content analysis: This is one of the most common methods to analyze
qualitative data. It is used to analyze documented information in the
form of texts, media, or even physical items. When to use this method
depends on the research questions. Content analysis is usually used to
analyze responses from interviewees.
Qualitative Data Analysis
Methods
Methods

Narrative analysis: This method is used to analyze content from
various sources, such as interviews of respondents, observations
from the field, or surveys. It focuses on using the stories and
experiences shared by people to answer the research questions.
Methods
Methods

Framework analysis. This is a more advanced method that
consists of several stages such as familiarization, identifying a
thematic framework, coding, charting, mapping, and
interpretation.
Methods
Methods

Discourse analysis: Like narrative analysis, discourse analysis is used to
analyze interactions with people. However, it focuses on analyzing the social
context in which the communication between the researcher and the
respondent occurred. Discourse analysis also looks at the respondent’s
day-to-day environment and uses that information during analysis.
Methods
Methods

Grounded theory: This refers to using qualitative data to explain why a
certain phenomenon happened. It does this by studying a variety of similar
cases in different settings and using the data to derive causal explanations.
Researchers may alter the explanations or create new ones as they study
more cases until they arrive at an explanation that fits all cases.
Methods
Methods

Coding can be explained as
he categorization of data. A ‘code’
can be a word or a short phrase
that represents a theme or an
idea.
Step 1: Developing and
Applying Codes.
The analytical and critical thinking skills of the
researcher plays significant role in data
analysis in qualitative studies. Therefore, no
qualitative study can be repeated to generate
the same results.
Step 2: Identifying
themes, patterns and
relationships.
Qualitative data analysis can also be
conducted through the following
three
three steps:
steps:

At this last stage, you need to link research findings
to hypotheses or research aims and objectives.
When writing the data analysis chapter, you can
use noteworthy quotations from the transcript in
order to highlight major themes within findings and
possible contradictions.
Step 3: Summarizing
the data.
three
three steps:
steps:

Step 1: Developing and Applying
Codes
Codes
WHAT IS CODE?
Code may be a word or short
phrase that symbolically assigns
a cumulative prominent and
sense-capturing portion of a text
or visual data.

Codes
Codes
There are three types of coding:
1. Open coding. The initial organization of raw data to try to
make sense of it.
2. Axial coding. Interconnecting and linking the categories of
codes.
3. Selective coding. Formulating the story through
connecting the categories.

Codes
Codes
There are three types of coding:
Coding can be done manually or using qualitative data
analysis software such as
NVivo, Atlas ti 6.0, Hyper RESEARCH 2.8, Max QDA and others.

Codes
Codes
https://www.youtube.com/watch?v=6_gZuEm3Op0

Step 2: Identifying themes, patterns
and relationships.
and relationships.
most popular and effective methods of
qualitative data interpretation
Word and phrase repetitions – scanning primary data
for words and phrases most commonly used by
respondents, as well as, words and phrases used with
unusual emotions;

and relationships.
and relationships.
Primary and secondary data comparisons – comparing
the findings of interview/focus group/observation/any
other qualitative data collection method with the
findings of the literature review and discussing
differences between them;

and relationships.
and relationships.
Search for missing information – discussions
about which aspects of the issue was not
mentioned by respondents, although you
expected them to be mentioned;

and relationships.
and relationships.
Metaphors and analogues – comparing primary
research findings to phenomena from a different
area and discussing similarities and differences.

Step 3: Summarizing the data
Step 3: Summarizing the data

DATA
DATA
ANALYSIS METHODS
ANALYSIS METHODS
Research Project
Finding the Answers to the Research Questions-Quantitative

Quantitative or Qualitative
data
data
Identify whether the
following statements is a
quantitative or qualitative
data

data
data
It is warm outside

data
data
The cup had a mass of
454 grams

data
data
The cake recipe
requires 3 cups of
flour

data
data
The shelf life of the
Papaya Pickle is 3
days

data
data
The cloth of our
table napkin feels
rough

data
data
One of the welding
rod measures 9 cm
long.

data
data
The temperature of
the oven increased
by 8°C.

data
data
Opening the wine
bottle makes a loud
pop sound.

data
data
The pastry in the
canteen smells
sweet.

data
data
Leonora earned 95%
on her Math quiz.

Types of Data
Types of Data
Qualitative or Categorical Data is
data that can’t be measured or
counted in the form of numbers.
These types of data are sorted by
category, not by number.
Qualitative or Categorical
Gender ( Male, Female)
Hair color ( Black, Brown, Gray, etc)
Nationality (Indian, American, Chinese,
etc)
These data consist of audio, images,
symbols, or text. The gender of a person,
i.e., male, female, or others, is qualitative
data.
Example

Types of Data
Types of Data
Nominal values represent discrete units and
are used to label variables that have no
quantitative value. Just think of them as
“labels.” Note that nominal data that has no
order. Therefore, if you would change the order
of its values, the meaning would not change.
Nominal Data
Example

Types of Data
Types of Data
Ordinal data have natural ordering where
a number is present in some kind of order
by their position on the scale. These data
are used for observation like customer
satisfaction, happiness, etc., but we can’t
do any arithmetical tasks on them.
Ordinal Data
Example

Types of Data
Types of Data
Quantitative data is also known as
numerical data which represents the
numerical value (i.e., how much, how often,
how many). Numerical data gives
information about the quantities of a
specific thing. Quantitative data can be used
for statistical manipulation.
Quantitative or numerical
Example
Height or weight of a person or
object
Room Temperature
Scores and Marks (Ex: 59, 80, 60, etc.)
Time

Types of Data
Types of Data
Discrete data can take only discrete
values. Discrete information contains
only a finite number of possible values.
Those values cannot be subdivided
meaningfully. Here, things can be
counted in whole numbers.
Discrete
Example

Types of Data
Types of Data
Continuous data represent
measurements and therefore their values
can’t be counted but they can be
measured. An example would be the
height of a person, which you can describe
by using intervals on the real number line.
Continuous
Example

Types of Data
Types of Data
It represents ordered data that is measured
along a numerical scale with equal distances
between the adjacent units. These equal
distances are also referred to as intervals. So
a variable contains interval data if it has
ordered numeric values with the exact
differences known between them.
Interval
Example

Types of Data
Types of Data
Like Interval data, ratio data are also
ordered with the same difference
between the individual units. However,
they also have a meaningful zero so
they cannot take negative values.
Ratio
Example
The temperature on a Kelvin scale
(0 degrees represent the total
absence of thermal energy)
Height ( zero is the starting point)
weight, length

Analyzing Quantitative Data
Analyzing Quantitative Data
Data Preparation
The first stage of analyzing data is data
preparation, where the aim is to convert raw data
into something meaningful and readable. It
includes four steps.

The purpose of data validation is to find
out, as far as possible, whether the data
collection was done as per the pre-set
standards and without any bias. It is a
four-step process, which includes…
Step 1: Data Validation

Typically, large data sets include errors. For
example, respondents may fill the fields
incorrectly or skip them accidentally. To make
sure that there are no such errors, the
researcher should conduct basic data checks,
check for outliers, and edit the raw research
data to identify and clear out any data points
that may hamper the accuracy of the results
Step 2: Data Editing
Step 2: Data Editing

This is one of the most important steps
in data preparation. It refers to
grouping and assigning values to
responses from the survey.
Step 3: Data Coding
Step 3: Data Coding

For example, if a researcher has interviewed 1,000
people and now wants to find the average age of
the respondents, the researcher will create age
buckets and categorize the age of each of the
respondents as per these codes. (For example,
respondents between 13-15 years old would have
their age coded as 0, 16-18 as 1,
18-20 as 2, etc.)
Step 3: Data Coding
Step 3: Data Coding

Quantitative Data Analysis
Quantitative Data Analysis
Methods
Methods
After these steps, the data is ready for
analysis. The two most commonly used
quantitative data analysis methods are
descriptive statistics and inferential
statistics.

Descriptive Statistics
Typically descriptive statistics (also known as descriptive analysis)
is the first level of analysis. It helps researchers summarize the
data and find patterns. A few commonly used descriptive statistics
are:
Mean: numerical average of a set of values.
 Median: midpoint of a set of numerical values.
 Mode: most common value among a set of values.

 Percentage: used to express how a value or group of
respondents within the data relates to a larger group of
respondents.
 Frequency: the number of times a value is found.
 Range: the highest and lowest value in a set of values.

Descriptive Vs. Inferential
Descriptive Vs. Inferential

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