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Data AnalysisData Analysis
Stages in Data Analysis
I. Data Processing
II. Data distribution
III. Tabulation
IV. Data Analysis
V. Data Interpretation
VI. Diagrammatic Presentation
Coding
Editing
Computer
feeding
Categorisation
Frequency
Distribution
Measurement

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Data Processing
• involves various manipulations necessary for
preparing data for analysis
1. Editing- a process of examining the collected raw data to
detect errors and omissions and to correct them when possible.
2. Coding- the process of assigning numbers or other symbols
to answers so that responses can be put into a limited number
of classes or categories.
3. Computer feeding-

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Data distributionData distribution
It is a form of classification of scores obtained for the
various categories of a particular variable.
Data analysisData analysis
It is the ordering of data into constituent parts in order
to obtain answers to research questions.
1. Categorisation
2. Frequency distribution
3. Measurement

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Interpretation
Done in two ways-
i. The relations within the study and its data are
interpreted.
ii. The results of the study and the inferences drawn
within the data are compared with the theory.
Diagrammatic representation
Numerical data is represented in the form of-
Graphs
Tables
Statement

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Types of Statistical Analyses Used in
Marketing Research
Statistical Analysis

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Types of Statistical Analyses Used in
Marketing Research
Statistical Analysis
• Five types of statistical analysis:
1. Descriptive analysis: used to describe the data set
2. Inferential analysis: used to generate conclusions
about the population’s characteristics based on the
sample data
3. Differences analysis: used to compare the mean of
the responses of one group to that of another group
4. Associative analysis: determines the strength and
direction of relationships between two or more
variables
5. Predictive analysis: allows one to make forecasts
for future events

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Understanding Data Via Descriptive Analysis
• Two sets of descriptive measures:
• Measures of central tendency: used to report a
single piece of information that describes the
most typical response to a question
• Measures of variability: used to reveal the
typical difference between the values in a set
of values

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Measures of Central Tendency
• Mode: the value in a string of numbers that
occurs most often
• Median: the value whose occurrence lies in the
middle of a set of ordered values
• Mean: sometimes referred to as the “arithmetic
mean”; the average value characterizing a set of
numbers

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Measures of Variability
• Frequency distribution: reveals the number
(percent) of occurrences of each number or set of
numbers
• Range: identifies the maximum and minimum
values in a set of numbers
• Standard deviation: indicates the degree of
variation in a way that can be translated into a
bell-shaped curve distribution

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Correlation
A statistical technique that is used for
measuring the relationship or
interdependence of two or more variables.
It does not indicate the causal relationship
between two variables.
Usually scatter diagrams are used to
represent the relationship between the two
variables

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Measuring Correlation with the help of
scatter diagrams

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Regression
• A statistical technique that relates the
dependent variable to one or more
independent variables.
• It is always used to predict the value of
one variable based on the value of
another variable

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Hypothesis testing
A hypothesis is an assumption about relations between
variables.
It is a tentative explanation of the research problem or
a guess about the research outcome.
Hypothesis testing: a statistical procedure used to
“accept” or “reject” the hypothesis based on sample
information

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Steps involved in hypothesis
testing
1. Formulate a hypothesis
2. Set up a suitable significance level
3. Choose a test criterion
4. Compute
5. Make decisions

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Formulate a hypothesis
• Set up two hypothesis- null hypothesis
alternate hypothesis
Set up a significance level
The confidence with which a null hypothesis is
rejected or accepted depends upon the
significance level.

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Select test criterion
• Involves selecting an appropriate
statistical technique.
• For a large sample (30 or more)- Z test
• For a small sample( less than 30)- t test
include the testing statistic and also its
standard error
Compute

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Make decisions
• to accept or reject the null hypothesis.
• if the computed value falls in the
rejection region – reject the null
hypothesis and vice versa

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Types of errors
DECISION
Accept Ho Reject Ho
Ho (true) Correct
decision
Type I Error
Ho( false) Type II Error Correct decision