Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes.
Research analysis: getting more from your datacxpartners
Analysis is an under-appreciated part of the research process, but it's actually where the magic happens. Good analysis takes the data as a starting point, and goes beyond it to discover the insights that others will have missed. These slides go through a core method for analysing qualitative data, allowing you to slot in techniques and activities for specific research objectives as required
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes.
Research analysis: getting more from your datacxpartners
Analysis is an under-appreciated part of the research process, but it's actually where the magic happens. Good analysis takes the data as a starting point, and goes beyond it to discover the insights that others will have missed. These slides go through a core method for analysing qualitative data, allowing you to slot in techniques and activities for specific research objectives as required
Data analysis chapter 18 from the companion website for educational researchYamith José Fandiño Parra
This is a slide show of chapter 18 from Educational Research: Competencies for Analysis and Applications. Primarily intended for instructor use in the classroom, it is also available for students’ study use or to review as an advance organizer before class lectures or discussions.
Statistics for Anaesthesiologists covers basic to intermediate level statistics for researchers especially commonly used study designs or tests in Anaesthesiology research.
(Individuals With Disabilities Act Transformation Over the Years)DSilvaGraf83
(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
ANOVA
ANOVA
• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
One-Way ANOVA Excel Output
Treatme
(Individuals With Disabilities Act Transformation Over the Years)DMoseStaton39
(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
ANOVA
ANOVA
• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
One-Way ANOVA Excel Output
Treatme
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
5. WHAT IS SPSS?
• SPSS Statistics is a software package used for statistical analysis.
• SPSS can be used for:
– Processing Questionnaire
– Reporting in tables and graphs
– Analyzing
• Mean, Median, Mode
• Mean Dev & Std. Dev.,
• Correlation & Regression,
• Chi Square, T-Test, Z-test, ANOVA, MANOVA, Factor Analysis, Cluster Analysis, Multidimensional Scaling etc.
• Founded in 1968 and acquired by IBM in 2009.
6.
7. WHAT IS HYPOTHESIS?
“The statement speculating the outcome of a research or experiment.”
• H0=There is no difference in performance of Div. A, B and C in Semester I
• Ha=Business Communication subject has been effective in developing communication skills of students
• H0=Biometric system has not improved the attendance of faculties
• Ha=Excessive fishing has affected marine life
• H0=There is no significant difference in salary of males and females in particular organization.
Here,
H0=Null Hypothesis
Ha=Alternate Hypothesis
8. WHAT IS LEVEL OF SIGNIFICANCE
When null hypothesis is true, you accept it.
When it is false, you reject it.
5% level of significance means you are taking 5% risk of rejecting null hypothesis when it
happens to be true.
It is the maximum value of probability of rejecting H0 when it is true.
9. TYPES OF STATISTICAL TESTS
Tests Meaning When it is used
Statistical tests
used
Parametric
Tests
Based on assumption that
population from where the
sample is drawn is normally
distributed.
Used to test parameters
like mean, standard
deviation, proportions
etc.
• T-test
• ANOVA
• ANCOVA
• MANOVA
• Karl Pearson
Non
parametric
Tests
Don’t require assumption
regarding shape of
population distribution.
Used mostly for
categorical variable or in
case of small sample
size which violates
normality.
• Chi Square
• Mann-Whitney U
• Wilcoxon Signed Rank
• Kruskal-Wallis
• Spearman’s
11. INTRODUCTION
• Significance of difference between means of two samples can be judged using:
– Z test (>30)
– T test (<30)
• Difficulty arises while measuring difference between means of more than 2 samples
• ANOVA is used in such cases
• ANOVA is used to test the significance of the difference between more than two sample means and
to make inferences about whether our samples are drawn from population having same means
Significance of difference of IQ of 2 divisions Z test or T Test
Significance of difference between performance of 5 different types of vehicles ANOVA
12. WHEN TO USE ANOVA?
Compare yield of crop from several variety of seeds
Mileage of 4 automobiles
Spending habits of five groups of students
Productivity of 4 different types of machine during a given period of time
Effectiveness of fitness programme on increase in stamina of 5 players
13. WHY ANOVA INSTEAD OF MULTIPLE T TEST?
• If more than two groups, why not just do several two sample t-tests to compare the
mean from one group with the mean from each of the other groups?
• The problem with the multiple t-tests approach is that as the number of groups
increases, the number of two sample t-tests also increases.
• As the number of tests increases the probability of making a Type I error also
increases.
14. ANOVA HYPOTHESES
• The Null hypothesis for ANOVA is that the means for all groups
are equal.
• The Alternative hypothesis for ANOVA is that at least two of
the means are not equal.
16. What is 1-way ANOVA and 2-way ANOVA?
• If we take only one factor and investigate the difference among its various categories having numerous possible
values, it is called as One-way ANOVA.
• In case we investigate two factors at the same time, then we use Two-way ANOVA
Training Type Productivity
Advanced 200
Advanced 193
Advanced 207
Intermediate 172
Intermediate 179
Intermediate 186
Beginners 130
Beginners 125
Beginners 119
One-way ANOVA
Gender Educational
Level
Marks
Male School 89
Male College 50
Male School 90
Male College 80
Female College 50
Female University 40
Female School 91
Female University 56
Two-way ANOVA
17. HOW ANOVA WORKS?
• Three methods used to dissolve a powder in water are compared by the time (in minutes) it
takes until the powder is fully dissolved. The results are summarized in the following table:
• It is thought that the population means of the three methods m1, m2 and m3 are not all
equal (i.e., at least one m is different from the others). How can this be tested?
18. • One way is to use multiple two-sample t-tests and
• compare Method 1 with Method 2,
• Method 1 with Method 3 and
• Method 2 with Method 3 (comparing all the pairs)
• But if each test is 0.05, the probability of making a Type 1 error when running three tests would
increase.
• Better method is ANOVA (analysis of variance)
• The technique requires the analysis of different forms of variances – hence the name.
Important: ANOVA is used to show that means are different and not variance are different.
19. • ANOVA compares two types of variances
• The variance within each sample and
• The variance between different samples.
• The black dotted arrows show the per-sample variation of the individual data points around the
sample mean (the variance within).
• The red arrows show the variation of the sample means around the grand mean (the variance
between).
20. STEPS FOR USING ANOVA
Null Hypothesis H0 : μ1= μ2= μ3=………= μk
Alternate Hypothesis Ha : μ1≠ μ2 ≠ μ3 ≠ ……… ≠ μk
1. Calculate mean of each sample (x̄1, x̄2, x̄3…… x̄k)
2. Calculate mean of sample means:
Where k=Total number samples
3. Calculate Sum of Square between the samples:
Where n1=Total number of item in sample 1
n2=Total number of item in sample 2
n3=Total number of item in sample 3 …………………….
Step 1 : State Null and Alternate Hypothesis
Step 2 : Compute Variance Between the samples
k
XXXX
X K
.......321
22
33
2
22
2
11 )(......)()()( xxnxxnxxnxxnSS kkbetween
21. 1. Calculate Sum of Square within the samples:
SSTotal = SSBetween + SSWithin
Step 3 : Compute Variance Within samples
22
33
2
22
2
11 )(....)()()( kkiiiiiiiiwithin xxxxxxxxSS
Step 4 : Calculate total variance
Step 5 : Calculate average variance between and within
samples
1
k
SS
MS Between
between
kn
SS
MS within
within
N=Total no of items in
all samples
K=Number of samples
22. Step 6 : Calculate F-ratio
within
between
MS
MS
Fratio
Step 7 : Set up ANOVA table
Source of
variation
Sum of
squares (SS)
Degree of
freedom (d.f)
Mean Squares F-Value
(Calculated)
Between
Samples
SS Between k-1 MS Between=
SS Between/k-1
F=MS Between/MS
Within
Within
Samples
SS Within n-k MS Within=
SS Within/n-k
Total SS Total n-1
23. Decision Rule: Reject H0 if
Calculated value of F > Tabulated value of F
Otherwise accept H0
Or
Accept H0 if
Calculated value of F < Tabulated value of F
Otherwise reject H0
Step 8 : Look for Table value of F
Steps:
1. Find out two degree of freedom (one for between and one for
within)
2. Denote x for between and y for within [F(x,y)]
3. In F-distribution table, go along x columns, and down y rows.
The point of intersection is your tabulated F-ratio
24. EXAMPLE
• Set up an analysis of variance table for the following per acre production
data for three varieties of wheat, each grown on 4 plots and state if the
variety differences are significant.
• Test at 5% level of significance
25. H0 = The difference between varieties is not significant
Ha = The difference in varieties is significant
26. Interpretation:
Calculated Value of F < Table Value of F
∴ Accept Null Hypothesis
Difference in wheat output due to varieties is not significant and is just a matter of chance.
27. EXAMPLE
• Ranbaxy Ltd. has purchased three new machines of different makes and
wishes to determine whether one of them is faster than the others in
producing a certain output.
• Four hourly production figures are observed at random from each
machine and the results are given below:
• Use ANOVA and determine whether machines are significantly different in
their mean speed.
Observations M1 M2 M3
1 28 31 30
2 32 37 28
3 30 38 26
4 34 42 28
31. TWO WAY ANOVA
• Two-way ANOVA technique is used when the data are classified on the basis of two factors.
• For example, the agricultural output may be classified on the basis of different varieties of seeds and
also on the basis of different varieties of fertilizers used.
• Two types of 2-way ANOVA
– Without repeated values
– With repeated values
40. WHAT IS RESEARCH PROPOSAL?
A research proposal is a document that provides a detailed description of the intended
program. It is like an outline of the entire research process that gives a reader a
summary of the information discussed in a project.
41. WHAT IS RESEARCH PROPOSAL?
• Research proposal sets out
– Broad topic you want to research
– What is it trying to achieve?
– How would you do research?
– What would be time need?
– What results it might produce?
42. PURPOSE OF RESEARCH PROPOSAL
• Convince others that research is worth
• Sell your idea to funding agency
• Convince the problem is significant and worth study
• Approach is new and yield results
43. ELEMENTS OF RESEARCH PROPOSAL
Introduction
Statement of Problem
Purpose of the Study
Review of Literature
Questions and Hypothesis
The Design – Methods & Procedures
Limitations of the Study
Significance of the Study
References
45. Color of Bike
Look
Masculine/Feminine
Mileage
Price
Maintenance Cost
Power
Speed
Control
Weight
Brand
Ease of delivery
Financial Assistance
Offer/Discounts Tyre size
Disc Brake
Smooth Handling
Service Centers
Design Cost Technical Comfort
FACTORS Unobserved
Observed
46. FACTOR ANALYSIS
“Factor analysis is a statistical method used to describe variability among
observed, correlated variables in terms of a potentially lower number of
unobserved variables called factors.”
47. EXAMPLE
Academic ability of student
Quantitative Ability Verbal Ability
1. Maths Score
2. Computer Program Score
3. Physics Score
4. Aptitude Test Score
1. English
2. Verbal Reasoning Score
48. PURPOSE OF FACTOR ANALYSIS
• To identify underlying constructs in the data.
• To reduce number of variables
• To reduce redundancy of data (E.g. Quantitative Aptitude)
49. APPLICATION OF FACTOR ANALYSIS
• Market Segmentation
• Product Research
• Advertising Studies
• Pricing Studies
52. WAYS OF FACTOR ANALYSIS
1. Confirmative Factor Analysis
– Factors and corresponding variables are already known
– On the basis of literature review or past experience/expertise
2. Exploratory Factor Analysis
– Algorithm is used to explore pattern among variables
– Then factors are explored
– No prior hypothesis to start with
53. CONDITIONS FOR FACTOR ANALYSIS
• Use interval or ratio data
• Variables are related
• Sufficient number of variables (min 4-5 variables for one factor)
• Large no of observations
• All variables should be normally distributed
54. STEPS IN FACTOR ANALYSIS
Formulate the Problem
Construct the Correlation Matrix
Determine the method of Factor Analysis
Determine Number of Factors
Estimate the Factor Matrix
Rotate the Factors
Estimating Practical Significance
56. EXAMPLE
• Basketballer or volleyballer on the basis of anthropometric variables.
• High or low performer on the basis of skill.
• Juniors or seniors category on the basis of the maturity parameters.
58. OBJECTIVE
• To understand group differences and to predict the likelihood
that a particular entity will belong to a particular class or group
based on independent variables.
59. PURPOSE
• To classify a subject into one of the two groups on the basis of
some independent traits.
• To study the relationship between group membership and the
variables used to predict the group membership.
60. SITUATIONS FOR ITS USE
• When the dependent variable is dichotomous or multichotomous.
• Independent variables are metric, i.e. interval or ratio.
• Example:
• Basketballer or volleyballer on the basis of anthropometric variables.
• High or low performer on the basis of skill.
• Juniors or seniors category on the basis of the maturity parameters.
61. ASSUMPTIONS
1. Sample size
– Should be at least five times the number of independent variables.
2. Normal distribution
– Each of the independent variable is normally distributed.
3. Homogeneity of variances / covariances
– All variables have linear and homoscedastic relationships.
62. ASSUMPTIONS
• Outliers
– Outliers should not be present in the data. DA is highly sensitive to the inclusion
of outliers.
• Non-multicollinearity
– There should be any correlation among the independent variables.
• Mutually exclusive
– The groups must be mutually exclusive, with every subject or case belonging to
only one group.
63. ASSUMPTIONS
• Variability
– No independent variables should have a zero variability in either of the groups
formed by the dependent variable.
64. To identify the players into different categories during selection process.
67. DEFINITION
• “Cluster analysis is a group of multivariate techniques whose primary purpose is to
group objects (e.g., respondents, products, or other entities) based on the
characteristics they possess.”
• It is a means of grouping records based upon attributes that make them similar.
• If plotted geometrically, the objects within the clusters will be close together, while
the distance between clusters will be farther apart.
68. CLUSTER VS FACTOR ANALYSIS
Cluster analysis is about grouping subjects (e.g. people). Factor analysis is about
grouping variables.
Cluster analysis is a form of categorization, whereas factor analysis is a form of
simplification.
In Cluster analysis, grouping is based on the distance (proximity), in Factor analysis it
is based on variation (correlation)
69. EXAMPLE
• Suppose a marketing researcher wishes to determine market segments in a community based on
patterns of loyalty to brands and stores a small sample of seven respondents is selected as a pilot
test of how cluster analysis is applied. Two measures of loyalty- V1(store loyalty) and V2(brand
loyalty)- were measured for each respondents on 0-10 scale.
70.
71. HOW DO WE MEASURE SIMILARITY?
• Proximity Matrix of Euclidean Distance Between Observations
Observation
Observations
A B C D E F G
A ---
B 3.162 ---
C 5.099 2.000 ---
D 5.099 2.828 2.000 ---
E 5.000 2.236 2.236 4.123 ---
F 6.403 3.606 3.000 5.000 1.414 ---
G 3.606 2.236 3.606 5.000 2.000 3.162 ---
72. HOW DO WE FORM CLUSTERS?
• Identify the two most similar(closest) observations not already in the same cluster and combine
them.
• We apply this rule repeatedly to generate a number of cluster solutions, starting with each
observation as its own “cluster” and then combining two clusters at a time until all observations are
in a single cluster.
• This process is termed a hierarchical procedure because it moves in a stepwise fashion to form an
entire range of cluster solutions. It is also an agglomerative method because clusters are formed by
combining existing clusters.
75. • Dendogram:
Graphical representation (tree graph) of the results of a hierarchical procedure. Starting with each
object as a separate cluster, the dendogram shows graphically how the clusters are combined at
each step of the procedure until all are contained in a single cluster
76. USAGE OF CLUSTER ANALYSIS
Market Segmentation:
Splitting customers into different groups/segments where customers have similar requirements.
Segmenting industries/sectors:
Segmenting Markets:
Cities or regions having common traits like population mix, infrastructure development, climatic
condition etc.
Career Planning:
Grouping people on the basis of educational qualification, experience, aptitude and aspirations.
Segmenting financial sectors/instruments:
Grouping according to raw material cost, financial allocation, seasonability etc.
79. MEANING
• Concerned with understanding how people make choices between products or
services or
• Combination of product and service
• Businesses can design new products or services that better meet customers
underlying needs.
• Conjoint analysis is a popular marketing research technique that marketers use to
determine what features a new product should have and how it should be priced.
80. • Suppose we want to market a new golf ball. We know from experience and from
talking with golfers that there are three important product features:
1. Average Driving Distance
2. Average Ball Life
3. Price
81. TYPES OF CONJOINT ANALYSIS
1. Choice Based
– Respondents select from grouped options
82. TYPES OF CONJOINT ANALYSIS
2. Adaptive Choice
– It is used for studying how people make decisions regarding complex products or services
– Packages adapt based on previous selections
– It gets ‘smarter’ as the survey progresses
84. TYPES OF CONJOINT ANALYSIS
3. Menu-based
1. Respondents are shown a list of features
and levels
2. They have to choose among options
3. Example: Airtel My Plan
86. 4. Full profile rating based
– Display series of product profile
– Typically rated on likelihood to purchase or
preference scale
87. 5. Self explicate
– Direct ask of features and levels
– Each feature is presented separately
for evaluation
– Respondents rate all remaining
features according to desirability
88. ADVANTAGES
• Estimates psychological tradeoffs that consumers make when evaluating several
attributes together
• Measures preferences at the individual level
• Uncovers real or hidden drivers which may not be apparent to the respondent
themselves
• Realistic choice or shopping task
• Used to develop needs based segmentation
89. DISADVANTAGES
• Designing conjoint studies can be complex
• With too many options, respondents resort to simplification strategies
• Respondents are unable to articulate attitudes toward new categories
• Poorly designed studies may over-value emotional/preference variables and
undervalue concrete variables
• Does not take into account the number items per purchase so it can give a poor
reading of market share
91. EXAMPLE
A researcher may give test subjects
several varieties of apple and have
them make comparisons on several
criteria between two apples at a time.
Once all the apples are directly
compared to each other variety, the
data is plotted on a graph that shows
how similar one type is to another.
92. MEANING
• Multidimensional scaling (MDS) is a means of visualizing the level of similarity of
individual cases of a dataset.
• Multidimensional scaling is a method used to create comparisons between things
that are difficult to compare.
• The end result of this process is generally a two-dimensional chart that shows a level
of similarity between various items, all relative to one another.
93.
94. APPLICATIONS OF MDS
• Understanding the position of brands in the marketplace relative to groups of
homogeneous consumers.
• Identifying new products by looking for white space opportunities or gaps.
• Gauging the effectiveness of advertising by identifying the brands position before
and after a campaign.
• Assessing the attitudes and perceptions of consumers.
• Determine what attributes the brand owns and what attributes competitors own.
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