Multivariate analysis involves simultaneously analyzing multiple variables to understand relationships. This document discusses key concepts in multivariate analysis including:
1. Defining multivariate analysis and when it is appropriate to use.
2. Describing specific techniques like multiple regression, discriminant analysis, logistic regression, MANOVA, canonical correlation analysis, conjoint analysis, factor analysis, cluster analysis, multidimensional scaling, and correspondence analysis.
3. Providing guidelines for selecting the appropriate technique based on the measurement scales and relationship between variables.
It also covers important considerations like measurement error, statistical power, and a structured approach to multivariate model building.
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Differential Calculus, limits, slope of tangent line to the curve, the derivative. This slide accompanies my lecture in Differential calculus in LPU Batangas
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LANDFILL SITE SELECTION BY USING PAIRWISE COMPARISON, RATING, RANKING AND TRA...IAEME Publication
Any multi criteria decision analysis process need to weigh the criteria and to know any weighting method the best to use according the nature and degree of complexity of the problem. There are four method for criteria weighting Rank, Rating, Pairwise and Trade-off methods. In this study, which was done in Najaf – Iraq used this methods to weigh the seventeen criteria used to select landfill site within integration of (GIS -MCDA) in Najaf governorate. There is no difference between the results within Pairwise or Ranking method only in size of landfill in west part of study area (five sites were selected in each try). Within trade-off and rating methods, there are more than five site were selected.
Application of Univariate, Bivariate and Multivariate Variables in Business R...Sundar B N
In this ppt you can find the materials relating to Application of Univariate, Bivariate and Multivariate Variables in Business Research. Also What is Variable, Types of Variables, Examples of Independent Variables, Examples of Dependent Variables, Common techniques used in univariate analysis include, Common techniques used in bivariate analysis include, Common techniques used in Multivariate analysis include, Difference B/w Univariate, Bivariate & Multivariate Analysis
Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is the interval in nature. The term categorical variable means that the predictor variable is divided into a number of categories.
DA is typically used when the groups are already defined prior to the study.
The end result of DA is a model that can be used for the prediction of group memberships. This model allows us to understand the relationship between the set of selected variables and the observations. Furthermore, this model will enable one to assess the contributions of different variables.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
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Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
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LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
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It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
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Memorandum Of Association Constitution of Company.pptseri bangash
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
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Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
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Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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Mba2216 week 11 data analysis part 03 appendix
1. Data Analysis Part 3 :
Appendix:
Multivariate Analysis
MBA2216 BUSINESS RESEARCH PROJECT
by
Stephen Ong
Visiting Fellow, Birmingham City University, UK
Visiting Professor, Shenzhen University
2. 1. Explain what multivariate analysis is and
when its application is appropriate.
2. Define and discuss the specific techniques
included in multivariate analysis.
3. Determine which multivariate technique is
appropriate for a specific research problem.
4. Discuss the nature of measurement scales
and their relationship to multivariate
techniques.
5. Describe the conceptual and statistical
issues inherent in multivariate analyses.
19–2
LEARNING OUTCOMES
3. What is it? Multivariate Data Analysis = all
statistical methods that simultaneously analyze
multiple measurements on each individual or
object under investigation.
Why use it?
Measurement
Explanation & Prediction
Hypothesis Testing
What is Multivariate Analysis?
4. 1-4
The Variate
Measurement Scales
Nonmetric
Metric
Multivariate Measurement
Measurement Error
Types of Techniques
Basic Concepts of Multivariate Analysis
5. The variate is a linear combination of variables with
empirically determined weights.
Weights are determined to best achieve the objective of the
specific multivariate technique.
Variate equation:
(Y’) = W1 X1 + W2 X2 + . . . + Wn Xn
Each respondent has a variate value (Y’).
The Y’ value is a linear combination of the entire set of
variables. It is the dependent variable.
Potential Independent Variables:
X1 = income
X2 = education
X3 = family size
X4 = ??
The Variate
6. 1 - 6
Types of Data and Measurement Scales
Data
Metric
or
Quantitative
Nonmetric
or
Qualitative
Nominal
Scale
Ordinal
Scale
Interval
Scale
Ratio
Scale
7. Nonmetric
Nominal – size of number is not related to the amount of the
characteristic being measured
Ordinal – larger numbers indicate more (or less) of the
characteristic measured, but not how much more (or less).
Metric
Interval – contains ordinal properties, and in addition, there are
equal differences between scale points.
Ratio – contains interval scale properties, and in addition, there
is a natural zero point.
NOTE: The level of measurement is critical in determining the
appropriate multivariate technique to use!
Measurement Scales
8. 1-8
• All variables have some error. What are the
sources of error?
• Measurement error = distorts observed
relationships and makes multivariate
techniques less powerful.
• Researchers use summated scales, for
which several variables are summed or
averaged together to form a composite
representation of a concept.
Measurement Error
9. 1-9
In addressing measurement error,
researchers evaluate two important
characteristics of measurement:
• Validity = the degree to which a
measure accurately represents what it
is supposed to.
• Reliability = the degree to which the
observed variable measures the “true”
value and is thus error free.
Measurement Error
10. 1-10
Type I error, or , is the probability of rejecting the null
hypothesis when it is true.
Type II error, or , is the probability of failing to reject the
null hypothesis when it is false.
Power, or 1- , is the probability of rejecting the null
hypothesis when it is false.
H0 true H0 false
Fail to Reject H0 1-
Type II error
Reject H0
Type I error
1-
Power
Statistical Significance and
Power
11. Effect size: the actual magnitude of the effect of
interest (e.g., the difference between means or
the correlation between variables).
Alpha ( ): as is set at smaller levels, power
decreases. Typically, = .05.
Sample size: as sample size increases, power
increases. With very large sample sizes, even
very small effects can be statistically significant,
raising the issue of practical significance vs.
statistical significance.
Power is Determined by Three
Factors
14. 1 - 14
Rules of Thumb 1–1
Statistical Power Analysis
• Researchers should always design the study to
achieve a power level of .80 at the desired
significance level.
• More stringent significance levels (e.g., .01
instead of .05) require larger samples to achieve
the desired power level.
• Conversely, power can be increased by choosing
a less stringent alpha level (e.g., .10 instead of
.05).
• Smaller effect sizes always require larger sample
sizes to achieve the desired power.
• Any increase in power is most likely achieved by
increased sample size.
15. Dependence techniques: a variable or set of
variables is identified as the dependent variable to
be predicted or explained by other variables
known as independent variables.
o Multiple Regression
o Multiple Discriminant Analysis
o Logit/Logistic Regression
o Multivariate Analysis of Variance (MANOVA)
and Covariance
o Conjoint Analysis
o Canonical Correlation
o Structural Equations Modeling (SEM)
Types of Multivariate
Techniques
16. Interdependence techniques: involve the
simultaneous analysis of all variables in the
set, without distinction between dependent
variables and independent variables.
o Principal Components and Common
Factor Analysis
o Cluster Analysis
o Multidimensional Scaling (perceptual
mapping)
o Correspondence Analysis
Types of Multivariate
Techniques
17. 1 - 17
Selecting a Multivariate Technique
1. What type of relationship is being examined –
dependence or interdependence?
2. Dependence relationship: How many variables are
being predicted?
What is the measurement scale of the
dependent variable?
What is the measurement scale of the predictor
variable?
3. Interdependence relationship: Are you examining
relationships between variables, respondents, or
objects?
18. 1 - 18
Two Broad Types of Multivariate
Methods:
1. Dependence – analyze dependent and
independent variables at the same time.
2. Interdependence – analyze dependent
and independent variables separately.
19. 1 - 19
Multiple
Regression
and Conjoint
Discriminant
Analysis
and Logit
MANOVA
and
Canonical
Canonical
Correlation,
Dummy
Variables
Metric Nonmetric Metric Nonmetric
Metric Nonmetric
Factor
Analysis
Cluster
Analysis
Nonmetric
MDS and
Correspon-
dence
Analysis
Selecting the Correct Multivariate Method
SEM CFA
Several
Dependent
Variables
One
Dependent
Variable
Metric
MDS
Multivariate
Methods
Dependence
Methods
Interdependence
Methods
Multiple
Relationships -
Structural
Equations
20. Multiple Regression
. . . a single metric dependent
variable is predicted by several
metric independent variables.
Y
X1
X2
21. 1 - 21
Discriminant Analysis
• What is it?
. . . single, non-metric (categorical)
dependent variable is predicted by
several metric independent variables.
• Why use it?
22. 1 - 22
Logistic Regression
A single nonmetric dependent variable is
predicted by several metric independent
variables. This technique is similar to
discriminant analysis, but relies on calculations
more like regression.
23. 1 - 23
MANOVA
Several metric dependent variables
are predicted by a set of nonmetric
(categorical) independent variables.
24. 1 - 24
CANONICAL ANALYSIS
Several metric dependent variables
are predicted by several metric
independent variables.
25. 1 - 25
. . . is used to understand respondents’
preferences for products and services.
In doing this, it determines the
importance of both:
attributes and
levels of attributes
. . . based on a smaller
subset of combinations of
attributes and levels.
CONJOINT ANALYSIS
27. 1 - 27
Structural Equations
Modeling (SEM)
Estimates multiple, interrelated
dependence relationships based
on two components:
1. Measurement Model
2. Structural Model
28. 1-28
Exploratory Factor Analysis
. . . analyzes the structure of
the interrelationships among a
large number of variables to
determine a set of common
underlying dimensions (factors).
29. 1 - 29
. . . groups objects
(respondents, products, firms,
variables, etc.) so that each
object is similar to the other
objects in the cluster and
different from objects in all
the other clusters.
Cluster Analysis
30. 1 - 30
1-30
Cluster Analysis of “Eating
Out” Questions
1. I eat at fast food restaurants at least once a week.
2. I prefer restaurants with the highest quality food.
3. I prefer restaurants that have quick service.
4. I prefer to eat at restaurants that have a nice atmosphere.
Objective: Identify groups that maximize ratio of
between groups variance large
within groups variance small
=
1 7
7-point Agree/Disagree Scale
31. 1 - 31
Constructs . . .
Trust
Commitment
Cooperation
Locus of Control
Job Satisfaction
Turnover
High
Medium
Low
Organizational
Commitment
Example: Cluster Analysis . . .
“Polar Extremes”
= remove middle group(s)
32. 1 - 32
Multidimensional Scaling
. . . identifies “unrecognized” dimensions
that affect purchase behaviour based on
customer judgments of:
• similarities or
• preferences
and transforms these into distances
represented as perceptual maps.
33. 1 - 33
Correspondence Analysis
. . . uses non-metric data and evaluates
either linear or non-linear relationships in
an effort to develop a perceptual map
representing the association between
objects (firms, products, etc.) and a set
of descriptive characteristics of the
objects.
35. 1 - 35
Stage 1: Define the Research Problem, Objectives, and
Multivariate Technique(s) to be Used
Stage 2: Develop the Analysis Plan
Stage 3: Evaluate the Assumptions Underlying the
Multivariate Technique(s)
Stage 4: Estimate the Multivariate Model and Assess
Overall Model Fit
Stage 5: Interpret the Variate(s)
Stage 6: Validate the Multivariate Model
A Structured Approach to
Multivariate Model Building:
36. 1 - 36
Variable Description Variable Type
Data Warehouse Classification Variables
X1 Customer Type nonmetric
X2 Industry Type nonmetric
X3 Firm Size nonmetric
X4 Region nonmetric
X5 Distribution System nonmetric
Performance Perceptions Variables
X6 Product Quality metric
X7 E-Commerce Activities/Website metric
X8 Technical Support metric
X9 Complaint Resolution metric
X10 Advertising metric
X11 Product Line metric
X12 Salesforce Image metric
X13 Competitive Pricing metric
X14 Warranty & Claims metric
X15 New Products metric
X16 Ordering & Billing metric
X17 Price Flexibility metric
X18 Delivery Speed metric
Outcome/Relationship Measures
X19 Satisfaction metric
X20 Likelihood of Recommendation metric
X21 Likelihood of Future Purchase metric
X22 Current Purchase/Usage Level metric
X23 Consider Strategic Alliance/Partnership in Future nonmetric
Description of HBAT Primary Database Variables
37. Further Reading
COOPER, D.R. AND SCHINDLER, P.S. (2011)
BUSINESS RESEARCH METHODS, 11TH EDN,
MCGRAW HILL
ZIKMUND, W.G., BABIN, B.J., CARR, J.C. AND
GRIFFIN, M. (2010) BUSINESS RESEARCH
METHODS, 8TH EDN, SOUTH-WESTERN
SAUNDERS, M., LEWIS, P. AND THORNHILL, A.
(2012) RESEARCH METHODS FOR BUSINESS
STUDENTS, 6TH EDN, PRENTICE HALL.
SAUNDERS, M. AND LEWIS, P. (2012) DOING
RESEARCH IN BUSINESS & MANAGEMENT, FT
PRENTICE HALL.