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Ketan Kotalwar
  M.A (Psychology) Part 1
Multivariate
 Analysis.
What is Analysis?


Analysis is the Process of Breaking a Complex Factor or
Substance into Smaller Parts to Gain a better
understanding of it.

This Process is Known as a Method of Studying The
Nature of Something or of Determining its Essential
Features and There Relations.
What is Multivariate Analysis?
The Multivariate Analysis is The Analysis Which have
More Than one Factors to Analyze at The Same Time.

The Multivariate Analysis involves The Observation and
Analysis of more than one Outcome Variable at a time.
In The Design and Analysis, the Technique is used to
Perform Statistical Studies across Multiple Dimensions
while taking into Account the Effects of all Variables on
The Responses of Interest.

Multivariate Analysis = Multi + Variate (Variables)
Analysis. Which is To Done upon Many Dimensions.
Types of Multivariate Analysis.

There are Many Types in Multivariate Analysis. In which
The Analysis Could be Done.
The Method To Study May Vary From Topic To Topic
Which is To Study.

01 Path Analysis
02 Multiple Regression Analysis.
03 Multiple Correlation Analysis.
04 Canonical Correlation Analysis.
01. Path Analysis.
         The Concept Path Analysis has been Developed by Sewell
Writes in 1918.

         It is a Statistical Technique to test Cause and Effect
Relationship Within Two Factors.
         It is used to Describe the Directed Dependency among The
Set of Variables.
         We Can Show The Path Analysis by the help of Diagrams, in
Which we Calculate it by Product Moment Correlation.

        In which There are 2 Factors,
         01. Predictor (I.V)
         02. Casual Factor (D.V)
Path Analysis.
02. Multiple Regression Analysis.
The Term Regression was First Used in 1908 by Pearson.

The Regression is to learn more about the Relationship between
Several Independent or Predictor Variables (I.V) and a Dependent
Variable (D.V).

In Short, The Multiple Regression is used for Predict a Single
Factor(e.g. Anxiety) by Many other Independent Variables Which
may Affect on it.

Where are Two Calculating Things.
01. Several Independent Variables.
02. Single Dependant Variable.
Multiple Regression Analysis.
03. Multiple Correlation Analysis.
Multiple Correlation Analysis Shows an Association
Between The Factors.

It is an Estimate of The Combine Influence of Two or
More (I.V) On The Single (D.V).

The Coefficient of Correlation (r) States The Strength of
The Two Factors. (single D.V), (several I.V) (which is
always positive).

The Closer r Value to The +1.00 Shows More Strongest
Relation in The Factors.

We Can Measure it By Analysis of Variance (ANOVA)
Multiple Correlation Analysis.
04. Canonical Correlation Analysis.
 The Canonical Correlation Analysis, Firstly Introduced
 by Harold Hotelling.

 The Term Canonical Correlation Suggest That The
 Correlation Between Each and Every Factor Lies into Two
 Different Sets of Scores.

 e.g. The Correlation Between Two Groups of Marks.
 Group1 and Group2.
 The Canonical Correlation Will Show The Correlation
 Between Each and Every Factor in Group A, and Each and
 Every Factor in Group B.
Canonical Correlation Analysis.
Thank You…

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Research meathodology

  • 1. Ketan Kotalwar M.A (Psychology) Part 1
  • 3. What is Analysis? Analysis is the Process of Breaking a Complex Factor or Substance into Smaller Parts to Gain a better understanding of it. This Process is Known as a Method of Studying The Nature of Something or of Determining its Essential Features and There Relations.
  • 4. What is Multivariate Analysis? The Multivariate Analysis is The Analysis Which have More Than one Factors to Analyze at The Same Time. The Multivariate Analysis involves The Observation and Analysis of more than one Outcome Variable at a time. In The Design and Analysis, the Technique is used to Perform Statistical Studies across Multiple Dimensions while taking into Account the Effects of all Variables on The Responses of Interest. Multivariate Analysis = Multi + Variate (Variables) Analysis. Which is To Done upon Many Dimensions.
  • 5. Types of Multivariate Analysis. There are Many Types in Multivariate Analysis. In which The Analysis Could be Done. The Method To Study May Vary From Topic To Topic Which is To Study. 01 Path Analysis 02 Multiple Regression Analysis. 03 Multiple Correlation Analysis. 04 Canonical Correlation Analysis.
  • 6. 01. Path Analysis. The Concept Path Analysis has been Developed by Sewell Writes in 1918. It is a Statistical Technique to test Cause and Effect Relationship Within Two Factors. It is used to Describe the Directed Dependency among The Set of Variables. We Can Show The Path Analysis by the help of Diagrams, in Which we Calculate it by Product Moment Correlation. In which There are 2 Factors, 01. Predictor (I.V) 02. Casual Factor (D.V)
  • 8. 02. Multiple Regression Analysis. The Term Regression was First Used in 1908 by Pearson. The Regression is to learn more about the Relationship between Several Independent or Predictor Variables (I.V) and a Dependent Variable (D.V). In Short, The Multiple Regression is used for Predict a Single Factor(e.g. Anxiety) by Many other Independent Variables Which may Affect on it. Where are Two Calculating Things. 01. Several Independent Variables. 02. Single Dependant Variable.
  • 10. 03. Multiple Correlation Analysis. Multiple Correlation Analysis Shows an Association Between The Factors. It is an Estimate of The Combine Influence of Two or More (I.V) On The Single (D.V). The Coefficient of Correlation (r) States The Strength of The Two Factors. (single D.V), (several I.V) (which is always positive). The Closer r Value to The +1.00 Shows More Strongest Relation in The Factors. We Can Measure it By Analysis of Variance (ANOVA)
  • 12. 04. Canonical Correlation Analysis. The Canonical Correlation Analysis, Firstly Introduced by Harold Hotelling. The Term Canonical Correlation Suggest That The Correlation Between Each and Every Factor Lies into Two Different Sets of Scores. e.g. The Correlation Between Two Groups of Marks. Group1 and Group2. The Canonical Correlation Will Show The Correlation Between Each and Every Factor in Group A, and Each and Every Factor in Group B.