Factor Analysis - Factor Loading, Factor Scoring & Factor Rotation (Research & Statistics)

Dr. Manishika Jain in this lecture explains factor analysis. Introduction to Factor Analysis: Factor Loading, Factor Scoring & Factor Rotation.

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Steps in Research Proposal @0:24
Research Topic @0:43
Review of Literature @0:56
Rationale and Need for the Study @1:18
Definition of Terms @1:24
Assumptions @3:03
Method, Sample and Tools @4:06
Probability Sampling @4:23
Non - Probability Sampling @4:34
Significance of Study @5:13
Technique for Data Analysis @5:18
Bibliography @5:42
Budget @6:28
Chapterisation @6:39
#Expenditure #Tabulate #Significance #Assumption #Literature #Rationale #Constitutive #Phenomena #Elucidate #Literature #Manishika #Examrace

Factor Analysis and PCA

Reduce large number of variables into fewer number of factors
Co-variation is due to latent variable that exert casual influence on observed variables
Communalities – each variable’s variance that can be explained by factors
Types of Factoring
• PCA – maximum variance for 1st factor; removes that and uses maximum for 2nd factor and so on…
• Common Factor Analysis – Same as factor analysis (only common variance – used in CFA)
• Image Factoring – correlation matrix; uses OLS regression matrix
• Maximum Likelihood Method – on correlation matrix
• Alpha Factoring
• Weight Square

Estimate communalities - each variable’s variance that can be explained by factor.
See factors are retained
Factor rotation - Procedure in which the eigenvectors (factors) are rotated in an attempt to achieve simple structure.
Factor loading - Relation of each variable to the underlying factor. Output of a simple factor analysis looking at indicators of wealth, with just six variables and two resulting factors
6 variables: Income, education, occupation, house value, public parks and crimes
2 factors: individual socioeconomic status and neighborhood socioeconomic status
Factor Score – if value of variables are given then factor values can be predicted
Interpretation

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