Exploratory Factor Analysis PPT (1)

# Exploratory Factor Analysis PPT (1) - Exploratory Factor...

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EDP 7110 Exploratory Factor Analysis

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2 Overview 1 What is factor analysis? 2 Assumptions 3 Steps / Process 4 Examples 5 Summary
3 What is factor analysis? 1 What is factor analysis? 2 Purpose 3 History 4 Types 5 Models

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4 Conceptual model of factor analysis FA uses correlations among many items to search for common clusters.
5 Factor analysis Is used to identify clusters of inter- correlated variables (called ' factors '). Is a family of multivariate statistical techniques for examining correlations amongst variables. Empirically tests theoretical data structures . Is commonly used in psychometric instrument development .

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6 Purposes There are two main applications of factor analytic techniques: 1. Data reduction : Reduce the number of variables to a smaller number of factors. 2. Theory development : Detect structure in the relationships between variables, that is, to classify variables.
7 Purposes: Data reduction Simplifies data structure by revealing a smaller number of underlying factors Helps to eliminate or identify items for improvement : redundant variables unclear variables irrelevant variables Leads to calculating factor scores

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8 EFA = Exploratory Factor Analysis explores & summarises underlying correlational structure for a data set CFA = Confirmatory Factor Analysis tests the correlational structure of a data set against a hypothesised structure and rates the “goodness of fit” Two main types of FA: Exploratory vs. confirmatory factor analysis
9 This course focuses on Exploratory Factor Analysis However, note that Confirmatory Factor Analysis (and Structural Equation Modelling) is generally preferred but is more advanced and recommended for graduate level. This lecture focuses on exploratory factor analysis

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10 Factor 1 Factor 2 Factor 3 Conceptual model - Simple model e.g., 12 items testing might actually tap only 3 underlying factors Factors consist of relatively homogeneous variables.
11 Question 1 Conceptual model - Simple model Question 2 Question 3 Question 4 Question 5 Factor 1 Factor 2 Factor 3 Each question loads onto one factor

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12 Question 1 Conceptual model - Complex model Question 2 Question 3 Question 4 Question 5 Factor 1 Factor 2 Factor 3 Questions may load onto more than one factor
Example: Essential facial features (Ivancevic, 2003)

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14 Six orthogonal factors, represent 76.5% of the total variability in facial recognition (in order of importance): upper-lip eyebrow-position nose-width eye-position eye/eyebrow-length face-width Example: Essential facial features (Ivancevic, 2003)
15 Assumptions Check 1 GIGO 2 Sample size 3 Levels of measurement 4 Normality 5 Linearity 6 Outliers 7 Factorability

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16 Assumptions Check 2.Sample size Min of 5 cases per variable is required for factor analyis. A sample of 100 cases is acceptable but sample size of 200++ are preferable.
17 Assumptions Check 6.Outliers Variable with a low squared multiple correlation with all other variables and

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• Fall '15
• Kaiser

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