Download presentation

Presentation is loading. Please wait.

1
**Lecture 3: Exploratory Factor Analysis**

Aims & Objectives To describe the basic factor model Understand pre-analysis checks, extraction & rotation To describe the basic process of conducting a EFA, and To show when and where EFA is most appropriately use

2
Basic principle of EFA EFA tries to explain a set of correlations among variables in terms of a set of smaller common factors and zero cross loadings

3
**Factor analysis of cognitive abilities**

Verbal Reasoning Sentence completion Comprehension Cloze test Mathematical & Spatial Spatial rotation Computation Find the figure

4
**Process Pre-analysis Extraction Rotation Collect data of the 6 test**

Sample size, N of items, Social desirability Create correlation matrix Extraction How many factors? Rotation How to best view the solution

5
**Pre-analysis checks: I**

Scaling Likert-type Dichotomous use Phi % correct Item selection Theory, a-priori structure (marker variables) Sampling To the population where the results are to be generalised

6
**Pre-analysis checks: II**

Ratios & Stable structure N (min = 100) N:P (2:1, 10:1) P:M (4:1) N:M (6:1) Social desirability Remove items correlating with social desirability

7
Sentence Compre Cloze Rotation Comput Figure 1 Compre. .55 .65 .70 .17 .10 .25 .28 .18 .60 .22 .35 .33 .77 .58

8
**Pre-analysis checks:III**

Bartlett test of Sphericity Matrix to be analysed Identity matrix V1 1 V2 0 1 V V V1 1 V V V KMO greater than .5

9
**Communalities The shared variance of a variable with a factor**

Initially need to estimate these. Largest column r in the diagonal SMC

10
**Number of factors to extract**

Eigenvalues 2 Actual data (eigenvalues) 1 Random eigenvalues Factors 1 n

11
**Rotation & simple structure**

FI FII FI FII Sentence Compreh Cloze Figure Rotation Coputat

12
Rotation - orthogonal FI(a) Verbal Math/spatial FII (a)

13
Rotate FI(a) Verbal FI (b) Math/spatial FII (a) FII (b)

14
Rotation - oblique FI(a) FII (a) Delta

15
**The basic factor model Common factors Loading Verbal Math/Spatial**

Unique factors Variables

16
**Naming and next steps Factor naming Reliability & validity**

Re-captured Item Technique (RIT) Markers Raters Reliability & validity

17
**Use & abuses of EFA Uses Problems IQ Personality & psychometrics**

Education Psychophysiology Factorial validity Data reduction? Problems Garbage In Garbage Out (GIGO)

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google