## AF Measure Analysis Issues IV: Redundancy, correlation, complementarity, subjective scales validation (uses of principal components & factor analyses)

 Jose Manuel Roche Factor analysis; latent variable analysis help to define weights and final indicators. Two types of factor analysis; exploratory and complementary Principal component analysis. Subjective scale validation.

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Video

Guide to video

00:00 Introduction

02:42 Outline of the lecture

03:51 Definition of factor analysis, relate to latent variable analysis

08:28 Example of Rustein and Jonston (2004) Wealth Index, principal component analysis

11:37 Other options besides factor analysis (data reduction)

16:58 Example of Lelli (2008), factor analysis vs. fuzzy set theory

Part 1: Exploratory and Confirmatory Factor Analysis

22:08 Definition of exploratory (unrestricted) factor analysis (EFA); calculation steps

27:54 Example of principle component analysis (Klasen, 2000), here weight are based on the correlations between indicators – a statistical solution (see also Normative Issues in Multidimensional Poverty Measurement for choice of weights)

33:30 Confirmatory factor analysis – restricted analysis. Look in Brown’s book both for exploratory and confirmatory reference Brown (2006)

39:50 Literature overview on the goodness of fit

Part 2: Empirical Implementation of Exploratory Factor Analysis

41:16 The different step of exploratory factor analysis (Brown (2006))

43:03 Step 1: the example of Roche 2008

44:46 Step 2: extraction method

46:30 Step 3: determination of appropriate number of factors; sources – Kaiser criterion, Analysis of Scree plot, parallel analysis, normative judgement

52:07 Step 4: method of rotation to obtain your simple structural model, orthogonal and oblique rotation

61:40 Example of Roche (2008), rotation results – construct 3 indices based on factor analysis, discuss weight (link to lecture on normative issues)

70:04 Step 5: interpretation an evaluation of the quality of the solution

70:19 Example of Roche (2008), results of different models

75:28 Example of Gallo, Cesar and J.M. Roche (2011), the implicit weight arising from different clusters of variables

76:24 Tetrachoric correlations

77:29 Example Gallo, Cesar and J.M. Roche (2011), results

Part 3: Subjective Scale Validation

81:11 Subjective scale validation

81:50 Psychometric evaluation

81:32 Example of Gagne et al (2009), autonomy and work

84:33 The process of subjective scale variation

84:57 Techniques for the different stages of scale validation

85:22 Crombach Alpha

86:05 Example of Steger et al (2006), subjective scale validation, the meaning of life

87:30 Example, subjective scale validation, psychological needs

88:57 Exploratory factor analysis results

91:20 Complementary factor analysis results

92:10 Strength and weaknesses of factor analysis

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