Explanatory Factor Analysis: Alpha and Omega Dominique Zephyr Applied Statistics Lab University of Kenctucky

Factor Analysis Factor analysis is a statistical method that allows us to understand large quantities of observable variables in terms of a smaller number of unobservable variables. In factor analysis, latent variables represent unobserved constructs that are referred as factors or dimensions.

Two types of factor Analysis Exploratory Factor Analysis (EFA) - It is exploratory when you do not have a pre-defined idea of the structure or how many dimensions are in a set of variables. Confirmatory Factor Analysis (CFA)- It is confirmatory when you want to test specific hypothesis about the structure or the number of dimensions underlying a set of variables. CFA is used to study how well a hypothesized factor model fits a new sample from the same population or a sample from a different population

The EFA Model Factor analysis (FA) aims to describe a set of Q variables x1, x2,..., xQ in terms of a smaller number of m factors and residuals e ( unique factors)

The EFA model where x i (i=1,…,Q) represents the original variables but standardized with zero mean and unit variance F 1, F 2,...,F m are m uncorrelated common factors, each with zero mean and unit variance; α i1, α i2,..., α im are the factor loadings related to the variable X i e i are the Q unique factors supposed independently and identically distributed with zero mean

EFA Extraction Methods Methods of extraction available in Stata pf : principal factor; the default pcf: principal-component factor ipf: iterated principal factor ml: maximum-likelihood factor We will use ml so that the results are comparable with CFA

EFA results Factor GSE1-GSE6, ml Factor | Eigenvalue Difference Proportion Cumulative Factor1 | Factor2 | Factor3 | Variable | Factor1 Factor2 Factor3 | Uniqueness GSE1 | | GSE2 | | GSE3 | | GSE4 | | GSE5 | | GSE6 | |

How many factors to retain? Kaiser criterion. Drop all factors with eigenvalues below 1.0. The simplest justification for this is that it makes no sense to add a factor that explains less variance than is contained in one individual indicator. Scree plot. This method, proposed by Cattell, plots the successive eigenvalues, which drop sharply and then level off. It suggests retaining all eigenvalues in the sharp descent before the first one on the line where they start to level off. Variance explained criteria. Some researchers simply use the rule of keeping enough factors to account for 90% (sometimes 80%) of the variation.

Kaiser Criterion Factor | Eigenvalue Difference Proportion Cumulative Factor1 | Factor2 | Factor3 | factor GSE1-GSE6, ml screeplot

Rotation The goal of rotational strategies is to obtain a clear pattern of loadings. The sum of eigenvalues is not affected by rotation, but changing the axes will alter the eigenvalues of particular factors and will change the factor loadings The most common rotation method is the “varimax rotation”

Oblimin Rotation Results factor GSE1-GSE6, factor(1) ml rotate, oblimin Factor analysis/correlation Number of obs = 700 Method: maximum likelihood Retained factors = 1 Rotation: orthogonal oblimin (Kaiser off) Number of params = 6 Schwarz's BIC = Log likelihood = (Akaike's) AIC = Rotated factor loadings (pattern matrix) and unique variances Variable | Factor1 | Uniqueness GSE1 | | GSE2 | | GSE3 | | GSE4 | | GSE5 | | GSE6 | |

Cronbach Coefficient Alpha The Cronbach Coefficient Alpha is the most common estimate of internal consistency of items in a model or survey How large the α must be: Nunnally (1978) suggests 0.7 as an acceptable reliability threshold. Some authors use.75 or.80 as a cut-off value, while others are as lenient as to go to 0.6

Alpha results alpha GSE1-GSE6, item std detail average item-test item-rest interitem Item | Obs Sign correlation correlation correlation alpha GSE1 | GSE2 | GSE3 | GSE4 | GSE5 | GSE6 | Test scale |

Bootstrapped CI for Alpha bootstrap r(alpha), reps(1000): alpha GSE1- GSE6, item std detail estat bootstrap, all | Observed Bootstrap | Coef. Bias Std. Err. [95% Conf. Interval] _bs_1 | (N) | (P) | (BC) (N) normal confidence interval (P) percentile confidence interval (BC) bias-corrected confidence interval

Omega after EFA matrix list c: sum(loadings)^2 c matrix list e: sum(error variances) r scalar omega= c[1,1]/(c[1,1]+e[1,1]). scalar list omega =

Bootstrapped CI for Omega capture program drop omegaefa program define omegaefa, rclass version 12.0 set more off preserve syntax [varlist] [if] [in] factor `varlist', factor(1) ml rotate, oblimin matrix c = e(L) matrix e = e(Psi)' * sum(loadings)^2 matrix one = J(rowsof(c),1,1) matrix c = one'*c*c'*one * sum(error variances) matrix e = J(1,rowsof(e),1)*e matrix list c matrix list e return scalar omega=c[1,1]/(c[1,1]+e[1,1]) restore end bootstrap omega=r(omega), reps(1000): omegaefa GSE1-GSE6 estat bootstrap, all

Bootstrapped Results Bootstrap results Number of obs = 700 Replications = 1000 command: omegaefa GSE1-GSE6 omega: r(omega) | Observed Bootstrap | Coef. Bias Std. Err. [95% Conf. Interval] omega | (N) | (P) | (BC) (N) normal confidence interval (P) percentile confidence interval (BC) bias-corrected confidence interval