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CFA and EFA

1)

Exploratory factor analysis (EFA) could be described as orderly simplification of interrelated

measures. EFA, traditionally, has been used to explore the possible underlying factor structure of a

set of observed variables without imposing a preconceived structure on the outcome (Child, 1990).

By performing EFA, the underlying factor structure is identified.

Exploratory Factor Analysis

C onfirmatory factor analysis ( C FA) is a statistical technique used to verify the factor structure of a set

of observed variables. CFA allows the researcher to test the hypothesis that a relationship between

observed variables and their underlying latent constructs exists. The researcher uses knowledge of 

the theory, empirical research, or both, postulates the relationship pattern a priori and then tests

the hypothesis statistically. The process of data analysis with EFA and CFA will be explained.

Examples with FACTOR and CALIS procedures will illustrate EFA and CFA statistical techniques.

Confirmatory Factor Analysis

CFA and EFA are powerful statistical techniques. An example of CFA and EFA could occur with the

development of measurement instruments, e.g. a satisfaction scale, attitudes toward health,

customer service questionnaire. A blueprint is developed, questions written, a scale determined, the

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ins  

¡ ¢ £   ent pilot teste ¤  

¥  data collected, and CF¦   

 completed. The blueprint identifies the factor

structure or what we think it is. However, some questions may not measure what we thought they 

should. If the factor structure is not confirmed, EF¦   

is the ne§  

t step. EF¦   

helps us determine what

the factor structure looks like according to how participant responses. Exploratory factor analysis is 

essential to determine underlying constructs for a set of measured variables.

2) Basic step for C  ̈  A 

1.  Developing a theoretically based model.

3  In confirmatory factor analysis can be illustrated by a synthesis of the 

principal components common factor analysis. 3  For example, brand awareness, brand loyalty and brand image are 

the sub-factor of brand equity.

2.  Checking the assumption.

3  Outlier

âëå outlier ë spss úâëå 

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3  Multivariate Normality. 

-  Select data file  

-   Analyze properties test normal and outlier

-  calculate estimate

-   View text assessment of normality

cr.

 Assessment of normality (Group number 1)

 Variable min max skew c.r. kurtosis c.r.

CL3 3.000 5.000 -.008 -.041 -.095 -.262

CL2 3.000 5.000 .086 .472 -.390 -1.067CL1 3.000 5.000 -.031 -.169 .148 .404

CS3 2.000 5.000 .132 .720 -.316 -.866

CS2 2.000 5.000 -.255 -1.394 -.182 -.499

CS1 2.000 5.000 .204 1.120 -.738 -2.020

PM1 3.000 5.000 -.101 -.554 -.884 -2.422

PM2 3.000 5.000 -.209 -1.142 -.807 -2.211

PM3 2.000 5.000 -.379 -2.077 -.362 -.990

SQ1 3.000 5.000 .122 .666 -.344 -.942

SQ2 2.000 5.000 -.164 -.898 .845 2.314

SQ3 3.000 5.000 .268 1.466 .237 .648SQ4 3.000 5.000 .166 .909 -.004 -.011

SQ5 3.000 5.000 .180 .984 .846 2.317

Multivariate 16.003 5.072

èâõèèõèúèåúë è -1.96  ÷è 1.96 õè normal

øå: ûü åâ Observations farthest from the centroid õå Mahalanobis d-

squared

 ø

 âõ üûãö

úúø

  spss

âõsave 

õ

úspss

 

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3.  Evaluating goodness-of-fit criteria

3  Absolute fit (GFI, RMSEA) 

GFI and AGFI must be greater than 0.9.

RMSEA should be less than 0.05 while 3  Incremental fit (TLI, NFI) 

NFI and TLI (NNFI) should be greater than 0.9. 3  Parimonious fit (CMIN/DF) 

n > 100 

It is the ratio of the chi-square divided by the degrees of freedom. Accepted at 1-3 or 1-5 

Ho: Model is fit to data. 

Ha: Model is not fit to data. 

P must be greater than 0.05 in order to accept Ho. CMIN/DF must be less than 2.00.

4.  Interpreting and modifying the model3  Unstandardized and standardized 3  Model respecification

2) Basic step for SEM

1. üõ estimate õúø ââ  .05 ø  p value â 

éâ hypothesis 

Ho: XX has no eff ect on YY

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Ha: XX has eff ect in YY

2 ø  run  model fit ü