Exploratory factor analysis dataset tosse r sav
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Exploratory Factor Analysis --- Dataset (TOSSE-R.sav). Presenter : Melody Date: June 1, 2013. Suitable for FA? Based on what? Stages of making a decision on the factors to be extracted What is the convergent validity? discriminant validity?

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Exploratory Factor Analysis --- Dataset (TOSSE-R.sav)

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Exploratory factor analysis dataset tosse r sav

Exploratory Factor Analysis --- Dataset (TOSSE-R.sav)

Presenter : Melody

Date: June 1, 2013


Exploratory factor analysis dataset tosse r sav

  • Suitable for FA? Based on what?

  • Stages of making a decision on the factors to be extracted

  • What is the convergent validity? discriminant validity?

  • Reliability. Overall reliability? Extracted factors’ reliability?

  • Interpretation of the factor structure label these extracted factors

  • Conclusion


Suitable for fa

Suitable for FA?

  • At the initial stage of preliminary checking:

  • Correlation R-Matrix

     These items are eyesores.

    Q6 (r = .271), Q7(r = .225), Q10 (r =.254), Q12 (r =.079), Q19 (r = - .095), Q20 (r = .171), Q23 (r = .281), Q25 (r =.176), Q26 (r = .151), and Q27 (r = .259)

     Why? The standard that the extent of association among items should be within 0.3~0.8 is not met.


Suitable for fa1

Suitable for FA?

  • Communalities table

    singularity  Q12 (factor loading value is 0.297)

  • Determinant value : 0.00000124 < 0.00001

     multicollinearity problem


Suitable for fa2

Suitable for FA?

  • At the initial stage of preliminary checking:

  • KMO value (= .894) > 0.5

  • Barlett’s test of sphericity: statistical sig.

  • Anti-image Correlation Matrix shows that values along diagonal line is larger than 0.5, and values off the diagonal line are dominantly smaller, which meet the Measure of sampling adequacy (MSA) criteria with 0.5 set as the minimum requirement.


Suitable for fa3

Suitable for FA?

  • Bland’s theory of research methods lecturers predicted that good research methods lecturers should have four characteristics (i.e., a profound love of statistics, an enthusiasm for experimental design, a love of teaching, and a complete absence of normal interpersonal skills).  supported or refuted?

  • These four characteristics are correlated to some degree.  Multicollinearity is understandable .


Suitable for fa4

Suitable for FA?

  • In terms of

  • KMO with statistical significance, an

    indicator of sampling adequacy,

  • Anti-image Correlation Matrix, meeting

    the Measure of sampling adequacy (MSA)

  • Communalities: most items have reached the minimum criterion 0.5, indicating that most items have reached the degree of being explained by common factors

     Suitable for FA, but some items had better be crossed out.


Stages of making a decision on the factors to be extracted

Stages of making a decision on the factors to be extracted

  • At the preliminary stage :

     an action taken: Q12 (singularity problem) and Q10 (comparatively low factor loading value =0.417< 0.5) deleted.

  • At the second stage:

    an action taken : the remaining items (26 items) are under EFA by resorting to abliminrotation approach. ( because of expected correlated underlying factors)


Stages of making a decision on the factors to be extracted1

Stages of making a decision on the factors to be extracted

  • At the second stage:

  • Pattern Matrix table

     Q21 and Q27 crossing-load on two

    components

     the loading values of Q1, Q9, and

    Q11 are suppressed due to their

    coefficient values below the

    threshold set as 0.4.


Stages of making a decision on the factors to be extracted2

Stages of making a decision on the factors to be extracted

  • At the second stage:

    Q21, Q27, Q1, Q9, and Q11 deleted.

    21 items are left for EFA again.

  • At the third stage:

  • determinant value (=0.000),slightly larger than the benchmark 0.00001.

  • Pattern Matrix : no crossing-loading variables.


Stages of making a decision on the factors to be extracted3

Stages of making a decision on the factors to be extracted

  • At the third stage:

  • KMO value is .868 with statistical significance

  • total variance of being explained : these extracted five components after rotation account for nearly 62 percent of variance

  • eigenvalue of each component >1

  • communalities: only one variable value, Q7 (= 0.478), is below the threshold value 0.5.


Stages of making a decision on the factors to be extracted4

Stages of making a decision on the factors to be extracted

  • Pattern Matrix : two items ---Q7 (.483), Q26(.438) --- factor loadings are not as high as other items loaded onto factors.

  • But in terms of convergent validity criteria flexibly varying with various sample sizes, these variables Q7,Q26 still with sufficient factor loading values (minimum benchmark 0.35~0.4 for sample size ranging from 250~200), if retained, can be justified.


Stages of making a decision on the factors to be extracted5

Stages of making a decision on the factors to be extracted

  • Kaiser’s criterion is not met

    communalities values after extraction > 0.7

    ( if the # of variables is less than 30 )

    sample size > 250

    average communality > 0.6

     retain all factors with eigenvalues above 1

  • Scree plot is the last resort to turn to if sample size is large (i.e., around 300 or more)

  • 21 items decided  five factors extracted


Convergent validity

Convergent Validity

  • refer to to what extent variables loaded within a factor are correlated  the higher loading, the better.

  • Factor structure :

  • check Pattern Matrix to know about the convergent validity

    (no crossing-loadings between factors )

     variables precisely loading on factors

  • check convergent validity in terms of sample size. In this case, the sample size is 239; the convergent validity is acceptable, for most variables are above the range of 0.35 to 0.4. in terms of loadings within factors.


Discriminant validity

Discriminant Validity

  • 2 ways to check discriminant validity

  • Check Pattern Matrix to see no crossing-loadings

  • Check Factor Correlation Matrix : correlations between factors do not exceed 0.7.


Exploratory factor analysis dataset tosse r sav

Discriminant Validity

Correlations between factors do not exceed 0.7


Exploratory factor analysis dataset tosse r sav

Overall Reliability of the 21 items in the dataset (TOSSE.sav.)

Larger than 0.7


Exploratory factor analysis dataset tosse r sav

Reliability of Comp 1> 0.7

Reliability of Comp 2

=. 0.7


Exploratory factor analysis dataset tosse r sav

Reliability of

Comp 3

> 0.7

Reliability of Comp 4 =. 0.7

Reliability of Comp 5 > 0.7


Interpretation of extracted 5 factors

Interpretation of extracted 5 factors

  • labels of the five factors:

  • Component 1: ‘Passion for Applying

    Statistics Knowledge’

  • Component 2 : ‘Apprehension for Teaching ’

  • Component 3: ‘Obsession with

    Successfully Applying Statistics to

    Experiment’

  • Component 4: ‘Preference for being alone’,

  • Component 5: ‘Passion for teaching

    Statistics’


Exploratory factor analysis dataset tosse r sav

Component 1: ‘Passion for Applying Statistics Knowledge’


Exploratory factor analysis dataset tosse r sav

Component 2 : ‘Apprehension for Teaching’


Exploratory factor analysis dataset tosse r sav

Component 3: ‘Obsession with Successfully

Applying Statistics to Experiment’


Exploratory factor analysis dataset tosse r sav

Component 4: ‘Preference for being alone’


Exploratory factor analysis dataset tosse r sav

Component 5: ‘Passion for teaching Statistics’


Conclusion

Conclusion

  • The extracted five factors refute Bland’s theory through the EFA, for

  • we are asked to test the theory of four personality traits

  • the labeling of Component 2 (Apprehension for Teaching) contradicts the labeling of Component 5 (Passion for teaching Statistics)

  • Individual Factor reliability ---Comp 2 / Comp 4 at the margin of 0.7, not above 0.7


Exploratory factor analysis dataset tosse r sav

  • Why don’t we first group the question items into four components in correspondence with the four characteristics proposed by Bland, and then run FA? CFA?


Conclusion1

Conclusion

  • When EFA is resorted to, very often an extracted factor loaded with some variables as a cluster is hard to be labeled. And thus several trials seem unavoidable until the labeling of a factor can comprehensively interpret the variables loaded on that factor.

  • As such, this dataset seems to be more like a CFA case because of the already-existing hypothesis about the underlying constructs (i.e., four personality traits).


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