factor analysis: PCA vs EFA

#1
Hello everyone,

I have recently conducted a validation study on a 7-item survey.
For the factor analysis, I have calculated
1. Determinant of the correlation matrix Det = 0.030
2. Bartlett test of sphericity Chi-square = 685.883 Degrees of freedom = 21 p-value = 0.000 H0: variables are not intercorrelated
3. Kaiser-Meyer-Olkin Measure of Sampling Adequacy KMO = 0.847

Afterwards I have conducted FACTOR ANALYSIS with principal component factor Factor analysis/correlation
Factor analysis/correlation Number of obs = 200
Method: principal-component factors Retained factors = 1
Rotation: (unrotated) Number of params = 7

--------------------------------------------------------------------------
Factor | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 | 3.86248 3.03265 0.5518 0.5518
Factor2 | 0.82983 0.10172 0.1185 0.6703
Factor3 | 0.72811 0.06234 0.1040 0.7743
Factor4 | 0.66577 0.19947 0.0951 0.8695
Factor5 | 0.46630 0.15041 0.0666 0.9361
Factor6 | 0.31589 0.18426 0.0451 0.9812
Factor7 | 0.13163 . 0.0188 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(21) = 689.39 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

---------------------------------------
Variable | Factor1 | Uniqueness
-------------+----------+--------------
fgsis_s_01 | 0.8639 | 0.2536
fgsis_s_02 | 0.8678 | 0.2469
fgsis_s_03 | 0.7614 | 0.4202
fgsis_s_04 | 0.5869 | 0.6555
fgsis_s_05 | 0.6471 | 0.5812
fgsis_s_06 | 0.5569 | 0.6898
fgsis_s_07 | 0.8425 | 0.2902
---------------------------------------


Given these findings can we asume that the measure is an unrestricted one factor solution? Or should I undergo a maximum likelihood or principal axis factoring?

Thanks!!