This is because the average eigenvalue is always 1.0 if you analyze correlations. Which components have sufficient eigenvaluesīy default, SPSS uses a cutoff value of 1.0 for eigenvalues.
FACTOR /VARIABLES Car01 Car02 Car03 Car04 Car05 Car06 Car07 Car08 Conf01 Conf02 Conf03 Conf05 Conf06 Comp01 Comp02 Comp03 Comp04 Tou01 Tou02 Tou03 Tou04 Tou05 Succ01 Succ02 Succ03 Succ04 Succ05 Succ06 Succ07 /MISSING PAIRWISE /PRINT INITIAL EXTRACTION ROTATION /FORMAT SORT BLANK(.3) /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION VARIMAX /METHOD=CORRELATION. Listwise exclusion limits our analysis to N = 369 complete cases which is (arguably) insufficient sample size for 29 variables.Ĭompleting these steps results in the syntax below. We'll exclude cases with missing values pairwise. However, we do want to adjust some settings under Rotation and Options. Let's first open the factor analysis dialogs fromĪnalyze Dimension Reduction Factor as shown below.įor our first analysis, most default settings will do. We're now good to go so let's proceed with our actual factor analysis. Another is that a larger sample size results in more statistical power and smaller confidence intervals. This is one reason for including some incomplete respondents. So for our example analysis we'd like to use at least 29 (variables) * 15 = 435 cases. We'd like to use at least 15 cases for each variable With our FILTER in effect, all analyses will be limited to N = 533 cases having 9 or fewer missing values. Note that only 369 out of N = 575 cases have zero missing values on all 29 variables. But let's first activate our filter variable by running the syntax below. precisely which statements measure which factors?Ī factor analysis will answer precisely those questions.do these 29 statements indeed measure 5 underlying traits or “factors”.The first research questions we'd now like to answer are We created filt01 which filters out any respondents having 10 or more missing values (out of 29 variables). Some negative statements were reverse coded and therefore had “(R)” appended to their variable labels mis01 contains the number of missing values for each respondent. These variables have already been prepared for analysis: Variables Car01 (short for “career ambitions”) through Succ07 (short for “successfulness”) attempt to measure 5 traits. The data -partly shown below- are in 20-career-ambitions-pca.sav. Which personality traits predict career ambitions?Ī study was conducted to answer just that.
SPSS FACTOR Output II - Rotated Component Matrix.SPSS FACTOR Output I - Total Variance Explained.SPSS Factor Analysis – Intermediate Tutorial By Ruben Geert van den Berg under Factor Analysis