Where we left off, I had created some parcels and was going to do a factor analysis later. Now, it’s later. If you’ll recall, I had not find any items that correlated significantly with the food item that also made sense conceptually. For example, it correlated highly with attending church services but that didn’t really have any theoretical basis. So, I left it as a single variable. Here is my first factor analysis.

proc factor data= parcels rotate= varimax scree ;
Var socialp1 – socialp3 languagep spiritualp spiritual2 culturep1 culturep2  food;

You can see from the scree plot here that there is one factor way at the top of the chart with the rest scattered at the bottom. Although the minimum eigen value of 1 criterion would have you retain two factors, I think that is too many, for both logical and statistical reasons.  The eigenvalues of the first two factors, by the way, were 4.74 and 1.10 .

scree plot

Even if you aren’t really into statistics or factor analysis, I hope that this pattern is pretty clear. You can see that every single thing except for the item related to food loads predominantly on the first factor.

factor patternThe median factor loading was .79, and the factor loadings ranged from .49 to .83 .

These results are interesting in light of the discussion on small sample size. If you didn’t read it, the particular quote in there that is relevant here is

“If components possess four or more variables with loadings above .60, the pattern may be interpreted whatever the sample size used .”

Final Communality Estimates: Total = 5.845142

socialp1 socialp2 socialp3 languagep spiritualp spiritual2 culturep1 culturep2 food
0.67438366 0.72223020 0.64287274 0.80080260 0.34260318 0.46790413 0.70885380 0.69821549 0.78727573

These communality estimates are also relevant but it is nearly 1 am and I have to be up at 6:30 for a conference call, so I’ll ramble on about this some more next time.


Name (required)

Email (required)


Speak your mind


WP Themes