Finding mixed cases in exploratory factor analysis
Emil O. W. Kirkegaard
Two methods are presented that allow for identification of mixed cases in the extraction of general
factors. Simulated data is used to illustrate them.
General factors can be extracted from datasets where all or nearly so the variables are correlated. At the
case-level, such general factors are decreased in size if there are mixed cases present. A mixed case is
an 'inconsistent' case according to the factor structure of the data.
A simple way of illustrating what I'm talking about is using the matrixplot() function from the VIM
package to R (Templ, Alfons, Kowarik, & Prantner, 2015) with some simulated data.
For simulated dataset 1, start by imaging that we are measuring a general factor and that all our
indicator variables have a positive loading on this general factor, but that this loading varies in strength.
Furthermore, there is no error of measurement and there is only one factor in the data (no group factors,
i.e. no hierarchical or bi-factor structure, (Jensen & Weng, 1994)). I have used datasets with 50 cases
and 25 variables to avoid the excessive sampling error of small samples and to keep a realistic number
of cases compared to the datasets examined in S factor studies (e.g. Kirkegaard, 2015). The matrix plot
is shown in Figure 1.
Figure 1: Matrix plot of dataset 1
No real data looks like this, but it is important to understand what to look for. Every indicator is on the
x-axis and the cases are on the y-axis. The cases are colored by their relative values, where darker
means higher values. So in this dataset we see that any case that does well on any particular indicator
does just as well on every other indicator. All the indicators have the same factor loading of 1, and the
proportion of variance explained is also 1 (100%), so there is little point in showing the loadings plot.
To move towards realism, we need to complicate this simulation in some way. The first way is to
introduce some measurement error. The amount of error introduced determines the factor loadings and
hence the size of the general factor. In dataset 2, the error amount is .5, and the signal multiplier varies
from .05 to .95 all of which are equally likely (uniform distribution). The matrix and the loadings plots
are shown in Figures 2 and 3.
Figure 2: Matrix plot for dataset 2
Figure 3: Loadings plot for dataset 2
By looking at the matrix plot we can still see a fairly simple structure. Some cases are generally darker
(whiter) than others, but there is also a lot of noise which is of course the error we introduced. The
loadings show quite a bit of variation. The size of this general factor is .45.
The next complication is to introduce the possibility of negative loadings (these are consistent with a
general factor, as long as they load in the right direction, (Kirkegaard, 2014)). We go back to the
simplified case of no measurement error for simplicity. Figures 4 and 5 show the matrix and loadings
The matrix plot looks odd, until we realize that some of the indicators are simply reversed. The
loadings plot shows this reversal. One could easily get back to a matrix plot like that in Figure 1 by
reversing all indicators with a negative loading (i.e. multiplying by -1). However, the possibility of
negative loadings does increase the complexity of the matrix plots.
Figure 4: Matrix plot for dataset 3
Figure 5: Loadings plot for dataset 3
For the 4th dataset, we make a begin with dataset 2 and create a mixed case. This we do by setting its
value on every indicator to be 2, a strong positive value (98 centile given a standard normal
distribution). Figure 6 shows the matrix plot. I won't bother with the loadings plot because it is not
strongly affected by a single mixed case.
Can you guess which case it is? Perhaps not. It is #50 (top line). One might expect it to be the same hue
all the way. This however ignores the fact that the values in the different indicators vary due to
sampling error. So a value of 2 is not necessarily at the same centile or equally far from the mean in
standard units in every indicator, but it is fairly close which is why the color is very dark across all
For datasets with general factors, the highest value of a case tends to be on the most strongly loaded
indicator (Kirkegaard, 2014b), but this information is not easy to use in an eye-balling of the dataset.
Thus, it is not so easy to identify the mixed case.
Now we complicate things further by adding the possibility of negative loadings. This gets us data
roughly similar to that found in S factor analysis (there are still no true group factors in the data).
Figure 7 shows the matrix plot.
Figure 6: Matrix plot for dataset 4
Just looking at the dataset, it is fairly difficult to detect the general factor, but in fact the variance
explained is .38. The mixed case is easy to spot now (#50) since it is the only case that is consistently
dark across indicators, which is odd given that some of them have negative loadings. It 'shouldn't'
happen. The situation is however somewhat extreme in the mixedness of the case.
Eye-balling figures and data is a powerful tool for quick analysis, but it cannot give precise numerical
values used for comparison between cases. To get around this I developed two methods for automatic
identification of mixed cases.
A general factor only exists when multidimensional data can be usefully compressed, informationally
speaking, to 1-dimensional data (factor scores on the general factor). I encourage readers to consult the
very well-made visualization of principal component analysis (almost the same as factor analysis) at
this website. In this framework, mixed cases are those that are not well described or predicted by a
Thus, it seems to me that that we can use this information as a measure of the mixedness of a case. The
1. Extract the general factor.
2. Extract the case-level scores.
3. For each indicator, regress it unto the factor scores. Save the residuals.
4. Calculate a suitable summary metric, such as the mean absolute residual and rank the cases.
Using this method on dataset 5 in fact does identify case 50 as the most mixed one. Mixedness varies
between cases due to sampling error. Figure 8 shows the histogram.
Figure 7: Matrix plot for dataset 5
The outlier on the right is case #50.
How extreme does a mixed case need to be for this method to find it? We can try reducing its
mixedness by assigning it less extreme values. Table 1 shows the effects of doing this.
Mixedness values Mean absolute residual
Table 1: Mean absolute residual and mixedness
So we see that when it is 2 and 1.5, it is clearly distinguishable from the rest of the cases, but 1 is about
the limit of this since the second-highest value is .80. Below this, the other cases are similarly mixed,
just due to the randomness introduced by measurement error.
Since mixed cases are poorly described by a single score, they don't fit well with the factor structure in
the data. Generally, this should result in the proportion of variance increasing when they are removed.
Thus the method is:
1. Extract the general factor from the complete dataset.
2. For every case, create a subset of the dataset where this case is removed.
3. Extract the general factors from each subset.
4. For each analysis, extract the proportion of variance explained and calculate the difference to
that using the full dataset.
Using this method on the dataset also used above correctly identifies the mixed case. The histogram of
Figure 8: Histogram of absolute mean residuals from dataset 5
results is shown in Figure 9.
Like we method 1, we then redo this analysis for other levels of mixedness. Results are shown in Table
Mixedness values Improvement in proportion of variance
Table 2: Improvement in proportion of variance and mixedness
We see the same as before, in that both 2 and 1.5 are clearly identifiable as being an outlier in
mixedness, while 1 is not since the next-highest value is .45.
Large scale simulation with the above methods could be used to establish distributions to generate
confidence intervals from.
It should be noted that the improvement in proportion of variance is not independent of number of
cases (more cases means that a single case is less import, and non-linearly so), so the value cannot
simply be used to compare across cases without correcting for this problem. Correcting it is however
beyond the scope of this article.
Comparison of methods
The results from both methods should have some positive relationship. The scatter plot is shown in
Figure 9: Histogram of differences in proportion of variance to the
We see that the true mixedness case is a strong outlier with both methods -- which is good because it
really is a strong outlier. The correlation is strongly inflated because of this, to r=.70 with, but only .26
without. The relative lack of a positive relationship without the true outlier in mixedness is perhaps due
to range restriction in mixedness in the dataset, which is true because the only amount of mixedness
besides case 50 is due to measurement error. Whatever the exact interpretation, I suspect it doesn't
matter since the goal is to find the true outliers in mixedness, not to agree on the relative ranks of the
cases with relatively little mixedness.1
I have implemented both above methods in R. They can be found in my unofficial psych2 collection of
useful functions located here.
Source code and figures are available at the Open Science Framework repository.
Jensen, A. R., & Weng, L.-J. (1994). What is a good g? Intelligence, 18(3), 231–258.
Kirkegaard, E. O. W. (2014a). The international general socioeconomic factor: Factor analyzing
international rankings. Open Differential Psychology. Retrieved from
Kirkegaard, E. O. W. (2014b). The personal Jensen coefficient does not predict grades beyond its
1 Concerning the agreement about rank-order, it is about .4 both with and without case 50. But this is based on a single
simulation and I've seen some different values when re-running it. A large scale simulation is necessary.
Figure 10: Scatter plot of method 1 and 2
association with g. Open Differential Psychology. Retrieved from
Kirkegaard, E. O. W. (2015). Examining the S factor in US states. The Winnower. Retrieved from
Templ, M., Alfons, A., Kowarik, A., & Prantner, B. (2015, February 19). VIM: Visualization and
Imputation of Missing Values. CRAN. Retrieved from http://cran.r-