Measuring Personality in the SOEP

The German Socio-Economic-Panel (SOEP) is a longitudinal study of German households. The core questions address economic issues, work, health, and well-being. However, additional questions are sometimes added. In 2005, the SOEP included a 15-item measure of the Big Five; the so-called BFI-S (Lang et al., 2011). As each personality dimension is measured with only three items, scale scores are rather unreliable measures of the Big Five. A superior way to examine personality in the SOEP is to build a measurement model that relates observed item scores to latent factors that represent the Big Five.

Anusic et al. (2009) proposed a latent variable model for an English version of the BFI-S.

The most important feature of this model is the modeling of method factors in personality ratings. An acquiescence factor accounts for general response tendencies independent of item content. In addition, a halo factor accounts for evaluative bias that inflates correlations between two desirable or two undesirable items and attenuates correlations between a desirable and an undesirable item. The Figure shows that the halo factor is bias because it correlates highly with evaluative bias in ratings of intelligence and attractiveness.

The model also includes a higher-order factor that accounts for a correlation between extraversion and openness.

Since the article was published I have modified the model in two ways. First, the Big Five are conceptualized as fully independent which is in accordance with the original theory. Rather than allowing for correlations among Big Five factors, secondary loadings are used to allow for relationships between extraversion and openness items. Second, halo bias is modeled as a characteristic of individual items rather than the Big Five. This approach is preferable because some items have low loadings on halo.

Figure 2 shows the new model.

I fitted this model to the 2005 data using MPLUS (syntax and output: https://osf.io/vpcfd/ ). The model had acceptable fit to the data, CFI = .962, RMSEA = .035, SRMR = .029.

Table 1 shows the factor loadings. It also shows the correlation of the sum scores with the latent factors.

Item#NEOACEVBACQ
Neuroticism
worried50.49-0.020.19
nervous100.64-0.310.18
relaxed15-0.550.350.21
SUM0.750.000.000.000.00-0.300.09
Extraversion
talkative20.600.130.400.23
sociable80.640.370.22
reserved12-0.520.20-0.110.19
SUM0.000.750.00-0.100.050.360.09
Openess
original40.260.41-0.330.380.22
artistic90.150.360.290.17
imaginative140.300.550.220.21
SUM0.000.300.57-0.130.000.390.26
Agreeableness
rude30.12-0.51-0.320.19
forgiving60.230.320.24
considerate130.490.480.29
SUM0.00-0.070.000.580.000.500.11
Conscientiousness
thorough10.710.350.30
lazy7-0.16-0.41-0.350.20
efficient110.390.480.28
SUM0.000.000.000.090.640.510.11

The results show that all items load on their primary factor although some loadings are very small (e.g., forgiving). Secondary loadings tend to be small (< .2), although they are highly significant in the large sample. All items load on the evaluative bias factor, with some fairly large loadings for considerate, efficient, and talkative. Reserved is the most evaluatively neutral item. Acquiescence bias is rather weak.

The scale scores are most strongly related to the intended latent factor. The relationship is fairly strong for neuroticism and extraversion, suggesting that about 50% of the variance in scale scores reflects the intended construct. However, for the other three dimensions, correlations suggest that less than 50% of the variance reflects the intended construct. Moreover, the remaining variance is not just random measurement error. Evaluative bias contributes from 10% up to 25% of additional variance. Acquiescence bias plays a minor role because most scales have a reverse scored item. Openness is an exception and acquiescence bias contributes 10% of the variance in scores on the Openness scale.

Given the good fit of this model, I recommend it for studies that want to examine correlates of the Big Five or that want to compare groups. Using this model will produce better estimates of effect sizes and control for spurious relationships due to method factors.

6 thoughts on “Measuring Personality in the SOEP

  1. Have you ever considered the wealth of data collected from the Strong Vocational Interest Blank. Strong measured occupational norms (profiles) for 400 occupational groups (mostly male) using a 200 item questionnaire. He could then predict what occupation a person would wind up in 10 years later and verified this in follow-up studies. His results were only slightly worse at predicting a person’s choices than the person himself. Holland later did a factor analysis and came up with 6 main occupational clusters which he named Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC), each of which characterizes a type of person who will naturally gravitate towards, choose, and enjoy a specific occupation or vocational area as well as personal interests that are also shared by each type. My point is that Strong had gathered data as far back as 1915 and Holland’s types “rhyme” with the OCEAN traits. If there is overlap between OCEAN and RIASEC you can then extend traits to occupational choice. For example: You can answer this question: “Are our Traits what attracts us to particular occupations?” A salesman is an Enterprising type and we know they are generally extroverts. Social Types are Open, conscientious and Agreeable and Artistic Types have Neurotic type traits and generally correlate negatively with the other 5 Types. It’s also the only Type that correlates negatively with all the other Types. My thesis in 1980 was on Hollands Theory so when I discovered OCEAN traits 15 years later I immediately noted the overlap of Types with Traits.

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    1. I would love to analyze the data, but I don’t think they are open. Regarding overlap with the Big Five, there are some expected correlations (artistic = openness) (social = agreeable) (enterprising = E), but the correlations are not that strong (pun not indended). I think vocational interest measures have some important unique variance that adds to their predictive validity. Personally, I think the Big Five are a good broad approach, but for applied questions lower order factors are better.

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