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Simulation Study on the Effects of Ignoring Clustering in Regression Analysis

Thommai, J. (2019) Simulation Study on the Effects of Ignoring Clustering in Regression Analysis.

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Abstract:In the following, a simulation study was conducted in order to examine the effects of clustering on the precision of the model parameter estimates. One assumes that estimates will appear to be biased when disregarding part of the random effect structure. When taking clustering effects out of the equation more information will be assumed than the data actually contains, which leads to an overstatement of the precision and to false claims about statistical significance. The other way around, an overestimation of the random effect structure will imply non-existing cor-relations between observations, which leads to an underestimation of the precision. In order to test this assumption, simulated data is used to examine the under- and overestimation of the precisions. Furthermore, we are interested in the bias of standard error estimates of the fixed effects, when mis-specifying the clustering effects. Statistical analysis of p-values and standard errors of the intercepts and fixed effects in the Linear Mixed Effect Model and Linear Model showed that failing to account for clustering effects leads to extreme and biased outcomes which in turn will lead to false conclusions. Moreover, it was found that when dealing with negatively clustered data, ANVOCA, LME and LM falsely proposed that there was no evidence for clustering.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology BSc (56604)
Link to this item:http://purl.utwente.nl/essays/78374
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