This thesis aimed to investigate and describe the influence that certain socioeconomical, demographical, and geographical variables had on the election results for the three parliamentary party groups in the 2018 Swedish parliamentary election on the municipal level. The study also aimed to compare the difference in effect of the variables between two different geographical study areas: Norrland and the Greater Stockholm area. The study has been conducted via a regression analysis.  The results indicated that income, education, population density and average age all have a noticeable influence on the election results for the different party blocks. Income was the factor with the overall largest influence on the election result. There was a difference in influence from different variables between the three different party blocks. The study also found that there was a difference in effect between Norrland and Greater Stockholm. Certain variables had more of an effect in Norrland, and vice-versa. Most notably, income and average age had the opposite effect in Norrland compared to Greater Stockholm. The reason for this is not clear, but differences in culture between the study areas may provide an explanation.

Industry bias is the bias associated with polling estimates, and that is caused by systematic errors. We aim to test for its presence in Swedish political polling from 2010 to 2022 and simulate industry bias as a consequence of nonresponse bias. We estimate industry bias per party as the mean error of the last polls per polling house before an election and calculate the probability of observing the number of polling underestimates and overestimates per party and election year. Our results indicate that industry bias is present to varying degrees in each Swedish parliamentary election year from 2010 to 2022. Moreover, our results indicate that polls with and without industry bias as a consequence of nonrespondent bias can be replicated.