Estimated coefficients for the multinomial logit model fitted to voting behaviour.

<p>Estimated coefficients for the multinomial logit model fitted to voting behaviour.</p

Correspondence analysis for the segments and reported votes.

<p>Correspondence analysis showing the graphical representation of the cross tabulation for the segments and reported votes over the years, using the adjusted poststratification weights applied to the posterior probabilities of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190598#pone.0190598.e006" target="_blank">Eq (5)</a>. The symbols for the segments are faded according to the explained inertia so that darker points fit better. No shading was done for the political parties. For clarity, each panel shows a different year, but the results are from the same single analysis. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190598#pone.0190598.s006" target="_blank">S2 Fig</a> shows all years in a single plot. The first dimension can be labeled ‘openness-to-change’ and the second dimension ‘universalism’. The closer a party is to a segment, the more likely it is that this segment will vote for the party. For instance, dimension one distinguishes the traditional segment (III) from the other segments; Segment III is more likely to vote for SGP or CU and less likely to vote for GL. The plots also show that the positions of the political parties relative to the segments changes over time.</p

Response styles.

<p>The distribution of each response style segment across (1) all rating categories and (2) all value segments. The size of each response style segment, as a percentage, is given in parentheses after its number in the vertical axis labels. The table on the left shows the response categories used by the respondents to indicate their answers. The darker the cell, the more the respective rating category was chosen. For instance, in response style segment 4 (RS4 in the text), category “2” was chosen for 52% of the items and category “3” for 32.2% of the items. The table on the right shows combinations of response style segments and value segments. It can be seen that response style segments and value segments are not independent; for instance, response styles segments 18 and 20, with many missing answers, are more prevalent in value segment VII.</p

Analysis of variance for multinomial logit models estimating the parties voted for.

<p>Analysis of variance for multinomial logit models estimating the parties voted for.</p

Schwartz’s value circumplex.

<p>Schwartz’s value circumplex, showing the relationship between human values.</p

Competing for the same value segments? Insight into the volatile Dutch political landscape

<div><p>Values are central to public debates today. Human values convey broad goals that serve as guiding principles in a person’s life and value priorities differ across people in society. Groups in society holding opposing values (e.g., universalism versus security) will make different choices when voting in an election. Whereas over time, values are relatively stable, the number and type of political parties as well as the political values they communicate and disseminate have been changing. Groups of people holding the same human values may therefore vote for another (new) party in a later election. We focus on analyzing the relationship between human values and voting in elections, introducing a new methodology to analyze how value profiles relate to political support over time. We investigate the Dutch multi-party political system over five waves of the European Social Survey, spanning 2002 until 2010. Whilst previous research has focused on individual values separately and focused on voters only, we (1) distinguish groups holding a similar set of opposing and compatible values (value profile) instead of focusing on single values in the the entire population; (2) incorporate a correction for differences in scale use in our model; (3) compare voting over time; (4) include non-voters, a growing group in Dutch society. We find evidence that specific value profiles are related to voting for a specific set of political parties. We also find that specific value profiles distinguish non-voters from voters and that voters for populist parties resemble non-voters.</p></div

Theoretical predictions of power per causal SNP (upper panel) and out-of-sample <i>R</i>² of the PGS (lower panel), for a trait that across studies has SNP heritability (<i>x</i>-axis) and cross-study genetic correlation (<i>y</i>-axis).

<p>Factor levels: 50 studies, sample size 5,000 individuals per study, 100k independent SNPs, and heritability arising from a subset of 1k independent SNPs.</p

Theoretical predictions of power per causal SNP (upper panel) and out-of-sample <i>R</i>² of the PGS (lower panel), for a trait with GWAS results from the number of studies (<i>x</i>-axis) with cross-study genetic correlation (<i>y</i>-axis).

<p>Factor levels: total sample size 250,000 individuals, 100k independent SNPs, and arising from a subset of 1k independent SNPs.</p

Theoretical predictions of power per causal SNP, for total sample size (<i>x</i>-axis) and CGR between two sets of studies (<i>y</i>-axis).

<p>Factor levels: 2 sets of 50 studies, CGR equal to 1 within both sets, 100k independent SNPs, and arising from a subset of 1k independent SNPs.</p

Predicted and observed number of genome-wide-significant hits and PGS <i>R</i>², for large-scale GWAS efforts to date for height, BMI, <i>EduYears</i>, and self-rated health, assuming 250k effective SNPs (i.e., independent haplotype blocks) of which 20k trait-affecting, using averaged GREML estimates from Table 1 for setting SNP heritability and CGR.

<p>Notes on the sources for the large-scale GWAS efforts are listed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006495#pgen.1006495.s009" target="_blank">S3 Table</a>.</p