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The article focuses on the aggregate vote choice is responsive both to party leader television appearances and to the timing and substance of polling information. It mentions the utility of Voting Advice Applications (VAA) data in detecting campaign effects that cannot be observed using more conventional sources of public opinion data. It also mentions that VAAs allow for time‐series cross‐sectional estimates that illuminate the dynamics of public opinion that are not possible using conventional polling data, along with the information on the effect of the polls on vote share is significant and positive for the New Democratic Party (NDP).

The proliferation of Voting Advice Applications (VAAs) offers voters a simplified means by which to estimate their alignment with the candidates contesting a given election. But are the outputs generated by such applications both reliable and valid? While they differ greatly in design and degree of sophistication, most VAAs share a distinctive element that serves as their defining feature: an aggregation algorithm. Aggregation algorithms are the source of a VAA's legitimacy and yet no formal framework for their evaluation has as yet been agreed upon. We posit a dimensionality reduction technique as a corrective to recognised shortcomings in the dominant approaches to VAA design. We then test our model within a proposed framework for evaluating the validity and reliability of VAA algorithms. [ABSTRACT FROM AUTHOR]