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In election times, millions of voters consult Voting Advice Applications (VAAs) to learn more about political parties and their standpoints. While VAAs have been shown to enhance political knowledge and increase electoral turnout, research also demonstrates that voters frequently experience comprehension problems when responding to the political attitude statements in a VAA. We describe two studies in which we test a new type of VAA, called Conversational Agent VAA (CAVAA), in which users can easily access relevant information about the political issues in the VAA statements by asking questions to a chatbot. Study 1 reports about an online experiment (N = 229) with a 2 (Type: traditional VAA/CAVAA) x 2 (Political sophistication: low/high) design. Results show that CAVAA users report higher perceived political knowledge scores and also answer more factual knowledge questions correctly than users of a regular VAA. Also, participants' CAVAA experience was evaluated better. In Study 2 (N = 180), we compared three CAVAA designs (a structured design with buttons, a non-structured design with an open text field, and a semi-structured design with both buttons and an open text field), again for higher and lower politically sophisticated users. While the three designs score equally high on factual and perceived knowledge indicators, the experience of the structured CAVAA was evaluated more positively than the non-structured version. To explore the possible cause for these results, we conducted an additional qualitative content analysis on 90 chatbot-conversations (30 per chatbot version). This analysis shows that users more frequently access additional information in a structured design than in a non-structured design, whereas the number of break-offs is the same. This suggests that the structured design delivers the best experience, because it provides the best trigger to ask questions to the chatbot.

Voting Advice Applications are online tools that provide users with a voting advice based on their answers to a set of political attitude questions. This study investigated to what extent VAA users understand the questions that lead to the voting advice, and what search and response behaviour they expose in case of comprehension difficulties. Two studies were conducted to investigate these issues: a cognitive interviewing study among 60 VAA users during the Dutch municipal elections in the city of Utrecht, and a statistical analysis of all answers provided by 357,858 users who accessed one of the 34 municipal VAAs during these same elections. Results of the two studies show a coherent picture: difficult concepts (e.g., tax names or municipal jargon), geographical locations (e.g., reference to a specific street), and vague quantifying terms (e.g., "more") all complicate the question. In case of comprehension difficulties, Study 1 shows that VAA users make little effort to solve their problems, for example by looking up difficult terms on the Internet. Instead, they draw inferences about what the question might mean and proceed to answer nonetheless. These are often neutral or no opinion answers, which seems to suggest that the meanings of those options are confounded. In Study 2, however, we found that the choice for either a neutral or no opinion response is not accidental: semantic meaning problems often result in no opinion answers, whereas pragmatic problems are related to neutral responses. We discuss the implications of these findings for survey theory and practice.

Voting Advice Applications are online tools that provide users with a voting advice based on their answers to a set of political attitude questions. This study investigated to what extent VAA users understand the questions that lead to the voting advice, and what search and response behaviour they expose in case of comprehension difficulties. Two studies were conducted to investigate these issues: a cognitive interviewing study among 60 VAA users during the Dutch municipal elections in the city of Utrecht, and a statistical analysis of all answers provided by 357,858 users who accessed one of the 34 municipal VAAs during these same elections. Results of the two studies show a coherent picture: difficult concepts (e.g., tax names or municipal jargon), geographical locations (e.g., reference to a specific street), and vague quantifying terms (e.g., "more") all complicate the question. In case of comprehension difficulties, Study 1 shows that VAA users make little effort to solve their problems, for example by looking up difficult terms on the Internet. Instead, they draw inferences about what the question might mean and proceed to answer nonetheless. These are often neutral or no opinion answers, which seems to suggest that the meanings of those options are confounded. In Study 2, however, we found that the choice for either a neutral or no opinion response is not accidental: semantic meaning problems often result in no opinion answers, whereas pragmatic problems are related to neutral responses. We discuss the implications of these findings for survey theory and practice.

"Voting Advice Applications are online tools that provide users with a voting advice based on their answers to a set of political attitude questions. This study investigated to what extent VAA users understand the questions that lead to the voting advice, and what search and response behaviour they expose in case of comprehension difficulties. Two studies were conducted to investigate these issues: a cognitive interviewing study among 60 VAA users during the Dutch municipal elections in the city of Utrecht, and a statistical analysis of all answers provided by 357,858 users who accessed one of the 34 municipal VAAs during these same elections. Results of the two studies show a coherent picture: difficult concepts (e.g., tax names or municipal jargon), geographical locations (e.g., reference to a specific street), and vague quantifying terms (e.g., 'more') all complicate the question. In case of comprehension difficulties, Study 1 shows that VAA users make little effort to solve their problems, for example by looking up difficult terms on the Internet. Instead, they draw inferences about what the question might mean and proceed to answer nonetheless. These are often neutral or no opinion answers, which seems to suggest that the meanings of those options are confounded. In Study 2, however, we found that the choice for either a neutral or no opinion response is not accidental: semantic meaning problems often result in no opinion answers, whereas pragmatic problems are related to neutral responses. We discuss the implications of these findings for survey theory and practice." (author's abstract)

"For decades, survey researchers have known that respondents give different answers to attitude questions worded positively (X is good. Agree-Disagree), negatively (X is bad. Agree-Disagree) or on a bipolar scale (X is bad-good). This makes survey answers hard to interpret, especially since findings on exactly how the answers are affected are conflicting. In the current paper, we present twelve studies in which the effect of question polarity was measured for a set of thirteen contrastive adjectives. In each study, the same adjectives were used so the generalizability of wording effects across studies could be examined for each word pair. Results show that for five of the word pairs an effect of question wording can be generalized. The direction of these effects are largely consistent: respondents generally give the same answers to positive and bipolar questions, but they are more likely to disagree with negative questions than to agree with positive questions or to choose the positive side of the bipolar scale. In other words, respondents express their opinions more positively when the question is worded negatively. Even though answers to the three wording alternatives sometimes differ, results also show that reliable answers can be obtained with all three wording alternatives. So, for survey practice, these results suggest that all three wording alternatives may be used for attitude measurement." (author's abstract)

Voting Advice Applications (VAAs) provide voting recommendations to millions of people. As these voting recommendations are based on users' answers to attitude questions, the framing of these questions can have far-reaching consequences. The current study reports on a field experiment in which the framing of the header above VAA statements (N = 17) was manipulated (condition 1: no header; condition 2: a right-wing header, e.g., finance; condition 3: a left-wing header, e.g., nature and environment). Visitors of a VAA developed for Utrecht, the fourth largest municipality in the Netherlands, were randomly guided to one of the versions of the tool in which the header type was varied. Results (based on N respondents = 27,404) show that providing a header (left-wing or right-wing) leads to more left-wing answers as compared a condition where there is no header above the attitude statement. This effect, however, is only observed for respondents with lower levels of political sophistication.

Voting Advice Applications (VAAs) fulfill different needs for different citizens. In national elections, the majority of users can be characterized as politically sophisticated citizens who use VAAs for entertainment purposes and confirmation of their party preference, but a significant minority uses VAAs to learn about politics and make an informed vote choice. VAAs might, however, play a different role in second-order elections, since in these elections campaign dynamics and information supply are very different. In the current research, we applied latent class analysis on user data from a widely used Dutch VAA (Kieskompas) for a supranational and several subnational elections in the Netherlands, to test if an extant typology of VAA users for national elections could be replicated. We find that the typology can be replicated for most of these elections, but also that the relative size of the groups of users differs across elections; in all second-order elections except for the provincial elections, more doubters and seekers are found relative to national elections. This suggests that VAAs are likely to have stronger mobilizing potential in these second-order elections.

Online Voting Advice Applications (VAAs) are survey-like instruments that help citizens to shape their political preferences and compare them with those of political parties. Especially in multi-party democracies, their increasing popularity indicates that VAAs play an important role in opinion formation for citizens, as well as in the public debate prior to elections. Hence, the objectivity and transparency of VAAs are crucial. In the design of VAAs, many choices have to be made. Extant research in survey methodology shows that the seemingly arbitrary choice to word questions positively (e.g., 'The city council should allow cars into the city centre') or negatively ('The city council should ban cars from the city centre') systematically affects the answers. This asymmetry in answers is in line with work on negativity bias in other areas of linguistics and psychology. Building on these findings, this study investigated whether question polarity also affects the answers to VAA statements. In a field experiment (N = 31,112) during the Dutch municipal elections we analysed the effects of polarity for 16 out of 30 VAA statements with a large variety of linguistic contrasts. Analyses show a significant effect of question wording for questions containing a wide range of implicit negations (such as 'forbid' vs. 'allow'), as well as for questions with explicit negations (e.g., 'not'). These effects of question polarity are found especially for VAA users with lower levels of political sophistication. As these citizens are an important target group for Voting Advice Applications, this stresses the need for VAA builders to be sensitive to wording choices when designing VAAs. This study is the first to show such consistent wording effects not only for political attitude questions with implicit negations in VAAs, but also for political questions containing explicit negations.

Voting Advice Applications (VAAs) are Web tools that are used to inform increasing numbers of voters during elections. This increasing usage indicates that VAAs fulfill voters' needs, but what these needs are is unknown. Previous research has shown that such tools are primarily used by young males and highly educated citizens. This suggests that VAAs are generally used by citizens who are already well-informed about politics and may not need the assistance of a VAA to make voting decisions. To analyze the functions that VAAs have for their users, this study utilizes unique user data from a popular Dutch VAA to identify different user types according to their cognitive characteristics and motivations. A latent class analysis (LCA) resulted in three distinct user types that vary in efficacy, vote certainty, and interest: doubters, checkers, and seekers. Each group uses the VAA for different reasons at different points in time. Seekers' use of VAAs increases as Election Day approaches; less efficacious and less certain voters are more likely to use the tool to become informed.