A Spiral of Skepticism? The Relationship Between Citizens’ Involvement With Campaign Information to Their Skepticism and Political Knowledge

Abstract

Scholars have emphasized the importance of an informed citizenry for a healthy democracy. As a result, research has examined whether campaign information fosters positive or negative democratic outcomes. This article examines the relationship between information seeking and skepticism. We also examine whether skepticism leads to democratically beneficial outcomes. We examine these relationships using survey data collected during the course of the 2012 Presidential Election. We found an over-time relationship between campaign information seeking and skepticism. We also found that skepticism leads to increased knowledge at the end of the election through information seeking.

Keywords

elections campaigns political behavior quantitative survey

In an ideal world, a vigorous, well-functioning democracy is based on the involvement of knowledgeable, engaged citizens. News media and campaign information play a critical role in informing citizens about the decisions and actions of political leaders and the positive or negative outcomes of those decisions and actions. As a result, scholars have found communication inextricably tied to an informed citizenry through campaign information, media use, online information seeking and deliberation, conversations with family and friends, and even exposure to political advertising.

Scholars examining whether campaign information contributes to beneficial outcomes often come to differing conclusions. Some authors contend that exposure to campaign-related information, such as news or political ads, contributes to individuals’ frustration with and disappointment in government because of the negativity present in political content. These scholars suggest that political information contributes to a spiral in which citizens’ frustration results in growing cynicism and apathy (Cappella & Jamieson, 1997; de Vreese & Elenbaas, 2008; Jackson, 2011). Other scholars, however, find that use of political information results in increases in efficacy and knowledge, resulting in a more engaged electorate (Eveland & Hively, 2009; Kenski & Stroud, 2006).

Scholars who focus on the potential positive impacts of media engagement frequently argue that exposure to campaign information may produce healthy skepticism instead of cynicism. Indeed, Pinkleton, Austin, Zhou, Willoughby, and Reiserand (2012) examine the potential for citizens’ skepticism to act as a healthier alternative to cynicism, reflecting a critical but open attitude toward news and information. They find skepticism contributes to decreased apathy and increased efficacy. Pinkleton et al. also suggest that skepticism may have indirect effects on important political outcomes through intervening variables such as satisfaction with campaign information. In essence, healthy skepticism could increase citizens’ use of political information, which results in a more informed citizenry that will contribute to the democratic process in a meaningful way.

This article aims to better understand the potential relationships between campaign information seeking and skepticism over time. To do so, we employ three-wave panel data collected during the 2012 Presidential Election. Most of the research investigating the relationships between skepticism and information seeking has relied on cross-sectional survey data or has been conducted in a lab setting. The three-wave panel data allow us to illuminate the extent to which information seeking contributes to skepticism in citizens over time. In addition, these data allow us to examine the reciprocal and potentially reinforcing relationships between these variables. We also extend this line of research by examining the indirect effects of skepticism on political knowledge through increased political information seeking.

News Media, Civic Engagement, and Political Decision Making

It is difficult to understand the interaction of public affairs information with other sources of political information and how this information affects citizens’ political decision making. According to the civic volunteerism and psychological engagement models of public affairs, strong democracies work better when informed citizens are engaged with the political process (e.g., Brady, Verba, & Schlozman, 1995; Verba, Schlozman, & Brady, 1995). These models emphasize the importance of personal political interest and sophistication in citizens’ political engagement. Recent Pew reports reveal that citizens have wider access to news content than ever before, and many are using new platforms to get news (Pew Research Center, 2015). Despite concerns that this wealth of information will lead to “slacktivism” (McCafferty, 2011) or users retreating to their own echo chambers (Bennett & Iyengar, 2008), a number of studies remain optimistic about the potential for information to positively impact the political system (Gil de Zúñiga, Jung, & Valenzuela, 2012; Schäfer, 2015). Research considering new sources of news content continues to find support for the civic volunteerism models. Scholars find that active citizens feel a greater sense of external efficacy and engage in information seeking to learn about the government and public affairs (Gil de Zúñiga, Puig-I-Abril, & Rojas, 2009; Pinkleton et al., 2012), which fosters informed political participation (e.g., Lee, Shah, & McLeod, 2013).

Despite the findings showing a positive relationship between campaign information and engagement, the extent that information can have a negative effect on the political system should be taken seriously. Research indicates that distrust weakens citizens’ belief that leaders are responsive to their concerns and undermines their voting satisfaction, which in turn could contribute to reduced political engagement (Moy & Pfau, 2000; Moy, Torres, Tanaka, & McCluskey, 2005). As a result, some scholars lament that the reciprocal, mutually reinforcing process of civic involvement and interpersonal trust that encourage citizens to undertake public affairs activities, characterized by social capital and related forms of political engagement, have been in decline (e.g., Jackson, 2011; Kleinnijenhuis, van Hoof, & Oegema, 2006; Putnam, 2000). Scholarship indicating that campaign information can play either a positive or negative role in the public affairs process indicates that more research is needed to understand the relationships among citizens’ public affairs engagement and important political outcome variables such as skepticism and political knowledge.

The Role of Cynicism and Skepticism in Information Seeking

The debate regarding whether or not exposure to campaign information is good for democracy often comes down to whether or not campaign information increases the public’s level of cynicism or skepticism. Cappella and Jamieson (1997) describe the concept of cynicism as a lack of confidence in, and a feeling of distrust toward, the political system, government officials, and related institutions, including the media. Cappella and Jamieson argue that politicians use conflict to attract media attention, which produces negative public affairs information that results in public cynicism. The increase in cynicism results in less media use related to public affairs, lower engagement in elections, and potentially reduced voter turnout. This argument has thrived within the political communication community. Indeed, both experimental and observational studies have demonstrated that negative media content increases cynicism. Specifically, much of the recent literature asserts that cynicism is most common when information is not framed around issues (de Vreese & Elenbaas, 2008; Pingree, Hill, & McLeod, 2013). In essence, these game-framed/publicity-framed news stories feed into negative reactions to politics more generally, which result in political disengagement.

However, not all scholars believe that campaign information contributes to a more cynical, disengaged citizenry. For instance, political scientists have examined the concept of skepticism (Mishler & Rose, 1997; Seligson & Carrión, 2002). They operationalize skepticism via individuals’ responses to questions about their policy positions. A skeptical individual is one who does not have strong opinions for or against various policies. These scholars view skepticism as beneficial because it is associated with being open to new information regarding these policies. Scholars in communication have also been interested in skepticism because it could be associated with people being more active, engaged citizens. In essence, this line of inquiry sees skepticism as a more positive, yet still questioning, reaction to media content. Cappella and Jamieson (1997) discuss skepticism as a response that would result in people wanting to seek out more information. In other words, after seeing political content, the public will question the veracity of the information. To determine whether the claims they saw were true or false, they will seek out additional information. Pinkleton and colleagues offer another operationalization of skepticism used in political communication (Austin & Pinkleton, 1999; Pinkleton et al., 2012; Yamamoto & Kushin, 2014). They suggest that when skeptics are dissatisfied with the information presented to them, they seek out additional information to confirm or disconfirm the claims in the message. Along those lines, Tsfati and colleagues (Tsfati, 2003, 2010; Tsfati & Cappella, 2003, 2005) create a measure of skepticism that utilized measures commonly associated with various media trust and credibility scales, and define skepticism as a “subjective feeling of mistrust towards the mainstream news media” (Tsfati & Cappella, 2003, p. 506). These studies find that skepticism is associated with lower use of mainstream news outlets. However, results also show that skepticism is associated with political engagement in the form of using nonmainstream news outlets (e.g., use of Internet sources). Based on these various lines of research, we expect a positive relationship between skepticism and seeking out campaign information during the course of an election. Therefore, we provide the following hypothesis:

H1: There will be a positive relationship between skepticism and information seeking over time, with greater skepticism associated with higher levels of information seeking.

While the above suggests a general over-time relationship, we are interested in the causal nature of this relationship. Because many of the previous studies have utilized cross-sectional data, they have been unable to examine the causal relationship between skepticism and information seeking. The theoretical arguments suggest that skepticism is driving information seeking. Pinkleton and colleagues (2012) and Cappella and Jamieson (1997) suggest that skepticism leads people to seek out additional information to substantiate or repudiate what they have learned, rather than dampen their desire to find new information, as might be the case if they were cynical. In this way, citizens’ skepticism contributes to additional information seeking. We therefore formulated the following hypothesis:

H2: Skepticism will lead to a subsequent increase in information seeking.

It could also be the case that campaign information could affect people’s level of skepticism. Little research to date has examined whether campaign information or media content leads to an increase or decrease in skepticism. However, research suggests that media content can affect people’s level of cynicism (Cappella & Jamieson, 1997; de Vreese, 2005; de Vreese & Elenbaas, 2008). The lack of research looking at the use of campaign information and skepticism prevents us from proposing a hypothesis. However, the research focused on cynicism suggests that campaign information could affect levels of political skepticism. Therefore, we propose the following research question:

RQ1: Will information seeking lead to higher levels of skepticism?

Finally, prior research suggests skepticism may work in a reinforcing process over time (Slater, 2007, 2015). That is, individuals who seek information may become skeptical and subsequently seek out more information. Skeptical citizens will recognize that media and politicians are likely to have constraints, motivating their desire to use additional information sources to confirm or disconfirm claims made in the information they receive (Pinkleton et al., 2012). In this sense, skepticism may work in a spiral process in which consuming campaign information contributes to increased skepticism, which may in turn lead to increased information seeking. The lack of research focused on the over-time relationship between information seeking and skepticism prevents us from making predictions about the potential reinforcing relationship between these variables over the course of a campaign. Therefore, we propose the following research question:

RQ2: Is there a reinforcing relationship between skepticism and information seeking?

Political Decision Making, Media, and Knowledge

In addition to examining spirals of skepticism, we test whether this healthy questioning of information contributes to increased levels of political knowledge (Delli Carpini & Keeter, 1996). A number of political communication studies have found that interpersonal and mediated communication play an important role in facilitating the acquisition of knowledge (Eveland & Hutchens, 2013). What is not understood is whether or not political attitude variables such as skepticism contribute to gains in political knowledge over time, or whether this relationship is mediated by other variables such as information seeking. However, theoretical work suggests that skepticism should have a positive influence on democratic outcomes such as knowledge. As explained above, prior research has indicated that there should be a positive relationship between skepticism and information seeking. Furthermore, increased cognitive processing of information has been found to increase political knowledge (Beam, 2014; Eveland & Thomson, 2006). Given that information seeking is generally indicative of higher levels of cognitive engagement with campaign information, it follows that increased information seeking due to higher levels of skepticism would lead to higher levels of knowledge at the end of a campaign. When considered together, this line of research suggests an indirect effect of skepticism on factual knowledge through information seeking. Therefore, we predict the following:

H3: There will be an over-time indirect effect of skepticism on factual knowledge through increased information seeking.

Method

Data presented in this study were collected in three waves from a national online panel of participants recruited by Qualtrics, an online survey software and sample provider. Panel members were randomly sampled from quota groups that matched national census characteristics. With the increased reliance on cellular phones as a primary communication device, random digit dialing is no longer an inexpensive and easy method to reach a probability sample (Schaffner, 2011). As a result, some scholars have examined the quality of quota samples. Studies have shown opt-in Internet panels using quota groups provide higher levels of error compared with traditional probability methods (Yeager et al., 2011), while others have found that using those samples makes little difference in terms of response quality (Ansolabehere & Schaffner, 2014). Table 1 provides a comparison between this sample’s demographic characteristics and national census data.

Table 1.

Survey Demographic and U.S. Demographic Summaries (n = 402).

Variable	Survey	U.S. Census
Sex: Female^a	45.6%	50.9%
Age^b	47.63	45-64^c
Hispanic^a	6.5%	12.5%
Race: White^a	83.0%	75.1%
Race: African American/Black^a	11.5%	12.3%
Race: Asian American/Asian^a	4.5%	3.6%
Race: Native American^a	0.8%	0.9%
Race: Other^a	2.5%	5.6%
Education^d	Associate degree	Some college
Income^d	50,000 to <75,000	US$45,467

Proportion.

Mean.

Median category excluding those under 18.

Median.

The first wave of data (n = 1,148) was gathered between October 1 and 3, 2012, just before the first U.S. presidential debate. The second wave of data (n = 669, 58.28% of Wave 1) was gathered between October 25 and 27, 2012, between the third and final U.S. presidential debate and the 2012 general election. The third wave of data (n = 404, 60.39% of Wave 2) was gathered between November 13 and 15, 2012, in the week following the general election, giving us retention rates of 58% and 60% for the subsequent waves of data collection. Only individuals who completed all waves of the panel are included in the analyses and statistics presented in this article.

Some may have concerns regarding the retention rate of our survey; however, the American Association for Public Opinion Research (AAPOR; 2013) has indicated that the relationship between response rates and survey quality is by no means related, especially when attempting to gather data over a short time frame. AAPOR suggests that the other indicators of quality must be considered when response rates are less than desired. Given that our sample statistics were quite close to census reports, we are confident with the quality of our data. However, to ensure that certain people were not dropping out of our survey, we examined differences on several variables between individuals who completed all three waves of the survey and those who dropped out during data collection. We found that those who dropped out are younger, t(1146) = 4.427, p < .05, and had lower incomes, t(1146) = 2.068, p < .05, than those who completed all waves. However, there was not a statistical difference for education levels, t(1146) = 1.445, p =.149, or reported interest in campaign information, t(1146) = 1.384, p = .167. More importantly, there were no statistical differences in reported levels of skepticism, t(1146) = 0.318, p =.751, or information seeking, t(1146) = 1.621, p =.105. Although we had attrition, the sample generally maintained its consistency regarding important variables for our study.

Endogenous Measures

Political information seeking

Consistent with previous research measuring individuals’ perceptions of election relevance and attentiveness to election information (e.g., Pinkleton et al., 1998), participants disclosed their engagement with campaign information by completing four items using a 7-point Likert-type scale coded as 0 for “strongly disagree” and 6 for “strongly agree.” To complete the information seeking items, participants indicated how much they agreed or disagreed with the following statements: “how interested they were in campaign information,” “how much attention they pay to campaign information,” “how actively they seek out campaign information,” and “how much they like to stay informed about the election” (see Table 2 for descriptive statistics for the focal variables used in the study).

Table 2.

Focal Variable Descriptive Summaries (n = 402).

Variable	Wave 1	Wave 2	Wave 3
Information seeking
M	4.21	4.30	4.30
SD	1.65	1.64	1.67
α	.96	.97	.97
Skepticism
M	4.57	4.59	4.59
SD	1.17	1.20	1.22
α	.96	.97	.97
Factual knowledge
M	4.67	4.73	4.85
SD	1.60	1.65	1.60

Skepticism

In keeping with earlier research measuring citizens’ critical but constructive attitudes toward the government and the media (e.g., Pinkleton et al., 2012), participants completed four items using a 7-point Likert-type scale coded as 0 for “strongly disagree” and 6 for “strongly agree.” The four items asked participants to indicate how much they agreed or disagreed with the following statements: “think about news stories before accepting them as believable,” “seek out additional information to confirm statements made by politicians,” “think about things elected officials say before accepting them,” and “critically evaluate what news stories say.”

Factual knowledge

Eight items asked participants to identify the characteristics and policy positions on various issues for each presidential candidate (Barack Obama and Mitt Romney) to measure factual political knowledge. Correct responses were identified as the answers provided by the campaigns and the candidates on each of their websites. Correct answers were coded as “1.” Incorrect and missing responses were coded as “0.” The items covered candidates’ religious affiliation, their position on increasing tax rates, their position on the impact of the Affordable Care Act on small businesses, and their proposed energy plan. The religious affiliation item was an open-ended question that we coded as being correct or incorrect. Correct responses for Obama included “Christian” and “Protestant.” Correct responses for Romney included “Christian” and “Mormon.” The remaining items were true/false questions.

Exogenous (Control) Variables

Additional variables included in our statistical models as controls were time, age, education, income, gender, ethnicity, political ideology, political interest, and cynicism. All of the control variables, except time, were assessed during Wave 1. Time was coded in weeks, where Wave 1 was coded as “0,” Wave 2 was coded as “3,” and Wave 3 was coded as “5” to reflect the number of weeks that had passed since the first wave of data collection. Age was measured with a single item asking participants their age as of their last birthday (see Table 1). Education was measured with a single item asking, “What is the last grade or class you completed in school?” Ordinal response options were coded from 0 to 8 in ascending order from “None” to “Postgraduate Training or School” (see Table 1). Income was measured with one item using a 9-point scale that ranged from less than 10,000 dollars a year to more than 150,000 dollars a year (see Table 1). Gender was measured with one item asking respondents their biological sex (see Table 1). Ethnicity was measured by asking participants their race. They were instructed to select all that apply. We coded participants who selected any race other than “White” as a minority (see Table 1). Political ideology was assessed by asking participants to respond to the statement, “I would describe my political views as . . .” using a scale of “very conservative” (0) to “very liberal” (4) (M = 1.79, SD = 1.12). Political interest was assessed by asking participants to respond to the statement, “In general, I am very interested in politics.” Response options ranged from “strongly disagree” (5) to “strongly agree” (1) (M = 2.34, SD = 1.03).

Finally, we included cynicism in our statistical models as an additional control variable. Consistent with previous work concerning citizens’ distrust of politicians and the government (e.g., Pinkleton et al., 2012), participants completed five items using a 7-point Likert-type scale coded as 0 for “strongly disagree” and 6 for “strongly agree.” The five items asked participants to indicate whether or not they thought “candidates in office are only interested in people’s votes, not their opinions,” whether “the government is run by a few big interests who look out for themselves,” whether “politicians put their own interests ahead of the public interest,” whether “politicians only care about themselves or special interests,” and whether “politicians lose touch quickly with the public after they get elected” (M = 4.60, SD = 1.27, α = .96).¹

Analysis Strategy

We use both multilevel modeling (MLM), in Stata 12, and structural equation modeling (SEM), in Mplus 7, to examine the relationships between our endogenous variables of interest. MLM allows us to examine how much variance exists in these variables within and between individuals. In essence, it provides information as to whether an over-time relationship exists between our primary variables of interest. To run this analysis, the individual is considered the Level 2, or nesting, variable, with the repeated measures of skepticism, information seeking, and factual knowledge nested underneath each participant as the Level 1 variables. We use SEM to examine the relationships between our endogenous variables of interest. We utilize the two-step approach to SEM in that we first examine the fit of a measurement model, which allows us to examine whether our observed variables are appropriately loading onto our latent variables of interest. We then examine the structural model, which allows us to examine the direction of proposed relationships. In our structural models, we included a set of demographic (age, gender, income, education, and ethnicity) and general political (ideology, political interest, and cynicism) variables that were measured during the first wave of our survey.

Results

Before testing our proposed hypotheses, we used MLM to assess the relationship within each variable by examining the intraclass correlation (ICC). The ICC provides information regarding over-time changes in the amount of variance that can be attributed to differences between individuals as opposed to within individuals for each variable. While the distribution we are using prevents a test of significance (see Gelman & Hill, 2007; Zhang & Lin, 2003), results show that a substantial amount of variance can be attributed to differences between individuals. As can be seen in Table 3, data show a substantial amount of variance due to between-individual differences, ranging from a low of 64.05% of variance attributed to the individual for factual knowledge to a high of 79.28% of the variance for information seeking. These findings show that there is still a substantial amount of within-individual variance remaining (ranging from 20.82% for information seeking to 35.95% for factual knowledge), which suggests that our participants are changing over time.

Table 3.

Multilevel Model Results Predicting Information Seeking and Knowledge.

	Political information seeking			Factual knowledge
	Empty	Controls	Full	Empty	Controls	Full
Constant	4.271*** (0.0767)	5.899*** (0.368)	4.641*** (0.361)	4.752*** (0.0703)	3.357*** (0.493)	2.239*** (0.543)
Time	—	0.0197 (0.0105)	0.0183 (0.0105)	—	0.0340* (0.0135)	0.0313* (0.0136)
Age	—	0.00423 (0.00388)	0.00334 (0.00352)	—	0.0106* (0.00520)	0.00976 (0.00507)
Education	—	0.0420 (0.0317)	0.0322 (0.0287)	—	0.106* (0.0425)	0.0979* (0.0414)
Income	—	0.0355 (0.0241)	0.0296 (0.0218)	—	0.0140 (0.0323)	0.00791 (0.0315)
Gender	—	0.162 (0.0997)	0.145 (0.0904)	—	−0.0480 (0.134)	−0.0726 (0.130)
White	—	0.0534 (0.126)	0.0395 (0.114)	—	0.271 (0.169)	0.260 (0.165)
Ideology	—	0.0399 (0.0441)	0.0406 (0.0400)	—	0.301*** (0.0591)	0.297*** (0.0576)
Political interest	—	−1.129*** (0.0500)	−0.971*** (0.0483)	—	−0.304*** (0.0670)	−0.120 (0.0786)
Cynicism	—	0.0221 (0.0277)	−0.0458 (0.0278)	—	−0.00177 (0.0362)	−0.0301 (0.0374)
Skepticism	—	—	0.297*** (0.0314)	—	—	0.110* (0.0432)
Information involvement	—	—	—	—	—	0.111** (0.0383)
Log likelihood	−1,877.35	−1,690.8	−1,650.3	−2,043.97	−2,008.1	−1,998.6
Deviance	3,754.7	3,381.6	3,300.6	4,087.94	4,016.2	3,997.2
AIC	3,760.69	3,405.61	3,326.58	4,093.94	4,040.24	4,025.12
BIC	3,775.98	3,466.75	3,392.81	4,109.23	4,101.38	4,096.45
ICC	.7928			.6405
Pseudo-R²		.408	.483		.093	.116

Note. Cell entries are unstandardized coefficients with standard errors in parentheses. AIC = Akaike information criterion; BIC = Bayesian information criterion; ICC = intraclass correlation.

p < .05. **p < .01. ***p < .001.

Table 3 presents the empty model, a model with controls, and a final model of MLM analysis. The results demonstrate an over-time relationship between political information seeking and our measure of skepticism. We chose political information seeking as our outcome variable (the pattern of results is identical if skepticism is utilized as the outcome variable). The fit statistics improve with each subsequent model, and our final model tells us whether an over-time relationship exists between the two variables. Results found a significant positive, over-time relationship between information seeking and skepticism (B = 0.297, SE = 0.031, p < .05; see column 3 of Table 3), which supports H1. In essence, there appears to be a relationship between individuals’ levels of skepticism and political information seeking that persists over time, rather than just existing at a cross-sectional level.

We now move to the SEM analyses to investigate the causal order of the over-time relationships tested in the MLM. The first step is to examine whether or not our various observed variables appropriately load onto our latent variables. The conceptual model used can be seen in Figure 1, but rather than directional paths, covaried paths are utilized (see Kline, 2005, Chapter 7). The result of this analysis suggests that we have acceptable fit (Hu & Bentler, 1999), χ²(231) = 924.30, p < .05, comparative fit index (CFI) = .91, Tucker–Lewis index (TLI) = .89, root mean square error of approximation (RMSEA) = .08, standardized root mean square residual (SRMR) = .07, and all observed variables significantly load onto their respective factors (all ps < .001). The results of the structural model found strong relationships between skepticism and political information seeking.² Wave 1 skepticism was associated with higher levels of political information seeking at Wave 2 (B = 0.275, SE = 0.058, p < .05). We found a similar result for the relationship between these two variables between Waves 2 and 3 of our survey (B = 0.112, SE = 0.046, p < .05). These results show support for H2.

Figure 1.

SEM model for political information seeking and skepticism.

The structural model shows that information seeking predicted subsequent levels of skepticism, which provides us information relative to RQ1. Our Wave 1 measure of political information seeking correlated with higher levels of skepticism at Wave 2 of our survey (B = 0.166, SE = 0.041, p < .05). However, the relationship was not significant between Waves 2 and 3 of our survey (B = 0.045, SE = 0.050, p = .374).

We also examined the potential reinforcing effect between these two variables. Results provide us with information relative to RQ2. First, we found a positive, statistically significant over-time indirect effect of political information seeking at Wave 1 of our survey on Wave 3 levels of political information seeking through skepticism (B = 0.019, SE = 0.009, p < .05, 95% confidence interval [CI] = [.003, .050]). For this indirect effect, use of political information led to higher levels of skepticism, which increased subsequent levels of information seeking. We did not find a significant over-time indirect effect of skepticism at Wave 1 of our survey on Wave 3 levels of skepticism through political information seeking (B = 0.012, SE = 0.014, p = .383, 95% CI = [−.019, .055]). The combination of these results suggests that skepticism and political information seeking may work in a reinforcing process in which each variable positively reinforces the other variable; however, this indirect effect is only significant when examining the effects of information seeking on skepticism.

Last, we examine whether skepticism led to higher levels of factual knowledge through political information seeking. As mentioned previously, the MLM results indicate that levels of factual knowledge changed during the election. Results from the full MLM in Table 3 show that both skepticism (B = 0.110, SE = 0.043, p < .05) and information seeking (B = 0.111, SE = 0.038, p < .05) are associated with higher levels of knowledge over time. We tested whether skepticism led to increased knowledge through higher information seeking using SEM, with knowledge as an observed variable. The results of the hybrid SEM analysis found an over-time indirect effect of skepticism through seeking out campaign information on levels of factual knowledge, which supports H3.³ Specifically, the results show that skepticism led to an increase in seeking out campaign information. This increase in information seeking led to a subsequent increase in factual knowledge (B = 0.078, SE = 0.024, p < .05, 95% CI = [.028, .159]).

Discussion

In this study, we attempt to shed light on the relationships between political information seeking and skepticism. Our results highlight the potential importance of skepticism in creating a public that attends to campaign information, which ultimately leads to increases in political knowledge. We found a positive relationship between information seeking and skepticism over the course of the 2012 Presidential Election. Moreover, an in-depth analysis of these results shows that skepticism leads to increased levels of information seeking, and vice versa. Our results also found indirect effects of information seeking on future information seeking via skepticism. Finally, our results showed that the increase in information seeking resulting from skepticism led to increased levels of factual knowledge at the end of the campaign.

This article makes several contributions to the existing literature on campaign information and skepticism. First, the results outline the potential benefits of skepticism in producing an engaged, informed citizenry. Our results provide evidence that skepticism encourages active information seeking. Previous studies have shown that skepticism predicts less satisfaction with media (Pinkleton et al., 2012), yet we demonstrate that this dissatisfaction does not reduce the desire to seek out additional information. Utilizing three waves of data, we are able to show that skepticism has a positive over-time effect on information seeking, where higher levels of skepticism are associated with higher levels of information seeking at subsequent time points. Thus, we confirm that skepticism is an important political decision-making variable, contributing to active information seeking.

Second, these results complement previous theories concerning skepticism and its relationship to involvement with campaign information (e.g., Cappella & Jamieson, 1997; Pinkleton et al., 2012). Although prior studies have suggested potential benefits of skepticism, our findings show evidence of these benefits in the form of increased factual knowledge. These findings move beyond prior cross-sectional studies and examine these relationships over time during the course of an election and document their contribution to informed public affairs decision making. Even if the civic volunteerism and psychological engagement models of public affairs have been faulted for being too idealistic (e.g., Kuklinski & Peyton, 2007), our research demonstrates that citizens’ skepticism is an important contributor to the development of election-relevant knowledge gain among citizens.

Specifically, skepticism does indeed seem to be an indicator of a healthy questioning of information that produces tangible benefits. To better understand this underlying process, future studies should expand on the variables included in conceptual models to better understand the underlying process that leads skepticism to increase information seeking. For instance, future studies should include additional mediating and moderating variables between skepticism and information seeking. It could be that skepticism triggers a particular emotional reaction such as anger or anxiety, which leads to active information seeking (Huddy, Feldman, & Cassese, 2007). Trust in government or efficacy may also serve as important moderating variables between information seeking and skepticism. It could be that high levels of trust combined with skepticism increase people’s level of seeking out political information. It should also be noted that our operationalization of skepticism is one of many that have been used in the broader political science literature. In particular, our measure focuses more on the evaluation of media content rather than attitudes toward policy positions (Mishler & Rose, 1997; Seligson & Carrión, 2002). Examining how these various conceptualizations of skepticism are related to information seeking and communication habits is something that should be addressed in future research.

Future studies should also extend the existing conceptual model to include specific information seeking behaviors and investigate whether campaign information or media use contributes to skepticism. Here we dealt with a general measure of information seeking, but examining the type of information source sought would be an important next step. In particular, much of the research examining cynicism has begun to look outside of traditional media to nontraditional sources (e.g., de Vreese & Elenbaas, 2008; Tsfati, 2010). Extending this research to examining specific media sources in addition to the use of social media outlets will be an important next step in this line of research. Future studies should also examine whether specific message characteristics contribute to higher levels of skepticism, similar to what scholars have done in their examination of media use and cynicism (e.g., de Vreese & Elenbaas, 2008; Pingree et al., 2013).

As with any research, our study also had limitations that may have affected our results. First, there are potential history effects that may have influenced the internal validity of our study. Election Day occurred between Waves 2 and 3 of our data collection. Therefore, this event may have affected our endogenous variables. However, with the introduction of early voting, the historical threat from voting has become more difficult to address. News stories estimated that between 35% and 40% of voters cast ballots before Election Day (Charles, 2012). An examination of our sample found that 23.5% of registered voters had cast their votes by the time we collected our second wave of data. Because people have more opportunities to vote, collecting any data around an election will pose a number of threats to validity, Election Day being just one. Of course, we cannot rule out any possible effects from Election Day. In addition, our measure of information seeking subsumes many different communication concepts into one variable. Our measure likely includes components of network news, partisan news, and information from the campaigns. Despite the potential measurement problems, we were still able to find a clear over-time trend in our results. Future studies can extend these findings testing the effects of certain types of campaign information, such as horse race coverage or political advertising.

Overall, these results speak to the potential benefits of campaign information on creating a well-informed, engaged public citizenry. Communication scholars continue to debate the extent to which campaign information leads to an engaged or disengaged citizenry. These results show additional evidence that there is hope that campaign information can have a positive contribution for democracy. However, we still strike a cautious note. Our results speak to a reinforcing process in which skepticism and seeking out political information work in a mutually reinforcing relationship in which skepticism begets information seeking with political information, which leads to greater skepticism. One issue concerns whether continued increases in skepticism ultimately contribute to increases in political participation through its association with efficacy (Pinkleton et al., 2012), or perhaps fade over time or even yield undesirable outcomes such as apathy. Although skepticism may be healthy at certain levels for certain lengths of time, it is also possible that skepticism will morph into a less positive outcome as campaign information continues to increase people’s level of skepticism. Alternatively, skepticism interacting with trust and various information outcomes may also help foster use of less ideal information such as partisan news outlets resulting in more partisan attitudes as a result of consuming highly partisan media. Despite these possibilities, this research currently demonstrates the potential contributions of skepticism to active information seeking reflected in citizens’ involvement and knowledge gain, potentially contributing to citizens’ knowledgeable participation in civic affairs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Myiah J. Hutchens (PhD, The Ohio State University) is an assistant professor in the Edward R. Murrow College of Communication at Washington State University. Her research interests include political communication and political discussion.

Jay D. Hmielowski (PhD, The Ohio State University) is an assistant professor in the Edward R. Murrow College of Communication at Washington State University. His research interests include political and environmental communication.

Bruce E. Pinkleton (PhD, Michigan State University) is a professor and acting dean of the Edward. R. Murrow College of Communication at Washington State University. His research interests include political communication and health communication.

Michael A. Beam (PhD, The Ohio State University) is an assistant professor in the School of Communication Studies at Kent State University. His research interests include political communication and new communication technologies.

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