Testing the Robustness of a Risk Information Processing Model
Robert J. Griffin Marquette University Milwaukee WI Maria Powell University of Wisconsin-Madison Madison WI Sharon Dunwoody University of Wisconsin-Madison Madison WI Kurt Neuwirth University of Cincinnati Cincinnati OH David Clark Marquette University Milwaukee WI Vladimir Novotny Northeastern University Boston MA
Presented to the Communication Theory and Methodology Division, Association for Education in Journalism and Mass Communication, at the August 2004 AEJMC Annual Convention, Toronto, Ontario, Canada. Address inquiries to Robert J. Griffin, College of Communication, Marquette University, P.O. Box 1881, Milwaukee WI 53201. Phone: (414) 288-6787. E-mail:
[email protected]
Model Robustness 2
Testing the Robustness of a Risk Information Processing Model by Robert J. Griffin, Maria Powell, Sharon Dunwoody, Kurt Neuwirth, David Clark, and Vladimir Novotny
ABSTRACT
Two survey data sets test a model of Risk Information Seeking and Processing, informed by Eagly and Chaiken’s (1993) Heuristic-Systematic model, that describes characteristics of individuals that predispose them to seek and process information about health and environmental risks in different ways. Results indicate that information insufficiency relates positively with active seeking and systematic processing of risk information and negatively with avoidance and heuristic processing of it. Individuals are more likely to process systematically if they believe that media channels contain essential validity cues.
Model Robustness 3
Testing the Robustness of a Risk Information Processing Model Research into the ways people seek and process information can enhance our understanding of communication. Much can be gained from learning more about how people learn in informal settings, such as when attending to the news or other informational media content, and how they reason with that information. Indeed, studies of everyday reasoning strategies suggest that these processes are commonly marred by incompleteness and bias.1 So, it seems worthwhile to investigate the conditions under which people will devote more effort to the tasks of gathering and critically assessing the value of information. It is also useful to focus on the nature of information seeking and processing as essential variables because a body of theory has grown in both psychology and communication studies to facilitate the use of these concepts, specifically Petty and Cacioppo's (1981) elaboration likelihood model (ELM) and, more recently, Eagly and Chaiken's (1993) heuristic-systematic model (HSM).2 This study examines information seeking and processing in the context of risks to health and the environment. In general, given the complicated nature of risk and the potentially serious consequences associated with some health and environmental hazards, it makes sense to seek the conditions under which individuals are more or less systematic in their search for and processing of risk information. Of course, an individual’s desire to seek and process risk information could ultimately stem from a number of background factors, such as various dimensions of risk perception (e.g., perceived level of risk and its seriousness), affective response to a risk (e.g., worry), and perceived social pressures to stay informed about a risk (Griffin, Dunwoody, & Neuwirth, 1999). These variables have already been found to be associated, directly or indirectly, with motivations to achieve sufficient information to deal with a risk (Griffin, Neuwirth,
Model Robustness 4 Dunwoody, & Giese, 2004). This study will concentrate on how information sufficiency motivation, as well as allied factors such as individuals’ processing abilities and beliefs about available channels of information, might lead to more effortful seeking and processing of risk information. It will do so by applying the part of the Model of Risk Information Seeking and Processing (Griffin, Dunwoody, & Neuwirth, 1999) that was based most directly on Eagly and Chaiken’s heuristicsystematic model (Chaiken, Giner-Sorolla, & Chen, 1996; Chen & Chaiken, 1999; Eagly & Chaiken, 1993). As a result, this study may also have more general applications to studies of information seeking and processing outside of risk contexts. Previous analysis based on the RISP model have found full or at least partial support for a relationship between information sufficiency motivation and more effortful seeking and processing of information about specific risks (Griffin, Neuwirth, & Dunwoody, 1998; Kahlor, Dunwoody, Griffin, Neuwirth, & Giese, 2003; Trumbo, 2002). The main goal of this study, however, is to test the robustness of the information processing heart of the model across five risks, employing data from two comprehensive, federally funded sample surveys that were guided by the RISP model. The data in one survey focus on the ways that adult residents of two Great Lakes cities – Milwaukee, Wis., on Lake Michigan and Cleveland, Ohio, on Lake Erie – seek and process information about risks related to the Great Lakes. Two of the risks entail potential for harm to personal health: eating Great Lakes fish and drinking tap water drawn from the Great Lakes. The third risk involves threats to the ecological integrity (health) of the Great Lakes themselves. The data in the other survey concern the ways that heads of households in two urban river watersheds in the Milwaukee area deal with information about flood risks (one watershed) and the ecological integrity of the streams (both watersheds). Testing the model by
Model Robustness 5 using environmental as well as health risks will indicate the model’s applicability to contexts other than health risks to the self. Model of Risk Information Seeking and Processing Heuristic and Systematic Processing.. The heuristic-systematic model, like ELM, describes dual forms of human processing of information, one more superficial and the other deeper and more effortful. By default and necessity, most people employ the principle of least effort in processing messages, judging their validity and making inferences or decisions to comply through superficial cues such as the length of the message, the use of a trusted spokesperson, or the use of statistical data. This “heuristic processing” of information, Eagly and Chaiken (1993) state, is "a limited mode of information processing that requires less cognitive effort and fewer cognitive resources" (p.327) than systematic processing. The latter, by comparison, is a much more comprehensive effort to analyze and understand information. In HSM terms, people tend to adopt the form of processing that they use for a given message based on (1) their capacity to process the information in each manner, and (2) their motivation to go beyond the more superficial (heuristic) processing to engage in systematic processing. Heuristic and systematic processing are considered to be separate processing strategies which can co-occur. According to the HSM formulation, a person's desire for sufficiency motivates systematic processing. The sufficiency principle, state Eagly and Chaiken (1993), "asserts that people will exert whatever effort is required to attain a 'sufficient' degree of confidence that they have accomplished their processing goals" (p. 330). For example, the personal relevance of the message topic to the individual elevates the amount of judgmental confidence people need to have (the "sufficiency threshold") in their own attitudes (e.g., Do they square with relevant facts? Are they defensible? Are they socially acceptable?) and/or the confidence they need to have in
Model Robustness 6 the validity of the message (Eagly & Chaiken, 1993). Information (In)Sufficiency. Based on Eagly and Chaiken’s concepts of sufficiency and judgmental confidence, the RISP model proposes that different people try to reach varying but subjectively satisfactory levels of confidence in the information that they hold about a given topic (“information sufficiency”), especially as the basis for developing their risk-related beliefs, attitudes, and behavioral intentions. Griffin, Dunwoody, and Neuwirth (1999) propose that the drive to overcome information insufficiency (e.g., to gain and hold enough information to deal with a risk in daily life) motivates individuals to process risk-related information more systematically and less heuristically. They also propose that the sufficiency drive can similarly motivate more active, non-routine seeking of information — that is, attempts to gather relevant risk information (e.g., calling the doctor) that go beyond habitual or routine channels a given individual might use for such information (e.g., watching the evening newscast) – and less avoidance. Figure 1 illustrates the variables of primary concern for this study, as derived from the model of risk information seeking and processing. Based on Eagly and Chaiken's (1993) motivational factor, the size of the gap between information held and that needed will ultimately affect the information-seeking and processing styles employed by individuals to learn more about the risk. However, information seeking and processing are also seen as dependent upon one's ability to learn more about the risk (based on HSM’s concept of capacity), one's existing knowledge structures, and the perceived usefulness and credibility of available information. Therefore, seeking (which includes avoidance) and processing are also affected by the variables termed "perceived information gathering capacity" and “relevant channel beliefs” in the RISP model.
Model Robustness 7 Perceived Information Gathering Capacity. Because the dependent variables of risk information seeking and processing are essentially communication behaviors, one's sense of selfefficacy (e.g., Bandura, 1986) or perceived behavioral control (e.g., Ajzen, 1988) in performing them were considered as important to measure as in other domains of behavior or behavioral intention. Information-gathering capacity should reflect an individual’s perceived ability to perform the information seeking and processing steps necessary for the outcome he or she desires, especially when an outcome requires more cognitive effort and non-routine gathering of information. Relevant Channel Beliefs. Beliefs about channels of risk information, including their trustworthiness and usefulness, could affect the information seeking and processing strategies people employ. In their study of how audiences relate to general and political news in the mass media, Kosicki and McLeod (1990) observed that people maintain various images of the media (e.g., that the media represent special interests, that they are accurate and responsible) that are affected by social structural, political and cultural factors. Furthermore, their evidence indicates that these images seem to affect the habitual information processing strategies that people develop.. Thus, the RISP model suggests that relevant channel beliefs might affect, directly or indirectly, the ways in which people seek and process risk information. Individual Characteristics. The full RISP model, shown miniaturized in the inset in Figure 1, also includes a role for demographic variables and other individual characteristics (e.g., past experience with a hazard) in the deep background of risk information seeking and processing. This analysis will control for a limited set of key demographic variables, such as age and education, which could eventually affect risk information seeking and processing as well as individuals’ subjective assessments of the amount of information they already possess about the
Model Robustness 8 risk, perceived information gathering capacity, and beliefs about risk information channels. Research Questions and Hypotheses Our research questions and hypotheses represent the relationships that the selected components of the RISP model have with risk information seeking and processing across the various paths. In particular, we will be looking for consistent relationships across paths, that is, relationships that repeat regardless of the risk topic studied in the various populations. The first research question (RQ1) is: Are there consistent relationships between demographic variables and risk information seeking and processing? RQ2 is: Are there consistent relationships between beliefs about the channels of risk information and individuals’ seeking and processing of information about risks? RQ3 is: Are there consistent relationships between individuals’ capacity to gather information (Perceived Information Gathering Capacity) and their seeking and processing of risk information? In particular, it is hypothesized that greater capacity will be associated with: (H1) More active seeking of risk information, (H2) Greater use of systematic processing of risk information. RQ4 is: Are there consistent relationships between individuals’ information insufficiency and their seeking and processing of risk information. It is hypothesized that information insufficiency will be associated: (H3) Positively with systematic processing, (H4) Negatively with heuristic processing, (H5) Positively with information seeking, (H6) Negatively with information avoidance.
Model Robustness 9 METHOD We employed two sample survey data sets to analyze these relationships, both of which examine audience seeking and processing of risk-related information: #
The “Great Lakes study,” a study of public use of risk information concerning health and environmental risks related to the Great Lakes that was conducted in two metropolitan areas bordering the Great Lakes, Milwaukee, Wisconsin, and Cleveland, Ohio;
#
The “Watershed study,” a study of public use of risk information concerning flooding and environmental risks related to urban rivers and their watersheds that was conducted in the Milwaukee area.
The Model of Risk Information Seeking and Processing was used as the risk communication theory base for both data sets. In each survey, respondents had been assigned to a specific “path” of questions through the questionnaire. In the Great Lakes study, one path was comprised of questions dealing with risks to human health from eating Great Lakes fish (“fish path”), a second path concerned risks to human health from drinking tap water drawn from the Great Lakes (“tap water path”), and a third path was composed of questions about environmental risks to the Great Lakes ecosystem (“lake environment path”). In the Watershed study, one path posed questions about the risks that river flooding poses to the nearby built and natural environment (“flood path”) and a second path asked about environmental risks to the river ecosystem (“river environment path”) within the local urban areas. Most questions in all five paths were identical in construction, except for systematic differences in the wording based on the different path topics. This parallel construction was designed to allow meta-testing of the model by combining or comparing data
Model Robustness 10 across different risks and populations. When respondents were presented with a series of items to be answered on the same kind of scale (e.g., five point, Likert-type, agreement scale), the starting item in the list was chosen randomly. Although both surveys are multi-wave and panel design, this analysis employs data from only the first wave of interviews. Both studies were funded by federal grants, and professional survey research organizations conducted the survey sampling and interviewing in each case. The firms also conducted a series of focus groups that preceded the first wave surveys and that guided the construction of the measures to be used. Both surveys also gathered data on a variety of other variables, most notably measures related to behavioral intentions and to other parts of the Model of Risk Information Seeking and Processing, which are not included in this analysis. In both studies, first-wave survey interviews averaged approximately 20 minutes apiece. Applicable human subjects and informed consent practices were followed throughout. Great Lakes Study The research sites for the Great Lakes study — Milwaukee on Lake Michigan and Cleveland on Lake Erie — each have diverse populations that draw their drinking water from the lakes, have relatively ready access to commercially caught and sport-caught fish from the lakes, and in general rely on the lakes for their environmental, aesthetic, recreational, and civic image qualities. The primary purpose of the Great Lakes study was to examine local adults’ seeking and processing of information about potential hazards from consuming Great Lakes fish. Questions about how respondents used information about the other two risks, potential hazards in the tap water and threats to the Great Lakes ecosystem, were added to the study for purposes of comparison and model building. Although not reported here, the study also gathered data to assess how respondents’ use of the risk information related to the behaviors they might perform
Model Robustness 11 in response to the risk. This research project was supported by a grant from the federal Agency for Toxic Substances and Disease Registry. Risk Issues Background. Two of the risks about which Great Lakes study respondents were about entail potential for harm to personal health: eating Great Lakes fish and drinking tap water drawn from the Great Lakes. The third risk is environmental, specifically, threats to the ecological health of the Great Lakes ecosystem. Different respondents were assigned to each of these three paths of questions: the fish path, the tap water path, and the lake environment path. Fish in the Great Lakes, like fish from other waters, can contain various chemicals, most notably polychlorinated biphenyls (PCBs). Human consumption of PCB-laden fish is a suspected cancer risk and has been associated with developmental problems in infants whose mothers had regularly eaten PCB-contaminated fish. Every year for the past quarter century, states surrounding the Great Lakes, including Wisconsin and Ohio, have issued advisories that warn people to avoid or limit consumption of certain sizes and varieties of fish and that suggest ways to prepare the fish to reduce exposure to chemical contamination. The second health risk of concern, potential hazards lurking in municipal drinking water, is of course not limited to the Great Lakes. Municipal drinking water can contain substances such as chemicals and lead, as well as organisms that occasionally slip past municipal water treatment systems. Probably the most salient outbreak of waterborne illness took place in 1993 in Milwaukee, one of the communities in this study. A tiny parasite, cryptosporidium, entered the city drinking water from Lake Michigan and produced the largest recorded outbreak of waterborne disease in the nation’s history. Milwaukee has since installed special monitoring and treatment equipment. The third risk path involves perception of, and concern about, the health of the Great
Model Robustness 12 Lakes ecosystem. The cumulative effects of dangerous emissions from industry, power plants, automobile exhausts and runoff from cities and farms has resulted in increased concentrations of toxins such as mercury, lead, dioxin, mirex and toxaphene. These toxins pose a risk to the lake ecosystem and, in many cases, to human health, and often have the additional effect of decreasing aesthetics and recreational opportunities. Sampling and Interviewing. From October 1996 to March 1997, the Wisconsin Survey Research Laboratory, a professional research organization associated with the University of Wisconsin-Extension, conducted a sample survey by telephone of 1,123 adult residents of the two metropolitan areas (579 in Milwaukee and 544 in Cleveland). Residences were contacted by random-digit-dialing (RDD) and respondents were chosen randomly within households. The combined response rate was 55.2% (61.3% in Milwaukee and 50% in Cleveland). Since applying the model to fish consumption risks was, by design, the primary goal of the Great Lakes study, the interviewers’ first questions were set up to net respondents for whom eating Great Lakes fish was a relevant personal matter. Respondents were assigned to the fish path if they had eaten a meal of Great Lakes fish that year or if they had made a decision to avoid these fish specifically because of health concerns. In all, 634 respondents (326 in Milwaukee, 308 in Cleveland) were assigned to the fish path. The balance of respondents were randomly assigned to the other two paths in the questionnaire, 252 to the tap water path (137 in Milwaukee and 115 in Cleveland) and 237 to the lake environment path (116 in Milwaukee and 121in Cleveland). Watershed Study The Watershed survey was part of a multidisciplinary and multimethod assessment of flood risks and ecological quality in two urban river watersheds (the watershed is the region that
Model Robustness 13 drains into the river). This research project included research by civil engineers, biologists, economists, and communication scholars. One of the purposes of the sample survey was to examine local adults’ seeking and processing of information about potential hazards from local river flooding and about risks to the ecological health of the rivers. Although not reported here, the survey also examined how respondents’ use of the information related to their “willingness to pay,” through taxes and others means, for community projects to improve river quality and hold the line on flood risks, and to perform other behaviors related to these problems. This research project was supported by a “STAR” (Science to Achieve Results) grant from the U.S. Environmental Protection Agency, National Science Foundation, and U.S. Department of Agriculture. The research sites for the Watershed survey are both in the greater Milwaukee area: residents who live in the 137-square-mile watershed of the Menomonee River, which begins northwest of the city and flows through developing and developed suburban areas and then through the central city; and those who live in the 27-square-mile watershed of Oak Creek, a much smaller body of water which flows through a more rural, but rapidly urbanizing, suburban area south of the city of Milwaukee. Water from each stream drains first into another river and then, after a fairly short run, into Lake Michigan. The Menomonee River is one of the major rivers flowing into and through the Milwaukee area. Along with being larger in population, the Menomonee River watershed has a somewhat more ethnically diverse population than the Oak Creek. Risk Issues Background. Urbanization tends to degrade the ecological and aesthetic qualities of streams near and within urban areas and also increases the probability of downstream flooding. In the survey, different sets of respondents were assigned to questions about each these
Model Robustness 14 two risks, the river environment path of questions and the flood path. Urban development can seriously affect the ecological health of urban rivers and their surrounding areas, and the Menomonee River and the Oak Creek are no exceptions. Over the years, the water has become warmer, less clean and less clear, the stream bottoms have become muddy and polluted with toxic chemicals and bacteria, the number and variety of fish that can live in the streams have decreased so that only rough fish (e.g., carp) could survive, the variety of wildlife in the area has declined, and many parts of the surrounding areas have become littered and polluted. In addition, some experts suggested that these streams should not be used for swimming and wading. As an area becomes more urban and less rural, there is also increased runoff; that is, more of the rain that falls on the land runs into streams and rivers instead of soaking into the land. Thus, the kind of flooding that damages peoples' homes becomes more frequent and the size of the area that is flooded expands so homes that are not currently at risk of flooding would face risks in the future. In urban areas, flooding can be a social, economic, and environmental issue for residents of the entire watershed – and not just of the floodplain (i.e., the area around the river that can become flooded) – because upstream development, which often spells economic advantage in suburban areas, can produce downstream hardship, often among those less well off. Flooding became a more salient issue in the Menomonee River region after two large (so-called “100-year”) floods affected downstream residents in 1997 and again in 1998, causing property damage and loss of homes. (A “100-year” flood is one that is so large that the odds of it happening in a given year are 1%.) Although experts expect urbanization to also produce an increased risk of damaging floods from the smaller and historically well behaved Oak Creek, focus groups conducted prior
Model Robustness 15 to the surveys indicated that residents of that watershed may have difficulty envisioning that happening and, therefore, would have difficulty responding in a considered way to questions about it. Thus, Oak Creek watershed respondents were assigned only to the river environment path of questions. Among the Menomonee River respondents, some were assigned to the river environment path of questions and some to the flood path. A third set of respondents was randomly assigned to answer questions from both the flood path and the river environment path. However, since only the first two sets of respondents were asked about their use of risk information, only they are included in this analysis. Sampling and Interviewing. From October 1999 to April 2000, the University of Wisconsin Survey Center, a professional research organization associated with the University of Wisconsin-Madison, conducted a sample survey by telephone of 999 adult heads of households in the two watershed areas. Respondents were chosen randomly from among the heads of households in each contacted residence. To ensure that all interviewees resided within one of the two watersheds, Survey Sampling, Inc., constructed a probability sample of telephone numbers from all the working telephone numbers listed within the census tracts in each watershed. Sampling techniques such as RDD, which would have included unlisted phone numbers, would have been fatally cumbersome to employ under these conditions. However, the potential bias is that up to 20% of the eligible households in the watersheds might have been excluded from the population sampled (based on a statewide estimate of 13% of households unlisted and the 5-7% phoneless). The overall response rate was 46% (48% in the Menomonee River watershed and 40% in the Oak Creek watershed). Of the 759 respondents who had been asked about their information use, 516 resided in the Menomonee River watershed and 243 in the Oak Creek watershed. About twice as many
Model Robustness 16 interviews were needed in the Menomonee River watershed as in the Oak Creek because Menomonee River respondents were to be divided into two paths of questioning. All Oak Creek watershed respondents were assigned to the river environment path. Respondents in the Menomonee River watershed were randomly assigned to the river environment path (n=213) or the flood path (n=303). This distribution of the number of respondents by path and watershed was designed to maximize analysis potential while minimizing costs. Measurement Information Sufficiency. Information sufficiency was measured by two self-report variables: 1) current knowledge about the risk and 2) the information sufficiency threshold. Respondents’ current level of knowledge was measured as follows: Now, we would like you to rate your knowledge about this risk. Please use a scale of zero to 100, where zero means knowing nothing and 100 means knowing everything you could possibly know about this topic. Using this scale, how much do you think you currently know about... [fish path] the risk from eating Lake (Michigan) (Erie) fish? [tap water path] the risk from drinking Lake (Michigan) (Erie) tap water? [lake environment path] threats to Lake (Michigan) (Erie)? [flood path] Menomonee River flooding? [river environment path] threats to the health of the (river) (creek)? Sufficiency threshold was measured as follows: Think of that same scale again. This time, we would like you to estimate how much knowledge you would need... [fish path] to deal adequately with the possible risk from eating Lake (Michigan)
Model Robustness 17 (Erie) fish in your own life? [tap water path] to deal adequately with the possible risk from drinking Lake (Michigan) (Erie) tap water in your own life? [lake environment path] to achieve an understanding of threats to the health of Lake (Michigan) (Erie) that is good enough for your purposes? [flood path] to achieve an understanding of Menomonee River flooding that is good enough for your purposes? [river environment path] to achieve an understanding of threats to the health of the (river) (creek) that is good enough for your purposes? Of course, you might feel you need the same, more, or possibly even less, information about this topic. Using a scale of zero to 100, how much information would be sufficient for you, that is, good enough for your purposes? Rather than using the calculated difference between current knowledge scores and sufficiency threshold scores to represent information insufficiency, that variable was assessed by regressing sufficiency threshold scores on current knowledge scores (see Cohen & Cohen, 1983). The term “information insufficiency” will be used to refer to a person’s perception that he or she needs more information to deal with the risk, that is, that his or her current knowledge is less than sufficient. Perceived Information Gathering Capacity. In the Great Lakes survey, capacity was measured by two items that asked individuals to respond, on a five-point, Likert-type scale, to the statements: “If I wanted to, I could easily get all the information I need about this topic” and “It’s hard for me to get useful information about this topic” (reverse-coded). The summated scale had a standardized alpha of .58.
Model Robustness 18 Based on some dissatisfaction about the performance of that capacity measure in the Great Lakes survey, a new and more comprehensive measure of capacity was developed for the Watershed survey. The new items were based in part on work by Maly et al. (1998) and, in part, on the results of focus group research conducted in preparation for the survey. Interviewees were asked to indicate their beliefs about getting more information about the topic of their path if they wanted to. Interviewees used five-point, Likert-type, agree-disagree scales to respond to the following items: “I would know what questions to ask of the experts,” “I would know where to go for more information,” “ I could readily take the time to gather any additional information I might need,” “Much of the information would be too technical for me to understand” (reverse coded), “ I would know how to separate fact from fiction,” and “I believe I could understand information on this topic if I make the effort.” This summated scale had a standardized alpha of .66. Unfortunately, the dissimilarities between the capacity operationalizations used in the two survey projects prohibited combining them in this analysis. Relevant Channel Beliefs. Questionnaires for both survey projects included a set of items designed to measure respondents’ beliefs about channels of risk information, especially their trustworthiness and their usefulness in helping the individual judge the quality of the information. Beliefs such as these could affect the information seeking and processing strategies that people employ. For this report, analysis concentrated on the six channel-belief items used by Griffin, Neuwirth, Giese, and Dunwoody (2002) in a previous test of the RISP model using the Great Lakes survey data.3 The six items represented beliefs that the media distort information, especially through bias and sensationalism (first factor), and that the media provide audiences with cues about the validity of the information they contain (second factor). Respondents were asked the extent to which they agreed or disagreed with each belief on a 5-point, Likert-type
Model Robustness 19 scale. For this report, data from the two survey projects were combined. Initial factor analysis (principal axis factoring, oblique rotation) found that the channel beliefs factor analyses were more comparable from one path to another if one distortion item, “the news stories are just a series of unconnected events that don't add up to much,” were left out of the analyses. The overall results can be found in Table 1 and, by path, in Table 2. The first factor, labeled “Distortion” (omega=.69)4, reflects beliefs about media attributes focusing on bias and sensationalism. The second factor, called “Validity Cues” (omega= .41), reveals a focus on patterns of media content thought to provide cues about the validity and meaningfulness of the information presented. The two factors have a fairly small negative correlation with each other (the more one believes that the media distort, the less he or she believes that they provide validity cues), r= -.13, p
