Tips for Creating Reading Responses and Concept Map

Tips for Creating Reading Responses and Concept Map

ORDER A PLAGIARISM FREE PAPER NOW

2 Media Effects Theories An Overview Patti M. Valkenburg and Mary Beth Oliver Theories and research on the effects of media emerged under the umbrella concept mass communication. This term arose during the 1920s as a result of the new opportunities to reach audiences via the mass media (McQuail, 2010). In early mass communication theories, mass not only refered to the “massness” of the audience that media could reach but also to homogeneous media use and homogeneous media effects, notions that are increasingly challenged in the contemporary media landscape (Valkenburg, Peter & Walther, 2016). In the past two decades, media use has become progressively individualized, and, with the introduction of Web 2.0, decidedly more personalized. It is no surprise, therefore, that media effects theories have undergone important adjustments in the past decades. And it is also no surprise that the mass has turned increasingly obsolete in contemporary media effects theories (Chaffee & Metzger, 2001). The aim of this chapter is to provide an overview of the most important media effects theories that have been coined in the past decades and to chart changes in these theories. We start by providing a definition of a media effects theory and explaining the differences between media effects theories and models. In the second section, we discuss the results of several bibliometric studies that have tried to point out the most prominent media effects theories in central communication journals, and, based on these studies we identify “evergreen” and upcoming theories. In the third section, we discuss the communalities between contemporary media effects theories along three potential characteristics of such theories: selectivity, transactionality, and conditionality. We end with a discussion of the future of media effects research, with a special focus on the necessity of the merger between media effects and computer-mediated communication theories. What Is a Media Effects Theory? As Potter (2011) rightly observes in his review of the media effects literature, few scholars have attempted to provide a formal definition of a media effect. We can add to this observation that even fewer scholars have formulated a definition of a media effects theory. Without such a definition, it is difficult to assess which theories qualify as media effects theories and which do not. But to be able to document wellcited media effects theories that have been developed over the years, we first and foremost need a definition of a media effects theory. We define such a theory as one that attempts to explain the uses and effects of media on individuals, groups, or societies as a whole. To be labeled a media effects theory, a theory at least needs to conceptualize media use (or exposure to specific mediated messages or stories) and the potential changes that this media use can bring about in individuals, groups, or societies (i.e., the media effect). We define media use broadly as the intended or incidental use of media channels (e.g., telephone, email), devices (e.g., smartphone, game console), content/messages (e.g., games, narratives, advertising, news), or all types of platforms, tools, or apps (e.g., Facebook, Instagram, Uber). Media effects are the deliberate and non-deliberate short and long-term individual or collective changes in cognitions, emotions, attitudes, and behavior that result from media use (Valkenburg et al., 2016). Some media effects theories that fit within this definition have previously been labeled as media effects models, oftentimes (but not always) because they are accompanied by a pictorial model to explain the processes or relationships between media use, media outcomes, and other relevant concepts, such as individual differences or social-context variables (e.g., the Elaboration Likelihood Model, Petty & Cacioppo, 1986; the Reinforcing Spiral Model; Slater, 2007). In other scholarly publications, the labels theory and model are used interchangeably. For example, in the previous edition of this book, some authors referred to the agenda setting model (Tewksbury & Scheufele, 2009, p. 21), whereas others referred to agenda setting theory (McCombs & Reynolds, 2009, p. 13). Although there are many conceptions about the differences between theories and models within and beyond the communication discipline, these conceptions do not seem to be helpful in distinguishing media effects theories from models. In fact, all media effects models that will be discussed in this chapter fit within our definition of media effects theories. Therefore, although we will use the original labels of existing models/theories (e.g., the Elaboration Likelihood Model versus cultivation theory), we will use these labels without distinction. Prominent Media Effects Theories In the past 20 years, five bibliometric studies have tried to single out the most prominent media effects theories in scholarly communication work (Bryant & Miron, 2004; Chung, Barnett, Kim & Lackaff, 2013; Kamhawi & Weaver, 2003; Potter, 2012; Walter, Cody & Ball-Rokeach, 2018). These bibliometric studies have content-analyzed a varying number of communication journals to document, within a certain time frame, which theories are most often cited in these journals. For example, Bryant and Miron (2004) analyzed one issue per year from three communication journals (Journal of Communication, Journal of Broadcasting and Electronic Media, and Journalism & Mass Communication Quarterly) from 1956 to 2000, Chung et al. (2013) analyzed all issues from four communication journals from 2000 to 2009 (Journal of Communication, Communication Research, Human Communication Research, and Communication Monographs), and Walter et al. (2018) analyzed all issues from one communication journal (Journal of Communication) from 1951 to 2016. The bibliometric studies all focused on the prevalence of mass communication theories rather than media effects theories specifically. Although both types of theories are sometimes used interchangeably, the focus of mass communication theories is decidedly broader than that of media effects theories. Generally, mass communication theories do not only conceptualize the effects of mass communication, but also its production, consumption, and distribution, as well as the (changes in) policies surrounding mass communication. For example, in Bryant and Miron’s (2004) analysis, mass communication was defined as “any scholarship that examined processes, effects, production, distribution, or consumption of media messages” (p. 663). In addition, whereas mass communication theories have traditionally embraced both postpositivist and critical or cultural approaches (Chaffee & Metzger, 2001), media effects theories are primarily associated with postpositivist approaches. Postpositivists derive their quantitative research methods from those developed in the physical sciences, but they do recognize that humans and human behavior are not as constant and homogeneous as elements in the physical world (Baran & Davis, 2010). Indeed, most chapters in this book rely on theories or discuss research that stem from postpositivist approaches. Some bibliometric studies did not only analyze (mass) communication theories, but all theories, including those that originated in cognate disciplines. For example, Bryant and Miron identified 604 theories in their analyzed journals, including theories such as feminist theory, attribution theory, and Marxism. Likewise, Potter (2012) found 144 different theories from within and beyond the communication discipline, including theories like the availability heuristic, cognitive dissonance, and self-perception (see also Potter & Riddle, 2007; Walter et al., 2018). According to Potter, these theories all described “some aspect of the media effects phenomenon” (p. 69). However, although all these theories may be helpful to explain media effects, in themselves they cannot be considered media effects theories as defined in this chapter. As discussed, a media effects theory at least needs to conceptualize media use and the individual or collective changes that this media use brings about. Despite the fact that the bibliometric studies used different classifications of communication theories and analyzed different communication journals, together they provide an indispensable picture of the use and development of media effects theories in the past decades. Because media effects theories did play such a dominant role in all bibliometric studies (Chung et al., 2013), we were able to reanalyze the results of these studies with an exclusive focus on the media effects theories that they identified. For example, of the 144 theories that Potter (2012) identified, about one-fifth qualify as media effects theories according to our definition. Table 2.1 lists the media effects theories that have been identified as most prevalent in the bibliometric studies. In ranking these theories, we opted to include the 1956– 2000 period reported by Bryant and Miron (2004) and the most recent years (2010–2016) from Walter et al.’s (2018) study so as to provide a picture of changes and trends within the discipline. However, in listing these theories, it is important to note that their ranking should be understood in general terms rather than as necessarily representing stark or significant differences. First, some of the theories listed were “tied” in terms of their frequencies. For example, in Bryant and Miron’s (2004) analysis, agenda setting and uses and gratifications had 61 citations each, and medium dependency and linear theory had 16 citations each; in Kamhawi and Weaver’s (2003) analysis, priming and knowledge gap theory were mentioned in fewer than 1.5% of the articles sampled. Second, even when theories differed in terms of their prevalence, some of these differences are so small as to warrant caution in their interpretation. For example, in Chung et al.’s (2013) analysis, cultivation theory was associated with 68 mentions, and agenda setting was associated with 65 mentions. Finally, in some analyses, different theories were sometimes grouped together with similar theories in a common category, thereby increasing their prominence in the rankings. For example, in Walter et al.’s (2018) study, the “narrative theory” was employed to refer to articles that employed theories or concepts such as transportation, entertainment education, and character identification. Table 2.1 Prominent Media Effect Theories Listed in Five Bibliometric Studies to Document Communication Theories Evergreen Media Effects Theories As Table 2.1 reveals, six media effects theories have held up fairly well over the past decades, and so they can rightly be named “evergreen theories.” These theories showed up as top-cited theories in both the earliest bibliometric study (time frame 1956–2000; Bryant & Miron, 2004), and in two to four bibliometric studies that covered subsequent periods: cultivation theory (Gerbner, 1969), agenda setting theory (McCombs & Shaw, 1972), diffusion of innovations theory (Rogers, 1962), uses and gratifications theory (Katz, Blumler & Gurevitch, 1973; Rosengren, 1974), social learning/social cognitive theory (1986), and media system dependency theory (Ball-Rokeach & DeFleur, 1976). Other theories that were identified as well-cited theories in the bibliometric studies are two-step flow theory (Lazarsfeld, Berelson & Gaudet, 1948), knowledge gap theory (Tichenor, Donohue & Olien, 1970), spiral of silence theory (NoelleNeumann, 1974), priming theory (Berkowitz, 1984), third-person effects (Davison, 1983), the Elaboration Likelihood Model (Petty & Cacioppo, 1986), framing theory (Entman, 1993), and the limited capacity model (Lang, 2000). Table 2.2 gives a short description of the well-cited media effects theories identified in the bibliometric studies, listed according to the dates in which they were originally coined. Table 2.2 Prominent Media Effects Theories and Their Google Citations Changes in the Prominence of Theories over Time When comparing the results of the five bibliometric studies summarized in Table 2.1, some theories appear to have lost their appeal over the years. One such theory is Lasswell’s (1948) model of communication that was listed as one of the top-cited theories in Bryant and Miron’s (2004) analysis but lost that status in the more recent bibliometric studies. The same holds for other classic, linear media effects models, such as Shannon and Weaver’s (1949) mathematical model of communication. Another theory that was present in Bryant and Miron, but which lost its influence after the 1970s, is McLuhan’s medium (or sense-extension) theory (McLuhan, 1964). By means of his aphorism, “the medium is the message,” McLuhan theorized that media exert their influence primarily by their modalities (e.g., text, aural, audiovisual) and not so much by the content they deliver. His theory probably lost its appeal among media effects researchers because research inspired by his theory often failed to produce convincing results (Clark, 2012; Valkenburg et al., 2016). Although no one can deny that modality is an essential feature of media and technologies (Sundar & Limperos, 2013), media effects are often a result of a combination of features, among which content plays a prominent role. It is probably no surprise that “Content is King” is still one of the more popular adages in modern marketing. Another change over time suggested by the bibliometric studies is the “cognitive turn” in media effects theories coined in the 1980s and 1990s. This increased attention to internal cognitive processes of media users is at least in part a result of the cognitive revolution in psychology that started in the 1950s in reaction to behaviorism (Gardner, 1985). Behaviorism (or stimulus-response theory) is a learning theory that argues that all human behaviors are involuntary responses to rewarding and punishing stimuli in the environment. What happens in the mind during exposure to these stimuli is a “black box” and is irrelevant to study. In the 1980s and 1990s, several media effects theories have tried to open the black box between media use and media outcomes (e.g., priming theory, Berkowitz, 1984; the limited capacity model, Lang et al., 1995; the Elaboration Likelihood Model, Petty & Cacioppo, 1986). At the time, scholars started to acknowledge that in order to validly assess whether (or not) media can influence individuals, they need to know why and how this happens. This new generation of theories acknowledged that media effects are indirect (rather than direct). More specifically, they argued that the cognitive mental states of the viewer act as a mediating (or intervening) variable between media use and media outcomes. Indeed, these new theories recognized that the mental states of the media user play a crucial role in explaining media effects. In the same period, some classic media effects theories were adjusted to better acknowledge cognitions in the media effects process, sometimes by the author him or herself and sometimes by others. For example, in Bryant and Miron’s bibliometric study, Bandura’s theory was still named social learning theory (Bandura, 1977). This early version of his theory had its roots in behaviorism, which is evident, for example, from its unconditional emphasis on rewarding and punishing stimuli to realize behavioral change. In the 1980s, Bandura modified his theory and renamed it social cognitive theory to better describe how internal cognitive processes can increase or decrease learning (Bandura, 1986). In addition, although cultivation theory is an all-time favorite and its name is still current, over the past few decades researchers have proposed numerous adaptations to the theory to better understand how, why, and when cultivation effects occur. For example, Shrum (1995) has argued for the integration of cultivation theory in a cognitive information processing framework. According to Potter (2014), the adaptations of cultivation theory are so numerous and extensive that its original set of propositions may have gotten glossed over. Indeed, there appears to be only minimal overlap between the macro-level, sociological cultivation theory that Gerbner (1969) proposed and the more recent micro-level, psychological interpretations of the same theory (Ewoldsen, 2017; Potter, 2014). Upcoming Media Effects Theories Although highly informative, together the five bibliometric studies either do not (Bryant & Miron, 2004; Kamhawi & Weaver, 2003; Potter, 2012) or only partly cover the past decade of media effects research (Chung et al., 2013; Walter et al., 2018). The most recent study by Walter et al. (2018) does cover publications that appeared up to 2016. But due to their study’s broader scope, they only focused on research papers and omitted theoretical papers from their analysis, whereas these latter papers typically are the ones in which new media effects theories are coined. Given the rapid changes in media technologies in the past decade, it is highly relevant to investigate whether this recent period has witnessed an upsurge in novel or adjusted media effects theories. After all, as media technologies change, “new theories may be needed with which to understand the communication dynamics that these technologies involve” (Walther, Van Der Heide, Hamel & Shulman, 2009, p. 230). To identify upcoming media effects theories, we conducted an additional bibliometric analysis, in which we included the same 14 communication journals as the most extensive earlier analysis did (Potter, 2012; see Potter & Riddle, 2007). To capture theories and research that are particularly relevant to newer communication technologies, we included an additional communication journal: the Journal of Computer Mediated Communication. To identify highly cited articles in these 15 journals, we used the “highly cited paper” option provided by the citation indexing service Web of Science (WoS). Highly cited papers in WoS reflect articles in the last ten years that were ranked in the top 1% within the same field of research (e.g., communication) and published in the same year (Clarivate Analytics, 2017). An advantage of this analysis is that, within the designated ten-year period, older and recent papers are treated equally. Whereas in regular citation analyses older papers typically outperform more recent ones, the algorithm of WoS controls for this “seniority bias.” Our analysis yielded 93 highly cited papers in these 15 journals.2 Of these papers, about half involved media effects papers, which underscores the relevance of media effects research in the communication discipline. Most of these effects papers were empirical papers that used one or more existing theories to guide their research. However, a small percentage (about 10%) either introduced a new media effects theory or extended one or more existing theories. Some of these theoretical papers focused on media use in general (e.g., the reinforcing spiral model, Slater, 2007; the Differential Susceptibility Model of Media Effects, Valkenburg & Peter, 2013). Others dealt with specific types of media use, such as exposure to news (e.g., framing theory, Entman, 2007; the communication mediation model, Shah et al., 2017), persuasive messages (e.g., the model of psychological reactance to persuasive messages, Rains, 2013), or communication technology (extensions of spiral of silence theory and two-step flow theory, Neubaum & Krämer, 2017; the uses and gratifications theory 2.0, Sundar & Limperos, 2013). A first noticeable trend revealed by the highly cited media effects papers is the emergence of theories that attempt to explain the uses and effects of media entertainment (for a similar observation, see Walter et al., 2018; Table 2.1). Some of these theories try to better understand this type of media use by focusing on cognitive and emotional processing. They try to explain, for example, why and how exposure to narrative entertainment leads to less resistance than traditional persuasive messages (the entertainment overcoming resistance model, Moyer-Gusé, 2008; Moyer-Gusé & Nabi, 2010). Other theories have tried to better understand the concept of enjoyment in response to media entertainment (Tamborini, Bowman, Eden, Grizzard & Organ, 2010), or the “eudaimonic gratifications” (i.e., mediarelated experiences associated with contemplation and meaningfulness) that people experience in response to thought-provoking and poignant entertainment (Oliver & Bartsch, 2010; Oliver & Raney, 2011). Another trend that can be inferred from the highly cited media effect studies is that the traditional gap between media effects and CMC (Computer-MediatedCommunication) studies seems to have narrowed somewhat in the past years. Traditionally, “media effects research” and “CMC research” were part of two subdisciplines of communication science that developed in separation and rarely interacted with each other. Media effects research was part of the mass communication subdiscipline, whereas CMC research belonged to the interpersonal communication subdiscipline. Over time, many authors have argued for bridging the gap between these two subdisciplines, oftentimes without much success (for a review see Walther & Valkenburg, 2017). However, the significant changes in media use in the past decade seemingly have been an important impetus for the merger between media effects and CMC theories. After all, whereas previously “media use” referred only to a handful of mass media such as newspapers, radio, film, and television, the current definition of media use, including the one in this chapter, also includes an array of media technologies that stimulate give-and-take interactions of individuals or groups with technologies (e.g., games) or other individuals (e.g., social media) and that traditionally belonged to “the realm” of CMC theories and research. In fact, several CMC studies in our collection of highly cited papers did investigate “media effects” that fall within our definition of such effects. For example, Walther, Van der Heide, Kim, Westerman and Tong (2008) found that CMC users’ perceptions of an individual’s online profile are affected by the posts of friends who may have posted on the profile. We consider such a scenario as an example of a media effect. Namely, people (i.e., the receivers) look at online profiles (i.e., media use), and the messages or posts that they see (i.e., the messages) affect their perceptions (i.e., the media effect). Similarly, Tong, Van Der Heide, Langwell and Walther (2008) investigated how exposure to the number of friends listed on online profiles (i.e., media use) influenced observers’ perceptions of these profiles (i.e., the media effect). Their study showed that this system-produced information significantly influenced the cognitions and attitudes of the receivers of these messages. Core Features of Contemporary Media Effects Theories The previous section revealed several changes in media effects theories over the past decades, such as the cognitive turn in these theories as of the 1980s and 1990s, the emphasis on media entertainment and emotional media processing, and the gradual integration of media effects and CMC research. Generally, the more recent theories appear to be more comprehensive than earlier ones. For example, they more often recognize the interaction between media factors (media use, media processing) and non-media factors (e.g., dispositional, situational, and social context factors), and they better acknowledge that media effects are indirect rather than direct. In the next sections, we discuss how contemporary media effects theories differ from the earlier ones. We focus on three related core features of these theories: selectivity, transactionality, and conditionality. Selectivity Paradigm Selectivity is one of the oldest paradigms in communication. Already in the 1940s, Lazarsfeld et al. (1948) discovered that individuals predominantly select media messages that serve their needs, goals, and beliefs. These early ideas have been further conceptualized into two theories: the uses and gratifications (Katz et al., 1973; Rosengren, 1974) and selective exposure theory (Knobloch-Westerwick, 2014). Both theories are generally based on three propositions: (1) individuals only attend to a limited number of messages out of the miscellany of messages that can potentially attract their attention; (2) media use is a result of dispositional (e.g., needs, personality), situational (e.g., mood), or social-context factors (e.g., the norms that prevail in the social environment); and (3) only those messages they select have the potential to influence them (Klapper, 1960). This influence of media use is named “obtained gratifications” in uses and gratifications theory and “media effects” in selective exposure theory. Early empirical research guided by uses and gratifications and selective exposure theory usually investigated only the first part of the media effects process. This research typically conceptualized media use as the outcome, whereas the consequences or “effects” of this media use were typically ignored. Therefore, these early theories do not fit within our definition of media effects theories. In the past decade, however, the selectivity paradigm has progressively become an integrated part of media effects theories, including the reinforcing spiral model (Slater, 2007); the SESAM model (Knobloch-Westerwick, 2014; see Chapter 10 in this volume) and the Differential Susceptibility to Media Effects Model (Valkenburg & Peter, 2013). Indeed, in Walter et al.’s (2018) bibliometric analysis, selective exposure appeared as a top theory only in the last time frame examined (2000–2016). Contemporary selective exposure theories conceptualize that media users, rather than media sources, are the center points in a process that may bring about media effects. This insight has important implications for media effects research. It means, for example, that individuals, by shaping their own selective media use, also (deliberately or not) partly shape their own media effects (Valkenburg et al., 2016). The selectivity paradigm is also part and parcel of CMC theories and research. For example, Walther, Tong, DeAndrea, Carr and Van Der Heide (2011) argue that the specific goal(s) that prompt an individual’s media consumption “shape attention to variations in the content and features of the topical information one consumes, affecting its interpretation and recall” (p. 187). However, although selectivity is clearly an important feature of CMC, it is still unknown whether CMC users are more (or less) able to contribute to their own media effects than users of more traditional media are. On the one hand, CMC users have more agency in their media selection than they had with traditional media. They can, for example, openly comment on incoming messages, thereby publicly discounting this information. They can also more easily avoid incongruent or conflicting messages, and, due to technological algorithms that use their preferences or search terms, co-create their own “filter bubbles” (Pariser, 2011). Due to this increased agency and selectivity, CMC users may thus have more opportunity than traditional media users to shape their own media effects. On the other hand, the blending of mass (e.g., a television program) and interpersonal messages (e.g., viewer comments on Twitter about this television program) in CMC environments could also stimulate a type of gratification (or effect) that have been named “process gratifications” (Stafford, Stafford & Schkade, 2004). Unlike content gratifications, process gratifications (or effects) are not so much driven by preexisting needs, goals, or beliefs of the media user, but they develop while using media. For example, individuals may start surfing the web with specific a priori needs, beliefs, or goals, but while interacting with technologies or other people they may develop different and unforeseen needs, goals, and beliefs, which in turn may lead to different and unforeseen media gratifications (or effects). Therefore, in contemporary media effects theories, media effects can best be understood as the result of an interaction between need-driven media use and situational, process-based media use (for a further discussion, see Sundar & Limperos, 2013). Transactionality Paradigm In the early days of the communication discipline, most mass media effects theories were linear, one-directional models of communication that pointed from senders (mass media) to receivers. Examples of linear media effects theories are cultivation theory (Gerbner, 1969), Lasswell’s (1948) communication model, and McLuhan’s (1964) medium theory (see the first column of Table 2.1). Unlike one-directional media effects theories, transactional theories conceptualize media use and media outcomes as reciprocally related. Like uses and gratifications and selective exposure theory, transactional media effects models embrace a user-oriented approach (e.g., Wang & Tchernev, 2012). They argue that (1) certain dispositions of media users (e.g., needs, goals, beliefs) can cause their selective media use; (2) which can, in turn, cause certain outcomes (i.e., the media effect); (3) which can, then, further cause selective media use. For example, adolescents’ aggressiveness may stimulate their use of violent media, which, in turn, may increase their aggressiveness, which may then further stimulate their violent media use (Slater, Henry, Swaim & Anderson, 2003). Transactional media effects theories are relatively recent in the communication discipline. The first transactional media effects model appeared in the early 1980s in Germany (Früh & Schönbach, 1982), but that model probably suffered from the rule of the restrictive head start. Transactional models are difficult to investigate and, at the time, both the expertise and the methods to empirically test such complex models were not widely available then. Subsequent transactional media effects models are Bandura’s (1986) social cognitive model, Anderson and Bushman’s (2002) General Aggression Model, and Slater’s (2007) reinforcing spiral model. Although transactionality is relatively new to media effects theories, it has always been a core paradigm of interpersonal communication theories, which, par excellence, attempt to explain the reciprocal influences from interaction partners on one another. However, interpersonal communication has been increasingly mediated through CMC devices. Moreover, in newer media environments, many traditionally one-directional mass communication processes, such as news and entertainment consumption, have become transactional: Message producers and consumers can now exert reciprocal influences on one another and can easily switch their roles from consumers to producers and vice versa. These transactional processes necessitate alterations to existing media effects theories. Such alterations have already been suggested, for example, for agenda setting theory (Lee & Tandoc, 2017), spiral of silence theory (Neubaum & Krämer, 2017), communication mediation theory (Shah et al., 2017), diffusion of innovations theory (Rice, 2017), and entertainment theory (Raney & Ji, 2017). Conditionality Paradigm Like the transactionality paradigm, the conditionality paradigm elaborates on the uses and gratifications and selective exposure theories. It postulates that media effects do not equally hold for all media users, and that media effects can be contingent on dispositional, situational, and social-context factors. Remarkably, already in the 1930s, the first large-scale empirical studies into the effects of media on children and young adults, the Payne Fund Studies, concluded: That the movies exert an influence there can be no doubt. But it is our opinion that this influence is specific for a given child and a given movie. The same picture may influence different children in distinctly opposite directions. Thus in a general survey such as we have made, the net effect appears small. (Charters, 1933, p. 16) However, despite these early empirical findings, many subsequent media effects theorists have been rather slow in acknowledging conditional media effects. Particularly early theories aimed at establishing linear, across-the-board effects of mass media. For example, although Gerbner’s (1969) cultivation theory did recognize that individuals differ in their interpretation of messages, it did not conceptualize such differences, but instead focused on the macro-level effects of mass-mediated message systems on the public (Potter, 2014). And even today, there seems to be a tendency to ignore individual differences in susceptibility to media effects. As Neuman (2018) recently observed: “Perhaps our paradigm would be strengthened if we recognized that media effects are neither characteristically strong nor are they characteristically minimal: they are characteristically highly variable” (Neuman, 2018, p. 370; see also Rains, Levine & Weber, 2018). However, despite Neuman’s (2018) recent criticism, in fact, most contemporary media effects theories do recognize conditional media effects, including the reinforcing spiral model (Slater, 2007), the communication mediation model (Shah et al., 2007, 2017), and the Elaboration Likelihood Model (Petty & Cacioppo, 1986). Most of these theories have proposed that conditional media effects are not only due to selective exposure but also to selective processing. For example, Valkenburg and Peter (2013) argue that dispositional, situational, and social context factors may have a double role in the media effects process: They not only predict selective exposure, but they can also influence the way in which media content is cognitively and emotionally processed. Individuals have the tendency, at least to a certain extent, to seek out content that does not deviate too much from their needs, goals, and beliefs (Knobloch-Westerwick, 2014). It is conceivable that the same factors that predict selective exposure can also influence the way in which media content is processed. It has been shown that people’s opinions on a given issue influence how they respond to media messages and characters. For example, in their now-classic study about the American series All in the Family, Vidmar and Rokeach (1974) found that high prejudiced viewers tended to be more sympathetic to Archie, the bigoted main character, whereas low-prejudiced individuals tended to be more sympathetic to Mike, the politically liberal main character of the series. Unfortunately, although in the past decades there has been ample research on selective exposure and selective recall, there has been relatively less attention to selective reception processes (Hart et al., 2009). Moreover, the scarce research that did focus on selective reception has mainly focused on individual differences in cognitive processing of media content and less on emotional processing. However, as our analysis of recent highly cited communication papers suggests, two decades after the cognitive turn in media effects theories, an emotional turn in these theories seems to have unfolded. Indeed, contemporary media effects theories increasingly recognize that emotional processes, such as identification with characters or emotional involvement in the narrative, are important routes to media effects (Moyer-Gusé & Nabi, 2010; Nabi, 2009; Slater & Rouner, 2002). Discussion Together, the five bibliometric studies that we attempted to integrate in this chapter and our highly cited paper analysis suggest that the use of theory in communication papers has increased significantly across time. For example, whereas Bryant and Miron (2004), who reported on the period from 1956 to 2000, found that only 26% of articles provided a theoretical framework, Potter and Riddle (2007), who reported on the period from 1993 to 2005, found that 35% of articles featured a theory prominently. Finally, Walter et al. (2018) observed that whereas in the 1950s only 9% of all empirical papers that appeared in the Journal of Communication featured a theory prominently, this percentage increased towards 65% in the 2010s. Although it is promising that the development of theory in communication journals has quantitatively increased over the years, it is even more important to establish whether it has improved in a qualitative sense. Some of the bibliometric studies are pessimistic about this qualitative development. For example, Walter et al. (2018) observed a “remarkable slowdown in new theory development” (p. 424) and “a general increase in theory use, yet a decrease in theory development” (p. 435). It must be noted, though, that Walter et al.’s analysis did not include theoretical articles and literature reviews in their bibliometric analysis, which together comprised 11% of their sample of papers. Their conclusions about the state of the field would undoubtedly have been more positive if they had included theoretical papers in their sample. Walter et al. (2018) based their conclusion on the fact that a number of theories, such as cultivation theory, social cognitive theory, and agenda setting theory, which we dubbed as evergreen theories, remained prominent in every decade after the 1970s. Several other authors have also observed that some theories have been used over and over again up until the present day (Ewoldsen, 2017; Katz & Fialkoff, 2017; Potter, 2014). One explanation for this phenomenon may be that these theories have managed to become part of the shared identity of media effects researchers, who, by referring to or adjusting these theories in their work, are able to communicate this identity. Another explanation may be the high “tolerance” of evergreen theories for multiple interpretations of their claims. Social cognitive theory, for example, is a comprehensive theory with broad concepts that are related to one another in complex ways. An unforeseen consequence of such theories is that they allow researchers to freely interpret or select parts of the theory to justify or explain their results. Some authors fear that the recurrent referral to these theories distorts what the theory originally proposed (Potter, 2014) or hides the progress that has been made in the understanding of media effects theories (Ewoldsen, 2017). Others have proposed the “retirement” of these old theories and replace them with newer ones that better explain contemporary media use and effects (Katz & Fialkoff, 2017). Indeed, we agree that it is important for the progress of the media effects field to develop new theories with new names rather than to selectively use claims of old theories to justify or explain expected or unexpected results. After all, true theoretical progress can only occur if certain claims of theories that do not hold are formally falsified. Despite the concerns of some authors about the progress in the media effects field, our analysis of recent highly cited communication papers suggests a somewhat more optimistic view. We found that about 10% of the highly cited papers in 15 communication journals published between 2007 and 2017 either introduced a new theory or significantly extended an existing one. These extensions of old theories, such as spiral of silence and diffusion of innovations, were partly due to the rapid changes in the new media landscape, which demands a rethinking of theories that originated in periods when the relation between media and audiences was predominantly anonymous and one-directional. In this chapter, we summarized several important theoretical trends over the past decade. One such trend is the development of theories that attempt to understand the effects of (narrative) media entertainment and the role of emotional processing in these effects. Another trend is that theories that were coined or extended in the past decade increasingly recognize the selectivity, conditionality, and transactionality of media effects. Finally, despite concerns about the lack of integration between mass and interpersonal communication, we did observe an increased tendency to merge media effects, interpersonal, and CMC theories in papers that investigate the uses and effects of messages communication via the internet and social media. Challenges and Opportunities for Future Media Effects Research We are encouraged by the development of media effects theories revealed in our analysis, and we look forward to the new theory development that will undoubtedly evolve in our changing media landscape, where most technologies are simultaneously rapidly new and rapidly old. Both the proliferation of new media technologies and the possibilities to instantaneously interact with other media users pose important challenges and opportunities for future researchers. Conceptualizing “Media Use 2.0.” First, we anticipate that newer theory development must confront how best to conceptualize what constitutes “media use.” Whereas foundational theories often used sweeping measures such as hours-a-day spent with television (e.g., Gerbner, 1969), newer theories need to account for a seemingly endless array of media platforms, even when focusing on a single “type” of media such as social networking sites. Moreover, given the mobility and multiplicity of media channels, the prevalence of media multitasking has reached new heights, and particularly among younger individuals (Voorveld & van der Goot, 2013). Consequently, watching a favorite television program may now also simultaneously involve chatting with other viewers on fan sites, posting one’s reactions to the program on social media, or searching online for information about the actors. Finally, evolving technologies facilitate media “use” well beyond the time boundaries of any single instance of media consumption. For example, although an individual may watch a given television program for a span of an hour, she may continue to “watch” the show for much longer by engaging with other viewers about the show, by watching replays and commentaries about the show on YouTube, or even expressing her thoughts about the program through self-generated media content such as mashups or fan fiction (Shade, Kornfield & Oliver, 2015). These examples are but a handful of the many ways that media use is changing, thereby highlighting the need to revise or develop new ways to conceptualize and measure how individuals now “use” media content and technology. New Methods to Assess Cognitive and Emotional Media Processing Related to the need to reassess how to measure media use, media effects theories may stand to benefit from the evolving use of newer means of assessing individuals’ emotional and cognitive processing of messages and resultant changes in beliefs, attitudes, affective states, and behaviors. Whereas traditional scholarship has typically relied on self-reports for studying media effects, many researchers are now turning to alternative techniques. For example, an increasing number of scholars are now examining the neural patterns associated with media use, pointing out its relevance in a host of areas including persuasion, stereotyping, health, and social interaction (see, for example, Falk & Scholz, 2018; Weber, Eden, Huskey, Mangus & Falk, 2015). Likewise, devices such as face readers and eye trackers are currently providing ample opportunity to assess changes in emotional responses to media messages and devices (e.g., Jennett et al., 2008; McDuff, Kaliouby & Picard, 2012; Russell, Russell, Morales & Lehu, 2017). Such measurement holds the promise of helping us develop theories about changes in emotions that occur during media use and what such changes imply for resultant media outcomes (Nabi & Green, 2014). Further, the opportunity to scrape and analyze big data and networks of information sharing will open many new avenues for media effects theorizing. Although numerous theoretical perspectives have acknowledged the sharing of media messages among individuals (e.g., two-step flow model, diffusion of innovations), network analysis of online communities represents ample opportunities to develop new or adjust existing communication theories. The Effects of “Mass Self-Communication” Finally, we eagerly anticipate the growth of media theory that grapples with the implications of the shift from mass communication to what O’Sullivan (2005) has named “masspersonal” and Castells (2007) “mass self-communication.” In traditional mass media effects theories, the influence process is unidirectional, from one generator of messages to recipients. Mass self-communication theories provide an extension to these theories, in that they do not only focus on the effects of messages on recipients but also on the effects of those messages on the generator him or herself. The effects of self-generated and self-modified media messages on the message generators themselves have garnered increasing interest among scholars with the emergence of interactive technologies. For example, research on the Proteus Effect demonstrates that people often adopt the characteristics of the avatars that they use to present themselves online (Yee & Bailenson, 2007). Likewise, research on customization of websites and user-interfaces shows that when individuals have the opportunity to select their own digitized environments (e.g., interests, colors, banners), they tend to feel greater affiliation for the environments and heightened perceptions of relevance and interactivity (e.g., Kalyanaraman & Sundar, 2006). Most recently, Valkenburg (2017) coined the phrase “self-effects” to refer broadly to the effects of messages on the cognitions, emotions, attitudes, and behaviors of the message generators themselves. She argued that in the context of social media, expressing an attitude, stating one’s opinion, or even selecting an avatar with a particular appearance may not only influence the cognitions, beliefs, and attitudes of message recipients, but also those of the message generators. Further, as discussed, given individuals’ tendencies to select media content that is congruent with their cognitions, beliefs, and attitudes, it is likely that messages which are self-generated and originate from their generator’s own beliefs may have an even stronger effect on the message generators themselves than on their message recipients. There is an apparent need for future communication research that investigates and compares the effects and effectiveness of messages on both recipients and message generators themselves. Conclusion In sum, our review of media effects theories leads us to end on an optimistic note. Whereas some reviews may suggest that our scholarship is somewhat slow to evolve, our review of media effects theories is heartening. Some theories have remained evergreen, and likely for good reason. Although some of these evergreen theories were developed in what may seem like a long-ago past, their fundamental assumptions about media and human psychology are likely applicable across a wide acreage of media landscapes. At the same time, newer theories, concepts, and foci are populating our scholarship, and reflecting a greater nuance of human experience and of its intersection with communication technologies. Undoubtedly, media effects as a focus of study is at the center of public discourse about interpersonal interaction, political exchange, and even the striving for well-being. We await the insights that will certainly arise from scholars who work toward our understanding of media in the emotional, cognitive, and behavioral lives of its consumers and creators. Notes 1 Some parts of this chapter are based on Valkenburg et al. (2016). This mostly holds for the section about the three core features of media effects theories, and Table 2.2, which offers an extension and update of a table that appeared in Valkenburg et al. (2016). 2 The list of highly cited articles in these journals can be obtained from Patti Valkenburg: p.m.valkenburg@uva.nl. Two out of the 14 journals that Potter (2012) analyzed (the Quarterly Journal of Speech and the Mass Communication Review) are not indexed in WoS; as a result, no highly cited papers from these journals could be included in our analysis. References Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review of Psychology, 53, 27–51. doi:10.1146/annurev.psych.53.100901.135231 Ball-Rokeach, S. J., & DeFleur, M. L. (1976). A dependency model of mass-media effects. Communication Research, 3(1), 3–21. doi:10.1177/009365027600300101 Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (2009). Social cognitive theory of mass communication. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (pp. 94–124). New York, NJ: Routledge. Baran, S. J., & Davis, D. K. (2010). Mass communication theory: Foundations, ferment, and future. Boston, MA: Cengage Learning. Berkowitz, L. (1984). Some effects of thoughts on anti- and prosocial influences of media events: A cognitive-neoassociation analysis. Psychological Bulletin, 95, 410– 427. doi:10.1037//0033-2909.95.3.410 Bryant, J., & Miron, D. (2004). Theory and research in mass communication. Journal of Communication, 54, 662–704. doi:10.1111/j.1460-2466.2004.tb02650.x Castells, M. (2007). Communication, power and counter-power in the network society. International Journal of Communication, 1, 238–266. doi:19328036/20070238 Chaffee, S. H., & Metzger, M. J. (2001). The end of mass communication? Mass Communication and Society, 4, 365–379. doi:10.1207/S15327825MCS0404_3 Charters, W. W. (1933). Motion pictures and youth: A summary. New York, NJ: Macmillan. Chung, C. J., Barnett, G. A., Kim, K., & Lackaff, D. (2013). An analysis on communication theory and discipline. Scientometrics, 95, 985–1002. doi:10.1007/s11192-012-0869-4 Clarivate Analytics. (2017). Essential science indicators: Selection and ranking of highly cited papers. Retrieved from https://support.clarivate.com/WebOfScience/s/article/Essential-ScienceIndicators-Selection-and-ranking-of-highly-cited-papers?language=en_US Clark, R. (2012). Learning from media: Arguments, analysis, and evidence. Charlotte, NC: Information Age Publishing. Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47, 1–15. doi:10.1086/268763 Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43, 51–58. doi:10.1111/j.1460-2466.1993.tb01304.x Entman, R. M. (2007). Framing bias: Media in the distribution of power. Journal of Communication, 57, 163–173. doi:10.1111/j.1460-2466.2006.00336.x Ewoldsen, D. R. (2017). Introduction to the forum on the retirement of concepts. Annals of the International Communication Association, 41, 83–85. doi:10.1080/23808985.2017.1289069 Falk, E., & Scholz, C. (2018). Persuasion, influence, and value: Perspectives from communication and social neuroscience. Annual Review of Psychology, 69, 329–356. doi:10.1146/annurev-psych-122216-011821 Früh, W., & Schönbach, K. (1982). Der dynamisch-transaktionale Ansatz: Ein neues Paradigma der Medienwirkungen [The dynamic-transactional approach: A new paradigm of media effects]. Publizistik, 27(1/2), 74–88. Gardner, H. (1985). The mind’s new science. New York, NJ: Basic Books. Gerbner, G. (1969). Toward “Cultural Indicators”: The analysis of mass mediated public message systems. AV Communication Review, 17, 137–148. doi:10.1007/BF02769102 Hart, W., Albarracin, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135, 555–588. doi:10.1037/a0015701 Jennett, C., Cox, A. L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., & Walton, A. (2008). Measuring and defining the experience of immersion in games. International Journal of Human-Computer Studies, 66, 641–661. doi:10.1016/j.ijhcs.2008.04.004 Kalyanaraman, S., & Sundar, S. S. (2006). The psychological appeal of personalized content in web portals: Does customization affect attitudes and behavior? Journal of Communication, 56, 110–132. doi:10.1111/j.1460-2466.2006.00006.x Kamhawi, R., & Weaver, D. (2003). Mass communication research trends from 1980 to 1999. Journalism & Mass Communication Quarterly, 80, 7–27. doi:10.1177/107769900308000102 Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37, 509–523. Katz, E., & Fialkoff, Y. (2017). Six concepts in search of retirement. Annals of the International Communication Association, 41, 86–91. doi:10.1080/23808985.2017.1291280 Klapper, J. T. (1960). The effects of mass communication. Glencoe, IL: Free Press. Knobloch-Westerwick, S. (2014). The selective exposure self- and affectmanagement (SESAM) model: Applications in the realms of race, politics, and health. Communication Research, 42, 959–985. doi:10.1177/0093650214539173 Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50, 46–70. doi:10.1111/j.1460-2466.2000.tb02833.x Lang, A., Dhillon, K., & Dong, Q. (1995). The effects of emotional arousal and valence on television viewers’ cognitive capacity and memory. Journal of Broadcasting & Electronic Media, 39, 313–327. Lasswell, H. D. (1948). The structure and function of communication in society. In L. Bryson (Ed.), The communication of ideas (pp. 37–51). New York: Institute of Religious and Social Studies. Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1948). The people’s choice: How the voter makes up his mind in a presidential campaign. New York, NJ: Columbia University Press. Lee, E. J., & Tandoc, E. C. (2017). When news meets the audience: How audience feedback online affects news production and consumption. Human Communication Research, 43, 436–449. doi:10.1111/hcre.12123 McCombs, M. E., & Reynolds, A. (2009). How the news shapes our civic agenda. In J. Bryant, & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 1–16). New York, NY: Routledge. McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36, 176–187. doi:10.1086/267990 McDuff, D., Kaliouby, R. E., & Picard, R. W. (2012). Crowdsourcing facial responses to online videos. IEEE Transactions on Affective Computing, 3, 456–468. doi:10.1109/t-affc.2012.19 McLuhan, M. (1964). Understanding media: The extensions of man. New York: Mentor. McQuail, D. (2010). McQuail’s mass communication theory. London, UK: Sage. Moyer-Gusé, E. (2008). Toward a theory of entertainment persuasion: Explaining the persuasive effects of entertainment-education messages. Communication Theory, 18, 407–425. doi:10.1111/j.1468-2885.2008.00328.x Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion. Human Communication Research, 36, 26–52. doi:10.1111/j.1468-2958.2009.01367.x Nabi, R. L. (2009). Emotions and media effects. In R. L. Nabi, & M. B. Oliver (Eds.), The SAGE handbook of media processes and effects (pp. 205–222). Thousand Oaks, CA: Sage. Nabi, R. L., & Green, M. C. (2014). The role of a narrative’s emotional flow in promoting persuasive outcomes. Media Psychology, 18, 137–162. doi:10.1080/15213269.2014.912585 Neubaum, G., & Krämer, N. C. (2017). Opinion climates in social media: Blending mass and interpersonal communication. Human Communication Research, 43, 464– 476. doi:10.1111/hcre.12118 Neuman, W. R. (2018). The paradox of the paradigm: An important gap in media effects research. Journal of Communication, 68, 369–379. doi:10.1093/joc/jqx022 Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24, 43–51. doi:10.1111/j.1460-2466.1974.tb00367.x O’Sullivan, P. B. (2005, May). Masspersonal communication: Rethinking the mass interpersonal divide. Paper presented at the annual meeting of the International Communication Association, New York, NY. Oliver, M. B., & Bartsch, A. (2010). Appreciation as audience response: Exploring entertainment gratifications beyond hedonism. Human Communication Research, 36, 53–81. doi:10.1111/j.1468-2958.2009.01368.x Oliver, M. B., & Raney, A. A. (2011). Entertainment as pleasurable and meaningful: Identifying hedonic and eudaimonic motivations for entertainment consumption. Journal of Communication, 61, 984–1004. doi:10.1111/j.1460-2466.2011.01585.x Pariser, E. (2011). The filter bubble: What the internet is hiding from you. London, UK: Penguin. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123– 205). New York, NJ: Academic Press. Potter, W. J. (2011). Conceptualizing mass media effect. Journal of Communication, 61, 896–915. doi:10.1111/j.1460-2466.2011.01586.x Potter, W. J. (2012). Media effects. Thousand Oaks, CA: Sage. Potter, W. J. (2014). A critical analysis of cultivation theory. Journal of Communication, 64, 1015–1036. doi:10.1111/jcom.12128 Potter, W. J., & Riddle, K. (2007). A content analysis of the media effects literature. Journalism & Mass Communication Quarterly, 84, 90–104. doi:10.1177/107769900708400107 Rains, S. A. (2013). The nature of psychological reactance revisited: A meta-analytic review. Human Communication Research, 39, 47–73. doi:10.1111/j.14682958.2012.01443.x Rains, S. A., Levine, T. R., & Weber, R. (2018). Sixty years of quantitative communication research summarized: Lessons from 149 meta-analyses. Annals of the International Communication Association, 42, 1–20. doi:10.1080/23808985.2018.1446350 Raney, A. A., & Ji, Q. (2017). Entertaining each other?: Modeling the socially shared television viewing experience. Human Communication Research, 43, 424–435. doi:10.1111/hcre.12121 Rice, R. E. (2017). Intermediality and the diffusion of innovations. Human Communication Research, 43, 531–544. doi:10.1111/hcre.12119 Rogers, E. M. (1962). Diffusion of innovations. New York, NJ: Free Press. Rosengren, K. E. (1974). Uses and gratifications: A paradigm outlined. In J. G. Blumler, & E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 269–286). Beverly Hills, CA: Sage. Russell, C. A., Russell, D., Morales, A., & Lehu, J.-M. (2017). Hedonic contamination of entertainment: How exposure to advertising in movies and television taints subsequent entertainment experiences. Journal of Advertising Research, 57, 38–52. doi:10.2501/jar-2017-012 Scheufele, D. A. (1999). Framing as a theory of media effects. Journal of Communication, 49, 103–122. doi:10.1111/j.1460-2466.1999.tb02784.x Shade, D. D., Kornfield, S., & Oliver, M. B. (2015). The uses and gratifications of media migration: Investigating the activities, motivations, and predictors of migration behaviors originating in entertainment television. Journal of Broadcasting & Electronic Media, 59, 318–341. doi:10.1080/08838151.2015.1029121 Shah, D. V., Cho, J., Nah, S., Gotlieb, M. R., Hwang, H., Lee, N.-J., … McLeod, D. M. (2007). Campaign ads, online messaging, and participation: Extending the communication mediation model. Journal of Communication, 57, 676–703. doi:10.1111/j.1460-2466.2007.00363.x Shah, D. V., McLeod, D. M., Rojas, H., Cho, J., Wagner, M. W., & Friedland, L. A. (2017). Revising the communication mediation model for a new political communication ecology. Human Communication Research, 43, 491–504. doi:10.1111/hcre.12115 Shannon, C., & Weaver, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press. Shrum, L. J. (1995). Assessing the social influence of television: A social cognition perspective on cultivation effects. Communication Research, 22, 402–429. doi:10.1177/009365095022004002 Slater, M. D. (2007). Reinforcing spirals: The mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory, 17, 281–303. doi:10.1111/j.1468-2885.2007.00296.x Slater, M. D., Henry, K. L., Swaim, R. C., & Anderson, L. L. (2003). Violent media content and aggressiveness in adolescents: A downward spiral model. Communication Research, 30, 713–736. doi:10.1177/0093650203258281 Slater, M. D., & Rouner, D. (2002). Entertainment-education and elaboration likelihood: Understanding the processing of narrative persuasion. Communication Theory, 12, 173–191. doi:10.1093/ct/12.2.173 Stafford, T. F., Stafford, M. R., & Schkade, L. L. (2004). Determining uses and gratifications for the internet. Decision Sciences, 35, 259–288. doi:10.1111/j.00117315.2004.02524.x Sundar, S. S., & Limperos, A. M. (2013). Uses and grats 2.0: New gratifications for new media. Journal of Broadcasting & Electronic Media, 57, 504–525. doi:10.1080/08838151.2013.845827 Tamborini, R., Bowman, N. D., Eden, A., Grizzard, M., & Organ, A. (2010). Defining media enjoyment as the satisfaction of intrinsic needs. Journal of Communication, 60, 758–777. doi:10.1111/j.1460-2466.2010.01513.x Tewksbury, D., & Scheufele, D. A. (2009). News framing: Theory and research. In J. Bryant, & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 17–33). New York, NY: Routledge. Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public Opinion Quarterly, 34, 159–170. doi:10.1086/267786 Tong, S. T., Van Der Heide, B., Langwell, L., & Walther, J. B. (2008). Too much of a good thing? The relationship between number of friends and interpersonal impressions on Facebook. Journal of Computer-Mediated Communication, 13, 531– 549. doi:10.1111/j.1083-6101.2008.00409.x Valkenburg, P. M. (2017). Understanding self-effects in social media. Human Communication Research, 44, 477–490. doi:10.1111/hcre.12113 Valkenburg, P. M., & Peter, J. (2013). The differential susceptibility to media effects model. Journal of Communication, 63, 221–243. doi:10.1111/jcom.12024 Valkenburg, P. M., Peter, J., & Walther, J. B. (2016). Media effects: Theory and research. Annual Review of Psychology, 67, 315–338. doi:10.1146/annurev-psych122414-033608 Vidmar, N., & Rokeach, M. (1974). Archie Bunker’s bigotry: A study in selective perception and exposure. Journal of Communication, 24, 36–47. doi:10.1111/j.14602466.1974.tb00353.x Voorveld, H. A. M., & van der Goot, M. (2013). Age differences in media multitasking: A diary study. Journal of Broadcasting & Electronic Media, 57, 392–408. doi:10.1080/08838151.2013.816709 Walter, N., Cody, M. J., & Ball-Rokeach, S. J. (2018). The ebb and flow of communication research: Seven decades of publication trends and research priorities. Journal of Communication, 68, 424–440. doi:10.1093/joc/jqx015 Walther, J. B., Tong, S. T., DeAndrea, D. C., Carr, C., & Van Der Heide, B. (2011). A juxtaposition of social influences: Web 2.0 and the interaction of mass, interpersonal, and peer sources online. In Z. Birchmeier, B. Dietz-Uhler, & G. Stasser (Eds.), Strategic uses of social technology: An interactive perspective of social psychology (pp. 172–194). Cambridge, UK: Cambridge University Press. Walther, J. B., & Valkenburg, P. M. (2017). Merging mass and interpersonal communication via interactive communication technology: A symposium. Human Communication Research, 43, 415–423. doi:10.1111/hcre.12120 Walther, J. B., Van Der Heide, B., Hamel, L. M., & Shulman, H. C. (2009). Selfgenerated versus other-generated statements and impressions in computermediated communication: A test of warranting theory using Facebook. Communication Research, 36, 229–253. doi:10.1177/0093650208330251 Walther, J. B., Van der Heide, B., Kim, S.-Y., Westerman, D., & Tong, S. T. (2008). The role of friends’ appearance and behavior on evaluations of individuals on Facebook: Are we known by the company we keep? Human Communication Research, 34, 28– 49. doi:10.1111/j.1468-2958.2007.00312.x Wang, Z., & Tchernev, J. M. (2012). The “myth” of media multitasking: Reciprocal dynamics of media multitasking, personal needs, and gratifications. Journal of Communication, 62, 493–513. doi:10.1111/j.1460-2466.2012.01641.x Weber, R., Eden, A., Huskey, R., Mangus, J. M., & Falk, E. (2015). Bridging media psychology and cognitive neuroscience. Journal of Media Psychology, 27, 146–156. doi:10.1027/1864-1105/a000163 Yee, N., & Bailenson, J. (2007). The Proteus Effect: The effect of transformed selfrepresentation on behavior. Human Communication Research, 33, 271–290. doi:10.1111/j.1468-2958.2007.00299.x Copyright 1994 by the American Psychological Association, Inc. Developmental Psychology 1994, Vol. 30, No. 2, 229-239 Young Children’s Perceptions of Television Reality: Determinants and Developmental Differences John C. Wright, Aletha C. Huston, Alice Leary Reitz, and Suwatchara Piemyat Five- and 7-year-old children judged factuality and social realism of favorite TV shows and test clips in pairs matched for content. In each pair one was news or documentary format, the other fictional drama. All children understood thatfictionalprograms were not factual. Children correctly discriminated the purposes and intended audience of news from those of documentaries. Children discriminated factuality by genre of program, and genre of program by formal production features and by content. Age and vocabulary scores (Peabody Picture Vocabulary Test—Revised; PPVT-R) predicted accuracy of factuality judgments, but TV viewing history over the past 2 years did not. By contrast, judged social realism was predicted by viewing history and very little by age and PPVT-R. Older children better understood that fictional characters do not retain their roles in real life and thatfictionalshows are scripted and rehearsed. The investigation reported in this article was designed to explore young children’s comprehension of the reality or unreality of television. Theorists and commentators from various perspectives cite fiction-reality distinctions when they discuss the effects of television on children. For instance, an extensive semiotic analysis of children’s comprehension of cartoons emphasized reality distinctions as a central basis for children’s cognitive organization of television messages (Hodge & Tripp, 1986). Interventions to increase media literacy often stress the fictional nature of television entertainment on the assumption that undesirable effects of television are diminished once children understand that it is not real (cf. Corder-Bolz, 1982). Reality (or unreality) is not, however, a simple dichotomy or unidimensional construct. It can be defined at different levels, ranging from the reasonable, if simplistic, to the abstractly metaphysical. Moreover, all television is not alike. Not only are some events that are shown on TV real (e.g., news) and others fictional, but there is also a wide range of genres and contents that vary in their factual status with respect to real-world events and that vary in their realism or similarity to real-life experiences of viewers. Even for adults,fiction-realitydistinction may be blurred by genres like “reality programs,” which show real John C. Wright, Aletha C. Huston, Alice Leary Reitz, and Suwatchara Piemyat, Department of Human Development, University of Kansas. This research was supported by grants from the Spencer Foundation and Grant MH-44311 from the National Institute of Mental Health. We wish to thank the Meninger Foundation, Topeka, Kansas, for providing space in which to test children and interview parents. We are especially grateful to the families who participated in this research over more than 2 years. We also thank Marilyn Bremer, Dennis Kerkman, Mabel L. Rice, David Rolandelli, Jean Siegle, Michelle St. Peters, and Rosemarie Truglio for assistance in the data collection and analysis phase of the longitudinal study. Denise Neapolitan and the late John Condry read earlier drafts of this article and made helpful suggestions for clarification. Correspondence concerning this article should be addressed to John C. Wright, Department of Human Development, University of Kansas, Lawrence, Kansas 66045. 229 events reenacted, and docudramas, in which the artistic license offictionwriters is applied to real-world events. Therefore, any attempt to determine what children understand about the reality of what they see on television must address multiple criteria for reality applied to a range of televised content. Judged Reality of TV: Taxonomy and Developmental Course Virtually all children in industrialized parts of the world are exposed to television from birth onward, and they begin paying attention to it quite early. When they are between 2 and 5 years old, they form some basic conceptions about the representational nature of the television medium and begin to understand how the content shown on television is related to events in the real world. Their concepts about television are based in part on more general comprehension of pretense, appearance, and reality in their everyday experiences with objects. Although 3year-olds understand pretense in the sense of using objects symbolically (Harris & Kavanaugh, 1993), they do not consistently appear to conceptualize the appearance and reality of an object as separate. Flavell’s (1986) investigations demonstrate, for example, that 3-year-olds assert that a rock-shaped sponge really is a rock as well as looking like one; older children make the appropriate distinction between the appearance of the object and its reality. Data on comprehension of television reality in the age range from 2 to 5 years are scant, but suggestive. In an intensive longitudinal study of 3 children from ages 2 to 5 years, 2-year-olds showed little understanding of the boundary between the television and the immediate perceptual environment (Jaglom & Gardner, 1981). When an egg broke on television, they tried to clean it up. By age 3 or 4, they understood the separation of the television and the real world; in fact, they overgeneralized the notion that nothing on television was real. At 4 or 5, they began to recognize some connections between the two (e.g., local news events that were shown really happened in the community). In an investigation of 3- and 4-year-olds’ understanding of televised images, 3-year-olds showed incomplete understanding 230 WRIGHT, HUSTON, REITZ, AND PIEMYAT of the representational nature of television stimuli (Flavell, Flavell, Green, & Korfmacher, 1990). For instance, when asked whether a bowl of popcorn shown on a television would spill if the TV were turned upside down, many 3-year-olds said yes. These children did not appear to believe that televised objects are literally inside the television set; they made the same assertions about still photographs. Instead, their responses seemed to reflect a basic lack of ability to differentiate conceptually between images and the objects they represent. By age 4, most children made the distinction correctly. These data suggest that the cognitive bases for comprehending the nature of television images are similar to those for comprehending appearance-reality distinctions in general: perspective taking and understanding that human beings have mental representations that can be different from the objective reality of perceived objects. Once children have a basic understanding of the representational nature of television images, they begin to differentiate certain types of television content. The process appears to proceed by identifying markers or attributes of a class of television content and separating that class from a largely undifferentiated remainder. Commercials were the first class type to be discriminated by the children at around age 3 to 3% years in Jaglom and Gardner’s (1981) study. Next came cartoons and Sesame Street, then news, children’s shows, and adult shows. These classes of television content (herein called genres) not only have characteristic content but also are marked by distinctive formats and forms of production. Cartoons are animated; Sesame Street has recognizable routines, musical themes, and logos. In news, adults sit at a table and look at the camera, often with a visual display behind them. Documentaries have an unseen narrator, and they often alternate between interviews and on*location footage. Situation comedies have laugh tracks. The television genres used by children to organize the TV world differ in their typical levels of reality. It seems likely, therefore, that children develop their understandings about television reality within the framework of genre (Klapper, 1981). They move from the literal mindedness of a magic window conceptualization to the overgeneralized notion that all TV is unreal, and thence to a differentiated understanding about the reality of different types of television. In one investigation (Condry & Freund, 1989), children in second, fourth, and sixth grades were shown 40 bits representing all sorts of television content. For each, the child was asked whether or not it was real (i.e., true and not pretend). The youngest children were accurate about thefictionalstatus of programs containing animation, puppets, or impossible feats and about the real status of news and documentaries, but they were less accurate about realisticfictionand situation comedies. To make matters more complicated, the meaning of reality also changes with age. Although several dimensions can be identified in existing literature, two appear fairly consistently. The first is /actuality—whether the events shown are true in the world outside television or are made up and scripted specifically for television. By late childhood, children become reasonably accurate in understanding that fictional programs do not typically show real-world events (Condry & Freund, 1989; Dorr, 1983; Fernie, 1981; Hawkins, 1977; Potter, 1988; Morison, & Gardner, 1978; Morison, Kelly, & Gardner, 1981). For instance, most 11 -year-olds know that an actor who plays a police officer does not occupy that role in real life (Dorr, 1985; Hawkins, 1977). Similarly, older children know when televised content is real (e.g., news and documentaries); for example, by age 9, children knew that the televised Challenger explosion was real (Wright, Kunkel, Pinon, & Huston, 1989). Little is known, however, about the development of such understanding in the preschool years. A second dimension of reality is social realism. Even though individuals know that a story is scripted and acted, they may judge it as real because they think the people and events are similar to those in the real world. Dorr (1983) referred to this dimension as a judgment of probability—how likely are the televised events to occur in the real world? As children move from middle childhood to adolescence, they are more apt to refer to probability than to possibility or factuality as a basis for judging reality. By adolescence, children’s social reality judgments include perceptions of utility (applicability of television lessons to one’s life), identification with characters, as well as similarity to real life (Potter, 1988). Studies with children younger than about 8 years, however, suggest that they do not understand the more abstract elements of social reality. They can compare television with their own experience, but questions about other aspects of social reality elicit inconsistent responses that suggest the questions have little meaning (Huston, Wright, Fitch, Svoboda, & Truglio, 1992). Cues for Reality Judgments Factual and fictional television programs can sometimes be distinguished on the basis of content (e.g., physically impossible events), but even more reliable cues may reside in the forms and formats used in production (Huston & Wright, 1983). For example, live broadcasts of events are characterized by poorquality sound and background noise, disfluencies in speech, and narration. These characteristics result from on-site recording and ad-lib, unscripted speech. Documentaries and news typically have a narrator, often as a voice-over during visual footage of an event or topic. The music in documentaries often designates particular content (e.g., nature programs). By contrast, dramatic stories have close-ups of actors, clear dialogue among characters, studio-quality sound, and dramatic music; comedies are often marked by a laugh track, freeze-frames, and other postproduction editing effects. Formal features denoting factual events can override content cues for fiction, leading adults to believe that highly unlikely events are true. One famous example from radio was the War ofthe Worlds broadcast by Orson Wells in 1939. Hundreds of people fled their homes after hearing the dramatic radio program with a news format reporting that an invasion from outer space had taken place. Existing evidence suggests that children learn form cues for factuality gradually during middle childhood. When children from ages 5 to 11 were asked how they know whether a television program was real orfictional,they typically named content features such as people flying (i.e., physically impossible events). With increasing age, children were more apt to name formal features as indicators of reality or program genre (Dorr, 1983; Hodge &Tripp, 1986; Morison etaL, 1981; Wright etal., 1989). These studies relied on verbal self-reports, but children may recognize the cues for fictional and real portrayals before they can describe them. Investigations of children’s comprehension of form cues denoting gender-appropriateness (e.g., abrupt TELEVISION REALITY cuts vs. fuzzy dissolves) and time changes (e.g., instant replays) demonstrated that children as young as 5 years have and use implicit knowledge of the meaning of such cues before they can describe that knowledge (Huston, Greer, Wright, Welch, & Ross, 1984; Rice, Huston, & Wright, 1986). Cognitive Development and Viewing History 231 noted fiction or reality) but content cues were minimal, and they were asked to judge reality, genre, and purpose. Although age differences were examined, cognitive developmental level was assessed more directly by a vocabulary test. Viewing history was measured over a 2-year period and was examined by types of programs viewed rather than simply as a total amount of television exposure. The determinants of children’s comprehension of television Method reality may be both cognitive developmental and experiential. Sample Wright and Huston (1983) proposed that children acquired knowledge about television forms and conventions as a result of The sample comprised 261 participants in a 2-year longitudinal study both cognitive developmental changes and experience with the of children’s television use. Of these, 122 were near their 5th birthday medium. For example, metacognitive developmental changes in (M age = 60.4 months; SD = 2.9) and 139 were near their 7th birthday (M age = 83.6 months; SD = 3.5) when they were interviewed. The children’s comprehension that other people have mental represample was predominantly White and represented a range of occupasentations different from their own, and in their perspectivetional status and parent education (see Pinon, Huston, & Wright, 1989, taking skills, probably form one basis for comprehension of for details of sample composition). cues for reality on television (Flavell et al., 1990). Therefore, all else being equal, cognitively advanced children should acquire such knowledge earlier than other children. To the extent that Procedure age is a proxy for cognitive development, the available evidence Children were brought to a research center by their parents for a series supports this hypothesis for children’s understanding of factuof tasks. During the series, one experimenter administered the Peabody ality, but not for social realism. There are no clear age changes Picture Vocabulary Test—Revised (PPTV-R; Dunn & Dunn, 1981) in perceived social realism; in fact, in one investigation, preand the interview about favorite programs. In a different room, another school children and adolescents thought television was less realexperimenter showed the film clips and questioned the children about them. The order of the two sets of procedures was counterbalanced istic than did children in middle childhood (Hawkins, 1977). across children. Exposure to different varieties of television ought to provide Reality of favorite programs. Two procedures were used to investia basis for learning typical content cues and the meanings of gate children’s reality perceptions. In the first, all of the children were television forms. Therefore, children with extensive and varied asked the names of their three favorite television programs. They were viewing experience might be expected to learn television conthen asked a series of questions about the reality level of the first-named ventions earlier than those with little experience. Indirect supfavorite, except when the question did not make sense in relation to that port for this notion comes from the finding that children were program (e.g., there was no known central character). In that case, some more accurate about the unreality of the Teenage Mutant Ninja questions were asked about the second- or third-named favorite. TUrtles than about other cartoons, presumably because it was a Four questions were designed to measure perceived factuality and one familiar favorite (Barrett & Ames, 1991). was designed to measure social realism. They are shown in Appendix A. Thefirstitem, fact, was a direct question about whether the events in Contrary to this hypothesis, however, for children in middle the program happened in real life or just on TV. The next two items, childhood, those who are heavy viewers of cartoons, situation magic window-job and magic window-character, asked about whether comedies, and action adventure programs generally consider television characters perform theirfictionalroles in real life. The fourth television more realistic (not more factual) than do those who are light viewers (Dorr, Kovaric, & Doubleday, 1990; Greenberg item, unscripted, concerned the extent to which the child believed the program was unplanned, unrehearsed, and spontaneous. One item, & Reeves, 1976; Hawkins & Pingree, 1982; Huesmann, Lasimilarity to real people, was designed to measure one aspect of social gerspetz, & Eron, 1984). In most of these studies, viewing experealism. All of the items were adapted from earlier measures by Hawrience was measured by brief, concurrent self-reports that did kins (1977) and Potter (1988) and were pilot tested for wording and not encompass the child’s history of experience with the meclarity of format.1 dium. None of them assessed viewing experience in the early Cues for reality. The purpose of the second procedure was to deterpreschool years. mine whether children could detect the form cues for reality and fiction, Purpose of Present Study The purpose of the present investigation was to explore comprehension of television reality among young children. Children who were near their 5th and 7th birthdays were questioned about perceived factuality and social realism. Previous studies indicated that children may be more skilled at making reality distinctions for specific programs with which they are familiar than for television in general (Dorr et al., 1990; Greenberg & Reeves, 1976). Therefore, they were asked about the reality of their favorite programs. Second, children were shown short clips of television footage in which formal features marked the genre (and thereby de- even when content cues were minimal. A subsample of sixty-two 5-yearolds and seventy 7-year-olds (randomly selected) were shown four pairs of videotaped clips, each lasting approximately 2 min. Each pair was matched closely for content, but one member of the pair was factual, and the other member wasfictional.Two of the factual clips were live broadcasts of news events: (a) live coverage of a space shuttle launch, 1 The wording of the questions and alternatives was determined on the basis of extensive pilot testing. In particular, the wording for social realism questions used in earlier studies did not appear comprehensible to 5-year-olds. They did seem to be able to make judgments about similarity to people in their own life experiences. Similarly, the term “kinda” as a midpoint on a Likert-type scale appeared to be clearer to young children than other possibilities. 232 WRIGHT, HUSTON, REITZ, AND PIEMYAT matched with a scene from Space Academy, and (b) live coverage of the wedding of Prince Charles of Britain, matched with The Royal Wedding drama. The other two real bits were documentaries: (c) a documentary about a Dr. Who convention, matched with a Dr. Who drama, and (d) a documentary about the making of The Wizard ofOz, matched with parallel scenes from The Wizard ofOz. The two members in any pair were always shown contiguously, but the order of members within pairs and the order of pairs were counterbalanced across subjects. After each clip, children were asked the series of questions shown in Appendix B. These questions were selected and refined on the basis of extensive pilot testing. Three of them duplicated the fact, unscripted, and similarity to real people items used in the procedure described earlier. One additional question, pretend, concerned whether the scene was pretend or not. Four questions were designed to assess more general perceptions of program genre and purpose: whether the program was news, whether it was intended for learning, whether it was serious, and whether it was intended for adult audiences. We expected that these attributes might characterize programs perceived as real, whereas their opposites (which were not news, were intended for fun, were funny, and were intended for kids) might characterize programs perceived as fictional. Children were asked about single clips rather than asked to compare members of a pair because pilot testing indicated that they had considerable difficulty making comparisons. After each clip, the experimenter asked the questions in Appendix B. She stated the three alternatives after each question. All children were given the PPVT-R (Dunn & Dunn, 1981)- It was selected as an overall indicator of intellectual level because it is brief, it correlates highly with other tests of general ability, and it does not require verbal production by the child. Television viewing history. Five 1-week television viewing diaries were completed by the parents during the previous 2 years (one every 6 months). The diary contained a report of viewing by all members of the household in 15-min intervals from 6 a.m. to 2 a.m. for each day. Diaries are generally accepted as the most valid method of measuring viewing short of direct observation (Miller, 1987). One investigation included a comparison of diary measures with videotapes of viewers made in the home during viewing (Anderson, Field, Collins, Lorch, & Nathan, 1985). Diaries slightly overestimated children’s viewing time, but the correlation between the two methods was .84 for preschoolers, indicating that diaries are a valid method of assessing individual differences. Each television program was classified according to the intended audience (child or adult) and whether or not it was intended to be informative. Children’s viewing frequencies were thus calculated for four types of programs: child audience informative, child audience noninformative, adult audience informative, and adult audience noninformative. Because viewing frequencies were positively skewed, they were converted to square roots of (X + 1). Results Perceived Reality of Favorite Programs Because children answered the factuality and realism questions for a favorite program, appropriate answers might vary depending on the nature of the program being considered. To control for program type, we classified all favorite programs as one of the following: children’s informative (educational) programs, cartoons, or adult fiction (comedy, action adventure, and drama). The most frequent children’s educational program named was Sesame Street. Children’s responses to thefivefactuality and realism questions were submitted to two-way analyses of variance (ANOVAs) with age group and program type as independent variables.2 To protect against an inflated alpha, we applied the Bon- ferroni correction (alpha/number of comparisons; Pedhazur, 1982, pp. 315-316). The corrected alpha level was .01. Those F ratios with p values between .05 and .01 are interpreted as borderline. The sample sizes vary slightly because of occasional refusal to endorse any particular answer. The means for each item appear in Table 1. Level ofunderstanding. Children’s level of performance depended on the question asked and the type of program being discussed. Most children were quite accurate when asked whether their favorite program happened in real life or just on TV. The overall mean for this item was 1.20 on a scale of 1.0 to 3.0. Responses varied by program type. Virtually all children said cartoons do not happen in real life, but children more often thought that educational programs occurred outside television. Adult fiction fell in between. The main effect of program type on the fact item was F(2, 206) = 3.36, p < .036. The mean levels on the other questions were closer to the midpoint of 2, and there was considerable variability with many children giving answers at each of the three levels for each question. Cartoons and educational programs were perceived as unrehearsed more frequently than was adultfiction:program type, F(2,203) = 5.99, p < .003. The people in educational programs and adult fiction were considered more similar to real life than those in cartoons: program type, F(2,203) = 4.22, p < .016. Age differences. Although the means for 5-year-olds were higher than those for 7-year-olds on all four measures of perceived factuality, the main effect of age reached the corrected alpha level only for the unscripted item, \bunger children believed that their favorite programs were unrehearsed more often than older children, F(l, 203) = 32.2, p < .001. There was a borderline age difference for magic window-jobs (the belief that a character on the show had the same job when he or she was not on TV), F(\, 215) = 5.15, p< .024. By contrast, older children more often perceived their favorites as having people who were similar to people in their own worlds than younger children did, F{\, 203) = 6.63, p < .011. There were no significant interactions of age with program type. Cognitive level and viewing history as predictor. Cognitive level, as indexed by the PPVT-R, and viewing history were considered as predictors of children’s perceptions of TV reality. All programs reported in the home-viewing diaries were classified into four groups: children’s informative, other children’s programs, adult informative, and other adult programs. For each child, the frequencies of viewing in each category were calculated from five 1-week viewing diaries. For each of the five dependent variables, three hierarchical regressions were performed, one for each program type. In each regression, the predictors were age group, PPVT-R score, and the frequencies of viewing four types of television programs during the past 2 years (in that order). The results are summarized in Table 2. PPVT-R score. High PPVT-R scores were expected to be associated with relatively low perceived reality scores. That is, children with more advanced intellectual development were expected to understand that television fiction was not real. That prediction was supported for ratings of adult programs on the 2 Sometimes, a child answered one question about one program and another question about another program or a character from another program. Therefore, each item had to be analyzed separately. 233 TELEVISION REALITY Table 1 Means and Standard Deviations ofJudged Reality Scores for Favorite Programs Program type named as favorite Cartoon Educational Age group n M SD n Adult audience M SD n M SD 1.10 1.00 1.06 0.42 0 31 67 1.13 1.29 1.29 0.50 0.67 0.93 0.97 35 81 2.09 1.68 1.80 0.95 0.84 0.92 0.93 33 79 1.79 1.85 1.83 0.99 0.92 0.83 0.79 30 68 2.03 1.29 1.51 0.85 0.52 0.68 0.92 31 66 1.71 2.18 2.03 0.82 0.77 Item 1: Fact 5-year-olds 7-year-olds Both 23 9 1.52 1.20 1.42 0.90 0.63 47 31 Item 2: Magic window-job 5-year-olds 7-year-olds Both 26 10 2.15 2.09 2.14 0.88 0.94 41 24 2.30 1.77 2.12 Item 3: Major window-character 5-year-olds 7-year-olds Both 22 10 2.09 1.36 1.85 0.97 0.81 47 28 1.66 1.59 1.64 1Item 4: Unscripted 5-year-olds 7-year-olds Both 24 7 2.21 1.25 1.97 0.83 0.71 45 31 2.28 1.83 2.10 Item 5:; Similarity to real people 5-year-olds 7-year-olds Both 23 8 1.81 2.22 1.94 0.85 0.67 46 31 1.54 1.70 1.60 Note. The possible range of scores on each item was 1.00 (unreal) to 3.00 (real). See Appendixes A and B for exact definitions. There are slight variations in sample sizes because children declined to answer in some instances. two items for which there were also age differences. Children with high PPVT-R scores were less likely to believe that adult programs were unscripted and less likely to think television characters in adult fiction performed the same job in real life. However, PPVT-R scores were positively associated with the belief that educational programs occurred in real life, not just on TV. These responses are not necessarily inaccurate; it is true that some of the material shown in such programs is from reallife footage. Viewing history. Viewing history was not a significant predictor in every analysis, but the relations that occurred were, for the most part, consistent. Children who thought cartoons were factual had been heavy viewers of child entertainment (Item 2, magic window-job). Children who thought educational programs were real had been heavy viewers of child informative programs (Item 2, magic window-job). That is, when children had watched a lot of either category—cartoons or child informative—they later perceived characters in that type of program as more real than when they had not often watched that category of programs. For adult fiction favorites, viewing histories for adult programs were predictive. Children who had watched a lot of adult entertainment (situation comedies and dramas) thought their favorite adult programs were rehearsed and similar to people in their lives. Children who had watched a lot of adult informational programs thought that characters in adult fiction were dissimilar to people they knew. Cues for Reality Judgments In the second procedure, children made judgments about four pairs of real andfictionalclips closely matched for content. In two of the pairs, the real segment was a live news broadcast of an event; in the other two, it was a documentary. Stimulus and Genre Differences For each dependent variable, we performed a 2 (age group) X 2 (stimulus type: real orfictional)X 2 (genre: news or documentary) ANOVA. The genre classification for the fictional stimuli was the same as that for the factual stimuli with which they were paired. Therefore, each cell of the design contained two stimuli and had a possible range of 0 to 4. The Bonferroni correction for eight analyses set the alpha level at .00625. All F ratios whose associated p values were between .05 and .00625 are reported but are interpreted as borderline. Perceivedfactuality. On all three items intended to measure perceived factuality, children judged the real stimuli as signifi- 234 WRIGHT, HUSTON, REITZ, AND PIEMYAT Table 2 Hierarchical Regressions (Beta Values) Predicting Perceived Reality of Favorite Programs (Divided by Type of Program) Predictor uepenoent variable Age group PPVT-R score Child inform Child entertain Adult inform .07 .27* .14 .03 -.01 -.01 -.22 .20 .06 -.17 R2 Adult entertain Cartoon” Magic windowjob Magic windowcharacter No script Realism Educational Fact Magic window- -.22 -.03 -.03 -.18 .24f -.12 -.03 -.12 job Magic windowcharacter No script Realism Adult Fact Magic windowjob Magic windowcharacter No script Realism .04 -.02 .22f .40* .18f 0 0 0 .24 -.05 -.19 .10 .37 -.01 -.37 .30 .63** -.01 -.28 .17 .39* .07 .29 .36f .43* .16f .10 -.24** -.40* -.48** .29 .02 -.06 -.35 -.22 .21 .14 .05 .06 -.09 .20 .39 -.13 -.34 .09 .01 -.12 .07 .13 .03 .05 -.24* -.20* -.05 .09 .01 .03 •12t 0 -.08 -.36** .04 .08 -.17 .01 -.41** .05 .16 .11 -.01 -.15 -.03 -.03* .12 -.24| .41** .16 .03 .36** .14t Note. PPVT-R = Peabody Picture Vocabulary Test—Revised. ” The fact variable for cartoons was not analyzed because there was little variance (see Table 1). *p
Purchase answer to see full attachment