924Behavioral science researchers long ago moved beyond the business of theorizing about and testing simple bivariate cause and effect relationships, since few believe that any effects are independent of situational, contextual, or individual-difference factors. Furthermore, we understand some variable's effect on another better when we understand what limits or enhances this relationship, or the boundary conditions of the effect-for whom or under what circumstances the effect exists and where and for whom it does not. Theoretical accounts of an effect can be tested and often are strengthened by the discovery of moderators of that effect. So testing for moderation of effects, also called interaction, is of fundamental importance to the behavioral sciences.A moderated effect of some focal variable F on outcome variable Y is one in which its size or direction depends on the value of a third, moderator variable M. Analytically, moderated effects reveal themselves statistically as an interaction between F and M in a mathematical model of Y. In statistical models such as ordinary least squares (OLS) regression or logistic regression, moderation effects frequently are tested by including the product of the focal independent variable and the moderator as an additional predictor in the model. When an interaction is found, it should be probed in order to better understand the conditions (i.e., the values of the moderator) under which the relationship between the focal predictor and the outcome is strong versus weak, positive versus negative, and so forth.One approach for probing interactions that we have seen used in the literature is the subgroup analysis or separate regressions approach, where the data file is split into various subsets defined by values of the moderator and the analysis is repeated on these subgroups. But this method does not properly represent how the focal predictor variable's effect varies as a function of the moderator, especially when additional variables in the model are used as statistical controls. For details about the problems with this method-a method we do not recommend-see Newsom, Prigerson, Schulz, and Reynolds (2003) and Stone-Romero and Anderson (1994).Fortunately, there are more rigorous and appropriate methods for probing interactions in linear models, two of which we will describe in this article. The first method we discuss, the pick-a-point approach, is one of the more commonly used. This approach involves selecting representative values (e.g., high, moderate, and low) of the moderator variable and then estimating the effect of the focal predictor at those values (see, e.g., Aiken & West, 1991;Cohen, Cohen, West, & Aiken, 2003;Jaccard & Turrisi, 2003). A difficulty with this approach is that, frequently, there are no nonarbitrary guidelines for picking the points at which to probe the interaction. An alternative is the Johnson-Neyman (J-N ) technique (Johnson & Fay, 1950;Johnson & Neyman, 1936;Potthoff, 1964) Researchers often hypothesize moderated effects, in which the effect of ...
The main purpose of this study was to shed light on methodological problems in the content analysis of media frames. After a review of 5 common methods, we will present an alternative procedure that aims at improving reliability and validity. Based on the definition of frames advanced by R. M. Entman (1993), we propose that previously defined frame elements systematically group together in a specific way. This pattern of frame elements can be identified across several texts by means of cluster analysis. The proposed method is demonstrated with data on the coverage of the issue of biotechnology in The New York Times. It is concluded that the proposed method yields better results in terms of reliability and validity compared to previous methods.
Celebrities frequently endorse products, brands, political candidates, or health campaigns. We investigated the effectiveness of such endorsements by meta-analyzing 46 studies published until April 2016 involving 10,357 participants. Applying multilevel meta-analysis, we analyzed celebrity endorsements in the context of for-profit and non-profit marketing. Findings revealed strong positive and negative effects when theoretically relevant moderators were included in the analysis. The most positive attitudinal effect appeared for male actors who match well with an implicitly endorsed object (d = .90). The most negative effect was found for female models not matching well with an explicitly endorsed object (d = −.96). Furthermore, celebrity endorsements performed worse compared to endorsements of quality seals, awards, or endorser brands. No publication bias was detected. The study has theoretical and practical implications, and provides an agenda for future research.
This paper provides a systematic analysis of media framing studies in the world's leading communication journals. A quantitative content analysis of 131 studies published in fifteen international journals demonstrates how frames are conceptualized and measured. Current problems in framing research include lack of operational precision, the descriptive focus of many analyses, neglect of visuals, and insufficient reporting of reliability.
This article reports a meta-analysis of 345 published studies to examine the media’s role in construction of a Muslim and Islamic identity. A quantitative analysis highlights the geographical focus, methods, theories, authorship, media types, and time frames of published studies. A qualitative analysis investigated the most prominent researched themes. Our findings suggest that a large majority of studies covered Western countries, while Muslim countries and Muslim media have been neglected. We also identified an evident lack of comparative research, a neglect of visuals, and a dearth of research on online media. We found that most studies investigated the themes of ‘migration’, ‘terrorism’, and ‘war’. Moreover, our meta-study shows that Muslims tend to be negatively framed, while Islam is dominantly portrayed as a violent religion. Implications of these findings are discussed.
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