The psychology of news influence and development of Media Framing Analysis

HowHow precisely do media influence their readers, listeners and viewers? In this paper, we argue that any serious study of the psychology of media influence must incorporate a systematic analysis of media material. However, psychology presently lacks a methodology for doing this that is sensitive to context, relying on generalised methods like content or discourse analysis. In this paper, we develop an argument to support our development of a technique that we have called Media Framing Analysis (MFA), a formal procedure for conducting analyses of (primarily news) media texts. MFA draws on elements of existing framing research from communication and other social scientific research while at the same time incorporating features of particular relevance to psychology, such as narrative and characterisation.

What drives the prediction of early reading? An analysis of stimulus and response-type

Purpose: Phonological accounts of reading implicate three aspects of phonological awareness tasks that underlie the relationship with reading; a) the language-based nature of the stimuli (words or nonwords), b) the verbal nature of the response, and c) the complexity of the stimuli (words can be segmented into units of speech). Yet, it is uncertain which task characteristics are most important as they are typically confounded. By systematically varying response-type and stimulus complexity across speech and non-speech stimuli, the current study seeks to isolate the characteristics of phonological awareness tasks that drive the prediction of early reading. Method: Four sets of tasks were created; tone stimuli (simple non-speech) requiring a non-verbal response, phonemes (simple speech) requiring a non-verbal response, phonemes requiring a verbal response, and nonwords (complex speech) requiring a verbal response. Tasks were administered to 570 2nd grade children along with standardized tests of reading and non-verbal IQ. Results: Three structural equation models comparing matched sets of tasks were built. Each model consisted of two 'task' factors with a direct link to a reading factor. The following factors predicted unique variance in reading: a) simple speech and non-speech stimuli, b) simple speech requiring a verbal response but not simple speech requiring a non-verbal-response, and c) complex and simple speech stimuli. Conclusions: Results suggest that the prediction of reading by phonological tasks is driven by the verbal nature of the response and not the complexity or 'speechness' of the stimuli. Findings highlight the importance of phonological output processes to early reading.