lecture 11-9

lecture 11-9 - November 9, 2004 Content Analysis Continued...

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November 9, 2004 Content Analysis Continued Coding : Transforming content into numerical categories -Conceptualize categories -Manifest content (visible, surface content) ex: acts of violence -Latent content (underlying meanings) you know it when you see it but you don’t know how to pin it down; ex: measure is the show had an anti-violence theme -Operationalize categories (how to code measures) -Establish reliability! (Have coders take a test sample to make sure there is inter-coder reliability) Limitations: -Purely descriptive: cannot conclude anything about effects -Large-scale studies can be labor-intensive and costly Experimental Research Purpose: to test hypothesis of cause and effect -Goal is to establish internal validity (know that x caused y and there is no other explanation) -Must be willing to sacrifice external validity Key elements to a true experiment - Manipulation of causal variables while controlling all other variables Independent variable (IV): -Divide into “conditions” -Ex: experimental group vs. control group -Treatment 1/ treatment 2/ treatment 3 Dependent variable (DV): -Compare measures (e.g., mean scores) across conditions and see if differences exists - Random assignment of participants to conditions Everyone must have an equal chance of ending up in either condition Why important? People could be different in groups as well- Ex: can’t test comm. 88 vs. comm. 87
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Makes groups equal before manipulation November 16, 2004 Experimental Research Continued -What do you not need to establish causality? Don’t need representative sample Types of Experimental Designs Design notation: X: manipulation/ treatment O: observation (measure for DV) R: random assignment True (Full) Experiments: Posttest only control group design R
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This note was uploaded on 04/10/2008 for the course AS AM 1 taught by Professor Zhao during the Fall '08 term at UCSB.

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lecture 11-9 - November 9, 2004 Content Analysis Continued...

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