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Comm 88 Week 9 Notes - Mullin


Comm 88 Week 9 Notes - Mullin Comm. 88

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All lecture and section notes from week 9 of Comm. 88 with Professor Mullin
Communication Research Methods
Dolly Mullin
Class Notes
UCSB, communication, Comm88, Comm, Mullin
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This 4 page Class Notes was uploaded by on Friday May 27, 2016. The Class Notes belongs to Comm. 88 at University of California Santa Barbara taught by Dolly Mullin in Spring 2016. Since its upload, it has received 14 views. For similar materials see Communication Research Methods in Communication Studies at University of California Santa Barbara.

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Date Created: 05/27/16
Tuesday, May 24, 2016 Week 9 Lecture 17 - May 24, 2016 - Within-Subjects Designs All previously covered designs are “between-subjects” designs • - Subjects are randomly assigned to different conditions • Within-Subjects Designs: - Every subject is in every conditions - Doesn’t work for most kind of research - By exposing people to multiple conditions they might understand what the experiment is about - Ex: pilot’s reaction time to warning lights • Post-test design: R X(red) O1(reaction time) group 1 R X(green) O2(reaction time) group 2 R X(yellow) O2(reaction time) group 3 - Problems: small number of people per group, lots of random error - In Within-Subjects design each pilot gets a chance at each condition (color of light) • 1st red light —> reaction • 2nd green light —> reaction • 3rd yellow light —> reaction - Now there’s 18 subjects in each condition • Greater power Less random error • - Problem: carry-over/order effects 1 Tuesday, May 24, 2016 • Practice effects: with each trial subjects get better • Fatigue effect: subjects reaction time slows down • First treatment/contrast effect: first treatment is contrasted with what you do next - Humorous ad shown before serious one and subjects now compare them - Solution: Counterbalance orders • Let subjects do each condition in different order (randomly) - Lab vs. Field Experiments • Laboratory Experiments: - Bring subjects into highly controlled setting Field Experiments: • - Different than field research - Manipulate IVs in the real world • Ex: littering studies; are people more likely to litter if there’s a confederate that litters? More natural setting/behavior leads to higher external validity • • Less reactivity (or none) • Hard to maintain experimental control - Content Analysis (Quantitative) • Systematically examining content of comm. Lecture 18 - May 26, 2016 - Content Analysis • Used to: - Describe how much/what kind of certain messages there are (e.g. sex on TV, types of tweets) 2 Tuesday, May 24, 2016 - Assess “image” of particular groups in media (e.g. stereotypes of race, gender, age, political party, etc.) - Compare media content to “real world” - Examine message change over time - Provide background for research on media effects • Content Analysis is also a method for coding/analyzing open-ended data in surveys and experiments - Important Procedures: • Sampling - Define population of interest • Ex) Primetime TV shows, Facebook discussions - Identify “unit of analysis” for coding (textbook: “recording” and “context” units) • Ex) For TV shows: Code each episode? Scene? Character? • Ex) For Facebook: Code each entry? Thread? Word? - Select representative sample (ideally) Closer sampling most common for CA • • Coding: Transforming content into numerical categories - Manifest content (visible, surface content) - Latent content (underlying meanings) • Establish reliability! - Limitations: • Purely descriptive - Cannot explain why the content is that why - Cannot conclude anything about effects of the message • Very reductionistic - Reduces content to “code-able” concepts only 3 Tuesday, May 24, 2016 Section - May 26, 2016 - Factorial Design • Factorials and Main Effects - Review of homework assignment 4


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