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Comm. 88 Week 3 Notes - Mullin


Comm. 88 Week 3 Notes - Mullin Comm. 88

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All Comm. 88 notes from lectures and section of week 3 with Professor Mullin
Communication Research Methods
Dolly Mullin
Class Notes
UCSB, Comm 88, communication 88, Comm, research methods
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This 6 page Class Notes was uploaded by on Friday April 15, 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 20 views. For similar materials see Communication Research Methods in Communication Studies at University of California Santa Barbara.


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Date Created: 04/15/16
Tuesday, April 12, 2016 Week 3 Lecture 5 - April 12, 2016 - The Research Process (continued) • Causal relationship between variables - Ex.) Violent TV viewing produces aggressive behavior - Different Methods for Testing Different Relationships • Survey/Observational Research (Researcher A) - Tests associations (just relationships/correlations) • Measure/observe some attitudes/behaviors and correlate them • Or compare existing groups of people on some measure - Great for external validity (are our findings valid for things outside the research lab) • Ability to generalize results to other people (if use representative sample) • …and to “normal life” settings (if observe or ask people about normal behavior) - Poor for causality! • Experimental Research (Researcher B) - Tests causal connections • Manipulate variables (separate people into groups and give different “treatment” to each group) • Control everything else • Measure effects - Great for internal validity (ability to establish that X causes Y) • *Not just connection between variables but also establishes time order • Rules out “confounding” variables (other explanations) - Poor for generalizability! 1 Tuesday, April 12, 2016 - Defining Concepts and Variables • Variables in Experimental Research (testing cause/effect) - Independent Variable (IV) • Variable manipulated by researcher • The “cause” in cause-effect relationship - Dependent Variable (DV) Variable affected/changed by the IV • • The “effect” or outcome - Example Hypothesis: greater physical attractiveness creates impressions of greater friendliness IV = Physical attractiveness (manipulate high vs low attractiveness) • • DV = Impressions of friendliness - Variables in Survey/Observational Research (testing associations) • Can’t be cause-effect so… - IV considered a “predictor” variable - DV is what is being predicted by IV (sometimes called “criterion” variable) • Example Hypothesis: the stronger a person’s “fan” identity the greater their participation in online forums - IV = Fan indication (rate how strongly connected to fandom) - DV = Fan forum participation (measure how often people post/read posts) • Could the IV and DV be the other way around in this survey? YES - Participation predicts identity —> same relationship - Types of Relationships Between Variables • Association between variables - X is connected/related to Y 2 Tuesday, April 12, 2016 • Ex.) The more TV violence children watch the more aggressive they are (more of something predicts more of something else) OR aggressive kids watch more violence than do non-aggressive kids (can also connect gender) • Causal Relationships between variables - X influences/affects/changes Y • Conceptualizing your variables - Defining what the concepts mean for purposes of investigation • Usually based on theory/prior research - Example variable: “fear”… what is it? • Operationalizing your variables - Deciding exactly how the concepts will be measured (or manipulated) in a study Lecture 6 - April 14, 2016 - Measurement - Operationalizing Variables • We have to first conceptualize - what is fear? Define it • Some tactics work on kids but not adults - Adults can better hide their emotions • Types of Measures - Physiological measures • BP, brain imaging, cortisol (stress hormone), heart rate, pupil dilation (good for arousal studies) - Behavioral measures • Observing people’s actual behavior; nonverbal gestures, time/money spent, actual posts on social media - Self-Report measures • Items on questionnaire • Doesn’t always work with kids 3 Tuesday, April 12, 2016 - “How does that make you feel?” … “Fine.” • Can still be on a scale (strongly agree/strongly disagree) • Measures should: - Have good conceptual fit with variables in the hypothesis/RQs - Capture variation - Minimalize social desirability effects Typically with self-report measures • - People’s memories are not very accurate - Do the participants tell the truth always? • No - questions like “how prejudiced are you?”… People don’t want to think they’re racist • People may alter response/behavior for an experiment/survey - Less of an issue with physiological measures • Levels of Measurement - Nominal (categorical/discrete) measurement: variable is measured merely with different categories • Nominal measures are for comparing differences - Between manipulated IV conditions in experiments - Between existing IV groups in surveys • Eye color, political party/ideology, ethnicity, TV use (high/low), yes/no questions - Ordinal measures: variable is measured with rank ordered categories • Rank of top 5 favorite TV shows, most to least important political issue • You can’t have a sense of how much more important one thing is over another 1… 2, 3, 4, 5 - But 2-5 are pretty equal 4 Tuesday, April 12, 2016 - Interval measurement: variable is measured with successive points on a scale with equal intervals • Ex.) On immigration policy position: “The US should increase border security”. Strongly oppose or strongly agree on a scale - Normally 5 or 7 - Numbers don’t mean anything unless scaled together - Ratio measurement: interval measurement with an absolute zero • Time in hours, weight in pounds, age in years, test scores • Interval and ratio are both continuous variables - Allow you to capture more variation - You want to get as much variation as possible - Can always collapse categories if need be - Allow you to compare means - Allows you to test continuous relationships between variables • The more X, the more Y —> positive The more X, the less Y —> negative • - Questionnaire Items as measures • Don’t use “survey” to describe a questionnaire - There are items on a questionnaire - Common for IVs and DVs in surveys - Common for DVs in experiments • IV is a manipulation into groups • Types of questionnaire items - Open-ended • Respondents give their own answer to questions • You don’t want to prime them with a response, but there is way more data to sift through at the end 5 Tuesday, April 12, 2016 - Closed-ended • Respondents select from a list of choices • Choices must be mutually exclusive • Choices must be exhaustive - All options must be there Section - April 14, 2016 - Conceptualization • A definition that fits your research, not necessarily a dictionary definition - Answers the question “what is it?” - Operationalization Taking your conceptual definition and turning it into something unique and specific • to your study - Two ways of looking at different variables: • Association: correlation or a relationship Causal: cause and effect (X causes Y) • - Review of previous study - brand placement/recall in hip-hop songs • How it can be improved 6


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