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Comm. 88 Week 7 Notes - Mullin


Comm. 88 Week 7 Notes - Mullin Comm. 88

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All lecture notes from Week 7 of Communication 88 with Professor Mullin
Communication Research Methods
Dolly Mullin
Class Notes
UCSB, communication, Comm88, Mullin
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This 6 page Class Notes was uploaded by on Friday May 13, 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 15 views. For similar materials see Communication Research Methods in Communication Studies at University of California Santa Barbara.

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Date Created: 05/13/16
Tuesday, May 10, 2016 Week 7 Lecture 13 - May 10, 2016 - Experimental Research (Recall from last time…) Key Elements to a True Experiment • - Manipulation of IV(s)… • Divide into different “conditions” - EX. IV) New painkiller drug; half of Ps get drug & other half do not… while controlling all other variables (Ss in each condition must be treated the same etc.) • Examine effects on Dependent Variable (DV) - Compare measures (mean scores) for Ss in each condition and see if differences exist • EX. DV) amount of perceived pain (e.g., 0-10 scale) - Random Assignment of participants (Ps) to conditions • Everyone must have an equal chance of ending up in either condition • Why is this important? - Makes group equal before manipulation - Types of Experiments • Design notation: - X : IV manipulation (treatment/induction) - O : Observation (measure for DV) - R : Random assignment - *TEXTBOOK ALERT: Page 253 says you can measure DV with a “survey”… NO! We call it a “questionnaire” or “measure” or measurement “instrument”, so as not to confuse it with SURVEY RESEARCH as a whole method! • True Experiments 1 Tuesday, May 10, 2016 - Posttest only control group design • R X O1 (group 1) • R O2 (group 2) - Variations: more groups, several different treatments - Example: IV — different types of ad appeals • R X1 (personal cancer story) O1 (group 1) • R X2 (cancer statistics) O2 (group 2) • R X3 (attack tobacco industry) O3 (group 3) • Example: - R X (anti-smoking ad) O1 (beliefs about smoking) - R (no anti-smoking ad) O2 (beliefs about smoking) This is the IV manipulation • This is the DV measure (compare O1 and O2). If you get difference • between group means (O1 vs O2), the IV caused it! - Pretest-posttest control group design • R O1 X O2 (group 1) • R O3 X O4 (group 2) - Possible problem: differences on O2 and O4 might be result of interaction of manipulation with pretest • Example: • R O1 (beliefs about smoking) X (anti-smoking ad) O2 (beliefs about smoking) • R O3 (beliefs about smoking) (no anti-smoking ad) O4 (beliefs about smoking) - This is the DV measure (before) - This is the IV manipulation - This is the DV measure (after) 2 Tuesday, May 10, 2016 • Again, if you get a difference between group means (O2 vs O4), the IV caused it! - Solomon Four-Group Design: Used when you need to know whether pretest had effect • R O1 X O2 (group 1) • R O3 O4 (group 2) • R X O5 (group 3) R O6 (group 4) • - Comparison between O2 and O5 - see if pre-test had effect - Comparison between O4 and O6 - see if - Pretesting: Should you or shouldn’t you? • Useful; to “check” on RA (random assignment), to get info on change • But; not necessary to establish causality - Bad idea if treatment/pretest interaction is likely - Example Study: Music & Learning • Research Question: Does listening to music (while studying) hinder of enhance learning? • IV = music, DV = learning - Possible experiment: • R X (music) O1 (test score) (group 1) M (mean score) = 67 • R (no music) O2 (test score) (group 2) M = 79 - Type of design? What can you conclude? - Now, what if we want to test for effects of ANOTHER IV?… - Factorial Designs • Purpose: To examine the effects of two or more IVs simultaneously 3 Tuesday, May 10, 2016 Lecture 14 - May 12, 2016 - Factorial Designs • To look at the effects of two or more IVs at the same time • “Factors” are the IVs - Each factor/IV has at least 2 levels (conditions) • Ex: Music factor - music/no music (while studying) Caffeine factor - caffeine/no caffeine (while studying) - 2x2 design: “Conditions” (below Music No Music and right) Caffeine These blocks are called “cells” No caffeine What if there’s more than two levels? 3x2 or 2x3 design: Pop music Classical Music No music Caffeine No caffeine What if you have more than 2 factors? • Music factor: pop/classical/none • Caffeine factor: caffeine/no caffeine • Class level: lower division/upper division - 3x2x2 design • Factorial designs test for 2 effects - Main effect for each IV - Interaction effect between IVs • Main Effects 4 Tuesday, May 10, 2016 - Effect of 1 IV on the DV • Only 1 individual IV - Ex: Music & caffeine 2x2 study • Main effect for caffeine - Lower scores with caffeine than without - To test for main effects, compare “marginal” means of DV for each factor/IV Music No music 50 60 Caffeine MEAN = 55 No Caffeine 70 60 MEAN = 65 MEAN = 60 MEAN = 60 • Main effect for caffeine - Greater learning without caffeine than with caffeine • No main effect for music - Studying with our without music makes no difference • Main effects do not tell the whole story • Interaction Effects - Unique effect of combo of IVs The effects of 1 IV depends on the level of other IVs • - Caffeine reduces learning, but only when combined with listening to music. Without listening to music, it has no effect - To test for interaction effect, look at cell means (shown in above table) 100 ) 88 r c t 76 e ( 64 V D 52 C affeine 5 40 Music No music IV Tuesday, May 10, 2016 • There is an interaction effect if the lines are NOT parallel - Caffeine has no effect without music, but brings down scores with music - About Factors • In one design, you can have __ as IVs: - Manipulated variables • Ex: music exposure, caffeine - Subject variables • Ex: gender, personality traits, TV use (high/low) • You can only make causal conclusions about manipulated variables - Not subject variables - You can only say there’s a relationship with subject variables • But this is just a survey with a factorial setup - Experimental Research • Key Elements: - Manipulation/control - Random assignment = internal validity • Threats to Internal Validity (in book pages 188-190) - If not a true experiment or if you do an improper experiment then alternative explanations for results become possible 6


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