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Comm. 88 Week 4 Notes - Mullin


Comm. 88 Week 4 Notes - Mullin Comm. 88

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Notes from week 4 lectures and section with Mullin. Includes a brief review of all material that may be tested on the midterm.
Communication Research Methods
Dolly Mullin
Class Notes
communication, Comm, Comm88, Mullin, UCSB
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This 9 page Class Notes was uploaded by on Friday April 22, 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 173 views. For similar materials see Communication Research Methods in Communication Studies at University of California Santa Barbara.


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Date Created: 04/22/16
Tuesday, April 19, 2016 Week 4 Lecture 7 - April 19, 2016 - Reminder: Midterm next Tuesday • Group office hours: Thursday, April 21 from 1:30-2:30 (SSMS 4105) • TA Q&A session (SSMS 1009) on Monday, April 25, TBA • Review guide and practice questions on GauchoSpace (and seating chart for midterm) - Measurement: Operationalizing Variables (both IVs and DVs) - Recall from last time… • Using Questionnaire Items as Measures - 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 answers to Qs - Closed-ended • Respondents select from list of choices - Choices must be mutually exclusive! (Can’t choose more than one) - Choices must be exhaustive! (There is an option for any attitude/opinion a person may have) • Some closed-ended formats: - Likert-Type items: • Respondents indicate their agreement with a particular statement - Example: “I feel uncomfortable when people are arguing.” Strongly disagree = 1, Strongly agree = 5 - Other response options also possible (oppose/favor; not at all/very much; almost never/almost always) 1 Tuesday, April 19, 2016 - Semantic Differential • Respondents make ratings between two opposite (bipolar) adjectives - Example: This candidate seems: honest 7:6:5:4:3:2:1 dishonest - Composite Measures • Use multiple items combined to measure one variable (i.e., create an index/scale) - Example: This candidate seems: honest 7:6:5:4:3:2:1 dishonest, trustworthy 7:6:5:4:3:2:1 untrustworthy, unbiased 7:6:5:4:3:2:1 biased - Add together (or average) scores on all three items into an overall credibility score - How Good Is Your Measurement? Reliability and Validity • - Textbook note: SKIP pages 188-193 (save for final) • Reliability of Measurement - Does your measure (of the variable) have consistency? • Assessing Reliability - For measures using questionnaire items: • Inter-item reliability (consistency between items) - Look at internal consistency of similar items in a scale/index • Want a high “item-total” score - Example: Candidate credibility - honest 7:6:5:4:3:2:1 dishonest - trustworthy 7:6:5:4:3:2:1 untrustworthy - unbiased 7:6:5:4:3:2:1 biased - funny 7:6:5:4:3:2:1 not funny 2 Tuesday, April 19, 2016 - What’s wrong with the “funny” item? Doesn’t go with the rest; will lower the reliability of the scale. If you remove the funny item, then all the items are indicators of the SAME basic quality of the candidate (credibility/trustworthiness) and will probably have a high internal consistency (high item-total score)…so, a good “Uni- dimentional” variable/concept - What if credibility involves more than just trustworthiness? (like expertise) • Would need “Multi-dimensional” scale: - honest 7:6:5:4:3:2:1 dishonest - trustworthy 7:6:5:4:3:2:1 untrustworthy - unbiased 7:6:5:4:3:2:1 biased - knowledgeable 7:6:5:4:3:2:1 not knowledgeable - experienced 7:6:5:4:3:2:1 inexperienced - competent 7:6:5:4:3:2:1 incompetent • Multidimensional “credibility”: 2 dimensions - trust + honest + unbiased = “trustworthiness” dimension - knowledge + experience + competence = “expertise” dimension - —> Evaluate reliability separately for each sub-scale - For measures using coders (e.g., behavioral observations): Inter-coder reliability • - Compare multiple coders • Intra-coder reliability - Compare multiple observations of the same coder • Validity of Measurement - Does your measure really capture the concept you intend to be measuring? • Assessing Validity 3 Tuesday, April 19, 2016 - Subjective types of validity: • Face Validity - The measure looks/sounds good “on the face of it” • Content Validity - The measure captures the full range of meanings/dimensions of the concept • Criterion-related/predictive validity: - The measure is shown to predict scores on an appropriate future measure • Ex: SAT scores (your “potential” to achieve) —> college GPA (your achievement) • Construct validity: - The measure is shown to be related to measures of other concepts that should be related (and not to ones that shouldn’t) • Ex: aggression scale <—> hostility scale Lecture 8 - April 21, 2016 - Sampling • How to select participants for a study - Or other units • Sample-subset of target population - Voters, FB users, married couples, juries, football fans, etc. - Or TV shows, magazine ads, blog posts, etc. • Sampling units - Individual persons - Groups - Social artifacts (ads, TV scenes, tweets, etc.) - Representative Sampling • *NOTE: NEED TO KNOW REPRESENTATIVE AND NON-REPRESENTATIVE 4 Tuesday, April 19, 2016 • Probability Sampling • Sample should be mini-versos of target population - Equal sample proportions to target populations (Democrats, Republicans, men, women, etc.) - The larger the sample, the more representative it is normally - Allows you to generalize for the larger population - The key is random selection: • Everyone in population has to have an equal chance of being in the sample • How representative is it? - You won’t always be dead on with your findings - There is always sampling error • Sample data will be slightly different from population because of chance alone • Random error/chance error/sample error • Statistically called margin of error - National poll ~1,000 people —> +/- 3% • Larger sample size = smaller margin of error - After about 1,000 people, your margin of error plateaus and doesn’t change much - Representative Sampling Techniques • Simple random sampling - select elements randomly from population - Assign each element a number and randomly select a number • Listed populations - random # from table • Phones - random digit dialing • Systematic random sampling - form a list of population and take random starting point, pick an interval, select every “nth” person off that list, cycle through the entire list - Similar results to simple random sampling 5 Tuesday, April 19, 2016 - Close to right proportions, but some sampling error • Stratified Sampling - for getting population proportions even more accurate - Divide population into subsets (strata) of particular variables • Usually for demographic variables (sex, race, political party) • Anything where you think groups will have different opinions/views on something - Randomly select from each strata to get right proportions of population • Need prior knowledge of population proportions - Increases representativeness because it reduces sampling error • But very time consuming Multistage Cluster Sampling - useful for populations not listed as individuals • - First randomly sample groups (clusters) then randomly sample elements within each cluster - Reduces costs - Sampling error at each stage and margin of error goes up - All Representative Sampling: • Will always have sampling error, but can generalize findings to larger target population (assuming the sampling is done properly) • What to avoid: - Systematic error (sampling bias) • Systematically over/under representing certain segments of a population - Caused by: • Wrong sampling face (where you are finding people) • Very low response rate • Importer weighting (wrong proportions in stratified sample) - Non-Representative Sampling: *READ ABOUT THESE IN BOOK • Cannot generalize results 6 Tuesday, April 19, 2016 • Typical of experiments and qualitative research Section - April 21, 2016 - Sampling • Subset of the population • Representative sampling - Random selection, sampling error - Simple Random, Systematic, Stratified, multistage closet • Sampling bias • Non-representative sampling - Convenience, purposive, volunteer, quota, network/snowball • Convenience sample: choosing people close to you/easily available to sample from, nearby • Purposive sample: purpose exists behind why you choose a certain sample, usually related to a specific reason as to why you are looking at the purposive sample in a certain way (people in a relationship, homosexual people, etc.) • Volunteer sample: sample members volunteer to take part in the study, people choose to participate for some reason (an incentive/reward is sometimes the reason) - *NOTE: ALL studies are voluntary, but not all studies use volunteer samples • Quota sample: selecting individuals to match a certain demographic, similar to a stratified sample but not the same - Taking a certain number of people who match a certain criteria needed for the study, finding a certain number of people to match the demographic RANDOMLY (asking a random person who is African American to participate just because they are African American) • Network/snowball sample: taking a certain participant and asking if they know anyone else who would like to/be able to participate in the study, asking if the participant knows anyone else who is in the same situation/criteria needed for the study 7 Tuesday, April 19, 2016 - Upsides of Non-Representative sampling: • Easier, cheaper • Not necessarily “bad”, just different - Remember, it IS NOT generalizable - Review of homework exercise: Hypothesizing Relationships Between Variables • Causality (cause and effect) MUST be found by an experiment, not a survey/ observation - Midterm Review - Brief Overview of Topics • Ways of Knowing (Epistemology) - “Everyday” ways of knowing Tradition/Tenacity • • Authority • Experience • Intuition/Logic - Problem overall • Overgeneralization - Science! • Characteristics of science (public, peer-review, etc.) • Goals of scientific research - Description, explanation, prediction • Research Process - The Wheel of Science Going from theory to observation is deduction, while going from observation to • theory is induction - Quantitative vs. Qualitative research, Basic vs. Applied - Theory in research 8 Tuesday, April 19, 2016 • Concepts and relationships - Relationships between variables • Associational vs. Causal • Survey vs. Experiment • Variables - Independent vs Dependent Variables Conceptual and operational definitions • - Measuring variables • Types of measures, levels of measurement - Questionnaire items • Open vs. closed ended items • Composite measures • Reliability and Validity • Sampling - Subset of the population - Representative sampling • Random selection, sampling error • Simple Random, Systematic, Stratified, multistage cluster - Sampling bias - Non-Representative sampling • Convenience, purposive, volunteer, quota, network/snowball 9


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