Communication Research Methods (Comm 88) Lectures 6 & 7
Communication Research Methods (Comm 88) Lectures 6 & 7
Popular in Course
verified elite notetaker
Popular in Communication
This 7 page Reader was uploaded by Kelsey Calef on Thursday May 1, 2014. The Reader belongs to a course at University of California Santa Barbara taught by a professor in Fall. Since its upload, it has received 112 views.
Reviews for Communication Research Methods (Comm 88) Lectures 6 & 7
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
Date Created: 05/01/14
Lecture 42215 Composite Measures Grouping conglomerate form of other measures Use multiple items for E variable combine those items into an index aka scale 0 Example variable Perceived credibility of a speaker I IE Professional dress vs hipster style 0 As a singleitem measure I The speaker I just heard is credible 765432l not credible I Above semantic differential measurement I Likert The speaker I just heard is credible Agree or disagree Semantic differential works better bc don39t need statement each time 0 As a composite measure I The speaker I just heard is credible 7l not credible knowledgable 7l not knowledgable experienced 7 l inexperienced honest 7l dishonest etc I Here all high scores mean more of each item 0 Unidimensional index all items added or averaged into E overall score I Options Unidimensional credibility add all items into one total credibility score Multidimensional credibility knowledge experience competence Expertise dimension trustworthiness honesty unbiased Trustworthiness dimension 0 Multidimensional index group different items into different subscales I Separating all the different dimensions of a Variable How Good is Your Measurement Reliability and Validity Reliability of Measurement 0 Are you measuring the concept consistently 0 For measures using questionnaire items I Interitem reliability Administer same items more than once e g testretest splithalf 0 Can get another set of subjects from same population get same subjects to take survey twice Look at intemal consistency of similar items in a scaleindex e g Cronbach39s alpha 0 Similar items should get similar scores 0 Cronbach39s alpha Numerical formula for finding whether interitem reliability is good or not good usually above 7 I Unidimensional credibility is likely to get LOW Cronbach39s alpha poor quality I Multidimensional credibility is likely to get higher reliability bc computed separately for each subscale Expertise dimension and Trustworthiness dimension 0 For measures using coders eg behavioral observations I Intercoder reliability Compare multiple coders I lntracoder reliability Compare multiple observations of same coder Validity of Measurement 0 Does your measure really capture the concept you intend to be measuring 0 Assessing validity I Subjective types of validation Face validity 0 The measure lookssounds good on the face of it Content validity 0 The measure captures the full range of meaningsdimensions of the concept Criterionrelated validation aka Predictive Validity 0 The measure is shown to predict scores on an appropriate criterionfuture measure 0 Example SAT scores your potential to achieve gt college GPA your achievement Construct validation 0 The measure is shown to be related to measures of other concepts that should be related and not to ones that shouldn39t 0 EX Verbal aggressiveness scale lt gt hostility scale FYI don39t worry about convergent discriminant Can a measure be reliable but not valid Yes Can a measure be valid but not reliable No If not consistent how can it be true Lecture 42414 Sampling How we select participants or other units for a study Sample A subset of the target population whowhat you want to report about IE teenagers college students voters FB users married couples juries football fans etc IE content analysis of TV shows magazine ads blog posts etc Representative sampling probability sampling 0 Intended to be miniature version of the target population 0 KEY is random selection I Everyone in population has equal chance of being included in sample I How representative is it 0 Will always be Sampling Error I Error variation Sample data will be slightly different from population because of chance alone AKA random error Statistically known as the margin of error 0 IE National poll N 1000 gt 3 Larger sample size smaller margin of error Representative Sampling Techniques 0 Simple random sampling I Select elements randomly from population Listed populations random 39s table Phones randomdigit dialing 0 Systematic random sampling I From a list of the population select every Nth element AND I Must have random start cycle through entire list Similar results as SRS 0 Example School population 60 girls 40 boys Pull 8 out of 24 names random start then every 3 Comes close to 60 40 Watch out for potential periodicity 0 Strati ed sampling I For getting population proportions even more accurate I Divide population into subsets strata of a particular variable Usually strati ed for demographic variables e g sex race political party I Select randomly from each strata to get right proportions of the population I Need prior knowledge of population proportions I Increases representativeness bc reduces sampling error for the strati ed variables I But more costly amp time consuming 0 Multistage cluster sampling I Useful for populations not listed as individuals First randomly sample groups clusters then randomly sample individual elements within each cluster Example Sampling high school athletes 15 stage Random sample high schools 2 stage Random sample athletes from those schools in sample Reduces costs But sampling error at each stage So for all of Representative Sampling techniques 0 Will always have sampling error 0 But can generalize to the larger target population assuming done properly 0 Caution Avoid Systematic Error I Systematically over or underrepresent certain segments of population I Caused by Improper weighting Very low response rate Wrong sampling frame Using nonrepresentative sampling methods Nonrepresentative sampling cannot generalize to a population 0 Convenience sample I Selecting individuals that are availablehandy 0 Purposive sample Select certain individuals for special reason their characteristics etc 0 Volunteer sample I People select themselves to be included 0 Quota sample I Select individuals to match demographic proportions population I Differs from strati ed in that individuals are not randomly chosen 0 NetworkSnowball sample I Select individuals who contact other similar individuals and so on
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'