Comm 88 Lecture Notes 5/1/14 to 6/3/14
Comm 88 Lecture Notes 5/1/14 to 6/3/14
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Date Created: 06/04/14
Lecture 5114 More Q39s for Final from this day BC low attendance Survey Research Primary Goals 0 lldentifydescribe attitudes or behaviors in a given population 0 lExamine relationships between the attitudebehavior variables measured I Does X predictrelate to Y Ex Does exposure to alcohol ads X predict teen drinking Y Tricky bc frequent drinkers could know ads better peer pressure in uence etc I Do many factors together predict Y Ex Do alcohol ads Xl parent drinking X2 peer drinking X3 and risk taking X4 together predict teen drinking Y 0 All factors related Administering Surveys 0 lSelfadministered questionnaire I Use of term survey refers to method actual questions are on questionnaire as items I Mail surveys online or e mail handouts diaries jelatively easy and inexpensive 110 interviewer in uence increased privacy anonymity X Must be selfexplanatory Example Media Use Diary is overly complicated hard to understand arduous 0 Can skew data Very low response rate of selfadmin Ways to increase response rate 0 Have inducements incentive 0 Make it easy to complete and return 0 Include persuasive cover letter andor do advance mailing I Substantially increases likelihood of response 0 Send followup mailings llnterview Surveys 0 More exible can probe for depth 0 Higher response rate BUT 0 More potential for interviewer in uence 0 Higher costs ielephone 0 Quickest results 0 Compared to facetoface reduced costs more privacy more efficiency 0 Compared to selfadmin more detail possible better response rate 0 But what about call screening and cell phones Role of time in surveys lCrosssectional studies 0 One sample at one point in time IE each variable is measured once lLongitudinal studies 0 More than one point in time measured 0 Variables measured more than one time to track changes over time I Panel same people each time I Trend different random samples from same population e g survey Americans every 5 years to study churchgoing poll lively voters over the course of an election campaign student alcohol study Samples do need to be representative I Cohort different samples but of same cohort Here group of people are anchored to each other because of something in time timebased characteristic IE when you39re bom when you graduated lived in NY during 9 l 1 e g survey class of 2012 every 5 years to check their employment since graduation Lecture 5614 How Do We Relate Variables in Survey Research Recall goals of survey Identifydescribe attitudes or behaviors in a given population Examine relationships between attbeh variables measured 0 Does X predictrelate to Y I Ex exposure to alcohol ads X predicts teen drinking Y 0 Do many factors together predict Y I EX Do alc ads Xl parent drinking X2 and peer drinking X3 predict teen drinking Y jepends on hypRQ and how your variables are measured When both IV amp DV are nominalcategorical discrete variables 0 All you can do is break down the percentages by category I EX YesN o MF supportopposeno opinion 0 Percent of people in 1 category who are also in another I EX Gallup survey on support for legalization of marijuana Q Do you think the use of marijuana should be made legal or not 44 Yes 54 No llould this be related to another variable How about gender lix RQ Does 1 IV predict support for legalization DV 0 By 2009 gender connection gone both closely equal in support 0 Political ideology makes difference I Does not address strength of opinion If IV is categorical but DV is intervalratio data continuous 0 lDifference statement in hypothesis I DV uses Likert semantic diff items etc 0 Compare mean average DV scores for the different IV categories I Ex Average of heavy teXter39s self esteem compared to average of light teXter39s self esteem I Same procedure as experimental where causality is added 0 Comparing means I Ex RQ Does political ideology IV predict support for legalization DV LV political ideology 0 Measured as categoricalnominal variable I consider myself check one Liberal Moderate Conservative 0 OR if IV originally measured as a continuous var but then collapsed to categorical I Ex I consider my political views to be Very liberal l 2 3 4 5 6 7 Very conservative I Must decide how high is a high score Above median conservative below median liberal LN support for legalization 0 As continuous var intervalratio I The recreational use of marijuana should be made legal Strongly agree 1 2 3 4 5 6 7 Strongly agree To relate vars For Ss in each IV category compute their mean score on the DV then compare means 0 EX Conservatives M 23 Moderates M 42 Liberals M 61 I Liberals are significantly more in favor of legalization Signi cant statistical test shows 6 l39s difference from 42 did not occur by chance 0 All of stats just asks if something happens by chance If both IV and DV are intervalratio data 0 lCompute a correlation I Statistical value that relates two or more continuous variables I Compute r value Pearson r r tells you type vs and magnitude strength of relationship 0 Type of relationship I Positive r as X increases Y increases Line looks like Also called direct relationship I Negative r as X increases Y decreases Line looks like Also called inverse relationship 0 Magnitude of relationship I r ranges from 0 to 1 100 lt 0 gt 100 I The further from 0 the stronger the relationship Lecture 5814 Survey research cont What can you conclude from surveycorrelational data lIAN conclude that variables are relatedassociated lIANNOT conclude that one variable causes the other 0 Why not 0 Remember To establish causality I Variables must be related X correlated with Y Okay so far surveys can show that e g increased time studying GPA increases increase of thin mag exposure decrease body image Must establish time order 0 IV happened before DV Must rule out other explanationscauses 0 lSo SurveyCorrelational Research has 2 causality problems I lCausal Direction Problem time orderl Does X cause Y or does Y cause X Chicken and egg problem I lThird Variable Problem Does some 3rd variable explain the XN relationship Getting closer to causality 0 To help solve 3 variable problem Partial correlation lvleasure potential 3 variables ltatistically partial out control for effects of those 3 variables 1 hen see if XN relationship still holds l artial correlation like Venn Diagram overlap of variables 0 3 variable can also overlap 0 Can mathematically block out this part of overlap once controlled for I Check if anything is left over 0 If XN rel still holds can rule out 3 variable as the cause I If 3 has lots of overlap the correlation of XN relationship disappears or is reduce substantially then the 3 variable explanation matters 0 To help solve causal direction problem I Need a longitudinal studyl Helps avoid time order problems lCrosslagged panel design 0 Time 1 Measure X and Y Variables 0 Time 2 Measure X and Y vars again later for the same people 0 Compute r39s for X and Y but across the times measured I Example Age 8 Timel Age 18 Time2 TV Violence X 1X1 Set of scores Aggression Y Y2 Scores r 31 r 0 no correlation lo measure both Variables for same people at different times then see which cross relationship holds 0 Example of recent study Effect of IM on adolescents friendships IV IM use DV friendship quality I Significant r for IM Use at time 1 and Quality of Friendship at time 2 I No significant r for Quality of Friendship at time 1 and IM Use at time 2 Mediating Variable intimate selfdisclosure in IM Research claim errors example 0 IE BREAD ALERT A recent headline read Eating or smelling baked bread may be hazardous to health I Some breadrelated research claims More than 98 of convicted felons are bread eaters More than 90 of Violent crimes committed with 24 hours of eating bread Fully half of all children who grow up in breadconsuming households score below average on standardized tests Gateway food item leading to pbj cold cuts 0 Researchers make these same mistakes when proposing data results with less ridiculous data in emotionally charged cases especially I All of these prior claims have way too many 3 Variable problems Lecture 51314 lEXperimental Research Purpose To test hypotheses of cause and effect Goal is to establish intemal Validity 0 IV has effect on DV only through control Willing to sacri ce extemal Validity 0 Remember to establish causality intemal Validity I Variables must be related I Must establish time order I Must rule out other explanationscause Key Elements to a True Experiment 0 Manipulation of causal Variablesl I Divide Independent Variable IV into conditions EX IV New painkiller drug half of Ss get drugother half do not while controlling all other Variables Ss in each condition treated the same etc Examine effects on Dependent Variable Ex DV amount of perceived pain eg likert scale 0 Random Assignment of participants to conditions I Everyone must have equal chance of ending up in either condition Could be assigned to either condition to each control non group by chance I Why important Makes groups equal before manipulation 0 From book Matching can be used with RA not as substitute I Subjects are paired with those who score similarly on variables related to experiment are then randomly split into control or noncontrol group True Types of Experiments 0 Design notation X IV manipulationtreatment 0 Observation measure for DV I Posttest only control group design R X 01 group 1 R Ol group 2 Example R O1 beliefs about smoking R O1 beliefs about smoking Compare them Occurs only after subjects are shown ad could realize goal of experiment If you get a statistically signi cant difference between group means on 01 the IV caused it Variations More groups several different treatments Example IV different types of ad appeals R X1 personal cancer story O1 group 1 R X2 cancer stats Ol group 2 R X3 tobacco industry Ol group 3 R Ol X 02 group 1 R O1 02 group 2 EXample R Ol smoking beliefs O2 smk beliefs R Ol smoking beliefs O2 smk Beliefs DV measure before 0 c pK Again if difference between group means on 02 the IV caused it Possible problem Differences on 02 could result of interaction of manipulation with pretest Looking for change with pretest I Soloman fourgroup design R X Ol grpl Lecture 51514 Threats to Intemal Validity From book Ways that variables other than IV could cause a change in DV If NOT a TRUE experiment or if do experiment improperly then gt 0 Altemative explanations become possible IE threaten intemal validity I PreExperiments Some manipulation of IV but no random assignment thus many threats to intemal Qalidity lOneshot case study X 01 group 1 0 Missing RA not even second group treats all in study same way only one period of time for one group examined no comparison 0 IE when a teacher wants to test if you smile the world smiles with you so has students smile all day and take notice of what happens I Could be many altemative explanations Smile at people already smiling could be lovely day there was free cake need control group by smiling at some not smiling at others lOne group pretestposttest design 0 l X 02 group 1 0 Like above only includes a nosmiling group but now have a before and after two points in time no control group 0 Possible altemative explanations Could be seasonal like for ads and sales black Friday natural uctuation from time 1 to time 2 time of day happen to already be smiling lStatic group comparison posttest only nonequivalent groupsl X 01 group 1 01 group 2 0 Have 2 separate groups but neither is controlled no random assignment 0 Go to one half of campus to smile other half of campus to not smile compare groups 0 Possible altemative explanations People in each group could be different already Threats Within preexperiments Selection bias 0 Altemative explanation is because of people selected or how they are divided 0 All 3 preexperiments aka quasi experiments have this problem 0 True experiments use RA lHistory effect 0 Something that happens in world outside of control of data is producing results I Can be as little as a sunny day or free cake day does not have to be huge event could be happing personally to a participant 0 True experiments needuse RA to make sure it is happening to 1 group then the other I Equal amounts of people in either condition so cake events don39t matter O ls only history effect if event impacts study results IE study on suicide celebrity kills self in middle of study could raise awareness of problem I Cannot stop an event from happening lReactivity Effectszl 0 Being reactive like social desirability effect 0 Becomes altemative explanation if participants are reacting to being studied rather than O O IVtreatment in uences DV Threatens intemal validity with fact that participants know what goal of study is lHawthorne effectl Experiment on worker productivity tried giving them more light looked at productivity after improved Onegroup pretestposttest design increased light improved again Brought lighting down would have expected productivity to be lower instead goes up Workers increased workload no matter what because they knew they were being observed Need control group to have difference but receive same amount of attention to avoid I lPlacebo effecq Reacting to thinking you39re getting something that will have speci c effect Control for it by actually using it in study 0 Group doing everything else equally with other in study I lDemand characteristics Thing subjects are responding to demand of artificial setting of study subjects think they know hypothesis respond accordingly 0 Know what they want you to say 0 Treat both groups same won39t figure it out So how to removecontrol these threats Conduct a TRUE experiment RA to proper conditions Be sure to treat groups equally All groups get equal time attention etc Threats related to pretesting or measures over timel These relate to Onegroup pretestposttest design within Preexperiments Testing effect sensitization 0 Pretest sensitizes subjects on topic affects answers on posttest IE smokers taking pretest about habits changes later response lMaturation 0 Comes from idea that people naturally change mature over time I IE reading program checks improvement of reading scores but over course of year kids will improve anyway 0 Natural uctuation is same Seasonal changes also a type of maturation lStatistical regression to the mean 0 Phenomenon that happens because of laws of chance but only applies when starting with extreme scores I IE fail SATs without any changes will improve next time around closer to mean than first time nowhere to go but up I Also occurs when starting high up nowhere to go but down statistical chance you will do worse 0 Chance that you will become closer to the mean whether increasing or decreasing 0 Only real problem if limiting sample to those with extreme scores lInstrumentationl 0 Changed how you were measuring something from timel to time2 IE calculate sales of type of shoe then check sales for whole department I Change guidelines definitions when they should be consistent lMortality attritionl 0 From timel to time2 some people will drop out I Those who drop may be different than those who stay I IE people seemed smilier on second round bc all the grumpy people dropped out How are these xed by true experiments 0 Need to account for what is happening to all groups Section 51614 ASK ABOUT AMOUNT OF RESEARCH STUDY CREDITS Lecture 52014 Recap True experiment 0 Manipulationcontrol random assignment intemal Validity If NOT a true experiment or if do exp Improperly then gt 0 Altemative Variables become possible explanations How to remoVe control pretesting threats 0 Conduct a TRUE experiment I RA to proper conditions Be sure to treat groups equally 0 All groups get equal time attention etc Threats to intemal Validity cont Experimenter39s EffectBias 0 Experimenter39s behavior or attitudes rather than treatment IV in uences DV I IE One group told they are handling smart rats for an experiment others told they are handling dumb rats but all rats are actually just randomly assigned then compared I Smart rats went faster in maze because experimenters treated them differently 0 How to control exp39er effects I Same thing again true exp etc but also Automate or script the experiment 0 Read uniformly possibly prerecorded or have ignorant exp39er 0 Only know the most basic instructions IE hand people a packet or have blind exp39er 0 The know there are 2 groups but do not know who was in which 0 Double blind is best What is being done in True Experiments that rule all these problems out Selection bias Use RA History effect Even if a change happens from timel to time2 it is happening equally to both groups washes out as explanation Exp39er effects Must make effort to make sure everything is uniform When studying test actual variables in these scenarios IE different than smiling example Example study music and leaming RQ Does listening to music while studying hinder or enhance leaming 0 Causal needs experiment to answer Possible experiment 0 IV Listening to music DV leaming R X Music 01 test score groupl M65 R No music 01 test score group2 M78 josttest only design 0 If data is those means can conclude Music hinders the group39s leaming cannot generalize 0 All study types examined have only 1 IV 1 DV What if we want to test for effects of ANOTHER IV Factorial Designs lmrpose 0 To examine the effects of 2 or more IV39s simultaneously Factors are IV39s 0 Each factor has at least 2 levels conditions I Example DV leaming test score Music factor While studying music no music AND Caffeine factor While studying caffeine no caffeine MUSIC NO MUSIC CAFFEINE NO CAFFEINE This is a 2X2 design 2 levels of Music X 2 levels of Caffeine easiest factorial design possible 0 Can have more POP MUSIC CLASSICAL MUSIC NO MUSIC CAFFEINE NO CAFFEINE This is a 3X2 design 3 levels of Music X 2 levels of Caffeine 0 Still only 2 factors Music and caffeine I What if more than 2 factors Music factor pop classical none Caffeine factor caff no caff Gender male female I Now 3X2X2 design Would look like 3x2 for men 3X2 for women with boxes Factorial designs test for 0 Main effects 0 Interaction effects Lecture 52214 lFactorial designs contl Main effects 0 The effect of one IV individually on the DV I Simplest I IE for the 2 music X 2 caffeine study Main effect for caffeine 0 Lower scores w caff than wo caff worsens leaming 0 Could get as many main effects as IV39s 2 I To test for main effects compare marginal means of DV for each factorIV DV leaming test score Music factor While studying music no music AND Caffeine factor While studying caffeine no caffeine MUSIC NO MUSIC CAFFEINE M 5 0 M 60 No CAFFEINE Marginal mean is combined mean for a factor 0 Need a mean for people who do caffeine do both averages 0 55 for caffeine group 65 for no caffeine groupthink I If there is a difference there is a main effect I Main effect for caffeine Greater leaming without caffeine than with or caffeine worsened test scores 0 For music match Vertically MUSIC NO MUSIC CAFFEINE M50 M60 NO CAFFEINE M 70 M 60 0 Both means for music are 60 0 No main effect for music I Studying with or without music made no difference But gt Main effects do not tell the whole story Interaction effects 0 The unique effect of the combination of IV39s 0 Different effects depending on different combinations I The effect of one IV depends on the levels of the other IVs Examples for a Music X Caffeine interaction Caffeine reduces leaming only when combined with listening to music without music it has no effect 0 To test for an interaction effect graph the l means MUSIC NO MUSIC CAFFEINE M 5 0 M 60 NO CAFFEINE M 70 M 60 Test scores on Yaxis l00 90 80 70 No caffeine 601 j 501 lt On Xaxis Put one IVfactor Music No Music There is an interaction effect if the lines are not parallel 0 So although caffeine lowered scores overall the effect was worse when combined with music Music actually improved scores when without caffeine For those without music caffeine did not matter With different data MUSIC NO MUSIC CAFFEINE M90 M 70 80 NO CAFFEINE M 70 M50 60 80 60 Test scores on Yaxis l00 901 2 80 701 H 60 l No caffeine line 50 lt On Xaxis Put one IVfactor Music No Music Main effect for caffeine main effect for music Lines are parallel no interaction If lines cross interaction A word about factors lV39s 0 In one design can have as lV39sfactors I Manipulated Variables Ex Music exposure caffeine I Subject Variables lix gender personality traits TV use hilo 0 Can only make causal conclusions about manip39d lV39s not Ss vars 0 If Q manip39d vars at all then it39s not an exp39t it39s a survey w factorialtype setup Factorial designs cont 0 ls IV manipulated RA If yes a true experiment If no a survey l QuasiExperiments 0 Closer to true experiments than preexperiments 0 Not true experiments no RA but have decent comparison groups 0 Nonequivalent Control Group Design Pretestposttest with quasiequivalent groups 01 X 02 grpl O1 O2 grp2 0 Use pretest scores to match groups before manipulation I Simulating random assignment IE went to 8am to do experimental condition then went to 10am to do control condition I If both groups are same can make a case for data 0 Time Series Designs I Track many observations over time before and after a manipulation Able to see trend over all as opposed to l pretest 1 post test I Singlegroup interrupted time series design 01 O2 O3 04 X O5 O6 O7 08 grpl jlmproves upon the onegroup pretestposttest Example Crime prevention program Politician nds Crimes reported Jan X Feb Actual data Crimes reported Jan X Feb I Solves some threats to intemal validity testing maturation I Variation take treatment away and measure again Could also compare 2 cities 1 w rec cen l Wo Lecture canceled 52714 Lecture 52915 Research paper still due next Tuesday this way can have time to prepare for nal Section tomorrow all about paper get a lot written tonight to get help LPE studytutor session next week Wed Jun 4 69pm in the Hub GO TO THIS We are skipping WithinSubjects Designs both in lecture and book Experimental Research cont one final issue Laboratory vs Field experiments 0 Laboratory Experiments I Bring Ss subjects into highly controlled setting can be anything as long as it is controlled I High control gt high intemal validity I Artificial setting gt low extemal validity I Must watch more carefully for exp39er and reactivity effects 0 Researchers themselves can treat subjects differently must treat them same 0 Reactivity is when subjects are aware something is being done to them I Field Experiments lvlanipulate lVs in the real world ltill an experiment need RA should not confuse with field research in general refers to qualtitative methods ix Littering studies 0 Interested in behavioral contagion to study common transgressions like cutting in line littering etc believe they can get away with it 0 To test iers placed on cars in parking lot randomly assigned people coming to parking lot in nontreatment or control group 0 wherein a confederate would be seen by control group subjects throwing ier out of car I The group that saw the confederate litter also had high littering Ex She Said No TV movie study social awareness type movie 0 Managed to use representative sampling in true experiment 0 Randomdigit dialed Americans who were randomly assigned to 2 conditions I Told to tum to movie channel in control other group told to watch whatever 0 Able to test subjects from within their home 0 Ultimately movie oversimpli ed rape issue back red in older male subjects not supporting cause I Very rare and expensive experiment 1lore natural settingbehavior gt higher extemal validity 0 Increasing extemal validity in terms of realism Less reactivity IE litter people weren39t even aware they were subjects Harder to maintain experimental control Matter of goals and feasability Content Analysis Quantitative Content analysis is by nature through numbers qualitative are identified as such Systematically quantitatively examining the content of communication 0 Often studies of media messages 0 Sometimes don39t even have subjects unless recording and analyzing conversations llsed to Describe how muchwhat kind of certain messages there are eg sex on TV types of tweets 0 IE magazine intemet ads and the ideal of women39s beauty prosocial behavior in animated disney lms motivational counseling call targeting obesityrelated behaviors among postpartum women concussion related traf c on Twitter Assess image of particular groups in media eg stereotypes of race gender age political party etc 0 IE rare portrayal of wealthy in media as good race in primetime ads coverage of the orida panther Compare media content to real world 0 Implication that media distorts reality so use reallife statistics to examine I IE gender race and age in video games compared to the US population I No one can say what right amount is but can use stats like population as reference point 0 Examine message changes over time I IE motherhood and sexuality in magazine articles over 20 years 0 Provide background for research on media effects Also a method for codinganalyzing openended data in surveysexp39s Entire studies can be just content analysis Important Issues 0 Sampling I De ne population of interest Ex primetime TV shows FB discussions I Identify unit of analysis for coding Ex number of sex scenes on I V I For TV shows code each episode Scene Each character I For FB pages code each entry Thread 0 Select representative sample ideally I Findings cannot make broad conclusions without are less meaningful without 0 Coding Transforming content into numerical categories I Conceptualize categories What is meant by stereotype portrayal sexism violence Manifest content visible surface content 0 IE can see an act of violence know an implication is of sex naked under sheets Latent content underlying meaning 0 IE theme in time travel movies based on correcting past Or that we should not mess with science I Operationalize categories IE difference between sexual kiss or familial kiss Distinction between being polite and being submissive I Establish reliability Can take multiples attempts Limitations 0 Purely descriptive I Cannot explain why the content is that way I Cannot conclude anything about effects of the messages I IE news media relying on Twitter is not representative of users or what people take from it 0 Very reductionistic I Idea that science reduces complex topic to a few key variables I Particularly noticeable when coding 1 thing in entire movie Reduces content to codeable concepts only Lecture 6314 Mon Jun 9 special office hours 3430 ssms 4105 TA QampA Wed Jun 11 45 ssms 1009 Qualitative Research Methods Qualitative types of message analysis Contrast with content analysis Analysis of content in nonnumerical approach Subjectively analyze comm messages e g media content conversations Researcher is allowed to give scholarly opinion not unlike pro film critic 0 Rhetorical criticism I Critique form content imagery delivery of speechespop culture I Without counting points out interesting terms or concepts usually starts with famous examples like MLK Hitler etc I EX use of metaphors in a presidential speech Content analysis can look at same stuff but must count it totally different I Ex themes in students drinking stories on FB I Goal greater understanding andor appreciation similar to literary criticism llritical theory aka cultural studies 0 Criticizing something39s role in society 0 Craft arguments about the cultural implicationsoppression esp of gender race class etc of media I IE hiphop dreamworlds39 documentary Marxist or feminist analysis of advertising images I No objectivity because these are positions and ideologies 0 Goal socialpolitical awareness amp social change Qualitative Studies of People other labels often used are interpretive ethnographic or eld research contrast with surveys and experiments Goal to develop rich understanding of peoples subjective experience Desire to see from others perspective can ask what someone is going through Some important features 0 Natural setting I Must be there unlike other methods 0 Researcher is not separate from participants researcher subjectivity I Can even become involved in advocating for activism 0 The subjects guide what is studied I Must be willing to focus on what subjects state is important 0 Inductive theorybuilding I Qualitative use inductive theorizing gt start with observation here Participant observation Participation while being with subjects you39re researching 0 Researcher participates to varying degrees in the events groups under study 0 Can be within the action or attempt to be ignored while there levels vary 0 Natives may or may not be aware of being studied Important issues 0 Typically purposive types of sampling case studies common I Seeking out certain people of interest IE looking for successful CEOs famous ComiCon attendees 0 Construction of detailed field notes amp records I Must happen in moment 0 Finished when achieve saturation I Like in chem when a solution will not hold anymore because so much is added sugar falls to bottom of tea after so much is added I More data will not add any new insight QualitativeField Interviewing lmstructured or semistructured 0 Openended Qs free to change 0 Getting de13 th is key I Trust necessary iypes of interviews 0 Ethnographic conversations I Naturally occurring conversations that can be provoked into more thorough interview 0 Indepth interview I Require lots of time Focus Group Groups discuss an issue in presence of moderator 0 About 515 people in group interview 0 Again openended questions 0 Leader should facilitate not control 0 Popular technique in marketing and political research The Trustworthiness of Data Qualitative research is NOT concerned with 0 Reliability and validity of measurement 0 lntemal and extemal validity Instead focus is on uali of researcher inte retations T 9 0 IE picking right examples from FB as opposed to cherrypicking for agenda 0 Should be credible trackable wellreasoned 0 Good to triangulate qual methods where multiple methods are used e g participant observations with depth interviews I Want richness of understanding but must be able to live with messiness
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