Complete set of notes for Comm 88
Complete set of notes for Comm 88 88
Popular in Communications
Popular in Communication
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Date Created: 11/02/14
Communication 88 Comm Research Methods Lecture 4114 Objectives Preparation for the major Preparation for consuming and creating knowledge Research paper Start early with group do things in order do NOT skip literature review Understand project Practice thinking speaking like scientist Lecture 4314 Ways of Knowing Aside from research how do you know some truths 0 IE Vegetables are good for you I Possible sources parents doctors studies media experience gov39t agencies ideologies 0 IE People who are similar to each other tend to like each other I For example values about money are very important thrifty vs expensive Ways of knowing epistemology science of knowing 0 What does it mean to know something 0 Everyday ways of knowing and their problems I Method of tradition tenacity Get carried down passed on over and over even if origin is unknown Truths are persistent pieces of knowledge don39t just go away Upside Do not have to do research when acquiring knowledge this way 0 IE Supreme court using precedent to decide what cases to judge Downside Might not be true or best interpretation 0 IE Waiting 30 minutes after eating to swim I Method of authority Taken to be true because some granted authority said so 0 IE surgeon general teacher parent professor doctor Upside Don39t have to research saves time has experience and professionalism Downside Not knowing if source is reliable can be wrong I Method of experience observation Know it39s true because seen with own eyes 0 Personal experience easy superficial noticing I IE Remember when I got food poisoning at that Taco Bell 0 Baconian Empiricism serious rigorous empirical experiments I IE Biohacking when chemists use their own bodies to test intake effects I IE If milk is past expiration label authority smelling it to check if it39s good Downside Could be situational or inaccurate observation misunderstood or selective observation noticing one pattem ignoring others I Method of intuition logic Gut feeling able to reason to get to truth common sense Platonic idealism Plato argued that you can39t trust your eyes but your brain by setting up series of logical premises to get to truth If A is B and B is C A is also C Downsides One39s common sense could differ from another 0 IE Both sides of politics believe theirs is common sense approach 0 What if A doesn39t actually equal B If premises are incorrect from start argument will still be wrong 0 Illogical reasoning All sh can swim I can swim so I am a sh Too many Bs on a test must be wrong 0 Problem for all of the above methods Overgeneralization I Kemel of information taken to mean pattem I IE Food poisoning at Taco Bell basis for never eating at any Taco Bell or Mexican again 0 Everyday ways of knowing can even lead to con icting ideas about trut I IE Absence makes the heart grow fonder or Out of sight out of mind or reconciling Absence extinguishes small in ames the great All can be con rmed with methods but are contradictory The Scienti c Method 0 Attempt to control or overcome problems in attaining knowledge 0 Combines platonic idealism with Baconian empiricism I Logic intuition gt constructing theories I Observation experience gt gathering data In communication science Use empirical observations to test theories about comm processes Unique characteristics of science 0 How is science different from other everyday ways of knowing I Scienti c research is public Published in peerreviewed journals O Submit study to journal have it checked by others in area of study make advised revisions can be reviewed again then possibly published Opportunity to replicate studies 0 IE Chemists went to media claiming ability to create cold fusion but other researchers could not replicate study Section 4414 How to read the accompanying text 0 Use to sum up and in other words in short etc and describing cues notes importance 0 Quizzes and outlines of chapters available online Don39t get bogged down with examples skim for key points I Portions like field research not as important for quantitative class as opposed to empiricism reasoning etc 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 S Q P 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 Lecture 41514 Variables 0 Independent xed predictor I In case of Exercisel example Hypothesisl is independent Variable 0 Dependent Outcome I In example the scores of student recall Different methods for different hypotheses 0 SurveyCorrelational Research eg Researcher A I Test correlational hypothesesRQs mere relationshipassociation Measure some Variables amp relate them compare existing groups etc I Great for extemal Validity Ability to generalize results to other people andor normal life settings I Poor for causality Measuring factors simultaneously cannot tell which came first 0 Experimental Research eg Researcher B I Tests causal hypothesesRQs Manipulate Vars grounds control everything else and measure effects eg Researcher B manipulated amount of Violence seen I Great for intemal Validity basically means causality Ability to establish that X causes Y rules out other explanation I Poor for generalizability The Research Process cont Defining concepts and Variables Variables in Experimental Research causal hyps 0 Independent Variable IV I The cause in causeeffect relationship I Variable manipulated by researcher 0 Dependent Variable DV I The effect or outcome I Variable affectedchanged by the IV Example hypothesis 0 Greater physical attractiveness will create impressions of greater friendliness I Is suggesting causal relationship IV physical attractiveness eg manipulate level of attractiveness DV impressions of friendliness eg ratings on friendliness scale I Ciquot be causeeffect so IV considered a predictor variable DV is what is being predicted by the IV sometimes called criterion variable Example hypothesis 0 Stronger an identity predicts greater participation in online fan forums I IV fan identification eg score on identification scale I DV fan forum participation eg measure how often people post or report reading posts etc I Could the IvsDVs be other way around Yes Not manipulating relegating cause but as researcher get to decide which is which depends on how study is set up De ning ConceptsNariables 0 Conceptual de nition I A working de nition of what the concept means for purposes of investigation usually based on theoryprior research Example variable fear what is it 0 Feeling unsafe reaction to threat possibly evolutionary in nature adrenaline rush from euphoria of not dying needs to be de ned further 0 Operational de nition I How exactly the concept will be measured in a study Eg fear de ned as sharp heart rate increase combined with nonverbal reaction Types of Measures 0 Physiological measures IE BP brain imaging Cortisol stress hormone even glycosulated hemoglobin scores for diabetes 0 Behavioral measures I IE Nonverbal gestures timemoney spent actual posts on social media 0 Selfreport measures I IE items on questionnaire Lecture 41714 Levels of Measurement Nominal categoricaldiscrete 0 Variable measured merely with different categories works best with separate groupings I IE yes or no Qs gender mf ethnicity TV violence rewardedpunished TV use hilo 0 Categories must be mutually exclusive Subjects cannot pick both I check all that apply just a way of asking many YN questions 0 Categories must be exhaustive All possible options must be offered should not force agreement with wrong answer that doesn39t re ect subject39s information 0 Nominal measures are for comparing differences Ordinal 0 Variable is measured with rank ordered categories I IE Rank top 5 favorite TV shows rank most to least important political issues I not very useful compared to Nominal Interval 0 Variable is measured with successive points on a scale with equal intervals aka equidistant points I IE Olympic medalists may win Silver but could have been only 1 point behind gold no way to tell how far apart Interval accounts for differences on continuum IE measure on immigration policy opinion US should build a fence along border Strongly oppose l 2 3 4 5 strongly favor Numbers themselves are just relative to each other Strongly oppose 2 l 0 1 2 strongly favor same scale as prior above 0 0 does not mean nothing here still represents a measurement vs ratio Ratio 0 Interval measurement with a true absolute zero point I Nothing has been measured yet absence of data I IE Time in hours weight in lbs age in years etc I IE Test scores if from 0 possible SAT not ratio bc you get points for signing name 0 means nothing in all these cases Interval and ratio measures are continuous variables vs difference of nominal and ordinal 0 Allow continuoustype hypotheses the more X the more Y etc I IE misunderstanding in This Is Spinal Tap with amp going to 11 Measures should capture variation 0 Use continuous vars for DV39s where possible have good conceptual fit with variables in the HypsRQ39s 0 Conceptual and operational definitions should be answered or reasonable to attempt by measurement minimize potential social desirability effects 0 Lying exaggeration political correctness Using Questionnaire Items as Measures Common for IV39s and DV39s in surveys Common for DV39s in experiments IV is a manipulation Types of Questionnaire Items 0 Openended I Respondents give their own answers to Qs 0 Closedended I Respondents select from list of choice exhaustive and mutually exclusive Some closedended formats 0 Likerttype items I item just 1 unique thing on survey or to make up parts of variable not always Q I Respondent indicate their agreement with a particular statement IE Parents should talk openly about sex with their children Strongly disagree 1 2 3 4 5 Strongly agree Have to know what you want high scores to represent can format results in such a way Other response options also possible opposefavor not at allvery much almost neveralmost always 0 Semantic Differential I Respondents make ratings between two opposite bipolar adjectives I IE My best friend is IntelligentUnintelligent I Can reverse code numbers to make easier to read again what you would want your high scores to re ect IE high score warmer more intelligent Lecture 5114 More Q39s for Final from this day BC low attendance Survey Research Primary Goals 0 Identifydescribe attitudes or behaviors in a given population 0 Examine 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 Selfadministered 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 Relatively easy and inexpensive No interviewer in uence Increased privacy anonymity BUT 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 Interview Surveys 0 More exible can probe for depth 0 Higher response rate BUT 0 More potential for interviewer in uence 0 Higher costs Telephone 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 Crosssectional studies v 0 One sample at one point in time Longitudinal studies 0 More than one point in time measured Communication 88 Final Book Readings General Features of Survey Research 1 A large number of respondents are chosen through probability sampling procedures to represent the population of interest 2 Systematic questionnaire procedures are used to ask prescribed questions of respondents and record their answers 3 Answers are numerically coded and analyzed Large Scale Probability Sampling I Large samples are used to ensure precise estimates of the population I National studies require lots of time money and personnel therefore many surveys involve smaller samples drawn from state local populations I Conducted with nonprobability samples I Small scale surveys are used if you have a low budget or some specialized research purpose Systematic Procedures Interviews amp Questionnaires I Procedures are standardized in order to enhance the reliability of the data I Structured Interview objectives are specific questions are written beforehand I Unstructured Interview objectives are general discussion is wide ranging and spontaneous I Semistructured Interview specific objectives but interviewer has some freedom in meeting these objectives Quantitative Data Analysis Depend on whether a survey s purpose is descriptive seeks to describe based on certain characteristics attitudes experiences or explanatory investigate relationships between 2 or more variables Secondary Analysis the analysis of survey data by analysts other than the primary investigator who collected the data Uses amp Limitations to Surveys Surveys are used to describe and explain purposes o Offer the most effective means of social description Can address a much broader range of research topics than experiments can Disadvantages o Cannot causally explain variables direction of causality and third variables can be an issue o Less adaptable o Susceptible to reactivity Survey Research Designs Cross Sectional Designs Data on a sample cross section of respondents chosen to represent a target population are gathered at essentially one point in time Limited by the amt and accuracy of the information that the respondents can capably report Weakness don39t show direction of causality and not great for studying process of change 0 Contextual Designs I Sample enough cases within particular groups to accurately describe certain characteristics of those contexts 0 Social Network Designs I Focus on the relationships among social actors and the transaction flows occurring along the connecting links Longitudinal Designs Provides stronger inferences about causal direction and patterns of change The same questions are asked at two or more points in time 0 Trend Studies I Repeated cross sectional design I Cohort Studies Cohort people who experience the same significant life event within a specified period of time birth year Tracing changes across cohorts in repeated cross sectional studies 0 Panel Studies I Reveal which individuals are changing over time the same respondents are surveyed again amp again Drawbacks timely costly attrition rates and reactivity Key point panel surveys measure changes in individuals over time trend surveys track general social changes and cohort surveys gauge changes in age groups Key point survey researches seek to minimize error due to construction of the sampling frame coverage error sample selection sampling error securing sampled respondents nonresponse error amp data collection measurement error Face to Face and Telephone Interviewing Advantages over questionnaires clarification greater response rate greater response variability Face to Face Interviewing Response rate is high Allows for visual aids permits long and complex questionnaires Disadvantage cost amp difficulty of locating respondents Answering survey questions requires that respondents 1 Comprehend the question 2 Retrieve info requested from memory 3 Formulate a response in accord with the questions and the info retrieved 4 Communicate a response deemed appropriate Questions must be truthful relevant non redundant and clear Materials Available to the Survey Designer Open Ended and Closed Ended Questions Open Ended free response allows respondents to answer in their own words 0 Advantages may provide information that the researchers never thought about a wealth of information o Disadvantage coding this info is timely and costly amp may produce error CloseEnded fixed choice respondent chooses a response from those provided 0 Advantages require less effort and less coding 0 Disadvantages difficult to develop good questions Direct and Indirect Questions Direct Question there is a direct clear relationship between the question that is asked and what the researcher wants to know Indirect Question link between researchers objectives and question are less obvious ex Do you believe your co workers would mind having a woman as a supervisor Opposed to direct would you mind having a woman as a supervisor BECAUSE respondent might not be willing to answer about himselfwill most likely answer truthfully when talking about someone else Response Formats Ordinal Response Scales Likert Response 0 Strongly Agree Strongly Disagree 0 Excellent Very good Good Fair Poor 0 Very satisfied Moderately satisfied A little dissatisfied Very dissatis 0 Complete Confidence 1 2 3 4 5 6 7 No confidence at all 0 Fun x Boring Existing Questions If possible researchers should use existing questions developed by reputable sources Writing the Items Using Language Effectively Items must be unambiguous easily read sufficiently brief clear amp precise Items should be easy to read accurately if terms have multiple meanings the definition should be provided Use appropriate vocabulary for the target population Doublebarreled question two separate ideas are presented together as a unit o Ex What factors contributed to your decision to marry and have children what if you are married but don39t have kids Leading questions suggest a possible answer or make some responses seem more acceptable than others leading people to respond in a certain way o Ex How often do you smoke marijuana OR do you agree with many senators that taxes should be increased The Frame of Reference Problem A frame of reference is what the respondents are thinking about referencing when they answer a question o Ex how satisfied are you with UCSB people who check relatively satisfied can be choosing that one because of social reasons scholarships personal reasons etc o You can fix this by asking a follow up why you do feel this way Funnel Sequence moves from a general question to progressively more specific questions o Avoids the possibility that asking specific questions first will in uence responses to more general questions Inverted funnel sequence begins with most specific question and ends with most general o It can ensure that everyone is thinking about the same points or circumstances before expressing general opinions Response Bias Problems Social Desirability people may respond to questions based on how they should be responding rather than how they actually feel o Fix this indirect questions careful placement and wording of sensitive questions assurances of anonymity Acquiescence Response Set the tendency for respondents to be very agreeable Position Biases o Primacy effect most likely to pick the response options listed earlier o Recency effect when you hear the responses people are more likely to list the answer that was listed last Key features of experiments manipulation amp control The Logic of Experimentation True experiments research designs that contain the basic requirements to permit strong inferences about cause and effect Preexperimental and quasiexperimental do not have these requirements Basic Features Manipulated independent variable followed by a measured dependent var 2 groups one receives experimental treatment other doesn39t Except for manipulation both groups are treated EXACTLY the same Subjects are randomly assigned to a group Test of statistical significance to test the likelihood that the results could have occurred by chance Matching and Random Assignment To eliminate prior differences between groups researchers use matching 0 Match subjects on a particular trait IQ GPA and then randomly assign these people into groups Random assignment is the process of placing subjects in experimental conditions and random sampling is selecting research subjects from an eligible population Internal and External Validity Measurement validity do the operational definitions of a variable measure what they were intended to measure Internal validity a study is high on internal validity when it provides sound evidence of a causal relationship External validity question of generalizability can the results from this experiment be generalized to other populations from replication lab experiments are typically high on internal validity but low on external Sampling in Experiments Most experiments use readily available populations in their sample college students and use specific artificial lab settings9 low external validity Broadening the subject population increases external validity Staging Experiments Pretesting9 Subject Recruitment acquisition of informed consent 9 Introduction to experiment Random assignment 9 Manipulation of IV 9 Measurement of DV manipulation check9 Debriefing Experimental realism when an experiment is found to have impact on subjects and to seem real to them ludgment experiments subjects make judgments about stimulus materials and have little direct impact to subjects Mundane realism the similarity of experimental events to everyday practices The Experiment as a Social Occasion Reactive measurement effects responses due to subjects awareness of being studied Demand Characteristics When subjects enter the lab they have expectations about what will happen When they agree to participate in an experiment they often willingly do whatever is asked of them regardless of how dumb stupid and dangerous Demand Characteristics the particular cues in an experimental situation that communicate to subjects what is expected amp what experimenter hopes to find Evaluation Apprehension When subjects are apprehensive and nervous about being in an experiment They try to change their answers to satisfy the researcher Experimenter Effects Experimenter s expectations can skew the results of the study Minimizing Bias Due to the Social Nature of Experimentation Detecting Demand Characteristics o Simply ask subjects about their perception of the experimental situation Provide subjects with a false story about what the experiment is about Measure the DV in a different setting than the IV Keep subjects unaware that they are participating in an experiment o Ask subjects to adopt role of faithful subject Detecting experimenter biases o Doubleblind technique prevents the experimenter from knowing which condition a subject is in o Have a single experimental session that includes all subjects 0 Reduce amt of Contact between experimenter and subjects OOO Experimentation outside the lab Field experiments tend to be higher in external validity and mundane realism than lab experiments Threats to Internal Validity An experiment has internal validity when you can make strong inferences about cause and effect When we can39t separate the effects of the IV from possible 3rd variables we say effects are confounded 1 History events in the subject39s environment other than the IV that occur during the course of the experiment that may affect the outcome environmental like a recent political event a remark by a subject Maturation any psych or physical change that takes place within subjects that occur with the passing of time becoming hungrytired growing intellectually developing health problems Testing changed in what is being measured that are brought about by the reactions to the process of measurement tests become easier after practice subjects remember how to do the test etc Instrumentation unwanted changes in characteristics of the measuring instrument procedure Statistical Regression tendency for extreme scores on a test to move closer to the mean the second time a test is administered for example the assertive training program in extremely shy rated individuals Selection if there are differences in the composition of the control amp experimental groups ex AA program vs hospital treatment in alcoholics if economic well being was a factor the hospital treatment will do better because more people well off will choose that program Attrition the loss of subjects from the experimental groups differential attrition when the conditions of an experiment have different dropout rates Pre experimental Designs Design 1 The OneShot Case Study X 0 0 Some treatment is administered and then the group is observed tested to determine treatment effects 0 Provides no basis for comparing the findings with other observations Design 2 The OneGroup PretestPosttest Design O1X 02 o Observing a group introducing treatment observing group again 0 Provides a basis of comparison but still invalid Design 3 The Static Group Comparison Group 1 X 01 Group 2 02 0 Provides a set of data with which to compare the post treatment scores 0 Provides the scores of a control group which helps to control for history testing and statistical regression 0 Selection is a threat because there is no RA True Experimental Designs Design 4 The PretestPosttest Design RA 01 X 02 O3 04 Measures the experimental group before and after the treatment Suffers from the external validity threat of testing interacting with the IV called testing X interaction or testingtreatment interaction Design 5 The PosttestOnly Control Group Design RA X 01 02 Similar to the Static Group design but includes RA By eliminating the pretest the design becomes more economical and it eliminates the possibility of an interaction between pretest amp experimental Design 6 The Solomon FourGroup Design RA 01 X 02 O3 O4 X 05 06 More expensive but provides much more information Factorial Experimental Designs When 2 or more IV are studied in a single experiment they are referred to as factors Main effect the overall effect of the factor by itself bar graph Interaction effects the effect of one factor affects another factor line graph 0 When lines are parallel there is not interaction effect Quasi Experimental Designs When RA or control is not possible use quasi 1 Separatesample pretestposttest design RA 01 X X 02 2 Nonequivalent control group designs 01 X 02 O3 O4 3 Interrupted TimeSeries Design 01 O2 O3 O4 X 05 O6 O7 08 0 Example looking at how a gun control law affected homicides you could take data from the past 4 years amp later 4 years after the law was passed 4 Multiple timeseries Design 01 O O O X 0 O O 0 O2 0 O O O O O 0 Sources of Available Data 1 Public documents official records Vital statistics data on births deaths marriages divorces etc Death records Emile Durkhem s study Suicide US Bureau of the Census Census data is commonly available in 3 forms aggregate data on social units such as states towns and census tracts 1 percent and 5 percent samples of individual level data and individual records released 72 yrs after each census 2 Private documents Harder to obtain than public records but provides lots of insight Diaries letters notes 3 Mass media Newspapers magazines TV radio films Can analyze verbal and visual content 4 Physicalnonverbal materials Works of fine art clothing household items and various artifacts 5 Social science data archives Repositories of data collected by various agencies and researchers Advantages of Research Using Available Data Nonreactive Measurement 0 Reactive Measurement changes in behavior that occur because of subject39s awareness that they are being studied 0 Available data avoids this problem because the data is collected without the knowledge of those who produced it Analyzing Social Structure 0 Allows researchers to analyze larger social units Studying an Understanding the Past Understanding Social Change Studying Problems Cross Culturally Improving Knowledge through Replication and Increased Sample Size Savings on Research Costs Disadvantages of Research with Available Data Searching for amp Procuring Available Data Measurement of Key Concepts Data Evaluation and Adjustment Assessment of Data Completeness Content Analysis I A set of methods for analyzing the symbolic content of any communication 0 Selecting and Defining Content Categories 0 Defining the Unit of Analysis I Recording units units of analysis 0 Deciding on a System of Enumeration o Carrying out the Analysis Potentials amp Limitations of Field Research I Main purpose of conducting field research is to get an insider39s view of reality I Methodological empathy seeing things from the observed person39s point of View I It39s exible so it does will with rapidly changing situations I Recommended when it39s essential to preserve whole events when a situation is complex when the focus is on the relationship between the person amp the setting I Field research is less costly than experimental research in the lab I Ethical reasons like staging a riot are not appropriate to conduct in the lab it takes time dependent on observational amp interpretive skills of researcher difficult to replicate may lack generalizability Research Design amp Sampling I Begins with 1 broad substantive amp theoretical questions 2 a methodological approach based on observation in natural settings I Sampling revolves around convenience and accessibility I Theoretical sampling sampling broad analytical categories that will facilitate the development of theoretical insights Field Observation I Nonparticipant Observation 0 Someone who attempts to observe people without interacting o Structured when explicit and present plans for selectionrecording encoding is used can be used to measure the duration frequency of patterns 0 Unstructured the extent that these processes are implicit amp emergent allows for the discovery of behavioral patterns I Participant Observation 0 A researcher actively participates in the daily lives of people o To fully understand the shared meanings of a group Field Interviewing I Informants participants who are interviewed to gain more insight into a situation I Formal in depth interviews are used once the researcher has ben in contact with the informant for an extended period of time Stages of Field Research I Problems 1 selecting a research setting 2 gaining access to the setting 3 presenting oneself 4 gathering info in the field 5 analyzing the info and developing a theoretical scheme for interpretation I Select a setting9 gain access9 present oneself 9 take field notes 9 develop analysis 9 leave field Observer participant roles I Complete observer easier to play one role than to try to balance two I Complete participant access may be gained to info that would be withheld from an observer 0 Both avoids problem of reactivity I Covert research clarifying points by questioning those observed Membership roles I Peripheral members only marginally part of the settings they observe I Active membership assume functional role in the setting but retain sight of its temporary nature I Complete membership becoming fully immersed in the setting and attaining full member status Ethics standards of right amp wrong Research Ethics applying ethical principles to scientific research Data Collection and Analysis I Scientists have moral and ethical guidelines to provide honest and true results I Research misconduct fabrication falsification and plagiarism Treatment of Human Subjects I Harm o Ex Zimbardo s prison experiment 0 Costbenefit analysis assessing the full extent of costs and benefits o Researchers should inform subjects should screen out participants who may be harmed measures to assess harm afterward should be given I Informed Consent o Researches should give participants the opportunity to make voluntary amp informed decisions about whether or not to take part I Deception o Debriefing after deception is used researchers should tell participants what was really up with the survey 0 Use deception as a last resort considering alternative methods and sensitive ways to debrief I Privacy 0 Anonymity amp confidentiality 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 Why Study Research Methods We are constantly reading reports in magazines TV class etc and it39s important to know when reports are falsified It39s important to be able to make judgments about the quality of the data and limits of the conclusions Methodological Approaches to the Social World To determine whether a research question is a legit object of social research depends on 0 Social phenomena it must involve people how they think act feel and interact with one another 0 Social research is scientificso it must be possible to answer the question by making appropriate observations 4 Research strategies o 1 experiments 2 surveys 3 field research 4 use of available data The sciences are united by its objectives its presuppositions its general methodology and its logic The Aim of Science Produce knowledge to understand and explain some aspect of the world around us What makes something scientific It is the how and why knowledge is accepted by the scientific community 0 Form or logical structure of knowledge amp the evidence on which it is based Science as a Product Scientific vs Nonscientific Questions Scientists try to describe and explain questions Scientific questions have to be observable and they have to answer the how and why things happen Scientific questions are questions that can be answered by making observations that identify the conditions under which certain events occur Knowledge as Description We must describe objects events before we can understand and explain the relationships among them Concepts abstractions communicated by words or other signs that refer to common properties among phenomena o Note one concept one meaning 0 There must be agreed upon ways of tying them to tangible objects and events o Scientists create concepts because they are useful for understanding Knowledge as Explanation and Prediction Explanations attempts to satisfy curiosity o Labeling when the appropriate term is given in response to a question about what something is 0 Defining giving examples new terms are clarified with familiar terms and images o Evoking empathy when people offer motives or other good reasons for their conduct o By appealing to authority o By citing a general empirical rule 6 only this is capable of explaining past behavior and predict the future Empiricalgeneralizations when explanations are derived from observations or hypotheses They describe explain and predict a particular phenomenon To answer questions about WHY things happen and to explain empirical generalizations or laws you use theories Theory set of interconnected propositions that have the same form as laws but are more general or abstract Theories are judged as superior if they 1 involve the fewest number of statements assumptions 2 explain the broadest range of phenomena 3 makes the most accurate predictions Knowledge as Understanding This sense is gained by describing causal processes that connect events Causal relationship a relationship in which a change in one event forces produces or brings about a change in another Tentative Knowledge Scientists never achieve complete understanding they reach tentative understanding Science as Process Theories Hypotheses Observations Deduction Observations Hypotheses 9 Theories Induction Example Durkheim39s Study of Suicide Logical Reasoning Deductive reasoning conclusion is absolutely certain if the evidence is true 0 General Specific top down processing I Ex All union members are Democrats oan belongs to the union Therefore loan is a Democrat Inductive reasoning conclusion is uncertain even if the evidence is true because its content goes beyond evidence o Specific9General bottom up processing I Hubert Walter amp Ioan who are union members are Democrats Therefore all union members are Democrats Key point deductive reasoning as when deriving a hypothesis from a theory is either valid or invalid inductive reasoning when generalizing from specific observations is more or less sound depending on the scope of the observations Empiricism A way of knowing or understanding the world that relies directly indirectly on what we experience through our senses Implies that appeals to authority tradition revelation and intuition cannot be used as scientific evidence Objectivity Observation that is free from emotion conjecture or personal bias To the scientist it means agreement on the results of a given observation intersubjective testability Control To employ procedures that effectively rule out all explanations except the one in which the researcher is interested Ex Double blind studies where the administers research assistants don39t know which group is which Methods using several independent observers withholding information from subjects employing instruments like tape recorders Key point scientific inquiry is guided by empiricism objectivity and control Science Ideal versus Reality Scientists personal values may in uence what they choose to study how they conduct their research and how they interpret evidence Origins of Research Topics 1 The structure and state of the scientific discipline 2 Social problems 3 Personal values of the researcher 4 Social premiums 5 Practical considerations Formation of a researchable problem depends on deciding what relationships among what variables of what units are to be studied Units of Analysis The objects or events under study o Ex Social roles individual people relationships orgs Variables Characteristics of units that vary take on diff values categories attributes for different observations o Ex Age gender marital status level of education income Social scientists study relationships among variables variables depicts and differentiate units of analysis Types of Variables o Explanatory variables ones that are the object of the study I Dependent one researcher is interested in explaining and predicting I Independent the in uencing variable 0 Extraneous Variables all others that don39t relate to the study I Antecedent variable that occurs before the independent and dependent variables I Intervening an effect of the independent variable and a cause of the dependent variable lurking variable 0 Control Variables I Held constant during observation I This variable cannot account for any variation that occurs in the explanatory variables Quantitative 0 Its values consist of numbers amp can be expressed numerically Qualitative 0 Discrete categories and nonnumerical differences between the categories Relationships among Quantitative Variables Positivedirect relationships as one increases the other increases Negativeinverse relationship as one increases the other decreases Correlation coefficient numerical measure of the strength and direction of linear relationships Statistically Signi cant associations that are not likely to have occurred by chance or random process The Nature of Causal Relationships Association the variables need to be related to one another Direction of in uence 0 The cause must precede the effect Elimination of all hidden 3 variables 0 Spurious relationship when a correlation has been produced by an extraneous third factor and neither of the variables involved in the correlation has actually effected each other 0 To control for this researchers must identify and control for extraneous variables KEY POINT an association between variables doesn39t mean they39re causally related Causal inferences require association evidence of direction of causality and elimination of all extraneous variables Formulating Questions and Hypotheses Hypothesis an expected but unconfirmed relationship between 2 or more variables Expressing testable hypotheses o If then conditional statements o Mathematical statements 0 Continuous statements the bigger the X the bigger the Y o Difference statements Research Purposes and Research Design To explore a phenomenon in order to formulate a more precise research problem for further study To describe a particular situation as completely precisely and accurately as possible To examine and formally to test relationships among variables Exploratory Studies when little is known about something Descriptive Studies describe some phenomenon Explanatory Studies formally seek answers to questions and hypotheses Stages of Social Research Stage 1 Formulation of a Research Question Stage 2 Preparation of the Research Design Stage 3 Measurement Stage 4 Sampling Stage 5 Data Collection Stage 6 Data Processing Stage 7 Data Analysis and Interpretation The process of assigning numbers or labels to units of analysis in order to represent conceptual properties The Measurement Process Conceptualization o The process of formulating and clarifying concepts Operationalization 0 Operational definition describe the research operations that specify the values or categories of a variable Operational Definitions in Social Research Manipulation operations designed to change the Value of a variable Measurement operations estimate existing values of variables Verbal Reports Aka self reports Composite measures 0 Indexscale responses to several questions combined KEY POINT many operational definitions are possible but rarely does any one perfectly represent a concept therefore it is usually preferable to use multiple indicators Observation Provides direct and unequivocal evidence of overt behavior but it also is used to measure subjective experiences feelingsattitudes Archival Records Existing recorded information Selection of Operational Definitions Survey verbal reports Field research observational measurement Experiments verbal reports ampor observation in addition to manipulation procedures The selection of operational definition should be guided by levels of measurement reliability and validity Levels of Measurement Nominal measurement o Categories of 2 or more gender race religious preference I Exhaustive there must be enough categories so that virtually all people will fit into one of the categories I Mutually exclusivity the people things being classified must not fit into more than one category Ordinal Measurement o Numbers indicate the rank order of cases on some variablethere are no intervals between the numbers ranking of favorite TV shows Interval Measurement 0 Has qualities or nominal amp ordinal with equal distances between the numbers you can tell the amt that exists between categories IQ test scores Ratio Measurement o Includes features from the three other levels plus an absolute zero point weight age income years of employment number of siblings Reliability and Validity Reliability concerned with stability and consistency Validity does the operational definition measure the concept that it is supposed to measure accuracy Sources of error Systematic measurement error factors that systematically in uence either the process of measurement or the concept being measured o Reactive measurement effect when the respondent39s sensitivity to a measure if affected by the process of obseravation 0 Social desirability effect when the respondent gives answers that are socially desirable instead of true answers Random measurement error result of temporary chance factors 0 Ex Tired bored respondent temporary upswings and downswings in health KEY POINT a completely valid measure is free of systematic and random error a completely reliable measure is free from random error but may contain systematic error Reliability Assessment Testretest Reliability if a respondent is tested twice the test should yield similar results 0 Problems the person responding or person recording may remember and simply repeat the responses they gave the first time OR something between the two responses could be affecting the results SplitHalf Reliability test questions are split into halves to test whether the respondent will have similar responses on both Internal Consistency researcher examines the relationships among all the items simultaneously rather than arbitrarily splitting the items Intercoder Reliability the extent to which observers agree on what they are observing Improving Reliability Exploratory studies preliminary interviews or pretests are ways to gain crucial information Adding items of the same type to a scale An item by item analysis Instructions to respondents may not be clear Validity Assessment Face validity does a measure seemlook like it39s measuring what it is supposed to measure Content Validity the extent to which a measure adequately represents all facets of a concept Criterionrelated Validity measuring instruments that have been developed for some practical purpose other than testing hypotheses or advancing scientific knowledge Construct Validity emphasizes the meaning of the responses to one s measuring instrument Convergent Validity does the measure agree with another measure that is supposed to measure the same thing Discriminant Validity is the measure distinct and different from other types of measures We seek knowledge or info about a whole class of similar objects events population We observe some of these sample Why Sample Need to test more than one person to notice variability in a population It39s costly and time consuming to sample the entire population A sample chosen on important criteria can yield more significant results than if you surveyed the entire population Population Definition Target population the population to which the researcher would like to generalize his or her results o The researcher determines what factors should be included and excluded age demographics race gender etc o Geographic and time referents are always included Sampling frame the set of all cases from which the sample is actually selected o Do this by list all cases OR provide a rule defining membership Sampling Designs Ideally the sample should be representative of target population Sampling design the part of the research plan that indicates how cases are to be selected for observation 0 Probability sampling all cases in the population are randomly selected and have a known probability of being included o Nonprobability sampling chances of selecting any case are not known because cases are nonrandomly selected Probability samples remove the possibility that investigator biases will affect the selection of cases AND by random selection the laws of math probability may be applied too estimate the accuracy of the sample Probability Sampling Random Selection Random refers to a process that gives each element in a set an equal chance of being selected Good representative samples always involve random sampling One procedure is the lottery method Every possible combination of cases has an equal chance of being selected To achieve this you need a complete list of the population amp random selection of cases Parameters characteristics of the population what we39re interested in knowing Statistics sample estimates of population parameters Sampling distribution the statistic calculated for all possible samples of a given size Sampling error the amount that a given sample stat deviates from the population parameter Standard error the stat measure of the average of such errors for an entire sampling distribution Confidence Interval the range of values within which the estimated population value is likely to lie Level of Confidence the probability ex 95 that the interval contains the population value The population is subdivided into two or more mutually exclusive segments strata based on categories of one more more combination of relevant variables Example Separate a population into demographics of age O 20 20 40 40 60 60 80 80 100 AND randomly sample 5 people from each demographic group When there are differences across strata stratified sampling ensures that these differences are accounted for and are not free to vary within the sample eliminate source of sampling error increase efficiency Used to guarantee that variable categories with small proportions of cases in the population are adequately represented in the sample Disproportionate Stratified Random Sampling selecting uneven samples from each strata ex Selecting 5 people from group one 10 people from group 2 20 people from group 3 Simple rand amp Strat both assume a complete list of pop is known Obtaining a sample in stages when the pop is unknown is cluster Population is broken down into groups o First randomly sample for clusters 0 Then either sample the entire cluster population single stage OR randomly sample again for a different criteria multistage o The first sample primary sampling units second sample secondary sampling units Reduces costs of data collection decreases travel costs Probability proportionate to size sampling make the selection of clusters or cases within clusters proportionate to the size of the cluster Selecting every k th case from a complete list or file of the population starting with a randomly chosen case from the first K cases on the list 2 requirements sampling interval K the ratio of the number of cases in the population to the desired sample size and a random start Example there are 2500 students divide 2500 by 100 to get 25 sampling intervals Select a random number between 1 and 25 and starting with that number select every 25th students thereafter suppose the number you chose was 19 you would then sample the 19th student 44th student 69th student 94th student etc Each case has an equal chance of being selected but each combination does not Problem amp advantage most lists are not random but are ordered in some way Nonprobability Sampling No random selection 0 Don39t control for investigator bias o Pattern of variability can39t be predicted from probability sampling theory can39t calculate sampling error or precision I Researcher simply selects a requisite number of cases that are conveniently available 0 Ex All the students in one classroom anyone who happens to pass the library I Easy quick inexpensive perfect for early stages of a study I Should be avoided if the goal is to make inferences about a pop I Investigator relies on their expert judgment to select units that are representative or typical of the population I Identify important sources of variation in the population and then select a sample that reflects this 0 Ex Researching which populations have a certain trait that you39re looking for and then going and sampling there I Weakness making an informed selection of cases requires knowledge of the population before the sample is drawn I Form of purposive that39s similar to stratified Divide the population into relevant strata and then give quotas for each category o Ex Categories are 1 2nd 3rd 4th years on campus You want to sample 25 of each category You stand outside the UCen and approach anyone until the quotas are filled Other Sampling Designs 1 Combined Probability and Nonprobability Sampling I Used to reduce cost of probability sampling I Common combinations multistage probability clusters amp quota sampling 2 Referral Sampling I Respondents who are initially contacted are asked to supply names and addresses of members of the target population I Network Sampling I Snowball Sampling chain referral Factors Affecting Choice of Sampling Design 1 What is the stage of research 2 How will the data be used 3 What are the available resources for drawing the sample 4 How will the data be collected Factors Determining Sample Size Population Heterogeneity 0 The degree of dissimilarity among cases with respect to a particular characteristic 0 The more heterogeneous the population with respect to the characteristic being studied the more cases required to yield a reliable sample estimate 0 Standard Deviation measures a population39s heterogeneity Desired Precision Sampling Design Number of Breakdowns planned Lecture 4814 Unique characteristics of science continued 0 Science is empirical More than simply noticing My friends who eat vegetables are healthier I Rigorous empiricism conscious deliberate observations Set up study with controlled factors Many observations are made 0 IE more studies repeated results can see a pattern I Science is objective Opposed to subjective which is biased Control remove personal biases Even so there is always a spin to objective research 0 Public side attempts to catch those viewpoints must be able to challenge Explicit rules standards amp procedures 0 Science is systematic amp cumulative I Builds on prior studies theory I Practically impossible to do totally original research Must ask what we know already I New knowledge modi es old 0 Goals of scientific research What can science tell us I Description Looking for social regularities of aggregates 0 Means seeking pattems things that happen regularly 0 Science not interested in a unique case I IE predictors of infidelity to con rm evidence of patterns I Aggregate deciding what factors will be used in research IE infidelity in freshman females I Science can tell is what is Handles description fairly well I Explanation Develop understanding of WHY pattems exist what causes what Can create a causal chain 0 Science can tell us why it is I Prediction Predict outcomes given certain factors If I know X Y Z I can tell what you39re likely to do Science can tell what will be within certain realm of probability 0 Can sometimes make good prediction without an explanation I Science CANNOT settle questions of value CANNOT tell us what should be right wrong good bad moral immoral Science get brought into debates but cannot address rightness or wrongness 0 IE abortion debate science can tell fetus development is used to support or refute both sides of debate but not if abortion should be considered okay 0 Science can only inform debate The Research Process Theories Hypotheses amp Research Questions 0 Wheel of Science cyclical process of research always ongoing building of knowledge 0 Theories gt Hypotheses gt Observations I In this order engaging in deduction Traditional science quantitative methods I Theories Something scientists posit may be true having reason to suspect 0 Can also be Observations gt Empirical generalizations gt Theories I In this order engaging in induction Humanistic interpretive qualitative methods To remember difference think of how gathering data inward to make point inductive Difference between quantitative and qualitative 0 Quantitative I Employ numerical measures amp data analysis I Adhere strongly to scientific goals amp principles objectivity empirical data etc I Examples Surveys Experiments Content Analysis 0 Qualitative I Also called interpretive research or field research I A humanistic form of social science Values some aspects of science especially empiricism 0 Data is very significant Having personal spin on it is okay Values researcher subjectivity I Examples Participant Observation Depth Interviewing Conversation Analysis I Note There is also purely humanistic research in communication called critical studies IE rhetorical criticism feminist analysis cultural studies etc What is the difference between Basic and Applied Research 0 Basic theoretical Research I Testing building theories conceptual ideas advancing what we know about a topic Valuable aside from having practical use IE Math is valuable for its own sake 0 Applied practical Research I Applying research to solve practical problems I IE testing effects of an ad campaign policy change new school program company technology etc Use math to build bridge 0 Matter of emphasis in any given study these descriptions are of extremes I Note Even the most theoretical research has practical value and even most applied research uses theoretical reasoning and arguments to form hypotheses etc Lecture 41014 Using Theories in Research 0 Theory an attempt to explain some aspect of social life I A scholar39s ideas about howwhy eventsattitudes occur I Includes set of concepts and their relationships Terms for thingsideasparts of the theory because they are conceptualized Researchers must define these terms 0 IE Social Cognitive Theory Bandura I Theory about how we leam mostly applied to children or in communication media effects I Theory that leaming happens through watching modeled behavior see parent brushing teeth child imitates Requires attention retention motor production motivation e g rewardspunishments 0 What are some concepts involved here I What does it mean to retain information I What is it to be modeled I Does it have to occur inperson I Definition of rewardpunishment I Definition of leaming etc I Concepts are studied as variables They have variations that can be measured 0 IE Motivation I How does it vary Rewarded vs punished model amount of rewardpunishment etc 0 IE Model I TV character vs parent TV hero vs villain degree of likeability or similarity to viewer etc 0 Scientific theories should be falsifiable I If not a study cannot actually test the theory I Able to be til empirically with data I There is some result that if you got it would show the theory is Q Note you can never prove theories true can only gain supportevidence 0 What about Soc Cog theory I Has been tested many times cannot say it39s right only that there is much evidence for it 0 What about theory of man made global warming I Evidence suggested in press temp change ice change droughts oods lackexcess of hurricanes etc many contradictions I Ends up being politics and less of a scientific theory Using Theories in Research 0 From prior findings andor theory we derive a testable hypothesis I A speci c prediction about the relationship between variables in your study I IE Using Soc Cog Theory to make a prediction about the effects of TV violence H1 TV violence viewing will produce more aggressive behavior than will non violent TV viewing What are the variables involved here 0 Definition of violence definition of aggressive behavior etc I What if theory or previous research does not lead to a specific prediction Or if previous findings con ictinconclusive Pose research question instead of hypothesis 0 Difference is that research question is not a statement but an actual question 0 IE To what extent will children imitate behavior of a TV character whom they do not likerelate to Will there be gender differences in children39s imitation of violence I If study has only research questions appears to be a lack of background knowledge Testing a hypothesis An example 0 Researcher A I Soc Cog Theory children leam behavior by watching models behave I Hypothesis watching TV violence will increase kids aggressive behavior I Given large grant to conduct research Finds out how much violent TV viewed How much aggression is shown on playground Random sample of subjects used Each scored and plotted on X Y axis shows positive linear relationship 0 Conclusion TV violence increases aggression I Possible problems Correlation relationship bw variables is not causation Could be another cause for aggression in subjects perhaps parental neglect leading to more violent viewing leading to aggression Cannot tell difference from aggression before and after chickenegg issue Problem with conclusion TV violence increases aggression gt should be TV violence is related to aggression 0 Researcher B I Catharsis Theory watching others behave allows purging of pentup feelings I Hypothesis watching TV will reduce kids aggressive behavior I Lack of funds for research Has subjects watch one of four clips 0 5 10 20 acts of violence in clip of varying degrees of violence Random assignment of subjects Number of hits on toys variety available without other kids toys used as weapons Plot on XY axis acts of aggression with degree of violent content viewed negative linear relationship 0 Conclusion TV violence decreases aggression I Many possible problems with lack of resources I However conclusion correct because controlled study accounts for behavior prior to viewing Technically should state correct for these lab conditions Types of Hypotheses amp Research Questions 0 Hyps and RQs can be I Causal state how 1 variable changesin uences another I Correlational state mere association between variables
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