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Reading- attitudes- scales - Coggle Diagram
Reading- attitudes- scales
Can be explicit or implicit scales in measuring attitudes- some completely explicit-people asked to agree or disagree on some statements- led to lots of use of attitude questionnaires around 1930s onwards- challenge not looking at scores across items but find link between specific item and certain behaviour Fishbein and Ajzen looked at both evaluative and belief component of attitude- created expectancy value model- each view underlying certain attitude weighted by strength of relationship to attitude object
Can be explicit or implicit scales in measuring attitudes- some completely explicit-people asked to agree or disagree on some statements- led to lots of use of attitude questionnaires around 1930s onwards- challenge not looking at scores across items but find link between specific item and certain behaviour Fishbein and Ajzen looked at both evaluative and belief component of attitude- created expectancy value model- each view underlying certain attitude weighted by strength of relationship to attitude object
Linkert scales- thought Thurstones one took too long- got a 5 part scale ranging from 5-1 on agreement with statements- scores totalled to give attitude score- yet in many cases responses to Qs won’t correlate equally to final scores- will drop those responses alongside any ambiguous items on scale where responses don’t differ between those w/ opposing views- rest summed up to give attitude score-
Where you can items of scale for 1/2 questionare where agree= positive and for other half that means negative eg 1= really negative now means really positive- removes bias of acquiescent response set
Guttman scale- scores on Thurstone/ Linkert don’t have unique meaning- 2 can receive same score (averaged or summed) but put quite diff items- believed can measure single unidimensional traits can be measured by statements in continuum ranging from least to most extreme of responses if agree with one item on scale believe you agree with all other statements before it and this is most extreme statement they’ll agree w/
hard to get unidimensional scale-believed people respond using multiple dimensions
dimension represents a particular aspect of human traits or behaviors that varies along a continuum- (praditus)
Osgoods semantic differential- didn’t use opinion statements like others- looked at connotations people see around word/ concept-believed underlying dimension= eval- if see word as good or bad-believed this eval relates to definition of attitude and so can use to see attitude scores- name concept eg nuclear power- give responses in evaluative 7 point scales good, bad, fun, unfun etc- average scores across scales
Doesn’t require writing attitude relevant Qs- more reliability as more scales are used- yet could be too simple - does look at eval meanings of concepts but not opinions which are main factor of other scales
Using attitude scales today- combo Linkert and semantic differential have measured complex evaluations- eg voters use semantic scale to eval various issues then use Linkert to answer how each candidate stands on these issues- combine 2 answers - predict who they’ll vote- yet recent polls been so reliable lessened people’s confidence in these polls as representation of voting behaviour
Linkert scale also helped w/ modern question area which - starts on basis attitude measured may have underlying dimensions- computer programmes allow for multivariate statistical methods eg factor analysis (looks at underlying structure of questionare data) - Linkert tests for unidimensionality via working out item- total score relation- instead factor analysis- looks at matrix of relations between alll pairs of items in scale- items= statement or Q with accompanying scale- seeing variance or covariance- eg hyperventilating, nausea, tiredness can be grouped into underlying factors eg depression and anxiety- so can estimate if single dimension or more explains range of patterns of response between each respondent
Factor analysis can reveal both interesting and subtle substructures- eg Glick and Fisk look into sexism found 2 subscales hostile and benevolent sexism- revealed covert mixed feelings (ambivalence) in ppts - on other note- when reversing Linkert scales find constant- biggest no. in scale +1 then minus number from constant- eg if 1-7 = 1= most extreme disagree 8-1= 7
Attitudes- especially more (evaluative or affective) ones? Cog= more scientific eg agreement that alcohol causes type 2 diabetes- affective more emotional eg alcohol is fun- can be measured via physical methods eg skin changes, pupil dilation, heart beat- faster could mean more intense attitude- advantage- ppt may not know attitudes are being measured and even if aware may not be able to control attitudes- other method for measuring (avoidance related feelings of threat or approach related feelings of challenge)= cortisol level in blood or saliva
Negatives- most sensitive to variables outside of attitudes - skin changes can happen due to novel or incongruous stimuli not to do w/ attitude, heartbea5 can increase w/ problem solving tasks nd decrease w/ vigilance tasks eg watching something- also don’t know what direction people can feel opposed or supportive w/ same intensity- alternative- can look at face muscles - Caciopoppo et al observed facial muscles when ppts heard speech they agreed or disagreed w/
Can go further- look at brain chemistry Levin look at ERP event- related brain potentials- people viewing white or black faces- looked more at white faces- saw black faces as more superficial- example of relative homogeneity effect- see in groups more differently and outgroup members as more the same
Unobtrusive measures- observation approach - look at people’s behaviour without interrupting processes measured or cause unnatural behaviour- eg amount of bottles in bins could look into attitude of town toward drinking, can look at public records- eg chemist reports of which doctors are giving specific drug/ look at gender roles- reports of which books with male or female lead characters are being checked out at library, can be internet data- eg what insta posts are people most drawn to/ which is most popular movie on Netflix rn
Other unobtrusive method- nonverbal behaviour- can see who’s more close to each other- also can look into emotions eg fear- when scared tend to sit/ stand closer to those you know
most likely self report measures= more likely- selling point of unobtrusive is that they have diff limitations- so could do both and look at correlation? Or potential obtrusive method w/ less bias= bogus pipeline technique- strap up ppts to ‘lie detector’ say will measure intensity/ direction of emotion- lead to believe no point in lying- be more overt w/ socially unacceptable/ undesirable attitudes or behaviour
Looking at cover attitudes- implicit measures- similar but different to unobtrusive- in implicit looks at behaviour people didn’t know they had whereas unobtrusive looks at behaviour people might want to conceal (social desirability)
looking at language- Anne Maase- ingroup positive bias and outgroup negative bias- use more abstract terminology rather than concrete in talking abt negative aspects for outgroup and opposite for positive aspects of same group- this relationship could be used to show prejudice toward particular group
Priming- (activation of accessible categories or schemas in memory that influence how we process new info) - can be used to look at how response time differs when underlying attitudes align w/ what’s right- white ppts took longer to say positive things about black images and same for black ppts about white images- can also see stereotyping when social category first- eg ppts shown photos of people asked if old or not old then given anagrams- some words relating to age some words not- showed som3 differences in time/ slowed down altogether- could be due to associations of elderly w/ slow
Implicit association test-reaction time test to measure attitudes- particularly those people may want to conceal- with research found ppts quicker to relate words that they have attitudes toward- eg if had negative experience with librarians quicker to relate them and word bad than if you had no feeling toward them- became popular in west- meta analysis found as study got more socially sensitive eg abt race instead of yoghurt- ppts more likely to do image management- in this study- found IAT much more predictive value for more socially sensitive than self report- but in more meta analyses found low correlation between IAT and explicit measures when looking at intergroup and ethnic bias - believed should predict better in interracial and inter- ethnic settings