IAT METHODOLOGY 1
ExaminingAge Stereotyping In Students Using IAT Methodology
ExaminingAge Stereotyping In Students Using IAT Methodology
ImplicitAssociation Test measures attitudes in many dimensions indirectly. Ituses many dimensions including old-young and safe-unsafe as comparedto the conventional way where only one dimension is considered suchas dislike/like. The traditional scale was difficult as not allattitudes were represented (Weiner, 2003).The introduction of theindirect measurement that was the Implicit Association Test hasalternatives preventing problems associated with the traditionalmethod (Intelecom, 2006). Implicit Association Test ismulti-dimensional and assesses attitudes placing value on researchand market targeting the consumers. It creates profiles that arecomplex creating room for tests, personal branding or brandpersonality. It is not affected by stimuli and uses brand identifierssuch product pictures, logos and signatures all used in measuringattitudes (Nelson, 2002). The indirect measure is dependentupon the brain activation or facial expression that interfere withattitudes. These indirect measures can use latency or physiologicalbased measures. Physiological measures employ techniques such asbrain imaging and eye tracking to observe mental activities and tasks(Zager, 2012). Latencies responses use standard forms of attitudeassessment including computers and testing environment (Kalat, 2011).The Implicit Association Test is a valid instrument in themeasurement as it captures the attitudes of the consumers. Age can beused to capture the attitude of consumers to influence them toacquire certain products. Implicit Association Test is sensitive todifferences that can be regarded as individual including age. Whileconsidering attitude accessibility, it can capture the automaticassociation that is different from other measures that are obvious(Wittenbrink & Schwarz, 2007). Implicit Association Test isa tool that is easy to use in assessing associations and is widelyused in research. It sorts tasks that require fast stimuli such aspictures and words. An example can have categories of men and womenwith two age groups. The categories will display in the computerscreens and the age groups maybe using popular personalities. If apositive Implicit association Test is noted, it is regarded as astrong connotation with the pairing category in the tasks that wereinitially combined (Schneider, 2004).1.1 TheoreticalBackground of IAT Theoretical background about ImplicitAssociation Test predicts and explains the relationships presentbetween behavior, relationships and events in several instances byconstructing reality models. The main aim of the Implicit AssociationTest was to allow multi-dimensional assessments that were valid,affective-evaluative components and economically feasible as opposedto using only one attribute dimension. Distinct attributes dimensionallows for consumer associations that are differentiated to a brandgenerating profile brands (Schmitz, 2010). An example is wherethere are participants picked randomly aged between 20-40 years.Among those participating, over 80% own cars. Their interest in carsis assessed by questions on whether they read magazines about cars,latest updates about car developments or attention to caradvertisements (Sargeant, 2011). In this case, a deductive approachhas been employed to modify and evaluate the relationships betweencar owners and how passionate they are about cars. The existingtheory has been modified to test the predictions between thephenomena’s that have been observed (Gregory, 2001) In such acase, the rationale has to be developed followed by the hypotheses, aframework base for the references including definitions, the researchdesign, interpretations, observations and generalization. Thismechanism serves as a path to help define the relationship that existbetween the variables that have been used (Schmitt, 2004).1.2Objectives of the study Theaims of the study:To help the researchers see the variablesthat are present in age stereotyping.It provides a frameworkthat is generalized that can be used in the analysis of data on agestereotyping.It is crucial when preparing research proposalsusing the experimental methods in age stereotyping labexperiments.METHODOLOGY The overall methodologyused in the study of gathering and analyzing data in order to achievethe research objectives is outlined in this section.2.1Research Design Rudman (2011), states that research designis the manner in which data is collected, measured, and analyzed inorder to achieve set objectives. It refers to the overall plan ofconducting the research and it helps to answer questions of theresearch and achieve the purpose of the study. In this context,therefore, the research design entailed the collection of relevantdata to determine age stereotyping in students. The collection ofdata was done within a laboratory set up where the participants wererequired to answer different questions, both explicit and implicit.The study used cross-sectional survey criteria because the study wasnot confined to the collection and description of the data, but alsosought to both establish the existence of certain relationships amongthe variables as well as determining the relationship between age andcompetence.2.2 Population Hofmann (2003),describes the population as a complete set of individuals, cases orobjects with some common observable characteristics. A givenpopulation has some characteristics that differentiate it from otherpopulations. The population to which the researcher want togeneralize the results of the study on is referred to as a targetpopulation. The target population (participants) of this study was agroup of students who expressed their opinions regarding the youngand the old. Giving each of the participating student a chance toexpress their opinions about the two age groups is essential inunderstanding stereotypes among students.2.3 DataCollection There are two main categories of data: secondaryand primary data. The study used primary data collected by psychologystudents in a laboratory setting. The data collected summarized thestudents explicit and implicit prejudice, stereotype valence, ageismand age-related prejudice.
2.4 DataAnalysis Procedure Teitz (2007) defined data analysis assystematically looking for patterns in the data collected andformulating ideas that account for those patterns. The process isbroken down into four related tasks namely organizing, developingideas, drawing and verifying conclusions. For quantitative data, thestudy used Implicit Association Test (IAT) that provides a D score asa measure of each participant’s biases. A D score of zero indicatesthere are no biases while a positive D score indicates biases towardsthe older people. The data analysis procedure aimed at gettinganswers to several implicit and explicit questions. The explicitquestions included: Did participants consider young people to be morecompetent than old people? Did participants consider old people tohave warmer traits than young people? Was there an overall bias? Theimplicit questions included: was there an overall implicit bias? Didparticipants respond more quickly to consistent rather thaninconsistent pairings? Was one bias for example young, greater thanthe other bias for example old? The last question considered by dataanalysis procedure is whether implicit attitudes predicted explicitattitudes.2.5 t-stat and p-value Theseboth showed the significance of individual model parameters. Thepositive or negative nature of the t-stat is regarded because it isan absolute value. These two statistics go hand in hand and they arenegatively related. The higher the t-stat, the more significant theparameter of interest during the lower the p value, the moresignificant the parameter of interest. When the t-stat is greaterthan 2.326, the results are statistically significant. When the pstat is less than 0.02, the individual parameter is statisticallysignificant. When an individual parameter is significant, the nullhypothesis is rejected (Breckler, 2006).3.0 Interpretationof Results After carrying out the study of age stereotypingin students, results were obtained. The results obtained in thelaboratory indicate the presence of competence bias, which was one ofthe individual elements being researched on. The null hypothesis (Ho)indicated that there was no competence bias but because the t-stat isgreater than 0.236 at 8.69 and the p value is less than 0.02 at 0.01,it should be rejected. The study also aimed at determining the warmthbias and the null hypothesis stated that there was no warmth bias.However, results indicate there was significant warmth bias with ap-value of 1 percent and a t-value of 0.493 and thus the nullhypothesis should be rejected. In terms of overall bias, theresults indicate it was not significant. The null hypothesisindicated that the overall bias was not significant and thus itshould not be rejected. This fact is proved by a t-stat that is lessthan 2.326 at 0.27 and a p value greater than 0.002 at 0.79. Theresults also indicate that individuals respond faster to consistentthan to inconsistent pairings with a t-value of -0.75 to reject thenull hypothesis. Finally, the results indicate that individuals whoproduce a high bias score on one task do not necessary produce a highbias score on a different task. The t-value of -1.40 and p value of0.17 indicate that the null hypothesis should not be rejected.
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