Mind Genomics WHO RUNS AMERICA Arthur Kover Howard Moskowitz Paolo Gentile Eugene Galanter Yasir Batalvi 2 As we enter the middle of the second decade in this new millennium, going from 2013 with some trepidation into 2014, many of sense an un-ease with America, a nation founded by Deists in 1776, but really founded a century before in the English optimism of the New Jerusalem. The hopes of the founding fathers were high, and realistically so. The Enlightenment promised increasing rights, first to landholders, then to men, then to women.Those days of the nations founding were heady ones,optimismreigning,dispellingthememoriesofEnglishoppression,butreallydispellingthe memories of oppressive history.America was to be the light of the nations, the new Israel in some minds,thelampthatwouldlightthewayto mans progression,evenuptothelevelofangels,but more realistically to the level of free and thinking men. All good things come to an end, or so folk wisdom tell us.If we move beyond such folk wisdom to consult historians looking for patterns, we need look only to Arnold Toynbee, the famed author of a study of history (REF). It was Toynbees view that all civilizations are more or less doomed, doomed because like any living organism, the civilization begins with the power and optimism of the young, proceeds through an adult phase, and become inevitably caught up in the mis-workings of its own internals,theslowmovementtowardage,infirmity,andultimatelydeathanddissolutions.Civilizationsaresubjecttotheirownarcs,theirownindividualagingprocesses,andthe accompanying ills and maladies.Toynbee looked at civilizations, and with his world view was not at all astonished at the decline of each. The general pattern was certain. The civilization would fall. The details were of interest to the historian. Is Toynbee right?Or, if not Toynbee, are the different blogs about America right, blogs and points of view that one can find all over the internet, blogs pointing out the different wrong actions taken by the American government, wrong actions attributed not so much to accident and error as to deliberate bad will, and a hidden desire to crash our country, and perhaps even our civilization. When we began this book author Arthur Kover had not articulated the position in this way, but simply guided again and again by his statement that somehow, something is wrong. But just what is it.Inspired by his sincere worry that America was fraying, we encouraged Arthur to think about this fraying as topics in Mind Genomics, and to move from thinking like a sociologist to thinking like an experimenter. And so this volume, a series of exploration of topic in social decay,done from the perspectiveofexperimentalscience,butdealingwithtopicsbestencounteredinthedescriptive science, rather than the experimental science, of sociology. Andnowonwardstothescience,tothetopics,tothecitizens,totheirminds,andtoasomewhat different way of picturing America. 3 On Methods Who Runs America Introduction ThegenesisofthisbookwasauthorKoversconcernwiththebreakdownoftheAmerican society, during first and now second decades of the 21st Century.Kovers education was sociology, interestedinlarge-scalesocialprocesses.YetKoverhadspentyearsintheadvertisingindustry, sensitive to the fact that the large is written in the small, that to understand the response of the many itmightnotbeabadideatolookatthemindsofindividuals.Koverwasalsoanempiricist,and sympathetictoexperiment.Itwouldbedatathatconvinced,aheritagefromhisdaysasasocial scientist,andapredilectionfromhisdaysinadvertising.Thesympathytoexperimentcamefrom advertising as well, and the strong bent towards experimentation held by author Moskowitz. And so begins our book, and our first chapter, on Methods. This first chapter provides the reader with a sense of how we as researchers, scientists, social observers, grapple with the task of understanding the breakdown of American society, the fraying of America. Thereaderwill,bynoaccidentofcourse,noticethatourbookiscouchedinstatistics,in models, in understanding what Moskowitz calls the algebra of the mind. Our ingoing assumption is thatbyunderstandingresponsestoteststimuli,thoseteststimulibeingsystematicvariationsof ideas, it will be possible to understand how people react to current situations in a profoundly deep way.Anditwillbefromthatdeepunderstandingthatwecomprehendimportantaspectsofthe Fraying of America. And now on to procedure. What exactly do we study? Wearestudyinghowpeoplerespondtocertainsocialissues,evensocialproblems.Furthermore we study how todays people think about todays problem. Theforegoingstatementaboutwhatwestudyseemssuperficial,incomplete.Wearenot talking about deep issues, or about theory, but just about people thinking about problems. In common parlance,therefore,wheresthesecretsauce?Justwhatarewedoingthatmakestheeffort worthwhile to do the research, and what is the reward for the reader? The effort to do the research is obvious. We are working in the scientific tradition, trying to understand how people respond to their world.The way we are doing the work makes it even more important. We are using a method from consumer research known as conjoint analysis.Rather than asking the respondent to tell us about how we feels, we make the task a bit more subtle, harder to game. We present the respondent with different vignettes, descriptions of the situation of today, and ask the respondent to rate the vignette on a scale. From the pattern of responses we deduce how the respondentisthinking,eveniftherespondentcannotarticulatehisthoughtsinadirect,simple manner. It is that approach which is novel, and which enables to probe deeply into the mind of the respondent. We will introduce the approach by explicating a study, specifically a study we call Who runs America? 4 Introduction to the Study Power attracts. As much as we would like to believe that we come that wonderful nave land ofJeanJacquesRousseau,wherearefedintheirnaturalstateofnavegoodness,thetruthofthe matter is simply quite different. As people, as homo sapiens go, this overly ambitious, sentient animal walking along, responds to power, seeks power, strives for power. In todays United States we know that some people are more powerful than others. Clearly, the President of the United States is high up there in the ranks of power. But just how high is he.What aboutothermembersoftheU.S.government.Andletsnowmovebeyondgovernmentto entertainment, to the world of business, organized religion, athlete, and so forth Sofarwehavebeentalkingaboutastraightforwardtask,oratleastataskthatcouldbe construed as being straightforward. One merely selects the names of the individuals, mixes them up in good research practice, to avoid what is called order error, wherein the names evaluated at the start of the interview receive higher than deserved scores, whereas the names evaluated at the end oftheinterviewreceivedlowerthandeservedscores.Theorderinterviewisprevalent,andyetis easy to dispose of. A much more profound problem is the adoption of different criterion for different kinds of names.When we deal with single names from different worlds, such as Rush Limbaugh and People who make billions from the housing crisis, we dont know whether the respondent can maintain a constant criterion while judging the power of such different interview. The raw materials silos and elements The essence of Mind Genomics comes in the form of simple language, of silos (general groups or buckets of ideas), and elements (relatively single-minded ideas).These elements may be simple people, as we will see in this chapter, or descriptions of objects, actions, motives, and so forth. One is limited only by ones imagination. We see our silos and elements in Table 1.1.This chapter deals with who runs America and how much do you trust them.Thus the silos and elements in Table 1.1 take on the simple form of a name or a description of a person or a group, and nothing more. There is no action, no antecedent situation, no action, no result, just simply proper names or descriptions of people. For these studies on The Fraying of America we make use of one particular set-up, the so-called 6x6 design. This design calls for six silos, each silo containing six related elements.The 6x6 designisparticularlyattractiveforthesestudiesbecauseitcoversawiderangeofideas(36 elements), and because it requires relatively little work on the part of the respondent, who ends up evaluated48vignettesortestcombinations.Tocover36elements,awiderangeofnames,in48 vignettes, a relatively straightforward design, and to have every respondent evaluate a unique set of elements, is no mean task. Table 1.1: The six silos and six elements per silo for the study on Who runs America? 5 Silo A People who are well known, but doing ordinary acts that excite interestA1Kim Kardashian A2Rush Limbaugh A3Jake Long A4Alex Rodriguez A5Bill Gates A6Rev. Pat Robertson Silo B Organizations or job titles with great political power B1Big international companies B2Chairman, Ford Motor Company B3Head of Fox News B4Chairman of the Federal Reserve Bank B5President, JP Morgan/Chase Bank B6President, Goldman Sachs investment bankSilo C People who have made a great deal of money C1Donald Trump C2People who made billions from government bailout of companies C3The "One Percent" who controls almost half of America's wealth C4People who made billions from the housing crisis C5Company presidents whose salaries are many million dollars C6Mark Zuckerberg (president of Facebook) Silo D Public servants D1President Obama D2Congress D3Supreme Court D4Lobbyists D5Agencies enforcing government regulations D6Secret connecters between big money and legislators Silo E Large organizations with perceived power E1Countries that export oil to the United States E2Oil company lobbyinggroupsE3The "Tea Party" E4American militaryE5Big retail companies who mainly buy cheap goods from China E6Big labor unions Silo F: Well known sociological groups F1The American people F2China F3College professors F4Jews F5The Catholic Church F6Fundamentalist religious groups Thereisareasonfortheselectionofpropernames,aswellasnounsdenotingspecific organizations.People respond easily to concrete phrases, lists of individuals and organization that have meaning.To the degree that we can encapsulate an idea in an individual, such as outrageous 6 wealthrepresentedinthenameDonaldTrump,wecanmovepasttheintellectualizationphrase, and perhaps move more towards the visceral reaction. Ontheotherhand,wedontsimplyworkwithnamesofpeople.Weworkwithnamesof organizations(e.g.,theCatholicChurch),andevendescriptions(e.g.,Fundamentalistreligious groups).Inaddcasesweusethespecificphrasetorepresentanidea,choosingtheformthatwe express that idea in the easiest was we can. Rating questions (Table 1.2) It is the rating question which can create a wonderful study about how we react to stimuli, or just as easily destroy the quality of the study. We may make a great deal of effort to select the proper test stimuli, those words and phrases appearing in Table 1.1.But how do we have the respondent convey his reaction to these stimuli. Here,aswebeginourdiscussionabout,andouranalysisofresponsestoteststimuli,its important to set the record straight. The key message here is that as much effort ought to be placed into selecting the right form of answer as one puts into selecting the right test stimuli. The scales that researchers develop become the respondents language. The respondents reaction to the test stimuli through these scales will make a great deal of difference to the results, allowing us to draw correct inferences or preventing correct inferences, allow a story to come out that tells us how things work, orpreventthestoryfromcomingoutbecausetheratingscalecannotexpresswhatweneeditto express. To this end, in our goal to understand who runs America we choose the simple questions in Table 1.2. We did not further specify what we meant by affecting what happens in America?Nor did we specify what we meant by trustworthy. We avoided the very common practice of labeling each scalepoints,producinganominalscale,whereeachscalepointisessentiallyadifferenttypeof answer. For us, the sale was anchored at both ends, and represented a range of magnitude. The scale pointswereassumedtorepresentequalgradationsofpsychologicalmagnitude,butthatequal interval property was simply assumed. Table1.2:Thetwoscales,power,andbelief,thatrespondentsusedtomeasuretheir perceptionofthestrengthofgroupsofnamesoncertainbehaviors,affectingtheUSand being trusted 1. How much does this group affect what happens in America? 1= No effect at all9= Major effect 2. How trustworthy is this group? 1=Not trustworthy at all ... 9= Very trustworthy Learning more about the respondent (Table 1.3) Mind Genomics studies focus on a specific, limited area of experience. As an example, take the studywepresenthere,onWhorunsAmerica.Wefocusonasetofpeopleandinstitutions.Asa consequence,wereallyknownothingelseaboutourresponses,otherthanthepatternoftheir responses to stimuli. Sincemuchofourinterestisindifferentialpsychology,inthedifferencesamongpeople, differences that may tell us about the nature of groups of individuals, such as divisions by ethnicity, age, even beliefs, we need to learn more about the respondent as a person. There are many facets of 7 people, so we must be content that no matter what we do, we will still learn only something about the person, not everything. We also have limits on time and on venue. We want to learn a lot about the respondent, but wehavewhatwillturnouttobejustalittletime,perhaps5-7minutes.AndourMindGenomics studies will be run on the web, so our venue is remote, and our information-gathering system is called self-administered.Wearenotpresentwiththerespondenttoasktherespondentquestions,to engage the respondent in a dialogue, to let the dialogue take us new places. Rather, we must make do with closed-end questions, allowing the respondent to add in new, unexpected answers, to questions that that already have been formulated. (These are called open-ended questionsthe questions are fixed, but the answers are not). In Mind Genomics the second part of the interview, that remaining 5-7 minutes, is given over to what we call the self-profiling classification. Table 1.3 shows an example of this questionnaire for the study on Who runs America.By its very nature the questionnaire is incomplete. The main focus of the interview is on the responses to the systematically varied messages, in our current study the combination of names. We give short but considered attention afterwards to who the respondent is, what the respondent does, what the respondent believes, recognizing that we are only probing the surface, gathering just a little bit of the information that we could gather. Table1.3:Theself-profilingclassificationquestionnaire.Therespondentcompletesthis questionnaire as the second half of the interview, usually taking 4-7 minutes to complete it. Q1: What is your GENDER? S1. Male S2. Female Q2: For DEMOGRAPHIC purposes only, which best describes your ETHNIC BACKGROUND?S1. Asian S2. Black/African American S3. Hispanic/Latino S4. White/Caucasian S5. Other Q3:Please indicate the AGE GROUP you belong to? S1. 18-20 S2. 21-29 S3. 30-39 S4. 40-49 S5. 50-59 S6. 60-65 Q4: What is your TOTAL ANNUAL household income before taxes? S1. Under $30,000S2. $30,000-$39,999S3. $40,000-$49,999S4. $50,000-$74,999 S5. $75,000-$99,999S6. $100,000-$124,999S7. $125,000 or over Q5: What is your current MARITAL status? S1. Married S2. Separated S3. Divorced S4. Widowed S5. Living with a partner S6. Single Q6: Do you have any children? S1. Yes S2. No Q7: How many children currently live in your household?S1. O S2. 1 S3. 2 S4. 3 S5. 4 or more 8 Q8: The United States will continue to grow in importance S1. Agree totally S2. Agree Somewhat S3. Neither agree nor disagree S4. Disagree Somewhat S5. Disagree totally Q9: Opportunities for individuals will grow in the United States S1. Agree totally S2. Agree Somewhat S3. Neither agree nor disagree S4. Disagree Somewhat S5. Disagree totally Q10: More people will need financial help in the next few years S1. Agree totally S2. Agree Somewhat S3. Neither agree nor disagree S4. Disagree Somewhat S5. Disagree totally Q11: The financial gap between the "haves" and the "have-nots" will grow smaller S1. Agree totally S2. Agree Somewhat S3. Neither agree nor disagree S4. Disagree Somewhat S5. Disagree totally Q12: Society generally ignores people who need help S1. Agree totally S2. Agree Somewhat S3. Neither agree nor disagree S4. Disagree Somewhat S5. Disagree totally Q13: Has your annual income increased or decreased in the past few years? S1. IncreasedS2. DecreasedS3.Stayed the same Q14: Do you expect that your annual income will increase or decrease in the next few years? S1. IncreaseS2. DecreaseS3.Stay the same Q15: Which of the following best describes your CURRENT EMPLOYMENT STATUS? S1. Employed full time for payS2. Employed full time but not paid (example: housewife)S3. Employed part timeS4. Not currently employed but lookingS5. Not currently employed but not lookingS6. RetiredS7.Student Q16: What is the HIGHEST LEVEL OF EDUCATION that you have completed? S1. Some high school or less S2. Completed high school S3. Some college S4. Completed college S5. Graduate school S6. Other education beyond high school (technical, nursing, etc.) Q17: Which religion, if any, are you most affiliated with? S1. Buddhist S2. Christian S3. Hindu S4. Jewish S5. Muslim S6. Other S7. None Q18: How religious are you? S1. Very religious S2. Somewhat religious S3. Not formally religious but a believer S4. Not formally religious and a non-believer 9 Q19: Which political group do you belong to? S1. DemocratS2. RepublicanS3. IndependentS4. OtherS5. None Q20: Which best describes your political beliefs? S1. LibertarianS2. ConservativeS3. In the middleS4. LiberalS5. RadicalS6. Not interested Q21: How long have you lived in your current community? S1. Less than a yearS2. About a yearS3. Between one and five yearsS4. Over five but less than ten yearsS5. Ten to twenty yearsS6. Longer Q22: How many really good friends do you have in the community you live?S1. No really good friendsS2. One or two S3. Four or fiveS4. Six or more Q23: How you feel about the community in which you live? S1. Really anchored here: this is my townS2. It's all rightS3. I'd rather besomewhere else S4. Can't wait to get out Creating the actual test vignettes by experimental design (Figure 1.1) Theactualteststimulievaluatedbyrespondentscomprisecombinationsofelements.The combinations are not random, not haphazard, despite what it might appear. When respondents see these combinations, for example the three-element vignette in Figure 1.1, its tempting to feel that the combinations have been put together in a non-systematic way. Nothing can be further from the truth, as we see below. 1.Each respondent will evaluate 48 vignettes. That number is fixed, when we deal with the test design we call a 6x6, i.e., six silos, each comprising six elements. 2.Each respondent will evaluate a different set of 48 vignettes. That is, the 48 combinations of elements seen by one respondent will differ from the 48 vignettes seen by another respondent. Over hundreds of respondents some vignettes will, of course, repeat. The particular way of creating these different permutationsofonebasicdesignhasbeencoveredinpublishedpapers(REF),andinpatent applications (REF) 3.In a respondents set of 48 vignettes, each element will appear in a statistically independent way, i.e., statistically independent of every other element. It is this statistical independence that will allow us tousethemethodofOLS(ordinaryleast-squares)regressiontodeconstructthevignetteintoits componentelements,ascertainingthecontributionofeachelementtotheratingassignedtothat vignette. 4.Theexperimentaldesigndictatesthateachelementappearfivetimesinthe48vignettes,absent thereforefrom43ofthe48vignettes.Theabsenceoftheseelementsfromsomevignettesadds another wonderful feature to the experimental design. The future is absolute value. That is, when we run the OLS regression and estimate the part-worth contribution of the element to the rating, a contributionwe callimpact,thatimpactvalue is estimated inanabsolute sense.Byabsolutewe mean that the impact value can be stored in a database, and compared to the impact values of other elements, from different studies. The actual numerical magnitude of the impact value is meaningful. 5.Each silo contributes to 30 of the 48 vignettes, and is therefore absent from 18 of the vignettes..6.Of the 48 vignettes, 36 comprise four elements, and 12 comprise three elements. 10 7.Althoughthevignetteisthereforeincompleteaccordingtothestructureofsilosthatwehave created, respondents do not seem to notice it. Indeed, respondents simply go about their task, grazing in the vignette, looking for relevant information Figure 1.1 shows us an example of a three-element vignette, one of the 12 of 48 vignettes for a respondentwhichcomprisesthreeelements.Thestructureofthevignetteissetuptomakethe respondents task easy: 1.The typeface is sans-serif, presenting the information boldly. 2.The elements are stacked, one atop the other. There is no connective device. Nor do we need one, to connectthe differentelements.Our studyisnotoftheparagraph itself,theconcatenationofideas into a single sentence, a concatenation that the respondent would have to undo, when parsing the sentence, prior to making a judgment. Rather, by putting the elements into stand-alone phrases we maketherespondentsjobeasier.Yes,therespondenthastoevaluatethecombinationasasingle idea, merging mentally the different elements. We make the job easier, by putting the elements into easy-to-read formats, and separating them.3.At the top right we see the number 1/74. This number is the progress number, telling the respondent that there are 74 different screens, and that this is the first of the 74. Respondents want to know that the interview will not be endless, take forever, and lead nowhere. Presenting the respondent with a number that changes gives the respondent reassurance that he is progressing through the interview, even though for the most part the respondent proceeds through with nary more than a glance as the progress number. 4.Thebottomofthescreenshowsthefirstratingquestion,Howmuchdoesthisgroupaffectwhat happens in America? To the right is the scale, and a box. The respondent merely points to the rating number, clicks, and the rating is registered. 5.After the respondent rates the vignette, the first rating scale is replaced by the second rating scale, dealing with trust How trustworthy is this group? The progress number does not change. 6.Its important to note that the respondent need not enter the rating. As soon as the rating is assigned, the number is transmitted to the server. Some critics would aver that this approach does not let the respondentreconsiderhisrating.Thatcriticismiscorrect,buttheoveralleffectistomakethe interview easier, less onerous, less effortful. Figure 1.1: Example of a three element vignette for Who runs America 11 Orienting the respondents (Figure 1.2) Bythetimeofthiswriting,middle/late2013,wereallquiteaccustomedtoconsumer surveys, to public opinion polls, to receiving phone calls during dinner time about buying something, being intercept at malls to answer just a few questions, and so forth. One might think that with the increasingthirstforknowledge,oratleastconsumermeasurement,displayedbycompanyand government alike, that we would be automatically at home in any survey, ready to take the survey, and of course breeze right through it. The reality is quite different, especially when we deal with the fuzzy area between interview andexperiment.Yes,we areaskingrespondentstoanswerquestions, muchas onewoulddo ina questionnaire.Butatthesametimewereinstructingrespondentstoevaluateratherunnatural combinations of ideas, such as the unnatural combination we see in Figure 1.1. FacedwiththecombinationsthatweseeinFigure1.1,thetypicalfirstresponseisforthe respondent to rate each of the three (or four) elements on the screen, element by element. Yet, there is only one rating scale. When these Mind Genomics studies were conducted on the computer, but in a supervised central location test (e.g., a back room in a shopping mall, set aside for research), we wouldoftendifferentseerespondentsaskingwheretheyshouldrateeachelement.Asa consequence, we built in an orientation page, to anticipate that question and other questions. Figure 1.2 shows us the orientation page, the first page the respondent sees after pressing on a link embedded in an invitation. The respondent is led to the screen, which gives some information (typically very little) about the study, introduces the rating scale,and tells the respondent how long the interview will last. Orientation pages have one purpose only, the introduce the respondent to the study, without telling the respondent too much.By introducing the respondent to the study, we mean explaining thetask,tellingtherespondenthowlongtheinterviewshouldlast(veryimportant),andtellthe respondent how to monitor his progress. 12 Figure 1.2: The orientation page for the Who runs America study Setting up the data for statistical analysis (Table 1.4) MindGenomicsgeneratesagreatdealofdata,someofthedatapertainingtothespecific vignettes evaluated by a respondent, other data coming from the self-profiling questionnaire wherein we find outmore about the respondent.We do a 1/3 of the job setting up the study, 1/3 of the job executing the study among respondents, leaving us with the last 1/3, namely analyzing what we have, both in terms of what the data tell us directly, and what the data teach us as we do modeling. Our task in this part of the chapter is to understand what data look like, how to prepare the data for analysis, and then how to drive towards the analysis with standard, off-the-shelf statistical routines. We begin with the format of the data.We present a little slice of the data in Table 1.4. The actual form of the data is a horizontal table, not a vertical table as we show in Table 1.4. We choose to present our data in a vertical format so that we can scroll down leisurely, looking at the different variables. Ordinarily, these variables would be columns rather than rows, making them difficult to read on a sheet of paper. In Table 1.4 we show the data for one respondent, for the first three vignettes presented to, and evaluated by the respondent. 1.UID. The abbreviation stands for the unique identification number, a number assigned to the panelist, and used only for that panelist for that particular study. All records generated by the panelist in that study will be accompanied by this particular UID number. The UID for this respondent is 422987 2.Silos/Elements.Thestudycomprisessixsilos,eachsilobeingeitherabsentfromthevignette,or contributing an element. The silos and elements are shown in the top part of the table 13 3.BinaryRecoding.InorderforOLS(ordinaryleast-squares)regressiontowork,theindependent variables,inourcasethe36elements,mustbeconvertedinameaningfulformatthatastatistical program can use. The binary recoding does that by creating 36 new variables, one variable for each element.Foranyvignettethevariabletakesonthevalue1whentheelementispresentinthe vignette,and0whentheelementisabsentfromthevignette.Scanningdownthesetof36new variables shows immediately which elements appear in the vignette, and which are absent.There are36rows,oneroweachelement.Table1.4showsusonlysixofthe36rows,therows corresponding to the first six elements. 4.Ratings.Rightbelowthere-codingweseefourrows,correspondingtothetworatingscales, expressed by the original 1-9 Likert Scales, and a re-coding of the scale values into the binary INT values. 5.Q1 refers to question 1, How much does this group affect what happens in America? 6.Q2 refers to question 2, How trustworthy is this group? 7.INT Affect refers to the binary recoding of responses to Question 1. a. When the respondent rated Question 1 as 1-6, i.e., the low part of the scale, INT Affect becomes 0. b. When the respondent rated Question 1 7-9, i.e., the high part of the scale, INT Affect becomes 100 8.INT Trust refers to the binary recoding of responses to Question 2. a.When the respondent rated Question 2 as 1-6, INT Trust becomes 0 b.When the respondent rated Question 2 as 7-9, INT Trust becomes 100 9.To ensure that the OLS regression does not crash, we add a small random number to each of these four variables, the two original ratings (Q1, Q2) and the two derived, binary ratings (INT values). 10. The classification portion of the questionnaire constitute the rest of the respondents record for that vignette. Note that since the respondent evaluated 48 vignettes, the self-profiling classification will be the same.In relational databases we would simply have a linkage between the construction of the 48 vignettes, the responses to the 48 vignettes, and the one self-profiling classification, referring to the respondent, and not to the vignette. Table 1.4: Example of the data matrix prepared for analysis. The table shows the construction of the first three vignettes, the respondents ratings, and the self-profiling classification, which is the same profile for a respondent across all 48 vignettes.Vig1Vig2Vig3 UID422987422987422987 Silos/Elements Silo AAbsentA4Absent Silo BB6AbsentB4 Silo CC3C5Absent Silo DD1AbsentD5 Silo EE2E6E3 Silo FAbsentF5F5 Binary RecodingA1000 A2000 A3000 A4010 A5000 A6000 14 RatingsQ1 Affect676 Q2 Trust334 INT Affect0.00100.000.00 INT Trust0.000.000.00 Classification questionsQ1 GenderFemaleFemaleFemale Q2 EthnicityWhiteWhiteWhite Results how do we look at the data, and what do we learn from the numbers? The distribution of ratings (Table 1.5) We be the analysis of the data by looking at the distribution of ratings on the 9-point scale. These ratings are often too granular to see patterns easily, and so we simplify the data first. For each of our two scales (Q1=Affect America, Q2=Trust), we divide the scale into three equal parts, low (1-3), medium (4-6), and high (7-9). By reducing the former nine point scale into a simpler three point scale, we can create a simple tabular presentation that may reveal a new story to us. The presentation of the new data appears in Table 1.5, where all the numbers are percents except N, the base sizes. Table 1.5 divides into two parts, Part A which looks at the level of Trust as the main focus, and Part B which looks at Who runs America as the main focus. Looking at Part A focusing on responses to trust 1.The key information is to be found in the row total.We see that across all 16,704 vignettes, most of the vignettes are rated 1-3 (low trust, Question 2), or 4-6 (medium trust, Question 2). Only 13% of the vignettes are rated 7-9 (high trust Question 2) 2.When we break out the trust ratings by their association with Affect, we find that when the Affect is low, i.e., the vignette is perceived not to affect America, then the trust is low. 3.When we look at those vignettes rated higher on Affect, either medium (4-6) or high (7-9), we find that trust is still low. 4.Thus it appears that trust does not depend upon the perception of the power held Looking at Part B focusing on trust 1.The key information is again to be found in the column total 2.Mostofthevignettesareratedasmedium(ratings4-6)orhigh(7-9)inabilitytoaffectAmerica (Question 1) 3.Again there is no clear relation between rated affect and the trust one has Table 1.4: Cross tabulation of ratings for Question 1 (Affects America, as a 3-point question) and Question 2 (Trust as a 3-point question). All numbers except N are percents Part A Looking at the rows (levels of Trust is the main focus) Trust Low Trust Med Trust High Trust Tot N Total42451310015 Affect Low722441001,718 Affect Med365861007,968 AffectHigh4235231007,018 N7,0727,4842,148 16,704 Part B Looking at the columns (Runs America is the main focus) Trust Low Trust - Med Trust - High TotalN Affect Low1753101,718 Affect Med416222487,968 AffectHigh423375427,018 Affect -Total100100100100N7,0727,4842,148 16,704 Building theINT Models (Tables 1.5 & 1.6 Figures 1.3 & 1.4)) The essence of the Mind Genomics effort can be found in the individual-level models relating the presence/absence of the 36 elements to the rating, and more specifically to the INT Value. Recall that the INT value is the rating of Affects America with ratings of 1-6 converted to the value 0 to denote does not affect America, or does so modestly, and ratings of 7-9 converted to the value 100 to denote does affect America. The experimental design allows us to estimate the contribution of every element to the rating, whether the rating be the original 9-poiont rating, or the transformed INT rating. Wefirstexpresstheequationwhoseparametersaretobeestimatedusingstatistical techniques. For Mind Genomics studies, at this early stage, we express the relation between ratings and elements by the simple linear function: Rating = k0 + k1(Element A1) + k2(Element A2) k36(Element F6) The foregoing equation tells us that we deconstruct the ratings assigned to the vignette into an additive constant, k0, and the part-worth contribution of each of the 36 elements. Table 1.5 shows us the statistical workup of our data, for one respondent, based upon the 36 elements, and the particular set of 48 vignettes evaluated by the respondent. We should look at Table 1.5 strictly for its ability to teach us what type of statistical analyses are done at the level of the OLS (ordinary least-squares) regression. Webeginfirstwiththesummarystatisticsforthisindividualmodel.Thesesummary statistics appear at the top of Table 1.5. 1.The dependent variable is INT1RUN the INT value of Question, who affects America. 2.The statistics begin with the 48 vignettes evaluated by one respondent 3.The Multiple R is a goodness of fit. It is high, with the value of R being 0.89. The multiple R (multiple Pearson correlation) varies from a low of 0 to a high of 1. 4.The Squared Multiple R is 0.80. This statistic, the square of the Multiple R, tells us the proportion of variability in the ratings accounted for by the equation. It is 80%, which on a raw basis means that 16 80%ofthevariationintheratingsfor48observations(our48vignettes)isaccountedforbythe equation. 5.The Adjusted Squared Multiple R is 0.14. Although we have a very high Squared Multiple R (0.80), a lot of that high value is due to the fact that we are fitting the data with an equation of 37 terms, the additive constant and 36 weighted predictors. The adjusted squared multiple R corrects our high guess, telling us that our data are good on a raw basis, but that a lot of that fit is due to the fact we are using many predictors. 6.Thestandarderrorofestimateisameasureoftheexpectedvariabilityofourestimation.The standarderrorofestimateis46,meaningthatwhenweestimatethelikelyscoreforthisONE respondent, we will do so with a great deal of error. Wenowmovetotheestimationoftheindividualcontributionsoftheelements,forthisone respondent.In a moment, after we explicate the data for the one respondent, we will look at the same typeofresults,butputtinginallofthedata,fromtheentiregroup,makingonepassthroughthe results. 1.Effect. These are the individual terms. 2.Coefficient individual terms. Each element, A1-F6, brings its own contribution. 3.Additive constant expected value of the individual model, in the absence of elements 4.Standard error the variability of the estimate. It should be small 5.T statistic the ratio of the coefficient to the standard error. Ideally it should be greater than 2.0. It will be greater than 2.0 when we run all the data together, but its not likely to be greater than 2.0 for an individuals data, except for the strongest performing terms 6.P value the likelihood that the t statistic would be obtain by chance alone. We would like to see low p values, around 0.05 or less. Again, we will see those lower p values when we combine all the data. Table 1.5: Output of statistical modeling by OLS (ordinary least-squares) regression, for the data from one respondent. Dependent VariableINT1RUNN48Multiple R0.89Squared Multiple R0.80Adjusted Squared Multiple R0.14Standard Error of Estimate46.25

EffectCoefficientStandard Error t statistic p-Value Additive constant-56.81115.20-0.490.63 A1-11.9543.78-0.270.79 A28.1758.120.140.89 A31.7551.870.030.97 A426.8060.240.440.67 A58.0551.260.160.88 A6-29.7051.27-0.580.57 B132.8760.510.540.60 B217.4855.590.310.76 B338.7756.510.690.51 B469.2955.641.250.24 17 B527.0147.990.560.58 B634.9248.960.710.49 C12.7841.890.070.95 C2-10.4054.42-0.190.85 C3-0.1155.840.001.00 C495.4654.351.760.11 C515.7372.480.220.83 C615.2638.140.400.70 D182.3864.471.280.23 D254.2971.690.760.46 D3-0.9767.77-0.010.99 D48.9747.510.190.85 D566.4949.961.330.21 D6-17.3852.22-0.330.75 E187.4147.761.830.09 E241.9752.010.810.44 E355.1354.701.010.34 E4112.2348.472.320.04 E529.7372.290.410.69 E651.9555.790.930.37 F118.1050.950.360.73 F2-25.6862.47-0.410.69 F32.6271.330.040.97 F4-24.9546.62-0.540.60 F534.1543.490.790.45 F626.7848.090.560.59 Wegetaslightlydifferentpictureofthingswhenwecombinethedatafromallofthe respondents,whichwecando.Ratherthanworkwith48vignettes,creatingamodelusing37 predictors,andbeingdunnedforusingsomanypredictorsrelativetothenumberofcasesor vignettes, now we become more conservative. We work with 16,704 cases, yet with the same number of predictors, an additive constant, and 36 coefficients. Our Multiple R drops down dramatically to 0.19,andourSquaredMultipleRdropsdownto0.04.Wearenotdunnedforhavingsomany predictors relative to the number of cases. Whatsmostencouragingisthatourstatisticsincreaseforthesolidityofourresults.We have t statistics of 2 or high (or -2 or lower) far more frequently, and correspondingly more p-values that are 0.05 or less. In a phrase, by combining all the data into one pass, we show much stronger results, at least on a statistical level. Table 1.5: Output of statistical modeling by OLS (ordinary least-squares) regression, for the data from all respondents, analyzed by one pass through the data. Dependent VariableINTEFF1

N16,704

Multiple R0.19

Squared Multiple R0.04

18 Adjusted Squared Multiple R0.03

Standard Error of Estimate48.53

EffectCoefficientStandard errort statisticP-value CONSTANT24.763.307.490.00 A1-5.371.55-3.460.00 A2-1.911.55-1.240.22 A3-3.451.55-2.220.03 A4-3.651.54-2.370.02 A55.341.553.440.00 A6-1.741.54-1.130.26 B17.191.544.680.00 B21.441.540.940.35 B30.981.530.640.52 B47.481.544.860.00 B55.681.543.700.00 B65.361.533.500.00 C12.021.531.320.19 C26.811.534.450.00 C312.981.548.440.00 C46.241.524.100.00 C57.451.534.890.00 C61.801.531.180.24 D123.121.5215.240.00 D211.681.517.730.00 D311.531.537.560.00 D44.021.532.620.01 D55.611.523.700.00 D64.011.532.630.01 E15.051.493.380.00 E26.221.504.150.00 E3-0.281.49-0.190.85 E48.091.505.390.00 E55.091.493.410.00 E64.831.503.220.00 F19.291.546.050.00 F26.771.564.340.00 F30.381.560.240.81 F4-0.011.55-0.010.99 F53.891.552.520.01 F61.731.551.120.26 19 Later on, when we look at the results, we will adopt an intermediate position compute the individual models and average them across the relevant respondents to estimate the likely response to the individual elements.This is a compromise strategy that lets us maintain our analysis at the individual respondent level, while at the same time getting a better estimate of how the population as a whole respond. We wont be very far off from the Grand Model when we average the individual INT Models, whether we are working with Question 1 (Affects America; see Figure 1.3), or working with Question 2 (Trust, Figure 1.4). In both cases we see a strong linear relation, with quite similar values of the impacts, i.e., the coefficients, for the same element. Figure1.3:Comparisonoftheimpactvalues(i.e.,coefficients)forthe36elementsfrom Question 1 (Affects America). The abscissa shows the estimated impact value from the Grand Model, the ordinate shows the estimate impact value from the average of all individual-level models. Each filled circle corresponds to an element. Figure1.4:Comparisonoftheimpactvalues(i.e.,coefficients)forthe36elementsfrom Question 2 (Trust). The abscissa shows the estimated impact value from the Grand Model, the ordinateshowstheestimateimpactvaluefromtheaverageofallindividual-levelmodels. Each filled circle corresponds to an element. 20 Demonstrating differences between responses to the two rating questions (Figure 1.5) In many consumer research studies the researcher uses a number of different questions to probe various aspects of the consumers attitudes towards a topic.Sometimes the demands of the research interview are so hard that the respondent ends up essentially answering all the questions in the same way. Is there any way to show that the answers to Question 1 about Who affects America differs from the answers to Question 2 How much do you trust? One way to do this plots the average impact values for the 36 elements, from Question 1 on Who affects America, against the average impact values for the same 36 elements, from Question 2 on How much do you trust? Figure 1.5 shows us the scatterplot. It is clear that we are dealing with different patterns of answers, even before we know the meaning of each element. Had we see more of a 45 degree line, we mightsuspectthatthepatternsofresponsestoQuestion2arequitesimilartothepatternof responses to Question 1. The patterns are clearly different from each other. Figure 1.5: Scatterplot, showing the relation between the impact values for the 36 elements, firstonQuestion1(AffectingAmerica),andthenonQuestion2(Trust).Eachelement corresponds to a filled circle. The scatterplot suggests quite different patterns. 21 Statistical consideration Does the constant change our conclusion (Figures 1.6, 1.7) Throughout this book we will be creating INT models with additive constants. The ingoing assumption we hold is that the response to the vignette can be deconstructed into the basic proclivity to rate the vignette yes (whether in terms of Affecting America, or Trust, respectively), and then the part-worth contribution of each element. We can also instruct the OLS (ordinary least-squares) regression program to estimate the 36 impact values, this time forcing the model or the equation through the origin, i.e., forcing the additive constant to be 0. If we were to adopt the INT Model without the additive constant, would be make a different set of decisions about Who Runs America, or Whom Do We Trust? Weanswertheforegoingquestionempirically,by creatingtwoGrandModels,onewithan additive constant, and one without an additive constant: With Constant: INT Value = k0 + k1(Element A1) + k2(Element A2) k36(Element F6) WithoutConstant: INT Value = k1(Element A1) + k2(Element A2) k36(Element F6) Figure1.6showsustheestimated36impactvalues,i.e.,coefficients,oneimpactvaluefor each element. We create Figure 1.6 by estimating the parameters of the Grand Model for Question 1, Affects,doingsotwice.WedothesamethinginFigure1.7,thistimeworkingwiththedatafrom Question2,Trust.Weseeaverystrongrelationbetweenthetwosetsofimpactvalues,those estimatedwithanadditiveconstantintheINTmodel,andthoseestimatedwithoutanadditive constant in the INT model.

22 The first estimation uses all the data from all vignettes, instructing the OLS regression to estimate the valueoftheadditiveconstant,k0.Thesecondestimationusesthesamedata,butinstructstheOLS regression to leave out the constant. The change in instructions generates in its wake a new set of impact values.Plotting the 36 impacts against each other shows a virtual perfect relation. The actual numbers fortheimpactsorcoefficientschange,butwewouldemergewiththesameconclusionsaboutwho affects America, and who is trusted. Figure 1.6: The coefficients for the Grand Model for Question 1, Affects America. The abscissa showstheimpactvalues,coefficients,forthe36elementswhenthemodelwasestimated using an additive constant. The ordinate shows the same set of impact values, this time with theOLSregressionmodelestimatedwithoutanadditiveconstant.Therelationisperfectly linear. 23 Figure 1.7: The coefficients for the Grand Model for Question 2, Trust. The abscissa shows the impactvalues,coefficients,forthe36elementswhenthemodelwasestimatedusingan additive constant. The ordinate shows the same set of impact values, this time with the OLS regression model estimated without an additive constant.The relation is perfectly linear. 24 The PER Model versus the INT Model (Figure 1.8 & 1.9) Using the 9-point rating scale allow us to obtain granular information about the respondents attitude. With nine points the respondent can show gradations of feeling. The PER Model uses this rating scale as the dependent variable in the OLS regression. In contrast, using the binary INT scale, 0/100, allows us to state that the respondent either findsthevignettetodescribeagroupwhichRunsAmericaordoesnot,oragroupthatthe respondents Trusts or not.We give up a great deal of granular information, transforming our data into simpler-to-understand numbers, numbers that convey a more black and white, yes/no situation. We looked at the Grand Models for Affect and for Trust, running those equations first using theoriginal9-pointratingsasthedependentvariable(i.e,.thePERModel),andthenusingthe transformed0/100ratingsasthedependentvariable(i.e.,theINTModel).TheINTmodel transformed ratings of 1-6 to 0, and ratings of 7-9 to 100. Figure 1.8 shows us that we get virtually the same pattern of impact values for Question 1, on Affecting America. Figure 1.9 shows us that we get virtually the same pattern of impact values for Question 2 on Trust, albeit with a little more variability.We conclude, therefore, that group data from many people lead to the same conclusions, no matter which dependent variable we use.The numbers differ and the actual meaning of the numbers differ (intensity of feeling for the PER model versusmembershipinagroupfortheINTModel).However,thepatternandsubstantive interpretationofthedatashouldremainthe same,nomatterwhetherweusetheoriginal9-point ratings or the transformed binary values. Figure1.8:Relationbetweentheimpactvalues,i.e.,coefficients,forthePERandINTGrand Models, for Question 1, Affects America.The pattern of impact values is the same for both models.

25 Figure1.9:Relationbetweentheimpactvalues,i.e.,coefficients,forthePERandINTGrand Models,forQuestion2,Trust.Thepatternofimpactvaluesisthesameforbothmodels, although a bit noisier than the pattern we see emerging from Question 1.

Modeling are the results really reliable (Figure2 1.10 1.13) One hallmark of good science is repeatability, the ability to get the same answer when one repeats the experiment, presuming all conditions remain the same.The scientific term for this is the reliability of ones research.Whether the experiment actually measures what it purports to measure is a different issue. Right now were focusing on whether the experiment can be repeated, and the same answers obtained.Thus reliability and validity of the experiment are not the same, although an experiment can never be more valid than the degree of its reliability. It can certainly be less valid. For Mind Genomics studies we assess reliability two ways, repeating the entire experiment separately with new people, and splitting the data in half and comparing the results of the two halves. In this section we look at both approaches for our study on Who Runs America, looking first at the reliability of Question 1 (Who affects America), and then at the reliability of Question 2 (Trust). Ourfirstanalysislooksatsplittingthesampleofvignettesintotwoparts,thefirst24 vignettes, and then the second 24 vignettes.Since the study comprised 48 unique vignettes for each respondent, it is a simple matter to segregate the two halves, run a Grand Model for each half, and compare the impact values.Each respondent contributes exactly half of his vignettes to each Grand Model. Theresultsarequiteclear;theimpactorcoefficientvaluesforthetwohalvesofthe experiment are quite close to each other. A straight line describes the relation between the impact values. The relation is a bit less noisy for Question 1, dealing with Who affects America, (Figure 1.10) and a bit more noisy for Question 2, dealing with Trust, (Figure 1.11) Its also worth noting that Question 2 generates a far narrower range of impact values for the INT model, as we will see in below. 26 Figure 1.10: The 36 impact values from the INT Model for Question 1, Who affects America. TheabscissashowstheestimatedvaluesfromtheGrandModelcomputedonthefirsthalf, vignettes1-24.Theordinateshowstheestimatedimpactvaluesfromthesecondhalf, vignettes 25-48. Figure1.11:The36impactvaluesfromtheINTModelforQuestion2,Trust.Theabscissa shows the estimated values from the Grand Model computed on the first half, vignettes 1-24. The ordinate shows the estimated impact values from the second half, vignettes 25-48. 27 In the summer of 2013 author Kover suggested that we repeat the study of 2012, following the precise format that we followed when the first study was run.The replication was focused on whetherwe couldfindanydramaticchanges intheinterveningyear.Thefirststudyhadrevealed largedifferencesintheimpactvalue,suggestingweweredealingwithmajordifferencesinhow people perceived well known individuals, organizations, as well some not so well known. What emerged from the replicated study was dramatic evidence of reliability, as shown by Figure 1.12 for the impact values from Question 1, and by Figure 1.13 for the impact values from Question 2. Figure1.12:The36impactvaluesfromtheINTModelforQuestion1,Affect.Theabscissa shows the estimated values from the Grand Model computed from all the vignettes, based on the data from 2012.The ordinate shows the estimated impact values from the Grand Model, based on the data from 2013.28 Figure1.13:The36impactvaluesfromtheINTModelforQuestion2,Trust.Theabscissa shows the estimated values from the Grand Model computed from all the vignettes, based on the data from 2012.The ordinate shows the estimated impact values from the Grand Model, based on the data from 2013. 29 Substantive results what stories do the data tell us? Who runs America, and just who is trusted (Table 1.6) Wemovenowfromstatisticstosubstance,fromlookingatthepropertiesofour measurement system to looking at what it can tell us about the minds of our respondents.A lot of our answers will appear in Table 1.6, which summarizes the study looking at the average of all 348 respondents, first on the INT scale for Affect America, and second on the INT scale for Trust.That is, the numbers in Table 1.6 show us the averages from 348 individual-level models. The story is in the numbers: 1.For Question 1, Affect America, the additive constant is 20, meaning that in the absence of elements aboutonerespondentoutoffivefeelsthatthevignettedescribesagroupwhorunsAmerica.Of course all vignettes comprise elements, by design 3-4 elements. So the additive constant is a baseline, to which we attach the contributions of the individual elements. 2.When looking at the elements for Question 1, we should be struck by the dramatically strong impact of President Obama (24, one of the strongest performing en 2 pionlements ever recorded in a Mind Genomics study. 3.There are other elements as well, not surprisingly the American people, the American military, and to a slightly less degree congress. 4.ThepeoplewhoshowtheleastabilitytoAffectAmericaare,asoneexpects,privateindividuals, whether entertainers, sportspeople, or even religious figures. 5.It is impossible to game the system, because each element appears five times in 48 vignettes, always against different backgrounds. Yet the respondent ratings are strong, consistent, and reliable from yeartoyear.Iftherespondentratingswerenotconsistent,wewould see alltheelements moving towards the center, which they do not.6.For Question 2, Trust, we see immediately a much more constrained range of impact values, with the highest trust associated with The American People (+5), and President Obama (+4) 7.The lowest trust values, -6, are the very rich, the One percent who controls almost half of Americas wealth. 8.Comparing the impact models for Affect and Trust, we are struck by some very large impact values forAffect;peoplereallybelievethatcertainindividualsandinstitutesaffectAmerica.Wearenot impressed at all by the corresponding responses to Trust. Just because one can exert power does not mean that the person is to be trusted. 9.Its important to note that this particular study was run in 2012. Table1.6:ParametersoftheINTModelforWhoRunsAmerica.Question1pertainstowho affects America, a sense of power. Question 2 pertains to Trust.Data from the total panel Who Runs America AffectTrust Base size = 348 (year run - 2012) Additive constant2016 D1President Obama 244 F1The American people 155 E4American military133 F2China 13-1 D3Supreme Court 112 B1Big international companies 10-3 30 D2Congress 10-2 E2Oil company lobbyinggroups9-2 C3The "One Percent" who controls almost half of America's wealth 9-5 C4People who made billions from the housing crisis9-2 C5Company presidents whose salaries are many million dollars 8-2 E6Big labor unions 80 E1Countries that export oil to the United States 7-2 E5 Big retail companies who mainly buy cheap goods from China 7-3 B5President, JP Morgan/Chase Bank 7-3 B4Chairman of the Federal Reserve Bank 7-1 B6President, Goldman Sachs investment bank6-2 C2People who made billions from government bailout of companies 6-2 F5The Catholic Church 50 D6Secret connecters between big money and legislators 5-1 A5Bill Gates 51 D4Lobbyists 5-1 F6Fundamentalist religious groups 4-1 D5Agencies enforcing government regulations 3-1 B3Head of Fox News 3-1 B2Chairman, Ford Motor Company 3-2 F3College professors 21 F4Jews 2-2 C1Donald Trump 1-1 E3The "Tea Party" 1-1 A2Rush Limbaugh 0-2 C6Mark Zuckerberg (president of Facebook) -1-1 A3Jake Long -10 A6Rev. Pat Robertson -21 A1Kim Kardashian -2-2 A4Alex Rodriguez -3-1 Who runs America Men versus Womens point of view (Table 1.7) InMindGenomicsstudies,agreatdealofinformationemergeswhenwelookat complementarygroups.Iftruthbetold,theinformationemergingbygenderdifferenceisor compelling when we take a slide of life, a social situation meaningful to the respondent. Gender differs are not as evident when we work with products and services, unless the products and services are designed with the genders in mind. Table1.7breaksoutthestrongperformingelementsbygenderforQuestion1(Affect America). 1.Malesarefarmorelikelytochoosethetopofthescale,ratingsof7-9.Thisgreaterproclivity manifests itself as a higher additive constant for Affect America, 28 for males, 12 for females 2.There are four elements which drive the perception of power, and the ability to affect America.3.Three of these four strong performing elements are equal among men and women 31 The American peopleChinaAmerican military 4.The fourth element, President Obama, is far stronger among women than among men 5.There are other strong elements, but mainly among women 6.Among neither men nor women do we see single individuals of political rank, nor entertainers, but ratherindividualsandassociationswhichouchontheworldofpowerpoliticsandbusiness organization Table 1.6: Who affects America differences between genders Who affects America? Tot Male Fem Base Size 2012348171177 Constant202812 Both males and femalesD1President Obama 241830 F1The American people 151615 F2China 131114 E4American military131115 Females alone D3Supreme Court 11516 E2Oil company lobbyinggroups9316 E6Big labor unions 8214 D2Congress 10714 C3The "One Percent" who controls almost half of America's wealth 9513 C4People who made billions from the housing crisis9413 E1Countries that export oil to the United States 7213 C5Company presidents whose salaries are many million dollars 8413 B1Big international companies 10912 B5President, JP Morgan/Chase Bank 7311 Who runs America how different ethnicities see it (Table 1.7) The pattern of Who runs America seems to be emerging more clearly from genders. Do we still get only political power andmonetary power when we dive deeper into our data. We have whites but also Asians, Hispanics and Blacks in our 2012 data, by planning. 1.We have readable numbers of respondents in the four ethnic groups, albeit not equal base sizes. But 2.We begin with the additive constant, low (10 ) for Whites, highest (29, 31) for Hispanic and Blacks. The additive constant tells us whether there a submerged sense that perhaps someone is running the show, has the power, can affect America. Maybe is colloquial terms its da man.If it is da man, then there is his numerical value 3.For the White response its all about the power and money groups that we saw. 4.For the Asian it is President Obama, The American People, China, Big International companies, and their presidents. 32 5.For the Hispanics is President Obama, The American Military, Big international companies and Oilylobbying groups,6.For the blacks is strongly President Obama, and much less the America military 7.Theseresponsepatternsmakeintuitivesense.Wemightnothaveselectthem,buttheycreatea pattern consistent without intuition about who these groups are, and what values they might have. Table1.7:StrongestperformingonQuestion(AffectAmerica)bytotal andfourself-defined ethnic groups Tot White Asian Hispanic Black Base Size 2012348129725388 Constant2010212931 D1President Obama 2431191724 F1The American people 15241686 E4American military131391415 F2China 13161798 D3Supreme Court 1121348 B1Big international companies 101313115 D2Congress 1020-168 E2Oil company lobbyinggroups9127173 C3 The "One Percent" who controls almost half of America's wealth 914766 C4People who made billions from the housing crisis912786 C5 Company presidents whose salaries are many million dollars 8131115 E6Big labor unions 888126 E1Countries that export oil to the United States 714-294 E5 Big retail companies who mainly buy cheap goods from China 758108 B5President, JP Morgan/Chase Bank 71111-54 B4Chairman of the Federal Reserve Bank 710554 B6President, Goldman Sachs investment bank610721 C2 People who made billions from government bailout of companies 610-1122 D6Secret connecters between big money and legislators 5105-13 D5Agencies enforcing government regulations 312-5-32 Who runs America how different age group see it (Table 1.8) Lets just look at the pattern of shaded cells in Table 1.8, which shows the strong performing elements for the three clusters of ages: 18-29, 30-39, and over 40.We some revealing patterns as we first look closely at the numbers and then stand away: 33 1.ThosewhoareolderfeelthatthereisagrayeminencerunningAmerica,somethingthat cannot be explained. They have the high additive constant. Those 18-29 have a low additive constant, and those in their most formative years in term of power, age 30-39 show the lowest additive constant, as if it were entire the elements, the groups that were identified. 2.The youngest respondents respond mainly to the most clearly powerful elements, President Obama, the American people, the American military, and China. 3.The next group seems more away of many groups that run America, including the big retail companies,theunionsandtheCatholicChurch.Thisgroupisbeginningtointegratethe structure of American society. 4.The olderrespondents,age40+ seemhavegonethroughthe middlegroupsacceptance of many other sources of power, but then has rejected a few, such the Catholic Church and the President of JP Morgan/Chase Table 1.8: Strongest performing on Question (Affect America) by total and three age groups Tot Age 18-29 Age 30-39 Age 40+ ry Base Size 20123481629591 Constant20211028 D1President Obama 24202928 F1The American people 15151812 E4American military1312189 F2China 13141310 D3Supreme Court 1171116 B1Big international companies 1071413 D2Congress 108817 E2Oil company lobbyinggroups921417 C3 The "One Percent" who controls almost half of America's wealth 941216 C4People who made billions from the housing crisis941214 C5 Company presidents whose salaries are many million dollars 87109 E6Big labor unions 84149 E1Countries that export oil to the United States 73148 E5 Big retail companies who mainly buy cheap goods from China 77123 B5President, JP Morgan/Chase Bank 75107 F5The Catholic Church 55101 D6Secret connecters between big money and legislators 54012 D4Lobbyists 50512 F6Fundamentalist religious groups 4444 D5Agencies enforcing government regulations 31012 34 Just how stable are the impact values- 2012 versus 2013 studies with the same elements (Table 1.9) In the foregoing section on the statistical properties of the modeling, using the Grand Models, we showed that the element impacts from the INT Models of 2012 matched the element impacts from theINTModelsof 2013. Theplotswerequitelinear,althoughthelinearity andreliabilitywasfar more pronounced for Question 1 on Affecting America, and a bit noisier, and less pronounced, but still linear, for Question 2 on Trust. Wemoveforwardinthissection,topresenttheimpactvalues,thistimebythemethodof creatingindividualmodelsforeachrespondentforQuestion1onAffectingAmerica,andthenon Question on Trust.Our original goal was not to demonstrate reliability. Rather, with the upheavals in Congress and the difficulties faced by the United States, we wanted to see whether there was any dramatic power shift. 1.What can be said is that for the four strongest performing elements in 2012, there is a slight perceived increase in power 20122013 President Obama 2933 The "One Percent" who controls almost half of America's wealth 2023 Congress1823 Supreme Court1822 2.Furthermore, there is a slight eroding of trust from 2012 to 2103 for those trusted most 20122013 President Obama 97 Supreme Court 76 The American people 77 American military75 Bill Gates 75 College professors 65 Agencies enforcing government regulations 42 Table1.9:Comparisonoftheimpactsofelementsfromthe2012andthe2013studies, respectively.Theimpactsaretheaverageestimatedvaluesfromtherespondentswho participate, i.e., from the individual-level models. 20122013 20122013 ElementAffect AffectTrustTrust D1 President Obama 2933 97 D3 Supreme Court 1822 76 E1 The American people 1616 77 E4 American military1414 75 A5 Bill Gates 1210 75 F2 College professors 75 65 D5 Agencies enforcing government regulations 1214 42 35 F4 The Catholic Church 117 44 F3 Jews 76 43 D2 Congress 1823 33 B4 Chairman of the Federal Reserve Bank 1413 33 B6 President, Goldman Sachs investment bank1212 31 E2 Oil company lobbyinggroups1213 30 D4 Lobbyists 1113 31 E5Big retail companies who mainly buy cheap goods from China 119 30 E6 Big labor unions 1111 32 C1 Donald Trump 94 30 F5 Fundamentalist religious groups 96 32 B2 Chairman, Ford Motor Company 88 33 C6 Mark Zuckerberg (president of Facebook) 86 32 B3 Head of Fox News 78 31 E3 The "Tea Party" 65 30 A6 Rev. Pat Robertson 54 32 A4 Alex Rodriguez 30 31 B1 Big international companies 1413 22 C5Company presidents whose salaries are many million dollars 1411 21 F1 China 1415 21 C2People who made billions from government bailout of companies 1312 21 C4 People who made billions from the housing crisis 1312 20 B5 President, JP Morgan/Chase Bank 1213 22 D6Secret connecters between big money and legislators 1114 20 E1 Countries that export oil to the United States 1112 21 A3 Jake Long 30 21 A1 Kim Kardashian 1-4 21 C3The "One Percent" who controls almost half of America's wealth 2023 11 A2 Rush Limbaugh 55 11 Segmenting the mind of the respondent (Table 1.10, Figure 1.10) Peopledifferfromeachother.Sociologists,consumerresearchers,politicalpollstersand many other professionals dealing with people realize that these differences can be traced, at least to someextent,backtowhothepeopleare,whethergender,age-cohort,ethnicgroup,andsoforth. Other researchers aver that the differences between people can be traced to other differences, so-called psychographic differences, based on the attitudes that a person holds. Another way to divide people focuses on their reaction to the local, to the here and the now.This way of dividing people is called mind-typing, or in its new incarnation, view-point analysis. Thereasonforthename change isquite simple,andislessthan meetsthe eye!Originally,typing tools had little research meaning, nor really had little of any business meaning either. With the advent 36 of computers, however, and the widespread use of keyboards, typing tool first became a method for identifying groups of people based upon a scoring system (as we will use here), but quickly became whatitsoundslikeatooltoteachonetypingonakeyboard.Thewidespreaduseofcomputers quicklyputanendtotheextravalue impliedbythetermtypingtool. Thatterm migratedtothe termviewpointidentifier,recognizingthateveryonehadaviewpointaboutthelargeandsmall thingsofhislife,sothatthephraseviewpointidentifierwouldnotberesponsibleforcreatinga whole new identity. Theobjectiveofmindtypingissimple,andoperationallyclear.Amindtypeisagroupof individualswhoshareacommonpatternofreactionstoasetofstimuli,thesereactionstypically beingobtainedbyresponsetoasmall,limited,andfocusedsetofstimuli.Thefocused,smallset, allowstheresearchertocreatetheseminds-setsegmentsorviewpointsusingsimple,prescribed methods,methodswhichstanduptotherigorofacademics,academicjournals,statistical considerations, as well as practical application. In order to discover the mind-type of a respondent, we work with the responses to a specified topic area, here the data from Question 1, Who affects America.Our objective in min-typing is to discover different viewpoints about the question. We know from the analysis of subgroup that there arethesegroup-to-groupdifferences.Someofthedifferencesarequitelargesuggestingdifferent patterns, others simply a slightly numerical difference but the patterns are similar. The actual process to discover these mind-types, these different segments, follows a simple statistical path, combining the rigor of statistics and the freedom of interpretation.We follow this particularpath,recognizingthatatsomepointsotherslightlydifferentmathematicalapproaches might be used, yielding in turn slightly different answers.The important thing to keep in mind is that themind-setsegmentationisaheuristic,awayofunderstandingthedatamoredeeply,andnota fixed-in-stone analytical approach that must be followed in all its mathematical and statistical details. 1.Begin with the relevant data.In our case we begin with the 348 individual PER Models. These models or equations relate the presence/absence of the 36 element to the rating of Question 1 (Who affects America).Recall that each element appeared 5x in a persons 48 vignettes, and that the experimental design allowed us to create an individual-level model for the respondent.So wehaveanumericalvalueforeachelementshowinghowthatelementdrivestheratingof Question 1. 2.Step 1 creates for us our working data matrix, 36 columns (one per element) and 348 rows (one per respondent). The numbers in the body of the matrix are the coefficients or impact values from thePERModel,whichgivesusgranularity.UsingtheimpactsfromtheindividualINTModels would give us less granularity. 3.Defineameasureofdistanceordissimilaritybetweenpairsofrows,i.e.,betweenpairsof respondents. Statisticians have developed a variety of such measures of distance, including the geometrical or Euclidean distance between pairs of rows, computed on the 36 elements.We use a slightly different measure of distance, known as the Pearson distance. The Pearson distance is definedasthequantity(1-R),whereRisthePearsoncorrelationbetweenthetworowsof36 numbers. R varies between a high of +1 for perfect linear relationship, to 0 signifying no linear relationship, to a low of -1 for a perfect inverse relationship. Thus when two respondents show the same pattern, the numerical value of R is +1, and the value of quantity 1-R is 0, denoting no distancebetweenthetworespondents.Inturn,whentworespondents showinversepatterns, the value of R is -1, and the value of quantity 1-R is 2 (11 = 2). 37 4.Theclusteringprogramnowdividesthefullsetofdata,inourcasethe348respondents,into complementary groups, first two groups or clusters, then three clusters, then four clusters, and so forth.Each set of clusters, the 2-cluster, the 3-cluster, the 4-cluster, is called a solution.The reason for calling the set of clusters a solution comes from the fact that that clustering solves a particular problem in mathematics and statistics, namely allocate the people or objects to a fixed setofgroupsorclusters,sothatthevariabilityacrossthedifferentpeoplewithinaclusteris smaller andthevariabilityofthe means oftheclustersonthedifferentelementsislarge.The specific ways of doing the allocation vary from one program to another. The criteria are different, but typically with the same starting conditions and the same clustering rules, a program will come up with a solution that is stable, i.e., that repeats when the process is tried, again and again with the same data. 5.Up to now we have dealt with the mechanics of clustering, of our mind-set segmentation. Now enter the subjective parts, the art, which ends up being of interpretation than of creation. Our clustering program has done its work, creating for us a 2-segment, a 3-segment, a 4-segment, and even higher order solutions, such as a 5-segment, and a 6-segment solution.Which solution is best?Each of the solutions is best in a mathematical sense, but what about other criteria. 6.Psychologicalcriteriaforsegmentation:Step5raisesthequestionofwhichsegmentation solutionisbest.Asweincreasethenumberofsegmentswefindeachsegmenttobemore defined, clearer. There is only one problem, parsimony.At some point we feel that there are too many segments. So we end up with two major criteria for segmentation, opposite ones, in fact. a.The first criterion is interpretability. We have to understand the segments, to be able to explain the segments simply. They have to make sense.They have to tell a story. b.The second criterion is parsimony. We have to end up with as few segments as possible. Our story has to be simple. Fewer segments mean simpler stories.People prefer a few groups, feeling that the data creates a nice, neat, cohesive, manageable package. 7.Mindfuloftheforegoing,wewillbecreatingmind-setsegmentationswithaneyetoboth parsimonyandinterpretability,focusingmoreonparsimonyasthecontrollingfactor,thetool which prunes our solution, making it at once fairly interpretable, but also manageable. 8.Our segmentation suggests a minimum of two mind-sets, again not based on who the respondent is,orevenwhattherespondentfeelsingeneralabouttodayssociety,butonthepatternof responses to the particular set of 36 elements. Table 1.10 shows us the two-segment break.We see the strongest performing elements for each segment. 9.When we look at the segmentation, we should keep in the mind that it is the strongest-performing elements which give the segment its identity. Segment 1 appears to be those respondents who feel that the traditional centers of power really run America. These are the Traditionalists.Also includedinthisgroupisChina,perhapsnotsurprisinglygiventheeconomicsofthepastfew years. This segment comprises the vast majority of the respondents (284 out of 348). They show the low constant of 18, meaning that for them its the elements which do the work. 10. Oursecondinterpretablesegment,whetherreadersmightormightnotagree,comprises individualswhoareknown,whetherforentertainment,sports,orbusiness.Includedinthis group is the American Military, the only group performing strongly in Segment 1. These might be 38 called the Followers. Segment 2 is small, comprising 64 respondents, a little less than 20% of the respondents.The additive constant for Segment 2 is higher, 31, meaning that about one in threeoftheserespondentsfeelthatsomeonerunsAmerica,butarentreallyspecificabout which people. If the person is well known, its likely that the person runs America. 11. Forbothsegments,thetrustvaluesarelinearlyrelatedtotheperceptionofpower.Thatis, as Figure 1.10 shows us, people tend to assign high trust values to those that they that they believe affect America. Perhaps the one exception to this might be China, a foreign country. Looking at the responses of Segment 1, who believe China does affect America, we see that in fact China is neither trusted nor distrusted.The bottom line here is how we began this section; people tend to assign high trust value to those that they believe run America. Table 1.10: The strongest performing elements for two mind-set segments. The table shows the impact value from the INT Models for total and segments, for both Runs America (Question 1), and Trust (Question). Segmentation was done on the basis of Question 1 Runs - Total Runs - Seg1 Runs Seg 2 Trust - Total Trust Seg 1 Trust Seg 2 Base Size34828464 34828464 Constant201831 161521 Segment 1 TraditionalistsD1President Obama 24294 46-5 F1The American people 15182 56-1 F2China 1316-3 -1-1-4 D3Supreme Court 1115-10 24-5 D2Congress 1014-6 -20-9 E4American military131313 332 B1Big international companies 101110 -3-3-4 Segment 2 FollowersA2Rush Limbaugh 0-416 -2-33 A4Alex Rodriguez -3-614 -1-13 A1Kim Kardashian -2-614 -2-33 A6Rev. Pat Robertson -2-514 1-15 E4American military131313 332 A5Bill Gates 5313 106 Figure 1.10: The relation between impact values for Trust (ordinate, Question 2) and Power (RunsAmerica,Question1).Therelationisessentiallylinear,althoughthetwosegments differ dramatically in whom they feel affects America most. 39 Summing up what have we learned in terms of method, and in terms of understanding our respondents. Appendix to Chapter 1 -Panel composition Total Total Sample348 Gender Q1:Male171 Gender Q1:Female177 Ethnic Q2:Asian72 Q2:Black/African American88 Q2:Hispanic/Latino53 Q2:White/Caucasian129 Q3:18-2024 Q3:21-29138 Q3:30-3995 Q3:40-4940 Q3:50-5940 Q3:60-6511 Q4:Under $30,000119 Q4:$30,000-$39,99956 Q4:$40,000-$49,99944 Q4:$50,000-$74,99970 Q4:$75,000-$99,99927 Q4:$100,000-$124,99913 Q4:$125,000 or over19 Q5:Married118 Q5:Divorced20 Q5:Living with a partner43 40 Q5:Single161 Q6:Yes144 Q6:No204 Q14:Salary Will increase193 Q14: Salary Will decrease70 Q14:Salary will stay about the same85 Q15:Employed full time for pay161 Q15:Employed full time but not paid (example: housewife)11 Q15:Employed part time74 Q15:Not currently employed but looking37 Q15:Not currently employed but not looking12 Q15:Student43 Q16:Completed high school29 Q16:Some college126 Q16:Completed college122 Q16:Graduate school55 Q18:Very religious60 Q18:Somewhat religious108 Q18:Not formally religious but a believer96 Q18:Not formally religious and a non-believe84 Q19: Party Democrat165 Q19: Party Republican46 Q19:Party Independent88 Q19: Party None39 Q20: Political Belief Libertarian19 Q20: Political Belief Conservative53 Q20: Political Belief In the middle103 Q20: Political Belief Liberal145 Q20: Political Belief Not interested25 Q21:Less than a year33 Q21:About a year26 Q21:Between one and five years85 Q21:Over five but less than ten years58 Q21:Ten to twenty years75 Q21:Longer71 Q22:No really good friends80 Q22:One or two136 Q22:Four or five78 Q22:Six or more54 Q23:Really anchored here: this is my town82 Q23:It's all right196 41 Q23:I'd rather be somewhere else46 Q23:Can't wait to get out24