Color poster with text, graphs, and tables.History is filled with examples of humans'
inherent tendency to infer cause-and-effect from a
mere association. In the abstract, the
implication is that when researchers state that
"Variable X" and "Variable Y" are correlated, people may mistakenly infer that "Variable X" causes "Variable Y."  This study hypothesized that the conflation of correlation with causation is common and that the order in which variables are presented has an influence on which variable is assumed to be the cause and which the effect; and that the influence of variable order is most robust when correlations are presented both visually and in text.University of Wisconsin--Eau Claire Office of Research and Sponsored Programs

Bebault, Hali

Morrison, Katelyn

English

Minds@University of Wisconsin

INFERENCES OF CAUSE-AND-EFFECT FROM CORRELATIONAL DATAKate Morrison and Hali BebaultDepartment of Psychology, University of Wisconsin-Eau ClaireDr. April Bleske-Rechek, Faculty Mentor66 66 667657 565275686148867865610102030405060708090100SampleOverallMen Women First year SecondyearThird year Fourthyear +0 1 2 3+ 0 1 2 3+Percent using causal language at least onceYear in school Number of psychology courses            Number of math/science coursesGenderRESULTSOVERVIEWPARTICIPANTS’ USE OF CAUSAL LANGUAGE BY GENDER, YEAR IN COLLEGE, AND EXPOSURE TO SCIENTIFIC THINKINGMost college students can easily complete the phrase,“Correlation does not imply…” with the word“causation.” But how well do they actually apply thisreasoning? History is filled with examples of humans’inherent tendency to infer cause-and-effect from amere association (e.g., the long-held inference, fromcorrelational data on authoritative parenting andchildren’s good behavior, that authoritative parentingleads to children’s good behavior). In the abstract, theimplication is that when researchers state that“Variable X” and “Variable Y” are correlated, peoplemay mistakenly infer that “Variable X” causes“Variable Y.”We hypothesized that the conflation of correlationwith causation is common. We speculated that theorder in which variables are presented has aninfluence on which variable is assumed to be thecause and which the effect; and that the influence ofvariable order is most robust when correlations arepresented both visually and in text. To test thesehypotheses, we generated five correlational scenariosand presented each scenario to students in one of sixdifferent versions. The versions were created bycombining two independent variables: (1) the orderof X and Y (X before Y, Y before X); and (2) the form ofpresentation (text only, scatterplot only, text andscatterplot combined). After reading, viewing, orreading and viewing each scenario, participantsrestated the findings in their own words.METHODA total of 230 students (85M, 145F) across the UWECcampus participated in this study as part of an in-classactivity. Students came from a variety of majors; 51%were first-year students, 23% were second-yearstudents, 16% were in their third year, and 10% werein their fourth year or beyond.Each participant responded to five sets of findings.The five scenarios were all presented in the sameformat for each participant, depending on thecondition s/he had been randomly assigned to. Thefive sets of findings involved the following pairs ofvariables:Women’s self-esteem (“x”)Women’s number of sex partners (“y”)Children’s IQ (“x”)Amount of spanking from parents (“y”)Level of confidence (“x”)Number of friends (“y”)Children’s level of disobedience (“x”)Parents’ marital conflict (“y”)Couples’ sexual satisfaction (“x”)Couples’ emotional intimacy (“y”)For half of participants, the variables werepresented in x-then-y order; for half, the variableswere presented in y-then-x order. Each of these twogroups of participants was split into threesubgroups. One group received the findings in textformat only, such as: “Researchers havedocumented that women’s self-esteem correlatesnegatively with their number of sex partners.Restate this finding in your own words.” A secondgroup received the findings in the form of a scatterplot only: “Researchers have documented thecorrelation shown above. Restate this finding in yourown words.” A third group received the findings in acombined format: “Researchers have documentedthat women’s self-esteem correlates negatively withtheir number of sex partners, as shown above.Restate this finding in your own words.”SCATTERPLOTS THAT PARTICIPANTS VIEWED05101520251 2 3 4 5 6Women's number of sex partnersWomen's self-esteem1ALow self-esteem High self-esteem01234567891070 75 80 85 90 95 100105110115120125130Frequency of spankingChild's IQ2A1234567891011121314151 2 3 4 5 6 7 8 9 10Number of close friendsLevel of confidence3AOur first hypothesis was supported: Participants frequently conflated correlation with causation. Overall, 66% of the sample used clear causal language in one or more of their restatements (seeexamples below). Men and women did not differ in their likelihood of using causal language, but younger students and students who had less exposure to psychology coursework and math-science coursework were more likely to use causal language. However, nearly one-half (48%) of students with 3 or more psychology classes still conflated correlation with causation.1234560 5 10 15 20 25Women's self-esteemWomen's number of sex partners1BLow self-esteemHigh self-esteem7075808590951001051101151201251300 1 2 3 4 5 6 7 8 9 10Child's IQ Frequency of spanking 2B123456789101 2 3 4 5 6 7 8 9 10 11 12 13 14 15Level of confidenceNumber of close friends3BORDER OF VARIABLES IN PARTICIPANTS’ RESTATEMENTSScenario Variable X Variable Y% using causal languageExamples of causal language% using non-causal, “fuzzy” languageExamples of non-causal, “fuzzy” language that may reflect causal thinking(written in order used more often)% of sample who used the same order in their restatement (Chance = 50%)1 Women’s self-esteemWomen’s number of sex partners26.5% When women have sex with an increasing number of sex partners it negatively affects their self-esteem.60.4% As women’s number of sex partners increases, their self-esteem decreases.65.5% implied that sexpartners precede self-esteem2 Children’s IQ Frequency of spanking33.9% The less a child is spanked, the higher IQ he/she will have.52.6% Children who get spanked more have lower IQs. 70.0% implied that spanking precedes low IQ3 Level of confidenceNumber of friends33.5% The more friends you have, the higher your confidence will be. 54.8% More confidence = more friends. 66.5% implied  that confidence precedes friends4 Children’s level of disobedienceParents’ maritalconflict39.6% Higher marital conflict leads to higher levels of child disobedience.47.0% Children who live in a conflicted household are more disobedient.74.4% implied that marital conflict precedes disobedience 5 Sexual satisfactionEmotionalintimacy29.1% If couples are more emotionally intimate, they will be more sexually satisfied.58.3% Couples have a higher level of sexual satisfaction when their level of emotional intimacy is higher.72.1% implied that emotional intimacy precedes sexual satisfactionEach participant received either scatterplot A or B for each of the five findings. The format of presentation (in textformat or scatterplot format) did not influence participants’ restatements of the findings; however, scatterplotsbetrayed some confusions. First, negative correlations were interpreted as a lack of an association by severalparticipants. Second, some participants misinterpreted the magnitude of the correlations, describing them as“weak” and “sporadic,” when in reality, all associations were very strong (1A & 1B, r = -.75; 2A & 2B, r = -.75; and3A & 3B, r = .78). Scatterplots for scenarios 4 and 5 are not shown here, but were also strong (r = .87 and .77).The order in which participants received the variables did not influence their restatements. Instead, the majority of participants used the same order, regardless of what order they hadbeen exposed to. For example, in scenario 4, 74% of participants listed parents’ marital conflict before children’s level of disobedience in their restatements, regardless of the order thetwo variables had been presented in. Thus, even though some participants’ statements weren’t clearly causal, students had a clear preference for which variable came first. The fact thatthe majority of participants placed marital conflict before children’s level of disobedience suggests that participants held preconceived notions that marital conflict precedes – or causes --children’s disobedience. In other words, many students used language that wasn’t clearly causal, but consistency in their restatements betrayed cause-and-effect inferences.DISCUSSIONACKNOWLEDGMENTSIn many psychology classes, instructorsdemonstrate that correlation does not implycausation through the well known example ofthe positive association between ice creamsales and murder rate. In this examplestudents have an easy time seeing theabsurdity in inferring that ice cream sales havean effect on the murder rate or that themurder rate has an effect on ice cream sales;they easily generate third variables, like warmweather, that explain the association. In mostquestions that social scientists are interestedin, however, the limitations of correlationaldata are not so easy to recognize. In thecurrent study, we presented students with fivecorrelational scenarios in which either variablecould be causal and in which it is possible togenerate third variables that could underliethe associations. We found that students didinfer causation from data described ascorrelational, likely because they hadpreconceived notions about the issuesinvolved. As Stanovich (2010) notes, “whenthe causal link seems obvious to us, when wehave a strong preexisting bias, or when ourinterpretations become dominated by ourtheoretical orientation, it is tempting to treatcorrelations as evidence of causation.” Thecurrent study showed evidence that themajority of students do this even in acircumstance where, if anything, they shouldhave been primed not to (i.e., they were in aclassroom, the study was introduced as beingabout “interpretations of data,” and thescenarios we gave were worded and displayedin a scientific format).We think the danger of inferring causationfrom correlation is bigger than some peoplemight think. Take, for example, the scenariowe gave participants in which children’s IQ isnegatively correlated with the frequency withwhich they are spanked. Some people mightargue that it makes sense to assume thatspanking precedes children’s IQ, as 70% of oursample did. However, there are severalreasons why common sense is not goodenough. First, researchers cannot manipulateeither IQ or spanking to test their causaleffects. Second, it may not occur to people,but it is just as plausible that low IQ can elicitspanking or that a third variable, such asfinancial duress or parental IQ, could explainboth low IQ and spanking. Third, history is fullof examples in which the public has invested inmistaken causal, but common sense,interpretations of correlation data. Theinference that erratic eye movements causereading disabilities from the mere associationbetween the two variables and the inferencethat poverty causes schizophrenia are twocases in point.Future research in our lab will focus on furtherunderstanding how people interpretassociations between two variables underdifferent circumstances. Specifically, we planto provide specific information about differentresearch designs and ask people to tell uswhich of a variety of possible conclusions theycould infer from the findings.We thank ORSP for supporting this research and faculty acrosscampus for allowing us to collect data in their classrooms:Monty Ernst, John Grauer, Valerie Guyant, BJ Hollars, TraciThomas-Card, Catya von Karolyi, and Jeffrey Wright.

Inferences of Cause-and-Effect from Correlational Data

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Inferences of Cause-and-Effect from Correlational Data

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