the roles of ability, personality, and interests in acquiring current events knowledge: a...

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08) 261–278
Intelligence 36 (20
The roles of ability, personality, and interests in acquiring currentevents knowledge: A longitudinal study☆

David Z. Hambrick a,⁎, Jeffrey E. Pink c, Elizabeth J. Meinz b,Jonathan C. Pettibone b, Frederick L. Oswald a

a Michigan State University, United Statesb Southern Illinois University Edwardsville, United States

c University of Virginia, United States

Received 3 July 2006; received in revised form 4 June 2007; accepted 22 June 2007Available online 1 August 2007

Abstract

The purpose of this study was to investigate sources of inter-individual differences in current events knowledge. The studyoccurred in two sessions. In the initial session, 579 participants completed tests to ability, personality, and interest factors, as well asprior knowledge of current events. Approximately 10 weeks later, participants completed tests to assess new knowledge of currentevents, acquired since the initial session. Structural equation modeling revealed positive effects of both ability and non-abilityfactors on prior knowledge, and in turn, a large positive effect of prior knowledge on new knowledge. Results are interpreted in thecontext of theories of human intelligence that integrate ability and non-ability traits.© 2007 Elsevier Inc. All rights reserved.

Keywords: higher-level cognition; individual differences; knowledge; abilities; interest; personality

The importance of knowledge has been stressed in anumber of recent definitions of intelligence. Ackerman(1996) described it as the “central ingredient of adultintellect” (p. 241), and Schank and Birnbaum (1994)stated: “The bottom line is that intelligence is a functionof knowledge. One may have the potentiality of intel-ligence, but without knowledge, nothing will become ofthat intelligence” (p. 102). Similarly, Ceci (1996) pro-

☆ The authors thank Alison Gillings, Carlee Hawkins, AngellaMacDonald, Kristin Stege, Parth Tikiwala, and Emelia Zerkel for thehelp with data collection and entry.⁎ Corresponding author. Department of Psychology, Michigan State

University, East Lansing, MI 48824, United States.E-mail address: [email protected] (D.Z. Hambrick).

0160-2896/$ - see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.intell.2007.06.004

posed, “Intelligence is a function of cognitive complex-ity, which in turn is dependent upon the operation ofcognitive processes on a specifiable knowledge structureand, conversely, cognitive processes are dependent uponthe sheer quantity of knowledge a person possesses…”(p. 27). Why, though, do some people know more thanothers? Exposure is an obvious requirement for acquir-ing knowledge: To acquire knowledge about some topic,a person must be exposed to information about that topic(e.g., Stanovich & Cunningham, 1992, 1993). But whyare some people more likely to seek out knowledge thanothers? Or given equivalent exposure, why do somepeople acquire more knowledge than others? Simply put,what are the factors that contribute, indirectly anddirectly, to individual differences in knowledge?

mailto:[email protected]

http://dx.doi.org/10.1016/j.intell.2007.06.004

262 D.Z. Hambrick et al. / Intelligence 36 (2008) 261–278

1. Perspectives on inter-individual differences inknowledge

More than a century after Spearman (1904) firstobserved it, the question of what psychometric g re-presents remains unanswered, as does the broaderquestion of how to define intelligence. Nevertheless,many theorists have proposed that intelligence com-prises the ability to learn. For example, in a 1921symposium (Thorndike, 1921), Woodrow stated, “Ishould first say that it is an acquiring capacity” (p. 207),and Henmon drew a distinction between two factors ofintelligence: “the capacity for knowledge and knowl-edge possessed” (p. 195). Later, Hebb (1942) distin-guished between a genetically-based Intelligence A andan experience-based Intelligence B. Around the sametime, Cattell (1943) proposed a similar view in hisdistinction between a general capacity for solving novelproblems–fluid intelligence (Gf)–and the skills ac-quired through the use of this capacity—crystallizedintelligence (Gc). More recently, in his landmark surveyof factor-analytic studies, Carroll (1993) found evi-dence for a three-stratum model, with a general factor(g) at the highest level (Stratum III); eight broadabilities, including Gf and Gc, at the next level (StratumII); and approximately 70 narrow abilities at the lowestlevel (Stratum I).

This idea that intelligence and learning are closelyrelated is supported by a large amount of evidence. Infact, Jensen (1998) concluded, “there is no generallearning factor…that is independent of psychometric g”(p. 276). At the level of more specific abilities, Gf andGc are typically reported to correlate in the .40 to .60range (see Horn & Noll, 1998; McGrew, 1997), whereGf is typically assessed with tests of reasoning and Gcwith tests of vocabulary, comprehension, and generalinformation. Gc has been observed to correlate espe-cially highly with knowledge in specific domains. Forexample, across four domains, Beier and Ackerman(2001) found that current events knowledge correlated.81 with Gc, compared to .45 with Gf. Beier andAckerman (2003) observed a similar pattern of results–Gc correlated much more strongly with knowledge in awide range of health-related domains than did Gf–andBeier and Ackerman (2005) found that Gc was astronger correlate of self-directed learning about twotopics (xerography and cardiovascular disease) than wasGf.

It appears, then, that Gc is predictive of acquiringknowledge in a given domain, presumably because theverbal skills and knowledge that Gc comprises have animpact on how well information is comprehended when

first encountered, how well it is integrated into long-term memory, or both. However, Gc is certainly not theonly factor that appears to play a role. Another isknowledge itself. Hambrick (2003), for example, foundthat the best predictor of acquiring new knowledgeabout college basketball over the course of a season wasprior knowledge of basketball—evidence for a “snow-ball” effect in knowledge acquisition. There also havebeen many reports of prior knowledge of some topicfacilitating retention of new information about thattopic. For example, Spilich, Vesonder, Chiesi, and Voss(1979) found that prior knowledge of baseball facilitatedretention of information from a passage about a baseballgame (see also Hambrick & Engle, 2002; Recht &Leslie, 1988; Walker, 1987), and in the study by Beierand Ackerman (2005) already mentioned, effects of Gcon learning were mediated through prior topic knowl-edge (see also Ackerman & Beier, 2006). One way tothink about how prior knowledge might operate directlyto facilitate acquisition of new knowledge stems fromthe common view of long-term memory as a network ofinterconnected concepts or “nodes” (e.g., Anderson,1983; McClelland & Rumelhart, 1981): When somenew piece of information is encountered, activationspreads throughout the network to associatively relatednodes, and the new piece of information becomes part ofthe existing knowledge structure through some bindingprocess.

1.1. The role of non-ability factors

Theory and evidence suggest that non-ability factorsalso contribute to knowledge acquisition. In his invest-ment theory of intelligence, Cattell (1971) conceptual-ized the growth of Gc in terms of a complex set ofinfluences, comprising not only Gf, but also personalityand interest traits, which jointly influence how Gf is“invested” in learning opportunities. Cattell thereforeobserved that the correlation between Gf and Gc shouldbe positive but generally far less than unity. Cattell alsorecognized a distinction between a Gc factor comprisingrelatively general skills acquired primarily during theschool years and knowledge acquired thereafter: “Thepracticing psychologist must realize that crystallizedability begins after school to extend into Protean formsand that no single investment such as playing bridge orskill in dentistry can be used as a manifestation by whichto test all people” (p. 121). In his theory of intelligence-as-process, personality, interest, and intelligence-as-knowledge (or PPIK), Ackerman (1996) expanded onthis idea in his distinction between intelligence-as-process (Gp) and intelligence-as-knowledge (Gk). Like

263D.Z. Hambrick et al. / Intelligence 36 (2008) 261–278

Cattell, Ackerman posited both ability and non-abilityinfluences on the growth of knowledge. However, Gkis much broader than Gc, encompassing specializedknowledge acquired through vocational and avocationalactivities.

Consistent with these proposals, there are a numberof different sources of evidence for the role of non-ability factors in knowledge acquisition. At least twomajor types of interest can be distinguished. Accord-ing to Hidi and Renninger (2006), situational interestis a transient state that is activated in the moment by anenvironmental stimulus, and promotes knowledge ac-quisition in activities like reading by directingattention toward relevant information and away fromirrelevant information (e.g., Hidi, 1990, 1995; McDa-niel, Waddill, Finstad, & Bourg, 2000). On the otherhand, individual interest can be thought of as a morestable predisposition to attend to certain objects orevents, and to engage in activities like reading about acertain topic (e.g., science). Individual interests havealso been implicated as a contributor to knowledgeacquisition. For example, Ackerman et al. observedcorrelations between vocational interest themes, whichmight be thought of as broad collections of individualinterests, and knowledge of various domains (e.g.,Ackerman, 2000; Rolfhus & Ackerman, 1996, 1999),and Reeve and Hakel (2000) found a positive averageintraindividual correlation between interest and knowl-edge levels across twelve domains, indicating thatpeople learn more about what they like. There havealso been a number of reports of individual interestfacilitating learning from text (e.g., Alexander, Jetton,& Kulikowich, 1995; Alexander, Kulikowich, &Schulze, 1994), and in a meta-analysis, Schiefele,Krapp, and Winteler (1992) observed an average cor-relation of .31 between interest and achievementacross a wide range of academic topics (see alsoTobias, 1994, for a review).

Certain personality characteristics may also contrib-ute to individual differences in knowledge. In an earlystudy, Cattell (1947) noted a positive correlation be-tween Gc and a cluster of personality traits that helabeled “intellectual/wide interests”. Around the sametime, Gough (1953) described a measure of IntellectualEfficiency (Ie), which included items like “I read at leastten books a year” and “I like to read about history”.Gough reported positive correlations between Ie and astandard assessment of intelligence, which included Gc-type measures, as did Gough and Weiss (1981). And,more recently, in a meta-analysis, Ackerman andHeggestad (1997) found moderate positive correlationsbetween Gc and two personality constructs: Openness

to Experience and Typical Intellectual Engagement.Openness to Experience–a factor of Costa andMcCrae's (1992) big-five personality structure–reflectsintellectual and artistic interests and has been alterna-tively interpreted as a Culture or Intellect factor (e.g.,Goldberg, 1999), whereas Goff and Ackerman's (1994)concept of Typical Intellectual Engagement refers to aperson's “interest in a wide variety of things and…pref-erence for a complete understanding of a complex topicor problem…” (p. 539). Finally, there have been a fewreports of positive correlations between Need forCognition–one's preference or propensity for intellec-tual engagement (Cacioppo & Petty, 1982)–and variousGc measures (e.g., Sadowski & Cogburn, 1997;Salthouse, Berish, & Miles, 2002). At least in part, allof these personality characteristics might be thought ofas reflecting a general interest in learning, and it seemsreasonable to suggest that they contribute to acquiringverbal abilities (Gc) through activities like reading,which then contribute directly to knowledge acquisition.General interest in learning may also contribute toindividual differences in knowledge independent of Gc(or other ability factors). In particular, people with astrong general interest in learning may be more likely todevelop individual interests in specific topics (e.g., art,science, politics), and hence to seek information aboutthese topics, than are people with a weaker interest inlearning.

2. The present study

The evidence just reviewed suggests that individualdifferences in knowledge arise from both abilityand non-ability factors. The present research extendsa study by Hambrick, Meinz, and Oswald (2007)concerning the relative contributions of these factors toindividual differences in knowledge of current events—the sort of knowledge that may be important foreveryday tasks such as deciding how to vote in anelection or how to invest money in the stock market.Hambrick et al. distinguished between distal andproximal predictors of knowledge acquisition. Distalpredictors reflect indirect causes originating fromgeneral dimensions of psychological functioning, suchas intelligence and personality, whereas proximalpredictors reflect more direct causes tied to particulardomains, such as individual interests and exposure.Hambrick et al. found evidence for two predictive“pathways”. In the ability pathway, Gf predicted Gc,which predicted current events knowledge. In the non-ability pathway, Need for Cognition predicted currentevents interest, and current events interest predicted


news exposure. In turn, news exposure predicted cur-rent events knowledge.

The major goal of this study was to address twolimitations of the Hambrick et al. (2007) study. The firstlimitation is that the predictor variables (ability and non-ability) and current events knowledge were measured atthe same point in time. Hambrick et al. were thereforelimited in their ability to draw causal conclusions aboutthe role of the predictor variables in acquiring knowledge.The present study used a longitudinal design. The majorquestion of interest was how ability and non-ability var-iables, along with preexisting current events knowledge,would influence acquisition of current events knowledgeover an extended period of time. An advantage of tradi-tional approaches to research on learning, in which ex-posure to some information is experimentally controlled(e.g., in a paired-associates task), is that individual dif-ferences in learning can be attributed to factors other thanexposure. Nevertheless, a limitation of this approach isthat the conditions of learningmay be different from thoseencountered outside of the laboratory, where learningtends to occur over an extended period of time and underself-paced conditions (Neisser, 1978). Hambrick (2003)conducted a longitudinal study of the acquisition ofknowledge in the domain of college basketball that in-volved two test sessions. In the first session, participantscompleted tests to assess potential predictors of knowl-edge acquisition, including ability factors and domain-specific knowledge and interest. Then, approximately2.5 months later, these same participants completed testsof basketball acquired over the just-completed season.The present study parallels this approach in the domain ofcurrent events.

The second limitation of the Hambrick et al. (2007)study is that assessment of potentially relevant predictorconstructs was limited. Two broad abilities were con-sidered in this earlier study—Gf and Gc. We expandedthe ability assessment to include tests of a third, short-term memory (Gsm).Gsmmight contribute to individualdifferences in current events knowledge, becauseinformation must presumably be held in immediateawareness before it can be transferred to long-termmemory. A specific question was whether Gf, Gc, andGsm would contribute incrementally to current eventsknowledge, beyond any influence of g. As alreadydiscussed, previous research suggests that Gc should behighly predictive of current events knowledge (Ackerman&Beier, 2006; Beier &Ackerman, 2001; Hambrick et al.,2007). Furthermore, within the Cattell–Horn–Carroll(CHC) framework, a number of studies have demonstrat-ed incremental validity of Gc and Gsm for outcomes likereading achievement (Evans, Floyd, McGrew, & Lefor-

gee, 2001; McGrew, Keith, Flanagan, & Vanderwood,1997; Vanderwood, McGrew, Flanagan, & Keith, 2002).Furthermore, Gustafsson and Balke (1993) found thatGcadded over g to the prediction of course grades across awide range of topics (see also Thorndike, 1991; Young-strom, Kogos, & Glutting, 1999). We also expandedassessment of personality variables. Hambrick et al. ob-served a non-ability influence on current events knowl-edge originating from Need for Cognition. However,Need for Cognition may be a manifestation of broaderdimensions of personality. In fact, in a longitudinal studydescribed by McCrae (2000), Openness to Experienceand Need for Cognition correlated strongly (r=.55), andthe correlation was even stronger for the Ideas facet ofOpenness (r=.68).

2.1. Sex differences in knowledge

An extensive literature documents a link between sexdifferences in ability and non-ability characteristics andsex differences in educational and career trajectory andachievement (e.g., Lubinski & Benbow, 1992; see alsoSpelke, 2005). In the general spirit of this research,another goal of this study was to investigate sex differ-ences in current events knowledge. In a recent study,Lynn and Irwing (2002) observed higher levels ofgeneral knowledge in males than females (see alsoLynn, Irwing, & Cammock, 2002; Wilberg & Lynn,1999). This sex difference could not be explained byeither Gf or an experience factor assumed to reflectacculturation. However, the assessment of experience inthis study was very limited, as it was based on only twoindicators (father's occupation and father's profession).Ackerman, Bowen, Beier, and Kanfer (2001) assessed abroader range of ability, personality, and interest factors,and they found evidence that these factors contributedto, but could not completely explain, sex differences inknowledge favoring males. In another relevant study,Hambrick (2003) found that although males tended toacquire much more knowledge about basketball over thecourse of a college season than females, there were nodirect effects of sex on basketball knowledge, in part dueto a mediating effect of basketball interest and exposure.A specific question of interest here was whether sexdifferences in current events knowledge, if observed,would be accounted for by sex differences in currentevents interest.

2.2. Overview

To recap, the major goal of the present study wasto investigate ability and non-ability influences on


acquisition of current events knowledge. A specificquestion of interest was how the ability and non-ability factors, along with preexisting knowledge,would impact acquisition of new knowledge. Anotherquestion was whether sex differences in currentevents interest would contribute to sex differences inacquisition of current events knowledge. To addressthese questions, we conducted a study involvingover 500 participants. The two sessions of the studywere separated by approximately 10 weeks betweenSeptember, 2004, and December, 2004. In Session 1,participants completed tests and questionnairesdesigned to measure ability, personality, interest,and exposure factors. Participants also completed atest to assess current events knowledge from theprevious two years (2002 and 2003). In Session 2,participants completed additional tests to assessknowledge of events that occurred in the periodsince Session 1. Data were analyzed using structuralequation modeling.

3. Method

3.1. Participants

Participants were recruited from Michigan StateUniversity and Southern Illinois University Edwards-ville. Five-hundred seventy-nine participants (74%female) completed Session 1, of which 536 (92.6%)returned for Session 2. Participants received credit inan introductory psychology course for volunteeringtheir time. We assume that the range of cognitiveability in this sample was restricted, given thatall participants were college students. However, therewas still a relatively wide range of ability in oursample, as ACT scores ranged from 13 to 33 (M=23.7,SD=3.3).1 Therefore, it appears that our samplewas selective, but not extremely so relative to allstudents who apply to college (M=20.8; SD=4.8; seewww.act.org).

1 The ACT is a widely used college entrance exam in the UnitedStates and is designed to assess broad academic achievement. Wewere not able to verify the self-reported ACT scores against universityrecords. However, there is no reason to suspect that there wassignificant degree of misreporting, given that ACT had a high positivecorrelation with the two other Gc measures, Synonym Vocabulary(r=.56) and Reading Comprehension (r=.64). Seventeen participantsreported an SAT score instead of an ACT score; we converted theseSAT scores into ACT scores using a table published by the CollegeBoard (see www.act.org).

3.1.1. Session 1

3.1.1.1. Procedure and materials. Session 1 occurredin September, 2004, and lasted approximately 2.5 h;participants were tested in groups (up to 70). Aftersigning an informed consent form, participants complet-ed a background questionnaire that asked for age, sex,ethnicity, and ACT score. Next, participants completed abattery of materials in a fixed order of (1) News ExposureQuestionnaire; (2) Need for News; (3) Thinking Disposi-tions; (4) News Headlines; (5) Word Span, (6) Digit Span,(7) Letter Span, (8) Matrix Reasoning; (9) ReadingComprehension; (10) Letter Sets; (11) Vocabulary;(12) Series Completion; (13) Letter Comparison; (14) Pat-tern Comparison; (15) Number Comparison; (16) CurrentEvents Knowledge.2

3.1.1.1.1. Cognitive ability. Participants complet-ed tests to measure four ability constructs: fluidintelligence (Gf), crystallized intelligence (Gc), andshort-term memory (Gsm). Where noted, items weredrawn from the following batteries: Air Force OfficerQualifying Test (Berger, Gupta, Berger, & Skinner,1990); Educational Testing Service Kit of Factor-Referenced Tests (Ekstrom, French, Harman, & Dar-man, 1976); and the Shipley Institute for Living Scale(Zachary, 1986).

Gf—(1) Series Completion (from Zachary, 1986):Each item consisted of a sequence of letters,numbers, or both, followed by blank spaces; thetask was to fill in the blank spaces with the logicalcontinuation of the sequence. Four minutes wereallowed for 20 items, and participants markedresponses on the test form. (2) Letter Sets (fromEkstrom et al., 1976): Each item consisted of five setsof letters; the task was to infer the rule that madethese letter sets similar and to identify the letter setthat did not fit this rule. Eight minutes were allowedfor 14 items, and participants marked responses on ascantron. (3) Matrix Reasoning: Items in this testwere drawn from Raven's Advanced ProgressiveMatrices (Raven, 1962). Each item consisted of a3×3 matrix in which each cell except the one in the

2 Given that the period between Session 1 and Session 2encompassed the final stages of the 2004 U.S. general election,participants completed additional tests and questionnaires to assesspolitics interest and exposure, and politics items were overrepresentedon the test of new current events knowledge (i.e., 30 items). Resultspertaining to these materials are to be described in a separate reportfocusing specifically on individual differences in politics knowledge.

http://www.act.org

http://www.act.org


lower right-hand corner contained a pattern; the taskwas to choose from among eight alternatives apattern that made logical sense for the missing ninthcell. Eight minutes were allowed for 14 items, andparticipants marked responses on a scantron. Foreach test, a participant's score was the proportioncorrect.Gc—(1) SynonymVocabulary (from Zachary, 1986):Each item in this test consisted a target word, printedin all capitals, followed by five lettered alternatives;the task was to choose the alternative that was similarin meaning to the target word. Five minutes wereallowed for 15 items, and participants markedresponses on a scantron. (2) Reading Comprehension(from Berger et al., 1990): Items in this test consistedof a short paragraph; the task was to select thealternative (from among four) that completed the finalsentence. Six minutes were allowed for 10 items, andparticipants responded on a scantron form. For eachtest, a participant's score was the proportion correct.Gsm—(1) Word Span (from Kane et al., 2004):Participants attempted to recall sequences of com-mon nouns, presented in all capital letters. Set sizesranged from four to nine words, with one trial foreach set size. (2) Digit Span (from Kane et al.):Participants attempted to recall sequences of digits(0–9). Set sizes ranged from five to nine digits, withone trial for each set size; digits repeated across butnot within trials. (3) Letter Span (from Kane et al.):Participants attempted to recall letters. Set sizesranged from five to nine letters, with one trial foreach set size; letters repeated across but not withinsets. Nine letters were used (B, F, H, J, L, M, Q, R,X). For each test, the stimuli were presented on aprojection screen for 1 s each, with a 1 s blankscreen between items. After presentation of the finalitem in a set, a prompt (RECALL) appeared in thecenter of the screen, and participants attempted torecall the items (words, digits, or letters) in the orderpresented. For each test, a participant's score wasthe number of items recalled in the correct serialorder.

3.1.1.1.2. Intellectual openness. We assessed de-gree of intellectual openness with a scale (ThinkingDispositions) that included items drawn from twosources. One source (18 items) was the Need forCognition scale (Cacioppo, Petty, & Kao, 1984). Theother source (10 items) was the Intellect facet scale fromthe International Personality Item Pool (2001), whichappears to measure the same construct as the Ideas facetscale of the NEO-PI-R (Goldberg, 1999). Each item was

a statement describing an attitude toward or propensityfor intellectual engagement (e.g., I prefer complex tosimple problems). Using a five-point scale with anchorsof 1 (Completely Inaccurate) and 5 (CompletelyAccurate), participants were to assign a value to eachitem reflecting the degree to which they believed thestatement was an accurate description of them. Half ofthe items were positively worded, and half are nega-tively worded. There was no time limit; most partici-pants finished within 10 min. Participants markedresponses on a scantron form. For each scale, a par-ticipant's score was the average rating.

3.1.1.1.3. News exposure. We assessed exposureto news media with a paper-and-pencil questionnaire(News Exposure Questionnaire from Hambrick et al.,2007). Participants were to estimate for a typical weekthe number of times they engaged in each of followingfive activities and how long they spent on each activity:(1) reading the newspaper, (2) reading news magazines,(3) watching news programs on television, (4) listeningto news programs on the radio, and (5) reading the newson the Internet. For each activity, the exposure estimatewas the frequency estimate multiplied by the timeestimate (i.e., minutes per week). Participants were alsoasked to list the newspapers, news magazines, newsprograms, and news websites they rely upon most.

As an additional assessment of news exposure,participants completed a newly developed scale (Needfor News, from Hambrick et al., 2007) in which each ofthe 12 items was a statement describing an attitudetoward or propensity for seeking news (e.g., It bothersme to feel like I'm behind on the news). Using a five-pointrating scale with endpoints of 1 (Completely Inaccurate)and 5 (Completely Accurate), participants were to ratethe degree to which each statement described them. Aparticipant's score was the average rating.

3.1.1.1.4. Current events interest. We assessedinterest in current events using an inventory (NewsHeadlines) in which each item was a news headlinefrom one of the following seven categories: (1) Arts/Entertainment, (2) Business/Economy, (3) Crimes/Accidents/Disasters, (4) U.S. Politics/Government,(5) World Politics/Government, (6) Science/Medicine,and (7) Sports/Recreation. There were nine items foreach category, for a total of 63 items. For each item,using a five-point scale with endpoints of 1 (Not at allInterested) and 5 (Very Interested), participants were toindicate how interested they would be in reading orhearing about the news story. The items were selectedfrom a larger pool of items administered by Hambricket al. (2007). For each category, a participant's score wasthe average rating.

Table 1Sample items from Current Events Knowledge test

Category Item Alternatives

Arts/Entertainment

Television host _ wasaccused of sexualharassment by an associateproducer working on hisshow.

a) Geraldo Riverab) Bill O'Reilly⁎

c) Jerry Springerd) Chris Matthews

Business/Economy

Which American retail giantrecently announced a mergerwith K-mart?

a) Targetb) Wal-Martc) Sears⁎

d) JC PenneyCrimes/

Accidents/Disasters

In September, this hurricanebattered the U.S. Gulf Coast,causing billions of dollars indamages In Florida andAlabama:

a) Nicoleb) Georgec) Lucyd) Ivan⁎

U.S. Politics/Government

Following the election, whodid President Bush appointSecretary of State?

a) Colin Powellb) Arlen Specterc) Condoleeza Rice⁎

d) Orrin HatchWorld Politics/

GovernmentAging world leader _ trippedand fell as he stepped off astage after making a public:appearance.

a) Muamarr Qadaffib) Fidel Castro⁎

c) Yassir Arafatd) Jacques Chirac

Science/Medicine

This medication was takenoff drugstore shelves bypharmaceutical companyMerck due to safety concernsof increased risk of heartattack and stroke:

a) Vioxx⁎

b) Viagrac) Zoloftd) Claritin

Sports/Recreation

A brawl involving bothplayers and fans erupted ina pro basketball gamebetween the Detroit Pistonsand ___.

a) Indiana Pacers⁎

b) L.A. Lakersc) Houston Rocketsd) Philadelphia 76ers

Note. ⁎=correct alternative.


3.1.1.1.5. Current events knowledge. We assessedprior (pre-study) knowledge of current events with amultiple-choice test consisting of questions about newsstories from 2002 and 2003. For each year, there werethree questions for each of the seven current eventscategories listed above (for a total of 42 items). Theitems were selected from a larger pool of items ad-ministered by Hambrick et al. (2007). For each category,a participant's score was the proportion correct of itemsattempted. Participants were encouraged to answer all ofthe questions, even if they had to guess. There was notime limit; most participants finished within 10 min.

3.1.2. Session 2

3.2.1.1. Procedure and materials. Session 2 occurredin December, 2004, and lasted approximately 45 min;participants were tested in groups (up to 50). First,participants completed a new version of the NewsExposure Questionnaire, in which they were asked toestimate time spent engaging in the news-seekingactivities described previously during the period be-tween Sessions 1 and 2. Next, participants completed amultiple-choice test to assess newly acquired currentevents knowledge; that is, knowledge acquired sinceSession 1. This test consisted of 90 questions aboutnews stories from the 10-week period between the dayafter the final participation date for Session 1 (Septem-ber 19, 2004) and the day prior to the first participationdate for Session 2 (November 29, 2004). Participantswere encouraged to answer all of the questions, even ifthey had to guess. For each category, a participant'sscore was the proportion correct of items attempted;missing values were assigned to participants who re-sponded to fewer than 80% of items. There was no timelimit; most participants finished within 35 min.

We wrote the current events questions to minimizethe possibility that participants could answer the ques-tions correctly without having been exposed to theinformation between Sessions 1 and 2 and withoutguessing. Consider the following item:

In ___, a Laotian man shot and killed six deerhunters; the shooter alleges that the hunters threatenedhim and made racist remarks.

a) Illinoisb) Minnesotac) North Dakotad) Wisconsin

The answer is Wisconsin, but each of the otheralternatives is plausible, because deer are legally hunted

in Illinois, Minnesota, and North Dakota. In short, wecannot completely rule out the possibility that priorknowledge enabled correct guessing on some of theitems, but it seems reasonable to assume that themajority of the questions assessed newly acquiredknowledge (see Hambrick et al., 2007, and Hambrick,2003, for further discussion of this issue). Additionalsample items appear in Table 1.

3.2.1.1. Item selection. Ideally, we would have admin-istered knowledge items to a pilot sample, and thenadministered the items with the best psychometriccharacteristics (e.g., item-total correlations) to a testsample. However, a constraint of this study is that wewanted to maximize the amount of time between Sessions1 and 2 for acquisition of current events knowledge, whileleaving enough time at the end of the semester for Session2. Consequently, there was not enough time for a pilot

Table 2Descriptive statistics for news exposure variables

Session 1 Session 2

M SD Sk. Ku. dsex M SD Sk. Ku. dsex

Newspaper min/week 36.5 47.3 2.03 4.44 − .28⁎⁎ 44.0 47.1 1.57 2.35 − .37⁎⁎Television min/week 84.3 103.6 2.60 9.72 − .11 57.7 80.0 2.12 4.36 − .28⁎⁎Internet min/week 33.8 60.0 2.56 6.62 − .41⁎⁎ 30.1 51.6 2.41 5.57 − .42⁎⁎Magazines min/week 7.9 21.3 3.08 9.02 − .04 3.9 12.9 3.78 13.90 − .13Radio min/week 21.8 45.3 2.98 9.55 − .13 11.4 30.9 3.77 15.08 − .03

Note. Because of high skewness and kurtosis values, data for these values were square-root transformed in subsequent analyses. Cohen's d statisticsreflect sex differences; negative values indicate lower averages in females (n=374) than in males (n=144).⁎⁎pb .01.


study. Therefore, as a compromise, participants complet-ed all of the items that we generated, but we anticipatedusing only a subset of these items in the final analysesbased on psychometric characteristics of the items. Itemswere selected in two steps. First, we selected items thatcould be clearly classified into one of the seven currentevents categories. The classification procedure was asfollows: Using short descriptions of the categories, 10raters were asked to give each item a primary classifica-tion, reflecting the most relevant category, and secondaryclassifications if any other categories were relevant. Therewas good agreement (70% or greater) on the primaryclassification for 79 of 90 items, leaving at least eightitems per category, except Business/Economy (six items).Second, for each category, we selected the six itemshaving the highest correlations with total correct for thecategory (i.e., item-total correlations). (We used allBusiness/Economy items.) Thus, as for prior knowledge,there were six items per category, for a total of 42 newknowledge items.

Table 3Correlations among news exposure variables

1 2 3 4

Session 11. Newspaper min/week −2. Television min/week .29 −3. Internet min/week .24 .11 −4. Magazines min/week .26 .24 .10 −5. Radio min/week .10 .21 .13 .12

Session 26. Newspaper min/week .59 .16 .21 .207. Television min/week .24 .53 .10 .208. Internet min/week .29 .13 .65 .139. Magazines min/week .16 .24 .08 .4610. Radio min/week .08 .18 .01 .07

Note. All variables are square-root transformed. Correlations with an absolu

4. Results

There were statistically significant differences(pb .01) between participants who returned for Session2 (n=536) and those who did not (n=51) for only 2 of39 variables in the data set (i.e., Letter Sets, Science/Medicine Interest). This small rate of statistical signif-icance suggests that attrition from Session 1 to Session 2was random. All subsequent analyses are based onlistwise deletion, including only those participants whocompleted both sessions.

4.1. Data preparation

Three steps were involved in preparing the data foranalysis. First, for each scantron-based test, excludingthe (timed) reasoning tests, we assigned missing valuesto participants who responded to fewer than 80% ofthe items. Second, we discarded participants who weremissing values on two or more variables. This resulted

5 6 7 8 9 10

−

.10 −

.22 .15 −

.10 .30 .16 −

.18 .19 .27 .10 −

.44 .08 .24 .05 .10 −

te magnitude greater than .09 are statistically significant (pb .05).

Table 5Descriptive statistics for personality and interest variables

M SD Sk. Ku. α dsex

Intellectual opennessNeed for cognition 3.3 .6 − .15 − .01 .87 .17Intellect 3.5 .6 − .09 .00 .78 − .24⁎⁎

Current events interestArts/Entertainment 3.4 1.4 .28 − .74 .93 .56⁎⁎

Business/Economy 2.7 1.1 .63 .31 .89 − .38⁎⁎Crimes/Accidents/Disasters 4.6 1.2 − .36 − .18 .92 .52⁎⁎

U.S. Politics/Government 3.1 1.3 .39 − .41 .92 − .08World Politics/Government 2.5 1.1 .87 .43 .92 − .38⁎⁎Science/Medicine 4.4 1.1 − .24 .03 .86 − .05Sports/Recreation 3.1 1.5 .57 − .47 .93 − .62⁎⁎

Note. Values reflect average ratings. Cohen's d statistics reflect sexdifferences, with negative values indicating lower averages in females(n=372) than in males (n=144).⁎⁎pb .01.

Table 6Descriptive statistics for Current Events Knowledge variables


Prior CE KnowledgeArts/Entertainment .73 .22 − .68 − .07 .46 − .52⁎⁎


in elimination of 18 participants. For 39 participants,each of whom had only one missing value, we regressedeach variable onto other variables of the same type(ability, interest, etc.) and replaced missing values withpredicted values. Finally, we screened the data foroutliers. We screened the data for univariate outliers on avariable-by-variable basis, replacing any value greaterthan 3.5 SD units from the variable mean (b .5% of thedata) with a less extreme value of 3.5 SD units from themean. We then inspected Mahalanobis D2 values toscreen the data for multivariate outliers, but there wereno extreme values. The final sample consisted of 518participants.

4.2. Descriptive statistics

As shown in Table 2, participants reported a widerange of news exposure. For example, estimates for the10-week period between Sessions 1 and 2 ranged from 0to 220 min/week for newspaper reading (M=44.0;SD=47.1) and from 0 to 369 min/week for televisionwatching (M=57.7; SD=80.0); on average, malesreported higher levels of news exposure than females(avg. d=− .22). There also was evidence that that esti-mates were moderately reliable. Across sessions, cor-relations between the two estimates for each activityranged from .44 to .65, and each variable correlatedpositively with Need for News, an independent index ofnews exposure (see Table 3): Session 1 (avg. r=.33) andSession 2 (avg. r=.29). Many exposure variables werenon-normal (skewness valuesN2 and kurtosis val-uesN10). For this reason, all exposure variables were

Table 4Descriptive statistics for ability variables


GfSeries completion .75 .10 − .65 1.01 .62 − .18⁎Letter sets .79 .20 −1.12 .93 .76 .07Matrix reasoning .59 .20 − .93 .81 .74 − .35⁎⁎

GcSynonym vocabulary .59 .17 .00 − .63 .60 − .44⁎⁎Reading comprehension .53 .24 − .05 − .77 .66 − .31⁎⁎ACT 23.7 3.3 − .02 − .36 − − .46⁎⁎

GsmWord Span .65 .14 − .17 − .21 .78 − .19⁎Digit Span .79 .14 − .48 − .30 .82 − .15Letter Span .70 .15 − .06 − .48 .85 − .28⁎⁎

Note. Means reflect proportion correct. Cohen's d statistics reflect sexdifferences, with negative values indicating lower averages in females(n=372) than in males (n=144).⁎pb .05, ⁎⁎pb .01.

square-root transformed for subsequent analyses. Skew-ness and kurtosis values were in the acceptable rangefollowing transformation.

Descriptive statistics for the ability, non-ability, andknowledge variables are displayed in Tables 4–6, and acorrelation matrix appears in Table 7. It can be seen inTable 4 that the reliability estimates were somewhat,though not extremely, low for some ability variables

Business/Economy .47 .23 .24 − .37 .32 − .57⁎⁎Crimes/Accidents/Disasters .42 .24 .35 − .48 .39 − .45⁎⁎U.S. Politics/Government .50 .27 .17 − .66 .57 − .51⁎⁎World Politics/Government .47 .26 .21 − .57 .49 − .68⁎⁎Science/Medicine .45 .24 .18 − .62 .42 − .33⁎⁎Sports/Recreation .39 .27 .72 − .16 .59 −1.31⁎⁎Total .49 .17 .47 − .30 .83 − .97⁎⁎

New CE KnowledgeArts/Entertainment .57 .24 − .01 − .57 .43 − .43⁎⁎Business/Economy .35 .18 .29 − .14 .01 − .02Crimes/Accidents/Disasters .54 .23 − .18 − .31 .34 − .24⁎⁎U.S. Politics/Government .65 .25 − .41 − .44 .50 − .33⁎⁎World Politics/Government .42 .24 .34 − .33 .47 − .61⁎⁎Science/Medicine .50 .19 .11 − .16 .13 − .17Sports/Recreation .44 .26 .48 − .22 .62 −1.13⁎⁎Total .50 .14 .33 − .16 .75 − .75⁎⁎

Note. CE=Current Events. Prior CE Knowledge was assessed inSession 1; New CE Knowledge was assessed in Session 2. Valuesreflect proportion correct. Cohen's d statistics reflect sex differences,with negative values indicating lower averages in females (n=374)than in males (n=144).⁎⁎pb .01.

Table 7Correlation matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Ability1. Series completion −2. Letter sets .48 −3. Matrix reasoning .43 .40 −4. Synonym vocabulary .27 .16 .25 −5. Reading comprehension .36 .26 .35 .55 −6. ACT .45 .30 .46 .56 .64 −7. Word Span .25 .24 .25 .30 .30 .34 −8. Digit Span .18 .21 .14 .10 .14 .21 .34 −9. Letter Span .24 .23 .18 .23 .17 .28 .43 .45 −

Personality and CE Interest10. Need for cognition .15 .09 .15 .30 .27 .26 .11 .02 .03 −11. Intellect .15 .06 .16 .40 .34 .38 .23 .05 .10 .77 −12. Arts/Entertainment .04 .07 .01 − .18 − .08 − .07 − .01 .06 .01 − .21 − .19 −13. Business/Economy − .02 − .08 − .05 .03 .02 .04 − .07 − .01 − .03 .24 .20 .06 −14. Crimes/Acc's/Dis's − .01 .05 − .02 − .09 − .06 − .07 − .05 − .04 − .04 .01 .01 .40 .34 −15. U.S. Politics/Gov't − .03 − .06 − .03 .11 .11 .08 .00 − .02 − .01 .32 .32 − .03 .69 .29 −16. World Politics/Gov't − .01 − .11 − .04 .18 .13 .11 − .01 − .03 − .03 .31 .30 − .09 .73 .30 .74 −17. Science/Medicine .05 .06 .10 .13 .20 .20 .02 − .03 − .06 .33 .33 − .05 .32 .38 .34 .38 −18. Sports/Recreation .12 .06 .11 − .02 .02 .08 .08 .07 .14 − .08 − .07 .32 .27 .31 .15 .13 .02 −

News Exposure19. Newspaper min/week .04 .04 .09 .15 .14 .11 .05 .03 .08 .18 .19 − .08 .23 .10 .30 .33 .13 .2120. Internet min/week .02 − .05 .02 .04 .05 .06 − .03 .04 .01 .06 .09 − .01 .24 .00 .26 .29 .07 .0921. Magazines min/week − .10 − .02 .00 − .02 .03 − .01 − .09 − .06 − .05 .11 .10 − .06 .20 .03 .20 .16 .09 .1022. Television min/week − .11 − .10 − .13 − .03 − .03 − .05 − .01 − .03 .01 .08 .09 − .04 .31 .10 .31 .30 .05 .1423. Radio min/week − .03 − .09 − .15 .00 − .03 − .02 − .04 − .03 − .02 .05 .05 − .07 .07 .01 .09 .11 .06 − .05

Prior CE Knowledge24. Arts/Entertainment .20 .01 .15 .32 .29 .29 .13 .00 .10 .11 .22 − .11 .12 − .04 .17 .22 .11 .1025. Business/Economy .10 − .02 .09 .32 .28 .29 .11 .01 .04 .24 .24 − .23 .20 − .06 .23 .31 .15 .0926. Crimes/Acc's/Dis's .02 − .02 .06 .30 .26 .25 .12 .05 .08 .14 .18 − .17 .14 − .03 .20 .31 .10 .0727. U.S. Politics/Gov't .10 − .03 .12 .39 .30 .31 .14 .00 .09 .20 .24 − .17 .21 − .06 .31 .35 .13 .1028. World Politics/Gov't .10 .00 .14 .42 .38 .36 .13 .01 .08 .22 .31 − .16 .21 − .05 .29 .37 .15 .0629. Science/Medicine .18 .11 .17 .33 .33 .32 .12 .06 .11 .18 .20 − .16 .11 − .05 .17 .18 .16 .0130. Sports/Recreation .06 − .02 .06 .20 .12 .15 .04 .10 .14 .04 .06 − .15 .16 − .03 .10 .15 .02 .46

New CE Knowledge31. Arts/Entertainment .13 .05 .10 .32 .32 .32 .14 − .03 .08 .08 .16 − .13 .10 .01 .16 .22 .10 .1132. Business/Economy − .04 − .05 − .01 .02 .02 .06 .03 − .04 .03 .05 .04 .04 .09 .02 .09 .08 − .03 − .0233. Crimes/Acc's /Dis's .07 .08 .02 .22 .23 .19 .10 − .04 .03 .05 .12 − .06 .10 .07 .13 .15 .11 .0734. U.S. Politics/Gov't .10 .03 .15 .32 .30 .32 .16 − .03 .08 .19 .25 − .17 .11 − .04 .23 .24 .13 .0035. World Politics/Gov't .02 .04 .09 .30 .29 .26 .10 − .03 .02 .16 .24 − .15 .21 − .03 .24 .33 .10 .0836. Science/Medicine .04 .01 .04 .28 .21 .23 .08 − .05 .06 .03 .14 − .13 .05 − .05 .10 .14 .09 .0237. Sports/Recreation .11 .02 .17 .21 .18 .22 .14 .15 .21 .05 .11 − .09 .17 − .04 .10 .16 .02 .48

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37Note. N=518. CE=Current Events. Correlations with an absolute magnitude greater than.09 are statistically significant (pb .01). All news exposureestimates are square-root transformed and reflect news exposure between Session 1 and Session 2.


(αsb .70), perhaps because the tests were administeredin large groups or contained a relatively small number ofitems. Nevertheless, the three variables for each abilityconstruct correlated positively and moderately with eachother (see Table 7). For this reason, we retained all of thevariables for subsequent analyses. Males tended tooutperform females on the tests of cognitive ability (avg.

d=− .25), but this advantage was consistently signifi-cant only for Gc (avg. d=− .43). As can be seen inTable 5, the two measures of intellectual openness hadacceptable reliability (αsN .70) and correlated highlywith each other (r=.77); averages for these variableswere higher for males than for females (ds=− .17 and− .24). The interest variables also had acceptable

Table 7 (continued)

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

−.30 −.19 .10 −.15 .16 .27 −.08 .05 .10 .24 −

.22 .19 .08 .13 .05 −

.17 .08 .08 .14 .04 .34 −

.16 .20 .15 .22 .02 .31 .37 −

.24 .17 .16 .17 .05 .48 .50 .45 −

.22 .22 .06 .08 .06 .31 .40 .43 .49 −

.13 .13 .07 .13 .08 .34 .32 .32 .40 .35 −

.31 .16 .11 .15 .03 .30 .30 .26 .36 .30 .21 −

.26 .09 .15 .14 .12 .42 .31 .29 .45 .36 .31 .31 −

.06 .06 .00 .10 .02 .15 .06 .10 .13 .11 .04 .01 .12 −

.18 .10 .10 .12 .03 .27 .25 .31 .30 .25 .20 .16 .32 .06 −

.25 .13 .00 .14 .01 .40 .35 .32 .44 .42 .31 .23 .39 .18 .30 −

.25 .18 .11 .25 .07 .34 .36 .39 .41 .42 .24 .26 .37 .15 .35 .38 −

.06 .09 .07 .10 .03 .21 .22 .25 .29 .25 .20 .17 .34 .03 .27 .28 .25 −

.27 .16 .06 .10 − .03 .28 .32 .28 .32 .28 .14 .60 .28 .05 .17 .25 .28 .17 −


reliability (αs= .86 − .93), and indicative of a generalinterest factor, there were moderate-to-strong correla-tions (avg. r=.45) among the variables for five of sevendomains: Business/Economy, Crimes/Accidents/Disas-ters, U.S. Politics/Government, World Politics/Govern-ment, and Science/Medicine (see Table 7). We foundthat Arts/Entertainment and Sports/Recreation interest

correlated more weakly with these interest variables.Averages were significantly (pb .01) higher for femalesin the Arts/Entertainment (d=.56) and Crimes/Acci-dents/Disasters (d=.52) domains, but were higher formales in the Business/Economy (d=− .38), WorldPolitics/Government (d=− .38), and Sports/Recreation(d=− .62) domains.

Fig. 1. Measurementmodel for current events (CE) knowledge variables. B/E=Business/Economy,C/A/D=Crimes/Accidents/Disasters, USP/G=UnitedStates Politics/Government, W P/G=World Politics/Government, S/M=Science/Medicine. Values are standardized regression weights; value on thebidirectional arrow is a correlation.


Finally, an inspection of Table 6 reveals that therewas a wide range of scores, both for prior knowledge(M=.49; SD= .17; Range= .14 to 1.00) and new knowl-edge (M=.50; SD=.14; Range= .14 to.93). Reliabilityestimates were quite low for some of the individualknowledge scales. Nevertheless, the overall scales hadacceptable reliability (αs= .83 and .75), and as shown inTable 7, the individual knowledge variables tended tocorrelate positively with each other: Prior CE Knowl-edge (avg. r=.36) and New CE Knowledge (avg.r=.24).3 Therefore, we retained all knowledge variablesfor subsequent analyses. Means were generally higherfor males than for females (avg. d=− .52), and for all butone comparison, sex differences were statisticallysignificant. Finally, the prior and new knowledge totalscorrelated very highly (r=.71).

4.2.1. Structural equation modelingThe remainder of this section reports structural

equation modeling (SEM) aimed at explaining variancein newly acquired current events knowledge. Through-out this section, we report a number of fit statistics. Theχ2 test indicates whether there was a significant differ-ence between the reproduced and observed covariancematrixes. Thus, non-significant χ2 values reflect a fit of

3 Note also that a low coefficient alpha for a knowledge scale indicatesonly that the items for that scale did not correlate highly with each other(i.e., were not internally consistent). The itemsmay still represent diverseand relevant content, and a low alpha does not preclude total scores forthat scale from correlating with total scores for other scales. For example,although the coefficient alpha for Science/Medicine knowledge (Session2) was low (.13), it correlated significantly with 11 of the 12 otherknowledge variables (avg. r=.23; see Table 9).

the model to the data. However, when moderate to largesample sizes are used, slight differences betweenreproduced and observed covariance matrices can resultin significant χ2 values. The comparative fit index (CFI)and non-normed fit index (NNFI) are less sensitive tosample size and reflect improvement in the fit of a modelcompared with a baseline model in which populationcovariances among observed variables are assumed tobe zero. The root mean squared error of approximation(RMSEA) reflects the average difference between theobserved and reproduced covariances. CFI and NNFIvalues of greater than .90, and RMSEA values less than.08, indicate acceptable fit (e.g., Browne & Cudeck,1993; Kline, 2005).

4.2.1.1. Confirmatory factor analyses. Two stepswere involved in the SEM. The first step was to performconfirmatory factor analyses to determine whether wewere successful in measuring the intended constructs.We used the Holzinger bifactor approach to model theability variables (see Jensen & Weng, 1994), first spe-cifying a model in which each indicator loaded onto ageneral factor (g). Factor loadings were generally high,but model fit was poor: χ2(27)=319.83 (pb .01),CFI= .78, NFI= .76, RMSEA=.15. We added factorsrepresenting three cognitive abilities (Gf, Gc, Gsm).Each variable had a positive loading on the generalfactor (.27–.77) and one specific factor: Gf (.24–.64),Gc (.35–.51), and Gsm (.40–.65). Model fit was ex-cellent: χ2(18)=34.35 (pb .05), CFI= .99, NFI= .97,RMSEA=.04.

For the non-ability variables, we tested a modelconsisting of three factors: Intellectual Openness,


Current Events Interest, and News Exposure. Fit of aninitial model was unacceptable: χ2(74) = 471.90(pb .01), CFI= .82, NFI= .79, RMSEA=.10. Inspectionof the parameter estimates suggested that this wasbecause two variables had very low loadings on theinterest factor: Entertainment (− .02) and Sports (.21).And, in fact, model fit was acceptable with these

Table 8Standardized direct, total indirect, and total effect estimates from structural e

Endogenous variable

CE Interest News Exposur

Exogenous variable β C.R. β C

gDirect effect – – – –Total indirect effect – – .24Total effect – – .24

GcDirect effect – – – –Total indirect effect – – .28Total effect – – .28

GfDirect effect – – – –Total indirect effect – – − .09 −Total effect – – − .09 −

GsmDirect effect – – – –Total indirect effect – – − .01Total effect – – − .01

Intellectual opennessDirect effect .36 6.90⁎⁎ − .10 −Total indirect effect – – .18Total effect .36 6.90⁎⁎ .08

CE InterestDirect effect – – .46Total indirect effect – – .19Total effect – – .65

News ExposureDirect effect – – – –Total indirect effect – – – –Total effect – – – –

Prior CE Know.Direct effect – – .54Total indirect effect – – – –Total effect – – .54

g Gc

Correlations β C.R. β C

Intellectual Openness .38 5.31⁎⁎ .34Current Events Interest − .07 −1.14 .07News Exposure − .27 −2.46⁎ − .29 −

Note. CE=Current Events. Direct effects reflect unmediated associations, totand total effects reflect the sum of direct and total indirect effects. β=standerrors were used to compute C.R.s for total indirect and total effects (when⁎pb .05; ⁎⁎pb .01.

variables omitted, χ2(51)=196.83 (pb .01), CFI= .92,NFI= .90, RMSEA=.07. The remaining variables hadpositive loadings on their respective factors, IntellectualOpenness (.85–.90), Current Events Interest (.36–.88),and News Exposure (.23–.49).

For the current events (CE) knowledge variables, wetested a model with separate factors for Session 1 (Prior

quation model

e Prior CE Knowledge New CE Knowledge

.R. β C.R. β C.R.

.44 4.52⁎⁎ .10 1.051.91 – – .39 3.21⁎⁎

1.91 .44 4.52⁎⁎ .49 2.45⁎

.52 3.78⁎⁎ .09 .641.82 – – .46 3.13⁎⁎

1.82 .52 3.78⁎⁎ .55 2.21⁎

− .17 −2.33⁎ .03 .301.58 – – − .15 −2.00⁎1.58 − .17 −2.33⁎ − .13 .83

− .02 − .28 − .10 −1.76− .24 – – − .02 − .22− .24 − .02 − .28 − .12 −1.20

1.42 − .10 −1.68 − .01 − .162.39⁎ .12 4.00⁎⁎ − .02 − .21.66 .02 .29 − .03 − .21

4.85⁎⁎ .35 6.67⁎⁎ − .17 −1.98⁎2.46⁎ – – .45 3.37⁎⁎

5.83⁎⁎ .35⁎⁎ 6.67⁎⁎ .28 2.86⁎⁎

– – .30 2.28⁎

– – – –– – .30 2.28⁎

3.55⁎⁎ – – .73 4.78⁎⁎

– – – –3.55⁎⁎ – – .73 4.78⁎⁎

Gf Gsm

.R. β C.R. β C.R.

3.36⁎⁎ − .10 −1.43 − .03 − .51.74 − .14 −1.83 − .07 −1.111.69 .02 .16 .09 .94

al indirect effects reflect the sum of all possible mediated associations,ardized regression weight. C.R.=critical ratio. Bootstrapped standardnot the same as direct effects).


CE Knowledge) and Session 2 (New CE Knowledge).Model fit was acceptable, χ2(34)=37.37 (ns), CFI=1.00,NFI=.97, RMSEA=.01. As shown in Fig. 1, the factorscorrelated highly (r=.89). However, when the correlationwas constrained to unity, model fit was reduced, χ2(35)=50.95 (pb .05), CFI=.99, NFI=.96, RMSEA=.03, andthe difference in the model fits was statistically signifi-cant: ▵χ2(1)=13.58, pb .01. We were therefore justifiedin treating the knowledge factors as distinct.

4.2.1.2. Structural models. The next step was to testfor effects of the predictor variables on newly acquiredcurrent events knowledge (New CE Knowledge). Ofparticular interest were the two pathways discussedpreviously. The ability pathway included direct effects ofthe general (g) and specific ability (Gf, Gc, Gsm) factorson Prior CE Knowledge and New CE Knowledge, and apositive effect of Prior CE Knowledge on New CEKnowledge. We predicted a positive effect of g on PriorCE Knowledge, and a positive effect of Prior CEKnowledge on New CE Knowledge. We also predicteda positive effect of Gc on Prior CE Knowledge, beyondthe influence of g. For the non-ability pathway, we pre-

Fig. 2. Structural equation model with ability and non-ability factors prC/A/D=Crimes/Accidents/Disasters, US P/G=United States Politics/GoverValues on single-headed arrows are standardized regression weights; value

dicted a positive effect of Intellectual Openness on Cur-rent Events Interest, a positive effect of Current EventsInterest on News Exposure, and a positive effect of NewsExposure on New CE Knowledge. We made no specificpredictions about relations between the ability and non-ability factors (e.g., Gf and Current Events Interest), andhence paths between these variables were bidirectional.

Direct, indirect, and total effects are summarized inTable 8, while parameters from the model that reachedstatistical significance (pb .05) are displayed in Fig. 2.There was a direct effect of g on Prior CE Knowledge(.44), and of Prior CE Knowledge on New CEKnowledge (.73). However, this was not the wholestory with respect to the ability factors, as there was adirect effect of Gc on Prior CE Knowledge (.52),beyond the influence of g.We also found evidence for anon-ability pathway. That is, there were direct effects ofIntellectual Openness on Current Events Interest (.35),Current Events Interest on News Exposure (.46), andNews Exposure on Prior CE Knowledge (.30), all aspredicted. Two unexpected findings were direct nega-tive effects of Gf on Prior CE Knowledge (− .17) and ofCE Interest on New CE Knowledge (− .17). The predictor

edicting current events (CE) knowledge. B/E=Business/Economy,nment, W P/G=World Politics/Government, S/M=Science/Medicine.s on double-headed arrows are correlations.


variables accounted for 83.9% of the variance in New CEKnowledge. Model fit was acceptable: χ2(395)=690.69(pb .01), CFI=.94, NFI=.87, RMSEA=.04.4

4.2.1.2.1. Sports/recreation and arts/entertain-ment. We ran another set of models to test for predictorsof knowledge in the Arts/Entertainment (A/E) and Sports/Recreation (S/R) domains. For each domain, the modelswere configured the same as in the preceding analysis,except that therewas a single indicator for both interest, andfor prior knowledge and new knowledge. There were pos-itive effects of g andGc on Prior A/E Knowledge (.27 and.25, respectively), and ofGc (.34) on Prior S/RKnowledge.In turn, there were positive effects of Prior S/R KnowledgeonNewS/RKnowledge (.43), and of PriorA/EKnowledgeon New A/E Knowledge (.20). There was also someevidence for non-ability contributions: a positive effect ofNews Exposure on New A/E Knowledge (.29), and pos-itive effects of S/R Interest on both Prior S/R Knowledge(.52) and New S/R Knowledge (.22). (All psb .01).

4.2.1.3. Sex differences. We performed a final analysisto investigate sex differences in current events knowledge.The specific question of interest was whether these sexdifferences would remain statistically significant aftercontrolling for sex differences in the ability and non-abilitypredictor variables. To answer this question, we added sexas a predictor variable to the structural equation modeldescribed previously. For statistical reasons, we decidednot to run a model with paths leading from sex to all of theability factors in the manner that they were previouslymodeled. Instead, we modeled g and Gc only for thisanalysis.5 Focusing on Gc was a logical choice, because

4 As already explained, for the model displayed in Fig. 2, we used the“best” 42 items (6 per category) from the 90-item test of current eventsknowledge given in Session 2. However, because the 48 excluded itemstapped relevant content, we re-ran the model with the total score on thetest as the single indicator for new knowledge. The results were similarto those displayed in Fig. 2: there were positive effects of Prior CEKnowledge (.62) and News Exposure (.37) on the total score.5 We scaled the ability factors by constraining the variance of each

factor to 1.0 instead of setting the factor loading of one indicator perfactor to 1.0.We did this because, hadwe used the latter approach, for oneof the three sets of ability indicators (e.g., Gc), it would have beennecessary to use one indicator to scale the specific ability and thenanother to scale the g factor, leaving only one freely estimated factorloading. This worked well, except that for the analysis of sex differences,it created a problem: when a latent factor is scaled by setting its varianceto 1.0, it cannot be treated as an endogenous factor that is predicted bysome variable (e.g., sex); it can only be exogenous (see Kline, 2005). Toget around this problem, for the analysis of sex differences, we left all ofthe ability variables in the model, but we specified only g and Gc. Wescaled the g factor by setting the factor loading of oneGf indicator to 1.0and theGc factor by setting the factor loading of oneGc indicator to 1.0.

Gcwas found to be a significant predictor of current eventsknowledge in the previous analyses, and because therewere consistent, sizeable sex differences in Gc but not theother abilities (cf. Table 4).

This analysis revealed that there were direct negativeeffects of sex on the ability variables–g (− .16) and Gc(− .20), psb .01–and the non-ability variables—Intellec-tual Openness (− .12), Current Events Interest (− .11), andNews Exposure (− .18), psb .05. (Mean levels werehigher for males than for females for all variables.)Critically, however, the negative effect of sex on Prior CEKnowledge (− .20) remained statistically significant(pb .01) even after taking sex differences in these otherfactors into account. (The effect of sex on New CEKnowledge was non-significant.) This was also true forthe effect of sex on Prior A/EKnowledge (− .16) and PriorS/R Knowledge (− .42). Overall, then, the factors con-sidered here contributed to, but could not completelyexplain, sex differences in current events knowledgefavoring males.

5. Discussion

Why do some people know more than others? Forexample, in an election, why do some people possessextensive knowledge of the issues (the economy, theenvironment, etc.), whereas others know little more thanthe candidates' party affiliations? More generally, whataccounts for individual differences in knowledge? Thepurpose of the present study was to test for effects ofability, interest, personality, and experience variables,along with prior knowledge, on knowledge acquiredunder naturalistic learning conditions. Previous researchhas considered some of these factors. For example, Beierand Ackerman (2001) looked at effects of a number ofdifferent predictors on current events knowledge,including ability and personality variables, but they didnot attempt to measure and model specific interests andexperience. Beier and Ackerman (2005) added topic-relevant experience as a predictor of knowledge ac-quisition, but they did not model personality and interestfactors as antecedents of this experience. Thus, towardthe goal of gaining a more complete understanding ofwhat underlies inter-individual differences in knowl-edge, the contribution of this study to the extant literatureis to consider, within a single model, a broader range ofinfluences than has been previously examined.

5.1. Major findings

There was evidence for an ability influence on acqui-sition of current events knowledge. The g factor we


modeled had a positive effect on prior current eventsknowledge, which had a strong positive effect on newknowledge. Furthermore, as others have demonstrated foreducational outcomes like school performance (e.g.,Gustafsson & Balke, 1993) and reading achievement(e.g., Evans et al., 2001; Vanderwood et al., 2002), therewas an effect ofGc on knowledge acquisition beyond theinfluence of g. Indeed, the effect ofGc on prior knowledgewas even larger than that of g, as was the total effect ofGcon new knowledge. We do not interpret this as evidenceagainst Jensen's (1998) claim that there is no generallearning factor independent of g, because there obviouslyare many types of learning that we did not consider in thisstudy (e.g., procedural learning). However, in line withprevious research (Ackerman & Beier, 2006; Beier &Ackerman, 2005), what this does suggest is that Gc maybe especially important for one form of learning—acquiring declarative knowledge. Our results also providefurther evidence for the idea that knowledge “begets”knowledge: Having knowledge makes it easier to acquirenew knowledge (see also Beier & Ackerman, 2005;Hambrick, 2003).

In contrast, fluid ability (Gf) had a non-significant onnew current events knowledge. This replicates results ofprevious research: after controlling forGc, Hambrick et al.(2007) observed a non-significant effect of Gf on currentevents knowledge, as did Beier and Ackerman (2001) intheir study of current events knowledge. Consistent withCattell's (1943) original conception of Gf, we speculatethat this is because Gf is important for acquiring the typeof knowledgemeasured in this study only for areas that arenovel. The samemay be true forGsm, which had no effecton new knowledge in this study. To take the example ofsports, the ability to reason (Gf)–which presumablydepends to some extent on how much information onecan hold in short-termmemory (Gsm)–may be required tounderstand how a game like hockey or baseball is played,but once this basic knowledge is in place, these factorsmay play little direct role in acquiring declarative facts likethe outcome of a particular game.We note that there was asmall negative effect of Gf on prior knowledge. We canthink of no obvious explanation for this finding, and canonly speculate that measures of fluid reasoning correlateweakly with something, perhaps a personality or interestvariable not measured here, that has a negative impact onacquiring current events knowledge.6

6 This finding may also reflect something unusual about theparticipants in this study, but it is worth nothing that the effect of Gfon current events knowledge in the Hambrick et al. (2007) study,while non-significant, was also negative (- .13) and was similar inmagnitude to this effect in the present study (- .17).

We also found that non-ability factors had indepen-dent effects on current events knowledge. Intellectualopenness, a personality variable assumed to reflect ageneral interest in learning, positively predicted interestin current events topics. This interest, in turn, positivelypredicted engagement in news-seeking activities, whichpositively predicted current events knowledge. Oneunexpected finding in the non-ability pathway was thatthere was a direct negative effect of current eventsinterest on new knowledge. Perhaps there was a subset ofparticipants in our sample who had low interest in currentevents, and who engaged in little news-seeking activity,but who inflated their interest ratings for reasons of socialdesirability. Nevertheless, the total effect of currentevents interest on new knowledge was robustly positive,and thus the overall picture that emerges from this studyis that, as determinants of knowledge growth, personalitytraits and interests influence how people spend their timeand what they learn. More generally, the evidence rein-forces the view that the adult intellect, with knowledge asits core component, cannot be fully understood withoutconsidering the role of non-ability factors (Ackerman,1996; Cattell, 1971).

5.2. Future directions

This study points out directions for research on anumber of phenomena concerning individual and dev-elopmental differences in knowledge. As a case in point,fluid-type abilities tend to decline in adulthood, whereasspecialized knowledge has been observed to increase(Ackerman, 1996). This is paradoxical if one accepts theview that Gf plays a direct role in knowledge acquisition.However, our results point to the possibility that Gc,which remains relatively stable in adulthood (e.g.,Salthouse, 2004), is more important than Gf for decla-rative learning. This stability, combined with a narrowingof interests and experience, may help to explain age-related increases in knowledge. On the other end of thelifespan, the impact of non-ability factors, interests inparticular, on knowledge acquisition may explain howchildren can acquire high levels of knowledge in domainslike chess well before fluid abilities have reached peaklevels (e.g., Chi, 1978; Schneider, Gruber, & Opwis,1993). On a final note, the results leave the issue of sexdifferences in current events knowledge unresolved. Thatis, there were direct effects of sex on current eventsknowledge even after controlling for the ability, person-ality, interest, and exposure factors. A goal for futureresearch is to develop more specific measures of interestand exposure to see whether males and females differ inthe types of information that they attend to and retain.


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