Learning outside the laboratory: Ability and non-ability influences on acquiring political knowledge

Learning and Individual Differences 20 (2010) 40–45

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Learning and Individual Differences j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i n d i f

Learning outside the laboratory: Ability and non-ability influences on acquiring political knowledge David Z. Hambrick a,⁎, Elizabeth J. Meinz b, Jeffrey E. Pink c, Jonathan C. Pettibone b, Frederick L. Oswald d a

Michigan State University, United States Southern Illinois University Edwardsville, United States University of Virginia, United States d Rice University, United States b c

a r t i c l e

i n f o

Article history: Received 6 November 2008 Received in revised form 23 September 2009 Accepted 27 October 2009 Keywords: Knowledge Intelligence Personality Interests

a b s t r a c t The purpose of this study was to identify sources of individual differences in knowledge acquired under natural conditions. Through its direct influence on background knowledge, crystallized intelligence (Gc) had a major impact on political knowledge, acquired over a period of more than 2 months, but there were independent influences of personality and interest factors, via exposure to political information through activities like reading the newspaper. We also found sex differences in political knowledge, favoring males, and these differences could not be explained in terms of any of the predictor variables we modeled. We discuss theoretical and practical implications of the results. © 2009 Elsevier Inc. All rights reserved.

1. Introduction Knowledge is necessary for success. Many occupations require a certain level of specialized knowledge for licensure—a person must pass the real estate exam to become a realtor, the bar exam to become an attorney, or a medical board to become a physician. While no exam is required for pursing a hobby like woodworking, gardening, or bridge, knowledge is the key to success, if not enjoyment. Knowledge is also called upon in tasks encountered in between work and play: repairing a car, cooking a meal, financial planning, and so on. Accordingly, the importance of knowledge has been stressed in recent theories of intelligence (e.g., Ackerman, 1996; Ceci, 1996; Sternberg, 1999).

1.1. Perspectives on knowledge Asked to define intelligence, Henmon (1921) distinguished between the “capacity for knowledge and knowledge possessed” (italics added, p. 195). Later, Cattell (1943) described this distinction more formally, defining fluid intelligence (Gf) as a general capacity for perceiving relations and crystallized intelligence (Gc) as the skills and knowledge acquired through this capacity. The existence of Gf and Gc has since been confirmed in dozens of studies, and is one of the most replicated findings in research on human intelligence (Horn & Noll, 1998). ⁎ Corresponding author. Department of Psychology, Michigan State University, East Lansing, MI 48824, United States. E-mail address: [email protected] (D.Z. Hambrick). 1041-6080/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.lindif.2009.10.013

Cattell (1971) articulated the prevailing view of the relationship between Gf and Gc in his investment theory of intelligence: “[Gc] arises and has its particular form as a result of investing a general capacity [Gf] in suitable learning experience” (p. 124). This view is modestly supported by evidence (e.g., Schmidt & Crano, 1974), but there is also evidence that Gc plays an important role in learning independent of Gf. For example, Beier and Ackerman (2001) found that current events knowledge in four areas correlated on average .81 with Gc, but only .45 with Gf (see also Ackerman & Beier, 2006). Furthermore, one of the best predictors of what a person will learn about a topic is prior knowledge. Spilich, Vesonder, Chiesi, and Voss (1979), for example, found that baseball knowledge correlated positively with memory for a story about a baseball game, and Hambrick (2003) found that knowledge of basketball predicted acquisition of new basketball knowledge over the course of a college season. Similarly, Price and Zaller (1993) found that political knowledge was a positive predictor of recall of current events news stories. Finally, non-ability factors contribute to what, and how much, people know. Openness to Experience—interest in a wide variety of things (Costa & McCrae, 1992)—has been found to correlate positively with various measures of general knowledge (see Hambrick, Pink, Meinz, Pettibone, & Oswald, 2008), and similar results have been reported for related constructs, including Need for Cognition (Cacioppo & Petty, 1982) and Typical Intellectual Engagement (Goff & Ackerman, 1994). At least in part, all of these factors can be assumed to reflect a general interest in learning—an intellectual openness— which presumably leads a person to develop specific interests, and ultimately to seek out information related to these interests.

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2. Psychometric model of learning

3.2. Procedure and materials

To integrate this evidence, Hambrick, Oswald, and Meinz (2007) described a psychometric model of learning, which includes two predictive “pathways.” On the one hand, the ability pathway includes Gf and Gc, and other cognitive abilities (e.g., memory). In this model, these factors are assumed to impact the efficiency of processes like making inferences, and memory encoding, storage, and retrieval. On the other hand, the non-ability pathway originates from intellectual openness. Intellectual openness predicts specific interests, which in turn predict knowledge, via exposure to information through activities such as reading. Hambrick et al. found evidence for both pathways. Hambrick et al. (2008) replicated this finding in a subsequent study, in which current events knowledge was assessed at two time points separated by more than 2 months, and found that prior knowledge had a large positive effect on new knowledge.

Participants were tested in two sessions, separated by approximately 10 weeks. Sessions 1 lasted 2 h, and Session 2 45 min. Participants were tested in groups of up to 70, and received credit in a psychology course for volunteering.

2.1. Political knowledge Here we extend the model to political knowledge. As a domain of knowledge, the term political refers to information about the structure, systems, and activities of a government; specific individuals and groups of individuals in government; and the issues with which these individuals and groups concern themselves. Campaign knowledge— one specific type of political knowledge—refers to information about issues pertinent to a particular election and the candidates running for the office at stake. As political scientists have noted (e.g., Delli Carpini & Keeter, 1993; Mondak, 2001), political knowledge is necessary for effective citizenship. It is of obvious importance for the task of voting, and it serves a communicative function in that it is necessary for conveying information to others about an election, for persuasive or educational purposes, and for comprehending information that others convey for these purposes. 2.2. Study design The goal of this study was to understand why some people know more about politics than others. The specific question was how ability, personality, and interest factors, along with background knowledge, influenced acquisition of campaign knowledge during a U.S. presidential race. The sample consisted of over 500 participants, and the study occurred in two sessions. The first session was in September, 2004. Participants completed tests of ability, personality, interest, exposure, and knowledge. The second session was 10 weeks later, in early December, after the November election. Participants completed a test with questions about political events that took place since Session 1.1

3.2.1. Session 1 After signing an informed consent form, participants completed a background form with demographic questions. The study materials were then administered in the same fixed order to all participants. (Please see Hambrick et al., 2008, for further details on the cognitive ability and personality tests.) 3.3. Ability The Gf tests were (1) Matrix Reasoning (Raven, 1962): choosing patterns to fill missing cells in matrices (8 min, 14 items); (2) Letter Sets (Ekstrom et al., 1976): choosing sets of letters different than other sets (8 min, 15 items); and (3) Series Completion (Zachary, 1986): completing alphanumeric series (4 min, 20 items). The Gc tests were (1) Synonym Vocabulary (Ekstrom et al., 1976): picking synonyms for target words (5 min, 15 items); and (2) Reading Comprehension (Berger, Gupta, Berger, & Skinner, 1990): picking sentences to complete short paragraphs (6 min, 10 items). For each test, the score was proportion correct. Self-reported ACT score was the third indicator of Gc. 3.4. Personality Participants completed a 28-item scale to measure intellectual openness. The items were taken from two sources—18 items from the Need for Cognition scale (Cacioppo, Petty, & Kao, 1984) and 10 items from the International Personality Item Pool (IPIP) identified as measuring Openness to Intellect (Goldberg, 1999). Each item was a statement describing a preference for intellectual engagement (e.g., I love to read challenging material). There was no time limit. 3.5. Exposure

3. Method

Participants completed a questionnaire in which they were asked to estimate the average number of days per week (and minutes per day) that they (1) read the newspaper, (2) watched a news program on television, (3) read news on the internet, (4) read a newsmagazine, and (5) listened to news on the radio. Participants also provided an estimate of minutes per week learning about the upcoming U.S. presidential election.

3.1. Participants

3.6. Politics interest

A total of 579 participants (74% female) completed Session 1, and 536 returned for Session 2. We excluded data for 18 participants because of a large number of missing values, leaving a final sample of 518. Participants were recruited from subject pools at two Midwestern universities. The average ACT score for the sample was 23.7 (SD = 3.3), compared to a national average of 20.8 (SD = 4.8), and scores in the sample ranged from 13 to 33 (maximum = 36). Thus, our sample represented a fairly wide range of cognitive ability.

Using a 5-point scale with endpoints of very low interest (1) and very high interest (5), participants rated their interest in U.S. politics and in the Bush vs. Kerry race.

1 The data reported in this article is from a large data set. The first report from this data set (Hambrick et al., 2008) focused broadly on current events knowledge. Here, we report previously unpublished results on politics interest, exposure, and knowledge.

3.7. Political knowledge Participants completed a 42-item multiple-choice test, and we created the political knowledge measures based on 10 items for each of the three branches of the U.S. government (legislative, executive, judicial), and 10 items for the electoral process and past elections (2 items were discarded because of near zero variability). (Sample items for this test and for the test of campaign knowledge described next appear in the Appendix A.)

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3.8. Campaign knowledge

Table 2 Descriptive statistics for campaign and news exposure variables.

We used a multiple-choice test to assess knowledge of the ongoing presidential campaign. The 52 questions covered all aspects of the presidential race, including important speeches and events; outcomes of primaries and caucuses; names of key figures in the campaigns and their roles; the candidates' backgrounds and stances on issues; controversies and scandals; and trivia such as campaign slogans. 3.8.1. Session 2 Participants completed answered the same questions about news/ campaign exposure as in Session 1, except the estimates were for the period since Session 1. They then completed a multiple-choice test that included 20 items to assess campaign knowledge disseminated in the media since Session 1. 4. Results We defined an outlier as a value greater than 3.5 SDs from the total sample mean for a given variable, and truncated any value that met this criterion (<.5% of the data) to the cutoff value. We regressed variables with missing values onto variables of the same type (e.g., political knowledge) and replaced the few missing values in the data set with predicted values (N = 518 for all analyses reported below). 4.1. Descriptive statistics and correlations Descriptive statistics are displayed in Tables 1 and 2, and a correlation matrix is shown in Table 3. The ability variables had acceptable reliability and correlated moderately with each other, as did the personality variables. There was a large amount of variability in campaign exposure (0–1000 + min/week), and the variables were highly skewed; for subsequent analyses, we used square-root transformed exposure variables, which were more normally distributed. Scores on the multiple-choice tests ranged from just above chance to near perfect (Ms = .38–.51). Correlations of the ability variables with the political knowledge variables were uniformly positive, but on average, the Gc correlations (avg. r = .41) were more than three times larger than the Gf correlations (avg. r = .13). The personality, interest, and exposure variables correlated positively with each other, and correlated with campaign knowledge in the expected manner, with stronger correla-

Session 1

Campaign min/wk Total news min/wk

Session 2

M

SD

dsex

M

SD

dsex

r12

42.14 154.54

116.53 149.50

.20 .35

59.58 131.92

91.92 124.95

.26 .51

.18/.48 .60/.63

Note. N = 518. Total news min/wk is the sum of the estimates for three activities: newspaper reading, internet surfing, and television watching. Under r12, the value on the left is the correlation between the Session 1 and Session 2 estimates in the original units (min/wk), and the value on the right is the correlation between the estimates after square-root transformation (all rs, p < .01). Positive effect sizes (Cohen's ds) indicate higher averages for males than females.

tions for campaign interest and campaign exposure than for the personality variables. Finally, despite having somewhat low reliability (αs = .45–.56), the political knowledge variables correlated strongly with each other (avg. r = .47) and with campaign knowledge (avg. r = .49), as expected.2

4.2. Structural equation modeling We built a structural equation model to test for relations among the variables. Two steps were involved. The first step was to perform a confirmatory factor analysis to establish a measurement model.3 The ability portion of the model included Gf and Gc (3 indicators each), while the non-ability portion included Intellectual Openness and Campaign Exposure factors (2 indicators each). The model also included Political Knowledge (4 indicators) and Campaign Knowledge (2 indicators). Model fit was excellent: χ2(89) = 182.27, CFI = .97, NFI = .94, RMSEA = .05.4 The second step was to test for relations among the latent variables, with the rating of Campaign Interest added as an observed variable. Results are displayed in Fig. 1. The ability pathway emerged as expected: Gf predicted Gc (.58), which predicted Political Knowledge (.95). In turn, Political Knowledge predicted Campaign Knowledge (.79).5 The nonability pathway was also evident: Intellectual Openness predicted Campaign Interest (.22), which predicted Campaign Exposure (.52). The last link in the model was Campaign Exposure to Campaign Knowledge (.16). The predictor variables accounted for approximately 70% of the variance in Political Knowledge, and approximately 84% of the variance

Table 1 Descriptive statistics.

Ability Matrix Reasoning Letter Sets Series Completion Synonym Vocabulary Reading Comprehension ACT Personality Need for Cognition Openness to Intellect Political interest U.S. politics Campaign '04 Political knowledge Legislative Judicial Executive Electoral

M

SD

α

dsex

.38 .51 .41 .41 .53 23.71

.18 .19 .21 .19 .24 3.32

.74 .76 .62 .60 .66 −

.39 −.07 .21 .49 .35 .47

.38 .51

.18 .19

.87 .78

.22 .26

2.37 3.00

.94 1.00

− −

.24 .07

.38 .51 .41 .41

.18 .19 .21 .19

.56 .45 .45 .50

.47 .36 .73 .55

Note. N = 518. Positive effect sizes (Cohen's ds) indicate higher averages for males than females.

2 Participants were recruited from universities in two states—Michigan (n = 374) and Illinois (n = 144). It is possible that candidates placed different emphasis on these states in their campaigns, leading to differences in exposure to campaign information. (Campaigning was perhaps heavier in Michigan—a “battleground” state—than in Illinois.) However, we compared the Michigan and Illinois samples in campaign knowledge (averaged across Sessions 1 and 2), and the difference was non-significant (t = 0.52). 3 We obtained three measures of short-term memory and three measures of perceptual speed (see Hambrick et al., 2008). However, we excluded these measures from the analyses, because they correlated essentially zero with the political/campaign knowledge measures. We also excluded measures of political knowledge reflecting recognition of names of politicians, appointees, and officials because of very low means for these measures. 4 The χ2 reflects whether there is significant deviation between the observed and reproduced covariance matrices, and thus non-significant values indicate good fit. The comparative fit index (CFI) and normed fit index (NFI), which are less sensitive to sample size than the χ2, reflect fit of the model relative to a baseline model in which all covariances are assumed to be zero; values greater than .95 indicate good fit. The root-mean squared error of approximation (RMSEA) reflects the average difference between observed and reproduced covariances; values less than .06 indicate good fit. 5 Although we measured Campaign Exposure and Campaign Knowledge at two time points, we decided against modeling Session 1 and Session 2 factors to simplify the model.

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Table 3 Correlation matrix.

1. MatRea 2. LetSet 3. SerCom 4. SynVoc 5. ReaCom 6. ACT 7. NeedCog 8. OpeInt 9. PolInt 10. CamInt 11. CamExp 12. LegKno 13. JudKno 14. ExeKno 15. EleKno 16. CamKno

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

−

.40 −

.43 .48 −

.25 .16 .27 −

.35 .26 .36 .55 −

.46 .30 .45 .56 .64 −

.15 .09 .15 .30 .27 .26 −

.16 .06 .15 .40 .34 .38 .77 −

.00 −.05 −.06 .14 .10 .13 .29 .30 −

.03 −.02 −.03 .08 .11 .09 .22 .25 .61 −

.06 .07 −.02 .15 .15 .16 .17 .23 .32 .27 −

.20 .11 .08 .43 .43 .39 .24 .30 .26 .19 .19 −

.16 .09 .19 .38 .36 .38 .20 .25 .22 .13 .12 .42 −

.11 .06 .09 .39 .34 .34 .20 .27 .27 .20 .25 .47 .44 −

.17 .07 .23 .50 .44 .48 .26 .32 .28 .22 .19 .45 .41 .53 −

.13 .17 .06 .46 .42 .40 .23 .26 .32 .39 .33 .48 .43 .47 .56 −

Note. N = 518 for all correlations. Values with an absolute magnitude greater than .09 are statistically significant (p < .05). MatRea, Matrix Reasoning; LetSet, Letter Sets; SerCom, Series Completion; SynVoc, Synonym Vocabulary; ReaCom, Reading Comprehension; NeedCog, Need for Cognition; OpeInt, Openness to Ideas; PolInt, Political Interest; CamInt, Campaign Interest; CamExp, Campaign Exposure (averaged across Sessions 1 and 2); LegKno, Legislative Knowledge (multiple-choice); JudKno, Judicial Knowledge (multiplechoice); ExeKno, Executive Knowledge (multiple-choice); EleKno, Election Knowledge (multiple-choice); CamKno, Campaign Knowledge (averaged across Sessions 1 and 2).

Fig. 1. Structural equation model with ability and non-ability factors predicting knowledge factors (only statistically significant paths are displayed, all ps < .05).

in Campaign Knowledge. Model fit was excellent: χ2(109) = 231.56, CFI = .96, NFI = .93, RMSEA = .05. Surprisingly, even after controlling for sex differences in the predictor variables, there was a significant male advantage for Political Knowledge (−.16), and the effect of Sex on Campaign Knowledge was mediated through Political Knowledge. That is, there was an indirect effect of Sex on Campaign Knowledge (−.16 × .79 = −.13, p < .05), but no direct effect (−.01, ns). Thus, participants who tended to be low in political knowledge (females) tended to learn less during the campaign than did a group of participants who tended to be higher in political knowledge (males).6

Fig. 1 does not test for interactive effects involving Gf. One specific possibility is that the “rich get richer”—that people high in Gf make better use of preexisting knowledge in learning than do people low in Gf. To test this possibility, we performed a multiple-groups analysis, splitting the sample into Low Gf and High Gf groups through a median split on a Gf composite (Ns = 256), and then estimating the model in Fig. 1 for the two groups (cf. Kline, 2005). In short, there were no statistically significant differences in the path coefficients for the Low Gf and High Gf groups (all χ2 difference tests ns), and thus there was no evidence for an impact of Gf on learning. 5. Discussion

4.3. Moderator analyses Gc had a large influence on acquisition of campaign knowledge, but the impact of Gf was negligible. Nevertheless, the model shown in

6 Mondak and Anderson (2004) found evidence to suggest that sex differences in political knowledge may be due to a greater willingness on the part of males (vs. females) to guess see also Mondak, 2001). However, in this study, there were almost no missing values on the political knowledge questions, because participants were encouraged to provide an answer, even if they had to guess.

Operating through background knowledge, Gc was as a potent predictor of campaign knowledge. By contrast, the direct effect of Gf on campaign knowledge was near zero. We do not believe that Gf is generally unimportant for learning, because research has convincingly established that it predicts acquisition of complex skills like airtraffic control (e.g., Ackerman, 1988). But consistent with previous reports (Hambrick, 2003; Hambrick et al., 2007, 2008), we do believe that Gf may be relatively unimportant for declarative learning— acquiring the sort of factual information that is important for a wide range of tasks. We also replicated our earlier finding of non-ability

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D.Z. Hambrick et al. / Learning and Individual Differences 20 (2010) 40–45

influences on learning. A personality factor that we assume reflects a general interest in learning (intellectual openness) had a direct effect on interest in politics, which influenced exposure to politics information through activities like reading the newspaper. We also found a large male advantage for political knowledge. This difference was not explained by sex differences in campaign interest or campaign exposure, and so the question of why males tend to outperform females on tests of political knowledge, and other types of knowledge (e.g., Lynn, Irwing, & Cammock, 2002), remains unanswered. Although our results concerning gender differences in political knowledge should be interpreted cautiously given that our sample consisted of college students, nearly all between the ages of 18 and 21, one possibility is that there a developmental process that our measures did not capture. Perhaps boys are more encouraged than girls to pursue their interests in academic topics, and this translates into knowledge differences later in life. Whatever the explanation, our results indicate that background knowledge is a potent predictor of learning in an area, and that anyone low in background knowledge should have a relatively difficulty time learning in that area. 5.1. Future directions A direction for future research is to identify mechanisms that account for effects of predictor variables on learning. One issue we are interested in is how exactly prior knowledge impacts acquiring new knowledge. For example, if knowledge is conceptualized as a semantic network, with interconnected concepts (“nodes”), can effects of knowledge on learning be linked to one or more specific properties of the network, such as its size, connectivity, or organization? And based on answers to questions like these, can techniques be designed to help people learn? If, for example, connectivity turns out to mediate the relationship between prior knowledge and new knowledge, then how can this property of the network be increased? We believe that the understanding gained from addressing questions like these may be of great practical benefit in settings like the workplace and classroom where knowledge is so critical for success. Appendix A Sample items from political and campaign knowledge tests (⁎correct answer) Political knowledge Legislative ___ is a term for any attempt to block or delay Senate action on a bill or other matter by debating it at length, by offering numerous procedural motions, or by any other delaying or obstructive actions. A) Hold B) Filibuster⁎ C) Cloture D) Blockade This congressman from Illinois is currently the Speaker of the U.S. House of Representatives: A) George Pataki B) Ted Kennedy C) Dennis Hastert⁎ D) John Ashcroft Executive What is the job of the Secretary of State? A) To oversee the defense of the United States B) To oversee the judicial systems of the 50 states

C) To take and interpret official notes from meetings of state D) To handle government affairs involving foreign countries⁎ Facing the economic struggles of the Great Depression, which president declared a new economic policy termed the “New Deal”? A) Harry S. Truman B) Franklin D. Roosevelt⁎ C) Herbert Hoover D) Calvin Coolidge Judicial The Fourth Amendment to the Constitution protects which rights of citizens? A) Right of assembly B) Right against cruel and unusual punishment C) Right to bear arms D) Right against unlawful search and seizure⁎ The landmark Supreme Court case of Plessy v. Ferguson… A) Gave African Americans equal rights under the law. B) Required “separate but equal” treatment for whites and African Americans.⁎ C) Freed slaves in Southern states. D) Established quotas for hiring minorities in federal positions. Electoral What color is typically associated with the Democratic party in elections? A) Blue⁎ B) Red C) Green D) Gold In 1980, who became the first female to run for Vice President on a major party ticket? A) Sandra Day O'Conner B) Jeanette Rankin C) Shirley Chisholm D) Geraldine Ferraro⁎ 2004 Campaign Knowledge Session 1 Who was the winner of the Iowa caucus? A) Wesley Clark B) John Edwards C) John Kerry⁎ D) Dennis Kucinich The Reform Party of the United States chose ___ to run on its party ticket. A) David Cobb B) Ralph Nader⁎ C) Peter Camejo D) John Buchanan One of the Kerry campaign's slogans was: A) America Can Do Better⁎ B) A Change Will Do You Good C) An Honest Leader, a Stronger America D) Restoring Honor and Dignity to the White House

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