1 Cognitive Performance Differentiates

Cognitive Performance DifferentiatesSelected Aspects of Psychosocial

Maturity in Adolescence

Nancy L. GalambosDepartment of Psychology

University of Alberta, Canada

Stuart W. S. MacDonaldKarolinska Institute

Sweden

Corey NaphtaliDepartment of Psychology

University of Victoria, Canada

Anna-Lisa CohenDepartment of Psychology

New York University

Cindy M. de FriasDepartment of Psychology

Stockholm University

Karolinska Institute

Sweden

This study examined relations between adolescents’ cognitive performance and

psychosocial maturity. Forty-eight adolescents in Grades 9 and 12 were measured on

intelligence (composite, crystallized, fluid), executive functioning (backward digit

span, Color Trails 2 (CT2), Stroop, everyday problem solving), and psychosocial

maturity (subjective age, problem behavior, psychological maturity). Significant re-

DEVELOPMENTAL NEUROPSYCHOLOGY, 28(1), 473–492Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Requests for reprints should be sent to Nancy L. Galambos, Department of Psychology, University

of Alberta, P–217 Biological Sciences Building, Edmonton, Alberta, Canada T6G 2E9. E-mail:

[email protected]

lations between aspects of cognitive performance and psychosocial maturity

emerged. Problem behavior was related to lower crystallized intelligence, whereas

psychological maturity was related to higher crystallized intelligence and better per-

formance on the CT2. Psychosocially mature adolescents had significantly higher

composite IQ scores than did pseudomature adolescents. Mature adolescents also

showed advantages in crystallized and fluid intelligence, and in performance on the

CT2, compared to their less mature counterparts (the combined group of immature

and pseudomature adolescents). The results suggest that cognitive abilities are re-

lated to psychosocial maturity.

Despite a long-standing assumption that the cognitive advances of adolescence

shape changes in social relations and behavior (Eccles, Wigfield, & Byrnes,

2003; Hill & Palmquist, 1978), very little research links adolescents’ cognitive

and psychosocial functioning (Galambos & Leadbeater, 2000). The absence of

knowledge on the cognitive correlates of psychosocial maturity in particular has

been identified as an important gap in the literature (Klaczynski, Byrnes, &

Jacobs, 2001; Steinberg & Cauffman, 1996). Psychosocial maturity refers to in-

dividuals’ general level of adaptive functioning and socioemotional competence

(Galambos & Costigan, 2003). Recent research showing age-related differences

in the cognitive functioning of adolescents (Anderson, Anderson, Northam,

Jacobs, & Catroppa, 2001; Demetriou, Christou, Spanoudis, & Platsidou, 2002)

highlights the potential for individual differences in cognitive performance to ex-

plain variation in adolescents’ psychosocial maturity. The aim of this investiga-

tion is to examine whether there is a relation between selected indicators of ado-

lescents’ cognitive performance and their psychosocial maturity.

ADOLESCENTS’ PSYCHOSOCIAL MATURITY

Psychosocial maturity encompasses attainments in several domains, including

independent functioning, effective interpersonal communication and interaction,

and social responsibility (i.e., contributing to the well-being of society;

Greenberger, Josselson, Knerr, & Knerr, 1975). Galambos and colleagues (Ga-

lambos, Barker, & Tilton-Weaver, 2003b; Galambos & Tilton-Weaver, 2000)

suggested that one way to conceptualize psychosocial maturity in adolescents is

to consider their constellation or pattern of scores on subjective age (how old

they feel relative to their chronological age), involvement in adultlike problem

behaviors such as drinking alcohol, and level of psychological maturity (i.e.,

self-reliance, identity, and work orientation). Indeed, in a sample of 10- to

17-year-olds, Galambos and Tilton-Weaver (2000) found three distinct maturity

status groups. One group scored high on subjective age (felt older than they

474 GALAMBOS ET AL.

were), high on problem behavior, and low on psychological maturity. These ado-

lescents were referred to as pseudomature or adultoid adolescents, so named be-

cause they felt mature but lacked genuine psychological maturity. A second clus-

ter of adolescents also emerged, labeled as immature; they felt younger than

their age, showed low levels of problem behaviors, and also scored low on mea-

sures of psychological maturity. A third group, the mature adolescents, felt

slightly older than their chronological age, engaged in low levels of problem

behaviors, and scored highest on indexes of psychological maturity. These clus-

ters were replicated in a large school-based sample of adolescents (Galambos et

al., 2003b).

This line of research identified important correlates of psychosocial maturity

status. Specifically, pseudomature adolescents were more likely than immature

and mature adolescents to have older brothers and friends, to be engaged in pop-

ular culture activities, and to be involved with peers. Compared to mature ado-

lescents, pseudomature and immature adolescents had a significantly stronger

desire to be older. Immature adolescents reported mattering less to their friends

than did pseudomature and mature adolescents. The general picture is one in

which pseudomature adolescents seem to be on the fast track to adulthood, but

without acquiring the responsibilities and behaviors typically associated with

genuine maturity, whereas immature adolescents are moving at a slower pace to

an unknown destination. Mature adolescents, on the other hand, appear to have a

good fit with their environments, growing up at a reasonable pace (Galambos et

al., 2003b; Galambos & Tilton-Weaver, 2000). Considering these patterns, an as-

sociated question is whether cognitive functioning is related to the components

of psychosocial maturity (subjective age, problem behavior, psychological matu-

rity) or differentiates the three psychosocial maturity statuses.

In this study, we contrast person and variable approaches to psychosocial matu-

rity (see e.g., Magnusson & Törestad, 1993; Peck & Roeser, 2003). The person ap-

proach considers individuals in a holistic manner, as organisms consisting of mul-

tiple attributes. The person is the unit of analysis, and individuals who share

similar profiles across a number of indicators are grouped and observed on other

characteristics. This contrasts with the traditional variable approach, which exam-

ines interrelations among single variables through, for example, correlation and re-

gression analysis. The variable approach assumes that the study of general interre-

lations among variables justifies inferences about how these variables function in

individuals. The approaches are complementary and may lead to different results

(Magnusson, 2003). In this study, a variable approach is taken by examining rela-

tions between cognitive performance and each component of psychosocial matu-

rity (subjective age, problem behavior, psychological maturity), whereas a person

approach is followed by exploring associations of cognitive performance with

psychosocial maturity status.

COGNITIVE PERFORMANCE 475

COGNITIVE PERFORMANCE AND PSYCHOSOCIALMATURITY IN ADOLESCENCE

In this study, adolescents’ crystallized and fluid intelligence and executive func-

tioning were of interest as indicators of cognitive performance. Crystallized intelli-

gence refers to the breadth and depth of acquired knowledge (e.g., vocabulary),

whereas fluid intelligence reflects the ability to reason and solve problems in novel

situations (Horn, 1968; Horn & Cattell, 1966). These two factors are often viewed

as anchors on a continuum of intelligence, where crystallized intelligence mea-

sures represent the products of cognition and fluid intelligence measures represent

cognitive processes. Although crystallized and fluid intelligence are important in-

dicators of general cognitive ability, measures of executive functions may be more

sensitive to specific cognitive deficits (Kolb & Whishaw, 2003; Spreen & Strauss,

1998). Thus, it is important to consider both general cognitive ability and executive

functioning as they relate to psychosocial maturity.

Executive functioning has been described as a multidimensional construct

consisting of “a variety of loosely related higher-order cognitive processes in-

cluding initiation, planning, hypothesis generation, cognitive flexibility, decision

making, regulation, judgment, feedback utilization, and self-perception that are

necessary for effective and contextually appropriate behavior” (Spreen &

Strauss, 1998, p. 171). There is no consensus on the definition and structure of,

interrelations among components of, and the best ways to measure executive

functions (Klenberg, Korkman, & Lahti-Nuuttila, 2001). Executive functions

may include or overlap with cognitive operations, such as working memory, the

suppression of habitual responses in the face of novel situations, inhibitory con-

trol, and attentional control (Barkley, 1997; Demetriou et al., 2002; Klenberg et

al., 2001; Lezak, 1995; Miyake et al., 2000; Spreen & Strauss, 1998). Problem

solving, which includes goal setting, initiating, planning, and strategic behavior,

is also considered by some to be a component of executive functioning (Ander-

son et al., 2001; Zelazo, Müller, Frye, & Marcovitch, 2003). With respect to se-

lection of tasks to measure executive functions, Miyake et al. (2000) recom-

mended a pragmatic approach in which multiple tasks are administered that

cover different aspects of executive functions, including the core components of

working memory and inhibitory control.

Three measures of executive functions in this study were selected because they

tapped into working memory and inhibition, and they have proven useful in previ-

ous research on executive functioning in adolescents (Anderson et al., 2001;

Demetriou et al., 2002; White et al., 1994). Backward digit span was used as a

measure of working memory. The Stroop test was selected for its properties as a

measure of attention and inhibition of automatic responses. Color Trails 2 (CT2)

was chosen because it assesses a complex set of abilities, including divided atten-

tion, processing speed, inhibitory control, and cognitive flexibility. The Everyday

476 GALAMBOS ET AL.

Problem-Solving Inventory (EPSI), a measure of everyday problem-solving abil-

ity (reasoning applied to real-life decisions; Cornelius & Caspi, 1987), was se-

lected to capture a more complex set of problem-solving skills than typically seen

in studies of executive function. The EPSI is sensitive to neuropsychological defi-

cits that may accompany frontal lobe dysfunction, and seems to detect impaired

social judgment (Dimitrov, Grafman, & Hollnagel, 1996). As such, it may provide

a window into whether everyday problem-solving ability is a cognitive bridge to

psychosocial maturity.

How is cognitive performance associated with psychosocial maturity? Although

no research relates adolescents’ cognitive performance to subjective age, intelli-

gence is associated with delinquency (which is conceptually similar to problem be-

havior at the extreme end of the problem behavior continuum). For example, IQ is

consistentlylower (byabout8points) indelinquentcompared tonondelinquentado-

lescents. Delinquency is also associated with lower verbal (crystallized) and perfor-

mance (fluid) IQs, although it is more strongly related to verbal IQ (Hirschi &

Hindelang, 1977; Lynam, Moffitt, & Stouthamer-Loeber, 1993; Moffitt & Silva,

1988). With respect to executive functions, White et al. (1994) reported that a “cog-

nitive impulsivity”compositeofmeasuresof theabilitytosustainattentionandto in-

hibit an automatic response (e.g., Stroop, Trail-Making Test) was associated with

early delinquency in a sample of at-risk boys. Moreover, in a community sample,

verbal, visuospatial–motor integration, and memory deficits were related to self-re-

ports of delinquency(Moffitt & Silva, 1988). Based on the delinquencyresearch, we

predicted that in a school-based sample of adolescents there would be a significant

negative association between cognitive performance (particularly in verbal IQ and

executive functioning) and problem behavior.

We also predicted a positive relation between indicators of cognitive perfor-

mance and psychological maturity. The enhanced intellectual and executive skills

that enable adolescents to avoid problem behavior may also enable appropriate in-

teractions with the environment in a way that promotes self-reliance, a high value

placed on work, and the achievement of a strong sense of identity. In support,

Cauffman and Steinberg (2000) reported a significant relation between adoles-

cents’ decision-making competence and aspects of psychosocial maturity. Spe-

cifically, adolescents who demonstrated better decision-making skills were more

competent social-perspective takers and were better able to control their impulsive

and aggressive behavior. Thus, we expected that higher scores on measures of in-

telligence, executive functioning (particularly those assessing inhibition), and ev-

eryday problem solving would predict higher psychological maturity. We also

speculated that better cognitive performance would be associated with an older

subjective age, as adolescents with advanced intellectual skills might view them-

selves as older or more mature than their peers.

Finally, we hypothesized that cognitive performance would differentiate the

mature and pseudomature groups of adolescents, with pseudomature adolescents


exhibiting poorer performance on measures of verbal intelligence, executive func-

tions, and everyday problem solving. Given that immature adolescents might be

less effective at self-regulation (Galambos, Barker, & Tilton-Weaver, 2003a), we

thought that they might perform below the level of mature adolescents on some as-

pects of cognitive ability.

METHOD

Participants

The participants were 48 adolescents in Grades 9 (n = 22; 11 girls, 11 boys) and 12

(n = 26; 16 girls, 10 boys). At the time of testing (February to May 2001), the mean

age of the ninth graders was 14.82 years (SD = 0.46) and the mean age of the

twelfth graders was 17.70 years (SD = 0.34). Eighty-seven percent self-identified

as White, with the remaining 13% indicating Asian, Asian mix, or Latino mix. All

participants had previously been involved in a longitudinal study of psychosocial

maturity (the Victoria Adolescence Project [VAP]; Galambos et al., 2003b) that

had begun 3 years earlier (Wave 1) when the participants were in Grades 6 or 9, and

continued 1 (Wave 2) and 2 years (Wave 3) later.

With respect to demographic characteristics of adolescents in this study (the

cognitive sample), 75% lived with two parents (including stepparents), 23% lived

in single-mother families, and 2% lived in joint custody situations. These figures

are very similar to families with adolescents in school in the region in which the

sample resided (McCreary Centre Society, 2000). According to adolescent reports,

85% of mothers and 92% of fathers were employed. Employed mothers had a

mean score of 46.42 (SD = 15.02), and employed fathers had a mean score of 55.31

(SD = 17.47) on the Blishen, Carroll, and Moore (1987) socioeconomic status

(SES) index for Canadian samples (mothers’ range: 26.99 to 101.32; fathers’

range: 27.92 to 101.74). Examples of occupations and their SES scores are sales

clerk, 30.93; secretary, 41.82; and civil engineer, 71.70. All but one mother had

completed high school, and 79% had some postsecondary experience (technical or

vocational, college, university) following high school completion. All but two fa-

thers had completed high school, and 71% had some postsecondary experience.

Although there is diversity in SES in this sample, mothers and fathers were more

likely to be employed and were more highly educated relative to the population of

parents with adolescents in school in the region (McCreary Centre Society, 2000).

Because the sample for this study was a small proportion of the 452 adolescents

who had participated in the initial wave of measurement 3 years earlier, we exam-

ined whether there were differences at Wave 1 between the larger sample and the

cognitive sample. There were no differences with respect to parents’ educational

backgrounds, or employed mothers’ SES scores. Employed fathers’ SES scores

478 GALAMBOS ET AL.

were higher for adolescents in the cognitive sample (M = 51.27, SD = 19.73) com-

pared to the larger sample (M = 44.84, SD = 14.78), t(373) = –2.58, p ≤ .05. There

were no significant differences between the larger sample and the cognitive sample

on Wave 1 scores for subjective age, problem behavior, and measures of psycho-

logical maturity (self-reliance, identity, and work orientation).

Procedure

Participants who completed the Wave 3 questionnaire of the VAP prior to their entry

into Grade 9 or 12 and who indicated a willingness to participate further were invited

by letter and telephone to take part in the cognitive study. Adolescents who received

parental consent and who provided their own consent attended a 1-hr individual test-

ing session on campus, in which cognitive measures were administered by a

same-sex research assistant. They received $20. Psychosocial maturity measures

(subjective age, problem behavior, and psychological maturity) were gathered via

questionnaires mailed home in Wave 3 of the VAP approximately6 to 9 months prior

to the cognitive assessment. These questionnaires were returned by mail.

Cognitive Measures

Kaufman Brief Intelligence Test (K–BIT). The K–BIT (Kaufman & Kauf-

man, 1990) is a brief measure of intelligence that is administered individually. Pre-

sentation is divided into three subsections: expressive vocabulary, which asks par-

ticipants to name pictured objects; definitions, which asks participants to generate

a word, given two clues (a brief definition, and selected letters from the word); and

matrices, which asks participants to choose one of several pictures that goes best

with a stimulus picture. The expressive vocabulary and definition scores were

summed to form a raw vocabulary score (range in this sample: 51 to 78). A raw ma-

trices score was also calculated (range: 27 to 48). The authors of the K–BIT argued

that the vocabulary and matrices indexes capture the constructs of crystallized

and fluid intelligence, respectively (Kaufman & Kaufman, 1990). An age-cor-

rected composite IQ score was calculated as the sum of standardized vocabulary

and matrices scores (range: 84 to 128). Composite IQ indexed general level of

intelligence.

Backward digit span. The Digits Backward sequence of the Digit Span

subtest of the Wechsler Adult Intelligence Scale–Revised (Wechsler, 1981) mea-

sured short-term memory. Participants were asked to recall a series of digits in re-

verse order. They were presented with two strings of digits from each length, be-

ginning with 2-digit strings and ending with 8-digit strings. The task was

discontinued if the participant failed to correctly recall both strings for a given

length. The participant’s score was equal to the number of digits in the longest


string in which he or she was able to correctly recall both strings. Higher scores in-

dicated better memory performance (range: 0 to 6).

CT2. The Color Trails Test (D’Elia, Satz, Uchiyama, & White, 1989) mea-

sures a complex set of cognitive skills, including sustained attention, perceptual

tracking, graphomotor skills, divided attention, sequencing, speed of processing,

and cognitive flexibility. There are two trials. In the first trial (Color Trails 1

[CT1]), participants use a pencil to connect a sequence of numbered circles (half of

which are pink, and half of which are yellow). In the second trial, each number ap-

pears twice, once in a pink circle and once in a yellow circle. Participants must

connect the numbered circles in order, but also alternate between pink and yellow

circles (CT2). CT2 is believed to be a more sensitive measure of frontal lobe func-

tioning than CT1 because it requires divided attention and sequencing skills.

Therefore, we used the score for the time (in seconds) that it took to complete CT2

(range: 34.16 to 135.56). A higher score indicates poorer (i.e., slower) perfor-

mance on this task.

Stroop interference. The Victoria version of the Stroop test (Regard, 1981)

measured the extent to which participants had difficulty focusing attention and in-

hibiting automatic responses. The Stroop task is composed of three parts. Part 1 in-

volves naming the color of 24 dots (colored blue, green, red, or yellow) as quickly

as possible (generating a baseline response latency score in seconds). In Part 2,

participants name the color of simple words (e.g., “when”), printed in blue, green,

red, or yellow, as quickly as possible. Part 3 involves naming as quickly as possible

the printed color of color words in which the word and the color of the word are in-

congruent (e.g., the word “blue” is printed in yellow). The third part of this task is

most difficult because it requires the inhibition of an automatic response (reading

the word) in favor of a novel response (naming the printed color of the word). We

used the Stroop interference score, which is calculated as the response latency for

Part 3 divided by the baseline score for Part 1 (range: 1.30 to 2.87). A higher score

indicates poorer performance (i.e., more interference) on this task.

Everyday problem solving. Adolescents completed a 24-problem version

of the EPSI designed by Cornelius and Caspi (1987). Participants were asked to

rate the effectiveness of four possible solutions to each of 24 problems that

might be experienced. Problems were drawn from the domains of home, friend,

and consumer (e.g., home: “Because of a lack of time you have let household

chores begin piling up”; friend: “You would like to get some friends to come

visit you more often”; consumer: “You are shopping for a CD player. A sales-

man at the store is trying to sell you a better quality product, but it is more ex-

pensive than you would like to pay.”) The four possible solutions to each prob-

lem represent problem-focused action (initiating direct action to cope with the

480 GALAMBOS ET AL.

problem), cognitive problem analysis (cognitive efforts to understand, appraise,

or reinterpret the situation), passive–dependent behavior (doing nothing to solve

the problem or depending on another person to solve it), and avoidant thinking

and denial (denying the situation and one’s personal responsibility in it, selec-

tively attending to other situations instead, or suppressing one’s emotions con-

cerning the situation). For example, possible solutions to the problem of pileup

in household chores are: “Try to cut down on your other activities until you have

completed the chores” (problem-focused action); “Decide what is most impor-

tant to do and consider different ways of spending your time” (cognitive prob-

lem analysis); “Let someone else do the chores for you” (passive–dependent be-

havior); and “Tell yourself that it is not worth being upset about” (avoidant

thinking and denial). The effectiveness of each solution is rated on a 5-point

scale ranging from (1) extremely ineffective or poor solution to (3) neither inef-

fective nor effective solution to (5) extremely effective or good solution.

To generate a total everyday problem-solving score, the adolescent’s ratings of

the effectiveness of 96 solutions (4 solutions × 24 problems) were correlated with

averaged judges’ ratings (23 young to old adults, with diverse backgrounds) of the

effectiveness of the same solutions, using the judges’ ratings and procedures from

Cornelius and Caspi (1987). Intraclass correlations for the judges’ ratings in the

Cornelius and Caspi study were in the .90s, demonstrating that the judges largely

agreed on effective solutions to the problems. In this study, the adolescent–judge

correlations ranged from .12, demonstrating little agreement with the judges, to

.81, showing a very high level of agreement on the effectiveness of various solu-

tions to the problems. The adolescent’s EPSI score is his or her mean correlation.

The mean score of .59 in our sample is nearly identical to that found (r = .60) in a

sample of normal adults ranging in age from 20 to 78 (Cornelius & Caspi, 1987).

EPSI scores relate moderately to measures of crystallized and fluid intelligence.

Psychosocial Maturity Measures

Subjective age. The mean of seven items (Galambos & Tilton-Weaver,

2000; Montepare, Rierdan, Koff, & Stubbs, 1989) measured how old adolescents

perceived themselves to be, relative to their chronological age. Items were rated on

a scale ranging from 1 (a lot younger than my age) to 4 (the age I am) to 7 (a lot

older than my age). Sample items for girls (boys’ version in brackets) are:

Compared to most girls [boys] my age, most of the time I feel ___; compared to

most girls [boys] my age, most of the time I look ___; my girl [boy] friends act to-

ward me as if I am ___. Higher scores indicate an older subjective age (M = 4.70,

SD = 0.77, range: 3.29 to 6.57) (α = .85).

Problem behavior. The mean of 23 items (Brown, Clasen, & Eicher, 1986;

Maggs, Almeida, & Galambos, 1995) (e.g., done something that your parents told


you not to do; became angry and broke things; smoked marijuana; started a fist

fight; took things worth $50 or more) made up the problem behavior measure. Ad-

olescents indicated the monthly frequency of these activities on a scale from 1

(never) to 5 (almost every day). Higher scores reflect higher levels of problem be-

havior (M = 1.48, SD = 0.48, range: 1.00 to 3.48) (α = .85). Small increments in

this measure indicate substantial differences in problem behavior. For example,

whereas a score of 1.00 indicates that the adolescent reported none of the 23 behav-

iors in the previous month, a score of 1.50 would be obtained by an adolescent who

reported engaging in 12 of the 23 behaviors once or twice in the past month. A

score of 3.00 would represent very serious levels of problem behavior (e.g., an ado-

lescent who reported all 23 behaviors 3 or 4 times in the last month).

Psychological maturity. Greenberger’s Psychosocial Maturity Inventory

(Greenberger & Bond, 1984) assessed the adolescent’s psychological maturity in

three domains: self-reliance (e.g., When things go wrong for me, it is usually be-

cause of something I couldn’t do anything about), identity (I change the way I feel

and act so often that I sometimes wonder who the “real” me is), and work orienta-

tion (I often leave my homework unfinished if there are a lot of good TV shows on

that evening). These subscales were selected because of their high loadings on a

single factor (Greenberger & Bond, 1984). Items (10 each for self-reliance and

identity and 9 for work orientation) were rated on a scale as follows: 1 (agree

strongly), 2 (agree slightly), 3 (disagree slightly), and 4 (disagree strongly). One

item was omitted from the original 10-item work orientation measure due to a low

item–total correlation. Alphas for self-reliance, identity, and work orientation

were .74, .64, and .67, respectively. An overall mean score was calculated from the

29 items (M = 3.07, SD = 0.33, range: 2.45 to 3.86), with higher scores indicating

higher psychological maturity (α = .82). A score of 3.00 (close to the mean in this

sample) would be obtained by an adolescent who “slightly” endorsed all items in-

dicative of psychological maturity, thereby demonstrating a reasonable, but not

high, amount of psychological maturity. A score of 2.5 (close to the lower bound in

this sample) would be obtained by an adolescent who agreed and disagreed equally

with items indicative of psychological maturity.

Maturity status. Following earlier research (Galambos et al., 2003b;

Galambos & Tilton-Weaver, 2000), the subjective age, problem behavior, self-reli-

ance, identity, and work orientation measures for all participants with complete

data for these five measures at Wave 3 of the VAP were submitted to a Ward’s

method of cluster analysis, using squared Euclidean distance as the measure of

similarity (Ward, 1963). This cluster analysis suggested that a three-cluster solu-

tion was most appropriate. Second, a k-means cluster analysis was conducted, us-

ing seed values obtained from the Ward’s method analysis, to divide the data into

three clusters (Aldenderfer & Blashfield, 1984). The three clusters generally repli-

482 GALAMBOS ET AL.

cated those found in earlier research in a different sample (Galambos &

Tilton-Weaver, 2000), as well as in Wave 1 of the VAP (Galambos et al., 2003b).

Figure 1 shows the characteristics of these three clusters for participants in this

study. The mature cluster (n = 21) scored above the mean on subjective age and all

three measures of psychological maturity, and below the mean on problem behav-

ior. The immature cluster (n = 21) scored below the mean on all five measures. The

pseudomature cluster (n = 6) scored above the mean on subjective age, well above

the mean on problem behavior, and below the mean on all three measures of psy-

chological maturity.

RESULTS

Preliminary Analyses

Table 1 presents means, standard deviations, and intercorrelations among the cog-

nitive measures. The composite IQ for the total sample was 104.67 and was related

significantly to all other cognitive measures in expected directions (as was K–BIT

vocabulary). Negative correlations of CT2 and Stroop interference with most other

measures were also found, in accordance with the expectation that CT2 and Stroop

interference assess deficits in executive functioning. CT2 and Stroop, however,

were unrelated.

Gender differences in scores on the cognitive and psychosocial maturity mea-

sures (subjective age, problem behavior, psychological maturity) were examined,

but none were found. Thus, gender was dropped from further consideration. Corre-


FIGURE 1 Maturity cluster patterns: Z scores on five measures by cluster (N = 48).

lations of employed mothers’ and fathers’ SES scores with the cognitive and

psychosocial maturity measures revealed no significant associations. Neither

mothers’ nor fathers’ SES were considered in further analyses.

Correlations of chronological age with the cognitive measures revealed a signif-

icant association between being older and having higher vocabulary (r = .37, p ≤

.05) and everyday problem solving (r = .43, p ≤ .05) scores. Correlations of chro-

nological age with the psychosocial maturity measures showed a significant asso-

ciation with problem behavior only (r = .33, p ≤ .05). Given these significant corre-

lations, subsequent analyses controlled for chronological age.

Cognitive Performance and Componentsof Psychosocial Maturity

Following a variable approach, we conducted three regression analyses to deter-

mine the best cognitive predictors (vocabulary, matrices, backward digit span,

CT2, Stroop interference, and everyday problem solving) of subjective age, prob-

lem behavior, and psychological maturity, respectively. Composite IQ was not

used as a predictor because of its part–whole relation with vocabulary and matri-

ces. One-tailed tests were conducted because our hypotheses were directional. The

set of predictors did not explain a significant percentage of variance in subjective

age, R2 = .11, F(7, 40) = .70, p = .67, but the models were significant for problem

behavior and psychological maturity.

The results of the regressions for problem behavior and psychological maturity

are presented in Table 2. The set of predictors accounted for 36% of the variance in

problem behavior. The coefficients for age, vocabulary, and matrices were signifi-

cant, indicating that younger adolescents and those with higher levels of fluid and

crystallized intelligence reported lower levels of problem behavior. With respect to

484 GALAMBOS ET AL.

TABLE 1Means, Standard Deviations, and Intercorrelations for Cognitive

Performance Measures

Cognitive Measure M SD 1 2 3 4 5 6

1. K–BIT composite IQ 104.67 10.24

2. K–BIT vocabulary 61.48 6.41 .67*

3. K–BIT matrices 36.52 4.72 .77* .19

4. Backward digit span 3.67 1.10 .33* .42* .14

5. CT2 (time) 64.87 19.98 –.41* –.33* –.36* –.41*

6. Stroop interference 1.92 0.44 –.41* –.30* –.21 –.30* .13

7. Everyday problem solving 0.59 0.15 .33* .38* .34* .07 –.32* .14

Note. N = 48. Higher scores on all but the CT2 and Stroop interference measures represent better

cognitive performance. K–BIT = Kaufman Brief Intelligence Test; CT2 = Color Trails 2.

*p ≤ .05.

psychological maturity, 30% of the variance was explained, with vocabulary and

CT2 making significant independent contributions. Better performance on the

K–BIT vocabulary and CT2 measures was associated with higher psychological

maturity. Given these results, the cognitive measures of vocabulary, matrices, and

CT2 were singled out for use in the person analyses.

Follow-up analyses. Inspection of the distributions for the cognitive and

psychosocial maturity measures revealed that CT2 and problem behavior were

positively skewed (skewness values were 1.43 and 1.90, respectively) and every-

day problem solving was negatively skewed (–1.45). Logarithmic transformations

(with reflection for everyday problem solving) were applied to reduce the skew

(new skewness values were .43, .94, and 1.09 for CT2, problem behavior, and ev-

eryday problem solving, respectively). All of the preceding regressions were rerun

with the transformed variables. The only difference in results was that the coeffi-

cient for matrices as a predictor of problem behavior dropped from –.26 (p < .05,

one-tailed) to –.21 (p < .10, one-tailed). This accompanied an increase in the mag-

nitude of the coefficient for the transformed variable for CT2 (β = .16 vs. .07), al-

though CT2 was not significant in either regression.

Cognitive Performance and Maturity Status

Following a person approach, a final set of analyses examined whether there was a

relation between cognitive performance and adolescents’ maturity status. First, a


TABLE 2Age-Partialed Regressions Predicting Problem Behavior and

Psychological Maturity From Cognitive Performance

Criterion Psychosocial Maturity Measure

Problem Behavior Psychological Maturity

Predictor B SE B B SE B

Chronological age .01 .00 .48* –.00 .00 –.03

K–BIT vocabulary –.03 .01 –.33* .02 .01 .40*

K–BIT matrices –.03 .02 –.26* –.01 .01 –.09

Backward digit span .07 .07 .15 –.07 .05 –.24

CT2 (time) .00 .00 .08 –.01 .00 –.35*

Stroop interference .08 .16 .07 –.10 .12 –.14

Everyday problem solving –.19 .51 –.06 .06 .37 .03

Note. N = 48. R2 = .36 for problem behavior; R2 = .30 for psychological maturity (both ps ≤ .05).

Higher scores on all but the CT2 and Stroop interference measures represent better cognitive perfor-

mance. K–BIT = Kaufman Brief Intelligence Test; CT2 = Color Trails 2.

*p ≤ .05, one-tailed.

one-way analysis of variance examined group differences in composite IQ, reveal-

ing an overall difference, F(2, 45) = 7.12, p ≤ .05. Follow-up tests (Tukey honestly

significant difference) indicated that mature adolescents had significantly higher

IQ scores (M = 109.43, SD = 11.40) than did pseudomature adolescents (M =

94.33, SD = 8.91). The difference between mature and immature adolescents (M =

102.86, SD = 6.12) did not reach statistical significance, p = .06.

Second, a direct discriminant function analysis examined whether vocabulary,

matrices, and CT2, which were identified as significant predictors of problem be-

havior or psychological maturity in the regression analyses, discriminated among

the three maturity status groups. Two discriminant functions were identified, to-

gether showing a reliable association between the cognitive measures and maturity

status,χ2(6) = 18.52, p≤ .05. After removal of the first function, the second function

did not uniquely discriminate among the groups, χ2(2) = 2.37, p = .31, and was not

considered further. The first function accounted for 88.8% of the between-group

variability, maximally separating the mature adolescents from the pseudomature

and immature (centroids for mature, pseudomature, and immature groups were .72,

–.89,and–.46, respectively).Observedcorrelationsbetween thecognitivemeasures

and the first discriminant function suggest that all three measures distinguished the

mature adolescents from the other two groups (see Table 3).

To examine whether these cognitive differences remained when controlling for

age, the immature and pseudomature groups were combined. A multivariate analy-

sis of covariance, with age as the covariate, compared performance on the three

cognitive measures in the mature and the combined immature–pseudomature

groups. The multivariate main effect of group was significant, Wilks’s F(3, 43) =

4.72, p ≤ .05, as were the univariate tests for vocabulary, F(1, 45) = 9.93, matrices,

F(1, 45) = 4.51, and CT2, F(1, 45) = 4.55 (all p ≤ .05). With age controlled, mature

adolescents had better vocabulary (adjusted M = 64.44 vs. 59.17) and matrices

486 GALAMBOS ET AL.

TABLE 3Discriminant Function Analysis Relating Cognitive Measures to

Psychosocial Maturity Status

Correlation of Predictor

With First Function

Univariate

F(2, 45)

Pooled Within-Group r

Predictor Matrices CT2

K–BIT vocabulary .81 6.86* .06 –.20

K–BIT matrices .55 3.84* –.27

CT2 (time) –.55 3.39*

Canonical R .55

Eigenvalue .44

Note. N = 48. Higher scores represent better cognitive performance, except for CT2. K–BIT =

Kaufman Brief Intelligence Test; CT2 = Color Trails 2.

*p ≤ .05.

scores (adjusted M = 38.19 vs. 35.22) and were faster at completing CT2 (adjusted

M = 57.96 vs. 70.25).

Follow-up analyses. The discriminant function analysis and the multi-

variate analysis of covariance were rerun, using the transformed variable for CT2.

There was no difference in the results.

DISCUSSION

What was the relation between cognitive performance and components of psycho-

social maturity? Analyses based on the variable approach found that, in line with

studies of delinquency, lower crystallized and fluid intelligence scores were asso-

ciated with higher engagement in problem behavior (e.g., Lynam et al., 1993;

Moffitt & Silva, 1988). The relation between fluid intelligence and problem behav-

ior, however, weakened when scores for problem behavior were transformed to re-

duce skewness. This superiority of verbal (crystallized) over fluid intelligence as a

predictor of problem behavior also accords with related research on delinquency

(Lynam et al., 1993). Clearly, verbal dimensions of intelligence are associated with

adolescents’ engagement in problem behaviors, even in a small, community-based

sample. Moffitt (1990) speculated that verbal ability is related to delinquency be-

cause it may contribute to developing behavioral self-control, establishing a fu-

ture-oriented cognitive style, and labeling emotions, communicating, and negoti-

ating in social situations. Similarly, higher levels of verbal intelligence may help

adolescents to avoid engaging in problem behavior.

Higher crystallized intelligence also predicted the component of psychological

maturity. To the extent that verbal deficits hinder self-regulation, the development

of a future orientation, and competence in social situations, it makes sense that

they might also stand in the way of achieving greater psychological maturity (i.e.,

self-reliance, identity, and work orientation). Executive functioning deficits might

also play a role, as poorer performance on the CT2 was associated with lower psy-

chological maturity. Because performance on the CT2 is dependent on a variety of

cognitive skills, it is difficult to know whether the association with psychological

maturity is attributable to one or more specific elements (e.g., speed of processing

or cognitive flexibility). Poorer cognitive flexibility, for example, could lead to dif-

ficulties in perceiving alternative life choices, or it could impair the accurate moni-

toring that is needed to develop the self-understanding of a psychologically mature

individual. The Stroop interference score, which may indicate cognitive flexibility,

however, did not attain significance as a predictor of psychological maturity, sug-

gesting that it could be other cognitive abilities (e.g., speed of performance) that

account for the relation between the CT2 and psychological maturity. Future re-

search could examine the nature of the relation between CT2 and psychological


maturity by determining which underlying cognitive abilities or deficits are re-

sponsible for this relation.

Cognitive performance was unrelated to the component of subjective age.

Subjective age is an indicator of perceived maturity that might be associated

more with noncognitive factors in adolescents’ lives, including pubertal status,

height and weight, associations with older siblings and friends, and behaviors

such as smoking and alcohol use (see, e.g., Galambos et al., 2003b; Galambos,

Kolaric, Sears, & Maggs, 1999; Galambos & Tilton-Weaver, 2000). It is also

likely that other constructs (e.g., self-awareness) could be associated with an ad-

olescent’s subjective age.

How did mature, immature, and pseudomature adolescents differ in cognitive

performance? Analyses following the person approach showed that at an average

composite IQ of 109, the mature adolescents were a full 15 points above the

pseudomature adolescents (M = 94), a greater difference than the typical 8-point

difference observed between delinquent and nondelinquent adolescents (e.g.,

Hirschi & Hindelang, 1977). The mature adolescents were 6 points above the im-

mature adolescents (M = 103), but this difference was not significant by conven-

tional standards. These results suggest that there may be considerable intellectual

advantages for the mature adolescents compared to the pseudomature adolescents.

The discriminant function analysis further pointed to the advantages experienced

by the mature group of adolescents; mature adolescents performed significantly

better than the combined group of immature and pseudomature adolescents on the

vocabulary and matrices measures and had faster scores on CT2. It is possible that

immature and pseudomature adolescents have some intellectual deficits that put

them at risk for lower psychosocial maturity. The fact that the mature and

pseudomature–immature adolescents differed on CT2, which taps abilities such as

divided attention, inhibitory control, cognitive flexibility, and speed of processing,

corresponds with the significant negative relation between CT2 and psychological

maturity that emerged in the variable analyses. Again, we are drawn to asking what

aspect or aspects of cognitive abilities indexed by the CT2 are responsible for the

difference between the mature adolescents and their less mature counterparts.

Elsewhere, we have argued that mature adolescents evidence the best fit with

their environments, pseudomature adolescents exhibit the poorest fit, and imma-

ture adolescents fit with only some aspects of their environment (Galambos et al.,

2003b; Galambos & Tilton-Weaver, 2000). These results lend support to the idea

that the mature adolescents may be in the best position cognitively to adapt to their

environments, whereas the immature and pseudomature groups may have a harder

time. Cognitive functioning is connected to psychosocial functioning, but the pro-

cesses by which this relation unfolds are unknown and await further research.

The results of the variable and person approaches to psychosocial maturity

were rather compatible. Crystallized intelligence was a key correlate of the sepa-

488 GALAMBOS ET AL.

rate components of problem behavior and psychological maturity, as well as a fac-

tor distinguishing the mature group from the other two. It is interesting, however,

that fluid intelligence was not strongly related to problem behavior or psychologi-

cal maturity, but was significant in distinguishing mature from imma-

ture–pseudomature adolescents. CT2 also distinguished mature from less mature

adolescents, parallel to its significant relation to the component of psychological

maturity. It was surprising that everyday problem solving was not significantly re-

lated to psychosocial maturity in either approach. With crystallized intelligence

controlled, only one index of executive functioning (CT2) emerged. These results

demonstrate how both approaches together can provide richer insights into under-

standing the puzzle of psychosocial maturity.

Limitations of this research include a small sample size and its cross-sectional

nature. Future research would do well to replicate the findings and to chart the rela-

tion between cognitive development and psychosocial maturity in longitudinal

studies. In addition, studies of the associations between cognition and

psychosocial functioning in adolescence have typically not included a broad range

of cognitive functioning variables. Whereas one set of studies has examined gen-

eral intellectual and neuropsychological functioning as related to delinquency, but

has not considered everyday problem-solving (e.g., Lynam et al., 1993; Moffitt &

Silva, 1988), another set has considered the relation between decision making and

psychosocial functioning, but has not incorporated other measures of cognitive

performance (e.g., Cauffman & Steinberg, 2000). To truly understand how adoles-

cents’ cognitive functioning develops over time and influences the decisions they

make, the behaviors they engage in, and their perceptions of self, studies incorpo-

rating multiple aspects of cognitive functioning (including lower and higher order

cognitive functioning) as well as multiple aspects of psychosocial functioning are

necessary. Moreover, the use of multilevel modeling to examine the interrelations

among these variables as they may change together over time would be a most ap-

propriate methodology. As much as we know about adolescents’ intellectual and

psychosocial development, there are many questions that remain to be answered.

Foremost among these questions is how cognitive and psychosocial functioning

develop in tandem across the period of adolescence.

ACKNOWLEDGMENTS

This research was funded by Social Sciences and Humanities Research Council of

Canada and University of Victoria Grant 410–97–0436 to Nancy L. Galambos.

Stuart MacDonald’s participation was supported by a research fellowship from the

Canadian Institutes of Health Research.

We thank Esther Strauss for her helpful comments on this article.


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