Intentional Self-Regulation and Positive Youth Development in Early Adolescence: Findings From the 4-H Study of Positive Youth Development

Abstract

In this research, the authors examined the development of intentional self-regulation in early adolescence, which was operationalized through the use of a measure derived from the model of selection, optimization, and compensation (SOC). This model describes the individual's contributions to mutually influential relations between the person and his or her context. Through use of data from a longitudinal sample of 5th and 6th graders who were participating in the 4-H Study of Positive Youth Development (PYD), structural equation modeling procedure, reliability analyses, and assessments of convergent, divergent, and predictive validity suggested that a global, 9-item form of the SOC measure was a valid index of intentional self-regulation in early adolescence. Scores for this index of SOC were related to indicators of positive and negative development in predicted directions. The authors discuss the idea that self-regulation is a global process in early adolescence that contributes to PYD.

Full text

Intentional Self-Regulation and Positive Youth Development in Early
Adolescence: Findings From the 4-H Study of Positive Youth Development
Steinunn Gestsdo´ttir and Richard M. Lerner
Tufts University
In this research, the authors examined the development of intentional self-regulation in early adolescence,
which was operationalized through the use of a measure derived from the model of selection, optimi-
zation, and compensation (SOC). This model describes the individual’s contributions to mutually
influential relations between the person and his or her context. Through use of data from a longitudinal
sample of 5th and 6th graders who were participating in the 4-H Study of Positive Youth Development
(PYD), structural equation modeling procedure, reliability analyses, and assessments of convergent,
divergent, and predictive validity suggested that a global, 9-item form of the SOC measure was a valid
index of intentional self-regulation in early adolescence. Scores for this index of SOC were related to
indicators of positive and negative development in predicted directions. The authors discuss the idea that
self-regulation is a global process in early adolescence that contributes to PYD.
Keywords: self-regulation, positive youth development, selection, optimization, compensation
The adolescent period is marked by the emergence of new
cognitive and emotional characteristics; the development of phys-
ical and sexual maturity; changing relationships with family,
teachers, and peers; and the formulation of a more differentiated
self-definition (identity), which influences all aspects of the young
person’s development. Brandtsta¨dter (1998) explained that adoles-
cence is characterized by the emergence of a notion of personal
future that becomes integrated into the process of self-regulation,
that is, selecting and enacting behaviors that may attain goals of
pertinence to the self. Normatively, self-regulation capacity attains
higher levels of functioning (Freund & Baltes, 2002) and becomes
a significant moderator of the person’s actions (Baltes, Linden-
berger, & Staudinger, 2006). As discussed by Baltes et al. (2006;
see also Freund & Baltes, 2002), in adolescence, as well as in other
portions of the life span, selecting goals—when coupled with
developing the means of obtaining goals and with adjusting goals
when opportunities for reaching them are blocked or lost—
constitutes a fundamental feature of a young person’s successful
interactions with his or her social ecology. However, Brandtsta¨dter
(1998) noted that, insofar as adolescence is concerned, the devel-
opment of self-regulation has not been adequately studied.
As is the case in regard to most contemporary theories within
developmental science, current theories of adolescent development
are framed by developmental systems models (see Lerner, 2006).
These models may be a useful frame for the study of self-
regulation in adolescence. These models are predicated on a rela-
tional metatheory that emphasizes that, as a consequence of the
integration of all levels of organization within the ecology of
human development, the key process of human development in-
volves mutually influential relations between an individual and his
or her context (represented as individual 43context relations;
Lerner, 2004; Overton, 2006). Termed developmental regulation,
this process of mutual influence connects levels ranging from
genes and cell physiology through individual mental and behav-
ioral functioning to society, culture, the designed and natural
ecology and, ultimately, history (Elder & Shanahan, 2006; Gott-
lieb, Wahlsten, & Lickliter, 2006; Valsiner, 2006).
When developmental regulations are mutually beneficial (to
both individual and context), they may be termed adaptive devel-
opmental regulations (Brandtsta¨dter, 2006). Such regulations align
levels associated with the individual (physiology, mental function-
ing, and behavior) with levels associated with his or her ecology
(e.g., peer and family relations as well as connections to schools
and to community institutions). Adaptive developmental regula-
tions result during adolescence in positive youth development
(PYD), a construct that has been operationalized within the youth
development literature through the five Cs; that is, the subscales of
Competence, Confidence, Connection, Character, and Caring
(Eccles & Gootman, 2002; Lerner, Lerner, et al., 2005; Roth and
Brooks-Gunn, 2003a, 2003b). A key idea linking goal-oriented,
self-regulation behavior with PYD is that as increases occur in the
capacity for selecting goals, for recruiting the means of reaching
them, or for making adjustments when goals are blocked, PYD (as
an index of adaptive self-regulation) should increase; by the same
reasoning, indices of problematic behavior should decrease. The
present research presents data supporting this idea.
Steinunn Gestsdo´ttir and Richard M. Lerner, Institute for Applied Re-
search in Youth Development, Eliot-Pearson Department of Child Devel-
opment, Tufts University.
This article is based, in part, on a dissertation submitted by Steinunn
Gestsdo´ttir to Tufts University in partial fulfillment of the requirements for
the PhD degree.
This research was supported, in part, by a grant from the National 4-H
Council to Richard M. Lerner. The authors thank Erin Phelps and Stacy
Zimmerman for their invaluable contributions to the research reported in
this article.
Correspondence concerning this article should be sent to Steinunn
Gestsdo´ttir, who is now at Iceland University of Education, Stakkahlid
Reykjavik 105, Iceland. E-mail: steinunng@khi.is
Developmental Psychology Copyright 2007 by the American Psychological Association
2007, Vol. 43, No. 2, 508 –521 0012-1649/07/$12.00 DOI: 10.1037/0012-1649.43.2.508
508

The attributes involved in, and the means through which the
adolescent contributes to, developmental regulations may be
termed self-regulation (Gestsdo´ttir, 2005). Self-regulation encom-
passes multiple forms of functioning, ranging from physiological
attributes of the organism (e.g., circadian rhythms) through bio-
psychological features of behavioral style (e.g., temperament) to
intentional thought processes involving self-selected, goal-directed
actions.
1
A key example of the latter type of actions is the model
of selection, optimization, and compensation (SOC) created by
Baltes, Freund, and colleagues (e.g., Baltes, 1997; Baltes & Baltes,
1990; Baltes et al., 2006; Freund & Baltes, 2002; Freund, Li, &
Baltes, 1999). The model describes how each person is presented
with a combination of expected and unexpected events throughout
the life span; these experiences require the person to negotiate
among resources in the environment, examine his or her own
abilities, and, consequently, choose appropriate goals and goal-
related strategies for attaining positive individual 43context
relations. The three components of the SOC model refer to the
self-regulatory processes that an individual uses to regulate his or
her relationship with the environment and to manage his or her
external and internal resources (Baltes, 1997; Baltes & Baltes,
1990; Freund & Baltes, 2002). Selection refers to how a person
identifies goals, optimization refers to the person’s attempts at
maximizing the chances of recruiting the resources necessary for
goal attainment, and compensation refers to the person’s ability at
modifying behaviors in the face of the blocking of or loss of
goal-directed actions.
Selection, then, requires choosing from a broad range of alter-
native goals, identifying an appropriate number of goals, and
forming a goal hierarchy, thereby guiding attention and organizing
behaviors. Optimization occurs after a goal system has been cre-
ated and requires a person to apply and refine appropriate, goal-
relevant means of achieving desired outcomes as well as monitor
a possible discrepancy between a present state and the goal being
sought. The investment of time and energy, through qualities such
as persistence and the focus of attention, are instances of optimi-
zation. For maintenance of adaptive developmental regulations,
compensation must occur if there is a loss in functional capacity
(e.g., through injury or aging) or if goal-relevant means are
blocked. Compensation involves means similar to those used in
optimization, such as practice, but such actions are aimed at
avoiding losses in the face of the loss of goal-relevant means rather
than approaching positive states. For example, if a person was
absent from school for a period of time because of illness, the
individual can seek alternative means, such as taking an extra
class, to maintain functioning (Baltes & Baltes, 1990; Freund &
Baltes, 2002; Wiese et al., 2000).
The intentional self-regulations specified within the SOC model
and in other action theoretical accounts of individual 43context
relations (e.g., Brandtsta¨dter, 2006; Brandtsta¨dter & Lerner, 1999;
Heckhausen, 1999, may be distinguished from organismic regula-
tion (cf. Eisenberg, Fabes, & Spinrad, 2006). The former instances
of self-regulation, which are the focus of this research, involve
contextualized actions that are actively aimed toward harmonizing
personal goals with demands and resources in the environment
with the goal of attaining better functioning and enhancing self-
development (Baltes, 1997; Baltes et al., 2006). Intentional self-
regulation is characterized by goal-directed behaviors that, al-
though potentially not conscious, are more readily available to
consciousness than are the processes and structures of organismic
regulation. The latter processes and structures are broad, consistent
attributes of a person that involve biologically based, physiological
structures and functions (e.g., temperamental style; Rothbart &
Bates, 2006) that contribute to the relationship that an individual
has with the environment (cf. Eisenberg et al., 2006, who uses the
terms effortful control and reactive control, respectively, to denote
these two features of self-regulation).
In turn, although not measured within the SOC model, organ-
ismic instances of self-regulation (e.g., involving hypothalamic
control of body temperature, circadian rhythms, pubertal timing,
and, as noted, temperamental attributes such as threshold of re-
sponsiveness or quality of mood) are under relatively less control
of the person and do not involve intentional or effortful effects of
the person at regulating his or her exchanges with the context
(Eisenberg et al., 2006). Organismic regulatory characteristics tend
to show relative continuity through the life span and contribute to
consistencies in behavior across situations and over time (Hooker
& McAdams, 2003; Susman & Rogol, 2004).
Self-Regulation in Adolescence
Although organismic regulation continues to contribute to the
person’s relationship with his or her environment throughout the
life span (Kagan, 1998), it may be expected that intentional self-
regulation will undergo significant developmental change in ado-
lescence. Few developmental phases are characterized by changes
that are as dramatic as those experienced during adolescence, a
period encompassing the second decade of life (Lerner & Stein-
berg, 2004). The individual-level changes that occur during ado-
lescence and the changing world in which the adolescent is em-
bedded means that the relationship between the young person and
his or her context is changing, as well, making the study of
self-regulation in early adolescence (Lerner, 1982; Lerner, Theo-
kas, & Jelicic, 2005) especially pertinent.
The SOC model is aimed at providing a comprehensive ap-
proach to describing the multiple processes that are involved in
goal setting and goal pursuit across the life span (Baltes, 1997).
Freund and Baltes (2002) have discussed the nature of intentional
self-regulatory functioning in adolescence insofar as the three
components of the SOC model are concerned. They suggested that,
consistent with Werner’s (1957, 1948) orthogenetic principle, the
processes depicted within the SOC model should, in childhood and
early adolescence, exist in a global and undifferentiated structure;
however, across the second decade of life (i.e., the adolescent
period; Lerner & Steinberg, 2004), this global structure should
differentiate into a structure with three identifiable (albeit ob-
liquely related) components that reflect the three facets of the SOC
model: selection (S), optimization (O), and compensation (C). That
is, Freund and Baltes noted that children and young adolescents
may have more limited abilities and opportunities for making goal
selections and for providing direction to their own development
than do older adolescents or adults and that they “expect that
individuals . . . acquire and refine their knowledge and expression
1
Labels other than “intentional” self-regulation, such as “effortful” and
“voluntary,” may also be used for depiction of the processes to which we
are pointing.
509
SELF-REGULATION AND PYD

of SOC-related behaviors . . . [so that] adults should show a
stronger preference for S, O, and C . . . than should adolescents or
children” (Freund & Baltes, 2002, p. 644).
Of course, even in early adolescence (e.g., 5th and 6th grades or
about 10 and 11 years of age), youth have opportunities to select
their goals (e.g., relating to leisure-time activities, allocation of
money available to them, and participation in within-school and
after-school structured activities) and to recruit the means (e.g., in
school, home, and community settings) of attaining these goals
(i.e., “optimizing,” in the terms of Baltes & Baltes, 1990). For
instance, young adolescents can earn extra money to purchase
desired goods or to participate in entertainment experiences, and
they can recruit friends to support their goals in school or in
after-school activities (Eccles & Gootman, 2002). Damon, Menon,
and Bronk (2003) discuss such purposive (goal-directed or selec-
tive) behaviors at this age level and point to the young adolescents’
capacities for pursuing what they describe as “noble purposes.” Of
course, the young adolescent may feel a need to compensate when,
for instance, he or she does not achieve the goal of making a sports
team or when his or her attendance at a music event is blocked
because the performance is sold out.
The SOC process may be observable, therefore, in early ado-
lescence. Nevertheless, an expectation of orthogenetic change in
intentional self-regulation across the adolescent decade may be
derived from the idea that the confluence of new cognitive abilities
and behavioral skills (e.g., Fischer & Bidell, 1998), new opportu-
nities for social exchanges (East et al., 1992; Eccles & Gootman,
2002), and changed social expectations placed on youth (Damon et
al., 2003) impel the young person to develop new approaches to
intentional self-regulation. As adolescents internalize the social
standards and behavioral mechanisms of those around them, they
may transform external bases of regulation into more internal,
mindful forms of self-regulation (Brandtsta¨dter, 2006). Such in-
ternalization enables them to make better interpretations, choices,
and decisions depending on the environment in which they are
interacting (Demetriou, 2000). Because intentional self-regulation
involves actions that are aimed at changing a part of a develop-
mental system (e.g., a person) toward a particular goal, a person
must be able to form representations of himself or herself and of
others that inform the person of past experiences, offer self-
evaluations, and provide directions for future actions. Only by
having such representations can he or she set and attain goals
(Demetriou, 2000). It can be expected that the internalization of
standards as well as a sense of identity and of personal future,
which are fundamental for the development of successful self-
regulation, will develop across adolescence and even into adult-
hood (Brandtsta¨dter, 1998).
Theories of intentional self-regulation, such as SOC, are theo-
ries of successful life management, and empirical evidence links
various forms of positive self-regulation with the absence of indi-
cators of negative development. Indeed, it has been hypothesized
that intentional self-regulation is the key means through which the
individual can contribute to his or her positive development
(Lerner, Dowling & Anderson, 2003). Low levels of self-
regulation have been linked to various forms of negative develop-
mental outcomes in adolescence. Raffaelli and Crockett (2005)
found that adolescents’ abilities at regulating attention, emotions,
and behavior was associated with sexual risk taking, especially
with the ability at minimizing the risk associated with being
sexually active. These findings are consistent with our expectation
that increased intentional self-regulatory abilities are inversely
related to problematic behaviors, presumably because, in regard to
the SOC model, there is a capacity for choosing healthy behaviors
and for manifesting related optimization and compensatory
behaviors.
Additional support for our expectation of a positive correlation
between SOC and PYD derives from extensive research pertinent
to the tripartite SOC model (Baltes, 1997; Baltes et al., 1998;
Baltes & Baltes, 1990; Baltes et al., 2006; Freund & Baltes, 2002);
this scholarship has indicated the reliability and validity of the
Freund and Baltes (2002) SOC measure and has demonstrated its
use in understanding successful regulation in adult and aging
populations. This utility has been manifested in regard to general
functioning and to domain-specific behaviors.
For instance, Wiese et al. (2000) found that participants who
reported using SOC behaviors scored higher on indicators of
overall successful life management and on measures of successful
occupational and partnership functioning. Freund and Baltes
(1998, 2002) found that self-reported SOC behaviors were posi-
tively related to various indicators of successful life management
and well-being, such as life satisfaction, positive relations, and a
sense of purpose in life. These studies are consistent with the
theory of SOC-related behaviors as strategies for successful life
management and for healthy development within and across peri-
ods of development, for example, for PYD among adolescents.
Accordingly, in the present research, we tested the idea that
SOC, as an index of adaptive self-regulation, is positively related
to PYD and negatively related to indices of problem behaviors
among youth developing across a 2-year period in early adoles-
cence. We assessed whether the structure of SOC may be better
represented as a global structure or as a tripartite one. On the basis
of Freund and Baltes’s (2002) ideas, we expected that a global
structure would exist within the early adolescent period that we
assessed in this research. Moreover, on the basis of the ideas of
Baltes et al. (2006) and Brandtsta¨dter (1998, 2006), we expected
that SOC processes would positively predict outcomes of adaptive
developmental regulation (PYD in the present research) and would
negatively predict indices of problems of development (indices of
depression, risk behaviors, and delinquency in the present
research).
However, prior to this research, the SOC model had not been
empirically tested, nor had the SOC measure been used, with early
adolescent samples, although it has been proposed as a promising
approach to understanding intentional self-regulation in adoles-
cence (Lerner, Freund, De Stefanis, & Habermas, 2001). Accord-
ingly, in this research, we explored the use of the SOC model and
the use of a measure developed by Freund and Baltes (2002) for
indexing the international self-regulatory actions depicted in the
model. We used data from the first two waves (Grades 5 and 6) of
the 4-H Study of Positive Youth Development (Jelicic et al., in
press; Lerner, Lerner, et al., 2005), a longitudinal study involving
annual assessments (beginning with 5th grade as Wave 1) of about
1,700 5th-grade youth from 13 U.S. states, to contribute simulta-
neously to several interrelated issues.
First, we assessed the structure of intentional self-regulation in
early adolescence as indexed by the SOC measure. We explored
whether, within the 5th and 6th grades, evidence exists for the
presence of the three components of intentional self-regulation
510
GESTSDO
´
TTIR AND LERNER

specified by Baltes (1997; Baltes et al., 1998, 2006; Freund &
Baltes, 2002; Freund et al., 1999); that is, selection, optimization,
and compensation, or if the structure of self-regulation exists as a
more global, undifferentiated phenomenon.
Second, we assessed the psychometric characteristics of Freund
and Baltes’s (2002) SOC measure. We appraised reliability,
as indexed by Cronbach’s (1951) alpha scores for internal
consistency.
Third, we assessed concurrent validity (through findings asso-
ciated with data from within the 5th and 6th grades, respectively)
and predictive validity (through longitudinal findings across the
5th to 6th grades) so that we could test the theoretical expectations
that better intentional self-regulation (i.e., higher SOC scores)
covaries positively with indicators of PYD and covaries negatively
with indicators of problem or risk behaviors.
Within the 4-H study data set, PYD is operationalized through
(a) scores for the five Cs of PYD, that is, scores for five first-order
latent variables identified by Lerner, Lerner, et al. (2005) and
discussed by Roth and Brooks-Gunn (2003a, 2003b) and Eccles
and Gootman (2002) as competence, confidence, character, con-
nection, and caring and (b) a score for a second-order latent
variable of PYD. Problem and risk behaviors are operationalized in
the 4-H data set by scores for depression, for risk behaviors
associated with substance use (i.e., tobacco, alcohol, and drug use),
and for what may be labeled as delinquent behavior (e.g., fighting
and property damage).
In sum, this research sought to (a) describe the psychometric
characteristics of the Freund and Baltes (2002) measure of SOC
use among the 4-H study participants assessed within the 5th and
6th grades and (b) increase understanding of whether, across a
2-year period within early adolescence, a relation exists between
intentional self-regulation and the positive and problematic devel-
opment of youth.
Method
The current investigation was conducted as part of the 4-H Study
of Positive Youth Development, which is a longitudinal investiga-
tion starting with 5th-grade youth in the United States and their
parents. The 4-H study is designed for testing a theoretical model
about the role of developmental assets in the promotion of PYD, as
conceptualized by the five Cs of PYD (the subscales of Compe-
tence, Confidence, Connection, Character, and Caring), and the
promotion of the sixth C (the scale of Contribution) as well as in
the diminution of problem and risk behaviors (Lerner, Lerner, et
al., 2005). Full details of the methodology of the 4-H study have
been presented in prior reports (Lerner, Lerner et al., 2005; Theo-
kas & Lerner, 2006; see also Jelicic et al., in press). Accordingly,
we present here those features of methodology pertinent to the
focus of this investigation.
Design
In the 4-H study, we used a form of longitudinal sequential
design (Baltes, Reese, & Nesselroade, 1977). Fifth graders, gath-
ered during the 2002–2003 school year (which was Wave 1 of the
study) were the initial cohort within this design, and this cohort
was the only one studied in Wave 1. However, for maintenance of
at least the initial levels of power for within-time analyses and for
assessment of the effects of retesting, all subsequent waves of the
study involved the addition of a retest control cohort of youth who
were in the current grade level of the initial cohort; this new cohort
was then followed longitudinally. Accordingly, in Wave 2 of the
study (6th grade for the initial cohort), a retest control group of 6th
graders who were new to the study were gathered; these youth
became members of a second longitudinal cohort. Similarly, each
subsequent wave of the study will introduce a new cohort, which
will then be followed longitudinally throughout the rest of the
study.
2
In the present report, which presents data from the first two
waves of the 4-H study, analyses involve three different subsets of
the overall set of study participants. First, the participants who
were studied longitudinally at the first two waves of testing were
used in analyses aimed at examining the predictive validity of SOC
from Wave 1 to Wave 2 and examining a possible change in levels
of SOC-related behaviors from Wave 1 to Wave 2. Second, all
5th-grade participants who were studied at Wave 1 (i.e., the initial
cohort involved in the study) were used in analyses aimed at
examining the factor structure of the SOC measure at the 5th-grade
level. Third, all 6th-grade participants (the participants from the
initial cohort that remained in the longitudinal sample for Wave 2
and the participants from the new cohort of 6th graders, introduced
into the study as members of the Wave 2 retest control group) were
used in analyses aimed at examining the factor structure in 6th
grade. Details about these groups of participants are provided in
the Participants section.
Participants
At Wave 1, participants came from sites located in 13 states that
provided regional, rural/urban, racial/ethnic, and religious diver-
sity. Schools were chosen as the main method for collecting the
sample. Assessment was conducted in 57 schools and in four
after-school programs. The sample consisted of 1,659 5th-grade
adolescents (48.5% boys, M ⫽ 10.9 years of age, SD ⫽ 1.13 years;
51.5% girls, M ⫽ 10.8 years of age, SD ⫽ 0.97 years).
In 6th grade, 854 youth who were in the initial cohort during
Wave 1 were retested (45.6% boys; 54.4% girls), and, in addition,
a retest control sample of 733 6th graders was added (39.3% boys;
60.7% girls). The combination of longitudinally studied (Wave
1–Wave 2) participants from the initial cohort and the retest
control participants from Wave 2 resulted in a total of 1,587
6th-grade participants at Wave 2 (42.4% boys; M ⫽ 12.1 years of
age, SD ⫽ 0.71 years; 57.6% girls, M ⫽ 12.1 years of age, SD ⫽
0.70 years).
At Wave 2, youth were sampled from 53 schools and five
after-school programs in 18 states across the nation. We asked
2
In the 4-H Study, we selected a yearly division because, in conducting
a school-based survey of the scope involved in the present study, we could
not gain access to the schools more than once a year and, because little is
known about the developmental pace or course during early adolescence of
the self-regulatory processes, we believed that a yearly division of the
x-axis was a reasonable starting point for indexing such changes. Other
longitudinal studies of the age levels and of the size of the present
investigation (e.g., Nesselroade & Baltes, 1974) have found that yearly
intertesting intervals have been sufficiently sensitive at testing for the
presence of such differentiation for the constructs in which they were
interested (e.g., Fitzgerald, Nesselroade, & Baltes, 1973).
511
SELF-REGULATION AND PYD

participants to describe their ethnicity. This information exists for
1,496 of the participants at Wave 1: 57.2% described themselves
as European American, 19.9% described themselves as Latino/a,
and 8.4% described themselves as African American. Other ethnic
groups were smaller. At Wave 2, information exists for 1,462
participants. Of those participants, 67.5% described themselves as
European American, 14% described themselves as Latino/a, and
6.4% described themselves as African American. The remaining
12.1% described themselves as belonging to other ethnic groups.
In regard to household annual income, 989 participants who
answered provided this information at Wave 1: 21.6% reported
household income of under $25,000, 38.4% reported household
income between $25,000 and $64,999, and 40% reported house-
hold income of $65,000 or more. Information on household in-
come existed for 968 participants at Wave 2: 19.6% reported a
yearly household income of less than $25,000; 39.7% reported a
yearly household income of $25,000 –$64,999; and 40.7% re-
ported a yearly household income of $65,000 or more.
Attrition in the 4-H sample is not randomly distributed across
schools. In Wave 2, some principals withdrew consent for their
schools’ participation, and, thus, these students dropped out before
we had the opportunity to ask them if they wanted to remain in the
study. For example, in one state, we were unable to collect data in
Wave 2, resulting in the loss of more than 250 participants.
Overall, we lost 561 participants in Wave 2 because of the absence
of principal or superintendent permission for continuing. In turn,
however, attrition from Wave 1 to Wave 2 for students whose
principals and superintendents allowed us to ask them to remain in
the study was only 10%.
The two groups of Wave 1 youth—those who continued into
Wave 2 and those who did not—were compared on several back-
ground and outcome variables.
3
It is likely that the youth who
continued in the study were slightly more advantaged, as indexed
by family income and mothers’ education; were European Amer-
ican; came from suburban areas; had parents who also participated;
and had slightly higher levels of PYD (as a result of higher
Competence and Connection scores). However, both groups had
equivalent levels of SOC scores.
Measures
The measures used in the present report pertain to assessment of
the actions associated with the SOC model of developmental
regulation (Freund & Baltes, 2002; Freund, Li, & Baltes, 1999)
and with features of positive and problematic development that
were expected to covary positively and negatively, respectively,
with the scores derived from the SOC measure.
Indexing intentional self-regulation: A measure of SOC. We
used the Selection, Optimization, and Compensation (SOC) Ques-
tionnaire (Freund & Baltes, 2002) to measure intentional self-
regulation. The Elective Selection (S) subscale represents the
development of preferences or goals, the construction of a goal
hierarchy, and the commitment to a set of goals. The Optimization
(O) subscale refers to acquisition and investment of goal-relevant
means for achieving one’s goals, and the Compensation (C) sub-
scale refers to the use of alternative means for maintaining a given
level of functioning when specific goal-relevant means are not
available anymore. As conceptualized by Freund and Baltes
(2002), researchers have used loss-based selection to describe an
aspect of regulation observed in older populations, for whom a
decline or loss of previous levels of functioning is a major chal-
lenge. Loss-based selection is certainly a feasible component of
adolescent self-regulatory behavior (e.g., when youth lose the
opportunity for participation in elite sports teams or lose friends
because of family relocation). However, the lack of an age fit
between (a) the design by Freund and Baltes (2002) of the item
pool used for measurement of loss-based selections and (b) the
developmental status of participants in the present study resulted in
the decision for omitting the Loss-Based Selection subscale items
from those used with the current sample. The original SOC mea-
sure, which was created in German for use with an adult popula-
tion, includes 48 items (12 items in each subscale of Elective
Selection, Loss-Based Selection, Optimization, and Compensa-
tion). A shorter version of this measure (with only 6 items per
scale) has equivalent psychometric characteristics (Freund &
Baltes, 2002), and, in deference to the need for not requesting more
attention and longer participation from the participants when a
shorter index would provide comparable measurement, the shorter
version of the SOC measure was used. Three subscales were used:
Elective Selection, Optimization, and Compensation. Thus, 18
items were used in the present version of the SOC measure. Table
1 presents these items.
The items in the SOC are forced-choice format, and each item
consists of two statements, one describing behavior reflecting S, O,
or C and the other describing a non-SOC-related strategy. The
SOC asks participants to decide which of the statements is more
similar to how they would behave. For example, an item from the
Elective Selection subscale is “I concentrate all my energy on few
things [Person A] OR I divide my energy among many things
[Person B].” A sample Optimization subscale item is “When I do
not succeed right away at what I want to do, I don’t try other
possibilities for very long [Person A] OR I keep trying as many
different possibilities as are necessary to succeed at my goal
[Person B].” A sample item from the Compensation subscale is
“Even if something is important to me, it can happen that I don’t
invest the necessary time or effort [Person A] OR For important
things, I pay attention to whether I need to devote more time or
effort [Person B].” Responses that are consistent with the use of a
SOC-related strategy are totaled, which then provides a score for
each individual on each subscale.
It has been found that the SOC measure has adequate psycho-
metric characteristics among adult samples. For instance, indices
of internal consistency reliability (Cronbach’s alphas) were re-
ported as .75 for Elective Selection, .70 for Optimization, and .67
for Compensation (Freund & Baltes, 2002). Freund and Baltes
(2002) report that SOC scores have good convergent and divergent
relations with other psychological constructs (e.g., goal pursuit,
thinking styles) and have positive correlations with measures of
well-being (Brandtsta¨dter & Renner, 1990; Freund & Baltes,
2002). The psychometric characteristics of the SOC measure
within the present data set are presented in the Results section.
Indexing PYD and risk/problem behaviors. As described in
Lerner, Lerner, et al. (2005), we used several measures derived
from the overall measurement model of the 4-H Study of PYD to
3
A table providing these data is available upon request from Steinunn
Gestsdo´ttir.
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index PYD, operationalized through the assessment of the five Cs
(the subscales of Competence, Confidence, Character, Connection,
and Caring; Eccles & Gootman, 2002; Roth & Brooks-Gunn,
2003a, 2003b), and risk/problem behaviors. Table 2 lists the mea-
sures (including their Cronbach’s alpha reliabilities) which were
included as part of a questionnaire administered to youth partici-
pants. Full details about these measures, including scoring instruc-
tions and descriptive statistics, are provided in Lerner, Lerner, et
al. (2005; see also Theokas & Lerner, 2006, and Jelicic et al., in
press). A brief summary of the features of these measures is
provided here.
We used the Self-Perception Profile for Children (SPPC; Har-
ter, 1983) to index several of the Cs of PYD. The SPCC was
developed for assessment of perceived Competence in regard to
six specific domains of functioning and to global self-worth. We
used three of the scales to index the Cs: (a) Academic Competence
(reflecting school performance); (b) Social Competence (empha-
sizing peer popularity); and (c) Self-Worth (indexing feelings of
self-esteem, in general).
In each scale, all items are scored from 1 to 4 (1 ⫽ low
perceived competence, 4 ⫽ high perceived competence). Overall
scores are then computed for each scale.
It has been shown that each of the six SPCC subscales has adequate
to good reliability and validity (␣s ⫽ .76–.90; e.g., East et al., 1992;
Harter, 1983; Talwar et al., 1986; Windle et al., 1986), and moderate
and significant correlations exist between (a) self and other ratings
and (b) scores on psychosocial standardized assessments (e.g., East et
al., 1992; Harter, 1982; Talwar et al., 1986; Windle et al., 1986).
In turn, and as detailed in prior publications (Lerner, Lerner, et
al., 2005; Jelicic, et al., in press; Theokas et al., 2005), we also
used several items from the Search Institute’s 156-item Profiles of
Student Life–Attitudes and Behaviors Survey (PSL-AB; Benson,
Leffert, Scales, & Blyth, 1998) to index several of the Cs of PYD
(Confidence: 6 items; Competence: 5 items; Character: 18 items;
Connection: 22 items). The response formats for all items are
Likert-type scales, with the majority measured on a 5-point scale
and with higher scores indicating better outcomes. In addition, we
included the four items of the Peer Support Scale (PSS; Armsden
Table 1
SOC Items Retained After Reliability Analysis at Waves 1 and 2
Item no. Item
Retained
at Wave 1
Retained
at Wave 2
Selection item
13 When I decide upon a goal, I stick to it. Yes Yes
18 I always pursue goals one after the other. Yes Yes
1 I concentrate all my energy on few things.
2 I consider exactly what is important for me.
11 I always focus on the one most important goal at a given time.
12 When I think about what I want in life, I commit myself to one
or two important goals.
Optimization item
3 I keep trying as many different possibilities as are necessary to
succeeding at my goal.
Yes Yes
6 When I want to achieve something difficult, I wait for the right
moment and the best opportunity.
Yes
a
7 I think about exactly how I can best realize my plans. Yes Yes
8 I make every effort to achieve a given goal. Yes Yes
10 When I start something that is important to me but has little
chance at success, I usually stop trying.
Yes Yes
14 When I want to get ahead, I also look at how others have done it.
Compensation item
5 For important things, I pay attention to whether I need to devote
more time or effort.
Yes Yes
15 When things don’t work the way they used to, I look for other
ways to achieve them.
Yes Yes
17 When something doesn’t work as well as usual, I look at how
others do it.
Yes Yes
4 When something does not work as well as before, I listen to
advisory broadcasts and books as well.
9 When things aren’t going so well, I accept help from others. Yes
a
16 When I can’t do something as well as I used to, then I ask
someone else to do it for me.
Note. The items listed here are the choices in the forced-choice questions that are consistent with the use of
a selection, optimization, and compensation (SOC) strategy.
a
All items were retained at both waves, except for Item 6 (only retained at Wave 1) and Item 9 (only retained
at Wave 2). These two items were not included in the nine-item version of the measure.
513
SELF-REGULATION AND PYD

& Greenberger, 1987), which assesses adolescents’ relationships
with friends, to index Connection. The response format ranges
from 1 (always true)to5(almost never true). Examples of items
include “I trust my friends” and “My friends care about me.” When
all items are reverse coded, higher scores indicate higher peer
support. In the present data set, the Cronbach’s alpha for the Peer
Support Scale was .89.
The five items of the Eisenberg Sympathy Scale (ESS; Eisenberg
et al., 1996) were used for assessing the degree to which partici-
pants feel sorry for the distress of others. This measure was
included as a measure of Caring. The ESS shows adequate reli-
ability (␣s ⫽ .63 to .73; Eisenberg et al., 1996, 1998), and there is
evidence for its validity. Teachers’ reports of sympathy were
significantly related to children’s reports of sympathy when watch-
ing a film in which the intent was inducement of sympathy (r ⫽
.38, p ⬍ .001) and to children’s physiological responses to the film
(Eisenberg et al., 1996). Sympathy scores also have been modestly
to moderately related to a variety of social competence measures,
including teachers’ ratings of social skills and peers’ reports of
popularity (Eisenberg et al., 1996; Murphy, Shepard, Eisenberg,
Fabes, & Guthrie, 1999). Examples of items include “I feel sorry
for people who don’t have the things I have” and “I feel sorry for
other kids who don’t have toys and clothes.” The response format
ranged from 1 (really like you)to3(not like you). High scores
indicate low levels of sympathy.
The Center for Epidemiological Studies–Depression Scale
(CES-D; Radloff, 1977) is a widely used self-report measure of
depressive symptomatology and was included as a measure of risk.
The measure has been used extensively in adolescence, and valid-
ity and reliability with populations in high school and junior high
school have been established (Radloff, 1991). For instance, Windle
et al. (1986) demonstrated the construct validity of the measure
with 6th graders.
Adolescents responded to 20 individual items and reported how
often they felt that way during the past week. Examples of items
include “I was bothered by things that usually don’t bother me”
Table 2
Measurement Model of the Five Cs and Positive Youth Development (PYD)
Measure
Cronbach’s alpha
coefficients
5th grade
(Wave 1)
6th grade
(Wave 2)
Confidence
Positive identity (Benson et al., 1998; Theokas et al., 2005) .70 .79
Self-worth (Harter, 1983) .69 .76
Competence
Academic competence (Harter, 1983) .65 .78
Grades
a
(self-reported; Benson et al., 1998; Theokas et al., 2005)
——
School engagement (Benson et al., 1998; Theokas et al., 2005) .56 .65
Social competence (Harter, 1983) .62 .74
Character
Personal values (Benson et al., 1998; Theokas et al., 2005) .89 .91
Social conscience (Benson et al., 1998; Theokas et al., 2005) .92 .93
Values diversity
a
(Benson et al., 1998; Theokas et al., 2005)
——
Interpersonal values and skills (Benson et al., 1998; Theokas et al., 2005) .68 .73
Caring .87 .90
Sympathy: Disadvantaged (Eisenberg et al., 1996)
Sympathy: Loneliness (Eisenberg et al., 1996)
Sympathy: Unfortunate (Eisenberg et al., 1996)
Sympathy: Pain (Eisenberg et al., 1996)
Sympathy: Rejection (Eisenberg et al., 1996)
Connection
Family (Benson et al., 1998; Theokas et al., 2005) .79 .85
School (Benson et al., 1998; Theokas et al., 2005) .78 .82
Community (Benson et al., 1998; Theokas et al., 2005) .87 .85
Peer support (Armsden & Greenberger, 1987) .89 .89
Risk/problem behaviors
Depression (CES–D; Radloff, 1977, 1991) .81 .84
Risk/problem behaviors (substance use; Benson et al., 1998; Lerner et al., 2005;
National Institute on Drug Abuse, 2000)
.65 .66
Delinquency (Benson et al., 1998; Lerner et al., 2005; National Institute on
Drug Abuse, 2000)
.98 .65
Note. CES–D ⫽ Center for Epidemiologic Studies–Depression.
a
Some of the measures comprise a single item; therefore, we do not report Cronbach’s alphas for these measures.
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and “I felt sad.” The response format ranged from 0 (rarely/none
of the time)to3(most/all of the time). Items are then summed for
a total score. Higher scores are indicative of higher depressive
symptomatology.
Finally, using additional items adapted from the PSL-AB and
items from the Monitoring the Future questionnaire (Bachman,
Johnston, & O’Malley, 2000), we constructed indicators of risk
and problem behaviors (pertinent to substance use and delin-
quency). Five items assessed the frequency of substance use (e.g.,
cigarettes, alcohol) in the last year. The response format ranged
from 1 (never)to5(regularly). Four items assessed the frequency
of delinquent behaviors. The response format ranged from 1
(never)to5(5 or more times). A sample item is “During the last
12 months, how many times have you hit or beat up someone?”
To obtain indices of the each of the Cs or of the risk/problem
behaviors, we weighted items equally to create a summary score
for each construct; in all cases, a higher score indicated either
greater presence ofaCormore frequent risk behaviors (substance
use or delinquency), respectively.
Procedure
Participants were tested in groups within their schools (in more
than 95% of the cases) or within after-school programs. In both the
5th and 6th grades, teachers or program staff gave youth an
envelope to take home to their parents. The envelope contained a
letter explaining the study, consent forms, a parent questionnaire,
and a self-addressed envelope for returning the parent question-
naire. Data collection was conducted by trained study staff or
assistants, who began all testing sessions by reading the instruc-
tions to the participants.
Results
In this research, we investigated the tripartite or global structure
of SOC through several factor analyses using structural equation
modeling procedures (i.e., LISREL 8.72; Jo¨reskog & So¨rbom,
1996). Internal consistency (Cronbach’s alpha) reliability was as-
sessed, as was concurrent and predictive validity (and, thus, the
predicted links between SOC scores and PYD and risk/problem
behaviors, were assessed, as well).
Measurement Characteristics of the SOC Measure in
Early Adolescence
We organize the presentation of these analyses pertinent to the
structure of SOC among our early adolescent participants by, first,
presenting findings pertinent to assessing the evidence for a three-
factor structure for SOC, one reflecting the structure of intentional
self-regulation identified among adults with this measure (Freund
& Baltes, 2002). We also report the internal consistency reliability
of the factors derived from the analyses and discuss whether
reliability can be improved by using item deletion procedures. In
turn, we then present findings pertinent to assessing the evidence
for a one-factor structure for the SOC measure, which, as pre-
dicted, would correspond to what Freund and Baltes (2002) discuss
as the expected, nondifferentiated status of intentional self-
regulation processes in early adolescence. We also report the
internal consistency reliability of the factors derived from these
analyses within Grades 5 and 6.
Testing a Three-Factor Model: 5th and 6th Grades
We applied a confirmatory factor analysis using LISREL 8.72
(Jo¨reskog & So¨rbom, 1996) to evaluate the SOC model for each
wave. We report five fit indices for each analysis: (a) minimum fit
function chi-square, (b) normed fit index (NFI), (c) comparative fit
index (CFI), (d) goodness-of-fit index (GFI), and (e) root-mean-
square error of approximation (RMSEA). A matrix of polychoric
correlations was analyzed with the weighted least-squared method
of estimation on polychoric correlation and asymptotic covariance
matrices, as recommended for models including dichotomous vari-
ables (Jo¨reskog & So¨rbom, 1996). First, we computed a three-
factor model to assess the fit of the 18 items with three latent
variables: selection, optimization, and compensation.
In regard to our computation at Wave 1 (5th grade) of a
three-factor model (based on 18 items), ␹
2
⫽ 469.22 (df ⫽ 132,
p ⬍ .001), NFI ⫽ .57, CFI ⫽ .64, GFI ⫽ .98, and RMSEA ⫽ .045
(confidence interval [CI] ⫽ .041–.049). In turn, for Wave 2 (6th
grade), computation of a three-factor model (based on 18 items)
resulted in a chi-square value of 492.38 (df ⫽ 132, p ⬍ .001),
NFI ⫽ .65, CFI ⫽ .71, GFI ⫽ .98, and RMSEA ⫽ .046 (CI ⫽
.042–.051). These findings do not suggest that there is an accept-
able fit between (a) a proposed tripartite structure of SOC, repre-
senting the S, O, and C components of the model, and (b) our data
from adolescents in 5th and 6th grades.
Testing internal consistency for the three-factor models. Con-
sistent with findings of the confirmatory factor analyses of the
three-factor model, internal consistency among the subscales (S,
O, and C) of the 18-item measure were very low at both waves. At
Wave 1 (i.e., for the overall sample of 5th grade participants),
Cronbach’s alphas associated with the Selection, Optimization,
and Compensation subscales were .21, .30, and .10, respectively.
The overall alpha for all 18 items included in the SOC measure
was also low (i.e., ␣⫽.34; N ⫽ 1,265). Internal consistency
reliability analyses of the three subscales of the measure for the
overall sample of Wave 2 participants again resulted in low alphas
(␣s ⫽ .26, .32, and .18, respectively) and also in a somewhat
higher but still low alpha (␣⫽.46) for all 18 items (N ⫽ 1,302).
Item deletion procedures. On the basis of the ideas of Freund
and Baltes (2002) regarding the orthogenetic course of the devel-
opment of the internal self-regulating processes included within
the SOC model, we did not expect that all items in an 18-item set
designed for use with adults would necessarily be useful for
indexing what we expected was the more global structure of SOC
in the early adolescent period. Accordingly, we assessed the factor
structure of SOC when items that lowered the internal consistency
of the measure (through having low item–total correlations) were
deleted. Specifically, if keeping an item in the measure resulted in
a lower alpha for the measure, the item was deleted. At Wave 1, 8
items were deleted through this process: Four items were deleted
from the Selection subscale, 3 from the Compensation subscale,
and 1 from the Optimization subscale. The 10 remaining items
yielded a higher overall alpha than did all 18 items (␣⫽.53; N ⫽
1,378), but the alphas for the three subscales of Selection, Opti-
mization, and Compensation were still low (i.e., ␣s ⫽ .25, .33, and
.24, respectively). Moreover, the reduced SOC subscales included
515
SELF-REGULATION AND PYD

only 2, 5, and 3 items, respectively, and, especially for the first and
the third subscale, the few items used for indexing the construct
may have contributed to the low alphas (see Table 1).
We conducted similar analyses at Wave 2. As with the Wave 1
data, 8 items were identified for deletion through this process. Of
the 10 items that were retained, 9 had also been retained during the
Wave 1 analyses (see Table 1). At Wave 2, an additional item was
retained from the Compensation subscale, whereas 1 item that had
been retained at Wave 1
4
was deleted from the Optimization
subscale. Although the alphas for the three SOC components
associated with the retained items were again low (␣s ⫽ .35, .47,
and .26 for the 2, 4, and 4 items indexing the three SOC processes,
respectively), the overall alpha for the 10 items was .63 (N ⫽
1,382), which is an alpha level found for some of the SOC
processes among adult samples (Freund & Baltes, 2002).
Accordingly, the alpha coefficients for the reduced, overall
version of the SOC measure (␣⫽.53 at Wave 1, ␣⫽.63 at Wave
2) approach the lower end of the distribution of these coefficients
found by Freund and Baltes (2002) among older samples. If,
however, the analyses of the concurrent and predictive validity of
the SOC measure resulted in confirmation of the predicted patterns
of covariation between (a) SOC scores and (b) indices of positive
and of problematic functioning, there would be reason for regard-
ing the SOC measure as a useful index of internal self-regulation
in early adolescence. In this regard, Schmitt (1996), in a discussion
of the issues involved in interpreting alphas, noted that alpha is a
lower bound estimate of reliability, and he stated that “when a
measure has other desirable properties . . . low reliability may not
be a major impediment to its use” (pp. 351–352). This view
underscores the importance of evaluating evidence for the validity
of the SOC measure within the present sample.
Testing a One-Factor Model in the 5th and 6th Grades
We again did a confirmatory factor analysis using LISREL 8.72
(Jo¨reskog & So¨rbom, 1996) to examine if, as expected, evidence
existed for the presence of a global structure for SOC. Two
different one-factor models were tested at each wave: First, we
tested the 10-item version, in which 1 item was different between
waves. Second, we tested a 9-item version of the model, in which
only the items that had been retained at both waves were included.
At both Waves 1 and 2, the items that changed between waves had
the lowest item–total correlation of all the items retained at that
wave. As before, we report five fit indices for each analysis.
In regard to our computations at Wave 1, for the 10-item,
one-factor model, ␹
2
⫽ 93.26 (df ⫽ 35, p ⬍ .001), NFI ⫽ .78,
CFI ⫽ .85, GFI ⫽ .99, and RMSEA ⫽ .036 (CI ⫽ .027–.045). The
t value for the item that was different from Wave 2 (Item 6) was
not significant, and the R
2
was very low (1%) compared with the
other variables, providing support for use of the 9-item model. For
the 9-item, one-factor model, ␹
2
⫽ 67.32 (df ⫽ 27, p ⬍ .001),
NFI ⫽ .83, CFI ⫽ .89, GFI ⫽ .99, and RMSEA ⫽ .034 (CI ⫽
.024 –.045), which presents a better fit to the data than does the
10-item model.
Similarly, in regard to our computations at Wave 2 for the
10-item, one-factor model, ␹
2
⫽ 113.18 (df ⫽ 35, p ⬍ .001),
NFI ⫽ .84, CFI ⫽ .88, GFI ⫽ .99, and RMSEA ⫽ .042 (CI ⫽
.033–.050). Again, although significant, the loading for the single
item that differed from Wave 1 (Item 9) was much lower than the
loadings of the other items, and the R
2
(8%) was very low com
-
pared with the R
2
for the other items. For the 9-item, one-factor
model, ␹
2
⫽ 92.22 (df ⫽ 27, p ⬍ .001), NFI ⫽ .85, CFI ⫽ .89,
GFI ⫽ .99, and RMSEA ⫽ .043 (CI ⫽ .034 –.053), which again
provides stronger support for the presence of a one-factor solution
on the basis of the nine items that were retained at both waves.
Confirmatory factor analyses for both the 9-item and the 10-
item versions at both waves showed a significantly better fit to the
data than did the LISREL analyses of the tripartite structure
presented earlier. The relatively strong support for a one-factor
model on the basis of the 9 items of the SOC measure, which
shows stability between waves, suggests that SOC strategies may
be expressed as one undifferentiated component among 10- and
11-year-old youth.
We have noted previously in this article the internal consistency
of the one-factor model at Waves 1 and 2 when a 10-item version
of the measure was used. Because the items that we deleted to
create the 9-item version of the measure at each wave had the
lowest item–total correlation of all the items, internal consistency
of the 9-item measure was almost identical to that of the 10-item
version (for the 9-item version, Wave 1 ␣⫽.55 and Wave 2 ␣⫽
.64; Ns ⫽ 1,389). Consequently, the alpha for the 9-item version
was, similar to that of the 10-item version, higher than the alpha
for all 18 items.
Taken together, then, the findings presented here suggest that, at
least as operationalized through this item set, intentional self-
regulation in early adolescence is manifested as a single, global
component rather than as three distinct processes in the 5th and 6th
grades. This view is consistent with the findings of Raffaelli et al.
(2005), in which assessments of the regulation of affect, behavior,
and attention in a longitudinal sample and the structure of self-
regulation provided support for a single-factor model of self-
regulation rather than for a more differentiated model.
In short, given the lack of empirical support within the present
data set for the presence of the three SOC components as well as
the results of past research (Raffaelli et al., 2005) and the present
analyses, the nine-item SOC measure was used as an index of
intentional self-regulation for the 5th and 6th grade participants in
the 4-H Study. Therefore, subsequent analyses were aimed at
validating the use of this measure by appraisal of its covariation
within and across time with indices of PYD and of risk/problem
behaviors.
Differences and Change in Levels of Self-Regulation
Table 3 provides means and standard deviations for the nine-
item SOC measure at Waves 1 and 2 for the whole sample and for
the sample differentiated by gender. The SOC score for each
participant was a sum across the nine items, with a range, there-
fore, of 0 –9. In addition, Table 3 provides means and standard
4
Item deletion on the basis of reliability analyses resulted in the reten
-
tion of 9 of the same items and 2 different items at Wave 1 and Wave 2.
Therefore, we conducted further analyses to compare these different ver-
sions of the measure—a 9-item version in which all items at Waves 1 and
2 were identical and a 10-item version in which 1 item differed between
waves. Confirmatory factor analyses comparing the fit for a one-factor,
9-item version of the measure and a one-factor, 10-item version are
reported in the Results section.
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deviations for the longitudinal sample only, as we used this sample
to assess the SOC scores across the two waves of testing.
We conducted a 2 (Gender) ⫻ 2 (Time: Wave 1, Wave 2)
between–within, mixed-model analysis of variance (ANOVA),
with the total SOC score as the dependent variable, to evaluate
gender differences and changes in SOC over time. In this analysis,
we found no significant main effects of time and gender and no
Time ⫻ Gender interaction.
Obviously, in using a repeated-measures ANOVA to detect
change, we used only the data of the longitudinal sample. As
discussed earlier, attrition between waves may result in differences
between the overall sample and the longitudinal sample. However,
the data reported in Table 3 indicate that there are no differences
in means on SOC between the longitudinal sample and the overall
sample.
SOC and Features of PYD and Problem/Risk Behaviors
SOC is a theory of successful life management and is expected
to be positively related to positive indicators of development and
negatively correlated with indicators of problematic functioning.
We noted earlier that Lerner, Lerner, et al. (2005) found that the
five, first-order latent variables represented by the Cs converge on
a second-order latent variable that can be labeled as PYD. In the
absence of prior studies examining the interrelations between the
Cs or between PYD and SOC, we concluded that assessing if there
were any individual Cs for which the predicted relations did not
hold was useful. Correlations among the Cs ranged from
r(1190) ⫽ .12 (between Competence and Caring) to r(983) ⫽ .47
(between Connection and Character) at Wave 1 and from
r(1290) ⫽ .23 (again, between Competence and Caring) to
r(1290) ⫽ .62 (between Competence and Confidence) at Wave 2.
All correlations were significant at p ⬍ .01.
Table 4 presents correlations between (a) SOC scores and (b)
indicators of positive and negative development within each time
point as well as SOC scores at Wave 1 and developmental indi-
cators at Wave 2. All correlations were significant and in the
predicted direction, suggesting that even if processes of self-
regulation still may be developing during this early portion of
adolescence, intentional self-regulation plays a role in multiple
aspects of healthy functioning in early adolescence. Of course,
correlations can be misleadingly low when one or both measures
have low reliability. As such, correlations were corrected for the
limited reliability of the SOC scales. As reported in Table 4, the
correlations between SOC and the Cs ranged from r(1399) ⫽ .22
Table 3
Reduced (Nine-Item) Version of the SOC Measures: Means and Standard Deviations for Boys
and Girls at Wave 1 and Wave 2 for the Overall Sample and for the Longitudinal Sample
Samples
Wave 1 Wave 2
MSD N MSD N
Overall sample 6.63 1.88 1,619 6.51 2.02 1,563
Boys 6.57 1.88 780 6.41 2.07 663
Girls 6.69 1.88 839 6.59 1.97 900
Longitudinal sample 6.69 1.86 840 6.57 2.00 839
Boys 6.67 1.81 381 6.59 1.97 381
Girls 6.71 1.89 459 6.56 2.03 458
Note. SOC ⫽ selection, optimization, and compensation.
Table 4
SOC and Indicators of Positive and Negative Development: Correlations Within the Same Wave of Assessment (Wave 1 and Wave 2)
and Among SOC at Wave 1 and Indicators of Development at Wave 2
Indicators
SOC at Wave 1
N
SOC at Wave 2
N
SOC at Wave 1
NIndicators at Wave 1 Indicators at Wave 2 Indicators at Wave 2
Indicators of positive development
Confidence .393
***
942 .356
***
1,227 .331
**
677
Competence .252
***
1,212 .342
***
1,313 .335
**
723
Connection .282
***
1,211 .284
***
1,544 .285
**
832
Character .338
***
996 .312
***
1,334 .257
**
731
Caring .217
***
1,399 .194
***
1,435 .139
**
823
PYD
a
.350
***
1,438 .373
***
1,563 .329
**
840
Indicators of negative development
Depression ⫺.261
***
1,373 ⫺.303
***
1,508 ⫺.205
**
814
Risk behaviors ⫺.122
***
1,467 ⫺.136
***
1,531 ⫺.119
**
826
Delinquency ⫺.178
***
1,443 ⫺.210
***
1,524 ⫺.145
**
819
Note. SOC ⫽ selection, optimization, and compensation.
a
Positive youth development (PYD) is a composite of confidence, competence, connection, character, and caring.
*
p ⬍ .05.
**
p ⬍ .01.
***
p ⬍ .001.
517
SELF-REGULATION AND PYD

to r(942) ⫽ .39, p ⬍ .001, at Wave 1 (between SOC and Caring
and between SOC and Confidence, respectively) and from
r(1435) ⫽ .19 to r(1227) ⫽ .36, p ⬍ .001, at Wave 2 (again,
between SOC and Caring and between SOC and Confidence). As
expected, correlations between SOC and the Cs were higher when
they were corrected for attenuation. For instance, corrected corre-
lations ranged from r(1399) ⫽ .32 to r(942) ⫽ .60, p ⬍ .001, at
Wave 1 (between SOC and Caring and between SOC and Confi-
dence, respectively) and from r(1435) ⫽ .25 to r(1227) ⫽ .49, p ⬍
.001, at Wave 2 (again, between SOC and Caring and between
SOC and Confidence).
Wave 1. As shown in Table 4, at Wave 1, correlations between
SOC and positive indicators were moderate, as they ranged from
r(1399) ⫽ .217, p ⬍ .001, for Caring, to r(942) ⫽ .393, p ⬍ .001,
for Confidence. We used Fisher’s r to z transformation to compare
correlations among SOC and indicators of positive development.
Caring had the weakest relation with SOC, as the correlation
between SOC and Caring was lower than that between SOC and
Confidence, SOC and Character, and SOC and PYD ( p ⬍ .01). We
found that few of the differences between correlations of the other
Cs and SOC were significant. The fact that there were relatively
few significant differences between the strength of correlations of
the positive indicators and the SOC measure may indicate that
intentional self-regulation is involved in all aspects of the healthy
development of the person.
On the other hand, when using Fisher’s r to z transformation, we
found that correlations between SOC and negative indicators were
lower than the correlations between SOC and positive indicators at
Wave 1. When comparing the correlations among SOC and the
three indicators of negative development, we found that depression
had a higher, negative correlation with SOC than did risk behav-
iors and delinquency ( p ⬍ .05). This difference may be due to the
low variability in risk behaviors within the sample (Lerner, Lerner,
et al., 2005; Theokas & Lerner, 2006). Alternatively, these results
may indicate that intentional self-regulation is more pertinent in
promoting positive attributes of a person and has less bearing on
reducing negative behaviors. Future waves of data collected in the
4-H Study, which will assess an age period that is typically marked
by an increase in risk behaviors, will provide more information
regarding the relationship between SOC and positive and negative
indicators of development.
Wave 2. As noted also in Table 4, correlations between SOC
at Wave 2 and positive indicators at Wave 2 were comparable to
the correlations observed at Wave 1, as they ranged from
r(1435) ⫽ .194, p ⬍ .001, for Caring to r(1563) ⫽ .373, p ⬍ .001,
for the second-order latent variable of PYD. Consistent with find-
ings from Wave 1, there were not many significant differences
between correlations of SOC and the six indicators of positive
development. Again, using Fisher’s r to z transformations, Caring
was found to have a significantly lower correlation to SOC than all
of the other Cs and the latent PYD variable ( p ⬍ .01). Differences
among correlations between SOC and the other Cs were less clear.
Again, consistent with Wave 1 findings, correlations between
SOC and all three negative indicators were significantly lower than
those between SOC and the positive indicators. In addition, de-
pression had a higher, negative correlation with SOC at Wave 2
than did risk behaviors and delinquency ( p ⬍ .05). When com-
paring correlations among SOC and indicators of positive and
negative development for the 5th and 6th grade participants, cor-
relations were not systematically higher or lower at either time
point.
SOC at Wave 1, indicators at Wave 2. Scores on SOC at Wave
1 were correlated to scores on negative and positive indicators at
Wave 2. Correlations were moderate, significant, and in the ex-
pected directions (see Table 4). This finding about the predictive
validity of the SOC measure provides further confirmation regard-
ing the effectiveness of the SOC measure in capturing a construct
of intentional self-regulation in early adolescence.
5
Discussion
Although there is theoretical and empirical evidence that self-
regulatory processes mature and develop during the 2nd decade of
life, research on self-regulation has suffered because of the relative
scarcity of longitudinal studies with a focus on the development of
such processes during late childhood and adolescence (Raffaelli et
al., 2005). Accordingly, the purpose of the present research was
exploration of the interrelation between intentional self-regulation
in the course of positive development and intentional regulation in
the course of problematic development during early adolescence.
Using data from the first two waves of data collection (5th and
6th grades) of the 4-H Study of PYD, in this research we aimed at
ascertaining the measurement characteristics of a measure devised
by Freund and Baltes (2002) for use in testing ideas associated
with a key theoretical model of intentional self-regulation—that is,
the Baltes and Baltes (1990; Baltes, 1997; Baltes et al., 1998,
2006) SOC model. We also assessed whether, with this measure,
we could gain increased understanding about the theoretically
expected positive relation, in early adolescence, between inten-
tional self-regulation and PYD and, in turn, the expected negative
relation between such regulation and problem or risk behaviors
among youth.
In regard to the psychometric features of the SOC measure used
for indexing intentional self-regulation among the 5th and 6th
grade participants of the present study, the results of factor anal-
yses that used structural equation modeling procedures and of
5
To ascertain if, in addition to within- and across-time covariation
between SOC scores and scores for the positive and negative outcome
variables, SOC scores predicted changes in these outcomes, partial corre-
lations were calculated between SOC at Wave 1 and the outcomes vari-
ables at Wave 2 (i.e., the scores for PYD, Depression, Risk Behaviors, and
Delinquency). Scores for these outcomes at Wave 1 were controlled in
these analyses. There was a low, significant correlation between the Wave
1 SOC score and the change scores. The correlation between SOC and the
PYD change score was r(775) ⫽ .19, p ⬍ .001, and the correlation between
SOC and the Depression change score was r(721) ⫽ –.13, p ⬍ .001.
Correlations between SOC and the Risk and Delinquency change scores
were lower but also significant, r(777) ⫽ –.09, p ⬍ .01, and r(759) ⫽ –.08,
p ⬍ .05, respectively. These findings suggest that the ability of using
self-regulatory behaviors, as reflected in the SOC model, predicts an
increase in PYD from Grade 5 to Grade 6 and a decrease in Depression,
Risk Behaviors, and Delinquency during this same time period. However,
these correlations, although statistically significant, are extremely low, and
we question whether they have psychological significance. We expect that
future waves of data (when SOC will be more fully developed and we will
see an increase in, and more variance associated with, problem behaviors)
will better allow us to evaluate the role that SOC plays in increased positive
behaviors and decreased negative behaviors.
518
GESTSDO
´
TTIR AND LERNER

associated analyses of internal consistency reliability indicated that
a one-factor solution involving nine items in both 5th and 6th
grades best fit the present data. These findings are consistent with
the idea that self-regulatory processes are not orthogenetically well
developed in 5th and 6th graders—that is, it does not appear that
the tripartite processes evident among young adults and older
individuals are differentiated in the early adolescent participants in
the present study (Freund & Baltes, 2002; Werner, 1957).
Although future waves of data collection within the 4-H Study
will be needed for ascertaining if and when the expected tripartite
structure of SOC emerges, other findings in the present report are
consistent with the purported links between intentional self-
regulation and adaptive relations between individuals and their
contexts (e.g., Baltes, 1997; Brandtsta¨dter, 1998, 2006; Heck-
hausen, 1999). That is, summary scores for the nine items were
related, in predicted ways, to indices of PYD and of risk/problem
behaviors. These associations—positive relations between inten-
tional self-regulation and indicators of positive development (the
Five Cs and PYD) and negative relations between SOC scores and
indices of risk/problem behaviors— existed at both waves of as-
sessment and between SOC scores at 5th grade and positive and
negative indicators at 6th grade. Considering that the expectation
may be that the SOC measure will capture processes that are still
developing among the participants of the current study, these
correlations provide good support for the relation between inten-
tional self-regulation and positive development. In addition, the
longitudinal findings suggest the predictive validity of the SOC
measure, indicating its potential use in further research about
intentional self-regulation in early adolescence.
Moreover, the pattern of theoretically expected, positive rela-
tions among scores for intentional self-regulation, the individual
Cs, and PYD shown in Table 4 have interesting implications for
additional research and for practice in youth development. The
positive relation among these constructs suggests that researchers
who are interested in links between self-regulation and only one or
a few of the five Cs may focus on these constructs without having
to index the overall PYD construct. In turn, practitioners can still
promote positive developmental outcomes and overall PYD, even
if a particular adolescent is showing, for instance, poor compe-
tence or few positive social connections. Although positive, the
fact that the interrelations among the Cs are not perfect suggests,
for practice, that there is enough variability in the system that all
is not lost for a given young person if he or she shows even quite
low scores on any given C. PYD can be promoted by focusing on
the other Cs.
Of course, it is expected that intentional self-regulation may still
be developing in early adolescence, and such development may
moderate the important influences that ecological characteristics,
such as a youth’s relationships in family, school, and peer group
settings (e.g., Theokas & Lerner, 2006), may have on youth
development. Thus, it can be expected that processes of intentional
self-regulation play a significant role in the lives of the young
adolescents who are participating in the 4-H Study. In short, the
present findings about SOC and PYD are consistent with previous
studies of self-regulation (through use of measures other than
SOC) in childhood and adolescence (Eisenberg et al., 2006) as
well as with SOC as a theory of successful life management
(Baltes, 1997; Baltes & Baltes, 1990; Freund & Baltes, 2002).
Limitations of the Current Study and Future Steps
The youth who participated in the 4-H study of PYD were
recruited through schools, and, as such, their participation de-
pended on permission from a superintendent and principal as well
as on consent from their parents. Therefore, the sample is one of
convenience and, although it provides regional, rural/urban, racial/
ethnic, and socioeconomic variation, it does not provide for gen-
eralizability in the manner associated with a sample that is repre-
sentative of the overall adolescent population in the United States.
In particular, it can be expected that schools that are stressed for
time and resources find it challenging to participate in a study such
as the 4-H Study, and parents who do not speak English as a first
language may be less likely to send in a signed consent form.
Moreover, the survey methodology that we used to assess partic-
ipants provides only a single means of appraising participants’
intentional self-regulating behaviors and the predicted positive and
negative covariates of these behaviors.
Furthermore, although intentional self-regulation plays an increas-
ing role in moderating an individual’s developmental trajectory as he
or she moves through adolescence and adulthood, conceptually, or-
ganismic regulation also makes an important contribution to how
adolescents contribute to their own development. The SOC model,
which measures intentional self-development, aims to capture only
one aspect of regulation. Future research will profit by seeking to
understand more about the links between intentional self-regulation
and organismic regulation in adolescence. Accordingly, a measure of
temperament will be added to future waves of data collection for the
4-H Study. These measures will allow examination of how tempera-
ment, as an instance of organismic self-regulation, and SOC, as an
index of intentional self-regulation, contribute, both independently
and together, to a person’s functioning.
In addition, as noted in the Method section, adolescents encoun-
ter loss of functioning (e.g., loss of hearing or loss of mobility) less
frequently than do older individuals; therefore, at the start of the
study, we decided to not include the Loss-Based Selection subscale
of the SOC measure. However, as we continue to explore the
development of self-regulatory processes across adolescence, we
plan to include this subscale in future waves of the study.
As previously noted, it is believed that adolescents have various
opportunities to affect their own development (Lerner, 1982;
Lerner et al., 2001) and, thus, to engage in the selection and
optimization processes of interest within the SOC model (e.g.,
Damon et al., 2003; Freund & Baltes, 2002). During this age
period, a person can select goals pertinent to recreational activity,
participation in after-school programs, and the use of financial
resources for purchasing goods and services as well as activities
pertinent to serving his or her family, school, or community
(Damon et al., 2003). In addition, at this age, it is expected that the
person take more responsibility for his or her own life, such as
choosing an occupational path, being responsible for school atten-
dance, contributing to his or her school or neighborhood (e.g., by
volunteering or by undertaking leadership roles), or resisting pres-
sure at engaging in risk behaviors. Such changes provide individ-
uals with increased opportunities at regulating their interactions
with their ecological settings. Still, we expect that opportunities for
contributing to their own developmental trajectories in a positive
way will differ among individuals and among groups of individ-
uals. Although beyond the scope of this article, we acknowledge
519
SELF-REGULATION AND PYD

the need for understanding how contexts—including family,
school, and community contexts— can create (or obstruct) oppor-
tunities for the young person to contribute to his or her own
positive development. Ongoing work is being done within the 4-H
Study of PYD, in which opportunities for such regulation in
different contextual settings are being investigated (see Theokas &
Lerner, 2006).
Although the current study reports data from two points in time,
the development of self-regulatory processes must be studied over
a longer period. The findings of this research suggest that some
developmental change may be observed between 5th and 6th
grades (e.g., the increased interconnectedness of the SOC items, as
represented in an increase in alpha from Wave 1 to Wave 2, may
indicate that the SOC strategies are becoming better integrated).
However, future waves of data will provide more complete infor-
mation about how processes of intentional self-regulation develop
in adolescence and will add to information regarding the reliabil-
ity, construct validity, and predictive validity of the SOC measure
when used across the adolescent years.
In sum, this study represents a first step toward creating an
understanding of how intentional self-regulation develops during
the adolescent years and of the relation between self-regulation
and the positive development of youth. The findings presented in
this research are consistent with the perspective of action theories
that, across the life span, people are both products and producers
of their environment and, thus, are active agents in their own
development. The current findings provide empirical evidence for
the importance of recognizing the active role that adolescents play
in their development. Future studies should focus on the develop-
ment of self-regulation over subsequent years in the adolescent
period and should identify the ecological and individual charac-
teristics that, if supported, foster adaptive regulation and thriving
among children and adolescents.
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Received October 22, 2005
Revision received October 19, 2006
Accepted November 20, 2006 䡲
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