Intentional Self-Regulation and Executive Functions: Overlap and Uniqueness in Predicting Positive Development Among Youth in Iceland

Abstract

This study examined relations between intentional self-regulation (ISR) and executive functions (EFs) among 84 Icelandic youth in Grade 6 (mean age 11.7 years; 51% girls) and their contributions to healthy and problematic outcomes a year later. ISR was indicated by the Selection, Optimization and Compensation model (SOC) and Self-Regulated Learning (SRL). EF was indicated by attention shifting, inhibition, and working memory (WM). Only WM was related to ISR. Furthermore, ISR tended to uniquely predict Grade 6 outcomes, but the inverse was less true. In Grade 7, EF but not ISR uniquely predicted national test scores, and symptoms of depression and anxiety, whereas ISR remained a stronger predictor of Positive Youth Development (PYD) and risk behavior. These findings suggest a limited relation between ISR and EF, and that ISR is important to the general well-being of youth, whereas EFs may play a role in the development of problems and academic achievement.

Keywords

intentional self-regulation executive functioning positive youth development academic achievement problem behaviors

Relational developmental systems metatheory is a framework that can be applied to most influental contemporary models of human development (Lerner et al., 2019). Central to relational theories is that every individual is embedded within a multi-layered system where development is determined by a bidirectional interaction between the person and their context (Brandstader, 2006; Overton, 2013). Within this person-context system, agentic control over the person-context relations represents a person’s contribution to their own development (Gestsdottir & Lerner, 2008). Much of the recent study of self-regulation has been informed by the relational developmental systems approach and places self-regulation as a fundamental aspect of human functioning and development (McClelland et al., 2015).

The study of adolescent development has undergone a major change in the last few decades, where young people’s strengths and healthy development has been at the focus of how we can understand and support thriving among young people. The Positive Youth Development (PDY) perspective has been at the forefront of this change and has generated an understanding of how young people’s positive development can be conceptualized and measured. Consistent with the relational developmental systems theories, within the PYD approach, self-regulation is typically viewed as a key strength among young people (Gestsdottir & Lerner, 2008). In studies of PYD, self-regulation has most frequently been conceptualized by intentional self-regulation (ISR). ISR involves actions that can be actively selected and controlled by a person and that aim to transform situations in accordance with desired future states (Brandtstädter, 2006; Gestsdottir & Lerner, 2008; Gestsdottir et al., 2017).

While the role of ISR in PYD has been studied quite extensively across a variety of cultures, another influential literature on the role of self-regulation in human functioning has taken a different approach to understanding self-regulation. Executive functions (EFs) refer to more narrowly defined and purely cognitive processes, such as the control of attention, inhibition of a response, and the utilization of working memory (McClelland et al., 2017). As such, compared to the ISR, which focuses on relations between the person and their context, EF refers purely to within-person processes.

Research on ISR and EFs have developed mostly independently of each other, partly because these bodies of work have emerged from different theoretical and methodological frameworks (developmental science and cognitive psychology, respectively; Duckworth & Kern, 2011; Hofmann et al., 2012). As Duckworth and Kern (2011) note, researchers studying self-regulation from different traditions rarely cite or use each other’s work, hindering progress and a comprehensive view of self-regulated action. Nevertheless, both these instances of self-regulation, ISR and EF, have been linked to various forms of adaptive outcomes across childhood and adolescence (see e.g., Gestsdottir et al., 2017; Pharo et al., 2011; Zelazo et al., 2017).

The current study contributes to a better-integrated view of self-regulation and its role in adaptive functioning by expanding previous studies of ISR and PYD and include EF as an additional indicator of self-regulation. Specifically, we explore the relation between ISR and EF among early adolescents in Iceland and the unique contribution of these measures to a range of developmental outcomes, including indicators of PYD, symptoms of anxiety and depression, risk behaviors, and academic achievement.

Intentional Self-regulation and Positive Youth Development

Models of PYD are strength-based conceptions of youth development. The Five Cs of PYD model is arguably the most influential model of PYD, which conceptualizes thriving as “Five Cs”; competence, confidence, connection, character, and caring (Lerner et al., 2005, 2019). According to this model, all youth possess strengths that can be nurtured. The Five Cs refer to characteristics that promote thriving among youth, i.e., as having a positive view of one’s own actions and abilities that allows a person to overcome challenges in life (competence), an overall positive self-worth (confidence), positive bonds with family, friends, and community (connection), respect for and promoting of societal norms and justice (character), and a sense of sympathy and empathy for others (caring). Based in developmental systems approach, the Five Cs of PYD model places bidirectional person-context relations as the fundamental process that drives thriving and development. Both the individual and the context contribute to the person-context relations, and ISR is a key individual asset that gives the person the power to beneficially influence the person-context interaction and, as such, their own healthy development.

A comprehensive work on ISR in adolescence comes from Gestsdottir, Lerner, and colleagues, who have operationalized ISR by using the Selection, Optimization, and Compensation (SOC) model devised by Baltes, Baltes, Freund, and colleagues (Baltes, 1997; Freund & Baltes, 2002). This model describes three self-management strategies; goal selection describes how people develop, elaborate, structure, and commit themselves to specific goals, giving direction to development. Once a goal has been selected, the person must act to attain the selected goals. Optimization involves seeking strategies or resources that are compatible with personal and/or social values to pursue a particular goal. Compensation also involves goal-directed actions but, unlike optimization, aims to avoid or minimize losses when goal-relevant means are lacking (Baltes, 1997; Freund & Baltes, 2002; Wiese et al., 2000). Many of the capacities necessary for the developmentally advanced ISR skills may for the most part emerge in adolescence and adulthood (Brandtstädter, 2006; Geldhof et al., 2017; Kopp, 1982; McClelland et al., 2017; Moilanen, 2007; Raffaelli et al., 2005). For instance, acquiring more-developed planning and metacognitive skills, an increased understanding of resources, a more realistic view of one’s own abilities and increased future orientation improves a young person’s ability to attain strived-for goals (e.g., relating to academic, occupational, and social outcomes; Brandtstädter, 2006; Geldhof et al., 2017). As such, this type of self-regulation is typically used to understand development in adolescence and adulthood. The concurrent and long-term relations between ISR and the 5 Cs of PYD (competence, confidence, character, caring, and connection) has now been validated with adolescent samples in multiple cultural contexts, e.g., in the U.S, Iceland, Canada, and Germany (Geldhof et al., 2015; Gestsdottir et al., 2015; Stefansson et al., 2018). These studies find that ISR is positively related to positive behaviors, including concurrent and longitudinal prediction of higher PYD scores, as well as being associated with the developmental trajectories characterized by high levels of PYD (Zimmerman et al., 2008). In addition, these studies consistently report that ISR is related negatively to problems such as risk behaviors and depression (Gestsdottir et al., 2009; Zimmerman et al., 2008). As such, ISR has been validated as an indicator of mindful, long-term self-regulation skills that contribute to a range of life goals of young people.

Another common conception of ISR in adolescence is the domain-specific construct of self-regulated learning (SRL). SRL involves ISR skills that are applied specifically to the academic context. As such, SRL describes how young people set learning goals, use appropriate strategies for attaining academic goals, evaluate the methods they have chosen to achieve their goals, and monitor the progress being made toward those goals (Bandura, 2006; Zimmerman & Schunk, 2001). There is an obvious overlap between the measures of SOC and SRL, e.g., in terms of goal selection (e.g., “I always focus on the one most important goal at a given time” vs. “Plan my schoolwork for the day”, respectively) and the management of resources and both are used in the current study as conceptions of ISR. The contribution of SRL to academic achievement has been demonstrated among youth in multiple cultures. Self-regulated learners have been found to be more flexible in search of solutions to problems (Fuchs et al., 2003), achieve higher school grades (e.g., see meta-analysis by Dent & Koenka, 2016) and less likely to drop out of school (Caprara et al., 2008). In addition, although SRL is conceptualized to capture ISR skills as applied to academic settings, it still assesses general ISR strategies and, as such, can be expected to be related positively to various adaptive developmental outcomes, including PYD, and negatively to problems, including risk and internalizing symptoms.

Executive Functions

There is no one theoretical approach to EF that can be seen as dominant in the study of EF across the lifespan (McClelland et al., 2017). However, multiple models of EF see it as an umbrella term that refers to a range of distinct, but interrelated, higher-order cognitive processes (Boelema et al., 2014; Scorza et al., 2015). Researchers studying EF frequently emphasize three foundational features: attentional control, inhibitory control, and working memory (Best & Miller, 2010; McClelland et al., 2017). Inhibition describes a person’s ability to stop a prepotent response in favor of a more adaptive response. Attentional control refers to the ability to shift and maintain attention, and working memory describes how an individual can retain and manipulate information over a short period of time (Best & Miller, 2010; Boelema et al., 2014; Duckworth & Kern, 2011; Scorza et al., 2015). Traditionally, EF skills have been measured in terms of responses to abstract, decontextualized problems that involve control of cognitive processes, such as focusing on the color of a word’s font when the word spells the name of a different color (e.g., Stroop, 1935). Performance on such tasks improves gradually from pre-school to adolescence, with the most rapid growth taking place during the pre-school years and the transition to adolescence (Best & Miller, 2010; Zelazo et al., 2017).

Although used more frequently with younger samples, there is a considerable body of research that relates EFs in adolescence to developmental outcomes, most notably to academic performance (see Zelazo et al., 2017, for a review). In particular, many studies have highlighted the role of EF skills in the acquisition of math, as math problems are frequently reasoning-based and, as such, require a high degree of metacognitive control and problem-solving skills (see Cragg & Gilmore, 2014, for a review). EF skills have also been found to play a role in reading comprehension, as reading for meaning involves the simultaneous use and coordination of a number of reading processes (e.g., decoding, semantic and syntactic analysis and inference making) while holding the just-read material in mind (Birgisdóttir et al., 2020; Cain et al., 2004; Swanson & Jermane, 2007). In addition, poor EF skills have consistently been related to behavioral disorders that interfere with learning. In particular, deficient EFs have been regarded as a core problem in attention-deficit hyperactivity disorder (ADHD; Roberts et al., 2015), which is primarily characterized by a lack of attentional and behavioral control (Barkley, 1997). Similarly, weaknesses in EF have been associated with risk behaviors during adolescence (Pharo et al., 2011; Romer et al., 2011).

Compared to the contribution of EF to academic achievement and behavioral problems, empirical evidence on the role of EF in internalizing symptoms, including that of depression and anxiety, is less clear. EFs are hypothesized to influence the ability to accurately represent, evaluate, and adaptively respond to cognitively and emotionally challenging situations (Schmeichel et al., 2008; Zelazo et al., 2017, for a review). Accordingly, some suggest that youth with poor EF skills may exhibit less flexible control over their environment and more limited coping skills, leading to higher risk of internalizing symptoms or problems (Martel et al., 2007). A range of studies among adults with clinical depression and anxiety have demonstrated impairment on a broad range of EF components (Hammar & Årdal, 2009, for a review). However, similar research including children and adolescents has reported mixed results (see Vilgis et al., 2015, for a review). There is also doubt on how discrepancies in EF between youth with and without mental health problems should be interpreted. For example, Han et al. (2016) demonstrated that while EF deficits did not characterized youth with concurrent anxiety, they played a predictive role in the development of such symptoms over time. However, due to the scarcity of studies exploring longitudinal associations between EF and internalizing symptoms in adolescence, further research is needed to cast light on the exact role EF plays in the emergence of internalizing symptoms.

Similarities and Differences Between Intentional Self-Regulation and Executive Functions

As evident from the definitions of ISR and EFs, these constructs both overlap and differ from each other. Both represent the regulation of one’s cognition and behavior (Best & Miller, 2010; Hofmann et al., 2012) but as described, ISR captures cognitive, motivational, and behavioral functions in relation to long-term goals, as compared to the purely cognitive, and in-the moment control that characterizes EFs. In the simplest term, EF skills, such as control of attention and inhibition of a response, can be viewed as processes that underlie and facilitate more complicated self-regulation skills (Best & Miller, 2010; Best et al., 2011; McClelland et al., 2017). Even advanced ISR skills that involve long-term goal setting and have a definite motivational component, rely on EF skills. For example, working memory helps represent the goals and standards that guide long-term goal-pursuit and allow people to compare goal standards and their own behaviors. Similarly, attentional control is necessary to ignore distracting and irrelevant stimuli and helps a person switch between different means to pursue a goal. Finally, inhibition skills reduce the chance of automatic responses or mindless behaviors when pursuing a goal (for more discussion see Hofmann et al., 2012; Scorza et al., 2015; but see also Wegner, 2002). This view of EFs as foundations for more advanced self-regulation is consistent with McClelland et al. (2017), who state that EFs represent “the fluid cognitive processes that underlie self-regulatory action” (p. 278).

ISR and EFs are mostly operationalized in different ways. Typically, most measures of ISR are introspective self-reports that ask participants to assess the extent to which they direct their attention to their most important goal (SOC; Freund & Baltes, 2002) or to assess their ability to organize their schoolwork (SRL; Bandura, 2006). This is very different from EF measures that capture in-the moment regulation of cognitive processes that are frequently assessed using computerized tasks where a person’s performance is measured in terms of correct responses or reaction times required to inhibit an automatic response, such as to press a computer key on the same side of the keyboard as the stimulus on the screen (e.g., the Amsterdam Neuropsychological Tasks (ANT); De Sonneville, 1999). A recent meta-analysis explored the association between introspective self-reports of self-control and reaction-time based tests of executive functioning, both of which are very focused on inhibitory control. Saunders et al. (2018) analyzed five datasets (N= 2641) and found that the field’s most widely used measure of self-control (The Self-Control Scale; Tangney et al., 2004) is unrelated to the most widely used measure of EF (Stroop; Stroop, 1935). The authors emphasized that while this finding means that conclusions drawn from studies using one type of measure cannot be generalized to findings using the other, the lack of a relation between the two conceptions of self-regulation does not invalidate either construct. Rather, such findings indicate that clearer differentiation needs to be made between different conceptions and measures of self-regulation and the implications each has for adaptive functioning.

Similarly, Duckworth and Kern (2011) conducted a meta-analysis of convergent validity by analyzing 282 studies that included at least two different measures of self-control, including EF measures and questionnaires. They reported small but statistically significant convergence between measures of self-control that were operationalized by self-report, other informants, or typical EF measures, noting strong evidence for convergent validity among questionnaire measures but low convergent validity among EF tasks. Consistent with other researchers, Duckworth and Kern (2011) concluded that self-control is a multidimensional construct that requires multiple assessment methods and approaches and that the optimal strategy for research is to include both task and questionnaire-based measures of self-regulation.

Finally, there are multiple questionnaires intended to capture the same underlying EFs as task-based measures do. However, even the relations between these two types of EF measures, questionnaires and reaction time-based assessments, are frequently limited (Toplak et al., 2013; Zelazo et al., 2017). Zelazo et al. (2017) conclude that:

…at the present time, it seems best to conclude that questionnaires and performance-based measures of EF measure different phenomena. Although these methods of assessment may yield complementary data, they certainly do not yield interchangeable data. EF questionnaires index parental and teacher impressions of children’s behaviors in everyday contexts. It is not clear whether these behaviors necessarily represent manifestations of underlying cognitive skills (behaviors are multiply determined). In contrast, performance-based EF tasks index a child’s EF skills under optimal testing conditions and may not be indicative of a child’s typical use of those skills in everyday contexts. (p. 37)

Interestingly, in spite of recent studies, and even meta-analyses, that compared the overlap between EFs and other conceptions of self-control or self-regulation, few studies have been published on the relation between EFs and ISR. Associations between EFs and SRL have been reported (e.g., Effeney et al., 2013; Garner, 2009), however, in those studies, EF has typically been measured by behavioral ratings. As reflected by the limited overlap between task- and questionnaire-based measures of EF discussed above, while these behaviors may imply the use of EF skills, they are not the same as those skills (Zelazo et al., 2017). Consequently, the specific relation between the cognitive control mechanisms of EF, and the mindful, longer-term regulation involved in ISR, remains largely unexplored. Such research is critical to understanding the positive development of youth, as both forms of self-regulation have been associated with various developmental outcomes.

The Current Study

The aim of this study is to expand current conceptual understanding of self-regulation and its role in the healthy functioning of youth. We achieve this aim by broadening the focus of previous studies of ISR and PYD and include another important conception of self-regulation, EF, and a wider range of indicators of healthy and problematic functioning.

It is evident that both ISR and EFs play an important role in youth’s successful adaption in a wide range of contexts. However, although EF is widely regarded as an important foundation of ISR, our understanding of the relation between these two constructs, and the uniqueness of EF and ISR skills in predicting different areas of healthy functioning, is limited, if not non-existent. As such, the current study assessed the association between ISR and EF and examined how each independently predicted key developmental outcomes in early adolescence in Iceland, both concurrently in Grade 6 and longitudinally from Grade 6 to Grade 7. We used two measures of ISR, the general measure of SOC (Freund & Baltes, 2002) and a domain-specific measure of self-regulated learning (SRL; Bandura, 2006). As frequently recommended, two measures were included for each EF variable (attention shifting, inhibition, and working memory): accuracy and reaction time (Best et al., 2011). To provide a comprehensive picture of the role that different conceptions of self-regulation may have in adolescent functioning, we included a wide range of outcomes that are important to support the thriving of youth: positive youth development, risk behaviors, symptoms of depression and anxiety, and indicators of academic achievement.

The following research questions guided the current investigation:

1. What is the relation between measures of ISR and EFs? To answer this question, SOC and SRL were related to three measures of EFs in sixth grade. No prior empirical research exists on the relation between EFs and ISR but, due to previous research examining questionnaire- and task-based measures of self-regulation, we expected the two constructs to be weakly correlated.

2. What are the concurrent and longitudinal relations of ISR and EF to indicators of PYD, problematic behaviors, and academic achievement? Based on prior research, both ISR measures were expected to relate positively to PYD and academic achievement and negatively to problems. The EF measures were also expected to correlate with all outcomes, but most strongly to indicators of academic achievement.

3. What is the unique predictive power of ISR versus EFs in PYD, problematic outcomes, and academic achievement? To answer this question, we fit linear regression models in which measures of ISR and EF in Grade 6 simultaneously predicted measures of PYD, symptoms of depression and anxiety, risk behaviors, and academic achievement in Grades 6 and 7. Paralleling our second hypothesis, we anticipated that ISR would predict PYD, problematic outcomes, and academic achievement in Grades 6 and 7, over and above EF. We also expected that EF would predict academic achievement in Grades 6 and 7, over and above ISR, but would be a less consistent unique predictor of PYD and problematic outcomes once the effects of ISR were taken into an account.

Method

Participants

Ninety-four children (48 girls and 46 boys) participated in the present study. Of the 94 children, 10 had been diagnosed with learning or developmental disabilities. As conditions such as ADHD or autism may influence relations between the variables under investigation (see e.g., Mash & Barkley, 2014), these children were excluded from further analyses such that 84 children remained in our final sample. The children were recruited from two schools and seven classrooms in Iceland’s capital city (Reykjavík) and were enrolled in grades 6 (mean age was 11.72 years, SD = 0.30) and 7 (mean age, 12.73, SD = 0.30) at time of testing. In each school there was one teacher per classroom. The majority of children came from high-middle class background and were monolingual Icelandic speakers (81%). Parents of 95% of the children provided background information. The mothers generally had high education levels, with over half having obtained a university degree (70%), 15% had completed secondary education, 6% had received vocational training and 9% had completed compulsory education.

Procedure

Data on ISR, EF and outcomes (grades, PYD, risk behaviors, and symptoms of depression and anxiety) were collected in the spring term of Grade 6. Outcome variables were collected again a year later. Data were collected during school hours by trained study staff in three separate school visits at Wave 1, and two school visits at Wave 2. A computerized test battery was used to assess executive function skills (see the Measures section below) and was administered individually in a single session. Self-report measures were obtained in a group setting in the children’s classroom in two 40-minute school visits, supervised by trained research staff. If children were absent during school visits, their teachers were asked to assist them later in completing the self-report questionnaires, which were then collected by study staff. Grades from national standardized achievement tests (in math and reading comprehension) were obtained from the Directorate of Education in Iceland when the children were in Grade 7. Grades from national tests were available for 85% of the participating children. In addition, information on various background variables (obtained from parents, teachers, and the children themselves) were available for analysis.

Active, informed consent was collected from parents, obtained in collaboration with school administrators. Verbal assent was obtained from child participants. The study was reviewed and approved by the Icelandic Data Protection Authority. Each participating class received a collection of books for the school library, and the classroom teachers received a book or a small gift for their participation.

Measures

Executive Functions

Selected parts of the Amsterdam Neuropsychological Tasks (ANT; De Sonneville, 1999, 2014) were administered to assess executive function skills. The ANT is a computerized neurocognitive test battery that is widely used for experimental purposes. The test battery has been shown to be valid, sensitive, and suitable for the assessment of a wide age-range (pre-school to adulthood) (see De Sonneville, 2014, for a review). Icelandic norms are not yet available, but the tasks can be used with and without age norms for research purposes. In the current study, executive functioning was operationalized into three subcomponents: Working memory, Inhibitory control, and Attentional shifting.

The tasks were presented on a laptop and administered by trained study staff in a quiet test area in the child’s school. Verbal instructions were given, with an example of the task displayed on the screen. Following practice runs to familiarize the child to the task, a test trial started. The children were instructed to respond as fast and as accurately as possible. Responses were made by clicking keys on the laptop. The order of task presentation was fixed.

Working Memory was assessed with the Memory Search 2D stimuli task of the ANT. The memory set size consisted of three colored targets in different shapes (e.g., red circle, green square) that were presented on the screen. Four 2-dimensional symbols in various shapes and colors were then presented on the corners of a virtual square. The child had to detect whether one of the predefined targets was among them, as quickly and accurately as possible. A total of 48 test runs were conducted. Both speed (mean reaction time in milliseconds on correct responses) and accuracy were used as a measure of performance on this task.

The Response Organization Objects (ROO) task of the ANT was used to evaluate both Inhibition and Attentional shifting. In the first part of the ROO task (Condition 1, the compatible part) a white fixation cross was presented on the screen. The child was asked to respond to a colored circle presented on either side of the cross, by executing a compatible response. Performance in the second part of the ROO task (Condition 2, the incompatible part) was used to assess Inhibition. A differently colored circle was now presented on the computer screen and the child instructed to press opposite keys. Here the reaction pattern was reversed and the child was required to execute an incompatible response to the one in Condition 1.

Performance in Condition three of the ROO task was used to measure Attentional shifting. In this condition, trials from Condition one and Condition two were mixed at random. The circles were shown on the left or on the right side of the screen, but they also changed color, thus requiring the child to switch between the two types of response sets. Each condition consisted of approximately 30 test runs.

The outcome measures for both Inhibition and Attentional Shifting were mean reaction time in milliseconds, calculated by subtracting the response time from Condition one of the ROO from the more difficult versions of the task (Conditions two and 3), and accuracy (number of correct responses). Thus, lower reaction times but higher accuracy scores indicated better executive functioning.

Intentional Self-Regulation

ISR was assessed by two measures, a global ISR measure of Selection, Optimization, and Compensation (SOC) and a domain-specific self-regulated learning measure (SRL).

Selection, Optimization, and Compensation. A nine-item version of the Selection, Optimization, and Compensation (SOC) questionnaire was used to assess intentional self-regulation (Freund & Baltes, 2002; Gestsdóttir et al., 2011). Respondents were asked how they decide what is important to them in life and how they go about achieving the goals they have set for themselves by answering items on a Likert type binary scale ranging from 1 (Not at all like me) to 5 (Just like me). An example of a SOC item is “I make every effort to achieve a given goal”. The nine-item version of the scale been translated and adjusted for use in Icelandic context and has shown good reliability and validity among Icelandic adolescents (Stefánsson et al., 2014; Stefansson et al., 2018). Cronbach’s Alpha for SOC in the present study was 0.84 in Grade 6 and 0.84 in Grade 7.

Self-Regulated Learning. Self-regulated learning was assessed with Bandura’s (2006) Self-efficacy for Self-regulated Learning. Participants rated their degree of confidence in doing various things that can be difficult for students, such as “Get myself to study when there are other interesting things to do” and “Always concentrate on school subjects during class”. Responses were made on a five-point scale ranging from 1 (Can’t do) to 5 (Certainly can do). The scale is widely used, and various studies have found it to be reliable and valid (Usher & Pajares, 2008; Zimmerman & Martinez-Pons, 1988). The measure was translated into Icelandic by two independent translators, and the two translations then reconciled by a researcher fluent in both languages and an expert in self-regulation theory. The scale was pretested with 40 Grade-6 students from a single school. The alpha coefficient obtained for the scale in the present study was 0.84 in Grade 6 and 0.83 in Grade 7. The SRL scale also correlated highly with the other ISR measure used in the current study (SOC; r= .59), supporting the construct validity of the Icelandic translation of the SRL scale.

Positive Youth Development

A 17-item version (the very short version, PVD-VSF; Geldhof et al., 2014) of the Positive Youth Development scale was used as a measure of positive youth development. The PVD-VSF measure includes items from all the subscales of the original scale assessing the five central dimensions (the Five Cs) of positive youth development: Competence, Confidence, Character, Connection, and Caring (Lerner et al., 2005). “I can ask adults for help” is a sample item for the Connection part of the PYD scale. The original scale has been translated and adjusted for use for Icelandic adolescents and demonstrated good reliability and validity (Gestsdottir et al., 2017). In the current study, scores for the individual Cs were summed for a composite PYD score (Cronbach’s alpha = 0.78 in Grade 6 and = 0.84 Grade 7).

Depression Symptoms

A shortened version of the Center for Epidemiological Studies Depression scale (CESD; Radloff, 1977) was used to assess depressive symptoms. The children reported on how often they had experienced particular symptoms during the past week using a four-point scale that ranged from 1 (None of the time) to 4 (Most of the time). Example items included: “I felt sad” and “I felt that everything I did was an effort”. Six items were summed for a total score, with higher scores indicating higher levels of depression symptoms. These items were translated into Icelandic by two independent translators, and the two translations then reconciled by a clinical psychologist fluent in both languages. The scale was pre-tested with 40 Grade-6 students from a single school. Cronbach’s alpha for the overall composite was 0.64 in Grade 6 and 0.83 in Grade 7.

Anxiety Symptoms

Symptoms of anxiety were assessed with items drawn from the Anxiety subscale of the Symptom Checklist-90-R (SCL-90-R), a widely used self-report measure (Derogatis & Savitz, 2000). Children indicated how often they had experienced particular symptoms over the past week by responding on a five-point scale ranging from 1 (Almost never) to 5 (Almost always). Example items include “How often did you experience a sudden feeling of being scared during the past week” or “How often did you experience nervousness during the past week”. The total sum of items was used as a single indicator estimate of the presence of anxiety, with higher scores indicating greater distress. Translation and pre-testing of the scale followed the same procedure as for the items assessing depression symptoms above. Cronbach’s alpha for the anxiety subscale of the SCL-90-R in the present study was 0.58 in Grade 6 and 0.85 in Grade 7.

Risk Behaviors

Four items from the European School Survey Project on Alcohol and other Drugs (ESPAD; Hibell et al., 2009) and five items from the risk-behavior scale on the ASEBA Youth self-report form (Achenbach, 1991) were used to measure risk behaviors. These items assess the frequency of alcohol and substance use and how many times the children have skipped classes, broken rules, or stolen something (i.e., frequency of delinquent behaviors). The ASEBA-YSR has been translated and published in Iceland and has shown good reliability and validity (Gudmundsson, 2006). The response format for these items ranged from 1 (Never) to 5 (10 or more times). Items were summed for a total score, with higher scores indicating higher levels of risk behavior. Cronbach’s alpha for substance use and delinquency behavior was 0.64 in Grade 6 and 0.61 in Grade 7.

Academic Achievement

Academic achievement was assessed via self-report in Grade 6 and by accessing grades from national standardized achievement tests in Grade 7. The children reported their grade point average (GPA) on a scale of 1–10 in an open-ended format. Grades from national tests in mathematics and reading comprehension were retrieved from the Directorate of Education in Iceland. The institute conducts yearly comparison exams in Mathematics and Icelandic. The exams are curriculum-based and administered simultaneously in all regular schools in Iceland under standardized testing procedures. The test of mathematical ability is comprised of measures of arithmetic skill, operations, geometry, and numerical aptitude. The reading comprehension test includes three texts (two narratives and one information text) of varying length and difficulty, each followed by multiple-choice questions. Grades from the national exams are normally distributed, ranging from 0 to 60, with an average score of 30 (SD = 10).

Covariates

Information about gender and age of the children and the educational level of their mothers was collected at the outset of the study and used as covariates in the present analysis.

Analytic Plan

Because missingness was relatively low in the present data (6.10%), we performed all analyses using a single dataset in which missing values were imputed using the EM algorithm. Of the 13 outcome variables examined in the present data, missingness was not significantly correlated with maternal education, child gender, child age, or which school the child came from. The only exceptions were that Grade 7 national test scores were significantly correlated with which school a child attended. The data imputation model included maternal education, child gender, child age, and which school the child came from, and these variables were also included as covariates in the model. Because children came only from two schools, the binary indicator of school also served as a fixed effect that accommodated nesting within schools.

Our first research question centered on determining the correlation between ISR and EF after controlling for gender, age, and maternal education. To answer this question, we examined partial correlations between all indicators of ISR and EF, controlling for the effects of gender, age, maternal education, and which school the child was enrolled in. Partial correlations have the same statistical significance as regression coefficients (Cohen et al., 2003) but have the interpretational advantage of not requiring the researcher to formally specify which variable is a predictor versus an outcome. Because we interpreted both ISR and EF as multidimensional concepts rather than unidimensional constructs as might be represented by latent factors, and because of the small sample size, examining semi-partial correlations was also advantageous over more-sophisticated approaches such as Confirmatory Factor Analysis.

Our second research question followed the same general format of the first, requiring us to examine the (partial) bivariate associations between indicators of self-regulation (ISR and EF) and key developmental outcomes. For the same reasons listed above, we examined partial correlations between the self-regulation indicators and measures of positive youth development, anxiety, depressive symptoms, and academic achievement. When assessing associations with developmental outcomes measured in Grade 7, we additionally partialled out the effect of the same construct measured the year prior.

The last research question asked whether EF or ISR uniquely (i.e., after controlling for the other) predicted key developmental outcomes. Due to the predictive nature of this hypothesis, we fit OLS regression models and assessed the unique variance explained by each set of predictors (i.e., EF vs. ISR) after controlling for the other. Stated more precisely, the unique effect of ISR was determined by examining the increase in R² between a model that contained only covariates and EF measures as predictors and a larger model that included covariates, indicators of EF, and indicators of ISR as predictors. Conversely, the unique effect of EF was assessed by comparing a model that contained covariates and measures of ISR as predictors to a larger model that additionally contained indicators as EF as predictors.

We focus our interpretation of the regression models on changes in R², which allowed us to assess the joint significance of all construct-relevant indicators rather than relying on partial regression coefficients for each indicator. As compared to coefficient-specific p values associated with Type I sums of squares, the change in R² related to a set of predictors does not depend on the order the predictors within a given set (e.g., all items measuring EF) were entered into the model. Conversely, coefficient-specific p values associated with Type III sums of squares may miss important variance explained by the overlap shared among multiple correlated predictors (e.g., multiple indicators of EF).

Results

Table 1 provides descriptive statistics for all target predictors and outcomes, including means, standard deviations, and percent missing. Table 2 contains partial correlations among the self-regulation measures (controlling for gender, age, and maternal education) and therefore addresses our first research question. As the table shows, the results indicate significant positive associations among same-type items (i.e., among the two ISR measures, among EF items assessing reaction time, and among EF items assessing accuracy). Correlations between EFs and the ISR assessments were generally not significant, although working memory reaction times were significantly and negatively correlated with both ISR measures. Because lower reaction times presumably indicate greater executive functioning, the observed negative correlations provide evidence that working memory is positively associated with intentional self-regulation.

Table 1.

Descriptive Statistics for Predictors and Outcomes (N = 84).

Variable	Mean	Std Dev (%)	Missing (%)
Grade 6
WM (# correct)	−0.17	0.17	0.00
WM (reaction time)	4851.51	1241.64	1.19
Inhibition (# correct)	29.13	1.033	1.19
Inhibition (reaction time)	109.38	58.86	1.19
Switch (# correct)	28.24	1.29	1.19
Switch (reaction time)	317.36	130.02	1.19
SOC	35.28	5.90	4.76
Self-regulated learning	23.60	4.92	3.57
Self- reported grades	8.77	0.72	4.76
Positive youth development	66.93	6.55	16.67
Risk behaviors	11.67	2.86	2.38
Depression symptoms	10.17	2.90	9.52
Anxiety symptoms	4.91	1.57	2.38
Grade 7
National test: Reading comp	38.49	9.46	15.48
National test: Math	35.06	7.32	15.48
Self-reported grades	8.57	0.74	9.52
Positive youth development	66.37	7.91	20.24
Risk behaviors	12.03	2.96	9.52
Depression symptoms	9.54	3.72	11.90
Anxiety symptoms	5.03	2.29	8.33

Note. WM = Working Memory; Switch = Attention Switching; SOC = Selection, Optimization, and Compensation.

Table 2.

Partial Correlations Among Self-Regulation Measures.

	1	2	3	4	5	6	7	8
1. WM (# correct)	1.00
2. WM (reaction time)	.13	1.00
3. Inhibition (# correct)	.09	.14	1.00
4. Inhibition (react. time)	−.04	.15	−.25*	1.00
5. Switch (# correct)	.19	.10	.40***	.03	1.00
6. Switch (reaction time)	−.08	.31**	−.06	.68***	−.09	1.00
7. SOC	−.17	−.22	.05	.14	.13	.04	1.00
8. Self-Regulated learning	−.20	−.36**	−.12	.18	−.04	.13	.61***	1.00

Note. WM = Working Memory; Switch = Attention Switching; SOC = Selection, Optimization, and Compensation. Gender, Age, Maternal Education, and School Attended were controlled.

*p < .05; **p < .01; ***p < .001.

The partial correlations in Tables 3 and 4 help answer our second research question and indicate that the ISR items were more consistently associated with the outcomes than were the EF items. These criterion correlations were especially strong for the SRL measure and the Grade 6 outcomes. Still, some EF measures correlated with some outcomes, especially when measured in Grade 7, where longer reaction times were positively associated with increase in risk behaviors (WM and Inhibition), symptoms of depression (WM) and academic achievement (switch attention). Furthermore, as discussed under limitations, reaction times for the more-inhibitory tasks (i.e., inhibition and attention switching) were also negatively correlated with Grade 7 depression and Grade 6 anxiety.

Table 3.

Partial Correlations Between Self-Regulation Measures and Grade 6 Outcomes.

	PYD	Risk	Depression	Anxiety	Grades
1. WM (# correct)	−.16	.03	−.07	.12	.09
2. WM (reaction time)	−.24*	.05	.05	.27*	.15
3. Inhibition (# correct)	−.08	−.09	.13	.14	−.04
4. Inhibition (reaction time)	.19	.13	−.14	−.25*	.02
5. Switch (# correct)	−.07	−.04	.04	.09	−.19
6. Switch (reaction time)	.10	.10	−.12	−.12	.14
7. SOC	.62***	−.16	−.23*	−.28*	.13
8. Self-Regulated learning	.72***	−.26*	−.30**	−.46***	.36**

Note. WM = Working Memory; SOC = Selection, Optimization, and Compensation; PYD = Positive Youth Development. Grades were self-reported. Gender, Age, Maternal Education, and School Attended were controlled.

*p < .05; **p < .01; ***p < .001.

Table 4.

Partial Correlations Between Self-Regulation Measures and Grade 7 Outcomes.

	Reading	Math	PYD	Risk	Depression	Anxiety
1. WM (# correct)	.14	.14	−.22	.11	.22	−.17
2. WM (reaction time)	.00	−.21	−.18	.29**	.30**	.07
3. Inhibition (# correct)	−.10	−.19	−.09	.04	.04	.10
4. Inhibition (reaction time)	−.12	−.14	.13	.22*	−.27**	.09
5. Switch (# correct)	.06	.01	−.07	.04	.07	.02
6. Switch (reaction time)	−.25*	−.23*	.17	.15	−.24*	−.01
7. SOC	−.03	.02	.34*	−.43***	−.22	.14
8. Self-Regulated learning	.14	.14	.37***	−.16	−.27*	.17

Math and Reading represent scores from national standardized tests. Gender, Age, Maternal Education, and School Attended were controlled. Separate models were run for each Grade 7 outcome in a way that controlled for the same outcome as measured in Grade 6.

Note. WM = Working Memory; SOC = Selection, Optimization, and Compensation; PYD = Positive Youth Development.

*p < .05; **p < .01; ***p < .001.

Last, we tested the additive effects of EF versus ISR using regression models that allowed us to examine changes in R². We provide results from the multiple regression models in Table 5, where entries for the complete model indicate the total R² explained by all predictors, entries for the unique effect of ISR indicate the difference in R² between a model that did versus did not contain measures of ISR as predictors, and entries for the unique effect of EF indicate the difference in R² between a model that did versus did not contain measures of EF as predictors.

Table 5.

Unique Predictive Power for Intentional Self-Regulation versus Executive Function (N = 84).

	R²/ΔR²	Significance	Removed # of Outliers
Grade 6 PYD			0
Complete model	.60
Unique effect of ISR	.43	F (2,71) = 38.25, p < .001
Unique effect of EF	.02	F (6,71) = 0.38, p = .89
Grade 6 risk behaviors			0
Complete model	.27
Unique effect of ISR	.07	F (2,71) = 3.54, p = .03
Unique effect of EF	.04	F (6,71) = 0.62, p = .71
Grade 6 depression symptoms			3
Complete model	.36
Unique effect of ISR	.17	F (2,68) = 8.96, p < .001
Unique effect of EF	.14	F (6,68) = 2.47, p = .03
Grade 6 anxiety symptoms			2
Complete model	.34
Unique effect of ISR	.12	F (2,69) = 6.34, p = .003
Unique effect of EF	.06	F (6,69) = 1.00, p = .43
Grade 6 self-reported grades			1
Complete model	.27
Unique effect of ISR	.16	F (2,70) = 7.65, p = .001
Unique effect of EF	.16	F (6,70) = 2.56, p = .03
Grade 7 national reading test			2
Complete model	.36
Unique effect of ISR	.03	F (2,68) = 1.44, p = .24
Unique effect of EF	.19	F (6,68) = 3.39, p = .006
Grade 7 national math test			0
Complete model	.39
Unique effect of ISR	.02	F (2,70) = 1.37, p = .26
Unique effect of EF	.13	F (6,70) = 2.41, p = .04
Grade 7 PYD			1
Complete model	.59
Unique effect of ISR	.08	F (2,69) = 6.89, p = .002
Unique effect of EF	.04	F (6,69) = 1.26, p = .29
Grade 7 risk behaviors			1
Complete model	.60
Unique effect of ISR	.13	F (2,26) = 10.79, p < .001
Unique effect of EF	.09	F (6,69) = 2.51, p = .03
Grade 7 depression symptoms			1
Complete model	.61
Unique effect of ISR	.01	F (2,69) = 0.69, p = .50
Unique effect of EF	.12	F (6,69) = 3.53, p = .004
Grade 7 anxiety symptoms			5
Complete model	.45
Unique effect of ISR	.03	F (2,65) = 1.84, p = .17
Unique effect of EF	.13	F (6,65) = 2.65, p = .02

Note. Gender, Age, Maternal Education, and School Attended were controlled. For Grade 7 variables with direct analogues measured in grade 6, autoregressive effects are also included in the model. For national test scores, we controlled for self-reported grades assessed in Grade 6.

As the Table shows, ISR tended to predict outcomes measured in Grade 6 when controlling for the EF measures, but the inverse was not typically true. When there was a significant effect for EF, the overall changes in R² per added predictor tended to be smaller than the changes associated with the two measures of ISR. The findings for Grade 7 outcomes were less clearly biased in favor of ISR, however. For example, EF but not ISR significantly predicted national test scores, Grade 7 depressive symptoms, and Grade 7 anxiety symptoms. ISR remained a stronger predictor of Grade 7 PYD and risk behavior. One reason these results may have been less consistent than those we observed for the Grade 6 outcomes is that the analyses for Grade 7 outcomes controlled for prior standing on the constructs of interest.

We examined outlier diagnostics for each complete regression model and report the number of outliers removed from each model in Table 5.

Discussion

Consistent with relational developmental systems metatheory, self-regulation has received enormous attention from researchers, and programs supporting self-regulation have been proposed as a fruitful way to support the healthy development of children and adolescents. There has been considerable support for the role of ISR in promoting the healthy development of youth, and a large literature suggests that EFs are related to a range of developmental outcomes, particularly academic achievement. However, the literature examining these two approaches to self-regulation remain largely disconnected. The goal of the current study was to clarify the nature and role of self-regulation in early adolescence by providing an assessment of the relation between ISR and EFs and their predictive relations to a wide range of developmental outcomes. The findings suggested that there was a relation between EFs, at least working memory reaction times, and the mindful, longer-term regulation involved in ISR, but that this relation was limited. In addition, ISR tended to uniquely predict outcomes measured in Grade 6 when controlling for the EF measures, but the inverse was not typically true. The findings for Grade 7 outcomes were less clearly biased in favor of ISR. For example, EF but not ISR uniquely predicted national test scores, and symptoms of depression and anxiety, but ISR remained a stronger predictor of Grade 7 PYD and risk behavior.

Are Intentional Self-Regulation and Executive Functions Related?

In this study, we explored relations between two conceptions of self-regulation, the long-term goal-setting ISR that is typically characteristic to adolescence and adulthood, and the in-the moment control captured by EFs. As we are not familiar with studies that directly compare these constructs, as defined and measured in this study, this question was exploratory by nature. The correlations between ISR and EF showed that neither measure of switch attention or inhibition (reaction time and accuracy) were related to the two ISR measures. Working memory reaction times, but not accuracy, were related to both SOC and SRL, though. This finding suggests limited overlap between cognitive, in-the-moment regulation of EF and longer-term ISR processes during adolescence. Although EFs may be precursors of ISR skills, successful ISR therefore is likely influenced by other less-cognitive skills such as motivation, self-evaluations, and forethought, than by the temporary control of attention and inhibitory control.

In light of the conceptual overlap between each of the EFs and ISR as explained earlier, the finding that only working memory was clearly related to our ISR measures was not expected but is nevertheless consistent with a large literature demonstrating the importance of working memory for various developmental outcomes (Best et al., 2011; Pharo et al., 2011; Romer et al., 2011; St Clair-Thompson & Gathercole, 2006).

How do Intentional Self-Regulation and Executive Functions Relate to Positive Youth Development, Problems, and Academic Achievement?

The associations linking EFs and ISR with our selected outcome measures suggests a more significant contribution of ISR to those outcomes, at least in the short term and when the associations do not control for prior levels of the outcomes. In Grade 6, partial correlations showed that one or both ISR measures (SOC and SRL) were related to all outcome measures (PYD, risk, symptoms of depression and anxiety, and self-reported grades), which is consistent with many prior studies of ISR, PYD, and risk (see e.g., Geldhof et al., 2015; Gestsdottir et al., 2009, 2015; Lerner et al., 2005; Zimmerman et al., 2008). Concurrent partial associations between the EF measures and the outcomes were more limited in Grade 6, however, where slower WM reaction times predicted lower levels of PYD and more frequent symptoms of anxiety.

In Grade 7, and after controlling for prior levels of the target constructs, the magnitude of observed associations between the ISR measures and outcomes tended to attenuate. An exception was the correlation between SOC and risk behavior, which became significant in Grade 7. This finding indicates that although deficits in ISR may not characterize youth exhibiting early signs of risk behavior in Grade 6, they can play a predictive role in the development of such problems. The findings further revealed that EF (attention switching reaction time) predicted growth in academic achievement, and reaction time aspects of working memory and inhibition also predicted increase in problematic outcomes (i.e., risk, and symptoms of depression) from Grade 6 to 7, but not concurrent levels of these outcomes in Grade 6. As such, at the onset of adolescence, deficits in EF may be stronger predictors of the development of risk behavior and internalizing symptoms than of concurrent levels. Han et al. (2016) reported comparable findings using a slightly older sample (11–16-year-olds) where cognitive flexibility deficits predicted an increase in anxiety symptoms over time, but not concurrent levels among 11–16-year-old adolescencts. As discussed, however, due to the scarcity of studies exploring longitudinal associations between EF and internalizing symptoms in adolescence, further research is needed to cast light on the exact role EF plays in the emergence of internalizing symptoms.

Findings from regression models that examined the unique prediction of ISR (both measures) versus EFs (all measures) of the outcome measures showed a similar pattern to that observed for the partial correlations. In Grade 6, ISR had unique effects on all outcomes. However, parallel to the findings of the bivariate analyses discussed above, the strength of these associations tended to drop in Grade 7. We interpret these drops as being related to the inclusion of autoregressive effects. As with the correlations, a major exception was that the association between ISR and risk behavior increased in magnitude (change in R²) in Grade 7. In addition, and similar to the findings of the bivariate analyses discussed above, the unique effects of EFs tended to be more prominent in Grade 7 than in Grade 6, with EF (but not ISR) predicting national test scores, and symptoms of depression and anxiety. ISR remained a stronger predictor of Grade 7 PYD and risk behavior, however.

When comparing the relations that the two ISR measures (SOC and SRL) had to the outcomes, SOC tended to be positively related to PYD and negatively related to measures of risk behaviors and problems (symptoms of depression and anxiety), but had no relations to indicators of academic achievement. This is consistent with ISR as primarily a catalyst of healthy development that is in line with personal goals, rather than supporting outcomes on academic tasks (Baltes, 1997; Gestsdottir et al., 2017). Also consistent with theory, SRL had clearer relations than SOC to self-reported grades in the Grade 6. Less obvious is the more consistent relation of SRL to all outcome measures, as compared to SOC. One reason for this finding may be that youth have had more opportunities to set and pursue long-term academic goals as compared to goals in other areas of functioning or general life goals. As such, the domain specific SRL skills may have developed more than the skills needed for long-term general SOC regulation. In addition, the SRL measure may be easier to answer for early adolescents than the global SOC measure, as the former asks about academic topics and behaviors that are quite specific. This would also be consistent with theories of cognitive development, as youth, who are entering Piaget’s (1936) stage of formal operation, may find it easier to answer specific, less abstract questions about goal setting and pursuit in relation to school than questions that do not relate to goals within a specific domain of functioning such as those specified in the SOC measure. If so, it is important to follow up this study with a study later in adolescence where SOC may become a more valid and reliable indicator of ISR.

Limitations and Future Directions

Several things should be kept in mind when interpreting the above results. First, the Icelandic population is quite homogenous with a relatively high educational level and financial equality (Ólafsson & Kristjánsson, 2017). Consequently, the findings need to be replicated across more diverse cultures. However, similar to the US and many parts of Europe, Iceland is embedded in a Western sociocultural orientation where autonomy is highly valued. This orientation prioritizes, for example, support for independent problem solving, choice, and participation of the child in decisions (Grolnick & Ryan, 1989; Rimm-Kaufman & Wanless, 2012). Accordingly, teachers and parents typically support such independence by encouraging children’s autonomy and separateness, which are considered adaptive in Western cultures, especially among the urban middle class (Kagitcibasi, 1996). The cultural context in Iceland therefore shares important features with other Western societies that may influence self-regulatory skills and behaviors, supporting the generalizability of the results. In fact, a recent cross-cultural study among youth in Iceland and the U.S. provided support for invariance of the ISR-PYD model across the two cultural contexts, as well as partial strong measurement invariance for PYD and partial weak measurement invariance for ISR across cultures (see Gestsdottir et al., 2017). Thus, although cultural differences can always be expected, we are confident that the findings of the current study can provide information that can be affirmed in other cultural contexts.

Second, there were three unexpected correlations between the predictors and the outcomes. Interestingly, these correlations all related to the reaction times for the more-inhibitory tasks of EF (i.e., inhibition and attention switching), which were negatively correlated with problems (i.e. depression and anxiety). These findings are difficult to explain. It may be that the children who have a tendency towards depression and anxiety found the tasks demanding and therefore wanted to complete them as quickly as possible (the tasks were presented in a fixed order: working memory, inhibition and attention shifting). Of course, these are speculations and therefore it is important that the findings of the current study will be replicated and caution exercised when interpreting the EF findings. Further studies are also needed to shed light on situations in which longer reaction times may not reflect deficits in EF.

Third, we emphasize that EF is a complicated construct that can be measured in a myriad of ways. Consequently, before conclusions can be drawn about the associations between EF and ISR – or lack thereof – further studies are needed that employ a wider range of EF measures and a wider operationalizations of ISR. For example, the EF measure used in the current study assessed three foundational EF constructs separately. It is important to replicate the current findings using measures that assess more complex EFs, such as those that require the integrated use of working memory, inhibition, and attention, or other components of EF. In addition, recent conceptions of EF have emphasized that EFs may vary in motivational or affective significance, i.e., that EFs can be viewed as purely cognitive (as done in the current study), or as more “hot” or affective, when they involve stimuli or outcomes that are emotionally or motivationally charged (Prencipe et al., 2011). Using measures of hot EFs may yield different outcomes than observed in the current study, for example in relations to the ISR measures, which have a motivational component (e.g., the control of emotional responses). Furthermore, the present study cannot answer the degree that emotive/motivational factors differed between tasks in ways that could manifest in reaction times taking on task-specific meanings.

Last, the measurement strategy employed for assessing EF (observational) versus ISR (self-report) indicates the possibility of a method effect. It remains possible that the more elevated associations between self-reported ISR and self-reported outcomes (e.g., PYD, anxiety, depressive symptoms) reflects a method effect rather than actual and systematic associations at the construct level. Future work must therefore consider a wider array of measures before firm conclusions can be drawn about the relative independence of EF and ISR, as well as their unique associations with developmental outcomes, presented in the current study.

Conclusions

The current study suggests that although EFs may be viewed as precursors of the more complex ISR skills, EFs and ISR may be relatively independent of each other in early adolescence. Interestingly, the findings generally support some previous, well established findings from studies that examined ISR and EFs separately. Most notably, ISR continues to be a predictor of PYD and risk, when EF measures were added to the model, and predicted growth in PYD across the two grades, suggesting that the ISR-PYD model is not affected by the addition of EFs. EF, on the other hand, did not a emerge as a predictor of PYD when ISR is included in the model, whereas it had relations to growth in academic outcomes and internalizing problems. As such, we have taken a step toward creating a more comprehensive, although complex, understanding of self-regulation by casting light on the association between two key self-regulation processes, EFs and ISR, in early adolescence and how they relate to various aspects of young people’s well-being and problem behaviors.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the research grant no: 141483-051 from The Icelandic Research fund (RANNÍS) to Steinunn Gestsdottir and Freyja Birgisdottir.

ORCID iD

Steinunn Gestsdottir

Author Biographies

Steinunn Gestsdottir, Professor, University of Iceland, Department of Psychology. Research interests: development of self-regulation across the lifespan, executive functions, academic achievement and positive youth development.

G. John Geldhof, Associate Professor, Oregon State University, College Of Public Health and Human Sciences. Research interests: development of self-regulation across the lifespan and relations between intentional self-regulation and positive developmental outcomes.

Freyja Birgisdóttir, Associate Professor, University of Iceland, Department of Psychology. Research interests: Development of literacy and relations to other aspects of development, including executive functions, intentional self-regulation and motivation.

Johanna Cortes Andresdottir is a doctoral student at the University of Iceland.

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