Examining the Measures:
Review of Self-Reg Components Captured in Current Measures Labelled “Self-Regulation”
Daisy Pyman HBA and Brenda Smith-Chant PhD
An examination of measures designed to assess self-regulation was undertaken to assess how well they are aligned with Shanker Self- Reg®. A literature search turned up 11 tools designed to provide measures of self-regulation. Analysis revealed that all tools primarily addressed prefrontal cortex functions and none adequately addressed all five domains of the Shanker Self-Reg® framework (biological, emotion, cognitive, social and prosocial). The cognitive domain was the focus of most of the tools reviewed, although the authors noted a historical trend towards conceptualizing self-regulation as a multi-domain concept. Full alignment with Self-Reg would require measures to address the dynamic nature of all five domains along with subcortical processes and the interplay between subcortical and neocortical systems and processes.
In Self-Reg, Shanker defines self-regulation as the ability to manage stressors across five domains: biological, emotion, cognitive, social, and prosocial, and then to subsequently recover (2016). This definition is based on the psychophysiological view of self-regulation developed by historic theorists and researchers through to current scientists (see Shanker, 2016 for a review). A stressor, by definition, is any stimulus that triggers a physiological response that serves to keep an internal homeostatic system (or systems) operating within its optimal functional range. The energy expended in such processes must be restored in order to avoid slipping into a state of allostatic overload, in which recovery is compromised and mood, behaviour, learning, and physical health are impaired.
One of the big challenges when doing research in this area is that there have been 447 different definitions of self-regulation (Burman, Green, and Shanker, 2015). These definitions belong to different disciplines (for example, education or mental health) that are focused on different contexts (for example, university learning or preschool behaviour). Many view self-regulation as a dispositional and domain-specific attribute (for example, a cognitive ability or set of cognitive abilities) that is relatively static (for example, emotional intelligence). Self-Reg, in contrast, sees the ability to manage stress as a fluid and dynamic process that is continually refined across the lifespan.
Measures developed to serve other disciplines often do not align with the psychophysiological view of self-regulation on which Self-Reg is based. The problem here is that those seeking to assess Self-Reg may gravitate towards published tools that are labelled as self-regulation, but are addressing something completely different. It is clear, then, that to advance the science of Self-Reg, we need to address the question: Are any of these tools, or a combination of these tools, or parts of these tools aligned with Shanker’s definition of self-regulation?
Measures of Self-Regulation and Self-Reg
To resolve this question, we undertook an analysis of the existing literature. The analysis involved an examination of measures that included the term self-regulation, self-regulating, self-regulatory, or self-regulated in the title. Using Google Scholar as our primary database, due to its comprehensiveness, we established inclusionary criteria for empirical research articles as follows: (a) the term self-regulation, or a variant (see above), must have been included in the title, (b) the paper had to have been published by more than one author or research group to eliminate any one-offs (tools developed for a single study but not used beyond), and (c) the measurement had to have been cited in an article within the last 20 years, to ensure that it was still in relatively current use. All articles were found using a search query for “self-regulation,” or a variant, in conjunction with one of the following terms: scale, inventory, assessment, or questionnaire. Measurements identified in this search were then submitted to an item-by-item analysis in order to identify which items, if any, tapped into biological, emotion, cognitive, social, and/or prosocial facets of Self-Reg. Additionally, the discipline or field for which the measure was primarily developed (for example, psycho-education, mental and physical health, development) was identified.
One limitation of this search was that biometric tools or assessments of stress and/or anxiety processes were not included in this analysis, although the theoretical underpinnings of Self-Reg lie in this area. For a review of biometric tools , including body- and brain-based measures, see “Measuring the Foundations of Self-Reg” (this volume). A review of tools that measure aspects of Self-Reg but do not use the term self-regulation is forthcoming.
Shanker (2016) identified five domains of stressors that require energy to be expended. We examined each of the measures to identify which domains of stressors were addressed by the assessment items. Items were identified as measuring biological stressors if they assessed either internal states (for example, quality of sleep, appetite and diet, feelings of illness/wellness, individual differences, disabilities) or potential sensory irritations (for example, eye fatigue, noise, visual screen-time, sensory processing issues). Items that assessed the experience of feeling or coping with emotion (for example, upset/unease, homesickness, test anxiety, excitement) were classified as tapping the emotion domain. By far the most often assessed was the cognitive domain. Items were identified as cognitive when they measured either cognitive processes (for example, attention, motivation, distractibility, metacognition) or thinking-based strategies or assessments (for example, self-efficacy, problem-solving, self-appraisals). The social domain included items that asked about the impact of interpersonal interactions on the individual (for example, personal and professional/educational relationships, social interactions with others, giving and receiving social cues). Finally, items were assessed as tapping the prosocial domain if they assessed the impact of empathy or social mores on the individual (for example, the impact of others’ stress or distress, feeling of societal expectations, cultural expectations). The items were classified according to domain by each author independently. Conflicts in the classification were resolved using consensus.
Measures were identified as “psycho-education” if they were designed to assess self-regulation skills in educational settings (for example, school, childcare, learning environments). Measures that were designed to measure self-regulation as a general life skill or for clinical populations (for example, those with addictions) were categorized as “mental health” tools. Finally, tools were categorized as Electrophysiology, T. F. o. t. E. S. o. C. t. N. A. S. o. P. (1996). Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. doi:10.1161/01.CIR.93.5.1043 “health” measures if they were designed for general medical use (for example, for those with illness or injury) or for use within fitness settings (for example, exercise, lifestyle). A summary of the domains of stressors assessed in each domain is outlined in Table 1.
Table 1: Representation of Shanker’s Five Domains of Self-Reg across Popular Self-Regulation Measures
Analysis of Measures
The analysis of the identified measures revealed a historical trend towards increasingly conceptualizing self-regulation as a multi-domain construct and considering the context of the behaviour within the tool. Measures developed in the 1980s focused predominantly on the metacognitive aspects of self-regulation alongside the application of cognitive strategies, both of which are cognitive domain factors. For example, in one of the first self-regulation measures developed by Zimmerman and colleague, the focus was solely on cognitive factors, such as self-control and self-monitoring abilities and beliefs, with little consideration of intra-personal factors impacting the ability to manage stressors and cope in a learning environment (Zimmerman & Martinez-Pons, 1988; the Self-Regulated Learning Interview Schedule).
As research on self-regulation advanced into the 1990s, the instruments that were developed started to hone in on measures of very specific cognitive sub-skills, as exemplified by the Self-Regulation Scale, which assesses attention-control (Diehl, Semegon, & Schwarzer, 2006), and the Self-Regulation Questionnaire (Brown et al., 1999), which assesses cognitive skills and abilities as a mental capacity across contexts. This focus on the cognitive domain is a fundamental feature underlying all the examined measures of self-regulation. One measure that does address the other domains is the Motivational Strategies for Learning Questionnaire (MSLQ), which measures cognitive factors in a learning context (for example, education), but considers factors impacting cognition across all five domains. For example, the confluence of the emotion and cognitive domains was tapped with Item Number 19 of the scale, which asked university students to respond on a likert scale whether they have, “an upset/uneasy feeling when [they] take an exam.” The impact of prosocial learning was assessed in the Item Number, “I think about how poorly I am doing compared to other students”. The limitation with the MSLQ is that the measure focuses predominantly on the cognitive impacts of these stressors, and pays little attention to the impacts of the other domains, where only one item of 81 itemed scale adequately assessed each of the remaining four domains. Moreover, the questionnaire is limited insofar as it conceptualizes cognition as an outcome and not a stressor in and of itself. Accordingly, while the measure considers multiple domains on the surface, it reflects a definition of self-regulation that is focused on managing or controlling stressors, rather than understanding the impact of stressors across domains or the impact of subcortical processes and conceptualizing “Self-Reg” through an understanding of the “triune brain.”
Many of the measures analyzed purport to measure biological/physiological, emotion, social, and prosocial factors (see, for example, the Self-Regulation Questionnaire-Prosocial). However, a review of these measures reflected only minimal or indirect assessments of these domains. For example, in the aforementioned Self-Regulation Questionnaire-Prosocial, stressors from the prosocial domain are only indirectly assessed by items asking about cognitive appraisals of this domain (for example, “Why do you keep a promise to a friend?”) with a choice selection provided for the rationale of the question. Here, the focus is on the thinking, or cognitive assessment, rather than the assessment of the prosocial factors that are impacting the individual. The minimal or indirect reflection of non-cognitive domains underplays not only the importance of considering stressors across domains, but also how these stressors impact coping and performance. Measures with this limitation are indicated in Table 1 using an asterisk.
One important observation that we made during our analysis is that these measures typically do not include items that look at self-regulation and the impact of stressors as a dynamic and evolving process. For example, few questionnaire items attempt to contextualize the impact of stressors across domains, such as indicating the impact of a biological stressor on problem-solving. One very rare exception from the Adolescent Self-Regulatory Inventory is “I have trouble getting excited about something that is really special when I am tired”, an item that looked at the interplay between emotion and biological factors.
Given that self-regulation in the reviewed measures refers to stress-management and is focused on cognitive performance as an outcome and not as a stressor as well, these tools do not reflect the Self-Reg model as conceptualized by Shanker (2016). A measure that is consistent with Self-Reg would require a five-domain model, not simply as some sort of additive measure, but rather to reflect how domains impinge on and amplify each other. For example, what might be an intolerable emotional or social stress when the individual is experiencing a period of low-energy (for example, fatigue) and high-tension (for example, high levels of stress overall) might be tolerable and even positively arousing when the individual has high levels of energy and low levels of tension. The context is an essential consideration and must be part of the assessment.
Many of the existing measures of self-regulation assess prefrontal cortical functions (cognitive processes such as mental strategies, planning, problem-solving, and self-monitoring). There are few measures that focus on subcortical systems (for example, stress responses). Self-Reg shows us that an adequate measurement of self-regulation must capture the interplay between neocortical and subcortical processes and the inter- (biological, emotion, and cognitive) and intra-processes (social and prosocial) impacting an individual.
Burman, J.T., Green, C.D., & Shanker, S. (2015). On the meanings of self-regulation: Digital humanities in service of conceptual clarity. Child Development, 86(5), 1507–1521. doi:10.1111/cdev.12395.
Brown, J.M., Miller, W.R., & Lawendowski, L.A. (1999). The Self-Regulation Questionnaire. in L. VandeCreek & T. L. Jackson (Eds.), Innovations in clinical practice: A source book (Vol. 17, pp. 281-289). Sarasota, FL: Professional Resource Press.
Carey, K.B., Neal, D.J., & Collins, S.E. (2004). A psychometric analysis of the self-regulation questionnaire. Addictive Behaviors, 29(2), 253–260. doi:10.1016/j.addbeh.2003.08.001
Cleary, T.J. (2006). The development and validation of the self-regulation strategy inventory—
Self-report. Journal of School Psychology, 44(4), 307–322. doi:10.1016/j.jsp.2006.05.002
Diehl, M., Semegon, A. B., & Schwarzer, R. (2006). Assessing attention control in goal pursuit: A component of dispositional self-regulation. Journal of Personality Assessment, 86(3), 306-317. doi: 10.1207/s15327752jpa8603_06
Hong, E., & O’Neil Jr., H.F. (2001). Construct validation of a trait self-regulation model. International Journal of Psychology, 36(3), 186–194. doi: 10.1080/00207590042000146
Moilanen, K.L. (2007). The adolescent self-regulatory inventory: The development and validation of a questionnaire of short-term and long-term self-regulation. Journal of Youth and Adolescence, 36(6), 835–848. doi:10.1007/s10964-006-9107-9
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. doi: 10.1037/0022-06188.8.131.52
Ryan, R.M., & Connell, J.P. (1989). Perceived locus of causality and internalization: Examining reasons for acting in two domains. Journal of Personality and Social Psychology, 57(5), 749. doi:10.1037/0022-
Schwarzer, R., Diehl, M., & Schmitz, G.S. (1999). Self-regulation scale. Retrieved from http://www.fu-berlin.de/gesund/skalen
Shanker, S. (2016). Self-Reg: How to help your child (and you) break the stress cycle and successfully engage with life. Toronto: Penguin Random House Canada.
Smith-Donald, R., Raver, C.C., Hayes, T., & Richardson, B. (2007). Preliminary construct and concurrent validity of the Preschool Self-Regulation Assessment (PSRA) for field-based research. Early Childhood Research Quarterly, 22(2), 173–187. doi:10.1016/j.ecresq.2007.01.002
Toering, T., Elferink-Gemser, M.T., Jonker, L., van Heuvelen, M.J., & Visscher, C. (2012). Measuring self-regulation in a learning context: Reliability and validity of the Self-Regulation of Learning Self-Report Scale (SRL-SRS). International Journal of Sport and Exercise Psychology, 10(1), 24–38. doi:10.1080/1612197X.2012.645132
Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80(3), 284-290. doi: 10.1037/0022-06184.108.40.2064