ORIGINAL RESEARCH published: 13 March 2020 doi: 10.3389/fpsyg.2020.00354 Young University Students’ Academic Self-Regulation Profiles and Their Associated Procrastination: Autonomous Functioning Requires Self-Regulated Operations Rafael Valenzuela 1 , Nuria Codina 1* , Isabel Castillo 2 and José Vicente Pestana 1 1 Department of Social Psychology and Quantitative Psychology, University of Barcelona, Barcelona, Spain, 2 Department of Social Psychology, University of Valencia, Valencia, Spain Students’ autonomous self-regulation requires not only self-motivation but also volition or transforming motivation into specific behavioral intentions and following through. Self-regulation includes self-motivation (i.e., goal setting, learning from mistakes) Edited by: and volitional regulation (i.e., strategic decision making). Furthermore, individual Miguel Melendro, differences, like trait-level perseverance, significantly influence both motivation and National University of Distance volition. Procrastination has been defined as a volitional self-regulation problem, which Education (UNED), Spain involves delaying what one had intended to do, in spite of being motivated, and Reviewed by: María Jesús Monteagudo, regardless of anticipating adverse consequences. Thus, it is a tendency toward University of Deusto, Spain dysregulated behavior - which may stabilize with age - in which subpar self- Piers Steel, University of Calgary, Canada regulation may lead to procrastination. As a form of dysregulation, procrastination *Correspondence: adversely affects young people’s autonomy and well-being by limiting their personal Nuria Codina growth. Previous research has confirmed a negative relationship between self- ncodina@ub.edu regulation and procrastination. However, more precision is demanded in: (a) examining Specialty section: the intertwined roles of motivational and volitional aspects of self-regulation for This article was submitted to procrastination, and (b) distinguishing between different medium, and between medium Educational Psychology, and high levels of self-regulation. Consequently, it has been suggested that this a section of the journal Frontiers in Psychology could be accomplished by means of person-centered analyses, aimed at identifying Received: 01 November 2019 distinct naturally occurring students’ self-regulation profiles. These profiles would Accepted: 14 February 2020 inform differentiated pedagogical approaches to promote self-regulation strategies Published: 13 March 2020 counteracting procrastination tendencies. We used cluster analysis to identify academic Citation: Valenzuela R, Codina N, Castillo I self-regulation profiles and analyze their relationships with procrastination. Participants and Pestana JV (2020) Young were 994 young university students from one public and one private university in University Students’ Academic Catalonia (41.0% men, 58.4% women, and 0.5% non-binary gender). Their age ranged Self-Regulation Profiles and Their Associated Procrastination: from 18 to 24 years (M = 19.69, SD = 1.41). Sampling method was intentional, with Autonomous Functioning Requires proportional quotas by sex, academic year, and area of knowledge. The instrument Self-Regulated Operations. Front. Psychol. 11:354. used for data collection incorporated the Short Spanish Self-Regulation Questionnaire doi: 10.3389/fpsyg.2020.00354 (SSSRQ), which includes four dimensions: perseverance, learning from mistakes, Frontiers in Psychology | www.frontiersin.org 1 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students goal setting, and decision making; and the Pure Procrastination Scale (PPS), which considers three dimensions: decisional procrastination, implemental delay and lateness. Results obtained by means of cluster analysis distinguished between high and low academic self-regulation profiles, and also between these and two different medium self- regulation profiles, each with specific emphases on particular volitional shortcomings (i.e., weaknesses in decision-making skills and perseverance). These profiles and their relations with procrastination dimensions allow a joint evaluation via structural equation modeling (SEM) to test cognitive motivational strategies (goal setting, decision making, learning from mistakes, and decisional procrastination) together with behavioral aspects (perseverance, implemental delay), considered in the constructs of academic self-regulation and pure procrastination. From this joint evaluation, guidelines are suggested for promoting autonomy among young university students to the detriment of procrastination, thereby – and in accordance with previous research – enhancing students’ well-being and growth. Keywords: autonomy, student, self-regulation, procrastination, youth, well-being INTRODUCTION development of a skill” but also later during its performance (Zimmerman, 1998, p. 73). Consequently, autonomy need Developing the capacity to exercise autonomy from a young satisfaction in learning would mean being able to autonomously age has been deeply studied with the self-determination theory endorse thoughts, feelings, and actions oriented toward reaching (SDT; Ryan and Deci, 2017), as well as with other psychological goals in academic learning. approaches. According to SDT, autonomous functioning is From a pedagogical standpoint, we argue that it is relevant to an indispensable aspect of people’s innate tendencies toward note that self-motivation, volition, and self-regulation of behavior psychological growth, integration, and well-being. Based on the are abilities based on operations, processes, and strategies and, original works of Heider (1958) and De Charms (1968), SDT as such, can be developed and improved through supportive defines autonomy as the need to self-regulate one’s experiences conditions of social contexts (Ryan and Deci, 2017). We think and actions, entailing a form of functioning associated with that schools where students’ autonomy is aided can help feeling volitional, congruent, and integrated (Ryan and Deci, them in deploying self-motivation processes and self-regulation 2017). By definition, autonomy depends on the capacity for strategies, as it is at this operational level that students can act self-reflective endorsement of one’s actions, and autonomy upon their autonomous functioning and improve it. need satisfaction depends on behavior being “self-endorsed or Educational research on self-regulation strives to understand congruent” with “authentic interests and values” (Ryan and Deci, proactive efforts in learning, such as personal initiative, 2017, p. 10). Autonomy corresponds to people who willingly resourcefulness, persistence, and responsibility (Zimmerman, comply or wholeheartedly consent to engage in behaviors based 1998; Dewitte and Lens, 2000). These proactive properties on motives which they would also endorse if reflecting upon of autonomous functioning can only arise if the learner is them autonomously (which do not need to be self-initiated or self-motivated and has competence for self-directed learning; lack external inputs) (Ryan and Deci, 2006, 2017). Contrarily, thus, most models of self-regulated learning include these people acting against their volition experience incongruence and two aspects: self-motivation; and cognitive, metacognitive, and conflict, thus limiting their well-being. behavioral strategies and processes (Pintrich and De Groot, 1990; Zimmerman, 1995, 1998). Given that these strategies and Autonomy Need Satisfaction Through processes can be learned and improved, self-regulation is “no Self-Regulation Operations longer viewed as a fixed characteristic of students but rather as While autonomy can be considered a formal or abstract need a set of context-specific processes that are selectively used to [e.g., “the need for self-regulation of experience and action” succeed in school” (Zimmerman, 1998, p. 74). (Ryan and Deci, 2017)], self-regulation can be understood as Zimmerman (1998) argues that students learn best when able the capacity for exercising the operations which the satisfaction to self-regulate major dimensions of their learning (motives, of this need entails. In other words, the need for being or methods, behaviors, environment, company, and time). This feeling autonomous can only be satisfied through the exercise occurs in a four-step cycle including: (1) self-evaluation, (2) of self-motivation, volition, and behavioral self-regulation. In goal setting and strategic planning, (3) implementation, and this regard, the construct of academic self-regulation has (4) outcome evaluation. Analogously to this cyclical model been defined – based on the operations it entails – as the suggested by Zimmerman (1998), recent research assessing “self-generated thoughts, feelings, and actions for attaining self-regulation in university students has found a four-factor academic goals,” which are “not only important during the structure comprising of perseverance, goal setting, decision Frontiers in Psychology | www.frontiersin.org 2 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students making, and learning from mistakes (Pichardo et al., 2014). outcomes such as learning, achievement, and procrastination In this model, learning from mistakes can be understood as (Wolters and Hussain, 2015). a last phase, which may blend into a renewed first phase of goal setting, with both of these phases representing a deliberative motivational dimension of self-regulation occurring Procrastination: Volitional before and after performance (Dewitte and Lens, 2000). Self-Regulation Failure Impairs Growth Whereas the intermediate phase of decision making may and Well-Being represent the cognitive, meta-cognitive, and behavioral aspects The notion that self-regulation entails not only self-motivation of self-regulated learning implementation strategies, in other and skill but also operations and strategies at the cognitive, words, “self-generated thoughts, feelings, and actions” aimed metacognitive, and behavioral levels has come partly from studies at academic success (Pintrich and De Groot, 1990; Dewitte focusing on volitional dysregulation, called procrastination, and Lens, 2000), spanning across initial goal pursuit, planning, a consistent failure to do what one is supposed to do in avoiding temptations, controlling attention, and dealing with order to reach their own goals (Lay, 1986). By showing that difficulties such as distal goals or fear of failure, while executing some students consistently outperformed their peers, regardless goal-oriented behaviors (Steel et al., 2018). of similar motivation and skill, early studies showed that Both the motivational dimension, occurring “before” procrastination was indeed a distinct problem. These findings and “after” the actual practice, and the strategic-operational stirred interest in volitional differences in self-regulation, which dimension, happening “during” implementation of behavior may account for academic success or failure, that is, differences in are indispensable for the cycle of self-regulation to influence dispositions or abilities to “follow through with one’s intentions” learning outcomes positively; hence, these two dimensions are (Dewitte and Lens, 2000). key considerations in models of self-regulated learning. Procrastination has been found to be unrelated to general Recent research has found that three elements are key to intelligence (Ferrari, 1991), and procrastinators report a similar understanding procrastination over time: pacing style (the pace number of study intentions than non-procrastinators, but the and time at which the person decides, plans, and carries latter enact more of those intentions (Dewitte and Lens, out courses of action), intention-action gap (the times the 2000). Similarly, research has stressed that intention-action gaps, person fails to enact their own intentions), and goal striving which is implementing a task with delay or being late, are (the effort exerted by the person over time to reach their explained by the failure to transform intention into action, goals) (Steel et al., 2018). Also, these three elements point but not by intentions, which show no differences between in the direction of distinct dimensions having to work well people who procrastinate and who do not (Steel et al., 2018). together over time, such as having a timely strategy (which These antecedents show that volitional problems, such as relates closely with constructs like goal setting, decision making, procrastination, can independently predict variations in critical and learning from mistakes), and exerting effort over time study outcomes, such as learning or performance, over and above (closely related with constructs such as perseverance). Thus, it motivation and skill, possibly even mediating between the latter is possible to expect that perseverance could be linked with goal and learning or achievement outcomes (Dewitte and Lens, 2000). striving, whereas goal setting, decision making, and learning However, procrastination has proven to be a construct from mistakes could potentially be linked with pacing style with multiple facets (Svartdal and Steel, 2017), including or intention-action gap through, for example, outlining the decisional procrastination, linked with subpar planning and goals early, with a clear planning and intention-action gap risk decision making (Mann, 1982; Mann et al., 1997); general assessment. Consequently, intention cannot be realized without procrastination, centered on behavioral implemental deLay strategy, nor will strategy ever yield results without perseverance (1986); and a lateness factor, linked with failing to meet deadlines or goal striving. (McCown and Johnson, 1989). Furthermore, it has been argued that self-regulation Furthermore, cyclical models of self-regulation (Zimmerman, includes personal characteristics such as “a person’s trait- 1998) suggest that dysregulations such as poor decision-making level perseverance and passion for long-term goals” (Duckworth skills or low perseverance may both lead to procrastination, et al., 2007), sometimes referred to as grit, which is argued low achievement and, thus, to frustration and lower well- to be an individual trait and has drawn attention from being. As a result, procrastination may adversely influence self- educational researchers given its consistent prediction of study evaluations and self-efficacy beliefs, and subsequent motivation and achievement outcomes (Wolters and Hussain, 2015). and goal setting. Thus, failures in self-regulation are at the Studies focusing on the relationships between perseverance core of academic procrastination and pose serious threats and preference for long term goals, self-regulation, motivation, to students’ academic achievement and subjective well-being and procrastination suggest that perseverance or grit may (Steel and Klingsieck, 2016). influence variables such as self-motivation (including, for These antecedents highlight the pedagogical urgency in instance, goal setting and learning from mistakes) and use of supporting young students’ autonomy, growth, and well-being cognitive and metacognitive strategies of self-regulated learning by helping them in overcoming volitional problems, such as (spanning across aspects like goal setting, goal pursuit, and the difficulty to transform their intentions or motives into decision making), which in turn may potentially act as mediators action (Lay, 1986), and the inability to create adequate mental between trait-level individual differences in perseverance and representations of the operations required to successfully tackle Frontiers in Psychology | www.frontiersin.org 3 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students the target activity (Dewitte and Lens, 2000). In this regard, decision making (which are supposed to allow for development students who procrastinate typically face one of two problems. and improvement) to decisional procrastination. Sometimes, they fail to transform their motivation into volition Self-motivation and use of self-regulated learning strategies by failing to convert their goals into precise implemental have received insufficient empirical research regarding intentions aimed at enacting specific goal-oriented behaviors, in students’ academic achievement (Wolters and Hussain, an adequate and timely manner; they also have been found to 2015). Consequently, we asked ourselves if only personal choose inadequate implemental intentions, goals, or methods, trait-like characteristics (such as perseverance) would account which grant little support for success. These two aspects, closely for procrastination variations, or if other distinct aspects of linked with goal setting, decision making, and learning form self-regulation, such as self-motivational aspects (goal-setting, mistakes (which may overlap in time), have also been approached learning from mistakes), or even strategic aspects (decision from a longitudinal perspective, in which goal choice and goal making), could also play specific roles. pursuit represent two distinct moments: choosing a goal and The present work hypothesized that self-regulation strategies setting up an initial plan to pursue it (Steel et al., 2018). and processes, such as decision making, goal setting, and learning from mistakes, could be expected to counteract adverse procrastination tendencies in young adults to some extent, by counteracting decisional procrastination over and THE PRESENT STUDY above the effects of personal trait-level differences, such This research combines the interpretations of data offered by as perseverance, which were also considered. Even though the SEM and the cluster analysis. As justified in the following decisional procrastination has been linked with personal aspects paragraphs – and demonstrated in the Results and Discussion of character (rather than context), explanations have also sections – the use of both methods allows the visualization of both included aspects like the need for cognition and excessive the combination between self-regulation and procrastination, (metacognitive) clutter, that distracts from decision making and also the identification of how both constructs can manifest in (Ferrari et al., 2018). In this regard, self-regulated learning the students. This could potentially offer, in future researches, the strategies have been found to play critical mediation roles possibility of implementing differentiated strategies according to between people’s beliefs about procrastination and decisional the profile of each individual. procrastination (De Palo et al., 2017), suggesting that, even if not the root of the problem, self-regulation strategies – especially time management – may be key in fostering its Full Structural Equation Model for prevention. Furthermore, excessive meta-cognition or clutter Self-Regulation and Pure Procrastination may be responsible for decisional procrastination, thus, it The main goal of the present study was to test a structural has been reported that decisional procrastination could be model of self-regulation and procrastination. Self-regulation alleviated by attention control (Fernie et al., 2016). Thus, we and procrastination are closely linked constructs (Steel and tested if decision making skills would counteract decisional Klingsieck, 2016; Steel et al., 2018). Self-regulation includes procrastination through aiding students in keeping their a dimension of perseverance, as well as self-motivational and attention under control, that is, focused on the task at hand, strategic aspects (Dewitte and Lens, 2000; Garzón-Umerenkova given that using cognition to decide and plan actions may help et al., 2017), whereas procrastination is a self-regulation failure them maintain focus. (Steel, 2010), which includes a distinct emphasis on implemental Furthermore, we tested if perseverance could positively or decisional delays, prompting the failure to meet deadlines, influence these motivational and strategic aspects of self- known as lateness factor (Svartdal and Steel, 2017). regulation and if, in turn, self-regulation strategies could However, the pathways through which procrastination may be influence decisional procrastination, independent of the effects counteracted by self-regulation are subject to much discussion of trait-level perseverance. (Dewitte and Lens, 2000; Svartdal and Steel, 2017). This is Thus, to analyze particular pathways connecting specific especially true given that self-regulation is based on conscious aspects of self-regulation with relevant dimensions of operations and strategies that can supposedly be learned and procrastination, we examined three distinct models of improved (Pintrich and De Groot, 1990; Zimmerman, 2008), self-regulation and pure procrastination via structural whereas procrastination has been argued to be an irrational equation modeling (SEM). deLay (1986), rooted in personal trait-like individual differences Taking into account a four-factor self-regulation model (Steel, 2010). (Pichardo et al., 2014; Garzón-Umerenkova et al., 2017) and Based on the measurement of three distinct facets of a three-factor pure procrastination model (Svartdal and Steel, procrastination (implemental delay, decisional procrastination, 2017), we hypothesized an integrated seven-factor model with the and lateness; Svartdal and Steel, 2017) we hypothesized a following characteristics. structural model in which procrastination variations were The four factors proposed by Pichardo et al. (2014) and predicted both by a pathway from trait-level perseverance (which validated by Garzón-Umerenkova et al. (2017) seem theoretically is supposed to be difficult to influence, given that it is considered a appropriate to investigate self-regulation processes in young trait) to implemental delay; and by a pathway from self-regulatory university students, given that these factors distinguish between strategies, such as learning from mistakes, goal setting, and a trait-like characteristic (perseverance), a self-motivation Frontiers in Psychology | www.frontiersin.org 4 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students dimension of deliberative nature (happening before-and-after delay or volitional self-regulation problem, within which it action), including goal setting and learning from mistakes, and is relevant to test: (a) pathways connecting self-regulation a strategic dimension, including decision making. dimensions (learning from mistakes, goal setting, decision Perseverance has been found to be a stable trait-like making, and perseverance) with procrastination dimensions characteristic linked with preference for long-term goals, which (decisional procrastination, implemental delay and lateness); and is expected to influence both student’s motivation and use (b) the potential mediating effect of decision-making strategies of self-regulation strategies; these theoretical links, however, between motivational aspects of self-regulation and decisional have received scarce empirical research (Pintrich and De procrastination. Groot, 1990; Wolters and Hussain, 2015). Consequently, we Thus, this model allows for a discussion of the margins hypothesized that perseverance would positively influence self- for conscious strategic self-regulated behaviors to counteract motivation, such as learning from mistakes (H1 ) and goal procrastination by comparing two distinct pathways connecting setting (H2 ), and the use of self-regulation strategies, such both personal trait like characteristics (perseverance) with as decision making (H3 ).Furthermore, learning from mistakes implemental delay; and self-conscious dimensions of (motivational deliberative state happening after action) has been self-regulation (self-motivation, including learning from reported to inform subsequent goal-setting and decision making mistakes and goal setting, and decision making) with (Zimmerman, 2008); thus we anticipated these paths in the model decisional procrastination. to be significant and positive: from learning from mistakes to goal setting (H4 ) and decision making (H5 ), as well as from goal Cluster Analyses setting to decision making (H6 ). Based on the two main pathways hypothesized to counteract As regards the connections between self-regulation and procrastination in our proposed model (perseverance to procrastination dimensions, and in line with theory (Lay, 1986; implemental delay, and strategic aspects of self-regulation to Svartdal and Steel, 2017), we hypothesized that perseverance decisional procrastination), we asked ourselves if groupings of would negatively and robustly influence implemental delay (H7 ), students would naturally occur based on their levels of self- given that, in order to be considered an irrational delay rooted regulation dimensions of goal setting, decision making, learning in individual differences, significant amounts of the variance of from mistakes, and perseverance (bearing in mind that only this aspect should be explained through a direct path from trait- the last factor is supposed to be a trait like characteristic, like perseverance, and this path should be independent of the whereas the first three factors are supposed to be possible to be strategic dimensions of self-regulation, because, contrarily, the learned and improved). latter can supposedly be learned and improved. As perseverance Prior research has pointed toward difficulty in distinguishing has been reported to influence not only behavioral dimensions between medium, and between medium and high levels of self- but also cognitive dimensions of self-regulated learning through regulation, thus suggesting the use of a person-centered approach a greater awareness of and conscious centredness on intended to address this issue (Garzón-Umerenkova et al., 2017). In action courses (Pintrich and De Groot, 1990; Wolters and line with this, it has been argued that procrastination-related Hussain, 2015), we also anticipated that perseverance would typologies may address the complex multifaceted nature of negatively and moderately influence decisional procrastination, procrastination in a more simple way, serving as orientation for given that perseverant students may maintain greater awareness pedagogues to inform their practice with empirically based and of intended action courses, helping them in finding criteria testable hypotheses (Steel and Klingsieck, 2016). for decision making from their already set goals and their Thus, we used a person-centered approach (i.e., cluster planning (H8 ). analysis) to examine different self-regulation profiles, using Furthermore, we anticipated that a strategic dimension of self- four self-regulation dimensions (Pichardo et al., 2014; Garzón- regulation (decision making) would not only be influenced by Umerenkova et al., 2017) as partition variables (i.e., goal setting, motivational dimensions of learning from mistakes (H4 ) and learning from mistakes, decision making, and perseverance), goal setting (H6 ), but that, in turn, it would influence students’ in order to examine distinct naturally occurring academic self- decisional procrastination negatively and robustly (H9 ), thus regulation profiles, with distinct shortcomings, among young alleviating implemental delay and lateness problems indirectly university students. Regarding self-regulatory strategies, we (see Figure 1). We expected decision making to influence anticipated that decision making skills would be critical in decisional procrastination negatively and robustly (H10 ). differentiating between students with medium levels of self- In regards to mediation effects, we anticipated that a strategic regulation, as strategic aspects of self-regulatory behaviors dimension of self-regulation (decision making) would act as a happen during practice (and not only at an intention level mediator for motivational aspects (learning from mistakes and before or after practice, in which procrastinators and non- goal setting) to exert their protective effects against decisional procrastinators have not been found to diverge). Furthermore, we procrastination, given that similar mediations of strategic self- used emerging profiles to examine between-group differences in regulation aspects between motivation and learning outcomes procrastination and its dimensions of decisional procrastination, have been suggested to be relevant in academic learning implemental delay, and lateness. (Pintrich and De Groot, 1990). Results from the full structural model and from the cluster The goal of this theory-driven model is to inform the analyses are discussed with the goal of informing pedagogues who discussion about procrastination defined as an irrational want to address volitional self-regulation problems in order to Frontiers in Psychology | www.frontiersin.org 5 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students FIGURE 1 | Standardized solution of the hypothesized structural model of the relationship between self-regulation and pure procrastination dimensions. All regression coefficients were significant (*p < 0.05, **p < 0.01). For clarity of presentation, this figure does not include observed variables. support young student’s autonomy and well-being in simplified (15/1999: Jefatura del Estado, 1999), and the Declaration of and group-specific adapted ways. Helsinki (World Medical Association, 2013). Instruments The instrument used for data collection incorporated two MATERIALS AND METHODS scales. Firstly, the Spanish Short Self-Regulation Questionnaire (SSSRQ), proposed by Pichardo et al. (2014), derived from Participants Brown et al. (1999) and validated through Rasch analysis, for Participants were 994 young university students from one public Spanish speaking participants, by Garzón-Umerenkova et al. and one private university in Catalonia (41.0% men, 58.4% (2017). Evidence for the reliability of this questionnaire has women, and 0.5% non-binary gender). Their age ranged from been provided in the original study (i.e., Garzón-Umerenkova 18 to 24 years (M = 19.69, SD = 1.41). Sampling method was et al., 2017) with alphas ranging from 0.71 to 0.81 in its four intentional, with proportional quotas by sex, academic year (first, dimensions and 0.87 in the total questionnaire. The SSSRQ second, or third), and area of knowledge. is composed of 17 items divided into four dimensions: goal setting (e.g., “I set goals for myself and keep track of my Procedure progress”); perseverance (e.g., “I have a lot of willpower”); We contacted regular teachers in one public and one decision making (e.g., “I have trouble making up my mind private university and asked them to allow researchers to about things”); and learning from mistakes (e.g., “I don’t address their students during class. Students voluntarily and seem to learn from my mistakes”). The answers are collected anonymously participated by completing standardized 12- on a Likert scale ranging from 1 (not at all like me) to 5 minute questionnaires, via an online platform, which was made (very much like me). available to them through a link for the duration of each data Secondly, we used the Pure Procrastination Scale (PPS: collection session. Steel, 2010; Svartdal and Steel, 2017) consisting of 12 items The ethical requirements of the Ethics Committee of stemming from the General Procrastination Scale (Lay, 1986), the University of Barcelona (University of Barcelona’s the Decisional Procrastination Questionnaire (Mann, 1982; Bioethics Commission, CBUB – Institutional Review Board Mann et al., 1997), and the Adult Inventory of Procrastination IRB00003099) were applied to the current study, which meant (McCown and Johnson, 1989), translated into Spanish by Díaz- that additional approval for the research was not required Morales et al. (2006), which has shown its relevance compared because the data obtained did not involve animal or clinical with other measures via factor analysis (Steel, 2010; Svartdal and experimentation. Additionally, this study complies with Steel, 2017). The PPS (Svartdal and Steel, 2017) includes three the recommendations of the General Council of Spanish dimensions: decisional procrastination (e.g., “I delay making Psychological Associations (Consejo General de Colegios de decision until it’s too late”), implemental delay (e.g., “I am Psicólogos), the Spanish Organic Law on Data Protection continually saying ‘I’ll do it tomorrow’.”), and lateness (e.g., Frontiers in Psychology | www.frontiersin.org 6 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students “I don’t get things done on time”). Evidence for the reliability implemental delay were above this value. The internal reliability of this scale has been provided in the original study (i.e., coefficients for all the scales and subscales were adequate. Svartdal and Steel, 2017) with alphas ranging from 0.83 to Cronbach’s alphas and composite reliability ranged from 0.68 0.87 in the dimensions and 0.92 in the total questionnaire. to 0.85, whereas AVEs ranged from 0.36 to 0.60 (Table 1). The The answers are collected on a Likert scale ranging from convergent validity of a construct can be considered adequate 1 (very seldom or not at all like me) to 5 (very often or when AVE is less than 0.50 but CR is higher than 0.60 very true of me). (Fornell and Larcker, 1981). Gender-differences (not tabulated) showed men’s greater Data Analysis tendency toward procrastination and were found for Statistical analyses were performed with IBM SPPS Statistics implemental delay (M men = 3.38, SDmen = 0.99; M women = 3.18, version 20 and IBM SPSS AMOS version 24. Data was screened SDwomen = 1.05; t = 2.891, p = 0.004), lateness (M men = 2.21, to delete unfinished and unengaged responses (excluding SDmen = 0.85; M women = 1.97, SDwomen = 0.81; t = 4.194, questionnaires reporting only two or less response categories p < 0.001) and overall pure procrastination (M men = 2.81, throughout one whole scale). Based on the resulting participant SDmen = 0.76; M women = 2.66, SDwomen = 0.77; t = 3.039, base (N = 994) we examined means, standard deviations, and p = 0.002). However, decisional procrastination showed bivariate correlations between study variables. Cronbach alpha no gender-differences. Regarding self-regulation, women coefficients, composite reliability (CR), and extracted mean showed greater scores than men in goal setting (M men = 3.37, variance (AVE) were also examined (Table 1). SDmen = 0.87; M women = 3.59, SDwomen = 0.83; t = −3.736, Three different self-regulation and pure procrastination p < 0.001) and perseverance (M men = 3.01, SDmen = 0.85; measurement models were compared via confirmatory factor M women = 3.23, SDwomen = 0.82; t = −2.523, p = 0.012), but lesser analysis (CFA) in order to proceed to a full structural equation scores in decision making (M men = 3.02, SDmen = 0.85; modeling (SEM) phase that would test the theoretical pathways M women = 2.88, SDwomen = 0.89; t = 2.280, p = 0.023). connecting both constructs. Furthermore, no gender-differences were found in learning Furthermore, we conducted Ward’s method hierarchical from mistakes. As a result, overall self-regulation scores showed cluster analyses, based on squared Euclidean distances, using no gender-differences. dimensions of self-regulation as partition variables, in order Table 2 shows bivariate correlations between study to identify naturally occurring groups of students with distinct variables. The four dimensions of self-regulation and the self-regulation profiles. Based on fusion coefficients and on the three dimensions of procrastination, respectively, showed proportions of variance explained in partition variables, we robust internal consistency. Because inter-factor correlations chose the four-cluster solution for further analysis. Subsequently, are below 0.85 and following Kline (2005) criteria, factor iterative K-means clustering analyses were conducted, using discrimination can be established among the instruments’ final cluster centers from the hierarchical clustering as initial dimensions, providing evidence of discriminant validity. Overall, cluster centers for the iterative method, in order to yield and as expected, self-regulation and its dimensions were robustly more precise groupings and confirm the stability of the and negatively correlated with pure procrastination and its solutions by means of Cohen’s kappa. The final cluster solution corresponding dimensions. is depicted in Figure 1, based on Z-values of the final cluster centers in partition variables. Lastly, between-groups differences in dimensions of self-regulation and procrastination Three Measurement Models and One Full were analyzed with ANOVA, as evidence contributing to SEM for Self-Regulation and Pure convergent validity. Procrastination Prior to analyses concerning the hypothesized structural model, we compared three measurement models (Table 3) to check the RESULTS factorial structure of the questionnaires. Measurement model 1 comprised seven factors, considering the four-factor self- Descriptive Statistics, Reliability and regulation model (Pichardo et al., 2014; Garzón-Umerenkova Bivariate Correlations et al., 2017), and the three-factor pure procrastination model Table 1 shows overall mean scores, standard deviations, (Svartdal and Steel, 2017). Measurement model 2 comprised five range, skewness, kurtosis, Cronbach’s alphas, average variance factors, considering four factors for self-regulation, but one single explained (AVE), and composite reliability (CR) for the study factor for pure procrastination. Measurement model 3 comprised variables. With respect to self-regulation dimensions, students’ four factors, considering three factors for pure procrastination, responses showed that values for the self-motivational aspects but one single factor for self-regulation. of goal setting and learning from mistakes were above the Measurement model 1 showed better fit to the data mean value of the questionnaire, while perseverance and the than models 2 or 3 (Table 3). However, five items did strategic aspect of decision making were under the mean not perform well and were dropped (see Appendix) in value. In relation to the dimensions of pure procrastination, line with recommendations from the authors of the SSSRQ students’ reported values in lateness were below the mean value questionnaire, arguing that a smaller number of items, reflectively of the questionnaire, whereas decisional procrastination and explaining equal or greater amounts of variation in a factor Frontiers in Psychology | www.frontiersin.org 7 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students TABLE 1 | Descriptive statistics and reliability of study variables (N = 994). M SD Min Max Skew Kurtosis Alpha AVE CR Self-Regulation 3.27 0.62 1.21 4.83 −0.26 −0.06 0.85 0.57 0.85 Goal setting 3.54 0.79 1 5 −0.52 −0.03 0.83 0.55 0.83 Perseverance 3.18 0.82 1 5 −0.12 −0.42 0.72 0.46 0.72 Decision making 2.91 0.93 1 5 0.00 −0.60 0.78 0.53 0.77 Learning from mistakes 3.45 0.88 1 5 −0.28 −0.54 0.82 0.60 0.82 Pure procrastination 2.73 0.76 1 5 0.15 −0.46 0.84 0.55 0.84 Implemental delay 3.25 1.02 1 5 −0.17 −0.82 0.81 0.51 0.81 Decisional procrastination 2.86 0.97 1 5 0.16 −0.61 0.70 0.44 0.70 Lateness 2.08 0.84 1 5 0.67 −0.10 0.68 0.36 0.68 Range variables: 1–5. AVE = average variance extracted; CR = composite reliability. TABLE 2 | Bivariate correlations between study variables (N = 994). 1 2 3 4 5 6 7 8 1. Self-Regulation 2. Goal setting 0.75** 3. Perseverance 0.73** 0.51** 4. Decision making 0.70** 0.32** 0.30** 5. Learning from mistakes 0.72** 0.39** 0.36** 0.33** 6. Pure procrastination −0.70** −0.50** −0.60** −0.53** −0.39** 7. Implemental delay −0.58** −0.45** −0.61** −0.33** −0.30** 0.84** 8. Decisional procrastination −0.63** −0.39** −0.41** −0.64** −0.35** 0.80** 0.48** 9. Lateness −0.48** −0.37** −0.42** −0.29** −0.31** 0.79** 0.53** 0.44** **p < 0.01 (2-tailed). TABLE 3 | Three measurement models and one full SEM for self-regulation and pure procrastination. CMIN/DF TLI CFI SRMR RMSEA 90% CI PCLOSE Model 1 3.912 0.889 0.903 0.0538 0.054 0.051–0.057 0.011 Model 2 5.622 0.824 0.841 0.0656 0.068 0.065–0.071 0.000 Model 3 9.333 0.683 0.710 0.0806 0.092 0.089–0.094 0.000 Model 1 B 3.846 0.915 0.929 0.0462 0.054 0.050–0.057 0.058 Full SEM 3.798 0.916 0.927 0.0470 0.053 0.049–0.057 0.081 may be seen as an improvement in measurement (Garzón- from mistakes. Furthermore, decisional procrastination was Umerenkova et al., 2017), if – desirably – at least three negatively and robustly influenced by decision making but with items are kept per factor (Bollen, 1989). These theoretically a smaller coefficient; perseverance also influenced decisional acceptable modifications (deleting five items) produced the procrastination negatively. Decisional procrastination positively final Model 1 B, increasing model fit significantly, as reported influenced implemental delay and, lastly, lateness was positively in Table 3. Thus, structural equation modeling (SEM) was influenced by decisional procrastination and implemental delay, employed to assess the hypothesized seven-factor structural the latter showing a stronger coefficient. model (see Figure 1). Table 4 shows standardized indirect effects and significance The SEM for the hypothesized model (see Figure 1) indicated levels of the hypothesized structural model. Perseverance that perseverance positively and robustly influenced both self- showed the biggest indirect coefficient on lateness, followed motivational aspects of self-regulation (learning from mistakes by decision making, both negatively influencing lateness. and goal setting), as well as the strategic aspect (decision Furthermore, decision making and perseverance also showed making). Perseverance also showed a negative influence on significant indirect effects on implemental delay. Decisional both decisional procrastination and implemental delay, the procrastination received indirect negative significant effects latter being more robust. Learning from mistakes positively from perseverance and from both motivational aspects of self- influenced both aspects of self-regulation (goal setting and regulation (learning from mistakes and goal setting). Lastly, decision making). Decision making was also positively influenced perseverance showed indirect positive effects on decision making by goal setting, although to a lesser extent than by learning and goal setting. Frontiers in Psychology | www.frontiersin.org 8 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students TABLE 4 | Indirect effects of the hypothesized structural equation model. Four Self-Regulation Profiles Independent – Dependent variables Standardized coefficient Table 6 shows final cluster centers for self-regulation dimensions, based on four-group cluster membership: a de-regulated cluster, Perseverance – Lateness −0.64* scoring low on all partition variables; a self-regulated cluster, Learning from Mistakes – Lateness −0.07* scoring high on all self-regulation dimensions; a low motivation Goal Setting – Lateness −0.05* cluster, characterized by subpar motivation (goal setting, learning Decision Making – Lateness −0.25* from mistakes) and low perseverance; and a low strategy cluster, Decisional Procrastination – Lateness 0.10* characterized by average motivation (goal setting, learning from Perseverance – Implemental Delay −0.10* mistakes) and medium perseverance, but low decision making. Learning from Mistakes – Implemental Delay −0.04** Figure 2 depicts Z-values in goal setting, perseverance, Goal Setting – Implemental Delay −0.02* decision making, and learning from mistakes for the four Decision Making – Implemental Delay −0.12* emerging self-regulation profiles. Low motivation students Perseverance – Decisional Procrastination −0.31** (alongside de-regulated) reported below-average goal setting Learning from Mistakes – Decisional Procrastination −0.21* and perseverance; low strategy students (alongside de-regulated) Goal Setting – Decisional Procrastination −0.13* reported below-average decision making. Perseverance – Decision Making 0.23** Figure 3 shows a line diagram depicting four self-regulation Learning from Mistakes – Decision Making 0.04** profiles based on their Z-scores in self-regulation dimensions Perseverance – Goal Setting 0.10** (partition variables). Post hoc tests (using Bonferroni for *p < 0.05; **p < 0.01. homogeneous variances and Dunnett’s T for non-homogeneous variances, as judged by Levene’s test) revealed significant differences (p < 0.001) in all partition variables between all Cluster Analysis: Self-Regulation Profiles pairs of groups, except between de-regulated students and low As a first step in the cluster analysis, we used Ward’s method strategy students in decision making (p = 0.163). Interestingly, hierarchical clustering, which combines the two most similar low strategy students scored higher than low motivation students clusters, based on their squared Euclidean distance, starting in all partition variables except decision making, where this with N = 994 single-person clusters, and ending with one pattern reversed itself, with decision making, thus, acting as single cluster containing all participants. We observed a clear the most important differentiating aspect between groups with jump in fusion coefficients (Table 5) when collapsing four medium levels of self-regulation. Lastly, overall self-regulation groups into three, arguing for the presence of at least four also revealed significant differences between all groups except natural groupings. between low motivation and low strategy (p = 1.000). As a second step, we applied K-means iterative clustering procedure for a four-group solution, using cluster centers resulting from the hierarchical clustering, as initial centers in a Procrastination Among Four subsequent iterative procedure, taking eight iterations until the Self-Regulation Profiles largest change in any of the cluster centers was less than 2% Table 7 shows means in procrastination and its dimensions of the initial smallest distance. The stability of both solutions for each of the four self-regulation profiles. De-regulated was tested through Cohen’s Kappa, which showed an acceptable students reported the highest mean scores of the four groups level (κ = 0.67). in implemental delay, decisional procrastination, lateness, and Between-group effects explained 40% of variations in overall pure procrastination, but even their reports of lateness were self-regulation, 38% in goal setting, 42% in perseverance, 72% in below a neutral score of three points, manifesting a skew decision making, and 47% in learning from mistakes. Between- in self-reporting. Low motivation students’ mean scores in group differences also accounted for 31% in overall pure procrastination dimensions were average, however, slightly procrastination variance, 39% in implemental delay, 44% in above in implemental delay and slightly below in decisional decisional procrastination, and a more modest 17% in lateness. procrastination. Inversely, low strategy students’ mean scores were slightly below-average in implemental delay, but above- average in decisional procrastination. TABLE 5 | Fusion coefficients. Figure 4 shows a line diagram depicting Z-values in pure procrastination and its dimensions for the four self-regulation Group number Fusion coefficient profiles. Post hoc tests (using Bonferroni for homogeneous 1 2906.02 variances and Dunnett’s T for non-homogeneous variances, 2 2098.70 as judged by Levene’s test) revealed significant differences 3 1827.56 (p < 0.001) in pure procrastination and all its dimensions 4 1580.98 between all pairs of groups, except between low strategy and 5 1453.45 low motivation students in implemental delay (p = 0.912) and 6 1356.97 in lateness (p = 0.884). Interestingly, low strategy students 7 1267.25 scored higher than low motivation students in decisional 8 1197.93 procrastination and overall pure procrastination, marking the Frontiers in Psychology | www.frontiersin.org 9 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students TABLE 6 | Final cluster centers on partition variables for four self-regulation profiles. De-regulated Low motivation Low strategy Self-regulated M SD Goal setting 2.70 3.42 3.71 4.17 3.54 0.79 Perseverance 2.43 2.85 3.32 3.93 3.18 0.82 Decision making 2.10 3.54 2.21 3.68 2.91 0.93 Learning from mistakes 2.50 3.27 3.72 4.11 3.45 0.88 N 224 232 259 279 994 relevance of subpar self-regulation strategies such as decisional be improved consciously. This strategic dimension of self- procrastination for differentiating among groups with medium regulation seems to be acting as a mediator between self- levels of procrastination. motivational antecedents (learning from mistakes, goal setting) and decisional procrastination. Noteworthy is the fact that the strongest predictor of decision making in the model was learning DISCUSSION from mistakes, which influenced both goal setting and decision making positively. In this regard, we want to stress that learning Pedagogues want to support young university student’s from mistakes and decision making are two dimensions of self- autonomy and well-being by helping them in overcoming regulation which cannot be detached from autonomy, given that volitional self-regulation problems like procrastination, their operations rest on it. which create frustration, as well as lessening performance, To a lesser extent, decisional procrastination was also academic achievement, self-efficacy beliefs, and subsequent directly influenced by perseverance and, in turn, decisional motivation. Teachers seek simplified and group-specific procrastination influenced implemental delay positively. strategies to understand and counteract students’ procrastination Furthermore, the lateness factor received the most robust causal tendencies. However, the discussion about the margins for path from implemental delay. These findings suggest that consciously counteracting implemental delay and decisional decisional delays may be the cause of lateness, and also serve as procrastination based on self-regulated learning deserves more grounds for implemental delays; the strongest lateness predictor, detailed attention. Consequently, our full SEM model allows however, was still perseverance-related implemental delay. for a discussion of the margins for conscious strategic action The model supports the argument that strategic decision- against procrastination. making skills may act as a partial mediator between motivational According to Model 1B, perseverance positively influences aspects of self-regulation and decisional procrastination. It both motivational self-regulation factors (learning from mistakes, also supports the belief that that both motivational and goal setting) and strategic aspects (decision making). More strategic self-regulation operations may act as partial mediators perseverant students, therefore, are expected to report higher between perseverance and decisional procrastination, allowing motivation for self-regulated learning. In turn, self-motivational for the discussion of potential conscious and intentional self- aspects also exert significant positive effects on the strategic regulation strategies to counteract procrastination tendencies and aspect of decision making. their adverse impact on young university students’ autonomy, Furthermore, comparing between resulting self-regulation growth, and well-being. clusters, perseverance, and motivational aspects (learning from Furthermore, the present study provides a person-centered mistakes, goal setting) increased or decreased concomitantly, analysis of four distinct students’ self-regulation profiles and their whereas decision making manifested high or low levels, associated procrastination. By means of a cluster analysis we independent of the levels of self-motivation and perseverance of identified a self-regulated and a de-regulated group, respectively, that cluster. It was the strongest variable to differentiate between with the highest and the lowest scores on all self-regulation medium-level profiles of self-regulation. dimensions. Furthermore, we also differentiated two medium The two most important paths connecting self-regulation level self-regulation groups, whose main diverging factors and procrastination dimensions were the strong negative path were distinct shortcomings, respectively, in perseverance and from perseverance to implemental delay, and the strong negative decision making. path from decision making to decisional procrastination. The Of the two medium self-regulation groups, low strategy first path serves as evidence that more perseverant students students scored above average in perseverance and in may remain more aware of their goals, preventing them from motivational aspects of self-regulation (learning from slipping into irrational implemental delays. However, as this mistakes, goal setting), but considerably below average in path roots on personal characteristics, it is still worthwhile to the strategic dimension of decision making (as low as de- explore alternative paths to counteract procrastination through regulated students). Consequently, they also scored higher in self-regulation strategies. decisional procrastination than did low motivation students. As The second path suggests that decisional procrastination a result, notwithstanding the optimistic outlook of their higher (and its contribution to implemental delay and lateness) may motivation (learning from mistakes, goal setting), low strategy be prevented via strategic decision-making skills, which could students’ overall pure procrastination scores were higher than Frontiers in Psychology | www.frontiersin.org 10 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students FIGURE 2 | Column diagram depicting Z-values in goal setting, perseverance, decision making, and learning from mistakes for four self-regulation profiles. FIGURE 3 | Line diagram depicting four self-regulation profiles based on their Z-values in partition variable. TABLE 7 | Four self-regulation profiles and their procrastination dimensions. De-regulated Low motivation Low strategy Self-regulated M SD Pure procrastination 3.45 2.10 2.68 2.83 2.73 0.76 Implemental delay 4.05 2.53 3.33 3.25 3.25 1.02 Decisional procrastination 3.68 2.17 2.55 3.17 2.86 0.97 Lateness 2.62 1.60 2.15 2.08 2.08 0.84 N 224 232 259 279 994 Frontiers in Psychology | www.frontiersin.org 11 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students FIGURE 4 | Line diagram depicting four self-regulation profiles based on their Z-values in pure procrastination and its dimensions. their low motivation peers’. In contrast, low motivation students necessary for target activities. Could this mean that there is (notwithstanding their lower motivation and perseverance), space for strategies of self-regulated learning to gain terrain scored similarly to their low strategy peers on implemental on this potential realm of irrational delay, through increasing delay and lateness, and lower on overall pure procrastination. and maintaining conscious centeredness on adequate goals and These findings stress the notion that procrastination is not strategies? Furthermore, it has been reported that procrastination a problem of motivation but rather a failure of strategic tendencies may interact with contextual-factors such as teacher volitional self-regulation. autonomy support or control (Codina et al., 2018). Thus, Combining the interpretations of the structural model (Model supporting young university students’ autonomy, by facilitating 1B) and cluster analyses, we provide an account of the pathways self-regulation, may promote timely goal setting, initial goal connecting self-regulation dimensions (learning from mistakes, pursuit, decision making, planning, and goal striving (Steel and goal setting decision making, perseverance) with procrastination Klingsieck, 2016; Steel et al., 2018). We argue that increased dimensions (decisional procrastination, implemental delay, goal centeredness and awareness of self-regulation strategies lateness). This account enables the discussion about particular may, therefore, potentially counteract procrastination tendencies; shortcomings in self-regulation operations (perseverance versus future research is needed, however, to further address this point. strategic and motivational aspects), linking these with specific An important issue to put to test by future studies would facets of procrastination (implemental and decisional delays). be if greater awareness of the self-motivational and strategic We understand that irrational delay occurs when conscious dimensions of self-regulation can be fostered through the volitional self-regulation operations fail; thus, we argue that support of young students’ autonomy, and by facilitating their providing young students with autonomy support and rationales, psychological processes of learning from mistakes, goal setting, aiding their design of autonomous strategies (including abilities and decision making. Given that making successful strategic for promoting both their motivation and decision making decisions requires careful self-examination and adequate mental skills), may help them maintaining conscious goal centeredness representations of tasks and person-context interactions, we and strategy engagement, thus, preventing the irrational slip argue that facilitating students’ self-awareness and enhancement into procrastination. of their own motivations, abilities, and strategies, may prevent It has been discussed that procrastination may be linked with them from slipping into the realm of irrational delay, by personal trait-like characteristics such as perseverance and low facilitating the inverse processes of autonomous self-regulation. ability for emotional regulation, which producer an irrational delay and thus leave little margin for other causal explanations or even for counteracting the behaviors through conscious behavioral regulations. In this regard, it may be true that – FINAL REMARKS in retrospective – procrastination is always rooted in irrational dysregulation, but the question of whether it can be consciously The four emerging self-regulation profiles and the seven-factor prevented – in our opinion – still holds sway. structural model connecting self-regulation and procrastination It has been argued that procrastination may be promoted by suggest that low strategy students (low in decision making) sub-par mental representations of required or viable operations may benefit from interventions that help them develop adequate Frontiers in Psychology | www.frontiersin.org 12 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students mental representations of tasks and person-context interactions, Commission, CBUB – Institutional Review Board IRB00003099) and consider their autonomous goals and past experiences. were applied to the current study, which meant that additional These students carry above-average self-motivation but below- approval for the research was not required because the data average strategic self-regulation, thus separating themselves from obtained did not involve animal or clinical experimentation. self-regulated students in regard to general self-regulation and Additionally, this study complies with the recommendations procrastination. of the General Council of Spanish Psychological Associations Findings also suggest that low motivation students’ above (Consejo General de Colegios de Psicólogos), the Spanish average reports of decision making may be insufficient Organic Law on Data Protection (15/1999: Jefatura del Estado, for their academic adjustment, given that these students 1999), and the Declaration of Helsinki (World Medical are also characterized by below average goal setting and Association, 2013). The patients/participants provided their learning from mistakes, and these variables exert significant written informed consent to participate in this study. indirect effects on decisional procrastination through decision making. Low motivation students’ self-regulation shortcomings stemmed greatly from their below average perseverance AUTHOR CONTRIBUTIONS and self-motivation, thus these students could benefit from support in building up a repertoire of autonomous RV contributed to the conception and design of the study, pursuits, which they value personally, and which depend on organized the database, performed the statistical analysis, and autonomous reflection upon previous goal-oriented experience wrote the first draft of the manuscript. NC conceived and (learning from mistakes). designed the research, drafted the work, and revised it critically Cyclical models of self-regulation suggest that, in order to for important intellectual content. IC was responsible for avoid frustration and a consequent decrease in subsequent supervising the analysis and interpretation of data gathered, motivation, it would be of cardinal importance that these students revising it critically. JP contributed to the conception and design accomplish their autonomous goals, thus, adequate guidance of the study, wrote sections of the manuscript, and revised it or support for the optimal selection and scaffolding of initially critically. All authors contributed to manuscript revision, read attainable goals may be key to increased academic success and approved the submitted version. among these students. DATA AVAILABILITY STATEMENT FUNDING This research was conjointly supported by the project The datasets generated for this study are available on request to REDICE18-2260 (funded by the Institut de Desenvolupament the corresponding author. Profesional the University of Barcelona), the project RTI2018- 094405-B-I00 (funded by the Ministerio de Ciencia, Innovación ETHICS STATEMENT y Universidades), and PsicoSAO/Research Group in Social, Environmental, and Organizational Psychology (2017 SGR 564; The Ethical requirements of the Ethics Committee of the Secretaria d’Universitats i Recerca del Departament d’Economia University of Barcelona (University of Barcelona’s Bioethics i Coneixement, Generalitat de Catalunya). REFERENCES Díaz-Morales, J. F., Ferrari, J. R., Díaz, K., and Argumedo, D. (2006). Factorial structure of three procrastination scales with a Spanish adult population. Eur. J. Bollen, K. A. (1989). Structural Equations with Latent Variables. New York, NY: Psychol. Assess. 22, 132–137. doi: 10.1027/1015-5759.22.2.132 John Wiley & Son. Duckworth, A. L., Peterson, C., Matthews, M. D., and Kelly, D. R. (2007). Grit: Brown, J. M., Miller, W. R., and Lawendowski, L. A. 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Conflict of Interest: The authors declare that the research was conducted in the doi: 10.1037/0022-0663.82.1.33 absence of any commercial or financial relationships that could be construed as a Ryan, R. M., and Deci, E. L. (2006). Self-regulation and the problem of human potential conflict of interest. autonomy: does psychology need choice, self-determination, and will? J. Pers. 74, 1557–1585. doi: 10.1111/j.1467-6494.2006.00420.x Copyright © 2020 Valenzuela, Codina, Castillo and Pestana. This is an open-access Ryan, R. M., and Deci, E. L. (2017). Self-Determination Theory. Basic Psychological article distributed under the terms of the Creative Commons Attribution License Needs in Motivation, Development, and Wellness. New York, NY: Guilford (CC BY). The use, distribution or reproduction in other forums is permitted, provided Publishing. the original author(s) and the copyright owner(s) are credited and that the original Steel, P. (2010). Arousal, avoidant and decisional procrastinators: do they exist? publication in this journal is cited, in accordance with accepted academic practice. No Pers. Individ. Diff. 48, 926–934. doi: 10.1016/j.paid.2010.02.025 use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Psychology | www.frontiersin.org 14 March 2020 | Volume 11 | Article 354 Valenzuela et al. Autonomous Self-Regulation and Procrastination in University Students APPENDIX The following are the scale construction decisions made in the research: (1) Implemental delay item: “I often find myself performing tasks that I had intended to do days before” was dropped, given that its squared multiple correlation was <0.3 (= 0.022) and its standardized regression weight on its factor was <0.4 (= 0.15); as a result, the alpha for implemental delay increased from 0.74 to 0.81. (2) Decision making item: “I have so many plans that it’s hard for me to focus on any one of them” was dropped, given that its squared multiple correlation was <0.3 (= 0.117), and its standardized regression weight on its factor was <0.4 (= 0.34). One more decision making item was dropped: “Little problems or distractions throw me off course,” given that its standardized residual covariance with two other items was >3.0 (= 6.348 with perseverance item: “I get easily distracted from my plans”; and 4.695 with goal setting item: “I have trouble making plans to help me reach my goals.” Worth is noting that in the Spanish version “off-course” is translated as “disoriented,” which might lead attention away from the notion of “action course” – as relevant in decision making – and toward the notion of “chaos and confusion,” consequently, interacting with perseverance and goal setting items. As a result of deleting these two items the alpha for decision making increased from 0.76 to 0.78. (3) Similarly, goal setting item: “If I make a resolution to change something, I pay a lot of attention to how I’m doing” was dropped, given that its standardized residual covariance with a perseverance item was >3.0 (= 3.953 with perseverance item: “I have a lot of will power”). Worth is noting that in the Spanish version the phrasing “resolution to change something” was translated as “determination of doing something,” subtracting the notion of “change,” thus, reflecting on an aspect less linked with self- motivation and more with strategy. (4) Lastly, another goal setting item: “I usually keep track of my progress toward my goals” was dropped, given that the absolute value of its standardized residual covariance with a decision making item was >3.0 (= −3,636 with item: “I have trouble making up my mind about things”). Worth is noting that the Spanish version of this goal setting item includes the notion of “study goals,” not just “goals”; and that this decision making item replaces the notion “make up my mind” – unavailable in Spanish – with the phrase: “decide about things,” thus interacting with goal setting dimension. Frontiers in Psychology | www.frontiersin.org 15 March 2020 | Volume 11 | Article 354