Within-students variability in learning experiences, and teachers' perceptions of students' task-focus

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Within-students variability in learning experiences, and teachers' perceptions of students' task-focus Malmberg, Lars-Erik; Lim, Wee H. T.; Tolvanen, Asko; Nurmi, Jari-Erik Malmberg, L.-E., Lim, W. H. T., Tolvanen, A., & Nurmi, J.-E. (2016). Within-students variability in learning experiences, and teachers' perceptions of students' task-focus. Frontline Learning Research, 4(5), 62-82. https://doi.org/10.14786/flr.v4i5.227 2016 ! ! ! ! ! Frontline!Learning!Research!Vol.4!No.!5!(2016)!62!<!82! ISSN!2295<3159!! ! Contact information: Lars-Erik Malmberg, Department of Education, University of Oxford, 15, Norham Gardens, OX2 6PY, Oxford, UK. Email: lars-[email protected]. DOI: http://dx.doi.org/10.14786/flr.v4i5.227! Within-students variability in learning experiences, and teachers' perceptions of students' task-focus Lars-Erik Malmberga, Wee H. T. Lima, Asko Tolvanenb and Jari-Erik Nurmib a University of Oxford, United Kingdom b University of Jyväskylä, Finland Article received 16 November / revised 7 July / accepted 7 September / available online 11 January! Abstract In order to advance our understanding of educational processes, we present a tutorial of intraindividual variability. An adaptive educational process is characterised by stable (less variability), and a maladaptive process is characterised by instable (more variability) learning experiences from one learning situation to the next. We outline step by step how we specify a multilevel structural equation model of state, trait and individual differences in intraindividual variability constructs, which can be appropriately fitted to intraindividual data (e.g., time-points nested in persons, intensive longitudinal data). In total 285 primary school students’ (Years 5 and 6) completed the Learning Experience Questionnaire using handheld computers, on average 13.6 learning episodes during one week (SD = 4.6; Range = 5-29; nepisodes = 3,433). We defined mean squared successive differences (MSSD) for each manifest indicator of task difficulty, competence evaluation and intrinsic motivation. We also demonstrate how to specify multivariate models for investigating convergent validity of the variability constructs. Overall, our study provides support for intraindividual variability as a construct in its own right, which has the potential to provide novel insight into students’ learning processes. Keywords: Intraindividual variability; multilevel structural equation model (MSEM); learning experience; ecological momentary assessment Malmberg)et)al) ) ) ! | FLR 63! ! 1. Introduction There is a growing interest in the study of students’ learning processes using diary and real-time data (Schmitz, 2006). These micro-longitudinal studies expand our knowledge about learning processes beyond what we can learn from single time-point cross-sectional studies, in at least three ways. First, there is considerable variation in students’ learning experiences, e.g., their engagement, beliefs, motivation, emotions, and performance from one situation to another (i.e., intraindividual variation), more so than there is variation between students (i.e., interpersonal variation; Schmitz & Skinner, 1993). Second, situationspecific learning experiences vary as a function of contextual features, such as perceived autonomy support (Tsai, Kunter, Lüdtke, & Trautwein, 2008), and extrinsic motivation (Malmberg, Pakarinen, Vasalampi, & Nurmi, 2015). This means that situation specific opportunities and constraints, such as provision of support and levels of expectation, form an integral part of students’ learning experiences. Third, students’ individual characteristics can moderate the relationship between experiences. Compared with relatively lower achievers, higher achievers had more stable control beliefs and perceived task ease from one situation to the next (Musher-Eizenman, Nesselroade, & Schmitz, 2002), and exerted more effort when confronted with difficult tasks (Malmberg, Walls, Martin, Little, & Lim, 2013). What we know less about is the intraindividual variability in students’ learning experiences from one situation to the next. Whilst intraindividual variation captures the differences between individuals’ experiences above or below their own average experience (i.e., an “individual standard deviation” of own “ups” and “downs”), intraindividual variability, inconsistency, or instability refers to the magnitude of short-term fluctuations in the order of the ups and downs from one time-point to the next (e.g., Jahng, Wood, & Trull, 2008; Kernis, Grannemann, & Barclay, 1989). This magnitude of intraindividual variability is larger when the shifts between highs and lows are more abrupt, occur more often, and the swings go from one extreme to the other. In the present study we go beyond previous real-time studies of students’ learning experiences in two ways. First, we propose a methodology for specifying a within-person variability construct alongside state and trait constructs, using state-of-the-art multilevel structural equation models (MSEM). The MSEM allows us to model latent constructs net of measurement error at two (or more) levels of data. Second, we include teacher perceived student task-focus as an indicator of convergent validity of students’ intraindividual variability. Accumulated research shows that students, who in the eyes of their teacher are generally task-focused, are: intrinsically motivated, deploy task-focused rather than task-avoidant behavioural engagement, exert effort, seek help when they need it, and persist when they encounter difficulties (Eccles, Wigfield, & Schiefele, 1998; Nurmi, Hirvonen & Aunola, 2008, Zimmerman, 2000). It would be important to know whether students who teachers regard as task-focused, are also more stable in their learning experiences, i.e., less variability in students’ perceptions of task difficulty, competence beliefs and intrinsic motivation from one learning situation to the next. To this end we provide a brief overview of intraindividual research in education, task-focused learning, a didactical example of the mean squared sequential difference (MSSD) index of intraindividual variation, and an MSEM specification. 1.1. Intraindividual resesarch to education There appears to be a surge in intraindividual research in education. Since the seminal diary studies by Schmitz and Skinner (1993) and Musher-Eizenman et al. (2002), an up-swing in the number of publications has been seen, for example Schmitz and Wiese (2006), and Tsai et al., (2008). Recent studies have used experience sampling of students’ academic emotions (Goetz, Frenzel, Stoeger & Hall, 2010), coping with boredom (Nett, Goetz & Hall, 2011) and metacognitive strategies (Nett, Goetz, Hall & Frenzel, 2012); ecological momentary assessment studies of effort exertion, competence beliefs and task difficulty (Malmberg, Walls et al., 2013); and contextual activity sampling of university students’ challenge, competence and emotions (Inkinen et al., 2013). Data in these studies were collected at multiple time-points in their natural settings, as close in time as possible to events, thus reducing retrospection bias (Wilhelm, Perrez, & Pawlik, 2012). The importance of the intraindividual perspective on learning experiences is threefold. These studies pave the way for understanding, first, learning processes as they occur in real-time; second, individual differences in such learning processes, and third, how teachers might differentially Malmberg)et)al) ) ) ! | FLR 64! ! support individual students. Taken together, an intraindividual approach to learning can help us understand both learning processes and the ways in which teachers can support these (Schmitz, 2006). 1.2. Intraindividual variation and variability In the research fields of personality and psychiatry, affect instability is characteristic of personality disorders (Jahng et al., 2008; Trull et al., 2008), with particular focus on negative mood (Eid & Langeheine, 2003), affect (Eid & Diener, 1999), mood and job satisfaction (Ilies & Judge, 2002), affect and mood instability (Jahng et al., 2008), short-term fluctuations in self-esteem (Kernis et al., 1989), and mood variability (McConville & Cooper, 1997). Expanding into other fields, recent studies of intraindividual variability include secure attachment (La Guardia, Ryan, Couchman, & Deci, 2000), temperament (Hooker, Nesselroade, Nesselroade, & Lerner, 1987), perceived control (Eizenman, Nesselroade, Featherman, & Rowe, 1997), and coping (Roesch et al., 2010). A range of techniques have been suggested for aggregating measures of within-person variability (for a review, see Jahng et al., 2008): the intraindividual standard deviation (or variance), first-order autocorrelation coefficients r, and the mean square successive difference (MSSD; von Neumann, Kent, Bellinson, & Hart, 1941). While the intraindividual standard deviation is intuitively appealing, it does not capture the frequency of change (Larsen, 1987). The MSSD calculates an aggregate that takes the sequential order of the events into account (Equation 1). MSSD =!! !!! !(x!+!1−!x!)! !!! !!!! (1), where xi + 1 is the lagged value of xi. The squared difference between xi + 1 and xi assures that the magnitude of the successive differences is captured. There are n-1 observations in the dataset (see Appendix 1). In a didactic simulation shown in Figure 1 we exemplify the conduct of the mean (M), standard deviation (SD), the mean square successive difference (MSSD), and the autocorrelation (r), in three scenarios (Panels A, B and C). For a similar simulation see Jahng et al. (2008). When we observe the raw data in Panel A (Figure 1) we find that the M and SD are the same as in Panel C, in which the data has been rank-ordered in descending order. The M and SD in Panel B, in which each data-point has been multiplied by two, are the same as multiplying the M and SD of those in Panel A by two. While the SD indeed captures variation, it is not sufficient for capturing the magnitude of variation. The stability over time captured by the autocorrelation r remains the same in Panels A and B, demonstrating that r does not capture the magnitude of change either. The autocorrelation coefficient r is different in Panel C demonstrating that the order of events matter. Finally MSSD differs in all three Panels demonstrating that it is both sensitive to magnitude (Panel B) and order of change (Panel C). In the present study we use MSSD for investigating intraindividual variability. In previous studies, a range of models for investigating lagged associations have been specified, including time-series and spectral analysis (Larsen, 1987; Ram et al., 2005), the mixed-effects location scale model (Li & Hedeker, 2012), generalized multilevel model (Jahng et al., 2008), and mixture distribution models (Eid & Langeheine, 2003). However, these models do not correct for measurement error in constructs. To do so, we calculated MSSD for each indicator of our latent constructs and modelled these using multilevel structural equation models (MSEM). Although time-series typically requires longer stretches of time-points, the MSSD method is suggested to be robust also for shorter time-series e.g., a number of time-points during each day (Ebner-Priemer, Eid, Kleindienst, Stabenow, & Trull, 2009). Malmberg)et)al) ) ) ! | FLR 65! ! Figure 1. Three example time-series and indices of intraindividual variability (cf. Jahng, et al., 2008). Note: Panel A represents one sample student for whom 29 situation reports were observed for intrinsic motivation (1 = low motivation, 4 = high motivation). Panel B represents each numerical value in Panel A multiplied by 2, so the scale now spans 2 to 8. Panel C represents the raw data from Panel A but now rank-ordered in descending order. M = Mean, SD = standard deviation, MSSD = the mean square successive difference, and r = autocorrelation. 1.3. Research questions and hypotheses a) What is the structural validity of the state, trait and intraindividual variability constructs? b) What is the association between trait and intraindividual variability constructs? c) How do trait and intraindividual variability constructs of students’ learning experiences converge with teacher-reported task-focus? Hypothesis 1: We expected convergence between teacher-reports of students' task-focus (Nurmi et al., 2008), higher level of task-focus positively and moderately associated with trait-levels of each construct, and negatively associated with variability constructs (i.e., higher task-focus less variability, lower task-focus more variability). 2. Method 2.1. Sample and procedure In total, 353 students in 16 classrooms in 11 schools participated in the Learning Every Lesson (LEL) study (for details see Malmberg, Woolgar, & Martin, 2013; Malmberg, Walls et al., 2013; Malmberg et al., 2015), with informed parental or guardian consent. The study was carried out in two quite diverse areas in southeast England, UK. Students were asked to complete the electronic Learning Experience Questionnaire (LEQ) for Personal Digital Assistant (PDA) at the end of each learning episode or at least once per lesson. Teachers or teaching assistants were asked to complete a brief one-page report of each student they taught. Teaching arrangements differed across the classes. In half of the classrooms, one teacher reported on all his or her students; in four classrooms two teachers reported on the students; in two Malmberg)et)al) ) ) ! | FLR 66! ! classrooms, there was a mix of students with one or two teacher reports; and in another two classrooms, two or three teachers reported. In order to investigate the correspondence between students’ and teachers’ views of the students, in the final study sample we included all observations for which both teacher and student reports for any given student were available. The intraclass correlation for teacher-reported task-focus was rICC = .08 between classrooms and rICC = .08 between teachers (Malmberg et al., 2015). In order to not burden the models with additional hierarchical levels, teacher reports were aggregated for each student, weighted for the number of experiences with each teacher. However, for the purpose of aggregating MSSD-indices of the lagged relationships between the time-points, we carried out analyses for those students who had at least five timepoints of data available (roughly the possible number of reports per day). There were 285 students who reported on 3,433 learning episodes: on average 13.6 learning episodes (SD = 4.6; Range = 5-29) combined with 434 teacher reports (139 students had one teacher report, 143 had two reports and 3 had three reports). Of these there were 126 boys (44.2%) and 159 girls (55.8%), 104 were in Year 5 (36.5%) and 181 in Year 6 (63.5%). They were 10.5 years old on average (SD = 0.64). 2.2. Student-reported measures Students’ learning experiences were measured using the validated LEQ (reliability, structural and external validity), covering sources of motivation, learning behaviour, competence evaluation and affect (Malmberg, Woolgar, & Martin, 2013). 2.2.1. Task difficulty Students completed a single item measuring task difficulty: “The learning task I was doing was”, on a four-point scale (1= very easy, 4= very hard). 2.2.2. Competence evaluation Students responded to two items indicating competence evaluation (Mα = .70; SDα = .18): “how well were you doing at this task” on a five-point scale (1 = poorly, 5 = very well), and “how much did you understand” on a four-point scale (1 = all of it, 4 = none of it; reverse-coded). 2.2.3 Intrinsic motivation Students were asked “why were you doing this task?” and responded to three items measuring intrinsic motivation: “I enjoyed it”, “I chose to do it”, and “I was interested in it”. When we split the data by day and learning experience, the average internal consistency was Mα = .85 (SDα = .09). 2.3. Teacher-reported measures Teachers reported on each student’s task-focused characteristics and behaviour. 2.3.1. Task-focus We used teacher-reports of each student’s task-focus in school in general. Task-focus was measured with six items modified from the Observer-rating Scale of Achievement Strategies (OSAS; Nurmi, & Aunola, 1998), and the Behavioural Strategy Rating Scale II (BSR-II; Aunola, Nurmi, Parrila, & OnatsuArvilommi, 2000; Zhang, Nurmi, Kiuru, Lerkkanen, & Aunola, 2011). Teachers were asked to think about each student’s behaviour and work habits in class, and respond on five-point scales (0 = not at all, 1 = rarely, 2 = sometimes, 3 = often, 4 = very often), to what extent each of the six statements characterise the way each student typically behaves in learning situations. Half of the items were positively worded (indicating taskfocus): “actively attempts to solve even difficult tasks”, “demonstrates initiative and persistence in activities and tasks”, and “tries hard to finish even difficult tasks”. The three negatively worded items (indicating task- Malmberg)et)al) ) ) ! | FLR 67! ! avoidance) were: “has a tendency to find something else to do, instead of focusing on the task at hand”, “gives up easily”, and “loses focus if a task or activity is not going well” (α = .88). We specified the construct so that higher values indicated more focus on tasks. Task-focus was strongly and positively related to academic performance (Malmberg et al., 2015). 2.4. Analytic Procedures We specified multilevel structural equation models (MSEM) in Mplus (Muthén & Muthén, 2012). At the within level we specified a latent state construct ξW1 using x1 to x3 as indicators (see Figure 2). At the between level, we specified a correspondence between level trait construct ξB1, equating factor loadings across the levels for metric invariance between the state and trait constructs (Morin, Marsh, Nagengast, & Scalas, 2014). We then specified a second between-level construct, which captures interindividual differences in intraindividual variability, ξB2 using k indicators. Figure 2. MSEM of statetrait and variability constructs Note: Indicators are raw data of time-points (t) nested in students (i). Circles above (at the between level, e.g., X1B) and below (at the within level e.g., X1W) the indicators depict latent constructs of decomposed betweenand within-level indicators respectively. There is one within-level latent construct (ξW1) and two between-level constructs (ξB1 and ξB2), with factor loadings (λ, one-headed arrows) linking constructs to level-specific indicators. Variances of latent constructs are indicated in double headed arrows (ψ). Residuals of indicators are also depicted with double headed arrows (ε), at the within-level measurement error. The mean-structure (triangle with 1 inside) is estimated at the between-level (i.e., cluster-intercepts, τ). In the dataset we created lagged variables (xkT+1) of each indicator (xkT) for each student. This gave 285 additional lines of data, one for each participant in our data-matrix, giving a total of nti = 3,718 lines of Malmberg)et)al) ) ) ! | FLR 68! ! data (see Appendix 1). We then, in Mplus, defined intraindividual squared deviations (xkT+1 - xkT)2 which we used as indicators (see Appendix 2). The scalar of the MSSD equation, ! !!!, was not necessary to apply as there are n-1 number of successive differences for each participant. Calculating the average of the successive differences is to divide the sum of the squared successive differences by n-1. We specified MSEMs, presented in Figures 3-5, for each construct using one (difficulty), two (competence), and three indicators (intrinsic motivation) for each latent construct separately (Models 1-3). We then illustrated how to specify three multivariate models, presented in Figure 6 for investigating convergence between trait and variability constructs, and between variability and task-focus (Models 4-6). We inspected indices of convergence (association of higher magnitude where expected) and divergence (lack of association where expected; Campbell & Fiske, 1959). Model fit was assessed by inspecting cut-offs for goodness of fit indices: ≤.06 for good model fit using the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual for the within (SRMRW) and the between level (SRMRB), and ≥.90 for acceptable and ≥.95 for good model fit for the Comparative Fit Index (CFI; Browne & Cudeck, 1993). Assuming MAR we treated missing data (4.8% of the missing data-points, in the dataset with the non-lagged variables) using the default FIML algorithm in MPlus (Muthén & Muthén, 2012). We used the robust maximum likelihood estimator (MLR) which corrects standard errors for non-normality. 3. Results In order to test structural validity of the state, trait, and variability-constructs of each learning experience, we present a univariate MSEM specified with one manifest indicator (task-difficulty), two indicators (competence evaluation), and three indicators (intrinsic motivation). To investigate the association between trait and variability-constructs we report on the correlation between these latent constructs. 3.1. Univariate models As shown in Fig 3, we illustrate how to specify our proposed model using a single item indicator. To identify this model we fixed a number of parameters: all factor loadings (at 1), and residuals (at 0). The pooled within-level variance was ψW1= 0.88 and between ψB1 = 0.26, showing that 22.5% of the variance of task difficulty resided at the between level. We note that the variance of the variability construct, ψB2 = 2.05, was larger than the variance of the trait construct. The association between trait-task-difficulty and variability in task-difficulty was ρ = 0.46, that is the more difficult tasks appeared on average during the week, the more variability in task-difficulty (i.e., larger ups and downs in task difficulty during the week). Malmberg)et)al) ) ) ! | FLR 69! ! Figure 3. Multilevel structural equation model of latent state, trait and intraindividual variability of task difficulty. Note: manifest indicators are diff = task-difficulty As shown in Fig 4, we illustrate how to specify our proposed model using two indicators. The pooled within-level variance was ψW1 = 0.32 and between ψB1 = 0.13, showing that 29.5% of the variance of competence beliefs resided at the between level. We note that the variance of the variability construct, ψB2 = 0.66, was larger than the variance of the trait construct. The association between trait intrinsic motivation and variability in intrinsic motivation was ρ = -0.72, that is the more competent students thought they were on average during the week, the less variable they thought their competences were during the week (i.e., smaller ups and downs in competence belief during the week). Malmberg)et)al) ) ) ! | FLR 76! ! References Aunola, K., Nurmi, J.-E., Parrila, R., & Onatsu-Arvilommi, T. (2000). Behavioral strategy relating scale II. Unpublished measurement instrument Jyväskylä: University of Jyväskylä, Finland. Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54, 199-231. doi: 10.1111/j.14640597.2005.00205.x Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Beverly Hills, CA: Sage. Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245-281. doi:10.3102/00346543065003245 Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitraitmultimethod matrix. Psychological bulletin, 56, 81-105. doi:10.1037/h0046016 Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125, 627–668. doi:10.1037/0033-2909.125.6.627 Ebner-Priemer, U. W., Eid, M., Kleindienst, N., Stabenow, S., & Trull, T. J. (2009). Analytic strategies for understanding affective (in)stability and other dynamic processes in psychopathology. Journal of Abnormal Psychology, 118, 195–202. doi:10.1037/a0014868 Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & MacIver, D. (1993). Development during adolescence: The impact of stage-environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48, 90-101. doi:10.1037/0003066X.48.2.90 Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In W. Damon & N. Eisenberg, (Eds.), Handbook of child psychology, 5th ed.: Vol 3. Social, emotional, and personality development (pp. 1017-1095). Hoboken, NJ.: John Wiley & Sons. Eid, M., & Diener, E. (1999). Intraindividual variability in affect: Reliability, validity, and personality correlates. Journal of Personality and Social Psychology, 76, 662-676. doi:10.1037/00223514.76.4.662 Eid, M., & Langeheine, R. (2003). Separating stable from variable individuals in longitudinal studies by mixture distribution models. Measurement: Interdisciplinary Research and Perspectives, 1, 179-206. doi:10.1207/S15366359MEA0103_01 Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J.W. (1997). Intraindividual variability in perceived control in an older sample: The MacArthur Successful Aging studies. Psychology and Aging, 12, 489-502. doi:10.1037/0882-7974.12.3.489 Georgiou, G., Manolitsis, G., Nurmi, J.-E., & Parrila, R. (2010). Does task-focused versus task-avoidance behavior matter for literacy development in an orthographically consistent language? Contemporary Educational Psychology, 35, 1−10. doi:10.1016/j.cedpsych.2009.07.001 Gill, P., & R. Remedios, R. (2013). How should researchers in education operationalise on-task behaviours? Cambridge Journal of Education, 43, 199-222. doi:10.1080/0305764X.2013.767878 Goetz, T., Frenzel, A. C., Stoeger, H., & Hall, N. C. (2010). Antecedents of everyday positive emotions: An experience sampling analysis. Motivation and Emotion, 34, 49-62. doi:10.1007/s11031-009-9152-2 Hamre, B. K., & Pianta, R. C. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76, 949-967. doi:10.1111/j.1467-8624.2005.00889.x Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: a metaanalysis. Review of Educational Research, 66, 99-136. doi:10.3102/00346543066002099 Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77, 81-112. doi:10.3102/003465430298487 Hirvonen, R., Tolvanen, A., Aunola, K., & Nurmi, J.-E. (2012). The developmental dynamics of taskavoidant behavior and math performance in kindergarten and elementary school. Learning and Individual Differences,22, 715-723. doi: 10.1016/j.lindif.2012.05.014 Malmberg)et)al) ) ) ! | FLR 77! ! Hooker, K., Nesselroade, D. W., Nesselroade, J. R., & Lerner, R. M. (1987). The structure of intraindividual temperament in the context of mother-child dyads: P-technique factor analyses of short-term change. Developmental Psychology, 23, 332-346. doi:10.1037/0012-1649.23.3.332 Ilies, R., & Judge, T. A. (2002). Understanding the dynamic relationships among personality, mood, and job satisfaction: A field experience sampling study. Organizational Behavior and Human Decision Processes, 89, 1119–1139. doi:10.1016/S0749-5978(02)00018-3 Inkinen, M., Lonka, K., Hakkarainen, K., Muukkonen, H., Litmanen, T., & Salmela-Aro, K. (2013). The interface between core affects and the challenge-skill relationship. Journal of Happiness Studies, 15, 891-913. doi:10.1007/s10902-013-9455-6 Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13, 354–375. doi:10.1037/a0014173 Kernis, M. H., Grannemann, B. D., & Barclay, L. C. (1989). Stability and level of self-esteem as predictors of anger arousal and hostility. Journal of Personality and Social Psychology, 56, 1013-1022. doi:10.1037/0022-3514.56.6.1013 La Guardia, J. G., Ryan, R. M., Couchman, C. E., & Deci, E. L. (2000). Within-person variation in security of attachment: A self-determination theory perspective on attachment, need fulfillment, and well-being. Journal of Personality and Social Psychology, 79, 367-384. doi:10.1037/0022-3514.79.3.367 Larsen, R. J. (1987). The stability of mood variability: a spectral analytic approach to daily mood assessments. Journal of Personality and Social Psychology, 52, 1195-1204. doi:10.1037/00223514.52.6.1195 Lee, W., & Reeve, J. (2012). Teachers’ estimates of their students’ motivation and engagement: Being in synch with students. Educational Psychology, 32, 727–747. doi:10.1080/01443410.2012.732385 Li, X., & Hedeker, D. (2012). A three-level mixed-effects location scale model with an application to ecological momentary assessment data. Statistics in Medicine, 31, 3192-3210. doi:10.1002/sim.5393 Malmberg, L.-E., Walls, T., Martin, A. J., Little, T. D., & Lim, W. H. T. (2013). Primary school students' learning experiences of, and self-beliefs about competence, effort, and difficulty: Random effects models. Learning and Individual Differences, 28, 54–65. doi:10.1016/j.lindif.2013.09.007 Malmberg, L.-E., Woolgar, C., & Martin, A. (2013). Quality of measurement of the Learning Experience Questionnaire for Personal Digital Assistants. International Journal of Quantitative Research in Education, 1, 275-296. doi:10.1504/IJQRE.2013.057689 Malmberg, L.-E., Pakarinen, E., Vasalampi, K., & Nurmi, J-E. (2015). Students’ school performance, taskfocus, and situation-specific motivation. Learning and Instruction, 39, 158-167. doi:10.1016/j.learninstruc.2015.05.005 Marsh, H. W. (1989). Age and sex effects in multiple dimensions of self-concept: preadolescence to early adulthood. Journal of Educational Psychology, 81, 417-430. doi:10.1037/0022-0663.81.3.417 McConville, C., & Cooper, C. (1997). The temporal stability of mood variability. Personality and Individual Differences, 23, 161-164. doi:10.1016/S0191-8869(97)00013-5 Morin, A. J. S., Marsh, H. W., Nagengast, B., & Scalas, L. F. (2014). Doubly latent multilevel analyses of classroom climate: An illustration. The Journal of Experimental Education, 82, 143-167. doi:10.1080/00220973.2013.769412 Musher-Eizenman, D. R., Nesselroade, J. R., & Schmitz, B. (2002). Perceived control and academic performance: A comparison of highand low-performing children on within-person change patterns. International Journal of Behavioral Development, 26, 540–547. doi:10.1080/01650250143000517 Muthén, L. K. & Muthén, B. O. (2012). Mplus statistical analysis with latent variables: User’s guide (Version 7). Los Angeles, CA: Muthén & Muthén. Nett, U. E., Goetz, T., & Hall, N. C. (2011). Coping with boredom in school: An experience sampling perspective. Contemporary Educational Psychology, 36,49–59. doi:10.1016/j.cedpsych.2010.10.003 Nett, U. E., Goetz, T., Hall, N. C., & Frenzel, A. C. (2012). Metacognitive strategies and test performance: An experience sampling analysis of students’ learning behavior. Education Research International. Article ID 958319, 16 pages. [online] http://kops.ub.uni-konstanz.de/handle/urn:nbn:de:bsz:352-206102 (Accessed 12 April 2013). Malmberg)et)al) ) ) ! | FLR 78! ! Nurmi, J.-E., & Aunola, K. (1998). Observer Rating Scale of Achievement Strategies (OSAS). Unpublished measurement instrument. Jyväskylä: University of Jyväskylä, Finland. Nurmi, J.-E., Hirvonen, R., & Aunola, K. (2008). Motivation and achievement beliefs in elementary school: A holistic approach using longitudinal data. Unterrichtswissenschaft, 36(3), 237–254. doi:10.3262/UW0803237 Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: Standardized observation can leverage capacity. Educational Researcher, 38, 109-119. doi:10.3102/0013189X09332374 Pianta, R. C., Belsky, J., Vandergrift, N., Hours, R., & Morrison, F. (2008). Classroom effects on children’s achievement trajectories in elementary school. American Educational Research Journal, 45, 365-397. doi:10.3102/0002831207308230 Ram, N., Chow, S.-M., Bowles, R. P., Wang, L., Grimm, K., Fujita, F., & Nesselroade, J. R. (2005). Examining interindividual differences in cyclicity of pleasant and unpleasant affect using spectral analysis and item response modeling. Psychometrika, 70, 773-790. doi:10.1007/s11336-001-1270-5 Reeve, J., Jang, H., Carrell, D., Jeon, S., & Barch, J. (2004). Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion, 28, 147-169. doi:10.1023/B:MOEM.0000032312.95499.6f Roesch, S. C., Aldridge, A. A., Stocking, S. N., Villodas, F., Leung, Q., Bartley, C. E. & Black, L. J. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behavioral Research, 45, 767-789. doi:10.1080/00273171.2010.519276 Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, N J: Princeton University Press. Schmitz, B. (2006). Advantages of studying processes in educational research. Learning and Instruction, 16, 433-449. doi:10.1016/j.learninstruc.2006.09.004 Schmitz, B. (2015). The study of learning processes using time-series analyses [Video file]. Available from http://www.education.ox.ac.uk/network-on-intrapersonal-research-in-education-nire/seminar1/bernhard-schmitz/ Schmitz, B., & Skinner, E. (1993). Perceived control, effort, and academic performance: Interindividual, intraindividual, and multivariate time-series analyses. Journal of Personality and Social Psychology, 64, 1010-1028. doi:10.1037/0022-3514.64.6.1010 Schmitz, B. & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31, 64 – 96. doi:10.1016/j.cedpsych.2005.02.002 Sheldon, K. M. & Elliot, A. J. (1998). Not all personal goals are personal: Comparing autonomous and controlled reasons for goals as predictors of effort and attainment. Personality and Social Psychology Bulletin, 24, 546-557. doi:10.1177/0146167298245010 Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85, 571-581. doi:10.1037/0022-0663.85.4.571 Trull, T. J., Solhan, M. B., Tragesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., & Watson, D. (2008). Affective instability: Measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of Abnormal Psychology, 117, 647-661. doi:10.1037/a0012532 Tsai, Y-M., Kunter, M., Lüdtke, O. & Trautwein, U. (2008). Day-to-day variation in competence beliefs: How autonomy support predicts young adolescents’ felt competence In H. W., Marsh, R. G., Craven, & D. M., McInerney (Eds.), Self-processes, learning, and enabling human potential: dynamic new approaches (pp. 119-143). Charlotte, NC:Information Age Publishing.. von Neumann, J., Kent, R. H., Bellinson, H. R., & Hart, B. I. (1941). The mean square successive difference. The Annals of Mathematical Statistics, 12, 153–162. doi:10.1214/aoms/1177731746 Walls, T. A., Barta, W. D., Stawski, R. S., Collyer, C, & Hofer, S. M. (2011). Time-scale dependent longitudinal designs. In B. Laursen, T. D. Little, & N. Card (Eds.), Handbook of developmental research methods (pp. 46-64). New York: Guilford Press. Malmberg)et)al) ) ) ! | FLR 79! ! Wilhelm, P., Perrez, M., & Pawlik, K. (2012). Conducting research in daily life. In M. R. Mehl, and T. S. Conner, (Eds.), Handbook of research methods for studying daily life (pp. 62-68). New York: Guilford Press. Zhang, X., Nurmi, J-E., Kiuru, N., Lerkkanen, M.-K., & Aunola, K. (2011). A teacher-report measure of children's task-avoidant behavior: A validation study of the Behavioral Strategy Rating Scale. Learning and Individual Differences, 21, 690–698. doi:10.1016/j.lindif.2011.09.007 Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82–91. doi:10.1006/ceps.1999.1016 Malmberg)et)al) ) ) ! | FLR 80! ! Appendix 1. Hand-calculation of the mean square successive difference (MSSD) This hand-calculation presents the values of Panel A in Figure 1. The columns below represent: timepoint = 28 time-points for one individual. Note that the numeric values of intrT+1 are replicated in the intrT column, only one row below each corresponding intrT+1 value. This gives a 29th time-point; intrT+1 = intrinsic motivation at time-point T+1; intrT = intrinsic motivation at time-point T. This variable was created by lagging the intrT+1 variable one time-point step. Time-point T becomes a predictor of time-point T+1; Δ = difference between intrT+1 and intrT; Δ2 = squared difference between intrT+1 and intrT; l = missing data created by the lag. time-point intrT+1 intrT Δ Δ2 1 4.0 l l l 2 3.5 4.0 -0.5 0.25 3 2.0 3.5 -1.5 2.25 4 2.5 2.0 0.5 0.25 5 4.0 2.5 1.5 2.25 6 2.0 4.0 -2.0 4.00 7 2.0 2.0 0.0 0.00 8 2.0 2.0 0.0 0.00 9 2.0 2.0 0.0 0.00 10 2.5 2.0 0.5 0.25 11 1.0 2.5 -1.5 2.25 12 2.0 1.0 1.0 1.00 13 2.5 2.0 0.5 0.25 14 2.5 2.5 0.0 0.00 15 2.5 2.5 0.0 0.00 16 2.0 2.5 -0.5 0.25 17 1.0 2.0 -1.0 1.00 18 2.5 1.0 1.5 2.25 19 2.0 2.5 -0.5 0.25 20 1.5 2.0 -0.5 0.25 21 1.5 1.5 0.0 0.00 22 2.5 1.5 1.0 1.00 23 1.5 2.5 -1.0 1.00 24 1.0 1.5 -0.5 0.25 25 2.0 1.0 1.0 1.00 26 1.0 2.0 -1.0 1.00 27 1.0 1.0 0.0 0.00 28 1.0 1.0 0.0 0.00 (29) l 1.0 l l M 2.05 2.05 -0.11 0.78 SD 0.83 0.83 0.89 1.01 r(T+1,T) !! 0.36 The MSSD is the average of Δ2 using n-1 (28-1=27) as denominator. r(T+1,T) is the autocorrelation between intrT+1 and intrT. Malmberg)et)al) ) ) ! | FLR 81! ! Appendix 2. Mplus code for single construct model (intrinsic motivation) TITLE: MSSD 28 June 2016 ; DATA: FILE IS "C:\variability.txt" ; VARIABLE: NAMES ARE studid sequence lag_n diff_t1 diff_t0 well_t1 well_t0 und_t1 und_t0 enj_t1 enj_t0 int_t1 int_t0 cho_t1 cho_t0 focus1 focus2 focus3 avoid1 avoid2 avoid3 ; !enj=enjoyment, int=interest, cho=choice, t1 = time T+1, t0 = time T USEVAR = enj_t1 int_t1 cho_t1 enj_var int_var cho_var ; !include three observed and three defined variables MISSING ALL (-9) ; BETWEEN enj_var int_var cho_var ; !defined variables are at level 2 CLUSTER = studid; !clustering by student DEFINE: enj_va = (enj_t1 - enj_t0)**2 ; !squared difference of enjoyment int_va = (int_t1 - int_t0)**2 ; !average squared difference of enjoyment cho_va = (cho_t1 - cho_t0)**2 ; !squared difference of interest enj_var = CLUSTER_MEAN (enj_va) ; !average squared difference of interest int_var = CLUSTER_MEAN (int_va) ; !squared difference of choice cho_var = CLUSTER_MEAN (cho_va) ; !average squared difference of choice CENTER (grandmean) enj_t1 int_t1 cho_t1 enj_var int_var cho_var ; !grand mean centre level 2 indicators ANALYSIS: TYPE = TWOLEVEL ; MODEL: %WITHIN% w_intr BY enj_t1 (a) int_t1 (b) cho_t1 (c) ; ! w_ = state-construct (within-level) ! factor loadings of within and between indicators are equated between level w_intr (var_w) ; !estimate variance, and use for calculating new parameter %BETWEEN% b_intr BY enj_t1 (a) int_t1 (b) cho_t1 (c) ; ! b_intr = trait-construct intr_var BY enj_var int_var cho_var ; ! variability construct b_intr WITH intr_var ; b_intr (var_b) ; Malmberg)et)al) ) ) ! | FLR 82! ! !estimate variance, and use for calculating new parameter int_t1*.05 (br1) ; ! estimate error variance MODEL CONSTRAINT: br1 > 0 ; !br2 > 0 ; NEW(var_comp); var_comp = var_b / (var_b + var_w) ; ! calculate intraclass correlation of latent constructs OUTPUT: STAND SAMPSTAT TECH1 TECH2 ; !