Achievement emotions and academic performance: longitudinal models of reciprocal effects

TLDR: The model was tested using five annual waves of the Project for the Analysis of Learning and Achievement in Mathematics (PALMA) longitudinal study, which investigated adolescents' development in mathematics, and showed that positive emotions positively predicted subsequent achievement and negative emotions negatively predicted achievement.

Abstract: A reciprocal effects model linking emotion and achievement over time is proposed. The model was tested using five annual waves of the Project for the Analysis of Learning and Achievement in Mathematics (PALMA) longitudinal study, which investigated adolescents’ development in mathematics (Grades 5–9; N = 3,425 German students; mean starting age = 11.7 years; representative sample). Structural equation modeling showed that positive emotions (enjoyment, pride) positively predicted subsequent achievement (math end-of-the-year grades and test scores), and that achievement positively predicted these emotions, controlling for students’ gender, intelligence, and family socioeconomic status. Negative emotions (anger, anxiety, shame, boredom, hopelessness) negatively predicted achievement, and achievement negatively predicted these emotions. The findings were robust across waves, achievement indicators, and school tracks, highlighting the importance of emotions for students’ achievement and of achievement for the development of emotions.

Figures

Figure 1. Basic structure of cross-lagged reciprocal effects models. Upper part: emotion and achievement. Lower part: positive affect, negative affect, and achievement. The models include cross-lagged effects, autoregressive effects, and directional paths from the covariates to emotion or affect and achievement at all waves. Correlations between the covariates and between residuals are not displayed.

Table 1 Alpha Coefficients and Pearson Product-Moment Correlations for Emotions and Achievement

Table 3 (continued)

Table 2 Reciprocal Effects Models for Emotion and Grades: Fit Indexes

Table 3 (continued)

Full text

Durham Research Online
Deposited in DRO:
29 March 2018
Version of attached le:
Accepted Version
Peer-review status of attached le:
Peer-reviewed
Citation for published item:
Pekrun, R. and Lichtenfeld, S. and Marsh, H.W. and Murayama, K. and Goetz, T. (2017) 'Achievement
emotions and academic performance : longitudinal models of reciprocal eects.', Child development., 88 (5).
pp. 1653-1670.
Further information on publisher's website:
https://doi.org/10.1111/cdev.12704
Publisher's copyright statement:
This is the accepted version of the following article: Pekrun, R., Lichtenfeld, S., Marsh, H.W., Murayama, K. Goetz,
T. (2017). Achievement Emotions and Academic Performance: Longitudinal Models of Reciprocal Eects. Child
Development 88(5): 1653-1670, which has been published in nal form at https://doi.org/10.1111/cdev.12704. This
article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Additional information:
Use policy
The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for
personal research or study, educational, or not-for-prot purposes provided that:
•
a full bibliographic reference is made to the original source
•
a link is made to the metadata record in DRO
•
the full-text is not changed in any way
The full-text must not be sold in any format or medium without the formal permission of the copyright holders.
Please consult the full DRO policy for further details.
Durham University Library, Stockton Road, Durham DH1 3LY, United Kingdom
Tel : +44 (0)191 334 3042 | Fax : +44 (0)191 334 2971
https://dro.dur.ac.uk

Running head: EMOTION AND ACHIEVEMENT
1
Paper accepted for publication in: Child Development
Achievement Emotions and Academic Performance:
Longitudinal Models of Reciprocal Effects
Reinhard Pekrun
Stephanie Lichtenfeld
University of Munich
Herbert W. Marsh
Australian Catholic University and University of Oxford
Kou Murayama
University of Reading
Thomas Goetz
University of Konstanz and Thurgau University of Teacher Education

Running head: EMOTION AND ACHIEVEMENT
2
Author Note
Reinhard Pekrun, Department of Psychology, University of Munich, Munich, Germany;
Stephanie Lichtenfeld, Department of Psychology, University of Munich, Munich, Germany;
Herbert W. Marsh, Institute for Positive Psychology and Education, Australian Catholic
University, Sydney, Australia, and Department of Education, University of Oxford, Oxford, UK;
Kou Murayama, Department of Psychology, University of Reading, Reading, UK; Thomas
Goetz, Department of Empirical Educational Research, University of Konstanz, Konstanz,
Germany, and Thurgau University of Teacher Education, Thurgau, Switzerland.
This research was supported by a LMU Research Chair grant awarded to R. Pekrun by
the University of Munich and four grants from the German Research Foundation (DFG) to R.
Pekrun (PE 320/11-1, PE 320/11-2, PE 320/11-3, PE 320/11-4). Parts of this paper were
presented at the annual meeting of the American Educational Research Association,
Philadelphia, PA, April 2014, and at the International Congress of Applied Psychology, France,
Paris, July 2014.
Correspondence concerning this article should be addressed to Reinhard Pekrun,
Department of Psychology, University of Munich, Leopoldstrasse 13, 80802 Munich, Germany.
E-mail: pekrun@lmu.de

Running head: EMOTION AND ACHIEVEMENT
3
Abstract
A reciprocal effects model linking emotion and achievement over time is proposed. The model
was tested using five annual waves of the PALMA longitudinal study, which investigated
adolescents’ development in mathematics (grades 5-9; N=3,425 German students; mean starting
age=11.7 years; representative sample). Structural equation modeling showed that positive
emotions (enjoyment, pride) positively predicted subsequent achievement (math end-of-the-year
grades and test scores), and that achievement positively predicted these emotions, controlling for
students’ gender, intelligence, and family socio-economic status. Negative emotions (anger,
anxiety, shame, boredom, hopelessness) negatively predicted achievement, and achievement
negatively predicted these emotions. The findings were robust across waves, achievement
indicators, and school tracks, highlighting the importance of emotions for students’ achievement
and of achievement for the development of emotions.
Keywords: achievement emotion, anxiety, academic achievement, mathematics
achievement, control-value theory

Running head: EMOTION AND ACHIEVEMENT
4
Research has shown that children’s and adolescents’ emotions are linked to their academic
achievement. Typically, positive emotions such as enjoyment of learning show positive links
with achievement, and negative emotions such as test anxiety show negative links (for
overviews, see Goetz & Hall, 2013; Pekrun & Linnenbrink-Garcia, 2014; Zeidner, 1998).
However, most of the available studies were correlational and do now allow any inferences about
the causal ordering of emotion and achievement over time. As such, it remains unclear how the
observed links should be interpreted. It is open to question if students’ emotions impact their
learning, if success and failure at learning influence the development of their emotions, if other
variables cause the association, or if several of these possibilities are at work. Given the need to
acquire knowledge about the antecedents of both students’ achievement and their emotions, this
is an issue of considerable theoretical and practical importance. To address this issue, the present
investigation went beyond merely observing correlations at a single point in time and attempted
to disentangle the temporal ordering of these constructs across multiple waves of data collection
and a developmental time span of several school years.
The investigation is based on a reciprocal effects model of emotion and achievement which
posits that the two variables reciprocally influence each other over time. This stands in contrast
to traditional unidirectional perspectives, which suggest that the link between emotion and
achievement is simply due to effects of emotions on students’ learning and performance. For
example, correlations between test anxiety and students’ achievement were interpreted as
indicating that anxiety impacts achievement, and test anxiety theories put forward various
suggestions about mediating mechanisms (e.g., cognitive interference, motivation; Zeidner,
1998, 2014). In a similar vein, in studies on affect and performance more generally, researchers
have been interested in the impact of moods and emotions on cognitive performance and created

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Running head: emotion and achievement 1 paper accepted for publication in: child development achievement emotions and academic performance: longitudinal models of reciprocal effects" ?

The model was tested using five annual waves of the PALMA longitudinal study, which investigated adolescents ’ development in mathematics ( grades 5-9 ; N=3,425 German students ; mean starting age=11. 7 years ; representative sample ). 

Q2. What future works have the authors mentioned in the paper "Running head: emotion and achievement 1 paper accepted for publication in: child development achievement emotions and academic performance: longitudinal models of reciprocal effects" ?

Nevertheless, several limitations should be considered when interpreting the study findings and can be used to suggest directions for future research. As such, although the present analysis used multi-wave longitudinal structural equation modeling and controlled for related variables and autoregressive effects, the possibility still exists that their findings are attributable to other variables that were not included in the study. To balance the benefits and drawbacks of different methodologies and make headway in this avenue of research, future studies should further pursue the approach taken herein while complementing this approach with experimental studies. Additionally, future research should explore if these findings generalize to emotions in achievement domains other than mathematics, 

Q3. What is the loss of fit for the metric and residual invariance models?

For positive and negative affect, the loss of fit was CFI < .008, RMSEA < .004,and SRMR < .005 for the metric, intercept, and residual invariance models, demonstratingsupport for invariance for these second-order constructs as well. 

Q4. What is the purpose of the proposed model of reciprocal effects?

In the proposed model of reciprocal effects, it is posited that effects of emotion on achievement are due to the influence of emotions on cognitive resources, motivation, and strategy use. 

Q5. What was the loss of fit for the metric and intercept models?

When constraining autoregressive effects, cross-laggedeffects, and factor residual variances to be equal across time intervals, the loss of fit was CFI <.003, RMSEA < .001, and SRMR < .003 for all of the models. 

Q6. What is the effect of doing well in school on students’ emotions?

More specifically, it appears that doing well in school can strengthen students’ positive emotions andreduce their negative emotions over time, whereas doing poorly in school undermines positive emotions and exacerbates negative emotions. 

Q7. What are the advantages of using grades as sources of students’ emotional development?

from the perspective of grades as sources of students’ emotional development, they could be seen as having almost perfectreliability---grades, rather than objective achievement, provide the feedback that shapes students’ perceptions of success and failure and any development based on these perceptions. 

Q8. What was the loss of fit for the metric invariance models?

The loss of fit for the scalarinvariance models was CFI < -.007,RMSEA < .004, and SRMR < .007 for all of theemotions, documenting that scalar invariance was supported as well. 

Q9. What are the dimensions of emotion that are used to describe human affect?

Using these dimensions renders four groups of emotions: positive activating (e.g., enjoyment, hope, pride), positive deactivating (e.g., relaxation, relief), negative activating (e.g., anger, anxiety, shame), and negative deactivating (e.g., boredom, hopelessness). 

Q10. What were the background variables included as covariates?

The background variables were included as covariates; for each of these variables, directional paths to all of the emotion variables and to all of the achievement variables were included. 

Q11. What scales were used to measure students’ emotions in math?

Participants responded on a 1 (strongly disagree) to 5 (strongly agree) scale, and the scores were summed to form the emotion indexes (Alpha range .86 to .92 across all scales and measurement occasions; see Table 1). 

Q12. What is the average influence of positive deactivating emotions on achievement?

notwithstanding individual differences regarding effects, the authors expect that the average overall influence of positive deactivating emotions on achievement is positive, and that the average overall influence of negative activating emotions is negative. 

Q13. What is the way to estimate missing values?

FIML has been found to result in trustworthy, unbiased estimates for missing values even in the case of large numbers of missing values (Enders, 2010) and to be an adequate method to manage missing data in longitudinal studies (Jeličič, Phelps, & Lerner, 2009). 

Q14. What is the effect of achievement on emotions?

Foreffects of achievement on emotion, this is to be expected, as success and failure are thought to impact the development of different positive and negative emotions in similar ways. 

Q15. What is the relationship between achievement and control?

Because emotions are posited to influence achievement and achievement, in turn, toinfluence emotion, the two constructs are thought to be linked by reciprocal causation over time. 

Q16. What is the significance of the present study?

The present study represents a significant advancement over previous research, because itdocuments reciprocal effects of emotion and achievement over time while controlling for general cognitive ability and critical demographic background variables. 

Q17. What is the relationship between perceived competence and control?

Perceived competence and control depend on students’ individual achievement history,with success strengthening control and failure undermining it.