Use longitudinal designs combining experimental manipulation with alternating measurement of motivation and achievement to establish causal reciprocity¶

Strategy · All strategies
Evidence · 4 claims (2 for, 2 mixed) · 4 studies (2 review, 2 associational), q1–q2 · 0 of 4 report an effect size · 3 claims rest on one study

Description¶

The review recommends that future studies meet three preconditions for causal inference in reciprocal relations: temporal order in both directions with alternation of measurements, correlation, and manipulation of one variable affecting the other at a later time point. It states that, to its knowledge, "no studies of the mutual relations between motivation and achievement also satisfy the third precondition of causality". It suggests new manipulations that raise perceived performance without raising performance per se, and notes double-blinded random assignment is easier in the lab than the classroom.

Design Implications¶

Context¶

Requirements¶

• Experimental manipulation of at least one variable, followed by repeated alternating measures of both motivation and achievement

Constraints¶

• Manipulating achievement independently from motivation is hard because instructional manipulations may raise motivation at the same time; random assignment poses practical problems in classrooms

Target Learners¶

• school students studied in longitudinal motivation-achievement research

Target Learning Goals¶

• reciprocal motivation-achievement relations

Claims¶

• Meta-analytic longitudinal evidence shows achievement affects later motivation about twice as strongly as motivation affects later achievement, with both pooled effects small [+M]
• The choice of longitudinal statistical model can determine whether reciprocal motivation-achievement relations are found [~M]
• Across basic and extended RI-CLPMs and CLPMs of five-wave secondary-school data, math self-concept and achievement show consistent reciprocal (bidirectional) directional ordering [+M]
• Simulation evidence shows RI-CLPM estimates are biased when the true model is a CLPM with lag-2 effects, and fit statistics cannot decide between the two [~W]

Related Strategies¶

• Supplement self-report questionnaires with process-oriented and online measures of motivation during learning

Examples¶

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Key Sources¶

• Vu, T., Magis-Weinberg, L., Jansen, B. R. J., van Atteveldt, N., Jansen, T. W. P. J., Lee, N. C., van der Maas, H. L. J., Raijmakers, M. E. J., Sachisthal, M. S. M., & Meeter, M. (2021). Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda. Educational Psychology Review. https://doi.org/10.1007/s10648-021-09616-7