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Student outcomes are influenced by different types of motivation that stem from external incentives, ego-involvement, personal value, and intrinsic interest. These types of motivation as described in self-determination theory each co-occur to different degrees and should lead to different consequences. These associations with outcomes are in part due to unique characteristics and in part to the degree of autonomy each entails. In the current meta-analysis, we examine these different types of motivation in 344 samples (223,209 participants) as they relate to 26 performance, well-being, goal orientation, and persistence-related student outcomes. Findings highlight that intrinsic motivation is related to student success and well-being, whereas personal value (identified regulation) is particularly highly related to persistence. Ego-involved motives (introjected regulation) were positively related to persistence and performance goals, but also positively related with indicators of ill-being. Motivation driven by a desire to obtain rewards or avoid punishment (external regulation) was not associated to performance or persistence but was associated with decreased well-being. Finally, amotivation was related to poor outcomes. Relative weights analysis further estimates the degree to which motivation types uniquely predict outcomes, highlighting that identified regulation and intrinsic motivation are likely key factors for school adjustment.
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Student Motivation and Outcomes 1
Student Motivation and Associated Outcomes: A Meta-Analysis from Self-Determination
Theory
Joshua L. Howard1, Julien S. Bureau2, Frédéric Guay2, Jane X.Y. Chong3, Richard M. Ryan4
1 Department of Management, Monash University, Melbourne, Australia.
2 Department of Educational Fundamentals and Practices, Université Laval, Canada
3 School of Psychological Science, University of Western Australia, Australia
4 Institute for Positive Psychology & Education, Australian Catholic University, Australia.
Corresponding Author:
Joshua L. Howard, Monash Business School, Department of Management, Monash University,
900 Dandenong Rd, Caulfield East, VIC, 3145, Australia. Email: josh.howard@monash.edu
This is the prepublication version of the following manuscript:
Howard, J.L., Bureau, J.S., Guay, F., Chong, J.X.Y., Ryan, R. M. (2021). Student motivation and
associated outcomes: A meta-analysis from self-determination theory. Perspectives on
Psychological Science. https://doi.org/10.1177/1745691620966789
©2020. This paper is not the copy of record and may not exactly replicate the authoritative
document published in Perspectives on Psychological Science.
Abstract
Student outcomes are influenced by different types of motivation that stem from external
incentives, ego-involvement, personal value, and intrinsic interest. These types of motivation as
described in self-determination theory each co-occur to different degrees and should lead to
different consequences. These associations with outcomes are in part due to unique
characteristics and in part to the degree of autonomy each entails. In the current meta-analysis,
we examine these different types of motivation in 344 samples (223,209 participants) as they
relate to 26 performance, well-being, goal orientation, and persistence-related student outcomes.
Findings highlight that intrinsic motivation is related to student success and well-being, whereas
personal value (identified regulation) is particularly highly related to persistence. Ego-involved
motives (introjected regulation) were positively related to persistence and performance goals, but
also positively related with indicators of ill-being. Motivation driven by a desire to obtain
rewards or avoid punishment (external regulation) was not associated to performance or
persistence but was associated with decreased well-being. Finally, amotivation was related to
poor outcomes. Relative weights analysis further estimates the degree to which motivation types
uniquely predict outcomes, highlighting that identified regulation and intrinsic motivation are
likely key factors for school adjustment.
Keywords: Motivation; Self-determination; Education; Student; Meta-analysis.
Student Motivation and Outcomes 2
Student Motivation and Associated Outcomes: A Meta-Analysis from Self-Determination
Theory
Within self-determination theory (SDT; Ryan & Deci, 2017), multiple types of
motivation are specified that each have unique characteristic phenomenology and dynamics.
These types of motivation can also be predictably ordered on a continuum of self-determination
(see Figure 1; Ryan & Deci, 2017, 2020; Howard, Gagné, & Bureau, 2017), varying from most
self-determined (i.e., intrinsic motivation), to partially self-determined (e.g., introjection), and
finally to an absence of self-determination (i.e., amotivation). Given that types of motivation
share a certain degree of self-determination, questions have been raised concerning the value of
measuring different types of motivation (i.e., a multidimensional approach) as opposed to a
single-dimensional approach which measures the general degree of experienced self-
determination (Chemolli & Gagné, 2014; Howard et al., 2017). Through meta-analytic
procedures, we aim to quantify the relative importance of different types of motivation in order
to examine how much each motive uniquely adds to our understanding of student functioning.
While SDT specifies one of the more complex and nuanced perspectives of motivation,
each type of motivation can also be aligned with other contemporary theories of motivation and
achievement. For example, SDT research began with a focus on intrinsic motivation, defined as a
psychological desire to enact behaviors for the pleasure, satisfaction, or excitement associated
with enacting the behavior itself (Ryan & Deci, 2019). Yet, intrinsic motivation has also been a
focus of other theories, including Krapp’s (2002) interest theory and Csikzentmihalyi’s theory of
flow (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014). Intrinsic motivation has also been a
central construct within Gottfried and colleagues’ developmental research on academic
motivation (e.g., Gottfried, Marcoulides, Gottfried, & Oliver, 2009) and Harter’s (2012) social
developmental perspective on motivation and identity.
In contrast to intrinsic motivation, extrinsic motivation describes the psychological state
evident when individuals are driven to achieve outcomes separable from the satisfactions
inherent in the behavior itself (Ryan & Connell, 1989). However, the behavior will have
different consequences for the individual depending on the type of contingency regulating it.
This leads to the specification of three commonly studied subtypes of extrinsic motivation:
identified, introjected, and external regulations. Identified regulation is a state that drives
individuals to enact behaviors based on perceived personal valued and meaningful, whether or
not these behaviors are inherently enjoyable. As such, within SDT, identified regulation is
considered a relatively self-determined form of motivation and is predicted to foster positive
learning attitudes and outcomes. Similarly, within expectancy-value theories (e.g., Wigfield,
Rosenzweig, & Eccles, 2017) such personal valuing of behavior is a positive motivating force in
student achievement.
Another internally driven, yet extrinsically focused form of motivation is described
within SDT as introjected regulation. This represents a state driven by internal, self-esteem
related dynamics such as guilt and shame avoidance as well as pride-seeking (Ryan & Deci,
2017). Introjected regulation is characterized by ego-involvement (Ryan, 1982) because the goal
Student Motivation and Outcomes 3
is to gain and maintain approval from the self and others. A number of theories have considered
introjection and ego-involvement in academic settings, for example, achievement goal theories
(Duda, 1989; Nichols, 1984), stability of self-esteem perspectives (Paradise & Kernis, 2002) and
theories focused on contingent self-esteem (e.g., Crocker, 2008). Research further suggests that
introjection often results from the application of contingent regard by parents and teachers for
academic outcomes (Roth, Assor, Niemiec, Ryan, & Deci, 2009). Finally, introjection has been
directly related to the concept of self-infiltration with Personality Systems Interaction Theory
(Kuhl, Quirin, & Koole, 2015; Ryan, 2018).
External regulation is the psychological state enacted when individuals seek out
externally controlled rewards and/or avoiding externally administered punishments. Although
SDT suggests that external regulation can drive short-term behavior, it also portrays external
regulation as a low-quality form of motivation that is often undermining of more self-determined
motives (e.g., Deci, Koestner, & Ryan, 1999). Nonetheless, there are approaches that embrace
external evaluations and incentives to promote learning such as high-stakes testing policies in
which external contingencies are expected to foster improved student achievement (Koretz,
2017; Levitt, List, Neckermann, & Sadoff, 2016; Ryan & Weinstein, 2009).
In addition to these intrinsic and extrinsic motivations, the category of amotivation refers
to a state in which neither intrinsic nor extrinsic factors energize action (Ryan & Deci, 2020).
Students experiencing amotivation either do not see the connection between their behavior and
the expected result and/or feel incapable of doing the work. As such, they are expected to
demonstrate little to no effort or persistence in school activities (Pelletier, Dion, Tuson, & Green-
Demers, 1999). Amotivation is thus associated with theories concerning low expectancy and/or
value (Wigfield et al., 2017), low self-efficacy (Schunk & DiBenedetto, 2016) and learned
helplessness (Abramson, Seligman, & Teasdale, 1978).
Although these five types of motivation are the most commonly applied in SDT research,
another type of extrinsic motivation, integrated regulation, is sometimes studied. Integrated
regulation is a form of extrinsic motivation that is highly self-determined. When driven by
integrated regulation, individuals assimilate the enactment of a behavior into their sense of self
such that the behavior becomes a fully congruent element of their identity. However, it is
commonly acknowledged that school students are too young to have integrated academic
demands into their identity (Deci, Ryan, & Guay, 2013), which explains difficulties in
empirically distinguishing integrated regulation from identified regulation in the educational
context (Howard et al., 2017). As such, it is not often measured in academic motivation scales.
Evaluating Both the Self-Determination and Specificity of Motives
The idea that motivation types are predictably ordered according to their degree of self-
determination raises an interesting and often overlooked issue concerning the specific effects of
different types of motivation along this continuum. That is, are motivation types truly distinct if
they can be described by their position on a single-dimensional continuum of self-determination?
If each type of motivation lies on a continuum, it would imply that motivation could be
understood by a unidimensional model, thereby questioning the necessity of a multidimensional
Student Motivation and Outcomes 4
approach. This issue has been the focus of several empirical studies in recent years with some
disputing the continuum structure of self-determination (Chemolli & Gagné, 2014), others
indicating a highly consistent and predictable ordering (Howard et al., 2017), while yet other
studies providing a nuanced interpretation incorporating both sides of this issue (Howard, Gagné,
Morin, & Forest, 2018; Litalien et al., 2017).
A meta-analysis examining the correlations among SDT regulation types found that,
regardless of the domain or scale used, each type of regulation is predictably ordered, thus
supporting the continuum structure of motivation (Howard et al., 2017). However, this previous
meta-analysis did not test whether motivation types related differently to outcome variables and
was limited solely to relations among motivation types. To examine this issue of
multidimensionality, understanding the associations between motivation types and students’
outcomes is required. If motivation types relate to outcomes in a linear fashion determined by
their place on the continuum of self-determination, this would indicate the central importance of
self-determination in understanding motivation. For example, it is generally assumed that the
more self-determined (or autonomous) a motivation type is, the more positively it will relate to
adaptive outcomes, and negatively to maladaptive ones. In contrast, less self-determined (or
controlled) motivation types will relate positively to maladaptive outcomes, but negatively to
adaptive ones. However, because SDT also specifies that each motivation type is defined by
unique characteristics (i.e. enjoyment, meaningfulness, ego-involvement, external pressures), and
will therefore have specific consequences, it is important to determine the patterns, magnitudes,
and reliability of their univariate relations with learners’ goals, academic outcomes, and well-
being.
Past research has occasionally highlighted the importance of unique motivation
characteristics in association with outcomes. For example, whereas some research suggests that
intrinsic motivation, being the most highly self-determined type of motivation, will predict more
desirable results (Guay & Bureau, 2018; Guay, Denault, & Renauld, 2017; Taylor et al., 2014), a
number of instances have been noted in which identified regulation appears more influential
(Burton, Lydon, D'alessandro, & Koestner, 2006; Gagné et al., 2015; Losier & Koestner, 1999).
Losier and Koestner (1999) found that voting behavior was more likely to be motivated by
identified regulation, reflecting a sense of the importance of the action, rather than by any
intrinsic enjoyment derived from the behavior. Results thus indicated that these types of
motivation do indeed capture distinctly different reasons for behaving. Many school tasks also
share this property of being important but not always enjoyable. While these studies provide a
proof of concept, it remains unclear when or for which education-relevant outcomes identified
regulation will demonstrate similar, or even stronger prediction than intrinsic motivation.
The exact roles of introjected and external regulations are also of interest in order to
clarify how these two controlled motives are differentially associated with outcomes (Gagné et
al., 2015; Ng et al., 2012). For example, in a meta-analysis of health-related behaviors,
introjection was found to be associated with some adaptive outcomes, some negative ones, and
showed some nonsignificant relations (Ng et al., 2012). As such, this previous meta-analysis
Student Motivation and Outcomes 5
demonstrated that introjection has varying effects but was not broad enough to identify a
consistent pattern of when and for which variables these effects should be expected. In the
academic domain, introjection may similarly play different roles depending on whether the
outcome concerns, for example, persistence, well-being, or the embracing of performance goals.
When considering external regulation, it is also unclear in which occasions it is an
effective motivator and when it does more harm than good. In contrast with introjection, which
theoretically represents partial internalization and should thus have mixed effects, external
regulation is considered a less positive influence. Typical relations to educational outcomes
range from nonsignificant relations with positive outcomes to small deleterious effects (Guay &
Bureau, 2018; Litalien, Guay, & Morin, 2015). Evidence from other domains of research, such as
workplaces, indicate that external motivating factors may yield positive relations with outcomes
such as quantity of work completed. For example, a meta-analysis by Cerasoli, Nicklin, and Ford
(2014) compared the effects of intrinsic motivation and extrinsic incentives (used as a proxy for
extrinsic motivation) in predicting the quantity and quality of workplace performance, finding
that extrinsic incentives positively predicted work quantity but not quality, whereas intrinsic
motivation predicted both quality and quantity. However, the scope of their meta-analysis was
restricted to these two broad categories of motivation and did not examine the contribution of
each regulation type specified by SDT which is relevant for a detailed understanding of human
motives. Likewise, a broad range of important outcomes fell outside the scope of their study,
leaving questions about the effects of motivation on well-being, for example, unexamined. These
questions are relevant to education considering the high levels of both external and introjected
motivation in school contexts (Ratelle, Guay, Vallerand, Larose, & Senécal, 2007).
Such issues concerning the relations between specific motivation types and outcomes are
particularly important given the correlated nature of the motivation types and their frequent use
by SDT researchers in aggregated forms, such as contrasts between autonomous and controlled
types of motivation and/or relative autonomy summary scores (Howard et al., 2020; Sheldon,
Osin, Gordeeva, Suchkov, & Sychev, 2017). The issue of multicollinearity is noteworthy as it
can result in spurious effects and the incorrect partitioning of explained variance, thereby
obscuring the true effects associated with regulation types in multiple regression. While many
studies tend to either ignore this multicollinearity or bypass it through composite variables in
which motivation types are combined, solutions to multicollinearity are available. Relative
weight analysis can be utilized to account for correlated predictors and is a particularly powerful
method when applied in conjunction with meta-analytically derived correlations. As such, this
analytic approach is well suited to testing the individual contributions of each motivation type in
multivariate models and will more clearly identify trends and patterns which are unlikely to be
noticed in individual studies or studies employing summary scores.
Assessing Multiple Outcomes Associated with Academic Motives
Outcomes in the current study are presented under five categories; academic
achievement, persistence (e.g., effort, continuance intention, dropout intention), well-being (e.g.,
anxiety, positive and negative affect), goal orientations (e.g., performance approach/avoidance
Student Motivation and Outcomes 6
and mastery approach/avoidance), and self-evaluation (e.g., self-esteem, self-efficacy, self-
image). Academic achievement is usually operationalized as grade point average and is
measured both objectively and by self-report. Variables within the persistence category reflect a
students intentions to participate in behaviors and include the variables of effort, continuance
intention, intention to exercise, participation in physical activity, engagement, absenteeism, and
intention to dropout. The well-being category includes the variables of anxiety, depression,
boredom, negative affect, positive affect, general life satisfaction, vitality, enjoyment, and social-
emotional functioning. Social-emotional functioning is defined in this study as an ability of
students to navigate interpersonal interactions is a positive and fulfilling manner. The goal
orientation category includes variables describing achievement goals (Elliot, 2013). Where
mastery goals focus on developing competence and mastery of school activities, performance
goals focus on how students are judged to have performed, especially in relation to others
(Scherrer, Preckel, Schmidt, & Elliot, 2020). For example, mastery approach describes a mindset
of engaging in a behavior with the intention of mastering the behavior, whereas mastery
avoidance describes avoiding self-perceptions of incompetence, often resulting in individuals
choosing easier tasks over more difficult ones. A performance approach orientation describes
students who aim to demonstrate high performance relative to others, whereas performance
avoidance describes a desire not to fail in a given behavior in front of others (Elliot & Hulleman,
2017; Scherrer et al., 2020). The self-evaluation category includes self-efficacy (belief in one’s
ability to accomplish a goal), self-esteem (respect or positive regard one has for themselves),
anxiety concerning physical self-image, as well as positive impressions of physical self-image.
The categories of self-evaluation and goal orientations can be considered either antecedents or
outcomes of motivation (e.g., Hein & Hagger, 2007; Ciani, Sheldon, Hilpert, & Easter, 2011).
For this reason, we refer to these variables as covariates rather than outcomes, but for analytic
consistency treat them as outcomes.
Last, a number of moderators were examined, including publication status, age, gender,
scale used, nationality, and context (classroom education vs. physical education). Self-
determination theory specifies that its representation of motivation is universal (Chirkov, 2009;
Ryan & Deci, 2020), and therefore should not vary substantially across contextual or individual
difference variables. As such, we test the degree to which this claim of universality holds true for
relations between motivation types and outcomes in the education context. We specifically test
whether these relations vary as a function of the country in which data were collected to explore
broad cultural differences. We also test more specifically for any differences in how motivation
is experienced by students according to age and gender. In each case, these exploratory analyses
are not expected to find substantial differences.
Methods
Inclusion Criteria
Inclusion in the current study was limited to samples meeting three criteria. First, samples
must have presented primary data collected using a validated motivation scale based upon the
SDT conceptualization of motivation. These scales are the Self-Regulation Questionnaire (SRQ),
Student Motivation and Outcomes 7
Perceived Locus of Causality Scale (PLOC), Academic Self-Regulation Questionnaire (ASRQ),
Academic Motivation Scale (AMS), Échelle de Motivation en Éducation (EME), Situational
Motivation Scale (SIMS), Behavioral Regulation in Exercise Questionnaire (BREQ, BREQ-2,
BREQ-2r), Behavioral Regulation in Sport Questionnaire (BRSQ), or Exercise Motivation Scale
(EMS). Studies that made slight adaptations to these scales (e.g., alterations to the premise
statement to reflect a specific context) were included. Second, samples must have presented data
collected from students in an educational context, ranging from primary school to university
education. Third, studies must have reported at least one correlation between a motivation type
and an outcome. Studies published in languages other than English were included when relevant
data was accessible.
Literature Search
Our literature search relied on three primary methods and is depicted in Figure 2. Firstly,
forward searches were conducted beginning at scale validation articles. The list of all possible
validated SDT-based motivation scales upon which to conduct this search was compiled and
approved by the authors prior to searches. This forward search consisted of collecting all articles
that cited a scale validation paper in either Google Scholar or Web of Science (n = 10,448). This
process was conducted by the first, second and fourth authors, as well as a trained research
assistant. All articles were independently examined at this stage by either the first or fourth
authors with duplicates being removed. Studies not reporting primary data from student samples
or associations with outcomes were also removed at this stage. Secondly, a search of major
databases (EBSCO & PsycINFO) was conducted by the first author using search terms “self-
determination” and “student” and again combining the search terms “student” and individual
scale names. All available dissertations, conference presentations, and grey literature were
included. Additionally, the Proquest Dissertation and Thesis Global database was searched with
the keywords “self-determination” and “student.All articles appearing in database search
results were assessed, compared against previously collected samples, and included if meting the
inclusion criteria and not duplicates. Of the 329 remaining articles, 55 did not provide all
necessary information. Corresponding authors of these articles were contacted via e-mail (n =
49), requesting the full correlation tables associated with published studies as well as any
additional unpublished data (including conference presentations and dissertations; response rate
= 43%), resulting in 14 additional samples. Finally, a request for unpublished data was posted on
multiple mailing lists (SDT, American Educational Research Association, Society of Personality
and Social Psychology, and Society for the Study of Motivation), resulting in an additional 17
samples.
The final database consisted of 344 samples (276 published, 68 unpublished), including a
total of 3,959 correlation coefficients from 223,209 participants (samples ranging from 21
26,607 participants, mean = 649). Of the samples, 232 were classroom-based, whereas 112 were
from physical education-based. The mean age across samples was 16.19 years old and the
average proportion of males in each sample was 45.94%.
Coding
Student Motivation and Outcomes 8
A coding spreadsheet was developed by the first author and approved by remaining
authors. Information pertaining to motivation variables (type of motivation, scale used, and
reliability) was coded, as was information relating to associated outcomes (scale used and
reliability). Demographic information relating to the sample was also coded including domain in
which the data were collected, country, language, year level at school (or categorical label when
exact year level was unavailable), mean age, percentage of males in the sample, and publication
status. All coding was shared between the fourth and first authors. The first author additionally
double coded approximately 10% of articles independently to establish inter-coder agreement
(Cohen’s = .94; McHugh, 2012). All divergent coding decisions were discussed between the
first and fourth authors and resolved through reexamination of the data.
Outcomes
All outcomes included in the collected studies were coded. In two instances, outcomes
measuring highly related constructs were combined when agreed upon by the authors (i.e.,
positive affect/happiness; anxiety/stress) in order to meet minimum required number of samples
for analysis. Variables that were not measured more than three times and could not be combined
with related constructs were not examined further (see Table S2). Additionally, variables that
were considered of low interest for education, specifically, body mass index and physical
performance were relegated to the supplementary materials (Table S3). Most variables were
measured through self-report and many outcomes included data from multiple different scales.
Student academic achievement was the only outcome commonly measured through both self-
report and objective measures. Given that previous research has demonstrated that this
distinction is likely to be important (Kuncel, Credé, & Thomas, 2005) and because many
samples were available, self- and objective-report academic achievement were analyzed
separately. As such, the final analyses included a total of 26 outcome variables.
Meta-Analytic Procedures
All meta-analytic calculations were conducted using the R software, specifically using
the robumeta package (Fisher & Tipton, 2015). Random effects models were used throughout as
the assumption of fixed effects models (i.e., attributing residual variance in outcomes to artifacts
rather than theoretically plausible moderators) is unlikely to be tenable (Borenstein, Hedges, &
Rothstein, 2007; Hunter & Schmidt, 2000). Correlations were initially corrected for scale
reliability before final meta-analytic correlations (ρ) and associated 95% confidence intervals
were calculated through inverse-variance weighting procedures. When scale reliability data was
not available, mean reliability scores were calculated for each measure and imputed. Non-
independent data was handled with robust variance estimator procedures (Hedges, Tipton, &
Johnson, 2010). This analytic method integrates dependent effects into a single, non-biased
estimate in order to avoid inflation of sample size and effect precision. When studies included
multiple time points, only time 1 correlations were included in order to avoid effects of
experimental manipulations and duplication of data. Outliers were examined through cumulative
analyses and one-study-removed analyses (Borenstein, Hedges, Higgins, & Rothstein, 2011).
Results from these tests did not find any substantial outliers and, as such, no studies were
Student Motivation and Outcomes 9
excluded from analyses based on this test. Specifically, the removal of a sample never
significantly changed the estimated association with outcomes, which was indicated by highly
similar point estimate and confidence intervals as well as by effects remaining within the
confidence intervals of the estimated true effect.
The degree of heterogeneity present in meta-analytic estimates was examined through the
Tau squared (T2) and I2 statistics. Specifically, whereas the T statistic is the estimated standard
deviation of the population level effect size, T2 is the population variance, which indicates the
amount of heterogeneity in the target association. The I2 statistic estimates the proportion of this
variance which can be attributed to true heterogeneity caused by moderating factors, as opposed
to artifacts such as sampling error and chance (Higgins, Thompson, Deeks, & Altman, 2003;
Higgins & Thompson, 2002). I2 scores greater than 75% indicate substantial heterogeneity which
could be explained by moderating factors, whereas 50% is considered moderate heterogeneity,
and 25% low heterogeneity (Higgins et al., 2003).
To compare the effect sizes between regulation types, we relied on the confidence
interval method proposed by Cumming and Finch (2005). Effect sizes with confidence intervals
that show >50% overlap indicates approximate equivalence between effects. Alternately, when
confidence intervals do not overlap at all, this is interpreted as a statistically significant
difference in effect equal to a probability of approximately .01. When confidence intervals
overlap but less than 50%, this indicates effects are statistically different at a probability of .05.
A number of tests were conducted to establish the moderating influence of age, gender,
publication status, scale, and context. Specifically, meta-regressions were conducted on the
continuous variables of age and gender (respectively operationalized as mean age and percentage
of males in the sample). Meta-regressions indicate how much of the unexplained heterogeneity
(I2) can be explained by the potential moderator. More specifically, meta-regression examines
the proportion of between-study variance in an effect, in this case an estimated correlation, which
can be explained by the moderating variable, in this case age or proportion of males in the
sample and is represented by the R2 analogue statistic. Additionally, the regression coefficient
describes the degree to which the estimated effect size will change in relation to a one-point
increase in the potential moderator variable. The statistical significance associated with this
effect is also provided. Furthermore, the effect of publication status was investigated in several
ways including trim and fill procedures (Duval & Tweedie, 2000), Egger’s test of the intercept
(Egger, Smith, Schneider, & Minder, 1997), and subgroup analysis. The trim and fill method
utilizes funnel plots to examine symmetry of observed effects in order to identify potential
missing studies, and thereby potential for publication bias. Results present the number of studies
estimated to be missing, the direction of missing studies (left or right of the mean), and the
corrected correlation if such studies were in fact conducted and included. Although adjusted
correlations from this analysis are not reliable replacement estimates, they nonetheless serve to
demonstrate if systematic exclusion of literature is present and in which direction these effects
are likely to be, and in doing so act as a type of sensitivity analysis (Duval & Tweedie, 2000).
Student Motivation and Outcomes 10
Egger’s test is a regression-based test of symmetry in which results significantly different from
zero indicate potential publication bias.
Finally, subgroup analysis was also applied to further examine publication bias in which
correlations were estimated for published and unpublished samples separately. Subgroup
analyses were also conducted based upon the motivation scale used (AMS vs. SRQ; Vallerand et
al., 1992; Ryan & Connell, 1989), which context the study took place (classroom education vs.
physical education), and the country in which samples were collected.
Additionally, relative weights analysis (RWA) was applied to meta-analytically derived
correlations in order to test the differential prediction capabilities of motivation types for each
outcome. RWA is designed to estimate the unique contribution predictors make towards the
prediction of an outcome once accounting for the correlated nature of predictors (Tonidandel &
LeBreton, 2011). In other words, when predictors are correlated, the amount of explained
variance in an outcome can be misattributed between predictors and lead to false conclusions
regarding the influence or importance of predictors. RWA adjusts for this issue of
multicollinearity and produces an estimated R2 for each predictor as well as a rescaled relative
weight describing the proportion of total variance uniquely explained in the outcome by each
predictor. RWA was conducted in the R software package (Tonidandel, & LeBreton, 2015).
Results
Correlations between types of motivation are presented in Table 1. Results conformed to
the expected pattern with larger correlations recorded between theoretically neighboring types of
motivation. The correlations between intrinsic motivation, integrated regulation, and identified
regulation were notably high (ρ = .85 to .90).
Motivation and Adaptive / Maladaptive Outcomes
To gain a broad understanding of results, outcomes were first divided into two composite
variables representing adaptive or maladaptive educational outcomes as displayed in Table 2,
and correlations between these two composites and motivation types were calculated
(graphically represented in Figure 3, Table S1 of the supplementary materials). When examining
adaptive outcomes, a general pattern can be observed as amotivation is the strongest negative
correlate, and relationships with motivation types become increasingly more positively as the
degree of self-determination increases, until reaching a moderately strong positive relationship
with intrinsic motivation. Specifically, amotivation was significantly associated with adaptive
outcomes (ρ = -.24), though external regulation was unrelated (ρ = -.01). Introjected regulation
(ρ = .17), identified regulation (ρ = .38), and intrinsic motivation (ρ = .41) were all positively
associated. Integrated regulation was also positively associated with adaptive outcomes (ρ = .23;
See Table S1), although this result was not significant, likely due to the very limited number of
samples including the motivation type (k = 6).
The reverse pattern of association is also observable for maladaptive outcomes which
displayed a moderately positive association with amotivation and decreased to a negative
association with intrinsic motivation. Specifically, amotivation was positively and significantly
associated with maladaptive outcomes at a corrected correlation of .28, along with external
Student Motivation and Outcomes 11
regulation (ρ = .18) and introjected regulation (ρ = .19). Identified regulation, on the other hand,
demonstrated a nonsignificant association with maladaptive outcomes (ρ = -.04) whereas
intrinsic motivation was found to relate negatively and significantly (ρ = -.13). Insufficient
samples were available to meaningfully estimate a correlation between integrated regulation and
maladaptive outcomes.
It is worth noting that introjected regulation was positively related to both adaptive and
maladaptive outcomes. Though these correlations are somewhat modest in magnitude, this
clearly indicates the theorized double-sided nature of introjected regulation. It is also interesting
to highlight that identified regulation and intrinsic motivation display highly similar associations,
with each relating positively to adaptive outcomes (ρ = .38 & .41 respectively). However, when
examining maladaptive outcomes, intrinsic motivation was significantly related -.13) whereas
identified regulation was not = -.04). This result is noteworthy as intrinsic motivation is the
only motivation type to significantly and negatively relate to globally defined maladaptive
outcomes.
Motivation and Specific Outcomes
In examination of specific outcomes and beginning with student academic achievement,
it can be seen in Table 3 (and Figure 4) that regardless of grades being self-reported or
objectively reported, amotivation is associated in a negative manner. External and introjected
regulations also display results similar across both reporting methods, with non-significant
results. While identified regulation and intrinsic motivation were both positively and
significantly related to academic achievement, the differences between these two types of
motivation were not significant. Interestingly, unlike with the previous types of motivation,
results with identified and intrinsic motivation differed between self-report and objective report.
Specifically, the effect size is substantially higher for self-report (identified ρ = .29, intrinsic ρ =
.32) compared to an objective report (identified ρ = .11, intrinsic ρ = .13). This indicates that
while both identified regulation and intrinsic motivation positively and equally related to
academic achievement, self-reports of academic achievement may inflate this relationship over
more objective measures.
When examining variables relating to student persistence (Table 4, Figures 5 & 6),
several common themes emerged. Firstly, amotivation was once again negatively associated
whether the outcome was defined as continuation intention, intention to exert effort, intention to
exercise, or engagement. Likewise, external regulation related non-significantly to each of these
variables. Introjected regulation displayed diverse results, with significant positive correlations
with effort (ρ = .25, k = 16), engagement (ρ = .26, k = 23) and intention to exercise (ρ = .25, k =
12). However, its relationship with continuation intention and dropout intention were
nonsignificant (ρ = .02 & -.03 respectively, k = 9 & 7). Together, these results seem to indicate
that introjection has a mixed relationship with future intentions to persist in education-based
activities. Interestingly, when comparing identified regulation and intrinsic motivation in relation
to persistence-based outcomes, results showed that effect sizes were larger for identified
regulation than intrinsic motivation, although notably, overlapping confidence intervals indicate
Student Motivation and Outcomes 12
this difference may not be significant (Cumming & Finch, 2005). This same difference in
absolute effect sizes favoring identified regulation was also noted for dropout intention, though
with negative correlations. This indicates that while both identified and intrinsic motives are
positively and significantly associated with student persistence, it is reasonable to expect
identified regulation may often be the strongest correlate. Objectively reported absenteeism was
unrelated to any motivation types, perhaps signaling the diverse causes of being absent from
school. Results associated with well-being outcomes are displayed in Table 5 (Figures 7 & 8).
Beginning with amotivation, results demonstrated significant negative associations with
indicators of positive well-being (i.e., positive affect, vitality, enjoyment, and social emotional
functioning) and significant positive associations with maladaptive well-being indicators (i.e.,
anxiety, depression, boredom, and negative affect). The only non-significant relationship was
that with general (life) satisfaction. External regulation often did not relate to well-being
outcomes significantly, but it was positively and significantly associated with anxiety (ρ = .12, k
= 20), and negative affect (ρ = .22, k = 20), and negatively with vitality (ρ = -.18, k = 10). Across
the range of well-being indicators, introjected regulation was positively and significantly
associated with positive affect (ρ = .13, k = 18), enjoyment (ρ = .26, k = 9), negative affect (ρ =
.16, k = 12), and anxiety (ρ = .13, k = 17), while remaining unrelated to all remaining outcomes.
Somewhat unexpectedly, identified regulation was unrelated to most negative indicators of well-
being (i.e. depression, negative affect), with anxiety presenting the only exception (ρ = -.12, k =
16). In contrast, identified regulation was positively and strongly related to positive indicators.
Intrinsic motivation, on the other hand, was negatively and significantly associated with anxiety
and negative affect and unrelated to depression, but positively associated with all positive
indicators of well-being. As such, it appears intrinsic motivation is a stronger correlate of well-
being, particularly regarding indicators of negative well-being, than identified regulation.
When examining results pertaining to goal orientation (See Table 6, Figure 9), it was
found that in relation to mastery-approach orientation, amotivation was negatively related (ρ = -
.22, k = 14) and external regulation unrelated. Introjection displayed a moderate positive
correlation (ρ = .33, k = 18), whereas identified and intrinsic motivations returned strong positive
relationships (ρ = .65 & .64 respectively, k = 16 for both). Mastery avoidance was unrelated to
amotivation, and positively related to all remaining types of motivation (ρrange = .30 to .40).
Likewise, performance approach was unrelated to amotivation but positively related to each
other type of motivation with introjected regulation being the strongest correlate (ρ = .46, k =
17). Finally, performance avoidance was positively related to all motivation types, including
amotivation, with introjected regulation again recording the strongest correlation (ρ = .43, k =
19). In summary of these results, it appears intrinsic motivation and identified regulation were
the strongest associates of mastery-approach goals, whereas both approach and avoidance
performance goals, which are focused on comparisons with others, were particularly associated
with introjection.
Student Motivation and Outcomes 13
Data were available and analyzed for several self-evaluation covariates including
students’ self-esteem, self-efficacy, and self-physical image evaluation (both positive and
negative; Table 7, Figure 10). First, self-efficacy related negatively and significantly to
amotivation (ρ = -.37, k = 13), non-significantly to external (ρ = -.02, k = 16) and positively and
significantly to introjected regulations (ρ = .18, k = 13), identified regulation (ρ = .43, k = 15)
and intrinsic motivation (ρ = .41, k = 11). Self-esteem, likewise, related negatively and
significantly to amotivation (ρ = -.38, k = 3), non-significantly with external regulation (ρ = .10,
k = 5) and positively and significantly with introjected (ρ = .23, k = 5) identified (ρ = .44, k = 5)
and intrinsic motivations (ρ = .34, k = 5). Interestingly, when examining positive and negative
physical image perceptions, amotivation was uniformly maladaptive (i.e., associated with
increased physical image anxiety and reduced positive physical image perception), external and
introjected regulations were both unrelated to positive physical image perception but
significantly positively related to physical image anxiety (ρ = .26, ρ = .22, respectively).
Identified and intrinsic motivations were both positively and significantly related to positive
physical image (ρ = .32, ρ = .36, respectively) whereas only intrinsic motivation was related
significantly to physical image anxiety (ρ = -.18, k = 5).
Relative Weights Analysis
Relative weights analysis was conducted to estimate the unique contribution each type of
motivation made towards outcomes once accounting for the correlated nature of motivation
(Table 8). Beginning with student academic achievement, for self-report data, both intrinsic and
identified types of motivation contributed substantial unique predictive capability (39% and 21%
respectively), while amotivation also accounted for 27% of the explained variance. Furthermore,
it was estimated that introjected and external regulations contributed very little unique
information (2% and 9% respectively). However, for data employing objective measures of
academic achievement, it appears that avoiding amotivation was the most important
consideration as its detrimental effect can account for 38% of variance, followed by intrinsic
motivation (25%) and identified regulation (20%).
When examining other outcomes, it appears that intrinsic motivation was, on average, the
single strongest predictor, capable of accounting for 30% of variance in outcomes. Identified
regulation also played an important role on average (28%) with amotivation uniquely
contributing 17% of predictive capability towards outcomes. External (12%) and introjected
(11%) regulations played less central roles in prediction, on average. It should be noted,
however, that results of this analysis pertain to direct effects of motivation on outcomes and do
not account for interaction effects. As such, while introjected and external regulation do not
appear to be strong unique predictors of most outcomes, they may still play more complex
interactive roles.
Interestingly, two patterns consistently emerged from these analyses. First, it appears that
factors relating to well-being including depression, negative affect, and psychological well-being
were primarily associated with intrinsic motivation (RWmean = 38%), and somewhat less so with
identified regulation (RWmean = 27%) and amotivation (RWmean = 17%). For these well-being
Student Motivation and Outcomes 14
outcomes, external and introjected regulations appeared to be uninfluential as simultaneous
predictors. This pattern of intrinsic motivation playing the strongest role also applied to the
outcomes of mastery avoidance goals (38%) and physical image anxiety (30%).
A second pattern also emerged relating to the persistence-based outcomes of effort,
intention to exercise, and continuance intention. For these outcomes, identified regulation was
estimated to uniquely contribute the most to prediction, accounting for an average of 34% of
variance, which was more than intrinsic motivation (RWmean = 26%) or any of the remaining
motivation types. These results provide evidence for the importance of identified regulation in
uniquely predicting outcomes relating to persistence and intention.
When examining performance goals, introjected regulation was the strongest predictor of
both performance approach and avoidance goals (30% & 41% respectively). Additionally,
whereas self-efficacy appeared most strongly predicted by identified regulation (34%), self-
esteem was predicted more strongly (and negatively) by amotivation (39%). External regulation
was not found to be the key predictor of any measured outcome.
Moderation Analyses
Turning to moderation analyses, we conducted trim and fill procedures as well as Egger’s
regression test to investigate publication bias through examination of the possibility of missing
studies. Given the known tendency of trim and fill tests to return false positives (Sterne,
Gavaghan, & Egger, 2000), we interpreted results from both tests, as well as the degree to which
possible missing studies would influence the effect size in order to identify publication bias.
Results generally demonstrated that the amount of suspected missing data was small and that for
most variables, publication bias was not present (see Table S4 for details and minor exceptions)
A single notable exception to this was seen in the association between intent to exercise and
external regulation which, when based upon collected data was estimated to be .03, although
once accounting for potentially missing studies was estimated to be negatively related (ρ = -.11).
Relationships between these variables should therefore be interpreted with some caution.
Subgroup analysis comparing published and unpublished results did not find any additional
differences (See Table S5).
When examining the influence of participants' age on the relationships between
regulation types and outcomes through meta-regression (see Table S4), many analyses returned
nonsignificant results (74 out of 100 results). However, a subtle yet statistically significant
pattern emerged when examining introjection. Specifically, meta-regression results regarding
well-being variables indicated that as students grow older, the positive relationship between
introjection and adaptive well-being outcomes decreased and the positive relationship between
introjection and maladaptive outcomes increased. Results also showed corresponding small but
significant effects for positive affect and general satisfaction such that introjection became less
strongly positively related to these variables as mean age of samples increased. Alternatively, the
positive relationship between introjection and negative affect demonstrated the inverse result
with this relationship becoming stronger as participant age increased. This effect was also
noticed for dropout intention (relationship increasing for older students), while the relation
Student Motivation and Outcomes 15
between introjection and self-reported academic achievement became weaker with age. A final
trend to be noted regards amotivation. Associations between amotivation and both self-efficacy
(negative) and negative affect (positive) decreased as student age increased. Alternatively,
correlations between amotivation and social-emotional functioning, positive affect, engagement
and physical activity all increased as student age increased, indicating that for these outcomes,
amotivation became less detrimental as students’ age increased.
When examining the moderating effects of gender, two trends appeared noteworthy.
First, the correlation between self-reported academic achievement and both identified and
intrinsic motivation increased as the proportion of males in a sample increased. Interestingly, no
differences were present when examining objectively recorded academic achievement. Second, a
pattern emerged in which engagement, vitality, and social-emotional functioning all displayed
lower correlations with amotivation as the proportion of males increased, indicating that
amotivation may have a less detrimental effect on males on these outcomes. These results are
reported in Table S4 of the online supplementary materials.
Finally, subgroup analyses were conducted to examine the moderating influence of the
scale used (Tables S6 & S7), the type of academic context (classroom vs. physical education;
Table S8), and the country in which samples were collected (Table S9). Results do not show any
clear pattern of difference, with 95% confidence intervals around point estimates overlapping in
most comparisons. Differences were noticed in that introjected regulation, identified regulation,
and intrinsic motivation all related more strongly and positively to engagement in the Eastern
samples. These results indicate that cultural influences may moderate the relationships between
motivation and some specific outcomes in a minor way. However, in general, the associations
between motivation and outcomes appear robust across cultural contexts. Nonetheless, the
relatively small number of samples collected in non-western countries suggests caution in
interpreting these findings. Discussion
In this meta-analysis, we examined the relations between various types of motivation as
defined within SDT and an array of outcomes in education, including indicators of academic
performance, persistence, well-being, goal orientations, and self-evaluative appraisals. In line
with theoretical expectations, adaptive outcomes were generally associated with more self-
determined forms of motivation, and less self-determined motives were generally associated with
more maladaptive indicators. In addition, several nuanced findings reflecting the specific
characteristics of these motivation types were demonstrated, as were moderating influences
based on student age. The theoretical implications as well as implications for teaching and
institutional practices are detailed below.
A central finding is that identified regulation appears to hold some unique and
meaningful characteristics pertinent to motivation beyond any of the other motivation types, as
evidenced by both correlations and RWA results (see Table 8). These results are interesting
because identified regulation is a self-determined form of extrinsic motivation that relates to
several outcomes more strongly (or as strongly) than intrinsic motivation. Although this pattern
Student Motivation and Outcomes 16
has been noted previously in some specific instances (e.g., Burton et al., 2006; Losier &
Koestner, 1999), we found that this pattern was particularly important regarding a range of
persistence related outcomes (see Table 8). This result can be aligned with theory considering
that intrinsic motivation is dependent on emotive states such as curiosity and enjoyment (Reeve,
1993), whereas identified regulation is likely to be more relevant to both interesting and
uninteresting tasks, thus adding an essential component in persistence-based outcomes. In
addition to providing relatively strong evidence for this under-documented effect, these results
also have substantial implications for SDT as they indicate that identified regulation contributes
some amount of unique information towards explaining student outcomes, above other
motivation types or general self-determination.
Results also indicate that ego-involving motives, described as introjected regulation in
SDT, play a very interesting dual role. Introjection positively relates to education-focused
behaviors including effort, engagement, and physical exercise, but will likely coincide with
notable negative side effects including anxiety and negative affect. These findings are thus
consistent with SDT’s view that introjection represents a partial internalization of values, and as
such can drive behaviors through internalized pressures and ego-involvement to some extent
(Ryan & Connell, 1989; Ryan & Deci, 2017). An especially important and novel contribution of
our analysis was showing the strong relations of introjected motives with performance goals,
both approach and avoidance. This is perhaps not surprising given that performance goals are
focused on social comparisons, which may be associated with both self and other approval
dynamics. Another set of findings unique to this meta-analysis was that the associations between
introjected regulation and many outcomes were moderated by age. Specifically, results indicated
that as students become older, the relation between introjection and adaptive outcomes decreases,
whereas the relation between introjection and maladaptive outcomes increases. It appears that
introjection plays an increasingly negative role as students mature. This result implies that
moving beyond introjected regulations (or internalizing them; Deci, Eghrari, Patrick, & Leone,
1994) might be considered an important developmental challenge for students. Though beyond
the scope of this study, this result may well generalize to other domains such as sport, work,
parent-child relationships, and health-care settings.
Our results also demonstrate that external regulation associates with outcomes in a very
interesting and somewhat unexpected way, which had not been outlined so clearly before.
Specifically, external regulation related to a range of maladaptive results (including higher
anxiety as well as reduced social-emotional functioning, physical well-being, and vitality), but
appeared unrelated to adaptive outcomes. In fact, across the 26 outcomes included in the current
study, in only one case (performance approach goals), did external regulation demonstrate
positive and significant associations with what may be considered an adaptive outcome. This is
contrary to past research examining the effects of incentives, usually monetary, on student
performance and persistence have argued for positive, or at least mixed, outcomes (e.g., Angrist
& Lavy, 2009; Fryer, 2011; Niu, 2016). In fact, current results indicate that motivating students
via such external means, insofar as that engenders external regulation, may risk reduced student
Student Motivation and Outcomes 17
well-being. This is highly notable as much of the past research on incentives, often stemming
from economic and management domains of research, does not include measures of well-being,
typically prioritizing student achievement (Angrist & Lavy, 2009; Fryer, 2011). As such,
detrimental effects associated with external regulation are not well documented outside of self-
determination theory.
Finally, because most general outcome categories (i.e., achievement, persistence, well-
being) are not exclusive to the education domain, one can expect that the current results are
transferable to domains beyond education such as workplaces, health-related behavior and
interpersonal relationships, to name a few. Recent meta-analytic research in different domains,
although not exactingly paralleling our methods, does in fact reveal findings that are generally
consistent with those of the present study, holding promise for such generalizability (e.g.,
Ntoumanis et al., 2020; Slemp, Kern, Patrick, & Ryan, 2018; Vasconcellos et al., 2019).
Implications for SDT
When considering theoretical implications for SDT, it is important to note that
theory-consistent results demonstrate the characteristics and importance of specific types of
motives, including for example, the strong connections between introjection and performance
goals, and between academic persistence and identified regulation. Such results largely support a
multidimensional perspective of motivation and indicate that any single factor is not likely to
capture all construct-relevant information inherent within SDT (Howard et al., 2020; Sheldon et
al., 2017). However, it must also be noted that more autonomous types of motivation were
generally associated with more desirable outcomes in a predictable and linear manner, indicating
the relatively strong influence from general self-determination. This conclusion aligns with
previous studies examining the structure of motivation in SDT through bifactor modeling
(Howard et al., 2018; Litalien et al., 2017). Once a general factor of self-determination was
extracted in these studies, each regulation factor contributed varying amounts in subsequent
prediction analyses with intrinsic and identified factors found to play substantially greater roles
than external and introjected factors in predicting many variables (Howard et al., 2018; Litalien
et al., 2017).
These findings also have implications for the commonly applied dichotomy of
autonomous/controlled motivations (Howard et al., 2020). Given our results indicate that
introjected and external regulations do not associate similarly with outcomes, and can even
associate in different directions, combining these motivations into a controlled motivation factor
carries risks and will be insensitive to these noted differences. Likewise, an autonomous
motivation factor will be insensitive to the unique associations between identified regulation and
persistence, as well as the more beneficial effects of intrinsic motivation on wellbeing. For these
reasons, the present results also indicate that a simple, unidimensional conceptualization of
motivation is unlikely to explain the most important educational outcomes in a satisfying way.
Results from both previous studies and the current meta-analysis indicate that while the degree of
self-determination is highly important, different motivation types within the SDT taxonomy also
relate to different outcomes beyond their level of self-determination.
Student Motivation and Outcomes 18
Practical Implications for Classrooms
These results have substantial implications for classrooms, especially for how teachers,
parents, and administrators attempt to activate or incentivize student participation in their
education through learning practices, engagement, and testing. While past research has
established the importance of autonomy support in facilitating motivation, the current results
demonstrate more clearly and precisely the importance of motivation, thereby completing the
motivational process from teacher and parent behaviors to student outcomes.
For example, it is known that implementation guidelines for fostering optimal student
motivation, that is a combination of both intrinsic motivation (i.e., enjoyment) and identified
regulation (i.e., meaningfulness), should be centered around teachers’ and parents’ autonomy-
supportive practices. These require consideration of the student as a person, meaning that their
feelings and preferences should be acknowledged (through empathetic interactions and provision
of choices meaningful to them) and that they are entitled to rationales explaining why school
tasks suggested to them are meaningful. It also implies minimizing the use of controlling
language and behavior. These positive interpersonal gestures have demonstrated in meta-analytic
investigation the ability to increase meaningfulness and intrinsic motivation among students and
to lead to adaptive academic outcomes (Vasquez, Patall, Fong, Corrigan, & Pine, 2016) and are
supported by the demonstrated effectiveness of interventions designed to increase autonomy-
supportive teaching practices (Cheon, Reeve, & Moon, 2012; Cheon & Reeve, 2015; Su &
Reeve, 2011). Combining current findings with this past research, parents and educators now
have access to precise estimates detailing the motivational pathway from autonomy supportive
practices, to student experienced motivational states, and associations with a wide array of
education focused outcomes. The present results also highlight more clearly the potential costs of
using external incentives and punishments (e.g., parents offering monetary rewards or teachers
punishing bad behavior), as these controlling environmental effects are likely to foster external
regulation which in turn relate negatively with student wellbeing while not associating with
persistence or performance. Additionally, overbearing or conditionally regarding teaching and
parenting practices are likely to increase student ego-involvement and introjected regulation
(Joussemet, Landry, & Koestner, 2008). Current results highlight the double-edged nature of
conditional regard as it relates positively with effort and engagement, but also to negative well-
being costs, and ultimately remains unrelated to academic achievement.
Current results may also have implication concerning how education systems are
designed more broadly. Specifically, Ryan and Weinstein (2009) have argued that high stakes
testing, with its focus on achieving suitable scores on standardized tests and the incentives and
punishments schools incur as a result, will foster an education environment of control. School
faculty will feel externally pressured to meet and exceed specific criteria, potentially
encouraging controlling teaching practices, and subsequently stifling the more autonomous
forms of motivation of students (Pelletier & Sharp, 2009). Current results highlight the flaw
inherent within such a system in that carrot and stick approaches, insofar as they tend to foster
external regulation, are unlikely to correlate with performance though will correlate with
Student Motivation and Outcomes 19
negative student well-being indicators. In other words, the pursuit of a narrowly defined criteria
of success in the form of test scores now appears potentially harmful to students’ long-term
development and educational success. The absence of positive motivational contributions from
high-stakes testing is particularly worrisome given their worldwide pervasiveness (e.g., SAT in
the US, NAPLAN in Australia, Baccalaureat in countries following the French system).
Limitations & Direction for Future Research
A first limitation worth noting is that heterogeneity could not be removed from point
estimates, even after moderation analyses. While current estimates successfully distinguish
between motivation types and their associations with outcomes, this remaining heterogeneity
indicates that point estimates may be further moderated by factors not included in this study and,
therefore, that effect sizes may still vary due to contextual, environmental, and individual
differences. The contextual influence of class subject, particularly non-academic subjects such as
music and art, could be notable. Future meta-analytic studies focusing on more specific contexts
and fewer outcomes would be well suited to mapping out these moderating influences. Secondly,
it must also be noted that data in the current study were cross-sectional and correlational,
precluding strong conclusions about causality. While this reflects the current literature, it also
limits our ability to infer meaning from current results and highlights the need for more rigorous
methodologies designed to test causality, potentially using time separation, repeated measures,
and experience sampling methodologies.
Another potential limitation is that very few samples in the current meta-analysis
included data for integrated regulation. This is because it is not often measured in the education
domain, likely due to theorizing that students are too young to internalize motives to this degree.
However, results that did include this motivation type showed that integrated regulation was
potentially important in reducing maladaptive student outcomes. This indicates the potential for
more research including this motivation type in education contexts as it may play a role not
otherwise accounted for by intrinsic or identified motives. Likewise, the range of outcomes,
while representative of the current literature, do not cover the full range of important education
outcomes. Specifically, greater attention could be paid to maladaptive student outcomes such as
disruptive and anti-social student behaviors as these are highly relevant to teachers and parents.
Likewise, growth scores would likely be a more nuanced indicator of student achievement then
GPA, and as such could be included in future studies.
Another direction for future research could include examination of potential interactions
between motivation types in predicting educational outcomes (Deci, Koestner, & Ryan, 1999,
2001). Although our results indicated that external and introjected regulations play relatively
minor roles directly predicting most outcomes in RWA, it may be the case that they are more
important predictors of others, or through interaction with other motives. The application of
person-centered analyses such as latent profile analysis and latent transition analysis to the study
of motivation is ideally suited to testing these complex interactions (e.g. Wang, Morin, Ryan, &
Liu, 2016). A final direction for future research would be a comparison of SDT against other
commonly applied theories of motivation in order to establish the relative importance of various
Student Motivation and Outcomes 20
theories. Such an approach could be essential to the integration of theories and even the
development of novel and more holistic theories of student motivation.
Conclusion
Through a comprehensive synthesis of research within the educational psychology
literature on types of motivation specified within SDT, the current results demonstrated the
importance of student self-determination in education contexts. In general, and as expected, more
autonomous types of motivation were associated with more desirable outcome. Furthermore,
results demonstrate the added importance of identified regulation in fostering persistence and
future intentions, as well as the detrimental effects on well-being likely to accrue from external
incentives and pressures insofar as they engender external regulation. The role of shame, pride,
and guilt (i.e., introjected regulation) is demonstrated as they show positive associations with
both adaptive and maladaptive education outcomes. The particularly damaging effect of
amotivation for students in general is also underscored. Taken together, these results provide
compelling evidence for the importance of fostering high quality student motivation, and of the
relative costs and benefits of specific types of academic motivation.
Data and Supplementary Materials:
The full dataset and all associated supplementary materials are made available through Open
Science Framework. All files can be found with the following link:
https://osf.io/ykfz5/?view_only=35f834deee2d459f8eb39c97d8ff3601
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Figure 1
Representation of Motivation in Self-Determination Theory. Adapted from “Testing a Continuum
Structure of Self-Determined Motivation: A Meta-Analysis,” by J.L. Howard., M. Gagné., & J.S.
Bureau, 2017, Psychological Bulletin, 143(12), p. 1347. Copyright 2017 by American
Psychological Association
Student Motivation and Outcomes 26
Figure 2
Flow Chart of Literature Search and Exclusion Procedures
Student Motivation and Outcomes 27
Figure 3
Graphical Representation of the Average Relationship between Motivation Factors and Adaptive
and Maladaptive Outcomes with 95% Confidence Intervals
Figure 4
Graphical Representation of Correlations between Motivation and Grade Point Average with
95% Confidence Intervals
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Amotivation External Introjected Identified Intrinsic
Adaptive Maladaptive
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Amotivation External Introjected Identified Intrinsic
Objective Self-report
Student Motivation and Outcomes 28
Figure 5
Graphical Representation of Correlations between Motivation and Adaptive Persistence
Outcomes
Figure 6
Graphical Representation of Correlations between Motivation and Maladaptive Persistence
Outcomes
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Amotivation External Introjected Identified Intrinsic
Effort Engagement Intent to Exercise
Physical Activity Continuance Intention
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Amotivation External Introjected Identified Intrinsic
Absenteeism Dropout Intention
Student Motivation and Outcomes 29
Figure 7
Graphical Representation of Correlations between Motivation and Adaptive Well-being
Outcomes
Figure 8
Graphical Representation of Correlations between Motivation and Maladaptive Well-being
Outcomes
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Amotivation External Introjected Identified Intrinsic
Positive Affect Satisfaction
Vitality Enjoyment
Social-Emotional Functioning
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Amotivation External Introjected Identified Intrinsic
Anxiety Depression Negative Affect
Student Motivation and Outcomes 30
Figure 9
Graphical Representation of Correlations between Motivation and Goal Orientations
Figure 10
Graphical Representation of Correlations between Motivation and Self-Evaluation Outcomes
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Amotivation External Introjected Identified Intrinsic
Mastery Approach Mastery Avoidance
Performance Approach Performance Avoidance
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Amotivation External Introjected Identified Intrinsic
Self-Efficacy Self-Esteem
Physical Image - Anxiety Physical Image - Positive
Student Motivation and Outcomes 31
Table 1: Correlations between Regulations
Intrinsic
Integrated
Identified
Introjected
External
Amotivation
Intrinsic
-
15
210
193
206
131
Integrated
.845
-
19
20
19
15
Identified
.878
.897
-
243
249
158
Introjected
.363
.382
.589
-
251
156
External
-.058
<.001
.333
.643
-
167
Amotivation
-.503
-.393
-.492
.017
.426
-
Note. Corrected correlations (ρ) below the diagonal. Number of samples included in calculation
above the diagonal.
Table 2: Classification of Outcomes into Adaptive and Maladaptive Categories
Outcomes
Adaptive
Maladaptive
Academic Performance
(Objective)
Academic Performance
(Self-Report)
Physical Image - Anxiety
Satisfaction (General)
Enjoyment
Absenteeism
Effort
Self-Esteem
Dropout Intention
Continuance Intention
Self-Efficacy
Anxiety
Intent to Exercise
Mastery Approach
Depression
Physical Activity
Performance Approach
Boredom
Engagement
Vitality
Negative Affect
Physical Image - Positive
Positive Affect
Social-Emotional Functioning
Note. Mastery avoidance and performance avoidance were not included in this classification as
they are neither clearly adaptive nor maladaptive.
Table 3: Meta-Analytic Correlations between Motivation Types and Academic Performance
Outcome
95% CI
Motivation
k
ρ
Lower
Higher
Sdt.
Error
T2
I2
Objective Academic Performance
Amotivation
24
-.21
-.27
-.15
.03
.01
89.28
External
33
-.03
-.08
.01
.02
.01
80.71
Introjected
30
-.01
-.05
.04
.02
.01
84.86
Identified
33
.11
.06
.17
.03
.02
93.80
Integrated
3
.04
-.27
.34
.07
.01
81.49
Intrinsic
23
.13
.07
.19
.03
.01
85.46
Self-Report Academic Performance
Amotivation
32
-.28
-.33
-.23
.02
.01
82.91
External
26
-.02
-.06
.03
.02
.01
85.11
Introjected
28
.07
-.01
.16
.04
.03
94.45
Identified
27
.29
.22
.35
.03
.02
91.70
Intrinsic
33
.32
.26
.39
.03
.02
90.06
Note. k = number of samples; ρ = correlation after correction for reliability and weighted by
samples size. Std Error = Standard error.
Student Motivation and Outcomes 32
Table 4: Meta-Analytic Correlations between Motivation Types and Persistence Outcomes
Outcome
95% CI
Motivation
k
ρ
Lower
Higher
Std. Error
T2
I2
Effort
Amotivation
13
-.41
-.51
-.31
.05
.04
95.10
External
17
-.08
-.20
.05
.06
.05
96.23
Introjected
16
.25
.15
.35
.05
.02
91.91
Identified
15
.51
.41
.62
.05
.04
94.52
Intrinsic
16
.54
.43
.64
.05
.03
94.30
Continuance Intention
Amotivation
6
-.27
-.44
-.10
.07
.02
90.50
External
10
-.02
-.15
.10
.05
.02
91.72
Introjected
9
.02
-.07
.11
.04
.01
84.45
Identified
10
.31
.20
.43
.05
.02
92.97
Intrinsic
7
.26
.08
.43
.07
.02
92.95
Intent to Exercise
Amotivation
6
-.26
-.52
.00
.10
.03
93.09
External
12
.03
-.11
.17
.06
.05
96.00
Introjected
12
.25
.14
.37
.05
.03
93.06
Identified
11
.51
.41
.61
.04
.02
88.23
Intrinsic
12
.43
.32
.55
.05
.02
94.08
Physical Activity
Amotivation
17
-.12
-.19
-.05
.03
.01
87.61
External
24
-.03
-.09
.03
.03
.02
88.36
Introjected
25
.14
.08
.20
.03
.02
90.26
Identified
25
.31
.23
.39
.04
.05
95.93
Intrinsic
23
.33
.26
.41
.04
.04
95.10
Engagement
Amotivation
13
-.43
-.53
-.32
.05
.03
94.50
External
22
-.10
-.20
.00
.05
.07
97.29
Introjected
23
.26
.19
.34
.04
.04
95.03
Identified
23
.57
.47
.68
.05
.05
96.18
Intrinsic
20
.62
.54
.70
.04
.03
93.83
Absenteeism
Amotivation
3
.17
-.08
.43
.05
.01
71.11
External
4
-.01
-.15
.14
.04
<.01
60.89
Introjected
4
-.07
-.18
.03
.03
<.01
30.08
Identified
4
-.09
-.19
.02
.02
<.01
0.00
Intrinsic
4
-.08
-.32
.16
.07
.01
82.96
Dropout Intention
Amotivation
5
.52
.09
.94
.15
.17
99.47
External
7
-.06
-.15
.02
.03
.01
89.42
Introjected
7
-.03
-.28
.21
.10
.04
97.55
Identified
7
-.27
-.43
-.12
.06
.04
97.60
Intrinsic
7
-.25
-.41
-.10
.06
.02
94.14
Note. k = number of samples; ρ = correlation after correction for reliability and weighted by
samples size. Std Error = Standard error.
Student Motivation and Outcomes 33
Table 5: Meta-Analytic Correlations between Motivation Types and Well-being Outcomes
Outcome
95% CI
Motivation
k
ρ
Lower
Higher
Sdt. Error
T2
I2
Anxiety
Amotivation
18
.26
.15
.37
.05
.05
96.94
External
20
.12
.06
.19
.03
.01
87.55
Introjected
17
.13
.04
.23
.05
.03
95.40
Identified
16
-.12
-.21
-.02
.04
.02
93.03
Intrinsic
16
-.15
-.26
-.05
.05
.04
96.47
Depression
Amotivation
4
.29
-.23
.81
.16
.09
97.86
External
6
.08
-.12
.27
.08
.04
95.47
Introjected
6
.05
-.09
.18
.05
.03
93.06
Identified
5
-.14
-.31
.02
.06
.03
94.46
Intrinsic
4
-.19
-.39
.00
.06
.03
95.18
Boredom
Amotivation
4
.58
.23
.93
.11
.06
96.81
External
3
-
-
-
-
-
-
Introjected
3
-
-
-
-
-
-
Identified
4
-.45
-.69
-.21
.07
.02
87.25
Intrinsic
3
-.48
-.75
-.22
.06
.01
83.21
Negative Affect
Amotivation
10
.34
.22
.47
.05
.02
84.84
External
13
.22
.11
.32
.05
.03
89.98
Introjected
12
.16
.04
.27
.05
.04
91.83
Identified
13
-.16
-.30
-.02
.07
.04
92.94
Intrinsic
15
-.29
-.44
-.14
.07
.05
95.19
Positive Affect
Amotivation
14
-.29
-.42
-.16
.06
.04
94.66
External
17
-.04
-.16
.08
.06
.07
96.39
Introjected
18
.13
.02
.25
.06
.05
94.44
Identified
18
.41
.28
.54
.06
.06
96.04
Intrinsic
14
.52
.37
.66
.07
.06
96.48
Satisfaction (General)
Amotivation
8
-.23
-.49
.04
.11
.07
97.32
External
11
.00
-.12
.12
.05
.02
91.91
Introjected
11
-.01
-.16
.14
.07
.11
98.40
Identified
10
.41
.24
.57
.07
.04
96.33
Intrinsic
9
.44
.31
.58
.06
.03
94.47
Vitality
Amotivation
8
-.36
-.47
-.25
.05
.02
93.30
External
10
-.18
-.30
-.05
.05
.03
94.14
Introjected
11
.13
.00
.27
.06
.04
95.75
Identified
11
.51
.34
.69
.08
.08
97.68
Intrinsic
10
.61
.46
.76
.07
.04
94.95
Enjoyment
Amotivation
7
-.39
-.67
-.11
.12
.11
98.32
External
9
-.10
-.32
.11
.09
.07
96.94
Introjected
9
.26
.02
.49
.10
.12
98.17
Identified
8
.56
.41
.71
.06
.03
92.58
Intrinsic
9
.69
.52
.85
.07
.03
94.07
Student Motivation and Outcomes 34
Social-Emotional Functioning
Amotivation
6
-.25
-.37
-.12
.05
.01
82.15
External
6
-.07
-.22
.08
.06
.02
87.76
Introjected
6
.05
-.07
.17
.05
.01
75.50
Identified
5
.23
.12
.33
.04
.01
64.78
Intrinsic
3
.31
.15
.46
.04
<.01
56.77
Note. k = number of samples; ρ = correlation after correction for reliability and weighted by
samples size. Std Error = Standard error.
Table 6: Meta-Analytic Correlations between Motivation Types and Goal Orientations
Covariate
95% CI
Motivation
k
ρ
Lower
Higher
Sdt. Error
T2
I2
Mastery Approach
Amotivation
14
-.22
-.41
-.02
.09
.09
97.59
External
17
.11
-.02
.25
.06
.08
96.87
Introjected
18
.33
.22
.44
.05
.06
95.91
Identified
16
.65
.51
.79
.07
.06
96.17
Intrinsic
16
.64
.56
.71
.03
.01
86.60
Mastery Avoidance
Amotivation
10
.08
-.07
.23
.07
.04
95.11
External
7
.30
.12
.48
.07
.04
95.16
Introjected
7
.40
.25
.54
.06
.03
93.09
Identified
7
.35
.15
.55
.08
.05
96.18
Intrinsic
5
.36
.18
.53
.06
.01
87.05
Performance Approach
Amotivation
18
.11
-.01
.24
.06
.06
96.62
External
17
.34
.27
.40
.03
.01
82.94
Introjected
17
.46
.38
.53
.03
.01
85.50
Identified
17
.28
.22
.34
.03
.01
82.75
Intrinsic
16
.25
.17
.32
.04
.01
85.47
Performance Avoidance
Amotivation
18
.21
.12
.30
.04
.03
93.29
External
19
.31
.22
.40
.04
.03
90.86
Introjected
19
.43
.33
.53
.05
.03
92.11
Identified
19
.23
.12
.34
.05
.04
94.12
Intrinsic
15
.20
.10
.29
.04
.03
91.08
Note. k = number of samples; ρ = correlation after correction for reliability and weighted by
samples size. Std Error = Standard error.
Student Motivation and Outcomes 35
Table 7: Meta-Analytic Correlations between Motivation Types and Self-Evaluation Covariates
Covariate
95% CI
Motivation
k
ρ
Lower
Higher
Sdt. Error
T2
I2
Self-Efficacy
Amotivation
13
-.37
-.49
-.26
.05
.04
94.06
External
16
-.02
-.14
-.14
.05
-.14
-0.14
Introjected
13
.18
.05
.31
.06
.04
94.95
Identified
15
.43
.31
.55
.06
.05
95.42
Intrinsic
11
.41
.24
.57
.07
.07
96.74
Self-Esteem
Amotivation
3
-.38
-.72
-.03
.08
.01
90.37
External
5
.10
-.13
.32
.08
.02
90.83
Introjected
5
.23
.01
.44
.07
.02
92.04
Identified
5
.44
.21
.67
.08
.04
95.68
Intrinsic
5
.34
.18
.51
.05
.02
88.36
Physical Image - Anxiety
External
6
.26
.17
.34
.03
<.01
45.74
Introjected
6
.22
.05
.40
.07
.02
85.11
Identified
5
-.05
-.28
.17
.08
.02
87.35
Intrinsic
5
-.18
-.29
-.07
.04
<.01
54.01
Physical Image - Positive
Amotivation
4
-.17
-.26
-.08
.03
<.01
34.82
External
5
-.08
-.22
.06
.05
.01
86.89
Introjected
5
.06
-.04
.16
.04
.01
78.95
Identified
5
.32
.19
.45
.05
.01
80.85
Intrinsic
5
.36
.23
.49
.05
.01
81.99
Note. k = number of samples; ρ = correlation after correction for reliability and weighted by
samples size. Std Error = Standard error.
Student Motivation and Outcomes 36
Table 8: Relative Weights Analysis of Motivation Predicting Outcomes
Outcome
R2
Amotivation
External
Introjected
Identified
Intrinsic
RW
%
RW
%
RW
%
RW
%
RW
%
Academic Achievement
Objective
.17
.07
38.00
.02
12.40
.01
3.90
.04
20.25
.04
25.44
Self-report
.30
.08
27.05
.03
9.52
.01
2.66
.06
21.40
.12
39.37
Persistence
Effort
.36
.05
15.17
.04
11.46
.04
11.79
.12
33.08
.10
28.51
Continuance Intentions
.27
.03
11.78
.02
8.58
.02
8.71
.12
44.66
.07
26.28
Physical Activity
.27
.03
9.88
.05
17.47
.03
9.56
.11
39.12
.06
23.98
Absenteeism
.15
.06
38.95
.02
14.05
.01
5.44
.03
20.95
.03
20.60
Engagement
.47
.05
11.62
.06
12.75
.05
10.87
.16
34.12
.14
30.63
Well-being
Anxiety
.16
.06
40.37
.01
7.92
.03
18.28
.02
14.06
.03
19.38
Depression
.44
.05
15.17
.04
11.46
.04
11.79
.12
33.08
.10
28.51
Negative Affect
.53
.13
25.21
.03
5.49
.05
8.74
.13
24.13
.19
36.43
Positive Affect
.53
.07
13.01
.03
4.86
.01
2.51
.14
25.99
.28
53.63
Satisfaction
.28
.02
6.91
.02
5.38
.04
14.00
.09
32.20
.12
41.50
Vitality
.49
.04
7.61
.09
17.80
.04
7.18
.18
35.83
.16
31.59
Enjoy
.52
.05
10.49
.02
3.58
.03
6.27
.14
27.40
.27
52.26
SE-Functioning
.33
.07
21.16
.02
6.10
.01
2.74
.09
25.61
.15
44.40
Goal Orientations
Mastery Approach
.65
.05
8.08
.05
6.98
.06
8.99
.29
44.30
.20
31.65
Mastery Avoidance
.40
.02
5.01
.08
19.05
.07
17.38
.08
20.12
.16
38.45
Performance Approach
.40
.02
4.31
.09
22.93
.12
30.40
.07
16.57
.10
25.79
Performance Avoidance
.26
.06
21.65
.03
13.25
.11
41.15
.03
11.93
.03
12.03
Self-Evaluations
Self-Efficacy
.24
.06
0.06
.02
7.01
.02
7.40
.08
34.10
.06
25.96
Self-Esteem
.24
.09
39.02
.02
9.18
.02
7.07
.06
26.28
.04
18.44
Physical Image Anxiety
.23
.05
21.86
.01
6.25
.05
20.44
.05
20.76
.07
30.69
Physical Image Positive
.27
.02
8.23
.04
16.79
.02
7.66
.11
40.98
.07
26.34
Average Relative Weight
17.42
10.88
11.52
28.13
30.95
Note. RW is an estimated R2 associated with each predictor. % is this same relative weight
converted to a percentage of total R2. Highlighting indicates strongest estimated predictor.
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School motivation is a multidimensional concept. It can be qualitatively defined by various sources of regulation as well as by the school subject to which it pertains. Based on self-determination theory, we postulate that motivation types vary in terms of quality (from lower to higher quality these types are: external, introjected, identified, and intrinsic) and that higher motivational quality predicts positive outcomes. In this study, we examined school subject differentiation in motivational quality and prediction patterns of academic achievement. Results from bi-factor ESEM examining differences in motivational quality within a subject (French, math, and English as a second language) showed that high general levels of motivation in math and English predicted achievement, and more so in the corresponding school subject. Intrinsic motivation for a school subject was generally positively associated with achievement, but only in the corresponding school subject, whereas introjected and external regulations for most school subjects negatively predicted achievement in the corresponding school subject, but also in the other ones. Results from bi-factor ESEM examining differences in motivation levels for distinct school subjects for a given motivation type showed that general levels of intrinsic and external regulations across school subjects predicted achievement positively and negatively, respectively, in all school subjects, while intrinsic motivation, but also identified regulation, had positive subject-specific associations with achievement. The specificity of intrinsic and identified motivations and non-specificity of introjected and external motivations point toward various recommendations in school motivation research and practice. While assessment of autonomous motivations should be subject-specific, assessment of controlled motivations could be general with no loss of predictive power.
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This chapter highlights the conceptual and practical differences between fixed‐effect and random‐effects models. Under the random‐effects model the goal is not to estimate one true effect, but to estimate the mean of a distribution of effects. Under the fixed‐effect model there is a wide range of weights whereas under the random‐effects model the weights fall in a relatively narrow range. Under the fixed‐effect model Carroll was assigned a relatively small proportion of the total weight, and had little influence on the summary effect. The selection of a computational model should be based on our expectation about whether or not the studies share a common effect size and on our goals in performing the analysis.