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72
Shen and Wingert are with Wayne State Univeristy, Kinesiology, Health & Sport Studies, Detroit, MI.
Li is with Ohio State University, Columbia, OH. Sun is with University of South Florida, Tampa, FL.
Rukavina is with Adelphi University.
Journal of Teaching in Physical Education, 2010, 29, 72-84
© 2010 Human Kinetics, Inc.
An Amotivation Model
in Physical Education
Bo Shen,1 Robert K. Wingert,1 Weidong Li,2
Haichun Sun,3 and Paul Bernard Rukavina,4
1Wayne State University, 2Ohio State University,
3University of South Florida, 4Adelphi University
Amotivation refers to a state in which individuals cannot perceive a relationship
between their behavior and that behavior’s subsequent outcome. With the belief
that considering amotivation as a multidimensional construct could reect the com-
plexity of motivational decits in physical education, we developed this study to
validate an amotivation model. In study 1 (N = 156), an exploratory factor analysis
provided preliminary support with the model comprising four dimensions: ability
beliefs, effort beliefs, values placed on the task, and characteristics of the task. In
study 2 (N = 499), the four-dimensional model was further corroborated through
a conrmatory factor analysis. Its construct validity and predictive validity were
also conrmed. Overall, the ndings lend evidence to the conceptual validation
of the four-dimensional structure of amotivation. Lack of motivation in physical
education may result from different reasons. The multifaceted nature of amotiva-
tion in physical education must be considered and instructionally addressed during
teaching and learning.
Keywords: amotivation taxonomy, self-determination theory, high school students
The area of motivation has been one of the most prolic areas of research in physical
education literature (Solmon, 2003). A number of theoretical frameworks have been
implemented to examine the inuence of various motivational factors on learning
and physical activity (Chen, 2001). Although the investigations are extensive, the
fact remains that many students lack motivation in physical education, especially
during their high school years. One of the most evident phenomena is that high
school students do not have the desire to choose physical education courses after
they have met the minimal physical education credit requirements for graduation.
The enrollment in physical education in high schools decreases at an average rate
of 32% yearly (Centers for Disease Control and Prevention, 2004). Needless to say,
understanding the reasons why students lack motivation or become amotivated is
An Amotivation Model 73
increasingly urgent for physical educators to design effective strategies to enhance
students’ enrollment in physical education and prevent sedentary lifestyles toward
adulthood.
According to Self-Determination Theory (SDT; Deci & Ryan, 2002), an indi-
vidual’s behavior can be effectuated through intrinsic motivation (enjoyment and
interest-related motives), extrinsic motivation (instrumental motives), and amotiva-
tion (an absence of motivation). These three broad theoretical types of motivation
can be located at some point on a continuum, representing the relative degree of
self-determination of behavior (Ryan & Connell, 1989). Individuals become more
self-determined as they internalize to a greater extent their reasons for executing
a given behavior, while they become less self-determined as they engage in an
activity as a means to an end. On this continuum, amotivation comprises the other
extreme with the least amount of self-determination and represents a complete lack
of volition with respect to the target behavior (Deci & Ryan, 2002).
Amotivated individuals cannot perceive a relationship between their actions
and subsequent outcomes of those actions (Pelletier, Fortier, Vallerand, & Briere,
2001). They may feel disintegrated or lacking control of their actions and will thus
invest little effort or energy in accomplishment of the actions. Amotivation has been
associated with boredom and poor concentration in class (Vallerand et al., 1993),
poor psychosocial adjustment, high perceived stress at school (Baker, 2004), and
school dropout (Pelletier et al., 2001).
In physical education, some researchers have also examined amotivation. For
example, Carlson (1995) used the term alienation to describe middle and high
school students’ amotivation. Through interviews with students and conducting class
observations, the author found that alienated students felt that physical education
was not personally important. Such students had low perceptions of competence
and adherence. As a result, they were more likely to be passively participating in
class, faking illness/injury, or missing classes. Similarly, Ntoumanis, Pensgaard,
Martin, and Pipe (2004) explored British school children’s perception of amo-
tivation in physical education using semistructured interviews. They found that
amotivation resulted from learned helplessness beliefs and was often displayed by
nonattendance, low involvement in the class, and low intention to be physically
active outside school.
Although previous studies in SDT have enhanced our understanding of
amotivation, most of them treated amotivation as a one-dimensional construct
or feeling of general helplessness. Given the complexity of school subjects and
context, Shen, McCaughtry and Martin (2008) argued that domain specicity
and different learning environments can inuence students’ motivation status sig-
nicantly. Because students may lack motivation for many different reasons with
distinct forms, conceptualization of amotivation as general helplessness was not
sufciently revealing the whole picture of motivational decits in school settings.
Legault, Green-Demers, and Pelletier (2006) proposed “…amotivation is itself
an entity, a complex and multifaceted process, which is not so much an absence
as a broad effect of unmet needs” (p. 580). The authors suggest that investigating
amotivation using a multidimensional framework may enrich our understanding
of the nature of lack of motivation and potentially facilitate educators to design
effective motivational strategies to enhance students’ involvement.
74 Shen et al.
Echoing this suggestion, a few researchers have explored different dimen-
sions of amotivation from a multifaceted perspective. Pelletier, Dion, Tuson, and
Green-Demers (1999), for example, investigated individuals’ reasons for their
lack of motivation toward environmental protective behaviors. They argued that
the concept of amotivation could be more precisely understood when three dimen-
sions are considered: amotivation because of strategy beliefs, ability beliefs, and
effort beliefs. Strategy beliefs stem from Bandura’s (1982) concept of outcome
expectancy and refer to individuals’ expectancies regarding the extent to which
certain strategies are effective in producing the desired outcomes. It is proposed
that one possible reason for amotivation is the belief that a specic behavior will
not be effective in attaining the goal. Ability beliefs are similar to the concept of
self-efcacy (Bandura, 1997) and refer to individuals’ expectations with respect to
their aptitude to perform a certain behavior. When perceived self-efcacy is high,
more ambitious challenges are pursued, and a greater goal commitment is applied.
In contrast, when self-efcacy is uncertain, failure is perceived as a likely outcome.
It is proposed that individuals may know that a particular course of action would
produce a desirable outcome, but may not believe that they have what it takes to
successfully carry out the required behaviors. This leads to amotivation (Pelletier
et al., 1999).
Effort beliefs refer to the desire to expend the energy required by a particular
behavior (Pelletier et al., 1999). Individuals may be reluctant to perform a behavior
if they are unable to sustain the necessary effort or if the behavior is difcult to
integrate into their lives. Under such circumstances, although individuals may be
aware of what is required to fulll the tasks and positively appraise their ability to
do so, low effort perception may create amotivation if they think that they cannot
exert the sustained effort required to perform and maintain the behavior.
Built upon Pelletier et al.’s (1999) study, Legault et al. (2006) developed and
conceptually validated a taxonomy of reasons that give rise to academic amotiva-
tion in school. Specically, Legault et al. examined the relevance of Pelletier et
al.’s amotivation dimensions in a school context and conrmed that ability beliefs
and effort beliefs would impact students’ amotivation directly while the inuence
of strategy beliefs is indirect via the mediation of other factors. More importantly,
Legault et al. (2006) recognized the uniqueness of school and highlighted that
value placed on the learning task and characteristics of the task in class can also
lead to amotivation.
Amotivation in school is associated with students’ values in relation to the
task. As an incentive for engaging in different tasks, low values placed on the task
may initiate amotivation, especially when the task is not an integral component or
of importance in a student’s life (Ryan 1995). Values affect behaviors by inuenc-
ing the perceived desirability of situations and experiences, and by contributing
to the organization of personal goals (Kasser, 2002). Wigeld and Eccles (2000)
suggest that devaluing school subjects may lead to serious motivational decits.
When students perceived their environments as delivering negative information
about the value of school or of a particular class or subject, they are more likely
to be lacking in motivation (Murdock, 1999). Researchers (e.g., Pintrich, 2003;
Wigeld & Eccles, 2000) recommend that task values need to be examined to better
understand motivational issues in school.
An Amotivation Model 75
Various situational inuences or specic features of a task can invoke feelings
of amotivation (Ntoumanis et al., 2004). Similar to the concept of situational inter-
est in interest-based motivation theory (Chen & Shen, 2004), characteristics of the
task refers to the appealing effects of learning tasks. Unappealing characteristics of
the learning task may lead to learning disengagement. Ainley, Hidi and Berndoff
(2002) noted that individuals must experience some form of pleasure or interest to
effectuate a behavior. If the qualitative experience of the activity does not engage
the knowledge or stimulation of students, then students will not favor it. Legault at
al. (2006) argued that although much amotivation stems from within the student,
many school tasks are not inspiring or interesting enough to make students feel
motivated to perform them. When students perceive the tasks as uninteresting, unin-
spiring, or lacking of stimulating qualities, amotivation may also occur (Carlson,
1995; Hidi & Harackiewicz, 2000).
With the dened multifaceted construct of amotivation in school, Legault et al.
(2006) generated the Academic Amotivation Inventory. The inventory was designed
to examine students’ reasons for not wanting to study or doing their school work.
Using exploratory factor and 1st and 2nd-order conrmatory factor analyses, the
authors offered support for an academic amotivation taxonomy comprising four
dimensions: ability beliefs, effort beliefs, characteristics of the task, and value
placed on the task. Furthermore, the proposed taxonomy was corroborated through
analysis in discriminant validity and construct validity with related psychological
and behavioral constructs (e.g., academic performance, self esteem, anxiety).
The aforementioned inventory was developed and assessed in classroom set-
tings. Little is known about the generalizability of this inventory to other settings.
To our knowledge, there has been no study that systematically and conceptually
investigated the dimensions of amotivation in physical education. The purpose
of this study, therefore, was to examine whether Legault et al.’s (2006) academic
amotivation taxonomy could be generalized into physical education settings and
whether the four dimensions of amotivation could be explained by a higher order
structure of general feeling of helplessness in physical education. Specically, two
sub studies were conducted to examine the reliability, validity, and generalizability
of the scores produced by the taxonomy in high school physical education settings.
The objective of study 1 was to provide preliminary evidence of the four-factor
structure of the amotivation construct in physical education. The objective of study
2 was to further test the hypothesized structure of the amotivation model.
Methods
Participants
This study was part of a larger project on motivation and its relation to educa-
tional outcomes among high school students in physical education. A total of
655 students from two suburban high schools in two different school districts
in a major midwest metropolitan area served as participants. The data collected
from School One were used to pilot test the validity and reliability of the scores
produced by the amotivation inventory, while the data collected from School Two
were used to evaluate the generalizability of the scores. All physical education
76 Shen et al.
teachers in both schools were certied and active members in the state physical
education association. Permission to conduct the study was obtained before the
investigation from the university review board, the school district, the partici-
pants, and their parents.
School One is a private high school with more than 1,000 students. All physical
education classes are single-sex. Participants in School One (N = 156; 85 boys,
71 girls) consisted of ninth (75.0%), tenth (13.5%), and eleventh (11.5%) graders
whose ages ranged from 14 to 18 years (M = 15.36, SD = 1.20). Ethnicity of the
participants included Caucasian (90.1%), African-American (5.6%), Hispanic-
American (3.1%), and others (1.2%).
School Two is a comprehensive public high school with more than 2,500
students. Unlike School One, all physical education classes are coeducational.
Participants in School Two (N = 499; 269 boys and 230 girls) consisted of ninth
(58.2%), tenth (22.1%), eleventh (12.4%), and twelfth (7.5%) graders ranging in
age from 14 to 20 years (M = 16.01, SD = 1.32). A majority of the students came
from lower-middle to middle class socioeconomic backgrounds. The sample was
highly representative of the ethnic demographics for the student population in the
area (80.1% Caucasian, 13.0% African American, 4.1% Hispanic American, and
2.9% Asian-American). Both schools had a one credit requirement in physical
education and health. Students usually took this credit during their rst year of
high school: ninth grade. Subsequently, they may take elective physical educa-
tion courses in specialized areas if they wish to continue their involvement. The
schools used a 90-min rotating block schedule. Students had physical education
classes every other day throughout the semester. The curriculum of the physical
education classes was focused on lifetime tness activities. With the exception of
swimming as a mandatory unit, students in both schools were offered the oppor-
tunity at the beginning of the semester to choose one physical activity from each
team sport (e.g., basketball, volleyball, soccer, etc.) and each individual sport
(e.g., weight training, dance, aerobics, etc.) category. Their nal grade was based
on health-related tness improvements and summative written tests on the units
covered (e.g., technique, rules, tness principles, etc.).
Measures
Amotivation in Physical Education. Amotivation Inventory-Physical Education
(AI-PE) adapted from Legault et al. (2006) was used to examine students’ reasons
for not wanting to participate in physical education. The AI-PE consists of 16
items and measured the four proposed dimensions of amotivation: Ability beliefs
(e.g., “Because I don’t have what it takes to do well in PE”); Effort beliefs (e.g.,
“Because I don’t have the energy to participate in PE”); Characteristics of the
task (e.g., “Because I nd that the sport/activity being played is boring”); and
Value placed on the task (e.g., “Because participating in PE is not valuable to
me”). Students were rst asked how often they experienced a lack of motivation
in physical education. Then, they were asked to rate, from 1 to 7 on a Likert-type
scale, the degree to which each statement corresponded with their reasons for
not wanting to participate in physical education (1 = does not correspond at all;
7 = corresponds exactly).
An Amotivation Model 77
To preserve the content validity of the amotivation taxonomy in physical edu-
cation, we took the following steps. First, we consulted with a panel of six profes-
sionals about the questionnaire items before data collection. The panel consisted
of one school district physical education coordinator and ve experienced high
school physical education teachers (each of them have taught physical education
over 15 years). The panel members were asked to read the items and evaluated their
consistency with the dimensions on a 5-point Likert scale (1 = very inaccurate/
inappropriate, 5 = very accurate/appropriate). They also examined the wording
and language usage of the instrument. Based on their suggestions, two items for
task characteristics and one item for effort beliefs were rewritten to better reect
the nature of students’ learning in physical education. Finally, all items scored
above 4.5 in average, suggesting that the questionnaire has an acceptable content
validity. Second, we ran a pilot test on 35 nonparticipating high school students in
their physical education classes to rene the AI-PE. The rst author administered
the questionnaire and encouraged students to ask questions if they had difculty
understanding instructions or items in the questionnaire. The students raised no
questions while completing the AI-PE.
In-Class Effort. The physical education teachers were asked to provide an overall
rating of each student’s levels of effort in physical education. For each student, his/
her PE teacher provided a single rating on a 7-point scale (1= no effort at all- 7=
high levels of effort). In the initial introduction, the physical education teachers
were informed that the scale was going to assess how hard the students tried to
improve their skills and whether they “do their best” during PE lessons. Ntoumanis
(2005) used this scale in physical education with similar aged participants.
Compared with students’ self-reports, teachers’ evaluations were more objec-
tive (Ntoumanis, 2005). Given that the teachers in this study demonstrated strong
commitment to teaching, we believe the ecological validity of this measure could
be assumed. However, it is worthwhile to notice that using a single item to measure
in-class effort made it impossible to evaluate the scale psychometric properties. In
the future research, a more comprehensive evaluation including self-reports, teacher
ratings, and behavioral observations with multiple-item measures is recommended
to better reect students’ performance in class.
Procedure
Using the same protocol in both schools, the AI-PE was administered to students by
the rst author and a graduate student in spring during regularly scheduled physi-
cal education classes in the gymnasium. The AI-PE took approximately 13 min to
complete. To diminish students’ tendency to give socially desirable responses, they
were ensured that their responses would not affect their grades and their teachers
would not have access to their individual responses. Students were also told that
there were “no right or wrong answers.” Finally, students were informed that lling
out the survey was voluntary and that they could withdraw at any time they wanted.
During the data collection, the teachers assisted in managing nonparticipated stu-
dents by assigning different tasks for them.
78 Shen et al.
Data Analysis
The response scores from students at School One were analyzed by an exploratory
factor analysis with varimax rotations. According to the theoretical dimensions of
amotivation, we specied the number of factors as four in the extraction. Because
the sample size in School One was relatively small (less than 200), we followed
Stevens’ (2001) recommendation in which the factor determination was based on
the criteria of factor loadings greater than or equal to .45 and without cross load-
ings. In addition, Cronbach’s alpha coefcients were used to examine the internal
consistency of test scores produced by the AI-PE. Following Kline (1998), we set
the internal consistency as acceptable with a Cronbach alpha value greater than
.70 in the current study.
Conrmatory Factor Analysis (CFA) was then employed to examine the con-
struct of the Amotivation Taxonomy with students’ scores in School Two. To evaluate
model t, several model-data-t values in the analysis were selected to assess the
model’s validity (Hu & Bentler, 1999). To evaluate the model’s absolute or parsimo-
nious t relative to the hypothetical model, the comparative t index (CFI) was used
with a value close to .95 or above indicating a good t. A Standardized Root Mean
Squared Residual (SRMR) was used to evaluate the model-data t by estimating the
overall discrepancy between the observed and model-implied covariances. An SRMR
value of .09 or less indicates an adequate t. To estimate the difference between the
hypothesized covariance matrix and the actual sample covariance matrix, the root
mean square error of approximation (RMSEA) was used to determine model-data
t with a value of .06 or less indicating a good t (Hu & Bentler, 1999).
Given the differences between the two schools in terms of school characters
(i.e., private versus public) and physical education context (i.e., single-sex versus
coeducational), we employed a multistep analysis of invariance procedure (Kline,
1998) to assess the generalizability and strength of the factor structure. In the
multistep modeling, the model structure in both school samples were added simul-
taneously in a system of equations. Then, all the parameters in the equation which
were equal across the two samples were constrained increasingly. In addition, we
also calculated the correlations between students’ amotivation and their in-class
effort to provide information regarding the predictive validity of the AI-PE. It was
hypothesized that all dimensions of amotivation would be negatively associated
with their in-class effort.
Results
The results of descriptive statistics are presented in Table 1. The four subdimen-
sions of amotivation across the two school samples had an average score below the
midpoint of the inventory. The standard deviations ranged from .88–1.61. Univariate
skewness and kurtosis ranged from -.90–1.22, indicating that the observed variables
in both samples were approximately normal. Further analysis showed that Mardia’s
coefcient (Mardia, 1970) was 4.67, suggesting that the multivariate normality
assumption for model testing was not violated.
To investigate the structure of the AI-PE, we performed an exploratory factor
analysis with the School One data. Results are displayed in Table 2. The four factors
with eigen values greater than or close to one accounted for a substantial portion
An Amotivation Model 79
(73.3%) of the total item variance. Factor loadings displayed a clean factor struc-
ture, which offered preliminary support for a four-dimensional conceptualization
of amotivation. Moreover, the magnitude of factor loadings was satisfactory (i.e.,
loadings on target factors ranged from .61 to .78). As can be seen in Table 1, the
amotivation dimensions were positively and moderately correlated. Cronbach’s
alpha coefcients for the four dimensions ranged from .82 to .91. All values
exceeded the minimum recommended value of .70, indicating that the scores
produced by the AI-PE had acceptable internal consistency in this population of
high school students.
To statically test the factor structure of amotivation, we performed a CFA with
maximum-likelihood estimation using the School Two data. We hypothesized that
the four-factor structure of the amotivation construct would be veried by grant-
ing the conceptualization of amotivation in terms of ability beliefs, effort beliefs,
characteristics of the task, and value placed on the task. The CFA was specied as
a typical measurement model where target loadings, item uniqueness values, and
factor variances and covariance were estimated. Results of the CFA yielded the
following t indices: CFI = .96, SRMR = .04, and RMSEA = .06, suggesting that
the hypothesized model ts the data well.
Because the rst-order model ts the data well, we tested whether the four
factors identied in the model could be explained by the higher order structure of
general amotivation (i.e., general feeling of helplessness in physical education). The
hierarchical structure of amotivation is presented in Figure 1. Results revealed that
the data t in the model adequately, CFI = .95, SRMR = .06, and RMSEA = .06.
In addition, all standardized factor loadings ranged from .65 to .85 and was sig-
nicant at the p < .01 level, indicating that all the items are indicators of the fac-
tors they are hypothesized to measure. Cronbach’s alpha coefcients for the four
dimensions ranged from .85 to .89.
Table 1 Correlation among Dimensions of Amotivation
in Physical Education
Dimension M/
SD
Alpha Skewness Kurtosis 1 2 3 4 5
School One (n = 156)
1. Ability beliefs 2.54/.88 .86 1.00 1.01 __
2. Task character 2.98/1.13 .82 .80 .21 .42 __
3. Value of task 3.04/1.35 .91 .99 .87 .44 .69 __
4. Effort beliefs 3.25/1.25 .83 1.21 .42 .48 .48 .53 __
5. In-class effort 4.67/1.50 __ -.90 -.67 -.37 -.35 -.41 -.40 __
School Two (n = 499)
1. Ability beliefs 2.91/1.20 .87 1.22 .94 __
2. Task character 3.76/1.50 .85 .88 .11 .50 __
3. Value of task 3.49/1.56 .89 1.11 .43 .51 .60 __
4. Effort beliefs 3.45/1.35 .87 1.12 .80 .58 .54 .59 __
5. In-class effort 4.80/1.61 __ -.60 -.37 -.33 -.33 -.38 -.33 __
Note. All correlations are signicant at the .01 level.
80 Shen et al.
Table 2 Dimensions of Amotivation in Physical Education
(Study 1: Exploratory Factor Analysis)
Item AB TC TV EB
Because I am not good at PE .68 .22 .24 .26
Because I don’t have what it takes to do well in PE .78 .06 .24 .21
Because I don’t have the knowledge/skill required to
succeed in PE
.78 .21 .15 .15
Because the tasks demanded of me in PE surpass my ability .70 .29 .10 .24
Because I nd that the sport/activity being played is boring .30 .71 .23 .22
Because I don’t like the sport/activity being played in PE .25 .77 .30 .11
Because I have the impression that it’s always the same
thing in PE everyday
.11 .61 .27 .25
Because the sport/activity in PE is not stimulating .19 .77 .24 .20
Because for me, PE holds no interest .22 .27 .74 .28
Because participating in PE is not important for me .18 .36 .76 .28
Because participating in PE is not valuable to me .28 .36 .65 .24
Because I have no good reason to participate in PE .30 .38 .61 .25
Because I’m not energetic enough for PE .27 .19 .23 .76
Because I’m a bit lazy .22 .28 .26 .67
Because I don’t like to invest the effort that is required for PE .33 .27 .30 .60
Because I don’t have the energy to participate in PE .25 .16 .20 .77
Eigen values 7.25 2.07 1.42 .99
Percentage of variance explained (%) 45.3 13.0 8.85 6.20
Note: AB = Ability Beliefs; TC = Task Characteristics; VT = Value of Task; EB = Effort Beliefs
Further, the multistep invariance analysis of the hierarchical structure revealed
that items from the AI-PE are equally valid for students in the two school settings.
Baseline multigroup model (school One and school Two together, unconstrained)
revealed that the data t the model well, CFI =. 96, RMSEA =.04, and SRMR=.06.
In following two consecutive steps, we (1) constrained the factor loadings to equiva-
lence across schools, and (2) constrained the covariance matrix to be invariant with
the factor loadings still constrained across schools. With imposed increasing equality
constraints, indexes of data-model t showed little loss moving from the baseline
model to the more stringent models. Those t indices, CFI =.95, RMSEA =.06,
and SRMR =.04, for the rst invariance model while CFI =.95, RMSEA =.06, and
SRMR=.05, for the second, indicated that the hypotheses of equal factor loadings
and covariance across the two samples were all tenable.
Lastly, to extend the predictive validity of amotivation in physical education,
we assessed correlations among the dimensions of amotivation and in-class effort
An Amotivation Model 81
assessed by the teachers. As hypothesized, all four dimensions of amotivation were
associated negatively with in-class effort, with correlation coefcient ranging from
-.33 to -.41, as reected in Table 1. The result indicated that students’ amotivation
might have inuenced their involvement in physical education class.
Figure 1. Hierarchical structure of amotivation in physical education
Note: All parameters are significant at the .01 level.
Task
Characters
Ability
Beliefs
Effort
Beliefs
Value of
Task
Amotivation
A 1
A 2
A 3
A4
E 1
E 2
E 3
E 4
V 1
V 2
V 3
V 4
T 1
T 2
32222
1
T 3
T 4
.79
.62
.77
.78
.73
.64
.63
.68
.76
.74
.81
.74
.66
.68
.59
.68
.80
.87
.83
.78
.60
.49
.56
.62
.80
.85
.65
.82
.60
.53
.76
.57
.76
.83
.84
.80
Figure 1 — Hierarchical structure of amotivation in physical education.
Figure 1. Hierarchical structure of amotivation in physical education
Note: All parameters are significant at the .01 level.
Task
Characters
Ability
Beliefs
Effort
Beliefs
Value of
Task
Amotivation
A 1
A 2
A 3
A4
E 1
E 2
E 3
E 4
V 1
V 2
V 3
V 4
T 1
T 2
32222
1
T 3
T 4
.79
.62
.77
.78
.73
.64
.63
.68
.76
.74
.81
.74
.66
.68
.59
.68
.80
.87
.83
.78
.60
.49
.56
.62
.80
.85
.65
.82
.60
.53
.76
.57
.76
.83
.84
.80
82 Shen et al.
Discussion
In terms of SDT, amotivation can be characterized as the state of motivational
decit (Deci & Ryan, 2002) or feelings of alienation and helplessness in physical
education (Ntoumanis et al., 2004). The purpose of this study was to develop and
validate a comprehensive taxonomy of amotivation in high school physical educa-
tion settings. The ndings provide convincing evidence for the multidimensional
nature of amotivation in physical education, suggesting that there are different
sources that may lead students to be amotivated.
Our exploratory factor analysis and CFA exhibited favorable psychometric
properties, supporting the construct validity of the scores produced by these high
school student populations. The results conrmed the appropriateness of using
the amotivation inventory in high school physical education. It is suggested that
students lacking motivation in physical education can be classied under the fol-
lowing four categories: their ability beliefs, effort beliefs, value placed on tasks,
and characteristics of the tasks.
The second-order CFA conducted in School Two specied amotivation in
physical education as a higher order construct with four subdimensions. This higher
order factor may indicate general amotivation, or the overall state of alienation,
and helplessness, as described in SDT (Deci & Ryan, 2002) in physical education.
Multistep invariance test of the amotivation taxonomy across schools revealed
that both the measurement and structural model were invariant. There were no
signicant factor loading or factor covariance differences detected between the
two schools. The generalizability of the scores produced by the AI-PE is further
demonstrated. Correlations among the four amotivation factors also supported the
proposed taxonomy. Moderate correlations suggest that the four subdimensions
of amotivation are components of the same higher order factor but represent four
independent constructs. In addition, Cronbach’s alpha coefcients in both School
One and School Two for the four dimensions were greater than .82, indicating the
scores from the AI-PE had reliable internal consistency for these two samples of
high school physical education students.
As hypothesized, the predictive validity correlations in School Two revealed a
general pattern in which all subdimensions of amotivation were negatively correlated
with teacher rated in-school effort, a benecial behavior construct in physical edu-
cation. The overall associations may reect the inuence of the common variance
shared by the four amotivation subdimensions, which represent the manifestation
of the latent general amotivation concept. Results from this study lend support to
the notion that students’ amotivation status may be directly associated with their
class involvement.
In summary, this study represents a rst attempt to apply the taxonomy of
amotivation to the high school physical education setting. The substantiation of the
taxonomy (AI-PE) makes an important contribution to physical education research
because it offers a theoretically sound and methodologically valid and reliable
test score for assessing students’ amotivation in high school physical education
settings. This study supports the appropriateness of the use of this instrument in
physical education settings, implying that a consideration of amotivation as a factor
in classroom learning may also be useful in models of learning and instruction in
physical education settings. Such efforts have potential to extend our understanding
An Amotivation Model 83
of this comprehensive body of knowledge by clarifying the processes involved in
amotivation in physical education. The taxonomy of amotivation in physical edu-
cation or AI-PE may help future researchers to assess and examine how students’
amotivation and self-determined motivation simultaneously affect their cognition,
affect, and behavior in physical education settings. The multifaceted nature of
amotivation in physical education must be considered and instructionally addressed
during teaching and learning.
Validation is a continuous process. It is suggested that future research should
continue examining the psychometric properties of the taxonomy with larger
and more diverse samples. We suggest that future research should examine how
consistent the scores produced by the AI-PE are over time (test-retest reliability)
and if the score can predict other outcome variables, such as students’ physical
activity involvement and knowledge/skill learning. In addition, we suggest that
additional multistep invariance analyses under different school level and physical
education curriculum are needed to better assess the generalizability of the AI-PE.
Finally, because social interaction is an important component of physical educa-
tion (Balderson & Sharpe, 2005), we suggest that future study consider social
factors in the amotivation taxonomy and examine the possible relationships of
social decits with other amotivation dimensions and overall amotivation concept
in physical education.
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