JOURNAL OF SPORT & EXERCISE PSYCHOLOGY, 2006, 28, 171-192
© 2006 Human Kinetics, Inc.
Assessing Multidimensional Physical Activity Motivation:
A Construct Validity Study of High School Students
Andrew J. Martin, David V. Tipler, Herbert W. Marsh,
Garry E. Richards, and Melinda R. Williams
University of Western Sydney
This study presents a new, multidimensional approach to physical activity motivation that is operationalized through four primary factors: adaptive cognitive
dimensions, adaptive behavioral dimensions, impeding cognitive dimensions,
and maladaptive behavioral dimensions. Among 171 Australian high school
students, the study assessed the structure of this four-factor framework (a
within-network construct validity approach) and also examined the relationships
between motivation and three key correlates: flow in physical activity, physical
self-concept, and physical activity level (a between-network construct validity
approach). The four-factor framework demonstrated within-network validity in
the form of reliable subscales and a sound factor structure. In terms of betweennetwork validity, relationships between the adaptive behavioral and cognitive
aspects of motivation and physical self-concept, flow, and activity levels were
found to be positive and significant, whereas significant inverse relationships
were found between impeding and maladaptive motivation dimensions and
flow and physical self-concept. Additional analysis utilizing multiple-indicator multiple-cause (MIMIC) modeling showed that during earlier adolescence
girls are more motivated than boys to engage in physical activity, but by later
adolescence boys are more motivated to do so. Results are interpreted in terms
of future directions for possible physical activity interventions aimed at increasing both the uptake and continuation of activity.
Key Words: physical health, adolescent health, well-being
Physical inactivity and increasingly sedentary lifestyles are significant health
risks in all countries (Amisola & Jacobson, 2003). One important part of physical
activity is the motivation to be active. The current study examines the structure and
role of motivation to undertake physical activity. In the context of this investigation, physical activity motivation is defined as the energy and drive to be physically active and the cognitions and behaviors that reflect this energy and drive (see
Martin, 2001, 2003).
The authors are with the SELF Research Centre, Bankstown Campus, University of
Western Sydney, Locked Bag 1797, Penrith South DC, NSW Australia 1797.
171
172 / Martin, Tipler, Marsh, et al.
Research into physical activity levels in the broader population confirms that
sedentary behaviors are problematic for health and well-being. Powell and Blair
(1994) found that at least a third of deaths due to coronary heart disease, colon
cancer, and diabetes could have been prevented in a given year if individuals were
more physically active. There is also mounting evidence that activity levels are
decreasing. Today’s young people spend a large amount of time watching television
or being involved in other sedentary behavior (Andersen, Crespo, Bartlett, Cheskin,
& Pratt, 1998); engagement in physical education classes at school is declining (U.S.
Dept. of Health and Human Services, 2000); and relatively few late adolescents
engage in physical activity for any significant period of time (Armstrong & Van
Mechelen, 1998).
Motivation Theory in Education and Physical Activity
Motivation has been identified as a key to both the initiation and the continuation of physical activity (Rejeski, 1992). Motivation across all domains, including
physical activity and education, is concerned with the energization and direction of
behaviors (Pintrich, 2003). Hence theories and strategies in the field of motivation
focus on the things that get people moving (Pintrich & Schunk, 2002). Both the
U.S. Surgeon General’s Report (U.S. Dept. of Health and Human Services, 1996)
and Active Australia strategy (Commonwealth Dept. of Health and Family Services,
1998) have emphasized the need to motivate people to convert intent into action.
Motivation has also been identified as a central construct in reducing dropout rates
in physical activity programs (Andrew & Parker, 1979).
However, while motivation is acknowledged as being critical, motivation
theory and research in the context of physical activity has been diffuse and fragmented. Dishman (1982) argues that the fragmentation of motivation theory is due
in large part to the innate complexities of exercise behaviors. Similarly, according
to Roberts (1992), much of the fragmentation in the research on physical activity
motivation stems from a mistaken belief that motivation in physical activity is a
relatively simple (and stable) construct. Those interested in increasing activity levels,
including coaches, teachers, and parents, have tended to adopt a unidimensional
approach to motivation. Such approaches assume that motivation is synonymous
solely with arousal, that motivation results from positive thinking, or that motivation
is an innate construct either present or absent in individuals. Cervone and Mischel
(2002) emphasized the limitations of these single theory approaches and called for
the adoption of a cross-disciplinary perspective, encompassing key motivational
constructs from different theories.
With this in mind, it is interesting to note that the field of educational psychology has proven to be a relatively fertile ground for integrated motivational theory
and practice. Recent research in the educational domain has explored the validity of
encompassing models of motivation (e.g., Pintrich, 2003), and it is suggested that
this area provides a fruitful direction for research on physical activity motivation.
In the educational domain, Martin (2001, 2003) has developed a multidimensional,
cross-theoretical, and integrated model of student motivation. This model was developed as an attempt to bridge the gap between diverse dimensions of theorizing and
attempts by educational practitioners to find a motivational framework that could
be easily communicated to students and parents. We suggest that his model offers
a starting point for a similarly integrated and cohesive approach to motivation in
Physical Activity Motivation / 173
physical activity. Martin’s model of student motivation is conceptualized in terms of
a primary four-factor structure consisting of adaptive cognitions, adaptive behaviors,
impeding cognitions, and maladaptive behaviors.
A Four-Factor Model of Motivation
Martin’s (2001, 2003) multidimensional concept of motivation which comprises adaptive and maladaptive cognitive and behavioral dimensions is reflected in
diverse lines of psychological inquiry: (a) Pintrich and DeGroot (1990) presented a
cognitive view of motivation encompassing motivational orientations and a behavioral component encompassing learning strategies. (b) In a model of strategy and
behavior proposed by Buss and Cantor (1989), individuals’ characteristic orientations and cognitions influence the behaviors they use to negotiate demands in their
environment. (c) Cognitive behavioral approaches to engagement emphasize that
cognitive activity affects behavior and that behavioral change can be effected through
cognitive change (Beck, 1995). (d) Researchers studying academic engagement
separate engagement into cognitive-affective and behavioral dimensions (e.g., see
Miserandino, 1996). (e) At an empirical level, it is apparent that different motivational
constructs reflect differing levels of motivation; for example, self-efficacy reflects highly
adaptive motivation (Bandura, 1997), anxiety impedes students’ engagement (Sarason
& Sarason, 1990), and behaviors such as self-handicapping reflect quite maladaptive
engagement (Martin, Marsh, & Debus, 2001a, 2001b, 2003).
Taken together, when considering the joint issues of motivational orientations,
learning strategies, cognitive-affective and behavioral dimensions, and differing levels
of adaptive and maladaptive dimensions from empirical and applied settings, motivation
can be characterized in terms of four groups: (a) adaptive cognitive dimensions, (b)
adaptive behavioral dimensions, (c) impeding cognitive dimensions, and (d) maladaptive behavioral dimensions.
Tipler and colleagues (Tipler, Martin, Marsh, Richards, & Williams, 2004)
recently adapted these concepts to the physical activity domain. On the basis of
the theoretical synergy between these two spheres of motivated behavior (Roberts,
1992), they demonstrated the feasibility of extending Martin’s model of student
motivation to the physical activity domain through the development of four primary
factors: (1) adaptive cognitions which reflect individuals’ positive attitudes and
orientations to physical activity (e.g., confidence in and valuing of physical activity); (2) adaptive behaviors which reflect positive behaviors and engagement with
physical activity (e.g., planning, management, and persistence in physical activity);
(3) impeding cognitions which reflect processes inhibiting physical activity motivation (e.g., uncertain control and fear of failure); and (4) maladaptive behaviors
which actually reflect reduced physical activity motivation (e.g., avoidance and
disengagement). Figure 1 presents this physical activity motivation framework. The
present study assessed the within-network and between-network construct validity
of this four-factor model of physical activity motivation.
Within- and Between-Network Approaches to Validity
We adopt a construct validation approach to the empirical assessment of this
four-factor model of physical activity motivation. Researchers in exercise psychology have increasingly emphasized the need to develop and evaluate instruments
174 / Martin, Tipler, Marsh, et al.
Figure 1 — Four-factor physical activity motivation framework.
within a construct validity framework (Ostrow, 1996). Studies that adopt the construct validity approach can be classified as within-network and between-network
studies.
Within-network studies explore the internal structure of a construct. Beginning
with a logical analysis of internal consistency of the construct definition, measurement instruments, and generation of predictions, they typically employ empirical
techniques such as exploratory factor analysis (EFA), confirmatory factor analysis
(CFA), and reliability analyses. Between-network studies attempt to establish a
logical, theoretically consistent pattern of relationships between constructs. This
research considers these relationships by adopting statistical procedures such as correlational, regression, or structural equation modeling (SEM) analyses to examine
relationships between measures and instruments.
Physical Activity Motivation and Between-Network Measures
The first component of the between-network study focuses on the relationship
between motivation and two psychological constructs—flow and physical self-concept—as well as the relationship between motivation and a behavioral measure of
physical activity levels.
Flow
Fun, enjoyment, and flow are consistently associated with the commencement
of and continued involvement in physical activity (Wankel, 1993). Csikszentmihalyi’s (1990) concept of flow encompasses enjoyment, and the ability to derive
meaning and enjoyment from total absorption in the present moment is at the heart
of the theory of flow (Csikszentmihalyi & Csikszentmihalyi, 1988). This has been
supported in the physical activity setting. Jackson considered the effect of flow
Physical Activity Motivation / 175
on elite figure skaters (Jackson, 1992) and elite athletes (Jackson, 1995), while
Dryden (1989) studied flow in relation to professional ice-hockey players. Both
authors found that athletes’ flow in the specific activity was correlated with their
performance level in that activity. Thus, flow has a clear conceptual framework
from which programs designed to foster positive physical activity experience can be
developed. Taken together, the research on flow suggests that it is an important correlate, and perhaps even an outcome of, physical activity motivation. Furthermore,
little research has assessed flow among adolescents and so the present study was an
important test of reliability and validity of this measure for adolescents.
Physical Self-Concept
Recently physical self-concept has become somewhat of a “hot” construct
(Marsh, 2002) that has been shown to make a difference. Indeed, physical selfconcept is positioned as a mediating variable that facilitates the attainment of a
myriad of related outcomes including physical activity levels. This is due to physical
self-concept being shown to influence key factors involved in activity levels such
as task choice, sustained effort, persistence, and motivation (Marsh, 2002). Marsh
has developed a measure of physical self-concept, the Physical Self-Description
Questionnaire (PSDQ; Marsh, Richards, Johnson, Roche, & Tremayne, 1994), the
major emphasis of which centers on the multidimensional nature of the physical
self-concept construct. The multidimensionality of physical self-concept, reflected
in Marsh et al.’s (1994) PSDQ instrument, has been widely endorsed in research.
Baumgartner and Jackson (1987), for example, evaluated measurement instruments
in physical activity and concluded that a unidimensional approach to such measurement was limiting.
Physical Activity Level
We use physical activity as an inclusive term encompassing “planned
physical activity for recreation, leisure, or fitness, with a specific objective such as
improving fitness, performance, health, or social interaction” (Bauman, Wright,
& Brown, 2001, p. 1) which includes a variety of physical activities. Marsh and
Johnson (1994) provided support for the construct validity of self-report measures
of physical activity used in the large, nationally representative Australian Health and
Fitness Survey (translating specific activities into METs, i.e., resting metabolic rate,
to better assess intensity levels as well as frequency). Expanding on this research,
we propose to use the items from the Active Australia survey to measure physical
activity. These items have recently been shown to have test-retest reliability and
validity (in comparison with the “gold standard” accelerometer measure) which is
as good, if not better, than other instruments used in Australia and around the world
(Brown & Bauman, 2000).
The Role of Gender and Age
In addition to exploring between-network validity through a set of hypothesized between-network constructs, we suggest that between-network validity is
also established through examining relationships between hypothesized predictors
and physical activity motivation. We suggest that two factors relevant to physical
activity are age and gender.
176 / Martin, Tipler, Marsh, et al.
In terms of the main effects of gender and age on physical activity, the data
reveal particular patterns. For example, males have been found to spend more time
in physical activity than females (Australian Institute of Health and Welfare, 2003).
And Caspersen, Pereira, and Curran (2000) found that physical activity levels of
active adolescents tend to decrease with age and continue to decline until one’s
mid-50s (Australian Institute, 2003). More revealing, however, is the interaction
between gender and age, and it is this that we suggest will be further indicative of
the between-network validity of the four hypothesized physical activity motivation
factors. Specifically, data show that by later adolescence males are more physically
active than females (Australian Institute, 2003). Indeed, other data on self-reports of
adaptive cognition and behavior demonstrate a decline for girls during adolescence
and a concomitant maintenance or increase for boys (referred to as the self-serving
bias; see Mezulis, Abramson, Hyde, & Hankin, 2004, for a review). This further supports the possibility for a parallel Gender × Age interaction in the present study.
Aims and Hypotheses of the Present Study
The present study had two primary aims. First, it sought to examine the
within-network validity of our four-factor model of physical activity motivation. It
was hypothesized that this model would reflect a sound factor structure (primarily
reflected in currently accepted levels of fit using appropriate fit indices) comprising four factors: adaptive cognitive dimensions, adaptive behavioral dimensions,
impeding cognitive dimensions, and maladaptive behavioral dimensions. It was
also hypothesized that these four factors would comprise reliable subscales (above
α = .75) that would be approximately normally distributed (skewness and kurtosis
between –1.96 and +1.96).
The second aim related to the between-network validity of our four-factor
model and comprised two components. The first component entailed an examination of the relationships between motivation and two psychological constructs in
physical activity (flow and physical self-concept), and one behavioral measure
(physical activity level). In relation to this, it was hypothesized that the adaptive
dimensions of motivation would be positively correlated (with at least modest correlations >.3, consistent with Martin, 2003, in the educational domain using similar
measures) with the three sets of between-network constructs, while the impeding
and maladaptive dimensions would be negatively correlated with these betweennetwork constructs, with the maladaptive dimension evincing relatively stronger
negative correlations (with at least modest correlations >.3, consistent with Martin,
2003) and the impeding dimension yielding relatively nonsignificant correlations
(rr between –.1 and +.1, consistent with Martin, 2003).
The second component entailed multiple-indicator multiple-cause (MIMIC)
modeling to explore the effects of age, gender, and their interaction on each of the
four motivation factors. In relation to this, it was hypothesized that the effect of
gender would vary with age group such that, compared to earlier adolescence, by
later adolescence the physical activity motivation of boys would be higher while
the physical activity motivation of girls would be lower. This would be reflected
in higher scores on the adaptive dimensions and lower scores on the impeding and
maladaptive dimensions of motivation. These effects, although consistent across
most factors, were predicted to be small to modest (Australian Institute, 2003).
Physical Activity Motivation / 177
Method
Participants and Procedure
As described earlier, adolescents represent a group for whom physical activity
is an important life component. Hence the sampling frame for this study focused on
adolescents. The research was approved by the university human ethics committee.
Letters of invitation were distributed to students, and participating students were
those for whom consent to their involvement was received. Teachers administered
the instrument to students during class. The rating scale was first explained and a
sample item was presented. Students were then asked to complete the instrument
on their own and return it to the teacher at the end of class.
The participants for the current study comprised 171 high school students in
Year 7 (n = 76), Year 8 (n = 37), Year 9 (n = 33), Year 10 (n = 9), and Year 11 (n
= 16) from two government schools in Sydney, Australia. The two schools were
located in a predominantly white lower middle class outer-suburban area of Sydney.
Eighty-eight percent of the sample was born in Australia and 92% spoke English at
home. Of the respondents, 56% (n = 96) were male and 44% (n = 75) were female.
The mean age of the respondents was 13.57 years, (SD = 1.38). Compared with
mean population physical activity levels (150 on the index of “sufficient” activity
levels on the Active Australia Survey), the present sample can be considered above
average (with a mean of 462 on the “sufficient” index) in physical activity levels
in their life (see Commonwealth Department, 1998, for guidelines on calculating
sufficient physical activity levels).
Instrumentation
In adopting a dual (within-network and between-network) approach the
present study employed four instruments. Both the within- and between-network
approaches utilize the new Physical Activity Motivation Scale (PAMS) instrument. The between-network approach explores the nature of relationships between
physical activity motivation and a conceptually relevant set of physical activity
between-network constructs: flow, physical self-concept, and physical activity level.
Respectively, these were assessed using the Short Flow Scale (Jackson & Eklund,
2002), the Physical Self-Description Questionnaire (Marsh et al., 1994), and the
Active Australia Survey (Australian Institute, 2003). Means, standard deviations,
skewness, kurtosis, and reliabilities for each measure are listed in Table 1.
The Physical Activity Motivation Scale (PAMS). For the purposes of the
present investigation, the PAMS was based on the Student Motivation and Engagement Scale (Martin, 2001, 2003) which measures high school students’ motivation in a four-primary-factor structure comprising adaptive cognitive dimensions,
adaptive behavioral dimensions, impeding cognitive dimensions, and maladaptive
behavioral dimensions. Adaptation for the PAMS involved retaining the four primary factors but rewording the component items and using a 7-point rating scale
(1 = strongly disagree, 7 = strongly agree). Following is a brief description of each
primary factor.
Adaptive cognitive dimensions reflect individuals’ positive attitudes and
orientations addressing confidence and valuing of physical activity (e.g., “If I try
hard, I believe I can do physical activity regularly”; “Regular physical activity is
3.44
–
Flow
Mean correlation
* < .05; **
*p
**p < .01
5.00
4.73
4.11
3.78
3.92
3.92
3.66
3.70
4.16
3.98
3.88
4.01
4.18
4.26
M
–
0.71
1.14
1.10
1.06
1.21
1.14
1.08
1.27
1.07
0.91
1.08
1.19
1.22
1.25
1.01
–
–.19
–.18
–.19
–.13
–.32
–.30
.11
–.08
.06
–.01
–.15
–.06
–.06
–.37
–.06
–
–.36
–.64
–.57
–.54
–.25
.19
–.51
–.57
–.22
–.43
–.31
–.52
–.81
–.38
–.64
Descriptive Statistics
Skew- KurSD
ness
tosis
–
.82
.92
.89
.85
.85
.81
.80
.88
.78
.70
.82
.84
.87
.89
.79
α
.49**
.76**
–
.91**
.06
–.16*
.36**
.51**
.51**
.46**
.20*
.65**
.60**
.66**
.60**
.52**
AC
Descriptive Statistics and CFA Correlations for all Instruments in the Study
Adaptive cognitive dimensions (AC)
Adaptive behavioral dimensions (AB)
Impeding cognitive dimensions (IC)
Maladaptive behav. dimensions (MB)
PSDQ Appearance
PSDQ Strength
PSDQ Endurance
PSDQ Flexibility
PSDQ Health
PSDQ Coordination
PSDQ Activity
PSDQ Sport
PSDQ Global physical
PSDQ Global esteem
Table 1
.44**
.75**
–
–
.24*
.01
.35**
.40**
.48**
.47**
.02
.64**
.52**
.61**
.53**
.39**
AB
–.11
.07
–
–
–
.90**
–.12
–.05
–.04
–.07
–.43**
–.04
–.06
–.07
–.07
–.34**
IC
Correlations
–.20*
–.06
–
–
–
–
–.12
–.13
–.19*
–.11
–.49**
–.15
–.23*
–.21*
–.16
–.49**
MB
.19*
.22**
.10
.17*
–.08
–.08
.18*
.21*
.26**
.21**
–.09
.16
.29**
.26**
.21**
.14
Physical
activity
178 / Martin, Tipler, Marsh, et al.
Physical Activity Motivation / 179
important to me”; “I feel very pleased with myself when I can do physical activity
on a regular basis”).
Adaptive behavioral dimensions reflect positive behaviors and engagement
with physical activity and comprise items assessing planning, management, and
persistence in physical activity (e.g., “I feel very pleased with myself when I stick
at regular physical activity”; “Before I start my physical activity I get a clear idea
of what I am going to do”; “When I’m physically active it’s usually in places where
I can do it best”).
Impeding cognitive dimensions of physical activity motivation reflect processes that inhibit physical activity motivation and include items reflecting fear of
failure, uncertainty about the conduct of physical activity, and anxiety (e.g., “I worry
that I don’t do enough physical activity”; “Often the main reason I’m physically
active is because I don’t want people to think that I’m unhealthy”; “When I’m not
physically active, I’m unsure how I can fit it into my life”).
The maladaptive behavioral dimensions of physical activity actually reflect
reduced physical activity motivation and comprise concepts such as avoidance and
disengagement (e.g., “I sometimes avoid physical activity so I have an excuse if I
don’t do well at sport, am not good at other physical activities or don’t lose weight”;
“I’ve pretty much given up doing any regular physical activity”).
The Flow Trait Scale (FLOW). The Flow Trait Scale assesses flow in
physical activity and is based on Csikszentmihalyi’s theory of flow (Jackson &
Csikszentmihalyi, 1999). The instrument, developed by Jackson and Marsh (1996),
measured 9 flow dimensions in activity: action-awareness merging, clear goals,
unambiguous feedback, concentration on task, sense of control, time transformation, autotelic experience, balance between challenge and skill, and loss of selfconsciousness. Research by Jackson and Marsh (1996) supports a single global
flow factor. This, and further research (Marsh & Jackson, 1999), has demonstrated
that models involving the single global flow factor have acceptable psychometric
properties (Jackson & Eklund, 2002). Moreover, this research has found that the
single flow measure performs equally as well as the 9-factor flow scale. Accordingly, the present study utilized the single dimension of flow as operationalized
using the short 9-item form of the flow scale.
Physical Self-Description Questionnaire (PSDQ). The PSDQ (Marsh et
al., 1994) is a 70-item test that measures 11 components of physical self-concept.
These components are health (e.g., “When I get sick I feel so bad that I cannot
even get out of bed”), coordination (e.g., “I feel confident when I do coordinated
movement”), physical activity (e.g., “Several times a week I exercise or play hard
enough to breathe hard”), body fat (e.g., “I am too fat”), sport (e.g., “Other people
think I am good at sports”), global physical (e.g., “I am satisfied with the kind of
person I am physically”), appearance (e.g., “I am attractive for my age”), strength
(e.g., “I am physically a strong person”), flexibility (e.g., “I am quite good at bending, twisting and turning my body”), endurance (e.g., “I can run a long way without
stopping”) and global esteem (e.g., “Overall, most things I do turn out well”). Each
PSDQ item is a simple declarative statement to which participants respond using
a 6-point Likert scale (1 = false, 6 = true). Marsh (2002) demonstrated that the
PSDQ scales are reliable (median coefficient alpha = .92 across the 11 scales) and
are stable over the short-term (median r = .83, at 3 months) and long-term (median
r = .69, at 14 months).
180 / Martin, Tipler, Marsh, et al.
Physical Activity Level. The Active Australia Survey (Australian Institute,
2003) is a self-report instrument designed to measure participation in physical
activity. We recognize there are other measures of physical activity levels such as
direct observation, physiological testing, electronic or mechanical devices (e.g.,
pedometers), indirect estimates of maximal or submaximal cardiorespiratory
oxygen uptake, and activity diaries (see Commonwealth Department, 1998, for
an overview). Although offering some unique advantages, each has its drawbacks
including high cost and time constraints (e.g., in the case of direct observation,
physiological testing, and oxygen uptake) or may unduly influence physical activity
itself (e.g., activity diaries).
Accordingly, the present study employed a recall-based self-report measure
limited to 1 week (comparable to other recent measures such as the International
Physical Activity Questionnaire). Comprising a short set of questions, the Survey
calculates the total amount of physical activity undertaken in 1 week based on the
number of activity sessions undertaken (e.g., “In the last week how many times did
you do any vigorous physical activity which made you breathe harder or puff and
pant?”), and the total time spent involved in the physical activity (e.g., “What do you
estimate was the total time that you spent doing this vigorous physical activity in
the last week?”). In the present investigation the answers to both questions resulted
in a final score (called Activity Level) for each participant. This instrument has
demonstrated acceptable validity and good to excellent reliabilities with intraclass
correlation coefficients ranging from .71 to .86 (Brown, Bauman, Timperio, Salmon,
& Trost, 2002) and has successfully been administered to teenagers (Australian
Institute, 2003). In 1997, 1999, and 2000 it was administered nationally and has
demonstrated criterion validity (Australian Institute, 2003; Brown et al., 2002;
Marsh & Johnson, 1994).
Statistical Analyses
Confirmatory Factor Analysis. Confirmatory factor analysis (CFA), performed with LISREL 8.72 (Joreskog & Sorbom, 2005), is the primary method used
to test the within- and between-network validity of the four-factor motivation model
and the other constructs. In CFA, the researcher posits an a priori structure and tests
the ability of a solution based on this structure to fit the data by demonstrating that
(a) the solution is well defined, (b) parameter estimates are consistent with theory
and a priori predictions, and (c) the χ2 and subjective indices of fit are reasonable
(Marsh, Hau, & Wen, 2004). Maximum likelihood was the method of estimation
used for the models.
This method is deemed most appropriate for small samples (Tabachnick &
Fidell, 1996). In evaluating goodness of fit of alternative models, the root mean
square error of approximation (RMSEA) is emphasized. Although the RMSEA is
apparently the most widely endorsed criterion of fit, also presented are the nonnormed fit index (NNFI), the incremental fit index (IFI), the comparative fit index
(CFI), the χ2 test statistic, and an evaluation of parameter estimates. For RMSEAs,
values at or less than .08 and .05 are taken to reflect an acceptably close fit and
an excellent fit, respectively (see Marsh, Balla & Hau, 1996). The IFI, NNFI, and
CFI vary along a 0 to 1 continuum in which values at or greater than .90 and .95
are typically taken to reflect acceptable and excellent fits, respectively, to the data
(McDonald & Marsh, 1990).
Physical Activity Motivation / 181
Considerations Relevant to Smaller Sample and Missing Data. Due to the
small sample, the NNFI rather than the NFI is used, as the NFI may underestimate
the fit of a model when in fact the model is good fitting in small samples (Bearden,
Sharma, & Teel, 1982). Notwithstanding this, because the NNFI can yield improper
fit values and large fit variability (Anderson & Gerbing, 1984), the incremental fit
index (IFI) is also included which has been found to reduce the variability in the
NNFI. In addition to incorporating appropriate fit indices and appropriate estimation
procedures to account for the relatively small sample, the present study also included
an inspection of residuals recommended by Joreskog and Sorbom (1989) as one
means of indirectly assessing the power through the average differences between
the sample variances and covariances and the estimated population variances and
covariances. Finally, the tests of significance in LISREL rest on student t-values
which are appropriate for samples larger than 120 (Joreskog & Sorbom, 2005), and
hence appropriate for tests of significance in the present study.
Taken together, through (a) a diverse set of fit indices that included appropriate penalties for increased parameters and the relatively small sample, (b) the ML
estimation method, (c) inspection of residuals (including RMSEA), and (d) tests
of significance appropriate for the present sample size, we attempted to redress at
least in part the problems posed by the relatively large number of parameters relative to the size of the sample.
For survey-based studies, the inevitable missing data can pose an important
problem, particularly when the amount of missing data exceed 5% (e.g., Graham
& Hoffer, 2000). A growing body of research has emphasized potential problems
with traditional pairwise, listwise, and mean substitution approaches to missing data
(e.g., Brown, 1994), leading to the implementation of the Expectation Maximization
Algorithm, the most widely recommended approach to imputation for missing data,
as operationalized using missing value analysis in LISREL. The EM Algorithm was
used to handle missing data. In the present sample, only 4.39% of the data were
missing and so the EM Algorithm was considered an appropriate procedure.
Multiple-Indicator Multiple-Cause (MIMIC) Models. As mentioned earlier, it is important to examine the effects of gender and age on physical activity
motivation. Kaplan (2000) suggested the MIMIC approach, which is similar to a
regression model in which latent variables (e.g., multiple dimensions of motivation)
are “caused” by discrete grouping variables (e.g., gender, age, Gender × Age) that
are represented by single indicators. One advantage of the MIMIC approach over
the standard approach is that it can handle cases in which sample size in a given
group may be too small to ensure stable estimates of variances and covariances.
Moreover, by representing group membership in appropriate ways, the MIMIC
approach allows the researcher to consider more familiar models of main effects
and interactions. This type of model also has the important advantage in that the
dependent variables are latent variables based on multiple indicators.
The present MIMIC model included the effects of gender, age (treated as a
continuous variable) and the Gender × Age interaction. Consistent with recommendations by Aiken and West (1991), age was zero-centered (put in deviation
score form so that the mean is zero) so as to reduce the multicollinearity between
age and the corresponding interaction term. The interaction term was calculated by
multiplying gender and the zero-centered age variable. Because MIMIC models are
ideally suited to accommodate ordinal and continuous variables, age was retained
182 / Martin, Tipler, Marsh, et al.
as an ordinal variable. This was considered preferable to truncating its variance
through dichotomizing. Notwithstanding this, when untangling the precise nature of
significant interaction effects that did emerge, we dichotomized age to lend clarity
to the plotting of the related figure.
Results
Within-Network Validity: Psychometric Properties
of the Four-Factor Model
Confirmatory factor analysis (CFA) was performed on students’ responses to
the 44-item physical activity motivation instrument assessing the four primary latent
factors (adaptive cognitive dimensions, adaptive behavioral dimensions, impeding
cognitive dimensions, and maladaptive behavioral dimensions). Consistent with
hypotheses, the CFA yielded a good fit to the data (χ2 = 1,546.23, df = 896, NNFI
= .95, IFI = .95, CFI = .95, RMSEA = .06). Factor loadings are listed in Table 2.
Taken as a whole, factor loadings were acceptable. However, some loadings (e.g., the
first loadings on the impeding factor) were lower than expected and are suggestive
of items that would require closer consideration in subsequent administrations.
Correlations for the sample are also presented in Table 2. As expected, results
indicate strong and significant correlations between adaptive cognitions and adaptive
behaviors, and between impeding dimensions and maladaptive dimensions. There
was a significant (albeit weak) negative correlation between adaptive cognitions
and maladaptive behaviors. Surprisingly, there was a significant but weak positive
Table 2
Factor Loadings and Correlations for CFA of the PAMS
Adaptive
cognitive
Adaptive cognitive dimensions 1
Adaptive cognitive dimensions 2
Adaptive cognitive dimensions 3
Adaptive cognitive dimensions 4
Adaptive cognitive dimensions 5
Adaptive cognitive dimensions 6
Adaptive cognitive dimensions 7
Adaptive cognitive dimensions 8
Adaptive cognitive dimensions 9
Adaptive cognitive dimensions 10
Adaptive cognitive dimensions 11
Adaptive cognitive dimensions 12
Adaptive behavioral dimensions 1
Adaptive behavioral dimensions 2
Adaptive behavioral dimensions 3
Adaptive Impeding
behavioral cognitive
Maladapt.
behavioral
.77
.70
.68
.71
.80
.75
.65
.66
.74
.72
.65
.55
.77
.70
.62
(continued)
Physical Activity Motivation / 183
Table 2 Cont.
Adaptive
cognitive
Adaptive behavioral dimensions 4
Adaptive behavioral dimensions 5
Adaptive behavioral dimensions 6
Adaptive behavioral dimensions 7
Adaptive behavioral dimensions 8
Adaptive behavioral dimensions 9
Adaptive behavioral dimensions 10
Adaptive behavioral dimensions 11
Adaptive behavioral dimensions 12
Impeding cognitive dimensions 1
Impeding cognitive dimensions 2
Impeding cognitive dimensions 3
Impeding cognitive dimensions 4
Impeding cognitive dimensions 5
Impeding cognitive dimensions 6
Impeding cognitive dimensions 7
Impeding cognitive dimensions 8
Impeding cognitive dimensions 9
Impeding cognitive dimensions 10
Impeding cognitive dimensions 11
Impeding cognitive dimensions 12
Maladaptive behavioral dimensions 1
Maladaptive behavioral dimensions 2
Maladaptive behavioral dimensions 3
Maladaptive behavioral dimensions 4
Maladaptive behavioral dimensions 5
Maladaptive behavioral dimensions 6
Maladaptive behavioral dimensions 7
Maladaptive behavioral dimensions 8
Adaptive Impeding
behavioral cognitive
Maladapt.
behavioral
.58
.66
.64
.64
.60
.57
.55
.55
.78
.43
.31
.38
.73
.64
.66
.67
.79
.47
.52
.55
.67
.62
.68
.70
.74
.64
.49
.60
.67
Cronbach’s alpha
.92
Adaptive cognitions
Adaptive behaviors
Impeding cognitions
Maladaptive behaviors
–
.91**
.06
–.16*
Factor Reliabilities
.89
.85
.85
Factor Correlations
* p < .05. ** p < .01
–
.24*
.01
–
.90**
–
184 / Martin, Tipler, Marsh, et al.
correlation between adaptive behaviors and impeding dimensions. The high correlations between the two adaptive dimensions and also the impeding and maladaptive
dimensions were suggestive of the need to examine a two-factor first-order structure
and also a two-factor higher order structure and the relative fit of these against
the hypothesized four-factor model. The two-factor first-order model fit the data
more poorly than the four-factor model, with a significant difference in chi-square
values (χ2 = 1,672.64, df = 901), as did the two-factor higher order structure (χ2 =
1,556.64, df = 897), albeit marginally. Hence, although the four-factor first-order
model yielded high factor correlations, it reflected the best fit and thus was retained
for subsequent analyses.
In order to examine the internal consistency of the four-factor model, we
performed four reliability analyses. The Cronbach alphas calculated for the adaptive cognitions, adaptive behaviors, impeding dimensions, and maladaptive dimensions are given in Table 2 and, as hypothesized, they demonstrate strong internal
consistency. Moreover, descriptive statistics for the four scales presented in Table
1 show, consistent with hypotheses, that the scales were approximately normally
distributed as indicated by the relatively low skewness and kurtosis values.
In summary, the within-network approach to construct validity shows there
was good support for the hypothesis that the four-factor model demonstrates sound
structure. Specifically, the hypothesized four-factor structure was good fitting, the
subscales demonstrated excellent reliabilities, and the measures were approximately
normally distributed.
Between-Network Validity I: Relationships Between
the Four Motivation Factors
The first dimension of between-network validity involved the relationships
between the four motivation factors and a set of between-network correlates hypothesized to be centrally relevant to physical activity motivation. The three sets of factors were physical self-concept, flow in physical activity, and actual physical activity
levels. CFA was conducted on the full set of motivation measures and all items in
the three between-network groups. The CFA yielded an acceptable fit to the data
(χ2 = 10,555.95, df = 6666, NNFI = .92, CFI = .92, IFI = .92, RMSEA = .06). This
CFA also yielded a correlation matrix that was used as the basis for this component
of between-network validity. Target correlations are presented in Table 1.
Results show that all 10 physical self-concept factors were positively and
significantly correlated with the adaptive cognitions of physical activity motivation,
as indeed was the global flow factor. The adaptive behaviors of physical activity
motivation were significantly related to physical activity levels, flow, and all physical self-concept measures (with the exception of health physical self-concept). In
the case of both adaptive cognitions and behaviors, significant correlations were in
the hypothesized positive direction. Interestingly, however, physical activity level
was not significantly related to adaptive cognitions.
The maladaptive dimensions of physical activity motivation were significantly
negatively correlated with the endurance, health, activity, sport, and global esteem
factors of physical self-concept. In all but one instance, impeding dimensions were
correlated negatively with between-network measures but, as hypothesized, the
strength of these correlations was weaker than those with the maladaptive dimension
and predominantly nonsignificant. Finally, physical activity level was positively
Physical Activity Motivation / 185
and significantly correlated with flow and with the appearance, strength, endurance,
flexibility, activity, sport, and global physical subscales of physical self-concept.
Interestingly, physical activity level was not correlated with health, coordination,
or global esteem.
When correlations for each dimension of motivation were averaged across
all between-network measures, the resulting pattern of correlations further supported the hypotheses: adaptive cognitions and behaviors were significantly and
positively correlated with physical activity between-network measures; impeding
dimensions were negatively correlated with these between-network measures but
not significantly so; and maladaptive dimensions were significantly negatively correlated with the between-network measures.
Between-Network Validity II: Gender and Age Effects
Using the MIMIC Approach
It was hypothesized that the effects of the Gender × Grade interaction would
unfold in predictable ways and that this would constitute another dimension of
between-network validity. A MIMIC approach was developed to examine the
relationship between gender, age, and the Gender × Age interaction for each of
the four motivation factors. This entailed structural equation modeling (SEM) also
using LISREL in which the two main effects and their interaction predicted the
four motivation factors. The SEM yielded a good fit to the data (χ2 = 1,686.34, df
= 1016, NNFI = .94, IFI = .95, CFI = .95, RMSEA = .06).
Three statistically significant interaction effects were found. In contrast to
previous research (Australian Institute, 2003), there were no main effects for gender
and age. Interaction effects were found for adaptive cognitions (β = .14, p < 0.1),
adaptive behaviors (β = .17, p < 0.05), and maladaptive dimensions (β = .14, p <
0.1). For the purposes of illustrating the interactions, raw mean score findings are
plotted in Figures 2a to 2c. In these follow-up inspections, age was recoded into
two categories, earlier adolescence (13 years and younger) and later adolescence
(14 years and older), and gender was logically retained as dichotomous. Consistent
with hypotheses, in terms of adaptive cognitions and adaptive behaviors, older
adolescent boys’ physical activity motivation was higher than that of younger
adolescent boys, while the reverse was true for older and younger adolescent girls.
A similar theme emerges for maladaptive behaviors in that older adolescent girls’
maladaptive behaviors were higher than those of younger adolescent girls, while
the reverse was the case for older and younger adolescent boys. Hence, in terms
of three of the four central physical activity motivation constructs, these findings
provide additional partial support for between-network validity.
Discussion
This study explored a new, multidimensional approach to physical activity
motivation that was operationalized through four primary factors: adaptive cognitive
dimensions, adaptive behavioral dimensions, impeding cognitive dimensions, and
maladaptive behavioral dimensions. Consistent with hypotheses, the four-factor
model demonstrated within-network validity in the form of reliable subscales and
a sound factor structure. Also consistent with hypotheses, the four-factor model
demonstrated between-network validity: The adaptive cognitive and behavioral
186 / Martin, Tipler, Marsh, et al.
a)
ADAPTIVE COGNITIONS
b)
ADAPTIVE BEHAVIORS
c)
MALADAPTIVE BEHAVIORS
Figure 2 — Gender × Age interaction.
Physical Activity Motivation / 187
dimensions of motivation were significantly and positively associated with physical
self-concept, flow, and activity levels (in the case of adaptive behaviors), whereas
significant inverse relationships were found between maladaptive motivation dimensions and flow and physical self-concept. Additional analysis using multiple-indicator multiple-cause (MIMIC) modeling showed that during earlier adolescence girls
are more motivated than boys to engage in physical activity but by later adolescence
boys become more motivated to do so.
Significance of the Findings for Research and Practice
This study approaches physical activity from a cross-disciplinary perspective
in drawing on motivational research from the educational domain, and specifically
on Martin’s (2001, 2003) model of student motivation and engagement. It was found
that this model (originally validated in the educational sphere) can generalize to the
physical activity domain. As such, the four-factor model offers a parsimonious and
intuitively appealing framework for the study of motivation in physical activity.
At the same time, the current study supports Roberts’ (1992) view that motivation
in the physical activity domain is both diverse and related to a wide variety of
psychological constructs. Previous research (see, for example, Jackson, Kimiecik,
Ford, & Marsh, 1998) has considered a number of these constructs together. The
current research, however, focuses for the first time on the relationships between
motivation, flow, and physical self-concept, and in turn on their relationships to
activity levels.
The primary implication of this conceptually and empirically valid framework is that physical activity motivation can be conceptualized in terms of a finite
and manageable set of factors (namely adaptive, impeding, and maladaptive
dimensions). As such, those involved in the physical activity domain (teachers,
coaches, parents) can conceptualize about and quantify adolescents in terms of
these constructs. This will allow for more targeted intervention strategies to be
designed to increase adolescents’ motivation to engage in physical activity during
a critical stage in the development of activity habits (Caspersen et al., 2000), and
which research has shown are likely to continue into adulthood (Goran, Reynolds,
& Lindquist, 1999).
This study has also demonstrated the reliability and validity of the flow
construct among a sample of adolescents by way of sound factor structure, good
reliability, approximately normal distribution, and feasible correlations with factors to which it is theoretically connected. Moreover, while previous research into
flow in physical activity has demonstrated the link between increased flow and
increased levels of activity (Jackson, 1996), the current study extends this link
to include motivation. Since the four-factor model offers a useable tool for the
assessment of motivation—and potentially the construction of motivational profiles
for adolescents—it is now possible to target those aspects of motivation that are
positively related to flow.
In the same way, the present study extends previous research that considered
physical self-concept to be a key psychological construct in physical activity (Marsh
et al., 1994). It is interesting to note that while the majority of physical self-concept
factors are positively related to the adaptive aspects of motivation, only a select
number of the same factors are negatively related to the maladaptive and impeding
dimensions of motivation. This suggests that practitioners might be better advised
188 / Martin, Tipler, Marsh, et al.
to focus on the positive aspects of motivation, rather than on the negative aspects,
in attempts to increase physical self-concept. Previous research (McAuley, 1992)
has found that it is easier to increase the positive aspects of motivation (such as
planning and confidence) than it is to overcome the negative aspects (such as disengagement).
Limitations of the Current Study and Directions
for Future Research
Future research can extend the current study by addressing a number of
limitations. First, the current study was based on self-report data. As with all selfreport-based quantitative studies, there are limitations in terms of data accuracy,
social desirability biases, and the accuracy of recall. It would be desirable for
future research to also be based on objective data relating to physical activity types
and levels from multiple sources, including coaches, physical activity teachers,
and parents. This would serve to validate the accuracy of the data collected from
the adolescents themselves. In addition, future research might incorporate actual
activity logs completed by participants, thus reducing the reliance on their ability
to accurately remember the amount and type of activity performed. In relation to
this, it is also important to note that the present sample was a physically active one
and so generalizations as to the motivational antecedents of inactive adolescents
must be made with caution.
Further on the instrumentation, it is important to recognize that some loadings
in the PAMS were lower than expected and would require closer consideration in
subsequent administrations of that instrument. It is also relevant to note the distinction between nomological validity and discriminant validity. Regarding the former,
the four factors demonstrated good utility in terms of principles relevant to physical
activity motivation. However, in terms of the latter, there were substantial correlations among factors which served to diminish its discriminant validity. Although
two-factor first and higher order models did not fit the data as well as the four-factor
model, further work may be needed to refine the items in a way that offers greater
distinctiveness between factors.
It is also important to recognize that the current study utilized a cross-sectional
design. In order to further explore and expand upon the relationships between the
psychological constructs and their impact on physical activity levels, future research
should implement causal modeling techniques along with longitudinal data collection. This would enable an extension of the model presented in this study and would
identify the causal relationships between the constructs. Intervention strategies
would then be developed based on the causal ordering of the specific aspects of the
psychological constructs examined. Moreover, whereas the present cross-sectional
study could only infer the effects of age over time, longitudinal data would provide
a better test of such effects.
Finally, it must be noted that the sample size was small relative to the number
of parameters estimated in the variance/covariance matrix. Although the present
study incorporated appropriate estimation procedures (maximum likelihood),
provided appropriate fit indices to account for the small sample (e.g., median IFI
= .95), demonstrated acceptable residuals (e.g., median RMSEA = .06 and fitted
residuals for PAMS CFA = –.82 to 1.01), and used tests of significance appropriate for the present sample size, clearly future work needs to be conducted among
Physical Activity Motivation / 189
larger samples. One obvious alternative to the present data analysis would be to
conduct analyses using scale or factor scores and not estimate latent variables
using item indicators. However, because it was considered important to estimate
the latent factors, model residuals, and conduct a more parsimonious approach to
statistical analysis (e.g., through one MIMIC model rather than numerous regression models), we opted for CFA and SEM via a large variance/covariance matrix
as the approach of choice.
To conclude, based on findings derived from the present study, coaches,
physical education teachers, and other practitioners are now better able to understand and hence target those aspects of motivation that hinder the physical activity
process and at the same time provide strong intervention to facilitate those aspects
of motivation, flow, and physical self-concept that are likely to increase physical
activity levels.
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Manuscript submitted: June 17, 2005
Revision accepted: February 17, 2006