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The Physical Activity and Leisure Motivation Scale: A confirmatory study
of the Malay language version
Yee Cheng Kueh
a,b
*, Garry Kuan
b,c
and Tony Morris
b
a
Unit of Biostatistics and Research Methodology, School of Medical Sciences, Universiti Sains Malaysia,
Kubang Kerian, Malaysia;
b
College of Sport and Exercise Science, Institute of Sport, Exercise and Active
Living, Victoria University, Melbourne, Australia;
c
Exercise and Sports Science, School of Health
Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
(Received 16 January 2016; accepted 3 April 2017)
The aim of this study is to validate a Malay language version of the Physical Activity and
Leisure Motivation Scale (PALMS) using a confirmatory approach. Data collection was
conducted in Kelantan, Malaysia. Participants were 634 university undergraduate students
(female 63%, male 37%), mean age of 21 years. Motivation for physical activity was
assessed using the 40-item PALMS, which measures eight motives. PALMS was first
translated into Malay using standard forward and backward translation procedures.
Participants then completed the PALMS-Malay (PALMS-M). Confirmatory factor analysis
(CFA) was conducted, using Mplus 7.3 software, on the 8-motive PALMS-M model. The
hypothesised model consisted of 40 observed items, and 8 latent variables. Employing CFA,
this model did not result in a good fit to the data. Further examination of CFA results
suggested modifications to the path model to improve fit indices. This modification included
deleting two problematic items (items 10 and 18) and co-varying the error terms for items
19 and 31. This resulted in improved fit indices (RMSEA = .041 (90% CI: .038, .044),
CIfitRMSEA = 1.000, CFI = .911, TLI = .901, SRMR = .052). The final measurement model
consisted of 38 items. The majority of the items were retained and were considered
acceptable for the present sample.
Keywords: physical activity; exercise; motivation; confirmatory factor analysis
Engaging in regular physical activity is crucial to maintain a healthy lifestyle. Physical inactivity
is claimed to be the leading cause of hypertensive illness and is estimated to cause two million
deaths per year (Word Health Organization, 2015a). With the extensive use of science and tech-
nologies in developed societies, people are becoming content with a sedentary lifestyle, including
their jobs, travel, and leisure activities, leading to insufficient physical activity, which is 1 of the
10 leading risk factors for death worldwide (World Health Organization, 2015b). Conversely,
regular exercise or physically active has showed promising links to the prevention and treatment
of heart disease, type 2 diabetes, hypertension, obesity, depression, osteoporosis, and cancer
(Armstrong, Bauman, & Davies, 2000; Riiser et al., 2014; Warburton, Nicol, & Bredin, 2006).
However, in countries like Malaysia, more than half of the adult population does not undertake
sufficient physical activity to gain health benefits (Nadaraj, 2013). Thus, it is imperative to motiv-
ate people to carry out more physical activity in their daily life.
© 2017 International Society of Sport Psychology
*Corresponding author. Email: yckueh@usm.my
International Journal of Sport and Exercise Psychology, 2019
Vol. 17, No. 3, 250–265, https://doi.org/10.1080/1612197X.2017.1321029
One of the most pronounced factors that stimulate and maintain individuals’participation in
physical activity is motivation. Various types of motivation have been shown to influence people’s
effort during exercise sessions and their intentions to continue exercising (Wilson, Rodgers,
Fraser, & Murray, 2004). Researchers have used self-determination theory (SDT; Deci &
Ryan, 2000) as a guideline in studying human motivation in physical activity (Duncan, Hall,
Wilson, & Jenny, 2010; Gardner & Lally, 2013; Gunnell, Crocker, Mack, Wilson, & Zumbo,
2014). SDT consists of two main components: intrinsic and extrinsic motivation. Individuals
who are intrinsically motivated to participate in a physical activity are those who are motivated
by factors related to the activity itself, such as enjoyment or skill development and mastery. Intrin-
sic motives promote participation in physical activity over a longer period of time than extrinsic
motives. Individuals, who are extrinsically motivated to participate in a physical activity due to
factors that are not related to the activity itself, such as rewards, improved health, and looking
good, are more likely to stop participating when those motives are not readily satisfied (Frederick
& Ryan, 1993). Therefore, by understanding the individuals’motivation for a physical activity,
healthcare practitioners can promote motivation for physical activity at the individual level and
also at the community level, helping to reduce illnesses caused by lack of physical activity.
Healthcare practitioners can develop suitable interventions to target people with different
motives for participating in physical activity, leading to increases in their physical activity
adherence.
In addition to SDT, Achievement Goal Theory (AGT; Nicholls, 1989) has also been used as a
guideline for motivation research in sport and exercise settings. Nicholls stated that the two main
components of goal states are task and ego orientations. He further explained that task-oriented
individuals focus on mastering a task, whereas ego-oriented individuals focus on their perform-
ance score in comparison to other opponents. Their goal is to beat their opponents to demonstrate
their ability. The conceptual framework of AGT has been widely applied by researchers in under-
standing motivation based on goal orientations in competitive sport (e.g. Almagro, Saenz-Lopez,
Moreno-Murcia, & Spray, 2015; Isoard-Gautheur, Guillet-Descas, & Duda, 2013; Waldron &
Krane, 2005) and recreational sport and exercise (e.g. Duda & Tappe, 1988; Escarti & Gutierrez,
2001; Xiang, McBride, & Bruene, 2003). However, researchers have argued that two goal orien-
tations in AGT are not sufficient to cover a wide range of goals people may have for participating
in physical activities (Rogers, Morris, & Moore, 2008; Whitehead, 1995).
A number of questionnaires have been developed to measure participation motivation in exer-
cise and physical activity. These questionnaires included the 28-item Sport Motivation Scale
(SMS; Fortier, Vallerand, Briere, & Provencher, 1995), the 44-item Exercise Motivation Inven-
tory (EMI; Markland & Hardy, 1993), the 69-item EMI-2 (Markland & Ingledew, 1997), the
32-item Exercise Motivation Scale (EMS; Li, 1999), the 30-item Participation Motivation Ques-
tionnaire (PMQ; Gill, Gross, & Huddleston, 1983), and its various versions, the 23-item Motiv-
ation for Physical Activity Measure (MPAM; Frederick & Ryan, 1993), the 30-item MPAM-
Revised (Ryan, Frederick, Lepes, Rubio, & Sheldon, 1997), and the 11-item Perception of
Success Questionnaire for Exercise (POSQ-E; Zizzi, Keeler, & Watson, 2006). These question-
naires have their strengths, but a number measure quite narrow motives because they are based
on specific theories, whereas others refer only to specific aspects of physical activity, such as exer-
cise (Molanorouzi, Khoo, & Morris, 2014). A recently developed measure of participation motiv-
ation is the Recreational Exercise Motivation Measure (REMM), developed by Rogers and
Morris (2003). The REMM provides information about individuals’motives for participation
in physical activity and covers a wide range of motives for participation in physical activity.
However, due to the sizeable length of the REMM (73 items), Morris and Rogers (2004)
reduced the number of items to 40 by taking the strongest five items in each subscale, based
on a range of psychometric factors. The subscales are mastery, physical condition, psychological
International Journal of Sport and Exercise Psychology 251
condition, affiliation, appearance, enjoyment, competition/ego, and others’expectations. They
renamed the measure the Physical Activity and Leisure Motivation Scale (PALMS). There are
some advantages of using PALMS over earlier physical activity motivation measures. The
PALMS was developed based on qualitative research that fits two major theoretical frameworks,
namely intrinsic–extrinsic motivation as described in SDT and task–ego orientation as described
in AGT. Although the motive statements in PALMS were generated in a recreational exercise
context, it is also suitable to be used in the sport context (Zach, Bar-Eli, Morris, & Moore,
2012). This 40-item PALMS is proposed to be more effective because of its succinct nature,
which should reduce the effects of boredom and fatigue (Morris & Rogers ).
The recommended physical activity for adults by World Health Organisation is at least 150
minutes of moderate-intensity physical activity throughout the week (World Health Organiz-
ation, 2015b). Globally, 1 in 4 adults is not active enough (World Health Organization,
2015a). Malaysian Dietary Guidelines recommended that individuals are encouraged to
perform at least 30 minutes of moderate-intensity physical activity at least five days a week
(Ministry of Health Malaysia, 2010). The majority of Malaysians do not meet this recommen-
dation for being physically active. Based on a Malaysian adult physical activity population
survey, Poh et al. (2008) reported that most Malaysians are not active, and only a small percen-
tage (14.4% of study population) participated in regular activity or had adequate physical
activity. Healthy lifestyles should begin in childhood. However, young adults are experiencing
a significant period of life, because they are likely to reduce their physical activity due to the
change in lifestyle from school to work and family. Thus, it is critical to activate strategies to
sustain and even increase physical activity among young adults, which will have a large positive
impact on young adults’future health.
There is a lack of information about the motivation underlying physical activity among uni-
versity students who mainly comprise young adults. We propose that enhancing motivation to
participate in physical activity could be an effective way to increase the duration of physical
activity among the young adult population. Therefore, it is important to understand what will
drive and sustain their motivation to undertake physical activity. This study aimed to translate
the PALMS into Malay language for use in the Malay population, and then examine the
reliability and validity of the translated version of PALMS-M among Malaysian university
students.
Method
Participants
A total of 634 university undergraduate students (male 37%, n= 234, female 63%, n= 400) par-
ticipated in this study. The mean age of the participants was 21 years (SD = 1.7), and their ethni-
city was Malay (59%), Chinese (25%), Indian (11%), and others (5%). All the participants were
undergraduate students enrolled in health-related degrees, such as nursing, speech therapy, diete-
tics, nutrition, and medicine. The participants reported their participation in physical and leisure
activities at least once a week, with at least 30 minutes per session, in the past seven days. The
main physical and leisure activities reported include jogging, cycling, badminton, basketball,
taekwondo, netball, and tennis. All participants were Malaysian and had strong comprehension
in Malay for reading, speaking, and writing.
In confirmatory factor analysis (CFA), larger samples generally produce more stable solutions
and are more likely to be replicable. Based on Hair, Black, Babin, and Anderson (2010), with a
number of factors larger than six, sample size requirements may exceed 500. In the present study,
PALMS-M consists of eight subscales or factors, so we considered that the sample size of 634 was
sufficiently large for a confirmatory study using CFA.
252 Y. C. Kueh et al.
Questionnaire translation
The original English language version of PALMS was translated into Malay language using the
following steps: (1) The second author forward translated the English version into Malay
language, based on the principle of retaining meaning, rather than literal word-for-word trans-
lation, (2) A local Malay who was bilingual in Malay and English back translated the Malay
version to English, (3) Five panels consisted of experts from the areas of sport sciences, sport psy-
chology, health psychology, and physical activity with more than 10 years experience in their
areas of expertise. All panel members were competent bilingual speakers of Malay and
English. They reviewed the English translation from Malay, and Malay translation from
English, comparing each item to the corresponding item on the original English version. They
noted any deviations in meaning and finalised the Malay version of PALMS. Then, expert
panels were asked to assess whether the contents were culturally appropriate to the Malaysian
population. The final version of PALMS-M was pre-tested among 10 undergraduate students
for clarity and comprehension. The students were asked to answer the questions and comment
on the wording and the presentation of the questionnaire. We found the result of the pre-test to
be good and no modification was necessary. The original items (in English) and the translated
version (in Malay) are presented in Appendix.
Data collection
Data collection was conducted between February 2015 and May 2015 at the Universiti Sains
Malaysia, Kelantan, Malaysia. A cross-sectional study design was employed on the self-report
PALMS-M questionnaire. The students were briefed regarding the study at the end of their lec-
tures. The Information to Participants sheet and PALMS questionnaire were distributed to the stu-
dents. Participants did not sign consent forms because their responses were non-identifiable. We
inferred consent from voluntary completion of the PALMS. Those who volunteered to participate
in this study completed the questionnaire and returned it to the researcher. A total of 700 PALMS-
M questionnaires were distributed to students, and the response rate was 91% with 650 question-
naires returned to the researchers. However, of these we found that 16 questionnaires were not
complete, so the final sample comprised 634 usable questionnaires with complete answers.
Measures
Demographic/physical and leisure activities information
Several demographic and physical and leisure activity questions were administered. These ques-
tions assessed personal attributes of the participants (including age, gender, and ethnicity), the
sport or physical and leisure activities in which participants were involved, and the hours per
week of pursuing the activities.
Physical Activity and Leisure Motivation Scale
The PALMS measures motives for participating in physical activity and leisure (Morris & Rogers,
2004). The PALMS questionnaire used in this study consisted of 40 items measuring the aspects
of mastery, physical condition, psychological condition, affiliation, appearance, enjoyment, com-
petition/ego, and others’expectations. Each factor consisted of five items, all scored on a 5-point
Likert scale rated from 1 (strongly disagree)to5(strongly agree). The PALMS has shown good
reliability with 4-week test–retest correlations ranging from .78 to .94 (Molanorouzi et al., 2014).
Internal consistency measured by Cronbach’s alpha for all eight factors was good, ranging from
0.78 to 0.82 (Molanorouzi et al.). Molanorouzi et al. examined the validity of PALMS using CFA
International Journal of Sport and Exercise Psychology 253
and reported that the eight factors in the 40-item scale had acceptable goodness-of-fit (Molanor-
ouzi et al.). In the present study, the Malay translated version of PALMS-M was used.
Ethics approval
The research was conducted in accordance with the Declaration of Helsinki and was approved by
the Universiti Sains Malaysia Human Research Ethics Committee. Participants were provided
with a research information sheet prior to the study. Implied consent was obtained when the par-
ticipants volunteered to complete and return the PALMS-M questionnaire to the researchers. Par-
ticipants were ensured of the confidentiality of their answers given in the questionnaire and were
informed about their right to withdraw from completing the questionnaire at any stage without
any penalty. Any personal identifiers were not included on the questionnaire.
Statistical analysis
Data analysis was conducted using Mplus 7.3. Data were screened for missing values prior to the
analysis. From the total of 650 questionnaires received, there were 16 responses with missing
values, where the 16 participants did not fully completed the questionnaire. These questionnaires
were excluded from analyses, leaving a total of 634 useable sets of observations that were used for
subsequent analyses. The maximum likelihood (ML) estimator is the common estimator used in
CFA, to estimate the fit of the model with the unbiased standardised estimates of path coefficients.
However, the data must meet the assumption of multivariate normality in order to use the ML
estimator. When the data are non-normal, an alternative estimator, such as MLM (known as
Satorra-Bentler chi-square) is used. The MLM estimator is robust to non-normality and it esti-
mates with standard errors and a mean-adjusted chi-square test statistic (Muthen & Muthen,
1998/2012). In the present study, the assumption of multivariate normality was not met
(Mardia multivariate skewness and kurtosis test with p-values less than .05). Therefore, the
MLM estimator was used in the subsequent CFA analyses.
The initial hypothesed measurement model was developed and tested in CFA. The initial
hypothesed measurement model consists of 8 latent variables (subscales of PALMS-M) and 40
observed variables (items in PALMS-M). Factor loadings of .40 and above, with significant p-
value and modification index were used as a guide to retain or remove the items from the measure-
ment model (Ford, MacCallum, & Tait, 1986; Wang & Wang, 2012). Evaluation of fitness was
carried out each time the model was re-specified or when there was removal of a problematic
item. Multiple fit indices should be used in determining whether to reject or retain a model
(Hair et al., 2010; Kline, 2011). Based on the 8-factor structure and 40 items measurement
model in the present study, the fit indices that were used in this study and the recommended fit
values were: the comparative fit index (CFI) and Tucker and Lewis index (TLI) with the
desired value of more than .90, the root mean square error of approximation (RMSEA) with
the desired value of less than .07 and Close fit (ClfitRMSEA) more than .05, and the standardised
root mean square (SRMR) with the desired value of less than .08 (Hair et al., 2010; Kline, 2011).
After the best fit measurement model was identified, the eight factors were assessed for con-
struct validity, which measures the extent to which the set of items actually reflects the theoretical
construct that the items are designed to measure (Hair et al., 2010). Construct validity in CFA
includes two components: convergent and discriminant validity. According to Hair et al., conver-
gent validity assesses whether the items that belong to the same factor shared a high proportion of
variance in common. Convergent validity was assessed using composite reliability (CR) and
average variance extracted (AVE). CR calculated by Raykov’s method was applied to measure
the reliability of the scale (Raykov & Marcoulides, 2015). CR has been recommended as a
254 Y. C. Kueh et al.
reliability test in the CFA measurement model instead of Cronbach’s alpha (Wang & Wang,
2012). This is because Cronbach’s alpha always underestimates the scale reliability when
measurement errors of the observed variables are uncorrelated (Raykov, 2001). The minimum
acceptable range of CR is .60 and above (Tseng, Dornyei, & Schmitt, 2006) and AVE is .50
and above (Fornell & Larcker, 1981). Discriminant validity is used to examine the extent to
which one factor is distinct from the other factors (Kline, 2011). Discriminant validity was
checked by inspecting the correlation between the factors in the model. Brown (2006) stated
that if the correlation coefficients between factors are not too high ≤.85, then discriminant validity
can be established.
Results
Measurement model PALMS-M
Results of all models tested are shown in Tables 1 and 2. The hypothesised measurement model
for PALMS-M consisting of 8 factors with 40 items, which is 5 items in each factor (Model 1), did
not result in a good fit to the data based on several fit indices (see Model 1, Table 1). The factor
loadings of the Model 1 for all items are presented in Table 2.
Examination of the CFA results suggested some modifications to the path model in order to
improve the fit indices. Item Q18 (To manage a medical condition), which had the lowest factor
loading with .160, was removed, then the model was re-specified, and the fit indices were re-
examined (see Model 2, Table 1). Although the fit indices improved to some extent, they still
did not represent a good fit of the model to the data. Item Q10 (Because it helps maintain a
healthy body) was identified as a problematic item because CFA results indicated that the
model fitness would improve significantly if item Q10 is removed. After consideration of practical
and theoretical issues in the present study, item Q10 was removed and then the fit indices were re-
examined (see Model 3, Table 1). The factor loadings are presented in Table 2. Again, the
improvement in fit indices was not considered to be sufficient. Further re-specification of the
model was carried out as suggested from the modification index in the CFA results. The parameter
with the highest modification index was noted between items Q19 (To do my personal best) and
Q31 (To keep current skill level). Adding the covariance between these items’error seems reason-
able as both items indicate the motive of the participants to master a physical activity. After adding
covariance between the error terms for items Q19 and Q31, the model resulted in a good fit to the
data (see Table 1). The final model (Model 4) was established with two items deleted (Q18 and
Q10) and adding a covariance between items’error for items Q19 and Q31. The factor loadings of
the final model were presented in Table 2.
Table 1. Summary of the models’fit indices.
Path models RMSEA (90% CI) CIfitRMSEA CFI TLI SRMR
Model 1 .049 (.047, .052) 0.645 .863 .850 .071
Model 2
a
.047 (.044, .049) 0.974 .882 .870 .058
Model 3
b
.042 (.039, .045) 1.000 .905 .895 .054
Model 4
c
.041 (.038, .044) 1.000 .911 .901 .052
a
Measurement model with Q18 deleted.
b
Measurement model with Q18 and Q10 deleted.
c
Measurement model with Q18, Q10 deleted and covariance between the item errors of Q19 and Q31 (covariance, r=
0.318, p< .001).
International Journal of Sport and Exercise Psychology 255
Table 2. Standardised factor loadings for model 1, model 2, model 3 and model 4.
Factors and items
Factor loadings
Model 1 Model 2 Model 3 Model 4
Mastery
Q5 0.620 0.620 0.621 0.634
Q16 0.692 0.692 0.692 0.682
Q19 0.592 0.592 0.593 0.551
a
Q24 0.658 0.659 0.656 0.645
Q31 0.561 0.562 0.562 0.509
a
Physical condition
Q10 0.539 0.538 ––
Q12 0.694 0.694 0.720 0.719
Q15 0.682 0.681 0.687 0.689
Q28 0.563 0.564 0.584 0.583
Q33 0.680 0.681 0.694 0.693
Psychological condition
Q2 0.464 0.464 0.467 0.468
Q9 0.667 0.667 0.665 0.665
Q14 0.777 0.776 0.776 0.776
Q22 0.706 0.706 0.705 0.705
Q35 0.740 0.741 0.742 0.742
Affiliation
Q4 0.691 0.692 0.691 0.691
Q8 0.649 0.649 0.649 0.649
Q20 0.575 0.575 0.576 0.576
Q30 0.640 0.640 0.640 0.640
Q38 0.808 0.808 0.808 0.808
Appearance
Q11 0.513 0.513 0.513 0.513
Q23 0.781 0.780 0.778 0.778
Q32 0.615 0.616 0.623 0.623
Q36 0.674 0.674 0.675 0.676
Q40 0.748 0.748 0.743 0.743
Enjoyment
Q3 0.735 0.735 0.735 0.735
Q13 0.706 0.706 0.706 0.706
Q25 0.743 0.743 0.743 0.743
Q34 0.621 0.621 0.621 0.621
Q37 0.790 0.790 0.790 0.790
Competition/ego
Q6 0.680 0.680 0.680 0.681
Q17 0.727 0.728 0.728 0.728
Q27 0.662 0.661 0.661 0.660
Q29 0.616 0.616 0.615 0.614
Q39 0.587 0.587 0.587 0.588
Others’expectations
Q1 0.650 0.657 0.656 0.656
Q7 0.678 0.692 0.693 0.693
Q18 0.160 –––
Q21 0.444 0.437 0.437 0.437
Q26 0.454 0.443 0.443 0.443
a
Covariance between the error terms of Q19 and Q31.
256 Y. C. Kueh et al.
Convergent and discriminant validity
Based on the final model, the CR was computed and values ranged from .648 to .846, which indi-
cated a moderate to good construct reliability. The AVE of each factor ranged from .324 to .671.
Although the AVE values for some factors were below the recommended value of .50, the CR
were above the recommended value of .60, so we can conclude that the convergent validity of
the construct is adequate (Fornell & Larcker, 1981).
The factor correlations that were not significant involved Others’Expectations with Physical
Condition, Others’Expectation with Psychological Condition, and Others’Expectations with
Enjoyment. However, other pairs were significant. All correlations were below the recommended
value of .85, which indicated good discriminant validity. The values of CR, AVE, and correlation
coefficients are shown in Table 3.
Discussion
The development of the PALMS-M is an important step in determining individuals’participation
in physical activity among the Malay-speaking population. In this study, we conducted a confir-
matory examination of the factor structure of PALMS-M. The original version of PALMS has
been found to be reliable, valid, and stable across time based on previous studies (Molanorouzi
et al., 2014; RoyChowdhury, 2012; Zach et al., 2012). Among the questionnaires that measure
motives for participation in physical activity, PALMS is a measure that considers a wider
range of motives than most questionnaires because it is based on empirical identification of
motives. Moreover, PALMS is related to the key motivational frameworks of SDT and AGT,
and PALMS measures motives across recreational and lifestyle physical activity, as well as com-
petitive sport (RoyChowdhury, 2012; Zach et al., 2012). Therefore, we translated the original
English version of PALMS into the Malay version of PALMS (PALMS-M) to suit the local popu-
lation where Malay is the most commonly spoken and well-understood language.
We performed confirmatory analyses on the 40 PALMS-M items to examine how well the data
collected from 634 Malaysian university students fitted the proposed 8-factor structure measure-
ment model. Two items were found to be problematic as they resulted either in a low factor
loading or caused a reduction in the goodness-of-fit. These two items were “Q10: Because it
helps maintain a healthy body”and “Q18: To manage medical condition”. After examining the
meaning of the items, we decided to omit the two items from the PALMS-M because deleting
both items would not affect the theoretical framework of the scale. Moreover, there are other
items that address similar aspects to the deleted items. For example, “Q15: To maintain physical
health”is very similar in wording and meaning to Q10. Therefore, omitting Q10 would not affect
the content of the scale. Q18 was not considered to be appropriate for university students because
the majority are young adults without any major health issues. Thus, it is likely that almost all
students responded to this item with “strongly disagree”, a score of 1, which decreased the dis-
crimination of the Others’Expectations subscale and, thus, impacted on the fit of the model.
Omitting Q18 from the data improved the fit of the measurement model in this sample. We pro-
posed that Q18 should be retained in future research and practice with populations that are less
homogeneous in age than the present sample, including a proportion of older adults and other
people with illnesses requiring physical activity as part of the treatment. The final measurement
model of PALMS-M with 38 items was found to be a good fitto the sample in the present study.
The results from the CFA process, in which we tested four models, confirmed that the
PALMS-M with 38 items represented 8 factors with a desirable goodness-of-fit for the data we
collected from this large sample of university undergraduate students. This finding is consistent
with previous studies in which researchers tested the factor structure of the original version of
International Journal of Sport and Exercise Psychology 257
Table 3. CR, average variance extraction (AVE), and factor correlation of final model for PALMS-M.
Variable CR AVE 1 2 3 4 5 6 7 8
1. Mastery .670 .369 1 0.767* 0.701* 0.483* 0.556* 0.816* 0.667* 0.252*
2. Physical condition .846 .671 1 0.792* 0.289* 0.592* 0.678* 0.339* –0.036
3. Psychological condition .807 .462 1 0.335* 0.480* 0.680* 0.395* 0.040
4. Affiliation .807 .459 1 0.326* 0.512* 0.630* 0.332*
5. Appearance .802 .453 1 0.468* 0.468* 0.256*
6. Enjoyment .843 .520 1 0.437* 0.062
7. Competition/ego .790 .430 1 0.610*
8. Others’expectations .648 .324 1
*Correlation is significant at the 0.05 level (two tailed).
258 Y. C. Kueh et al.
PALMS with 40 items using CFA (Molanorouzi et al., 2014; RoyChowdhury, 2012). Molanor-
ouzi et al. and RoyChowdhury reported the 8-factor structure of the original English language
version of PALMS. In both studies, all 40 items remained in the final model. Interestingly, Mola-
norouzi et al. conducted their study in Malaysia, using the English language version in metropo-
litan Kuala Lumpur, where many people speak good English (Molanorouzi, 2015; Molanorouzi
et al., 2014). In rural areas of Malaysia, the Malay language version is more suitable because
Malay is the first language spoken by most of the population. Researchers have also been reported
using the translated version of PALMS in Japanese and Mandarin languages (Machida, Yamada,
Araki, & Tsuchiya, 2013; Wang, Morris, Khoo, Hu, & Tang, 2013). Although these studies noted
minor variations in the results of CFAs, all produced at least adequate fit to the 40-item, 8-factor
PALMS, indicating promising robustness of that model of PALMS across diverse cultures and
languages. The study reported here, which is the first to examine the Malay version, PALMS-
M, adds to that pattern of generally robust tests of the 8-factor model.
The present study was conducted with undergraduate university students as the study popu-
lation, whereas previous studies included participants with a wider range on demographic vari-
ables, such as age, education, and occupation (Molanorouzi et al., 2014; RoyChowdhury,
2012). This may explain the discrepancy with other studies of the current finding, which
focused specifically on young adult, undergraduate students. In the present study, two items
were removed, whereas in the Molanorouzi et al. and RoyChowdbury studies, all 40 items
remained in the CFA model. The two items were removed after recognising that they were not
appropriate in the present study sample which was undergraduate students, who were homo-
geneous in terms of age and education level. Therefore, we propose that researchers and prac-
titioners should continue to use the original PALMS-M with 40 items in future research with a
wider socio-demographic range in Malaysia and the psychometric properties should be continued
tested in different population. However, the present result still supports the initial validity of the 8-
factor structure of the PALMS-M, showing that the questionnaire measures a wide range of
motives for participation in various sports, physical activities, and leisure contexts.
The present study utilised the confirmatory approach to examine and confirm the factor struc-
ture of PALMS-M. One of the benefits of using CFA in factor analysis is that it can be used to
assess the measurement model validity based on the proposed measurement theory. The
PALMS-M was proposed to comprise of eight factors with five items in each factor except for
physical condition and others’expectations with four items each. After confirming the factor
structure of PALMS-M, it was appropriate to assess the construct validity. Construct validity is
the extent to which a set of measured items actually reflects the theoretical factors in a construct,
in other words, it is measuring the accuracy of measurement (Hair et al., 2010). The two com-
ponents of construct validity we have assessed in the present study were convergent validity
and discriminant validity of PALMS-M. The AVE reflects the average of the squared factor
loading for each factor. In this study, the AVE accounted for loadings in the range 0.324–
0.671. Although the AVE for some factors in this study was less than .50, provided that the
CR is higher than .60, the convergent validity of the construct is still acceptable (Fornell &
Larcker, 1981; Huang, Wang, Wu, & Wang, 2013) . The construct reliability based on CRs in
this study ranged from .648 to .846, which were above the recommended value of .60 as
suggested by Tseng et al. (2006). The present study also provided strong evidence of discriminant
validity, because correlations between the factors were below the recommended value of .85. The
discriminant validity result indicated that each factor in PALMS-M is unique, not highly overlap-
ping other factors, and each factor captures some phenomena that other factors do not. The present
study fills a gap by confirming the construct validity or the accuracy of the measurement, which
has not been addressed in previous studies of PALMS (Molanorouzi et al., 2014; RoyChowdhury,
2012).
International Journal of Sport and Exercise Psychology 259
Recent publications suggest that PALMS can be used to advise about suitable types of phys-
ical activity based on people’s main motives (Molanorouzi, Khoo, & Morris, 2015a,2015b).
These studies showed that the primary motives measured by PALMS do predict the amount of
physical activity of participants. Therefore, it should be useful to know the motives of people
for participation in physical activity measured by PALMS and PALMS-M. This information
will help in developing interventions to increase the amount of physical activity people do
based on advising individuals about activities that will satisfy their primary motives for doing
physical activity. The PALMS-M, which is a Malay language version, will be useful for health
care providers, health planners, physical educators, and exercise psychologists in Malaysia to
identify the motives for participation of their clients, whose main spoken language is Malay.
Then they can use counselling techniques like motivational interviewing and physical activity
consultation to help individuals identify appropriate types of physical activity for long-term par-
ticipation, promoting a healthy lifestyle. The application of PALMS-M in a provincial area of
Malaysia should also provide insights that are transferable to many similar cultures around
Asia and in other regions, such as parts of Africa and South America.
We acknowledge that there were some limitations in the present study. Firstly, the data were
collected in a single university. This may limit the generalisability of the findings to other univer-
sity students. A second limitation is the use of self-reported data in the form of paper-based
survey. Self-report is subject to measurement bias or response bias which may decrease the accu-
racy of the completing data. Another limitation is that participants may be subject to social desir-
ability responding, where they may answer questions in a manner that is intended to make
themselves look good (He & van de Vijver, 2013). Given that participants did not provide
their names, so they knew there was no way the researchers could identify them, there would
reduce the chance of responding in that way. In addition, participants were encouraged to
respond honestly to all items related to their motives for participating in physical activities and
leisure.
Further, results of the present and previous studies on motives for participation in physical
activity, using self-report questionnaires such as PALMS have shown acceptable reliability and
validity (Molanorouzi et al., 2014; RoyChowdhury, 2012; Zach et al., 2012). Also, in the early
validation studies with the original English version of PALMS, the short-form Marlowe-Crown
Social Desirability Scale (Reynolds, 1982) showed no correlation with any of the subscales of
PALMS, indicating that responses to PALMS in the context of validation research were not influ-
enced by social desirability (RoyChowdhury, 2012).
In the present study, PALMS-M has shown good construct validity and confirmed 8-factor
structure, which is consistent with previous studies of PALMS. However, it is important to
further examine the replicability of PALMS-M in other populations, such as among people
with more diverse age, education, occupations, and health. It will be interesting to know
whether the 8-factor structure of PALMS-M is replicated in more diverse Malay-speaking popu-
lations. Researchers should also examine the change and stability of PALMS and PALMS-M
across time by applying longitudinal studies. There are many advantages of longitudinal measure-
ment, including that they can be more informative than cross-sectional studies, allowing research-
ers to examine change and/or variability processes, test the extent of measurement invariance, and
examine potential causal relationships (Koch, Schultze, Eid, & Geiser, 2014).
Conclusion
The final measurement model for the PALMS-M questionnaire in the present sample consists of
38 items. The majority of the items were retained and items were considered a sound fit to the
sample in this study. Future research on motives for participation can use the PALMS-M to
260 Y. C. Kueh et al.
examine the motives for engaging in any form of physical activity and leisure, interpreting their
responses within the 8-factor framework of subscales among populations where the major
language is Malay.
Acknowledgements
The authors wish to thank all the study participants, who were involved in the present study.
Funding
The present study was supported by the short-term grant from Universiti Sains Malaysia (304/PPSP/
61313082).
References
Almagro, B. J., Saenz-Lopez, P., Moreno-Murcia, J. A., & Spray, C. (2015). Motivational factors in young
Spanish athletes: A qualitative focus drawing from self-determination theory and achievement goal per-
spectives. The Sport Psychologist,29, 15–28.
Armstrong, T., Bauman, A., & Davies, J. (2000). Physical activity patterns of Australian adults: Results of
the 1999 national physical activity survey (Cat. No. CVD 10). Canberra: Australian Institute of Health
and Welfare.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guilford Press.
Deci, E. L., & Ryan, R. M. (2000). The “what”and “why”of goal pursuits: Human needs and the self-deter-
mination of behavior. Psychological Inquiry,11 , 227–268. doi:10.1207/S15327965PLI1104_01
Duda, J., & Tappe, M. (1988). Predictors of personal investment in physical activity among middle-aged and
older adults. Perceptions and Motor Skills,66, 543–549.
Duncan, L. R., Hall, C. R., Wilson, P. M., & Jenny, O. (2010). Exercise motivation: A cross-sectional analy-
sis examining its relationships with frequency, intensity and duration of exercise. International Journal
of Behavioral Nutritin and Physical Activity,7.doi:10.1186/1479-5868-7-7
Escarti, A., & Gutierrez, M. (2001). Influence of the motivational climate in physical education on the inten-
tion to practice physical activity or sport. European Journal of Sport Science,1(4), 1–12.
Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied
psychology: Critical review and analysis. Personnel Psychology,39, 291–314. doi:10.1111/j.1744-6570.
1986.tb00583.x
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and
measurement error. Journal of Marketing Research,18, 39–50. doi:10.2307/3151312
Fortier, M., Vallerand, R., Briere, N., & Provencher, P. (1995). Competitive and recreational sport structures
and gender: A test of their relationship with sport motivation. International Journal of Sport Psychology,
26, 24–39.
Frederick, C., & Ryan, R. (1993). Differences in motivation for sport and exercise and their relations with
participation and mental health. Journal of Sport Behavior,16, 124–146.
Gardner, B., & Lally, P. (2013). Does intrinsic motivation strengthen physical activity habit? Modeling
relationships between self-determination, past behaviour, and habit strength. Journal of Behavioral
Medicine,36, 488–497. doi:10.1007/s10865-012-9442-0
Gill, D., Gross, J., & Huddleston, S. (1983). Participation motivation in youth sports. International Journal
of Sport Psychology,14,1–14.
Gunnell, K. E., Crocker, P. R., Mack, D. E., Wilson, P. M., & Zumbo, B. D. (2014). Goal contents, motiv-
ation, psychological need satisfaction, well-being and physical activity: A test of self-determination
theory over 6 months. Psychology of Sport and Exercise,15, 19–29. doi:10.1016/j.psychsport.2013.
08.005
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New
Jersey: Pearson Prentice Hall.
He, J., & van de Vijver, F. J. R. (2013). A general response style factor: Evidence from a multi-ethnic study in
the Netherlands. Personality and Individual Differences,55, 794–800. doi:10.1016/j.paid.2013.06.017
Huang, C. C., Wang, Y. M., Wu, T. W., & Wang, P. A. (2013). An empirical analysis of the antecedents and
performance consequences of using the moodle platform. International Journal of Information and
Education Technology,3, 217–221. doi:10.7763/IJIET.2013.V3.267
International Journal of Sport and Exercise Psychology 261
Isoard-Gautheur, S., Guillet-Descas, E., & Duda, J. L. (2013). How to achieve in elite training centers
without burning out? An achievement goal theory perspective. Psychology of Sport and Exercise,14,
72–83.
Kline, R. B. (2011). Principles and practice of structural equation modelling (3rd ed.). New York, NY: The
Guilford Press.
Koch, T., Schultze, M., Eid, M., & Geiser, C. (2014). A longitudinal multilevel CFA-MTMM model for
interchangeable and structurally different methods. Frontiers Psychology,5.doi:10.3389/fpsyg.2014.
00311
Li, F. (1999). The exercise motivation scale: Its multifaceted structure and construct validity. Journal of
Applied Sport Psychology,11, 197–115. doi:10.1080/10413209908402953
Machida, M., Yamada, H., Araki, K., & Tsuchiya, H. (2013). A preliminary examination of the relationship
between physical activity motivation and self-efficacy among physical activity participants in Japan.
Paper presented at the meeting of the International Society of Sport Psychology 13th World Congress
of Sport Psychology, Beijing, China.
Markland, D., & Hardy, L. (1993). The exercise motivations inventory: Preliminary development and val-
idity of a measure of individuals’reasons for participation in regular physical exercise. Personality
and Individual Differemces,15, 289–296. doi:10.1016/0191-8869(93)90219-S
Markland, D., & Ingledew, D. (1997). The measurement of exercise motives: Factorial validity and invar-
iance across gender of a revised exercise motivation inventory. British Journal of Health Psychology,
2, 361–376. doi:10.1111/j.2044-8287.1997.tb00549.x
Ministry of Health Malaysia. (2010). National coordinating committee on food and nutrition: Malaysian
dietary guidelines. Putrajaya: Author.
Molanorouzi, K. (2015). Measuring motives for physical activity in adults (Doctoral dissertation). Retrieved
from http://studentsrepo.um.edu.my/4947/1/THESIS-KEIVAN_MOLANOROUZI.pdf
Molanorouzi, K., Khoo, S., & Morris, T. (2014). Validating the physical activity and leisure motivation scale
(PALMS). BMC Public Health,14, 1074. doi:10.1186/1471-2458-14-909
Molanorouzi, K., Khoo, S., & Morris, T. (2015a). The relationship between motivation for physical activity
and level of physical activity over time. International Journal of Medical, Health, Biomedical,
Bioengineering and Pharmaceutical Engineering,9(8), 609–612.
Molanorouzi, K., Khoo, S., & Morris, T. (2015b). Motives for adult participation in physical activity: Type of
activity, age, and gender. BMC Public Health,15(66). doi:10.1186/s12889-015-1429-7
Morris, T., & Rogers, H. (2004). Measuring motives for physical activity. Paper presented at the International
Sport Science Congress, Seoul, Korea.
Muthen, L. K., & Muthen, B. O. (1998/2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthen &
Muthen.
Nadaraj, V. (2013). Malaysians eat out, work long hours and hardly exercise. Retrieved from http://www.
establishmentpost.com/malaysian-workers-eat-work-long-hours-hardly-exercise/
Nicholls, J. G. (1989). The competitive ethos and democratic education. Cambridge, MA: Harvard
University Press.
Poh, B. K., Safiah, M. Y., Tahir, A., Siti Haslinda, M. D., Siti Norazlin, N., Farina, Z., & Mohd Hasyami, S.
(2008). Physical activity of adults aged 18 to 59 years, Malaysian adult nutrition survey 2003 (Vol. 6),
Putrajaya: Ministry of Health Malaysia.
Raykov, T., & Marcoulides, G. A. (2015). Scale reliability evaluation under multiple assumption violations.
Structural Equation Modeling,00,1–12. doi:10.1080/10705511.2014.938597
Raykov, T. (2001). Bias of Cronbach’s alpha for fixed congeneric measures with correlated errors. Applied
Psychological Measurement,25, 69–76.
Reynolds, W. M. (1982). Development of reliable and valid short forms of the Marlowe-Crowne Social
Desirability Scale. Journal of Clinical Psychology,38, 119–125.
Riiser, K., Ommundsen, Y., Smastuen, M. C., Londal, K., Misvaer, N., & Helseth, S. (2014). The relation-
ship between fitness and health-related quality of life and the mediating role of self-determined motiv-
ation in overweight adolescents. Scandinavian Journal of Public Health,42, 766–772. doi:10.1177/
1403494814550517
Rogers, H., & Morris, T. (2003). An overview of the development and validation of the recreational exercise
motivation measure (REMM). In R. Stelter (Ed.), New approaches to exercise and sport psychology:
Theories, methods and applications. Proceedings of the 11th European Congress of sport psychology.
CD-ROM (3 page full paper). Copenhagen: University of Copenhagen.
Rogers, H., Morris, T., & Moore, M. (2008). A qualitative study of the achievement goals of recreational
exercise participants. The Qualitative Report,13, 706–734.
262 Y. C. Kueh et al.
RoyChowdhury, D. (2012). Examining reasons for participation in sport and exercise using the Physical
Activity and Leisure Motivation Scale (PALMS) (Doctoral dissertation). Retrieved from http://vuir.vu.
edu.au/19943/1/Debadeep_RoyChowdhury.pdf
Ryan, R., Frederick, C., Lepes, D., Rubio, N., & Sheldon, K. (1997). Intrinsic motivation and exercise adher-
ence. International Journal of Sport Psychology,28, 335–254.
Tseng, W. T., Dornyei, Z., & Schmitt, N. (2006). A new approach to assessing strategic learning: The case of
self-regulation in vocabulary acquisition. Applied Linguistics,27, 78–102. doi:10.1093/applin/ami046
Waldron, J., & Krane, V. (2005). Whatever it takes: Health compromising behaviors in female athletes.
Quest,57(3), 315–329.
Wang, J., Morris, T., Khoo, S., Hu, W., & Tang, Y. (2013). Psychometric Validation of the Physical Activity
and Leisure Motivation Scale (PALMS) in a Chinese Sample. Paper presented at the International Society
of Sport Psychology 13th World Congress of Sport Psychology, Beijing, China.
Wang, J., & Wang, X. (2012). Structural equation modeling applications using Mplus. Chichester: John
Wiley & Sons.
Warburton, D., Nicol, C. W., & Bredin, S. (2006). Health benefits of physical activity: The evidence.
Canadian Medical Association Journal,174, 801–809. doi:10.1503/cmaj.051351
Whitehead, J. (1995). Multiple achievement orientations and participants in youth sport: A cultural and
developmental perspective. International Journal of Sport Psychology,26, 431–452.
Wilson, P. M., Rodgers, W. M., Fraser, S. N., & Murray, T. C. (2004). Relationships between exercise regu-
lations and motivational consequences in university students. Research Quarterly for Exercise and
Sport,75, 81–91. doi:10.1080/02701367.2004.10609136
World Health Organization. (2015a). Physical inactivity a leading cause of disease and disability, warns
WHO. Retrieved from http://www.who.int/mediacentre/news/releases/release23/en/
World Health Organization. (2015b). Physical activity. Retrieved from http://www.who.int/mediacentre/
factsheets/fs385/en/
Xiang, P., McBride, R., & Bruene, A. (2003). Relations of parents’beliefs to children’s motivation in an
elementary physical education running program. Journal of Teaching in Physical Education,22(4),
410–425.
Zach, S., Bar-Eli, M., Morris, T., & Moore, M. (2012). Measuring motivation or physical activity: An
exploratory study of PALMS –the physical activity and leisure motivation scale. Athletic Insight,4,
141–152.
Zizzi, S. J., Keeler, L. A., & Watson, J. C., II. (2006). The interaction of goal orientation and stage of change
on exercise behavior in college students. Journal of Sport Behavior,29, 96–110.
International Journal of Sport and Exercise Psychology 263
Appendix. Factors and Items in the PALMS-M and the English version
Item number Item in Malay (English) Factor/subscale
Q5 Untuk menjalankan aktiviti dengan lebih bagus (To get better
at an activity)
Mastery
Q16 Untuk meningkatkan kemahiran yang sedia ada (To improve
existing skills)
Mastery
Q19 Untuk melakukan yang terbaik yang saya boleh (To do my
personal best)
Mastery
Q24 Untuk mendapatkan kemahiran yang baru atau aktiviti baru
(To obtain new skills/activities)
Mastery
Q31 Untuk mengekalkan tahap kemahiran semasa saya (To keep
current skill level)
Mastery
Q10 Kerana ia membantu saya mengekalkan badan yang sihat
(Because it helps maintain a healthy body)
Physical condition
Q12 Untuk menjadi cergas (Be physically fit) Physical condition
Q15 Untuk mengekalkan kesihatan fizikal (To maintain physical
health)
Physical condition
Q28 Kerana ia dapat mengekalkan kesihatan saya (Because it
keeps me healthy)
Physical condition
Q33 Untuk meningkatkan kecergasan kardiovaskular (To improve
cardiovascular fitness)
Physical condition
Q2 Kerana ia dapat membantu saya berehat (Because it helps me
relax)
Psychology condition
Q9 Untuk menangani tekanan dengan lebih baik (To better cope
with stress)
Psychology condition
Q14 Untuk menghilangkan tekanan (To get away from pressures) Psychology condition
Q22 Kerana ia mampu melegakan tekanan (Because it acts as a
stress release)
Psychology condition
Q35 Untuk menenangkan fikiran saya daripada perkara lain (To
take mind off other things)
Psychology condition
Q4 Kerana saya suka meluangkan masa saya dengan orang lain
(Because I enjoy spending time with others)
Affiliation
Q8 Untuk melakukan aktiviti bersama-sama dengan orang lain
(To do activity with others)
Affiliation
Q20 Untuk melakukan sesuatu yang sama dengan rakan-rakan (To
do something in common with friends)
Affiliation
Q30 Untuk berbual dengan rakan-rakan semasa bersenam (To talk
with friends exercising)
Affiliation
Q38 Untuk bersama-sama dengan rakan-rakan saya (To be with
friends)
Affiliation
Q11 Supaya otot kelihatan lebih bagus (To define muscle, look
better)
Appearance
Q23 Untuk memperbaiki bentuk badan saya (To improve body
shape)
Appearance
Q32 Untuk memperbaiki penampilan saya (To improve
appearance)
Appearance
Q36 Untuk mengurangkan berat badan supaya kelihatan lebih
bagus (To lose weight, look better)
Appearance
Q40 Untuk mengekalkan badan yang langsing dan tegap. (To
maintain trim, toned body)
Appearance
Q3 Kerana ia adalah menarik (Because it’s interesting) Enjoyment
Q13 Kerana ia membantu saya berasa gembira (Because it makes
me happy)
Enjoyment
Q25 Kerana ia adalah menyeronokkan (Because it’s fun) Enjoyment
(Continued)
264 Y. C. Kueh et al.
Appendix. (Continued).
Item number Item in Malay (English) Factor/subscale
Q34 Kerana saya suka bersenam (Because I enjoy exercising) Enjoyment
Q37 Kerana saya gembira sewaktu melakukannya (Because I
have a good time)
Enjoyment
Q6 Kerana saya melakukan aktiviti yang lebih baik daripada
orang lain (Because I perform better than others)
Competition/Ego
Q17 Untuk menjadi yang terbaik dalam kumpulan (To be best in
the group)
Competition/Ego
Q27 Untuk bekerja dengan lebih kuat daripada orang lain (To
work harder than others)
Competition/Ego
Q29 Untuk bersaing dengan orang lain (To compete with others
around me)
Competition/Ego
Q39 Untuk menjadi lebih cergas daripada orang lain (To be fitter
than others)
Competition/Ego
Q1 Untuk menyara kehidupan (To earn a living) Others’expectations
Q7 Kerana saya dibayar untuk melakukannya (Because I get
paid to do it)
Others’expectations
Q18 Untuk menguruskan keadaan kesihatan perubatan saya (To
manage medical condition)
Others’expectations
Q21 Kerana orang lain memberitahu bahawa saya perlu bersenam
(Because people tell me I need to)
Others’expectations
Q26 Kerana ia telah disyorkan oleh doktor atau fisioterapi saya
(Because it was prescribed by doctor, physio)
Others’expectations
International Journal of Sport and Exercise Psychology 265
