oaching journal NATIONAL COACHING ACADEMY
MEASURING MOTIVATION FOR PHYSICAL ACTIVITY USING PALMS SCALE: A BRIEF GUIDELINE FOR COACHES 1
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GARRY KUAN YEE CHENG KUEH2 NGIEN SIONG CHIN3
Exercise and Sports Science Programme, School of Health Sciences, Universiti Sains Malaysia, Kelantan, Malaysia Unit of Biostatistics and Research Methodology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia Physical Education and Health Department, Institute of Teacher Education, Tun Abdul Razak Campus, Kota Samarahan, Kuching, Malaysia.
ABSTRACT
The PALMS (Physical Activity and Leisure Motivation Scale) scale is a validated measure in determining the motive of an individual’s participation in physical activity. This article provides a step by step guideline in obtaining the profile of motivations using PALMS scale as a measuring tool. A common data management software, Microsoft Excel was used for demonstration. This article provides coaches with the steps for data entry, mathematical functions used in Microsoft Excel for analysing the data. The items within each subscale of PALMS were presented to aid the user in interpreting the results from PALMS scale. This article aims to guide coaches in reporting the results from PALMS scale and find the implications of the results. Thus, appropriate action can be taken to target the specific area of motivation in physical activity or sport for an individual or a group.
INTRODUCTION
One of the most crucial factors that maintain and stimulate individuals’ participation in physical activity is motivation. People who are motivated by the activity itself, such as enjoyment or skill mastery tend to participate over a longer period than people who engage in an activity due to other factors such as rewards or to look good (1). Therefore, knowing the individuals’ motivation for a sport activity can create awareness that will not only benefit on an individual level, but also for coaches who would like to develop an effective interventions to motivate their athletes for mastery and to enhance their skill in their particular sport. To promote and motivate people adhere in certain sports activity, researchers have tried to understand why people engage in various forms of physical activity (2,3, 4). Most sport researchers attempted to develop measures that are focused predominantly on sport and exercise in competition level (5, 6, 7). Therefore, it is important to have measures that can capture an individual’s participation motivation in the most natural way, reflect a range of motives for participation in sports activity, both competitive and non-competitive. Measures for which there appears to be the consensus among researchers, concerning reliability and validity, are the PALMS scales for assessing the motivation of physical activity. PALMS is the abridged version of the REMM (Recreational Exercise Motivation Measure), measuring an individual’s motivation to participate in physical activity. However, due the sizeable length of the REMM which contains 73 items may not apply in applied contexts. Therefore, the PALMS were developed based on the five strongest items on each of the eight factors in REMM which produces a 40-item measure. This shorter version of REMM is claimed to be more effective, and it helps to reduce the detrimental effects of boredom and fatigue (8).
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oaching journal NATIONAL COACHING ACADEMY
MEASURING MOTIVATION FOR SPORT
It is important to understand what motivates the athlete to undertake any form of sports activity they pursue. According to Deci and Ryan (9), motivation is defined as the energy and direction of behaviour. Energy in the component of motivation reflects the amount of effort devoted to a particular sports activity, the direction of behaviour refers to the individual’s unique level of personal interest in the task. The motivation may differ for different athletes. It will be worthwhile to know what will drive and sustain individuals’ motivation in their sport. It is imperative to measure the level of motivation among the athletes in order to implement an appropriate intervention for promoting a higher level of achievement in the sport they pursue.
THE PALMS SCALE
The PALMS scale measures the motives for participating in physical / sport activities and leisure. It comprises of 40 items and reflecting eight subscales. The eight subscales are (1) competition/ego, (2) appearance, (3) other’s expectations, (4) affiliation, (5) physical condition, (6) psychological condition, (7) mastery, and (8) enjoyment. Each subscale of the PALMS contains four items, measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores reflect a higher level of motivation. The PALMS were validated in a sample of 678 recreational exercise participants with age ranged from 9 to 89 years (10). The PALMS have good internal consistency with Cronbach alpha values for each of the eight subscales of PALMS varied from .63 and .96.
DATA MANAGEMENT
To identify the level of motivation of participation in physical activity, the total score of each subscale of PALMS should be calculated. First, we need to allocate the items that belong to their respective subscales. Table 1 provides the item number and the subscales they belong to in PALMS. As mentioned earlier, there are eight subscales in PALMS with five items in each subscale. Therefore, we need to allocate the scoring of the five items into their respective subscales. Table 1: PALMS subscales and it specific items SUBSCALES
ITEM’S NO.
Mastery
5, 16, 19, 24, 31
Physical condition
10, 12, 15, 28, 33
Affiliation
4, 8, 20, 30, 38
Psychology condition
2, 9, 14, 22, 35
Appearance
11, 23, 32, 36, 40
Other expectation
1, 7, 18, 21, 26
Enjoyment
3, 13, 25, 34, 37
Competition/ego
6, 17, 27, 29, 39
After identifying the items and its respective subscales, the next step is to calculate the total score of the five items within its subscales. Total score of the five items can be obtained by summing participants’ scoring on the items within its subscales. For example, to obtain the score of mastery, the score of item no. 5, 16, 19, 24, 32 are summed. A similar procedure is used to obtain the total scores for other subscales.
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oaching journal NATIONAL COACHING ACADEMY
A sample response to some of PALMS items was demonstrated in Figure 1. The score given for each response for each item is based on the scale chosen. As mentioned earlier, PALMS’s items measured on a 5-point Likert scale. The scales are: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), 5 (strongly disagree). From this sample, answer selected for item 1 was strongly disagree is recorded as 1, answer selected for item 2 was neutral is recorded as 3 and so forth. Figure 1 : Sample answer for items PALMS Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
1. To earn a living
1
2
3
4
5
2. Because it helps me relax
1
2
3
4
5
3. Because it’s interesting
1
2
3
4
5
4. Because I enjoy spending time with others
1
2
3
4
5
5. To get better at an activity
1
2
3
4
5
6. Because I perform better than others
1
2
3
4
5
When the PALMS measure is applied to a large group of athlete to measure the overall motivation level of their physical activity participation, manually calculate the scores of each response are time-consuming. The scoring for each subscale of the PALMS can be obtained simply using computer software such as Microsoft Excel, which is available in most of the computer. Statistical software such as the IMB Statistical Package for Social Science (SPSS) is useful as well especially some statistical analyses are involved. However, this article would demonstrate how to utilise Microsoft Excel in exploring the scoring of PALMS since it is most accessible by the computer users.
EXPLORING THE DATA USING MICROSOFT EXCEL
The advantages of using software such as Microsoft Excel to manage a large responses’ data are time saving, and precise calculation of the scoring can be obtained. It can also serve as a full database of respondents on the PALMS measure for future references. For example, if there are 100 athletes responded on the PALMS measure, there would be 100 responses in hard copy (or in the papers). If all the responses of the measure from all the 100 athletes could be entered into Microsoft Excel, it will be more convenient and time saving when we need to retrace information about a particular athlete’s motivation level. Figure 2 illustrates an example of a dataset entered into Microsoft Excel. To use the Microsoft Excel, first open the software and give a label for each column in the first row. In Microsoft Excel, the row represents an individual athlete or responder and column represent the items of the questionnaire which normally consists of name, demographic information, background of the sport, and PALMS’s items. For example, in the first row, we can have an ID for each athlete, their name, demographic information (e.g., gender, age), background of the sport (e.g., type of sport, competition experiences), then followed by items of the PALMS (e.g., item 1, item 2, etc.). Alternatively, you can use the subscales’ name to label the PALMS’s items and to arrange the items from the same subscale next to each other (see Figure 2-A). After last items of each subscale, it is preferable to label the total subscale’s scoring. For example, after item no. 31 (e.g., Mastery31 in Figure 2-A) the next column should be label as Mastery score, and so on.
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oaching journal NATIONAL COACHING ACADEMY
Figure 2 : Sample of data entry in Microsoft Excel
Once labeling on the first row of Microsoft Excel sheet is done, you may start to enter all the information in the questionnaires completed by the athletes into the data sheet. After the data entry is completed, you may start to explore the motivation levels among your athletes using the mathematical functions that are available in Microsoft Excel. Most commonly used mathematical functions in Excel are SUM (e.g., total), AVERAGE (e.g., mean), STDEV (e.g., standard deviation). In obtaining the total score of each subscale of the PALMS measure, place the cursor in the empty cell at the end of the row of the last items of subscale. For example, you can calculate the total score for mastery of participant of the first row (athlete with ID no. 1) by typing the following formula into the cell, “=SUM (G2: K2)” (see Figure 2-B) and then press the key “enter” (see Figure 2-C). The alphabets (e.g., G to K) are corresponding to the items’ column, and the number (2) is corresponding to the row item number. To get the total score for mastery of all participants, you may just simply copy the formula that you have just created and paste it into the rest of the row under the column of the mastery score (or under “L2”). Once you have obtained the total score of mastery for all the athletes, you may want to know the mean score of athletes on this particular motivation factor. The function in Microsoft Excel for the mean is “AVERAGE”. In order to obtain the mean of mastery, type the formula “=AVERAGE” into the cell after the last ID of the mastery score column (e.g., column L in Figure 2) and press the key, open bracket “(”, put your cursor in the first cell under column mastery score, athlete with ID 1 and drag across the column until the last athlete with ID 100. Next press the key, close bracket “)” and then press the key “enter”. The mean score of mastery for 100 athletes will appear in the corresponding cell. You may use the same methods as described above to calculate the total score for each subscale of PALMS and its mean.
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oaching journal NATIONAL COACHING ACADEMY
DATA INTERPRETATION
There is no specific range or cut off point of the total scores defined by the authors who developed the measure. However, in order to describe the scoring of each subscale, you may use the midpoint as cut off point to explain the condition of motivation among the athletes. For example, the total score for subscale mastery can be range from 5 to 25, therefore the midpoint will be 15. This can give us some insight into the motivation of athletes in this particular subscale is higher or lower at the midpoint. However, most of the time, we may use the scores of PALMS’s subscales to determine whether, as an individual or as groups, pre-designed intervention program should be conducted or not to improve their motivation level. This is because research has showed that motivation level was associated with athlete’s performance in sport (6,11).
CONCLUSION
The PALMS has shown to be a comprehensive and reliable measure of participation motivation, with high internal consistency and criterion validity. Therefore, PALMS can be used to extract valuable information regarding athletes’ motivation in the sport they pursue, and this information can guide coaches in understanding the wide range of motives athletes have for participating in sport. By knowing the motive of the athlete’s participation in sport, coaches may want to improve their motivation level by implementing an appropriate intervention that will help to motivate their participation and thus enhance their performance in sport.
PRACTICAL IMPLICATION
The scale provides a helpful guideline for coaches to understand the motivation of their athletes in participating in sport. By understanding their athletes’ intrinsic and extrinsic motivation, coaches can implement planned, structured, and accelerated programmes that can specifically cater for their athletes’ interest and motives to excel in their sports. Thus, this in turn will enhance their motivation for competition and practice, leading to positive performance outcomes.
APPLIKASI PRAKTIKAL UNTUK JURULATIH
Skala soal selidik ini menyediakan garis panduan yang berguna bagi membantu jurulatih memahami motivasi disebalik atlet mereka menyertai sukan. Dengan memahami motivasi intrinsik dan ekstrinsik atlet, jurulatih boleh melaksanakannya perancangan yang lebih berstruktur, dan program yang khusus untuk membantu atlet mencapai kecemerlangan dalam sukan mereka, seiring dengan kepentingan dan motif atlet masing-masing. Ini seterusnya dapat meningkatkan motivasi mereka untuk menyertai pertandingan dan juga menjalankan latihan, demi mencapai prestasi pertandingan yang positif.
ACKNOWLEDGEMENT
The present study was supported by the short-term grant of Universiti Sains Malaysia (304/PPSP/61313082).
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