Title

The prediction of physical activity levels of Hong Kongprimary six and secondary three students from theirattitudes toward physicalactivity: a partial test of Ajzen'stheory ofplanned behavior

Author(s) Hui, Shun-wing.; 許順榮.

Citation

Issue Date 2001

URL http://hdl.handle.net/10722/36489

Rights The author retains all proprietary rights, (such as patentrights) and the right to use in future works.

The prediction of physical activity levels of Hong Kong primary six and secondary

three students from their attitudes toward physical activity: A partial test of Ajzen's

Theory of Planned Behavior

Hui Shun Wing

Dissertation presented in partial-fulfilment of the requirements of the degree of Master

of Science in Sports Science，The University of Hong Kong.

September，2001

• <c:s> 冷 f x

Declaration

I hereby declare that this dissertation represents my own work and that it has not been

previously submitted to this University or any other institution for any other

qualification.

(signature)

(September, 2001)

Acknowledgments

There are many people who kindly contributed their guidance, support and

encouragement to the completion of my dissertation. I deeply appreciate their

considerable time and effort.

I owe special thanks to my supervisor. Dr. K. J. Lindner. I am fortunate to have

him as a mentor. He provided invaluable ideas and constructive criticism throughout

this dissertation. Without him, this work may have never been completed.

I would also like to offer my sincere thanks to Kenneth Cheung, Terence Fong,

Eric Lam, Sam Lam, Vince Poon and my colleagues who helped me to administer and

collect the questionnaires from their students.

Abstract of thesis entitled

The prediction of physical activity levels of Hong Kong primary six and secondary

three students from their attitudes toward physical activity: A partial test of Ajzen's

Theory of Planned Behavior

submitted by

Hui Shun Wing

for the degree of Master of Science at the University of Hong Kong

September，2001

The aim of this study was to examine the utility of attitudes toward physical

activity (ATPA) for predicting the physical activity (PA) levels within the Hong Kong

primary six (P6) and secondary three (S3) students. More specifically，attitude (one of

the variables in the theory of planned behavior (TPB)) was employed to assess its

utility for predicting PA levels. Three hundred P6 and S3 students (average age range,

11 to 17 years) completed the revised Children's Attitudes Toward Physical Activity

(CATPA) inventory (Schutz，Smoll, Carre, & Mosher，1985) and a modified

Self-administered Physical Activity Checklist (SAPAC) (Sallis et al.，1996) in order to

assess their ATPA and their PA levels, respectively.

Results of multiple regression indicated that Hong Kong school children's ATPA

was a significant predictor of their PA levels. The eight subdomains of CATPA could

significantly predict PA levels of P6 and S3 students and account for 7-8% of the

variability in the measure of PA levels. In addition，the predictive power of CATPA to

PA levels for P6 students and for boys was stronger than that for S3 students and for

girls, respectively. The social growth and vertigo were two subdomains that

contributed significantly to the prediction of PA levels.

The findings of the present study revealed that attitude was a weak, but

significant predictor in the TPB. The TPB should be fully examined to test its

predictive utility in sport and exercise domains，especially in Hong Kong. In addition,

it was important to emphasize the social growth and vertigo subdomains of CATPA in

planning programs and curriculum for P6 and S3 students in order to motivate them to

participate in PA.

Table of Contents

Page

Declaration i

Acknowledgments i i

Table of Contents i i i

List of Appendices vi

List of Tables vii

Abbreviations ix

Chapter 1 Introduction

1 • 1 Motivation for the study

1.2 Purpose of the study

1.3 Hypotheses

1.4 Operational definitions

1.5 Delimitations

1.6 Limitations

1.7 Significance of the study

Chapter 2 Review of Literature

2.1 Measurement of attitudes

2.1.1 Thurstone-Chave

2.1.2 Likert

2.1.3 Semantic differential technique

2.2 Measurement of ATPA

2.2.1 The Kenyon's attitude inventory

2.2.2 The revised CATPA inventory

2.2.3 The Children's Attraction to Physical Activity (CAPA) 15

scale

2.3 Studies using the Kenyon's ATPA inventory and the revised 17

CATPA inventory

2.4 Measurement of PA 20

2.4.1 The SAPAC 23

2.4.2 Previous Day Physical Activity Recall (PDPAR) 25

2.5 Studies on the children's PA levels 26

2.5.1 Age-related trends 26

2.5.2 Gender-related trends 28

2.6 Relationship between attitude and behavior 29

2.7 Relationship between the ATPA and participation in PA 32

2.8 Overview of the TPB 35

2.9 Using attitudes to predict PA participation 3 8

2.10 Summary 44

Chapter 3 Methods and Procedures

3.1 Subjects 45

3.2 The instruments 45

3.3 Scoring 47

3.3.1 The modified SAPAC 47

3.3.2 The revised CATPA inventory 49

3.4 Dependent variables 50

3.5 Independent variables 50

3.6 Procedures 50

3.7 Data analysis 52

Chapter 4

4.1

Results

The students' ATPA 53

4.1.1 Gender differences 55

4.1.2 Grade level differences 55

4.2 The students' PA levels 56

4.2.1 Gender differences 56

4.2.2 Grade level differences 57

4 • 3 Predicting PA levels by CATPA 5 7

4.3.1 Gender differences in prediction 59

4.3.2 Grade level differences in prediction 61

4.3.3 Prediction for P6 boys and girls 63

4.3.4 Prediction for S3 boys and girls 66

4.4 Summary 68

Chapter 5 Discussion

5.1 The local students' ATPA 70

5.2 The local students' PA levels 72

5.3 The prediction of PA levels by CATPA 74

Chapter 6 Conclusions, Implications, and Recommendations

6.1 Conclusions 81

6.2 Implications 82

6.3 Recommendations 83

References 85

Appendices 114

List of Appendices

Page

Appendix 1 : Chinese version of the SAPAC and the revised CATPA inventory 114

Appendix 2 : The SAPAC (modified version) and the revised CATPA 120

inventory

Appendix 3 : The SAPAC (original version) 125

Appendix 4 : METs scoring table 127

Appendix 5 : Application letter to school principal 128

Appendix 6 : Parental consent letter 129

Appendix 7 : Instructions for the physical education teacher 130

List of Tables

Page

Table 1 : The mean and standard deviation scores of CATPA by gender and 54

grade.

Table 2 : The mean and standard deviation scores of SAPAC by gender and 56

grade.

Table 3 : Prediction of min of MVPA, MVPA METs, and weighted MVPA 58

METs by the eight subdomains of CATPA.

Table 4 : Multiple regression of min of MVPA, MVPA METs, and weighted 59

MVPA METs by the eight subdomains of CATPA.

Table 5 : Prediction of min of MVPA, MVPA METs, and weighted MVPA 60

METs by the eight subdomains of CATPA for boys and girls.

Table 6 : Multiple regression of min of MVPA, MVPA METs, and weighted 61

MVPA METs by the eight subdomains of CATPA for boys and girls.

Table 7 : Prediction of min of MVPA, MVPA METs, and weighted MVPA 62

METs by the eight subdomains of CATPA for P6 and S3 students.

Table 8 : Multiple regression of min of MVPA, MVPA METs, and weighted 63

MVPA METs by the eight subdomains of CATPA for P6 and S3

students.

vii

Table 9 : Prediction of min of MVPA, MVPA METs, and weighted MVPA 64

METs by the eight subdomains of CATPA for P6 boys and girls.

Table 10: Multiple regression of min of MVPA, MVPA METs, and weighted 65

MVPA METs by the eight subdomains of CATPA for P6 boys

and girls.

Table 11: Prediction of min of MVPA, MVPA METs, and weighted MVPA 67

METs by the eight subdomains of CATPA for S3 boys and

girls.

Table 12: Multiple regression of min of MVPA, MVPA METs，and weighted 68

MVPA METs by the eight subdomains of CATPA for S3 boys and

Abbreviations

Page

2. TRA

3. TPB

4. ATPA

5. CATPA

6. P6

7. S3

8. SAPAC

9. CAPA

10. MANOVA

11. CATCH

12. PACI

13. PDPAR

14. PBC

15. MET

Physical activity

Theory of reasoned action

Theory of planned behavior

Attitudes toward physical activity

Children's attitudes toward physical activity

Primary six

Secondary three

Self-administered physical activity checklist 6

Children's attraction to physical activity 15

Multivariate analysis of variance 18

Child and adolescent trial for cardiovascular 24

health

Physical activity checklist interview 24

Previous day physical activity recall 25

Perceived behavioral control 37

Multiple of the resting metabolic rate. 48

"One MET is equal to the resting

oxygen consumption of approximately

3.6 ml/kg"1/min'1" (McAxdle，Katch, &

Katch, 1991，p.l67)

16. Min of MVPA Minutes of moderate to vigorous PA 49

17. MVPA METs Physical activity MET score 49

18. Weighted MVPA METs Weighted activity MET score 49

19. SPSS Statistical packages for the social sciences 52

CHAPTER 1

INTRODUCTION

1.1 Motivation for the study

Regular participation in physical activity (PA) is beneficial to health and

well-being in both adults and children (Powell, Caspersen, Koplan, & Ford, 1989;

Sallis & Patrick, 1994; U. S. Department of Health and Human Services, 1998).

Specifically，it has long been accepted that regular appropriate PA is important to

school-age children to maintain nonnal growth and development (Bar-Or, 1983;

Bar-Or & Malina, 1995) and develop lifelong PA habits (Armstrong & McMamxs，

1994; Pate, Baranowski, Dowda, & Trost，1996; Simons-Morton, Parcel，O'Hara,

Blair, & Pate, 1988; Yang, Telama，Leino，& Viikari，1999).

Unfortunately, few young people are taking advantage from the known

physiological and psychological benefits of regular exercise. Several studies have

reported that both boys and girls reduce their levels of PA strikingly as age or grade in

school increases and girls are less physically active than boys (Armstrong & Welsman,

1997; Health Education Authority, 1997; Pratt，Macera, & Blanton, 1999; Sallis &

Owen, 1999; U. S. Department of Health and Human Services，1998). Research

evidence in Hong Kong also suggests that most of the children exhibit low levels of

PA (Johns & Ha, 1999; Lindner, 1995，1998, 1999; Macfarlane，1997，1999;

McManus & Armstrong，1996; Ng，1996).

Recognizing these low levels of PA, research has focused on investigating the

determinants of PA participation in children. Although researchers have discovered a

long list of variables that affect involvement in PA (see Sallis, Prochaska, & Taylor,

2000，for a review), the study of attitudes is of high importance since it is believed that

the individuars behaviors are in accordance with them (Cooper & Croyle, 1984;

Kraus, 1995). Bentler and Speckart (1979) even indicated that attitude exerts a

significant direct effect on exercise behavior. Researchers have also pointed out that

attitude had a great impact on the study of PA behavior (Hagger，Cale，& Almond,

1997) and the study of attitudes and their relation to participation in PA has interested

sport and exercise scientists for a long time (Doganis & Theodorakis，1995).

However，the absence of a theoretical perspective was one of the major

criticisms of the study of attitudes (Smith & Biddle，1999). This descriptive approach

to the study of attitude "led researchers to question whether attitudes actually did

predict behaviors and to suggest an 'attitude-behavior discrepancy'" (Smith & Biddle,

1999，p.270).

Over the past three decades, several models have been proposed to explain the

relationship between attitudes and behavior (e.g.，the theoiy of reasoned action (TRA)

(Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975) and the theory of social

behavior (Triandis，1977)). The theory of planned behavior (TPB) (Ajzen, 1985，1988,

1991; Ajzen & Madden, 1986) is one of the models that has been found effective for

the prediction of behavior. The model received substantial attention in the area of

social psychology (Olson & Zanna，1993; Tesser & Shaffer，1990) and PA settings in

particular (Papaioannou & Theodorakis, 1996). It was chosen because of its

documented predictive validity in the exercise domain (Godin, 1993; McAuley &

Coumeya, 1993) and other behavioral domains (cf. Ajzen, 1991). Moreover，

considerable empirical evidence has supported the utility of the TPB in predicting and

explaining both the intention to exercise and actual exercise behavior (Blue, 1995;

Godin, 1993; Hausenblas, Carron & Mack, 1997).

According to Fishbein and Middlestadt (1987)，understanding the variables that

influence behavior was the first step in the development of educational intervention to

modify that behavior. Understanding predictors of students' PA participation could

assist teachers and parents to find effective intervention approaches for encouraging

students to participate in PA regularly (Deng, 1998). Therefore, studies of the

predictors of students5 PA participation attract the attention of both researchers and

practitioners when seeking to motivate students to participate in regular PA (Deng,

1998). In fact, students5 attitudes toward PA (ATPA) provide valuable information

about what students think and feel about PA (Subramaniam & Silverman, 2000).

Although there has been a good deal of research examining the relationship

between children's attitudes toward PA (CATPA) and their PA behavior (e.g., Smoll,

Schutz, & Keeney，1976; Long & Haney, 1986), much of this work has yielded

inconsistent findings (Doganis & Theodorakis，1995). In order to have a better

understanding of the relationship between attitude and behavior, researchers examined

the predictive utility of all the variables in the TPB and reported that notable

differences were found in the contribution of attitude across gender (Biddle, Goudas，

& Page, 1994) and grade level (Mummery, Spence, & Hudec, 2000). In contrast，

several studies found no significant gender differences in the predictor variables in the

TPB (Van Ryn，Lytic, & Kirscht, 1996; Wankel, Mummery, Stephens, & Craig, 1994).

To date, only a few studies have been conducted in Hong Kong to investigate the

relationship between the CATPA and their PA involvement. For instance, Fung (1997)

studied the relationship between secondary one and two students' participation level

and their ATPA. However, a relative dearth of research has attempted to examine the

predictive utility of the CATPA in predicting the PA levels based on the TPB by using

primary and secondary students as subjects. Will there be any differences in

comparison with the foreign studies' findings?

h2 Purpose of the study

The present study had two main purposes. The first was to investigate the

predictive utility of attitude (one of the constructs in the TPB) in predicting PA levels

in a sample of Hong Kong primary and secondaiy students. The second was to

compare the predictive power of CATPA to PA levels between genders and grade

levels.

1.3 Hypotheses

The following hypotheses were tested at the 0.05 level of significance:

1. The eight subdomains of CATPA will significantly predict PA levels for Hong

Kong school children.

2. The predictive power of CATPA to PA levels for boys is stronger than for girls.

3. The predictive power of CATPA to PA levels for the primary six (P6) students is

stronger than for the secondary three (S3) students.

1.4 Operational definitions

Attitude: It was defined as "a mental and neural state of readiness，organized

through experience, exerting a directive or dynamic influence upon the individual's

response to all objects and situations with which it is related" (Allport, 1967，p.8). It is

“a psychological tendency that is expressed by evaluating a particular entity with

some degree of favor or disfavor" (Eagly & Chaiken, 1993，p.l). In this study, it was

measured by the Chinese version of the questioimaire (Appendix 1，Part two) which

was adapted and translated from the revised CATPA inventory (Schutz，Smoll，Carre

& Mosher, 1985) (Appendix 2，Part two).

PA: It was defined as "bodily movement produced by the contraction of skeletal

muscle that increases energy expenditure above the basal level" (U. S. Department of

Health and Human Services, 1998, p.20). The term in this study included not only

extra-curricular sports programs but also the physical activities which were

undertaken by the students during their leisure-time. However，physical activities

which are done during the physical education lessons are excluded because they are

usually planned beforehand and the lessons are mandatory for the students. It was

measured by the Chinese version of the questionnaire (Appendix 1，Part one) which

was modified and translated from the Self-administered Physical Activity Checklists

(SAPAC) (Sallis et al.，1996) (Appendix 3).

1.5 Delimitations

1. The sample was limited to 300 P6 and S3 school children.

2. The subjects were limited to boys and girls aged 11 to 17.

3. Only one self-administered questioimaire was used to examine the ATPA and the

PA level of the students.

4. Owing to the great diversity of physical activities，only 26 activities were

selected in the modified SAPAC in accordance with their popularity and

accessibility to Hong Kong school children. However, the subjects were allowed

to list up to 5 other activities in an open-ended item of the checklist.

1.6 Limitations

1. A l l the data in this study were self-reported. For example, the use of retrospective

self-reports, not objective instruments (such as direct observation or activity

monitoring) as a measure of PA levels. It was difficult to control the authenticity

of responses.

2. A convenience rather than random sample was used, which might not be

reflective of the population.

3. No inquiiy was made into factors such as the socioeconomic status and previous

experience which might affect the subjects' attitudes.

4. The ability of all the subjects to read and comprehend the statements in the

inventory could not be controlled. The statements might not be well understood

by all the respondents. The possible misunderstanding might affect the results of

the study.

5. Confirmatory factor analysis was not performed to examine whether the same

factor structure for the eight subdomains of the revised CATPA inventory is

applicable to Chinese students.

1.7 Significance of the study

Results of this study were thought to be useful in the following aspects:

It was hoped that findings from this study would provide the basis for further

investigation on a larger scale.

It was hoped that findings from this study would provide a picture for the degree

of contribution of attitude in the prediction of PA behavior in the context of the

TPB.

Understanding predictors of students' PA participation could assist teachers as

well as parents in designing PA interventions for encouraging students to get

involved in PA regularly (Deng, 1998). Teachers could also acquire the latest

information about changes in students' participation in PA by examining related

predictors (Deng, 1998). The degree of the contribution of the eight subdomains

of CATPA toward the PA participation levels is important in future curriculum

and educational planning. If the subdomains of CATPA contribute substantially to

the PA levels, physical educators should do more to develop and enhance these

subdomains for students so as to motivate them to participate in PA.

CHAPTER 2

REVIEW OF LITERATURE

For the purpose of this study，nine areas of literature were reviewed: (1)

measurement of attitudes, (2) measurement of ATPA, (3) studies using the Kenyon's

ATPA inventory and the revised CATPA inventory, (4) measurement of PA, (5) studies

on the children's PA levels, (6) relationship between attitude and behavior, (7)

relationship between the ATPA and participation in PA, (8) overview of the TPB, and

(9) using attitudes to predict PA participation.

2.1 Measurement of attitudes

Attitude, like personality trait, motivation and some other psychological

constructs, is hypothetical and inaccessible to direct observation (Ajzen, 1988). It

must be inferred from overt responses or indicators (Himmdfarb，1993). Most

common methods used to infer attitudes rely upon verbal responses to questionnaire

items (Ajzen, 1988). However, the measurement of attitudes has been characterized by

controversy and a variety of approaches. The following are some of the major attitude

scales.

2.1.1 Thurstone - Chave

In 1928，Thurstone wrote one of the earliest articles about attitude measurement

and developed one of the first popularly used formal scaling techniques of attitude

measurement (Schuessler, 1993). The Thurstone5s instrument has been used to help

students analyze their educational problems (Demmons，1993). In 1929，Thurstone

and Chave developed the methods of equal-appearing intervals for attitude

measurement (Eagly & Chaiken, 1993).

The Thurstone scale was made up of a number of independent statements of

beliefs about a particular problem. Each statement was numerically based on its

position on a continrnxm which ranges between five areas of expressed feelings: very

unfavorable, unfavorable, neutral, favorable, and very favorable (Demmons, 1993).

Subjects were asked to agree or disagree with each statement. The subject's attitude on

the problem was evaluated by checking the statements which the subject agreed with.

The score was the mean or median of the scale values of the statements that the

subject endorsed (Eagly & Chaiken, 1993).

2.1.2 Likert

In 1932，Likert developed his ‘‘method of summated ratings since he believed

that Thurstone's techniques were too cumbersome and time-consuming" (Eagly &

Chaiken, 1993, p.52). He attempted to create an attitude instrument that was a scale in

itself. Likert's method was "the first approach which measured the extent or intensity

of the subject's agreement with each item，rather than simply obtaining a "yes-no"

response" (Oskamp, 1991，p.54).

10

The Likert scale was made up of a set of belief statements concerning certain

issues. Subjects were asked to respond to the scale by marking one of the five

positions (i.e.，strongly agree, agree, undecided, disagree, or strongly disagree) (Eagly

& Chaiken, 1993). The position indicated was the score for that item. If the statement

was positively worded，the positive position mounted on higher score (e.g.，agree = 4).

Contrarily, if the statement was negatively worded，the scoring was reversed (e.g.，

agree = 2) (Demmons, 1993). The attitude score was calculated by summing up all

item scores.

2.13 Semantic differential technique

Osgood，Suci，and Tannenbaum (1957) developed the best-known multi-item

measure of attitude, the semantic differential method. "In contrast to the above

methods of constructing attitude scales, Osgood's semantic differential is actually a

scale in itself...This has the great advantage that one does not have to construct and

try out a new scale every time one wants to study a new topic” (Oskamp, 1991, p.59).

No doubt this method was the most popular way of measuring attitudes in confronting

a variety of research problems (Eagly & Chaiken, 1993).

The semantic differential consisted of a set of bipolar adjective scales such as

good-bad, harmM-beneficial, pleasant-unpleasant, desirable-undesirable, and

awful-nice (Ajzen, 1988). Each of adjective pair was placed on opposite ends of a

11

7-pomt scale, and respondents were asked to indicate their evaluation of the attitude

object by placing a mark on one of the seven positions. Responses were scored from 1

on the negative side of each scale to 7 on the positive side (Demmons, 1993). The

scoring was reversed for negative statements. Scores on individual bipolar scales were

summed or averaged to get a total attitude score for each subject (Eagly & Chaiken,

1993).

2.2 Measurement of ATPA

There are a limited number of inventories that measured ATPA. The following

inventories have been used very extensively.

2.2.1 The Kenyon's attitude inventory

Kenyon (1968a) reported that most inventories or scales did not account for all

the general characterization of PA. These scales typically focused on attitudes toward

physical education, games or sports (Kenyon, 1968a). In order to overcome this

problem, Kenyon (1968b) developed attitude scales representing each of the

dimensions of a multidimensional model for characterizing PA. The inventory

assessed six subdomains (social experience, catharsis, health and fitness, pursuit of

vertigo, aesthetic experience, and ascetic experience) of ATPA (Kenyon, 1968b). The

six subdomains of Kenyon's ATPA inventory had been shown to be internally

consistent, with Hoyt reliabilities ranged from 0.72 (for the social experience) to 0.89

12

(for the pursuit of vertigo) among 215 female and 353 male college students (Kenyon,

1968b).

Researchers have used this inventory in sport and PA settings, such as assessing

attitudes among athletic groups (Alderman, 1970)，comparing attitudes between

athletes and non-athletes (Hendry & Douglass, 1975)，studying factors related to

attitude change (Sidney & Shephard, 1976)，and comparing ATPA of delinquent and

non-delinquent junior high school age girls (Stxaub & Felock, 1974). However, the

inventory has only been frequently used with high school and college age subjects.

Kenyon (1968a and 1968b) failed to develop an instrument for assessing CATPA.

Because of the lack of an appropriate testing instrument, attitudes of elementary

school children toward PA have not been investigated.

2.2.2 The revised CATPA inventory

Simon and Smoll (1974) adapted the Kenyon's ATPA inventory (1968b) used for

elementary school students (grade 4 to 6) and renamed it，the CATPA inventory. The

inventory measured the disposition of children toward the multidimensional construct

of PA (Patterson & Faucette, 1990a). It "closely follows the form and content of

Kenyon's scales but substantial changes in wording made this instrument appropriate

for reading competencies in grades 4 through 6” (Schutz et al., 1985，p.257). Strong

internal consistency (Hoyt reliabilities ranging from .80 to .89 and test-retest

13

reliabilities of approximately .60) was established for all subdomains (Simon & Smoll,

1974).

Wood (1979) studied the data on 1752 subjects (903 males, 840 females, ages

10-12 years) to evaluate the psychometric properties of the CATPA inventory (cited in

Schutz et al.，1985). There were four phases in Wood's analysis of this inventory: “(a)

item analysis of the discriminating power of each of the eight bipolar adjectives of the

semantic differential scales and the degree to which they are comprehended, (b)

assessment of the internal consistency of the six CATPA scales, (c) evaluation of the

need for reweighting CATPA data, and (d) examination of the factor structure of the

CATPA domains" (Schutz et al., 1985，p.257). The results showed that three pairs

(bitter-sweet, dirty-clean, steady-nervous) were deleted from the inventory since they

were not well understood by the subjects (Schutz et al.，1985).

Based upon the above results a revised instrument was developed by Carre,

Mosher, and Schutz (1980) (cited in Schutz et al., 1985). They shortened the inventory

and improved it psychometrically so that CATPA could be measured more accurately.

They tested the CATPA inventory with 1015 grade 7 students and 864 grade 11

students. Six modifications were made: (1) the semantic differential scales were

reduced from eight to five bipolar adjective pairs，(2) the 7-point scale was replaced

by a 5-point scale for each adjective pair, (3) an CI do not understand this idea，

14

response category was added to each subdomain in order to reduce ambiguity of

interpreting a midpoint response, (4) the phrase 'Taking part in' was added to the

description of each attitude subdomain so as to add an action element to the statement,

(5) the social subdomain was split into two separate dimensions since it was

hypothesized that the descriptive phrase for the original subdomain contained two

concepts for which an individual could hold different attitudinal dispositions, and (6)

the health and fitness subdomain consisted of two factors (value and enjoyment) and

should be scored separately (Schutz et al., 1985).

The two latest versions of the CATPA inventory had been developed, the first

one for children in grade 3 and the second for grades 7 and 11 (Schutz et al.，1985).

The alpha reliability coefficients for the revised CATPA inventory for grades 7 and 11

ranged from .76 (health) to .91 (aesthetic) for grade 7 subjects (n = 1038 boys and

girls), and from .77 (health) to .94 (aesthetic) for grade 11 participants (n = 857 boys

and girls) (Ostrow, 1996).

2.2.3 The Children's Attraction to Physical Activity (CAPA) scale

Brustad (1991) reported that many researchers had relied heavily upon

adult-generated theories and measures to conduct research with the youth in both sport

and exercise domains. He criticized the CATPA inventoiy which was not a

developmentally based research measure and developed the CAPA scale (Brustad,

15

1993) to measure children's interest in exercise and PA.

The content of the CAPA scale was child-generated. Open-ended discussions

were held with third and fourth grade school children to identify various dimensions

of PA that they found attractive or unattractive (Brustad, 1998). The CAPA contained

22 items and five dimensions of attraction to PA: (a) vigorous PA, (b) liking of games

and sports, (c) importance of PA，(d) peer acceptance in games and sports, and (e) fiin

of physical exertion (Brustad, 1993). Items are presented “in a structured-alternative

format, and the subjects respond to each item using a four-choice response formaf'

(Ostrow, 1996，p. 122). CAPA is "conceptually similar to CATPA in that a

multidimensional perspective is taken toward PA，and considerable individual

variability is assumed" (Brustad, 1998，p.468). The internal reliability was acceptable

(alpha value = .60) and the alpha levels for each of the five subscales on the CAPA

ranged from .62 to .78 (Brustad, 1993). Unfortunately, the CAPA was utilized to

measure only grade four to six children's interest in exercise and PA settings (Brustad,

1993，1996). Further testing among children from a broader age range was not

conducted.

Kenyon's ATPA scale (1968b) and Brustad's CAPA scale (1993) were not used

in this study for three Teasons. First, the simplified wording in the revised CATPA

inventory made the statements clearer and easier to understand and reduced the

16

possibility of ambiguous or inconsistent interpretation. Second, it was considered

more time saving and psychometrically superior (Schutz, Smoll，& Wood, 1981) while

yielding equivalent results (Schutz & Smoll, 1977) in comparison with the Kenyon's

ATPA inventory. Finally, the CAPA was designed for children in grades four to six

which was not suitable for the S3 subjects in the present study.

2.3 Studies using the Kenyon's ATPA inventory and the revised CATPA

inventory

Research that used the Kenyon's ATPA and the revised CATPA inventories

“provided sufficient evidence of construct validity" (Schutz et al., 1985，p.258). Also,

Schutz and Smoll (1977) had established the concurrent validity of the CATPA

inventory relative to the ATPA inventoiy. Both of the inventories have been used

extensively in examining gender differences in ATPA.

Alderman (1970) investigated the ATPA of 136 Canadian athletes (81 males and

55 females) by Kenyon's (1968b) inventory. Significant differences were found

between male and female subjects in subdomains of social experience, pursuit of

vertigo and aesthetic experience. The athletes showed the strongest ATPA as an

aesthetic experience but a very low or weak response toward PA as an ascetic

experience.

Another study involved fourth and fifth grade children as their subjects.

17

Patterson and Faucette (1990a) examined ATPA of 215 boys and 199 girls by using the

revised CATPA inventory (Schutz et al” 1985). Multivariate analysis of variance

(MANOVA) showed significant differences in attitudes for boys and girls on

subdomains of aesthetic and vertigo. Boys had significantly higher scores on the latter,

while girls had significantly higher scores on the former. Patterson and Faucette

(1990b) also examined CATPA in classes taught by physical education specialists

versus nonspecialists by employing the CATPA inventory (Simon & Smoll, 1974).

Discriminant function analysis showed a significant difference in the CATPA between

two groups, with the cathartic domain being the best predictor.

Birtwistle and Brodie (1991) investigated 291 (144 boys and 147 girls)

secondary and 316 (149 boys and 167 girls) primary school children's ATPA and their

perceptions of physical education. They found that girls had significantly more

positive ATPA than boys but no differences in attitudes were found between the

socio-economic levels. Results also showed that girls had significantly more positive

attitudes than boys in the aesthetic domain. Differences were also found in social

growth and vertigo scores. All subjects ranked health and fitness objectives highly,

with a similar pattern of pupils，perceptions of physical education.

Researchers have also exaxnined the relations of the CATPA inventory with overt

behavior. Smoll et al. (1976) found a highly significant relationship between CATPA

18

and PA involvement. They suggested that grade 4 to 6 children are primarily involved

in those activities for which they hold the most positive attitudes. Additionally, Schutz

et al. (1985) reported a moderate attitude-behavior relationship, "indicating that the

ATPA and CATPA inventories are at least as strong as most social-psychological

measures in accounting for attitude-behavior relationships" (p.259). Furthermore,

Theodorakis, Doganis，Bagiatis, and Gouthas (1991) utilized the CATPA - grade 3

scale (Schutz et al.，1985) to assess the efficacy of TRA in predicting exercise

behavior of 56 children (10 to 11 years old). The results indicated that this attitude

instrument, in conjunction with the TEA，satisfactorily contribute to the explanation

of exercise behavior. Finally, Schutz and Smoll (1986) conducted a longitudinal study

(2-year period of time) in examining the stability of attitudes for 132 grade 10 children.

They reported that "even in the presence of stability of both attitudes and involvement,

the strength of the attitude-behavior relationship continues to be low" (Schutz &

Smoll，1986，p. 195).

Other researchers have investigated the relationship between attitudes and

exercise adherence behavior (Dishman，Ickes，& Morgan, 1980; Long & Haney, 1986;

Shephard，Berridge, Montelpare, Daniel, & Flowers, 1987). McCready and Long

(1985) have also exaxnined the relationship between exercise adherence and the

combined effects of locus of control and ATPA by using the revised CATPA inventory

19

(Schutz et al.，1981). They found that two attitude measures (social continuation and

catharsis) were the best predictors of exercise adherence for 61 females fitness

program participants (ages 15 to 57).

Only a few studies have investigated the relationship between ATPA and PA

levels for school children by using the revised CATPA (grades 7 and 11) inventory

(Schutz et al., 1985), especially in Hong Kong. For example, Fung (1997) investigated

the relationship between CATPA and their PA participation level. He found that the

eight subdomains of CATPA contributed 22% to the secondary one and two students'

PA participation; the aesthetic and catharsis subdomains were most influential in

contributing to the students' participation level. However, he examined the

relationship between CATPA and their PA participation level, neither based on

theoretical models nor using primaiy students as the subjects.

2.4 Measurement of PA

PA is difficult to measure since it is a complex behavior. It varied daily and was

not as stable as other traditional-type behaviors (Sallis & Owen, 1999). The

assessment of PA in youth is even more problematic (Armstrong, 1991; Baranowski &

Simons-Morton, 1991; Freedson et al., 1997; Saris, 1986; Welsman & Armstrong,

1997).

There are several types of techniques that have been used to assess PA in a

20

variety of population (e.g., self-report, activity monitors, pedometers, heart rate

monitors，doubly labeled water, and indirect calorimetry) and others that have been

developed specifically for children (direct observation) (Welk, Corbin, & Dale, 2000).

Each of the measures has specific advantages and disadvantages that must be

considered when selecting an instrument. When assessing the suitability of a

measuring technique for children, some important criteria should be considered. For

example, the technique used should be socially acceptable, it should not burden the

child with cumbersome equipment and it should not influence the child's normal PA

pattern (Armstrong, 1991; Welsman & Armstrong, 1997). A number of excellent

reviews of PA assessments for children had shown the relative merits of the different

techniques (Baranowski & Simons-Morton, 1991; Baranowski et al., 1992; Pate, 1993;

Riddoch & Boreham，1995; Rowlands, Eston, & Ingledew, 1997).

Although there is currently no 'gold standard' measure of PA (Ainsworth,

Montoye, & Leon，1994)，self-report measures are the most widely used method

(Freedson & Melanson, 1996; Health Education Authority, 1997; Palmer, Burwitz,

Smith，& Collins，1999; Sallis，1991; Sallis & Saelens, 2000). Self-reports are

"convenient to administer, cost-effective, unobtrusive and non-reactive when

compared to other measures" (Cale，1994，p.440). In addition, "self-report measures

may cover details of activity for the previous days, weeks，months or even years and

21

can be used to measure a variety of physical activity variables over time and from one

source" (Cale, 1994，p.440).

According to Sallis (1991)，there are four major types of self-report measures: (1)

self-administered recall，where children report their own activities on a pre-printed

form; (2) interview-administered recall，where an interviewer conducts a structured

interview session with a child in a one-on-one setting; (3) diary，where the child lists

PA throughout the day on a diary form; (4) proxy reports, where the teachers or

parents report the child's activity using any of the three previously mentioned formats.

Among these four types of self-report measures, self-administered recall questionnaire

was found to be one of the most valid and practical tool for data collection (Caspersen,

1989). Sallis, Buono, Roby, Micale，and Nelson (1993) also indicated that adequate

reliability and validity were reported in PA recall for the fifth grade children.

Recently, Sallis and Saelens (2000) reviewed some PA self-reports that were

developed or had been used in 1990s and reported that the validity correlations for

self-administered recalls ranged from .07 to .88. While a number of different

approaches had been tested，several reviews (Pate, 1993; Sallis, 1991; Sallis, et al.,

1993) generally accepted that "previous-day recall instruments offer the most promise

for use with children" (Welk et al., 2000，p.66). The repeated 24-hour activity recall

had been shown to provide the most reliable and valid reports from children

22

(Baranowski et al., 1984; Sallis et al.，1993). A number of different instruments are

now available for this type of assessment (e.g.，Sallis et al., 1996; Weston，Petosa，&

Pate, 1997).

However, it must be noted that numerous limitations of self-reports had also

been discussed (Ainsworth et al., 1994). The definitions of PA variables and the

human cognitive process (ability to recall) were two main sources of errors in

accurately measuring PA (Baranowski, 1988). Numerous concerns arose with

self-report questionnaires or activity checklists including inaccurate recall of activity

and the inability to capture all types of PA for children (Freedson et al.，1997).

Self-reports were also susceptible to several sources of bias. For example, “social

desirability bias can lead to over-reporting of PA (Wamecke, Johnson, Chavez,

Sudman, O'Rourke, Lacey，& Horm，1997). Recalling PA is a highly complex

cognitive task (Baranowski, 1988)，and instruments vary in their cognitive demands，，

(cited in Welk, et al., 2000，p.59). Nevertheless, a number of investigators had found

self-report of exercise behavior to be both reliable and valid (Blair et al., 1985; Godin

& Shephard, 1986).

2.4.1 The SAPAC

The SAPAC is a 1-day self-report recall using checklist format (Sallis et al”

1996). The original checklist "required children to report minutes during previous day

23

they spent in 21 common physical activities that represented a range of intensities，

plus sedentary pursuits" (Sallis et al.，1996，p.840) (Appendix 3). It was developed to

measure PA outcomes in the Child and Adolescent Trial for Cardiovascular Health

(CATCH) multicenter school-based health promotion intervention study for fifth grade

children. The subjects reported the minutes they spent in each activity during three

segments of the previous day: before school，during school，and after school (Sallis et

al.，1996). The SAPAC provided information about three dimensions of PA: type,

intensity, and duration.

The intraclass correlations between the SAPAC and Physical Activity Checklist

Interview (PACI) demonstrated substantial similarity with coefficients ranging

from .64 (minutes of moderate to vigorous PA) to .79 (number of activities) (Sallis et

al., 1996). This result was within the range of test-retest reliabilities for other PA

self-report instruments (Sallis et al., 1996). To validate the recalls, heart rate

monitoring and Caltrac accelerometer were used. SAPAC was moderately and

significantly related with the two objective measures in both boys and girls. The

observed correlation between the SAPAC and Caltrac accelerometer and heart rate

monitoring was .30 and ,60, respectively. The heart rate correlations, ranging from .50

to .60，were higher than those in other published PA self-reports of fifth-grade children

(Sallis et a l , 1996). They concluded that SAPAC was more cost-effective in

24

comparison with the PACI (administered by interview) and received moderate support

for validity in all gender and ethnic subgroups.

2.4.2 Previous Day Physical Activity Recall (PDPAR)

The PDPAR is an instrument for measurement of PA in grades 7 to 12 students.

It is a self-report questionnaire that "required recall of the previous day's activities for

the after school hours (3:00 - 11:30pm) and their relative intensities" (Weston et al,

1997，p. 138). The instrument is divided into seventeen 30-inin intervals and used

contextual cues to improve the quality of the data recorded (Weston et al., 1997). The

instrument provides a numbered list of activities grouped into the following categories:

"eating, sleeping, bathing, transportation, work/school, spare time, play recreation,

and exercise/workout" (Weston et al., 1997，p.138).

The subjects are required to complete the activity mode portion by recording the

code number corresponding to the activity in which they were participated in during

the specific 30-min period. Then they rate the intensity levels (very light，light，and

hard) for the reported activity (Weston et al.，1997). The correlation between the

PDPAR and pedometer and Caltrac accelerometer was .88 and .77, respectively. The

test-retest reliability correlation coefficient for the PDPAR administered twice in one

hour was .98 (Weston et al., 1997). However, Trost, Ward, McGraw and Pate (1999)

reported that the PDPAR did not appear to provide useful estimates of moderate PA in

25

fifth-grade children.

The SAPAC was utilized in this study because it provided moderate validity for

all gender and ethnic groups. Moreover, SAPAC provided a fiill picture in quantifying

a whole day of PA behavior rather than the after-school (8.5 hours) period by PDPAR.

2.5 Studies on the children's PA levels

Due to the variation in definition of activity levels and methodology for

estimating PA levels, it is difficult to inteipret and compare the studies on children's

PA levels. But it is possible to examine a remarkably consistent age and gender-related

trends within and across studies (Armstrong & Welsman，1997; Pate, Long, & Heath，

1994).

2.5.1 Age-related trends

Increasing numbers of boys，and even more girls begin to drop out of active play

and participation in PA during adolescence (Armstrong & McManus，1994; Butcher,

1985; Fox, 1994; Rowland, 1990; Zakarian, Hovell, Hofstetter, Sallis, & Keating,

1994). Sallis (1993) had reviewed nine studies and reported that PA levels declined at

a rate of about 2.7% per year for boys and 7.4% per year for girls during school-age

years. Overall, there appeared to be a 50% decrease of PA between 6-16 year olds

(Rowland，1990). The total activity time gradually decreased from the ages of 11-13

till 17-18 for both girls and boys (Verschuur & Kemper, 1985). Researchers also found

26

that age was significantly and negatively correlated with the % time spent in moderate

and vigorous activity (Welsman & Armstrong, 2000)

Telama, Laakso, and Yang (1994) conducted a longitudinal study in Finland and

concluded that PA had its peak at the age of 12，after which age it was reduced

considerably. They also reported that PA during youth was a significant but weak

predictor of the PA that takes place 9 years later. The best predictors were the school

grade for physical education and participation in organized sports.

Riddoch and Boreham (1995) suggested that a young child's natural state of

activity might gradually deteriorate during adolescence，as children were habituated

into a sedentary lifestyle. They pointed out that young people spent much of their time

indoor at school and at home (doing homework, watching television，playing on

computers, etc.) and that this sedentary lifestyle might become the norm as they got

older.

In Hong Kong, the effect of age on PA participation has also been reported.

Research showed that PA participation declined with increasing age from 24.5%

(11-13 year group) and 26.2% (14-15 year group) to 20.7% (16 or above year group)

(Ng, 1984，cited in Speak，Lindner, & Li, 1994). Lindner (1998) also examined the

rates and frequencies of sport participation between age and gender for Hong Kong

school children. The result indicated that “boys were slightly below the frequencies in

27

western countries and showed little decline over age levels, whereas the girls had quite

low participation frequencies and a sharp decline with age" (Lindner, 1998，p.27).

2.5.2 Gender-related trends

Gender differences in PA levels have been reported in numerous studies. Several

reports have shown a decline in adolescent PA of both females and males (Blair et al”

1985; Stephens，Jacobs, & White, 1985; Sallis，1993; Verschuur & Kemper, 1985). It

has been reported girls decrease their PA levels more than boys during adolescence

(Lindner, 1998; Ng，1984，cited in Speak, Lindner，& Li, 1994; Verschuur & Kemper,

1985). The decline of female participation was caused by a decreased number of hours

in physical education lesson and a drop in playing outdoors games (Verschuur &

Kemper, 1985). Klesges, Eck, Hanson, Haddock，and Klesges (1990) indicated that

society placed more of an emphasis on PA for boys than for girls which could account

for the decline in activity among females.

Males between the ages of 6 and 17 have been estimated to be 15-25% more

physically active than females of the same age (Sallis, 1993). During childhood and

adolescence both boys and girls reduce their PA as they grow older but the rate of

decline is 2.5 times greater in girls than in boys (Sallis, 1993).

Riddoch and Boreham (1995) have summarized data from large population

studies. These data indicate that PA levels peak in children aged around 13 to 14，and

28

then markedly decline. Boys are normally reported to be more active than girls，but

this difference is not significant when moderate activity alone is compared, indicating

that boys participate more often in vigorous activities than girls.

Significantly higher levels of PA in boys compared to girls in older children and

adolescents were extensively documented (Armstrong & Welsman，1997)，but

observational studies have indicated no sex differences in the PA patterns of 5 to

11-year-old British school children (Sleap & Warburton, 1992). In contrast，American

studies have suggested that sex differences exist in as young as 3 to 4-year-old

children (Baranowski, Thompson, DuRant, Baranowski, & Puhl, 1993; DuRant et

al.，1993). Welsman and Armstrong (1997) found that sex differences in PA are

present in British children from 5 years old by using objective heart rate monitoring.

Armstrong, Balding, Gentle and Kirby (1990) demonstrated that boys' PA levels do

not decline significantly but girls' PA levels decrease progressively during their

secondary education. Welsman and Armstrong (1997) revealed that boys' activity

patterns are considerably higher during their initial primary education but a decline is

observed as they approach secondary education. The decline in girls' PA appears to be

a continuous trend which seems to start in the early years of primaxy education.

2.6 Relationship between attitude and behavior

Some theorists have agreed that there is a relationship between attitude and

29

behavior - behaviors reflect the attitudes (Bandura, 1986; Fishbein & Ajzen, 1975).

The expectation that attitudes enable us to predict behavior was a major reason for

being studied.

The relationship between attitudes and behavior has been the topic of

considerable debate since 1930's (Eagly & Chaiken, 1993). Virtually the only

empirical study of attitudes and behavior in this era was conducted by LaPiere (1934).

He concluded that attitudes could be easily and quantitatively measured but were

largely irrelevant to the prediction of behavior.

As time passed, there was 汪 growing sense that inconsistency between attitudes

and behavior was more common than was previously thought. The greatest challenge

to the attitude-behavior consistency was posed by Wicker's (1969) review of 47

empirical studies of the two components. He concluded that attitudes were generally

"unrelated or only slightly related to overt behaviors" (p.65) and that there was "little

evidence to support the postulated existence of stable，underlying attitudes within the

individual which influence both his verbal expressions and his actions" (p.75).

In contrast, several studies helped explain the relationship between attitude and

behavior (e.g., Bentler & Speckart，1979，1981). Kahle and Berman (1979) advocated

that attitudes exerted a significant direct effect on behavior. They reported that

attitudes had causal priority over behaviors. Bern (1972) and Kelman (1974)

30

suggested that attitudes and behavior affected each other - an attitude could shape

behavior and that behavior could build attitudes.

However, the inter-relationships between attitude and behavior are indeed

complex. Researchers suggested two major types of explanations for the apparently

widespread inconsistency between attitudes and behavior. They were "methodological

explanations, which attributed the inconsistency to poor methodology or measurement

techniques, and moderator variable explanations, which argued that the level of

consistency depends on other variables" (Kraus, 1995，p.60). The literature also

suggested that such inconsistencies might result from: (a) problems of attitude

definition and measurement (Kenyon, 1968a), (b) personal factors - such as interfering

attitudes, competing behaviors and the individual's response level (degree of apathy)

(Fishbein, 1967)，and (c) situational factors - such as group pressures, societal norms

and expected rewards or consequences (Fishbein, 1967). Fishbein (1967) maintained

that the situational and personal variables might be more important in determining

behavior than the individuars attitude.

"Research efforts over the past three decades have reconfirmed the importance

of attitude as the prime theoretical construct in social psychology and they have

verified the relevance of attitude measurement as an indispensable tool for our

understanding of social behavior" (Ajzen, 1993，p.53). Numerous investigations have

31

supported the attitude-behavior link by showing that "attitudes correlate strongly with

behavior when they are assessed at the same level of generality or specificity as the

behavioral criterion in terms of action, target, context, and time elements" (Ajzen,

1996，p.385).

2.7 Relationship between the ATPA and participation in PA

It was often assumed that PA participation would be influenced by attitudes

towards，knowledge of, and beliefs about PA (Health Education Authority, 1997).

However, research findings indicated that it was not necessarily the case. For example,

ATPA was not strongly related to PA participation (Butcher, 1983; Ferguson, Yesalis,

Pomrehn, & Kirkpatrick, 1989; Smoll & Schutz，1980)，accounting for less than 50%

of the variance in PA participation (Godin, & Shephard, 1986; Schutz & Smoll, 1986).

Appropriate attitudes or knowledge were weakly related with the PA behavior because

there were other factors to consider (Flora，Maibach，& Maccoby, 1989).

The research findings on attitudes and knowledge of PA have been inconsistent.

Knowledge, beliefs and attitudes about PA were reported as weak or inconsistent

correlates of PA (Garcia，Pender, Antonakos, & Ronis, 1998; Sallis et al.，1992).

Schutz and Smoll (1986) indicated that ATPA were found less stable in 10-12-year-

old children and the relationship between adolescents' attitude and PA involvement

was weak. They concluded that favorable PA attitudes did not necessarily indicate

32

active PA behavior. Similar findings were also found by Godin and Shephard (1986).

Brustad (1991) noted that a physically active lifestyle might not be developed even

though an individual possessed favorable attitudes toward, extensive knowledge about

benefits of，physical activity. Fox (1994) explained that the reason for weak

relationships between attitudes and actual PA was that PA became a choice of behavior

for adolescents and the act of decision-making was entirely personal. The perceived

and experienced beliefs, attitudes and values were used to base adolescents5 decisions

about PA.

A number of studies were conducted to determine the relationship between

ATPA and exercise participation in a wide range of age groups，from school children

(Greenockle, Lee, & Lomax，1990) to the rural elderly (Mobily et al, 1987). Smoll et

al. (1976) examined the nature and degree of the relationships among 127 boys and

137 girls in grade 4 to 6 children's ATPA, degree of primary involvement in different

physical activities and level of motor proficiency in running, jumping and throwing.

Canonical correlation analysis showed a strong linear relationship between the CATPA

domain and the involvement plus motor proficiency domain. Further analyses

indicated that the basis for the significant association was the strength of the

relationship between the CATPA and involvement.

Riddle (1980) examined the Fishbein's behavioral intention model by using the

33

beliefs，attitudes, and behavioral intentions of male and female toward regular jogging.

The subjects, 296 male and female joggers and non-exercisers, completed the

questionnaires. One way ANOVA showed that there were significant differences

between the joggers' and nonexercisers' beliefs. The result also supported the theory

upon the model based on. That is，"relationship between the intention to jog and the

jogging behavior was high (r ~ .82) and behavioral intention was predicted from an

attitudinal and a normative component alone (R = .742)" (Riddle, 1980, p.663).

However, it might be too specific to focus on the jogging only，other physical

activities should also be included. Deeter (1988) also found a positive relationship

between attitudinal commitment and the frequency and duration of high intensity

activities. The author concluded that the attitude of commitment could be used to

predict students，PA involvement even though other variables needed to be taken into

account.

Theodorakis et al. (1991) also examined the TRA to predict exercise behavior of

56 children (27 boys and 29 girls) aged 10 to 11. The CATPA (Schutz et al.，1985)

inventory and a questionnaire which was constructed using the TRA were utilized to

examine the efficacy of the instruments to predict exercise behavior of children. The

result showed that adding the subjects' past behavior as an external variable in the

analysis could increase the reasoned action model's efficacy. Also, attitude toward

34

behavior and subjective norm contributed significantly to the prediction of intention.

The beta weights for attitude toward behavior and subjective norm were .31 and .27，

respectively.

However, most of the studies mentioned above had suffered because several of

them had not based on appropriate theory. Researchers believed that predicting,

understanding and explaining PA behavior should be conducted within a theoretical

framework because it "enables us to build knowledge and understand how and why

people might be motivated or amotivated to adopt and/or maintain exercise" (Biddle

& Nigg，2000).

2.8 Overview of the TPB

Several models have been proposed to account for the relationship between

attitudes and behavior. One of these models that has been found effective is the

planned behavior model (Ajzen, 1985，1988，1991; Ajzen & Madden，1986).

Empirical research has provided evidence to support the TPB in a variety of

experimental and naturalistic settings (e.g., Ajzen & Driver, 1991，1992; Beck &

Ajzen, 1991) and shows that it has successfully predicted a variety of behaviors

(Ajzen，1988，1991，1996; Conner & Sparks, 1996; Godin & Kok，1996). The TPB

has been widely applied in behavioral domains such as students' class attendance and

academic achievement (Ajzen & Madden, 1986)，dishonest actions (Beck & Ajzen,

35

1991)，weight loss (Bagozzi & Kimmel, 1995; Schifter & Ajzen, 1985), sleeping,

listening to an album, taking vitamins and others (Madden, Ellen, & Ajzen, 1992).

A number of studies have also applied the TPB to sport and physical activity

settings (e.g., Gatch & Kendzierski, 1990; Godin, Valois，& Lepage，1993; Godin,

Vezina, & Leclerc, 1989; Greenockle et al.，1990; Kimiecik, 1992; Theodorakis, 1992).

[n addition, the theory is used to understand the behavioral determinants of exercise

and PA in a wide range of age groups ranging from school-aged children and

adolescents (Craig, Goldberg, & Dietz, 1996)，adults (Kimiecik, 1992) to the elderly

(Coumeya, 1995). The usefulness of the theory has been both supported

(Dzewaltowski, Noble, & Shaw, 1990; Gatch & Kendzierski, 1990; Kimiecik, 1992;

Wankel et al., 1994) and refuted (Godin et al.，1993; Yordy & Lent, 1993). The theory

has been received increased attention in the sport and exercise psychology fields (e.g.，

Coumeya & McAuley, 1993; Dzewaltowski et al., 1990).

The TPB is an extension of the TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen,

1975) which "predicts volitional behavior from a person's intention to actually

perform a target behavior" (Kimiecik, 1992，p. 193). It provides details on the

determinants of an individual's decision to perform a particular behavior. These two

models were utilized “to provide parsimonious explanations of informational and

motivational influences on behavior5' (Conner & Armitage, 1998，p. 1430). Both of the

36

models imply that an individual makes behavioral decisions based on careful

consideration of available information.

In fact, the TPB is the same as the TRA but with the additional variable of

perceived behavioral control (PBC). The TPB proposes that the subject's intention is

the main determinant of behavior because “it captures certain motivational factors

such as how hard one is willing to try，’ (Coumeya, 1995，p. 81). It is a function of three

conceptually independent variables. First is a personal factor，termed attitude, focuses

on a positive or negative evaluation of the outcomes in performmg the behavior.

Second is a social one termed subjective norm. It reflects the perceived social pressure

that individuals may feel to perform or not perform the behavior. Third is PBC which

indicates the perceived ease or difficulty of performing the behavior (Coumeya, 1995).

Ajzen (1985) suggests that PBC is influenced by both internal and external factors.

Internal factors include variables such as skills, abilities, and individual differences in

willpower while external factors include “time，opportunity, and dependence on other

people，，(p.27). The notion of PBC is closely similar to Bandura's concept of

self-efficacy beliefs (Ajzen，1991; Ajzen & Madden, 1986) and to Triandis's concept

of ‘facilitating conditions' in his model of interpersonal behavior (Ajzen, 1985).

According to the TPB, PBC has both a direct effect on behavior and an indirect

effect on behavior through intentions (Ajzen, 1991; Ajzen & Madden，1986). PBC

37

may also be an immediate determinant of behavior i f the behavior is not completely

volitional (i.e., not free of practical constraints) (Ajzen, 1991; Ajzen & Madden, 1986).

The summarized proposition of TPB is that people “will intend to perform a behavior

when they evaluate it positively, believe that important others think they should

perform it, and perceive it to be under their own control，，(Coumeya & McAuley, 1995，

p.501).

The theory also specifies the determinants of the three variables mentioned

above. Attitude is determined by behavioral beliefs that refer to the perceived

advantages and disadvantages associated with the behavior. Subjective norm “is

determined by normative beliefs that center on whether specific individuals or groups

who are important to the individual think individual should perform the behavior"

(Coumeya, 1995，p.81). Finally, control beliefs underlie PBC and "focus on

opportunities and resources available for performing the behavior" (Coumeya，1995,

p.81).

2.9 Using attitudes to predict PA participation

While much earlier research was conducted to identify the factors associated

with participation in and withdrawal from physical activities, a number of relatively

recent investigations has focused mainly on the prediction of either intention to

participate or actual exercise participation (Doganis & Theodorakis, 1995). Many of

38

these studies were based on theoretical models aiming at predicting behavior from a

number of variables (Doganis & Theodorakis, 1995). It must be noted that the relative

importance of attitude, subjective norm, and PBC in the prediction of intention; and

that of intention and PBC in the prediction of behavior, were expected to vary across

behaviors and populations (Ajzen & Driver, 1992，p.210).

Researchers had claimed that attitude could not predict exercise behavior

(Dishman et al•，1980; Hale，1976). Dishman and Gettman (1980) concluded that

ATPA，as measured with the Kenyon (1968b) inventory, had little influence on actual

exercise behavior. They wrote: "...many people may believe exercise is a good thing，

but few people exercise. This discrepancy has been noted previously..., and although

a variety of reasons has been presented for its prevalence..., it may simply reflect the

inadequacy of attitude as a predictor of behavior in general" (p.306).

In contrast, Terry and O'Leary (1995) found that people's attitudes had a direct

effect on actual behavior. A number of researchers had found a direct effect of

attitudes on behavior without being mediated by intention (e.g., Manstead, Proffitt, &

Smart, 1983). As noted by Eagly and Chaiken (1993)，such results possibly reflected

that an intention to perform certain behavior might not mediate between attitudes and

behavior. In other words，subjects might sometimes spontaneously act in accordance

with their attitudes (Fazio, 1990).

39

Attitude had also found to be a significant predictor of behavioral intention (e.g.，

Biddle et al., 1994; Collete，Godin, Bradet, & Gionet, 1995; Coumeya & McAuley,

1995; Dzewaltowski et al.，1990; Terry & O'Leary, 1995; Theodorakis, 1994; Wankel

et al., 1994) but the magnitude of this relationship had varied considerably across

studies.

A meta-analysis by Hausenblas et al. (1997) showed that intention had a large

effect on exercise behavior, and attitude had a large effect on intention. The effect of

attitude (the effect size: 1.22) was twice as bigger as subjective norm (the effect

size: .56). The direct effects of attitude and PBC on intention had also been well

documented (Godin, 1993，McAuley & Coumeya, 1993).

Yordy and Lent (1993) examined the utility of three theoretical models (TRA，

TPB, and social cognitive models) in predicting exercise intentions and behavior. The

sample consisted of 284 college students. Within the TPB, attitude accounted for a

veiy large portion of predictive variance (R2 change = .58)，with subjective norm

adding a small but significant increment to the equation (R2 change = .01). The PBC

did not explain any further significant variance. Kemer and Grossman (1998)

investigated the efficacy of the TPB in predicting intention to exercise and amount of

exercise by using 73 members of fitness center as the subjects. The result indicated

that 26.6% of the variance of intention to exercise was contributed by both fitness

40

attitude and subjective norm. The contribution of fitness attitude (part r = .36) was

slightly greater than that of subjective norm (part r = .33).

Gatch and Kendzierski (1990) found that the PBC added significantly to the

prediction of intention to participate in an aerobics program beyond that obtained by

the two variables (attitude and subjective norm) of the TRA. The multiple correlation

of the three predictor variables with intention was .55，whereas the corresponding

value was .50 for the two original variables in the TRA. In this sample of 100 female

university students, attitude was the best single predictor of intention, followed by

PBC and subjective norm (respective regression coefficients = .30, .25，and .23).

Similar results were reported by Kimiecik (1992) in a study of 332 corporate

employees. Adding PBC to the original variables of the TRA increased the multiple

correlation from .59 to .66. Attitude was the best predictor (/3 = 65), followed by PBC

(/3=.35) and subjective norm (/3=01). Subjective norm was not significantly related

to intention in either the TRA or the TPB analysis.

In sum, the contribution of attitude and subjective norm has been shown to vary

in the prediction of intention to exercise (Gatch & Kendzierski, 1990; Wankel et al.，

1994; Yordy & Lent, 1993). The variance of intention to exercise explained by both

attitude and subjective norm has ranged from an R2 of .18 (p<.05) (Dzewaltowski et

al., 1990) to an R2 of .59 (p<.05) (Kimiecik, 1992). That is, attitude has been shown

41

to explain as little as 18% (p < .05) of the variance of intention to exercise

(Dzewaltowski et al.，1990). In contrast, Yordy and Lent (1993) and Kimiecik (1992)

showed that ATPA explained as much as 58% (pC.OOl) and 59% (pC.OOl) of the

variance of exercise, respectively.

Other studies had found that PBC contributed to the prediction of exercise

intentions over the effects of attitude and subjective norm. Dzewaltowski et al. (1990)

revealed that the more positive an individuars PBC for PA participation, the stronger

the intention to perform PA. However, PBC did not contribute to the prediction of

behavior expressed as the total energy expenditure spent over the 4-week period. Few

studies showed that the social norms contributed more strongly to predict intention

than attitude did (Greenockle et al.，1990; Theodorakis, 1992).

Ajzen (1988) indicated that variations in the relative contributions of the three

components in the TPB could be influenced by demographic variables, such as gender

or age. Wankel and Muimnery (1993) integrated TPB items into a large population

survey of over 4000 Canadians. In predicting PA intention, it was found that across the

different age and gender groups, variance in intention accounted for by attitudes,

subjective norm，and PBC ranged from 25-35%. In 狂 forther analysis of the same

survey (n = 3679)，analyzing across four age groups set as 19 years and under, 20-39,

40-59，and 60 years and over, Wankel et al. (1994) showed that PBC predicted

42

intention differently across these age groups. Specifically, as age increased, PBC and

subjective norm became more important, and attitude less important.

Limited research had been conducted using the TPB to gain a better

understanding of the PA determinants in children or youth. Early research conducted

by Godin and Shephard (1986) used the TRA to study the factors that influence

exercise intention in Grade 7 and 9 participants. Results showed that measures of

attitude toward exercise and subjective norm explained 33% of the variance in

exercise intention. No differences between age or grade level were reported. Craig et

al. (1996) applied the TPB to studying the psychosocial correlates of PA in children in

Grades 5 and 8. Using the measures of attitude, subjective norm and PBC were able to

explain 30% of the variance in PA intention. Attitude, followed by PBC made the

largest contribution to the prediction. Consistent with the findings of Godin and

Shephard (1986)，the measure of subjective norm failed to contribute in predicting PA

intention.

Bungum，Dowda, Weston，Trost, and Pate (2000) examined associations

between psychosocial factors and PA in grade 8 to 12 school children. Regression

models indicated that ATPA predicted moderate to vigorous activity among males but

not females, although females' ATPA were significantly more favorable than males'.

These findings were consistent with those of Trost et al. (1996)，who noted several

43

important gender differences in the determinants of PA in preadolescent children.

They reported that beliefs about PA outcomes predicted PA behavior in boys but not

girls，while enjoyment of school physical education and perception of the mother's PA

(active vs. sedentary) predicted activity behavior in girls but not boys.

2.10 Summary

Although a number of studies have been conducted to assess the efficacy of the

TPB in predicting exercise or PA intention as well as behavior, there has been a

relative dearth of research in PA or exercise examining whether there are differences

across gender and grade level in the contribution of the variables in the TPB to the

participation in PA. On the other hand, it is generally accepted that attitudes have a

significant direct effect on behavior (Bentler & Speckart, 1979，1981; Kahle &

Berman, 1979; Kxaus, 1995). Thus, research into the predictive utility of the attitude

in predicting the primary and secondary school students' PA levels is still important

even after all these studies.

CHAPTERS

METHODS AND PROCEDURES

3.1 Subjects

The subjects in this study comprised 214 primary and 194 secondary school

children (206 boys and 213 girls) ranging in age from 11-15 and 13-17, respectively.

Three primary and three secondary schools were selected by convenience sampling.

These schools included five aided co-educational schools and one government

co-educational schools. One of the schools was located on Hong Kong Island and the

others were located in the New Territories.

Among the 419 subjects, 408 subjects were selected after deletions for missing

data or outliers. Moreover, 108 of the subjects were found that they had physical

education classes the day before. According to the definition of PA in Chapter One，

physical activities which were done during the physical education lessons were

excluded because they were usually planned beforehand and the lessons were

mandatory for the students. Therefore, 108 subjects were further eliminated and

finally 213 primary and 87 secondary school children's responses (149 boys and 151

girls, 11 to 17 years of age) entered the analysis.

3.2 The instruments

Two self-administered questionnaires were used in this study. The revised

45

CATPA inventory (Schutz et aL，1985) and the modified SAPAC (Sallis et al., 1996)

were employed to assess the CATPA and their PA level respectively. The alpha

reliability coefficients for the revised CATPA inventory for grades 7 and 11 ranged

from .76 (health) to .91 (aesthetic) for grade 7 subjects (n = 1038 boys and girls), and

from .77 (health) to .94 (aesthetic) for grade 11 participants (n = 857 boys and girls)

(Ostrow, 1996). The test stability coefficients also ranged from .80 to .87 over a 2-year

period of time (6-month test-retest intervals) among grades 10 and 11 students (Schutz

& Smoll, 1986). The construct validity of the seven hypothesized attitude subdomains

was supported through principal component factor analyses among the grade 7 (n =

1038 boys and girls) and grade 11 (n = 857 boys and girls) students (Ostrow, 1996).

For the SAPAC, the validity coefficient (weighted mean) was .60 and .32 in

comparison with the heart rate and Caltrac accelerometer measure, respectively (Sallis

& Saelens, 2000). The test-retest reliability ranged from .64 (minutes of moderate to

vigorous PA) to .79 (number of activities) in comparison with the PACI on the same

day (Sallis et al•，1996).

Both of the questionnaires were combined into one single questioimaire which

consisted of two parts (Appendix 2，Part one and two). The first part asked questions

related to PA participation on the previous day, such as type, duration and intensity of

specific activities. It was adapted from the SAPAC but some modifications had been

46

made to suit the design of this study. Firstly, some activities (e.g., American football)

were deleted from the list of the PA since they were uncoimnon in Hong Kong in

comparison with other activities. In return, several activities were added into the list

(e.g.，table tennis, hockey, athletics and wu shu) because these activities were more

common. As a result, the form consisted of a list of 26 physical activities and space for

listing up to five other activities. Secondly, the section for reporting television/video

viewing and video/computer game playing was deleted because that was not the aim

of this study.

The second part inquired about the subjects' ATPA. The revised CATPA

inventory was adapted from Schutz et al. (1985). Both questioimaires were translated

into Chinese and checked for accuracy and clarity through back translation by a

professional translator.

3.3 Scoring

3.3,1 The modified SAPAC

In the modified SAPAC, the subjects reported the minutes they spent in each

activity during three time periods of the previous day: before, during and after school

(Sallis et al” 1996). Four types of data were collected on this checklist: “(a) number of

activities，(b) minutes in physical activities that the subjects participated, (c) overall

volume of PA，and (d) weighted intensity of PA” (Fung, 1997，p.40).

47

The subjects were instructed to report engagement in an activity only i f they

spent 5 minutes or more “at one time". The total minutes of PA were the second

summary measure. The overall volume of PA was computed with MET values taken

from a published compendium (Ainsworth et al” 1993). “MET is a multiple of resting

metabolic rate, so MET values reflect the energy cost of activities" (Sallis et al.，1996，

p. 843).

A given child could participate in an activity vigorously or with a minimum of

exertion. The child's rating of intensity, based on symptoms of exertion (i.e., breath

hard or feel tired), might reflect individual differences in intensity. In this study,

different weighting rules were applied to activities of different intensities in order to

obtain weighted activity MET scores. "If an activity was light to moderate, heavy

breathing was not expected, so activities in the 1.1-5.9 MET range were multiplied by

1.1 if they reported being out of breath "some" of the time and by 1.25 if they reported

being out of breath “most，，of the time. For activities that were hard (i.e., 6 METs or

higher, based on the categorization of Jacobs, Ainsworth, Hartman, and Leon (1993))，

heavy breathing was expected at least some of the time. If they reported being out of

breath “most，，of the time, the MET score was multiplied by 1.25. However, if they

reported being out of breath “none，，of the time，the activity was less vigorous than

expected, and the MET value was multiplied by 0.75" (Sallis et al., 1996，p.843). A

48

METs scoring table is shown in Appendix 4.

In summary, the self-reported data were represented by the following four

variables: “(a) number of activities reported; (b) minutes of moderate to vigorous PA

(min of MVPA); (c) PA MET score (MVPA METs) (min of activity x MET value); (d)

weighted activity MET score (weighted MVPA METs) (min of activity x MET value

x intensity rating)" (Sallis et al•，1996，p.843).

3.3.2 The revised CATPA inventory

The revised CATPA inventory developed by Schutz et al. (1985) was based on

Kenyon's (1968a) multidimensional model of PA. The inventory consists of 8

subdomains: social growth, social continuation, health and fitness for value, health

and fitness for enjoyment, vertigo, catharsis, aesthetic, and ascetic (Schutz et al”

1985). Subjects are required to rate these subdomains of PA on a 5-point semantic

differential scale with five bipolar adjectives. They place a tick in the space that best

reflects the expressed attitude toward each pair. Each subdomain “is scored using a

5-point scale，with 5 always being associated with the positive adjective and 1 with

the negative adjective of the word pair" (Schutz et al” 1985，p.263). The word pairs 1，

4，and 5 were put in reverse order in comparison to word pairs 2 and 3.

The scores from 5 word pairs were added to yield a total score out of 25 for all

subdomains except health and fitness. "This subdomain is scored as health and fitness:

49

value (with a maximum score of 10，based on the word pairs good-bad and of no

use-useM), and as health and fitness: enjoyment (with a maximum score of 15, based

on the remaining three word pairs，not pleasant-pleasant, nice-awfiil, happy-sad). For

intersubdomains comparisons，these two health and fitness components should be

rescaled to a value out of 25 by multiplying the scores by 2.5 and 1.67” (Schutz et al.，

1985，p.263).

3.4 Dependent variables

The min of MVPA, MVPA METs and weighted MVPA METs were selected as

the criterion (dependent) variable to be measured.

3.5 Independent variables

The eight subdomains of CATPA acted as predictor (independent) variables.

Moreover, sex and grade level were also chosen as independent variables during the

prediction.

3.6 Procedures

Selected schools were contacted by letter to gain initial approval from the

principal (Appendix 5) and the students' parents for student participation in the study

(Appendix 6). After the initial contact, their physical education teachers were

contacted to make arrangements for the administration of the questioimaires. A

briefing of the administration procedures of the questionnaires and an instruction sheet

50

(Appendix 7) were given to the teacher concerned beforehand. Each school selected

two classes in P6 or S3 to take part in the study.

During the physical education classes, children who participated in the study

completed the questionnaires in their classrooms. Before the subjects had started Part

one, the teacher read aloud the administration instructions to the whole class

(Appendix 7). Also，the children were given a brief introductory presentation (about 5

minutes) to remind them the definition of PA and to think of all the physical activities

they did yesterday. They reported the starting and ending of the previous school day，

along with the amount of time spent in physical education class and/or recess. To

provide some guidelines for estimating time, students were asked to name events that

were longer than 5 minutes (e.g., having recess) and shorter than 5 minutes (e.g.,

watching TV commercials) by using open-ended questions (Fung, 1997). Then they

were asked to report the time they were "actually" active and excluding the time for

rest or waiting to play.

After the subjects had completed Part one，the teacher read aloud the

administration instructions and a sample page of the inventory was shown at the

overhead projector to demonstrate how to fill in the inventory in Part two. The teacher

also read the items in Part two so that the subjects did not solely rely on their own

reading skills to interpret the ideas. During the lesson, the teacher walked around the

room to check students' responses and provided further explanations when needed.

3.7 Data analysis

The completed questionnaires were checked, numbered, and coded and the data

were entered into computer files. A l l data were analyzed with the Statistical Packages

for the Social Sciences (SPSS) version 8.0. Descriptive data about the CATPA and

their PA levels were shown through calculation of means and standard deviations.

MANOVA was utilized to examine whether there was any significant difference in the

eight subdomains of CATPA and the PA levels between boys and girls，P6 and S3

students, respectively. Multiple regression analyses were performed in order to

examine the contribution of eight subdomains of CATPA for predicting PA levels. The

predictive power of CATPA was also compared between boys and girls, P6 and S3

students, respectively.

52

CHAPTER 4

RESULTS

The purpose of this study was to examine the predictive power of the ATPA of

primary and secondary students to their PA levels. In addition, it was a purpose of the

study to determine whether there are any gender and/or grade differences in predicting

PA levels by the CATPA.

This chapter was divided into three sections. The subjects' ATPA data were

presented in the first section, the second section showed the subjects5 PA levels, and

the third section was devoted to the analysis of multiple regression designed to answer

the research questions related to predicting PA levels.

4.1 The students' ATPA

For the revised CATPA inventory, possible scores for each subdomain ranged

from the lowest score of 5 to the highest score of 25. Table 1 summarizes the CATPA

scores by gender and grade.

53

Table 1. The mean and standard deviation scores of CATPA by gender and grade.

Subdomains Boys Girls Primaiy 6 Secondary 3

Mean SD Mean SD Mean SD Mean SD

Social Growth 19.97 3.30 19.50 3.61 20.33* 3.40 18.27 3.18

Social Continuation 19.75 3.53 19.34 3.57 20.07* 3.59 18.28 3.11

Health and Fitness: 22.47 3.86 21.89 3.73 22.29 3.76 21.90 3.89

Value

Health and Fitness: 20.00 4.53 19.42 4.01 20.21* 4.46 18.47 3.51

Enjoyment

Vertigo 16.38 4.62 16.46 4.02 16.52 4.70 16.18 3.23

Aesthetic 17.25 4.77 18.09 4.33 18.09* 4.94 16.63 3.31

Catharsis 19.16 4.66 18.33 4.23 19.21* 4.58 17.60 3.96

Ascetic 16.47 5.02 15.98 4.24 16.35 4.85 15.91 4.09

*p<0.05

The mean scores for eight subdomains were above the neutral score of 15 points.

The health and fitness: value subdomain had the highest mean score for boys and girls.

The ascetic subdomain had the lowest mean score for boys and girls.

Like the CATPA scores for boys and girls, the mean scores for eight subdomains

were also above the neutral score of 15 points for P6 and S3 students. The health and

54

fitness: value subdomain had the highest mean score for P6 and S3 students. The

ascetic subdomain had the lowest mean score for P6 and S3 students.

4.1.1 Gender differences

MANOVA was used to test the mean difference between the levels of the two

independent variables (gender and grade) for the eight dependent variables (the eight

subdomains of CATPA). The results showed that there was no significant difference

between boys and girls overall (Wilks Lambda = .986, F (8，289) = .500，p > .05).

4.1.2 Grade level differences

MANOVA was used to test the mean difference between the levels of the two

independent variables (gender and grade) for the eight dependent variables (the eight

subdomains of CATPA). The results showed that there was significant overall

difference between P6 and S3 students (Wilks Lambda = .897，F (8，289) — 4.131, p

< .05). Among the eight subdomains, the social growth, social continuation, health

and fitness: enjoyment, aesthetic, and catharsis subdomains scores in P6 were

significantly higher than that in S3 (Table 1). MANOVA was also used to examine the

gender and grade interaction, and a significant interaction effect was found (Wilks

Lambda = .933，F (8，289) = 2.584，/?〈.05). Both the health and fitness: value and

aesthetic subdomains scores in P6 and S3 indicated significant interactions (p<.05).

55

4.2 The students' PA levels

The SAPAC was a one-day recall questionnaire which was used to assess the

three dimensions of PA (type, intensity and duration). The subjects' participation

profile in PA is summarized in Table 2.

Table 2. The mean and standard deviation scores of SAPAC by gender and grade.

Variables Boys Girls Primary 6 Secondary 3

Mean SD Mean SD Mean SD Mean SD

No. o f Activities 1.79 1.61 1.84 1.76 1.97* 1.79 1.44 1.34

M i n o f M V P A 67.45* 77.91 57.83 70.07 62.03 69.17 64.02 85.39

M V P A METs 378.11* 497.58 259.89 309.76 298.81 348.55 367.07 550.27

Weighted M V P A METs 392.28* 531.49 277,32 332.28 312.85 365.79 387.21 597.02

*p<0.05

On average, there was no significant difference between the boys and girls in the

number of activities that they participated.

4.2.1 Gender differences

MANOVA was utilized to test the mean difference between the levels of the two

independent variables (gender and grade) for the four dependent variables (number of

activities, min of MVPA, MVPA METs, and weighted MVPA METs). The results

56

showed that there was a significant difference between boys and girls (Wilks Lambda

=.893，F (4，293) = 8.751, p<.05). The min of MVPA, MVPA METs, and weighted

MVPA METs for boys were significantly higher than for girls.

4.2.2 Grade level differences

MANOVA was also used for testing the mean difference between the levels of

the two independent variables (gender and grade) for the four dependent variables

(number of activities, min of MVPA, MVPA METs, and weighted MVPA METs). The

results showed that there was significant difference between P6 and S3 students

(Wilks Lambda = .911，F (4，293) = 7.121,/><.05). The number of activities that P6

students participated in on the previous day were significantly higher than S3 students.

When examining the gender and grade interaction, MANOVA indicated a significant

interaction effect (Wilks Lambda = .950，F (4, 293) = 3.864,尸〈.05). A l l four

dependent variables (number of activities, min of MVPA, MVPA METs, and weighted

MVPA METs) in P6 and S3 indicated significant interactions of gender and grade

level (/?<.05).

4.3 Predicting PA levels by CATPA

Multiple regression analyses were performed to determine the contribution of

attitude components to the prediction of the PA levels. The analysis was computed by

using the min of MVPA, MVPA METs, and the total weighted MVPA METs as the

57

dependent variables and the eight subdomains of CATPA as the independent variables.

Results are shown in Table 3.

Table 3. Prediction of min of MVPA, MVPA METs, and weighted MVPA METs by

the eight subdomains of CATPA.

Variables D f F Sig. R R 2

Min of MVPA 8，291 3.072 .002* .279 .078

MVPA METs 8，291 2.981 .003* .275 .076

Weighted MVPA METs 8，291 2.679 .007* .262 .069

*p<0.05

The linear combination of CATPA measures was significantly related to the min

of MVPA and MVPA METs. The sample multiple correlation coefficient indicated that

approximately 8% of the variance of the min of MVPA and MVPA METs in the

sample could be accounted for by the linear combination of CATPA measures. The

linear combination of CATPA measures was also significantly related to the weighted

MVPA METs. The sample multiple correlation coefficient indicated that

approximately 7% of the variance of the weighted MVPA METs in the sample could

be accounted for by the linear combination of CATPA measures.

The eight subdomains were analyzed by stepwise multiple regression. In

58

predicting the min of MVPA, the MVPA METs and the weighted MVPA METs, the

social growth and vertigo subdomains had the strongest influence. Overall, only these

two subdomains were below the .05 significant level (see Table 4).

Table 4. Multiple regression of min of MVPA, MVPA METs, and weighted MVPA

METs by the eight subdomains of CATPA.

Subdomains Min of MVPA MVPA METs Weighted MVPA

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Social Growth .211 2.687 .008* .168 2.130 .034* .163 2.064 .040*

Vertigo .156 2.426 .016* .167 2.593 .010* .156 2.415 .016*

*p<0.05

4.3.1 Gender differences in prediction

For boys, the linear combination of CATPA measures was significantly related to

the min of MVPA and MVPA METs. The sample multiple correlation coefficient

indicated that approximately 14% and 13% of the variance of the min of MVPA and

the MVPA METs in the sample could be accounted for by the linear combination of

CATPA measures, respectively. The linear combination of CATPA measures was also

significantly related to the weighted MVPA METs. The sample multiple correlation

59

coefficient indicated that approximately 12% of the variance of the weighted MVPA

METs in the sample could be accounted for by the linear combination of CATPA

measures. However，the linear combination of CATPA measures was not significantly

related to the min of MVPA, the MVPA METs, and the weighted MVPA METs for the

girls. Table 5 shows the results for boys and girls.

Table 5. Prediction of min of MVPA, MVPA METs, and weighted MVPA METs by

the eight subdomains of CATPA for boys and girls.

Boys Girls

Variables df F Sig. R R2 df F Sig. R R2

Min of MVPA 8，140 2.788 .007* .371 .137 8，142 1.270 .264 .258 .067

MVPA METs 8, 140 2.630 .010* .361 .131 8, 142 1.373 .213 .268 ,072

Weighted MVPA METs 8,140 2.367 .020* .345 .119 8,142 1,321 .238 .263 .069

*p<0.05

The eight subdomains were analyzed by multiple regression. In predicting the

min of MVPA for boys，the social growth and vertigo subdomains had the strongest

influence. For the MVPA METs, the vertigo subdomain for boys also had the strongest

influence. In predicting min of MVPA and the weighted MVPA METs for girls, the

60

vertigo subdomain had the strongest influence. Overall，all of the subdomains

mentioned above were below the .05 significant level (see Table 6).

Table 6. Multiple regression of min of MVPA, MVPA METs, and weighted MVPA

METs by the eight subdomains of CATPA for boys and girls.

Subdomains Min of MVPA MVPA METs Weighted MVPA

METs

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Boys

Social Growth .233 2.247 .026* .164 1.578 .117 .151 1.442 .151

Vertigo .182 2.072 .040* .185 2.103 .037* .171 1.928 .056

Girls

Vertigo .166 1.650 .101 .214 2.133 .035* .205 2.040 .043*

*p<0.05

4.3.2 Grade level differences in prediction

For P6 students, the linear combination of CATPA measures was significantly

related to the min of MVPA and the MVPA METs. The sample multiple correlation

coefficient indicated that approximately 10% and 11% of the variance of the min of

MVPA and the MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures, respectively. The linear combination of CATPA

61

measures was also significantly related to the weighted MVPA METs. The sample

multiple correlation coefficient indicated that approximately 11% of the variance of

the weighted MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures (Table 7).

Table 7. Prediction of min of MVPA, MVPA METs, and weighted MVPA METs by

the eight subdomains of CATPA for P6 and S3 students.

Primary 6 Secondary 3

Variables df F Sig. R R2 df F Sig. R R2

Min of MVPA 8，204 2.836 .005* .316 .100 8，78 1.937 .066 .407 .166

MVPA METs 8，204 3.103 .003* .329 .108 8,78 2.077 .048* .419 .176

Weighted MVPA METs 8,204 2.984 .003* .324 .105 8，78 2.003 .057 .413 .170

*p<0.05

However, the linear combination of CATPA measure was not significantly

related to the three dependent variables except the MVPA METs for S3 students. The

linear combination of CATPA measures was significantly related to the MVPA METs.

The sample multiple correlation coefficient indicated that approximately 18% of the

variance of the MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures (Table 7).

62

The eight subdomains were analyzed by multiple regression. In predicting the

min of MVPA, the MVPA METs and the weighted MVPA METs for P6 students, the

social growth and vertigo subdomains had the strongest influence. In predicting min

of MVPA and the weighted MVPA METs for S3 students，the catharsis subdomain had

the strongest influence. Overall, all of the subdomains mentioned above were below

the .05 significant level (see Table 8).

Table 8. Multiple regression of min of MVPA，MVPA METs, and weighted MVPA

METs by the eight subdomains of CATPA for P6 and S3 students.

Subdomains Min of MVPA MVPA METs Weighted MVPA METs

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Primary 6

Social Growth .260 2.869 .005* .265 2.938 .004* .271 3.003 .003*

Vertigo .219 2.866 .005* .250 3.283 •OOP .234 3.068 .002*

Secondary 3

Catharsis •257 1.717 .090 .326 2.191 •031* .335 2.243 .028*

*p<0.05

4.3.3 Prediction for P6 boys and girls

When dividing the subjects by grade and sex, multiple regression analyses were

63

performed again to determine the contribution of attitudes component to predict PA

levels. For P6 boys，the linear combination of CATPA measures was significantly

related to the min of MVPA and the MVPA METs. The sample multiple correlation

coefficient indicated that approximately 19% and 18% of the variance of the min of

MVPA and the MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures, respectively. The linear combination of CATPA

measures was also significantly related to the weighted MVPA METs. The sample

multiple correlation coefficient indicated that approximately 17% of the variance of

the weighted MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures. However, the linear combination of CATPA

measures was not significantly related to the min of MVPA, the MVPA METs, and the

weighted MVPA METs for the P6 girls (Table 9).

Table 9. Prediction of min of MVPA, MVPA METs, and weighted MVPA METs by

the eight subdomains of CATPA for P6 boys and girls.

Boys (Primary 6) Girls (Primary 6)

Variables df F Sig. R R2 df F Sig, R R2

Min of MVPA 8,95 2.705 .010* .431 .186 8, 100 1.007 .436 .273 .075

MVPA METS 8，95 2.535 .015* .419 .176 8, 100 1.113 .361 .286 .082

Weighted MVPA METS 8，95 2.483 .017* .416 .173 8，100 1.058 .399 .279 .078

*p<0.05

The eight subdomains were analyzed by multiple regression. In predicting the

min of MVPA for P6 boys，the social growth, social continuation, and vertigo

subdomains had the strongest influence. For the MVPA METs, the social growth

subdomain for boys had the strongest influence. For the weighted MVPA METs, the

social growth and vertigo subdomains for boys had the strongest influence. In

predicting min of MVPA and the weighted MYPA METs for P6 girls, the vertigo

subdomain had the strongest influence. Overall, all of the subdomains mentioned

above were below the .05 significant level (see Table 10).

Table 10. Multiple regression of min of MVPA，MVPA METs, and weighted MVPA

METs by the eight subdomains of CATPA for P6 boys and girls.

Subdomains Min of MVPA MVPA METs Weighted MVPA METs

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Boys

Social Growth .280 2.398 .018* .270 .304 .023* .271 2.307 .023*

Social Continuation -.246 -2.172 .032* -.128 -1.126 .263 -.123 -1.079 .283

Vertigo .265 2.528 .013* .287 2.722 .008 .266 2.523 .031*

Girls

Vertigo .221 1.811 .073 .262 2.152 .034* .252 2.068 .041*

^<0.05

65

4.3.4 Prediction for S3 boys and girls

For S3 boys, the linear combination of CATPA measures was significantly

related to the min of MVPA and the MVPA METs. The sample multiple correlation

coefficient indicated that approximately 40% and 41% of the variance of the min of

MVPA and the MVPA METs in the sample could be accounted for by the linear

combination of CATPA measures，respectively. The linear combination of CATPA

measures was also significantly related to the weighted MVPA METs. The sample

multiple correlation coefficient indicated that approximately 39% of the variance of

the weighted MVPA METs in the sample could be accounted for by the linear

combmation of CATPA measures. However, the linear combmation of CATPA

measures was not significantly related to the min of MVPA, the MVPA METs, and the

weighted MVPA METs for S3 girls (Table 11).

66

Table 11. Prediction of min. of MVPA, MVPA METs, and weighted MVPA METs by

the eight subdomains of CATPA for S3 boys and girls.

Boys (Secondary 3) Girls (Secondary 3)

Variables df F Sig. R R2 df F Sig. R

Min of MVPA 8,36 2.973 .012* .631 .398 8，33 .799 .607 .403 .162

MVPA METs 8,36 3.095 .009* .638 .408 8，33 .732 .663 .388 .151

Weighted MVPA METs 8,36 2.878 .014* .625 .390 8，33 .739 .657 .390 .152

*p<0.05

The eight subdomains were analyzed by multiple regression. In predicting the

min of MVPA for S3 boys, the social growth, health and fitness: value, and catharsis

subdomains had the strongest influence. For the MVPA METs and the weighted

MVPA METs, the health and fitness: value, and catharsis subdomain for boys had the

strongest influence. Overall，all of the subdomains mentioned above were below

the .05 significant level (see Table 12).

67

Table 12. Multiple regression of min of MVPA, MVPA METs, and weighted MVPA

METs by the eight subdomains of CATPA for S3 boys and girls.

Subdomains Min of MVPA MVPA METs Weighted MVPA

METs

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Boys

Social Growth .430 2.273 .029* .342 1.826 .076 .320 1.684 .101

Health and -.379 -2.272 .029* -.413 -2.495 .017* -.423 -2.52 .016*

Fitness: value

Catharsis .514 2.650 .012* .573 2.976 .005* .570 2.919 .006*

*p<0.05

4.4 Summary

In this study, there was no significant difference between the boys and girls with

respect to their ATPA but the ATPA scores of P6 students were significantly higher

than those of S3 students. Moreover，the PA levels for boys and P6 students were

significantly higher than the girls and S3 students, respectively.

The results also showed that the Hong Kong school students' PA levels were

significantly predicted by the eight subdomains of CATPA and therefore the first

hypothesis was not rejected. The social growth and vertigo subdomains had the

68

strongest influence in predicting PA levels.

When examining the prediction of PA levels by gender, the predictive power of

CATPA to PA levels for boys was significantly stronger than that for girls and hence

the second hypothesis was not rejected. Both social growth and vertigo subdomains

had contributed significantly to the prediction of children's PA levels.

When examining the prediction of PA levels by grade level, the predictive power

of CATPA to PA levels was significant for P6 students but not for S3 students and

hence the third hypothesis was not rejected. Both social growth and vertigo

subdomains had contributed significantly to the prediction of P6 students' PA levels.

Catharsis subdomain had contributed significantly to the prediction of S3 students' PA

levels.

69

CHAPTERS

DISCUSSION

Explaining PA in all its complexity is a difficult task, as is explaining any aspect

of human behavior (Sallis & Owen，1999). The primary purpose of the present study

was to investigate to what extent the students' ATPA contributes to their PA levels.

5.1 The local students5 ATPA

An examination of the CATPA mean scores for eight subdomains revealed that

both boys and girls reported positive ATPA, as reflected by scores generally above the

middle of the range of possible scores. Similar findings were obtained by Tsang and

Chan (1993)，Chung and Leung (1998), and Hagger et al. (1997). Data obtained in all

these studies were encouraging to current physical educators since favorable attitudes

were believed to play an important role in motivating students to participate in PA.

The result of this study found no significant difference between boys and girls in

their ATPA. This finding also matched the results reported by Keogh (1962)，Johnson

and Pargman (1987)，and Hagger et al. (1997), but was contrary to the results reported

by Alderman (1970)，Birtwistle and Brodie (1991)，Chung and Leung (1998),

Patterson and Faucette (1990a)，and Speak and Lindner (1996). However，it was

difficult to make comparisons between this study and the previous ones because these

studies used different inventories for measuring ATPA.

70

The absence of significant gender difference in CATPA might be attributed to

similar beliefs of physical activities. Attitudes are formed through beliefs (Silverman

& Subramaniam, 1999). An individual's belief is developed from his/her past

experiences and the belief developed will further lead to the development of attitudes

(Fung, 1997). Johns and Ha (1999) reported that the physical and social environment

factors in Hong Kong influence the extent and level of children's PA. Hong Kong

children have limited assess to high quality sport facilities and physical activities

outside physical education lessons (Fu, 1994; Fung, 1997). As a result, similar

attitudes were formed through their similar experiences in physical activities.

Nowadays, most of the physical activities are becoming gender-integrated in

order to provide equal opportunities for both sexes. Also，there has been a great

increase in organized programs for girls. These might contribute to the finding of

similarities between boys and girls with regard to participation attitudes. Sivan and

Roberston (1996) also found that there were some similarities in both the content and

the context of sport activities between males and females. The most popular sport

activities to both sexes were badminton, cycling, basketball and swimming.

On the other hand, there were significant differences in ATPA scores for S3 and

P6 students on the social growth，social continuation, health and fitness: enjoyment,

aesthetic, and catharsis subdomains. P6 students had a significantly higher ATPA

71

scores that corroborated findings of other studies (e.g., Chung & Leung, 1998; Fu，

1993，1994; Haladyna & Thomas, 1979; Schempp，ChefFers, & Zaichkowsky, 1983).

The significant differences in ATPA between P6 and S3 students could be explained

by the increasing emphasis on academic and career success in Hong Kong (Lindner,

1999). The S3 students might be more willing to use their time for study or other

sub-culture activities rather than for participating in PA.

As Fu (1994) pointed out, "children have an innate desire to move freely and

will take advantage of any free time in school to play" (p.73). However, “with the

onset of puberty, children are becoming more self-conscious and vulnerable to peer

pressure, especially in front of the opposite sex" (p.73-74). Therefore, the ATPA of

secondary school students was still favorable but not as much as their younger

counterparts.

5.2 The local students' PA levels

The results from MANOVA also showed that there were significant differences

between boys and girls in min of MVPA, MVPA METs, and the weighted MVPA

METs. Boys had a significantly higher duration and intensity of physical activities

than girls. This finding concurred with the findings reported by Armstrong (1989)，

Armstrong et al. (1990)，Armstrong, Balding, Gentle, Williams and Kirby (1990) and

Lindner (1998). It must be noted for the min of MVPA that boys and girls reported in

72

this study were less than the amounts reported in Sallis et al. (1996) study. The big

difference was due to different definitions of PA. In Sallis et al. (1996) study, physical

activities which were done in the physical education lessons were included but they

were excluded in this study. Thus a lower min of MVPA for the Hong Kong children

was expected.

For the MVPA METs and the weighted MVPA METs, the value reported for

boys and girls were also much lower than the value reported by Sallis et al. (1996).

This indicated that the local students spent lesser amounts of energy in performing

physical activities. The result of this study was supported by Macfarlane (1997) and

Wong (1997)，who found that Hong Kong children possessed the lowest levels of

habitual PA in comparison with other countries. McManus and Armstrong (1996) and

Chui (1997) also reported that very few children in Hong Kong experienced those

levels of PA associated with the promotion of aerobic fitness，especially the girls in

primary school.

A significant difference in number of activities between P6 and S3 students was

also found in this study. P6 students had a significantly higher in number of activities

they participated than S3 students. However, the number of activities that reported for

P6 and S3 students were much lower than the study by Sallis et al. (1996).

Indeed, Hong Kong children had a smaller variety of physical activities to

73

choose in comparison with the children in the United States. This might be attributed

to the lack of open space or sport facilities and resources in Hong Kong. In addition,

the children in Hong Kong would like to participate in a few activities which are

promoted or advocated by mass media as well as their physical educators. This

proposition deserved further research attention.

5.3 The prediction of PA levels by CATPA

The results from multiple linear regression analysis supported the basic tenets of

Ajzen's (1988) theoretical approaches to the prediction of behavior. That is，in

accordance with Ajzen's prediction, an attitude towards the target behavior

(participation in PA) was a significant predictor in the TPB. In this study, the PA levels

of Hong Kong P6 and S3 students could be significantly predicted by the eight

subdomains of CATPA. Therefore, the first hypothesis was supported.

However, the contribution of the 8 subdomains of CATPA to the prediction of

children's PA levels was not strong. The 8 subdomains could only account for

approximately 7-8% of the variance of children's PA levels which was expressed in

terms of min of MVPA, MVPA METs, and weighted MVPA METs. In fact，it was a

rather weak predictor because the remaining 92-93% of the variance in PA

participation is still unknown. The direct measures of subjective norm and PBC were

not included in this study, the TPB was not fully operationalized in accordance with

74

the recommendations of Ajzen (1988，1991). This study showed that attitude was a

weak but significant predictor in the TPB.

There were three factors that could explain the weakness. Firstly, this study

measured CATPA and PA levels by self-report questioimaire. The social desirability

bias could influence the subjects to over-report the PA levels. Secondly，as Ajzen

(1991) stated, the TPB is open to further elaboration if further important proximal

determinants were identified. Some researchers had proposed that adding exogenous

variables to the TPB would increase its predictive ability. For example, researchers

(Chamg, Piliavin, & Callero，1988; Sparks & Shephard，1992) introduced the concept

of role identity as a mediating factor in attitude-behavior relationship. Another

variable that had been shown to enhance the attitude-behavior relationship was

attitude strength (Liska，1984; Raden, 1985). Theodorakis (1994) and Theodorakis,

Bagiatis, and Goudas (1995) noted that the variables of role identity and attitude

strength increased the predictive ability of the TPB. Thirdly，the other two variables in

the TPB, as well as facilitating factors (accessibility，availability of resources and

environmental) (Godin & Shephard, 1990) which might have influence upon the Hong

Kong children were not assessed in this study. Therefore future study is needed to

examine the predictive ability of the TPB fully.

Ajzen (1988) indicated that variations in the relative contributions of the three

75

components in the TPB could be influenced by demographic variables, such as gender.

The results of this study showed that the predictive power of CATPA to PA levels was

significant for boys but not for girls. The second hypothesis in this study was also

supported by the data.

Similar were the results from a study by Biddle et al. (1994) who reported

significant gender differences in the predictor variables for the actual and intended PA.

In their study, subjects were fiill time employees in a university campus. They found

that 11% and 2% of the variance in strenuous PA was explained for men and women,

respectively. Moreover，attitude significantly predicted strenuous PA for men but not

for women. Mummery et al. (2000) also reported that attitude was the strongest

predictor of activity intention among boys, while PBC was the largest contributor to

predict intention for girls. However, the present findings were inconsistent with some

previous researchers (e.g., Bozionelos & Bennett, 1999; Van Ryn, et al., 1996; Wankel,

et al., 1994) who found no significant gender differences in any of the predictor

variables of TPB.

The results of this study which were consistent with previous findings

(Mummery et al., 2000) also showed that the predictive power of CATPA to PA levels

for P6 students was stronger than for the S3 students. In other words, the third

hypothesis in this study was also supported.

76

Mummery et al. (2000) investigated the efficacy of the TPB in predicting PA

intentions among Canadian children (grade 3，5，8，and 11). Although they examined

the intention rather than behavior, they found that the contribution of attitude to

predicting PA intention for grade 5 subjects was significantly greater than for grade 8

and 11. Wankel and Mummery (1993) also examined the relative importance to

behavioral intention of the three direct predictors of the TPB for the various

population groups. They pointed out that "perceived behavioral control increased in

importance relative to the other two predictors across age while attitude decreased in

relative importance. Subjective norm/social support although generally the weakest

surpassed attitude as a predictor of intention in the oldest age group" (p.173). The

present findings did not match the results reported by Craig et al. (1996)，who found

that the predictive power of CATPA to PA level for grade 8 students (r = Al) was

slightly stronger than for grade 5 students (r = .43).

The beta weights for social growth and vertigo were significant (p<.05) in this

study. These two subdomains contributed significantly to the prediction of the

children's PA levels. Both of the subdomains were positively related to the min of

MVPA, MVPA METs，and the weighted MVPA METs. This means that Hong Kong

school children who want to meet new people and to seek excitement tend to have a

higher degree of participation in PA.

77

When dividing the subjects by grade and sex，the predictive power of CATPA to

PA levels for boys in S3 was significantly stronger than the boys in P6. CATPA was a

large contributor to the prediction of PA levels for boys in S3 in comparison with boys

in P6. The results contradicted the findings of Mummery et al. (2000), who indicated

that attitude was the largest contributor to predicting intention for boys in grade 5

compared with grade 11. In the present study, the social growth, social continuation,

and vertigo were three subdomains which contributed significantly to the P6 boys’ PA

levels. Meanwhile，the social growth, health and fitness: value, and catharsis

subdomains contributed significantly to the S3 boys' PA levels.

The present results revealed that experience of social exchange, excitement and

personal challenge were important contributors to the prediction of children's PA

levels. Griffin (1998) suggested that sport was a primary context for social

development, especially for many youngsters. Young people “think of sport

participation as the best way to achieve popularity" (Griffin，1998, p.28). Researchers

also reported that peers played an important role in determining adotescenf s PA levels

(Anderssen & Wold, 1992; Brennan & Blealdey, 1997; Greenockle et al., 1990).

Indeed，children liked challenges and they were drawn to anything that provided fun

and excitement (Griffin，1998). For some young people, "there is little else other than

sports that offers the possibility of these kind of experiences" (Griffin, 1998，p.34).

78

The catharsis subdomain was also one of the significant predictors for S3 boys' PA

levels. Nowadays, the school children still face greater stress from the examination

and their academic performance. Participation in PA might be a good mean to release

tension.

From a theoretical perspective, the results of this study provided support for the

TPB in that attitude was a weak, but significant predictors for the exercise behavior.

According to the TPB, the direct determinant of behavior was the intention or PBC

(when actual control over the behavior is low). However, the measures of subjective

norm and PBC were not included in this study. Therefore a whole picture for the

degree of contribution of three variables in the TPB in the prediction of PA behavior

was not provided. What is needed is a long-term study using within-subjects

longitudinal designs to find out the factors which contribute to the local students' PA

levels.

From a practical perspective, although ATPA was found to relate weakly to PA

behavior, teachers and parents should not discount the importance of positive ATPA. It

was because "attitude influences whether we begin or continue with certain

activities - and whether we achieve in certain areas" (Silverman & Subramaniam,

1999，p.97). However, further investigations are needed to deterniine whether

subjective norm (the social pressure on the subject to perform or not to perform the

79

behavior) and PBC (the perception of how easy or difficult to perform a behavior) in

the TPB play key roles in predicting the children's PA levels or not. If it is true, the

professionals in the field thus seem to provide 狂 conducive situation or environment

which emphasizes these two aspects in planning the programs and curriculum.

80

CHAPTER 6

CONCLUSIONS，IMPLICATIONS, AND RECOMMENDATIONS

6.1 Conclusions

The purpose of this study was to assess the power of CATPA to predict the PA

levels of Hong Kong primary and secondary school children under the established

theoretical framework of the TPB (Ajzen, 1985, 1988, 1991). Based on the data

collected as well as within the limitations of this study, the following conclusions were

1 • The school children in this study showed a favorable ATPA. No gender difference

was found in ATPA between boys and girls. The result could be attributed to the

similarity of experiences that they gained in PA. When dividing the subjects by

grade level attained, the P6 students' ATPA scores were significantly higher than

those of S3 students. The subjects also reported relative low PA levels in

comparison with the Western countries.

2. The results of this study showed that the eight subdomains of CATPA could

collectively significantly predict PA levels of Hong Kong school children.

However, only 7-8% of the variability in the measure of PA levels could be

explained by the eight subdomains of CATPA. Indeed，participation in PA is

determined or influenced by a variety of factors (Dishman & Dunn, 1988; Sallis

81

et al.，1992). No single determinant or category of deteiminants has been found

to be the sole explanation of young people's PA levels.

3. The predictive power of CATPA to PA levels for P6 students and for boys was

significantly higher than for S3 students and for girls, respectively. In addition,

the predictive power of CATPA for boys in S3 was significantly stranger than for

boys in P6. Generally, the social growth and vertigo were two subdomains which

contributed significantly to the prediction of school children's PA levels.

6.2 Implications

The present results revealed that the CATPA could significantly predict the PA

levels of the subjects but the eight subdomains of CATPA could account for only 7-8%

of the variance in the children's PA levels. The remaining 92-93% was still

unexplained. According to the TPB, subjective norm and PBC are two other predictor

constructs for the prediction of a person's intention to perform a behavior. These

variables might make a larger contribution to the prediction. Moreover, PA

participation in young people “is influenced by a great number of interacting

biological, psychological, social，and environmental factors" (Aimstrong & Welsman,

1997，p.257)，and those factors might be important for different groups at different

periods of time (Dishman & Dunn，1988; Sallis，et al.，1992). Some researchers have

proposed that adding exogenous variables to the TPB to improve its predictive ability

82

(Conner & Axmitage, 1998; Theodorakis, 1994).

The present results also indicated that the social growth and vertigo subdomains

played key roles in the prediction of PA levels in Hong Kong school children. It is

desirable to emphasize the social growth and vertigo subdomains of CATPA in

planning programs and curriculum for P6 and S3 students. Indeed, other significant

predictors associated with PA levels for children (such as parental influence,

accessibility and availability of resources) should not be ignored.

6.3 Recommendations

A longitudinal study (using within-subjects) of students' ATPA and their PA

levels should be conducted to determine i f there is any change in attitudes and PA

levels as students' progress from primary to secondary school. Further study is needed

to find out the factors which contribute greatly to PA participation of Hong Kong

school children, especially for girls and S3 students.

Moreover, more studies should be conducted to examine the full TPB to test its

predictive ability in sport and exercise settings in the future, especially in Hong Kong.

Based on the findings of the present study, it would seem reasonable that future

investigations with the population of school children should focus on further

development of this theory and examine how well it can predict and explain PA

behavior.

83

One must consider the relative small sample size of S3 students (213 primary in

comparison with and 87 secondary school children) that may have had an impact on

the results. Moreover, subjective self-report measures may be compromised by

inaccurate recall or response biases. It would be valuable to replicate these findings

while employing objective indices of PA behavior and distributing the samples

(primary and secondary students) more equally. To increase the validity and

generalizability, more reliable methods such as random sampling should be adopted.

Based on the findings of the present study, it must be emphasized that no causal

relationship between the students' ATPA and their PA levels are warranted. If this

causal relationship was found in the future study, it would seem that enhancement of

ATPA might be more effective in promoting PA in boys and P6 students than girls and

S3 students, respectively. The high contribution for the social growth and vertigo

subdomains to the prediction of school children's PA levels implies that providing a

social environment and exciting physical activities that reinforces PA may be a

particularly effective strategy to boys and P6 students but not girls and S3 students in

order to motivate them to participate in PA.

84

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113

APPENDICES

APPENDIX 1 Part one: Chinese version of the SAPAC

香港大學教育學院

體 育 及 運 動 稱 部

(“)體育活動自我評核問卷（中文修訂版）

目的：本問卷是調查你在昨天的體育活動量，體育活動是指各種類型的活動，包

括：運動比賽，正規練習及非正規的活動，例如捉人遊戲、足球、緩步跑

和踏單車等0

塡寫程序：

1 )本問卷不是一項測驗，因此並沒有對或錯的答案。

2)最重要的是細閱指示，並誠實及準確地回答各問題。

3 )本問卷之調查結果絕不會影響你各科的成績，而全部資料將於調查報告完成

後銷毀。

多謝各同學合作！

甲部

個 人 麵 ：

1 ) 性 別 ： 男 女

2)年齡（計至2000年12月31日）： 0 4

1 1

1 1 1 5

1 2 1 6

1 3

1 7

3 ) 就 讀 年 級 ： 小學六年級 中學三年級

- 般 難

1)昨天你有沒有上體育課？ 有 沒有

2)如果答有，那體育課的時間有多久？ 分鐘

3 ) 昨 天 ， 你 有 沒 撤 小 息 ？ ( *被罰或替老師做事’観答沒有）有沒有

4)如果答有，那麼小息的時間有多久？

(第—次小息） 及鐘（第二次小息）

114

活動資料

1 )昨天你有沒有參與下列各項體育活動？如有，請於B、！>、F三欄內塡上你

的活動時間（如少於5分鐘，貝!J不用塡寫）。

2)請於C、E、G三欄內註明該項活動有否引致你呼吸急速或感到疲倦？

如沒有，請塡‘N，，小部分時間有請塡‘S,，大部分時間有請塡‘M, o

A•活動類型 B.上學前 (分鐘)

C. N，S andM

D.在校內 (分鐘)

E. N，S andM

R放學後 (分鐘)

GN5S andM

1 踏轉（非競賽性） 2 游泳（非競賽性） 3 田徑：賽跑練習

4 田徑：田賽練習（如跳遠、推鉛球） 5 體操：例如單、雙槓、彈網、翻筋斗

6 體能活動：如掌上壓、仰臥起坐

7 籃球：半場或全場比賽 8 棒球/壘球 9 足球

10 排球 11 辟 球

12 羽毛球 13 網球

14 壁球

15 曲棍球 16 球類遊戲：如數字球、射龍門、射籃

17 遊戲：如互相追逐、捉迷藏

18 溜冰、滾軸溜冰

19 跳繩 20 跳舞 21 健身舞 22 武術、空手道、跆拳道

23 戶外家務：如園藝、洗車

24 室內家務：如拖地板、吸麈、打掃

25 步行：如回校或回家

26 跑步：非田徑練習

27 其他：例如體育課、校隊練習或以上 未提及的，請註明活動類型

28 29 30 31

115

如果你不明白方格內的意見，請在此方格內•塡上“〃，並繼續回答下一S。

APPENDIX 1 Part two: Chinese version of the revised CATPA inventory

(二）青少年對體育活動態度問卷調查

目的：本問卷旨在調查你對體育活動的態度及意見，體育活動是指各種類型的活

動，包括：運動比賽，正規練習及非正規的活動，例如捉人遊戲、足球、

緩步跑和踏單車等。

塡寫程序：

1 )本問卷不是一項測驗’因此並沒有對或錯的答案。

2)請閱讀每題方格內所提出的意見，然後在每組字詞中最適當的位置上加

上“一”，以表示你對那意見的感覺。

3)果你不明白方格內的意見，請在“•”內加上，並繼續回答下一題。

4)請細閱指示，並誠實及準確地回答各問題。

5 )本問卷之調查結果絕不會影響你各科的成績，而全部資料將於調查報告完成

後銷毀。

多謝各同學合作！

甲部

請在下列各題每組字詞中最適當的位置上加上〜”，以表示你對各題方格內的

1•你對下列方格內的意見有何感覺？

參與細動能提供給你認識細

緊記方格內的意見

:的

；2?

*2?

*2?

6W

-6W

用

決

厭

壞

有

愉

討

SJ

用

快

趣

樂

沒

愉

有

快

3 4

116

如果你不明白方格內的意見，請在此方格內•塡上“ 一 ”，並繼續回答下一題°

你對下列方格內的意見有何感覺-

髏育活動延續社交關係

參與艘育活動能提供給你與朋友一起相聚的機會 ；

1 , , - • 〖

緊記方格內的意見

n

r —

一

l̂ill

¾¾ n

沒用的

不愉快的

有趣的

快樂的

有用的

愉快的

討厭的

mxmm 如果你不明白方格內的意見，請在此方格內•塡上 並繼續回答下一題

3•你對下列方格內的意見有何感覺？

髖育活動贱《康與艘雄 1

n

II

—

丨

<

_

m灌

mmm :¾

j

.

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feo

0

•

•IIM

/

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w

J 你

1

！〜

令

Is*

勤

r> vr̂

.

.

.

. .

*̂N

i -^̂-

緊記方格內的意見

的

的

的

的

齋

用

K

厭

力

壞

有

愉

討

钯

好

的

的

的

的

用

快

趣

樂

沒

愉

有

快

2

4 5

117

有用的

愉快的

討厭的

使 人 赔 的

如果你不明白方格內的意見，請在此方格內•塡上，並繼續回答下一題。

5.你對下列方格內的意見有何感覺？

如果你不明白方格內的意見，請在此方格內•塡上…，並繼續回答下一題。

緊記方格內的意見

4.你對下列方格內的意見有何感覺

：育活動令人緊張刺激，但有些危險

【是危險的‘因為你要快速地移動身想

緊記方格內的意見

？

^

6M

6W

An

Ap

用

快

趣

樂

沒

愉

有

Ik

的

的

的

I

用

陝

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义

壞

有

愉

討

使

好

的

的

的

的

用

快

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樂

沒

愉

荀

使

118

沒用的

不愉快的

有趣的

快樂的

緊記方格內的意見

有用的

愉快的

討厭的

使 人 鮮 的

如果你不明白方格內的意見，請在此方格內•塡上“v ”，並繼續回答下一題。

你對下列方格內的意見有何感覺？

緊記方格內的意見

如果你不明白方格內的意見，請在此方格內•塡上 。

問卷完

6•你對下列方格內的意見有何感覺？ , ： - … ： ：.；；； ；；7： . . ： ： . ； ： ； ： ； . . . . . . . . ‘

1" , . ； , “

1! ! "

1!

1： "

| , _. . . . . .

1 二.:!.；.

1

十...，； •： .... •‘-..: • ： ： ： • . . . ； ： ； . ： … ；;• ; ； ' ； ； ：•： ' . . .""‘ ... ：“：厂；...•.,•-：„ , I-'./-'；.；'："： .... ....... ： v.i . . - . ： , ： . . . , . .： ... ... .. •' . : , . .... ......,:.1 v,;,' -r .

髏 育 活 動 可 消 除 緊 張 的 情 緒 隱糧層̂ ：：:

參 與 髏 育 活 動 可 減 低 壓 力 或 令 你 避 開 一 些 你 將 面 對 的 問 題

的

的

的

直

用

快

厭

人

壞

有

愉

討

使

好

的

的

的

的

用

快

趣

樂

沒

愉

有

快

119

APPENDIX 2 Part one: The SAPAC (modified version)

The University of Hong Kong Faculty of Education

Physical Education and Sports Science Unit

Part 1: The self-administered physical activity checklists (SAPAC) (Modified version)

Purpose: To measure your physical activity level on previous day. Physical activity

refers to all kinds of activities, including sports，formal exercises, and informal activities，such as tag, soccer, jogging and cycling.

Procedures: 1. This is not a test, so there are no right or wrong answers. 2. Please read the instructions and answer all the questions as honestly and

accurately as you can - this is very important. 3. The results of this questionnaire will absolutely not affect your grades in any

subjects, and will be totally deleted after the completion of the investigation.

Thank you for your cooperation!!

Section A Personal Particulars: Please circle your answer in the following questions.

1. Sex: Male Female 2. Age (up to 31-12-2000): 9 10 11 12 13 14 15 16 17 3. Grade: Primary 6 Secondary 3 4. Did you participate in physical education class yesterday? Yes No 5. If yes, how many minutes would the class last? Minutes 6. Did you participate in recess yesterday? Yes No 7. If yes, how many minutes of recess did you have?

minutes for first recess minutes for second recess

120

Section B

1. Did you participate in the physical activities listed below? If yes, write down the number of minutes you were actually performing the activities in column B，D and F. (Only if you did the activity for 5 minutes or more)

2. Please also state whether the activity caused you to breathe hard or feel tired. Put an "N" for none of the time; “S，，for some of the time and “M，，for most of the time.

A. Activity B. Before school (Mins)

C. N，S and M

D. During School (Mins)

E. N，S and M

R After school (Mins)

G N s S and M

1 Bicycling (non competition) 2 Swimming (non competition) 3 Athletics: running events 4 Athletics: field events 5 Gymnastics: bars，trampoline 6 Exercise: push-ups, sit-ups 7 Basketball (half or full court) 8 Baseball/Softball 9 Soccer

10 Volleyball 11 Table tennis 12 Badminton 13 Tennis 14 Squash 15 Hockey 16 Ball games: dodge ball, shooting basket 17 Games: chase, hide and catch 18 Skiing and Roller skating 19 Rope jumping 20 Dancing 21 Aerobic dancing 22 Wu shu, karate, tae kwan tao 23 Outdoor chores: gardening, car washing 24 Indoor chores: mopping, vacuuming,

sweeping 25 Walking: to home or to school 26 Running: non athletics training 27 Others: (physical activity classes, lessons,

or teams) 28 29 30 31

121

APPENDIX 2 Part two: The revised CATPA inventory

The University of Hong Kong Faculty of Education

Physical Education and Sports Science Unit

PART 2: The revised children's attitudes toward physical activity (CATPA) inventory

Purpose: To find out how you feel about physical activity. Physical activity refers to

all kinds of activities, including sports，formal exercises, and informal activities, such as tag, soccer，jogging and cycling.

Procedures: 1. This is not a test, so there are no right or wrong answers. 2. Read the idea in the box for each question and then put a “ v，，on each word pairs

to show how you feel about the idea. 3. If you do not understand the idea in the box put a ” v " in the do not understand

box and go to the next page.

4. Please answer all the questions as honestly and accurately as you can — this is very important.

5. The results of this questionnaire will absolutely not affect your grades in any subjects, and will be totally deleted after the completion of the investigation.

Thank you for your cooperation!!

Please put a “ v ” on each word pairs to show how von feel about the idea.

1) How do you feel about the idea in the box? PHYSICAL ACTIVITY FOR SOCIAL GROWTH

Taking part in physical activities which give you a chance to meet new people. Always think about the idea in the box.

If you do not understand this idea，mark this box and go to the next question.

good bad of no use useM

not pleasant pleasant nice awful

happy sad

122

2) How do you feel about the idea in the box?

PHYSICAL ACTIVITY TO CONTINUE SOCIAL RELATIONS Taking part in physical activities which give you a chance to be with your friends.

Always think about the idea in the box.

If you do not understand this idea, mark this box and go to the next question.

1. good bad 2. of no use useful 3. not pleasant pleasant 4. nice awM 5. happy sad

3) How do you feel about the idea in the box?

PHYSICAL ACTIVITY FOR HEALTH AND FITNESS Taking part in physical activities to make your health better and to get your body in

better condition.

Always think about the idea in the box.

If you do not understand this idea, mark this box and go to the next question.

1. good bad

2. of no use useful 3. not pleasant pleasant 4. nice awful

5. happy sad

4) How do you feel about the idea in the box?

PHYSICAL ACTIVITY AS A THRILL BUT INVOLVING SOME RISK Taking part in physical activities that could be dangerous because you move very fast

and must change direction quickly.

Always think about the idea in the box.

If you do not understand this idea, mark this box _ and go to the next question.

1 • good bad 2. of no use usefixl 3. not pleasant pleasant

4. nice a w M

5. happy s a d

123

5) How do you feel about the idea in the box?

PHY SIC A L ACTIVITY AS THE BEAUTY IN MOVEMENT Taking part in physical activities which have beautiful and graceful movements.

Always think about the idea in the box.

If you do not understand this idea，mark this box and go to the next question.

1. good bad 2. of no use useful 3. not pleasant pleasant 4. nice awful 5. happy sad

6) How do you feel about the idea in the box? PHYSICAL ACTIVITY FOR THE RELEASE OF TENSION

Taking part in physical activities to reduce stress or to get away from problems you might have.

Always think about the idea in the box.

If you do not understand this idea，mark this box and go to the next question.

1. good bad 2. of no use useM 3. not pleasant pleasant 4. nice awfol 5. happy sad

7) How do you feel about the idea in the box? PHYSICAL ACTIVITY AS LONG AND HARD TRAINING

Taking part in physical activities that have long and hard practices. To spend time in practice you need to give up other things you like to do.

Always think about the idea in the box.

If you do not understand this idea，mark this box |.

1 • good bad 2. of no use usefUl 3. not pleasant pleasant

4. nice a w f i l 1

5. happy s a d The End

124

APPENDIX 3 The SAPAC (original version)

Section A

Personal Particulars: Please circle your answer in the following questions.

1. Sex: Male Female

2. Age (up to 31-12-2000): 9 10 11 12 13 14 15 16 17

3. Grade: Primary 6 Secondary 3

4. Did you participate in physical education class yesterday? Yes No

5. If yes，how many minutes would the class last? Minutes

6. Did you participate in recess yesterday? Yes No

7. If yes，how many minutes of recess did you have?

Minutes for first recess minutes for second recess

125

Section B

1. Did you participate in the physical activities listed below? If yes, write down the number of minutes you were actually performing the activities in column B，D and R (Only if you did the activity for 5 minutes or more)

2. Please also state whether the activity caused you to breathe hard or feel tired. Put an “N” for none of the time; “S，，for some of the time and “M” for most of the time.

C.None,Some,Most E. None,Sonie,Most G Noiie,Some,Most A. Activity B. Before

school (Mins)

N，S and M

D. During School (Mins)

N，S and M

F. After school (Mins)

N, S and M

1 Bicycling 2 Swimming laps 3 Gymnastics:bars,beam,tumbling,trampoline 4 Exercise: push-ups, sit-ups jumping jacks 5 Basketball 6 Baseball/Softball 7 Football 8 Soccer 9 Volleyball

10 Racket sports: badminton, tennis 11 Ball playing: four square, dodge ball,

kickball 12 Games: chase, tag，hopscotch 13 Outdoor play: climbing trees, hide-and-seek 14 Water play: swimming pool, ocean, or lake 15 Jump rope 16 Dance 17 Outdoor: mowing, raking, gardening 18 Indoor chores; mopping, vacuuming,

sweeping 19 Mixed walking/ running 20 Walking 21 Running 22 Others: physical activity classes, lessons, or

teams 23 24 25

Before School After School

T.V.Mdeo H.l hours _ — hours

H.2 一 hours hours

Video Games & Computer Games...

H.3 hours —— hours

H.4 —hours — — hours

126

APPENDIX 4 METs scoring table A . Activity METs

Scores

Weighting Rules

1 Bicycling (non competition) 4.0 Some x 1.1 Most x 1.25 2 Swimming (non competition) 6.0 None x 0.75 Most x 1.25 3 Athletics: running events 8.0 None x 0.75 Most x 1.25 4 Athletics: field events 5.0 Some x 1.1 Most x 1.25 5 Gymnastics: bars, trampoline 4.0 Some x 1.1 Most x 1.25 6 Exercise: push-ups, sit-ups 4.5 Some x 1.1 Most x 1.25 7 Basketball (half or full court) 8.0 None x 0.75 Most x 1.25 8 Baseball/Softball 5.0 Some x 1.1 Most x 1.25 9 Soccer 7.0 None x 0.75 Most x 1.25

10 Volleyball 3.0 Some x 1.1 Most x 1.25 11 Table tennis 4.0 Some x 1.1 Most x 1.25 12 Badminton 4.5 Some x 1.1 Most x 1.25 13 Tennis 7.0 None x 0.75 Most x 1.25 14 Squash 12.0 None x 0.75 Most x 1.25

15 Hockey 8.0 None x 0.75 Most x 1.25

16 Ball games: dodge ball, shooting basket 4.5 Some x 1.1 Most x 1.25

17 Games: chase，hide and catch 5.0 Some x 1.1 Most x 1.25 18 Skiing and Roller skating 7.0 None x 0.75 Most x 1.25 19 Rope jumping 8.0 None x 0.75 Most x 1.25 20 Dancing 4.5 Some x 1.1 Most x 1.25 21 Aerobic dancing 6.0 None x 0,75 Most x 1.25 22 Wu shu, karate, tae kwan tao 10.0 None x 0.75 Most x 1.25 23 Outdoor chores: gardening, car washing 4.5 Some x 1.1 Most x 1.25 24 Indoor chores: mopping, vacuuming,

sweeping

2.5 Some x 1.1 Most x 1.25

25 Walking: to home or to school 4.0 Some x 1.1 Most x 1.25 26 Running: non athletics training 7.0 None x 0.75 Most x 1.25

27 Others: (physical activity classes, lessons,

or teams)

28 29

30

31

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APPENDIX 5 Application letter to school principal

Dear Principal,

Application for permission to collect research data

My name is Hui Shun Wing and I am currently conducting a research study named: The prediction of physical activity levels of Hong Kong primary six and secondary three students from their attitudes toward physical activity: A partial test of Ajzen's Theory of Planned Behavior. It is a partial Mfillment to my part-time master degree course in physical education and sports science at the University of Hong Kong. I hereby request for your approval for me to collect the data in two classes in primary six / secondary three at your school.

The students will be given the children's attitudes toward physical activity inventory and self-administered physical activity checklists. The combined forms will take approximately 30 minutes to complete and will be administered during a physical education class. There are no risks to your students participating in the study.

If permission is gained, I shall contact the physical education teacher in your school to make the further arrangement. If you have any questions, please do not hesitate to contact me 26404033 (office). I will be happy to speak with you. Enclosed is the questionnaire that will be used in my study. Thank you for your attention.

Yours sincerely, Hui Shun Wing.

End.

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APPENDIX 6 Parental consent letter

Dear Parent or Guardian,

My name is Hui Shun Wing and I am currently conducting a research study on students' attitudes toward physical activity and their physical activity levels in partial fulfillment of the requirements for a master degree in sports science at the University of Hong Kong. Your child is invited to act as subjects in this project

Your child will be asked to complete children's attitudes toward physical activity inventory and self-administered physical activity checklists. There are no risks to your child participating in this study. The responses of this questionnaire will not impact the grades in any subjects. You can be assured that all information obtained with this study will remain confidential.

If you have any concerns for this study, please feel free to contact Mr. Hui at 26404033 (office). Please have your child return the lower part of this page with your signed consent. I truly appreciate your attention to this project.

Yours sincerely, Hui Shun Wing.

(cut here)

(keep top portion, return bottom) Permission Slip

I have read the above and understand the nature of this study. I agree to have xny

child, participate in this study described above.

Student Name: Parent/Guardian Signature: Date:

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APPENDIX 7 Instructions for the physical education teacher

First of all，thank you so much for agreeing to assist me in conducting my research. Your participation is invaluable!

Yours students will be completing children's attitudes toward physical activity inventory and self-administered physical activity checklists in a combined form.

To insure objectivity, please follow closely to all instructions. It is very important that all students are given the same instructions.

Preliminary information 1. The questionnaire will be given to primary six / secondary three students in any

two classes in your school. 2. Have the students complete the questionnaire anytime between now and Friday，2

March 2001. 3. It will be conducted during your students' regularly-schedules physical education

lessons. 4. The students will need a pen or pencil.

Test day 1. Distribute the questioimaire to the students and say: "Do not put your name on

your questionnaire. It has totally 6 pages and divides into two parts. Check it now”.

2. Read the purpose and instructions in Part one to the students and remind them to read each type of activity in Section B.

3. After all the students completing the Part one，read the following instructions and show a sample page (presented as an overhead) to entire class.

4. For each question in Part two，"there is a box，and in the box there is an idea. Down below the box are five different pairs of words. You will be marking these word pairs to show how you feel about the idea. Read the idea in the box (refer to the visual aid)，for example, REFEREE. Now go down to the first pair words — Good-bad. How do you feel about Referees? If you think they are veiy good，you would put a “ v，，here (mark at the end of the scale by good) or, if you think that they are very bad, you would put a “ V ” here (mark at the end of the scale by bad). If you think that referees are pretty good but not super good you would put a “ V，，here (indicate) or if you thought that referees were sort of bad but not really bad you would put a “ v，，here (indicate). If you think that referees are neither good nor bad (i.e.，a neutral feeling) then put a “ V ” in the middle. If you

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do not understand the idea in the box put a “ V ” in the do not understand box on the middle of the question. Then go to the next question. If you understand the idea in the box but not the word pair, leave the word pair the blank and go on to the next word pair. Do you have any questions?" (Schutz et al., 1985，p.262)

5. "It is important for you to remember several things. First of all, put your “ V ”

right in the middle of the space. Second, there are five pairs of words on each question, so how many “ V ”s will you have on each question? (Five).

6. When I tell you to begin，go through the questions page by page. Read the idea in the box on each question and fill in how you feel about all of the word pairs before you go on to the next question. Don't go back to a question after you have finished it; and don't try to remember how you answered the other questions. Think about each word pair by itself. As you go through the questionnaire go fairly quickly; don't worry or think too long about any word pair. Mark the first thing that comes into your mind, but don't be careless. Remember, the idea in the box at the top of each question is a new idea, so think only about that idea. When you ate all finished, put down your pen/pencil and go back through all the questions to make sure that you haven't left anything out by mistake. After you have finished checking, turn your questionnaire over and wait until everyone is finished. If you have any questions raise your hand and I will come around and help you. You may begin’’ (Schutz et a l , 1985，p.262).

7. After all the students completing both Part one and Part two, collect all questionnaires.

§ . •

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