’1. ) Ir
fizyexvu‘
. . v

. . .19.
1.4....» w. .5.
”Mn“... 35 7. av

. _
.{x
i. 3: 30:7
1 ...|.I... ..
v .:.......: v...
y

 

 

 

 

has.
.3.
n. \T’f}! ‘

. «V532.

13.39.!)

.3... 9.5 it... v.13 .

.5: v3.3“: a.“ E
1 a 1...;

. .mfi.

. $1.3...) ‘
v. ‘12,: i

.3... in z.‘
.s!..i 11
O.

.13.!
I. .ltx2yét
.1 Y x

. ‘ i 52.2. . . _ .. .i. .
. -4 , 1...; i. .. $ .v 321?...111
I . .._ ,. 1.- .2 2.7.. V 35:53.» .591? .f 4 . . .3... .1. ”a a as: finaévu
mvs.‘....ux.. 33%“. :r _ . . afgémmw; at. w..." m... ”Epfimmnfi , dwziumfl 3. 543?”. a} ..

. . ‘ I , .n . V In. ‘al‘ . ‘A. < f

 

.v
5...: . ‘ , . ‘ ‘
. u Ruiz) . . I I

 

I “‘59“? LIBRARY
2 Michigan State
20d» University

This is to certify that the
dissertation entitled

ANALYSIS OF LEISURE EXPERIENCE AND SUBJECTIVE
WELLBEING

presented by
Ariel Rodriguez

has been accepted towards fulfillment
of the requirements for the

PhD. degree in Park, Recreation and Tourism
Resources

MW

 

 

Major Professor’s Signature

M 4.970%

Date

 

MSU is an Affirmative Action/Equal Opportunity Institution

- n.—.-3-.-.-.-0-.-I-I-O-0-0-o-o-o-I-.-o-o-.-o-o-l-0-o-.-n-o-o-o-n-o-o-o-I-a-o-o-o-I-o-

PLACE IN RETURN Box to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2/05 p:/ClRC/Dale0ue.indd-p.1

 

ANALYSIS OF LEISURE EXPERIENCE AND SUBJECTIVE WELLBEING
By

Ariel Rodriguez

A DISSERTATION
Submitted to
Michigan State University

In partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Park, Recreation and Tourism Resources

2006

ABSTRACT
ANALYSIS OF LEISURE EXPERIENCE AND SUBJECTIVE WELLBEIN G
By
Ariel Rodriguez
The continued importance of leisure, as an important domain of life, and the
dearth of empirically analyzed theoretical models which explain the relationship between
leisure experience and subjective wellbeing have provided an impetus for this current
study. Towards this end, three models, each of which was based on Tinsley and Tinsly’s
(1986) original causal effects of leisure experience model were empirically tested. The
models consisted of six factors: leisure experience, physical activity level, autonomy,
physical health, mental health, and life satisfaction. Confirmatory factor analysis was
employed to test for unidimensionality of the factors, and path analysis was employed to
test the fit of the model to the data. Data were collected from a random sample of
registered voters from a Midwestern community (n=633; 21.3% response rate). Study
results provided some support for the three models, but only the final model fit the data
the best. Specifically, the final model indicated that leisure experience had a positive and
direct influence on physical activity. Physical activity had a positive and direct influence
on autonomy. Autonomy had a positive and direct influence on physical health, mental
health, and life satisfaction. Physical health had a positive and direct influence on mental
health and life satisfaction, and mental health had a positive and direct influence on life

satisfaction.

This dissertation is dedicated to my family. Your

patience and love have been paramount in my life successes.

iii

ACKNOWLEDGMENTS

Few important endeavors are the product of only one individual. This dissertation
is by no means an exception. I would like to acknowledge several individuals whose
efforts directly contributed to this dissertation.

I would like to thank my dissertation committee: Dr. Richard Paulsen (chair), Dr.
James Bristor, Dr. Margot Kurtz, Dr. Ralph Levine, and Dr. Christine Vogt. Their
wisdom, guidance, and support were vital to my dissertation success. As a whole, the
committee worked in a cohesive manner to efficaciously assist my progress. There were
times throughout the dissertation process that I could have failed, but their sheer
compassion and support helped me to overcome each obstacle.

Individually, each member diligently assisted me in my research efforts. Dr. Kurtz
was critical in helping me systematically organize my dissertation thought processes.
This provided a solid foundation for my dissertation. Dr. Levine’s understanding of
complex modeling techniques was vital to my dissertation. Dr. Bristor’s knowledge of the
overall leisure field and “eagle eyes” were also invaluable. Dr. Vogt, who is better known
as Superwoman around the office, served as an inspiration to me and as an example of
the researcher I wish to be. As for Dr. Paulsen, it is sufficient to say he has served as an
example of the type of person I wish to be. I am so grateful for his mentorship.

To my fi'iends who consistently supported me, I will be eternally indebted.
Whether it was going to the movies, having a fun family dinner, or simply sharing a little
Chianti, they were critical in helping me keep a manageable stress level.

Finally, to Julie Hall and SELCRA, thank you for giving me a chance.

iv

TABLE OF CONTENTS

LIST OF TABLES ......................................................... vii

LIST OF FIGURES ........................................................ ix

CHAPTER I

INTRODUCTION ......................................................... 1
Leisure and Subjective Wellbeing ................................... 3
Theoretical Model ...................................................... 4
Problem Statement ...................................................... 7
Definitions ............................................................... 7
Hypotheses ............................................................... 8
Assumptions ............................................................ 9
Delimitations ............................................................ 9
Limitations ............................................................... 9

CHAPTER II

LITERATURE REVIEW ................................................. 11
Literature Review Challenges ........................................ 11
Leisure ................................................................... l3
Psychological Needs ................................................... 1 6
Health .................................................................... 22
Life Satisfaction ........................................................ 27
Causal Effects of Leisure Experience Model ....................... 31

CHAPTER III

METHODOLOGY ......................................................... 34
Protection of Privacy .................................................. 34
Population and Sample ................................................ 35
Instrumentation ......................................................... 39

CHAPTER IV

ANALYSIS ................................................................. 45
Measurement Model ................................................... 45
Causal Model ........................................................... 59
Hypotheses .............................................................. 7O

CHAPTER V

CONCLUSIONS .......................................................... 72
Summary of Findings .................................................. 72
Discussion .............................................................. 74
Implications ............................................................. 82
Recommendations ..................................................... 86

APPENDIX A .............................................................. 93

APPENDIX B .............................................................. 96

APPENDIX C .............................................................. 108

APPENDIX D .............................................................. 1 12

REFERENCES .............................................................. 114

vi

LIST OF TABLES

Table 1. Study respondents, life satisfaction, physical health, and

mental health by socio-demographics ............................. 38
Table 2. Study respondents’ activity participation distribution .......... 41
Table 3. Summary of factor indicators ...................................... 50

Table 4. Physical health corrected obtained correlations and residuals.. 51
Table 5. Mental health corrected obtained correlations and residuals. .. 52

Table 6. Life satisfaction corrected obtained correlations and
residuals .......... . .................................................... 53

Table 7. Revised life satisfaction corrected obtained correlations and
residuals .............................................................. 5 4

Table 8. Test of fit results for external consistency analysis ............ 56

Table 9. Summary of corrected observed correlations and residuals
from the final external consistency analysis ...................... 57

Table 10. Summary of factor reliabilities ................................... 58

Table 11. Path analysis uncorrected and corrected observed
correlation matrix .................................................. 61

Table 12. Original model corrected observed correlations and path
coefficients ......................................................... 63

Table 13. Original model residual matrix ................................... 63

Table 14. Theoretically revised model corrected observed correlations
and path coefficients ............................................... 65

Table 15. Theoretically revised model residual matrix .................... 65

Table 16. Final model corrected observed correlations and path

coefficients ......................................................... 69

vii

Table 17. Final model residual matrix ...................................... 69

viii

LIST OF FIGURES

Figure 1. Theoretically revised causal effects of leisure experience path model.. 6

Figure 2. Original Tinsley and Tinsley (1986) causal effects of leisure
experience model .............................................................. 32

Figure 3. Final model .................................................................... 67

ix

Chapter I
INTRODUCTION

The quality of individuals’ lives continues to be a source of empirical and
rhetorical debate (Diener, 1984; Land, 1983). Quality of life has been studied at the
global (Diener, Diener, & Diener, 1995; Inkeles, 1993), national (Andrews & Withey,
1974, 1976; Campbell, 1981; Campbell, Converse, & Rodgers, 1976; Flanagan, 1978,
1982; Land, Lamb, & Mustillo, 2001), state (Michigan Land Use Leadership Council,
2003), community (Allen & Beattie, 1984; National Research Council, 2002; Sirgy,
Rahtz, Cicic, & Underwood, 2000), and individual levels (Michalos, 2003). In academia,
debate has focused on objective and subjective components of quality of life (Bramston,
Pretty, & Chipuer, 2002; Vitterso, 2003). Most of the earlier research on quality of life
focused on objective components of quality of life, or tangible conditions of life (e. g.,
Gross Domestic Product) (Cummins, 2000).

In the 1970’s, researchers began to focus on social-psychological states (i.e.,
peoples’ feelings, attitudes, expectations, aspirations, and values) regarding their quality
of life (Land, 2000). For example, according to Campbell (1981), “. . .we cannot
understand the psychological quality of a person’s life simply from a knowledge of the
circumstances in which that person lives” 0). 1). This shift came from a realization that
although people were getting wealthier, their happiness did not continue to increase
(Diener & Biswas-Diener, 2002; Diener & Seligman, 2004). In other words, it was no
longer sufficient to only study objective measures of quality of life, or objective

wellbeing. Research delimited to the subjective component of quality of life has often

focused on subjective wellbeing, human development, and constructs which influence or
correlate with subjective wellbeing or human development (Vitterso, 2003). Subjective
wellbeing encompasses four independent constructs: positive affect, negative affect,
overall life satisfaction, and salient domains of life (Diener, 2000; Diener, Suh, Lucas, &
Smith, 1999).

Two proposed theories have provided a framework to better understand the
overall relationship of the independent components of subjective wellbeing: bottom-up
theories and top-down theories. According to bottom-up theories, subjective wellbeing is
a sum of many small parts (Diener, 1984; Diener, Suh, Lucas, & Smith, 1999). These
small parts include the various life domains such as family, safety, health, community
and leisure (Allen, 1990, 1991; Allen & Beattie, 1984; Andrews & Withey, 1976;
Campbell, 1981; Cummins, 1996; Flanagan, 1978, 1982; Iwasaki, 2003; Marans,
Dillrnan, & Keller, 1980; Marans & Mohai, 1991; Michalos, 2003; Sirgy, 2001, 2002;
Tinsley & Eldredge, 1995; Tinsley & Tinsley, 1986). This is in direct contrast to top-
down theories where it is concluded that, “global features of personality are thought to
influence the way a person reacts to events. For instance, a person with a sanguine
temperament might interpret a large number of events as positive” (Diener, 1984, p. 565).
Empirical testing of both theories has yielded varying results, but overall results have
failed to reject either of the theoretical models (Feist, Bodner, Jacobs, Miles, & Tan,
1995; Headey, Veenhoven, & Wearing, 1991; Heller, Watson, & Ilies, 2004; Iwasaki &
Smale, 1998; Sirgy, 2002). Therefore, it has been concluded that subjective wellbeing is

both a cause and an effect.

Leisure and Subjective Wellbeing

Leisure as an important domain of life has been researched for many years, and as
such, has been included in various national indexes measuring aspects of quality of life
(Hagerty et al., 2001). Specifically, empirical efforts have focused on better
understanding the relationships between leisure and other domains of life, such as health
(Coleman & Iso-Ahola, 1993; Hills & Argyle, 1998; Iso-Ahola & Park, 1996; Iwasaki,
2003; Michalos, Zumbo, & Hubley, 2000; Wendel-Vos, Schuit, Tijhuis, & Kromhout,
2004), community (Allen, 1991; Allen & Beattie, 1984; Marans, Dillman, & Keller,
1980), work (Trenberth, Dewe, & Walkey, 1999; Zuzanek, Robinson, & Iwasaki, 1998),
and family (Holman & J acquart, 1988). In addition, research efforts have focused on the
relationship between leisure and overall life satisfaction (Lloyd & Auld, 2002; Ragheb &
Griffith, 1982). Because of these and other efforts, a better understanding of the
relationship between leisure and subjective wellbeing has been established. Overall, the
relationship between leisure and subjective wellbeing is a complex relationship which is
often described as positive and indirect. In other words, leisure is often perceived to have
a beneficial effect on subjective wellbeing, but its effect is mediated by other variables,
such as satisfaction of psychological needs (Tinsley & Tinsley, 1986), leisure satisfaction
(Lloyd & Auld, 2002), and leisure coping strategies (Iwasaki, 2003). Theoretical models
have been pr0posed to better understand this complex relationship, but this study is
delimited to one model, the causal effects of leisure experience model (Tinsley &

Tinsley, 1986).

Theoretical Model

Models based upon bottom-up theories are more common in leisure research than
models which take a top-down theoretical approach (Allen & Beattie, 1984;
Csikszentmihalyi, 1997; Tinsley & Tinsley, 1986). A possible reason is because leisure
researchers commonly believe that participation in general activities have both positive
and/or negative outcomes to participants’ overall subjective wellbeing (Driver, Tinsley,
& Manfredo, 1991; Roj ek, 1999). One theoretical model proposed to understand the
complexity of leisure and subjective wellbeing was developed by Tinsley and Tinsley
(1986), the causal effects of leisure experience model. Although the theoretical model has
been relatively influential in the leisure field (e. g., the model has been cited in over 80
empirical studies), it has never been empirically tested. Tinsley and Tinsley (1986)
themselves cautioned that, “The following propositions and corollaries represent a
beginning rather than a finished theoretical model. We urge the development of multiple
methods to measure those constructs and relate them to one another” (p. 9). This need for
competing theoretical models of leisure and subjective wellbeing was reemphasized by
Mannell and Kleiber (1997).

The causal effects of leisure experience model is a non-recursive model which
was proposed to better understand the relationship between leisure experience,
satisfaction of psychological needs, physical health, mental health, life satisfaction and
personal growth. Specifically, the model posits that leisure experiences positively
influence a person’s psychological needs. The fulfilhnent of a person’s psychological
needs positively influences a person’s physical health, mental health, and life satisfaction.

Moreover, physical health and mental health have a direct and positive impact on life

satisfaction, and they have a reciprocal relationship (i.e., physical health influences
mental health and mental health influences physical health). Finally, when a person has
high life satisfaction, this positively impacts their personal growth (Tinsley & Tinsley,
1986).

A review of the literature indicates that there are some important issues that
should be noted when testing the model. First, leisure has often been operationalized in a
manner which is not conducive to understanding the relationships proposed in the model.
In order to measure leisure experience, measures should take into consideration the
subjective identification of an experience as opposed to objectively assuming a leisure
experience when a person has participated in a general activity. For example, when a
person participates in a general activity and feels their experience is leisure, there are
additional potentially salutary effects such as increased perception of freedom and
intrinsic motivation (Esteve, San Martin, & Lopez, 1999). Therefore, efforts to test the
model should measure leisure experience from a subjective perspective with efforts to
understand the experience as opposed to solely focusing on frequency of participation.

Second, different activities have different effects on and subjective wellbeing
components. For instance, activities which are physically demanding have the added
benefit of positively influencing a person’s level of physical health compared to activities
which are not physically demanding (Iso-Ahola, 1997).

Third, there is little evidence for the reciprocal relationship of physical health and
mental health (Hayes & Ross, 1986). Instead, the literature supports the premise that
physical health influences mental health and not necessarily the other way around

(Aneshensel, Frerichs, & Huba, 1984). Therefore, efforts to test the model should modify

the reciprocal relationship between physical health and mental health to indicate only a
direct relationship from physical health to mental health.

A theoretically revised model, which takes into consideration the three model
issues, is presented in Figure 1. The model purports that participation in activities which
are physically demanding has a direct positive effect on both physical health and mental
health. In addition, physical health will have a direct positive effect on mental health.
Additionally, leisure experience will have a direct positive effect on psychological needs
(i.e., autonomy). This study will be delimited to one of the more significant psychological
needs, autonomy (Ryan, 1995; Ryan & Deci, 2000; Sheldon, Elliot, Kim, & Kasser,
2001). As a psychological need, autonomy has been identified as one of the key outcomes
of a leisure experience (Neulinger, 1981). Satisfaction of psychological needs will have a
direct positive effect on mental health. Finally, psychological needs, physical health, and

mental health will have a direct positive effect on life satisfaction.

Physrcal Physical

Act1v1ty E211 th

I

 

    
    

   

     
 

Life
Satisfaction

 

   
  
 

  
  

   

Leisure Satisfaction of Mental
Experience Psychological Health
Needs

Figure I. Theoretically revised causal effects of leisure experience path model.

Problem Statement
The dearth of empirically analyzed theoretical models which explain the
relationship between leisure experience and subjective wellbeing (Mannell & Kleiber,

1997) has provided an impetus for this current study. Specifically, the problem of this

study was to empirically analyze a theoretically revised version of Tinsley and Tinsley’s

(1986) causal effects of leisure experience model.

Definitions

1. Autonomy — “. . .an inner endorsement of one’s actions, the sense that they emanate
from oneself and are one’s own” (Deci & Ryan, 1987, p. 1025)

2. Leisure experience — a subjectively interpreted experience of leisure at any level of
intensity (Tinsley & Tinsley, 1986)

3. Life satisfaction — the global judgment of a person’s life (Diener, 1984; Pavot &
Diener, 1993)

4. Mental health — a summary measure composed of mental health, vitality, social
functioning, and role-emotional (Ware, Kosinski, Dewey, & Gandek, 2001)

5. Needs — “. .. the nutriments or conditions that are essential to an entity’s growth and
integrity” (Ryan, 1995, p. 410)

6. Path analysis — “. . .a procedure for systematically combining the use of partial and
multiple correlation to study the causal relations among a set of variables” (Hunter &
Gerbing, 1982, p. 290)

7. Physical activity — “an umbrella term describing any bodily movement produced by

the skeletal muscles resulting in energy expenditure” (Fox, Boutcher, Faulkner, &

Biddle, 2000, p. 8)

8. Physical health —— a summary measure composed of physical functioning, role-
physical, bodily pain, and general health (Ware, Kosinski, Dewey, & Gandek, 2001)

9. Subjective wellbeing — an umbrella term describing an individual’s perception of their
wellbeing. The term encompasses four independent constructs: positive affect,
negative affect, overall life satisfaction, and salient domains of life (Diener, 2000;
Diener, Suh, Lucas, & Smith, 1999)

Hypotheses

1. Physical health (PH) and mental health (MH) are positive significant predictors of life
satisfaction (L8).

2. Physical health (PH) is a significant positive predictor of mental health (MH).

3. Satisfaction of psychological needs (SPN) is a significant positive predictor of life
satisfaction (LS) and mental health (MH).

4. Leisure experience (LE) is a significant positive predictor of satisfaction of
psychological needs (SPN).

5. Physical activity (PA) is a significant positive predictor of physical health (PH) and
mental health (MH).

6. The model hypothesis:

H0: the data are consistent with the model (Error = 0).

H1: the data are not consistent with the model (Error ¢ 0).

Assumptions
1. Study participants are knowledgeable about what leisure is and what it is not in their

lives.
2. Voter registration sampling frame is representative of adults in the geographical area

of study.
3. Humans strive for certain fundamental qualities of experience.
Delimitations

The data used in this study were part of a larger municipal park and recreation
master plan needs assessment. As such, there were three parameters in this study. First, a
sample of 800 individuals were taken from each of the four municipalities included in the
needs assessment, regardless of the total population of each municipality, in order to have
sufficient study respondents to compare results between municipalities. Second, only a
limited number of questions specific to the study problem were posed in the needs
assessment. Third, data were collected during March and April, which are relatively
colder months which may influence physical activity levels and are months during which
families may travel during Spring Break.
Limitations
The results of this study may have been limited by five factors. First, one-item

indicators were used for both leisure experience and physical activity level. Second, the
study sample included socio-demographics which were different from the study
population. Therefore, generalizability of the study results should be limited to
populations similar to the study sample. Third, data were collected at one point in time,

limiting the ability to indicate a true cause-effect relationship. Fourth, the causal effects

of leisure experience model does not take into consideration biological or additional
psychological needs which may also influence a person’s physical health, mental health,
and life satisfaction. Finally, there are additional mediating variables, such as leisure
satisfaction (Lloyd & Auld, 2002) and leisure coping strategies (Iwasaki, 2003), which

were not considered in this study.

10

Chapter [1
LITERATURE REVIEW

The purpose of this chapter was to provide a basic understanding of each of the
constructs in the causal effects of leisure experience model and of the expected
relationships or links between the constructs. Therefore, the literature review consisted of
studies on (1) leisure, (2) psychological needs, (3) health, (4) life satisfaction, and (5) the
causal effects of leisure experience model. The relationship between each construct was
discussed after the construct was described. For example, the relationship between health
and leisure was discussed afier health was described. Likewise, the relationship between
life satisfaction and leisure was discussed afterlife satisfaction was described. The order
in which each construct was described was based on their presupposed causal order as
indicated in the original Tinsley and Tinsley (1986) causal effects of leisure experience
model. Prior to this, some challenges associated with the literature review were
discussed.
Literature Review Challenges

The interpretation of study factor relationships (i.e., leisure experience,
psychological needs, physical health, mental health, and life satisfaction) was
complicated by two factors. First, the model itself had not been tested to the author’s
knowledge. This was concluded only after an exhaustive search in the literature which
included going through each of the 86 sources (i.e., articles and texts) which cited the
original article by Tinsley and Tinsley (1986). Because the model had never been tested,

efforts were made to understand how the various factors had been operationalized in past

11

research. Unfortunately, operationalizations often used of these factors differed from
study to study. For instance, one study may operationalize leisure as an amount of free
time, another as a level of activity during one’s free time, another as satisfaction with
activities, and yet another as an experience. It becomes difficult to understand the
relationship of leisure to any other construct when all the factors are identified as
“leisure.”

Difficulties were also presented when the most frequently used
operationalizations were not measuring the factor used in this study. For example, leisure
has predominantly been operationalized as an activity or free time (Esteve, San Martin, &
Lopez, 1999). Part of the reason for this is because of difficulties in operationalizing
“leisure experience,” given the method (i.e., surveys) of collecting data often used in
these studies. To understand what activities a person participates in, a researcher could
provide a list of activities for a study participant to check. Moreover, the study participant
could be asked about frequency and satisfaction with their activity participation.
Likewise, a study participant could be asked how much free time they perceive they have
or how satisfied they are with the amount of free time they have. When it comes to
leisure as an experience, few instruments are available (with the exception of Ellis and
Witt’s [1984] Leisure Diagnostic Battery and Esteve, San Martin and Lopez’s [1999]
scales of leisure experience).

The few available instruments are quite extensive, encompassing various
components of the leisure experience, but the practicality of the instruments for smaller
studies is questionable. Specifically, the instruments are best applied when focused upon

the leisure experiences of one general activity (Esteve, San Martin, & LOpez, 1999).

12

Therefore, larger sample sizes would be needed to compare among different general
activities and surveys themselves would run the risk of increasing fatigue levels of study
participants if other instruments were included in the surveys. As a result, very little
literature is available regarding the relationship of leisure experience to subjective
wellbeing constructs such as physical health, mental health and life satisfaction.

Second, a field is not dedicated to leisure and life satisfaction, quality of life, or
subjective wellbeing, nor has an invisible college (e. g., Kuhn, 1996) been established.
Leisure researchers are generally more interested in other topics (an exception is research
by Iwasaki, Mannell and their colleagues), and quality of life researchers are generally
not interested in leisure (an exception is Argyle, Michalos and their colleagues).
Therefore, efforts to understand the relationships of the various constructs proposed in
this study were interdisciplinary and went beyond that of the leisure or quality of life
fields. This provided a great challenge, as each factor required an intensive review of the
literature within the respective field where the factor was most frequently researched. In
spite of this effort, there were several gaps in the literature specific to the relationships
between the various constructs tested in this study. These gaps were noted throughout the
literature review.

Leisure

Leisure has been defined in a variety of ways. The three most common ways in
which leisure has been defined are leisure as time, an activity, and as an experience
(Mannell & Kleiber, 1997). As time, leisure is synonymous with free time or time that
one has beyond work or obligatory activities (i.e., discretionary time). As an activity,

leisure is synonymous with an activity that one does during one’s free time. Thus, if a

13

person plays a game of basketball after work, the basketball activity is considered leisure.
As an experience, leisure is often synonymous with a state of mind one experiences when
one participates in an activity which one perceives to be freely chosen (i.e., the person
felt they could participate in the activity) and intrinsically motivating (i.e., the person
wanted to participate in the activity) (Iso-Ahola, 1980; Neulinger, 1981; Tinsley &
Tinsley, 1986).

Leisure can be operationalized subjectively or objectively, regardless of the
manner in which it is defined. For instance, leisure as free time can be measured
objectively by having a researcher indicate the amount of free time the researcher thinks a
subject has, or it can be measured subjectively with the subject indicating to the
researcher how much free time they perceive they have. The purpose of the study can
help a researcher identify whether objective or subjective measures may be more
appropriate. For example, if a study is focusing on the effects of psychological needs on
one’s life satisfaction, as is the case in this study, then a definition of leisure which
focuses on the leisure experience might be more appropriate given the extensive literature
associating different leisure experiences with different psychological needs (Driver,
Brown, & Peterson, 1991; Tinsley, 1984; Tinsley & Eldredge, 1995). Moreover, leisure
as an experience could be measured from an objective perspective, but the validity of this
practice has often been questioned given the various intrinsic differences that influence a
person to feel that an experience is leisure as opposed to not being leisure (Samdahl,
1991; Shaw, 1986).

In trying to understand the psychological needs fulfilled by leisure, other

definitions of leisure could be used, but their limitations are important to note. For

14

instance, it could be argued that a person with less free time might not have as many
opportunities to fulfill as many psychological needs during their free time as a person
who has more free time. Therefore, it may be concluded that a person with more free time
would have a higher level of physical health, mental health and life satisfaction because
of their ability to fulfill more psychological needs. Although this relationship makes
intuitive sense, it assumes that a person with more free time will use it in a manner which
is conducive to fulfilling their psychological needs. Examples for why this assumption
may not be true could be found in the leisure constraints literature.

Jackson and Scott (1999) identified three types of constraints, intrapersonal,
interpersonal, and structural, which may influence a person’s ability to use their free time
in a manner which is conducive to satisfying their psychological needs. Intrapersonal
constraints are those intrinsic elements that predispose an individual to view an activity
as leisure or not. The “constraints exist when, as a result of abilities, personality needs,
prior socialization, and perceived reference group attitudes, individuals fail to develop
leisure preferences” (Jackson & Scott, 1999, p. 308). Interpersonal constraints “are those
barriers that arise out of social interaction with friends, family and other” (Jackson &
Scott, 1999, p. 308). Structural constraints are “those factors that intervene between
leisure preference and participation” (Jackson & Scott, 1999, p. 307). An example of this
might be the lack of a swimming facility close enough for community members who
enjoy swimming to swim.

It could also be argued that a person who participates more frequently in activities
has a higher probability of satisfying their psychological needs than a person who

participates less fi'equently in activities. This comparison makes the assumption that an

15

individual derives positive outcomes when they participate in the activity. Tinsley and
Johnson (1984) indicated some of the issues associated with this assumption. When only
the frequency of participation is known, this “tells us little about why the individual
participates in the activity or about the psychological nature of the individual’s
experience when participating in the activity” (Tinsley & Johnson, 1984, p. 235). In other
words, there are various elements related to activity participation that may influence what
an individual actually gets out of participating in an activity. For example, if two
individuals both play basketball three times a week, would their participation have a
positive impact on their level of physical health, mental health, and life satisfaction?
Tinsley and his colleagues (Tinsley, Barrett, & Kass, 1977; Tinsley & Eldredge, 1995;
Tinsley & Kass, 1978, 1979) would argue that more information is needed on whether the
individuals experienced leisure during their activity participation. They propose that it is
only when a person perceives their activity participation as leisure that certain
psychological needs are fulfilled.
Psychological Needs

The psychological needs of humans have been a topic of research interest for
many years (Freud, 1920; Maslow, 1970; McDougall, 1908; Murray, 1938). There are
two recognized definitions of “needs.” In the first definition, which is the more common
of the two, “need is equated with virtually any motivating force, including one’s desires,
goals, wants, or values — whether these are implicit or self-attributed” (Ryan, 1995, p.
410). An example of this would be when a teenager indicates to her parents that she
needs a car. In the second definition, and the one which is more commonly used in

psychological needs research and which was used in this study, “needs refers to the

16

nutriments or conditions that are essential to an entity’s grth and integrity. A plant
needs sunlight and water to grow. Similarly a person, as a biological entity, needs food,
water. . .to thrive” (Ryan, 1995, p. 410).

Two reasons have been identified for the continued interest in the topic of
psychological needs. First, psychological needs “readily suggest psychosocial
interventions. That is, once identified, psychological needs can be targeted to enhance
personal thriving” (Sheldon, Elliot, Kim, & Kasser, 2001, p. 325). Second, it is believed
that helping an individual grow will ultimately have a positive impact on society at large
(Sirgy, 2002). In leisure studies, the focus has been on psychological needs that are
fulfilled from activities during a person’s free time and from leisure experiences. For
instance, what are the psychological needs that can be fulfilled from a person who jogs
during their free time as opposed to someone who attends plays during their free time?
Driver and his colleagues and Tinsley and his associates have provided much of the
foundation for what is currently known about psychological needs fulfilled during one’s
leisure. Their research on this topic spans more than three decades (Driver, Tinsley, &
Manfredo, 1991).

Leisure and psychological needs. Driver and his colleagues focused much of their
research on the outcomes or perceived positive benefits of participating in outdoor
activities. They believed that individuals participated in various outdoor activities to
fulfill needs that went unmet in other areas of an individual’s life. By participating in
outdoor activities, individuals could attain different positive outcomes (i.e., psychological
benefits) to fulfill these needs. Driver and his colleagues identified 19 psychological

benefits fulfilled through participation in outdoor activities: enjoy nature, physical fitness,

17

reduce tension, escape physical stressors, outdoor learning, share similar values,
independence, family relations, introspection, be with considerate people,
achievement/stimulation, physical rest, teach/lead others, risk taking, risk reduction, meet
new people, creativity, nostalgia, and agreeable temperatures (Driver, Tinsley, &
Manfredo, 1991).

Tinsley and his associates focused on activities that are commonly done during
individuals’ daily lives (e. g., playing cards, jogging, watching television, and reading). In
their earlier studies, Tinsley and his associates identified 27 psychological needs that
were specific to activities where leisure was experienced, but not others (e.g.,
achievement, creativity, self-esteem, and social service) (Tinsley, Barrett, & Kass, 1977;
Tinsley & Kass, 1978). Moreover, they identified 17 psychological needs which were
satisfied to nearly the same degree by all activities perceived as leisure (e.g., autonomy,
relaxation, and self-control) (Tinsley, 1984; Tinsley, Barrett, & Kass, 1977). In a more
recent study, Tinsley and his associates researched the extent to which various activities
were perceived to fulfill 11 psychological needs: exertion (need for vigorous physical
activity), affiliation (need to be with and relate to others in a cooperative, enjoyable way),
enhancement (need to improve one’s skills and develop one’s talents to the fullest), self-
expression (need to try one’s own ideas and to experiment with unique and unusual
approaches), nurturance (need to support, encourage, and help others), compensation
(need to experience things that are missing from one’s job or daily life), sensibility (need
for intellectual and aesthetic stimulation), conscientiousness (need to behave responsibly
and exercise personal restraint), status (need to receive attention and feel important and

influential), challenge (need to prove one’s self by meeting challenges), and hedonism

18

(need to experience pleasure and avoid pain or unpleasantness) (Tinsley & Eldredge,
1995). The result from this research effort was the development of an activity
classification system which was based on fulfilled psychological needs.

Activity classification systems. The practice of grouping activities into
classification systems generally simplifies data analysis by providing comprehensive
groups for the activities studied instead of individually analyzing each activity, but the
strategies commonly used to create these classification systems have been cautioned
against (Tinsley & Eldredge, 1995; Tinsley & Johnson, 1984). The most common
strategy in creating classification systems is to factor analyze activity participation
frequencies (Tinsley & Eldredge, 1995). The dilemma with this strategy is that
participation frequencies do not provide information about the experience during the
participation in these activities; they only indicate how often a person participates in an
activity. Therefore, conclusions of the relationships between these classification systems
and physical health, mental health, and life satisfaction are difficult to interpret because
individual activities have been shown to satisfy different psychological needs (Tinsley,
Barrett, & Kass, 1977; Tinsley & Eldredge, 1995; Tinsley & Kass, 1978). A more
appropriate strategy to creating activity classification systems is to base these systems on
the psychological needs fulfilled by each activity (Tinsley & Eldredge, 1995).

Tinsley and Eldredge (1995) identified 12 clusters based on the psychological
needs fulfilled by 82 general activities. The 12 clusters identified included agency,
novelty, belongingness, service, sensual enjoyment, cognitive stimulation, self-
expression, creativity, competition, vicarious competition, relaxation, and residual

(cluster containing miscellaneous activities). Participants in their study (n=3,771) were

19

knowledgeable about the one activity in which they provided information to reduce the
possibility of a participant answering based on stereotypes of the specific activity.
Detailed information about all the clusters can be found in Tinsley and Eldredge (1995),
but a brief description of the four clusters that were used in the present study were
provided. The four clusters were selected, as activities within these clusters were
common to most homes and communities of individuals in the study population.

The four clusters used in the present study included agency, service, creativity and
relaxation. Activities in the agency cluster had high scores in exertion and challenge, but
relatively low sensibility scores (Tinsley & Eldredge, 1995). The high scores in exertion
and challenge indicate that there were great physical demands in achieving difficult goals
(Tinsley & Eldredge, 1995). Moreover, the low sensibility scores indicate that intellectual
and aesthetic stimulation generally is not an outcome of participation in these activities
(Tinsley & Eldredge, 1995). Given the high exertion levels, activities within the agency
cluster should be positively associated with a person’s physical health, mental health, and
life satisfaction (Biddle, Fox, & Boutcher, 2000; Hayes & Ross, 1986; Iso-Ahola, 1997;
Wankel & Berger, 1991). For example, when an individual participates in an activity that
is physically demanding, this may increase endorphins and regulate norepinephrine
release which helps decrease depression (Hayes & Ross, 1986). For instance, in a recent
experiment in which 26 men with heart failure were randomized into a 6-month exercise
training program or to a control group, a significant decrease in anxiety and depression
was observed in the study participants who participated in the exercise program

(Koukouvou et al., 2004). Moreover, participation in physically demanding activities has

20

been found to help increase cardiorespiratory fitness, muscular strength, muscular
endurance, and flexibility (Wankel & Berger, 1991).

Activities in the service cluster were characterized by having high nurturance,
conscientiousness, affiliation, and status scores, but low challenge scores (Tinsley &
Eldredge, 1995). This indicates that the activities in this cluster fulfill a person’s sense of
personal responsibility to help others (Tinsley & Eldredge, 1995). Because of the low
challenge, activities in this cluster may promote boredom if the skill level of the person
doing the activity is too high (Csikszentmihalyi, 1990).

Activities in the creativity cluster were characterized by having high scores on
sensibility and average to above average scores in status, challenge, and hedonism
(Tinsley & Eldredge, 1995). This indicates that the activities in this cluster fulfill a
person’s need for self-expression, challenge and intellectual and aesthetic stimulation
(Tinsley & Eldredge, 1995).

The activities in the final cluster, relaxation, were characterized by having low
scores in enhancement, self-expression, and conscientiousness (Tinsley & Eldredge,
1995). This suggests that the activities in this cluster fulfill a person’s need to simply
relax while doing activities which are fairly routine and familiar (Tinsley & Eldredge,
1995).

Autonomy. When compared to other psychological needs (e.g., self-esteem, self-
actualization, security, and money-luxury), autonomy was identified as one of the most
significant psychological needs (Ryan, 1995; Ryan & Deci, 2000; Sheldon, Elliot, Kim,
& Kasser, 2001). “Autonomy connotes an inner endorsement of one’s actions, the sense

that they emanate from oneself and are one’s own” (Deci & Ryan, 1987, p. 1025). This

21

means that the more autonomous an action or behavior is the more it is sanctioned by the
individual as opposed to external forces. Autonomy is one of the key elements of a
leisure experience, despite which activity a person participates in (Driver, Tinsley, &
Manfredo, 1991), and individuals who have high autonomy have been found to also be
high in self-esteem, positive affect, and psychological health and be less likely to be self-
derogatory, experience negative emotions (e.g., shame or guilt), or experience boredom
(Deci & Ryan, 1987, 1995; Ryan, 1995).
Health

The construct of health has various definitions. Five models have been provided
to help distinguish these definitions. The five models include the medical model, holistic
model, wellness model, environmental model, and eclectic model. The medical model
defines health as the absence of disease, illness and disability. The holistic model defines
health as physical, mental, and social wellbeing (i.e., the health of the whole person). The
wellness model defines health from a more subjective perspective where excellence in
health and progress toward a future state of health is sought. In other words, a person’s
interpretation of their own health is the key component of the definition. The
environmental model interprets health as a product of harmony or homeostasis with the
environment, where the environment is the proper standard for defining health. The
eclectic model is a category for all other health definitions that have not been developed
and which may provide fruitful perspectives in the future (Larson, 1991).

Health-related quality of life. Health-related quality of life (HRQL) refers to
research which focuses on health in relation to quality of life. Specifically, those aspects

that might be affected positively or negatively in clinical studies and in clinical situations

22

are emphasized (Rejeski, Brawley, & Shumaker, 1996). The construct of health as a
domain of quality of life has been loosely defined, so no one definition of health has been
predominant (F ayers & Machin, 2000). Yet, there is general consensus that HRQL is
composed of at least two components. First, HRQL emphasizes a subjective assessment
from the point of view of individual (e. g., study participant or patient) being evaluated,
which is similar to the wellness model perspective. Second, HRQL is multidimensional,
which encompasses components similar to the holistic and medical model perspectives.
For example, Rejeski, Brawley and Shumaker (1996) proposed six elements which
encompass HRQL. These elements include global indices of health-related quality of life,
physical firnction (including difficulties with activities of daily living and other
performance-related domains, physical self-concept, and health-related perceptions),
physical symptoms/states (including pain discomfort, fatigue, energy, and sleep),
emotional function (including depression, anxiety, anger/hostility, self-esteem, and
mood/affect), social function (including social dependency, leisure-time pursuits, and
family/work roles), and cognitive function (including memory, attention, and problem-
solving/decision making).

Leisure and health. There are very few studies which have empirically tested the
relationship between leisure experience and health, but expected differences should be
influenced by whether a person experiences leisure in an active or passive activity (Iso-
Ahola, 1997). As aforementioned in this literature review, when a person participates in
an activity which is physically demanding, such as jogging or swimming, their level of
physical health is positively affected. Specifically, regular participation in physical

activities helps sustain joint structure and function, muscle strength, and appropriate body

23

weight (Miilunpalo, 2001; Wankel & Berger, 1991). There is also evidence that physical
activity has a salutary effect on morbidity levels. For instance, “. . .there is consistent
evidence that people with sedentary jobs, decreased participation in leisure or recreational
activities, or minimal total physical activity have an increased risk of colon cancer”
(Pedersen & Clemmensen, 1997, p. 190). In addition to salutary effects on physical
health, physical activity also has positive effects on mental health. For example, physical
activity has been associated with reducing anxiety and tension as well as depression
(Biddle, Fox, & Boutcher, 2000; Wankel & Berger, 1991). For instance, meta analytic
studies which have focused on the relationship between exercise (i.e., “. . .a subset of
physical activity that is volitional, planned, structured, repetitive and aimed at
improvement or maintenance of an aspect of fitness or health [Fox, Boutcher, Faulkner,
& Biddle, 2000, p. 7]) and depression have found effect sizes of 0.53-0.72 (Mutrie,
2000). According to Cohen (1977), effect sizes smaller than .20 are relatively
insignificant, between .20 and .50 are relatively small, between .50 and .80 are relatively
medium, and effect sizes over .80 are relatively large. In addition, exercise as an adjunct
therapy has been suggested for schizophrenia, developmental disorders, somatoform
disorders, substance abuse disorders (e.g., alcohol and drug addiction), smoking
cessation, and sleep apnea (Fox, Boutcher, Faulkner, & Biddle, 2000).

Because few studies have been done on actual leisure experience and health, it
was difficult to predict this relationship. On the other hand, since participation in general
activities is associated with psychological needs which have been shown to influence
mental health and life satisfaction, it could be hypothesized that leisure experience

influences mental health and physical health if the activity was physically demanding. In

24

a recent study using the same data as were used for the current study, this hypothesis was
tested. Differences were found in physical and mental health when persons felt their
physical activity participation was leisure compared to those who did not. Specifically,
individuals who jogged/walked for exercise and felt their experience was leisure had
significantly higher levels of physical health than those who did not jog/walk for exercise
and had significantly higher levels of mental health than study participants who
jogged/walked for exercise, but did not feel they had a leisure experience. Moreover,
individuals who weight lifted and felt their experience was leisure had significantly
higher levels of physical health than both those who weight lifted and did not perceive
their experience as leisure and those who did not weight lift. In addition, individuals who
weight lifted and felt their experience was leisure had significantly higher levels of
mental heath than individuals who did not weight lift. In the same study, there were no
significant differences in levels of physical health or mental health in individuals who
participated in non-physically activities such as baking/cooking, listening to the radio,
and visiting a spiritual/religious facility (Rodriguez, 2006).

Physical health and mental health. As aforementioned, an increase in physical
health has positive effects on one’s mental health by helping reduce anxiety, tension, and
depression (Biddle, Fox, & Boutcher, 2000; Wankel & Berger, 1991). On the other hand,
a decrease in mental health (i.e., an increase in depression, anxiety and hostility) has been
associated with increased physical illness (e. g., coronary heart disease and cancer)
(Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002). Researchers have noted both direct
and indirect pathways in which decreased mental health could affect physical dysfrmction

(i.e., physical health). Indirectly, individuals with mental illnesses “. . .are more likely to

25

have health habits that put them at greater risk, including poorer sleep, a greater
propensity for alcohol and drug abuse, poorer nutrition, and less exercise, and these
health behaviors have cardiovascular, immunological, and endocrinological
consequences” (Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002, p. 92). Directly,
increased mental illness has been associated with increased production levels of
proinflammatory cytokines [i.e., “. . .protein substances released by cells that serve as
intercellular signals to regulate the immune response to injury and infection” (Kiecolt-
Glaser, McGuire, Robles, & Glaser, 2002, p. 88)]. Dysregulations of proinflammatory
cytokines (specifically interleukin 6) has been associated with greater distress in older
women. Moreover, it has been associated with unhealthy behaviors. For example,
individuals who smoke, are less physically active, and have higher body mass indexes
generally have higher levels of interleukin-6 (Kiecolt—Glaser, McGuire, Robles, & Glaser,
2002). On the other hand, there has been limited support for the effects of interleukin-6
on physical functioning:

Although IL-6 [interleukin-6] has been shown to predict onset of disability

in older persons and both IL-6 and CRP [C-reactive protein] are associated

with mortality risk, these markers of inflammation have only limited

associations with physical performance, except for walking measures and

grip strength at baseline, and do not predict change in performance 7 years

later in a high-functioning subset of older adults. (Taaffe, Harris, Ferrucci,

Rowe, & Seeman, 2000, p. M705)
Therefore, although increased mental illness may affect levels of proinflammatory

cytokines, this does not necessitate long-term physical impairment. Moreover, the

26

empirical evidence relating mental health to physical health is largely associational,
which does not necessitate causality (Hayes & Ross, 1986). The issue of causality was
empirically analyzed in a longitudinal study where data were collected from adults every
four months during a 12—month period (n=744). It was found that physical illness (i.e.,
low level of physical health) increased depression levels in both male and female study
participants (Aneshensel, Frerichs, & Huba, 1984).
Life Satisfaction

An individual’s life satisfaction refers to the global judgment of their life (Diener,
1984; Pavot & Diener, 1993). This judgment is individualistic and is often based on a
person’s self-imposed standards and the degree to which standards are satisfied (Pavot &
Diener, 1993). Individuals who are able to decrease the gap between their current
situation and where they wish to be indicate higher life satisfaction (Pavot & Diener,
1993). Life satisfaction is often considered synonymous with happiness (V eenhoven,
1991), and it is often referred to as the cognitive component of subjective wellbeing (i.e.,
evaluation of an individual’s life) (Diener, 2000; Diener, Suh, Lucas, & Smith, 1999).
Other components of subjective wellbeing include positive affect (i.e., experiencing
many pleasant emotions and moods), negative affect (i.e., experiencing few unpleasant
emotions and moods), and significant domains of life (e.g., material wellbeing, health,
productivity, intimacy, safety, place in community, and emotional wellbeing) (Cummins,
1996, 1997; Diener, 2000; Diener, Suh, Lucas, & Smith, 1999).

Each subjective wellbeing component is unique, so evaluation of each individual
component can provide a useful contribution to the overall subjective wellbeing body of

knowledge. For instance, “affective components are often responses to immediate factors

27

and of short duration, whereas life satisfaction ratings can reflect a long-term
perspective” (Pavot & Diener, 1993, p. 165). In addition, “a person’s conscious
evaluation of her or his life circumstances may reflect conscious values and goals. In
contrast, affective reactions may reflect unconscious motives...” (Pavot & Diener, 1993,
p.165)

Leisure and life satisfaction. Participation in general activities is often considered
positively related to life satisfaction (Lloyd & Auld, 2002). For example, in a random
sample of Copenhagen residents (n=12,028), it was found that individuals who
participated in demanding physical activities (e.g., jogging) were less prone to stress and
life dissatisfaction than individuals who lived sedentary lifestyles (Schnohr, Kristensen,
Prescott, & Scharling, 2005). But given the diversity of general activities, it is difficult to
make this claim of positive relationship with much confidence. Specifically, few would
argue that using illegal drugs (a general activity) is positively associated with a person’s
life satisfaction.

A claim that can be made across general activities is there are some common
variables discussed in the literature which mediate the relationship between general
activity participation and subjective wellbeing components include leisure satisfaction
(Lloyd & Auld, 2002; Ragheb, 1989), psychological needs (Tinsley & Tinsley, 1986),
and leisure coping strategies (Iwasaki, 2003). Because general activities are often
mediated by different variables, lower correlations should be expected. For instance,
when an exogenous variable (A) is indirectly related to an endogenous variable (C)
because it is mediated by another endogenous variable (B), the correlation can be

expected to be smaller than if it was directly related because the correlation between A

28

and C is the product of the path coefficients BA and CB (i.e., rAc=rABch) (Hunter &
Schmidt, 2004). As such, studies which have measured the association between leisure
and life satisfaction often find smaller correlations. For example, in a study with a sample
of 3,500 adults from the Netherlands which looked at the relationship between number of
hobbies a person participates in and their happiness found a relatively small positive
correlation when the data were collected in 1993 (r=0.1 1, p<.05) and again in 1997
(r=0.l3, p<.05) (Boelhouwer & Stoop, 1999). Similar results were found for other
general activities including volunteering (1993, r=0.08, p<.05; 1997, r=0.08, p<.05),
organizational membership (1993, r=0.09, p<.05; 1997, r=0.09, p<.05), and recent
holiday trip (1993, r=0.11, p<.05; 1997, F021, p<.05) (Boelhouwer & Stoop, 1999). In
another study, which sampled 752 older adults (age 60 and over), frequency of television
watching was significantly negatively correlated (r=-O.23, p<.01) with life satisfaction
(Rahtz, Sirgy, & Meadow, 1988).

Despite these research efforts, few studies have focused on the relationship
between leisure experience and life satisfaction. One recent study which used the same
data as were used for the current study, attempted to understand this relationship. In that
study, differences were found in life satisfaction when persons felt their physical activity
participation was leisure compared to those who did not. Specifically, individuals who
jogged/walked for exercise and felt their experience was leisure had significantly higher
life satisfaction than those who did not jog/walk for exercise. Moreover, individuals who
weight lifted and felt their experience was leisure had higher levels of life satisfaction
than individuals who did not weight lift. In the same study, there were no significant

differences in life satisfaction in individuals who participated in non—physical activities

29

such as baking/cooking, listening to the radio, and visiting a spiritual/religious facility
(Rodriguez, 2006).

Psychological needs, health and life satisfaction. According to the need theory,
when individuals satisfy their needs, this has a salutary effect on their subjective
wellbeing (Diener & Lucas, 2000). For example, individuals who have high autonomy
have been found to also be high in self-esteem, positive affect, and psychological health
and be less likely to be self-derogatory, experience negative emotions (e.g., shame or
guilt), or experience boredom (Deci & Ryan, 1987, 1995; Ryan, 1995). In a study by
MacLeod and Conway (2005), positive affect of individuals (n=84) in a small community
in London was found to be significantly positively correlated (r=0.40, p<.001) with life
satisfaction. Moreover, negative affect in the same sample was significantly negatively
correlated (r=-0.31, p<.01) with life satisfaction. Similarly, Sheldon, Ryan, Deci and
Kasser (2004) found that in a sample of 221 undergraduates at the University of
Missouri, positive affect positively correlated with life satisfaction (r=0.61), and negative
affect negatively correlated with life satisfaction (r=-0.38). Moreover, there was a
significant main effect for relative autonomy on expected happiness (i.e., life satisfaction)
(0:050, p<.01) in different sample of undergraduates (n=714) (Sheldon, Ryan, Deci, &
Kasser, 2004). The findings that high positive affect and low negative affect are strong
predictors of life satisfaction is consistent with the other literature (Schimmack,
Radhakrishnan, Oishi, & Dzokoto, 2002; Suh, Diener, Oishi, & Triandis, 1998).

Health and life satisfaction. Individuals who have more salutary levels of physical
health and mental health generally indicate having higher levels of life satisfaction (Dear,

Henderson, & Korten, 2002). For example, in a national (Australia) household sample of

30

10,641 adults, it was found that “life satisfaction was consistently higher in persons in
good physical health” and “life satisfaction was better in persons with a low neuroticism
score. . .and a high Kessler-IO score [the K-10 Symptom Scale tests for non-specific
psychological symptoms; the higher the score, the fewer symptoms]” (Dear, Henderson,
& Korten, 2002, p. 504). Similar results were found in a study using a sample of 80
undergraduate students (King, Richards, & Stemmerich, 1998). Specifically, a negative
correlation was found between depression and life satisfaction (r=-0.59; p<.001) (King,
Richards, & Stemmerich, 1998).
Causal Effects of Leisure Experience Model

The causal effects of leisure experience model is a non-recursive model which
was proposed to better understand the relationship between leisure experience,
satisfaction of psychological needs, physical health, mental health, life satisfaction and
personal growth (Figure 2, p. 32). Specifically, the model postulates that leisure
experiences positively influence a person’s psychological needs as it helps fulfill a
person’s psychological needs. The fulfillment of a person’s psychological needs
positively influences a person’s physical health, mental health, and life satisfaction.
Moreover, physical health and mental health have a direct and positive impact on life
satisfaction, and they have a reciprocal relationship (i.e., physical health influences
mental health and mental health influences physical health). Finally, when a person has
high life satisfaction, this positively impacts their personal development (Tinsley &
Tinsley, 1986). The model itself has never been empirically tested, but it has continued to

be cited in the leisure literature in part because it makes intuitive sense.

31

      
         

Physical
Health

M

 
   
  
  

Satisfaction of
Psychological
Needs

   
 

 
   
 

Leisure
Experience

Life
Satisfaction

       
       
   
   

Personal
Growth

 

 
 

  

 

 

KN

Mental
Health

Figure 2. Original Tinsley and Tinsley (1986) causal effects or leisure experience model.

Model issues. Although the model makes intuitive sense, there are some
fundamental problems with the model. First, the causal effects of leisure experience
model was a culmination of years of research (Tinsley, 1984; Tinsley, Barrett, & Kass,
1977; Tinsley & Kass, 1978, 1979, 1980a, 1980b; Tinsley & Tinsley, 1982; Trafton &
Tinsley, 1980) which focused mainly on the psychological outcomes and motivations of
activities and experiences. The model’s development was guided by the need theory.
“Need theory rests on the assumption that there are universal human needs and that
people will experience feelings of SWB [subjective wellbeing] to the extent that these
. needs are met” (Diener & Lucas, 2000, p. 42). Mannell and Keiber (1997) indicate that
the causal effects of leisure experience model is a personal growth theory because it
ultimately focuses on personal growth, but their explanation of what constitutes a
personal grth theory is consistent with what Diener and Lucas (2000) refer to as a need
theory. Specifically, Mannell and Keiber (1997) indicate that personal growth is achieved
when important human needs are fulfilled, which is consistent with the need theory. This

clarification is significant as it indicates that the model is susceptible to similar

32

limitations as all models which are founded upon the need theory. The most critical
conceptual limitation of the need theory is that efforts to produce exhaustive lists of
biological and psychological needs have not received much empirical support (Diener &
Lucas, 2000). Although efforts have been made to produce exhaustive lists of
psychological needs (e.g., Sheldon, Elliot, Kim, & Kasser, 2001), the gap noted by
Diener and Lucas (2000) still exists in the literature. Specific to the causal effects of
leisure experience model, the model does not take into consideration biological or
physical needs which may also influence a person’s physical health, mental health, and
life satisfaction. One of the reasons for this is because leisure experience is often
associated with fulfilling psychological needs, such as autonomy and self-actualization.

Second, the model proposes a reciprocal relationship between physical health and
mental health. This is a dilemma because of the minimal empirical support for this
relationship. Instead, it is physical health which has been determined to cause mental
health (Aneshensel, Frerichs, & Huba, 1984).

Third, the model does not take into consideration the type of activities in which
leisure is experienced. In other words, the model assumes that all leisure experiences
have the same indirect and direct effects on subjective wellbeing components. This is a
dilemma because non-physical activities do not have as profound an impact on physical
health as physical activities (Iso-Ahola, 1997). On the other hand, there is a great amount
of support for the positive relationship of participation in physical activities and increased

levels of physical health.

33

Chapter III
METHODOLOGY

This study empirically tested the direct, indirect, and spurious effects of perceived
leisure experience, physical activity satisfaction of psychological needs, physical health,
mental health, and life satisfaction of residents in a Midwestern community. The
procedures or methodology used to acquire and analyze the information for this study are
presented in this chapter.
Protection of Privacy

The information collected fiom study participants was provided confidentially.
This indicates that linking data from the subject to the identification of the subject was
possible. Therefore, appropriate precautions were taken to ensure that this linkage was
not possible by anyone other than the principal investigator and the research assistant. For
example, data provided by study participants and study participants’ personal
identification information (i.e., name, address, and gender) were kept in separate datasets.
Moreover, once the data collection phase was complete, the list with study participant’s
personal identification information was destroyed, thus modifying the protection of
privacy to anonymous (i.e., no one, including the researchers, are able to associate
responses or other data with individual subjects). All procedures and instruments used in
the study were approved prior to data collection by the MSU internal review board,
University Committee on Research Involving Human Subjects (UCRIHS) (Appendix A,

p. 94).

34

Population and Sample

The population of this study is comprised of residents in a Midwestern
community. This geographical region includes four suburban municipalities, one city and
three townships. The community is an affluent area with a median household income of
$67,400. There is also a high level of education with more than 60.0% of community
residents having some college education (no degree), associates, bachelor’s, graduate, or
professional degree (US. Census Bureau, 2000).

A sampling frame consisting of registered voters was used to represent the
population. This was deemed appropriate since the study models focused on factor
changes at the individual level. Because a registered voter sampling frame was used,
there was the possibility that more a household may have obtained more than one survey
if there was more than one person in the household who was a registered voter. This
probability increased for the two communities in which only part of the townships were
used because of the higher probability of being selected in these communities.

A stratified random sample which consisted of 800 registered voters from each
municipality for a total sample of 3,200 registered voters were mailed using a modified
Dillman (2000) method consisting of three waves. The first wave included a cover letter,
survey, and postage paid return envelope (Appendix B, p. 97). The second wave was a
postcard reminder sent to nonrespondents (Appendix B, p. 97). The third wave was a
replica of the first wave, but with a revised cover letter, and it was sent to nonrespondents
(Appendix B, p. 97).

Surveys were mailed and data were collected throughout March and April of

2005, and a total of 633 completed surveys were returned yielding a 21.3% response rate

35

(225 surveys were returned as bad addresses and 21 were returned as refusals). Overall,
the largest socio-demographics represented included study respondents who were female
(58.9%), 45-54 years old (28.8%), White (98.1%), married (78.9%), did not have children
in the household (60.1%), and had household incomes of $100,000 and over (39.3%).
Given the low response rate, there was a higher probability that the sample might
be different from the study population. Therefore, the chi-square goodness of fit test was
employed to test whether the observed frequencies differed from their expected values

(obtained from the US. Census Bureau, 2000). Results indicated that the sample differed
significantly from the population by gender [380) = 24.65, p < .001; more women than
expected], education [78(5) = 227.39, p < .001; more higher education degrees and fewer
high school graduates or GED than expected], marital status [x2(3) = 63.82, p < .001;
more married and fewer single, never married than expected], age [x2(4) = 82.23, p <
.001; more 45-54, 55-64, and 65 and over and fewer less than 35 and 35-44 than
expected], income [x2(4) = 63.59, p < .001; more $100,000 and over and fewer under
$24,999 and 825,000-849,999 than expected], and whether there were children at home
[78(1) = 32.29, p < .001; more with no children in the home than expected].

Although the sample differed from the population regarding the analyzed socio-
demographics, differences in physical health, mental health, and life satisfaction of study
participants (see Table 1, p. 38) were generally consistent with the literature. For
example, individuals who were younger generally had higher physical health than those

who were older (Campbell, 1981). In addition, participants who had higher education

levels and household incomes generally had higher levels of life satisfaction than those

36

 

participants who had lower levels of education and household incomes (Diener &

Biswas-Diener, 2002).

37

Table I. Study respondents, life satisfaction, physical health, and mental health by soda-demographics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Physical Health a Mental Health b Life Satisfaction c
N % Mean SD. Mean SD. Mean SD.

Overall 633 100 50.0 8.5 52.0 8.2 26.1 6.4
Gender

Male 259 41.1 49.0 * 9.1 52.6 8.1 25.5 * 6.3

Female 371 58.9 50.7 * 8.0 51.7 8.3 26.6 * 6.4
Age (1

Less than 35 81 13.2 52.3 * 6.8 49.6 7.9 24.6 6.1

35-44 131 21.3 52.3 * 6.9 51.6 7.7 26.8 6.1

45-54 177 28.8 50.5 8.7 51.8 8.6 25.6 6.7

55-64 120 19.5 48.5* 9.4 53.5 7.9 26.6 6.4

65 and over 105 17.1 46.7* 8.5 52.7 8.8 26.4 6.5
Highest Education Attained e f}

Less than 12th Grade 12 1.9 45.3 9.3 48.4 7.7 22.4 9.0

 

High School Graduate/GED 72 11.3 48.6 9.0 52.0 9.1 24.8* 7.1

 

Some College (no degree) 150 23.6 49.1 8.8 49.9"I 10.0 24.2" 6.8

 

Associate DegLee 55 8.6 50.2 7.9 53.5 6.2 27.1* 5.6

 

Bachelor's Degree 210 33.0 51.0 8.0 52.1 7.4 26.9* 5.6

 

Graduate/Professional Degree 137 21.5 50.5 8.6 538* 6.9 27.6* 6.2

 

2004 Household Income h i j kl

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

$24,999 and under 41 7.4 44.6“ 10.9 488* 9.7 21.9”“ 7.3
$25,000-$49,999 75 13.5 48.7 8.3 47.8”“ 11.5 22.5“ 7.6
$50,000-$74,999 120 21.5 49.4* 9.0 52.4* 7.9 255* 5.9
$75,000-S99,999 102 18.3 49.6* 8.0 52.4* 7.1 26.5* 5.4
$100,000 and over 219 39.3 51.6* 7.8 52.9* 6.9 27.9* 5.5
Marital Status m n
Single, Never Married 57 8.9 51.4 7.5 50.1 8.5 22.9“ 6.4
Widowed 31 4.8 46.9 11.3 48.1* 10.4 22.4* 8.1
Married 505 78.9 50.0 8.4 52.6““ 7.8 26.9“ 6.0
Divorced or Separated 47 7.3 50.9 8.4 51.0 9.8 242* 7.5
Children
No children at home 392 60.1 48.7* 8.8 52.1 8.8 25.7 6.6
Children at home 260 39.9 52.0* 7.6 51.7 7.4 26.6 6.1

 

 

*=p<.05 (2-tailed; comparisons made for each socio-demographic within each SWB factor)
a=scores range from 0-100; higher scores indicate a higher level of physical health
b=scores range from 0-100; higher scores indicate a higher level of mental health
c=scores range from 5-35; higher scores indicate a higher level of life satisfaction

=age less than 35 & 35.44 > 55-64 > 65 and over for PH
e=Graduate/Professional Degree > Some College (no degree) for MH
f=Graduate/Professional Degree > Some College (no degree) and High School Graduate/GED for LS
g= Bachelor's Degree and Associate Degree > Some College (no degree) for LS
h=$ 100,000 and over, $75,000-$99,999 and $50,000-S74,999 > under $24,999 for PH
i=$lO0,000 and over > $25,000-$49,999 and under $24,999 for MH
j=$75,000-$99,999 and $50,000-$74,999 > $25,000-$49,999 for MH
k=$100,000 and over > 550,000-874,999 > $25,000-$49,999 and under $24,999 for LS
l=$75,000-$99,999 > $25,000-$49,999 and under $24,999 for LS
m=Married > Widow for MH
m=Married > Single, never married, divorced or separated, and widow for LS

38

Instrumentation

Activity participation and leisure experience. Study participants were asked to
indicate how many times they participated, within the past four weeks, in a set of
predetermined activities. Predetermined activities from six of the 12 clusters were used in
the overall study (based on Tinsley & Eldredge, 1995) (Appendix C, p. 109). The clusters
included the agency cluster (jogging/walking for exercise, downhill skiing, swimming
and weight lifting), service cluster (attending religious/spiritual facility and visiting
friends/relatives), sensual enjoyment cluster (attending plays, dining out, and going to the
movies), creativity cluster (baking/cooking and photography), competition cluster (cards
and computer games), and relaxation cluster (bingo, radio listening, and watching
television). In addition to the predetermined activities, study participants were provided
four spaces for four additional activities which they have participated within the past four
weeks.

In addition to indicating the number of times they participated in each activity, the
participants were also asked to indicate whether they perceived the activity to be leisure
for them during the same amount of time (i.e., past four weeks) (Appendix C, p. 109).
Therefore, leisure experience was measured subjectively from the participants’
perspective as opposed to assuming that their participation in the activity was necessarily
a leisure experience. Specifically, participants answered the question, “Do you think this
is leisure?” for each of the predetermined activities. This strategy is consistent with
findings which indicate that an understanding of subjective leisure experience can be
understood by asking participants whether they think an experience is leisure or not

(Samdahl, 1991). Moreover, an overall frequency of leisure experience was obtained,

39

consistent with recommendations by Mannell (1980); “. . .a more meaningful analysis
would result from an examination of the relationships between the frequency and
intensity of leisure experiences (rather than the number or types of activities) and an
individual’s current state of well-being” (p. 85).

Table 2 contains a distribution of responses by activities and participation type
(i.e., no participation, participation perceived as leisure, or participation not perceived as
leisure). There were differences by activities and participation type. For example, for
activities such as downhill skiing and bingo, the vast majority of study participants did
not participate in the activity within the past four weeks (84.7% and 96.8% respectively).
For other activities, the majority of individuals who did indicate they participated in the
activity within the past four weeks also indicated they perceived the activity was leisure,
such as the case with dining out and watching television (88.9% and 92.8% respectively).

Physical activity scale. A physical activity scale was created using the
predetermined activities as well as activities which were added by the study participants
in the four spaces provided. All activities were used to determine overall physical activity
level for study participants. In order to classify the level of intensity for each activity, an
objective process which included weighing activity frequencies by the appropriate
metabolic equivalent task (MET) then summing each study participant’s total MET was
employed. To obtain the METs for each activity, the activities in the study were
compared to a list of over 600 activities which contained appropriate METs for each
activity (Ainsworth et al., 2000). The definition of a MET is the ratio of work metabolic
rate to a standard resting metabolic rate (Ainsworth et al., 2000). In other words, one

MET equals the energy (oxygen) used by the body as a person sits quietly (CDC, 2006).

40

Moderate activities increase energy expenditure by at least three METs per hour (CDC,

2006). It has been recommended by the Centers for Disease Control and Prevention

(CDC) and the American College of Sports Medicine (ACSM) that adults should

accumulate at least 30 minutes of regular, moderate-intensity physical activity on most

days of the week (Ainsworth et al., 2000). Examples of moderate activities include golf,

yoga, horseback riding, and hunting. Vigorous activities increase energy expenditure by

at least six METs (CDC, 2006). Examples of vigorous activities include jumping rope,

stationary bike, and soccer. Appendix D (p. 113) includes a list of all the activities which

METs were provided for in this study. The activities were grouped by whether they were

a predetermined activity or not, and they were presented in alphabetical order.

Table 2. Study respondents ' activity participation distribution

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Overall No Participation Participation + NL Participation + L

Agency Activities N % N % N % N %

Downhill skiing 367 100 31 1 84.7 2 0.5 54 14.7

Jogging/walking for exercise 502 100 72 14.3 92 18.3 338 67.3

Swimming 372 100 285 76.6 7 1.9 80 21.5

Wflht lifting 394 100 219 55.6 64 16.2 111 28.2
Service Activities

Attending “1’9““ 475 100 122 25 7 172 36 2 181 38 1

spiritual facility ' ' '

Visiting friends/relatives 572 100 311 84.7 2 0.5 54 14.7
Sensual Enjoyment Activities

Attendirg plays 413 100 300 72.6 1 0.2 112 27.1

Dinirg out 577 100 7 1.2 57 9.9 513 88.9

Goingto the movies 484 100 158 32.6 5 1.0 321 66.3
Creativity Activities

Bakm/cooking 495 100 61 12.3 272 54.9 162 32.7

Photography 415 100 171 41.2 38 9.2 206 49.6
Competition Activities

Cards 427 100 196 45.9 3 0.7 228 53.4

Computer games 411 100 187 45.5 3 0.7 221 53.8
Relaxation Activities

Bing 348 100 337 96.8 2 0.6 9 2.6

Radio listening 531 100 21 4.0 101 19.0 409 77.0

Watching television 570 100 9 l .6 32 5.6 529 92.8

 

 

 

Note. NL=Not perceived as leisure; L=Perceived as leisure.

41

Leisure experience scale. Participation in different activities has been found to
affect psychological needs differently (Tinsley & Eldredge, 1995), but when an activity is
subjectively perceived as a leisure experience, despite what the activity is, it has positive
implications for a person’s psychological need of autonomy. Therefore, a leisure
experience scale was created using all the predetermined activities as well as the
activities which were added by the study participants in the four spaces provided. There
were a total of 20 possible activities a study participant could indicate as a leisure
experience. Therefore, the frequency of each activity was weighed by whether the study
participant felt their participation was a leisure experience or not. Using this strategy
assumes that each frequency was perceived as a leisure experience when the study
participant indicated that participation in the specific activity was leisure. Afterwards,
each leisure experience frequency was summed to obtain a total leisure experience
frequency (i.e., leisure experience scale).

Autonomy satisfaction. A modified subscale of the Recreation Experience
Preference Scales (REP) (Driver, Tinsley, & Manfredo, 1991; Manfredo, Driver, &
Tarrant, 1996) was used to measure study participant’s level of autonomy satisfaction
(Appendix C, p. 109). The REP was originally used to analyze the importance individuals
place on leisure specific outcomes. This would then be used to understand why
individuals participated in specific activities and in specific settings. The instrument was
modified to measure levels of satisfaction as opposed to levels of importance. Therefore,
the instrument is composed of three items on a seven-point Likert scale (l=completely
dissatisfied; 7=completely satisfied). The REP has shown to have Cronbach alphas of

over 0.75 (Driver, Tinsley, & Manfredo, 1991), and it has been found to have both

42

concurrent validity (Tinsley, Driver, & Kass, 1982) and construct validity (Rosenthal,
Driver, & Waldman, 1982).

Physical and mental health. Physical and mental health were measured using a
short version of the SF-36®, the SF-8TM Health Survey (Ware, Kosinski, Dewey, &
Gandek, 2001) (Appendix C, p. 109). The SF-36® has been recognized as a good
instrument to measure health-related quality of life in the general population as it meets
two important criteria of good health-related quality of life instruments. First, the
instrument measures health from a subjective perspective (Rej eski, Brawley, &
Shumaker, 1996). Second, the instrument measures various elements associated with
health including a global indices of health-related quality of life, physical function,
physical symptoms/states, emotional function and social function (Rejeski, Brawley, &
Shumaker, 1996). The SF-8TM measures physical and mental health using the same eight
constructs as the SF-36® (i.e., general health, physical functioning, role physical, and
bodily pain for physical health and vitality, social functioning, mental health, and role
emotional for mental health), but it uses single-items to measure the eight constructs. In
order to make SF-8TM results comparable to those of the SF-36®, results are scored using
a norm-based scoring method. Therefore, scores range from 0-100. Detailed procedures
are provided in Ware et a1. (2001). Psychometric analysis of the SF-8TM have shown it to
have strong reliability and convergent validity with the SF-36® (Ware, Kosinski, Dewey,
& Gandek, 2001).

Life satisfaction. The Satisfaction with Life Scale (Diener, Emmons, Larson, &
Griffin, 1985) was used to measure the satisfaction individuals had with their overall life

(Appendix C, p. 109). The instrument is composed of five items on a seven-point Likert

43

scale (1=strongly disagree; 7=strongly agree). Item scores were summed to provide a
possible range of scores (5=lowest, 35=highest). The instrument has shown to have good
validity and reliability (Larson, Diener, & Emmons, 1985; Pavot & Diener, 1993;

Shevlin, Brunsden, & Miles, 1998; Shevlin & Bunting, 1994).

Chapter IV
ANALYSIS

This study empirically tested the direct, indirect, and spurious effects of perceived
leisure experience, physical activity, satisfaction of psychological needs (i.e., autonomy),
physical health, mental health, and life satisfaction of residents in a Midwestern
community. The steps used to analyze the data (i.e., testing of the measurement model
and the structural model) and the results of the analysis are discussed in this chapter. The
overall steps used to analyze the data were heavily influenced by the work of Hunter and
Gerbing (1982). Moreover, a dissertation by Gomez (1999) was instrumental in
comprehending specific analysis steps. In addition to the study steps and results, study
hypotheses were examined.

Measurement Model

Testing the unidimensionality of indicators in a measurement model is done using
three criteria. First, the face validity of the indicators must be established. Face validity
refers to whether the indicators seem to measure the same factor. According to the
researcher of the current study and the researchers from each of the instruments, each of
the indicators seemed to measure the respective factor which they were measuring.

The second criterion in determining the unidimensionality of indicators is to test
indicators using the internal consistency theorem. There are two steps to test for internal
consistency. The first step involves estimating the parameters riT from the data. The
second step is to “see if the product rule reproduces the inter—item correlations to within

sampling error” (Hunter & Gerbing, 1982, p. 277).

45

When testing the parameters, it is important to remember that only models with
four or more indicators could be analyzed using the internal consistency theorem because
these models were over-identified. Models with three indicators are just-identified while
models with one or two indicators are under-identified. In order to test models which are
just-identified, the assumption of equal factor loadings must be made. Under-identified
models cannot be tested. Therefore, only the Satisfaction with Life Scale (five
indicators), and the physical (four indicators) and mental health (four indicators)
summaries from the SF-8TM were tested for internal consistency. The technique used to
estimate the factor loadings, the centroid multiple groups analysis, was developed by
Hunter and Gerbing (1982). The technique includes estimating factor loadings by
summing the indicator correlations and dividing by the square root of the sum of the
correlation matrix,

k k
1'1T = ,Zrli/f Zrlijls (1)

(=1 1:]:

where T is the true score.

To obtain a stable solution, the estimates must be iterated (i.e., factor loadings
must be reestimated). This is done by squaring the factor loadings (i.e., communalities or
reliabilities) and placing them in the diagonals of the correlation matrix. By using
communalities in the diagonals of the correlation matrix, this corrects for attenuated
correlations due to measurement error as perfect reliability is not assumed (Hunter &
Gerbing, 1982). The iterations are continued until the solution stabilizes. It usually takes
three iterations to stabilize solution, which is why the program developed to test for

internal consistency by Hunter and Hamilton (1992) called CFA limits the number of

46

iterations to three. Stabilized factor loadings are then used to estimate predicted
correlations (Equation 1).

The product rule for internal consistency is provided in Equation 2, and it
indicates that if two indicators are from the same factor, then the correlation between the
two variables should equal the product of the factor loadings of these two variables,

rij = fir In (2)
where T is the true score or the factor and riT and ro are factor loadings.

Testing of the product rule was completed using both the CFA program as well as
Microsoft Excel which was used to assist with testing confidence intervals of predicted
and observed correlations. This is explained further in the criteria for decision-making of
the internal consistency theorem. The program CFA is a least squares, oblique multiple
groups program which focuses on performing confirmatory factor analysis. Specifically,
the program estimates inter-factor (i.e., internal consistency) and factor-factor (i.e.,
external consistency) correlations as well as cluster reliabilities (i.e., standard score
coefficient alpha; Spearman-Brown reliability).

The third criterion in determining unidimensionality is to test for external
consistency (also called parallelism). The steps involved in testing for external
consistency are essentially the same as for internal consistency. Moreover, the CFA
program and Microsoft Excel were used to assist with the steps. In the first step, the
parameters must be estimated from the data. This is performed as indicated for internal
consistency. In the second step, the inter-factor correlations are checked to see if they are
within sampling error of the predicted correlations. The primary difference is that where

internal consistency predicted how indicators of one factor should correlate with each

47

other, external consistency predicts how indicators of one factor should correlate with
indicators of another factor. As such, this step should only be performed once internal
consistency within the factors has been established and is only possible when there is
more than one factor being analyzed. The product rule (see Equation 3) for external
consistency posits that the correlation of two separate factors is the product of their factor
loadings and the correlation between the factors,

1'xy = I.xT I.yU I‘TU (3)
where T is the true score for construct T, U is the true score for construct U, and TU is
the correlation between T and U corrected for attenuation (i.e., the correlations divided by
the product of the factor loadings).
Internal Consistency Results

Three initial internal consistency tests were performed (i.e., physical health scale,

mental health scale, and satisfaction with life scale). Four statistical techniques were
employed to help test the fit of the measurement models: chi-square, chi-square ratio,
Root Mean Square Residual (RMSR) and confidence intervals around the observed and
predicted correlations. Emphasis was placed on the confidence intervals. The first test
used to determine whether the residual was equal to zero was the chi-square test. In order
to determine that residuals were zero within sampling error, chi-squares should have been
small and not significantly different from zero (i.e., p>.05). Chi-square was used to test
the overall fit of the measurement model (i.e., inter-factor and factor-factor). Since chi-
square is a direct function of the sample size, the probability of rejecting the null
hypothesis increases as the sample size increases. Given that this study had a relatively

large sample size (i.e., over 600), three additional tests were also employed to help

48

 

determine the fit of the data to the measurement models. The second test is known as the

chi-square/degree of freedom ratio (Munro, 2005). Recommended ratios often range from

3:1 to 5:1 as cutoffs for good fit (Munro, 2005). The RMSR is the third test, and it is

calculated by calculating the square root of the mean of the squared residuals. Root

means square residuals greater than 0.10 constitute justification to reject the model

(Munro, 2005). The fourth test was the confidence interval tests (CI). Specifically, the .
researcher was interested in whether the observed correlation fell within the CI of the
predicted correlation and if the predicted correlation fell within the CI of the observed
correlation. If either the observed or predicted correlation fell out of the respective CI,
this would provide evidence that a specific indicator would not make a model fit the data
or that the indicator’s removal would make the model fit the data better. A summary table
with all the indicators, including the indicators removed a posteriori, is presented in Table

3.

49

Table 3. Summary of factor indicators

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Indicators e Mean SD

1 SF8 General Health b 50.45 6.93
2 SF8 Physical Functioning c 48.50 7.67
3 SF8 Role Physical c 49.29 6.92
4 SF 8 Bodily Pain c 51.41 8.09
5 SF8 Vitality a 51.58 7.32
6 SF 8 Social Functioninga_ 50.22 6.99
7 SF8 Mental Health c 51.16 7.33
8 SF 8 Role Emotional c 48.74 6.09
9 In most ways my life is close to my ideal c 5.06 1.47
10 The conditions of my life are excellent c 5.19 1.41
11 I am satisfied with my life c 5.43 1.36
12 So far I have received the important things 1 want in life a 5.59 1.35
13 If I could live my life over, I would change almost nothing c 4.84 1.74
14 Be your own boss c 5.00 1.41
15 Think for yourself c 5.65 1.22
16 Be free to make your own choices c 5.68 1.23
17 Leisure experience scale (1 67.08 34.64
18 Physical activity scale (1 201.70 106.09

a=Indicator eliminated in internal consistency analysis

b=Indicator eliminated in external consistency analysis

c=Indicators used in the final confirmatory factor analysis

d=One-item indicators

c=Indicators 1-4 (physical health), 5-8 (mental health), 9-13 (life satisfaction), 14-16

(autonomy)

 

Physical health. The four physical health indicators were found to have internal
consistency. This decision was influenced by the results of the chi-square, chi-square/df
ratio, RMSR and the CI tests. A chi-square of 8.54 (5 degrees of freedom; p>.05)
indicated that overall, the model residuals did not depart from zero. A chi-square/df ratio
of 1.71 was below the recommended 3:1 ratio. The RMSR of 0.01 was below the
recommended 0.10. A confidence interval test was employed to determine whether the
conclusion from the chi-square test would be supported by the additional test.

The obtained correlations (bold; bottom left) and the residuals (italicized; top

right) for the four physical health indicators were presented in Table 4. The largest

50

residuals came from items 1-2 and items 3-4. Both had residuals of 0.02. Although small,
it is important to check whether the residuals significantly differ from zero given the
sample (n=633). Given that 0.50 (obtained correlation) fell within 0.47 and 0.58 (CI of
the predicted correlation) and that 0.52 (predicted correlation) fell within 0.44 and 0.56
(CI of the obtained correlation), 0.02 (0.52-0.50) was within sampling error. This also
provided support for the unidimensionality of the indicators. Therefore, all four indicators

were retained.

Table 4. Physical health corrected obtained correlations and residuals

 

1 2 3 4

-0. 02 0. 01 0. 01

0.50 0. 0] 0. 01

0.58 0.79 -0. 02
0.46 0.62 0.65

 

Mental health. The four mental health indicators were found to not have internal
consistency. This decision was influenced by the results of the chi-square, chi-square/df
ratio, RMSR and the CI tests. A chi-square of 62.88 (5 degrees of freedom; p<.05)
indicated that the indicators were not internally consistent. The chi-square/df ratio
supported this conclusion. Specifically, a chi-square/df ratio of 12.58 was above either
the recommended 3:1 or 5:1 ratio. On the other hand, the RMSR, 0.05, was lower than
the recommended 0.10. A confidence interval test was employed to determine which
indicator(s) had the largest residual.

The obtained correlations (bold; bottom left) and the residuals (italicized; top

ri ght) for the four mental health indicators were presented in Table 5. It was found that
the obtained correlation between indicator 7 and 8 (0.69) did not fall within the

Confidence interval of the predicted correlation (0.56 and 0.66). Likewise, the predicted

51

correlation (0.61) also did not fall within the observed correlation’s confidence interval
(0.65 and 0.73). Moreover, the obtained correlation between indicator 5 and indicator 6
(0.54) did not fall within 0.41 and 0.53 (CI of the predicted correlation) and 0.47
(predicted correlation) also did not fall within 0.49 and 0.59 (CI of the obtained
correlation). Therefore, an error of 0.07 (0.54—0.47) was not within sampling error. The
results indicated that all four indicators were not unidimensional. Upon further inspection
of the correlations, indicators 7 and 8 correlated more highly with each other than with
indicators 5 and 6. Moreover, indicators 7 and 8 were more consistent with how mental
health has been operationalized in subjective wellbeing literature (i.e., mental health as
lack of mental illness and emotional dilemmas). Therefore, indicators 7 (mental health)
and 8 (role-emotional) were used in subsequent analysis. Although the internal
consistency test cannot be employed with two indicators, an external consistency test can

be employed with two indicators.

Table 5. Mental health corrected obtained correlations and residuals

 

5 6 7 8

0.07 -0.05 -0.03

0.54 -0. 03 -0. 04

0.39 0.53 0.08
0.48 0.61 0.69

 

Life satisfaction. The five life satisfaction indicators were not internal
Consistency. This decision was influenced by the results of the chi-square, chi-square/df
ratio, RMSR and the CI tests. A chi-square of 51.19 (9 degrees of freedom; p>.05)
indicated that the indicators were not internally consistent. The chi-square/df ratio

Supported this conclusion. Specifically, the chi-square/df ratio of 5.69 was above the

52

recommended 3:1 and 5:1 ratios. On the other hand, the RMSR of 0.03 was below the
recommended 0.10. A confidence interval test was employed to determine which
indicators had the largest residuals.

The obtained correlations (bold; bottom left) and the residuals (italicized; top
right) for the five life satisfaction indicators were presented in Table 6. Although the
observed correlation between indicator 12 and indicator 13 (0.64) fell within 0.54 and
0.64 (CI of the predicted correlation) and the predicted correlation (0.59) fell within 0.59
and 0.69 (CI of the obtained correlation), the residual of 0.05 (0.64-0.59) were very close
to not being within the confidence intervals.

Upon further inspection of the residuals, indicator 12 had several of the larger
residuals associated with it. Therefore, item 12 was removed, and the model was tested
again for internal consistency (Table 7). Results indicated that the removal of indicator 12
made the model fit with a chi-square of 7.48 (5 degrees of freedom; p>.05), chi-square/df
of 1.50 and RMSR of 0.01. In addition, inspection of the residuals indicates very small

residuals.

Table 6. Life satisfaction corrected obtained correlations and residuals

 

9 10 l l 12 1 3

0.04 0. 00 -0. 03 -0. 01

0.81 0.01 -0.03 -0. 02

0.79 0.82 0. 0] -0. 02

0.68 0.69 0.75 0.05
0.62 0.62 0.65 0.64

53

Table 7. Revised life satisfaction corrected obtained correlations and residuals

 

9 10 1 1 l3

0. 02 -0. 0] 0. 01

0.81 0.00 -0. 01

0.79 0.82 0. 0]
0.62 0.62 0.65

 

Note. Indicator 12 was removed.

Autonomy, leisure experience, and physical activity. As aforementioned,
autonomy, leisure experience, and physical activity could not be tested for internal
consistency since the factors were either just-identified or under-identified.

External Consistency Results

The test of external consistency was first performed with 13 indicators. In total,
three indicators were removed after the tests for internal consistency (Table 3, p. 50).
Moreover, three indicators of autonomy were added to the correlation matrix. Among the
13 indicators there were a total of 78 correlations ((13*13)-13)/2). Therefore, about four
errors (78/20) should be expected to be large just by chance. Moreover, residuals greater
than 0.07 can be expected to considered large (i.e., observed correlations are outside of
the predicted correlation confidence interval or the predicted correlations are outside of
the observed confidence interval).

Similar to the tests for internal consistency, the chi-square, chi-square/df ratio,
RMSR and confidence intervals were used to test the fit of the models. If residuals were

significantly different from zero for the confidence intervals, this indicated that a specific

54

indicator correlated more highly with another construct than with the construct it was
theoretically supposed to correlate.

The general statement of parallelism [i.e., external consistency] is that the

items in a unidimensional cluster have similar patterns of correlations with

(1) items in other clusters or (2) other traits [i.e., factors]. In fact, the

correlations for items of the same quality should be equal (to within-

sampling error) across all other variables. (Hunter & Gerbing, 1982, p.

279)

First round. In the first round of testing for external consistency, the chi-square
for each factor indicated that the residuals were not significantly different from zero,
except for physical health which had a chi-square of 86.88 (9 degrees of freedom; p<.05),
a chi-square/df ratio of 9.65 (above the recommended 3:1 ratio), and a RMSR of 0.09
which was close to the recommended 0.10 cutoff. Alter inspection of the residuals, it was
found that most of the larger residuals were associated with the first indicator of physical
health (i.e., general health). Specifically, this item had eight large residuals associated
with it. Therefore, this indicator was removed.

Second round. The second round of testing for external consistency was first
performed with 12 indicators (first indicator, general health, for physical health was
removed) (Table 3, p. 50). Among the 12 indicators there were a total of 66 correlations.
Therefore, about three errors were expected to be large just by chance.

After removing the first physical health indicator (general health), none of the chi-

square tests indicated that the residuals were significantly different from zero. Moreover,

55

all x2/df ratios were below the recommended 3:1 ratio and all RMSRs were below 0.10.
A summary of these results is presented in Table 8.

Despite these findings, confidence intervals of residuals for obtained and
predicted scores were analyzed. The results of this analysis are provided in Table 9 (p.
57). Observed correlations were provided in the bottom left, and residuals (difference
between observed and predicted correlations) were provided on the top right. Lined boxes
separated individual tests for external consistency. Factor loadings were also presented.
Overall, only one residual stood out as a large residual (0.09). Given the number of
correlations, this was expected and therefore did not influence the overall decision that

the residuals were not significantly different from zero.

Table 8. Test of fit results for external consistency analysis

 

Factor x2 (df) P-value Gradient/F lat x2/df RMSR
Physical health 6.12 (6) 0.41 Gradient 1.02 0.04
Mental health 6.61 (3) 0.09 Flat 2.20 0.02
Life satisfaction 7.27 (9) 0.61 Gradient 0.81 0.02
Autonomy 9.59 (6) 0.14 Gradient 1.60 0.03

 

Note. Leisure experience and physical activity were under-estimated. Therefore, external consistency
could not be analyzed for these factors. Also, gradient/flat indicates whether the reliabilities were flat
(e.g., 0.08, 0.08, 0.08) or gradient (e.g., 0.09, 0.08, 0.07, 0.06). Program CFA provides different chi-
square results for each. RMSR were calculated for each factor’s overall residuals.

56

Table 9. Summary of corrected observed correlations and residuals from the final external consistency
analysis

1
2
3
4
5
6
7
8
9

10
11
12
F1
F2
F3
F4

 

1

PH
2

MH
4 5

6

LS
7 8

9

10

AUT
l l

12

 

 

1.00
0.79
0.62
0.27
0.35
0.31
0.31
0.29
0.20
0.04
0.10
0.12
0.87
0.37
0.33
0.12

1.00
0.65
0.33
0.44
0.31
0.35
0.30
0.22
0.01
0.11
0.13
0.91
0.46
0.35
0.11

1.00
0.29
0.35
0.27
0.28
0.25
0.16
0.05
0.03
0.04
0.71
0.39
0.28
0.05

 

-0.09 -0.01
-0.04 0.07
0.01 0.06

0.01
0.00
0.04

0.01 -0.01
0.04 -0.01
0.03 0.01

-0.04
-0.03
-0.03

-0.02
-0.06
0.00

0.03
0.04
-0.03

0.03
0.04
-0.03

 

1.00

0.69 1.00
0.45 0.46
0.46 0.49
0.51 0.48
0.32 0.35
0.18 0.13
0.16 0.19
0.19 0.22
0.36 0.46
0.83 0.83
0.51 0.52
0.24 0.24

 

0.00
0.01

-0.01 0.04
0.02 0.01

-0.04
-0.01

0.03
-0.02

-0.01
0.02

-0.03
0.00

 

1.00
0.81
0.79
0.62
0.30
0.29
0.31
0.36
0.55
0.88
0.40

1.00

0.82 1.00
0.62 0.65
0.29 0.30
0.29 0.30
0.30 0.31
0.38 0.34
0.57 0.60
0.90 0.91
0.39 0.40

1.00
0.24
0.21
0.23
0.23
0.40
0.70
0.30

 

0.06
0.05
0.05
0.05

0.00
0.00
0.01
-0.02

-0.04
-0.06
-0.05
-0.04

 

1.00
0.45
0.56
0.04
0.19
0.33
0.62

1.00
0.66
0.10
0.21
0.32
0.73

1.00

Factor Loadings

F1
0.87
0.91
0.71
0.36
0.46
0.36
0.38
0.34
0.23
0.04
0.10
0.12

F2
0.37
0.46
0.39
0.83
0.83
0.55
0.57
0.60
0.40
0.19
0.21
0.25

F3
0.33
0.35
0.28
0.51
0.52
0.88
0.90
0.91
0.70
0.33
0.32
0.34

F4
0.12
0.11
0.05
0.24
0.24
0.40
0.39
0.40
0.30
0.62
0.73
0.90

 

0.12
0.25
0.34
0.901

 

1.00
0.49
0.38
0.11

0.49
1.00
0.62
0.29

0.38
0.62
1.00
0.44

0.1 1
0.29
0.44
1.00

 

Note. PH=Physica1 health; MH=Mental health; LS=Life satisfaction; AUT=Autonomy. Boxed residuals
and factor loadings were used to test for parallelism.

57

 

Reliability

Factor reliabilities were calculated using the program CFA. As aforementioned, a
standardized coefficient alpha was used to produce reliabilities. It is important to note
that reliabilities were calculated only after unidimensionality was determined using the
three criteria used in this study (i.e., face validity, internal consistency, and external
consistency). This is consistent with recommendations by Hunter and Gerbing (1982).
Specifically, Hunter and Gerbing (1982) indicated that, “coefficient alpha produces an
unbiased estimate of the reliability of the cluster score only if the scale is
unidimensional” (p. 281). A summary of results is presented in Table 10. Overall, the

four factors had acceptable reliabilities (i.e., all reliabilities were 0.79 or higher).

Table 10. Summary of factor reliabilities

 

Factor Standard score
coefficrent alpha
Physical health 0. 87
Mental health 0. 82
Life satisfaction 0.91
Autonomy 0.79

 

Note. Perfect reliabilities for leisure experience and physical activity
were assumed given single indicator measurement.

58

Causal Model

After the measurement models were tested and the indicators were found to be
unidimensional, the process of testing the causal model or path analysis could begin. The
procedures used to test the causal model were not very different from those of the
measurement model. For example, for both models,

the researcher must (1) construct the model, (2) estimate the values of

the parameters of the model from the data, the observed correlations

among the variables in the model, and (3) test the fit of the model to the

data by comparing the observed correlations with the correlations among

the variables predicted by the model. GIunter & Gerbing, 1982, p. 268)
Model Construction

Construction of the model, including any modification or respecification of the
model links, were done with a theoretical framework influencing the construction or
modifications of the model (Anderson & Gerbing, 1988). For example, modification of
the original causal effects of leisure experience model were discussed in detail in Chapter
2 (i.e., Literature Review, p. 11).
Estimation of Parameters

The next two steps (i.e., estimation of parameters or path coefficients and testing
the fit of the model) in the path analysis were completed using path analysis software
called PATH, which was developed by Hunter and Hamilton (1995), and Microsoft Excel
(used to calculate chi-square/df ratio, RMSR, and confidence intervals around the
obtained and predicted correlations). PATH is a least squares path analysis program

which estimates path coefficients using multiple regression (i.e., ordinary least squares).

59

Multiple correlations for all endogenous variables were computed, but since exogenous
variables were not predicted in the model, zeros were provided for all exogenous
variables. The difference between endogenous and exogenous variables is that
endogenous variables have their causal antecedents specified within the model which is
being considered (Anderson & Gerbing, 1988). On the other hand, the causal antecedents
of exogenous variables are outside of the model which is being considered (Anderson &
Gerbing, 1988). PATH also generated the residuals (i.e., discrepancies between observed
and reproduced correlations) and tests the model fit using chi-square goodness of fit.
Moreover PATH computed the standard error for the deviation in each non-
predetermined correlation (Hunter & Hamilton, 1995). This was then used to, “compute a
z-value which has a standard normal distribution if the path model correctly predicts that
correlation. An overall chi-square test is computed from the cell-by-cell analysis” (Hunter
& Hamilton, 1995, p. 3).

An estimated parameter (i.e., path coefficient) is equivalent to a standardized
regression coefficient indicating the direct effect of an independent variable on a
dependent variable. In PATH, path coefficients are calculated using observed correlations
corrected for attenuation (Table 11, p. 61). Uncorrected correlations are corrected for
attenuation using factor reliabilities (Table 10, p. 58). Path coefficients between two
exogenous variables (e.g., LE and PA in Figure l) are equivalent to correlation
coefficients corrected for attenuation. If a dependent variable (Y) is influenced by two
causal antecedents (X1 and X2), then the path coefficients for X1 and X2 are the beta
weights in the regression of Y onto X1 and X2. When there are three or more variables

(e.g., if Y mediated the relationship between X and Z), the path is the sum of the products

60

of the direct, indirect, and spurious effects’ path coefficients. In the example presented,

pZX would be the product of pXY and pYZ. (Hunter & Gerbing, 1982).

Table 1]. Path analysis uncorrected and corrected observed correlation matrix

 

 

Factor 1 2 3 4 5 6
1. LE 0.44 0.06 -0.01 -0.01 0.05
2. PA 0.44 0.14 0.05 0.02 0.13
3. AUT 0.05 0.12 0.14 0.19 0.32
4. PH -0.01 0.05 0.12 ' 0.65 0.35
5. MH -0.01 0.02 0.15 0.55 0.41
6. LS 0.05 0.12 0.27 0.31 0.35

 

Note. Uncorrected correlations on bottom left. Correlations corrected for attenuation using program
PATH located on top right.

Path Analysis Results

Path analysis results for three different path models were presented in this section.
The first model was the equivalent of the original causal effects of leisure experience
model proposed by Tinsley and Tinsley (1986) (Figure 2, p. 32). The second model tested
was the theoretically revised causal effects of leisure experience model (Figure 1, p. 6).
The last model tested was the final model (Figure 3, p. 67).
Original Model

The original causal effects of leisure experience model proposed by Tinsley and
Tinsley (1986) was tested first (Figure 2, p. 32). In order to test the original model using
the study data, two modifications were made. The first discrepancy focused on the link
between physical health and mental health. The original model proposed a reciprocal
relationship whereas the tested relationship indicates that physical health exerts a causal

influence on mental health. This rationale was discussed in Chapter 2 (i.e., Literature

61

Review, p. 11). The second discrepancy focused on the omission of personal
development from the analysis. Data on personal development were not collected in this
study.

The original model was over-identified by three paths [(25-5/2)-7]. The observed
correlations and the path coefficients are presented in Table 12. Overall, the global fit
indicators (i.e., x2=0.52, p>.05; x2/df=0.l7; RMSR=0.01) indicated a good fit between
the original model and the data. Moreover, individual link analyses supported the
conclusion that the original model fit the data. Specifically, the individual link between
PH and LE had a 2 value of -.03 (p>.05), the individual link between MH and LE had a 2
value of -0.33 (p>.05), and the individual link between LS and LE had a z value of 0.56
(p>.05). In addition, the obtained correlation (0.05) for the largest residual (i.e., PH and
LE) was within the CI of the predicted correlation (-0.06 and 0.10) and the predicted
correlation (0.02) was within the CI of the obtained correlation (-0.03 and 0.13) (Table
13).

Although global and local fit indices indicated there was a good fit between the
model and the data, the path coefficient between LE and AUT was relatively weak (0.06)
(Table 12). Since LE was indicated as a direct effect of AUT, the path coefficient should
have been higher (i.e., the critical value of a correlation coefficient for a sample of 500

and an alpha of .05 is 0.09).

62

Table 12. Original model corrected observed correlations and path coefi‘icients

 

 

Factor 1 2 3 4 5
1. LE 0.06 0.00 0.00 0.00
2. AUT 0.06 0.14 0.09 0.25
3. PH 001 0.14 0.64 0.14
4. MH -0.01 0.19 0.65 0.27
5. LS 0.05 0.32 0.35 0.41

 

Note. Corrected observed correlations on bottom left. Path coefficients on top right.

Table 13. Original model residual matrix

 

 

Factor 1 2 3 4 5
1. LE
2. AUT 0.00
3. PH -0.02 0.00
4. MH -0.02 0.00 0.00
5. LS 0.03 0.00 0.00 0.00

 

Note. Residual equals the difference between the corrected obtained correlation and the
predicted correlation for the corrected obtained correlation.

63

Theoretically Revised Model

The theoretically revised model was tested second as an alternative model (Figure
1). This model departed from the original model in four ways: (1) physical activity level
was added to the causal model, (2) the direct link between satisfaction of psychological
needs to physical health was eliminated, (3) the reciprocal link between physical health
and mental health was converted to a direct link between physical health to mental health,
and (4) personal development was omitted.

The theoretically revised model was over-identified by six paths [(36-6/2)-9]. The 1
observed correlations and the path coefficients are presented in Table 14 (p. 65). Overall,
the global and individual fit indicators indicated mixed results. Specifically, the global fit
indicators (i.e., x2=10.88, p>.05; x2/df=1.81; RMSR=0.06) indicated the data were
consistent with the model (i.e., the chi-square was not significant, the x2/df was less than
the recommended 3:1 ratio, and the RMSR score was less than 0.10 (Munro, 2005). On
the other hand, individual link analyses did not support the conclusion that the
theoretically revised model fit the data. Specifically, the individual link between AUT
and PH had a 2 value of 2.04 (p<.05). In addition, the individual link between AUT and
PA had a 2 value of 1.68 (p>.05).

Although p>.05, the z value is very close to 1.96 indicating that the CI test might
support the conclusion that the residuals equaled zero within sampling error (Table 15, p.
65). Indeed, neither of the two larger residuals retained the null of residuals being equal
to zero. Specifically, the obtained correlation (0.14) for the largest residual (i.e., PH and
AUT) was not within the CI of the predicted correlation (-0.08 and 0.08) and the

predicted correlation (0.00) was not within the CI of the obtained correlation (0.06 and

64

0.22). In addition, the obtained correlation (0.14) for the second largest residual (i.e., PA
and AUT) was not within the CI of the predicted correlation (-0.03 and 0.13) and the
predicted correlation (0.05) was not within the CI of the obtained correlation (0.06 and
0.22).

In addition to the problems with the individual links, various path coefficients
were too small to indicate direct links. For example, the direct link between LE and AUT
was too small (0.06). In addition, the links between PA and PH and PA and MH were too

small (0.05 and 0.02 respectively) to indicate direct links.

Table I4. Theoretically revised model corrected observed correlations and path coeflicients

 

 

Factor 1 2 3 4 5 6
1. LE 0.00 0.00 0.06 0.00 0.00
2. PA 0.44 0.05 0.00 -0.03 0.00
3. PH 001 0.05 0.00 0.64 0.14
4. AUT 0.06 0.14 0.14 0.10 0.25
5. MH -0.01 0.02 0.65 0.19 0.27
6. LS 0.05 0.13 0.35 0.32 0.41

 

Note. Corrected observed correlations on bottom left. Path coefficients on top right.

Table 15. Theoretically revised model residual matrix

 

 

Factor 1 2 3 4 5 6
1. LE
2. PA 0.00
3. PH -0.03 0.00
4. AUT 0.00 0.11 0.14
5. MH -0.02 0.01 0.01 0.09
6. LS 0.03 0.11 0.04 0.04 0.02

 

Note. Residual equals the difference between the corrected obtained correlation and the predicted
correlation for the corrected obtained correlation.

65

Final Model

The final model was a theoretical and statistical attempt to present a model that
could best fit the data taking into consideration all six factors (i.e., leisure experience,
physical activity, autonomy, physical health, mental health, and life satisfaction) (Figure
3). The original model fit the data better than the theoretically revised model, so it
naturally was more influential than the theoretically revised model when forming the
final model. On the other hand, both models had dilemmas which provided insight on the
relationships between the factors.

The original model had two overall dilemmas. First, the path coefficient between
LE and AUT was too weak (i.e., the critical value of a correlation coefficient for a sample
of 500 and an alpha of .05 is 0.09). Second, the model did not take into consideration PA
levels. The theoretically revised model had three overall dilemmas. First, in addition to
the path coefficients between LE and AUT, the path coefficients between PA and MH
and PA and PH were too weak. Second, the individual links between AUT and PH and
AUT and PA had a 2 value of close to or over 1.96 indicating bad fit between the
individual links. This conclusion was supported by the confidence interval tests. Third,
the correlation between LE and PA was relatively strong indicating that there might be a

direct link between the two factors.

66

 

 
      
     
     

Physical
Health

     
    

    
  

Satisfaction of
Psychological
Needs

   
 

   
     
   

Leisure
Experience

Physical
Activity

Life
Satisfaction

 

Figure 3. Final model.

In the final model (Figure 3), there was only one exogenous variable, leisure
experience. Leisure experience was indicated as having a causal influence on physical
activity. There was some support from the literature for this statement. First, leisure
experience has been associated with various positive attitudes and emotions such as firn
and enjoyment (Lee, Dattilo, & Howard, 1994). When an individual has a positive
attitude towards a specific behavior, this increases the probability that they will continue
to participate in the activity (Ajzen, 1991). Therefore, it was probable that leisure
experience influences physical activity level.

The next direct link was from physical activity to autonomy. From the results of
the traditional model, it was clear that the path coefficients between autonomy and
physical activity indicated a possibility that this link was direct. There was some support
in the leisure motivation literature for this direct link. Specifically, autonomy was found

to be one of the various psychological needs fulfilled when individuals participated in

67

 

different activities, including physically active activities (Driver, Tinsley, & Manfredo,
1991)

The direct link from autonomy to physical health was eliminated from the original
model because strong empirical evidence for the link was not available. However, there
was some evidence (Deci & Ryan, 1987) which is discussed in more detail in Chapter 5.
In addition, the results from the confidence interval test of the unspecified link between
autonomy and physical health from the theoretically revised model provide support for a
direct link.

The final six links were the same for the original model and the theoretically
revised model. Therefore, they will not be discussed in detail in this section as their
rationale was discussed in Chapter 2 (i.e., Literature Review, p. 11).

The final model was over-identified by seven paths [(36-6/2)-8]. The observed
correlations and the path coefficients are presented in Table 16 (p. 69), and the residuals
are presented in Table 17 (p. 69). Overall, the global fit indicators (i.e., x2=2.67, p>.05;
x2/df=0.38; RMSR=0.02) indicated a good fit between the final model and the data.
Moreover, individual link analyses supported the conclusion that the final model fit the
data. Specifically, the individual link between AUT and LE had a 2 value of -0.05
(p>.05), the individual link between PH and LE had a 2 value of -0.31 (p>.05), the
individual link between PH and PA had a 2 value of 0.55 (p>.05), the individual link
between MH and LE had a 2 value of —0.34 (p>.05), the individual link between .MH and
PA had a 2 value of —0.05 (p>.05), the individual link between LS and LE had a 2 value

of 0.55 (p>.05), and the individual link between LE and PA had a 2 value of 1.36 (p>.05).

68

 

Table 16. Final model corrected observed correlations and path coefi‘icients

 

 

Factor 1 2 3 4 5 6
1. LE 0.44 0.00 0.00 0.00 0.00
2. PA 0.44 0.14 0.00 0.00 0.00
3. AUT 0.06 0.14 0.14 0.09 0.25
4. PH -0.01 0.05 0.14 0.64 0.14
5. MH -0.01 0.02 0.19 0.65 0.27
6. LS 0.05 0.13 0.32 0.35 0.41

 

Note. Corrected observed correlations on bottom left. Path coefficients on top right.

Table 17. Final model residual matrix

 

 

Factor 1 2 3 4 5 6
1. LE
2. PA 0.00
3. AUT 0.00 0.00
4. PH -0.02 0.03 0.00
5. MH -0.02 0.00 0.00 0.00
6. LS 0.03 0.06 0.00 0.00 0.00

 

Note. Residual equals the difference between the corrected obtained correlation and the predicted
correlation for the corrected obtained correlation.

e
.
b
.3
r
.
A
i ‘7
.
c
.3
.e
B
I.
l
>«
.
,4’
_..
-

69

 

_u :9 ‘vas'uiniutr

Hypotheses

Support for study Hypothesis 1-3 was provided by analyzing factor-to-factor path
coefficients (i.e., standardized regression coefficients) since each hypothesis predicted the
direct link from one factor to another. Support for Hypothesis 4 and 5 was provided by I
multiplying the direct and indirect correlations between the factors to obtain a direct
effect since the links were indirect. Hypothesis 6 focused on whether the theoretically
revised model was found to be consistent with the data. Therefore, global and local fit of
the model were used to indicate whether Hypothesis 6 was supported.
Hypothesis 1

Hypothesis I predicted that physical health (PH) and mental health (MH) were
significant positive predictors of life satisfaction (LS). The data provided support for
Hypothesis 1. Specifically, the PH—LS path coefficient (0.14) and the MH-LS path
coefficient (0.27) were ample and indicated a positive relationship between the factors.
Hypothesis 2

Hypothesis 2 predicted that physical health (PH) was a significant positive
predictor of mental health (MH). The data provided support for Hypothesis 2.
Specifically, the PH-MH path coefficient (0.64) was ample and indicated a positive
relationship between the factors.
Hypothesis 3

Hypothesis 3 predicted that satisfaction of psychological needs [i.e., autonomy
(AUT)] was a significant positive predictor of life satisfaction (LS) and mental health

(MH). The data provided support for Hypothesis 3. Specifically, the AUT-MH path

70

 

‘.
:
.
71
a
o
a
..
...‘
o
‘_.
.-
....
"‘1
3..
..-
.r-
'2
.a
Y.
.7.
0..
ae
-.
.m,
5'.-
~
..
V
I
.
...
2
o
‘5
r.
'r
D
J.
.3
.:'
..
;.
‘.
o
-
~ -
.4
J.
O
..
'4
4
a
‘1
a.
a.
E
5
.:;.
'r.
J
"I-J.
”i

 

coefficient (0.10) and the AUT—LS path coefficient (0.25) were ample and indicated a
positive relationship between the factors.
Hypothesis 4

Hypothesis 4 predicted that leisure experience (LE) was a significant positive
predictor of satisfaction of psychological needs [i.e., autonomy (AUT)]. The data
provided partial support for Hypothesis 4. Specifically, the LE-AUT correlation (0.06)
was not ample, but it indicated a positive relationship between the factors.
Hypothesis 5

Hypothesis 5 predicted that physical activity (PA) was a significant positive
predictor of physical health (PH) and mental health (MH). The data provided partial
support for Hypothesis 5. Specifically, the PA-PH path coefficient (0.05) was not ample,
but it indicated a positive relationship between the factors. Moreover, the PA-MH path
coefficient (0.02) was not ample, but it indicated a positive relationship between the
factors.
Hypothesis 6

Hypothesis 6 predicted that the model fit the data. Specifically, the hypothesis
was referring to the theoretically modified model. The data provided partial support for
Hypothesis 6, but the support was not as convincing as expected. For example, the global
and individual fit indicators indicated mixed results. Specifically, while the global fit
indicators (i.e., x2=10.88, p>.05; x2/df=1.81; RMSR=0.06) indicated the data were
consistent with the model, the individual link analyses did not support the conclusion that
the theoretically revised model fit the data. Specifically, the individual link between AUT

and PH had a z value of 2.04 (p<.05).

71

 

Chapter V
CONCLUSIONS

The impetus for the current study was the dearth of empirically analyzed
theoretical models which explain the relationship between leisure experience and
subjective wellbeing (Mannell & Kleiber, 1997). Toward partial fulfillment of this
empirical gap, the purpose of the current study was to empirically analyze a theoretically
revised version of Tinsley and Tinsley’s (1986) causal effects of leisure experience
model. The efforts toward this end are summarized in this chapter. Moreover, a
discussion and implications of the study findings as well as recommendations for future
research are also discussed in this chapter.

Summary of Findings

A path analytic approach was implemented in this study to analyze the data.
Results from the confirmatory factor analysis indicated that the one indicator of physical
health, two indicators of mental health, and one indicator of life satisfaction needed to be
removed in order to obtain unidimensional indicators (Table 3, p. 50). Moreover,
reliabilities for each factor were above 0.75.

Three causal models were tested in this study. The first model was the equivalent
of the original causal effects of leisure experience model proposed by Tinsley and
Tinsley (1986) (Figure 2, p. 32). The original model proposed that leisure experience
(exogenous variable) had a positive and direct effect on satisfaction with psychological
needs (i.e., operationalized as autonomy), which in turn had a positive and direct

influence on physical health, mental health, and life satisfaction. Physical health had a

72

 

positive and direct influence on mental health and life satisfaction, and mental health had
a positive and direct influence on life satisfaction. Global and local fit indices indicated
that the model fit the data. The model had two dilemmas. First, the path coefficient
between leisure experience and autonomy (i.e., psychological need) was too small.
Second, the model did not take into consideration physical activity levels.

The second model tested was the theoretically revised causal effects of leisure
experience model (Figure 1, p. 6). The model proposed two exogenous variables, leisure
experience and physical activity level. Leisure experience had a positive and direct effect
on satisfaction with psychological needs which in turn had a positive and direct influence
on physical health, mental health, and life satisfaction. Physical activity level had a
positive and direct effect on physical health and mental health. Physical health had a
positive and direct influence on mental health and life satisfaction, and mental health had
a positive and direct influence on life satisfaction. Global fit indicators indicated a good
fit between the data and the model. On the other hand, individual link analyses did not
support the conclusion that the theoretically revised model fit the data.

There were three primary dilemmas with the theoretically revised model. First,
three path coefficients were too small to be considered ample. Specifically, the path
coefficients between leisure experience and autonomy, physical activity and mental
health, and physical activity and physical health were too small. Second, the individual
links between autonomy and physical health and physical activity had a 2 value of close
to or over 1.96 indicating bad fit between the individual links. This conclusion was
supported by the confidence interval tests. Third, the correlation between leisure

experience and physical activity was relatively large indicating that there might be a

73

direct link between the two factors. Global and local fit indices indicated that the model
fit the data. Moreover, the path coefficients were ample.

The last model tested was the final model (Figure 3, p. 67). The final model
proposed that leisure experience (exogenous variable) had a positive and direct influence
on physical activity. Physical activity had a positive and direct influence on autonomy.
Autonomy had a positive and direct influence on physical health, mental health, and life
satisfaction. Physical health had a positive and direct influence on mental health and life
satisfaction, and mental health had a positive and direct influence on life satisfaction.

The final model differed from the original model in that it proposed physical
activity as a mediating variable between leisure experience and autonomy. The final
model differed from the theoretically revised model in that it modified the relationship
between leisure experience and physical activity, physical activity and physical health
and mental health, and autonomy and physical health.

Discussion

The findings from this study generally supported the six hypotheses originally
proposed in this study, but specific to Hypothesis 6, there was little empirical support.
Since the overall theoretically revised model did not fit the data, the final model was
proposed. This discussion focuses on the discrepant links between the theoretically
revised model and final model.

Physical activity level and physical health and mental health. The relationships
between physical activity level and physical health and mental health were not found to
be ample enough to indicate a direct link, despite the substantial amount of empirical

evidence indicating otherwise. Part of the reason why a strong relationship was not found

74

can be attributed to the instrument used to measure physical activity. Specifically, the

instrument measured level of physical activity in a variety of activities, including

watching television, baking/cooking, and jogging/walking for exercise, which are

generally done during a person’s “free time.” Recent literature has indicated that not

taking into consideration levels of physical activity during a person’s job may

significantly underestimate physical activity levels (Bates et al., 2005). Specifically, _ ,1
“. . .particularly those with jobs involving walking, do a substantial amount of walking not

captured by traditional leisure-time activity surveys” (Bates et al., 2005, p. 46).

Two theoretical frameworks provide some support for this possible limitation to
the instrument, spillover and compensatory theory. Spillover theory purports that an
individual’s time outside of work reflects time spent while at work (Staines, 1980). For
example, if a person were active at work, it would be expected that time away from work
would also be spent in an active manner. Compensatory theory purports that an
individual’s time outside of work is contrary to time spent while at work (Staines, 1980).
For example, if a person were active at work, it would be expected that time away from
work would be spent in a passive manner, compensating for an individual’s need to relax.

The second reason why the instrument may not have been able to detect a
relationship between physical activity and physical health and mental health is because of
the short amount of time (i.e., four weeks) the instrument was measuring. It could be
argued that insufficient time was given for changes to physical and mental health to
develop to a noticeable point by study participants. On the other hand, if study
participants were required to provide detailed information for more than four weeks, the

accuracy of the data would have suffered given potential recall biases. Specifically,

75

higher levels of participation are often reported as opposed to actual participation levels
the longer the recall period (Tarrant, Manfredo, Bayley, & Hess, 1993).

The third reason why the instrument may not have been able to detect a
relationship between physical activity and physical health and mental health is because it
was limited to attaining only frequency information. In order to increase physical fitness
[i.e., “a set of attributes that people have or achieve that relates to the ability to perform
physical activity (Blair, Kohl, Gordon, & Paffenbarger Jr., 1992, p. 101)] a minimum
level of intensity and duration are needed for each exercise session (Blair, Kohl, Gordon,
& Paffenbarger J r., 1992). Specifically, activities with low levels of intensity must be
sustained longer in order to have a positive impact on physical fitness (Blair, Kohl,
Gordon, & Paffenbarger J r., 1992). The instrument used to measure physical activity
level did not take into consideration level of intensity or duration of each activity session.

Leisure experience and autonomy. The relationship between leisure experience
and autonomy was also not found to be ample enough to indicate a direct link. There are
two potential alternative explanations for this finding. First, the instrument used to
measure leisure experience was a new instrument which was not tested for reliability or
validity since it was a one-item indicator of leisure experience. Because only one-item
indictor was used, other dimensions of the leisure experience, such as quality, duration,
intensity, and memorability (Mannell, 1980, 1999), were not measured. Therefore, there
was the possibility that the instrument used did not accurately or consistently measure
leisure experience. Second, the construct of leisure experience is often associated with a
variety of different values. In other words, it is difficult to identify which value is being

assessed when a subjective measure is being used. When subjective measures of leisure

76

experience are used, common values often associated with leisure experience (i.e.,
intrinsic motivation and perceived freedom) are often assumed if independent measures
for these values are not employed.

On the other hand, if study participants perceived leisure experience to be more
closely related to positive attitudes and emotions such as fun and excitement (Lee,
Dattilo, & Howard, 1994), it would be difficult to argue against the results given that
little empirical research is available to predict the nature of the relationship between fun
or excitement and autonomy.

Leisure experience and physical activity level. The relationship between leisure
experience and physical activity level indicated an ample positive direct link. Therefore, a
decision was made to test the factors as direct links, but which of the two would be
considered the causal antecedents was the question? It could be argued that physical
activity level was a causal antecedent of leisure experience. Specifically, by definition, a
leisure experience must occur from some activity. Therefore, it could be established that
physical activity level had a positive effect on leisure experience. The primary dilemma
with this logic is that leisure experiences are experienced from numerous types of
activities, passive and active. Moreover, there is no evidence the researcher is aware of
which supports the proposition that an increase in physical activity increases a person’s
leisure experience perception.

On the other hand, if it were established that leisure experience had a causal
influence on physical activity level, then there would some strong empirical support from
the literature, albeit indirect. If, as aforementioned, leisure experience were associated

with various positive attitudes and emotions such as fun and enjoyment (Lee, Dattilo, &

77

 

Howard, 1994), then it could be argued that Ajzen’s (1991) theory of planned behavior
could explain the proposed relationship. The theory of planned behavior posits that a
behavior is influenced by a variety of factors including intention, attitudes, subjective
norms, and perceived behavioral control towards the behavior (Ajzen, 1991). In other
words, when an individual has a positive attitude towards a specific behavior, this
increases the probability that they will continue to participate in the activity (Ajzen,
1991). Therefore, it was probable that leisure experience influenced physical activity
level.

Although leisure experience may be a causal antecedent of physical activity level,
the path coefficient was not high enough to infer that leisure experience was the only
causal antecedent of physical activity. Literature on the determinants (i.e., correlates) of
physical activity provide support for three categories of causal antecedents to physical
activity: personal characteristics [e.g., past program participation (positive association),
perceived health (positive association), self-motivation (positive association), overweight
(negative association), smoking (negative association), health and exercise knowledge
(negative association), and blue-collar occupation (negative association)], environmental
characteristics [e.g., spouse support (positive association), perceived available time
(positive association), access to facilities (positive association), and disruptions in routine
(negative association)], and activity characteristics [e.g., perceived discomfort (negative
association)] (Dishman, Sallis, & Orenstein, 1985; Sallis, 1990).

Physical activity level and autonomy. The relationship between physical activity
level and autonomy indicated an ample positive direct link. This determination of causal

antecedence was difficult since there was some support for either factor being a causal

78

antecedent. For example, there was empirical evidence indicating that inducing sense of
control (i.e., autonomy) increased mental alertness, happiness, and activity in a sample of
91 ambulatory older adults who resided in a nursing home (Langer & Rodin, 1976).

On the other hand, more recent studies have not found similar conclusions.
Specifically, a synthesis of studies which measured the relationship between locus of
control (i.e., autonomy) and exercise, indicated largely weak or inconclusive findings
(Biddle & Mutrie, 1991). A possible explanation for the discrepancy between findings is
that lack of control may be a more significant factor influencing activity in individuals
who are institutionalized as opposed to individuals who are not. Individuals who are not
institutionalized have a host of additional factors which may influence their physical
activity level (Jackson & Scott, 1999). A summary by Biddle and Mutrie (1991) puts
autonomy as a causal antecedent to physical activity into perspective, “It would be an
over-simplification to suggest that internals will act in a certain way simply because of
their control beliefs. Differences in their interests and values associated with the behavior
could easily override an internal or external orientation” (p. 108). Although Biddle and
Mutrie’s (1991) may seem to contradict Ajzen’s (1991) theory of planned behavior which
indicates that locus of control is an important causal antecedent to behavior, causal
antecedents to behavior in the theory of planned behavior do not necessarily carry
equivalent weight. Specifically, the synthesis by Biddle and Mutrie (1991) provided
support for greater weight to be given to other causal antecedents in the theory of planned
behavior when the dependent variable is physical activity or exercise.

If autonomy was not a causal antecedent of physical activity level, could physical

activity level be a causal antecedent of autonomy? Over three decades of research by

79

Driver and his colleagues and Tinsley and his associates would indicate this relationship
is indeed possible. Specifically, there is sufficient evidence indicating that participation in
activities (e. g., hiking and cycling) satisfies numerous psychological needs, including
autonomy (Driver, Tinsley, & Manfredo, 1991).

Autonomy and physical health. The relationship between autonomy and physical
health indicated an ample and positive link. There was some empirical research focusing
on the direct link between autonomy and physical health, but overall little research has
focused on the direct link. One of the few empirical studies indicating a direct effect was
conducted by Schulz (1976). Specifically, findings indicated that loss of control (i.e.,
autonomy) was part of the explanation for, “. .. feelings of depression and helplessness,
as well as accelerated physical decline” (Schulz, 1976, p. 563) in a sample of 40 retired
individuals living in a retirement home. In another study, autonomy was found to be
positively correlated with various elements of physical health (i.e., role physical r=0.18,
p<.05; bodily pain r=0.17, p<.05; general health r=0.19, p<.05) in a sample of 225
registered nurses who worked at least 32 hours a week in a New Zealand general hospital
servicing a city and surrounding rural areas (Budge, Carryer, & Wood, 2003).

On the other hand, additional empirical support has been found providing support
for an indirect relationship between autonomy and physical health. Specifically, there is
empirical support that the relationship is mediated by positive affect. As aforementioned,
individuals who have high autonomy have been found to also be high in positive affect
and be less likely to be self-derogatory, experience negative emotions (e.g., shame or
guilt), or experience boredom (Deci & Ryan, 1987, 1995; Ryan, 1995). For example, in a

recent study, positive affect was negatively correlated (r=-0.14, p<.01) with morbidity

80

symptoms in a study of 67 students from an introductory psychology class (Reis,
Sheldon, Gable, Roscoe, & Ryan, 2000). In the same study, autonomy had a positive
correlation (r=0.28, p<.05) with positive affect and a negative correlation (r=-0.06; ns)
with morbidity symptoms (Reis, Sheldon, Gable, Roscoe, & Ryan, 2000). The study
findings are consistent with the premise that positive affect mediates the relationship
between autonomy and physical health. Specifically, the correlation between autonomy
(A) and positive affect (B) was ample, and the correlation between positive affect (B) and
morbidity symptoms (C) was also ample, but the correlation between autonomy (A) and
morbidity symptoms (C) was not ample. Smaller correlations can be expected (e.g.,
between A and C) when constructs are mediated by another construct (e.g., B) (Hunter &
Schrrridt, 2004).

Empirical research has also supported the premise that positive affect is a causal
antecedent of general health (a component of physical health) (Benyamini, Idler,
Leventhal, & Leventhal, 2000). Specifically, positive affect had a positive standardized
regression coefficient ([3=.28; p<.05) predicting general health and controlling for
demographics (i.e., age, gender, education), medical status (e.g., medical history and
medications), current impairments (i.e., functional disability and negative affect), and
positive physical and social functioning (e. g., exercise, working, and social support) in a
sample of 851 elderly residents of a retirement community (Benyamini, Idler, Leventhal,
& Leventhal, 2000). Additionally, in a sample of 119 older adults, positive affect had a
positive correlation (r=0.l8, p<.05) with high-density lipoprotein cholesterol (HDL)

(Ryff, Singer, & Love, 2004).

81

An alternative argument to autonomy being a causal antecedent to physical health
was the converse relationship (i.e., physical health being a causal antecedent to
autonomy). For instance, it could be intuitively argued that a minimal level of physical
health is needed to feel a sense of autonomy. For example, a person with severe physical
limitations might need assistance to do even the most basic of activities of daily living
(ADLs). This might then influence a person’s sense of autonomy. Given current
advancements in technology for persons with even severe disabilities (e. g., electronic
wheelchairs), this alternative explanation has become less probable. Even if other
elements of physical health, such as pain, are taken into consideration, current
advancements in pain management also have made this alternative explanation less
probable.

Implications

Practical implications. The data collected in this study were used to assist both
the current dissertation and a five-year multi-jurisdictional master plan which
encompassed two municipalities and one recreation authority. Relative to the master plan,
the data were used to establish a baseline measure of physical health, mental health, and
life satisfaction of area residents to provide local decision makers current information on
significant outcomes of municipal services. The collection of subjective wellbeing data is
rare in traditional park and recreation needs assessments. Traditionally, needs
assessments focus on constructs such as current activity participation, satisfaction with
current municipal park and recreation services, and barriers to recreation participation
(McLean, Bannon, & Gray, 1999). Although measurement of these constructs is

important to municipal leisure services, they do not directly measure a vital goal of many

82

 

municipal park and recreation departments, the improvement of local residents’ quality of
life. The addition of subjective wellbeing measures in municipal park and recreation
needs assessments can also be used to analyze how municipal leisure services, along with
other leisure services (e. g., nonprofit and commercial leisure providers), influence
subjective wellbeing measures. Moreover, when data on subjective wellbeing measures
are collected over time, long-term changes in resident subjective wellbeing can be
analyzed.

In this study, it was found that physical activity had an indirect relationship to
physical health, mental health, and life satisfaction. These study findings have
implications for municipal leisure services. Specifically, findings from this study support
the continued provision of programs which promote physical activity by municipal park
and recreation departments. Moreover, the findings of this study support the importance
of leisure service providers working closely with health practitioners to promote the
increase of physical activity. For example, in addition to providing services to the general
public, municipal leisure service providers can play an important role for post outpatients
of physical illnesses (e.g., diabetes, cancer, HIV/AIDS, arthritis, osteoporosis, and lower
back pain) and psychological illnesses [e.g., developmental disorders, somatoform
disorders, substance abuse disorders (e.g., alcohol and drug addiction), smoking
cessation, and sleep apnea] (Biddle, Fox, Boutcher, & Faulkner, 2000).

In addition to providing leisure services, municipal park and recreation
departments often evaluate their programs through summative and formative evaluations.
When providing leisure services which promote physical activity, it will be important for

municipal leisure service providers to evaluate the extent to which these programs

83

 

influence program participants’ autonomy and the extent to which participants’ feel their
experience was leisure. The former is important since satisfaction with autonomy was
found to mediate the relationship between physical activity and physical health, mental
health, and life satisfaction. In other words, as autonomy increased, so did the physical
health, mental health, and life satisfaction of study participants. The latter is important as
leisure experience was positively correlated with physical activity level (i.e., as leisure
experience increased, so did physical activity level). Therefore, understanding the extent
that leisure is experienced in physical activities may help to better understand future
participation levels in physical activities.

Conceptual implications. The findings from this study provide some insight into
the relationship between leisure and subjective wellbeing. First, study results provide
some support for Tinsley and Tinsley’s (1986) causal effects of leisure experience model
and the final model. There are two implications from this finding. First, when leisure is
operationalized as an experience, it has a positive direct effect on physical activity levels,
and high activity levels were associated with high levels of autonomy. The findings from
this study, along with several other research efforts, have provided support indicating a
positive direct effect of autonomy on various other subjective wellbeing factors (i.e.,
physical health, mental health, and life satisfaction). Therefore, leisure, as an experience,
should continue to be considered a critical factor in future research aimed at
understanding determinants of positive subjective wellbeing. Second, the finding
provides support for the self-determination theory (SDT) which posits that autonomy
significantly influences subjective wellbeing factors (Ryan & Deci, 2000, 2001). This

was important since SDT also posits that two other psychological needs, competence and

84

relatedness, are also positive predictors of subjective wellbeing (Ryan & Deci, 2000,
2001). Understanding the relationship between leisure experience, physical activity and
these additional psychological needs will help to better understanding the true
relationship between leisure and subjective wellbeing.

The second finding that has significant conceptual implications is the finding that
physical activity level did not have a positive direct effect on physical health or mental
health despite research indicating a direct effect (Blair, Jacobs, & Powell, 1985). Instead,
a positive indirect effect was found. If autonomy was found to mediate the relationship
between physical activity level and physical health and mental health, are there other
salient mediating variables, such as those posed by SDT? Future research is needed to
answer this question.

The third finding that has significant conceptual implications is that physical
activity level had a positive direct effect on autonomy. This finding provides support for
the literature indicating that participation of various activities affects various
psychological needs (Driver, Tinsley, & Manfredo, 1991; Tinsley & Eldredge, 1995).

The fourth finding that has significant conceptual implications is that autonomy
had a positive direct effect on physical health. Although this finding is consistent with
other research (Schulz, 1976), the mechanism by which this occurs is insufficiently
identified in the literature. There is some support that the effect of autonomy on physical
health is mediated by positive affect, but additional research which analyzes this
relationship is needed.

Study significance. There are two additional significant aspects of this study

which were not directly related to the results of the study. First, this study updated the

85

literature on the direct and indirect links between leisure experience, autonomy, physical
health, mental health, and life satisfaction. In doing so, a new foundation of knowledge
has been laid on our past understanding of the relationship between leisure experience
and subjective wellbeing.

Second, this study bridged the knowledge between the various disciplines (i.e.,
leisure, psychology, medicine, and quality of life) in which the various factors analyzed
in this study have been researched. As such, this study serves as a template for how future
research concerning the relationship between leisure experience and subjective wellbeing
may proceed, that is, in an interdisciplinary manner.

Recommendations

If this study were conducted again, a number of procedural, instrumental, and
conceptual recommendations should be taken into consideration.

Procedural. Five procedural issues hindered the potential of this study. All, but
the first issue noted, were in part due to the delimitations of the study. First, the study
sample included socio-demographics which were different from the study population.
Therefore, generalizability of the study results was limited to populations similar to the
study sample. Generalizability to general populations has been an issue with many leisure
studies which focus on quality of life. For example, Ragheb (1993) indicated that studies
which have focused on leisure and wellbeing “were either restricted to specific age
groups such as older people, used a small sample, or conceptualized wellness as solely
psychological, without taking other components into account such as physical and social
wellbeing” (p. 13). As such, future studies using samples more representative of the

general population may be needed to generalize results to the general population.

86

Second, data were collected throughout March and April of 2005. Since March
and to a lesser extent April were cold months (i.e., temperatures averaged in the 20’s and
30’s), physical activity levels may have been negatively affected by the weather.
Therefore, future studies should also focus on collecting data throughout the four seasons.
In addition to being cold months, the response rate of the study may have suffered
because of school breaks during March and April. Specifically, many families in the
community may travel during Spring Break.

Third, although a stratified random sample was used in this study, the probability
of being selected for each study respondent was not equal. Specifically, because the study
was part of a larger needs assessment, a sufficient number of respondents from each of
the four municipalities were needed for comparisons between municipalities. Future
studies should be aware of this potential issue and implement a stratified random sample
where each individual sampled has the same probability of being selected when possible
(i.e., probability proportional to size sampling).

Fourth, two of the four communities were not defined by political boundaries.
Therefore, census population data were not available for these two communities. As such,
accurate weighing of the data was not possible. Consequently, the generalizability of the
results to the population may have been influenced if the sample differed significantly
from the population, as the data were not weighed by the appropriate socio-demographic
proportions. Future researchers should be cognizant of this potential issue in their study
and make attempts to acquire population data when available.

Finally, data were collected at one point in time. This limited the study in two

ways. First, the ability to indicate a true cause-effect relationship was compromised.

87

Instead, proposed causal relationships using empirical literature was used to indicate
cause and effects among study factors. Second, collecting data in one point in time does
not allow for analysis of the long-term effects of subjective wellbeing constructs and
leisure experience. For example, what are the effects of having high levels of life
satisfaction on leisure experiences? Answers to questions such as this and issues such as
true cause and effects often require longitudinal or experimental study designs.
Instruments. There were four issues with the study instruments (i.e., factor
measures) which should be considered if this study were to be replicated. First, despite
the fact that a measure of subjective leisure experience was implemented in this study, it
was a relatively crude measure given the complexity of a leisure experience. In other
words, it only used one-item to measure leisure experience. A similar issue arose from
the physical activity level scale used in the study. For instance, the measure did not take
into consideration levels of physical activity during a person’s job, which may have
influenced the level of physical activity calculated for each study participant. The
inclusion of additional measures for leisure experience and physical activity would
increase the validity of the instruments since few one-item instruments sufficiently
measure complex human phenomena. This is particularly important since both the leisure
experience and physical activity measures are newer instruments. In addition, the use of
one-item instruments forced the research to assume perfect reliability of the instruments,
and it did not allow for internal and external consistency tests to verify the validity of the
instruments. It would therefore behoove future researchers to consider the different
elements of the leisure experience and physical activity when drawing comparative

conclusions which take into consideration these constructs.

88

Second, the difficulties of operationalizing leisure experience subjectively were
discussed in this study. If the relationship between leisure experience and subjective
wellbeing is going to be more accurately predicted, a definition of leisure experience is
needed that takes into consideration at least four elements: 1) subjectivity, 2) a focus on
psychological needs, 3) dynamic, and 4) fluid. The importance of the measure being
subjectively identified was discussed in Chapter 2 (Literature Review) and elsewhere
(Samdahl, 1991). The importance of focusing on psychological needs was discussed by
Iso-Ahola (1999),

...the nature and process of leisure is fundamentally motivational. Human

beings are born with basic innate (psychological) needs that are the main

energizers of human growth and potential. . .the basic needs not only

define leisure for people but also direct their involvement in activities

under various conditions. Thus, it does not matter even if there are

hundreds of different types of leisure activities [i.e., activities perceived as

leisure]: they all are potentially leisure activities and serve the same

psychological functions for their participants. (p. 35)

The dynamic nature of the definition implies that one or more psychological need(s) can
be interpreted as a leisure experience. Leisure literature focusing on the various
psychological needs which compose a leisure experience have noted the psychological
needs of intrinsic motivation and perceived freedom (i.e., autonomy) as two key
psychological needs which compose the leisure experience (N eulinger, 1981).
Additionally, the psychological needs which compose the leisure experience may differ

in a number of elements including the quality, duration, intensity, and memorability of

89

the psychological needs (Mannell, 1980, 1999). The fluid nature of the definition implies
that a subjective interpretation of what constitutes a leisure experience changes with time
and potentially, activity. In other words, what a person perceives as a leisure experience
today may be different from what they perceive as leisure tomorrow. Moreover, simply
because an activity is commonly perceived as a leisure experience by an individual, this
can change depending on the life circumstances surrounding the experience (Iso-Ahola,
1999)

Third, although prior research indicated the Satisfaction with Life Scale had good
validity and reliability (Larson, Diener, & Emmons, 1985; Pavot & Diener, 1993;
Shevlin, Brunsden, & Miles, 1998; Shevlin & Bunting, 1994), it was found in this study
that the instrument had good reliability, but one of the indicators did not pass the internal
consistency theorem indicating that it was not influenced by the same construct or factor
as the other four indicators. Future users of the Satisfaction with Life Scale should
continue to test the instrument for validity and reliability in their studies.

Fourth, the selection of the SF -8TM was based on prior research which indicated
that the instrument had both good reliability and convergent validity with the SF-36®
(Ware, Kosinski, Dewey, & Gandek, 2001). Although it was found that the instrument
had good reliability, the summary scale for mental health did not pass the internal
consistency test, and the summary scale for physical health did not pass the external
consistency test. Summary scales’ internal consistency concerns have been expressed
elsewhere (Wilson, Parsons, & Tucker, 2000), so additional research with the SF-8TM is

warranted.

90

Conceptual. Three conceptual issues should be taken into consideration if this
study were to be replicated. First, biological or physical needs which may also influence a
person’s physical health, mental health, and life satisfaction were not taken into
consideration in this study. Moreover, additional psychological needs, such as
relatedness, competence, and self-esteem, have also been found to be salient in positive
human development (Fox, 2000; Ryan & Deci, 2000). Specifically, self-determination
theory (SDT) purports that all three psychological needs (i.e., autonomy, relatedness, and
competence) are needed to positively influence a person’s positive development (Ryan &
Deci, 2000). Additionally, self-esteem has been noted as a key indicator of emotional
stability (Fox, 2000). “High self-esteem has been related to a range of positive qualities
such as life satisfaction, positive social adjustment, independence, adaptability,
leadership, resilience to stress...” (Fox, 2000, p. 88). Therefore, future efforts integrating
a more holistic perspective of needs may help in better understanding the true
relationship between leisure experience and subjective wellbeing constructs.

Second, there are additional mediating variables, unrelated to needs, such as
leisure satisfaction and stress, which may influence the relationship between leisure
experience and subjective wellbeing (Iwasaki, 2003; Lloyd & Auld, 2002). Future efforts
should focus on broadening knowledge of how other important mediating variables may
influence the relationship between leisure experience and subjective wellbeing.

Third, there is currently little empirical research focusing on the direct link
between autonomy and physical health, so future research in needed given the salience of

both these factors to subjective wellbeing.

91

 

APPENDICES

92

 

APPENDIX A

93

Principal Investigator Assurance of An Exempt Project

Name of Principal Investigator: Richard Paulsen

Title of Project: SELCRA COMMUNITY PARK AND RECREATION SURVEY

IRB #: X05-062

The University Committee on Research Involving Human Subjects (UCRIHS) has deemed this
project as exempt, in accord with the federal regulations for the protection of human subjects. As
an exempt project, the IRB will not be further involved with the review or continued review of
the project, as long as the project maintains the properties that make it exempt.

Since the IRB is no longer involved in the review and continued review of this project, it
is the Principal Investigator who assumes the responsibilities for the protection of human
subjects in this project and ensures that the project is performed with integrity and within
accepted ethical standards, particularly as outlined by the Belmont Report (see exempt
educational materials).

The Principal Investigator assumes the responsibility for ensuring that research subjects
be informed of the research through a documented or undocumented consent process, if
appropriate.

The Principal Investigator assumes the responsibility to maintain confidentiality of the
subjects and the data, and maintain the privacy of the subjects and protection of the data
through appropriate means. If data is anonymous, the investigators will make no attempt
to identify any individuals.

The Principal Investigator assumes responsibility for assuring that human subjects will be
selected equitably, so that risks and benefits of research are justly distributed.

The Principal Investigator assumes the responsibility that co-investigators and other
members of the research team adhere to the appropriate policies for the protection of
human subjects, maintain confidentiality and privacy, and adhere to accepted ethical
standards.

If the Principal Investigator adds additional investigators to an exempt project, he/she
may inform UCRIHS of the additions. This may be of particular importance to graduate
students if the MSU Graduate School requires proof of IRB approval.

Any complaints from participants regarding the risks and benefits of the project must be
reported to UCRIHS. Any information, unexpected or adverse events that would increase
the risk to human subjects and cause the category of review to be upgraded to Expedited
or Full Review must also be reported to UCRIHS.

Since the Principal Investigator and co-investigators are charged with human subject
protection and adhering to ethical principles in exempt research, it is appropriate that
investigators be trained in human subject principles. The Principal Investigator and all
members of the research team are required to complete MSU IRB educational
requirements or equivalent.

94

0 Any change in the project that may raise the project from exempt to an expedited or full
review category must be presented to UCRIHS. If there is any question about a change
in the project, the Principal Investigator should consult the Chair of UCRIHS. Failure to
submit changes that raise the project out of the exempt category will be considered non-
compliance and will be subject to investigation and action by UCRIHS.

By signing below, the Principal Investigator assures that he/she will abide by the terms of this
assurance and the UCRIHS exempt policy.

 

 

Signature of Principal Investigator Date

95

 

APPENDIX B

96

Cover Letter: First Wave (modified to fit MSU Graduate School requirements)

<<date>>
<<subj ect first name>> <<subj ect last name>>
<<subject address>>; <<city>>, <<state>> <<zip code>>

SELCRA Community Park and Recreation Survey
Dear <<subj ect first name>>:

The Southeastern Livingston County Recreation Authority (SELCRA) would like to continue
delivering quality programs, services, and facilities to Brighton area residents. Researchers from
Michigan State University are conducting a study to assist them in accomplishing these goals.
You have been chosen randomly to represent residents from the Brighton area, so your
participation is of great importance to us.

The survey will take approximately 10-15 minutes to complete. Your participation is entirely
voluntary, so you may decide not to fill out the survey or to stop filling out the survey at any time
without any type of penalty. By completing the survey and returning it to Michigan State
University, you indicate your consent to participate in the survey. All responses will be kept
confidential, and your privacy will be protected to the maximum extent allowable by law. Survey
results will be reported only as group information so that no individual’s responses can be
identified. Once the study is complete, the survey list of names and addresses will be destroyed.

If you return a completed survey by March 31, 2005, you will be eligible to enter one of five
drawings. The drawings include a season pass to Huron Clinton Metro Parks, $50 toward a
SELCRA program registration, a $20 gift card for a local movie theater, a $20 gift card for a local
bowling alley, or a $20 pass book for Meijer Skate Park. Drawing winners will be notified by
April 15, 2005.

If you have any questions about this study, please contact Dr. Richard Paulsen by phone: (517)
355-9578, fax: (517) 432-3597, email: paulsen@msu.edu, or regular mail: 131 Natural
Resources, East Lansing, MI 48824.

If you have questions or concerns regarding your rights as a study participant, or are dissatisfied
at any time with any aspect of this study, you may contact — anonymously, if you wish -— Peter
Vasilenko, Ph.D., Chair of the University Committee on Research Involving Human Subjects
(UCRH-IS) by phone: (517) 355-2180, fax: (517) 432-4503, c-mail: ucrihs@msu.edu, or regular
mail: 202 Olds Hall, East Lansing, MI 48824.

Please fill out the enclosed survey, and mail it today.

Sincerely,

Dr. Richard D. Paulsen, Associate Chair
Department of Community, Agriculture, Recreation and Resource Studies (CARRS)

Julie L. Hall, Director
Southeastern Livingston County Recreation Authority (SELCRA)

97

Postcard Reminder: Second Wave (modified to fit MSU Graduate School requirements)

Postcard (Front)

MICHIGAN STATE

U N l V E R S l T Y
SELCRA Community Survey

Department of Community, Agriculture,
Recreation and Resource Studies (CARRS)
131 Natural Resources Building

East Lansing, MI 48824

 

<<name>>

<<address>>
<<city>> <<state>> <<zip>>

Postcard (Back)

Dear <<name>>:

Two weeks ago a survey seeking your opinions about parks and recreation services in the Brighton area
was mailed to you. In fact, you may have received more than one survey in your household as we
randomly selected voters in the Brighton area.

If you have already mailed us your completed survey, we would like to thank you for your time and
effort. If not, please do so today as the completion of every survey is vital to the success of SELCRA’s
efforts. SELCRA is currently working on a park and recreation master plan to serve Brighton area
residents, so we are especially grateful for your help because it will assist SELCRA in better serving
Brighton area residents.

If you did not receive a survey, or if you need another copy, please contact Julie Hall or Cheryl Royster
at (810) 299-4140, and one will be mailed to you today.

Dr. Richard D. Paulsen
Community, Agriculture, Recreation and Resource Studies
Michigan State University

98

Cover Letter: Third Wave (modified to fit MSU Graduate School requirements)

<<date>>
<<complete name>>
<<complete address 1>>
<<complete address 2>>

SELCRA Community Park and Recreation Survey
Dear <<first name>>:

During the past month, we have sent you two mailings about an important research study we are
conducting for the Southeastern Livingston County Recreation Authority (SELCRA). In fact, you
may have received more than one survey in your household as we randomly selected voters in
the Brighton area. If you have already mailed us your completed survey, we would like to thank
you for your time and effort.

The information will be used in a comprehensive park and recreation master plan for the Brighton
area. The purpose of which is to help SELCRA continue delivering quality programs, services,
and facilities to Brighton area residents.

The study is drawing to a close. So if you have not turned in your completed survey, please do so
today as the completion of every survey is vital to the success of SELCRA’s and your local
govemment’s efforts.

The survey will take approximately 10-15 minutes to complete. Your participation is entirely
voluntary, so you may decide not to fill out the survey or to stop filling out the survey at any time
without any type of penalty. By completing the survey and returning it to Michigan State
University, you indicate your consent to participate in the survey. All responses will be kept
confidential, and your privacy will be protected to the maximum extent allowable by law. Survey
results will be reported only as group information so that no individual’s responses can be
identified. Once the study is complete, the survey list of names and addresses will be destroyed.

If you have any questions about this study, please contact Dr. Richard Paulsen by phone: (517)
355-9578, fax: (517) 432-3597, email: paulsen@msu.edu, or regular mail: 131 Natural
Resources, East Lansing, MI 48824.

If you have questions or concerns regarding your rights as a study participant, or are dissatisfied
at any time with any aspect of this study, you may contact — anonymously, if you wish — Peter
Vasilenko, Ph.D., Chair of the University Committee on Research Involving Human Subjects
(UCRIHS) by phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu, or regular
mail: 202 Olds Hall, East Lansing, MI 48824.

Please fill out the enclosed survey and mail it today.

Sincerely,

Dr. Richard D. Paulsen, Associate Chair
Department of Community, Agriculture, Recreation and Resource Studies (CARRS)

Julie L. Hall, Director
Southeastern Livingston County Recreation Authority (SELCRA)

99

 

Study Questionnaire (First and Third Wave)
(modified to fit MSU Graduate School requirements)

SELCRA

Community Park and
Recreation Survey

 

 

 

The information from this survey will be used to help Southeastern Livingston County
Recreation Authority (SELCRA) provide quality programs, services and facilities for you and
other members of your household. This information will also be used in the development of a
park and recreation master plan for SELCRA.

Your participation is entirely voluntary. Your responses will be kept confidential and at no time
will your name be identified with any results. Return of the survey will be considered as your
consent to participate in the study.

If you have any questions about the survey, please contact
Dr. Rlchard Paulsen at Michigan State Unlverslty
(517) 355-9578

Department of Community, Agriculture,
Recreation and Resource Studies
Michigan State University
131 Natural Resources Bldg.
East Lansing. MI 48824

MICHIGAN STATE

UNIVERSITY

 

100

Introduction: This questionnaire addresses topics of concern to both citizens and policy
makers in the Brighton Area. There are three sections to this questionnaire. The first section
asks questions about general activities you and members of your household participate in. The
second sectlon asks questions about specific SELCRA activities. The third section asks
questions about your general health and life satisfaction. as well as general demographic
questions. All the data collected will be kept in the strictest confidence.

SECTION I:

General Activity Participation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1. Please indicate the total number of times YOU participated in each of the following activities during
the PAST MONTH (4 weeks) AND E whether YOU think each activity is leisure. Four spaces have
been provided to add your activities if the list doesn't mention activities you do.

Do YOU think Do YOU thlnk
Ttl’nti'e’sfll: this is leisure? 20:18": this is leisure?
(Please a r (Please H your
General Activities m. M 4 "$00833” General Activities m. m ‘ "spam.”
melts? weeks?
Yes No Yes No

AWing plays ..__.. C1 C1 Visiting friends/relatives __ Cl C]

Baking/cooking _ D C] Attending religious/spiritual facility __ CI Cl

Bingo __ Cl C] Watching television __ CI Cl

Cards __ D D Jogging/Walking for exercise __ Cl C!

Computer games __ Cl 13 Swimming __ D C]

Dining out _ Cl C! Weight lifting D 0

Going to the movies __ D D Other (specify) __ D 0

Photography __ C1 C1 Other (specify) __ D [:1

Radio listening __ D D Other (specify) __ D D

Skiing: Downhill Cl C! Other (specify) D D

 

 

 

 

 

 

 

 

 

 

 

 

 

 

‘ 2. Please indicate whether you or members of your household participated in the following

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

activities durum the PAST YEAR (12 months). Please Z all that apply)
General Activities P'fiffmgm' Water and Sport Activities gfilg‘mg
Scenic Driving 13 Fishing Cl
Picnicking CI Swimming CI
Walking/Jogging/Running Cl Skiing Cl
Photography Cl Soccer 0
Nature Walks Cl Lacrosse 0
Camping Cl Volleyball Cl
Gardening CI Bicycling Cl
Bingo CI Baseball/Softball CI
Golf Cl Basketball 0
Art Shows/Galleries Cl Rollerblading/Skateboarding [J
Festival/Special Events C! Other (Please specify 1 D
Volunteer/Community Service D l J

 

 

 

 

101

General Activity Participation Benefits and Barriers

 

 

3. Please indicate how important each of the following statements is for you. (Please circle a response for

 

 

 

 

 

 

 

 

 

 

 

 

each statement below)

During the past month (4 weeks), how Important has No, A, A.

it been for you to... important Neutral lm nt
Get exercise 1 2 3 4 5 6 7
Be with others who enjoy the same things you do 1 2 3 4 5 6 7
Be your own boss 1 2 3 4 5 6 7
Develop your skills and abilities 1 2 3 4 5 6 7
Keep physically fit 1 2 3 4 5 6 7
Think for yourself 1 2 3 4 5 6 7
Be with people Who have similar interests 1 2 3 4 5 6 7
Do something with your family 1 2 3 4 5 6 7
Be free to make your own choices 1 2 3 4 5 6 7
Feel good after being physically active 1 2 3 4 5 6 7
Be with people having similar values 1 2 3 4 5 6 7

 

 

 

 

 

 

 

 

 

4. During the past month (4 weeks), has there been any reason(s) which has prevented you from
participating in general recreation activities more often. (Please 21 all that apply)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

There is no opportunity near my home C] it is difficult to find others to participate with D
i don't know where I can take part in the activity C] i am not at ease in social situations CI
I don't have physical abilities CI The cost of transportation is too high Cl
l'm too busy with my family 0 l'm too busy with my work (school) C!
The cost of equipment. material and supplies is D Admission fees or other charges. for CI
too high . faculities and programs are too high
i don't feel safe or secure D l have too many home chores Cl
I don't know where I can learn the activity D Recreation facilities are pooriy maintained D
My skills are not good enough 0 Other (please specify ) Cl
5. Please indicate your level of satisfaction with each of the following statements. (Please circle a
response for each statement below)
3:3flatmattizrtitmfitmf" mm m... m
Get exercise 1 2 3 4 5 6 7
Be with others who enjoy the same things you do 1 2 3 4 5 6 7
Be your own boss 1 2 3 4 5 6 7
Develop your skills and abilities 1 2 3 4 5 6 7
Keep physically fit 1 2 3 4 5 6 7
Think for yourself 1 2 3 4 5 6 7
Be with peeple who have similar interests 1 2 3 4 5 6 7
Do something with your family 1 2 3 4 5 6 7
Be free to make your own choices 1 2 3 4 5 6 7
Feel good after being physically active 1 2 3 4 5 6 7
Be with people having similar values 1 2 3 4 5 6 7

 

 

 

 

 

 

 

 

102

SECTION II:

 

SELCRA Leisure Programs, Services and Facilities

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6. In the past 12 months. have you OR other members in your household participated in a
program. service or event organized by SELCRA? (Please 2' ONE)
F D Yes D No (skip to question 8) Cl Don't Know (skip to question 8)
7. (if yes) Please indicate whether you or other members of your household participated in the
following SELCRA activities during the PAST YEAR (12 months).(Please Hall that a ply)
SELCRA Programs ”figfl’g‘” SELCRA Programs 3mg
Youth basketball Cl Youth track CI
Youth baseball D Youth volleyball CI
Youth flag football 0 Little Bit sports 0
Youth fioor hockey D PeeWee t-ball CI
Youth golf lessons Ci Adult basketball Cl
Youth skateboarding Ci Adult softball Cl
Youth soccer Cl Adult volleyball Cl
Youth softball CI Dog obedience class D
Youth tennis lessons Cl Camps/Clinics Cl

 

 

 

 

 

 

8. Please indicate which rating best represents your experience with SELCRA programs. services or
events in the following aspects? (Please circle a response for each statement below

 

 

 

 

 

 

Terrible Neutral Excellent 2:29:
Quality of programs 1 2 3 4 5 6 7 DK
Cost of programs 1 2 3 4 5 6 7 DK
writbifity of programs (camps. sports leagues. special 1 2 3 4 5 6 7 DK
Quality of services (registration, customer relations...) 1 2 3 4 5 6 7 DK
Overall experience with SELCRA 1 2 3 4 5 6 7 DK

 

 

 

 

 

 

 

 

 

 

9- Please indicate how often you or other members of your household have visited the following
recreation sites or facilities in the Brighton Area (Please 9' all that apply)

 

 

 

 

 

 

 

 

 

Visited Visfioa l-ieve
in past In past visited. but Have
4 12 not in past never Don't

weeks months 12 months visited Know
Metro parks (e.g., Kensington. Huron Meadows) Ci D D D D
State recreation areas (e.g., Brighton. island Lake) [3 D D D 0
Brighton school gyms/indoor facilities D D D D 0
Brighton school fields 0 D D D CI
Imagination station (downtown Brighton playground) 0 Ci D D 0
Senior Center (Miller School) E) Cl D D D
Private facilities (e.g., Brighton Athletic Club. Family Fitness,
country clubs. ms) CI 0 D D D
Meijer Skate Park C! D E] Cl 0

 

 

 

 

 

 

 

10. SELCRA offers the majority of its programs at Brighton Area School facilities. How would you rate
the quality of the athletic fields at these facilities? (Please BONE)
Ci Excellent Ci Very good Ci Good Cl Fair

 

 

Cl Poor 0 Don't Know

 

103

 

 

 

 

 

 

 

 

‘ 11. There are many different sources of information that people use to find out about recreation
programs. services and facilities. Please indicate how likely you are to use information from the
sources listed below. (Please circle a response for each statement below)

I Definitely
information Sources Would Not I Definitely
Use Neutral Would Use
Advice from Family or Friends 1 2 3 4 5 6 7
Newspaper Advertisements 1 2 3 4 5 6 7
SELCRA Website 1 2 3 4 5 6 7
SELCRA Brochure Mailing 1 2 3 4 5 6 7
Flyers in Brighton Schools 1 2 3 4 5 6 7
Calling SELCRA 1 2 3 4 5 6 7

 

 

 

 

 

 

 

 

 

 

Future Community Park and Recreation Programs,

 

Services, or Facilities in the Brighton Area

 

 

 

 

 

 

 

 

 

 

 

12 Please indicate your level of agreement with the following statements. (Please circle a response for
each statement below)
During the next 5 years. i would like to see SELCRA focus their 3m” ”on,”
efforts on... pm "QM fl
Acquiring land 1 2 3 4 5 6 7
Building new athletic facilities 1 2 3 4 5 6 7
Providing more youth programs 1 2 3 4 5 6 7
Providing more adult programs 1 2 3 4 5 6 7
Repair and maintenance of Brighton School outdoor facilities 1 2 3 4 5 6 7
Upgrades and new equipment for Meijer Skate Park 1 2 3 4 5 6 7
Cooperative agreements with local governments 1 2 3 4 5 6 7
Cooperative agreements with Brighton Area Schools 1 2 3 4 5 6 7
Cooperative agreements with Huron Clinton Metro Parks 1 2 3 4 5 6 7
system and/or Department of Natural Resources

 

 

 

 

 

 

 

 

 

 

Potential Funding Options

 

 

 

 

 

 

 

 

 

13. SELCRA currently recovers 80% of its costs through the fees charged for programs. The other 20%
is provided by local municipalities. SELCRA is a regional partnership between Brighton Township.
the City of Brighton. Genoa Township. and Green Oak Township to provide quality recreation.
SELCRA is considering a number of options to fund potential future land acquisition,
development, upgrade, and maintenance. Please indicate your level of support for each potential
fundigg optiowlease circle a response for each statement below)

Strongly MW
Oppose Neutral Su port

Grants from MDNR (Michigan Department of Natural 1 2 3 4 5 6 7

ResourcesyMDOT (Michigan Department of Transportation)

Grants from local foundations 1 2 3 4 5 6 7

Private donations 1 2 3 4 5 6 7

Potential small parks and recreation millage (not to exceed .5 mill) 1 2 3 4 5 6 7

Potential sale of bonds (borrowing money) 1 2 3 4 5 6 7

Raise fees 1 2 3 4 5 6 7

Form a friends of SELCRA Foundation that would be 1 2 3 4 5 6 7

responsible for fundraising

 

 

 

 

 

 

 

 

 

 

104

SECTION III:

General Health and Life Satisfaction
These data WIII be kept in the strictest confidence and used for statistical purposes only

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

14. Overall. how would you rate your health during the past 4 weeks? (Please 21 ONE)
Cl Excellent Ci Very good B Good D Fair Ci Poor Cl Very Poor
15. During the past 4 weeks. how much did physical health problems limit your usual physical activities
(such as walking or climbing stairs)? (Please 2' ONE)
- . Cl Could not do
Cl Not at all C] Very little Cl Somewhat Cl Quite a lot physical activities
16. During the past 4 weeks. how much difficulty did you have doing your daily work. both at home and
away from home. because of your physical health? (Please 21 ONE)
- . 0 Could not do
D Not at all Cl Very little Cl Somewhat Cl Qurte a lot daily work
17. How much bodily pain have you had during the past 4 weeks? (Please 5 ONE)
Cl None C) Very mild D Mild Ci Moderate Cl Severe Cl Very Severe
18. During the past 4 weeks. how much energy did you have? (Please z ONE)
Cl Very much Cl Quite a lot Cl Some Cl A little 0 None
19. During the past 4 weeks. how much did your physical health or emotional problems limit your usual
social activities with family or friends? (Please er ONE)
. . Cl Could not do
Cl Not at all Cl Very little :1 Somewhat Ci Quite a lot social activities
20. During the past 4 weeks. how much have you been bothered by emotional problems (such as
feeling anxious, depressed or irritable)? (Please 27 ONE)
'3 Not at all C! Slightly Cl Moderately Cl Quite a lot Cl Extremely
21. During the past 4 weeks. how much did personal or emotional problems keep you from doing your
usual work. school or other daily activities? (e.g., visiting friends, relatives. etc.)? (Please E ONE)
. . Cl Could not do
0 Not at all 0 Very little Cl Somewhat Cl Qurte a lot daily activities
22. Please indicate your level of agreement with each of the following statements (Please circle a
response for each statement below)
Di Neutral Am
lnmostwaysmylifeisclosetomyideal 1 2 3 4 5 6 7
The conditions of my life are excellent 1 2 3 4 5 6 7
i am satisfied with my life 1 2 3 4 5 6 7
So far I have received the important things i want in life 1 2 3 4 5 6 7
lfl could live my life over, I would change almost nothing 1 2 3 4 5 6 7

 

 

 

 

 

 

 

 

 

105

 

 

 

General Demographics

These data Will be kept in the strictest confidence and used for statistical purposes only

 

 

 

 

23. How many adults (18 years and older). including you. live in your home? (persons)
24. How many children (under 18 years old) live in your home? children
Child 1 (age) Child 2 __ (896) Child 3 (age)
Child 4 (age) Child 5 _ (800) Child 6 (age)
25. Are you? (Please BONE) D Male Cl Female
26. In what year were you born in? 19_ __ (year)
27. Which ethnic group are you a member of? (Please 3 ONE)
0 White CI American Indian or Alaska Native
D African American 0 Asian
Cl Spanish/HispaniCILatino Ci Other (please speci'iy l
28. What is the highest degree or level of school you have completed? (Please 2 ONE)
Cl Less than 12" Grade 0 Some College (no degree) Cl Bachelor's Degree
0 High School Graduate Cl Associate Degree Cl Graduate or Professional
or GED Degree
29. What is your current marital status? (Please 2 ONE)
Ci Single, Never Married Ci Married
D Widowed Cl Divorced or Separated
30. Are you? (Please Bail that apply)
Cl Employed Full-Time Cl Homemaker D Retired
Ci Employed Part-Time Cl Student Cl Temporarily unemployed
D Self-Employed C] Other (please specify)
31. What is your approximate 2004 annual household income before taxes? (Please 2 ONE)
Cl Under-$24,999 Cl 550,000-574,999 Cl $100,000 and over
0 325,000-349,999 D 575,000-399,999
32. When did you move into your present residence? (year)
33. How many years have you lived in the Brighton Area? (years)
34. Do you own or rent your residence? (Please z ONE)
Cl Own Cl Rent

 

 

 

106

 

is there anything else you would like to share with us? Please write in the space below.

 

 

 

 

 

 

 

 

 

 

 

 

Everyone who returns a completed questionnaire by March 31, 2005 will be entered into one
of five drawings. Drawing winners will be notified by April 15, 2005.

Of the five drawings, please indicate the one SELCRA drawing you would like to enter. (Please
ZONE).

 

$50 toward a SELCRA program registration
$20 gift card for local movie theater

$20 gift card for local bowling alley

$20 pass book for Meijer Skate Park
Season pass for Huron Clinton Metro Parks

DDDDD

 

 

 

Thank you for your time in completing this questionnaire.
Please place this completed survey in the enclosed, self-addressed
envelope, and mail it today.

SELCRA

 

 

107

 

 

APPENDIX C

108

Study Instruments

Used to measure leisure experience scale and physical activity scale

 

1. Please indicate the total number of times YOU participated in each of the following activities

during the PAST MONTH (4 weeks) AND 2' whether YOU think each activity is leisure.

Four spaces have been provided to add your activities if the list doesn’t mention activities you

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

do.
Do YOU Do YOU
Total # think this is Tom) # think this is
of times leisure? oftimes leisure?
Ace???“ in the (Please g General Activities in the (Please 3
ct v1 res past 4 you, P0514 your
week: .7 responses) weeks? responsey
Yes No Yes No
Attending plays —-— CI CI Visiting {fiends/relatives -— CI CI
. . Attending religious/spiritual
Baking/cooking —— CI CI facility — CI CI
Bingo —— C] 0 Watching television -—-— U E]
Cards CI CI Jogging/Walking for CI CI
exercise
Computer games — E] El Swimming —— CI CI
Dining out — E) El Weight lifting — CI CI
Going to the CI CI Other (specify) CI CI
movies
Photography —— CI D Other (spcc1fy) — ['3 CI
Radio listening CI CI Other (specrfy) CI CI
Skiing: Downhill Ci D Other (SPCC‘fY) Cl C]
Autonomy Indicators

 

Please indicate your level of satisfaction with each of the following statements. (Please circle a
response for each statement below)

 

 

 

 

 

During the past month (4 weeks), how satisfied have Completely Completely
you been with how often you have been able to... Dissatisfied Neutral Satisfied
Be your own boss 1 2 3 4 6 7
Think for yourself 1 2 3 4 6 7
Be free to make your own choices 1 2 3 4 6 7

 

 

 

 

 

 

 

 

109

 

 

Physical Health Indicators

 

14.

Overall, how would you rate your health during the past 4 weeks? (Please ZONE)

CI CI Very .
Excellent good D GOOd D Pa“ 0 Poor 0 Very Poor

 

 

15.

During the past 4 weeks, how much did physical health problems limit your usual physical
activities (such as walking or climbing stairs)? (Please ZONE)

Cl Not at all I? ttlY: ery CI Somewhat CI Quite a lot

Cl Could not do
physical activities

 

 

16.

During the past 4 weeks, how much difficulty did you have doing your daily work, both at
home and away from home, because of your physical health? (Please ZONE)

CI Not at all 0 Very C] Somewhat CI Quite a lot 0 Could not do daily
lltfle work

 

 

 

17.

How much bodily pain have you had during the past 4 weeks? (Please ZONE)

Cl None gig/cry CI Mild 0 Moderate Cl Severe CI Very Severe

 

Mental Health Indicators

 

18.

During the past 4 weeks, how much energy did you have? (Please ZONE)

0 Quite a CI Some 0 A little Cl None

Cl Very much lot

 

 

 

19.

During the past 4 weeks, how much did your physical health or emotional problems limit
your usual social activities with family or friends? (Please ZONE)

Cl Not at all I? m: cry Cl Somewhat Cl Quite a lot

CI Could not do social
activities

 

 

20.

During the past 4 weeks, how much have you been bothered by emotional problems (such
as feeling anxious, depressed or irritable)? (Please ZONE)

Cl Not at all Cl Slightly Cl Moderately Cl Quite a lot 0 Extremely

 

 

 

21.

During the past 4 weeks, how much did personal or emotional problems keep you from
doing your usual work, school or other daily activities? (e.g., visiting friends, relatives,
etc.)? (Please ZONE)

Cl Very
Cl Not at all little

Cl Could not do daily

Cl Somewhat Cl Quite a lot activities

 

110

 

 

 

Life Satisfaction Indicators

 

Please indicate your level of agreement with each of the following statements (Please

circle a response for each statement below)

 

 

 

 

 

 

 

 

 

Strongly Strongly

Disa Iree Neutral Agree
In most ways my life is close to my ideal I 2 3 4 5 6 7
The conditions of my life are excellent 1 2 3 4 5 6 7
I am satisfied with my life 1 2 3 4 5 6 7
So far I have received the important things I want in life 1 2 3 4 5 6 7
If I could live my life over, I would change almost 1 2 3 4 5 6 7
nothing

 

 

 

 

 

 

lll

 

 

APPENDIX D

112

Used to calculate activi scale
Predetermined Activities METs Additional Activities
P 1.5
Re' ' ' ' F ' 1.5 Maintenance
' 2.5 ' '
1.5
1.5
Games 1 .5 azzercise
out 1.5
' ' 6.0 ' Box
to the Movies 1.5
' for Exercise 6.0
2.5
1.0 ountain B
6.0 useum Visit
Friends/Relatives l .5
Television 1 .0
‘ 6.0 Instrument
Additional Activities METs Piano
e. Pilates 6.5 la with Kids
3.5
&Crafts e. ' ' ' 2.0
' Events 1.5 ' Old Car
Shows 2.0 '
8.0
ible 1.8 ' Machine
‘ ' 8.0
2.5
3.0 In
2.5

     

fiball

For Small Children e. ' 3.0
ircuit T ' ' 8.0
' i.e Rock or Mountain 8.0
e. Internet 1.5
1.5 ai-Chi
8.0 On Phone
4.5 ennis
8.0 acation
4.8 ideo Game
Golf 3.0 ' ' Galleries
' Trainer e. Nordic Track 9.0
Class 6.5 olunteer
3.0 alk In Woods
4.0 ‘
4.5 ' TV
ealth Club Exercise e. Curves 5.5 orkout e. General Exercise
' 3.3 ard W
8.0
MET Source: Ainsworth, et al. (2000)

113

 

REFERENCES

114

 

REFERENCES

Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J .,
et a1. (2000). Compendium of physical activities: An update of activity codes and
MET intensities. Medicine & Science in Sports & Exercise, 32(9 Suppl), S498-
8516.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decisions Processes, 50, 179-211.

Allen, L. R. (1990). Benefits of leisure attributes to community satisfaction. Journal of
> Leisure Research, 22(2), 183-196.

Allen, L. R. (1991). Benefits of leisure services to community satisfaction. In B. L.
Driver, P. Brown, J. & G. L. Peterson (Eds), Benefits of leisure (pp. 331-350).
State College, PA: Venture Publishing Inc.

Allen, L. R., & Beattie, R. J. (1984). The role of leisure as an indicator of overall
satisfaction with community life. Journal of Leisure Research, 16(2), 99-109.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A
review and recommended two-step approach. Psychological Bulletin, 103(3),
411-423.

Andrews, F. M., & Withey, S. B. (1974). Developing measures of perceived life quality:
Results from several national surveys. Social Indicators Research, 1, 1-26.

Andrews, F. M., & Withey, S. B. (1976). Social indicators ofwell-being: America's
perceptions of life quality. New York: Plenum Press.

Aneshensel, C. S., Frerichs, R. R., & Huba, G. J. (1984). Depression and physical illness:
A multiwave, nonrecursive causal model. Journal of Health and Social Behavior,
25(4), 350-371.

Bates, J. H., Serdula, M. K., Khan, L., Kettel, Jones, D. A., Gillespie, C., & Ainsworth,
B. E. (2005). Total and leisure-time walking among US. adults: Should every
step count? American Journal of Preventive Medicine, 29(1), 46-50.

115

 

Benyamini, Y., Idler, E. L., Leventhal, H., & Leventhal, E. A. (2000). Positive affect and
function as influences on self-assessments of health: Expanding our view beyond

illness and disability. Journal of Gerontology: Psychological Sciences, 553(2),
P 1 07-P1 1 6.

Biddle, S. J. H., Fox, K. R., & Boutcher, S. H. (Eds.). (2000). Physical activity and
psychological well-being. New York: Routledge.

Biddle, S. J. H., Fox, K. R., Boutcher, S. H., & Faulkner, G. E. (2000). The way forward

for physical activity and the promotion of psychological well-being. In S. J. H.
Biddle, K. R. Fox & S. H. Boutcher (Eds.), Physical activity and psychological

well-being (pp. 154-168). New York: Routledge.

Biddle, S. J. H., & Mutrie, N. (1991). Psychology of physical activity and exercise: A
health-related perspective. New York: Springer-Verlag.

Blair, S. N., Jacobs, D. R. J ., & Powell, K. E. (1985). Relationships between exercise or
physical activity and other health behaviors. Public Health Reports, 100(2), 173-
1 79.

Blair, S. N., Kohl, H. W., Gordon, N. F., & Paffenbarger Jr., R. S. (1992). How much
physical activity is good for health? Annual Review of Public Health, 13, 99-126.

Boelhouwer, J., & Stoop, I. (1999). Measuring well-being in the Netherlands: The SCP
index from 1974 to 1997. Social Indicators Research, 48(1), 51-75.

Bramston, P., Pretty, G., & Chipuer, H. M. (2002). Unraveling subjective quality of life:
An investigation of individual and community determinants. Social Indicators

Research, 59, 261—274.

Budge, C., Carryer, J ., & Wood, S. (2003). Health correlates of autonomy, control and
professional relationships in the nursing work environment. Journal of Advanced

Nursing, 42(3), 260-268.

Campbell, A. (1981). The sense of well-being in America: Recent patterns and trends.
New York: McGraw-Hill Book Company.

Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life:
Perceptions, evaluations, and satisfaction. New York: Russell Sage Foundation.

116

 

 

Centers for Disease Control and Prevention. (2006). Physical activity for everyone:
Measuring physical activity intensity [Electronic Version]. Retrieved January 16,
2006 from http://www.cdc.gov/nccdphp/dnpa/physical/measuring/index.htm.

Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York:
Academic Press.

Coleman, D., & Iso—Ahola, S. E. (1993). Leisure and health: The role of social support
and self-determination. Journal of Leisure Research, 25(2), 111-128.

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York:
Harper & Row, Publishers, Inc.

Csikszentmihalyi, M. (1997). Activity, experience, and personal growth. Champaign, IL:
Human Kinetics.

Cummins, R. A. (1996). The domains of life satisfaction: An attempt to order chaos.
Social Indicators Research, 38, 303-328.

Cummins, R. A. (1997). Comprehensive quality of life scale: Adult. Burwood,
Melbourne: School of Psychology, Deakin University.

Cummins, R. A. (2000). Objective and subjective quality of life: An interactive model.
Social Indicators Research, 52, 55-72.

Dear, K., Henderson, 8., & Korten, A. (2002). Well-being in Australia: Findings from the
National Survey of Mental Health and Well-being. Social Psychiatry and
Psychiatric Epidemiology, 3 7, 503-509.

Deci, E. L., & Ryan, R. M. (1987). The support of autonomy and the control of behavior.
Journal of Personality and Social Psychology, 53(6), 1024-1037.

Deci, E. L., & Ryan, R. M. (1995). Human autonomy as the basis for true self-esteem. In
M. H. Kemis (Ed.), Efficiency, agency, and self-esteem (pp. 31-47). New York:
Plenum Press.

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542-575.

117

 

Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a
national index. American Psychologist, 55(1), 34-43.

Diener, E., & Biswas-Diener, R. (2002). Will money increase subjective well-being?
Social Indicators Research, 5 7, 1 19-169.

Diener, E., Diener, M., & Diener, C. (1995). Factors predicting the subjective well-being
of nations. Journal of Personality and Social Psychology, 69(5), 851-864.

Diener, E., Emmons, R. A., Larson, R. J., & Griffin, S. (1985). The satisfaction with life
scale. Journal of Personality Assessment, 49(1), 71-75.

Diener, E., & Lucas, R. E. (2000). Explaining differences in societal levels of happiness:
Relative standards, need fiilfillment, culture, and evaluation theory. Journal of
Happiness Studies, I, 41-78.

Diener, E., & Seligman, M. E. P. (2004). Beyond money: Toward an economy of well-
being. Psychological Science in the Public Interest, 5(1), 1-31.

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three
decades of progress. Psychological Bulletin, 125(2), 276-302.

Dillman, D. A. (2000). Mail and internet surveys: The tailored design method (2nd ed.).
New York: John Wiley & Sons, Inc.

Dishman, R. K., Sallis, J. F., & Orenstein, D. R. (1985). The determinants of physical
activity and exercise. Public Health Reports, 100(2), 159-171.

Driver, B. L., Brown, R, J ., & Peterson, G. L. (Eds.). (1991). Benefits of leisure. State
College, PA: Venture Publishing, Inc.

Driver, B. L., Tinsley, H. E. A., & Manfredo, M. J. (1991). The paragraphs about leisure
and recreation experience preference scales: Results from two inventories
designed to assess the breadth of the perceived psychological benefits of leisure.
In B. L. Driver, P. Brown, J. & G. L. Peterson (Eds.), Benefits of leisure (pp. 263-
286). State College, PA: Venture Publishing Inc.

118

 

Ellis, G., & Witt, P. A. (1984). The measurement of the perceived freedom in leisure.
Journal of Leisure Research, 16(1), 1 10-123.

Esteve, R., San Martin, J ., & Lopez, A. E. (1999). Grasping the meaning of leisure:
Developing a self-report measurement tool. Leisure Studies, 18, 79-91.

Fayers, P. M., & Machin, D. (2000). Quality of life: Assessment, analysis, and
interpretation. Chichester: Wiley.

F eist, G. J ., Bodner, T. E., Jacobs, J. F., Miles, M., & Tan, V. (1995). Integrating top-
down and bottom-up structural models of subjective well-being: A longitudinal
investigation. Journal of Personality and Social Psychology, 68(1), 138-150.

Flanagan, J. C. (1978). A research approach to improving our quality of life. American
Psychologist, 138-147.

Flanagan, J. C. (1982). Measurement of quality of life: Current state of the art. Archives
of Physical Medicine and Rehabilitation, 63, 56-59.

Fox, K. R. (2000). The effects of exercise on self-perceptions and self-esteem. In S. J. H.
Biddle, K. R. Fox & S. H. Boutcher (Eds.), Physical activity and psychological
well-being (pp. 88-117). New York: Routledge.

Fox, K. R., Boutcher, S. H., Faulkner, G. E., & Biddle, S. J. H. (2000). The case for
exercise in the promotion of mental health and psychological well-being. In S. J.
H. Biddle, K. R. Fox & S. H. Boutcher (Eds.), Physical activity and psychological
well-being (pp. 1-9). New York: Routledge.

Freud, S. (1920). Beyond the pleasure principle. London: Hogarth.

Gomez, E. (1999). Reconceptualizing the relationship between ethnicity and public
recreation: A proposed model. Michigan State University, East Lansing.

Hagerty, M. R., Cummins, R. A., Ferris, A. L., Land, K. C., Michalos, A. C., Peterson,
M., et al. (2001). Quality of life indexes for national policy: Review and agenda
for research. Social Indicators Research, 55, 1-96.

119

 

Hayes, D., & Ross, C. E. (1986). Body and mind: The effect of exercise, overweight, and
physical health on psychological well-being. Journal of Health and Social
Behavior, 27, 387-400.

Headey, B., Veenhoven, R., & Wearing, A. (1991). Top-down versus bottom-up theories
of subjective well-being. Social Indicators Research, 24, 81-100.

Heller, D., Watson, D., & Ilies, R. (2004). The role of person versus situation in life
satisfaction: A critical examination. Psychological Bulletin, 130(4), 574-600.

Hills, P., & Argyle, M. (1998). Positive moods derived from leisure and their relationship
to happiness and personality. Personality and Individual Difi’erences, 25(3), 523-
535.

Holman, T. B., & Jacquart, M. (1988). Leisure-activity patterns and marital satisfaction:
A further test. Journal of Marriage and the Family, 50(1), 69-77.

Hunter, J. E., & Gerbing, D. W. (1982). Unidimensional measurement, second order

factor analysis, and causal models. Research in Organizational Behavior, 4, 267-
320.

Hunter, J. E., & Hamilton, M. A. (1992). CFA.BAS: A program in BASICA to do
confirmatory factor analysis. East Lansing, MI: Michigan State University.

Hunter, J. E., & Hamilton, M. A. (1995). PATH: A program in BASICA. East Lansing,
MI: Michigan State University.

Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and
bias in research findings (2nd ed.). Thousand Oaks, CA: Sage Publications.

Inkeles, A. (1993). Industrialization, modernization and the quality of life. International
Journal of Comparative Sociology, 34, 1-23.

Iso-Ahola, S. E. (1997). A psychological analysis of leisure and health. In J. T. Haworth
(Ed.), Work, leisure and well-being (pp. 131-144). London: Routledge.

120

Iso-Ahola, S. E. (1999). Motivational foundations of leisure. In E. L. Jackson & T. L.
Burton (Eds.), Leisure studies: Prospects for the twenty-first century (pp. 35-51).
State College, PA: Venture Publishing, Inc.

Iso-Ahola, S. E. (Ed.). (1980). Social psychologicalperspectives on leisure and
recreation. Springfield, IL: Charles C. Thomas.

Iso-Ahola, S. E., & Park, C. J. (1996). Leisure-related social support and self-
determination as buffers of stress-illness relationship. Journal of Leisure
Research, 28(3), 169-187.

Iwasaki, Y. (2003). Examining rival models of leisure coping mechanisms. Leisure
Sciences, 25, 183-206.

Iwasaki, Y., & Smale, B. J. A. (1998). Longitudinal analyses of the relationships among
life transitions, chronic health problems, leisure, and psychological well-being.
Leisure Sciences, 20, 25-52.

Jackson, E. L., & Scott, D. (1999). Constraints to leisure. In E. L. Jackson & T. Burton,
L. (Eds.), Leisure studies: Prospects for the twenty-first century (pp. 299-321).
State College, PA: Venture Publishing, Inc.

Kiecolt-Glaser, J. K., McGuire, L., Robles, T. F., & Glaser, R. (2002). Emotions,
morbidity, and mortality: New perspectives from psychoneuroimmunology.
Annual Review of Psychology, 53, 83-107.

King, L. A., Richards, J. H., & Stemmerich, E. (1998). Daily goals, life goals, and worst
fears: Means, ends, and subjective well-being. Journal of Personality, 66(5), 713-
744.

Koukouvou, G., Kouidi, E., Iacovides, A., Konstantinidou, E., Kaprinis, G., &
Deligiannis, A. (2004). Quality of life, psychological and physiological changes
following exercise training in patients with chronic heart failure. Journal of
Rehabilitation Medicine, 36(1), 36-41.

Kuhn, T. S. (1996). The structure of scientific revolutions (3rd ed.). Chicago, IL:
University of Chicago Press.

Land, K. C. (1983). Social indicators. Annual Review of Sociology, 9, 1-26.

121

Land, K. C. (2000). Social indicators. In E. Borgatta, F. & R. V. Montgomery (Eds.),
Encyclopedia of sociology (pp. 2682-2690). New York: Macmillan.

Land, K. C., Lamb, V. L., & Mustillo, S. K. (2001). Child and youth well-being in the
United States, 1975-1998: Some findings from a new index. Social Indicators
Research, 56, 241-320.

Langer, E. J ., & Rodin, J. (1976). The effects of choice and enhanced personal
responsibility for the aged: A field experiment in an institutional setting. Journal
of Personality and Social Psychology, 34(2), 191-198.

Larson, J. S. (1991). The measurement of health: Concepts and indicators. New York:
Greenwood Press.

Larson, R. J., Diener, E., & Emmons, R. A. (1985). An evaluation of subjective well-
being measures. Social Indicators Research, 1 7, 1-17.

Lee, Y., Dattilo, J ., & Howard, D. (1994). The complex and dynamic nature of leisure
experience. Journal of Leisure Research, 26(3), 195-21 1.

Lloyd, K. M., & Auld, C. J. (2002). The role of leisure in determining quality of life:
Issues of content and measurement. Social Indicators Research, 5 7, 43-71.

MacLeod, A. K., & Conway, C. (2005). Well-being and the anticipation of future positive
experiences: The role of income, social networks, and planning ability. Cognition
and Emotion, 19(3), 357-374.

Manfredo, M. J ., Driver, B. L., & Tarrant, M. A. (1996). Measuring leisure motivation:
A meta-analysis of the Recreation Experience Preference Scales. Journal of
Leisure Research, 28, 188-213.

Mannell, R. C. (1980). Social psychological techniques and strategies for studying leisure
experiences. In S. E. Iso-Ahola (Ed.), Social psychological perspectives on leisure
and recreation (pp. 62-88). Springfield, IL: Charles C. Thomas Publishers.

Mannell, R. C. (1999). Leisure experience and satisfaction. In E. L. Jackson & T. Burton,
L. (Eds.), Leisure studies: Prospects for the twenty-first century (pp. 235-251).
State College, PA: Venture Publishing, Inc.

122

Mannell, R. C., & Kleiber, D. A. (1997). A social psychology of leisure. State College,
PA: Venture Publishing, Inc.

Marans, R. W., Dillman, D. A., & Keller, J. (1980). Perceptions of life quality in rural
America: An analysis of survey data from four studies. Ann Arbor, MI: Institute
for Social Research.

Marans, R. W., & Mohai, P. (1991). Leisure resources, recreation activity, and the quality
of life. In B. L. Driver, P. Brown, J. & G. L. Peterson (Eds.), Benefits of leisure
(pp. 351-363). State College, PA: Venture Publishing Inc.

Maslow, A. H. (1970). Motivation and personality (2nd ed.). New York: Harper & Row,
Publishers.

McDougall, W. (1908). Introduction to social psychology. London: Methuen.

McLean, D. D., Bannon, J. J ., & Gray, H. R. (1999). Leisure resources: Its
comprehensive planning (2nd ed.). Champaign, IL: Sagarnore Publishing.

Michalos, A. C. (2003). Essays on the quality of life (Vol. 19). Boston, MA: Kluwer
Academic Publishers.

Michalos, A. C., Zumbo, B. D., & Hubley, A. (2000). Health and the quality of life.
Social Indicators Research, 51, 245-286.

Michigan Land Use Leadership Council. (2003). Michigan‘s land, Michigan's fiiture.
Retrieved April 30, 2004, from http://www.michiganlanduse.org/finalreport.htm

Miilunpalo, S. (2001 ). Evidence and theory based promotion of health-enhancing
physical activity. Public Health Nutrition, 4(2B), 725-728.

Munro, B. H. (2005). Statistical methods for health care research (5th ed.). Philadelphia,
PA: Lippincott Williams & Wilkins.

Murray, H. (1938). Explorations in personality. New York: Oxford University Press.

123

Mutrie, N. (2000). The relationship between physical activity and clinically defined
depression. In S. J. H. Biddle, K. R. Fox & S. H. Boutcher (Eds.), Physical
activity and psychological well-being (pp. 46-62). New York: Routledge.

National Research Council. (2002). Community and quality of life: Data needs for
informed decision making. Washington, DC: National Academy Press.

Neulinger, J. (1981). T a leisure: An introduction. Boston: Allyn & Bacon, Inc.

Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological
Assessment, 5(2), 164-172.

Pedersen, B. K., & Clemmensen, I. H. (1997). Exercise and cancer. In B. K. Pedersen
(Ed.), Exercise immunology (pp. 171-201). Austin, TX: R. G. Landes Company.

Ragheb, M. G. (1989). Step-wise regression analysis of leisure domains and the reported
contribution of leisure activities to individuals' well-being: An exploratory study.
Society and Leisure, 12(2), 399-412.

Ragheb, M. G. (1993). Leisure and perceived wellness: A field investigation. Leisure
Sciences, 15(1), 13-24.

Ragheb, M. G., & Griffith, C. A. (1982). The contributions of leisure participation and
leisure satisfaction to life satisfaction of older persons. Journal of Leisure
Research, 14(4), 295-306.

Rahtz, D. R., Sirgy, J. M., & Meadow, H. L. (1988). Elderly life satisfaction and
television viewership: Replication and extension. Paper presented at the American
Marketing Association Winter Educators' Conference, Chicago, IL.

Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000). Daily well-
being: The role of autdnomy, competence, and relatedness. Personality and Social
Psychology Bulletin, 26(4), 419-435.

Rejeski, W. J., Brawley, L. R., & Shumaker, S. A. (1996). Physical activity and health-
related quality of life. Exercise and Sport Sciences Review, 24, 71-108.

124

Rodriguez, A. (2006). Comparisons of leisure and activity participation in relation to
health and life satisfaction. Manuscript submitted for publication, Michigan State
University, East Lansing, MI.

Rojek, C. (1999). Deviant leisure: The dark side of free-time activity. In E. L. Jackson &
T. Burton, L. (Eds.), Leisure studies: Prospects for the twenty-first century (pp.
81-95). State College, PA: Venture Publishing, Inc.

Rosenthal, D. H., Driver, B. L., & Waldman, D. (1982). Construct validity of instruments
measuring recreationists' preferences. Leisure Sciences, 5(2), 89-108.

Ryan, R. M. (1995). Psychological needs and the facilitation of integrative processes.
Journal of Personality, 63(3), 397-427.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of
intrinsic motivation, social development, and well-being. American Psychologist,
55(1), 68-78.

Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of
research on hedonic and eudaimonic well-being. Annual Review of Psychology,
52(141-166).

Ryff, C. D., Singer, B. H., & Love, G. D. (2004). Positive health: Connecting well-being
with biology. Philosophical Transactions of the Royal Society of London, 359,
1 3 83-1 394.

Sallis, J. F. (1990). Determinants of exercise behavior. In J. O. Holloszy & K. B. Pandolf
(Eds.), Exercise and sport sciences reviews (pp. 307-330). Baltimore, MD:
Williams & Wilkins.

Samdahl, D. M. (1991). Issues in the measurement of leisure: A comparison of
theoretical and connotative meanings. Leisure Sciences, 13, 33-49.

Schimmack, U., Radhakrishnan, P., Oishi, S., & Dzokoto, V. (2002). Culture,

personality, and subjective well-being: Integrating process models of life
satisfaction. Journal of Personality and Social Psychology, 82(4), 582-593.

Schnohr, P., Kristensen, T. S., Prescott, E., & Scharling, H. (2005). Stress and life
dissatisfaction are inversely associated with jogging and other types of physical

125

activity in leisure time: The Copenhagen City Heart Study. Scandinavian Journal
of Medicine and Science in Sports, 15, 107-112.

Schulz, R. (1976). Effects of control and predictability on the physical and psychological
well-being of the institutionalized aged. Journal of Personality and Social
Psychology, 33(5), 563-573.

Shaw, S. M. (1986). Leisure, recreation or free time? Measuring time usage. Journal of
Leisure Research, 18(3), 177-189.

Sheldon, K. M., Elliot, A. J ., Kim, Y., & Kasser, T. (2001). What is satisfying about
satisfying events? Testing 10 candidate psychological needs. Journal of
Personality and Social Psychology, 80(2), 325-339.

Sheldon, K. M., Ryan, R. M., Deci, E. L., & Kasser, T. (2004). The independent effects
of goal contents and motives on well-being: It's both what you pursue and why
you pursue it. Personality and Social Psychology Bulletin, 30(4), 475-486.

Shevlin, M. E., Brunsden, V., & Miles, J. N. V. (1998). Satisfaction with life scale:
Analysis of factorial invariance, mean structures and reliability. Personality and
Individual Diflerences, 25, 911-916.

Shevlin, M. E., & Bunting, B. E. (1994). Confirmatory factor analysis of the satisfaction
with life scale. Perceptual and Motor Skills, 79, 13 16-131 8.

Sirgy, J. M. (2001). Handbook of quality-of-life research: An ethical marketing
perspective (Vol. 8). Boston, MA: Kluwer Academic Publishers.

Sirgy, J. M. (2002). The psychology of quality of life (Vol. 12). Boston, MA: Kluwer
Academic Publishers.

Sirgy, J. M., Rahtz, D. R., Cicic, M., & Underwood, R. (2000). A method of assessing
residents' satisfaction with community-based services: A quality-of-life
perspective. Social Indicators Research, 49, 279-316.

Staines, G. L. (1980). Spillover versus compensation: A review of the literature on the
relationship between work and nonwork. Human Relations, 33(2), 111-129.

126

Suh, E. M., Diener, E., Oishi, S., & Triandis, H. C. (1998). The shifting basis of life
satisfaction judgments across cultures: Emotions versus norms. Journal of
Personality and Social Psychology, 74(2), 482-493.

Taaffe, D. R., Harris, T. B., Ferrucci, L., Rowe, J ., & Seeman, T. E. (2000). Cross-
sectional and prospective relationships of interleukin-6 and C-Reactive protein

with physical performance in elderly persons. Journal of Gerontology: Medical
Sciences, 55A(12), M709-M715.

Tarrant, M. A., Manfredo, M. J., Bayley, P. B., & Hess, R. (1993). Effects of recall bias
and nonresponse bias on self-report estimates of angling participation North
American Journal of Fisheries Management, 13, 217-222.

Tinsley, H. E. A. (1984). The psychological benefits of leisure counseling. Society and
Leisure, 7(1), 125-140.

Tinsley, H. E. A., Barrett, T. C., & Kass, R. A. (1977). Leisure activities and need
satisfaction. Journal of Leisure Research, 9(2), 110-120.

Tinsley, H. E. A., Driver, B. L., & Kass, R. A. (1982). Reliability and concurrent validity
of the recreation experience preference scales. Journal of Educational and
Psychological Measurement, 41(3), 897-907.

Tinsley, H. E. A., & Eldredge, B. D. (1995). Psychological benefits of leisure
participation: A taxonomy of leisure activities based on their need-gratifying
properties. Journal of Counseling Psychology, 42(2), 123-132.

Tinsley, H. E. A., & Johnson, T. L. (1984). A preliminary taxonomy of leisure activities.
Journal of Leisure Research, 16(3), 234-244.

Tinsley, H. E. A., & Kass, R. A. (1978). Leisure activities and need satisfaction: A
replication and extension. Journal of Leisure Research, 10(3), 191-202.

Tinsley, H. E. A., & Kass, R. A. ( 1979). The latent structure of the need satisfying
properties of leisure activities. Journal of Leisure Research, 11(4), 278-291.

Tinsley, H. E. A., & Kass, R. A. (1980a). The construct validity of the leisure activities
questionnaire and of the paragraphs about leisure. Educational and Psychological
Measurement, 40, 219-226.

127

Tinsley, H. E. A., & Kass, R. A. (1980b). Discriminant validity of the leisure activity

questionnaire and the paragraphs about leisure. Educational and Psychological
Measurement, 40, 227-233.

Tinsley, H. E. A., & Tinsley, D. J. (1982). A holistic model of leisure counseling. Journal
of Leisure Research, 14(2), 100-116.

Tinsley, H. E. A., & Tinsley, D. J. (1986). A theory of the attributes, benefits, and causes
of leisure experience. Leisure Sciences, 8(1), 1-45.

Trafion, R. S., & Tinsley, H. E. A. (1980). An investigation of the construct validity of

measures of job, leisure, dyadic and general life satisfaction. Journal of Leisure
Research, 12(1), 34-44.

Trenberth, L., Dewe, P., & Walkey, F. (1999). Leisure and its role as a strategy for
coping with work stress. International Journal of Stress Management, 6(2), 89-
103.

US. Census Bureau. (2000). Table DP-l. Profile of General Demographic
Characteristics: 2000. from http://censtats.census.gov/data/MI/

Veenhoven, R. (1991). Questions on happiness: Classical topics, modern answers, blind
spots. In F. Strack, M. Argyle & N. Schwarz (Eds.), Subjective wellbeing: An
interdisciplinary perspective (pp. 7-26). London: Pergamon Press.

Vitterso, J. (2003). Flow versus life satisfaction: A projective use of cartoons to illustrate
the difference between the evaluation approach and the intrinsic motivation
approach to subjective quality of life. Journal of Happiness Studies, 4, 141-167.

Wankel, L. M., & Berger, B. G. (1991). The personal and social benefits of sport and
physical activity. In B. L. Driver, P. J. Brown & G. L. Peterson (Eds.), Benefits of
leisure (pp. 121-144). State College, PA: Venture Publishing, Inc.

Ware, J. E., Kosinski, M., Dewey, J. E., & Gandek, B. (2001). How to score and interpret
single-item health status measures: A manual for user of the SF -8T M Health
Survey. Lincoln, RI: QualityMetric Incorporated.

128

 

Wendel-Vos, G. C. W., Schuit, A. J ., Tijhuis, M. A. R., & Kromhout, D. (2004). Leisure
time physical activity and health-related quality of life: Cross-sectional and
longitudinal associations. Quality of Life Research, 13, 667-677.

Wilson, D., Parsons, J ., & Tucker, G. (2000). The SF-36 summary scales: Problems and
solutions. Social and Preventive Medicine, 45(6), 239-246.

Zuzanek, J ., Robinson, J. P., & Iwasaki, Y. (1998). The relationships between stress,
health, and physically active leisure as a function of life-cycle. Leisure Sciences,
20, 253-275.

129

 

 

   

llllllllllllllllll