Walden University
COLLEGE OF SOCIAL AND BEHAVIORAL SCIENCES
This is to certify that the doctoral dissertation by
Lisa Samuel
has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.
Review Committee
Dr. Andrea Miller, Committee Chairperson, Psychology Faculty Dr. Tom Diebold, Committee Member, Psychology Faculty
Dr. Suzanne Manning, Committee Member, Psychology Faculty Dr. Peter Anderson, School Representative, Psychology Faculty
Chief Academic Officer
David Clinefelter, Ph.D.
Walden University 2010
ABSTRACT
Eating, Health Behaviors, and Cognitive Style
by
Lisa Kristine Samuel
M.B.A., University of Phoenix, 2005 B.A., Florida Metropolitan University, 1998
Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy Psychology
Walden University August 2010
ABSTRACT
Researchers have documented relationships between negative eating behaviors, such as
binge eating, and health related outcomes such as obesity. Obesity is a chronic illness
which increases the probability of developing high blood pressure, type 2 diabetes, and
heart disease. Even with increasing rates of obesity, research has remained focused upon
the treatment of obesity or behavioral weight-loss therapies rather than health behaviors
and decision making styles that may contribute to this epidemic. Using the Theory of
Planned Behavior Questionnaire, the Kirton Adaption-Innovation instrument, and the
Eating Disorders Questionnaire-6, the purpose of this study was to determine any
relationships between theory of planned behavior variables, adaption-innovation
variables, and body mass with eating behavior variables of dietary restraint (DR), eating
concern (EC), shape concern (SC), and weight concern (WC). The convenience sample
consisted of 137 participants without clinical health disorders ranging in ages 18 through
64. After first entering BMI into the model, hierarchical multiple regressions indicated
significant relationships between attitude towards overeating with DR, EC, SC, and WC;
perceived behavioral control with EC, SC, and WC; intention to manage eating behavior
with EC, SC, and WC; and BMI with SC and WC. The implications for positive social
change include a better understanding of how motivational influences can predict certain
behavioral features of eating habits and how this may have the potential to minimize the
consequences of negative eating behaviors, such as chronic diseases, that are associated
with the growing population of overweight and obese individuals in society.
Eating, Health Behaviors, and Cognitive Style
by
Lisa Kristine Samuel
M.B.A., University of Phoenix, 2005 B.A., Florida Metropolitan University, 1998
Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy Psychology
Walden University August 2010
UMI Number: 3411946
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UMI 3411946
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DEDICATION
This dissertation is dedicated to my family. To my husband and best friend, Phil,
you have inspired me, supported me, and encouraged me through this journey and I am
eternally grateful to have you in my life. To my children, Hagan and Ryland, your smiles
inspire me every day, and I hope that you will look at my journey as encouragement to
pursue and achieve your personal dreams.
ii
ACKNOWLEDGMENTS
The support, effort, and patience of many individuals have made this journey
possible. First, I would like to thank Dr. Andrea Miller, my dissertation chairperson. Her
guidance, positive support, wisdom, and clarity have been instrumental in this process
and I am sincerely grateful for the time and effort she put forth. Secondly, I would like to
thank my dissertation committee members Dr. Tom Diebold, who guided me through
every step of the methodology process with expertise and patience, Dr. Suzanne
Manning, who contributed to my doctoral learning experience with her insightful
comments on this dissertation, and Dr. Anderson, who provided clarity and precision
throughout this process. I am truly grateful to have had such a gifted team. Additionally, I
would like to thank Dr. M. J. Kirton for spending valuable time with me to discuss his
adaption-innovation theory.
I would also like to acknowledge my parents, Jack and Phyllis Finney. I would
like to thank Phyllis for her support, and without her I would not have been able to make
the many trips away from my home and my family to complete this dissertation. I also
want to thank my Dad for believing in me from the very beginning, for his affirmation
throughout this process, and for teaching me throughout my life the value of always
learning something new.
Finally, I would like to thank my husband, Dr. Philip Samuel. There are not words
to describe how much his unending support has meant to me. I thank him for the endless
hours of discussions, just listening to me, and for holding my hand through this process.
iii
TABLE OF CONTENTS
LIST OF TABLES ............................................................................................................. vi LIST OF FIGURES .......................................................................................................... vii CHAPTER 1: INTRODUCTION TO THE STUDY...........................................................1
Introduction ....................................................................................................................1 Summary of Literature ...................................................................................................2 Theory of Planned Behavior ..............................................................................2 Adaption-Innovation Theory ..............................................................................3 Motivational and Biological Applications to Eating Behaviors ........................4 Eating Behaviors and Binging ...........................................................................6 Problem Statement .........................................................................................................7 Nature of Study ..............................................................................................................8 Research Question .............................................................................................8 Hypotheses .........................................................................................................8 Purpose of Study ..........................................................................................................10 Operational Definitions ................................................................................................10 Assumptions, Limitations, Scope, and Delimitations of Project .................................13 Significance of Study ...................................................................................................13 Professional Application ..................................................................................14 Knowledge Generation ....................................................................................15 Positive Social Change Implications ...........................................................................16 Summary ......................................................................................................................18
CHAPTER 2: LITERATURE REVIEW ...........................................................................18
Introduction ..................................................................................................................19 Organization of Chapter ..................................................................................19 Strategy for Literature Review .........................................................................20 Content .............................................................................................................20 Qualtiative and Quantiative Methodologies ....................................................20 Binge Eating.................................................................................................................21 Eating Behaviors and Food Choices ...............................................................22 Food Selection and Binge Eating ....................................................................25 Psychological and Sociological Implications ..................................................27 Theory of Planned Behavior ........................................................................................30 Health Behaviors .............................................................................................34 Binge Behaviors ...............................................................................................35 Adaption-Innovation Theory of Problem Solving Style ..............................................36 Summary ......................................................................................................................42
CHAPTER 3: RESEARCH METHOD .............................................................................44 Organization of Chapter ...............................................................................................44
iv
Research Design and Approach ...................................................................................44 Setting and Sample ......................................................................................................45
Population and Sampling Method .................................................................. 45 Sample Size ..................................................................................................... 45 Participants and Characteristics .................................................................... 46
Instruments and Materials ............................................................................................46 Body Mass Index ............................................................................................. 47 Eating Disorder Examination Questionnaire, EDE-Q6 ................................. 48 Theory of Planned Behavior Questionnarie ................................................... 52 Kirton Adaption-Innovation Inventory ........................................................... 56 Background Data Questionnaire .....................................................................61
Data Collection and Analysis.......................................................................................61 Null Hypotheses (H0) ...................................................................................... 61 Nature of Scales .............................................................................................. 63 Protection of Participant’s Rights .................................................................. 63
Summary…… ..............................................................................................................64 CHAPTER 4: RESULTS ...................................................................................................65
Introduction ..................................................................................................................65 Data Screening and Cleaning .......................................................................................65 Assumptions and Pretest Analyses ..............................................................................66 Outliers ..................................................................................................................66 Multicollinearity, Normality, Linearity, and Homoscedasticity ............................67 Sample Characteristics .................................................................................................68 Data Analyses ..............................................................................................................69 Reliability Analysis ................................................................................................69 Descriptive Statistics ..............................................................................................70 Hierarchical Multiple Regression Analyses ..........................................................72 Primary Research Question and Hypotheses Evaluation .............................................79 Additional Findings and Observations ........................................................................81 Observed Consistencies and Inconsistencies .........................................................82 Summary ......................................................................................................................83
CHAPTER 5: DISCUSSION ............................................................................................84
Introduction and Overview of Study............................................................................84 Interpretation of Findings ............................................................................................85 Interpretation of Hierarchical Regression Analyses .............................................86 Theoretical Considerations ....................................................................................89 Implications for Positive Social Change ......................................................................93 Implications for Health Institutions .......................................................................96 Implications for Health Organizations ..................................................................97 Implications for Indviduals and Society ................................................................98 Recommendations for Action ....................................................................................100 Limitations and Recommendations for Future Study ................................................102
v
Clinically and Non-Clinically Significant Eating Behaviors ...............................103 Seasonal Eating Behaviors ..................................................................................104 Coping Strategies .................................................................................................106
Conclusion .................................................................................................................108 REFERENCES ................................................................................................................110
APPENDIX A: BODY MASS INDEX CALCULATION ..............................................128
APPENDIX B: EATING DISORDER EXAMINATION QUESTIONNAIRE .............129
APPENDIX C: THEORY OF PLANNED BEHAVIOR QUESTIONNAIRE ...............133
APPENDIX D: KIRTON ADAPTION-INNOVATION INVENTORY ........................135
APPENDIX E: BACKGROUND DATA QUESTIONNAIRE .......................................136
APPENDIX F: CONSENT FORM ..................................................................................137
CURRICULUM VITAE..……………………………………………………...…….…139
vi
LIST OF TABLES
Table 1. Correlations: IVs by IVs ..................................................................................... 67 Table 2. Demographic Characteristics of Study ............................................................... 68 Table 3. Descriptive Statistics for Variables ..................................................................... 71 Table 4. EDE-Q6 Percentile Ranks for EDE-Q6 Global and Subscale Scores ................ 72 Table 5. Summary of Hierarchical Regression Analyses for Variables Predicting Dietary Restraint ................................................................................................. 74 Table 6. Summary of Hierarchical Regression Analyses for Variables Predicting Eating Concern.................................................................................................... 75 Table 7. Summary of Hierarchical Regression Analyses for Variables Predicting Shape Concern .................................................................................................... 77 Table 8. Summary of Hierarchical Regression Analyses for Variables Predicting Weight Concern .................................................................................................. 78
vii
LIST OF FIGURES
Figure 1. Binge analysis .....................................................................................................24
Figure 2. Theory of planned behavior ................................................................................31
Figure 3. Cognitive schema ...............................................................................................37
Figure 4. Cognitive style distribution curve ......................................................................40
CHAPTER 1:
INTRODUCTION TO THE STUDY
Introduction
The process of eating food provides both biological and psychological feelings of
gratification; however, excessive eating or binge eating often results in the development
of obesity (Alonso-Alonso & Pascual-Leone, 2007). Obesity results in health disorders,
such as bulimia nervosa, anorexia nervosa, and body dysmorphia, as well as
psychological distresses (Fairburn & Brownell, 2002; Plowman, 2008). Research has
facilitated the development of treatment programs for obesity, and has assessed the
cognitive styles and personality characteristics associated with clinical eating disorders
(Kaye, Bastiani, & Moss, 1995; Treasure, Tchanturia, & Schmidt, 2005). Recently,
obesity has become the focus of research in the United States in areas such as
understanding compulsivity and impulsive behaviors with a greater focus on health
related disorders (Patte, 2006). For example, The American Obesity Association [AOA]
(2008) estimated that 64.5% of Americans are obese. This report also found obesity to be
a chronic illness which increases the probability of developing high blood pressure, type
2 diabetes, and additional heart diseases, and estimated that obesity will overtake
smoking as the leading cause of death due to health related disorders.
Cognitive processes such as thinking patterns and emotional responses have been
investigated for most clinical eating pathologies (Johansson, 2006). However, there is a
lack of research surrounding non-clinical eating disorders such as binge eating or over-
eating behaviors. Individuals overeat for a variety of reasons, such as not eating enough
2
during the day, overeating because of social situations, or eating simple carbohydrate-
laden foods as a reward or treat. These behaviors often results in obesity (Sysko, Devlin,
Walsh, Zimmerli, & Kissileff, 2007). In addition to the contributing factors of eating
excessively or improperly and having a sedentary lifestyle, factors such as a person’s
environment, individual behavior, culture, and socioeconomic status can contribute to
obesity (Center for Disease Control, 2009b). Clinical eating disorders—defined as
anorexia nervosa, bulimia nervosa, or eating disorders not otherwise specified—are
known to cause significant health problems (National Institute of Health, 2008).
However, non-clinical eating behaviors that affect average adults such as binge eating or
overeating also contribute to weight related health disorders. This study focused on a
planned eating behaviors and decision-making styles with regard to reported eating
behaviors.
Summary of Literature
A review of the literature is expanded on in chapter 2. This summary provides an
overview of the concepts of the theory of planned behavior, the adaption-innovation
theory, motivational applications to cognitive eating behaviors, and biological factors
associated with eating behaviors.
Theory of Planned Behavior
The theory of planned behavior (TPB) has been used in studies on eating
behaviors (e.g., Armitage, Conner, Loach, & Willetts, 1999). These studies have assessed
eating behaviors through self-reports and body mass indexes, and have revealed a
connection between individuals’ attitudes and classical conditioning, observational
3
learning, and social comparison (Baron, Byrne, & Branscombe, 2006). These studies
have also found a prevalence of ambivalent attitudes with regard to non-clinical eating
behaviors. Contributing to these findings, studies using the positive-incentive theory
suggest people eat because of the psychological response to feeling that food is
pleasurable (Baron et al., 2006; & Pinel, 2006). However, significant gaps exist in the
research on binge eating behaviors. Research surrounding non-purging binge eating has
focused on either treating obesity through behavioral weight-loss therapies, with little
attention on the cognitive factors associated with binge eating (DeAngelis, 2002). This
study will address this gap by using the TPB to verify applicability to eating behaviors. .
Adaption-Innovation Theory
The adaption-innovation theory, through the use of the Kirton Adaption-
Innovation inventory (KAI), measures cognitive style (Kirton, 1976). This psychometric
instrument has been developed and extensively tested and it demonstrates a relationship
between the cognitive styles of innovation and adaption (on a bipolar scale) and a
person’s preferred approach to problem solving (Hutchinson & Skinner, 2007). There are
three subscores in this psychometric instrument: sufficiency of originality, which
measures the manner in which a person generates ideas; efficiency, which measures the
concept of a person’s problem solving methods or processes; and rule/group conformity
which focuses on how style, being more or less adaptive or innovative, affects the
structures in which problem solving occurs. These subscores create an overall KAI score.
A person who scores as being more highly adaptive is more likely to make
decisions based on reliability, methodology, efficiency, and in a systematic method
4
(Kirton, 2003). A person who scores as being more highly innovative is more likely to
make decisions by addressing the situation from an undisciplined or unpredictable
manner and may make behavioral decisions differently or unexpectedly. This theory and
the associated psychometric instrument, KAI, have been applied in multiple dissertations
and research programs to measure the difference in personal style and behaviors (Kirton,
2003). For example, Saggin (1996) proposed a relationship between those suffering from
anorexia having a more adaptive cognitive decision making style and those suffering
from binge eating having a more innovative cognitive decision making style.
Understanding a person’s cognitive decision making style may help to understand
reactions to binge eating situations.
Motivational and Biological Applications to Eating Behaviors
Understanding motivation is an important part of the adaption-innovation theory
and the TPB. A person’s behavior towards eating and exercise is usually more the result
of internal belief systems rather than the influence of an environmental factor (Ajzen &
Holmes, 1974). Therefore, before a person takes on a behavioral modification program he
or she may benefit from being educated on psychological concepts of eating behavior
such as the positive-incentive theory, and should understand how this theory interacts
with motivation as well as biological responses to eating and expending energy (Fairburn
& Brownell, 2002).
Biologically, two theories explain the concept of weight gain, loss, and
maintenance: glucostatic and lipostatic theories. These theories both suggest that the
human body has a natural weight and glucose range, also referred to as a set-point. When
5
a person’s weight or glucose range varies, perhaps because of diet or exercise, the body
will eventually regulate to the original set point (Carrier, 1994). These theories state that
eating a meal or gaining and losing weight are all done with the effort of returning to a
homeostatic body state (Pittas et al., 2005). The glucostatic theory is based on the idea
that the body regulates itself for the short term by the blood glucose level and that as the
level of blood glucose is depleted a person psychologically and physically will begin to
prepare for his or her next meal, and on consuming the meal the body returns to its set
point (Panksepp, Tonge, & Oatley, 1972).
The lipostatic theory is based on a similar idea regarding regulation except this
theory is based on fat storage and long-term regulation. This theory suggests that eating
and metabolism are biologically activated if there is a deviation from the body’s weight
set-point (Baile et al., 2000). The lipostatic theory assumes that the relative stability of an
adult’s body weight is because leptin manages the stability of the body regardless of
short-term behavioral differences in food consumption or exercise behaviors (Baile,
Della-Fere, & Martin, 2000). This theory states that the body has its own place of
stability with regard to total weight and amount of fat, and that the body will eventually
return to that state regardless of environmental influences (Baile et al., 2000). If humans
all have a natural tendency to return to our set point then the obesity epidemic as well as
other eating disorders such as bulimia and anorexia, would not likely exist in such
extreme fashion.
Factors associated with hunger include the understanding of how eating behavior
is managed and maintaining a healthful eating lifestyle. Feelings of hunger can be driven
6
by motivational factors and cognitive decisions about eating patterns that are not
associated with the current level of homeostasis. For example, the eating experience itself
can be associated with grazing behaviors, binge eating, or gorging at one meal setting
(Oxford University Press, 2007). A person may cognitively be aware that he or she
should not overeat but some experience a momentary pleasure from the food and possibly
lose motivation to maintain a healthy eating regimen and continue to overeat (Oxford
University Press, 2008). The person may experience guilt and shame after the binge
eating comes to completion (Oxford University Press, 2008).
The idea of eating out and celebrating an occasion with a special meal is not a
new concept. However, many researchers note that these occasions are built on mannered
rituals, the bringing together of family and friends, and notably, structured meals often
results in binge eating (Chaney, 2002). A person may experience different social
pressures to lose weight or obtain a body image that is unrealistically thin and he or she
cognitively makes a decision to obsessively diet or restrict their next meal. This
motivation stems from social influences (Schnieder, Gruman, & Couts, 2005), and may
impact a person’s ability to restrict meals to avoid binge eating (Sirois, 2004).
Eating Behaviors and Binging
This study covers a wide range of eating behaviors in the general population.
This includes non-clinical disorders and binge eating is one such disorder that has been
associated with obesity. Reported binge eating disorder affects over 3% of the adult
population of the United States and 75% of individuals with obesity suffer from this
maladaptive eating behavior (Reuters, 2008). However, these data may not reflect the
7
true number of individuals who binge eat, as over 35 % of the population is currently
considered to be obese (CDC, 2009). Those who binge eat typically do not participate in
purging or excessive exercise to compensate for unusually high caloric intake; nor would
they consume laxatives—typically behaviors associated with bulimia nervosa (BEDA,
2009).
Binge eating can result in obesity, which is currently measured using the body
mass index. Body mass index, or BMI, is a standard measurement that is calculated using
a person’s height and weight, using the formula (weight (lb) / [height (in)]2) x 703, to
derive a body fat percentage to determine obesity (Hairon, 2006). The National Institute
of Health (2008) stated that a BMI of 30.0 or above is considered obese. The Center for
Disease Control (CDC) used the body mass index statistics in the United States and
concluded that obesity has risen from 15% to 33.9% in the last 24 years (2008c).
Researchers have shown that BMI has a relationship with obesity related health disorders.
For example, an increase in BMI has been associated with an increased risk for the
development of many chronic health conditions such as hypertension, coronary artery
disease, stroke, type 2 diabetes, and some cancers (Baum & Posluszny, 1999).
Problem Statement
Researchers have noted the importance of understanding cognitive processes
associated with eating behaviors and health outcomes (Johansson, 2006; Wethington,
2008). The TPB has been studied with a variety of health and eating behaviors (Armitage,
Conner, Loach, & Willetts, 1999) and the adaption-innovation theory has been applied to
understanding how cognitive style impacts personal decisions (Kirton, 2003). Yet these
8
theories have not been examined as they relate to non-clinical eating disorder
components. Therefore, the problem is that while the dangers of binge eating and
overeating are known, cognitive style and the cognitive processes associated with
planned behavior, as they apply to non-clinical eating behaviors, have not been
investigated. If a link between these variables and eating behaviors can be established,
health professionals will better understand how decisions regarding negative eating
behaviors occur.
Nature of Study
The nature of the study is described by defining specific research questions,
hypotheses, and the purpose of the study. Chapter 3 provides a detailed discussion of the
study design, hypothesis, variables, and methodology.
Research Question
This research study was quantitative and focused on understanding any
relationships between variables from the theory of planned behavior, the adaption-
innovation theory, and eating behaviors. The specific research question was if eating
behavior is affected by body mass, perceived behavioral control, attitude, subjective
norms, intentions, sufficiency of originality, efficiency, and rule/group conformity.
Hypotheses
In order to answer the research question the following hypotheses were tested in
this study:
9
Null Hypothesis (Ho):
Null 1: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and dietary
restraint as measured by EDE-Q6 (R = 0).
Null 2: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and eating concern
as measured by EDE-Q6 (R = 0).
Null 3: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and shape concern
as measured by EDE-Q6 (R = 0).
Null 4: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and weight concern
as measured by EDE-Q6 (R = 0).
10
Purpose of Study
The purpose of this study was to assess the combined effects of individual
problem solving styles (sufficiency of originality, efficiency, and rule/group conformity)
and planned behavior (attitudes, subjective norms, behavioral intentions, perceived
behavioral control), after first controlling for body mass, on eating behaviors. There may
be positive reinforcement from eating behaviors that derive from social influences as well
as psychological gratifications. The TPB posits a relationship between social influences,
personal control over behaviors, and belief systems. The adaption and innovation theory
draws connections with problem solving styles and cognitive processes. Binge eating
behaviors are associated with obesity and can lead to psychological and health related
disorders. However, there is no consistent research to assess how a person makes
problem solving decisions regarding eating behaviors.
Operational Definitions
Attitude: Attitude is a personal feeling about certain behavior that has been built
on throughout a person’s lifetime based on experiences, observations, and information
acquired about the behavior (Higgins & Marcum, 2005). Attitude will be measured using
the TPB questionnaire.
Binge eating disorder (BED): BED is the consumption of an objectively large
amount of food or eating in a mannerism that was not intended (APA, 2000). BED will
be assessed using the EDE-Q6.
Body Mass Index (BMI): BMI is a measurement that is calculated from a person’s
weight and height which can be used as an indicator for potential weight related health
11
disorders (Center for Disease Control, 2008a). BMI will be measured using a
mathematical calculation that creates a raw score.
Dietary restraint: This is a process in which a person severely restricts caloric
intake with the hope of achieving weight loss; however, this behavior often results in
binge eating during the refeeding period (Fairburn & Brownell, 2002). Dietary restraint
will be measured using the EDE-Q6.
Eating concern: This is a characteristic in which an individual is preoccupied with
thoughts about eating, weight, and eating around others (Fairburn & Brownell, 2002).
Eating concern will be measured using the EDE-Q6.
Efficiency: This is a cognitive style metric that measures the concept of a person’s
problem solving methods or processes (Kirton, 1999). Efficiency will be measured using
the KAI inventory.
Individual cognitive style: Individual cognitive style is the preferred manner in
which a person undertakes problem solving methods (Kirton, 2003). This will be
measured using the overall KAI inventory score.
Intentions: Intentions are how a person combines his or her attitudes and
subjective norms and thereby determines how to tackle a problem (Ziefelmann et al.,
2007). Intentions will be measured using the TPB Questionnaire.
Perceived behavioral control (PBC): PBC defines the ability for a person to feel
control over the ability to perform a specific behavior and follow through on achieving
goals (Ajzen, 2008). PBC will be measured using the TPB Questionnaire.
12
Restraint: This is a characteristic in which an individual is preoccupied with
thoughts about avoiding eating, avoiding food, having and empty stomach, and adhering
to self set dietary rules (Fairburn, 2008). Restraint will be measured using the EDE-Q6.
Rule/group conformity: This is a cognitive style metric that measures how style,
being more or less adaptive or innovative, affects the structures in which problem solving
occurs (Kirton, 1999). Rule/group conformity will be measured using the KAI Inventory.
Shape concern: This is a characteristic in which an individual is preoccupied with
thoughts about fear of weight gain, discomfort of seeing body, feelings of fatness, and
concern with overall shape (Fairburn, 2008). Shape concern will be measured using the
EDE-Q6.
Subjective norms: Subjective norms are beliefs about what other people in their
social circle such as spouses, neighbors, or peers, would have regarding any given
behavior such as dieting or achieving a thin ideal (Ajzen & Holmes, 1974). Subjective
norms will be measured using the TPB Questionnaire.
Sufficiency of originality: This is a cognitive style metric that measures the
manner in which a person generates ideas (Kirton, 1999). Sufficiency of originality will
be measured using the KAI Inventory.
Weight concern: This is a characteristic in which an individual is preoccupied
with thoughts about weight, the importance of weight, and desire to lose weight
(Fairburn, 2008). Weight concern will be measured using the EDE-Q6.
13
Assumptions, Limitations, Scope, and Delimitations of Project
One assumption of this study was that the general population to be surveyed does
not suffer from clinical eating disorders. This assumption could be challenged by non-
reported eating disorders from the surveyed population. Additionally, the survey design
relied on self-reporting which could have a potential bias to underreport binge eating
behavior. Further, the participants in the survey were from Colorado which has the lowest
rate of obesity (less than 20%) in the United States which may reduce the significance of
the results in comparison to other states (CDC, 2008c).
The participants were from the general population of Boulder County, Colorado.
Males and females ranging in ages from 18-65 who do not reside in a hospital or mental
health facility were asked to participate in the survey. The gender, ethnicity, and
educational levels reflect a random sample of the general population as described by the
U.S. Census Bureau for Boulder County (2008). The availability of the sampling frames
and potential respondents is reflected in the population group selection (Creswell, 2003).
The time of study for the survey may additionally affect the results as it was
conducted during a time period in the United States that included Hanukah, Christmas,
and New Year’s celebrations. This timing may influence the data as these celebrations, as
well as many not mentioned, are associated with social situations that include eating large
meals which may not otherwise occur during regular calendar dates (Brown, 2000).
Significance of Study
This study investigated the variables from KAI, TPB, and BMI with respect to
four eating behavioral components which are restraint, eating concern, shape concern,
14
and weight concern. This study adds to the body of knowledge regarding the mannerism
in which a person can engage in or maintain healthy behaviors, and in so doing,
contributes to improving strategies associated with avoiding negative eating behaviors.
Professional Application
Psychologists recognize that many current social issues are health related and may
be resolved with the application of research findings to behavior modification programs
(Roth & Armstrong, 1990). The social problem of unhealthy eating behavior in the
United States is a serious issue and is associated with high death rates as an estimated
280,000 to 325,000 adults in the United States die each year from causes related to
obesity (CDC, 2009). Obesity related diseases, physical and mental disabilities, and
increases in healthcare expenditures are additionally significant social concerns for
professionals (American Obesity Association, 2007). Medical research confirms that poor
diet contributes to obesity which is the second leading cause of death in the United States
(Mokdad et al., 2004).
Poor eating behaviors also result in greater social psychological disabilities such
as poor self image and self esteem, and psychopathologies such as social anxiety and
depression (Center for Disease Control, 2008b). Phares, Steinber, and Thompson (2004)
noted multiple cases of depression and low self-worth in young people that were directly
associated with dysfunctional body image perceptions associated with obesity and
explained that there was a high risk for these disorders to become lifelong dysfunctions.
Another reason that the social problem of unhealthy eating behavior must be addressed is
that medical spending in 2000 attributed to obesity and overweight related disorders was
15
approximately $117 billion according to the National Health Accounts (NHA) data and
this amount is increasing annually (Center for Disease Control, 2009). The application of
the research findings in this study contribute to the health psychology profession by
increasing the knowledge of how variables from cognitive-behavioral models are linked
to eating behaviors.
Knowledge Generation
People are constantly making dietary changes and long-term resolutions for their
eating patterns and habits each year all with the hopes of losing weight and improving
overall health (Costin, 1998). However, many people who do not have self-efficacy and
motivation regarding their ability to control their eating may find that they always fall
short of their goals. In fact, 95% of those who diet regain some of their lost weight within
five years (Costin, 1998). Unfortunately, these dieting failures are most often blamed on
the set-point theory as the dieters feel they have no control and are destined to return to
their body’s natural weight (Gabel & Lund, 2002).
However, the weight regain may be better explained by understanding motivation
and style. Motivation can be modeled using TPB variables and style can be modeled by
KAI variables. These theories propose to predict behavior with the knowledge and
understanding that each person is unique and therefore, has unique decision making
preferences, attitudes, subjective norms, perceived behavioral control, and intentions
(Ajzen & Fishbein, 1980; Kirton, 2009).
The TPB demonstrates that a person can take intention and act on it which thereby
results in a specific behavior or outcome (Ajzen & Fishbein, 1980). For example, this
16
behavior could suggest that a person’s eating and nutritional behavior is a result of their
intention to stay on a diet. Or, that if a person has an attitude that diets do not work and
subjective norms that it is normal to be somewhat overweight, it is unlikely that he or she
will successfully make a dietary lifestyle change (Armitage et al., 1999).
Similarly, using the adaption-innovation theory, a person with an adaptive style
may be more likely to follow a meticulous pattern of solving dietary problems and may
not find success with changing dietary lifestyle. A person with an innovative style may be
less careful about sticking to a diet. On the failure of a diet a person may likely look at
the outcome as reaffirmation that the set-point theory or the lipostatic theory is correct,
and that his or her weight is not something that can be changed or maintained (Matheson
& Crawford-Wright, 2000). This research contributes knowledge regarding how the
theory of planned behavior and the adaption-innovation theory can be used to better
understand behaviors and decisions that contribute to eating behaviors.
Positive Social Change Implications
This research has significant importance to the health conscious community and
health care systems overall as it can contribute to the future development of behavioral
modification programs to reduce weight-related health disorders. One side effect of
obesity is depression and poor quality of life (Daniels, 2006). This research can address
the obesity epidemic by contributing to the development of healthy eating behavioral
programs and research surrounding human behaviors that contributes to decreasing health
problems (Baum & Posluszny, 1999). When physicians or psychologists assess a
person’s overall health, existing eating behavior and decision making styles should be
17
taken into consideration as this can help determine the likelihood of success a person has
for changing eating behaviors (Carrier, 1994).
Positive social change results in positive transformations for humans and social
conditions that can come in the form of a change in family systems, the individual, and
the community. Research has demonstrated there are stereotypes surrounding binge
eating and associated obesity which include being lazy, stupid, incompetent, or that these
individuals should be avoided by members of society who believe obese people are of a
lower class (Klaczynski, Goold, & Mudry, 2004). With these negativities associated with
obesity it seems clear that those suffering with binge eating need support from their
community, even if it is only to help them develop skills and strategies to cope with the
negativity they could face in this society (Lindsay, Sussner, Kim, & Gortmaker, 2006).
The implications for positive social change from this research furthers research regarding
poor eating behaviors and includes the potential to minimize the negative influences and
contributors to obesity and harness the potential positives of understanding the
relationships of cognitive decision making and health behaviors. This research benefits
the health conscious community and health care systems with the contribution to the
future development of behavioral modification programs to reduce weight related health
disorders. There will be an improvement to the human condition by assisting
communities with managing the obesity epidemic by contributing to the development of
healthy eating behavioral programs. Additionally this research contributes to institutions
by potentially reducing secondary illnesses and health care costs associated with binge
eating and obesity.
18
Summary
To prevent diseases and psychological disorders, a better understanding is needed
on the relationships between binge eating, obesity, and overeating eating behaviors. The
key point of this study was to identify any relationships or non-relationships with how a
person internally and externally makes decisions and acts on those decisions with regard
to eating behaviors. Chapter 2 defines the approach to the literature review and the
theories described prior are investigated. The lack of research surrounding binge eating
disorders and eating behaviors in association with psychological decision making
behaviors is addressed. Additionally, chapter 3 defines the methods and strategy
associated with the data collection process and statistical design. A new approach to
measuring why individuals make decisions regarding eating behaviors, including the
personal decisions associated with the intentions and problem solving styles, is
investigated and described. Chapter 4 describes the findings of the study and chapter 5
discusses the applications of this research including how it contributes to the
implementation of positive social change in the area of prevention-related health
behavioral programs.
CHAPTER 2: LITERATURE REVIEW
Introduction
Although research has established a link between binge eating behaviors and
obesity or weight-related health disorders, little attention has been devoted to the
relationship between eating behaviors and problem-solving styles and planned behavior.
Binge eating disorders are traditionally investigated from a clinical standpoint in which
the behavior has resulted in bulimia nervosa (Fingeret, Warren, Cepeda-Benito, &
Gleaves, 1996), or by assessing dietary management, treatment programs, psychosocial
interactions, physical risks, medication, and clinician skill in the treatment process
(Treasure, Tchanturia, & Schmidt, 2005). The purpose of this chapter is to discuss the
roles of decision making and planned behaviors on eating behaviors. These assessments
underscore the need to expand research in this area to promote alternative means to
resolve binge eating behaviors and associated health-related disorders.
Organization of Chapter
This review defines current literature regarding binge eating behavior, the TPB,
and the adaption-innovation theory. The chapter begins with a broad overview of how
binge eating has been researched and includes research surrounding how social
influences affect eating behavioral decision-making strategies and body mass. Next, the
TPB is discussed with a focus on current research surrounding eating behaviors. The
adaption-innovation theory is then examined to address previous research on the
relationship between cognitive decision making style and behavior. This chapter builds
on the literature to demonstrate the need to conduct research to understand how eating
20
behavior in the adult population may be associated with internal and external decision-
making processes.
Strategy for Literature Review
Information in this review was obtained from a multitude of scholarly journals
and primary author books. The majority of the literature was obtained from EBSCOhost
databases which include Mental Measurements Yearbook, PsycARTICLES, SocINDEX,
Health Source: Nursing/ Academic Edition, Academic Search Premier, and CINAHL
Plus. The review focuses on articles published in the last ten years but does include
several later references from the original authors of the TPB and the adaption-innovation
theory. Search terms include, but are not limited to the following: binge eating,
overeating, TPB, cognitive style, adaption and innovation, body mass index, problem
solving, obesity, food addictions, food behavior, social eating, dietary restraint, diets,
food intake, health attitudes, hunger, health behavior, eating disorders, body image, and
decision making.
Content
The scope of this literature review includes binge eating and general eating
behaviors as they are associated with psychological decision making processes, and
behaviors regarding food selection processes for binge eating and obesity issues,
psychological and social implications, the TPB, health behaviors, binge behaviors, and
adaption-innovation theory of problem solving style. Some clinical eating disorders and
general obesity related disorders are out of the scope of this research including general
knowledge regarding overeating behaviors, caloric intake, exercise behaviors, and
21
biological and genetic related eating disorders or obesity in addition to others are
excluded from review. Additionally, cognitive behavioral treatments and
psychopharmacological treatments are out of scope of this research.
Quantitative and Qualitative Methodologies
Literature related to the use of differing methodologies to investigate the
outcomes of interest has been reviewed. Qualitative research is quite prevalent in the area
of addressing eating behaviors as many researchers use ethnographical techniques or one
on one interview techniques. For example, clinician based interviews have been used to
assess psychopathology in eating disorders (First, Spitzer, Gibbon, & Williams, 1997).
Additionally, binge eating behaviors have been qualitatively documented during
diagnostic interviews (Mitchell & Peterson, 2008). Medical practitioners have also
incorporated qualitative analyses into identifying any potential barriers for obesity
focused assessments (Fairburn & Brownell, 2002). For the purpose of this literature
review the quantitative methodology was focused on although multiple qualitative studies
have been cited. This is based on the selected predictor and criterion variables which are
quantitative in nature.
Binge Eating
Eating disorders have been popularized with the increase in television and media
exposure surrounding young females with body image disorders and anorexia and
bulimia are now household terms (Serdar, 2005). For most individuals the more
prominent eating disorder is that of overeating or deviating from a normal eating pattern
which can lead to obesity. Fairburn (2008) defines regular eating as consisting of a
22
pattern of consuming breakfast, a small midday snack, lunch, a small afternoon snack,
dinner, and a small evening snack. Binge eating, in contrast, is defined as an episode of
uncontrolled eating that is usually triggered by an event, a mood, or by breaking a dietary
rule. Binge eating often results in feelings of uncomfortable fullness after eating, shame,
and guilt. Those who binge may feel uncomfortable fullness after consumptions. Binge
eating disorder was introduced in 1992 and has been used to describe excessive eating
without purging the food to lose weight, often resulting in obesity (Academy for Eating
Disorders, 2008). Yet binge eating has not been officially recognized as an eating
disorder in the American Psychiatric Association’s (2000) Diagnostic and Statistical
Manual (DSM- IV-TR), as it is considered to be in the category of Eating Disorder Not
Otherwise Specified (EDNOS). Although binge eating is more common among women
than men, it is a challenge that affects Hispanics Americans, African Americans, and
European Americans fairly equally (Regan & Cachelin, 2006).
Eating Behaviors and Food Choices
A binge eating episode is defined as having a sense of lack of control over a
period of eating which includes a consumption of food that is traditionally larger than
what would be considered to be normal by others in the same situation (Fairburn, 1995).
Binge eating can also represent the deviation from an eating plan or program associated
with health requirements such as avoiding simple carbohydrates when diabetic or
avoiding salt with hypertension diseases (Sohn, 2008). Foods that are most often
consumed in a binge eating episode noted in those with clinical disorders include but are
not limited to ice cream, popcorn and salty foods, cheese, cereal, candy, and donuts; the
23
range of caloric intake can vary from 1,200 to 11,500 over a period of 15 minutes to 8
hours (Mitchell, Pyle, & Eckert, 1981). Although these boundaries have been specified,
less research is available for those suffering with overweight-related binge eating who eat
a variety of foods in a rapid manner after they have skipped meals or avoided specific
foods for a period of time due to dieting.
Binge eating has also been associated with behaviors such as breaking a dietary
rule. These behaviors could include eating something considered to be fattening or salty,
eating alone, having premenstrual tension, drinking alcohol, or having a lack of a dietary
routine (Abraham & Beumont, 1982; see Figure 1). Body image dissatisfaction is also
associated with having higher incidences of dieting, unhealthy eating behaviors, and
binge eating (Neumark-Sztainer, Paxton, Hannan, Haines, & Story, 2006). Women with
binge eating disorder rate body image dissatisfaction as higher influences on their
behaviors than do men; however, men also rate body image dissatisfaction as
contributing factors to binge eating disorder behind depression and self-esteem (Grilo &
Masheb, 2005; Grilo et al., 2005). Age of onset of binge eating or individual age does not
seem to be a predictor of binge eating disorder or adult obesity (Masheb & Grilo, 2008).
Yet, there are biological and social reasons associated with eating behaviors that may
develop in early childhood that contribute to lifelong decisions about eating.
24
Figure 1. Note. From “Cognitive behavior therapy and eating disorders (p. 140) by C. G. Fairburn, 2008, New York, NY: The Guilford Press. Copyright 2008 by Fairburn. Reprinted with permission. Food Selection and Binge Eating
Food selection and availability also play a role in understanding eating behaviors.
Humans are no longer dependent on eating readily and obsessively when food is made
available to us in an effort to survive thanks to mass agriculture, so it is important to be
aware of consumption behaviors. One particular less formal type of dining, often called a
buffet, offers a different view of the implications eating behaviors and decision-making
processes. Dietary diversity traditionally is looked on as a benefit to diet maintenance and
overall nutritional health (Toray & Cooley, 1997). However, there are also negative
implications to diet diversity. For example, if people are presented with a wide variety of
high-caloric foods low in nutritional value they will eat more than they normally would if
they were only presented with one option (Kennedy, 2004). The same concept applies
from a positive-incentive perspective because the desirability to eat one food decreases
Binge Analysis • Breaking a dietary rule • Being disinhibited (e.g. alcohol) • Under eating for a period of time • Adverse event or mood
BINGE EATING
25
upon consumption. However, when presented with a wide variety of food options, such
as a cafeteria, the positive-incentive desire to indulge in the rest of the foods is not as
strong as with the first item, but it still exists and contributes to overeating (Nayga, 2000).
The pleasure of each food and the positive incentive of the value of taste for each new
food will decrease (Pinel, 2006). Although the human stomach can hold one liter of food
comfortably, in many situations, such as being presented with a variety of food options, it
can be pushed to hold two liters even though there are chemical and stretch receptors that
are signaled when overeating occurs (Toray & Cooley, 1997).
Biological factors also contribute to binge eating. The brain codes food choices in
the orbitofrontal cortex and assigns a value with the level of reward a person experiences
when consuming a specific food (Zald, 2008). This area of the brain responds to tastes,
the visual appearance of food, aromas, and texture and makes decisions regarding food
selection. Recent research demonstrated that binge eating occurred in patients after self-
reported satiety; these patients had various levels of abnormal functioning in their
orbitofrontal cortex (Woolley et al., 2007). These findings suggest this area of the brain
places greater value on the immediate reward of certain food selections over long-term
rewards such as long-term health and weight management (Zald, 2008).
Social psychologists have noted non-biological instances that contribute to binge
eating behaviors. For example, the positive incentive theory suggests that individuals eat
out of habits, social stimuli, the physical appearance and smell of food, and other reasons
unrelated to biologically-induced hunger (Pinel, 2006). Food selections in binge eating
26
behaviors seem consistent with this theory. Eating patterns are often based on
consumption habits and are not always based upon eating behavioral goals.
Many individuals are not aware of the external factors that mold their eating
habits. For example, in a study conducted by the University of Toronto, 120 female
college students were observed eating either alone or with friends (Liebman, 1995). The
students who ate alone consumed 375 calories, whereas the students who ate with friends
consumed over 700 calories, suggesting that social factors influence how much someone
eats and the social influence usually results in increased consumption. In an additional
study, a group of college students was given unlimited access to mini pizzas and they
were allowed to consume as many of the pizzas as they wanted in the group setting while
they watched television together (Herman et al., 2005). The results showed that members
of the group ate similar amounts of pizza during the timeframe, again suggesting that the
group environment dictated the eating behavior.
Social influences can also positively influence eating behaviors. Neumark-
Sztainer, Wall, Story, and Fulkerson (2004) used logistical regression in a population of
4746 ethnically diverse adolescent females and noted that 18.1% of those females who
ate 1-2 meals per week with their family reported eating disorders whereas only 8.8% of
females who ate 3-4 meals with their family reported similar behaviors. Additionally, in a
study by Vartanian, Herman, and Wansink (2008) two groups participated in a study
which measured their awareness of the influences that dictated their food selection
process. Variables such as eating partners, hunger, taste, satiety, free will and behavior of
co-eater were taken into consideration to assess the determinants of food consumption for
27
each individual. The results suggested that although the individuals were able to
determine what factors contributed to their partner’s eating behavior they were unable to
recognize these behaviors in their own eating behaviors. This suggests that subconscious
social cues may also play a role in eating behaviors.
Psychological and Sociological Implications
Fairburn (2008) noted that many individuals who participate in a binge do not
necessarily eat an extreme amount of food nor do they always experience guilt. Rather,
they may feel an overwhelming awareness of their body image as a result which could
result in excessive temporary dieting which increases the risk for repeat binge episodes or
they could be disinhibited, such as being under the influence of alcohol, which
contributes to the episode. A person is considered to have binge eating disorder if quality
of life is affected but, this can also be caused by emotional issues that initiate a binge
eating period.
For example, Chua, Touyz, and Hill (2004) demonstrated that the induction of a
negative mood after viewing a sad film did promote overeating in 40 obese female
participants by assessing hunger motivation, dietary restraint, and food intake. In
comparison with individuals with normal body mass indexes, overweight binge eaters
had great concern with body image but had a tendency to over eat when they were in a
negative mood (Eldredge & Agras, 1994). Negative affect has further been demonstrated
to contribute to binge eating and abnormal eating behaviors (Lyubomirsky, Casper, &
Sousa, 2001).
28
In addition to the external social environment, social groups have proven to be
significant influences on whether or not a person binges eats. Using two sorority groups
Crandall (1988) found that the members of the sorority binged in equal frequencies and
amounts compared to the mean of the other members of the group. Although this study
noted the influence of social norms on eating behaviors, it did not address any cognitive
decision-making styles for the individuals. Decision-making processes and social
influences that do contribute to binge eating include a person’s role in the immediate
family, cultural influences, early life experiences with in the family, community, and
social class (Wethington, 2008).
Although social situations can influence eating behaviors, many individuals feel
that binge eating behavior is a result of poor self-esteem or depression (Mond & Hay,
2008). Obese individuals self-report that binge eating behaviors are often a result of an
inability to manage the social pressures in society to be thin (Sorbara & Geliebter, 2001).
These stereotypes and social pressures can be damaging and can encourage additional
episodes of binge eating that contribute not only to the negative psychological health of
the individual but also impacts negative physical health.
Much research has been dedicated to understanding how binge eating is related to
overall health, obesity, and body mass index (BMI). The cycle of binge eating results in
challenges maintaining a healthy BMI, risks to being obese, challenges losing weight,
and weight regain (Elfhag & Rössner, 2004) although binge eating has been reported
approximately equally in women at all levels of the body mass index scale (Shisslak et
al., 2006). Weight maintenance, which means a person does not regain weight that was
29
successfully lost prior, is influenced by many variables such as social factors, personal
motivation, realistic goal measurement, eating restraint, and binge eating. Binge eating
specifically has been found to be related to weight regain over a five year period in
patients who have had multiple forms of obesity related surgery (Pekkarinen, Koskela,
Huikuri, & Mustajoki, 1994).
All of these health implications can be related to motivation and problem solving
styles. However, limited research has been conducted in these areas including the theory
that assesses planned behavior and relationships with motivation. Rather, the majority of
research focuses on the implications of cognitive behavioral therapy and
psychopharmacological solutions (Fairburn, 2008; Grilo, Masheb, & Wilson, 2006; Kaye,
Bastiani, & Moss, 1995). Guided self-help programs using cognitive behavioral therapies
have demonstrated success for treating BED but have not proven successful as a first step
for individuals with BED who are considered obese (Grilo & Masheb, 2005).
Cognitive behavioral therapies such as self-help programs and motivational
interviewing are considered to be the treatments of choice for BED according to a study
by Dunn, Neighbors, and Larimer (2006). In this study 90 undergraduate college
students received either motivational enhancement therapy or a self-help manual to
promote their readiness to change eating behaviors. Using repeated measures ANOVA
there was an increase in both groups for being able to abstain from binge eating episodes
temporarily. However, this does not contribute to understanding the intentions of the
individuals who abstained or did not abstain nor do these studies contribute to
understanding how people make decisions about eating behaviors. With all of the focus
30
on therapeutic interventions and lack of research in surrounding prevention, it is
important to understand underlying theories of overall health behaviors.
Theory of Planned Behavior
Factors that have been noted to prevent eating disordered behaviors include
general knowledge about nutrition, an understanding of eating pathology, dieting
behaviors, thin-ideal internalization, and body dissatisfaction (Fingeret, Warren, Cepada-
Benito, & Gleaves, 2006). However, understanding how a person’s internal cognitive
decision making process with regard to how it applies to poor eating behaviors has had
limited discussion. The TPB is one theory that can be used to investigate a person’s
intention and perceived behavioral control when applied to health behaviors.
The TPB is an extension of a model called the theory of reasoned action which
was developed by Fishbein and Ajzen (1975). The original theory of reasoned action
suggested that individuals systematically assessed a variety of inputs before making a
decision whether or not to act on or avoid acting on a certain behavior. These inputs
include individual beliefs, social influence, attitude towards a behavior, importance of
attitude and subjective norms, and the person’s overall intention for the attitude. This
theory was extended by Ajzen with the addition of the concept of perceived behavioral
control (Ajzen, 1988). The addition of perceived behavioral control as a component can
measure the effect a person’s experience with acting on a specific behavior has upon the
current ability to perform the behavior (see Figure 2).
31
Figure 2. Note. From “Constructing a theory of planned behavior questionnaire” by I.
Ajzen, 2009, TPB Model, Retrieved February 15, 2009 from: www.people.umass.edu.
Copyright 2006 by I. Ajzen. Reprinted with permission.
Armitage et al. (1999) noted that the TPB has been studied in relationship to a
variety of social psychology issues including eating behaviors and binge drinking.
Psychologically this theory is similar to understanding how a person measures locus of
control; however, it also measures a person’s feeling of control over a behavior rather
than just the internal control of events. Even with compelling evidence regarding the
dangers of unhealthy eating behaviors many individuals still demonstrate ambivalence
regarding changing their eating behavior and as this is often a result of personal decisions
and social influences (Snow, 2000).
The TPB was created to understand the interactions of beliefs, attitudes, and
social influences on a person’s final behavior with regard to personal intentions (Ajzen,
2008). The model has three tiers. The first tier is that a person will have behavioral
32
beliefs surrounding whether or not a specific behavior will result in an outcome which
impacts personal attitudes towards a behavior (Armitage et al., 1999). The second tier
addresses normative beliefs (which are perceived behavioral expectations of individuals
the person feels is important) and subjective norms (which are the perceived social
pressure to perform the specific behavior) as they apply to an initial behavioral belief
(Ajzen, 2008). The third tier consists of control beliefs and perceived behavioral control
which is a person’s internal and external feeling regarding the ability to execute a specific
behavior (Armitage et al., 1999). This theory has been popularized with the use in a
variety of social issues that are related to personal behavior such as understanding the
spread of HIV, measuring health behaviors for those with chronic illnesses, and
understanding goal directed behaviors for drug abuse recovery treatments (Young, 1991).
The TPB can be applied to personality and attitudes regarding healthy eating
behavior which is often formed through the media’s usage of agenda setting or the ability
to frame the issue with a specific angle to influence public opinion to achieve a certain
body image (Halliwell & Harvey, 2006). Although neuropsychological and satiety issues
are associated with eating behavior, personalities and attitudes toward poor eating
behaviors have been changed through educational programs and behavioral modification
(Ozelli, 2007). One such example was demonstrated by Carpenter, Finely, and Barlow
(2004) in a pilot study in which three groups of individuals were compared. Individuals
had poor eating behaviors according to the USDA’s Health Eating Index and either
received weekly nutritional educational and training, internet based nutritional
educational training, or no educational training. The results demonstrated a significant
33
improvement in eating behavior and a change in the associated attitude towards changing
their behavior in the group that received weekly nutritional education and training
(Carpenter et al., 2004). Although measuring behavior such as in the Carpenter, Finely,
and Barlow (2004) study can reflect a change in attitude, the measurement of an attitude
before a specific treatment, such as nutritional education or behavioral modification, the
TPB is often not applied in binge eating or obesity related studies (Reid, 2006).
Conner, Povey, Sparks, James, and Shepard (2003) used the TPB to assess
attitudinal ambivalence with regard to maintaining eating behaviors. They used an
increase in ambivalence towards healthy eating behaviors as the dependent variable and
attitudes and intentions, attitudes and behavior, and perceived behavioral control as
independent variables. By performing correlation studies based on results from two TPB
designed Likert scales, the study found that those participants who demonstrated higher
ambivalence with their healthy eating behaviors were more likely to have weaker
relationships between the independent variables and the outcome of healthy eating
behavior (Conner et al., 2003).
The TPB has been used to predict a wide variety of behaviors such as exercise
intentions (Shen, McCaughtry, & Martin, 2008), sexual behaviors (Myklestad & Rise,
2008), vegetable consumption in children (Pawlak & Malinauskas, 2008), smoking
behaviors (Nehl et al., 2009), and intentions for healthy eating (Tiejian et al., 2009).
The TPB has also demonstrated results in areas such as predicting the consumption of
dairy products by the elderly using attitudes, subjective norms, and perceived behavioral
control as variables (Kyungwon, Reicks, & Sjoberg, 2003) as well as addressing a variety
34
of health behaviors associated with exercise and dietary change. Additionally, behaviors
regarding the participation in physical activities have noted the influence of attitudes on
participation (Kiviniemi, Voss-Humke, & Seifert, 2007). However, attitudes do not
always demonstrate the willingness or intentions to eat in a healthful manner (Fila &
Smith, 2006). This is often caused by the lack of understanding how individuals make
decisions in specific situations. The TPB has not assessed the cognitive decision making
styles associated with how a person internally feels regarding health behaviors such as
binge eating.
Health Behaviors
When a person feels that a health threat exists, which is considered to be a
vulnerability to the consequences of a health related action, behavior is often modified to
avoid the perceived consequence (Brannon & Feist, 2004). This can be a reaction to a
combination of feelings of self-efficacy regarding the ability to change behavior as well
as a combination of internalizing the cost versus gain benefit a person believes will
equate to the change in behavior. The TPB incorporates internal decision making issues
regarding health behaviors such as what a person feels is a predictor of health, what a
person can to do pursue tasks associated with obtaining good health, social learning
theories that reinforce behaviors, personal intentions, internal perceptions of the future
consequences of continued behaviors as well as what a person feels can be achieved
giving existing capabilities (Sirois, 2004).
Conner, Norman, and Bell (2002) looked to examine the power of the TPB as it
applies to healthy eating. In their longitudinal study using questionnaires they looked to
35
understand the intentions associated with eating a healthy diet. The results demonstrated
that the TPB was predictive of healthy eating intentions for time periods up to 6 years.
This suggests stability in this instrument and can be beneficial for understanding
intentions and actual behavior.
Binge Behaviors
Non-eating binge behaviors have been examined, using the TPB, in this area.
Stewart, Brown, Devoulyte, Theakston, and Larsen (2006) noted that self-reporting binge
drinkers who drank for emotional relief rather than social pressures were also more likely
to have binge eating behaviors and they found that the root cause of the binge eating and
drinking were similar in nature. Additionally, poor internally reported self-control and
self-efficacy issues often lead to binge drinking and binge eating (Williams &
Ricciardelli, 2003).
Collins and Carey (2007) used longitudinal models to examine how the TPB
could predict drinking behaviors in college students. They noted that intentions should
predict behavior and, in their study, attitudes were a consistent predictor for binge
drinking. Additionally, a study using correlation and regression tested associations
between attitudes, perceived behavioral control, subjective norms, and beliefs and
perceived behavioral control reached significance for binge drinking behaviors. (Norman
et al., 1998).
As of June, 2010, this literature review found over 3,100 journal articles that have
cited the TPB. However, the quantities of research articles that include references to
binge eating behavior are significantly limited. Rather, the instances in which binge
36
eating behaviors or obesity issues are referenced in articles it is in the form of an
independent variable rather contributing to the outcome of the TPB. This study is
looking to demonstrate any relationship between the contributions of the TPB on eating
behaviors.
Adapted motivational interviewing and other therapeutic techniques have been
proven to demonstrate some success with assisting individuals with binge eating disorder
in the process of restraining from binge eating and improved the ability to feel control
over their decision making process (Cassin, von Ranson, Heng, Brar, & Wojtowicz,
2008). Yet, there is limited research available that demonstrates any relationship between
problem solving style and a person’s ability to change their eating behavioral styles. In
order to understand how attitudes and intentions contribute to actual eating behaviors it is
important to factor in the role of cognitive decision making style. Unfortunately, limited
research has examined the TPB with obesity disorders, dieting, or weight loss (Gardner &
Hausenblas, 2002).
Adaption-Innovation Theory of Problem Solving Style
The adaption-innovation theory of problem solving style assesses the relationship
between problem solving and creativity using a cognitive function schema (Kirton,
2003). This theory focuses on understanding the relationship between cognitive function,
which includes cognitive resource (knowledge, skills, and experience) and cognitive
affect (needs, values, and beliefs) in conjunction with cognitive effect which is the level
that a person is born with (such as intelligence) and preferred decision making style
(which is either more or less adaptive or innovative). These major theories are
37
additionally influenced by a person’s preferred style and coping behaviors and the social
effect of their culture and opportunities (Figure 3). These elements contribute to the
manner in which a person makes decisions which may influence eating behaviors.
Figure 3. Note. From “Certification Course (p. 27) by M. J. Kirton, 2008, Occupational
Research Centre: Pennsylvania State University. Copyright 2008 by Kirton. Reprinted
with permission.
The adaption-innovation theory of problem solving style uses the KAI inventory
to measure a person’s style, which is a part of the cognitive effect that all individuals are
born with and does not change throughout the lifespan (Kirton, 2008). Each individual
has a specific score which is a measure of the manner in which the diversity of problem
solving and managing changes can be incorporated into both a person’s lifestyle as well
as in a group context (Kirton, 2008). These scores range from 32, being the most highly
adaptive, to 160, being the most highly innovative (Figure 4). Cognitive style, which is
38
determined using this measure, affects how a person learns and solves problems in a
creative manner.
All individuals problem solve and the manner in which they do such may affect
the success they have with managing change, such as a new eating style, as well as
resulting in a person’s need to cope with a change that does not fit within the style. If a
person must behave in a manner that is not consistent with preferred style, then a person
must perform a behavior that is considered to be a coping skill. For example, resistance to
an idea that is not within a person’s preferred style may be met with objections or
resistance (Kirton, 2008).
Coping behavior can be evaluated by assessing how much effort it takes to
execute a behavior based on how close or far the behavior is related to a person’s
cognitive style. Coping occurs when it is necessary to perform a behavior that is outside
of a person’s preferred style (Kirton, 1995). Individuals will do the minimum amount of
coping as possible because there is a negative psychological cost associated with coping.
Binge eating has been associated with depressive symptomology in women due to the
repetitive coping skills required when a person has an eating disorder (Harrell & Jackson,
2008). Therefore, understanding a person’s preferred problem solving style may be
associated with how comfortable the person is with managing a dietary program or
refraining from binge eating behaviors.
When individuals have to conform to a certain behavioral style of eating, such as
maintaining a strict diet, it may result in feelings of having to cope. This could influence
the success or failure of a healthy eating program as a person who is highly innovative
39
may find it harder to maintain a strict diet that has many rules or they may sense a feeling
of boredom with a very rigorous diet. Alternatively, a person who is highly adaptive may
not be comfortable with a dietary style that is very flexible and does not have clearly
defined parameters (Kirton, 2008).
There is not a better or worse style; rather it is a measure of how comfortable a
person feels with change. A person who is considered to be more innovative is more
likely to be seen by others as being unconventional in thinking style, undisciplined,
nonconforming, bold, risk seekers, flexible, abrasive, and often impractical (Bagozzi &
Foxall, 1995). Alternatively, a person who is considered to be more adaptive is more
likely to be seen by others as being more sensitive to risky ideas, focused on doing things
better rather than differently, prudent, conforming, methodological, disciplined, and
perform better in situations surrounded with structure (Bagozzi & Foxall, 1995, Figure
4).
40
Figure 4. Note. From “Certification Course (p. 76) by M. J. Kirton, 2008, Occupational
Research Centre: Pennsylvania State University. Copyright 2008 by Kirton. Reprinted
with permission.
Many studies have applied the adaption-innovation theory over the years in a
variety of practices resulting in mean scores for various occupations (Kirton, 1996). For
example, bank branch managers, civil servants, plant managers, cost accountants,
programmers, and maintenance engineers have mean scores ranging from 80-90 which
places them on the more adaptive spectrum of the scale. On the more innovative side of
the KAI scale, engineers, research and development managers, and fashion buyers have
mean scores ranging from 101-110. This demonstrates that cognitive style is associated
with work preferences.
41
Additionally, the impact of problem solving style has been investigated in nursing
programs (Adams, 1993), marketing and intelligence planning (Bhate, 1999), musical
compositional development styles of students (Brinkman, 1999), managerial skill
assessments (Buttner, Gryskiewicz, & Hidor, 1999), and problem solving within the
health services (Flanagan, 2007). All of these studies have confirmed that preferred
decision making style is a critical component of personal performance in the workplace
environment.
However, this theory not only applies to preferred working environments, it is
equally applicable in understanding personal behavioral styles. In a study by Hutchinson
and Skinner (2007) the relationship between self-awareness, self-consciousness and
cognitive style was investigating using a population of 55 undergraduate students. Using
multiple regression analyses, students who scored more highly innovative on the KAI
inventory demonstrated lower levels of social anxiety and self-monitoring whereas the
students who scored more highly adaptive demonstrated increased public self-
consciousness and higher private self-consciousness. This is significant in that it
suggests that preferred style is associated with internal decision-making processes.
Cognitive behavioral analyses have noted an association between those who have
binge eating disorder and an analysis of being driven towards perfectionism, self-imposed
standards, and extreme self-evaluative view points (Dunkley, Blankstein, Masheb, &
Grilo, 2003). This is consistent with research using the Adaption-Innovation theory
which demonstrates that cognitive style has a relationship with maladaptive eating
behaviors (Saggin, 1996). Specifically, in a study by Saggin (1996), it was noted that
42
anorexic patients would rigorously adhere to a diet regimen even if it meant risking life
and health. Conversely, binge eaters were less likely to adhere to a dietary program and
would often lapse from their diet for a lengthy time period. Saggin (1996) divided
patients into three groups which were anorexic (n = 8), bulimic (n = 9), and binge eaters
(n = 19). Upon administering the KAI inventory which measures adaptive and innovative
style, the results demonstrated that the anorexic group had a mean KAI score of 76.75,
bulimics had a mean score of 102.66, and binge eaters had a mean score of 111.11. What
this study demonstrated was that anorexic patients had scores that were significantly
more adaptive than the mean score for the general female population (M = 91) and binge
eating patients scored significantly more innovative. This suggests that there is an
opportunity to understand preferred eating behaviors once the preferred problem solving
style is determined. However, to the knowledge of the researcher and based on the
literature review, there has never been an investigation regarding potential relationships
between the innovation-adaption theory with regard to non-clinical eating behaviors
which makes this research pertinent.
Summary
There is significant evidence linking the relationships between self-efficacy,
dieting cycles, body image, and binge eating (Cain, Bardone-Cone, Abramson, Vohs, &
Joiner, 2008). However, there is a lack of understanding associated with the cyclical
behavior of motivation, control, and psychological influences results in negative eating
behaviors such as binges (McDonald, 2003). Although the social influences associated
with binge eating behaviors have been defined, there is a stigma associated with binge
43
eating that results low self-esteem and depression. Additionally, psychologically-related
eating disorders of this nature have been associated with high rates of mortality due to the
obesity related diseases. These findings underscore the need for this research (Newman et
al., 1996). Further investigations to measure perceived behavioral control, attitude,
subjective norms, intention, sufficiency of originality, efficiency, rule/group conformity,
BMI, dietary restraint, eating concern, shape concern, and weight concern will result in
positive social change. The next chapter delineates the proposed research design to assess
these factors.
CHAPTER 3: RESEARCH METHOD
Organization of Chapter
This chapter presents the research design, the setting and sample, and the three
instruments for data collection: the Eating Disorder Examination Questionnaire (EDE-
Q6), TPB Questionnaire, and KAI Inventory. It also outlines the other supplemental
materials including height, weight, and a background data questionnaire used in the
research. Each instrument is discussed in terms of the type of instrument, the concepts
measured by instrument, how scores are calculated and their meaning, the assessment of
reliability and validity of instrument, the process needed to complete instrument by
participants, where raw data will be stored, and a detailed description of data that
comprise each variable in the study. Lastly, the data collection and analysis process is
discussed including an explanation of descriptive analyses used in the study, the nature of
scale for each variable, the statements of hypotheses related to each research question, a
description of analytical tools used, a description of data collection process, and the
protection of human subjects.
Research Design and Approach
This study employed a quantitative design, and used criterion measures and
predictor variables obtained at a single point in time. Due to the sensitive nature of
measuring eating behaviors, it was not ethically justifiable to manipulate health
behaviors, psychological intention, or the natural decision-making processes of human
participants. Therefore, correlation studies were used in the design instead of treatment or
experimental design. An advantage of the use of a correlation design using self-reported
45
surveys is that multiple factors that have not been previously investigated can be assessed
with relative efficiency. A disadvantage of this design is that causality cannot be
determined.
Setting and Sample
Population and Sampling Method
The sample was recruited via convenience sampling techniques (Creswell, 2003).
The population of interest included men and women between 18 and 65 years of age who
were not residing in a hospital or mental health facility, and who volunteered to be
surveyed. No participants were excluded based on gender, ethnicity, occupation, or
education level. The method of sampling was one of convenience (Creswell, 2003) using
available populations from universities, grocery stores, churches, local businesses, or
mailed forms in the greater Boulder, Colorado area. Interested individuals were provided
contact details to participate in the study.
Sample Size
In this study, multiple variables from the general population were investigated so
a non-random sample of convenience was employed. For this study, the alpha level (α)
was set to .05 and the power level was .80. Effect sizes were determined using Cohen’s
(1992) criteria where f2 = 0.02 (small effect), f2 = 0.15 (medium effect), and f2 = 0.35
(large effect). The effect size was set at a medium effect (f2 = 0.15) based on a literature
review using similar KAI and TPB models (Hutchinson & Skinner, 2007; Goldsmith &
Matherly, 1987; Ajzen, 2006). Additionally, a small to medium effect size has been
recommended in a meta-analysis performed by Lipsey and Wilson (1993) in the areas of
46
psychological, educational, and behavioral research. There were 8 predictor variables
which are BMI, perceived behavioral control, attitude, subjective norms, intention,
sufficiency of originality, efficiency, and rule/group conformity. Therefore, to have
adequate power to reach statistical significance for the combined effect of 8 predictors,
the recommend sample size was 108 participants who fully completed the survey. A total
of 140 participants fully completed the survey.
Participants and Characteristics
The eligibility criteria for study participants were that they were not receiving
medical treatment for eating disorders and that they were willing to participate on an
anonymous and voluntary basis. The characteristics of the selected sample were that
volunteers were interested in participating in a study that examines eating behaviors, and
intend to either change or remain in their specific eating behavioral style. The participants
ranged in ages from 18 through 65 and did not report suffering from any terminal
illnesses nor residing in a hospital or mental health facility.
Instruments and Materials
Three instruments were used in this study in the form of surveys in addition to
height, weight, and other background data. The three instruments were the Eating
Disorder Examination Questionnaire, the TPB Questionnaire, and the KAI Inventory, and
a background data questionnaire are described in detail below. Body mass index was
calculated from height and weight dimensions. All surveys were conducted using pen and
paper and were formatted in a self-report design.
47
Body Mass Index
A currently accepted measure to assess a person’s body fatness is the body mass
index referred to as BMI (CDC, 2008). This type of instrument is a tool that is used to
screen for possible weight problems for adults using standard weight categories for
adults: underweight, normal, overweight, and obese (Mei et al., 2002).
BMI scores were calculated using height and weight measurements. The
calculation for pounds and inches measures is: (weight (lb) / [height (in)]2) * 703. Often
the raw score is classified in four categories which are underweight (BMI = less than
18.5), normal (BMI = 18.5 to 24.9), overweight (BMI = 25.0 – 29.9) and obese (BMI =
30.0 or greater).
In empirical research, correlation coefficients for height and weight using the
BMI have been 0.99 and 0.96 (p = 0.0001) (Nakamura, Hoshino, Kodama, & Yamamoto,
1999); in addition, the Center for Disease Control noted that calculating BMI as a
screening tool is one of the best methods to assess the general public to determine obesity
or being overweight (2009). The BMI measurement concluded that using 95%
confidence intervals demonstrated a higher risk for health issues such as coronary heart
disease (Willet et al., 1995). Although research demonstrates the reliability and validity
of the BMI, there are still challenges with the fact that a person with a BMI over 25
would be considered obese, a category which would inadvertently include healthy
athletes. Despite these limitations in measurement, athletes were not the focused
population of this study.
48
The process needed to complete the instrument by participants was contained in
two questions requesting the height in inches and the weight in pounds using paper and
pencil. The variables of height and weight were entered into a SPSS datafile and the
participants’ names were coded numerically. The raw data were presented in tables and
maintained by the researcher in a secure locked location in the research lab to be
available on request only to qualified professionals.
Eating Disorder Examination Questionnaire, EDE-Q6
The Eating Disorder Examination Questionnaire, referred to as the EDE-Q6, is a
self-reported version of the original Eating Disorder Examination Edition 16.0D. The
EDE-Q6 is scored in the same manner but allows for a similar assessment without the
longer qualitative interview process and interpretation (Fairburn, 2008). This type of
instrument is quantitative and focused on self reported behaviors that have occurred
within the last four weeks.
The concepts measured by the EDE-Q6 are based on subscales which were
specified in the categories of restraint, eating concern, shape concern, and weight
concern. These four subscales are the criterion variables. The EDE-Q6 also has a
category that measures the frequency of occurrence. These questions, which are items
13-18, are not necessary to calculate a global EDE-Q6 score and thus were not included
within the four subscale criterion variables (Fairburn, 2008).
The restraint category measures the variables of empty stomach, dietary rules,
restraint, avoidance of eating, and food avoidance. The scale of restraint consists of five
items and the instrument item numbers for this subscale are 1, 2, 3, 4, and 5. Some
49
example items include in the past 28 days “have you had a definite desire to have an
empty stomach with the aim of influencing your shape or weight” and “have you gone for
long periods of time (8 waking hours or more) without eating anything in order to
influence your shape or weight”? The range of the score is 0-6. The possible response
options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score = 2), 13-15 days
(score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days (score = 6). This
subscale is specifically calculated by adding each score together and then the sum is
divided by the total number of items forming the subscore. The community norm for this
subscale is M = 1.251, SD = 1.323 (Fairburn, 2008). A lower score would imply a less
symptomatic focus on eating restraint where as a higher score would imply a greater
symptomatic focus on eating restraint. The Cronbach’s alpha value for this subscale is .84
(Luce & Crowther, 1999).
The eating concern category measures guilt about eating, fear of losing control
over eating, social eating, preoccupation regarding eating, and secretive eating. The scale
of eating concern consists of five items and the instrument item numbers for this subscale
are 7, 9, 19, 20, and 21. Some example items include in the past 28 days “has thinking
about food, eating, or calories made it very difficult to concentrate on things you are
interested in (for example, reading, working, following a conversation” and “have you
had a definite fear of losing control over eating”? The range of the score is 0-6. The
possible response options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score =
2), 13-15 days (score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days
(score = 6). This subscale is specifically calculated by adding each score together and
50
then the sum is divided by the total number of items forming the subscore. The
community norm for this subscale is M = 0.624, SD = 0.859. A lower score would imply
a less symptomatic focus on eating concern whereas a higher score would imply a greater
symptomatic focus on eating concern. The Cronbach’s alpha value for this subscale is .78
(Luce & Crowther, 1999).
The shape concern category measures feelings of fatness, flat stomach,
preoccupation with shape, importance of shape, fear of weight gain, discomfort of
visualization of body, and avoidance of body exposure. The scale of shape concern
consists of eight items and the instrument item numbers for this subscale are 6, 8, 10, 11,
23, 26, 27, and 28. Some example items include in the past 28 days “have you had a
desire to have a totally flat stomach” and “has your shape influenced how you think
(judge) yourself as a person”? The range of the score is 0-6. The possible response
options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score = 2), 13-15 days
(score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days (score = 6). The
range of the score is 0-6. This subscale is specifically calculated by adding each score
together and then the sum is divided by the total number of items forming the subscore.
The community norm for this subscale is 2.149 (SD = 1.602) (Fairburn, 2008). A lower
score would imply a less symptomatic focus on shape concern where as a higher score
would imply a greater symptomatic focus on shape concern. The Cronbach’s alpha value
for this subscale is .93 (Luce & Crowther, 1999).
The weight concern category includes the importance of weight, the desire to lose
weight, dissatisfaction with current weight, reaction to recommended weight loss advice,
51
and preoccupation with weight. The scale of weight concern consists of five items and
the instrument item numbers for this subscale are 8, 12, 22, 24, and 25. Some example
items include in the past 28 days “has your weight influenced how you think about
(judge) yourself as a person” and “have you had a strong desire to lose weight”? The
range of the score is 0-6. The possible response options are listed on a scale of 0-6 and
the participant selects the number in accordance to not at all (score = 0), slightly (score =
2), moderately (score = 4), or markedly (score = 6) going from left to right. This subscale
is specifically calculated by adding each score together and then the sum is divided by the
total number of items forming the subscore. The community norm for this subscale is
1.587 (SD = 1.369) (Fairburn, 2008). A lower score would imply a less symptomatic
focus on shape concern where as a higher score would imply a greater symptomatic focus
on shape concern. The Cronbach’s alpha value for this subscale is .89 (Luce & Crowther,
1999).
The process for assessment of reliability and validity of the EDE-Q6 has been
obtained by a literature review of over 40 publications from the Centre for Research on
Eating Disorders at Oxford (2009). Specifically, Luce and Crowther (1999) investigated
the internal consistency and the test-retest reliability of the EDE-Q including the overall
score and the subscales. Using Pearson correlation coefficients the researchers
determined that all of the correlations measuring behavioral features, such as binge
eating, were statistically significant. Additionally, Cronbach alphas were used to
investigate the internal consistency of the four subscales and the exceeded recommended
levels while Pearson correlation demonstrated statistical significance when investigating
52
the stability of the results over time. The EDE-Q has also been demonstrated using a
general population of women with ages ranging from 18-45 in a test-retest interval of 315
days (Mond, Hay, Rodgers, Owen, & Beaumont, 2004). This study demonstrated the
instrument had Pearson correlations, when assessing attitudinal features, of 0.57 for the
restraint subscale and 0.77 for the eating concern subscale. Additionally, the instrument
had a high internal consistency with a Cronbach’s alpha coefficient of 0.93 for the global
scale.
The process needed to complete the instrument by participants was a pen and
paper and the data were entered into a SPSS datafile. The participants’ names were
coded numerically. The raw data were presented in tables and maintained by the
researcher in a secure locked location in the research lab to be available on request to
qualified professionals.
Theory of Planned Behavior Questionnaire
The TPB questionnaire measures relationships between attitude, subjective norms,
perceived behavioral control and intention (Ajzen 2006). This instrument is a self-report
survey design that uses a Likert-scale measurement system to predict health behaviors.
Specifically, the total measurement addresses whether or not a person can perform a
specific health behavior. For the purpose of this research four predictor variables from the
TPB will be measured. They are attitude, which measures how much the person is in
favor of performing the behavior, subjective norms, which measures the social pressure a
person feels to perform a behavior, perceived behavioral control, which measures the
53
internal control a person believes exists over the behavior and intention, which measures
the likelihood a person will demonstrate the specific behavior.
Attitudes measure a person’s overall evaluation of the behavior of binging, or
overeating (Francis et al., 2004). The attitude subscale consists of 3 items and the
instrument item numbers for this subscale are 1, 2, and 3. The questions are arranged in a
possible response option, ranging from left to right, describing how participant’s attitude
ranges on a scale of 1-7. This subscale is specifically calculated by adding each score
together to form an overall attitude sum composite subscore. The participant will place
an X on one of the 7 dots. The range of the sum composite subscore is 3-21. Some
example items include on a scale of 1-7, with a 1 being extremely worthless and a 7 being
extremely useful, “healthy eating on a regular basis is”, and on a scale of 1-7, with a 1
being not important at all and a 7 being very important, “maintaining a healthy diet is”. A
lower score would imply a poor attitude towards healthy eating and a higher score would
imply a positive attitude towards not overeating. The Cronbach’s alpha value for this
subscale is .83 (Conner & Norman, 2002; Francis et al., 2004).
Subjective norms measure a person’s own estimate of social pressure to overeat or
abstain from eating (Francis et al., 2004). The subjective norms subscale consists of 3
items and the instrument item numbers for this subscale are 4, 5, and 6. The questions are
arranged in a possible response option, ranging from left to right, describing how
participant’s attitude ranges on a scale of 1-7. The participant will place an X on one of
the 7 dots. This subscale is specifically calculated by adding each score together to form
an overall subjective norm sum composite subscore. The range of the sum composite
54
subscore is 3-21. Some example items include on a scale of 1-7, with a 1 being not
important and a 7 being very important, “people that are important to me think that
keeping a healthy weight is”, and on a scale of 1-7, with a 1 being not important at all and
a 7 being very important, “what my doctor or health care provider thinks I should do to
eat healthy is”. A lower score would imply a low social pressure towards healthy eating
and a higher score would imply higher social pressure towards not overeating. The
Cronbach’s alpha value for this subscale is .84 (Conner & Norman, 2002; Francis et al.,
2004).
Perceived behavioral control measures the extent in which a person has a feeling
of being able to control how much they eat (Francis et al., 2004). The perceived
behavioral control subscale consists of 3 items and the instrument item numbers for this
subscale are 7, 8, and 9. The questions are arranged in a possible response option, ranging
from left to right, describing how participant’s attitude ranges on a scale of 1-7. The
participant will place an X on one of the 7 dots. This subscale is specifically calculated
by adding each score together to form an overall perceived behavioral control sum
composite subscore. The range of the sub composite subscore is 3-21. Some example
items include on a scale of 1-7, with a 1 being strongly agree and a 7 being strongly
disagree, “my weight or shape is in my control”, and on a scale of 1-7, with a 1 being
strongly agree and a 7 being strongly disagree, “the decision to stick to a diet program is
beyond my control”. A lower score would imply a person feels unable to manage control
of their weight whereas a higher score would imply a person feels has the internal control
55
to manage weight. The Cronbach’s alpha value for this subscale is .74 (Conner &
Norman, 2002; Francis et al., 2004).
Intention is a proximal measure of behavior towards a person’s eating behavior
(Francis et al., 2004). The intention subscale consists of 3 items and the instrument item
numbers for this subscale are 10, 11, and 12. The questions are arranged in a possible
response option, ranging from left to right, describing how participant’s attitude ranges
on a scale of 1-7. This subscale is specifically calculated by adding each score together
to form an overall intentions sum composite subscore. The participant will place an X on
one of the 7 dots. The range of the sub composite subscore is 3-21. Some example items
include on a scale of 1-7, with a 1 being extremely difficult and a 7 being extremely easy,
“for me, intending to eat healthy on a daily basis is”, and on a scale of 1-7, with a 1 being
strongly disagree and a 7 being strongly agree, “I intend to maintain healthy eating
behaviors on a daily basis”. A lower score would imply a person does not intend to
manage eating behaviors whereas a higher score would imply a person does intend to
manage eating behaviors. The Cronbach’s alpha value for this subscale is .82 (Conner &
Norman, 2002; Francis et al., 2004).
A brief form of the questionnaire was used for the purpose of this research as the
goal is an analysis to predict variance in behavioral intentions. Therefore, this format
resulted in a questionnaire that has three questions for each of the four predictor variable
items as recommended by the Constructing Theory of Planned Behavior Questionnaires
Manual (2004).
56
The process needed to complete instrument by participants was a pen and paper
responses to a total of 12 questions. The hard copy raw data were calculated using SPSS
and the participants names were coded numerically. The raw data were presented in
tables and maintained in a secure locked location in the research lab to be available on
request to qualified professionals.
Kirton Adaption-Innovation Inventory
The KAI is an instrument that is a self-report questionnaire that asks the
participant to rate on a bipolar scale how easy or difficult it is to present oneself
consistently over a long period of time with a specific style of behavior. This type of
instrument is quantitative and requires certification to administer which is obtained by
attending a week long training session as well as passing a certification test. Each
questionnaire is numerically identified and registered by the Occupational Research
Center in the United Kingdom and may not be administered in electronic format. The
concepts measured by this instrument are focused on the manner in which individuals use
creativity to solve problems and manage change (Kirton, 1999).
Scores and their meaning are calculated through a scoring method that is based on
three sub scores which, when totaled result in one final score for the inventory. The
possible range of scores for this inventory is between 32, being highly adaptive, through
160, being highly innovative. The population mean is 96 with male scores being normally
distributed at 91 and female scores being normally distributed at 98 (Kirton, 1999).
Research has also demonstrated relationships with professionals and their mean scores.
For example, in the area of marketing, finance, or fashion buyers have a mean score
57
ranging from 104-110 where as accountants, programmers, and plant managers have a
mean score ranging from 80-90 (Kirton, 1999).
The KAI has been studied in multiple populations to confirm its construct validity
(Goldsmith, 1985). Bagozzi and Foxall (1995) performed a confirmatory factor analysis
which demonstrated satisfactory levels of reliability as well as strong evidence for
convergent and discriminate validity using postgraduate students in the United Kingdom,
Australia, and the United States.
There are three subscores that compile the overall KAI score which are
sufficiency of originality, efficiency, and rule/group conformity (Kirton, 1976, 1999,
2003). The first concept is sufficiency of originality (SO). SO measures the manner in
which a person generates ideas. Innovators have a tendency to generate large amounts of
ideas in comparison with those who are more adaptive. These ideas are often paradigm
breaking and may result in problem solving solutions that may not be readily accepted by
others, may seem unsound, bizarre, or even outside of the scope of the problem entirely.
Additionally, those who are more innovative tolerate a higher failure rate of their ideas
although the quantity of their ideas is large. Adaptors solve problems differently. Their
level of SO reflects idea generation approach that is focused more on improvements to
the current problem rather than the out of the paradigm idea generation style of
innovators. Although adaptors generate fewer ideas than innovators, they expect a higher
success rate from their ideas.
The SO subscale consists of 13 items and the instrument item numbers for this
subscale are 3, 5, 11, 12, 13, 16, 18, 19, 21, 23, 24, 26, and 31. The questions are
58
arranged in a possible response option, ranging from left to right, from very hard, hard,
easy, and very easy on a 17 point dotted scale. The participant will place an X on any
location of the continuum scale that contains 17 dots. Each X can be converted to a raw
score between 1-5. SO scores range from 13 through 65 (M = 41, SD= 9). This subscale
is specifically calculated by adding each score together to form an overall SO sum
composite subscore. Some example items include how easy or difficult do you find it to
present yourself, consistently, over a long period of time as “a person who when stuck
will always think of something” or “a person who has fresh perspectives on old
problems”. A lower score would imply a more adaptive style of ideas created inside the
paradigm within a consensually agreed structure. A higher score would imply a more
innovative style of ideas formed, usually outside the paradigm, with less regard for
consensually agreed structure. The Cronbach’s alpha value for this subscale is .81
(Kirton, 2009).
The second concept measured by KAI is efficiency (E). Not to be confused with
SO which measures the style of idea generation, E measures the concept of a person’s
problem solving methods or processes. The efficiency concept helps clarify the manner in
which a person problem solves with those being more innovative likely be less
methodological and to pay less attention to the detail of solving the problem and accept a
higher level of risk with the proposed solution. Adaptors prefer to work closely with the
existing system that surrounds the problem in a rigorous and methodological way to
improve the current structure while tolerating much less risk in their solutions.
59
The E subscale consists of 7 items and the instrument item numbers for this
subscale are 4, 14, 15, 17, 22, 25, and 28. The questions are arranged in a possible
response option, ranging from left to right, from very hard, hard, easy, and very easy on a
17 point dotted scale. The participant will place an X on any location of the continuum
scale that contains 17 dots. Each X can be converted to a raw score between 1-5. E
scores range from 7 through 35 (M = 19, SD = 6). This subscale is specifically calculated
by adding each score together to form an overall E sum composite subscore. Some
example items include how easy or difficult do you find it to present yourself,
consistently, over a long period of time as “a person who enjoys detailed work” or “a
person who is methodological and systematic”. A lower score would imply a more
adaptive style of working within an existing system to solve a problem whereas a higher
score would imply a more innovative style of looking outside of a system to solve a
problem. The Cronbach’s alpha value for this subscale is .76 (Kirton, 2009).
The third concept measured by KAI is rule/group conformity (R). This concept
focuses on how style, being more or less adaptive or innovative, affects the structures in
which problem solving occurs. Adaptors are more likely to accept group conformity and
look for collaboration in problem solving processes. They prefer rules and guidelines for
solving problems. Innovators are more likely to have less regard for rules, guidelines, or
structure when solving problems. They may be more comfortable bending or breaking
rules in order to solve a problem or make a decision.
The R subscale consists of 12 items and the instrument item numbers for this
subscale are 2, 6, 7, 8, 9, 10, 20, 27, 29, 30, 32, and 33. The questions are arranged in a
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possible response option, ranging from left to right, from very hard, hard, easy, and very
easy on a 17 point dotted scale. The participant will place an X on any location of the
continuum scale that contains 17 dots. Each X can be converted to a raw score between
1-5. R scores range from 12 through 60 (M = 36, SD = 9). This subscale is specifically
calculated by adding each score together to form an overall R sum composite subscore.
Some example items include how easy or difficult do you find it to present yourself,
consistently, over a long period of time as “a person who conforms” or “a person who
holds back ideas until they are obviously needed”. A lower score would imply a more
adaptive style in which a person prefers to work within a group and have group cohesion
whereas a higher score would imply a more innovative style in which a person prefers to
initiate changes that may result in going outside of the rule/group structure. The
Cronbach’s alpha value for this subscale is .82 (Kirton, 2009).
The process needed to complete instrument by participants was a pen and paper
answer to a total of 33 questions, one which is not graded, resulting in a final total of 32
questions. These responses were anchored to a carbon copy that compiles them into a
score of 1 through 5. The instrument takes approximately 15 minutes to complete
(Kirton, 1999). The data from each KAI inventory was entered into a SPSS datafile and
the participants names were coded numerically and maintained by the researcher in a
secure location in the research lab in a double locked environment to be available on
request to qualified professionals.
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Background Data Questionnaire
A general demographics survey was also administered in addition to the consent
form. This instrument was comprised of fill in the blank questions to gather information
to assist in the data collection process. The questionnaire gathered information regarding
age, gender, and ethnic group. The process needed to complete instrument by participants
was a pen and paper answer to a total of three questions. The data from the questionnaire
was entered into a SPSS datafile and the participants names were coded numerically and
maintained by the researcher in a secure locked location in the research lab to be
available on request to qualified professionals.
Data Collection and Analysis
The specific research question was if each of four components of eating behavior
are affected by the variables of BMI, perceived behavioral control, attitude, subjective
norms, intentions, sufficiency of originality, efficiency, and rule/group conformity. In
order to answer this research questions the following hypotheses were tested.
Null Hypotheses (Ho)
Null 1: In a hierarchical multiple regression there will be no significant relationship
between the predictor variables (perceived behavioral control, attitude, subjective norms,
and intentions as measured by TPB, and sufficiency of originality, efficiency, and
rule/group conformity as measured by KAI, and BMI) and dietary restraint as measured
by EDE-Q6 (R = 0).
Null 2: In a hierarchical multiple regression there will be no significant relationship
between the predictor variables (perceived behavioral control, attitude, subjective norms,
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and intentions as measured by TPB, and sufficiency of originality, efficiency, and
rule/group conformity as measured by KAI, and BMI) and eating concern as measured by
EDE-Q6 (R = 0).
Null 3: In a hierarchical multiple regression there will be no significant relationship
between the predictor variables (perceived behavioral control, attitude, subjective norms,
and intentions as measured by TPB, and sufficiency of originality, efficiency, and
rule/group conformity as measured by KAI, and BMI) and shape concern as measured by
EDE-Q6 (R = 0).
Null 4: In a hierarchical multiple regression there will be no significant relationship
between the predictor variables (perceived behavioral control, attitude, subjective norms,
and intentions as measured by TPB, and sufficiency of originality, efficiency, and
rule/group conformity as measured by KAI, and BMI) and weight concern as measured
by EDE-Q6 (R = 0).
For the purpose of this study a hierarchical multiple regression model was
performed. As demonstrated by prior research, hierarchical regression was appropriate
for this type of research since there are more than two variables that are going to be
measured to obtain predictions regarding eating behaviors (Ajzen, 2008; Hair, Anderson,
Tatham, & Black, 1998; Francis et al., 2004; Gravetter & Wallnau, 2007; & Hutchinson
& Skinner, 2007). A hierarchical regression assesses multiple predictor variables that
may or may not generate a model that demonstrates a best fitting equation for a criterion
variable. The coefficient of determination (R), obtained from the analysis, can provide an
explanation for the proportion of variability in criterion variables accounted by the
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variability in predictor variables. Hierarchical regression adds terms to the regression
model in stages. At each stage, an additional term or terms are added to the model and the
change in R is calculated. A hypothesis test is done to test whether the change in R is
significantly different from zero. For the present study, BMI was first entered into the
model and then TPB and KAI were entered into the model as a set.
Nature of Scales
The eight predictor variables included in this study were BMI, perceived
behavioral control, attitude, subjective norms, intention, sufficiency of originality,
efficiency, and rule/group conformity. All predictor variables are expressed in ordinal
scales of measurement. Each variable from the TPB (perceived behavioral control,
attitude, subjective norms, and intention) can take a value from 3 through 21. The SO
variable from the KAI inventory can take a value of 13 through 65. The E variable from
the KAI inventory can take a value of 7 through 35. The R variable from the KAI
inventory can take a value of 12 through 60. The criterion variables EDE-Q6 also form
ordinal measures of scale. Each of the variables, namely dietary restraint, eating concern,
shape concern, and weight concern, can take an ordinal value from 0 through 6.
Protection of Participant’s Rights
The protections of participants’ rights were a vital part of this research study.
Participants completed an informed consent form prior to the administration of the
surveys and their identity was protected as they were numerically identified. The data
were collected and maintained in a private research location that has a lock on the file
cabinet as well as the main entrance which is only accessible to the researcher. As the
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participation in this study was voluntary and the participants were not associated with the
researcher on any personal or professional manner the protection of the participants’
rights were, and are, maintained. Although no unforeseeable psychological distresses
arose, if they do the participants will be provided with a list of local medical facilities as
well eating disorder treatment resources. Participants were able to withdraw from the
study at any time with no penalty.
Summary
It has been demonstrated that a great deal of research has focused on cognitive-
behavioral therapy for the treatment of binge eating disorders; however, that effort has
not completely solved the challenge of understanding how binge eating behaviors occur
(Wilfey et al., 2008). Normal individuals who demonstrate some levels of binge eating
disorders have higher lifetime rates of social maladjustment, anxiety, and mood disorders
(Stice et al., 2000). The following two chapters discuss the findings of the research as
they are related to the hypotheses and research question, the overall data analysis process,
and the outcomes. Interpretations of the findings, recommendations for further studies,
and how they relate to positive social change are also addressed.
CHAPTER 4: RESULTS
Introduction
This chapter presents the process of data screening, the demographic data for the
participants, and the results of the hierarchical regression analyses. The purpose of this
study was to assess the combined effects of individual problem solving styles (sufficiency
of originality, efficiency, and rule/group conformity) and planned behavior (attitudes
towards overeating, subjective norms, behavioral intentions to manage eating behavior,
perceived behavioral control), after first controlling for body mass index, on eating
behaviors. This study proposes a relationship between the predictor variables (perceived
behavioral control, attitude towards overeating, subjective norms, and intentions to
manage eating behavior as measured by TPB, and sufficiency of originality, efficiency,
and rule/group conformity as measured by KAI, and BMI) and eating behaviors as
measured by EDE-Q6. In order to investigate this relationship, the specific research
question was presented: Is eating behavior affected by body mass index, perceived
behavioral control, attitude towards overeating, subjective norms, intention to manage
eating behavior, sufficiency of originality, efficiency, and rule/group conformity?
Data Screening and Cleaning
The data collection instruments and the process of data collection followed all the
guidelines described in chapter 3. A total of 145 participants responded to the
questionnaire employed in this study. After data collection, the data were visually
screened and five surveys were excluded from the sample. Three of these surveys were
missing responses on an entire page of the survey. Kirton (1999) explained that
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participants who frequently answer the KAI instrument with ten or more 3s (the median
point on the response scale) often are unwilling to commit to honestly disclosing
cognitive style and thus, according to Condition 3 of scoring the KAI instrument, results
in a score that is unreliable and must be dismissed (Kirton, 2008). Two completed
surveys had ten or more 3s, including 1 or 2 omitted responses, in the KAI section of the
questionnaire and were therefore dismissed.
The data from the remaining 140 questionnaires were entered into an SPSS
version 15.0 data set document. Male participants were coded using the value “1” and
female participants were coded using the value “2”. Participants’ ethnicities were coded
as European American = 1, African American = 2, Hispanic American = 3, Asian
American = 4, and Native American = 5.
Assumptions and Pretest Analyses
Prior to accepting the results of a multiple regression analysis a number of
assumptions regarding the data collected should be checked. These considerations
include the following: outliers, multicollinearity, normality of residuals, homoscedasticity
of residuals, and reliability analyses.
Outliers
To determine whether the remaining data included any outliers, a regression
analysis was conducted that involved all variables. The procedure produced the
maximum value of 40.592 for the Mahalanobis distance. This distance is evaluated
against chi-square at a p value of .001 for a degree of freedom equal to the number of
variables. In this case there were a total of nine variables and the critical value calculated
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was 27.88. Therefore, any case with a value greater than 27.88 was considered to be a
multivariate outlier. Three cases in data rows 69, 98, and 105 fell into this category and
thus reduced the sample size to 137.
Multicollinearity, Normality, Linearity, and Homoscedasticity
An analysis of the relationships among the independent variables is required when
using multiple regression modeling so correlations were checked between the eight
independent variables. Pearson correlations between the IVs ranged from r(137) = .005,
p = .479 to r(137) = .650, p < .001. Table 1 contains additional relevant correlation
statistics.
Table 1
Correlations: IVs by IVs Variables BMI A SN PBC I SO E R BMI
1.000
A
-.021 1.000
SN
-.018 .448* 1.000
PBC
-.240* .154* .315* 1.000
I
-.169* .404* .323* .650* 1.000
SO
-.020 -.027 .031 .334* .221* 1.000
E
-.023 -.010 .005 .139 .167* .313* 1.000
R -.052 -.011 .029 .201* .081 .366* .502* 1.000 Note. N= 137. BMI = Body Mass Index; A = Attitude; SN = Subjective Norm; PBC = Perceived Behavioral Control; I = Intentions; SO = Sufficiency of Originality; E = Efficiency; and R = Rule/Group Conformity. * p <.05.
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The Variance Inflation Factor (VIF) values were all under 5, making the possibility of
collinearity between independent variables unlikely. Further, a review of Scatterplots of
the Standardized Residuals by the Regression Standardized Predicted Values revealed
randomly scattered residuals around the horizontal line which demonstrated relatively
homogenous distributions for all variables.
Sample Characteristics
The final sample contained 137 participants; 55.5% were female and 44.5% were
male. The ages ranged from 18 years old to 64 years old (median = 39). The majority of
respondents were European American (85.4%) followed by Hispanic Americans (8.0%)
and included African Americans (2.9%), Asian Americans (2.9%), and a Native
American (0.7%). Table 2 provides additional sample characteristics.
Table 2
Demographic Characteristics of Study Sample (N = 137) Characteristic N Percent Gender Male 61 44.5Female 76 55.5Total
137 100.0
Ethnicity European American 117 85.4African American 4 2.9Hispanic American 11 8.0Asian American 4 2.9Native American 1 .7Total 137 100.0
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Data Analyses
Reliability Analysis
Cronbach’s alpha coefficient for internal consistency reliability should be
calculated for any scales or subscales one may be using (Gliem & Gliem, 2003).
Therefore, Cronbach’s alpha coefficient for internal consistency and reliability for all
subscales in this study were calculated using SPSS.
The EDE-Q6 contains four subscales. The dietary restraint subscale consisted of 5
items (α = .775), the eating concern subscale consisted of 5 items (α = .867), the shape
concern subscale consisted of 8 items (α = .919), and the weight concern subscale
consisted of 5 items (α =.790). The Cronbach’s alphas for these subscales are within
acceptable range.
The TPB questionnaire contains four subscales. The attitude subscale consisted of
3 items (α = .764), the subjective norm subscale consisted of 3 items (α = .842), the
perceived behavioral control subscale consisted of 3 items (α = .829), and the intention
subscale consisted of 3 items (α =.765). The Cronbach’s alphas for these subscales are
within acceptable range.
Lastly, the KAI inventory contains three subscales. The rule/group conformity
subscale consisted of 12 items (α = .732), the efficiency subscale consisted of 7 items (α
= .824), and the sufficiency of originality subscale consisted of 13 items (α =.717). The
Cronbach’s alphas for these subscales are within acceptable range.
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Descriptive Statistics
Descriptive statistics for the variables are depicted in Table 3. The total sample of
the predictor variables reported a mean BMI score of 26.02 (SD = 5.17) with the potential
range of scores of less than 18.5 being underweight and greater than 30 being obese ,
mean Attitude score of 16.84 (SD = 3.52) with the range of scores being 3 to 21, mean
Subjective Norm score of 15.62 (SD = 3.97) with the range of scores being 3 to 21, mean
Perceived Behavioral Control score of 15.09 (SD = 4.17) with the range of scores being 3
to 21, mean Intention score of 13.48 (SD = 3.79) with the range of scores being 3 to 21,
mean Sufficiency of Originality score of 42.70 (SD = 7.02) with the range of scores being
13 through 65, mean Efficiency score of 20.48 (SD = 6.13) with the range of scores being
7 through 35, and mean Rule/Group score of 35.43 (SD = 7.23) with the range of scores
being 12 through 60.
The total sample of the criterion variables reported mean Dietary Restraint score
of 1.55 (SD = 1.42) with the range of scores being 0 through 6, mean Eating Concern
score of .741 (SD = 1.07) with the range of scores being 0 through 6, mean Shape
Concern score of 2.23 (SD = 1.71) with the range of scores being 0 through 6, and mean
Weight Concern score of 1.74 (SD = 1.45) with the range of scores being 0 through 6.
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Table 3
Descriptive Statistics for Variables N Minimum Maximum Mean SD Body Mass Index 137 17.37 50.07 26.02 5.178Restraint 137 0.00 5.00 1.55 1.422Eating Concern 137 0.00 4.80 .74 1.079Shape Concern 137 0.00 6.00 2.23 1.711Weight Concern 137 0.00 5.40 1.74 1.452Attitude 137 3.00 21.00 16.84 3.524Subjective Norms 137 3.00 21.00 15.62 3.971Perceived Behavioral Control 137 4.00 21.00 15.09 4.173Intention 137 3.00 21.00 13.48 3.793Sufficiency of Originality 137 25.00 58.00 42.70 7.023Efficiency 137 8.00 33.00 20.48 6.135Rule/Group Conformity 137 17.00 52.00 35.43 7.238
The EDE-Q6 contains the four criterion variables, described prior in the study,
and also calculates an overall mean global score by adding the subscore totals together
and then dividing by four. In this research, the total sample reported a mean EDE-Q6
global score of 1.56 (N = 137, SD = 1.21). Table 4 presents descriptive data and
percentile ranks for the EDE-Q6 global score and four subscale scores for this research
sample.
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Table 4
EDE-Q6 Percentile Ranks for EDE-Q6 Global and Subscale Scores (N = 137) GS R EC SC WC
Percentile rank 5 0.02 0.00 0.00 0.00 0.00 10 0.12 0.00 0.00 0.12 0.00 15 0.29 0.00 0.00 0.25 0.20 20 0.43 0.00 0.00 0.50 0.40 25 0.58 0.20 0.20 0.75 0.40 30 0.71 0.40 0.20 0.87 0.60 35 0.82 0.60 0.20 1.25 0.80 40 0.99 0.80 0.20 1.62 1.04 45 1.30 1.00 0.20 1.87 1.22 50 1.31 1.20 0.40 2.00 1.60 55 1.50 1.40 0.40 2.11 1.60 60 1.69 1.96 0.40 2.25 1.80 65 1.86 2.00 0.60 2.87 2.00 70 2.03 2.40 0.60 3.12 2.40 75 2.25 2.40 0.80 3.50 2.80 80 2.57 3.00 1.08 3.75 3.20 85 3.08 3.32 1.40 4.50 3.40 90 3.38 3.64 1.84 5.15 4.00 95 4.06 4.20 3.80 5.37 4.60
Note. GS = mean global score, R = dietary restraint subscale, EC = eating concern subscale, SC = shape concern subscale, WC = weight concern subscale.
Hierarchical Multiple Regression Analyses
A hierarchical multiple regression analysis was performed on each of the four
criterion variables to test the hypothesis. To determine the relative relationship between
the predictor variables and eating behavior, variables were entered using a hierarchical
block approach. Body mass index was entered first to account for as much variance as
possible in the criterion variable. Subsequently, the remaining predictor variables were
entered to account for any remaining variance.
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The first component of eating behavior this study examined was dietary restraint.
In the first regression model body mass index was not found to be statistically significant,
R2 = .015, F(1, 135) = 2.007, p = .159. When perceived behavioral control, attitude,
subjective norms, intentions, and sufficiency of originality, efficiency, and rule/group
conformity were entered into the equation, the change in variance accounted for a
significant proportion of the dietary restraint variance after controlling for the effects of
body mass index, R2 change = .148, F(7, 128) = 3.102, p = .003. Table 5 provides the
regression summary.
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Table 5
Summary of Hierarchical Regression Analysis for Variables Predicting Dietary Restraint (N = 137)
Variable
B
SEB
β
Sig.
Step 1
Body Mass Index
.033 .023 .121 .159
Step 2
Body Mass Index
.021 .023 .077 .361
Attitude
.118 .040 .292 .003
Subjective Norm
.051 .034 .142 .134
Perceived Behavioral Control
-.069 .040 -.201 .089
Intention
-.014 .044 -.038 .750
Sufficiency of Originality
.010 .019 .048 .608
Efficiency
-.023 .022 -.100 .301
Rule/Group Conformity
.012 .019 .062 .528
Note. R2 = .015 for Step 1; ΔR2 = .148 for Step 2 ( p < .05). In the second model it was found that attitude towards overeating significantly predicted
dietary restraint (β = .292, p =. 003).
The second component of eating behavior this study examined was eating
concern. In the first regression model body mass index accounted for 5.2% of the eating
concern variability, R2 = .052, F(1, 135) = 7.340, p = .008. When perceived
behavioral control, attitude, subjective norms, intentions, and sufficiency of originality,
efficiency, and rule/group conformity were entered into the equation, the change in
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variance accounted for a significant proportion of the eating concern variance after
controlling for the effects of body mass index, R2 change = .253, F(7, 128) = 6.993, p
< .001. Table 6 provides the regression summary.
Table 6
Summary of Hierarchical Regression Analysis for Variables Predicting Eating Concern (N = 137)
Variable
B
SEB
β
Sig.
Step 1
Body Mass Index
.047 .017 .227 .008
Step 2
Body Mass Index
.022 .016 .106 .168
Attitude
.060 .027 .197 .029
Subjective Norm
.025 .023 .091 .292
Perceived Behavioral Control
-.083 .028 -.319 .003
Intention
-.084 .031 -.296 .007
Sufficiency of Originality
.003 .013 .018 .835
Efficiency
.019 .015 .107 .225
Rule/Group Conformity
-.010 .013 -.064 .473
Note. R2 = .052 for Step 1; ΔR2 = .253 for Step 2 ( p < .05).
In the second model it was found that attitude towards overeating significantly predicted
eating concern (β = .197, p =. 029), as did perceived behavioral control (β = -.319, p =
.003), and intention to manage eating behavior (β = -.296, p = .007).
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The third component of eating behavior this study examined was shape concern.
In the first regression model body mass index accounted for 8.8% of the shape concern
variability, R2 = .088, F(1, 135) = 13.023, p < .001. When perceived behavioral
control, attitude, subjective norms, intentions, and sufficiency of originality, efficiency,
and rule/group conformity were entered into the equation, the change in variance
accounted for a significant proportion of the shape concern variance after controlling for
the effects of body mass index, R2 change = .315, F(7, 128) = 10.882, p < .001. Table
7 provides the regression summary.
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Table 7
Summary of Hierarchical Regression Analysis for Variables Predicting Shape Concern (N = 137)
Variable
B
SEB
β
Sig.
Step 1
Body Mass Index
.098 .027 .297 .000
Step 2
Body Mass Index
.060 .023 .183 .011
Attitude
.169 .040 .349 .000
Subjective Norm
.039 .034 .090 .261
Perceived Behavioral Control
-.129 .041 -.315 .002
Intention
-.124 .045 -.276 .007
Sufficiency of Originality
-.010 .019 -.041 .597
Efficiency
-.022 .023 -.077 .343
Rule/Group Conformity
.011 .020 .045 .586
Note. R2 = .088 for Step 1; ΔR2 = . 315 for Step 2 ( p < .05).
In the second model it was found that body mass index significantly predicted shape
concern (β = .183, p = .011), as did attitude towards overeating (β = .349, p < .001),
perceived behavioral control (β = -.315, p = .002), and intention to manage eating
behavior (β = -.276, p = .007).
The fourth and final component of eating behavior this study examined was
weight concern. In the first regression model body mass index accounted for 12.3% of the
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weight concern variability, R2 = .123, F(1, 135) = 18.866, p < .001. When perceived
behavioral control, attitude, subjective norms, intentions, and sufficiency of originality,
efficiency, and rule/group conformity were entered into the equation, the change in
variance accounted for a significant proportion of the weight concern variance after
controlling for the effects of body mass index, R2 change = .366, F(7, 128) = 13.095, p
< .001. Table 8 provides the regression summary.
Table 8
Summary of Hierarchical Regression Analysis for Variables Predicting Weight Concern (N = 137)
Variable
B
SEB
β
Sig.
Step 1
Body Mass Index
.098 .023 .350 .000
Step 2
Body Mass Index
.062 .018 .222 .001
Attitude
.155 .032 .377 .000
Subjective Norm
.010 .034 .090 .261
Perceived Behavioral Control
-.105 .032 -.302 .001
Intention
-.135 .035 -.353 .000
Sufficiency of Originality
-.005 .015 .022 .761
Efficiency
-.019 .018 -.078 .299
Rule/Group Conformity
-.011 .015 -.055 .473
Note. R2 = .123 for Step 1; ΔR2 = . 366 for Step 2 ( p < .05).
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In the second model it was found that body mass index significantly predicted weight
concern (β = .222, p = .001), as did attitude towards overeating (β = .377, p < .001),
perceived behavioral control (β = -.302, p = .001), and intention to manage eating
behavior (β = -.353, p < .001).
Primary Research Question and Hypotheses Evaluation
This research addressed the following primary question: Are each of four
components of eating behavior affected by the variables of BMI, perceived behavioral
control, attitude towards overeating, subjective norms, intention towards eating behavior,
sufficiency of originality, efficiency, and rule/group conformity. Based on the
presumption that eating behaviors are affected by cognitive style and motivation, four
hypotheses were formulated and their corresponding null forms are presented below.
Null Hypothesis (Ho):
Null 1: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and dietary
restraint as measured by EDE-Q6 (R = 0).
The results of the hierarchical regression showed that the combined effects of the
eight predictor variables did significantly predict dietary restraint and therefore the null
hypothesis is rejected.
Null 2: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
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subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and eating concern
as measured by EDE-Q6 (R = 0).
The results of the hierarchical regression showed that the combined effects of the
eight predictor variables did significantly predict eating concern and therefore the null
hypothesis is rejected.
Null 3: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and shape concern
as measured by EDE-Q6 (R = 0).
The results of the hierarchical regression showed that the combined effects of the
eight predictor variables did significantly predict shape concern and therefore the null
hypothesis is rejected.
Null 4: In a hierarchical multiple regression there will be no significant
relationship between the predictor variables (perceived behavioral control, attitude,
subjective norms, and intentions as measured by TPB, and sufficiency of originality,
efficiency, and rule/group conformity as measured by KAI, and BMI) and weight concern
as measured by EDE-Q6 (R = 0).
The results of the hierarchical regression showed that the combined effects of the
eight predictor variables did significantly predict weight concern and therefore the null
hypothesis is rejected.
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Therefore, to address the research question, attitude towards overeating affects the
eating components of dietary restraint, eating concern, shape concern and weight
concern; perceived behavioral control affects the eating components of eating concern,
shape concern and weight concern; intention towards eating behavior affects the eating
components of eating concern, shape concern, and weight concern; and, BMI affects the
eating components of shape concern and weight concern.
Additional Findings and Observations
Additional findings and observations of the data related to the results that should
be discussed is the overall results of the EDE-Q6 as they relate to the clinically
significant range of eating disorders. A clinically significant eating disorder score or
negative eating behavior pattern can be determined by a total score that is greater than or
equal to 4.0 on the dietary restraint subscale, eating concern subscale, shape concern
subscale, weight concern subscale, or the mean global score (Fairburn & Cooper, 1993;
Fairburn, Cooper, Doll, & Davies, 2005; Luce, Crowther, & Pole, 2008).
Using the cut-off value of ≥ 4.0 for clinical significance, 8% of the sample (n =
11) scored in clinical significance range on dietary restraint, 3% of the sample (n = 5)
scored in clinical significance range on eating concern, 17% of the sample (n = 24)
scored in clinical significance range on shape concern, 11% of the sample (n = 16) scored
in clinical significance range on weight concern, and 5% of the sample (n = 7) scored in
clinical significance range on the global scale. The total sample that reported one or more
subscale scores in clinical significance range was 20% (n = 28).
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Observed Consistencies and Inconsistencies
Several aspects of the findings relate to observed consistencies and
inconsistencies among the individual participant survey responses. One such observation
was noted with male respondents. If an individual reports a score of zero on any of the
EDE-Q6 subscales it is interpreted as an absence of the eating behavior feature, and a
score of 1 is interpreted as a feature almost, but not quite, absent (Fairburn, 2008). On
reviewing the raw data, it was noted that 13 male participants reported individual answers
on the EDE-Q6 of all zeros with a few reported scores of 1. Further, five male
participants had overall EDE-Q6 global mean scores of 0.00. Alternatively, the top 20
highest EDE-Q6 global mean scores were reported by female participants. Only one
female participant in the sample population reported individual answers on all the EDE-
Q6 subscales of all zeros. All the remaining female participants in the sample reported at
least one individual score as a 2 or higher. This could be explained in that female
participants in this sample have more negative eating behaviors or an increased
awareness regarding their eating behaviors, whereas male participants have less negative
eating behaviors or less awareness regarding their eating behaviors. Or this could be
alternatively interpreted that women are more comfortable disclosing any issues or
concerns they may have regarding eating behaviors, whereas men are less likely to
disclose any eating concerns or behaviors. Research using the Eating Disorders
Examination has noted that women are more likely than men to report negative eating
behaviors associated with emotional responses (Tanofsky, Wilfley, Spurrell, Welch, &
Brownell, 1998). However, there are relatively few studies in this research area and
83
therefore these aspects of this research must be considered merely observations and not
findings.
Summary
This chapter described data screening, assumptions and pretest analyses, sample
characteristics, and reported the demographic statistics for the survey participants.
Additionally a description of the data analyses and the results of the hierarchical
regression analyses were presented. These results were used to answer the research
question through the study hypotheses that attitude towards overeating affects the eating
components of dietary restraint, eating concern, shape concern and weight concern;
perceived behavioral control affects the eating components of eating concern, shape
concern and weight concern; intention towards eating behavior affects the eating
components of eating concern, shape concern, and weight concern; and, BMI affects the
eating components of shape concern and weight concern.
Lastly, additional findings and observations from the research were addressed.
Chapter 5 summarizes the study, discusses the conclusions and implications, addresses
the positive social change implications of the study, and presents recommendations for
future action and further study.
CHAPTER 5: DISCUSSION
Introduction and Overview of Study
This chapter begins with a brief overview of why and how the study was
conducted. It further provides an interpretation of the findings and how they relate to the
theoretical framework and implications for positive social change. Lastly, the chapter
discusses limitations of the present research and provides recommendations for action
and further research.
The CDC estimates that in 2009 health care costs associated with obesity had
risen to over $147 billion per year based on research comparing normal weight
individuals and obese individuals’ inpatient, non-inpatient, and prescription drug
spending (Finkelstein, Trogdon, Cohen, & Dietz, 2009). More Americans than ever are
considered to be obese and the health problems associated with this are evident (Baskin,
Ard, Franklin, & Allison, 2005). Although there are medical, genetic, and physiological
reasons for obesity, psychologists are interested in investigating the cognitive factors
associated with eating behaviors which contribute to this epidemic. However, there has
not been consistent research to assess how a person incorporates problem solving
decisions and planned behavior components with eating behaviors. While many
researchers have looked at how people become obese, little is known about what might
motivate a person’s eating behaviors. The purpose of this study was to assess the
combined effects of individual problem solving styles (sufficiency of originality,
efficiency, and rule/group conformity) and planned behavior (attitudes, subjective norms,
behavioral intentions, perceived behavioral control), after first controlling for body mass,
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on eating behaviors. The specific research question was if eating behavior is affected by
body mass, perceived behavioral control, attitude, subjective norms, intention, sufficiency
of originality, efficiency, and rule/group conformity.
In order to fulfill the objective of answering this question, a final sample of 137
participants from Colorado, ranging in ages 18-65, fully completed the voluntary surveys.
The surveys contained three instruments which were the EDE-Q6, the TPB
Questionnaire, the KAI Inventory, as well as a Background Data Questionnaire and BMI
questionnaire. Four hypothesis were investigated using hierarchical multiple regression
analyses. The null hypotheses were that there would be no significant relationships
between the predictor variables (perceived behavioral control, attitude, subjective norms,
and intentions as measured by TPB, and sufficiency of originality, efficiency, and
rule/group conformity as measured by KAI, and BMI) and the criterion variables of
dietary restraint, eating concern, shape concern, and weight concern, as measured by
EDE-Q6.
Interpretation of Findings
As described in chapter 4, all four hierarchical multiple regressions were
statistically significant and the null hypotheses were rejected. Therefore, it was found that
attitude towards overeating affects the eating components of dietary restraint, eating
concern, shape concern and weight concern; perceived behavioral control affects the
eating components of eating concern, shape concern and weight concern; intention
towards eating behavior affects the eating components of eating concern, shape concern,
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and weight concern; and, BMI affects the eating components of shape concern and
weight concern.
Interpretation of Hierarchical Regression Analyses
The first hierarchical regression was performed on the eating behavioral
component of dietary restraint. Dietary restraint is a process that addresses a person’s
restriction of calories with the goal of losing weight, or maintaining a current weight
(Fairburn & Brownell, 2002). In this analysis, all the combined predictor variables
accounted for a significant proportion of the dietary restraint variance after first
controlling for the effects of body mass index. The predictor variable of attitude toward
overeating significantly predicted dietary restraint. An increase in positive attitude
towards eating healthy and not overeating was associated with an increase in concern
with dietary restraint. Dietary restraint is associated with a person having rigid food rules,
eating specific food items, and dietary imperatives with an overall goal of restricting
weight gain but this behavior does not necessarily indicate a reduction in binge eating or
weight gain (White, Masheb, & Grilo, 2009). Having a negative attitude towards eating
healthy does imply that a person places less restriction on their dietary intake which could
contribute to negative eating behaviors. For example, if a person does overeat, an
increase in dietary restraint can contribute to an increase in binge eating behaviors (Lowe,
Thomas, Safer, & Butryn, 2007), whereas a positive attitude towards not overeating
results in a higher awareness of personal dietary restraint.
The second hierarchical regression was performed on the eating behavioral
component of eating concern. Eating concern is a characteristic in which a person is
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preoccupied with the process of eating and food consumption (Fairburn & Brownell,
2002). In this analysis, all the combined predictor variables accounted for a significant
proportion of the eating concern variance after first controlling for the effects of body
mass index. The predictor variables of attitude towards overeating, perceived behavioral
control, and intention to manage eating behaviors significantly predicted eating concern.
An increase in positive attitude towards not overeating was associated with an increase in
eating concern. An increase in perceived behavioral control of eating behavior was
associated with a decrease in eating concern, and an increase in intention to manage
eating behavior was associated with a decrease in eating concern. Obese individuals or
those who report binge eating behaviors traditionally have higher levels of eating concern
(Darby, Hay, Mond, Rodgers, & Owen, 2007). This research suggests that if an
individual does not feel they have control over eating, and does not have the intention to
eat healthy, they are more likely to display negative eating behaviors by not
demonstrating concern over what foods are consumed.
The third hierarchical regression was performed on the eating behavior
component of shape concern. Shape concern is a characteristic that reflects a person’s
preoccupation with his or her bodily shape, fear of weight gain that will impact shape, or
feelings of fatness (Fairburn & Brownell, 2002). In this analysis, all the combined
predictor variables accounted for a significant proportion of the shape concern variance
after first controlling for the effects of body mass index. The predictor variables of body
mass index, attitude towards overeating, perceived behavioral control, and intention to
manage eating behavior significantly predicted shape concern. An increase in a person’s
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body mass index was associated with an increase in concern with shape. An increase in
positive attitude towards not overeating was associated with an increase in concern with
shape. An increase in perceived behavioral control of eating behavior was associated with
a decrease in shape concern, and, an increase in intention to manage eating behavior was
associated with a decrease in shape concern. Individuals with binge eating disorder and
those who are considered to be morbidly obese have reported higher than average
concerns with shape while reporting a sense of loss of control, or a lack of self-efficacy,
over their eating behaviors and intentions to manage satiety control (Hsu et al., 2002)
which is consistent with these research findings.
The fourth hierarchical regression was performed on the eating behavior
component of weight concern. Weight concern is a characteristic in which a person is
preoccupied with the importance of his or her weight and desires to lose weight (Fairburn
& Brownell, 2002). In this analysis, all the combined predictor variables accounted for a
significant proportion of the weight concern variance after first controlling for the effects
of body mass index. Again, the predictor variables of body mass index, attitude towards
overeating, perceived behavioral control, and intention to manage eating behavior
significantly predicted weight concern. An increase in a person’s body mass index was
associated with an increase in concern over weight. An increase in positive attitude
towards not overeating was associated with an increase in concern with weight. An
increase in perceived behavioral control of eating behavior was associated with a
decrease concern with weight, and an increase in intention manage eating behavior was
associated with a decrease in concern with weight. Higher levels of weight concern have
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been noted in a variety of studies with binge eating disordered participants who report a
sense of loss of control with the ability to manage weight; this behavior is also consistent
with the research regarding weight concern (Hsu et al., 2002).
Theoretical Considerations
The cognitive style subscales of sufficiency of originality, efficiency, and
rule/group conformity were not independently detected as significant predictors in this
study. Perhaps the sample size precluded finding meaningful comparisons of weak verses
strong predictors. Alternatively, as Kirton (2008) suggests, individual cognitive style may
not be a determinant with eating behaviors as individuals often use coping skills to
manage decisions that are uncomfortable by nature. This theory will be addressed in
further detail in the limitations and recommendations for future study section.
In this research study, an individual’s body mass index was significant in
predicting shape and weight concern. A higher body mass index produced a higher shape
and weight concern. The research results in this study demonstrate consistency with
additional research regarding body mass index and shape and weight concerns. Watkins,
Christie, and Chally (2008) found that BMI was significantly correlated with negative
body image and, specifically, significant differences were found with weight and shape
concern. This is consistent with this research study’s findings.
The overall results of this research study are consistent with the conceptual and
theoretical framework of the Theory of Planned Behavior. The TPB makes an attempt to
understand how individuals internalize behaviors and then act on them (Christian &
Armitage, 2002) and the TPB is considered to be the leading model in the field of health
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psychology for investigating health behavioral relationships (Armitage & Conner, 2000).
Using this model, it is possible to predict behaviors and ultimately design intervention
strategies that can either reinforce positive behaviors or modify existing behaviors to
enhance a person’s overall health.
The predictor variable of attitude toward overeating plays an important role as
individuals who positively evaluate eating healthy, or avoid overeating behaviors, were
more likely to demonstrate concern regarding current eating behavior. Therefore, a
person is more likely to perform positive health and eating behaviors when there is an
internal belief system that affects the behavioral decision making process to personally
achieve the desired outcome, or positive eating behavior (Bandura, 1997; Fishbein &
Ajzen, 1975). Attitudes toward overeating consistently had a positive correlation with all
of the components of the eating behaviors as measured by the EDE-Q6, which implies
that individuals who had a more positive belief about the consequences of overeating
demonstrated higher levels of concern when they reflected on their past eating behavior.
Conversely, individuals who had an attitude reflecting an indifferent or lack of concern
about the consequences of negative eating behaviors also demonstrated a lack of concern
about their personal eating behaviors. Individuals with an attitude that overeating and
binge eating may have negative consequences demonstrated there may be a connection
between attitude and a personal awareness and concern of body shape, body weight,
awareness of the need to monitor dietary restraint, and a concern of the impact of
unrestrained eating. This connection implies that a positive attitude is associated with an
awareness of overall eating behavior in a healthful way.
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Intentions are also important factors and are often tied with attitudes when
investigating a person’s motivation to perform a particular behavior by addressing how
much effort and time a person is willing to commit to that behavior (Rivas & Sheeran,
2004). In this research intention was negatively correlated with eating concern behaviors
and this suggests that the greater the person’s internal motivation to avoid binge eating,
the less concern he or she had with this eating behavior, perhaps because the person felt a
greater sense of self-efficacy with eating behaviors. Additionally, an increase in intention
to manage eating behavior was associated with a decrease in shape and weight concern,
which may be related to a person’s ability and intentions to set and operationalize goals
for specific behavioral performance regarding both shape and weight (Gollwitzer &
Schall, 1998). These findings suggest that such individuals may have fewer concerns with
these eating behavioral characteristics (Bagozzi, 1992).
Perceived behavioral control is often the most significant predictor in a variety of
research studies described prior (Conner, Norman, & Bell, 2002; Conner et al., 2003;
Gardner & Hausenblas, 2001; Rivas & Sheeran, 2004). In this study, perceived
behavioral control was negatively correlated with eating behaviors. Perceived behavioral
control is a measure of the power that an individual believes he or she has over a
behavior. This finding implies that the greater internal control a person has over his or her
eating behavior, the less concern the individual has with how he or she eats and his or her
weight or shape.
Although the subjective norm subscale was not independently detected as a
significant predictor in this study, this finding is consistent with multiple studies of the
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TPB (Armitage & Conner, 2001; Ajzen, 1988; Christian & Armitage, 2002; & Conner,
Norman, & Bell, 2002). This finding implies that social groups and peer groups alone
may not provide enough support or influence for an individual to make effective eating
behavior decisions. Additionally, research using the Eating Disorder Examination
Questionnaire has noted that women with scores in the clinically significant range did not
have any difference in socioeconomic status, cultural affiliation, education level, or
satisfaction levels with their social and family groups compared with those who had
scores that were not in the clinically significant range (Soh et al., 2007). Personal
motivation to perform a behavior and the ability to have a positive attitude, or an ability
to gain a positive attitude towards a behavior, has a greater contribution to actually
performing a specific health behavior (Conner & Norman, 1998).
In conclusion, the theory of planned behavior suggests that individuals are likely
to perform a particular health action if they believe the behavior will lead to outcomes
that they value. This study demonstrated consistencies with current conceptual and
theoretical frameworks in the context of how it applies to eating behaviors and addressed
and answered the research question, rejecting the null hypotheses, that attitude towards
overeating affects the eating components of dietary restraint, eating concern, shape
concern and weight concern; perceived behavioral control affects the eating components
of eating concern, shape concern and weight concern; intention towards eating behavior
affects the eating components of eating concern, shape concern, and weight concern; and,
BMI affects the eating components of shape concern and weight concern. This
understanding, when applied with the theories described prior, is useful to enable
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researchers, health professionals, and individuals to develop interventions and cognitive
strategies to alter underlying unhealthy behaviors as they relate to overeating and binge
eating (Armitage & Conner, 2001).
Implications for Positive Social Change
This research study, as described in chapter 1, was motivated by several
opportunities for positive social change to minimize the negative influences and
contributors to obesity. By understanding the relationships of the cognitive factors
associated with eating behaviors described in chapter 4, this research has the potential to
contribute to tangible improvements in the psychological health and well being for
individuals and families suffering with obesity, contribute to the development of obesity
related programs in health institutions, increase health promotion in public health
organizations, and potentially decrease health problems by reducing secondary illnesses
related to binge eating and obesity.
The implications for positive social change specifically include having a better
understanding that attitudes, perceived behavioral control, body mass index, and
intentions can predict the certain behavioral features of eating habits and may have the
potential to minimize the consequences of negative eating behaviors, such as chronic
diseases, that are associated with the growing population of overweight and obese
individuals in society.
In order facilitate positive social change with the obesity epidemic it is critical to
understand the reasons why the rate of individuals who are overweight or obese is
steadily rising. The findings from this research are noteworthy in that the sample
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population for this research study came from the greater Boulder, Colorado area and this
region is noted for being one of the leanest in the nation. However, the obesity rate in
Colorado is growing faster than the U.S. average obesity growth rate, respectively rising
89% from 1995 to 2008 compared with 67% nationwide (Colorado Department of Public
Health and Environment, 2010). This discouraging news may be better understood by
noting the trend of misperception of what is considered a healthy weight and body mass
index status. For example, in this research study, the mean body mass index score of the
population studied was 26, which is in the overweight category. However, the mean
scores for all the behavioral features of eating habits that contribute to negative eating
behavior were consistently low, implying that the participants did not perceive that there
was a need or reason for personal dietary restraint, concern with weight or shape, or
concern specifically with personal eating behaviors. This finding is consistent with
research that demonstrates that there are increasing numbers of overweight individuals
who fail to recognize that their weight or eating behaviors may be a cause for concern
(Johnson, Cooke, Croker, & Wardle, 2008).
Perhaps this can be attributed to the growing misperception of what classifies an
individual as being overweight or obese, and therefore at risk for multiple chronic
diseases such as cardiovascular diseases, type 2 diabetes, stroke, and hypertension. Miller
et al. (2008) noted that individuals who consider themselves to be active or self-reporting
themselves as normal weight, although their BMI classifies them as being either
overweight or obese, misperceive their risk for chronic diseases and strokes.
Additionally, those who fall into the overweight status tend to perceive themselves as
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being in the normal range or in a healthy weight status and are unlikely to change eating
behaviors that may contribute to additional weight gain (Johnson-Taylor, Fisher,
Hubbard, Starke-Reed, & Eggers, 2008). An increase in the societal levels of being
overweight, which currently constitutes over 66% of the population of the United States
and encompasses multiple ages and ethnic groups (Kolodinsky & Reynolds, 2009;
Masheb & Grilo, 2001; Wang, Liang, & Chen, 2009), leads to an increased acceptance of
higher weight and body fat due to social comparison (Francis, Ventura, Marini, & Birch,
2007; Johnson et al., 2008). If an individual who is overweight or obese has low eating,
shape, and weight concern that individual may be unlikely to see overall personal eating
habits as a concern which could eventually lead to health problems and the individual
may not be motivated to make a behavioral change which could prevent such obesity and
overweight related illnesses.
This current research study contributes to positive social change as it could assist
with the reduction in the proportion of individuals in higher risk body mass indices (BMI
greater than 25) who could significantly benefit from moderate weight loss that will
reduce their risk for weight related diseases (Miller et al., 2008) by raising awareness of
the eating habits of overweight individuals. However, overweight individuals are unlikely
to make changes if they do not feel they are at risk or are not motivated to make a
behavioral change (Caperchione, Duncan, Mummery, Steele, & Schofield, 2008).
Therefore, understanding a person’s attitude, perceived behavioral control, and intentions
towards eating behavior may better assist with the facilitation of the changes necessary to
reduce an individual’s risk for certain negative eating habits. And, given the current
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focus on strategies for reducing overweight and obesity related disorders in this country,
an understanding of psychologically-related barriers to positive eating behavior may aid
in the development of future interventions and health related social marketing programs
that specifically target awareness for the overweight and obese populations.
Implications for Health Institutions
Health care systems and the manner in which medical care is provided are
undergoing significant transformations. Medical treatment traditionally has been
provided using evidence based diagnoses once symptoms present themselves; however,
understanding how the role individual human behavior contributes to healthcare is often
not incorporated into the overall health care system (Oldham, 2009). This research
demonstrates that although eating behaviors are complex, understanding the role that
attitudes, intentions, and self-efficacy/ perceived behavioral control have with individual
behaviors and health actions could contributes to a broader understanding that there are
multiple variables that influence eating behavior above and beyond an individual’s body
weight. This concept is valuable to both health psychologists and public health
department professionals as it may introduce the understanding that obesity and binge
eating behaviors that contribute to this epidemic are not limited to physiological
components alone. Discussions regarding these concepts may offer many opportunities
for health care systems and policy makers to collaborate with psychologists to uncover
individuals’ motivations and cognitive styles when developing holistic health care
programs with the goal to reduce the obesity epidemic. If a patient with negative eating
behaviors approaches a physician or psychologist for a radical procedure, such as gastric
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bypass surgery, or is looking to undertake a significant dietary change program, it may be
beneficial for the medical or psychological professional to consider the patients’ existing
eating behaviors and health decision making styles to determine the likelihood of success
a person has for changing eating behaviors before a behavioral change strategy is
recommended. This positive social change could be accomplished by investigating the
motivational variables described in this study.
Additionally, there are psychological interventions that can benefit from
understanding the relationships between attitudes toward eating, perceived behavioral
control, and intentions to manage eating behavior. For example, many psychologists
assess overeating and binge eating behaviors by addressing the specific types of food and
amounts of food consumed, eating patterns, weight history, and body image satisfaction
or dissatisfaction (Mitchell & Peterson, 2008) or they suggest psychoeducational
programs that focus on body weight regulation, coping with urges to binge or overeat,
education programs regarding nutrition, or psychological factors such as family dynamics
that may contribute to underlying causes of overeating (Fairburn, 1995). Although these
techniques have had success, incorporating the role that attitudes, intentions, and self-
efficacy/ perceived behavioral control have with individual behaviors could be beneficial
with existing dietary behavioral therapeutic interventions.
Implications for Health Organizations
The results of this research has implications for positive social change for the
health conscious community and health care organizations by contributing to the
development of behavioral modification programs to reduce weight related health
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disorders. This can be achieved by incorporating an understanding of how cognitive
health behaviors and decision-making processes are related to eating behaviors. Many
formal weight loss programs measure the success of their participants by looking at
metrics such as overall weight loss; however, measuring factors that contribute to
individual motivation to change eating behaviors often is not addressed (Adams, 2008).
Additionally, by increasing the awareness of the motivational factors that contribute to
eating behaviors, health promotion, which is the process of enabling people to have an
increased level of control over their individual health and health outcomes (World Health
Organization, 2010), can be improved and can allow for a greater success rate in many
formal and informal health conscious organizations and communities. The outcomes of
this research demonstrate that although the body mass index of an individual contributes
to increased concerns about personal shape and weight, psychological components such
as attitude towards eating, perceived behavioral control towards eating behavior, and
intention to manage eating behaviors are substantial and should not be overlooked.
Incorporating a shortened version of the Theory of Planned Behavior questionnaire into
many existing eating behavioral modification programs could provide insight into the
existing motivation a person has to perform a certain behavior, such as changing negative
eating patterns (Armitage & Christian, 2004).
Implications for Individuals and Society
Individuals who suffer from non-clinical eating disorders such as binge eating,
overeating, and obesity often experience negative stereotypes such as not having self-
control, being lazy or unwilling to change, having a lack of education, or having general
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incompetence in comparison to non obese individuals (Klaczynski, Goold, & Mudry,
2004). This research has identified several factors that contribute to eating behaviors that
allow an individual suffering from these challenges to have greater insight into how
personal decisions and intentions contribute to eating behaviors.
Personal motivation, which can be derived from attitudes, self-efficacy, and
intentions towards a behavior, are contributors to a person’s eating behaviors and overall
concern about how eating affects them personally. The results of this research can
provide a practical application of health promotion at the individual level if personal
motivation is used to predict negative eating behaviors, such as binging and overeating
with an understanding of an individual’s psychological control mechanisms (Conner &
Sparks, 1998). If a person, a family unit, and a community group can recognize the
complexities associated with eating behaviors and work to first understand the
contributing factors to negative eating behaviors, changes in perceptions and stereotypes
regarding obesity and overeating behaviors may be changed one small step, and family
unit, at a time. If an individual or a social group, such as a family unit or local
community, are able to understand the underlying contributors of a negative attitude
toward eating healthy, why there is a low sense of perceived behavioral control to
manage eating behaviors, and how this impacts a lower intention to change eating
behaviors prior to initiating a health behavioral change program, the potential for new
positive eating behaviors may increase. Accomplishing change is possible at both the
individual and group level through volunteer community outreach and educational
programs, through local school systems, and through church groups as well as other
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social networking forums. This change in perception could result in the positive social
change needed to adjust the social stigma that the obesity epidemic is limited to excessive
caloric consumption and lack of exercise alone.
Recommendations for Action
Addressing the challenge of obesity related health disorders is enormous. In order
for a positive social change to occur, the issue needs to be addressed at both the macro
level, such as our physical and mental healthcare systems, at the corporate at government
levels, and in our media. Additionally, at the micro level, the problem needs to be
addressed in local communities, with local health care providers, local education centers,
and with individual neighborhoods and families. Although these changes require a large
effort by a variety of professionals and community members, individual actions can start
to make a difference.
Even though the physical and psychological dangers of binge eating, overeating,
and obesity have been researched substantially, cognitive style and the cognitive
processes associated with planned behavior, as they apply to non-clinical eating
behaviors, are not systemically being incorporated into weight loss and weight
maintenance programs or health educational and preventative programs. As a link
between these variables and eating behaviors was established in this research, health
professionals have an opportunity to incorporate these findings to gain a better
understanding of how decisions regarding negative eating behaviors manifest at the
individual level. Health practitioners can investigate the predictor variables in this study
to assess individuals' attitudes, beliefs, decision making styles, and expectations when
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formulating a diet or a health behavioral modification program. This could be
accomplished by specifically identifying an individual’s attitudes towards overeating,
perceived behavioral control over the influence eating behavior, and intentions to manage
eating behaviors factors that to first address the cognitive attributes that can hinder or
help an eating behavioral change program. If actions can be taken to change an
individual’s outlook and self-efficacy regarding ability to manage eating behavior prior to
a systematic dietary change, the weight loss and health strategies may be more
successful.
One recommendation for action is for these results to be disseminated to a variety
of sources that may assist in elevating the need to understand the cognitive factors
associated with overeating and binge eating into their existing efforts to reduce the
impact of obesity on society. Individuals and groups that need to pay attention to the
results of this study include health psychologists, social psychologists, clinicians,
researchers, and physicians. These results also should be disseminated to special interest
groups, such as the National Weight Control Registry, CDC's Division of Nutrition,
Physical Activity, and Obesity (DNPAO), and the Colorado Health Foundation and
Colorado Department of Public Health and Environment, both of which are funding
through Live Well Colorado and the DNPAO. The authors of the EDE-Q6, the TPB, and
the KAI should additionally be informed of the results of this study. This researcher will
contribute to the dissemination of these results by sending electronic copies of this
research to the above mentioned parties as well as working to publicize these results in
journal articles and local community publications.
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Upon the dissemination of this research, an additional call to action would be for
health organizations that may have traditionally relied on weight and body mass index
and metrics for weight loss/control strategies to reassess their evaluation and treatment
methods. For example, it is challenging to identify any single approach in formal weight
loss programs that claims to be effective in maintaining long-term weight control
(Adams, 2008). Future interventions and health programs aimed at obesity related
conditions should incorporate weight loss and maintenance strategies that address the
attitudes, intentions, and perceived behavioral control measurements for individuals in an
effort to understand the personal decision making styles that contribute to eating
behaviors. Additional recommendations for action are included in the following section.
Limitations and Recommendations for Future Study
Research studies are often limited by various restrictions such as time limitations,
available sample, and financial capabilities of the researcher. One major limitation to this
study was the available sample, which was limited to Boulder, Colorado. Although the
demographics of the sample were reflective of the overall population, the study was
limited from an ethnic standpoint as the majority of the respondents were European-
American. Colorado is also the only state in the United States that, according to the
Center for Disease Control (2009), has less than 20% of the population that is considered
to be obese. This limits the generalizability of the study. It may be assumed that eating
behaviors are impacted by the predictor variables in this study in most adults; however,
this assumption needs to be validated. Future research could expand on this study into
more diverse ethnic regions of the United States.
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Further, cognitive level was not incorporated into this research design. Cognitive
level can be defined as the cognitive resources and potential cognitive capacity that an
individual has with regard to eating behaviors (Kirton, 2008). For example, measuring
the participants’ education level, personal knowledge about nutrition, health behaviors, or
the physical, mental, and social effects of obesity or binge eating were not incorporated
into this study.
Additionally, as the KAI inventory is not in the public domain, costs associated
with the inventories limited the sample size which may have resulted in an inability to
discriminate significance with the predictor variables of sufficiency of originality,
efficiency, and rule/group conformity. As the sample size in this research was modest,
increasing the sample size may provide an opportunity to further research specific to the
KAI inventory. Lastly, there are three specific areas that generate a new round of
questions which are clinically and non-clinically significant eating behaviors, seasonal
eating behaviors and coping strategies associated with eating behaviors.
Clinically and Non-Clinically Significant Eating Behaviors
In this research study, the availability of two independent samples for eating
behaviors in the clinically significant and non-clinically significant range could introduce
the following research question. Is there a difference in the population means of BMI,
attitude, subjective norm, perceived behavioral control, intention, sufficiency of
originality, efficiency, and rule/group conformity between individuals with clinically
significant eating behaviors and individuals with non-clinically significant eating
behaviors? On completing the data analyses in this research sample, 109 participants
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reported no clinically significant scores and 28 participants reported clinically significant
scores in this study. An independent two samples t-test for equality of means was
performed to determine any differences between the participants with EDE-Q6 scores
that had scores in the clinical range and non-clinical range. Reporting equal variances
not assumed, the results indicate a significant difference in means for body mass index, t
(135) = -2.40, p = .021; attitude, t (135) = -2.95, p = .004; perceived behavioral control, t
(135) = 3.85, p ≤ .001; and intention, t (135) = 2.52, p = .016.
Although the EDE-Q6 is considered to be the standard assessment instrument for
binge eating and overeating behaviors (Grilo, Masheb, & Wilson, 2001), it is still
difficult to precisely identify the specific negative eating behaviors that occur as the
results are based on self-reporting historical eating behaviors. However, as Colorado is
considered to be one of the healthiest states and has the one of the lowest levels (20% or
less of the population) of obesity in the country (Centers for Disease Control and
Prevention, 2010), it is interesting to note that 20% of the total sample also reported an
eating behavior that was in the clinical significant range of the EDE-Q6. Although this
information is an observation and cannot be supported with in this current study, it is an
area of interest worthy of further study.
Seasonal Eating Behaviors
A holiday can be defined as a day in which someone celebrates a religious event,
a day free from work, or a commemoration of an event (Merriam-Webster, 2010).
Although holidays occur throughout the entire year, there are certain seasons in the
United States in which multiple holidays are celebrated, such as the months of November
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through January, in which Thanksgiving, Hanukkah, Christmas, New Year’s Day and
many other celebrations occur. As these dates occurred near or during the data collection
period of this research, it is important to investigate the relationship between eating
behaviors and holidays and how this may or may not have impacted the implications of
this research. The holiday season is often social in nature and can present situations in
which individuals are faced with multiple poor food choices, such as dishes that are
higher in calories and fats compared with what a person would normally eat during a
non-holiday period. When faced with a multitude of poor food choices, individuals who
have a history of binge eating and overeating behaviors often have a higher level of
personal observation with the amount of food they consume and have a heightened
awareness of a need to control eating behaviors, often directly after an excessive holiday
eating experience or binge eating experience (Phelan et al., 2008). This is often
demonstrated with increased dietary restraint after the eating experience. Alternatively, a
decrease in awareness of eating behaviors during holiday periods is associated with
greater weight gain (Phelen et al., 2008).
Multiple research studies have reported mild body weight changes that occurred
over the entire holiday season (Hull, Radley, Dinger, & Fields, 2006; Ma et al., 2006; &
Yanovski et al., 2000). The typical weight gain reported during the holiday period is
approximately less than 1 pound, or 0.4 kilograms (Yanovski et al., 2000). Although this
is not a significant amount of weight, individuals who are already reporting obese body
mass indices tend to keep the weight gain throughout the year and this may contribute to
long term weight gain (Watras, Bucholz, Close, Zhang, & Schoeller, 2007). Future
106
research could be expanded to address the implications of eating behaviors before,
during, and after the holiday season to investigate any differences or changes in self-
reporting on the EDE-Q6. Additionally, an increase in weight gain is often associated
with stress, availability of excessive food in social situations, and perceived needs that
surround the eating occasion (Vue, Degeneffe, & Reicks, 2008). Investigating the
relationships between stress, social eating and the variables of the Theory of Planned
Behavior (attitude, subjective norm, intention, and perceived behavioral control) offers a
venue for future research.
Coping Strategies
Successful weight loss losers, who are defined by the National Weight Control
Registry (2010) as persons over the age of 18 who have maintained a 30 pound weight
loss for one year or longer, use different strategies that enable them to keep weight off in
comparison to normal weight individuals or those who are not able to keep weight off.
For example, Phelan et al. (2008) demonstrated in a study that individuals who have
successfully lost weight and do not regain weight over the holidays use strategies such as
increased physical activity, increased awareness and control over eating, and were more
likely to be strict in maintaining their dietary routines before, after, and during what they
considered to be high risk periods of eating. Kirton refers to this type of behavior, as
applied to the adaption-innovation theory, as a coping strategy (1994).
A coping strategy is a behavior that requires an individual to perform in a manner
that is not necessarily natural, or comfortable, with their preferred cognitive style. The
adaption-innovation theory notes that an individual’s preferred style is extremely hard to
107
change but performing a behavior can be flexible (Kirton, 2008). When a person must
participate in a behavior, such as self-monitoring eating behavior or planning a long-term
eating behavioral change, and the behavior is not consistent with a person’s preferred
style, the person experiences stress and inefficiency when performing the behavior
(Stum, 2009).
Coping behaviors, such as self-monitoring eating behaviors, are learned, and self-
monitoring of eating and increased awareness of eating behaviors is critical to successful
long-term weight loss (Wing, Tate, Gorin, Raynor, & Fava, 2006). Coping behaviors may
play a significant role in the success of long term eating behaviors. Although the KAI
predictor variables were included in this study, independent statistical significance was
not detected. However, future research needs to ask different questions about self-
monitoring eating behaviors by incorporation the adaption-innovation theory to
understand if a person with an adaptive style may be more likely to follow a meticulous
pattern of solving dietary problems compared with a person who has a more innovative
style who may be less careful about maintaining a dietary lifestyle change. On first
understanding a person’s motivations and intentions towards an eating behavioral change
program, research addressing the specific area of cognitive style could contribute to
improved success rates of obesity prevention, succession of binge eating behaviors, and
long term dietary improvements. Future research in this area should systematically
explore the relationships between the adaption-innovation theory and specific styles of
dietary restraint and self-monitoring eating behaviors.
108
Conclusion
This study contributes to the literature by being the first to focus on the
relationship between the variables of BMI, perceived behavioral control, attitude,
subjective norms, intentions, sufficiency of originality, efficiency, and rule/group
conformity and eating variables of dietary restraint, eating concern, shape concern and
weight concern. The predictor variables of attitude towards overeating, perceived
behavioral control, and intention to manage eating behaviors indicate that a relationship
exists between cognitive and motivational behaviors with factors that contribute to eating
behaviors, such as overeating and binge eating. This research provides a concrete step
towards looking at cognitive factors that contribute to negative eating behaviors.
Approximately 280,000 to 325,000 adults in the United States die each year from
causes related to obesity (CDC, 2009). Experts do not expect these numbers to decrease;
rather they expect them to continue to increase along with a rise in secondary illnesses
such as heart disease, diabetes, body dissatisfaction and depression symptoms (Goldfield
et al., 2010). This research study is beneficial for clinicians and researchers in
interpreting the relationship between eating behaviors and a person’s motivations and
cognitive decision-making processes. The findings that there are relationships between
attitude towards overeating with dietary restraint, eating concern, shape concern and
weight concern; perceived behavioral control with eating concern, shape concern and
weight concern; intentions to manage eating behavior with eating concern, shape
concern, and weight concern; and, BMI with shape concern and weight concern
109
are important for health professionals and society as a whole. Further insights are likely
to be produced from this research to understand what cognitive and health behavioral
factors contribute to eating behaviors. Understanding that it is not just genetics or lack of
self control that contributes to overeating behaviors, but that attitude, intentions, and
perceived behavioral control play an important role in overeating behaviors opens many
possibilities for positive social change to occur. By managing negative eating behaviors
not only from a physiological perspective, but also from a psychological perspective, an
understanding of the cognitive mechanism that underlie overeating and binge eating
behaviors can contribute to the reduction of obesity rates with the goal that the secondary
illnesses associated with obesity will subside.
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APPENDIX A:
BODY MASS INDEX CALCULATION
Public Domain
What is your weight at present? (Please give your best estimate.) ____________
What is your height? (Please give your best estimate.) ____________
The BMI is considered public domain, and can therefore be used for educational
purposes without permission from its author.
APPENDIX B:
EATING DISORDER EXAMINATION QUESTIONNAIRE VERSION 6
Christopher G. Fairburn and Sarah Beglin
Eating Questionnaire
Instructions: The following questions are concerned with the past four weeks (28 days) only. Please read each question carefully. Please answer all the questions. Thank you. Questions 1 to 12: Please circle the appropriate number on the right. Remember that the questions only refer to the past four weeks (28 days) only.
On how many of the past 28 days…
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
1 Have you been deliberately trying
to limit the amount of food
you eat to influence your
shape or weight (whether or not
you have succeeded)?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
2 Have you gone for long periods of time (8 waking hours or more) without eating
anything at all in order to influence
your shape or weight?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
3 Have you tried to exclude from your diet any foods that you like in order to influence your shape or weight (whether or not
you have
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
130
succeeded)? 4 Have you tried to
follow definite rules regarding your eating (for
example, a calorie limit) in order to influence your
shape or weight (whether or not
you have succeeded)?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
5 Have you had a definite desire to have an empty
stomach with the aim of influencing
your shape or weight?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
6 Have you had a definite desire to have a totally flat
stomach?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
7 Has thinking about food, eating, or calories made it very difficult to concentrate on things you are
interested in (for example, working,
following a conversation, or
reading)?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
8 Has thinking about shape or weight made it very difficult to concentrate on things your are
interested in (for example working,
following a conversation, or
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
131
reading)? 9 Have you had a
definite fear of losing control over eating?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
10 Have you had a definite fear that you might gain
weight?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
11 Have you felt fat? No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
12 Have you had a strong desire to
lose weight?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
19 On how many days have you eaten in secret?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
20 On what proportion of the times that you have eaten have you felt guilty because of the effect on your shape or weight?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
21 How concerned have you been about other people seeing you eat?
No days
1-5 days
6-12 days
13-15 days
16-22 days
23-27 days
Every day
Questions 22-28: Please circle the appropriate number on the right. Remember that the questions only refer to the past four weeks (28 days).
Over the past 28 days…
Not at
all,
1 Slightly
3 Moderate
5 Marked
22 Has your weight influenced how you
think (judge yourself) as a
0 1 2 3 4 5 6
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person? 23 Has your shape
influenced how you think about (judge) yourself as a person
0 1 2 3 4 5 6
24 How much would it upset you if you had
been asked to weight yourself once a week (no
more, or less, often) for the next four
weeks
0 1 2 3 4 5 6
25 How dissatisfied have you been with
your weight?
0 1 2 3 4 5 6
26 How dissatisfied have you been with
your shape?
0 1 2 3 4 5 6
27 How uncomfortable have you felt seeing
your body (for example, seeing
your shape in the mirror, in a shop
window reflection, while undressing or
taking a bath or shower)?
0 1 2 3 4 5 6
28 How uncomfortable have you felt about others seeing your
shape or figure
0 1 2 3 4 5 6
The EDE-Q6 is considered public domain, and can therefore be used for educational purposes without permission from its author. Permission for use is granted from the University of Oxford Department of Psychology: http://www.psychiatry.ox.ac.uk/research/researchunits/credo/assessment-measures-pdf-files/EDE-Q6.pdf.
APPENDIX C:
THEORY OF PLANNED BEHAVIOR QUESTIONNAIRE
I. AJZEN
Theory of Planned Behavior: Eating Behavioral Questionnaire
Please answer each of the following questions by circling the number that best describes
your opinion. Some of the questions may appear to be similar, but they do address
somewhat different issues. Please read each question carefully.
Predictor Variables: Attitudes (direct measures of attitude * 2 outcome expectations) 1. For me eating healthy and not overeating on a regular basis is
Worthless :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Useful
2. Overeating impacts my feelings about myself in a
Positive way :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Negative way
3. Maintaining a healthy diet is
Not important :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important
Subjective Norms (normative beliefs * 2 motivation to comply) 4. People that are important to me think that keeping a healthy weight is
Not important:___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important
5. People that are important to me think that overeating or binge eating is
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Normal in some situations Harmful
6. What my doctor or health care provider thinks I should do to eat healthy is
Not important:___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important
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Perceived Behavioral Control (self-efficacy * 2 influence of controllability) 7. For me controlling my eating behavior is
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Extremely difficult Extremely easy
8. The decision to stick to a diet program is beyond my control
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Strongly agree Strongly disagree
9. My weight or shape is in my control
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Strongly disagree Strongly agree
Behavioral Intentions (intention performance, general intention, intention simulation) 10. For me intending to eat healthy on a daily basis is
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Extremely difficult Extremely easy
11. I intend to maintain healthy eating behaviors on a daily basis
:___1__:___2__:___3__:___4__:___5__:___6__:___7__:
Strongly disagree Strongly agree
12. If I intend to maintain a consistent diet that results in overall health I find it
Extremely hard :___1__:___2__:___3__:___4__:___5__:___6__:___7__:Extremely easy
The TpB questionnaire is considered public domain, and can therefore be used
for educational purposes without permission from its author. This instrument is published
with permission from University of Massachusetts:
http://www.people.umass.edu/aizen/pdf/tpb.questionnaire.pdf
APPENDIX D:
KIRTON ADAPTION-INNOVATION INVENTORY
M. J. Kirton
Due to copyright laws the Kirton Adaption-Innovation Inventory may not be
published for the general public and requires certification for administration which has
been completed by this researcher.
APPENDIX E:
BACKGROUND DATA QUESTIONNAIRE
L. Samuel
What is your age? __________________
What is your gender? ______________________
What is your ethnicity? ____________________
APPENDIX F:
CONSENT FORM
Walden University, L. Samuel
You are invited to take part in a research study that investigates non-clinical eating behaviors with personal eating decision making styles. You were chosen for the study because you reside in the state of Colorado. This form is part of a process called “informed consent” to allow you to understand this study before deciding whether to take part. This study is being conducted by a researcher named Lisa K. Samuel, who is a doctoral student at Walden University. Background Information: The purpose of this study is to learn more about how individuals make everyday decisions regarding eating choices so that health psychologists can assist in creating positive wellness plans. Procedures: If you agree to be in this study, you will be asked to:
• Complete the surveys enclosed • Return them within three days to the researcher
Voluntary Nature of the Study: Your participation in this study is voluntary. This means that everyone will respect your decision of whether or not you want to be in the study. As this is confidential and participants are selected randomly, no one will treat you differently if you decide not to be in the study. If you decide to join the study now, you can still change your mind during the study. If you feel stressed during the study you may stop at any time and request assistance from the researcher to find assistance. You may skip any questions that you feel are too personal. Risks and Benefits of Being in the Study: The risks of being in this study include the uncovering of personal decision making styles that may contradict personal beliefs. However, the benefits of this study include understanding whether or not personal eating behaviors may or may not contribute to health disorders, understanding what your personal cognitive decision making style is as it contributes to multiple factions in life, and how you manage desires versus intentions which can assist in long term goal development.
138
Compensation: The compensation for this study is that you will receive personalized feedback regarding your preferred cognitive style, body mass index, and intentions regarding eating behaviors. However, this information can only be returned to you if you choose to provide your address or an e-mail address. This information is typically provided for a fee and the feedback is for information purposes only. The researcher is not soliciting any subsequent enrollment in any fee-based services and the feedback is not intended for diagnosis or treatment. Confidentiality: Any information you provide will be kept confidential. The researcher will not use your information for any purposes outside of this research project. Also, the researcher will not include your name or anything else that could identify you in any reports of the study. Contacts and Questions: You may ask any questions you have now. Or if you have questions later, you may contact the researcher via 303-604-6080 or [email protected]. If you want to talk privately about your rights as a participant, you can call Dr. Leilani Endicott. She is the Walden University representative who can discuss this with you. Her phone number is 1-800-925-3368, extension 1210. Walden University’s approval number for this study is 11-30-09-0342645 and it expires on November 29, 2010. The researcher will give you a copy of this form to keep. Statement of Consent: I have read the above information and I feel I understand the study well enough to make a decision about my involvement. By signing below, I am agreeing to the terms described above.
Printed Name of Participant
Date of consent
CURRICULUM VITAE
LISA K. SAMUEL EDUCATION Ph.D. Candidate, Psychology, 2006- Present Walden University, Denver, CO Masters Degree in Business Administration, September, 2005 University of Phoenix, Phoenix, AZ
Bachelors Degree in Business Administration, July, 1998 Florida Metropolitan University, Clearwater, FL
Associates Degree in Medical Science, July, 1995 Webster College, New Port Richey, FL EXPERIENCE
Ph.D. Candidate, Walden University, 4.O GPA The Health Psychology Ph.D. specialization focuses on the complex relationships among psychological, social, and biological factors implicated in health, illness, and well being.
SIX SIGMA ACADEMY, Scottsdale, AZ, October 1999- August 2005
Director, Operations, August 2003 to July 2005
Researched market trends, e-business and new market opportunities and managed market research teams, sales research process, resource and consulting services management, training material development and distribution teams. Managed new product development teams with multiple training curriculums utilizing adult learning theory. Allocated resources and provided recommendations on performance reviews. Developed deployment best practices, managed intellectual property and project databases including client specific examples, case studies, material translations, tools and templates. Provided deployment initialization support and coordinated operation support activities for major corporations throughout their Six Sigma deployments including Merrill Lynch, Visteon, Air Liquide, Rhodia P.I., DuPont, IndyMac Bank, Ford Motor Company, Grupo Antolin, Tyco International, IKON Office Supplies, Westinghouse, Albertsons, Johnson Controls, and Anthem.
New Market Development / Research Manager, October 1999 – August 2003 Identified and analyzed market opportunities for building new business models. Performed detailed research on prospective clients, analyzed and designed deployments and pricing structures, performed market research and financial
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analysis of potential business opportunities, plans and proposals. Developed and executed competitive intelligence process, designed client centric sales presentations and proposals. Managed and developed a case study library and client sensitive documentation. Developed sales contact list database and standardized corporate pricing model. Created a knowledge management structure for the organization. BAYONET POINT FOOT HEALTH CENTERS, Port Richey, FL Podiatric Surgical Nurse, 1995-1999 Office manager for three podiatric surgeons and three surgical facilities responsible for a team of 28 nurses. Lead surgical nurse for all podiatric surgeries. Additional certifications included Hazardous Material Manager, Registered Phlebotomist, Registered X-ray Technician, Registered Podiatric Surgical Assistant, ICD9 Certified Insurance Code Specialist and Certified Medical Transcriptionist. UNITED STATES NAVY, Barbers Point, HI Maintenance and Ground Support Operations, 1992 – 1995 Maintained pre and post flight operations for a squadron of P3 Orion aircraft both domestically and internationally. Received Southwest Asia Service medal, National Defense Service Medal, Overseas Duty Ribbon, and Honorable Discharge.
PROFESSIONAL CERTIFICATIONS, MEMBERSHIPS AND ACHIEVEMENTS
Walden International Corps – Social Changers Without Borders, 2010 Psi Chi, The International Honor Society in Psychology, 2010 KAI Certification Course (registered), Kirton Adaption-Innovation
Inventory, Dr. Kirton, Penn State University, December 11, 2008 Protecting Human Research Participants Certification (45805), National
Institutes of Health (NIH), January, 2008 American Psychological Association (APA), 2006 Association for Psychological Science (APS), 2006 Master Black Belt Certification, Six Sigma Academy International,
February, 2005 Supply-Chain Operations Reference Model v.6.0 certification, Supply
Chain Council, May 28, 2004 C3 (Creating a Customer Centered Culture) Certification, IMTC3, April 6,
2004 Achieving Performance Excellence and Metric Development Certification,
American Productivity and Quality Center (APQC), October 2003 Knowledge Management Certification, American Productivity and Quality
Center (APQC), October 2003
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Theory of Inventive Problem Solving (TRIZ) Certification, PQR Group, February 20, 2003
Certified Six Sigma Academy Black Belt, May 18, 2001
PAPERS and PRESENTATIONS (Samuel, L. K. is the researcher’s name and Modesitt, L.K. is the researcher’s prior name)
Samuel, L. K. (2010). Good Psychology. Website and Blogsite. www.goodpsych.com.
Samuel, L. K. (2009). Eating, Health Behaviors, and Cognitive Style. Poster Presentation, Walden Residency. Sheraton Hotel, January 22, 2009.
Modesitt, L. K. & Samuel, P. (2007). Linking Lean Six Sigma. The African Business Review, September-October, 4-6.
Modesitt, L. K. & Samuel, P. (2005). Linking Six Sigma Projects to Strategic Imperatives. European Business Review, May-June, 30-33. Samuel, P., Modesitt, L.K., & Finney, J. E. (2004). Creating New Markets Using Six Sigma. G100 Insights, Fall 2004, 20-25. Modesitt, L. K., & Samuel, P. (2004). Next Generation Six Sigma: A New Age in Process Improvement Strategies? Pharmaceutical Manufacturing and Packaging Sourcer, Autumn 2004, 18-22. Samuel, P., & Modesitt, L. K. (2004). Executing for Corporate Breakthrough Success: The Evolution of Six Sigma. Proceedings of the First International Conference on Six Sigma, 48-62. Samuel, P., Modesitt, L. K., & Sollenberger, D. (2004). Next Generation Six Sigma for Corporate Breakthrough Success. European CEO, September- October, 20-22. Samuel, P., & Modesitt, L. K. (2004). Six Sigma for Corporate Revenue Growth. The Growing Business Handbook: Kogan Page Limited, 405-409.