Eating, Health Behaviors And Cognitive Style by Dr. Lisa Samuel 2010

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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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.

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.

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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 iii

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 iv

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

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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

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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

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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 overeating 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 carbohydrateladen 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 AdaptionInnovation 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 adaptioninnovation 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 nonreported 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 decisionmaking 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 Binge Analysis

• Breaking a dietary rule • Being disinhibited (e.g. alcohol) • Under eating for a period of time

BINGE EATING

• Adverse event or mood

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

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 selfreported 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. NeumarkSztainer, 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, CepadaBenito, & 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 selfconsciousness 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 (EDEQ6), 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

60 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.

61 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,

62 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

63 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

64 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 cognitivebehavioral 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

66 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

67 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 BMI 1.000

SN

PBC

I

SO

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*

E

R

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