Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Song, Claire
Master of Science - MSc
University of British Columbia
BACKGROUND: Depression is one of the main public health challenges among Canadian adolescents. However, research suggests that Chinese adolescents in Canada may experience or express depressive symptoms differently compared to White adolescents, and this may influence the way Chinese adolescents answer items in the Center for Epidemiologic Studies Depression Scale (CES-D). Additionally, research suggests that gender and the strength of ethnic identity may influence the rate of depression among Chinese adolescents only, and how they report depressive symptoms. OBJECTIVE: This study aims to evaluate the measurement equivalence of the CES-D among Chinese and White adolescents living in Canada, and to conduct further investigation of the impact of strength of ethnic identity on responses to CES-D items among Chinese adolescents. METHOD: Data were collected from 961 high school students aged 13–17 years participating in the British Columbia Adolescent Substance Use Survey. The Ordinal Logistic Regression method was used to test for Differential Item Functioning (DIF) on items in the CES-D across Chinese and White adolescents. RESULTS: The initial set of DIF analyses identified non-uniform DIF in CES-D Item 7 between Chinese and White adolescents (χ² = 41.972, R²= 0.046). After controlling for gender and strength of ethnic identity, the results remained the same. In the second set of analyses, CES-D Item 8 demonstrated the statistically significant differential functioning in Chinese participants across different levels of ethnic identity, uniform DIF among Chinese adolescents (χ² = 10.779, R²= 0.044). Controlling for gender did not alter the results. CONCLUSION: The result of this study suggests that there may be a systematic difference in experience or expression of depression among Chinese adolescents. Specifically, CES-D Item 7 did not appear to discriminate the level of depression among Chinese adolescents. Additional research is needed to understand the complex process of DIF to replicate and investigate the process associated with detected DIF in this study.
EXPLORING POTENTIAL IMPACT OF CHINESE ETHNICITY ON THE MEASUREMENT OF DEPRESSION AMONG ADOLESCENTS USING THE CENTER FOR EPIDEMIOLOGIC STUDIES DEPRESSION SCALE by Claire Song B.Sc.P.N., Brandon University, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2019 © Claire Song, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: EXPLORING POTENTIAL IMPACT OF CHINESE ETHNICITY ON THE MEASUREMENT OF DEPRESSION AMONG ADOLESCENTS USING THE CENTER FOR EPIDEMIOLOGIC STUDIES DEPRESSION SCALE submitted by Claire Song in partial fulfillment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Dr. Chris Richardson, School of Population and Public Health Supervisor Dr. Bruno Zumbo, Measurement, Evaluation, and Research Methodology Supervisory Committee Member Dr. Anne Gadermann, School of Population and Public Health Supervisory Committee Member Dr. Arminee Kazanjian, School of Population and Public Health Additional Examiner Additional Supervisory Committee Members: Supervisory Committee Member Supervisory Committee Member iii Abstract BACKGROUND: Depression is one of the main public health challenges among Canadian adolescents. However, research suggests that Chinese adolescents in Canada may experience or express depressive symptoms differently compared to White adolescents, and this may influence the way Chinese adolescents answer items in the Center for Epidemiologic Studies Depression Scale (CES-D). Additionally, research suggests that gender and the strength of ethnic identity may influence the rate of depression among Chinese adolescents only, and how they report depressive symptoms. OBJECTIVE: This study aims to evaluate the measurement equivalence of the CES-D among Chinese and White adolescents living in Canada, and to conduct further investigation of the impact of strength of ethnic identity on responses to CES-D items among Chinese adolescents. METHOD: Data were collected from 961 high school students aged 13–17 years participating in the British Columbia Adolescent Substance Use Survey. The Ordinal Logistic Regression method was used to test for Differential Item Functioning (DIF) on items in the CES-D across Chinese and White adolescents. RESULTS: The initial set of DIF analyses identified non-uniform DIF in CES-D Item 7 between Chinese and White adolescents (χ2 = 41.972, R2= 0.046). After controlling for gender and strength of ethnic identity, the results remained the same. In the second set of analyses, CES-D Item 8 demonstrated the statistically significant differential functioning in Chinese participants across different levels of ethnic identity, uniform DIF among Chinese adolescents (χ2 = 10.779, R2= 0.044). Controlling for gender did not alter the results. iv CONCLUSION: The result of this study suggests that there may be a systematic difference in experience or expression of depression among Chinese adolescents. Specifically, CES-D Item 7 did not appear to discriminate the level of depression among Chinese adolescents. Additional research is needed to understand the complex process of DIF to replicate and investigate the process associated with detected DIF in this study. v Lay Summary Research suggests that adolescents of the Chinese culture may express or experience depressive symptoms differently compared to White adolescents, and the level of strength of their ethnic identity may impact how Chinese adolescents express depressive symptoms. This thesis uses the Ordinal Logistic Regression Differential Functioning Item (DIF) method to analyze potential differences in the Center for Epidemiologic Studies Depression Scale, a depression testing tool, across and within ethnic groups. The purpose of this thesis is to evaluate ethnicity DIF and the strength of ethnic identity DIF across Chinese and White adolescents, and among Chinese adolescents exclusively. The results of this thesis contribute to the examination of whether CES-D items equally assess and measure depressive symptoms regardless of ethnicity, strength of ethnic identity or gender. vi Preface This thesis uses data from the British Columbia Adolescent Substance Use Survey (BASUS), a large prospective cohort study of British Columbia adolescents’ attitudes about and experience with substance use and mental/physical health issues. The BASUS study was led by Dr. Chris Richardson (University of British Columbia). Participants were consented, and questionnaire data were obtained prior to Claire Song’s involvement. This study was approved by all participating school districts as well as by the Behavioural Research Ethics Board of the University of British Columbia (Application number: H08-02841-A011). The Canadian Institutes of Health Research (CIHR) funded the BASUS study (Project number 07-3631). The study design was developed jointly by the student and supervisor. The literature review in Chapter 1 was conducted by Claire Song. All analyses were performed by Claire Song with the assistance of Dr. Chris Richardson. The thesis was completed with the guidance of the thesis supervisory committee, Drs. Chris Richardson, Anne Gadermann (University of British Columbia), and Bruno Zumbo (University of British Columbia). vii Table of Contents Abstract ............................................................................................................................................................................. iii Lay Summary ................................................................................................................................................................... v Preface ................................................................................................................................................................................ vi Table of Contents ........................................................................................................................................................... vii List of Tables ..................................................................................................................................................................... x List of Figures .................................................................................................................................................................. xi List of Abbreviations .................................................................................................................................................... xii Acknowledgements ....................................................................................................................................................... xiii Dedication ....................................................................................................................................................................... xiv Chapter 1 Overview and Background ...................................................................................................................... 1 Chapter 2 Literature Review ....................................................................................................................................... 6 2.1 Depression .............................................................................................................................................................. 6 2.1.1 Depression among Adolescents ................................................................................................................ 8 2.1.2 The Center for Epidemiologic Studies Depression Scale (CES-D) ........................................... 10 2.1.2.1 The CES-D and Adolescence ............................................................................................. 12 2.2 Depression among Ethnic Groups ............................................................................................................... 13 2.2.1 Chinese Adolescents as a Vulnerable Group for Depression ...................................................... 14 2.2.2 Ethnic Identity and Distinct Culture of China ................................................................................ 16 2.2.3 Strength of Ethnic Identity .................................................................................................................... 18 2.2.4 The CES-D and Ethnic Groups ............................................................................................................ 21 2.3 Gender .................................................................................................................................................................. 22 2.3.1 Measurement of the Strength of Ethnic Identity ............................................................................ 24 viii 2.4 Differential Item Functioning ....................................................................................................................... 24 2.4.1 Validity, Item Bias and Item Impact .................................................................................................. 25 2.4.2 Different Types of DIF ............................................................................................................................ 29 2.5 Ordinal Logistic Regression DIF ................................................................................................................. 32 2.6 Research Questions .......................................................................................................................................... 34 Chapter 3 Methods and Analysis ............................................................................................................................ 36 3.1 Data ........................................................................................................................................................................ 36 3.1.1 British Columbia Adolescent Substance Use Survey (BASUS) ................................................. 36 3.1.2 Ethical Considerations ............................................................................................................................ 37 3.2 Measures .............................................................................................................................................................. 38 3.3 Analysis ................................................................................................................................................................ 42 3.3.1 Missing Value Analysis ........................................................................................................................... 42 3.3.2 Descriptive Analysis ................................................................................................................................. 43 3.3.3 Ordinal Logistic Regression - Differential Item Functioning ..................................................... 44 3.3.3.1 Ordinal Logistic Regression - Question I-A .................................................................... 44 3.3.3.2 Ordinal Logistic Regression - Question I-B .................................................................... 45 3.3.3.3 Ordinal Logistic Regression - Question II-A .................................................................. 46 3.3.3.4 Ordinal Logistic Regression - Question II-B .................................................................. 48 3.3.4 Software for Data Analysis .................................................................................................................... 49 Chapter 4 Results ......................................................................................................................................................... 50 4.1 Missing Value Analysis ................................................................................................................................... 50 4.2 Descriptive Statistics ........................................................................................................................................ 51 4.3 Differential Functioning Item Analyses ..................................................................................................... 55 4.3.1 DIF Analysis - Question I-A .................................................................................................................. 57 ix 4.3.2 DIF Analysis - Question I-B ................................................................................................................... 63 4.3.3 DIF Analysis - Question II-A................................................................................................................. 67 4.3.4 DIF Analysis - Question II-B ................................................................................................................. 71 Chapter 5 Discussion ................................................................................................................................................... 76 5.1 Ethnicity DIF ...................................................................................................................................................... 77 5.2 Strength of Ethnic Identity DIF ................................................................................................................... 78 5.3 Limitations and Future Research Suggestions ........................................................................................ 80 References ....................................................................................................................................................................... 83 Appendices .................................................................................................................................................................... 113 Appendix A. Missing Variable Analysis for the CES-D items ................................................................ 113 Appendix B. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses ................ 115 Appendix C. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses with Chinese Only ............................................................................................................................................................ 119 Appendix D. Results of Binary and Ordinal Logistic Regression of Ethnicity DIF Analyses ...... 123 Appendix E. Results of Binary and Ordinal Logistic Regression of Strength of Ethnic Identity DIF Analyses ............................................................................................................................................................ 127 x List of Tables Table 3-1 The CES-D Items …………………………………….………………………….………...40 Table 3-2 MEIM Scale………………………………………………………….………………….…41 Table 4-1 Descriptive Statistics………………………….…………………………………………....52 Table 4-2 Comparison of the mean CES-D by Ethnicity and Gender ………………………………..53 Table 4-3 Comparison of Mean CES-D Score (When Participants Scored≧24 on CES-D)……….…54 Table 4-4 Comparison of Mean MEIM Score by Ethnicity…………………………………………...55 Table 4-5 Three-Binary Variables of Item 2…………………………………………………………..57 Table 4-6 Result of Ethnicity DIF Analysis among White and Chinese Adolescents………………...58 Table 4-6.1 Result of Ethnicity DIF analysis for Item 4….………………………….………………..60 Table 4-6.2 Result of Ethnicity DIF Analysis for Item 7…………………………….………………..62 Table 4-7 Result of Ethnicity DIF Analysis with Gender and MEIM.......……………………………64 Table 4-7.1 Result of DIF Secondary Analysis for Item 7..…………………………………………..66 Table 4-8. Result of Strength of Ethnic Identity DIF Analysis among Chinese Participants…………68 Table 4-8.1 Result of Strength of Ethnic Identity DIF Analysis for Item 8..…………………………70 Table 4-9 Result of Strength of Ethnic Identity DIF Analysis with Gender…………………………..72 Table 4-9.1 Result of Strength of Ethnic Identity DIF Analysis with Gender for Item 8……………..75 xi List of Figures Figure 2-1 Conceptual Framework of Item Bias and Item Impact…………...……………………….28 Figure 2-2 Uniform and Nonuniform DIF……………….………………………...…….……………30 Figure 3-1 Initial Analysis of Differential Item Functioning…………………...……….…………….45 Figure 3-2 Secondary Analysis of Differential Item Functioning………………….…………….……46 Figure 3-3 Initial Analysis of DIF for Chinese Adolescents Only...….……………………………….47 Figure 3-4 Secondary Analysis of DIF for Chinese Adolescents Only…………………………..……48 Figure 4-1 Ethnicity Differential Item Functioning Plot of Item 4 with Not Displaying DIF………...61 Figure 4-2 Ethnicity Differential Item Functioning Plot of Item 7……………………………………62 Figure 4-3 Differential Item Functioning Plot of Item 7 Adjusting for Gender and Total MEIM Score …………..………………………………………………………………………..……………….....67 Figure 4-4 Strength of Ethnic Identity Differential Item Functioning Plot of Item 8…………………71 Figure 4-5 Differential Item Functioning Plot of Item 8 Adjusting for Gender……………………....74 xii List of Abbreviations APA American Psychiatric Association ARC Arthritis Research Center BASUS The British Columbia Adolescent Substance Use Survey BC British Columbia CES-D Center for Epidemiologic Studies Depression Scale CIHR Canadian Institutes of Health Research DIF Differential Item Functioning df Degrees of Freedom DSM The Diagnostic and Statistical Manual of Mental Disorder IRT Item Response Theory LOESS Locally Estimated Scatterplot Smoothing LR Logistic Regression M-H Mantel-Haenszel MEIM The Multigroup Ethnic Identity Measure SES Socioeconomic Status SIBTEST The Simultaneous Item Bias Test STD Standardization Method TAPs Think Aloud Protocols WHO World Health Organization xiii Acknowledgements Throughout my academic journey at the University of British Columbia, I have received a great deal of support and assistance. I would like to show my sincere gratitude to my supervisor, Dr. Chris Richardson, who believed in me throughout the process. His encouragement made this thesis possible. I especially thank him for his countless hours and efforts spent in reviewing the thesis draft, days and nights, on weekdays and weekends. My profound gratitude is extended to Dr. Anne Gadermann, for providing her expertise in psychometrics and very thorough feedback that truly enhanced the quality of this thesis. I owe enduring thanks to Dr. Bruno Zumbo for structuring the use of Ordinal Logistic Regression to detect DIF, and his dedicated work inspired the design of this thesis. I would also like to thank Dr. Arminee Kazanjian for her role in providing her expertise in Sociology, and immigrant health. I also want to offer my special thanks to my mentor, Professor Katherine Pachkowski (Brandon University), for guiding me to higher education, and for supporting me and encouraging me to overcome many challenges. Many thanks to Kristen Tardio. I learned a great deal of writing knowledge and skills from her mentorship, which helped me tackle numerous difficulties I encountered in putting this thesis together. Finally, I would like to thank everyone who participated in the British Columbia Adolescent Substance Use Survey. xiv Dedication I would like to dedicate this thesis to God. I also want to dedicate this thesis to my parents, Hyunsook Byun (변현숙) and Youngsohk Song (송영석), my brother Heon, my grandparents, Sinhyun Kim (김신현) and Wooryang Byun (변우량), and my life partner, Brandon Lee. I love you all. 1 Chapter 1 Overview and Background Mental illness represents one of the most pressing challenges for global health. In 2017, the World Health Organization reported that approximately 320 million people were living with depression, with 4.7% of Canadians experiencing a depressive disorder in the previous 12 months (World Health Organization [WHO], 2017). In 2018, WHO reported that depression is contributing 5.9% of total disability in Canada. This represents an increase of about 18% since 2005, with slightly higher increases among children and adolescents (WHO, 2017). Furthermore, data from Statistics Canada indicates that 1 out of 5 Canadians will experience a mental health problem or illness in a given year, and 1 in 10 Canadians aged 12 and older are diagnosed with either mood disorder or anxiety disorder (Mental Health Commission of Canada, 2013; Pearson, Janz, & Ali, 2013; Findlay, 2017; Statistics Canada, 2018). Substantial increases in depression have also been observed in the United States, where health insurers have reported an approximate 50% increase in rates of major depression in the past 5 years for children and adolescents (Blue Cross Blue Shield, 2018). The recognition of mental health as a major concern for adolescents in Canada has led to the implementation of many programs to identify and support individuals with various forms of mental illness (e.g., adolescents-focused health care centers, mobile and internet-based applications to assess symptoms and support treatment-seeking for mental illness, training of secondary school nurses or counselors, and media strategies to raise awareness of mental illness) (Torous, Nicholas, Larsen, Firth, & Christensen, 2018; Wei, Kutcher, & Szumilas, 2011). As part of these efforts, many government health authorities, hospitals, and major universities in Canada 2 have developed surveillance and screening strategies targeting depression among adolescents using self-reported measurement tools such as the Center for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977). The increased use of screening tools, particularly in an online format, has been recognized as a means of overcoming the stigma associated with treatment-seeking for suspected mental health problems and improving access to basic mental health assessments and treatment advice (Torous et al., 2018; Armontrout, Torous, Cohen, McNiel, & Binder, 2018). Key to the success of surveillance and screening efforts is the use of assessment tools that have been validated in the populations in which they are being applied. Without evidence of the validity of instruments in different populations, researchers cannot be sure whether differences in the observed scores are due to true differences in the condition being assessed or are the result of biased assessments. This has led researchers to recommend that similarity in the interpretation of individual assessment items across different populations should be empirically evaluated before comparing test results across different groups (Horn & McArdle, 1992; Beekman et al., 1997; Schmitt & Kuljanin, 2008). For example, in the context of depression screening and surveillance, research indicates that the experience of depression may differ across groups defined by gender and ethnicity (Gelin & Zumbo, 2003; Neufeld, Dunn, Jones, Croudace, & Goodyer, 2017). Gender differences in rates of depression and the expression of depressive symptomatology have been described in the literature, with most of the findings indicating that females tend to experience more depressive symptoms than males (Rai, Zitko, Jones, Lynch, & Araya, 2013; Albert, 2015; WHO, 2017). For example, in the United States, the prevalence of major depression among females is approximately two times higher than among males (Blue Cross 3 Blue Shield, 2018). In 2017, the prevalence of depression among females in Canada was 5.8%, while it was 3.6% for males (Knoll & MacLennan, 2017). In addition to higher rates of depression among females, researchers have also found evidence that the diagnosis and/or expression of depressive symptoms may vary by gender, especially among adolescents (Bennett, Ambrosini, Kudes, Metz, & Rabinovich, 2005; Verhoeven, Sawyer, & Spence, 2013; Slocum, Gelin, & Zumbo, 2004). For example, Bennett et al. (2005) found that adolescent females report more guilt and poor body image, whereas adolescent males show a tendency towards reporting greater levels of anhedonia (i.e., reduced ability to experience pleasure). Additionally, Slocum, Gelin and Zumbo (2004) found differences in the psychometric functioning of CES-D Item 17 (‘crying spells’) between female and male participants. A reduced tendency to endorse the experience of crying spells was found among males compared to equally depressed females (Gelin & Zumbo, 2003). In addition to gender, researchers have also investigated differences in the experience and expression of depression across groups defined by ethnicity. Over the past few decades, the field of ethnic identity research has grown considerably, with research indicating that in cities or countries where cultures are mixed or blended, immigrants have been identified as at-risk for depression (Zuvekas & Fleishman, 2008). As the make-up of the Canadian population became increasingly recognized as a ‘cultural mosaic’, research and policy began to carefully consider the dynamics among different ethnic groups, including the mental health challenges of adolescents with minority backgrounds (Moodley, 1995; Palmer, 1976; Babb, 2018). The relationship between gender and ethnicity on the experience of depression has been explored in several studies examining culturally specific gender roles (e.g., Tolman & Porche, 2000; Spence, Helmreich, & Stapp, 1975). Studies have also reported statistical evidence 4 indicating that the expression of depression may vary across different ethnicities, particularly among Asian cultures (Mojtabai, Olfson, & Han, 2016; Patil, Porche, Shippen, Dallenbach, & Fortuna, 2018; Burke, McArthur, Daryanani, Abramson, & Alloy, 2018; Chris Hahm et al., 2014; Dennis et al., 2018; Chen, Haas, Gillmore, & Kopak, 2011). For example, Asian adolescents are often recognized as a vulnerable group for depression, with female Chinese adolescents in the United States reporting the highest level of depressive symptomology. Chinese immigrants in Canada have also been found to have high rates of depression compared to native-born Canadians (Dennis et al., 2018). These findings highlight the need for research on the relationship between membership in an ethnic group, gender and the assessment of depression. This is especially the case among Asian cultures where research has shown significant differences in the expression of depressive symptomology in addition to Asians being less likely to be screened for depression (Chris Hahm et al., 2014). Although the validity of screening tools such as the CES-D across different genders and ethnic groups continues to be debated (Henry, Grant, & Cropsey, 2018; Callahan & Wolinsky, 1994), the CES-D has been used in many large-scale screening and surveillance programs (Kessler, Brown, & Broman, 1981; Crawford, Cayley, Lovibond, Wilson, & Hartley, 2011). However, given the potential influence of both gender and ethnicity on the experience and expression of depressive symptoms, research is needed on how these factors influence the validity of scores on the CES-D, especially among adolescents. Furthermore, it is important to consider both ethnic group membership and the strength of ethnic identity, as the latter may also influence the expression of depressive symptoms (Gummadam, Pittman, & Ioffe, 2016; Lee, 2005). 5 Psychometricians and mental health researchers have used a variety of methods to examine the validity of screening tools to identify item bias in an effort to establish measurement invariance across specific groups. Differential Item Functioning (DIF) represents one particularly useful approach to investigating measurement invariance. DIF is an item-based statistical analysis that compares the psychometric characteristics of individual items in assessment tools across predefined groups such as gender or ethnicity (Holland & Wainer, 2012). There are a variety of statistical approaches to test for DIF. One of them is the logistic regression (LR) approach, described by Zumbo (1999), which has been selected for the current research. LR approaches are based on a three-step process to detect DIF, where individual item responses are predicted by group membership after adjusting for differences on the total score of the assessment. In addition to testing for item bias, DIF analyses based on the application of multivariate regression models support the inclusion of variables representing additional group characteristics that may influence item responses. In this way, one can investigate DIF across ethnic groups while adjusting for the effect of additional variables, such as gender and strength of ethnic identity, and in so doing gain a more nuanced understanding of the processes that may be influencing DIF across specific groups. The purpose of this body of research is to build on previous research examining the impact of ethnicity on the assessment of depression by testing for DIF on the CES-D across ethnic groups (Chinese and White) while accounting for gender and strength of ethnic identity. Chapter 2 will provide a review of the relevant literature and present the specific study hypotheses; Chapter 3 will present the research design as well as describe the study sample and planned statistical methods. Chapter 4 will present results and Chapter 5 will discuss the findings, identify potential limitations of current research, and recommend directions for future research. 6 Chapter 2 Literature Review This chapter provides a review of relevant literature on depression with a focus on the experience of depression among Canadian adolescents, use of the CES-D as a screening tool, and the need for research on the validity of the CES-D when used in sub-populations of adolescents defined by ethnicity, particularly Chinese adolescents, and gender. This is followed by a review of the strength of ethnic identity and its possible relationship with experience of depression among Chinese adolescents in Canada. In addition, a statistical method, Differential Item Functioning (DIF), is introduced to identify bias in assessment tools and explore potential mechanisms associated with its manifestation. 2.1 Depression The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) identifies depression as a common and serious mood disorder (American Psychiatric Association [APA], 2013). Depression is associated with persistent sadness/hopelessness and apathy. Co-occurring symptoms also include chronic pain or other physical discomfort (APA, 2013). Depression includes a group of more specific diagnostic disorders such as major depressive disorder and seasonal affective disorder. The DSM-5 contains specific established clinical criteria that are used to diagnose depression. A person is diagnosed with depression if they report recent experiences of either depressed mood or apathy as well as five or more of the following symptoms within the same two-week period: unintentional changes in weight, altered cognitive and physical functions, feeling of fatigue, changes in thinking or concentration ability, and recurrent suicidal ideations (APA, 2013). 7 In terms of impact, depression is one of the most common mental health disorders and can cause chronic or permanent disabilities. Depression is a major public health challenge as it is one of the main contributors to the global burden of disease (Marcus, Yasamy, van Ommeren, Chisholm, & Saxena, 2012). More recently, the World Health Organization (WHO) projected that depression will be ranked as the first leading cause of global burden by 2030 (WHO, 2012). Depression has also been identified as a public health challenge as it affects personal health and is often accompanied by substantial social and economic impacts (Baxter et al., 2014; Cassano & Fava, 2002). Another important reason for depression becoming a global public concern is because it is associated with other disorders such as anxiety as well as potentially self-destructive behaviors. For example, nearly 1 million people commit suicide every year, with depression identified as one of the main risk factors (WHO, 2012). This significant number of preventable deaths illustrates how the failure to detect and treat depression can lead to continuing deterioration, culminating in the taking of one’s own life. In 2018, WHO reported that 89% of the total health burden in Canada is due to mental illness. The annual cost of depression in Canada within 2016 alone was estimated at 30 billion dollars, with the related condition of anxiety costing approximately 20 billion dollars (Southerland & Stonebridge, 2016). This estimation includes the loss of resources and personnel in the labor force due to decreased productivity and a range of other illnesses/disabilities associated with depression. This estimation is similar across the globe. For example, a study conducted by the World Health Organization and The Lancet estimated that the global economy is losing approximately 900 billion dollars annually due to people in the workforce displaying signs of depression (Chisholm et al., 2016). Although substantial, this cost can be dramatically reduced by 8 the implementation of existing evidence-based strategies that facilitate the early detection and treatment of depressive symptoms (Picardi et al., 2016). 2.1.1 Depression among Adolescents Depression is the leading cause of disability worldwide and this disability is not limited to adults. One out of 10 Canadians aged between 15 and 24 has experienced depression in his or her lifetime; this age group had the highest prevalence of depression in 2012 (WHO, 2017; Pearson et al., 2013; Findlay, 2017). The rate of depression is quickly rising in the general population (about 33% from 2013 to 2016) with even higher increases among adolescents, where diagnoses of depression have risen by 65% for female adolescents and 47% for male adolescents (Blue Cross Blue Shield, 2018). Recent Canadian research explored 6-month prevalence of mental health disorder and found that 7.5% of Canadian adolescents reported the prevalence of major depressive disorders (Georgiades, Duncan, Wang, Comeau, & Boyle, 2019). The prevalence of major depressive disorders was higher among female adolescents (10.2%) compared to male adolescents (5.0%) (Georgiades et al., 2019). Some scholars argue that these statistics do not indicate actual increased incidence of depression, but rather are the result of better detection and diagnosis of depression as overall mental health stigma reduced (Anxiety and Depression Association of America, 2015). Although it is often assumed that mental illness primarily affects adults, adolescence has been recognized as a critical phase of human development during which depression often emerges (Costello, He, Sampson, Kessler, & Merikangas, 2014). Additionally, the onset of depression in adolescence is associated with a higher rate for continuum or relapse of depression in adulthood (Kim-Cohen et al., 2003). Adolescents also report more prolonged periods of depressive symptoms 9 when compared to adults and seniors (Pearson et al., 2013). The experience of depression among many adolescents is not an acute and short-term/seasonal challenge, but rather the beginning of a chronic and persistent health issue that has the potential to impact their health and quality of life well into adulthood and old age (Kim-Cohen et al., 2003). Recent surveillance efforts in Canada indicate that over one-third of students in Ontario reported moderate to serious psychological symptoms including symptoms of depression in 2015 (Boak, Hamilton, Adlaf, & Mann, 2017), and, much like in adults, strong evidence has been found of its relationship with other health issues such as anxiety disorders. Similarly, the Canadian Community Health Survey ascertained the highest rates of anxiety disorders in people between the ages of 15 and 24 (Pearson et al., 2013). Furthermore, numerous studies have reported on the strong association between severe depression and suicide in adolescents (Kral, 2016; Oliffe et al., 2016; Vasiliadis, Lesage, Latimer, & Seguin, 2015). Especially among female adolescents, depression is identified as the most associated factor, which increases the risk of suicide up to 20-fold (Hallfors et al., 2004). Many scholars have concluded that the best way to decrease the prevalence and the burden of depression is to take a preventive approach that begins in early developmental stages well before adulthood (Muñoz, Cujipers, Smith, Barrera, & Leykin, 2010). Despite these recommendations, depression has remained relatively understudied in adolescents. There are multiple approaches to gain better understanding about factors related to depression and how these factors may influence depression among adolescents (Thornton, 2017). Research indicates that early detection and the implementation of appropriate treatment improves the course of depressive disorders (Halfin, 2007). Earlier detection followed by immediate medical therapy has been associated with a reduced risk of relapse and projected 10 decreases in the overall cost of care required (Halfin, 2007). Furthermore, Le and Boyd (2006) reported that early detection and initiation of pharmacological/psychological intervention prevents the onset of severe depressive disorders. Among adolescents, recent evidence also demonstrates that early detection and treatment leads to improvements in health outcomes of depressive disorders (Siu, 2016). As notable literature demonstrates, these improvements serve as an important motivator for screening programs targeting depressive symptoms in the general population, and among adolescents in particular. 2.1.2 The Center for Epidemiologic Studies Depression Scale (CES-D) One of the most widely used tools to screen for depression is the Center for Epidemiologic Studies Depression Scale (CES-D), which was developed in 1977 by Lenore Sawyer Radloff (Radloff, 1977; 1991). The CES-D was designed to measure symptoms similar to the DSM diagnostic criteria (Van Dam & Earleywine, 2011). For example, the CES-D includes items assessing sadness, anhedonia, changes in appetite/sleep/concentration, guilt, physical changes and suicidal ideations (Radloff, 1977). The rationale behind the development of the CES-D was to produce a cost- and time-efficient assessment tool for use in general population settings (Radloff, 1977). The self-report format of the CES-D provided a means of easily screening large numbers of the general population for current depressive symptomology (with a focus on depressed mood) at a time when many assessment tools had to be administered in a clinical setting (Radloff, 1977). The CES-D was not based on a psychological framework, but rather was an extension of several existing depression detection measures, including the Beck Depression Inventory (BDI) and Zung’s Depression Scale (Radloff, 1977; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961; Zung, 1965). Radloff (1977) tested the CES-D in household interviews and in clinical settings in 11 the United States. Radloff (1977) originally recruited research participants aged 18 and older from communities in the United States. Although the CES-D was described as acceptable for use in general populations, the initial study samples were composed of White participants. History shows the peak period of immigration into the United States began in the 1970’s (Hipsman & Meissner, 2013), and the timing of the scale development may be the reason diverse ethnic samples were not included. The CES-D is composed of 20 items derived from previously developed assessments of depression questionnaires including symptoms such as restless sleep, poor appetite, and feeling lonely. The 20 items of the CES-D have been shown to have high internal consistency and satisfactory test-retest reliability (Radloff, 1977). For each item, respondents provide an answer to address how often they experience certain symptoms on a Likert-type scale between 0 and 3, where 0 represents rarely or none of the time, 1 represents some or little of the time, 2 represents moderately or much of the time and 3 represents most or almost all the time. After reversing the scores on several items, the items scored are summed to create a total score ranging from 0 to 60, with higher scores indicating greater depressive symptomology (Radloff, 1977). Despite the use of the total CES-D score to interpret the depressive symptoms, few researches attempted to explore the dimensionality of the CES-D and reported CES-D items may be grouped into different categories (Mackinnon, McCallum, Andrews, & Anderson, 1998; Zhang et al., 2015; Jiang et al., 2019). However, the suggested categories differ on how many categories should be used and which items should be under each category. Furthermore, other research supported the unidimensionality of the CES-D; therefore, we are going to treat the CES-D as a single measure of depressive symptoms (Fong et al., 2016; Wood, Taylor, & Joseph, 2010). 12 General recommendation for the cutoff point for clinically concerning depressive level is 16; however, this cutoff point can vary by participants’ age, gender and other characteristics (Weissman, Sholomskas, Pottenger, Prusoff, & Locke, 1977; Vilagut, Forero, Barbaglia, & Alonso, 2016). In addition to the 20 CES-D items, standard sociodemographic characteristics such as age, gender, education and occupation were also recommended for inclusion in assessments (Radloff, 1977); however, ethnic background was not included as one of the recommended supplementary sociodemographic items. 2.1.2.1 The CES-D and Adolescence Adolescence is a developmental stage where maturation in both physical and mental health, including cognitive functioning, occurs. During this developmental period, adolescents frequently feel intense emotions that fluctuate rapidly from one extreme to another (Kuppens et al., 2012). Interpretation of their responses to a question can be difficult, and using screening tools developed for adults requires that these tools be independently validated in adolescent populations. In addition to Radloff, many other researchers have examined the validity of applying the CES-D in adolescent populations (e.g., Garrison, Schlunchter, Schoenbach, & Kaplan, 1989; Garrison, Addy, Jackson, McKEOWN, & Waller, 1991; Schoenbach, Kaplan, Wagner, Grimson, & Miller, 1983). In particular, researchers have identified the need for different cutoff scores for adolescents (Roberts, Lewinsohn, & Seeley, 1991; Mojarrad & Lennings, 2002; Chabrol, Montovany, Chouicha, & Duconge, 2002; Cuijpers, Bolujit, & van Straten, 2008). Although few of these studies identify underlying reasons, they all recommend higher cutoff scores for the CES-D when using the tool among adolescents. 13 2.2 Depression among Ethnic Groups There has been an increase in efforts to investigate the extent of depression among ethnic groups in Canada, particularly among new immigrants. Studies report that rates of depression have increased among immigrants and that the development of depression is associated with stressors, including changes in environment, changes in social support systems and stigma involving negative experiences based on discrimination (Bhugra, 2003; Robert & Gilkinson, 2012; Salami, 2017). Additionally, Hermansson, Timpka and Thyberg (2002) articulated that language barriers contribute to unemployment among immigrants, which increases risk for depression. In terms of specific populations of immigrants, Lai (2000) reported higher rates of depression among Chinese immigrants in Canada when compared to Canadian-born individuals. Similar findings showed Chinese immigrants are more prone to severe mental health concerns compared to native-born Canadians (Robert & Gilkinson, 2012). Despite the evidence of an increased risk of depression among Chinese immigrants and the potential for this to lead to more severe mental illness, there are very few studies of depression among Chinese adolescents in Canada. The majority of current literature on depression among Chinese immigrants is focused on Chinese residents or Chinese immigrants living in the United States, and even though Canada shares many similarities with the United States, the policies and demographical characteristics of immigrants are very different in the United States than Canada, which emphasizes the need for research in Canada (Lim, Yeh, Liang, Lau, & McCabe, 2008; Okamura et al., 2016). Given that the immigrant experience in the US is described as a melting pot, where immigrants band together to make a new population, new culture and new societies, the results of these studies may not apply to Chinese immigrants living in Canada, where ethnic groups have sustained their uniqueness while functioning as part of the whole (Palmer, 1976; 14 Chudek, Cheung, & Heine, 2015; Lam, 2016). This has created a significant knowledge gap with respect to our understanding of depression among Chinese immigrants in Canada, particularly among adolescents. Key to addressing this gap is research on the validity of screening tools to assess depressive symptoms in surveillance efforts. 2.2.1 Chinese Adolescents as a Vulnerable Group for Depression China is the second most common country of birth for foreign-born people in Canada (Statistics Canada, n.d.). In 2016, nearly 1.8 million people in Canada reported Chinese ancestry (Statistics Canada, n.d.). Furthermore, close to half of foreign-born adolescents immigrated to Canada from a country in Asia, and it is projected that almost half of all children in Canada will have an immigrant background by the year 2036 (Statistics Canada, n.d.). Generally, adolescents from immigrant families are characterized as vulnerable for depression (Costigan, Koryzma, Hua, & Chance, 2010). Immigrant adolescents frequently encounter numerous hardships associated with immigration, including changes in environment, conflicts between different sets of cultural norms and discrimination. Asian immigrants in particular have been found to experience challenges related to perceived discrimination, acculturative stress, legal status concern, English proficiency, and low socioeconomic status (Li & Anderson, 2016). In addition to challenges related to discrimination, many Asian immigrants also struggle against a stereotype associated with the model-minority myth, a generalization that all Asian immigrants are self-sufficient and well-educated (Islam et al., 2010). Studies have shown that experience of these challenges, especially discrimination, is more problematic during adolescence, as this is a critical period in which adolescents explore their identities and are increasingly sensitive to others’ perceptions of them (Fine & Sirin, 2008). 15 Despite these challenges, immigrants who are unaccustomed to industrialized countries usually report better mental health outcomes than the native-born population. This finding can be explained by the concept of “healthy immigrant effect,” in which the population of immigrants is often viewed as a homogeneous group. Some economists reported that the number of immigrants in the city explains the decrease in median household income and the increasing rate of poverty in the United States, especially within regions presenting high rates of immigrants (Chang, 2003). Negative health outcomes, including related to mental health, are known to be more common with marginalized populations, as well as groups with low socioeconomic status that often include ethnic minorities (Bartley, Blane, & Montgomery, 1997; Blumenshine, Egerter, Barclay, Cubbin, & Braveman, 2010). However, in the late 1960s, the term “immigrant paradox” was introduced to explain two contradicting epidemiological phenomena (Karno & Edgerton, 1969; Markides & Coreil, 1986). The term "immigrant paradox" is defined as “the contrasting observations that immigrants usually experience similar or better health outcomes than the native-born populations despite socioeconomic disadvantage and barriers to health care use” (Urquia, O’Campo, & Heaman, 2012). This counter-intuitive concept explains that most immigrants in industrialized countries are economically disadvantaged when compared to the native-born population. Research on Canadian immigrants also supported the “healthy immigrant effect” on depression (Ali, 2002; Elamoshy & Feng, 2018; Georgiades et al., 2019). This healthy immigrant effect can be explained by the immigration selection process where migrant candidates who are both physically and mentally healthier than others, are more likely to be selected as eligible immigrants in Canada (Knowles, 2016). However, it is noted that the healthy 16 immigrant effect fades as the immigrants spend more years in migrated countries (Elamoshy & Feng, 2018). 2.2.2 Ethnic Identity and Distinct Culture of China A unique feature of Chinese culture is its more externally oriented focus compared to Western cultures (Nisbett, 2010). People influenced by Chinese culture tend to act or think based on others’ views, whereas people from Western cultures tend to rely on internal orientation such as self-awareness and self-value. For example, in a situation where a person acts against cultural values or norms, Chinese tend to feel shame while people from Western cultures tend to feel guilt. Shame and guilt are feelings related to failure to meet standards and norms about what is good, right, appropriate and desirable (Lewis, 1974). The difference is that guilt associates with one’s own negative self-evaluation (Smith, Webster, Parrott, & Eyre, 2002), whereas shame involves negative evaluation by others (Lewis, 1974). Guilt has an internal orientation, while shame has an external orientation. Therefore, shame is related to the fear of revealing a defective aspect of oneself to others. On the other hand, guilt is related to the fear of not satisfying one’s own standards (Benedict, 1946). Moreover, Chinese who feel shame often engage in avoidance and withdrawal behaviors (Lutwak, Panish, Ferrari, & Razzino, 2001). Generally, Chinese tend to be quiet and shy, and emotional outbursts are discouraged. These characteristics and suppression of emotions influence how Chinese express psychological distress (Kramer, Kwong, Lee, & Chung, 2002) and may also influence how they experience and express depression. A manifestation of an externally oriented culture is behavior related to ‘saving face’, which is a form of action that is not congruent with self-conscious or facts. The action of ‘saving face’ is widespread in many Asian cultures, and it is considered a common characteristic of 17 psychosocial behavior in China (Ting-Toomey, 1988; Ting-Tommey & Kurogi, 1998; Oetzel et al., 2001; Ting-Tommey & Oetzel, 2002). The intention of this culture is to improve others’ or oneself status (Li, Bronfman, & Zhou, 2017). Markus & Kitayama (1991) state that Chinese culture considers common relationships and harmony with others; therefore, Chinese people are often very sensitive to others’ perceptions in their efforts to maintain social harmony. People who are under the influence of Chinese culture are also often taught to suppress their own emotions and feelings, whether positive or negative, to maintain good relationships within social groups (Tsai, 2007). The tendency for Chinese to suppress emotions may influence their way of expressing depressive symptoms; it may cause Chinese to answer some CES-D items differently compared to equally depressed non-Chinese. Chinese culture also places value on modesty and self-effacement as cultural virtues and inhibits the expression of positive emotion (Iwata & Buka, 2002; Jang, Kim & Chiriboga, 2005; Lindridge & Wang, 2008). It is believed that the open expression of happiness may endanger the harmony within the group. Emphasizing positive emotions such as happiness or a sense of satisfaction are thought to lead to jealousy and envy by others (Bond 1991; Heine, Lehman, Markus, & Kitayama, 1999; Uchida, Norasakkunkit, & Kitayama, 2004; Yeung & Tung, 1996). Additionally, personal happiness is perceived to be tainted and incomplete (Uchida et al., 2004), so many Chinese do not have a strong desire to express positive personal emotions as this may adversely affect group harmony. In addition to differences related to the expression of happiness, Chen (1995) and Zhang (2007) reported that somatic forms of depressive symptoms such as fatigue and loss of appetite are more frequently reported in China because such symptoms are more acceptable in Chinese culture. The suppression of many outward emotions, especially 18 happiness, combined with the increased tendency to report somatic symptoms of depression suggests a need for research on the validity of depression assessments that were originally developed for use in Western cultures. Several studies have reported evidence of Chinese respondents suppressing emotions and feelings when providing responses to the CES-D. This suppression of emotions is thought to be an effort to appear humble and courteous, part of a more general belief that expressing direct feelings can destroy harmony in Chinese culture (Norem & Cantor, 1986; Chen, 1995; Noh, Kaspar, & Chen, 1998; Norem & Chang, 2002). The tendency of Chinese participants to withhold positive emotions is thought to contribute to a reluctance to endorse positive affect items in the CES-D (Norem & Cantor, 1986; Noh et al., 1998; Norem & Chang 2002). Cheung and Bagley (1998) argued that Chinese people are not used to acknowledging positive mood such as happiness compared to Westerners. Bond and Hwang (1986) suggested that Chinese express low positive affect as a result of socialization during childhood that focuses on harmony within the group. These findings indicate that significant bias may be present in Chinese respondents’ depression assessments and highlight the need for investigations of the validity of the CES-D among Chinese adolescents and the relationship between ethnic identity and the expression of depressive symptoms. 2.2.3 Strength of Ethnic Identity Many of the previously mentioned psychological and sociological findings originate from investigations of Chinese culture among participants living in China, where they are constantly encouraged to practice their culture and are surrounded by people who hold similar cultural beliefs. However, whether these findings speak to Chinese living as immigrants requires investigation. Acculturation is a psychological and cultural change commonly experienced by immigrants 19 exposed to more than one culture (Berry, 2015). This phenomenon is a long-term process with consistent socialization on individual levels and group levels (Berry, 2015). This process is viewed not as a loss of one’s culture but rather as a continuation of conflict and negotiation between multiple cultures, with the degree of acculturation varying across individuals (Berry, 2005). Chinese immigrants who maintain a strong Chinese identity will therefore be more likely to demonstrate distinct features of their native culture (including preferences related to the experience/expression of emotion), whereas Chinese adolescents who have reached a point of advanced acculturation in Canada may be influenced by Chinese cultural beliefs to a much lower extent. Given the general consensus in the literature that both immigrants and adolescents represent vulnerable populations, immigrant adolescents may be at particularly high risk for depression. However, immigrant adolescents do not always demonstrate significantly higher rates of depression. Some scholars have reported that immigrant adolescents possess a higher level of psychological adjustment when compared with non-immigrant adolescents, and these scholars have argued that strength of ethnic identity is one important factor that protects immigrant adolescents from developing depressive symptoms (Smokowski, Evans, Cotter, & Webber, 2014; Lee & Zane, 1998). This perspective is supported by the many studies reporting that adolescents who participate in their traditional cultural practices have increased psychological well-being (Soto, Baezconde-Garbanati, Schwartz, & Unger, 2015; Chandler & Lalonde, 1998; Currie, Wild, Schopflocher, Laing, & Veugelers, 2013; Zimmerman, Ramirez-Valles, Washienko, Walter, & Dyer, 1996; Phinney, & Alipuria, 1990). 20 Ethnic identity is a complex construct with many different definitions among scholars (Phinney, 1992). However, most researchers agree that it describes a sense of belonging to one’s own culture and the magnitude of engagement in cultural practices. It is often viewed as a process of advancement of identity within minority ethnic and racial groups, and it is reflected as a cycle of self-investigation (Ong, Fuller-Rowell, & Phinney, 2010). Given that the concept of ethnic identity is highly complex, developing a tool to measure ethnic identity has been challenging (Ong, Fuller-Rowell, & Phinney, 2010). However, one tool that has gained supportthe Multigroup Ethnic Identity Measure (MEIM)is designed for use in any ethnic group. The MEIM has been used widely to assess the construct in a wide range of ethnicities (Phinney & Ong, 2007). Identifying one’s own culture is viewed as a positive developmental effect through reinforcing self-esteem and conformity to cultural community norms (Phinney & Alipuria, 1990). One quantitative research study investigated communities that proactively protect and restore ethnic identity; the study of these communities presented lower self-destructive behavior rates in adolescents, which includes symptoms of depression (Chandler & Lalonde, 1998). The positive impact of a strong ethnic identity appears to be homogeneous across all immigrant adolescents: Studies have ascertained ethnic identity is a protective factor against depression among Europeans, Africans, and Latin Americans (Smokowski et al., 2014; Lee & Zane, 1998). Although having a strong ethnic identity appears to have a similar protective impact across cultures, it is important not to assume that all members in a specific immigrant group have the same sense of ethnic identity. Research indicates that people sharing the same ethnicity often have differing strengths of ethnic identity, especially within immigrant groups (Schimmele & Wu, 21 2015). Berry (1997) identified four different degrees of immigrants conforming to dominant cultures, each with varying impacts on their ethnic identity. The different levels of affiliation with one’s own ethnic identity as an immigrant range from strongly adapting to dominant culture without preserving own cultural distinctiveness to strong retention of own cultural distinctiveness (Berry, 1997). According to this theory, Chinese adolescents who are actively exploring and practicing Canadian culture, and not exploring and committing to Chinese culture, will likely not express somatic depressive symptoms and positive emotions. However, Chinese adolescents retaining Chinese cultural distinctiveness will be prone to expressing somatic items on the CES-D. This theory highlights the importance of examining the impact of strength of ethnic identity among Chinese adolescents in relation to the depression assessment provided by the CES-D. 2.2.4 The CES-D and Ethnic Groups When using depression screening tools, it is critical that the scores from the screening tools reflect levels of depression and not differences related to cultural disparities in the experience of depression or in responses to specific items. The concept of cultural equivalence refers to whether or not tool items that are interpreted in the same way across different cultures (Kim, Landis, & Cain, 2013). The validity of depression assessments thus depends on the accuracy of the tool when used in different immigrant populations (Yang, Jia, & Qin, 2015). For example, the Hamilton Rating Scale is no longer widely used because many scholars questioned the accuracy of this scale, noting inconsistent interpretation of scores and of symptom severity. The American Psychiatric Association (2000) recommended a total score of 23 and above as a mark of very severe depressive symptoms while Furukawa, Akenchi, Azuma, Okuyama, & Higuchi (2007) interpreted a score of 23 to 27 as moderate. The CES-D has demonstrated a generally consistent 22 interpretation of scores and of symptom severity in a range of different cultures (Black, Toombs, & Kisely, 2018; Zhang et al., 2012; Cosco et al., 2019). However, several studies have raised concerns about the CES-D’s validity, including internal consistency of CES-D items when used in cultures other than the dominant culture of the United States where the tool was developed (Li, Cheung, Chung, & Ho, 2010; Zhang et al., 2011; Cheng, Yen, Ko, Yen, 2012; Wang et al., 2013). It is also worth noting that Radloff (1977) emphasized that for the CES-D to be utilized in epidemiologic studies, the scale must possess proper reliability and validity within the subgroups of the population. 2.3 Gender To minimize the impact of potential bias in statistical studies, it is important to acknowledge and address effect modifiers (Greenland & Morgenstern, 1989). An effect modification is defined as heterogenous in the degree of an effect measure across each stratum (Rothman, Greenland, & Lash, 2008). It is addressed to recognize the difference of the specific effect of an exposure in different populations (Corraini, Olsen, Pedersen, Dekkers, & Vandenbrouke, 2017). Several factors may vary the stratum-specific effect measures of the relationship between ethnic identity and the experience of depression. Many mental health illnesses, particularly depression, have been shown to be influenced by gender (Oliffe & Greaves, 2011). Sex and gender-based analysis is therefore often recommended as an important consideration when conducting research on mental health issues (Oliffe & Greaves, 2011; Canadian Institutes of Health Research, 2018). Even though the terms ‘sex’ and ‘gender’ have different meanings, many studies have used these terms interchangeably (Canadian Institutes of Health Research, 2018). While sex is widely used as a biological term 23 characterizing physical and physiological features, gender is a complex sociocultural concept. Gender can be described as including self-defined gender identity, socially constructed roles, and self-reported gender relations. Gender is defined and expressed through a complex interaction of social, psychological and physiological systems that begin to significantly differentiate during adolescence. (Canadian Institutes of Health Research, 2018). As described earlier, there is clear evidence of gender differences in both the prevalence of depression and the expression of depressive symptomatology. Researchers have noted that during adolescence, females are more likely to suffer from depressive symptoms than males; however, there does not appear to be a single explanation for this phenomenon (Nolen-Hoeksema & Hilt, 2013). These findings have led other researchers to conduct in-depth investigations of gender differences in CES-D responses (Callahan & Wolinsky, 1994; Van de Velde, Bracke, Levecque, & Mueleman, 2010). Van de Velde, Bracke, Levecque, & Mueleman (2010) reported significant gender differences in responding to CES-D items after adjusting for possible measurement bias. These results demonstrate the importance of addressing potential gender differences while using the CES-D. Historically, many studies have used the CES-D to investigate depression among diverse populations based on geographic environment, ethnic group, age, occupation, illness type and culture (O’Hara, Kohout, & Wallace, 1985; Andresen, Malmgren, Carter, & Patrick, 1994; Hann, Winter, & Jacobsen, 1999; Tak, Driscoll, Bernard, & West, 2007). However, very few published studies have examined the reliability and validity of the CES-D in these different populations. Radloff (1991) and Garrison et al. (1989) did report that the CES-D is appropriate for use in adolescent populations. Although the CES-D was recommended for use in both males and females, several studies have found statistical evidence of measurement inequivalence for different groups. 24 For example, Stommel et al. (1993) showed different response patterns of female versus male participants. Gelin and Zumbo (2003) found that the odds of females responding to Item 17 in the CES-D (“I had crying spells”) is about 9 times that of males. This evidence indicates that there is a need for additional research on the validity of the CES-D across groups defined by gender, ethnicity and age. 2.3.1 Measurement of the Strength of Ethnic Identity For limiting the impact of potential bias in statistical study, it is important to acknowledge and address factors that may bias the results (McNamee, 2003). Information bias is defined as “a distortion in the measure of association caused by a lack of accurate measurements of key study variables” (Alexander, Lopes, Ricchetti-Masterson, & Yeatts, 2015). Due to a lack of accurate information, any Chinese participants might be assumed to have similar strengths of ethnic identity. As described earlier, there is evidence suggesting that strength of ethnic identity is associated with both the prevalence of depression and the expression of depressive symptomatology. Therefore, in this study, we treat the MEIM scale as the measurement of the strength of ethnic identity that can be used to examine the impact of that strength on research examining the validity of assessment tools across different ethnic groups. 2.4 Differential Item Functioning One statistical approach used to examine the validity of measurement tools in different populations is to test for differential functioning in test items across predefined groups. Analyses based on differential item functioning (DIF) began to appear regularly in academic literature in the 1990’s. Prior to the 1990’s, the term ‘item bias’ was used, especially in studies investigating factors 25 that might influence test performance in different groups of participants (Angoff, 1993). DIF is indicated when members of different groups (e.g., males versus females) possess different statistical probabilities of endorsing a questionnaire item after matching on the total score (Zumbo, 1999). In other words, DIF analyses compare a focal group (e.g., Chinese adolescents) with a reference group (e.g., White adolescents) on item performance after conditioning (or controlling) of the overall performance on the measured traits (Holland & Wainer, 2012). When DIF occurs, it indicates that there is possible test bias, as the probability of endorsing a test item is associated with group membership in addition to the level of the latent construct being measured. Many scholars emphasized that DIF is required but not sufficient to indicate the existence of item bias (Zumbo, 1999; Ackerman, 1992). 2.4.1 Validity, Item Bias and Item Impact The use of tests or measurement tools such as the CES-D is very common in psychological and medical research as well as in public health surveillance programs. When the scores of a test or assessment play a central role in making a decision or diagnosis, the reliability and validity of the test needs to be investigated. The presence of item bias in a diagnostic test can result in either underdiagnosis or undiagnosed of a condition for a specific group. With the growing importance of testing in research and surveillance efforts, many medical researchers have investigated the validity of tools by examining measurement invariance and item bias using approaches such as DIF. Measurement invariance is defined as a statistical condition how a measurement instrument is to perform/ to measure the same construct across different groups (Bialosiewicz, Murphy, & Berry, 2013). Bias is defined as “any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease” (Schlesselman, 26 1982). For example, item bias occurs when members of a specific group are more likely to endorse an item compared to participants of another group because some characteristics of the test item are irrelevant to the purpose of the test (Zumbo, 1999). Item bias occurs when, for example, participants from group A are less likely to endorse hospital initial intake questions than participants from group B due to the cultural sensitivity of test items or the gender of intake personnel, which are not relevant to the test’s purpose. Bias has been characterized as “a source of invalidity that keeps some examinees with the traits or knowledge being measured from demonstrating the ability” (Shepard, Camilli, & Williams, 1985, p.79). Item bias is therefore a potential threat to the validity of tests and the inferences based on the test scores. Zumbo (1999) iterated that the concept, method and process of validation is key to evaluating measures, and highlighted that without validation, any inferences made from a measure are meaningless. Messick (1995) stated “The principles of validity apply not just to interpretive and action inferences derived from test scores as ordinarily conceived, but also to inferences based on any means of observing or documenting consistent behaviors or attributes.” These views suggest that validity is therefore not a property of the measurement tool but rather of the inferences made from scores derived from its application (Cizek, Rosenberg, & Koons, 2008; Zumbo, 1999; Messick, 1995). If score-based inferences are not equally valid across all relevant test participants, decisions based on score inferences will be biased (Langenfeld, 1997). The consequences of testing are very important, especially in medical diagnostic applications (Cizek et al., 2008). Messick (1988) stated it is not the apparent misuse of biased tools that is the issue, but rather the use of the yet- ratified interpretation of decisions derived from tools that can be traced back to test invalidity. 27 When there are many attempts to understand item bias and its relationship with testing tools, item impact has not been fully explored, even though it is an important concept when examining the validity of specific tests. In 1999, Zumbo defined item impact as “evident when examinees from different groups have differing probabilities of responding correctly to an item because there are true differences between the groups in the underlying ability being measured by the item”; also, in 2017, the concept was revised as “the difference in the item response that is attributable to the group’s difference in the measured construct based on an unbiased group comparison” (Wu, Liu, Stone, Zou, & Zumbo, 2017). In most recent definitions, there was an attempt to clarify what “true differences” means. For example, medical students may be more likely to answer biology questions correctly compared to students majoring in music. In this case, item impact occurs because medical students’ interests and performance skills are different than those of students who are majoring in music. Ackerman (1992) argued that item impact is “unavoidable” and that we should not view this matter in “a pejorative manner.” Wu et al. (2017) also emphasized the fact that item impact cannot be detected when item bias is present because “the true difference is obfuscated by groups’ differential responding.” To distinguish item impact from item bias, Wu et al. (2017) proposed a framework (see Figure 2-1) where item bias is examined first while controlling for covariates, and item impact is investigated when item bias has not been detected. 28 Figure 2-1 Conceptual Framework of Item Bias and Item Impact In psychological testing, the absence of bias is an important aspect of test validity. There are multiple statistical techniques to address test validity and measurement invariance by detecting potential bias; for example, multiple group confirmatory factor analysis (MG-CFA) is often used 29 to examine measurement bias and scale-level equivalence using a set of items comprising a test (Zumbo, 2003; Carle, Weech-Maldonado, Ngo-Metzger, & Hays, 2012). However, Zumbo and Koh (2005) stated that even if scale-level analysis reported measurement invariance, item-level bias can still be present. To analyze item-level equivalence, DIF is widely used. The presence of DIF in a test indicates that one or more items are measuring a “nuisance dimension” or other dimension (e.g., gender) that is unrelated to the primary dimension (e.g., depression) being measured by the remainder of the test (Ackerman, 1992). Detecting items that demonstrate DIF is critical to evaluating the validity of test scores. Zhang (2009) argued that statistically identifying items that are functioning differently is relatively straightforward compared to searching for the mechanisms underlying DIF. Furthermore, Scheuneman (1987) claimed that DIF is a consequence of a complex interaction between the item and testing participants and that it is rare that single variables will fully explain the detected DIF. 2.4.2 Different Types of DIF There are two different types of DIF: uniform and non-uniform. Uniform DIF is a simpler version of DIF, where there is no interaction between the ability level (i.e., amount of construct being measured) and group membership. Uniform DIF occurs when the ability level is consistent across analysis (Walker, 2011). In other words, the extent of DIF does not vary over the range of ability levels. In contrast, non-uniform DIF shows an interaction between ability level and group membership (Swaminathan & Rogers, 1990). In other words, the extent of DIF here varies according to the level of ability. See Figure 2-2 for a graphical description of uniform and non-uniform DIF. 30 Figure 2-2 Uniform and Nonuniform DIF 31 There are several different statistical techniques to quantify the magnitude of DIF: Item Response Theory (IRT) methods, Mantel-Haenszel (M-H) statistics, the Simultaneous Item Bias Test (SIBTEST) procedure, Standardization method (STD), and the logistic regression (LR) procedure (Thissen, Steinberg, & Wainer, 1993; Holland & Thayer, 1988; Shealy & Stout, 1993; Dorans & Kulick, 1986; Dorans, Schmitt, & Bleistein, 1992; Swaminathan & Rogers, 1990). For IRT methods, there are different approaches based on a range of IRT models that use a matching variable that is an estimate of latent ability rather than the observed score (Clauser & Mazor, 1998). Latent ability is an unobservable variable such as intelligence or depression, which tests are designed to measure using responses to a set of question items (Zumbo, 1999). Mantel-Haenszel statistics use matched observed variables to compare focal and reference groups. M-H procedures estimate the constant odds ratio that yields a measure of effect size for evaluating the magnitude of DIF (Holland & Thayer, 1988). The SIBTEST procedure is an alternative statistical method for detecting DIF. The matching criteria for the SIBTEST examine the significance based on the ratio of the weighted difference in the proportion correct for the focal and reference groups to its standard error (Clauser & Mazor, 1998). Similar to the M-H procedure, the Standardization method computes the standardized difference in the proportion correct (Clauser & Mazor, 1998). The use of logistic regression to detect DIF is similar to the M-H DIF procedure with respect to the ease of computation and interpretation, and it provides a measurement of magnitude for DIF. The main strength of the logistic regression approach compared to the other methods is the ability to detect non-uniform DIF (Zumbo, 1999). Swaminathan and Rogers (1990) introduced the use of logistic regression and demonstrated its ability to detect non-uniform DIF compared to M-H methods. Another strength of the logistic regression approach is the ability to adjust for 32 covariates. Zumbo (1999) and Swaminathan and Rogers (1990) built several regression models by adding variables and interaction terms. By adding additional covariates to the logistic regression model, it is possible to address potential confounding factors that may be influencing the results of the DIF analyses. 2.5 Ordinal Logistic Regression DIF Zumbo (1999) described LR DIF as a three-step process. In the first step, the total test score is entered into the regression model to predict individual item responses. This model can be thought of as examining the extent to which the total score predicts response to an individual item in the test. In the second step, a covariate representing group membership is added to the model. This model can be thought of as examining the extent to which group membership predicts response to an individual item in the test after adjusting for total scores on the test. The third step involves testing a regression model that also includes an interaction term between total test score and group membership. This model can be thought of as testing whether the impact of group membership on the individual item responses varies according to the level of total score. According to Gadermann, Chen, Emerson, and Zumbo (2018), “An assumption of all DIF methods is that there is empirical support for the items comprising the scale to be summed to a total score”. In other words, the scale is unidimensional. To test for the presence of DIF, a Chi-square test for logistic regression is computed from the third step to the first step. The subtracted Chi-square value is compared to its distribution function with two degrees of freedom, as the third step should have a statistical model that has two more degrees of freedom than the first step model (Zumbo, 1999). In addition to comparing Chi-33 square values, the method proposed by Zumbo (1999) and Jodoin and Gierl (2001) described that researchers also examine the difference among pseudo R-squared values in between each step. Comparing pseudo R-squared values can be thought of as a measure of effect size in that the change in pseudo R-squared values represents the amount of variation in each item response that can be explained by group membership after conditioning on total scores. Building on recommendations made by Zumbo (1999) and Jodoin and Gierl (2001), Gelin and Zumbo (2003) stated that those described two criteria must be met for an item to be flagged as displaying DIF. Most DIF methods were designed for binary scored items. However, Zumbo (1999) introduced a new ordinal logistic regression DIF for ordinal items based on the original LR DIF by Zumbo and Thomas (1997).  First model Logit p = 𝑏0 + 𝑏1 𝐴𝑏𝑖𝑙𝑖𝑡𝑦  Second model Logit p = 𝑏0 + 𝑏1 𝐴𝑏𝑖𝑙𝑖𝑡𝑦 + 𝑏2 𝐺𝑟𝑜𝑢𝑝  Third model Logit p = 𝑏0 + 𝑏1 𝐴𝑏𝑖𝑙𝑖𝑡𝑦 + 𝑏2 𝐺𝑟𝑜𝑢𝑝 + 𝑏3𝐴𝑏𝑖𝑙𝑖𝑡𝑦 ∗ 𝐺𝑟𝑜𝑢𝑝 Zumbo (1999) stated the advantages of using the ordinal logistic regression DIF as follows: (a) using ordinal logistic regression has the advantage of using the same modeling strategy for binary and ordinal items, (b) this common statistical model for binary and ordinal items should ease the process of implementation in an organization where DIF analyses are not yet common, and 34 (c) the Zumbo-Thomas DIF effect size method can be extended to ordinal logistic regression; hence, unlike the other methods (e.g., generalized M-H or logistic discriminant function analysis), one has a test statistic and a natural corresponding measure of effect size. 2.6 Research Questions Given the evidence of the impact of ethnicity on the experience and expression of depression, it is necessary to examine the validity of depression screening tools for different ethnic groups. Previous literature suggests that Chinese and Western cultures differ in terms of depressive expression in that Chinese respondents are more likely to express somatic items, and less likely to express positive emotions (Chen, 1995; Zhang, 2007). In addition to differences across specific ethnic groups of adolescents (e.g., Chinese and White), the literature also suggests Chinese adolescents with a stronger ethnic identity will likely express higher somatic symptoms of depression as compared to equally depressed Chinese adolescents with a weaker sense of ethnic identity (Berry 1997, 2005, 2015). The main purpose of this study is, therefore, to investigate the validity of the CES-D screening tool by examining measurement invariance across ethnic groups. Measurement invariance will be assessed by testing for differential item functioning on the items in the CES-D by ethnicity group (i.e., Chinese and White) as well as by strength of ethnic identity within Chinese respondents. The specific questions of this research are as follows: I. To examine the measurement invariance across ethnicities in the CES-D among Canadian adolescents. A. When conditioning on the CES-D total score, does the probability of an item’s response differ between Chinese and White adolescents? B. Does considering gender and strength of ethnic identity in the DIF analyses influence the detection of DIF in the CES-D? 35 II. To investigate the validity of the CES-D while considering the impact of strength of ethnic identity among Chinese adolescents. A. To what extent is the CES-D an equivalent measure of depressive experience/symptoms when comparing Chinese adolescents with stronger ethnic identity and those with weaker ethnic identity? B. Does considering gender influence the detection of DIF in the CES-D among Chinese adolescents? 36 Chapter 3 Methods and Analysis 3.1 Data The data analyzed for this research was obtained from the British Columbia Adolescent Substance Use Survey (BASUS). BASUS is a large prospective cohort survey that began in 2009 with a focus on examining the relationship among high school students’ psychosocial functioning, their attitudes and the use of substances including tobacco, alcohol and cannabis. Every six months, participants self-reported on a wide range of questions regarding family, psychological development, personal health and well-being, community, school and peers. 3.1.1 British Columbia Adolescent Substance Use Survey (BASUS) BASUS followed approximately 3000 students from 65 different schools across five health authorities in British Columbia (BC), assessing them from grade eight until grade eleven. Although core content related to substance use was included in every wave of the survey, some assessments of psychosocial functioning were not included in all survey waves. The data for this thesis was collected in Wave 5 of the BASUS study, which included the MEIM score and CES-D Scale. A total of 1592 students participated in Wave 5, of which 319 reported their ethnicity as Chinese and 897 reported as Whites. Participants indicating that they were both Chinese and White were treated as Chinese, resulting in a final sample containing 319 Chinese participants and 832 White participants. The BASUS Research Team members contacted school district superintendents across BC to receive approval to contact individual public secondary school principals and conducted recruitment. BASUS staff conducted presentations in participating schools, promoted the study and/or had other school staff distribute information packages to students as part of their school 37 orientation materials in September. The information packages included a pamphlet describing the study, an information letter for guardians, and study promotional materials. Other promotion methods included advertisements in student newspapers and school posters. The survey was easily accessible; students could participate by visiting the BASUS website. Students registered with the online survey system, and they were presented with a consent form. Upon consenting, the students went through eligibility screening questions. The eligibility criteria were 1) minimum age of 13, and 2) attending a BC secondary school. The mean participation rate was one-fifth among all potentially eligible students in participating schools. There were numerous potential influences contributing to the variation in response rates, especially a lack of time and resources to promote the survey to students stemming from an ongoing labour dispute between teachers and the school boards. 3.1.2 Ethical Considerations There are several ethical considerations with this study. In addition to the informed consent upon registering for the online survey, participants were given the opportunity to ask questions, to which the BASUS Research Coordinator and/or the Primary Investigator responded. In order to maintain confidentiality, the online survey system including all raw data was stored on a secure server at the Arthritis Research Centre (ARC) of Canada in Vancouver, British Columbia. Participants were provided resource information for adolescents’ services, such as counselling resources, on the BASUS survey homepage that served as an access portal for the BASUS survey. The BASUS study was approved by participating school districts, secondary schools and the UBC Behavioural Ethics Board (application number: H08-02841-38 A011). The Canadian Institutes of Health Research (CIHR) funded the BASUS study (project number 07-3631). 3.2 Measures A short set of demographic questions was included in the web-based survey. Participants self-reported gender, age, grade and ethnic background. Participants identified their gender as either male or female. Ethnicity. Participants completed a checklist of ethnic and racial descriptors frequently used in many government surveys. The survey contained the following options for ethnicity descriptors: Aboriginal, Chinese, Japanese, Korean, Filipino, South East Asian, West Asian, Latin American, Black, White/Caucasian and Other. If participants were not able to identify their ethnicities in any of the above-stated categories, they were able to check Other and specify what ethnicity they identify as using an open text box. In this study, we selected participants who identified as White/Caucasian as a reference group and Chinese as a focus group. Depression. The 20-Item Center for Epidemiologic Studies Depression Scale (CES-D) is a widely used tool developed to assess depressive symptoms originally developed by Radloff (1977). It is a self-report questionnaire (see Table 3-1 for a list of items) where participants report weekly frequency of depressive symptoms using a 4-point Likert-type scale that includes the following descriptions: 0. Rarely or none of the time (less than 1 day) 1. Some or a little of the time (1–2 days) 2. Occasionally or a moderate amount of time (3–4 days) 3. Most or all of the time (5–7 days) 39 The CES-D total score is generally interpreted as a measure of overall depression symptomatology, with higher scores indicating the presence of more symptomatology (Radloff, 1977). Researchers have examined the factors structure of the CES-D items, with results ranging from unidimensional to five or more factors being present (Wood, Taylor, & Joseph, 2010; Cheng, Yen, Ko, & Yen, 2012; Verhoeven, Sawyer, &Spence, 2013; Wang et al., 2013). Items comprising construct also vary by age, gender, and cultural characteristics (Wood et al., 2010; Cheng et al., 2012; Verhoeven et al., 2013; Wang et al., 2013). To determine the number of individuals with scores suggesting they are at risk for clinical depression, we used cutoff scores for adolescents presented in Roberts et al. (1991). Generally, scholars and health care professionals use a CES-D cutoff score of 16 to assess medically depressive symptoms. However, this cutoff score has been validated in the general population of adults, and many papers raised questions regarding its validity to detect clinical symptoms in adolescents. Roberts et al. (1991) has suggested 24 as a cutoff CES-D score for adolescents and showed improved sensitivity and specificity; this cutoff score has been used widely for adolescents (Closson et al., 2018). 40 Table 3-1 The CES-D Items Item Number Item content1 CES-D 1 I was bothered by things that usually do not bother me CES-D 2 I did not feel like eating; my appetite was poor CES-D 3 I felt that I could not shake off the blues even with help from my family or friends CES-D 4 I felt that I was just as good as other people CES-D 5 I had trouble keeping my mind on what I was doing CES-D 6 I felt depressed CES-D 7 I felt that everything I did was an effort CES-D 8 I felt hopeful about the future CES-D 9 I thought my life had been a failure CES-D 10 I felt fearful CES-D 11 My sleep was restless CES-D 12 I was happy CES-D 13 I talked less than usual CES-D 14 I felt lonely CES-D 15 People were unfriendly CES-D 16 I enjoyed life CES-D 17 I had crying spells CES-D 18 I felt sad CES-D 19 I felt that people dislike me CES-D 20 I could not get going 1 Response Option: 0 = Rarely or none of the time (less than 1 day) 1 = Some or a little of the time (1–2 days) 2= Occasionally or a moderate amount of time (3–4 days) 3 = Most or all of the time (5–7 days) 41 Ethnic Identity. The Multigroup Ethnic Identity Measure (MEIM) is a 6-item self-report survey designed to assess ethnic identity (Phinney, 1992; Phinney & Ong, 2007). It uses a 5-point Likert-type scale ranging from ‘strongly agree’ to ‘strongly disagree’ (see Table 3-2). Two subscales are embedded in this survey: 1) the ethnic identity search or exploration factor, and 2) the commitment factor (Phinney, 1992; Phinney & Ong, 2007). However, many studies report that the 6 items can also be represented using a single factor (Umaña-Taylor, Vargas-Chanes, Garcia, & Gonzales-Backen, 2008). For the purposes of this research, we are using the single factor approach to score the 6 items. Table 3-2 MEIM Scale Exploration Item 1 I have spent time trying to find out more about my ethnic group, such as its history, traditions, and customs Item 2 I participated in cultural practices of my own group, such as special food, music, or customs Item 3 I have often talked to other people in order to learn more about my ethnic group Commitment Item 4 I have a strong sense of belonging to my own ethnic group Item 5 I understand what my ethnic group membership means to me Item 6 I feel a strong attachment towards my own ethnic group Note. Response Option: 1 = Strongly Disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly Agree Demographic Variables. Demographic variables that have been shown to affect symptoms of depression are often routinely included in public health research to account for potential biases. For the purposes of the research, we considered gender, age, and parents’ educational levels. In this paper, self-reported binary sex (e.g., male or female) is interpreted in terms of gender. 42 Participants were asked to identify as male or female, and the available response options for age ranged from 12 years old or younger to 19 years old. 3.3 Analysis 3.3.1 Missing Value Analysis Missing data is a well-known problem in research, including in the medical field. There are multiple methods of handling this missing data, with many methods incorporating the imputation of missing data using some form of prediction (Graham, Cumsille, & Elek-Fisk, 2003). When missing data is considered random conditionally on specific population characteristics that is related to the reason for missingness, then it is called missing at random (MAR) (Schafer & Graham, 2002). When MAR is present, simple techniques such as only analyzing available complete cases can lead to biased results. Missing data in the form of MAR may not represent a random sample, and there is a risk of selection bias (Schafer & Graham, 2002). As a precaution, we used multiple imputation techniques to determine if the results from our analysis complete cases yielded different parameter estimates in the DIF analyses, as multiple imputation is assumed to provide unbiased results in MAR cases (Donders, Van Der Heijden, Stijnen, & Moons, 2006). According to Donders et al. (2006), imputation methods are derived from random sampling and its underlying assumptions. In statistics, it is assumed that in a random sampling study, the result is not relevant to the characteristics of the sample population used in the study. Similar results should be consistently obtained when different random samples are analyzed. For this research, 10 imputations were chosen in combination with a conservative data filtering approach: only keeping cases with a maximum of 20% missing data in the CES-D and MEIM 43 items, which in this case is the maximum of one item missing out of MEIM 6 items. A previous sensitivity analysis concluded that more than 10% of the value missing led to a comparatively less consistent result (Barzi & Woodward, 2004). Multiple imputation is done by creating multiple copies of the dataset where missing values are replaced by imputed values sampled from a previously predicted distribution, and usually 5 to 10 copies of the dataset is considered sufficient (Spratt et al., 2010). The 10 imputed datasets were generated using the Missing Values program of IBM SPSS 25. We set the method to be selected automatically by SPSS, and the Linear Regression model was selected to impute missing data. 3.3.2 Descriptive Analysis A combination of descriptive univariate and bivariate analyses were used to compare means and proportions for the total sample and for each ethnic group on key demographic and variables of interest (e.g., CES-D and MEIM scores). We used a p-value of 0.05 to analyze statistical significance. In traditional DIF testing, researchers typically use three main variables to detect DIF: group membership, a conditioning variable (usually the total score from the assessment) and the interaction between group membership and the conditioning variable. Investigating the relationship between each test item response and group membership, the conditioning variable and the interaction between group membership represents the first set of DIF analyses. In addition to this set of analyses, we performed a second set of DIF analyses that adjusted for covariates that might bias the results of the analysis. Based on our review of the literature, we included gender and strength of ethnic identity as covariates in our DIF analyses. 44 3.3.3 Ordinal Logistic Regression - Differential Item Functioning Ordinal logistic regression is widely used for depression measurement to investigate DIF. Zumbo (1999) extended the original Zumbo-Thomas DIF effect size method for binary logistic regression to contexts requiring the use of ordinal logistic regression models (e.g., when the item responses being examined contain more than 2 ordered response categories). Zumbo (1999) stated that to make a statistical decision on the presence of DIF, two criteria should be met: 1. The two degrees of freedom Chi-square test for DIF must have a p-value equal or less than 0.01 (i.e., the change within the Chi-square test is larger than 9.21). 2. The corresponding effect size measure (i.e., R-squared value attributable to DIF) must have an R-squared value of at least 0.035. 3.3.3.1 Ordinal Logistic Regression - Question I-A To answer our research question I-A “Does ethnicity DIF exist for the CES-D when comparing Chinese adolescents and White adolescents?”, the initial DIF models were built as follows (see Figure 3-1 for each step): y = 𝑏0 + 𝑏1𝑇𝑂𝑇𝐴𝐿 + 𝑏2𝐸𝑇𝐻𝑁𝐼𝐶𝐼𝑇𝑌 + 𝑏3𝑇𝑂𝑇𝐴𝐿 ∗ 𝐸𝑇𝐻𝑁𝐼𝐶𝐼𝑇𝑌𝜄 + 𝜀𝜄 where y is a natural log of the odds ratio and the 𝜀𝜄 is distributed with mean zero and variance 𝜋23⁄ . This equation can also be expressed as 𝑙𝑜𝑔𝑖𝑡 [𝑃(𝑌 ≦ 𝑗)] = 𝛼𝑗 + 𝑏1𝑇𝑂𝑇𝐴𝐿 + 𝑏2𝐸𝑇𝐻𝑁𝐼𝐶𝐼𝑇𝑌 + 𝑏3𝑇𝑂𝑇𝐴𝐿 ∗ 𝐸𝑇𝐻𝑁𝐼𝐶𝐼𝑇𝑌𝜄, j=1,2,…, c-1, where c is the number of categories in the ordinal scale, and 𝛼𝑗 is a separate parameter for categories (Zumbo, 1999). 45 For each model, yi is the participant’s answer to the ith CES-D item. Step one predicts the ith item response by TOTAL, which is the CES-D total score in this study. The CES-D total score is the sum score of all 20 items. The second step uses group membership, ETHNICITY in addition to the previous model. ETHNICITY is a binary variable of either White or Chinese. Whites are defined as a reference group and Chinese are defined as a focal group. For the last step, the interaction term between TOTAL and ETHNICITY was added to the logistic equation. 3.3.3.2 Ordinal Logistic Regression - Question I-B As outlined in the research objectives, we included two additional covariates in the statistical analysis for the detection of DIF outlined in the previous section to determine if their inclusion influenced the results. The selection of the covariates was based on a literature review and descriptive analysis where they showed a statistically significant relationship with CES-D scores. The final model thus included adjustments for MEIM total mean scores and gender as covariates in our second DIF analysis. Figure 3-1 Initial Analysis of Differential Item Functioning Step #1: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 One first enters the conditioning variable, CES-D total score was used in this paper. Step #2: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟐𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀 The group variable is entered: Ethnicity (Whites or Chinese) Step #3: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟐𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀+ 𝒃𝟑𝑻𝑶𝑻𝑨𝑳 ∗ 𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀 The group by ability interaction term is entered into the equation. 46 To answer our research question I-B “Does considering gender and strength of ethnic identity in the DIF analyses influence the detection of DIF in the CES-D?”, the secondary LR DIF model was built as follows (see Figure 3-2 for each step): yi = 𝑏0 + 𝑏1𝑆𝑒𝑥 + 𝑏2𝑀𝐸𝐼𝑀 + 𝑏3𝑇𝑂𝑇𝐴𝐿 + 𝑏4𝐸𝑡ℎ𝑛𝑖𝑐𝑖𝑡𝑦 + 𝑏5𝑇𝑂𝑇𝐴𝐿 ∗ 𝐸𝑇𝐻𝑁𝐼𝐶𝐼𝑇𝑌𝜄 + 𝜀𝜄 where yi is the participant’s answer to the ith CES-D item and 𝜀𝜄 is distributed with mean zero and variance 𝜋23⁄ . 3.3.3.3 Ordinal Logistic Regression - Question II-A To answer our third research question II-A “Does strength of ethnic identity DIF exist for the CES-D when comparing Chinese adolescents with stronger ethnic identity and those with Figure 3-2 Secondary Analysis of Differential Item Functioning Step #1: yi = 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙 + 𝒃𝟐𝑴𝑬𝑰𝑴+ 𝒃𝟑𝑻𝑶𝑻𝑨𝑳 One first enters the conditioning variable, CES-D total score while adjusting for covariates: MEIM and sex. Step #2: yi = 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙 + 𝒃𝟐𝑴𝑬𝑰𝑴+ 𝒃𝟑𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟒𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀 The group variable (Ethnicity) is entered while adjusting for covariates: MEIM and sex. Step #3: yi= 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙+ 𝒃𝟐𝑴𝑬𝑰𝑴+ 𝒃𝟑𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟒𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀+ 𝒃𝟓𝑻𝑶𝑻𝑨𝑳 ∗𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀 The interaction term is entered into the equation while adjusting for the same covariates. 47 weaker ethnic identity?”, the initial DIF models were built as follows (see Figure 3-3 for each step): y = 𝑏0 + 𝑏1𝑇𝑂𝑇𝐴𝐿 + 𝑏2𝐸𝑇𝐻𝑁𝐼𝐶 𝐼𝐷𝐸𝑁𝑇𝐼𝑇𝑌 + 𝑏3𝑇𝑂𝑇𝐴𝐿 ∗ 𝐸𝑇𝐻𝑁𝐼𝐶 𝐼𝐷𝐸𝑁𝑇𝐼𝑇𝑌𝜄 + 𝜀𝜄 where the 𝜀𝜄 is distributed with mean zero and variance 𝜋23⁄ . Figure 3-3 Initial Analysis of DIF for Chinese Adolescents Only Step #1: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 One first enters the conditioning variable, CES-D total score was used in this paper. Step #2: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟐𝑬𝑻𝑯𝑵𝑰𝑪 𝑰𝑫𝑬𝑵𝑻𝑰𝑻𝒀 MEIM total mean score was entered as the second adjusting variable Step #3: yi = 𝒃𝟎 + 𝒃𝟏𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟐𝑬𝑻𝑯𝑵𝑰𝑪𝑰𝑻𝒀 + 𝒃𝟑𝑻𝑶𝑻𝑨𝑳 ∗ 𝑬𝑻𝑯𝑵𝑰𝑪 𝑰𝑫𝑬𝑵𝑻𝑰𝑻𝒀 The ability interaction term is entered into the equation. This analysis is similar to the first analysis conducted in this study, but the selected participants are limited to Chinese adolescents. We also replaced the categorical group membership variable with a continuous measure of the strength of ethnic identity, in this case the MEIM total score. The MEIM total score is treated as a continuous variable and will be adjusted in the second step. The interaction term between the CES-D total score and the MEIM total mean score will be introduced in the third step. 48 3.3.3.4 Ordinal Logistic Regression - Question II-B To answer our research question II-B “Does considering gender influence the detection of DIF in the CES-D among Chinese adolescents?”, the secondary LR DIF model was built as follows (see Figure 3-4 for each step): y = 𝑏0 + 𝑏1𝑆𝑒𝑥 + 𝑏2𝑀𝐸𝐼𝑀 + 𝑏3𝑇𝑂𝑇𝐴𝐿 + 𝑏4𝐸𝑇𝐻𝑁𝐼𝐶 𝐼𝐷𝐸𝑁𝑇𝐼𝑇𝑌 + 𝑏5𝑇𝑂𝑇𝐴𝐿∗ 𝐸𝑇𝐻𝑁𝐼𝐶 𝐼𝐷𝐸𝑁𝑇𝐼𝑇𝑌𝜄 + 𝜀𝜄 where the 𝜀𝜄 is distributed with mean zero and variance 𝜋23⁄ . Figure 3-4 Secondary Analysis of DIF for Chinese Adolescents Only Step #1: yi = 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙 + 𝒃𝟐𝑻𝑶𝑻𝑨𝑳 One first enters the conditioning variable, CES-D total score while adjusting for covariates: gender. Step #2: yi = 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙 + 𝒃𝟐𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟒𝑬𝑻𝑯𝑵𝑰𝑪 𝑰𝑫𝑬𝑵𝑻𝑰𝑻𝒀 The Ethnic Identity is entered while adjusting for covariates: gender. Step #3: yi= 𝒃𝟎 + 𝒃𝟏𝑺𝒆𝒙 + 𝒃𝟐𝑻𝑶𝑻𝑨𝑳 + 𝒃𝟒𝑬𝑻𝑯𝑵𝑰𝑪 𝑰𝑫𝑬𝑵𝑻𝑰𝑻𝒀 + 𝒃𝟓𝑻𝑶𝑻𝑨𝑳 ∗𝑬𝑻𝑯𝑵𝑰𝑪 𝑰𝑫𝑬𝑵𝑻𝑰𝑻𝒀 The interaction term is entered into the equation while adjusting for the same covariates. For each model, yi is the participant’s answer to the ith CES-D item. The second designed analysis is very similar to the initial analysis with Chinese adolescents. The only difference is that in each model, gender is included as a co-variate. 49 3.3.4 Software for Data Analysis The SPSS version 25 (IBM Corp. Released 2017) was used for cleaning and screening of data, logistic regressions, inferential analysis, and presentation of descriptive statistics. 50 Chapter 4 Results 4.1 Missing Value Analysis A total of 1592 students participated in Wave 5 of the BASUS study. Of those, 1151 students who were either White or Chinese were retained for this paper. The dataset (n=1151) was screened for missing values. Out of a total 1151 participants, 294 participants had at least one missing values in either the CES-D items or the MEIM. Two proportion test using Z score was conducted for missing values. Using 5% of significance level, we failed to reject the null hypothesis (p=0.77) and concluded that there is not a statistically significant difference between the proportion of Chinese and White groups. The CES-D item with the most missing responses (7.6%) was Item 20 (“I could not get going”), followed by Item 4 (“I felt that I was just as good as other people”), with 6.3% missing a response. A test was conducted to determine which items had significant differences in the proportion of missing responses across ethnic groups (see Appendix A for these results). One hundred ninety students with either more than five missing responses in the CES-D (more than 20% of the 20-item CES-D) or more than two missing responses in the MEIM (20% of the 6-item MEIM) were excluded from the dataset due to high levels of missing data, which resulted in the retention of 961 participants with no excessive missing values. The variables measuring parental educational level was excluded from the analysis as nearly 100 of cases were missing. 51 4.2 Descriptive Statistics Table 4-1 represents descriptive statistics for the retained dataset for this study. Of the 961 participants, 72.1% were White (n=693), and 27.9% were Chinese (n=268). Of the 693 Whites, 415 (59.9%) reported as female and 278 (40.1%) reported as male. Out of 268 Chinese, 176 (65.7%) were female and 92 (34.3%) were male. Table 4-1 also shows the age distribution among participants. While there were 3 White students of age 17, none of the Chinese students were of the same age as the Whites. The statistical result from the Chi-square test shows a significant difference in age distributions between Whites and Chinese (p<0.001). Both maternal and paternal education also showed a statistically significant difference between White and Chinese adolescents (p<0.001). While 77.9% of White mothers reported some post-secondary school education or higher, 65.4% of Chinese mothers completed high school and pursued post-secondary or higher education. The trend was similar for paternal education levels (see Table 4-1). 52 Table 4-1 Descriptive Statistics Overall Study Sample Ethnicity N (%) (N=961; 100%) Chinese (N=268; 27.9%) Whites (N=693; 72.1%) Gender Male 370 (38.5%) 92 (34.3%) 278 (40.1%) Female 591 (61.5%) 176 (65.7%) 415 (59.9%) Result of Association χ2 = 2.734, p=0.098 Age 13 years old 23 (2.4%) 14 (5.2%) 9 (1.3%) 14 years old 260 (27.1%) 86 (32.1%) 174 (25.1%) 15 years old 596 (62.0%) 151 (56.3%) 445 (64.2%) 16 years old 79 (8.2%) 17 (6.3%) 62 (8.9%) 17 years old 3 (0.3%) 0 (0.0%) 3 (0.4%) Result of Association χ2 =20.606, p<0.001* Maternal Educationii Undergraduate degree or higher 389 (44.4%) 68 (30.6%) 321 (49.0%) Some post-secondary 244 (27.8%) 55 (24.8%) 189 (28.9%) Completed high school 189 (21.6%) 63 (28.4%) 126 (19.2%) Less than completed high school 55 (6.3%) 36 (16.2%) 19 (2.9%) Result of Association χ2 =66.921, p<0.001* Paternal Education iii Undergraduate degree or higher 380 (44.1%) 83 (38.8%) 297 (45.9%) Some post-secondary 268 (31.1%) 49 (22.9%) 219 (33.8%) Completed high school 148 (17.2%) 53 (24.8%) 95 (14.7%) Less than completed high school 65 (7.5%) 29 (13.6%) 36 (5.6%) Result of Association χ2 =31.144, p<0.001* CES-D total score Mean (SD) 13.92 (10.95) 14.56 (10.08) 13.67 (11.26) Result of Association t= 1.121, p=0.072 MEIM total score Mean (SD) 2.98 (0.94) 3.21 (0.87) 2.89 (0.95) Result of Association t= 4.719, p<0.001* ii Total 84 cases missing (46 cases missing from Chinese, and 38 cases missing from Whites) iii Total 100 cases missing (54 cases missing from Chinese, and 46 cases missing from Whites) 53 The average CES-D score is slightly higher among Chinese adolescents (see Table 4-1). Table 4-2 demonstrates the mean CES-D score stratified by ethnicity and gender. Both Chinese and Whites showed a higher mean score in females than males. An independent two-sample t-test showed a p-value=0.72; therefore, we cannot conclude that there is a statistically significant difference in CES-D total scores between Chinese and White populations. For gender, we ran an independent two-sample t-test to examine the association between gender and CES-D total score. It was concluded that there is a statistically significant difference in CES-D total score between females and males (p<0.001). Table 4-2 shows a 11.07 CES-D total mean score for the male population, while females have a 15.71 mean score. The trend remains similar after stratification by ethnicity. Both Chinese and White females have higher mean scores compared to males. Table 4-2 Comparison of the mean CES-D by Ethnicity and Gender Ethnicity Gender N (%) CES-D Mean (SD) Chinese Male 92 (32.9%) 12.56 (9.3) Female 176 (67.1%) 15.60 (10.35) Total 268 (26.5%) 14.56 (10.08) Whites Male 278 (40.0%) 10.57 (9.63) Female 415 (60.0%) 15.75 (11.8) Total 693 (73.5%) 13.67 (11.27) Total Male 370 (38.1%) 11.07 (9.58) Female 591 (61.9%) 15.71 (11.38) Total 961 (100%) 13.92 (10.95) Result of Association F statistics=15.014, p<0.000* Following Roberts et al. (1991), we used 24 as a cutoff CES-D score for adolescents. As reported in Table 4-3, the mean CES-D scores are very similar within gender and ethnicity. A t-54 test and two-way ANOVA test both concluded that there is no statistically significant difference of CES-D mean scores between ethnicity and gender. Table 4-3 Comparison of Mean CES-D Score (When Participants Scored≧24 on CES-D) Ethnicity Gender N (%) Chinese Male (n=92) 10 (10.9%) Female (n=176) 34 (19.3%) Total (n=268) 44 (16.4%) Whites Male (n=278) 30 (10.8%) Female (n=415) 91 (21.9%) Total (n=693) 121 (17.5%) Total Male (n=370) 40 (10.8%) Female (n=591) 125 (21.2%) Total (n=961) 165 (17.2%) Result of Association F statistics=1.005, p=0.392 Table 4-1 and Table 4-4 present the mean MEIM score between White and Chinese adolescent. The findings are congruent with the literature review. An independent two-sample t-test showed p-value<0.01; indicating that there is a statistically significant differences in MEIM total mean score between Chinese and White adolescents. 55 Table 4-4 Comparison of Mean MEIM Score by Ethnicity MEIM Total MEIM Commitment MEIM Exploration Chinese (N=268; 27.9%) Mean 3.21 3.32 3.10 Standard Deviation 0.87 0.92 0.98 Whites (N=693; 72.1%) Mean 2.89 3.05 2.73 Standard Deviation 0.95 1.02 1.02 Total (N=961; 100%) Mean 2.98 3.12 2.83 Standard Deviation 0.94 1.00 1.02 Result of Association t= 4.719, p<0.001* Note. See Table 3-2 for detailed item questions for MEIM exploration and commitment factors. 4.3 Differential Functioning Item Analyses In ordinal logistic regression, the parallel-lines assumption needs to be met. The parallel lines assumption refers to the assumption that the slope parameters should not change for different categories of a dependent variable. The null hypothesis for this assumption is that the location parameters are the same across response categories, and this null hypothesis should not be rejected in the test for parallel lines. In the DIF analyses, the test of parallel-lines indicated that following ordinal regression models did not meet the parallel-lines assumption: Items 2, 4, 7, 8, 10, 12 and 16. The parallel lines assumption is known to be sensitive in applications involving large sample sizes and continuous independent variables. Williams (2006) stated “in a very large sample, even substantively trivial violations of the parallel-lines assumption can be statistically significant”. O’Connell (2006) explained the test of parallel lines “nearly always results in very small p values, particularly when the number of explanatory variables is large (Brant, 1990), the sample size is large (Allison, 1999; Clogg & Shihadeh, 1994), or continuous explanatory variables are included in the model (Allison, 1999).” In the current study, our sample size is 56 reasonably large, and the CES-D score is a continuous independent variable. However, when the result of parallel-lines test is significant, it can be helpful to examine the data using a series of separate logistic regression models to investigate how the odds ratios for the total CES-D score vary across different dichotomous cutoffs in the dependent variable (Strand, Cadwallader, & Firth, 2011). We therefore computed separate sets of logistic regression models using dichotomized ordinal dependent variables for the items that violated the parallel-lines assumption. We examined the odd ratios for each dependent variable across the separate splits in the data to determine how consistent they are. As an example, Table 4-5 demonstrates the three different binary variables associated with CES-D Item 2. We ran separate logistic regressions for each variable with total CES-D scores predicting the dichotomized responses (see Appendix D for detailed results.) The p-values for the total CES-D coefficient from all three logistic regressions were significant (p<0.001), and the exponent coefficients were similar across categories of the dependent variable. Then, we ran the test of parallel-lines with only MEIM score as a predictor of CES-D item response. For this analysis, we used ordinal logistic regression, and failed to reject the null hypothesis for the test of parallel lines for Items 2, 4, 7, 8, 10, 12, 15 and 16; all other items showed insignificant results. This demonstrates the CES-D score is not predicting for items that violated the assumption, as we failed to reject the assumptions with the MEIM score. For the current research, examining the separated odd-ratios for a set of different dichotomous logistic regressions in order to report the statistically significant results in all splits are concluded as sufficient enough to explain the violation of assumptions, as well as to continue examining the ordinal logistic regressions. 57 Table 4-5 Three-Binary Variables of Item 2 Yes No Item 2-1 Response option ‘0’ Response option ‘1’, ‘2’ or ‘3’ Item 2-2 Response option ‘0’ or ‘1’ Response option ‘2’ or ‘3’ Item 2-3 Response option ‘0’, ‘1’ or ‘2’ Response option ‘3’ The test of parallel line assumptions was also conducted for the DIF analyses of Chinese participants. It was noted Item 4, 7, 8, 12 and 16 violated the parallel-lines assumption, and similar investigation of the change in coefficients across dichotomized categories of the dependent variable was carried out with similar results to the previous analyses of DIF across ethnic groups (see Appendix E for detailed findings). Similar results suggest that it is reasonable to include the interpretation of the ordinal logistic regression model. 4.3.1 DIF Analysis - Question I-A This paper initially conducted the analyses to detect ethnicity DIF for the CES-D items comparing White and Chinese adolescents. The initial DIF analysis indicated that Items 4, 7, 8, 11, 16 and 20 showed at least one DIF criteria (see Table 4-6). 58 Table 4-6 Result of Ethnicity DIF Analysis among White and Chinese Adolescents Item DIF Step Nag R2 χ2 df 𝛥 χ 2 𝛥 R2 CESD 1 Step 1 0.362 356.584 1 Step 2 0.366 360.969 2 Step 3 0.366 360.981 3 4.397 0.004 CESD 2 Step 1 0.278 259.027 1 Step 2 0.282 263.871 2 Step 3 0.284 265.497 3 6.470 0.006 CESD 3 Step 1 0.575 645.038 1 Step 2 0.576 645.451 2 Step 3 0.576 645.507 3 0.469 0.001 CESD 4 Step 1 0.262 263.360 1 Step 2 0.274 276.665 2 Step 3 0.275 277.738 3 14.378 0.012 CESD 5 Step 1 0.288 295.489 1 Step 2 0.295 303.016 2 Step 3 0.295 303.143 3 7.654 0.007 CESD 6 Step 1 0.641 765.993 1 Step 2 0.642 766.447 2 Step 3 0.643 768.021 3 2.028 0.002 CESD 7 Step 1 0.092 84.134 1 Step 2 0.119 111.416 2 Step 3 0.138 129.984 3 45.850 0.046 CESD 8 Step 1 0.209 205.497 1 Step 2 0.221 218.059 2 Step 3 0.222 218.940 3 13.443 0.013 CESD 9 Step 1 0.504 440.998 1 Step 2 0.508 445.277 2 Step 3 0.508 445.927 3 4.929 0.004 CESD 10 Step 1 0.416 420.449 1 Step 2 0.417 420.558 2 Step 3 0.418 422.496 3 2.047 0.002 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 59 Table 4-6 Result of Ethnicity DIF Analysis among White and Chinese Adolescents (Continued) Item DIF Step Nag R2 χ2 df 𝜟 χ 2 𝜟 R2 CESD 11 Step 1 0.277 276.361 1 Step 2 0.293 294.612 2 Step 3 0.293 294.852 3 18.491 0.016 CESD 12 Step 1 0.443 477.642 1 Step 2 0.447 483.419 2 Step 3 0.448 483.940 3 6.298 0.005 CESD 13 Step 1 0.308 308.979 1 Step 2 0.308 309.554 2 Step 3 0.308 309.677 3 0.698 0.000 CESD 14 Step 1 0.558 677.565 1 Step 2 0.559 678.363 2 Step 3 0.559 678.418 3 0.853 0.001 CESD 15 Step 1 0.341 336.351 1 Step 2 0.342 336.620 2 Step 3 0.345 340.319 3 3.968 0.004 CESD 16 Step 1 0.444 475.164 1 Step 2 0.451 485.351 2 Step 3 0.451 485.434 3 10.270 0.007 CESD 17 Step 1 0.407 374.072 1 Step 2 0.410 378.046 2 Step 3 0.412 380.300 3 6.228 0.005 CESD 18 Step 1 0.614 764.398 1 Step 2 0.616 768.732 2 Step 3 0.617 769.091 3 4.693 0.003 CESD 19 Step 1 0.499 568.903 1 Step 2 0.499 569.805 2 Step 3 0.499 569.816 3 0.913 0.000 CESD 20 Step 1 0.489 526.718 1 Step 2 0.502 545.522 2 Step 3 0.502 546.179 3 19.461 0.013 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 60 Following Zumbo’s (1999) ordinal logistic regression method, we computed the Chi-square value and the pseudo R-squared value to detect the occurrence of DIF. CES-D Item 4 showed a Chi-square difference of 10.982 from step 1 to step 3. Difference of a Chi-square value larger than 9.21 is one of the criteria of DIF, and Item 4 qualified for this condition. However, the pseudo R-squared difference did not meet the DIF standard identified in Zumbo (1999). The Zumbo-Thomas effect size model specified the pseudo R-squared difference should be equal to or greater than 0.035. The detailed results of the Item 4 analysis are shown in Table 4-6.1. In step 2, the Group predictor, either Whites or Chinese, was statistically significant using p-value 0.05 as a cutoff for significance. The Chi-square difference between step 2 and step 1 is less than 9.21, indicating no uniform DIF. In step 3, the Interaction, Total Score X Group, predictor was statistically significant, and the difference in Chi-square between step 3 and step 1 is significant, indicating a possible DIF. The difference in pseudo R-squared rejected the detection of DIF. Table 4-6.1 Result of Ethnicity DIF analysis for Item 4 Item 4 Total Score Group Interaction χ2 Pseudo R2 (Nagelkerke) 𝛥 χ2 𝛥 R2 Step 1 240.246 0.259 Estimate 0.094 - - Step2-Step 1 (uniform) p-value 0.000 - - 8.99 0.009 Step 2 249.236 0.268 Estimate 0.094 0.435 - Step3-Step 2 (nonuniform) p-value 0.000 0.003 - 1.992 0.002 Step 3 251.228 0.270 Estimate 0.099 0.742 -0.020 Step3-Step1 p-value 0.000 0.004 0.163 10.982 0.011 61 Figure 4-1 Ethnicity Differential Item Functioning Plot of Item 4 with Not Displaying DIF CES-D Item 7 (“I felt that everything I did was an effort”) met both of the criteria for DIF. With a change of 2 degrees of freedom, the Chi-square has a change of 41.972 (which is greater than 9.21) and a pseudo R-squared change of 0.045. When considering all DIF criteria, Item 7 demonstrated a statistically significant differential functioning between White and Chinese participants. The detailed results of the Item 7 analysis are shown in Table 4-6.2. In step 2, the Group predictor was statistically significant and the Chi-square difference between step 2 and step 1 is indicating the existence of uniform DIF. In step 3, the Interaction predictor was statistically significant, and the difference in Chi-square between step 3 and step 2 is significant, indicating a CES-D Total Score Differential Item Functioning Item 4 __ Whites __ Chinese Note. Used the LOESS fitting with 50% *Item 4 met the difference in Chi-square criterion, but not the effect size criterion for DIF. 62 non-uniform DIF. The difference in pseudo R-squared also indicated the detection of DIF. A plot of the predicted Item 7 responses for each ethnic group can be found in Figure 4-2. Table 4-6.2 Result of Ethnicity DIF Analysis for Item 7 Item 7 Total Score Group Interaction χ2 Pseudo R2 (Nagelkerke) 𝛥 χ2 𝛥 R2 Step 1 83.470 0.097 Estimate 0.052 - - Step2-Step 1 (uniform) p-value 0.000 - - 23.094 0.025 Step 2 106.564 0.122 Estimate 0.052 0.682 - Step3-Step 2 (non-uniform) p-value 0.000 0.000 - 18.878 0.021 Step 3 125.442 0.143 Estimate 0.066 1.606 -0.060 Step3-Step1 p-value 0.000 0.004 0.163 41.972 0.046 Figure 4-2 Ethnicity Differential Item Functioning Plot of Item 7 CES-D Total Score ㅡ Whites ㅡ Chinese Differential Item Functioning Item 7 Note. Used the LOESS fitting with 50% *Item 7 met both criteria for DIF detection. 63 Item 7 demonstrates non-uniform DIF. As shown in Figure 4-2, the groups differ in their slopes of logistic regression on Item 7; therefore, Item 7 is said to have non-uniform DIF. In other words, Item 7 affects participants in specific ranges of the total scores but not in a consistent manner. (Figure 4-2 shows noticeably different shapes and cross.) Item 7 favors the Chinese group with a range of 0 to mid-20s for the total score; at points of a total score between 25 and 40, the difference flips to favor White participants. Note that the approximate point where two curves cross is close to Roberts et al.’s (1991) CES-D cutoff score. 4.3.2 DIF Analysis - Question I-B For further investigation, we added relevant covariates to the previous analysis to detect ethnicity DIF. Table 4-7 contains the results of the DIF analysis that included the covariates gender and MEIM. The secondary DIF analysis indicated that Items 4, 7, 8, 11, 16 and 20 met at least one of the DIF criteria. As with the initial analysis, only Item 7 showed statistically significant differential functioning between White and Chinese participants, meeting both criteria. Item 7 showed a difference of Chi-square 36.397 and pseudo R-squared 0.04, which is the same as the primary DIF analysis. 64 Table 4-7 Result of Ethnicity DIF Analysis with Gender and MEIM Item DIF Step Nag R2 χ2 df 𝜟 χ 2 𝜟 R2 CES-D 1 Step 1 0.385 371.402 3 Step 2 0.388 374.578 4 Step 3 0.388 374.619 5 3.217 0.0027 CES-D 2 Step 1 0.276 249.048 3 Step 2 0.280 253.192 4 Step 3 0.282 255.187 5 6.139 0.0059 CES-D 3 Step 1 0.591 648.278 3 Step 2 0.592 649.454 4 Step 3 0.592 649.472 5 1.194 0.0007 CES-D 4 Step 1 0.271 264.473 3 Step 2 0.285 281.129 4 Step 3 0.287 282.354 5 17.881 0.0157 CES-D 5 Step 1 0.282 278.503 3 Step 2 0.288 285.564 4 Step 3 0.288 285.720 5 7.217 0.0062 CES-D 6 Step 1 0.642 743.604 3 Step 2 0.643 744.514 4 Step 3 0.644 746.469 5 2.865 0.0016 CES-D 7 Step 1 0.101 90.226 3 Step 2 0.124 112.527 4 Step 3 0.143 130.261 5 40.035 0.0417 CES-D 8 Step 1 0.221 210.485 3 Step 2 0.238 229.603 4 Step 3 0.240 230.891 5 20.406 0.0188 CES-D 9 Step 1 0.498 420.517 3 Step 2 0.502 425.060 4 Step 3 0.503 425.771 5 5.254 0.0049 CES-D 10 Step 1 0.419 412.332 3 Step 2 0.420 412.627 4 Step 3 0.421 414.526 5 2.194 0.0018 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 65 Table 4-7 Result of Ethnicity DIF Analysis with Gender and MEIM (Continued) Item DIF Step Nag R2 χ2 df 𝜟 χ 2 𝜟 R2 CESD 11 Step 1 0.284 275.316 3 Step 2 0.300 293.386 4 Step 3 0.300 293.468 5 18.152 0.0159 CESD 12 Step 1 0.451 471.490 3 Step 2 0.457 479.993 4 Step 3 0.457 480.178 5 8.688 0.0063 CESD 13 Step 1 0.310 301.317 3 Step 2 0.311 301.984 4 Step 3 0.311 302.129 5 0.812 0.0007 CESD 14 Step 1 0.565 666.622 3 Step 2 0.566 667.694 4 Step 3 0.566 667.694 5 1.072 0.0006 CESD 15 Step 1 0.350 333.938 3 Step 2 0.350 334.157 4 Step 3 0.353 337.507 5 3.569 0.0031 CESD 16 Step 1 0.450 467.643 3 Step 2 0.458 478.479 4 Step 3 0.458 478.680 5 11.037 0.0081 CESD 17 Step 1 0.454 413.640 3 Step 2 0.459 419.713 4 Step 3 0.461 422.096 5 8.456 0.0073 CESD 18 Step 1 0.625 759.148 3 Step 2 0.628 765.463 4 Step 3 0.629 765.884 5 6.736 0.0036 CESD 19 Step 1 0.503 555.619 3 Step 2 0.504 556.720 4 Step 3 0.504 556.832 5 1.213 0.0008 CESD 20 Step 1 0.487 506.042 3 Step 2 0.501 526.722 4 Step 3 0.502 527.605 5 21.563 0.0153 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 66 The detailed results of Item 7 analysis are shown in Table 4-7.1. In step 2, the Group predictor, either Whites or Chinese, was statistically significant using p-value 0.05 as a cutoff for significance. The Chi-square difference between step 2 and step 1 is larger than 9.21, indicating uniform DIF. In step 3, the Interaction, Total Score X Group, predictor was statistically significant, and the difference in Chi-square between step 3 and step 1 is significant, indicating a possible DIF. The difference in pseudo R-squared supports the detection of DIF. Figure 4-3 shows the graph for each White and Chinese participant. Unlike Figure 4-2, the prediction of Chinese response for Item 7 is consistent across the different total CES-D scores. Whether the participants scored high or low in ability, the total score in this case, their predictability of Item 7 response appears to consistently follow a primarily horizontal trend at a response of 1 in the plot. Table 4-7.1 Result of DIF Secondary Analysis for Item 7 Item 7 Total Score Covariate 1 (Gender) Covariate 2 (MEIM) Group Interaction χ2 Pseudo R2 (Nagelkerke) 𝛥 χ2 𝛥 R2 Step 1 91.477 0.109 Estimate 0.050 -0.295 0.201 - - Step2-Step 1 (uniform) p-value 0.000 0.027 0.003 - - 17.499 0.019 Step 2 108.976 0.128 Estimate 0.050 -0.267 0.156 0.608 - Step3-Step 2 (non-uniform) p-value 0.000 0.047 0.023 0.000 - 17.899 0.021 Step 3 127.875 0.149 Estimate 0.064 -0.244 0.149 1.551 -0.061 Step3-Step1 p-value 0.000 0.070 0.031 0.000 0.000 36.398 0.04 67 Figure 4-3 Differential Item Functioning Plot of Item 7 Adjusting for Gender and Total MEIM Score 4.3.3 DIF Analysis - Question II-A The secondary set of DIF analyses were conducted to detect strength of ethnic identity DIF. Firstly, the DIF analysis was conducted only among Chinese participants. Instead of testing for DIF across ethnic groups, we tested for DIF across participants’ levels of strength of ethnic identity by using the MEIM total mean score in the regression models. The difference in Chi-square and in pseudo R-squared for the models are reported in Table 4-8. CES-D Total Score Figure 4-3 Item 7 Graph with Predicted Response DIF CESD item 7 Differential Ite Functioning Item 7 Note. Used the LOESS fitting with 50% *Item 7 met both criteria for DIF detection. * 68 Table 4-8. Result of Strength of Ethnic Identity DIF Analysis among Chinese Participants Item DIF Step Nag R2 χ2 df 𝜟 χ 2 𝜟 R2 CES-D 1 Step 1 0.373 104.572 1 Step 2 0.389 106.471 2 Step 3 0.392 107.573 3 3.001 0.0187 CES-D 2 Step 1 0.316 81.543 1 Step 2 0.318 79.696 2 Step 3 0.321 80.492 3 1.051 0.0043 CES-D 3 Step 1 0.598 188.411 1 Step 2 0.591 178.051 2 Step 3 0.596 180.039 3 8.372 0.0025 CES-D 4 Step 1 0.200 54.395 1 Step 2 0.200 52.737 2 Step 3 0.205 54.281 3 0.114 0.0045 CES-D 5 Step 1 0.301 85.861 1 Step 2 0.293 80.442 2 Step 3 0.293 80.553 3 5.308 0.0083 CES-D 6 Step 1 0.552 170.528 1 Step 2 0.546 161.732 2 Step 3 0.547 162.118 3 8.410 0.0048 CES-D 7 Step 1 0.002 0.470 1 Step 2 0.001 0.302 2 Step 3 0.003 0.829 3 0.359 0.0016 CES-D 8 Step 1 0.217 60.199 1 Step 2 0.254 69.499 2 Step 3 0.256 70.112 3 9.913 0.0389 CES-D 9 Step 1 0.472 121.754 1 Step 2 0.479 119.902 2 Step 3 0.484 121.673 3 0.081 0.0125 CES-D 10 Step 1 0.454 129.443 1 Step 2 0.450 125.462 2 Step 3 0.450 125.525 3 3.918 0.0039 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 69 Table 4-8. Result of Strength of Ethnic Identity DIF Analysis among Chinese Participants (Continued) Item DIF Step Nag R2 χ2 df 𝜟 χ 2 𝜟 R2 CES-D 11 Step 1 0.210 54.767 1 Step 2 0.222 56.025 2 Step 3 0.224 56.528 3 1.761 0.0134 CES-D 12 Step 1 0.390 115.594 1 Step 2 0.387 110.799 2 Step 3 0.388 110.849 3 4.745 0.0020 CES-D 13 Step 1 0.299 82.780 1 Step 2 0.292 77.691 2 Step 3 0.292 77.718 3 5.062 0.0072 CES-D 14 Step 1 0.528 175.259 1 Step 2 0.538 173.530 2 Step 3 0.539 174.034 3 1.225 0.0108 CES-D 15 Step 1 0.420 121.338 1 Step 2 0.416 115.492 2 Step 3 0.417 115.657 3 5.681 0.0033 CES-D 16 Step 1 0.382 112.809 1 Step 2 0.389 111.587 2 Step 3 0.396 113.973 3 1.164 0.0143 CES-D 17 Step 1 0.403 96.901 1 Step 2 0.405 93.942 2 Step 3 0.412 95.701 3 1.200 0.0084 CES-D 18 Step 1 0.607 206.997 1 Step 2 0.613 202.391 2 Step 3 0.613 202.578 3 4.419 0.0063 CES-D 19 Step 1 0.500 158.312 1 Step 2 0.517 159.755 2 Step 3 0.521 161.708 3 3.396 0.0214 CES-D 20 Step 1 0.486 136.517 1 Step 2 0.476 127.769 2 Step 3 0.478 128.624 3 7.893 0.0078 70 Following Zumbo’s (1999) ordinal logistic regression method, we computed Chi-square value and pseudo R-squared value to detect the occurrence of DIF. CES-D Item 8 (“I felt hopeful about the future”) met both of the criteria for DIF. With a change of 2 degrees of freedom, the Chi-square has a change of 10.779 (which is greater than 9.21) and a pseudo R-squared change of 0.044. When considering all DIF criteria, Item 8 demonstrated a statistically significant differential functioning in Chinese participants when adjusting for the MEIM total mean score. A detailed breakdown of the DIF analyses for Item 8 is presented in Table 4-8.1 and we note that these results suggest that Item 8 appears to demonstrate uniform DIF, as the change in R-squared is minimal from step 2 to step 3. Table 4-8.1 Result of Strength of Ethnic Identity DIF Analysis for Item 8 Item 8 Total Score MEIM Score Interaction χ2 Pseudo R2 (Nagelkerke) 𝛥 χ2 𝛥 R2 Step 1 46.735 0.194 Estimate 0.087 - - Step2-Step 1 (uniform) p-value 0.000 - - 10.477 0.043 Step 2 57.212 0.237 Estimate 0.093 -0.429 - Step3-Step 2 (non-uniform) p-value 0.000 0.003 - 0.302 0.001 Step 3 57.514 0.238 Estimate 0.068 -0.537 0.008 Step3-Step1 p-value 0.147 0.026 0.582 10.779 0.044 To further support the interpretation of the findings for Item 8, we used a median split (using a total MEIM score of 19) to create two groups of Chinese participants: High MEIM Chinese, and Low MEIM Chinese. We then ran the DIF analyses with the dichotomous MEIM group variable replacing the continuous MEIM score in the models. The plot of the predicted responses for the two MEIM groups in Figure 4.4 indicates that they appear to differ in their slopes 71 of logistic regression on Item 8; therefore, Item 8 appears to have non-uniform DIF. In other words, strength of ethnic identity appears to affect participants in specific ranges of the CES-D total scores but not in a consistent manner. (Figure 4-4 shows slightly different shapes and cross.) Figure 4-4 Strength of Ethnic Identity Differential Item Functioning Plot of Item 8 4.3.4 DIF Analysis - Question II-B Further examination was done to investigate additional detection of strength of ethnic identity DIF while adjusting for covariates. The results of the DIF analyses with the inclusion of gender as a covariate are presented in Table 4-9. The secondary DIF analysis indicated that only Item 8 met at least one of the DIF criteria. Item 8 reported a difference of Chi-square 11.553 and pseudo R-squared 0.046, which is similar to the initial question II-A DIF analysis. Note. Used the LOESS fitting with 50% 72 Table 4-9 Result of Strength of Ethnic Identity DIF Analysis with Gender Item DIF Step Nag R2 χ2 df 𝜟 χ2 𝜟 R2 CES-D 1 Step 1 0.380 106.813 2 Step 2 0.399 109.846 3 Step 3 0.401 110.745 4 3.932 0.02149 CES-D 2 Step 1 0.319 82.231 2 Step 2 0.320 80.322 3 Step 3 0.322 81.013 4 1.218 0.00384 CES-D 3 Step 1 0.599 188.867 2 Step 2 0.592 178.567 3 Step 3 0.597 180.709 4 8.158 0.00196 CES-D 4 Step 1 0.206 56.176 2 Step 2 0.208 55.312 3 Step 3 0.213 56.736 4 0.560 0.00693 CES-D 5 Step 1 0.301 85.878 2 Step 2 0.293 80.444 3 Step 3 0.293 80.558 4 5.320 0.00832 CES-D 6 Step 1 0.554 171.360 2 Step 2 0.549 162.789 3 Step 3 0.550 163.322 4 8.038 0.00386 CES-D 7 Step 1 0.046 11.564 2 Step 2 0.051 12.554 3 Step 3 0.052 12.855 4 1.291 0.00653 CES-D 8 Step 1 0.225 62.617 2 Step 2 0.265 72.851 3 Step 3 0.268 73.663 4 11.046 0.04240 CES-D 9 Step 1 0.473 122.098 2 Step 2 0.480 120.280 3 Step 3 0.486 122.238 4 0.140 0.01316 CES-D 10 Step 1 0.454 129.624 2 Step 2 0.450 125.687 3 Step 3 0.451 125.759 4 3.865 0.00374 Note. Highlighted cell indicating significant values of the Chi-square and pseudo R-squared. 73 Table 4-9 Result of Strength of Ethnic Identity DIF Analysis with Gender (Continued) Item DIF Step Nag R2 χ2 df 𝜟 χ2 𝜟 R2 CES-D 11 Step 1 0.212 55.187 2 Step 2 0.225 56.960 3 Step 3 0.227 57.557 4 2.370 0.01562 CES-D 12 Step 1 0.399 118.952 2 Step 2 0.400 115.447 3 Step 3 0.400 115.450 4 3.502 0.00176 CES-D 13 Step 1 0.300 83.293 2 Step 2 0.293 78.234 3 Step 3 0.293 78.271 4 5.022 0.00699 CES-D 14 Step 1 0.532 176.999 2 Step 2 0.544 176.269 3 Step 3 0.544 176.613 4 0.386 0.01269 CES-D 15 Step 1 0.432 125.505 2 Step 2 0.425 118.565 3 Step 3 0.426 118.884 4 6.621 0.00545 CESD 16 Step 1 0.386 114.323 2 Step 2 0.394 113.364 3 Step 3 0.402 116.046 4 1.723 0.01586 CES-D 17 Step 1 0.433 105.697 2 Step 2 0.434 102.019 3 Step 3 0.439 103.489 4 2.208 0.00587 CES-D 18 Step 1 0.616 211.869 2 Step 2 0.625 208.641 3 Step 3 0.626 208.933 4 2.936 0.00949 CES-D 19 Step 1 0.502 159.249 2 Step 2 0.520 161.167 3 Step 3 0.524 162.925 4 3.676 0.02204 CES-D 20 Step 1 0.492 138.740 2 Step 2 0.483 130.176 3 Step 3 0.485 131.120 4 7.620 0.00677 74 Figure 4-5 Differential Item Functioning Plot of Item 8 Adjusting for Gender As with the previous analyses, we used a total MEIM score of 19 to split Chinese participants into two groups: High MEIM total scored Chinese and Low MEIM total scored Chinese. According to Table 4-9, Item 8 demonstrates uniform DIF. Figure 4-5 shows curves overlapping between mid-10s to late-10s of total CES-D scores. The groups were similar in their slopes of logistic regression on Item 8; therefore, Item 8 can be said to have uniform DIF. In other words, Item 8 affects participants in a consistent manner. (Figure 4-5 shows similar shapes and no cross.) Note. Used the LOESS fitting with 50% 75 Table 4-9.1 Result of Strength of Ethnic Identity DIF Analysis with Gender for Item 8 Item 8 Total Score Covariate (Gender) MEIM Score Interaction χ2 Pseudo R2 (Nagelkerke) 𝛥 χ2 𝛥 R2 Step 1 49.488 0.204 Estimate 0.091 0.435 - - Step2-Step 1 (uniform) p-value 0.000 0.094 - - 11.193 0.046 Step 2 60.681 0.250 Estimate 0.097 0.498 -0.444 - Step3-Step 2 (non-uniform) p-value 0.000 0.060 0.002 - 0.360 0.001 Step 3 61.041 0.251 Estimate 0.070 0.503 -0.562 0.008 Step3-Step1 p-value 0.135 0.059 0.020 0.548 11.553 0.047 Note. Gender: 1=Male, 0=Female 76 Chapter 5 Discussion In Canada, many programs have been implemented in the recognition of mental health as a major concern for Canadian adolescents, and as part of these efforts, the Center for Epidemiologic Studies Depression Scale (CES-D) has been used as a self-reported measurement tool for depression (Torous et al., 2018; Wei et al., 2011; Radloff, 1977). The increased use of screening tools, particularly in an online format, has been recognized as a means of overcoming the stigma associated with treatment-seeking for suspected mental health problems and improving access to basic mental health assessments and treatment advice (Torous et al., 2018; Armontrout et al., 2018). The increased use of screening tools calls for measurement equivalence analysis; vital to the success of surveillance and screening efforts is the use of assessment tools that have been validated in the population prior to application. Given the evidence of the impact of ethnicity on the experience and expression of depression, it is necessary to examine the validity of depression screening tools for different ethnic groups. Previous literature suggests that Chinese and Western cultures differ in terms of depressive expression in that Chinese respondents are more likely to endorse somatic items (Chen, 1995; Zhang, 2007). In addition to differences across specific ethnic groups of adolescents (i.e., Chinese and Whites), the literature also suggests Chinese adolescents with a stronger ethnic identity will likely express higher somatic symptoms of depression as compared to equally depressed Chinese adolescents with a weaker sense of ethnic identity (Berry 1997, 2005, 2015). The main objective of this study was to add to the existing literature on the validity of the CES-D by examining the impact of ethnic identity on the assessment of depression by testing for DIF on the CES-D across ethnic groups (Chinese and Whites) while accounting for gender and 77 strength of ethnic identity. To accomplish this objective, the ordinal logistic regression method for DIF detection developed by Zumbo (1999) was used to test for DIF in a sample of White (n=693) and Chinese (n=268) adolescents who completed the CESD as part of the prospective BASUS study. Zumbo (1999) stated DIF is a statistical finding that is necessary but insufficient to identify item bias. It was advised to perform follow-up item bias analyses to determine the presence of item bias. DIF provides a convenient starting point for the study of item bias (Wang & Lane, 1996). Decisions of detecting DIF must be made regarding the statistical results, and interpretations of the results must be placed within the context of the overall tool development process (Clauser & Mazor, 1998; Zieky, 1993). 5.1 Ethnicity DIF The DIF analysis determined whether the Chinese and White groups, after being matched on total score, had different probability of endorsing items on the CES-D scale. For this analysis, 20 items were examined for DIF. Of these, 6 (30%) showed significant changes in the Chi-square values. However, only one item showed a change in the pseudo R-squared value that met the criteria outlined by Zumbo (1999). More specifically, Item 7 (“I felt that everything was an effort”) had a change of 41 in the 2-degree Chi-square, and 0.04 in the pseudo R-squared value. This item was found to have non-uniform DIF as the addition of the interaction term was associated with relatively large changes in both the Chi-square and the pseudo R-squared values. This result did not change after the addition of the MEIM mean score and gender into the model suggesting that these factors did not influence the detection of DIF in this analysis. The non-uniform DIF found for Item 7 (“efforts”) suggests that Chinese adolescent are more likely to endorse higher levels of feeling that ‘everything was an effort’ compared to equally 78 depressed White adolescent until the total CES-D score reaches the mid-20’s. This trend changes when they reach the clinically concerning total CES-D score of 24, after which White adolescent appear more likely to report more expression of effort compared to equally depressed Chinese adolescent. This result is supported by the literature in that Item 7 is asking about somatic features of depressive symptoms and Chinese adolescent are more likely to report somatic symptoms compared to White adolescents, as it is more acceptable in Chinese culture. However, further research is needed to examine the change of trends after cutoff points of the CES-D, as the current literature review does not support these findings. Although the literature supports the interpretation of Item 7 by Chinese adolescents, it is also important to note that the relatively flat predicted item response curve for Chinese participants suggests that this item does not discriminate Chinese adolescents according to their level of depression. This suggests that the DIF observed in this study may be due to a poorly discriminating item. 5.2 Strength of Ethnic Identity DIF Another DIF analysis conducted on Chinese adolescents only examined if the strength of ethnic identity influences Chinese adolescents to systematically answer items on the CES-D Scale differently or in the same way compared to White adolescents. When we used a Chinese sample group to detect DIF using their MEIM total scores, we treated the MEIM total scores as a continuous variable. Initially we planned to treat the MEIM total score as a binary variable using the median as a cutoff for ‘high MEIM’ and ‘low MEIM’ cases; however, using the MEIM as a binary would lead to loss of excessive and essential information because a MEIM total score of 19 79 and 30 would be treated in the same category as ‘high MEIM’. Therefore, we treated it as a continuous variable to adjust for detecting DIF. The findings and conclusions drawn from the DIF analysis were also examined in light of the existing literature. Most notably, the DIF analysis failed to show support for distinct Chinese culture and acculturation theory. According to Berry (1997; 2015), acculturation has different stages, and if an individual has stronger ethnic identity, he/she will be more likely to retain distinct cultural differences even if he/she are exposed to a new cultural environment. Hence, according to this logic, items measuring a somatic feature of depression should generally score higher in Chinese adolescents who demonstrate stronger ethnic identity. Yet, no items in somatic construct detected DIF; however, Item 8 (“hopeful”) showed uniform DIF in accordance with DIF analysis with and without controlling for gender. Item 8 is designed to measure anhedonia as a depressive symptom. This finding also reflects the literature review examined in Chapter 2, as most Chinese refrain from expressing positive emotions where it may threaten group harmony and cause jealousy in others. Items detected for DIF did not change when controlled for a covariate. Taken together, the results of this study hold notable implications for the use of the CES-D given that Chinese adolescents demonstrated a systematic difference in expression/experience of depression. Specifically, this study shows that the CES-D that is intended to measure depressive symptoms should be stable across White and Chinese adolescents and different strengths of ethnic identity. Moreover, it is important to establish measurement equivalence in prior to implementing the CES-D across different ethnic groups in order to make valid inferences. 80 5.3 Limitations and Future Research Suggestions The study was evaluated with respect to its particular strengths and limitations. Some missing data were noted in the BASUS Wave 5 survey. The multiple imputation was conducted for variables used in this current research and provided virtually similar results with list-wise deleted analysis providing support for the results (see Appendix B and Appendix C for detailed results). It is also important to note that although the DIF analysis yielded evidence of DIF for several items, the results of the DIF analysis only addressed a small facet of the underlying questions surrounding issues of bias. That is, a comprehensive treatment of bias remains incomplete unless other extraneous variables are accounted for in the analysis: for example, for the purpose of the DIF analysis, such variables include socioeconomic status (SES), different migratory patterns, and years of residence in Canada post-immigration. SES was not included in the DIF analysis due to large missing values, and it may not be ‘missing at random’. Socioeconomic status may play an important role in detecting differential item functioning where immigrants are the targeted group of study. Some studies suggested the potential lack of household income among immigrants and its association with negative mental health; however, additional research is needed. Initially, we designed the study to consider the parental education level as an indicator of socioeconomic status; however, we excluded it from our analysis due to large missing values, and socioeconomic status is in need of further research when the targeted group is a combination of immigrants and native-born population. Other variables to measure, such as duration of immigration and different migratory patterns, were not available for the current study. However, it is not known if these omitted variables confounded the associations that were selected for analysis. 81 Other limitations addressed the generalizability of the study findings to populations in provinces other than British Columbia. British Columbia is considered a highly multicultural province and is known to have a larger Chinese population due to geographical advantages. Therefore, there is insufficient evidence to conclude that the CES-D DIF results are generalizable to other provinces or cities, especially where Chinese immigrants are not common. Future research may use a respectively large random sample for the result to be representative of a national population. One of the main assumptions of DIF analysis is unidimensionality. A unidimensional measurement scale means that all items in a test are measuring a single concept. A study was conducted to examine the degree of dimensions of the CES-D and confirm the unidimensionality of the CES-D (Wood, Taylor, & Joseph, 2010). However, some researchers recommended conducting a preliminary factor analysis in order to test this assumption prior to testing for DIF (Scott et al., 2010). Future research should therefore integrate tests dimensionality to investigate the unidimensionality of the CES-D items prior to testing for DIF in the CES-D items. If the unidimensionality assumption is not met, factor analysis may be used to identify the factor structure of the items and the DIF analysis conducted on items within each in factor. Lastly, many researchers recommended the use of the purification method after detecting DIF items (Zumbo, 1999; Clauser & Mazor, 1998; Holland & Thayer, 1988; French & Maller, 2007). French and Maller (2007) stated, “Purification attempts to identify a set of non-DIF items from the instrument under evaluation to be used as the matching criterion in DIF detection.” The purification process includes deleting items displaying DIF and recalculating the total score for a matching criterion to assess other items (Zumbo, 1999). In this study, we decided not to employ the purification process, for it may not be necessary when only one item displays DIF, whereas 82 removing one item out of 20 would not affect the precision of detecting less validity of a matching criterion. However, we would recommend use of further analysis to understand rationale behind the detection of DIF by using either qualitative or quantitative, or both, methods. Recommendations for future research and development were also identified. Mainly, the application of the CES-D for assessing depression symptoms needs to consider ethnicity differences and the potential impact of strength of ethnic identity for immigrants. Additionally, an effort to understand the nuances of the process behind the DIF is needed. Potential future research might include Think Aloud Protocols (TAPs) to gain insight into why certain items in the CES-D function poorly in Chinese adolescents. TAPs are used to examine qualitative aspects of DIF by investigating the extended surface characteristics of the DIF detected items. 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Medical Care, 46(9), 915-923. doi: 10.1097/MLR.0b013e31817919e5 113 Appendices Appendix A. Missing Variable Analysis for the CES-D items Item N=1151 Subtotal (%) Total (%) CES-D 1 Chinese (N=319) 25 (7.8%) 51 (4.43%) Whites (N=832) 36 (4.3%) CES-D 2 Chinese (N=319) 23 (7.2%) 61 (5.3%) Whites (N=832) 38 (4.6%) CES-D 3 Chinese (N=319) 31 (9.7%) 71 (6.2%) Whites (N=832) 40 (4.6%) CES-D 4 Chinese (N=319) 31 (9.7%) 72 (6.3%) Whites (N=832) 41 (4.6%) CES-D 5 Chinese (N=319) 26 (8.2%) 63 (5.5%) Whites (N=832) 37 (4.6%) CES-D 6 Chinese (N=319) 22 (6.9%) 52 (4.5%) Whites (N=832) 30 (4.6%) CES-D 7 Chinese (N=319) 27 (8.5%) 66 (5.7%) Whites (N=832) 39 (4.6%) CES-D 8 Chinese (N=319) 25 (7.8%) 57 (5.0%) Whites (N=832) 32 (4.6%) CES-D 9 Chinese (N=319) 22 (6.9%) 58 (5.0%) Whites (N=832) 36 (4.6%) CES-D 10 Chinese (N=319) 32 (10%) 69 (6.0%) Whites (N=832) 37 (4.6%) 114 Appendix A. Missing Variable Analysis for the CES-D items (Continued) Item N=1151 Subtotal (%) Total (%) CES-D 11 Chinese(N=319) 21 (6.6%) 45 (3.9%) Whites (N=832) 24 (4.6%) CES-D 12 Chinese(N=319) 17 (5.3%) 36 (3.1%) Whites (N=832) 19 (4.6%) CES-D 13 Chinese(N=319) 22 (6.9%) 49 (4.3%) Whites (N=832) 27 (4.6%) CES-D 14 Chinese(N=319) 20 (6.3%) 42 (3.6%) Whites (N=832) 22 (4.6%) CES-D 15 Chinese(N=319) 23 (7.2%) 51 (4.4%) Whites (N=832) 28 (4.6%) CES-D 16 Chinese(N=319) 20 (6.3%) 39 (3.4%) Whites (N=832) 19 (4.6%) CES-D 17 Chinese(N=319) 32 (10%) 58 (5.0%) Whites (N=832) 26 (4.6%) CES-D 18 Chinese(N=319) 19 (6.0%) 42 (3.6%) Whites (N=832) 23 (4.6%) CES-D 19 Chinese(N=319) 22 (6.9%) 46 (4.0%) Whites (N=832) 24 (4.6%) CES-D 20 Chinese(N=319) 39 (12.2%) 87 (7.6%) Whites (N=832) 38 (4.6%) 115 Appendix B. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses4 Question I-A Analysis Question I-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate CES-D 1 Step 1 0.126** 0.126** 0.126** 0.124** Step 2 0.126** 0.126** 0.127** 0.124** Step 3 0.125** 0.125** 0.126** 0.123** CES-D 2 Step 1 0.106** 0.106** 0.101** 0.104** Step 2 0.107** 0.107** 0.102** 0.105** Step 3 0.102** 0.102** 0.097** 0.100** CES-D 3 Step 1 0.202** 0.203** 0.203** 0.200** Step 2 0.203** 0.203** 0.204** 0.201** Step 3 0.202** 0.202** 0.203** 0.200** CES-D 4 Step 1 0.095** 0.094** 0.092** 0.092** Step 2 0.095** 0.094** 0.093** 0.093** Step 3 0.099** 0.098** 0.097** 0.097** CES-D 5 Step 1 0.103** 0.104** 0.101** 0.104** Step 2 0.105** 0.105** 0.102** 0.105** Step 3 0.104** 0.104** 0.101** 0.104** 4 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity ** p<0.001 116 Appendix B. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses5 (Continued) Question I-A Analysis Question I-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate CES-D 6 Step 1 0.233** 0.234** 0.234** 0.234** Step 2 0.233** 0.234** 0.234** 0.235** Step 3 0.240** 0.241** 0.241** 0.241** CES-D 7 Step 1 0.050** 0.050** 0.049** 0.049** Step 2 0.050** 0.050** 0.049** 0.049** Step 3 0.064** 0.063** 0.062** 0.062** CES-D 8 Step 1 0.082** 0.081** 0.082** 0.082** Step 2 0.082** 0.081** 0.083** 0.083** Step 3 0.079** 0.078** 0.079** 0.080** CES-D 9 Step 1 0.179** 0.179** 0.176** 0.180** Step 2 0.180** 0.180** 0.177** 0.181** Step 3 0.184** 0.184** 0.183** 0.186** CES-D 10 Step 1 0.145** 0.146** 0.144** 0.145** Step 2 0.145** 0.146** 0.144** 0.145** Step 3 0.140** 0.141** 0.139** 0.139** 5 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity ** p<0.001 117 Appendix B. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses6 (Continued) 6 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity ** p<0.001 Question I-A Analysis Question I-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate CES-D 11 Step 1 0.100** 0.099** 0.101** 0.099** Step 2 0.102** 0.102** 0.102** 0.101** Step 3 0.103** 0.104** 0.103** 0.103** CES-D 12 Step 1 0.145** 0.145** 0.147** 0.148** Step 2 0.145** 0.145** 0.148** 0.149** Step 3 0.143** 0.143** 0.146** 0.147** CES-D 13 Step 1 0.110** 0.110** 0.112** 0.113** Step 2 0.110** 0.110** 0.112** 0.113** Step 3 0.109** 0.109** 0.111** 0.112** CES-D 14 Step 1 0.192** 0.194** 0.192** 0.192** Step 2 0.193** 0.194** 0.192** 0.193** Step 3 0.194** 0.195** 0.192** 0.193** CES-D 15 Step 1 0.121** 0.121** 0.125** 0.126** Step 2 0.121** 0.121** 0.125** 0.127** Step 3 0.114** 0.114** 0.119** 0.119** 118 Appendix B. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses7 (Continued) CES-D 16 Step 1 0.146** 0.146** 0.148** 0.147** Step 2 0.147** 0.147** 0.150** 0.148** Step 3 0.148** 0.148** 0.152** 0.150** CES-D 17 Step 1 0.142** 0.142** 0.138** 0.140** Step 2 0.142** 0.143** 0.139** 0.141** Step 3 0.137** 0.137** 0.133** 0.134** CES-D 18 Step 1 0.220** 0.220** 0.219** 0.218** Step 2 0.221** 0.222** 0.221** 0.220** Step 3 0.224** 0.224** 0.224** 0.222** CES-D 19 Step 1 0.171** 0.171** 0.171** 0.170** Step 2 0.171** 0.172** 0.171** 0.171** Step 3 0.171** 0.171** 0.170** 0.170** CES-D 20 Step 1 0.165** 0.166** 0.165** 0.168** Step 2 0.169** 0.170** 0.169** 0.172** Step 3 0.166** 0.167** 0.165** 0.169** 7 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity ** p<0.001 Question I-A Analysis Question I-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate 119 Appendix C. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses with Chinese Only8 CES-D 1 Step 1 0.142** 0.142** 0.140** 0.140** Step 2 0.147** 0.146** 0.145** 0.144** Step 3 0.106* 0.101 0.107* 0.101* CES-D 2 Step 1 0.127** 0.128** 0.125** 0.126** Step 2 0.126** 0.127** 0.124** 0.125** Step 3 0.090 0.091 0.091* 0.091* CES-D 3 Step 1 0.232** 0.232** 0.231** 0.230** Step 2 0.229** 0.232** 0.227** 0.231** Step 3 0.313** 0.321** 0.315** 0.322** CES-D 4 Step 1 0.088** 0.086** 0.091** 0.088** Step 2 0.086** 0.085** 0.089** 0.087** Step 3 0.140* 0.116* 0.141* 0.116* CES-D 5 Step 1 0.113** 0.114** 0.113** 0.113** Step 2 0.111** 0.114** 0.111** 0.114** Step 3 0.099* 0.099* 0.099* 0.099* 8 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity Question II-A Analysis Question II-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate 120 Appendix C. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses with Chinese Only9 (Continued) CES-D 6 Step 1 0.212** 0.213** 0.210** 0.211** Step 2 0.208** 0.214** 0.207** 0.213** Step 3 0.242** 0.255** 0.247** 0.258** CES-D 7 Step 1 0.007 0.008 0.003 0.004 Step 2 0.006 0.008 0.002 0.004 Step 3 -0.017 -0.023 -0.016 -0.023 CES-D 8 Step 1 0.092** 0.092** 0.095** 0.095** Step 2 0.096** 0.091** 0.099** 0.094** Step 3 0.065 0.054 0.063 0.054 CES-D 9 Step 1 0.177** 0.178** 0.176** 0.177** Step 2 0.179** 0.184** 0.179** 0.183** Step 3 0.253** 0.263** 0.257** 0.267** CES-D 10 Step 1 0.169** 0.171** 0.170** 0.172** Step 2 0.169** 0.172** 0.170** 0.173** Step 3 0.156* 0.157* 0.157* 0.157* 9 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity Question II-A Analysis Question II-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate 121 Appendix C. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses with Chinese Only10(Continued) CES-D 11 Step 1 0.091** 0.090** 0.092** 0.091** Step 2 0.094** 0.089** 0.096** 0.091** Step 3 0.068 0.059 0.069 0.060 CES-D 12 Step 1 0.139** 0.138** 0.143** 0.143** Step 2 0.134** 0.137** 0.140** 0.142** Step 3 0.143* 0.147* 0.142* 0.146* CES-D 13 Step 1 0.117** 0.117** 0.119** 0.118** Step 2 0.115** 0.117** 0.116** 0.118** Step 3 0.108 0.104 0.108* 0.104* CES-D 14 Step 1 0.194** 0.197** 0.193** 0.195** Step 2 0.200** 0.198** 0.198** 0.197** Step 3 0.166* 0.167* 0.171** 0.169** CES-D 15 Step 1 0.155** 0.155** 0.162** 0.162** Step 2 0.153** 0.155** 0.159** 0.162** Step 3 0.135* 0.136* 0.134* 0.136* 10 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity Question II-A Analysis Question II-B Analysis Item DIF Step Initial Estimate Pooled Estimate Initial Estimate Pooled Estimate 122 Appendix C. Initial and Pooled Estimates for the CES-D Total Score of DIF Analyses with Chinese Only11(Continued) CES-D 16 Step 1 0.139** 0.138** 0.142** 0.142** Step 2 0.139** 0.137** 0.142** 0.141** Step 3 0.077 0.076 0.076 0.076 CES-D 17 Step 1 0.163** 0.163** 0.161** 0.161** Step 2 0.165** 0.170** 0.164** 0.168** Step 3 0.091 0.099 0.096 0.102 CES-D 18 Step 1 0.233** 0.235** 0.231** 0.233** Step 2 0.238** 0.237** 0.237** 0.235** Step 3 0.259** 0.261** 0.264** 0.264** CES-D 19 Step 1 0.184** 0.184** 0.182** 0.182** Step 2 0.190** 0.183** 0.189** 0.182** Step 3 0.128 0.126 0.129* 0.127* CES-D 20 Step 1 0.184** 0.184** 0.188** 0.189** Step 2 0.180** 0.184** 0.185** 0.189** Step 3 0.136 0.147* 0.139* 0.150* 11 Step #1: CES-D total score. Step #2: CESD total score + Ethnicity Step #3: CESD total score + Ethnicity + CESD*Ethnicity Question II-A Analysis Question II-B Analysis Item DIF Step Initial Estimate Pooled Estimate Estimate P-value 123 Appendix D. Results of Binary and Ordinal Logistic Regression of Ethnicity DIF Analyses Item 2 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.93 (0.008) <0.001* 0.911 Response ≤ 1 𝑜𝑟 > 1 -0.112 (0.009) <0.001* 0.894 Response ≤ 2 𝑜𝑟 > 2 -0.113 (0.013) <0.001* 0.893 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.453 0.797 2 With Total CES-D Score 11.382 0.003* 2 Item 4 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.144 (0.011) <0.001* 0.866 Response ≤ 1 𝑜𝑟 > 1 -0.099 (0.008) <0.001* 0.906 Response ≤ 2 𝑜𝑟 > 2 -0.049 (0.007) <0.001* 0.952 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 4.830 0.089 2 With Total CES-D Score 109.105 <0.001* 2 124 Appendix D. Results of Binary and Ordinal Logistic Regression of Ethnicity DIF Analyses (Continued) Item 7 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.095 (0.008) <0.001* 0.909 Response ≤ 1 𝑜𝑟 > 1 -0.036 (0.006) <0.001* 0.964 Response ≤ 2 𝑜𝑟 > 2 -0.010 (0.008) 0.187 0.990 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.647 0.724 2 With Total CES-D Score 96.837 <0.001* 2 Item 8 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.122 (0.011) <0.001* 0.886 Response ≤ 1 𝑜𝑟 > 1 -0.080 (0.007) <0.001* 0.923 Response ≤ 2 𝑜𝑟 > 2 -0.043 (0.08) <0.001* 0.958 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.697 0.706 2 With Total CES-D Score 44.752 <0.001* 2 125 Appendix D. Results of Binary and Ordinal Logistic Regression of Ethnicity DIF Analyses (Continued) Item 10 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.132 (0.010) <0.001* 0.876 Response ≤ 1 𝑜𝑟 > 1 -0.141 (0.011) <0.001* 0.869 Response ≤ 2 𝑜𝑟 > 2 -0.186 (0.023) <0.001* 0.830 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.378 0.828 2 With Total CES-D Score 8.182 0.017* 2 Item 12 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.179 (0.012) <0.001* 0.836 Response ≤ 1 𝑜𝑟 > 1 -0.144 (0.011) <0.001* 0.866 Response ≤ 2 𝑜𝑟 > 2 -0.065 (0.011) <0.001* 0.937 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 2.866 0.239 2 With Total CES-D Score 53.627 <0.001* 612 12 This is the result of test of parallel line for Step 3: the CES-D score + Ethnicity + Interaction Term. In previous step, the test of parallel lines cannot be performed as the log-likelihood value of the general model is smaller than that of the null model. 126 Appendix D. Results of Binary and Ordinal Logistic Regression of Ethnicity DIF Analyses (Continued) Item 16 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.183 (0.012) <0.001* 0.833 Response ≤ 1 𝑜𝑟 > 1 -0.135 (0.010) <0.001* 0.873 Response ≤ 2 𝑜𝑟 > 2 -0.099 (0.011) <0.001* 0.906 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.284 0.868 2 With Total CES-D Score 22.607 <0.001* 2 127 Appendix E. Results of Binary and Ordinal Logistic Regression of Strength of Ethnic Identity DIF Analyses Item 4 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.135 (0.022) <0.001* 0.874 Response ≤ 1 𝑜𝑟 > 1 -0.087 (0.015) <0.001* 0.917 Response ≤ 2 𝑜𝑟 > 2 -0.041 (0.015) <0.005* 0.960 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 0.407 0.816 2 With Total CES-D Score 20.265 <0.001* 2 Item 7 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.071 (0.018) <0.001* 0.932 Response ≤ 1 𝑜𝑟 > 1 0.007 (0.012) 0.584 1.007 Response ≤ 2 𝑜𝑟 > 2 0.030 (0.016) 0.060 1.030 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 8.725 0.013 2 With Total CES-D Score 37.919 <0.001* 2 128 Appendix E. Results of Binary and Ordinal Logistic Regression of Strength of Ethnic Identity DIF Analyses (Continued) Item 8 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.144 (0.025) <0.001* 0.866 Response ≤ 1 𝑜𝑟 > 1 -0.097 (0.009) <0.001* 0.908 Response ≤ 2 𝑜𝑟 > 2 -0.053 (0.013) <0.001* 0.948 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 1.509 0.470 2 With Total CES-D Score 12.155 0.002* 2 Item 12 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.173 (0.023) <0.001* 0.841 Response ≤ 1 𝑜𝑟 > 1 -0.170 (0.023) <0.001* 0.843 Response ≤ 2 𝑜𝑟 > 2 -0.055 (0.021) 0.009* 0.946 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 5.640 0.06 2 With Total CES-D Score 11.821 0.003* 2 129 Appendix E. Results of Binary and Ordinal Logistic Regression of Strength of Ethnic Identity DIF Analyses (Continued) Item 16 Logistic Regression (Binary) 𝝱 (Standard Error) P-value 𝑒β Response ≤ 0 𝑜𝑟 > 0 -0.201 (0.026) <0.001* 0.818 Response ≤ 1 𝑜𝑟 > 1 -0.132 (0.019) <0.001* 0.876 Response ≤ 2 𝑜𝑟 > 2 -0.077 (0.020) <0.001* 0.926 Logistic Regression (Ordinal) Result of Test of Parallel Line 𝑥2 P-Value Degree of Freedom With MEIM Total Mean Score 3.747 0.154 2 With Total CES-D Score 12.081 0.002* 2
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Exploring potential impact of Chinese ethnicity on the measurement of depression among adolescents using the Center for Epidemiologic Studies Depression Scale
Text