Psychometric Properties of the Geriatric Anxiety Scale in Community-Dwelling, Clinical, and Medical Samples of Older Adults. Anne Elizabeth Mueller

Transcription

1 Psychometric Properties of the Geriatric Anxiety Scale in Community-Dwelling, Clinical, and Medical Samples of Older Adults by Anne Elizabeth Mueller B.A., Saint Louis University, 2008 M.A., University of Colorado Colorado Springs, 2010 A dissertation submitted to the Graduate Faculty of the University of Colorado Colorado Springs in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Psychology 2014

2 This dissertation for Doctor of Philosophy degree by Anne Elizabeth Mueller has been approved for the Department of Psychology by Daniel L. Segal, Chair Leilani Feliciano Brandon Gavett Amy Silva-Smith Brian Yochim Date

3 iii Mueller, Anne Elizabeth (Ph.D., Psychology) Psychometric Properties of the Geriatric Anxiety Scale in Community-Dwelling, Clinical, and Medical Samples of Older Adults Dissertation directed by Professor Daniel L. Segal This study examined the psychometric properties of the Geriatric Anxiety Scale (GAS; Segal, June, Payne, Coolidge, & Yochim, 2010) in three samples of older adults using both classical test theory (CTT) and item response theory (IRT) techniques. Study One was conducted in a community-dwelling sample of older adults. Factor analysis revealed that a one- or two- factor solution best fit the data. The GAS also had excellent reliability and adequate convergent validity with other measures, though it lacked discriminant validity with a measure of depression. Study Two examined the psychometric properties in a clinical sample of older adults, and the GAS performed largely similar in this population. Study Three utilized a medical sample of older individuals, and found that the GAS had moderate relationships to self-reported subjective health status. Study Four used IRT to examine the item properties of the scale in all three samples, and a short form (GAS-10) was also created. Two items were flagged for differential item functioning (DIF), but the degree of DIF was negligible. Women scored significantly higher than men on the GAS and subscales, and adults younger than 80 scored significantly higher on the Cognitive subscale than adults 80 and up. Results from the studies indicated that the GAS has strong psychometric properties. Implications and future directions of study are discussed.

4 TABLE OF CONTENTS CHAPTER I. INTRODUCTION 1 Item Response Theory...13 Existing Measures of Anxiety: Strengths and Limitations Geriatric Anxiety Scale: Overview and Preliminary Psychometric Properties..18 Statement of Problem and Purpose of the Study..22 II. STUDY ONE..27 Method...27 Results III. STUDY TWO,,,,..44 Method Results 45 IV. STUDY THREE..52 Method.. 52 Results...54 V. STUDY FOUR Method.. 60 Results..63 VI. GENERAL DISCUSSION..77

5 v REFERENCES..93 APPENDICES A. Geriatric Anxiety Scale Version B. Scoring Instruction.104 C. Geriatric Anxiety Scale D. Geriatric Anxiety Scale 10 Item Version (GAS-10)...106

6 vi TABLES Table 1. Means, Standard Deviations, and Ranges for All Demographic Information and All Measures Cronbach s Alpha Coefficients for GAS Total Scale and Subscales Principal Axis Factoring with Direct Oblimin Rotation in Study Correlations between Demographic Variables, GAS, PHQ-9, BHS, SISE, 3LS, and SF-36 Physical Functioning Subscale in Study Correlations Between GAS items and PHQ-9 in Study 1 (N = 275) Correlations among GAS Items and SF-36 Physical Functioning Subscale Scores in Study 1 (N = 270) Correlations among Demographic Variables, GAS, Subscales, GDS, and GAF in Study 2 (N = 136) Correlations among GAS items and GDS in Study 2 (N = 99) Correlations among Demographic Variables, GAS, MoCA, GAI, BAI, PHQ-9, and SF-36 total Scale Scores in Study 3 (N = 38) Correlations among GAS, Subscales, BAI, GAI, PHQ-9, and SF-36 Subscales in Study IRT Calibration for GAS Items Standard Score Distribution for GAS Total Scale Scores (N = 542) Score Distribution for GAS Subscale Scores Score Distribution for GAS-10 (N = 556).75

7 vii FIGURES Figure 1. Screeplot for principal axis factoring on 25 GAS items (Study 1) Screeplot for principal axis factoring on 25 GAS items (Study 2) Test information function for 24-item GAS Test (left panel) and DIF-item (right panel) characteristic curves by age (young-old versus old-old) Test (left panel) and DIF-item (right panel) characteristic curves by sex Test Information Function for GAS-10.70

8 CHAPTER 1 INTRODUCTION Anxiety disorders are among the most ubiquitous and debilitating mental disorders in older adults. In fact, anxiety disorders in older adults are common, with a prevalence estimate ranging from 3.2 to 14.2% depending upon diagnostic criteria (i.e., DSM-III or DSM-IV) and age cutoff (i.e., 55 and up versus 65 and up; Wolitzky-Taylor, Castriotta, Lenze, Stanley, & Craske, 2010). Generalized Anxiety Disorder (GAD), a common anxiety disorder across the lifespan, includes the following criteria according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, American Psychiatric Association, 2000): excessive anxiety and worry more days than not for at least six months, difficulty controlling this worry, and at least three anxiety symptoms (restlessness, irritability, muscle tension, sleep disturbances, difficulty concentrating, being easily fatigued). These symptoms must also cause clinically significant impairment in functioning (e.g., social, occupational) and/or distress. Sub-syndromal anxiety symptoms in late life are even more widespread than anxiety disorders, with a prevalence ranging from 15% to 52.3% in community samples (Bryant, Jackson, & Ames, 2008). Indeed, a significant number of older adults are impacted by anxiety. As this subpopulation will steadily increase in years to come, the number of people who experience anxiety will subsequently increase as well. As will be discussed in this review, anxiety is associated with a multitude of dire outcomes in late life.

9 2 Accurate and timely assessment of anxiety is paramount, as this is a precursor for appropriate treatment. However, there are several challenges unique to anxiety in older adults which complicates anxiety assessment in this specific population. The Geriatric Anxiety Scale (GAS; Segal, June, Payne, Coolidge, & Yochim, 2010) is a self-report assessment tool designed specifically for use with older adults to address such challenges. Initial studies have suggested that the GAS is a reliable and valid tool (Segal et al., 2010; Yochim, Mueller, June, & Segal, 2011). The purpose of the current study was to further investigate the psychometric properties of the Geriatric Anxiety Scale in three samples of older adults, with the intention of increasing its utility for both clinicians and researchers alike. There is a unique set of challenges associated with the assessment of anxiety in late life. Such challenges highlight the necessity to utilize screening tools that address these issues and are well-validated for use with this particular population. Anxiety is often co-morbid with physical health conditions, which complicates the manner in which older adults perceive their symptoms, describe their symptoms, and seek treatment for these symptoms. Research has indicated that approximately one-third of adults with a somatic health problem also experiences anxiety and depression (Stordal, Bjelland, Dahl, & Mykletun, 2003). Specifically, anxiety occurs in high rates alongside health conditions such as arthritis (Brock et al., 2011; Murphy, Sacks, Brady, Hootman, & Chapman, 2012), chronic obstructive pulmonary disease (Cully et al., 2006), diabetes, and gastrointestinal health concerns (Wetherell, Ayers, Nuevo, Stein, Ramsdell, & Patterson, 2010). Anxiety is also associated with cognitive impairment (Yochim, Mueller, & Segal, 2012), urinary incontinence, sleep problems, and detrimental health behaviors such as

10 3 smoking, physical inactivity, poor diet, and alcohol abuse (DeLuca et al., 2005; Mehta et al., 2003; Strine, Chapman, Kobau, & Balluz, 2005). The co-occurrence of anxiety with medical problems is troublesome as it is associated with increased functional impairment, more physician visits (Kroenke et al., 2007), and decreased health-related quality of life (Porensky et al., 2009). This combination can result in increased healthcare costs and ineffective treatment. For example, an older adult may attribute particular symptoms of anxiety (e.g., feeling jumpy, fatigue) to his or her medical conditions and/or medications, and seek out services from his or her physician instead of a mental health clinician. Not surprisingly, co-morbid anxiety with chronic medical problems has been associated with reports of heightened somatic symptoms, even after controlling for the severity of the medical condition itself (Katon, Lin, & Kroenke, 2007). Thus, the presence of anxiety in conjunction with medical illness often results in heightened utilization of medical services (Porensky et al., 2009), whereas medical treatment alone may not be optimal nor sufficient in providing total patient care. Another way this co-morbidity between health conditions and anxiety may impact assessment is that heightened somatic symptoms may result in inflated scores on anxiety measures which contain many somatic items. Anxiety also increases the risk for individuals to develop significant health concerns and disability throughout the lifespan. For instance, anxiety is associated with the onset of cardiovascular medical conditions, such as the development of coronary heart disease (Suls & Bunde, 2005) and acute coronary syndrome (Rozanski, Blumenthal, & Kaplan, 1999). Thus, those with anxiety are at a higher risk for mortality in late life. Van Hout et al. (2004) found that older men with anxiety disorders had an 87% higher risk of mortality over seven years than older men without anxiety disorders, even after

11 4 controlling for co-morbid depression, smoking, alcohol use, and body mass index. Moreover, the presence of anxiety symptoms is associated with an increased risk of disability in completing activities of daily living, despite the presence of emotional support, psychotropic medication, and physical activity (Brenes et al., 2005). Whereas some physical health conditions in late life may be unavoidable, excessive anxiety, in contrast, is a treatable condition (i.e., Ayers, Sorrell, Thorp, & Wetherell, 2007). Given these findings, anxiety is clearly a serious concern in late life. Unfortunately, anxiety in medical settings is highly prevalent but largely undetected. For example, in a study of 965 adults in primary care settings, 19.5% were found to have at least one diagnosable anxiety disorder (Kroenke et al., 2007). In a study of adults with arthritis and co-morbid anxiety and/or depression who regularly attended medical appointments, only half of individuals had sought out any help for their mental health symptoms (i.e., not just behavioral health services; Murphy et al., 2012). Alarmingly, the detection rate of GAD by physicians is as low as 1.5% (Calleo et al., 2009). Physicians often rely on patient self-report to diagnose anxiety, and the older patient may ascribe their symptoms to other factors such as physical illness and/or depression (Segal, Qualls, & Smyer, 2011). Additionally, some older patients may erroneously perceive their anxiety symptoms as expectable or normal in the context of later life, and thus not report them to their doctors. Taken together, these factors stress the need for appropriate and brief assessment tools to be administered routinely in medical settings to coordinate appropriate treatment and to maximize patient well-being. Furthermore, the overlap between somatic symptoms of anxiety and symptoms of underlying physical health conditions emphasizes the importance of assessing other

12 5 aspects of anxiety (i.e., cognitive, affective) in addition to assessing somatic symptoms. Failure to do so may result in an incomplete and/or inaccurate assessment, misdiagnosis, and ineffective treatment. Another major challenge to assessment is the co-morbidity of anxiety with other mental health problems. Although anxiety exists independently of other mental health complications, it is often co-morbid with other psychopathology. Most notably, anxiety in older adults is highly co-morbid with depressive symptoms (Beekman et al., 2000). This could be in part due to the overlapping symptom criteria of both conditions (i.e., sleep disturbances, fatigue, difficulty concentrating). In a study of over 3,000 older adults, anxiety symptoms occurred in 43% of the people who reported depression (Mehta et al., 2003). Furthermore, in a population-based sample of community-dwelling older adults, anxiety disorders were noted in 23% of participants who also met the full diagnostic criteria for Major Depressive Disorder (Cairney, Corna, Veldhuizen, Herrmann, & Streiner, 2008). The co-morbidity of anxiety and depression is problematic as it has been associated with more severe anxiety symptomatology (Hopko et al., 2000), lower levels of well-being, greater functional impairment (Cairney et al., 2008), as well as poorer responsiveness to both pharmacological and psychological therapies (Andresscu et al., 2007). These findings demonstrate the gravity of having more than one psychiatric condition, and emphasize the importance of early and accurate detection of anxiety symptoms as well as depression in order to initiate treatment as soon as possible. However, the co-morbidity of anxiety with depression increases the likelihood of detection and psychopharmacological treatment in primary care settings (Calleo et al.,

13 6 2009). This is likely due to the increased severity of symptoms, although both conditions are largely unrecognized in medical settings. Another issue unique to the assessment of anxiety in later life is the role of cognitive impairment, a common condition among older adults. Individuals with cognitive impairment often present with co-occurring symptoms of anxiety, which may complicate the manner in which anxiety is experienced and communicated (Wolitzky- Taylor et al., 2010). For example, individuals with more severe cognitive impairment may experience anxiety but lack the cognitive skills needed to describe and seek help for such symptoms, stressing the need for caregivers to be aware of such symptoms. Additionally, some symptoms of anxiety overlap with cognitive impairment (i.e., difficulty concentrating, agitation), increasing the risk of misdiagnosis of either condition. The relationship between anxiety and cognitive impairment may be bidirectional, such that anxiety symptoms may exacerbate cognitive impairment, but awareness of cognitive impairment can lead to anxiety as well. There are also cultural considerations in late-life anxiety assessment. Though the lifetime prevalence rates of all anxiety disorders does not significantly differ among various ethnic groups (Jimenez, Alegria, Chen, Chan, & Laderman, 2010), there is vast diversity both among and within various ethnic groups in regards to the manner in which anxiety is expressed and treated. The prevalence of generalized anxiety disorder and social phobia is lower among Afro-Caribbean older adults than non-latino white older adults. Additionally, the prevalence of generalized anxiety disorder is higher among older Asian and Latino immigrants than US-born Asian and Latino immigrants (Jimenez et al., 2010). These subgroups may have greater difficulty in accessing behavioral health

14 7 care due to language barriers, socioeconomic status, and stigma. However, ethnic identity has a protective effect for some minority groups. Williams, Chapman, Wong, and Turkheimer (2012) reported that higher levels of ethnic identity was associated with lower levels of anxiety and depression in African American adults, but this association was not found in European American adults. This suggests that ethnic identity is an important area of assessment to consider with minority clients. Kim and colleagues (2011) reported that self-reported mental health and psychiatric diagnoses is not consistent among minority groups. The researchers found that self-reported mental health was more related to anxiety disorders in non-hispanic white individuals than Hispanic, African-American, and Asian individuals. Thus, though anxiety disorders are more prevalent in non-hispanic white populations, it could be that screening measures which assess self-reported mental health are not sensitive to symptoms in individuals from minority groups. Compared to younger adults, older adults may be reticent to identify their symptoms as anxiety and subsequently seek treatment specifically for those symptoms. For example, in an epidemiological study of older adults, less than a quarter of older adults with GAD elicited mental health services (Mackenzie, Reynolds, Chou, Pagura, & Sareen, 2011), again underscoring the need for routine screening in other contexts (e.g., primary care). Furthermore, older adults are less accurate than younger adults at identifying symptoms of anxiety (Wetherell et al., 2009). Older adults often attribute somatic symptoms of anxiety (e.g., fatigue, sleep troubles, muscle tension) to physical illnesses versus identifying them as symptoms of a mental health problem, leading them to elicit help from their physician rather than a specialized mental health clinician,

15 8 particularly in the presence of other medical conditions. Additionally, older adults may interpret affective symptoms of anxiety (i.e., decreased mood, worry) as depression (Segal et al., 2011), which could also impact the manner in which older adults seek treatment. In addition, the experience of anxiety in late life may differ from the experience of anxiety in earlier stages of life. This presents concerns regarding the content validity of screening measures intended for use with younger adults, as some items on such measures may not be appropriate (Kogan, Edelstein, & McKee, 2000). For example, older adults report more concerns regarding health than younger adults, and younger adults tend to report higher levels of work-related concerns (Diefenbach, Stanley, & Beck, 2001). Other common areas of worry for older adults include loss of independence and functional status, looking incompetent in front of others, and forgetting information in front of others (Brock et al., 2011; Ciliberti, Gould, Smith, Chorney, & Edelstein, 2011). Inadequate content validity of assessment tools increases the risk of misdiagnosing anxiety in late life, as some older adults with anxiety may not endorse items designed for use with younger adults. Thus, development of age and cohort appropriate assessment measures is imperative. Wolitzky-Taylor and colleagues (2010) also raised concerns regarding the differential experience of anxiety in late life versus earlier life. In their review of the current literature on late-life anxiety, they found that in general, the symptom presentations of anxiety disorders are similar to that of younger adults; however, they highlighted that the current knowledge of anxiety disorders in older adults is bound by the diagnostic classification system itself. Older adults may experience unique symptoms

16 9 and/or patterns of symptoms which are not currently included in the DSM-IV-TR and thus not routinely assessed, resulting in an inaccurate estimate regarding how many older adults experience anxiety. For example, the current diagnostic system lacks age-specific examples of maladaptive avoidance behaviors (i.e., avoiding situations due to excessive fear of falling, excessive checking of blood pressure; Mohlman et al., 2011). There is also controversy regarding the use of the word excessive in assessment, as older adults may not view their concerns as exceeding the norm for their age (Mohlman et al., 2011). Using more sensitive language (i.e., How nervous are you compared to other people you know? ) may be warranted. Therefore, there is a need for assessment tools designed specifically for older adults, as well as better guidelines for diagnosing anxiety disorders in this unique population. Research has identified variables which differentially impact the incidence of anxiety in late life. Such variables are important to consider in anxiety assessment as they may be useful in identifying individuals who are at-risk for experiencing this condition. These variables also influence normative data which may increase the accuracy of assessment tools in correctly identifying individuals who experience anxiety symptoms. The following section will describe two major variables identified in the literature which appear to influence the occurrence of late life anxiety and their impact on assessment. Though anxiety disorders are common among older adults, they are generally reported as less prevalent in older adults than in younger adults (Flint et al., 2010; Jorm, 2000; Owens, Hadjistavropoulos, & Asmundsmon, 2000). This finding is similar to the relationship between other mental health problems (i.e., depression) and age. It is also

17 10 important to bear in mind that older adults are heterogeneous in respect to various age cohorts. Research has identified differences in anxiety prevalence between the youngold and old-old. In a cross-sectional, community-based, epidemiologic study, Gum, King-Kallimanis, and Kohn (2009) discovered that adults 75 years of age and older were less likely than those between the ages of 65 to 69 to be diagnosed with an anxiety disorder. Data from the Berlin Aging Study also suggest that the prevalence of anxiety disorders continues to decrease in the very old (Schaub & Linden, 2000). The prevalence of anxiety disorders (using DSM-III-R criteria) in adults aged was 4.3%, whereas the prevalence in adults 85 years old and older was 2.3%. In sum, anxiety generally occurs less frequently in older adults than in younger adults, and occurs even less frequently in those who are considered very old. However, a multitude of adults in late life experience anxiety regardless of age disparities in prevalence. There are multiple hypotheses as to why the prevalence of anxiety tends to decrease with advancing age. Findings from cross-sectional studies may reflect underlying cohort differences versus true age effects (Segal et al., 2011). Additionally, individuals with anxiety are more likely to be functionally impaired (Kroenke et al., 2007), in which they may reside in assisted living facilities and thus be excluded from epidemiological research (Segal et al.) or die earlier than those without anxiety (van Hout et al., 2004). Furthermore, as mentioned previously, the experience of anxiety in late life may be qualitatively different than anxiety in earlier stages of life. As such, the differences in prevalence rates across the lifespan may be partly due to the diagnostic criteria used to identify anxiety. Therefore, the finding that anxiety decreases with age

18 11 may be due to diagnostic criteria and assessment tools better suited for younger adults rather than older adults (Flint et al., 2010) as well as other methodological factors. Sex is another variable which has been identified as a risk factor for anxiety. Specifically, women tend to report higher levels of anxiety than men, a finding that is reported consistently in the literature. For instance, Gum et al. (2009) found that community-dwelling individuals who were diagnosed with an anxiety disorder were more likely to be female. Furthermore, female sex has been associated with a greater likelihood of anxiety chronicity in older adults (De Beurs, Beekman, van Dyck, & van Tilburg, 2000), such that anxiety tends to persist in older women compared to older men. Similarly, Owens et al. (2000) found that men above the age of 60 reported the lowest levels of anxiety in comparison to younger men and older women. Other researchers have identified female sex as an independent risk factor for heightened anxiety symptoms (e.g., Lowe & Reynolds, 2005; Potvin et al., 2011). Despite these findings, there is some evidence to suggest that sex differences in anxiety dissipate in very late adulthood. In a study of community-dwelling adults aged 82 to 87, Pachana, McLaughlin, Leung, Byrne, and Dobson (2011) found no significant sex differences in both anxiety and depression after controlling for cognitive status, health, and level of education. Moreover, Brock and colleagues (2011) found that women above the age of 80 tended to report fewer worries than the younger women in their sample. Thus, though some research has drawn attention to sex differences in anxiety prevalence, these differences may become less salient with increasing age. A number of variables have been proposed to explain sex differences in anxiety. For example, Leach, Christensen, Mackinnon, Windsor, and Butterworth (2008) found

19 12 that women tended to have poorer physical health, be more physically inactive, and have more interpersonal problems than men, and reported that these variables mediated the relationships among sex, anxiety, and depression. Thus, sex differences in anxiety could be attributed to other factors which are associated with female sex. An additional explanation could be that women are more emotionally aware and thus willing to report or seek help for their anxiety symptoms than men, resulting in artificial discrepancies in anxiety levels. As can be noted from the previous sections, certain demographic variables differentially impact the incidence of anxiety in older adults. Thus, particular demographic groups (i.e., younger women) can be expected to obtain higher scores on measures such as the GAS. Such variations could be the result of true differences in prevalence rates, but could also reflect measurement bias. Measurement bias occurs when a particular group of individuals has an unequal chance of endorsing an item than another group of individuals, despite being matched upon the variable of interest. For example, men and women with the same level of anxiety should have the same likelihood of endorsing a particular item on a measure in the same manner. Thus, if an assessment tool is biased against a certain variable, variations in prevalence rates would reflect this measurement error instead of actual group differences. If a measure has no detectable bias but differences between groups remain, the differences are more likely to reflect actual variations between groups. Clearly, measurement bias has serious implications for the conclusions drawn from assessment tools, both clinically and in research. Several researchers have found item biases in various measures of anxiety. For instance, Van Dam, Earleywine, and Forsyth (2009) found that removing a single item from the

20 13 Anxiety Sensitivity Index ( It scares me when I feel faint ) eliminated significant gender difference in scores but did not alter the internal consistency of the measure. Leach, Christensen, and Mackinnon (2008) found that the Goldberg Anxiety and Depression scales were free from gender-biased items, though the factor structures of the measures became more similar between men and women when certain items were removed (i.e., items regarding sleep). This has serious implications for scale development, such that individuals could appear more or less anxious than they are based upon item properties, placing them at risk for misdiagnosis. Thus, though age and sex appear to have an impact upon the incidence of anxiety in late life, item bias must be taken into account in scale construction. Item Response Theory IRT is a set of statistical models used to measure latent variables (e.g., anxiety), and posits that responses on a given item are a function of both person and item properties (Edwards, 2009). According to IRT, individuals who have a greater level of the latent trait should have a higher probability of endorsing a particular item measuring that trait. Analyses are often presented as item characteristic curves (ICCs), plots which indicate the likelihood of endorsing an item (e.g., symptom) as the level of the underlying trait (e.g., anxiety) changes. The underlying trait is represented as theta (θ). More steep slopes in ICCs indicate that the item under scrutiny is better able to discriminate among people with high or low levels of the latent trait (represented as the discrimination parameter, a). The threshold parameter (also known as item severity or difficulty parameter; b) indicates the trait level at which the likelihood of endorsing a given response choice is 50%. A higher threshold parameter indicates that the individual must

21 14 have higher levels of the latent trait to have a 50% likelihood of endorsing the response choice. Each item has an information function, depicted in item information curves (IICs), which provide data about how much information the item yields about the threshold parameter. In IICs, a steeper slope indicates that the item provides more information about the threshold but over a more restricted range across the latent trait. A less steep slope indicates that the item provides less information over a more broad range. Item information functions are combined to create a test information function (TIF). Within the IRT framework, the reliability of a test increases with the inclusion of better or more informative items. Additionally, standard error is determined by calculating the square root of the inverse of information. According to IRT, both information and standard error are believed to vary across all trait levels, such that a particular item or sum of items may be more informative for an individual with a higher level of a trait compared with an individual with a lower level of the trait. Thus, IRT analyses are pertinent to scale development as items that do not provide reliable information about a person s standing on a latent trait can be identified and either re-written or removed from the measure. IRT can also be used to analyze item bias or measurement invariance (also known as differential item functioning [DIF]). If an item is biased against a certain group characteristic (e.g., age, sex), then the ICCs for that item will differ, despite the groups being matched on the latent trait. IRT modeling techniques are an alternative to classical test theory (CTT), which has historically been popular among psychologists in scale development (Embretson & Reise, 2000). While CTT statistical techniques are performed on a combined set of items, IRT states that properties of the item are linked directly to test behavior. In other

22 15 words, IRT can provide information regarding the latent variable under scrutiny by analyzing each individual item of the test, whereas CTT examines the properties of the test as a whole. IRT is growing in popularity among psychologists, particularly in regards to clinical assessment (Reise & Waller, 2009), and is considered a favorable alternative or supplement to CTT. Existing Measures of Anxiety Strengths and Limitations In a systematic review of the literature, Therrien and Hunsley (2011) reported that the majority of anxiety assessment tools used in research with older adults lack sufficient psychometric evidence to justify their use with this population. Although there are a multitude of empirically-validated measures of anxiety designed for use with younger adults, there are few assessment tools designed specifically for older adult populations. However, some existing measures of anxiety, while not intended for use with older adults, have empirical support for use with this population. The following section will detail some measures which are commonly used with older adults, including strengths and limitations of each measure. Beck Anxiety Inventory (BAI). The BAI (Beck, Epstein, Brown, & Steer, 1988) is a popular self-report measure of anxiety intended for use with adults of all ages. It contains a list of 21 symptoms which are rated dimensionally from 0 (not at all) to 3 (severe). Though the BAI is not intended specifically for older adults, Kabacoff, Segal, Hersen, and Van Hasselt (1997) found that the BAI had high internal consistency, convergent, divergent, and factorial validity in a sample of older adults in a psychiatric outpatient setting. Similar findings have been found in samples of older adults in medical samples (i.e., Wetherell & Areán, 1997).

23 16 However, the BAI may not be ideal for use with older adults as it contains many somatic symptoms of anxiety. In fact, the majority of the 21 items on the BAI pertain to somatic symptoms. Older adults may experience such symptoms for reasons other than anxiety (i.e., physical illness), but could endorse high levels of anxiety as defined by the measure. Thus, someone could screen positive for anxiety but not actually experience this condition. Furthermore, the BAI was designed to exclude DSM symptoms of depression which overlap with anxiety (i.e., difficulty concentrating, sleep troubles, fatigue). Though this may help the clinician to distinguish whether the respondent is experiencing either anxiety or depression, these symptoms are key diagnostic features of anxiety and should be included in any routine assessment of anxiety symptoms. Thus, though the BAI has demonstrated adequate psychometric properties in older adult populations, it contains limitations which may restrict its clinical utility with older adults. Geriatric Anxiety Inventory (GAI). The GAI (Pachana, Byrne, Siddle, Koloski, Harley, & Arnold, 2007) is a self-report measure designed specifically for use with older adults. Its intent is not to diagnose anxiety disorders per se, but rather to measure anxiety symptomatology in older people. This may be considered a strength of the measure given the inherent problems in the DSM and the prevalence of sub-syndromal anxiety in older individuals. The GAI contains 20 statements (i.e., I find it hard to relax, I think of myself as a worrier, Little things bother me a lot ) in which the respondent answers yes or no. Pachana et al. also noted that the GAI was designed to assess few somatic symptoms to minimize the risk of medical problems inflating scores on the measure. The GAI has demonstrated sound psychometric properties, including high internal consistency as well as convergent validity with other measures of anxiety (Pachana et

24 17 al.). It has utility in various settings and contexts in which older adults receive services, such as home care (Diefenbach et al., 2009), and has been translated and validated in several different languages. A short form of the GAI has also been created and validated in a community-dwelling sample of Australian women (Byrne & Pachana, 2011). Despite these strengths, the GAI also has several limitations which may restrict its clinical utility. For instance, the majority of items on the scale include statements pertaining to worry and other cognitive aspects of anxiety (i.e., anticipating the worst, difficulty making decisions). This may be problematic as an individual may experience anxiety but their symptoms may not be predominantly cognitive in nature. Additionally, the GAI utilizes a simple dichotomous response format. Though this may be preferable for older adults with cognitive impairment and other limitations, a dimensional rating system provides more information regarding the severity of the symptoms experienced by the client. Overall, the GAI has strong psychometric properties as well as other ideal features, but it also contains several features which may restrict its utility in assessing anxiety in certain groups of older adults. Adult Manifest Anxiety Scale-Elderly Version (AMAS-E). The AMAS-E (Reynolds, Richmond, & Lowe, 2003) is a 44-item self-report measure of anxiety designed for use with senior populations. The AMAS-E intends to measure chronic, manifest anxiety, which refers to how one generally thinks, feels and/or acts. It contains three subscales: Fear of Aging, Physiological Anxiety, and Worry/Oversensitivity. The AMAS-E also contains a Lie scale, which provides a validity estimate which distinguishes the measure from other anxiety assessment tools. Research has provided evidence for the construct validity of the measure, as well as the factor structure (Lowe &

25 18 Reynolds, 2006). However, similar to the GAI, the AMAS-E utilizes a dichotomous response format. Additionally, the scale intends to measure chronic anxiety, which may not apply to seniors who experience anxiety as a result of an acute life circumstance (i.e., changes in health/functional status, loss of spouse). Little validation research has been conducted on the AMAS-E outside of the laboratory of the measure s developer. Older Adult Social Evaluative Scale (OASES). The OASES (Gould, Gerolimatos, Ciliberti, Edelstein, & Smith, 2012) is a self-report assessment tool designed to measure social anxiety in older adults. The measure consists of 37 items in which the respondent is asked to rate how uncomfortable he or she would feel in a given situation on a scale ranging from 0 (not at all) to 3 (severely). The respondent is also asked to rate how often he or she avoids the given situation on a scale ranging from 0 (never) to 3 (usually). Preliminary research indicates that the OASES has excellent internal consistency, convergent validity, and divergent validity (Gould et al.). The main limitation of the OASES is that it does not intend to provide a measure of general anxiety in older adults and thus is limited in its utility for such a purpose. Geriatric Anxiety Scale: Overview and Preliminary Psychometric Properties The GAS (Segal et al., 2010) is a 30-item self-report measure of anxiety symptoms designed for use with older adults. The GAS is presented in Appendix A and the scoring instructions are presented in Appendix B. There are 25 items assessing symptoms of anxiety and 5 items which assess common aspects of worry among older adults. The items were selected from a larger pool of items based upon item endorsement frequency in a sample of older adults (Segal et al.). There are several qualities which distinguish the GAS from other measures of anxiety and were intended to address

26 19 limitations of existing assessment tools. First, the GAS was designed to include the full spectrum of anxiety disorder symptoms as listed by the DSM-IV-TR (APA, 2000), including symptoms which overlap with depression (in contrast to the BAI). This is unique to the GAS as other anxiety assessment tools did not originate from DSM-IV-TR symptoms, and indicates the GAS could potentially correspond more accurately with anxiety diagnoses from the DSM-IV-TR than measures that do not contain such symptoms. Second, the GAS contains three conceptually-derived subscales which are intended to holistically assess anxiety: Somatic, Affective, and Cognitive. This feature of the GAS allows the clinician or researcher to determine what type of symptoms are particular challenges for the individual. It may also help the clinician rule out other conditions which could impact their symptom presentation versus true anxiety symptoms. For example, if an individual scores highly on the Somatic subscale but does not endorse Cognitive or Affective symptoms, the clinician may wish to inquire further about this discrepancy and rule out physical health conditions which could mimic anxiety symptoms. Another distinctive attribute of the GAS is the gradated rating scale. Each item on the GAS is rated dimensionally, with potential responses ranging from 0 (not at all) to 3 (all of the time). This allows the respondent to endorse the severity of his or her symptoms, which may provide additional data to the clinician for further inquiry. Two studies have been published regarding the psychometric properties of the GAS. Segal et al. (2010) examined the validity and internal consistency of the measure in both community-dwelling and clinical samples of adults over the age of 60. In the community-dwelling sample, the GAS was administered along with the Geriatric Depression Scale, State-Trait Anxiety Inventory, Beck Anxiety Inventory, and Adult

27 20 Manifest Anxiety Scale-Elderly Version). Segal et al. found that the GAS total score had excellent internal consistency (Cronbach s α =.93). The internal consistency of the subscales ranged from good to excellent (Somatic α =.80; Affective α =.82; Cognitive α =.90). Additionally, the GAS total score and subscale scores correlated significantly with other measures of anxiety, providing evidence of convergent validity. However, the authors also found that the GAS significantly correlated with GDS total scores as well, raising concerns regarding the ability of the GAS to differentiate anxiety from depression. Within the clinical sample of older adults, Segal et al. found similar internal consistency coefficients for the GAS total score (α =.93) and the subscales (Somatic α =.80; Affective α =.82; Cognitive α =.85). Convergent validity was noted in that the GAS total score and its subscales correlated significantly with each other, as would be expected. The GAS also demonstrated divergent validity with the Global Assessment of Functioning (GAF) scale scores from the DSM-IV-TR, such that there were significant negative correlations noted with the GAS total score, subscales, and the GAF. Furthermore, neither the GAS total score nor the subscales correlated significantly with education, additional evidence of discriminant validity. The authors concluded that the GAS demonstrated strong preliminary evidence for convergent and divergent validity as well as reliability. The study did not examine the impact of age or sex on measurement bias. Additionally, the authors noted that the sample sizes were not large enough to conduct factor analyses to examine the underlying factor structure of the GAS, and thus the subscales remained conceptually-designed instead of empirically-based. Another limitation is that medical problems were not assessed, and thus the impact of medical burden on GAS scores was not known.

28 21 Yochim et al. (2011) further examined the psychometric properties of the GAS in a community-dwelling sample of 117 older adults. The convergent, divergent, and discriminant validity of the GAS was assessed in comparison to the Beck Anxiety Inventory and the Geriatric Anxiety Inventory. They also examined the ability to which the GAS could identify clinically significant anxiety as determined by the cut-scores of the Beck Anxiety Inventory. Additionally, Yochim et al. examined the impact of medical burden on GAS scores by utilizing a self-report measure of health conditions. As in Segal et al. (2010), the GAS was found to have excellent internal consistency (α =.90), and significantly correlated with other measures of anxiety (evidence of convergent validity). A noteworthy finding was that the GAS correlated more strongly with the GAI (r =.69, p <.01) and BAI (r =.61, p <.01) than the GAI and BAI did with each other (r =.36, p <.01). The GAS correlated weakly with reading ability and processing speed, suggesting discriminant validity. Similar to Segal et al., the GAS correlated strongly with measures of depression. Furthermore, those who reported clinically significant anxiety as determined by the BAI cutoff scores reported significantly more anxiety on the GAS than those who did not report significant anxiety on the BAI. Furthermore, Yochim et al. found that the GAS total score correlated with self-reported medical burden, as did the BAI. Not surprisingly, the Somatic subscale correlated with medical burden most strongly (r =.38, p <.01), though the Affective and Cognitive subscales correlated with medical burden as well (r =.22, p <.05 and r =.28, p <.01, respectively). The GAS total score was not significantly correlated with sex. The correlations between GAS subscales and sex were not reported. One limitation of the study was that it did not include a clinical sample and thus the analyses were limited to community-dwelling older

29 22 adults. Factor analysis was not conducted with this sample, nor was measurement bias assessed. Statement of Problem and Purpose of Study Anxiety is a significant concern for older adults, and there are a multitude of challenges unique to assessing anxiety in this population. As such, failure to accurately detect anxiety in older adults has serious consequences for senior populations. Given the significant number of older adults who are impacted by either sub-syndromal or clinically significant anxiety, the need for brief, psychometrically sound assessment tools for use in this specific population is imperative. Current measures of anxiety have a number of limitations which may restrict their applicability for use with older adults and increase the risk of misdiagnosing anxiety in this population, though they are used frequently (Therrien & Hunsley, 2011). The Geriatric Anxiety Scale was created to address such limitations, and initial research indicates that it is a promising measure for use with older people (Segal et al., 2010; Yochim et al., 2011). However, research on the psychometric properties of the GAS is in its early stages. The present study sought to further investigate the psychometric properties of the GAS in three distinct samples of older adults: community-dwelling, clinical, and medical. The purpose of this study was to identify the psychometric characteristics of the existing scale and, if needed, suggest modifications to maximize the utility of the measure for use both clinically and for research purposes. This study is the first to use item response theory to examine the scale properties of an anxiety measure in an older population. The present study had the following aims: 1) Assess the reliability and validity of the GAS and its subscales;

30 23 2) Examine the underlying factor structure of the GAS (both exploratory and confirmatory); 3) Examine the ability of the GAS to discriminate anxiety from depression; 4) Examine the extent to which subjective health well-being impacts scores on the GAS; 5) Identify age, sex, and education differences in GAS scores and item functioning; 6) Utilize IRT to examine the item properties of the GAS; 7) Determine the extent to which measurement bias impacts item endorsement on the GAS in regards to age, sex, and education; 8) Utilize item response theory (IRT) to create a short form of the GAS with adequate psychometric characteristics; 9) Establish descriptive labels for scores on the GAS (mild, moderate, severe) to assist with score interpretation. Study 1. The analyses in Study 1 were conducted in a large community-dwelling sample of older adults. The reliability of the GAS and each subscale was also examined (Aim 1), and it was hypothesized that the GAS total score and subscales would each yield sufficient internal consistency (α =.70 or higher). The factor structure of the GAS was examined to determine whether the items load onto their corresponding subscales (Aim 2). It was hypothesized that principal axis factoring will confirm a three-factor solution, consistent with the three conceptually derived subscales (Somatic, Cognitive, and Affective). Convergent validity of each subscale and the total scale score was assessed (Aim 3), and it was predicted that the GAS total score and each subscale would have

31 24 sufficient validity. An additional aim of Study 1 was to examine the ability of the GAS to discriminate anxiety from depression (Aim 4), and it was expected that items which discriminate anxiety from depression would be identified. The final aim of Study 1 was to examine the relationship among the GAS, its subscales, and items with a measure of subjective health status (Aim 5). It was expected that the GAS and its subscales would be related to lower ratings of subjective health status, and that the somatic scale and its items would have the strongest relationships with subjective health ratings. Study 2. Similar analyses were conducted in Study 2, but this study utilized a clinical sample of older adults. The reliability of the overall scale and each subscale was examined (Aim 1). The factor structure of the GAS was examined using principal axis factoring (Aim 2). It was hypothesized that this analysis would have similar results to those in Study 1. It was hypothesized that the total scale and each subscale would have adequate internal consistency (α =.70 or higher). The convergent validity of each subscale and the total scale score was assessed (Aim 3). It was predicted that the GAS and each subscale would have adequate convergent validity. This study also examined the ability of the GAS to differentiate anxiety from depression (Aim 4) and it was expected that items that sufficiently discriminate anxiety from depression would be identified. The results from each of the aforementioned analyses were compared to the results of Study 1 to determine if differences in psychometric properties exist between the two samples. It was expected that the results will be largely similar as Study 2, although it was expected that the clinical sample would have elevated scores on the GAS.

32 25 Study 3. This study explored the psychometric properties of the GAS in a medical sample of older adults. The psychometric properties of the GAS and its subscales were examined in this sample, including reliability (Aim 1). Convergent validity and divergent validity was also examined (Aim 2). It is hypothesized that the GAS in this sample will yield sufficient psychometric properties in all analyses. The relationship between medical burden and GAS scores was also examined (Aim 3). It was expected that those who experience more functional impairment would endorse more items on the GAS, especially somatic items. Study 4. This study combined data from Studies 1, 2 and 3 and employed both CTT and IRT techniques. Exploratory and confirmatory factor analysis was performed on the data to determine the unidimensionality criteria for IRT (Aim 1). As the GAS is intended to be a clinically useful measure of anxiety, it was expected that items should be able to discriminate individuals with high and low levels of anxiety. It was also expected the test information curve peak would be above the mean level of anxiety. DIF by age, sex, and education was assessed (Aim 2). This aim was exploratory and no specific hypotheses were generated. A short form was created by identifying and retaining the items which provide the greatest information and have the highest discrimination parameters, while maintaining the integrity of the subscales (Aim 3). It was expected that the short form would have adequate reliability and validity and function similar to the full version of the GAS. Age, sex, and education differences were assessed at the group level (Aim 4), with the expectation that individuals who are younger, less educated, and female would score higher on the GAS than individuals who are older, more educated, and male.

33 26 An additional aim was to establish descriptive categories (mild, moderate, severe) for the GAS, subscales, and short form (Aim 5).

34 CHAPTER II STUDY ONE Method In this study, two existing datasets from community-dwelling samples of older adults were combined to create one larger dataset. Sample 1. Data were collected from 123 older adults over the age of 60. Participants were volunteers from the community who participated in a larger study of cognitive functioning and mental health. All participants provided informed consent and were financially compensated for their time. Sample 2. Data were collected on 284 community-dwelling older adults recruited from the El Paso county voter registry. Participants provided informed consent prior to their participation in the study. Combined dataset. Merging these datasets yielded a sample of 407 older adults. Their ages ranged from 60 to 96 (M = 73.78, SD = 7.14), and 57.5% (n = 234) were female. The majority of participants were European American (n = 361). Participants were generally well educated (years of education M = 14.98, SD = 2.94). Other demographic information is presented in Table 1.

35 28 Table 1 Means, Standard Deviations, and Ranges for All Demographic Information and All Measures Study 1 N Mean SD Possible Range Range Age Education (Years) GAS Total Scale GAS Cognitive GAS Affective GAS Somatic BAI GAI GDS BDI-II BHS SISE LS SF PHQ Study 2 Age Education (Years) GAS Total Scale GAS Cognitive GAS Affective GAS Somatic GDS GAF Study 3 Age Education (Years) GAS Total Scale GAS Cognitive GAS Affective GAS Somatic BAI MoCA GAI PHQ SF-36 Total Scale

36 29 Table 1 Continued Study 4 Age Education (Years) GAS Total Scale GAS Cognitive GAS Affective GAS Somatic GAS Note. GAS = Geriatric Anxiety Scale, BAI = Beck Anxiety Inventory, GAI = Geriatric Anxiety Inventory, GDS = Geriatric Depression Scale, BDI-II = Beck Depression Inventory, Second Edition, BHS = Beck Hopelessness Scale, SISE = Single-Item Self- Esteem Scale, 3LS = Three-Item Loneliness Scale, SF-36 = 36-Item Health Survey, GAF = Global Assessment of Functioning, PHQ-9 = Patient Health Questionnaire, GAS-10 = Geriatric Anxiety Scale 10 Item Version. Measures Geriatric Anxiety Scale (GAS). The GAS (Segal et al., 2010) is a self-report measure of anxiety symptoms designed for use with older adult populations. Participants are asked to rate symptoms of anxiety or stress by indicating how often they have experienced each symptom during the past week on a Likert-type scale that ranges from 0 (not at all) to 3 (all of the time). Possible scores range from 0 to 75, with higher scores indicating the presence of more severe anxiety. The GAS was administered to all older adults included in the combined community-dwelling sample. Beck Anxiety Inventory. The BAI (Beck et al., 1988) is a self-report measure of anxiety intended for use with adults of all ages. It contains a list of 21 symptoms which are rated from 0 (not at all) to 3 (severe). Possible scores range from 0 to 63, with higher scores indicating more severe anxiety. Though the BAI is not gero-specific, the measure has adequate psychometric properties in older adult samples (i.e., Kabacoff et al., 1997; Wetherell et al., 1997). The BAI was administered to Sample 1 only.

37 30 Geriatric Anxiety Inventory. The GAI (Pachana et al., 2007) is a 20-item selfreport assessment tool. Participants are asked to respond yes or no to statements regarding their experience with anxiety during the past week. The internal consistency of the GAI is high, as is its convergent validity with other measures (Pachana et al.). Possible scores range from 0 to 20 with higher scores indicating the presence of more severe anxiety. The GAI was administered to Sample 1 only. Geriatric Depression Scale (GDS). The GDS (Yesavage et al., 1983) is a widely used self-report measure of depressive symptoms. It contains 30 items in which participants are asked to respond yes or no to each question. Possible scores range from 0-30, with higher scores indicating the presence of more depressive symptoms. The GDS is a reliable and valid measure of depression in older adults (Yesavage et al.), and has adequate internal consistency, test-retest reliability, and concurrent validity with diverse measures of depression in diverse populations (Marty, Pepin, June, & Segal, 2011). The GDS was administered to Sample 1 only. Beck Depression Inventory Second Edition (BDI-II). The BDI-II (Beck et al., 1996) is a self-report measure containing 21 items which correspond with the DSM criteria for major depressive disorder. Participants are asked to respond on a 4-point Likert-type scale, ranging from 0 to 3. Possible scores range from 0 to 63, and higher scores indicate more severe levels of depression. The BDI-II was administered to Sample 1 only. Beck Hopelessness Scale (BHS). The BHS is a 20-item self-report measure that assesses pessimism and hopelessness (Beck, Weissman, Lester, & Trexler, 1974). Participants respond to questions on a 5-point Likert scale, ranging from 1 (rarely or

38 31 none of the time) to 4 (most or all of the time). Higher scores on the BHS indicate greater hopelessness or frequency of negative expectancies for the self or for the future, with possible scores ranging from 20 to 80. The BHS has been validated among depressed older adult outpatients (e.g., Hill, Gallagher, Thompson, & Ishida, 1988) and utilized in research studies with older adults (i.e., Serrano, Latorre, Gatz, & Montanes, 2004). This measure was administered to Sample 2 only. Single-Item Self-Esteem Scale (SISE). The SISE is a one-item scale assessing self-esteem (Robins, Hendin, & Trzesniewski, 2001). Respondents rate how much they agree with the statement, I see myself as someone who has high self-esteem, on a 5- point scale ranging from 1 (strongly disagree) to 5 (strongly agree). This measure was administered to Sample 2. Three-Item Loneliness Scale (3LS). The 3LS is a 3-item self-report measure of loneliness (Hughes, Waite, Hawkley, & Cacioppo, 2004). Respondents rate the frequency of loneliness, ranging from 1 (hardly ever) to 3 (often). Higher scores indicate more loneliness, with possible scores ranging from 3 to 9. The 3LS has been validated in research studies with older adults (Hughes et al., 2004). This measure was administered to Sample 2 only. Patient Health Questionnaire (PHQ-9). The PHQ-9 is a self-report measure of depressive symptoms, based on DSM diagnostic criteria for Major Depressive Disorder (Kroenke, Spitzer, & Williams, 2001). Respondents indicate how often they experienced each symptom over the previous two weeks on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). Higher total scores indicate greater severity of depression, with possible scores ranging from 0 to 27. The PHQ-9 has demonstrated good reliability and

39 32 validity among the general population (Martin, Rief, Klaiberg, & Braehler, 2006). This measure was administered to Sample 2 only. RAND 36-Item Health Survey 1.0 (SF-36). The SF-36 is a self-report questionnaire measuring self-perceived health and functional status (Ware & Sherbourne, 1992). It contains 36 items assessing eight domains of health: 1) limitations in physical activities due to health problems; 2) limitations in social activities due to physical or emotional problems; 3) limitations in role obligations due to physical health problems; 4) pain; 5) mental health; 6) limitations in role activities due to emotional problems; 7) vitality; and 8) general perceptions of health. Possible scores for each variable range from 0 to 100, and higher scores indicate better health. The SF-36 is widely used in epidemiological research, and has demonstrated adequate psychometric properties in older adult samples (Mishra et al., 2011). Only the Physical Functioning subscale of this measure was administered to Sample 2. Procedure Sample 1. The measures administered to this sample were included in a two hour battery of cognitive tests and mental health questionnaires. Testing occurred in either a research lab at the university, or in a testing room at a mental health clinic. Sample 2. Participants in this sample were mailed a packet of questionnaires which they were asked to complete and return to the principal investigator. Completion of the packet took approximately 30 minutes.

40 33 Statistical Analyses All analyses were conducted using PASW 18.0 using an alpha level of.05. The reliability of the overall scale as well as each subscale was assessed by calculating Cronbach s alphas for the GAS total score and each subscale (Aim 1). For Aim 2, the factorial structure of the GAS was examined by conducting an exploratory factor analysis (principal axis factoring, PAF) on the merged sample of 407 older adults. Before performing PAF, the suitability of the data for factor analysis was assessed. The inter-item correlation matrix was examined to identify correlations.30 and above. Bartlett s test of sphericity was inspected, with a statistically significant value (p <.05) required for the analyses to be considered appropriate for the data (Bartlett, 1954). Additionally, the Kaiser-Meyer-Oklin (KMO) value was examined, with a value of at least.60 ideal for the analyses (Tabachnick & Fidell, 2007). Factors with an eigenvalue of at least 1.0 were retained for further analysis. The scree plot was also examined to assist in determining the ideal number of factors within the data, testing the hypothesis that the three subscales (Somatic, Affective, and Cognitive) will yield three corresponding factors. Convergent validity (Aim 3) was assessed by correlating each subscale with the total scale score and with each other. In addition, correlations were calculated with the PHQ-9, BHS, SISE, and 3LS. To determine whether the GAS measures a construct distinct from depression (Aim 4), PCA was performed on the combined items of the GAS and the PHQ-9 (using data from Sample 2 only). This strategy was also used by Wetherell and Areán (1997) in the development of the Beck Anxiety Inventory to determine the discriminant validity of the BAI. As previous research has indicated that the GAS is highly correlated with

41 34 measures of depression (Segal et al., 2010; Yochim et al., 2011), it was expected that many items will load onto the same factor as PHQ-9 items. Correlations were also calculated among individual GAS items and the PHQ-9 total score. Items which correlated less strongly with the PHQ-9 were included in a short-form version of the GAS which should theoretically be less correlated with measures of depression. Finally, the relationship of the GAS with self-rated physical functioning was assessed (Aim 5). The GAS, its subscales, and individual items were correlated with the SF-36. Results Aim 1: Reliability analysis. Cronbach s alpha was calculated on the total scale and subscales to assess the reliability of the scale scores (see Table 2). The reliability of the overall scale was excellent (Cronbach s α =.91). The reliability of the subscales were good (Cognitive α =.84, Affective α =.83, Somatic α =.79). Table 2 Cronbach s Alpha Coefficients for GAS Total Scale and Subscales Community Sample (Study 1) N = 398 Clinical Sample (Study 2) N = 136 Medical Sample (Study 3) N = 38 Combined Sample (Study 4) N = 581 Total Scale Cognitive Affective Somatic (Items 1-25) Subscale Subscale Subscale Aim 2: Exploratory factor analysis. To examine the factor structure of the GAS, the 25 items of the measure were subjected to principal axis factoring (PAF). Prior to performing PAF, the suitability of data for factor analysis was assessed. The sample

42 35 size (N = 384) was sufficient for PAF. The Kaiser-Meyer-Oklin value was.91, exceeding the recommended value of.60 (Tabachnick & Fidell, 2007), and Bartlett s Test of Sphericity (Bartlett, 1954) reached statistical significance, supporting the factorability of the correlation matrix. PAF revealed the presence of 6 factors with eigenvalues exceeding 1, explaining 34.64%, 6.8%, 5.5%, 4.9%, 4.9%, and 4.1% of the variance, respectively. Collectively, the factors explained 60.96% of the variance. The screeplot (Figure 1) revealed a clear break after the first factor, with a much smaller break after the second factor. Figure 1. Screeplot for principal axis factoring on 25 GAS items (Study 1). As the factors were significantly correlated with one another, a direct oblimin rotation was used to assist in the interpretation of the data. The data were analyzed using both a one and two factor solution. In the one-factor solution, all items significantly loaded onto the single factor. The interpretation of the two factor solution suggests that affective and cognitive items tended to load onto the first factor, and somatic items loaded onto the second factor (see Table 3). There was medium positive correlation