Important Levels of Measurement
My study seeks to measure depression in patients with rare cancer types, so ordinal and interval levels of measurement are important. Variables that can be assessed incrementally are often measured at the ordinal level. They can be rank-ordered from highest levels to lowest levels. Unlike nominal levels of measurement, ordinal data propose that the variables can be presented in an order (Trochim, 2006). The psychological construct of depression can be ranked in terms of relative amounts (Brockopp, & Hastings-Tolsma, 2003). For example, depression can be ordered as mild, moderate, or severe. However, by operationalizing depression based on the scores of the Beck Depression Inventory Fast Screen for Medical Patients (BDI-FastScreen), the ordinal levels of measurement, mild, moderate, and severe, become interval level measurements.
Whereas the ordinal level of measurement characterizes continuous variables or increments that can be ordered from high to low, the interval level of measurement allows for a degree of difference between the increments. The data related to depression can be ordered according to the intensity of individuals' experiences (Brockopp, & Hastings-Tolsma, 2003). At ordinal levels, however, depression data cannot be associated with meaningful differences and distances between the categories of mild, moderate, and severe. No consistent numbers can be associated with the distance between the categories, so measuring depression cannot fulfill the criteria for interval levels of measurement unless it is operationalized based on scores from the BDI-FastScreen (Brockopp, & Hastings-Tolsma, 2003). The opererationalization of the ordinal levels of data according to the BDI-FastScreen allows me to capture more complex data on central tendency that are not available to ordinal levels of measurement. For example, it is possible to calculate the median and mode in a rank-ordered data, however, the interval level of measurement allows the calculation of a mean, as well as a range and standard deviation. Because of the additional information provided by the interval level of measurement gained by operationalizing levels of depression based onBDI-FastScreen scores, a more complex statistical analysis will be possible. This will enable my research study to provide more complex information on the variables (Frankfort-Nachmias & Nachmias, 2008).
Ensuring Content, Empirical, and Construct Validity
Consulting the literature is an important component to choosing and assessing an instrument that will actually measure what the researcher intends to measure (Brockopp & Hastings-Tolsma, 2003). Other researchers can provide examples of the successful use of instruments that have been of value in similar settings. A researcher's assessment of content validity will rely on prior research as well as the expert opinions of other scientists. If an instrument has not been used to measure the construct in a similar setting, it will be difficult to determine its content validity (Brockopp & Hastings-Tolsma, 2003).
I will be using the BDI-FastScreen, which has been utilized by healthcare professionals in medical and clinical settings, to assess behavioral and somatic symptoms associated with medical issues (Scheinthal, Steer, Giffin, & Beck, 2001). It is a reliable and valid information source when used to determine and differentiate depressive symptoms that result from medical conditions from depressive symptoms from other sources (Hennessey & Pallone, 2003; Whiston & Eder, 2003). It has been normed on populations similar to the population of focus in my study.
Empirical or predictive validity is the extent to which scores on one assessment correspond to the same behaviors measured with other assessment instruments. For an assessment to be empirically valid, statistical evidence must suggest the instrument measures what it is meant to measure (Trochim, 2006). The BDI-FastScreen was correlated with two other assessment instruments thatmeasure symptoms of depression and with the diagnostic criteria for depression in the Diagnostic and Statistical Manual of Mental Disorders IV-TR (DSM-IV-TR). The correlations were r = .62 with the Hospital Anxiety and Depression Scale and r = .86, when correlated with the Beck Anxiety Inventory for Primary Care. Correlation with the DSM-IV-TR was r = .69 (Hennessey & Pallone, 2003). Although the correlation to the less recent DSM may not continue to be relevant, the empirical validity of this instrument is based on these correlations (Hennessey & Pallone, 2003; Whiston & Eder, 2003).
Construct validity is a primary concern for researchers (Schotte, Maes, Cluydts, De Doncker, & Cosyns, 1997). Assessing the validity of how well an instrument measures what it is supposed to measure is a critical component to the success of the researcher and the research (Cronbach, & Meehl, 1955). If the assessment measures something other than the construct of focus, the results may be misleading or meaningless. When assessing construct validity, it is critical to ensure that the instrument measuring the variable is actually measuring the variable intended for measurement and not some other construct (Cronbach, & Meehl, 1955). For example, if the goal is to measure depression, it would not be prudent or beneficial to measure fear.
Since the BDI-FastScreen was correlated with other assessments designed to measure depressive symptoms, I am confident with the construct validity demonstrated by this assessment. The BDI-FastScreen has been used to measure depression in a variety of medical conditions (Whiston & Eder, 2003) such as in patients with multiple sclerosis (Benedict, Fishman, McClellan, Bakshi, & Weinstock-Guttman, 2003), geriatric patients (Scheinthal, Steer, Giffin, & Beck, 2001), patients with chronic pain (Poole, Bramwell, & Murphy (2009) and cancer patients (Alacacıoğlu, Öztop, & Yılmaz, 2012).
Reliability is an estimation of the extent to which a measurement instrument will yield the same results upon reassessment (Trochim, 2006). The reliability of an instrument contributes to the level of usability for empirical research (Whiston, 2009). Further, it refers to the replicability andstability of a measurement and whether it will result in the same assessment in the same individuals when repeated (Frankfort-Nachmias & Nachmias, 2008). When determining the reliability of an assessment, a reliability coefficient of at least .80 indicates a trustworthy level of reliability (Trochim, 2006). In effect, the reliability of an instrument estimates the extent to which variance in response is real variance rather than an error in the implementation of the instrument.
If an assessment is reliable, its results are stable and relatively true (Whiston, 2009). Utilizing the BDI-FastScreen will provide a true and stable assessment of depression in the participants. The authors of the BDI-FastScreen based its reliability on four patient groups used as normative samples (Whiston & Eder, 2003). The test manual for the BDI-FastScreen supplied limited information on the assessment's reliability, however, the coefficient alphas for the four groups were .86; .85; .88; and .86 (Hennessey & Pallone, 2003). To gauge the assessment's test-retest reliability in my study, participants will be assessed with the BDI-FastScreen upon becoming participants in the study, after six weeks, and after twelve weeks of support group participation. This will ensure the test is a reliable indication of the participants' levels of depression.
Strengths and Limitations of the BDI-FastScreen
The BDI-FastScreen is a user-friendly assessment for screening depressive symptoms in adolescents and adults with medical conditions. One critical benefit is that the BDI-FastScreen determines these symptoms as they relate to medical issues (Hennessey & Pallone, 2003; Whiston & Eder, 2003). In addition, this instrument is a pencil and paper self-report that is easily scored, and is a reliable resource for evaluating depressive symptoms in medical patients (Whiston & Eder, 2003). It has been shown to be an adequate measure in a variety of patient groups and across a wide range of ages and both sexes (Whiston & Eder, 2003). The BDI-FastScreen is one of the few assessments that is capable of identifying depressive symptoms in patients and differentiates between normal psychological reactions to cancer diagnoses and major depressive disorders (Whiston & Eder, 2003). The BDI-FastScreen is easy to administer and has the potential to assist clinicians in their overall client conceptualization. In addition, it eliminates clinician bias and the overestimation of the patient's improvement (Trivedi et al., 2004; Kramer, Owen, Wilson, & Thrush, 2003).
Whiston and Eder (2003) suggested the high correlation between the Beck Anxiety Inventory for Primary Care and the BDI-FastScreen may mean that both instruments evaluate anxiety rather than depression. If this were the case, measuring depression accurately would not be accomplished by the use of this assessment. It may be that additional information and clarity is necessary to determine whether the BDI-FastScreen measures depression or anxiety. In addition, there has been some discussion as to whether the samples utilized limited the generalizeability of the instrument, since all participants were from the greater Philadelphia area, and the 268 participants randomly chosen were from only four medical settings.
Until additional studies are completed with representative samples this assessment instrument may not be representative of wider populations. In addition, no test-retest analysis was reported for the BDI-FastScreen and that deficiency may cause researchers to question this assessment's reliability (Hennessey & Pallone, 2003). For my study, it is essential that the depressive symptoms identified in the data collection are related to a medical condition rather than to other sources. The BDI- FastScreen was chosen for the purpose of identifying depressive symptoms as they relate to medical conditions. If the assessment does not assess levels of depression related to a cancer diagnosis, my research may be misleading or meaningless. The BDI-FastScreen may be subject to reporting bias from test takers minimizing responses or over reporting the severity of symptoms, which would reduce the test's validity (Trevedi et al., 2004).
An Appropriate Scale
The BDI-FastScreen is a seven item criterion-referenced instrument. Scores range from 0 to 21, with higher scores indicating an increased severity of depressive symptoms. The clinical interpretation of scores is attained by criterion-referenced methods and suggests scores between 4 and 8 are indicative of a mild major depressive disorders (MDD); scores ranging from 9-12 indicate moderate MDD; and scores of 13-21 suggest severe MDD (Hennessey & Pallone, 2003). The BDI-FastScreen incorporates a Likert scale to capture information on the responders' level of depressive symptoms. Likert scaling is a unidimensional scaling method (Trochim, 2006). This scale commonly utilizes questionnaires to collect information by allowing responders to provide answers that vary in intensity (Carifio & Perla, 2007). A Likert scale captures a range of intensity that represents responders' experiences and feelings since they can specify their level of agreement to the question or statement (Frankfort-Nachmias & Nachmias, 2008). Because the Likert scale contains a greater number of measurements within the assessment, these scales are more likely to be more reliable than single item assessments (Jamieson, 2004). This scale measures attitudes on the ordinal and interval levels of measurement (Frankfort-Nachmias & Nachmias, 2008). This scale is appropriate for my study since one of the primary goals is to obtain measurements of depressive symptoms as reported according to participant experiences and attitudes.
Scales allow researchers to define and categorize variables. According to Frankfort-Nachmias and Nachmias (2008), scales have properties which determine their appropriateness for use. Specific scales are utilized under certain conditions and with certain tests (Frankfort-Nachmias & Nachmias, 2008). Likert scaling assumes that distances on each item are equal, so ordinal and interval data are well-measured by this scale. Further, the Likert scale is designed to measure attitudes, which seems appropriate for my research study. Using a scale that is not appropriate for a test can result in inaccuracies and useless data (Frankfort-Nachmias & Nachmias, 2008). The Likert scale allows test-takers to respond to questions according to their personal experience. In addition, the results of a Likert scale, which is utilized in the BDI-FastScreen assists researchers in collecting ordinal data and transforming it into interval level data.
In my study, participants' levels of depression will be assessed with the BDI-FastScreen upon becoming participants, and at 6 weeks, and at 12 weeks of participation in either the face-to-face support group or the online, disease-specific support forum. My goal is to determine whether patients with rare cancers have lower depressive symptoms with participation in online, disease-specific forums than they do with participation in face-to-face support groups. The BDI-FastScreen will measure depressive symptoms three times during participation in the study.
The population used for the BDI-FastScreen were medical patients from a variety of clinical settings. The BDI-FastScreen was normed on four groups (Whiston & Eder, 2003). The first was a group of 50 patients who had been referred to psychiatrists for consultation after hospitalization for a medical condition. The second group consisted of 94 patients referred from a family practice setting, and the third group was made up of 100 pediatric patients that ranged in age from 12 to 17 who were scheduled for routine medical appointments, and the fourth group consisted of 120 patients from a university clinic. This instrument was specifically designed for screening depressive symptoms related to medical circumstances (Hennessey & Pallone, 2003; Whiston & Eder, 2003). The BDI-FastScreen provides information for a unique population in which symptoms of depression from medical conditions must be differentiated from depressive symptoms from other sources. The population in this study is cancer patients between the ages of 40 and 60 who have been diagnosed with a rare cancer at least six months prior to becoming a participant in this study, and have participated in a face-to-face support group, or an online disease-specific online support forum for at least three months. A rare cancer type is defined as a cancer that makes up less than 1% of all cancer diagnoses.
Although measuring variables can be a complicated task, selecting a valid and reliable assessment instrument is foundational to a valid research design (Sechrest, 2005). Determining appropriate levels of measurement is essential as is ensuring content, empirical, and construct validity as well as reliability. These characteristics contribute to the usability and the practical implementation of the research. Strengths and limitations exist for most assessment instruments, and it is important to identify how they might affect the study's results. For my study, the BDI-FastScreen is a reliable and valid assessment instrument for measuring depressive symptoms in cancer patients. It utilizes a Likert scale, which adequately measures attitudes on ordinal and interval levels of measurement (Frankfort-Nachmias & Nachmias, 2008). The BDI-FastScreen was developed as a screening tool for depressive symptoms in the unique population of medical patients.
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