Quantitative Research Plan: Instruments And Measurements Essay

Type of paper: Essay

Topic: Measurement, Assessment, Validity, Instrument, Reliability, Scale, Measure, Face

Pages: 7

Words: 1925

Published: 2020/09/09

Introduction

Quantitative studies often involve the manipulation or measurement of variables. A variable refers to an entity that takes on different values. Variables may be defined in various ways. For example, independent variables are manipulated by the environment and influence the dependent variable (Johnson, n.d). In contrast, researchers measure dependent variables but do not manipulate them. In the proposed study, the independent variable is the group of cancer patients while the dependent variable is the depressive symptoms that affect the patients. The BDI-FS measurement tool will analyze the dependent variable in the present study.
Sechrest (2005) has observed that the variable measurement, as well as the selection of valid measures, can be a difficult task. The determination of the appropriate measurement levels for variables improves the feasibility of the analysis process. Moreover, a reliable assessment instrument allows the researchers' data to represent the various variables adequately. Consequently, the correct representation of variables determines the study’s internal validity (Sechrest, 2005). The present paper describes the measurement levels crucial to a quantitative study and explains their significance. In addition, it explains the maintenance of reliability, as well as the empirical, content, and construct’s validity for the required measurement. Further, it discusses the limitations and strengths of the measurement instrument, the BDI-FastScreen (BDI-FS), in terms of validity and reliability.

Important Measurement Levels

My investigation seeks to examine depression among patients with rare carcinoma types. Hence, ordinal and interval measurement levels will be significant. Usually, variables assessed incrementally are evaluated at the ordinal level. The variables can be arranged from the relatively lower levels to the highest levels. Further, ordinal data shows that the presentation of variables can be performed in a particular order. Brockopp and Hastings-Tolsma (2003) explain that the depression’s psychological construct can be ranked based on relative amounts. For example, it can be ordered as moderate, mild, or severe. Nonetheless, the ordinal measurement levels of moderate, mild, and serious become interval-level measurements through the operationalization of depression in terms of the scores of the BDI-FS.
Whereas the ordinal measurement level characterizes continuous increments ordered from low to high, the interval measurement level allows a degree of difference in the increments. Moreover, the depression data can be ordered in terms of the intensity experienced by individuals (Brockopp & Hastings-Tolsma, 2003). However, at ordinal levels, the depression data cannot be linked to useful distances and differences between the moderate, mild, and severe categories. Since no consistent numbers are related to the distances between the provided categories, the measurement of depression fails to fulfill the criterion for the interval measurement levels. Consequently, Brockopp and Hastings-Tolsma (2003) contend that the measure must be operationalized using the BDI-FS’s scores. The use of the BDI-FS to operationalize the ordinal data levels is highly efficient. Hence, it allows me to capture the relatively more complex data on the central tendency that is unavailable to the ordinal measurement levels. For example, the mode and median of a rank-ordered data can be computed efficiently. However, the interval measurement level facilitates the calculation of the mean, the range, and the standard deviation. The additional information offered by the interval measurement level through the operationalization procedure allows a relatively more sophisticated statistical analysis. Further, Frankfort-Nachmias and Nachmias (2008) explain that it enables the provision of more complex information regarding the variables.

Ascertaining the Various Validity Types

According to Brockopp and Hastings-Tolsma (2003), consulting the literature is crucial to the selection and assessment of an instrument that can measure the variables under investigation effectively. Other researchers, therefore, can offer examples of tools that have been used successfully in similar settings. In addition, the investigator's analysis of content validity relies on other scientists’ prior research and expert opinions. Thus, it may be cumbersome to determine the content validity of an instrument that has not been used in a given setting. Several healthcare experts in different clinical and medical settings have utilized the BDI-FS successfully (Neitzer, Sun, Doss, Moran, & Schiller, 2012).Therefore, it is a valid and reliable information source for determining and differentiating various depressive symptoms.

Empirical Validity

Predictive or empirical validity describes the level at which the scores obtained from an assessment match the behaviors measured using other assessment instruments. Hennessey & Pallone (2003) correlated the BDI-FS with other depression-measuring tools. In addition, Hennessey & Pallone (2003) compared the BDI-FS with DSM-IV-TR and observed a correlation of r = 0.69. Consequently, the correlations influence the empirical validity of the BDI-FS.

Construct Validity

The assessment of construct validity is significant in the measurement of variables. Hence, the variable measurement that is different from the construct of focus may cause misleading results. For example, if the objective involves the measurement of depression, it would be unwise to measure unrelated variables such as fear.
The correlation of the BDI-FS with other assessments makes me confident of the assessment’s construct validity. The BDI-FS has measured depression in medical conditions that include the in-patients suffering from end-stage renal malady (Neitzer et al. 2012), carcinoma patients (Alacacioglu, Oztop, & Yilmaz, 2012), and patients experiencing continuing pain (Poole, Bramwell, & Murphy (2009).Ensuring Reliability
Reliability provides estimates of the level at which a measurement instrument yields similar results after a reassessment. Hence, an instrument’s reliability contributes to the usability level of the empirical research (Whiston, 2009). Moreover, reliability describes the stability and replicability of a measure (Frankfort-Nachmias & Nachmias, 2008). During the determination of an assessment’s reliability, a reliability coefficient of above 0.8 may indicate a trusty reliability level.
Stable and relatively correct results are found during a reliable assessment (Whiston, 2009). Thus, the BDI-FS can provide a stable depression assessment. According to Hennessey and Pallone (2003), the BDI-FS’s reliability has been explored using four patient categories that were used as normative samples. Although the BDI-FS’s test manual supplied little information regarding the assessment's reliability, the coefficient alphas obtained from the four groups were 0.86, 0 .85, 0.86, and 0 .88 (Hennessey & Pallone, 2003). My study will work out the reliability level of the assessment’s test-retest. The BDI-FS will assess the participants twice in the intervals of six weeks to ensure the test’s reliability in signaling the depression levels.

BDI-FS’s Limitations and Strengths

Strengths
A critical benefit linked to the BDI-FS is the determination of depressive symptoms in various illnesses (Hennessey & Pallone, 2003). BDI-FS is also user-friendly during the symptoms’ assessment performed on adolescents and adults. Moreover, the measurement instrument is easily scored and provides a reliable resource for the evaluation of depressive signs. Whiston and Eder (2003) have shown that the BDI-FS is an efficient measure across a broad range of age groups, as well as patient groups. In addition, it is among the few assessments capable of determining depressive symptoms, as well as differentiating between the severe depressive disorders and the normal psychological reactions (Whiston & Eder, 2003). Further, the BDI-FS is easily administered and assists medical practitioners in the overall conceptualization of their clients. It also eliminates clinician bias, as well as the over reckoning of patients’ healing progress (Trivedi et al., 2004).

Limitations

Unfortunately, a high figure in the BAI-PC’s correlation with the BDI-FS instruments may indicate that the two measuring tools assess anxiety instead of depression signs (Whiston & Eder, 2003). Additionally, the correlation suggests that the BDI-FS may fail to measure depression accurately. Thus, there is a need to determine whether the instrument measures anxiety or depression.
Until additional investigations involving representative samples are completed, the assessment instrument may fail to represent the wider populations adequately. Further, no test-retest analysis for the BDI-FS instrument was reported in the previous investigations (Hennessey & Pallone, 2003). In my study, therefore, it is imperative that the reported depressive symptoms match a particular medical condition. Consequently, if the assessment fails to evaluate the depression levels linked to a carcinoma diagnosis, the present study might yield misleading results. Sometimes, the BDI-FS assessment may experience a reporting bias from the test takers. Trivedi et al. (2004) suggest that such preconceptions over-report the symptoms’ severity, hence, reducing the test's validity.

Appropriate Scale

The BDI-FS is criterion-referenced and involves seven items. Scores used in the assessment range from zero to twenty-one, with higher scores showing an increased severity of the various depressive symptoms. Criterion-referenced methods help to attain the scores’ clinical interpretation. The techniques, according to Hennessey and Pallone (2003), suggest that the scores between four and eight indicate Mild Major Depressive Disorders (MDD), but the scores between nine and twelve show moderate MDD. Nevertheless, the scores between thirteen and twenty-one indicate severe MDD. A Likert scale that captures the information regarding the responders' depression level is incorporated into the BDI-FS. Carifio and Perla (2007) add that the scale uses a one-dimensional scaling method, as well as questionnaires for collecting information with varying intensity. Further, the Likert scale shows intensities’ range that symbolizes the responders' feelings and experiences. Therefore, it allows the respondents to particularize their agreement level to a specified statement. Since the Likert scale comprises a large number of measurements, it is more reliable than a single item assessment (Jamieson, 2004). In addition, it measures attitudes at the interval and ordinal measurement levels (Frankfort-Nachmias & Nachmias, 2008). Consequently, the Likert scale is suitable for this study because one of my primary objectives involves obtaining the measurements of depressive symptoms from the participants’ attitudes and experiences.
Scales are significant because they allow researchers to categorize and define variables. Frankfort-Nachmias and Nachmias (2008) explain that scales have attributes that determine their suitability for use. Particular scales are employed under certain circumstances and with specific tests. Since the Likert scale assumes equal distances between various items, it measures the ordinal and interval data adequately. Moreover, the Likert scale measures attitudes that are appropriate for this investigation. The Likert scale is also suitable for my research because it allows responses based on the test-takers’ personal experiences. Further, the Likert scales’ results, which are utilized in the BDI-FS assessment, assist researchers in the gathering of ordinal data, as well as its transformation into the interval level data.
In this study, depression levels will be analyzed using the BDI-FS instrument on the first day, in the sixth week, and during the twelfth week of participation. The test-takers will participate online, in face-to-face forums or disease-specific support groups. My aim will involve determining whether particular patients with a rare carcinoma experience minimal depressive symptoms during online participation and in the disease-specific forums. The results will be compared to their involvement in the face-to-face forums. Next, the BDI-FS will analyze the depressive symptoms thrice during the respondents’ participation.

Population involved in the Study

In the BDI-FS, Whiston and Eder (2003) utilized the population of medical patients from different clinical settings. The BDI-FS instrument was normed on four categories. The first comprised fifty patients that had been referred to various psychiatrists after hospitalization. The second consisted of ninety-four patients from a family practice background. The third category comprised of one-hundred pediatric patients aged between twelve and seventeen, who had been scheduled for medical appointments. The last group consisted of one hundred and twenty patients from a specified university clinic. The instrument was designed particularly to screen the depressive symptoms resulting from several maladies (Whiston & Eder, 2003).
Primarily, BDI-FS offers information concerning a unique population. In a population, the depressive symptoms resulting from illnesses must be separated from the depressive signs caused by other phenomena. The population in this investigation comprises of cancer patients whose ages range between forty and sixty. The patients should have shown a specific and rare carcinoma within six months prior to the study. Such a rare malignancy makes up less than one percentage of all cancer cases. In addition, the test-takers should have engaged in face-to-face groups, online forums, or illness-specific support groups for over three months.

Conclusion

Although the measurement of variables is a complicated task, the selection of a valid and reliable tool for assessment is critical to an accurate research design. Further, the determination of appropriate measurement levels is significant because it ensures empirical, construct and content validity, as well as data reliability. Such attributes promote the usability and the feasible implementation of the study. Nevertheless, different strengths and limitations influence many assessment instruments; hence, it is essential to determine their effect on the study results. In my investigation, the BDI-FS provides a well-grounded and reliable assessment of the depressive symptoms among cancer patients. The instrument utilizes a Likert scale that adequately measures the attitudes on interval and ordinal measurement levels. Moreover, the BDI-FS was designed specifically for the depressive symptoms resulting from unique maladies. Thus, the instrument is crucial to this study’s success.

References

Alacacioglu, A., Oztop, I., & Yilmaz, U. (2012). The Effect of Anxiety and Depression on Quality of Life in Turkish Non Small Lung Cancer Patients. Turkish Thoracic Journal, 13(2), 50-55. doi:10.5152/ttd.2012.12
Brockopp, D. Y., & Hastings-Tolsma, M. T. (2003). Chapter 6, Answering the research question: Quantitative designs. In Fundamentals of Nursing Research (pp. 193-220). Boston: Jones & Bartlett.
Carifio, J., & Perla, R. J. (2007). Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes. Journal of Social Sciences, 3(3), 106-116. doi: 10.3844/jssp.2007.106.116
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research Methods in the Social Sciences (7th ed.). New York: Worth.
Hennessy, J., J. & Pallone, N., J. (2003). Review of the BDI-FS for Medical Patients. In B.S. Plake, J.C. Impara, & R.A. Spies (Eds.), The seventeenth mental measurements yearbook. Lincoln, NE: Buros Institute of Mental Measurements. Retrieved from http://web.ebscohost.com.ezp.waldenulibrary.org/
Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217-1218. doi: 10.1111/j.1365-2929.2004.02012.x
Johnson, B., (n. d.). Chapter 2: Quantitative, Qualitative, and Mixed Research. University of South Alabama. Retrieved from http://www.southalabama.edu/coe/bset/johnson/lectures/lec2.htm
Neitzer, A., Sun, S., Doss, S., Moran, J., & Schiller, B. (2012). Beck Depression Inventory-Fast Screen (BDI-FS): an efficient tool for depression screening in patients with end-stage renal disease. Hemodial Int. 16(2):207-213. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/22754932
Poole, H., Bramwell, R., & Murphy, P. (2009). The utility of the Beck Depression Inventory Fast Screen (BDI-FS) in a pain clinic population. European Journal of Pain, 13(8), 865-869. doi: 10.1016/j.ejpain.2008.09.017
Sechrest, L. (2005). Validity of Measures Is No Simple Matter. Health Services Research, 40, 1584-1604. doi: 10.1111/j.1475-6773.2005.00443.x
Trivedi, M. H., Rush, A. J., Ibrahim, H. M., Carmody, T. J., Biggs, M. M., Suppes, T & Kashner, T. M. (2004). The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: A psychometric evaluation. Psychological Medicine, 34(1), 73-82.
Whiston, S. C. (2009). Principles and applications of assessment in counseling (3rd ed.). Belmont, CA: Brooks/Cole, Cengage Learning.
Whiston, S., C. & Eder, K. (2003). Review of the BDI-FS for Medical Patients. In B.S.Plake,J.C. Impara, & R.A. Spies (Eds.), The seventeenth mental measurements yearbook. Lincoln, NE: Buros Institute of Mental Measurements. Retrieved from http://web.ebscohost.com.ezp.waldenulibrary.org/

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