Psychology week 6 assignment psych

3

Dissertation Instrument Selection

Barbara Maclure

Keiser University Online

Psychometrics

Dr, Kelly Schmitt

04/13/2025

Dissertation Instrument Selection

Establishment of proper validity and reliability measures forms an essential requirement in developing a dissertation focusing on psychological constructs to protect research findings. In my dissertation, I am considering assessing the constructs of anxiety and resilience in college students. When it comes to addressing and assessing these constructs there is a need to ensure that one uses standardized measures, in my case two constructs that will be used are “Beck Anxiety Inventory (BAI) together with the Connor-Davidson Resilience Scale (CD-RISC).” It is important to note that much research that has been done in the past does use these measures and this in the process demonstrates strong measurement properties and therefore are compatible with my own research.

Self-Report Beck Anxiety Inventory (BAI) research tool features twenty-one items to determine the intensity of anxiety symptoms. Research that has been done on this construct have shown that BAI displays remarkable consistency in assessment with clinical and non-clinical samples. Numerous studies involving college students reveal the BAI demonstrates strong reliability and validity through the correlation of r = .75 for one-week test-retest results according to Ismail et al. (2023). The tool works well for conducting anxiety measurements during short timespans thus making it an ideal tool when studying cross-sections or brief longitudinal designs. When used as a 25-item variant the Connor-Davidson Resilience Scale (CD-RISC) provides an evaluation of individual resilience capabilities related to stress and adversity manageability. Due to its perfection in measuring college student data the CD-RISC exhibits great internal consistency and excellent test-retest reliability scores exceeding .89 and .87 respectively (Rezaeipandari, 2022).

In considering the application of the above selected tools in my dissertation research, it is very important to address and acknowledge the issues that might come up because of missing data. Self-report questionnaires in psychological research led to more frequent instances of missing responses between participants. The presence of missing data has the potential to ruin valid findings. According to Howell (2013) the identification of missing data mechanisms remains crucial because it contains three categories known as “Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). MCAR defines an absence of relationship between “missingness” probability and observed or unobserved information.” On the contrary, MAR defines that the “missingness” may be related to observed variables but not the missing values, while MNAR defines that the “missingness” may be related to unobserved data and may lead to systematic bias.

Proper identification of the nature of missing data guides the choice of statistical techniques for overseeing them. For example, If the data are MAR, sophisticated techniques such as multiple imputation or FIML would be preferable since they are better at parameter estimation. To prevent missing data in the first place, pilot testing of instruments, attention checks, and easy-to-use survey design are essential strategies.

References

Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Wadsworth Cengage Learning.

Ismail, N. H., Nik Jaafar, N. R., Woon, L. S. C., Mohd Ali, M., Dahlan, R., & Baharuddin, A. N. A. P. (2023). Psychometric properties of the Malay-version beck anxiety inventory among adolescent students in Malaysia. Frontiers in psychiatry, 13, 989079. https://www.frontiersin.org/articles/10.3389/fpsyt.2022.989079/full?amp;amp

Rezaeipandari, H., Mohammadpoorasl, A., Morowatisharifabad, M. A., & Shaghaghi, A. (2022). Psychometric properties of the Persian version of abridged Connor-Davidson Resilience Scale 10 (CD-RISC-10) among older adults.  BMC psychiatry,  22(1), 493. https://link.springer.com/article/10.1186/s12888-022-04138-0