Operationalization is used for turning abstract concepts into quantifiable values. This paper aims to analyze and assess the operationalization methods used to evaluate the levels of motivation, mood, anxiety, and happiness in different quantitative research papers. Two scholarly articles for each variable were chosen for the assessment. The selected articles vary in approaches, context, and possible applications to cover a broad range of possible operationalization methods.
Motivation
A study by Håvold & Håvold (2019) examines the influence of different kinds of power on motivation in hospitals. Motivation is defined as a “behavioral, affective and cognitive process that influences the willingness of employees to do their work to achieve personal and organizational goals” (Håvold & Håvold, 2019). Considering the small sample size, the variable type, and the model complexity, applying the partial least square (PLS) Structural equation modeling method is appropriate. Aside from the study limitation due to the small sample, it is consistent and well-organized, suggesting a widely applicable definition of the variable and an appropriate choice of modeling method.
In the research by Li & Tsai (2017), motivation is associated with students’ behaviors in accessing learning materials. The Motivated Strategies for Learning Questionnaire (MSLQ) and automated learning behavior software measured students’ motivation levels and learning behavior during at-home learning. However, the applyed methodology did not account for how long and in what way students engaged with the learning material (Li & Tsai, 2017). As a result, the study shows several evaluation inconsistencies, like similar motivation levels between groups with different behavior patterns. The study has not shown a reliable assessment of students’ motivation.
Mood
A study by Brown et al. (2019) explores how Animal-Assisted Activities (AAA) influence mood among patients and staff of two psychiatric units. The mood was assessed by an open-ended question to identify subjects’ feelings associated with AAA (Brown et al., 2019). The Visual Analog Mood Scale (VAMS) was used, measuring eight specific mood scales: Afraid, Confused, Sad, Angry, Energetic, Tired, Happy, and Tense. The authors justifiably claim that for the study the use of VAMS, validated for assessing mood states, is appropriate (Barrows & Thomas, 2018). The variable assessment, thus, can be considered adequate.
A study by Zhu et al. (2018) aimed to determine the effects of creative writing workshops for patients with cancer on their mood. A validated Emotion Thermometer Scale (ETS) was employed to predict changes in several parameters reflecting patients’ mental health. The ETS presents a comprehensive mood-type scale consisting of five points: “distress,” “anxiety,” “depression,” “anger,” and “need help.” The emotion thermometer (ET) is a validated screening tool for evaluating mood (Harju et al., 2018). Choosing the ET for the study to avoid time-consuming and stressful evaluation procedures is acceptable for operationalizing the mood variable in this case.
Anxiety
Grock et al. (2018) evaluated dental students’ anxiety related to endodontics treatment. The study aimed to characterize the anxiety levels of students who had begun to perform endodontics treatment and assess the degree of confidence to perform the different stages. The Numeric Rating Scale (NRS) was applied to determine students’ anxiety levels, ranging from “minimum” to “maximum.” The State-Trait Anxiety Instrument (STAI) was employed to assess both temporary and long-term anxiety. Applying STAI for anxiety operationalization and assessment is appropriate since it is a reliable and robust instrument for this purpose (Thomas & Cassady, 2021). Therefore, the variable assessment in this study can be considered rational.
Research by Tarrant et al. (2018) has shown the influence of virtual reality therapy on patients’ anxiety levels. Anxiety was assessed by measuring the global power shifts in Alpha and Beta brain activity and comparing them to participants’ self-evaluation. Results demonstrated a clear correlation between brain activity and subjective reports of anxiety. The study combined the GAD-7 self-report scale, STAI, and the EEG data for assessing and comparing individually reported and physiologically evident anxiety levels. These tools were justifiably chosen as validated and commonly used for similar applications (Thomas & Cassady, 2021; Doi et al., 2018). The chosen variable’s definition and assessment can be considered appropriate and beneficial for further applications.
Happiness
A study by Kun & Godanezc (2019) evaluated teachers’ happiness by associating it with five measurable factors: resilience, self-efficacy, optimism, hope, and overall workplace happiness. The authors used the Brief Resilience Scale, The General Self-Efficacy Scale, the Life Orientation Test, the Adult Hope Scale, and the Subjective Happiness Scale. According to the authors, Cronbach α of.70 and above for all of these indicate their reliability (Kun & Godanezc, 2019). The main limitation of this study is that the data was measured via self-reported questionnaires, and the data’s nature does not allow authors to infer causality. Therefore, even though the presented happiness operationalization in the positive psychology framework is valid, the variable’s assessment in this study fails to produce valuable results.
A study by Leerattanakorn & Wiboonpongse (2017) assessed happiness on a seven-point scale based on an individual’s household income, income aspiration, religiousness, and attitude toward one’s financial situation. The authors chose the definition and measurement of the happiness variable relevant to the cultural specifics of Thai farmers. In that context and considering the consistency of the results, the chosen approach is adequate. However, it is questionable whether same operationalization method can be applied for wider use outside of the local cultural framework.
Conclusion
The selected articles show the variety of operationalization methods and approaches that can be applied to a wide range of topics from medicine to economics. The possible applications of research results, however, depend on the context of the chosen subject, including cultural, geographical, and professional context. The correct choice of the variable definition and assessment model for a study is vital to produce appropriate results.
References
Barrows P.D., & Thomas S.A. (2018). Assessment of mood in aphasia following stroke: Validation of the Dynamic Visual Analogue Mood Scales (D-VAMS). Clinical Rehabilitation, 32(1), 94–102. Web.
Brown, S., Snelders, J., Godbold, J., Moran-Peters, J., Driscoll, D., Donoghue, D., Mathew, L., & Eckardt, S. (2019). Effects of animal-assisted activity on mood states and feelings in a psychiatric setting. Journal of the American Psychiatric Nurses Association, 26(6), 1–13. Web.
Doi, S., Ito, M., Takebayashi, Y., Muramatsu, K., & Horikoshi, M. (2018). Factorial validity and invariance of the 7-Item Generalized Anxiety Disorder scale (GAD-7) among populations with and without self-reported psychiatric diagnostic status. Frontiers in Psychology, 9, 1–6. Web.
Grock, C. H., Luz, L. B., Oliveira, V. F., Ardenghi, T. M., Bizarro, L., Ferreira, M. B. C., & Montagner, F. (2018). Experiences during the execution of emergency endodontic treatment and levels of anxiety in dental students. European Journal of Dental Education, 22(4), 1–9. Web.
Harju, E., Michel, G., & Roser, K. (2019). A systematic review on the use of the emotion thermometer in individuals diagnosed with cancer. Psycho-Oncology 28(9), 1803–1818. Web.
Håvold, J. I., & Håvold, O. K. (2019). Power, trust and motivation in hospitals. Leadership in Health Services, 32(2). Web.
Kun, A., & Godanezc, P. (2019) Workplace happiness, well-being and their relationship with psychological capital: A study of hungarian teachers. Current Psychology, 41, 185–199. Web.
Li, L.-Y., & Tsai, C.-C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286–297. Web.
Leerattanakorn, N., & Wiboonpongse, A. (2017). Happiness and community-specific factors. Applied Economics Journal, 24(2), 34–51.
Tarrant, J., Viczko, J., & Cope, H. (2018). Virtual reality for anxiety reduction demonstrated by quantitative EEG: A pilot study. Frontiers in Psychology. Web.
Thomas, C. L., & Cassady, J. C. (2021). Validation of the state version of the state-trait anxiety in a university sample. SAGE Open, 11(3). Web.
Zhu, J., Hussain, M., Joshi, A., Truica, C. I., Nesterova, D., Collins, J., Saunders, E. F. H., Hayes, M., Drabick, J. J., & Joshi, M. (2019). Effect of creative writing on mood in patients with cancer. BMJ Supportive & Palliative Care, 10(1), 64–67. Web.