Psychometric Assessments: Performance Dependence

Introduction

Academia and the workplace are two prime examples of fairly stimulating and competitive environments. For many, enrolling in tertiary education and entering the workforce is both mentally and emotionally challenging as students and employees experience a fair amount of pressure to succeed. Academic and workplace performance are rather broad terms that are operationalized differently depending on the perspective and study design. Yet, they can be generally explained as self-efficacy and goal-orientedness. Academic and career success is associated with a variety of factors, be it external such as a person’s background and internal, for instance, their stress-resilience. On a larger scale, the present research seeks to develop a reliable instrument to predict performance in the university and the workplace. Namely, the authors will attempt to apply psychometric assessment to two different demographics, high school students and recent graduates, to use the result as an indicator of their prospects.

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Literature Review

Van Dooren, Bouckaert, and Halligan (2015) define performative action as action with a certain degree of intent as opposed to mere behavior. Hence, there are two dimensions to performance: the action itself and the value judgment that is to evaluate to what extent the intention was realized by the actor. At the same time, it is possible to distinguish two different perspectives on measuring performance in regards to the quality of skills and the quality of achievements (Landy, Zedeck, & Cleveland, 2017). Many educators, researchers, and policy-makers struggle to put forward a definitive framework that would unite the said two approaches in which practical outcomes would be on par with formal achievements.

Academic and professional success are two notions with overlapping aspects, which, however, need to be operationalized and supported by theoretical frameworks separately. Researchers often hesitate to define academic performance due to its complexity. For instance, Cox, Imrie, and Miller (2014) reason that the US higher education offers credible measures for student learning. Yet, there is little to no consensus as to what exactly a student needs to know and do to be considered academically successful (Cox et al., 2014). One of the recent studies that embarked on dissecting the phenomenon of school success down to its most basic constituencies was conducted by York, Gibson, and Rankin (2015). According to the authors, the rigorous literature meta-analysis showed that the first definition encompassed a variety of generally accepted outcomes. The second definition involved six components: achievement, a sense of accomplishment, skills and competencies, persistence, meeting learning goals, and career success (York et al., 2015). Thus, the theoretical framework proposed by York ties together different aspects of learning concerning actions, value judgment, and future impact.

Job performance (JP) is probably the most extensively researched variable in the literature on human resources management. JP also constitutes an important concept in industrial and organizational psychology. Akin to the theoretical framework of academic success, researchers distinguish two sides to JP: behaviors and outcomes. Admittedly, not any behavior contributes to improved JP: according to Kappagoda, Othman, Zainul, and Alwis (2014), an employee needs to display engagement that can be observed. Here is where value judgment comes into play: JP is often defined externally as a set of expectations imposed by the employer.

Dalal, Bhave, and Fiset (2014) draw attention to the changing work environment. They emphasize the fact that while traditionally JP was evaluated based on task completion, in this day and age, what matters is not separate assignments but a larger impact and organizational outcomes. Thus, professional success includes both task performance and contextual performance: while the former correlates with the assigned role, the latter is aligned with the extra role that an employee might assume. Yet, drawing from the theoretical framework of JP and outlining practical measures applicable to a variety of organizations is fairly challenging. For instance, Campbell and Wiernik argue that each of the popular means of assessment (supervisor feedback, simulation, and technologically enhanced) might be both advantageous and disadvantageous.

In an attempt to understand what affects academic and job performance, many researchers focus on contributing factors, seeking to find a correlation between them and the outcomes. Kappagoda et al. (2014) showed that there is a partial correlation between attitude toward work and job performance and an unambiguously positive relationship between psychological capital (PsyCap, stress resilience, and self-confidence) and JP. Abbas and Raja (2015) came to the same conclusions: their study revealed an association between sufficient PsyCap, decreased job stress, and enhanced job performance. Hence, there is a body of evidence that psychological assessment might be reliable when it comes to predicting work outcomes.

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As for academic success, personal protective qualities such as stress resilience were instrumental to end-of-the-year achievements. In a study by Allan, McKenna, and Dominey, those students who scored high on most of the categories in the test (Competency, Trust, Spirit, Changed, Control) were receiving better grades. A study by Beauvais, Stewart, DeNisco, and Beauvais (2014) introduced emotional intelligence as one of the factors contributing to academic outcomes. The researchers offered nursing students a test that went beyond measuring stress resilience and embarked on spiritual well-being. As the findings showed, mental wellness and the ability to detect problems and seek help were critical to ensuring high grades and satisfaction.

Problem Statement

Literature research revealed a few knowledge gaps that the present research seeks to address. It has become evident that while both academic and job performance have been extensively studied to a variety of contributing factors, there is a place for a longitudinal study that would employ a psychometric assessment. The literature research did not reveal a comprehensive tool for personality assessment being applied to school and professional success. Therefore, the problems discussed in this study are to investigate the following:

  1. Can a psychometric assessment during high school predict students’ academic performance in the next four years?
  2. Can a psychometric assessment after graduation from college predict employees’ job performance in the next four years?
  3. Is it possible to develop a psychometric test that would apply to both academic and job performance assessments?
  4. Is it possible to develop an objective psychometric test that would operate on self-perception and yet, be reliable in making a prognosis concerning future performance?

Methodology

For this study, an observational method, which is otherwise known as unobtrusive, will be applied. Observation allows for collecting data that the researchers observe in a specific setting – in the case of the present study, it is an academic and corporate environment. The rationale behind the chosen study design is quite clear: observing participants serves several purposes at once. First, it allows for detailed description revealing another layer of reality; second, contextualization helps to put an abstract concept in a real-life setting and receive empirical outcomes. Lastly, by observing, the researchers will avoid early apply of theories, especially given that the present study is longitudinal. The methods pertain to the research questions since observation will help to prove whether predictions were right.

The researchers will contact schools and universities within the municipal area and possibly in the neighboring towns via email. The main problem withdrawing a sample might be a low response rate and two-stage enrollment since first, school authorities are contacted, and later, students need to give permission. Moreover, the duration of the study would require commitment which might discourage some of the potential participants. Since convenience sampling is far from ideal, the validity will be ensured by making it stratified. To conclude, the researchers will need data on GPA and self-evaluation for students and supervisor feedback and self-evaluation from employees. The self-evaluation tool will draw on literature research and will be adapted to meet the study’s needs.

Data Analysis

For each demographic (high school students and graduates), the researchers will prepare three sets of data:

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  1. Independent variable. Results of the psychometric test will be a value reflecting the suitability and adaptability of a person to a stimulating and competitive environment.
  2. Dependent variable. GPA or supervisor feedback for students and employees respectively.
  3. Dependent variable. Self-evaluation test results for students and employees alike.

The dependent variables will be measured continuously every year so that performance would be expressed as not just a random occurrence but a pattern. By the end of Year 4, for each of the dependent variables, a mean value will be calculated to use for later data processing. Since there is one predictor (psychometric assessment) and two possible outcomes (formal results and self-evaluation of motivation and attitude), multivariate regression analysis will be employed. The study will have its limitations that will be addressed in the respective section. First, the sample size might not be large and randomized enough to make the findings inferential. Second, the validity of the results might be undermined by volunteer bias: more motivated or interested students will step forward to partake.

Timeline of Study

Year 1

  • Literature research;
  • Study design and methodology development;
  • Psychometric assessment test creation or adaptation depending on the literature research;
  • Developing rules and policies to ensure participants’ safety and confidentiality;
  • Contacting schools and universities to inquire whether their students might be interested in participation;
  • Drawing a sample of at least 200 high school students and 200 university graduates;
  • Psychometric testing in person or via email.

Year 2

  • Data collection.
  • University students: learning assessment at the end of the first year based on the outlined criteria – GPA and self-evaluation of attitude and motivation.
  • Employees: job performance assessment based on the outlined criteria – supervisor feedback and self-evaluation of attitude and motivation.

Year 3

  • Repeat Year 2.
  • Assessment tool adaptation if necessary.

Year 4

  • Repeat Year 2.
  • Data analysis and computation;
  • Concluding and outlining the findings’ implications and recommendations for future research.

The research may be conducted part-time during seven years, in which case the actions planned out for Year 2 will be repeated for Years 3 through 7.

References

  1. Allan, J. F., McKenna, J., & Dominey, S. (2014). Degrees of resilience: Profiling psychological resilience and prospective academic achievement in university inductees. British Journal of Guidance & Counselling, 42(1), 9-25.
  2. Abbas, M., & Raja, U. (2015). Impact of psychological capital on innovative performance and job stress. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 32(2), 128-138.
  3. Beauvais, A. M., Stewart, J. G., DeNisco, S., & Beauvais, J. E. (2014). Factors related to academic success among nursing students: A descriptive correlational research study. Nurse education today, 34(6), 918-923.
  4. Campbell, J. P., & Wiernik, B. M. (2015). The modeling and assessment of work performance. Annual Review of Organizational Psychology and Organizational Behavior 2(1), 47-74.
  5. Cox, K., Imrie, B. W., & Miller, A. (2014). Student assessment in higher education: A handbook for assessing performance. Abington, UK: Routledge.
  6. Dalal, R. S., Bhave, D. P., & Fiset, J. (2014). Within-person variability in job performance: A theoretical review and research agenda. Journal of Management, 40(5), 1396-1436.
  7. Kappagoda, U. W. M. R., Othman, P., Zainul, H., & Alwis, G. (2014). Psychological capital and job performance: The mediating role of work attitudes. Journal of Human Resource and Sustainability Studies, 02(02),102-116.
  8. Landy, F., Zedeck, S., & Cleveland, J. (2017). Performance measurement and theory. Abington, UK: Routledge.
  9. Van Dooren, W., Bouckaert, G., & Halligan, J. (2015). Performance management in the public sector. Abington, UK: Routledge.
  10. York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research & Evaluation, 20(5), 1-20.
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