Operationalizing Variables

Motivation
Clancy et al. (2017) hold that motivation is not an observable entity but rather a latent
variable or construct. It is, therefore, difficult to measure the variable directly. Morsink et al.
(2019) used a 64-item prototype scale to measure the level of motivation among children and
adolescents based on their perception of a task. The researchers also used the Strengths and
Difficulties Questionnaire to measure internalizing and externalizing problems (Morsink et al.,
2019). Some of the items on the scale asked what motivates the respondents to engage in a given
task. Some of these include, “I work to avoid thinking about my problems.” There was also, “I
work because I like to work on one thing and forget everything else around me” (Morsink et al.,
2019). There was also the item that stated that “I work to avoid getting bored.” These are just a
few examples of the items on the scale. These helped the researchers determine the respondents’
level of motivation. The scale was effective in measuring motivation because it focused on the
motivational significance as opposed to internal traits associated with motivational processes.
In another study, Hammoudi (2020) measured the level of motivation in mathematics and
the self-concept of students in higher education. In the study, the researcher used the Motivation
Strategies for Learning Questionnaire (MSLQ), a 55-item instrument to measure motivation
(Hammoudi, 2020). The MSLQ included concepts such as academic self-efficacy and intrinsic
motivation. The items in the survey instrument measured intrinsic and extrinsic motivation
(Hammoudi, 2020). It included survey questions that asked about the level of enjoyment of
mathematics, perception of success in the subject, expectations of future income and career
progression in mathematics-related fields, and the necessity of mathematics as a subject. Based

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on the responses provided for each of the items on the scale, the researcher was able to measure
the level of mathematics motivation among the respondents.
The two articles assess motivation successfully. Each used a different scale, but the
survey instruments contained items whose responses aided the researchers in effectively
measuring motivation. For example, Hammoudi (2020) asked students whether they think that
Mathematics is important in their daily life, in their attainment of a college degree, and in high
school graduation. Each of these items sought responses that helped the researcher determine the
motivation behind the respondents’ study of the subject.
Leadership
Researchers in the field of leadership agree that effective leadership is key in determining
the success of an organization. Madanchian et al. (2017) conducted a study evaluating effective
leadership as a variable. In the study, the researchers operationalized effective leadership by
examining the consequences of the leader. They used several outcomes to analyze the
effectiveness of a leader. The outcomes include increased subordinate performance. If teams put
in charge of a leader perform better, then the implication is that the individual is an effective
leader (Madanchian et al., 2017). Another outcome is advanced subordinate job satisfaction. By
assessing the general job satisfaction level among subordinates, it is possible to tell whether or
not a leer is effective. Advanced commitment among subordinates is another outcome used to
assess the effectiveness of a leader. If a team is more committed, then the leader is effective, and
vice versa holds.
The researchers also used the leader’s ratings and their ability to act innovatively as an
outcome to determine their effectiveness. Further, if a leader is promoted to higher positions,

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then they are effective in their work. Improved group performance and increased commitment to
organizational goals among subordinates are indicators of commitment (Madanchian et al.,
2017). The researchers used a six-item scale to measure the effectiveness of a leader based on the
opinion of subordinates and their willingness to work with and for the leader. The scale asks
team members or subordinates to rate their leader on several issues, such as their ideals, their
admiration, and their ability to get along with the leader. Based on the results, an individual was
classified either as an effective or ineffective leader.
Further, Khoshhal and Guraya (2016) studied leadership but operationalized the variable
in a different way. The researchers used the leadership practices inventory (LPI) model of
leadership to measure leadership. The measurement of the variable was grounded on five
behavior-based exceptional leadership practices. First an individual that challenges the process.
The researchers argue that a leader is ready to accept the challenge, take risks, and innovate by
experimenting with various thoughts (Khoshhal & Guraya, 2016). Secondly, a leader inspires a
vision. He or she is an individual capable of creating a sketch for the future of the organization.
The third practice used to measure leadership is the ability to enable others to act. A leader is one
who empowers subordinates to act by delegating duties and sharing the necessary information
with them.
The fourth practice of a leader is an individual who models the way. Leaders are
consistent and lead by example, whether it is intentional or not. They encourage their
subordinates and boost their confidence Khoshhal & Guraya, 2016). The fifth practice of a leader
is encouraging the heart. Leadership is demonstrated by building a supportive social network for
the team members to thrive. Leaders openly attribute team members’ success to their efforts.

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During the study, Khoshhal and Guraya (2016) used this model to measure leadership and
determine instances when effective leadership is demonstrated and those when it is not.
Happiness
Happiness is considered one of the most important goals in life. It has thus been studied
widely in many studies interested in well-being ad overall quality of life. However, happiness is
a latent variable that can only be measured by assessing other related constructs. In a study,
Medvedev & Landhuis (2018) found that happiness has often been mistaken for feelings of
pleasure in various previous studies. The researchers argued that being happy is different from
feeling happy. In their study for happiness, they used the Oxford Happiness Questionnaire,
which contains 29 items to measure happiness (Medvedev & Landhuis, 2018). The respondents
completed the questionnaire, and scores were analyzed to determine happiness. Some of the
items in the questionnaire included, ‘I laugh a lot, ‘I am very happy,’ and ‘I have very warm
feelings towards almost everyone. In the study, the researchers found that psychometric
properties and not an individual’s environment had the most impact on one’s level of happiness
(Medvedev & Landhuis, 2018). The use of the items on the scale was effective in measuring
happiness.
Further, Sender et al. (2021) hold that defining happiness is not easy. Happiness is not
only challenging to measure but to define as well. In the article, the researchers aimed at
measuring happiness at work since it is an area that has not been given much attention in the
scholarly field (Sender et al., 2021). The researchers hold that questionnaires may not provide an
accurate measure of happiness as they are self-reports that often rely on memory. They thus used
sentiment analysis to measure happiness. They used the Happy Level, which is a measuring
instrument that represents a person’s level of happiness based on their own narrative. The way in

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which happiness is measured using the Happy Level is effective because the respondent is
allowed to provide their own recollection of events and how they made them feel. The researcher
does not impose any questions on the respondent, nor does he or she decide on what to ask. The
respondent is allowed to tell their story and includes memorable events as well as those that are
significant. Each of the articles provides different ways to measure happiness, and this allows
future researchers to choose either depending on their evaluation.
Mood
The mood is a psychometric property whose measurement mainly relies on self-reports. It
is a latent variable that relies on the individual’s disclosure to understand or even measure.
Andrade Fernández and Rodríguez (2018) conducted a study in which they aimed at measuring
the mood of athletes. They used the Profile of Mood States (POMS) questionnaire. The survey
instrument contained 65 items that sought to collect information on respondents’ depressed
mood, tension, confusion, fatigue, vigor, and anger (Fernández & Rodríguez, 2018). Each of the
items asked participants to describe their feelings at the time of answering the questionnaire. The
items thus helped the researchers collect data describing the participants’ moods at the time of
the study. The POMS questionnaire is an effective tool for measuring mood. It provides
participants with an opportunity to respond to questions based on their own evaluations. The
self-reports thus provide the correct information, and it is thus a good measure of mood.
The mood is measured using different tools and instruments. Another study by Hennessy
et al. (2021) used mixed methods to study mood variation when listening to music during the
early stages of Covid-19. The researchers used the Interpersonal Reactivity Index (IRI) to
measure empathic concern, fantasy, and personal distress (Hennessy et al., 2021). The State and
Trait Anxiety Index (STAI) was used to measure anxiety and emotional state. The study also

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included the use of the Patient Health Questionnaire (PHQ), a tool that helped measure depressed
mood and severity of depression. Lastly, the researchers used the Brief Music and Mood
Regulation Questionnaire (Hennessy et al., 2021). The instrument contains 21 items that measure
mood. The tools effectively helped the researchers identify ways in which people’s moods varied
during the first stages of the pandemic. The instruments thus effectively helped measure mood, a
latent variable that largely relies on self-reports to measure. Each of the instruments used asked
respondents to self-assess and then provide an account of how they felt during the study. It
helped to capture the mood of the respondents. Further, since each of the instruments used items
that were worded differently, it was possible for the researchers to capture a wide variation of
mood changes and a wide range of data for analysis.

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References

Andrade Fernández, E. M., & Rodríguez Salgado, D. (2018). Factor structure of mood over time
frames and circumstances of measurement: Two studies on the Profile of Mood States
questionnaire.
Clancy, R. B., Herring, M. P., & Campbell, M. J. (2017). Motivation measures in sport: A
critical review and bibliometric analysis. Frontiers in psychology, 8, 348.
Hammoudi, M. M. (2020). Measurement of students’ mathematics motivation and self-concept
at institutions of higher education: evidence of reliability and validity. International
journal of mathematical education in science and technology, 51(1), 63-86.
Hennessy, S., Sachs, M., Kaplan, J., & Habibi, A. (2021). Music and mood regulation during the
early stages of the COVID-19 pandemic. PloS one, 16(10), e0258027.
Khoshhal, K. I., & Guraya, S. Y. (2016). Leaders produce leaders and managers produce
followers: a systematic review of the desired competencies and standard settings for
physicians’ leadership. Saudi medical journal, 37(10), 1061.
Madanchian, M., Hussein, N., Noordin, F., & Taherdoost, H. (2017). Leadership effectiveness
measurement and its effect on organization outcomes. Procedia Engineering, 181, 1043-
1048.
Medvedev, O. N., & Landhuis, C. E. (2018). Exploring constructs of well-being, happiness and
quality of life. PeerJ, 6, e4903.
Morsink, S., Sonuga‐Barke, E., Van der Oord, S., Van Dessel, J., Lemiere, J., & Danckaerts, M.
(2019). Measuring individual differences in task‐related motivation in children and

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adolescents: Development and validation of a new self‐report measure. International
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Sender, G., Carvalho, F., & Guedes, G. (2021). The happy level: A new approach to measure
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