Measure for Dissertation: Emotional Effects and Workplace

This essay delves into the crucial measurement section of a dissertation, emphasizing its significance in transforming data into quantifiable variables. Focusing on the dissertation topic, "The Emotional Effects of Colorism and Hair Texture, and Its Influence on Impostor Syndrome in Black Women in the Workplace," the essay highlights the importance of finding a suitable measure to analyze the extent to which these factors affect individuals. It discusses the advantages of using existing, validated measures over creating a personal one, citing the benefits of established approaches for data analysis and future research. The essay explores constructs like color and workplace interests, suggesting the application of categorical and quantitative variables and the use of existing instruments for measurement, while also considering the potential of creating a new instrument to measure the constructs and guarantee validity and reliability.

Running Head: MEASURE FOR DISSERTATION
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Measure for Dissertation
Name
Institution
Date

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The Emotional Effects of Colorism and Hair Texture, and Its Influence on Impostor Syndrome in
Black Women in the Workplace
One of the key areas of a dissertation is the measurement section which comes
immediately after the collection and sampling of data. This is the section under which the data,
may it be people, events, objects, or any other form of information is turned into a numbers for
the measurement purposes. This is known as measuring the dissertation variables and despite
been the most challenging part of doing a doctoral research or a dissertation; it is one of the most
significant processes (Watson, 2015). One of the advantages that come with creating a measure
for the dissertation is that the process helps in dictating the descriptive information necessary for
the tasks ahead especially the discussion, analysis, and conclusion sections. This works as a
guarantee for the future processes of the work given in such a way that the adoptions and
assumptions made in the following sections can be explained.
In most cases, the measure of these data, which is called the adopted variables provide a
cross reference towards the adoption of formulae and other significant steps into concluding
prove behind the dissertation (Schwichow et al., 2016). For the topic in my dissertation, The
Emotional Effects of Colorism and Hair Texture, and Its Influence on Impostor Syndrome in
Black Women in the Workplace, finding a measure for my data provides a chance to apply the
statistics necessary to find out what extent that these people are feeling the effect. This also
provides a chance for the development of a validated and tested approach towards finding out
what these people go through as far as the issues of colorism and hair texture are concerned. The
validation that comes with finding a specific measure for the data that is collected is a crucial
part since this also provides a future reference for other researchers to apply in topics that are
related to this.

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Besides this, the significance of finding a measure for dissertation data is also a crucial
part since it is from this that the data collected and sampled is verified (Osanloo & Grant, 2016).
This means that the basic errors and assumptions that cannot be held up for the topic are
eradicated while at the same time others are adopted to furnish the research. There are also some
disadvantages that are associated with the issue of having a personal measure of the data in a
dissertation. When one has their own measure, it means that they do not have the consideration
of validation in their process (Tanujaya, 2016). This means that the measure and formulae
adopted in the data analysis and sampling is not considerable and also not confirmed as a
validated approach that can be relied on.
The other disadvantage comes in the process of reviewing and future adoptions. Since
this measure is not a validated approach, cases of having inconsiderable samples and
assumptions are a common case which means that the research does not a future reference in the
topic (Heale & Twycross, 2015). Besides the cases of variables that do not exist, it is always
wise to rely on instruments and measure approaches that already exist or the ones that other
researchers have already tested and used to a success so as to minimize the issues of fake
analysis and issues of future consideration.
Personally, it would be better to use the pre-existing measures rather than having my own
measure for the constructs of interest. The reason for this is because having pre-existing measure
guarantees a lot of considerable approaches for my dissertation. First, it means that the variables
that I will present will hold the expectations that I need from my research and the data I have
collected. Secondly, this means that I will be able to address the following sections especially the
data analysis and discussions of the results in an already validated and tested approach. Some of

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the constructs that may arise however include the issue of color and the specific interests towards
the workplace I am researching on.
Some of the existing instruments that are already available and ones that could measure
and assess these constructs include the application of categorical variables with a mixture of
variables that are quantitative in nature (Simmering et al., 2015). This means that the two
approaches will accommodate the scales of measurements for the constraints which include
Nominal, Ordinal, Interval, and Ratio.
For the constraints, I think that it would be better to create my own instrument to measure
them. This is because I would be able to adopt a process and an approach that assigns the data I
have to the specific variables associated with the variables. This will also guarantee some aspects
of validity and reliability of the research.

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References
Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-
based nursing, 18(3), 66-67.
Osanloo, A., & Grant, C. (2016). Understanding, selecting, and integrating a theoretical
framework in dissertation research: Creating the blueprint for your
“house”. Administrative issues journal: connecting education, practice, and
research, 4(2), 7.
Schwichow, M., Croker, S., Zimmerman, C., Höffler, T., & Härtig, H. (2016). Teaching the
control-of-variables strategy: A meta-analysis. Developmental Review, 39, 37-63.
Simmering, M. J., Fuller, C. M., Richardson, H. A., Ocal, Y., & Atinc, G. M. (2015). Marker
variable choice, reporting, and interpretation in the detection of common method
variance: A review and demonstration. Organizational Research Methods, 18(3), 473-
511.
Tanujaya, B. (2016). Development of an Instrument to Measure Higher Order Thinking Skills in
Senior High School Mathematics Instruction. Journal of Education and Practice, 7(21),
144-148.
Watson, R. (2015). Quantitative research. Nursing Standard (2014+), 29(31), 44.