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use the original post and references to respond back to both classmates SEPERATELY
ORIGINAL POST
Given the specific study topic of the level of relationship
of employee satisfaction with company performance for a high-tech corporation,
the level of measurement must be determined, as well as the measure of central
tendency is best suited to the summary statistic.
Employee
Satisfaction:
i.
Level of Measurement
Job satisfaction is assessed using a Likert scale and an
ordinal number system. Each item on the Likert scale corresponds to one of four
alternatives: strongly agree, agree, disagree, and strongly disagree. Such
metrics are standardized and determine the amount to which clients are
prohibited from cheating; consequently, an order is being established during
the grading process (Voordt & Jensen,
2023). This technique of measurement allows an organization
to regularly evaluate employees’ feelings and attitudes about the environmental
organization and utilize the instrument to make evidence-based decisions on
employee well-being and engagement issues.
ii.
Summary Statistic:
The ordinal nature of Likert scale responses makes the
median the outstanding statistic for gauging central tendency. Since it does
not get skewed by outliers like the mean does, the median is a better measure
of representative data and a more accurate representation of the general
responses (Hayes, 2014).
Company
Performance
i.
Level of Measurement:
Most companies measure their performance using KPIs such as
revenue growth rate, market share percentage, innovation rates (number of
patents or new items generated), and customer satisfaction indices (Lubis et al., 2023). Each of these KPIs is used at the
ratio level of measurement since it has a real zero point and scale continuity.
ii.
Summary Statistic:
As a measure of central tendency, the mean is anticipated
to work best for ratio data. When dealing with continuous variables like
revenue growth, percentage changes in market share, the mean is undeniably the
most relevant statistic (Hristov &
Chirico, 2019).
Statistical
Analysis:
i.
Correlation Analysis
Since both employee satisfaction and company
performance can be measured, correlation analysis can establish a link between
the two measurements. Given that the data on employee satisfaction is ordinal,
a statistical test such as Spearman’s rank correlation coefficient could be
applied.
ii.
Regression Analysis
Regression analysis can be used to identify the predicted
link between employee happiness and corporate success in respect to other
control variables. In this context, a multiple regression analysis would be
ideal to determine how employee satisfaction explains the various KPIs
concurrently.
Conclusion
For the analysts to interpret the data correctly in
the context of the relationship between employee satisfaction and company
performance in a technology company, it is necessary to know which variable is
at the measurement level and to use the correct measure of the central
tendency.
References
Voordt, T. V. D., & Jensen, P. A. (2023). The impact of
healthy workplaces on employee satisfaction, productivity and costs. Journal
of Corporate Real Estate, 25(1), 29-49.
Lubis, Z., Zarlis, M., & Aulia, M. R. (2023).
Performance Analysis of Oil Palm Companies Based on Barcode System through Fit
Viability Approach: Long Work as A Moderator Variable. Aptisi
Transactions on Technopreneurship (ATT), 5(1), 40-52.
Hayes, A. F. (2014). Introduction
to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. https://ci.nii.ac.jp/ncid/BB1323391X
Hristov, I., & Chirico, A. (2019).
The role of Sustainability Key Performance Indicators (KPIs) in implementing
sustainable strategies. Sustainability, 11(20),
5742. https://doi.org/10.3390/su11205742
ROSHY POST
In Module 1, researcher identified
three variables, Leadership Style (LS), Emotional Intelligence (EI) and General
Cohort (GC).
Researcher examined the
impact of leadership style, emotional intelligence, and generational cohort on
organizational innovation, it is important to correctly determine the level of
measurement for each variable and choose the appropriate measure of central
tendency. Leadership styles (LS) can be classified nominally or ordinally,
depending on whether they are treated as distinct categories or ranked orders,
with Mode and Median being appropriate measures of central tendency (Gravetter
& Wallnau, 2016). The Mean is the best way to summarize emotional
intelligence (EI), which is typically measured on an interval scale using
standardized assessments. It provides a comprehensive average score (Healey,
2014). Nominally classified generational cohorts should be analyzed using the
mode to identify the most common group within the sample (McCrum-Gardner,
2008). Organizational innovation, which is frequently measured as a ratio
variable using metrics such as the number of new products introduced or patents
filed, is best summarized by the mean, which provides a clear average level of
innovation within the organization (Trochim, 2007).
For Leadership Styles (LS), measurement can be
nominal or ordinal, depending on how LS is categorized. If LS is
categorized into distinct types (e.g., transformational, transactional),
measurement is Nominal. If it is ranked or ordered, it is ordinal. In this
study, researchers plan to use distinct types. Therefore, the measurement
selected for the study is nominal. To identify the most frequently occurring
type, Mode is the appropriate central tendency.
Emotional
Intelligence (EI) is often measured using standardized scales (e.g., EQ-i) that
provide scores or ranks. Therefore, the measurement is Interval for this
study. To consider all the scores and provide an average, Mean is
selected for the central tendency.
Generational
Cohorts(GC) are categorized into distinct types (e.g., Baby
Boomers, Generation X, Millennials). Therefore, the measurement is Nominal. To
identify the most frequently occurring type, Mode is the appropriate central
tendency.
Organizational
Innovation can
be quantified using metrics such as the number of new products introduced,
patents filed, or other measurable outputs, which have a true zero point and
meaningful intervals. Therefore, the measurement is Interval. To find the
average of ration data of innovation across the organization, Mean is the
appropriate central tendency.
References
Gravetter,
F. J., & Wallnau, L. B. (2016). Statistics for the Behavioral Sciences.
Cengage Learning.
Healey, J.
F. (2014). Statistics: A Tool for Social Research. Cengage Learning.
McCrum-Gardner,
E. (2008). Which is the correct statistical test to use? British Journal of
Oral and Maxillofacial Surgery, 46(1), 38-41.
Trochim, W.
M. (2007). The Research Methods Knowledge Base.
https://www.researchgate.net/publication/243783609_The_Research_Methods_Knowledge_Base
SHERNETHEA POST
In
the ever-evolving landscape of financial services technology, the progress
toward gender equity has not kept pace with the speed of innovation, resulting
in a lag in women’s career progression. Career progression, a cornerstone of
workforce development, is an individual’s trajectory throughout their
professional life (Dolton et al., 2005). In U.S. financial services information
technology (IT) organizations, achieving equitable career progression for women
is a noble aspiration and a critical factor for organizational success and
credibility (Rogish et al., 2023). The research question driving this study is:
What are the predictors of higher-level position titles among women in U.S.
financial services IT organizations? To answer this, the study will employ
survey instruments, providing an understanding of the relationship between the
dependent variable (the attainment of higher-level position titles such as Vice
President, Executive Director, and Managing Director) and independent
variables:
Mentoring/Mentorship
(MS)
Organizational
Culture (OC)
Leadership
Development Programs (LDP)
Gender
Diversity Human Resource Policy (GDHRP)
Professional
Network and Support Systems (PN &SS)
Several authors (Zikmund et al., 2012) have discussed
levels of measurement nominal, ordinal, interval, or ratio. Nominal assigns a
value for identification (Zikmund et al., 2012) . Ordinal is a ranking scale
that arranges things in order (Zikmund et al., 2012). Interval captures the
concept’s difference (e.g., by how much) (Zikmund et al., 2012). Ratio is the
absolute quantities (Zikmund et al., 2012). Central tendency refers to the
concentration or clustering of data around numerical values (mean, median, and
mode), as described by McClave et al. (2012, p.63). Here is how this study
categorizes the levels of measurements and central tendencies for the dependent
and independent variables:
Mentoring/Mentorship
(MS): This variable seeks to understand the presence or absence of
mentoring/mentorship throughout women’s career progression in U.S.
financial services information technology (IT) organizations. This
independent variable is nominal, and the mode (the most common measurement
in the data collected) is appropriate as a summary statistic (McClave et
al., 2012).
Organizational
Culture (OC): This variable measures the participants’
perceptions of U.S. financial services’ information technology (IT)
organizational culture. The study will use the Likert scale to measure the
participants’ perceptions (either supportive of women’s career progression
or unsupportive) (Likert Scales, n.d.). This independent variable
is ordinal, and the median is appropriate as a summary statistic (McClave
et al., 2012).
Leadership
Development Programs (LDP): This variable identifies whether the participants
participated in a leadership development program. This independent
variable is nominal, and the mode (the most common measurement in the data
collected) is appropriate as a summary statistic (McClave et al., 2012).
Gender
Diversity Human Resource Policy (GDHRP): This
variable rates the effectiveness of the
GDHRP policies in U.S. financial services information technology
(IT) organizations (either
effective or ineffective). This
independent variable is ordinal, and the median is appropriate as a
summary statistic (McClave et al., 2012).
Professional
Network and Support Systems (PN & SS): This
variable measures whether the professional network and support systems
helped the participants’ career progress in U.S. financial services
information technology (IT) organizations. The study will use the Likert
scale to measure the effectiveness (strongly agree, agree, neutral, or
disagree) of the professional network and support systems (Likert
Scales, n.d.). This independent variable is ordinal, and the median is
appropriate as a summary statistic (McClave et al., 2012).
For the
dependent variable (the attainment of higher-level position titles), this
measure is categorical (e.g., the different position titles). This
variable is nominal, and mode is appropriate as a summary statistic
(McClave et al., 2012)(McClave et al., 2012).
Reference
Dolton,
P., Makepeace, G., & Marcenaro-Gutierrez, O. D. (2005). Career progression:
Getting-on, getting-by and going nowhere: education economics. Education
Economics, 13(2), 237–255.
https://saintleo.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ691087&site=ehost-live&scope=site
Likert
scales: Definition, benefits & how to use them.
(n.d.). Qualtrics. Retrieved May 12, 2024, from
https://www.qualtrics.com/uk/experience-management/research/likert-scales/
McClave,
J. T., Benson, P. G., & Sincich, T. T. (2012). Statistics for
Business and Economics (12th edition). Pearson.
Rogish,
A., Shemluck, N., Hazuria, S., & Danielecki, P. (2023, June 8). Advancing
women leaders in the financial services industry, 2023 update: A global
assessment. Deloitte Insights.
https://www2.deloitte.com/us/en/insights/industry/financial-services/women-leaders-financial-services.html
Zikmund,
W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2012). Business
Research Methods (9th edition). Cengage Learning.
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