Discussion Thread: Variables, Research Questions and Data Coding
Lucianna Easton
BUSI 820: Quantitative Research Methods
January 17, 2025
Author Note
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Lucianna Easton. Email:
D1.1. Compare the terms active independent variable and attribute independent variable.
What are the similarities and differences?
Active independent variable is a variable that research is actively manipulating in an
experiment to analyze the impact and effect on whatever the depending variable is. Attribute
independent variable is a variable that the researcher cannot manipulate, but is singularly
classified or categorized, for example a variable like gender (Morgan et. al., 2019). It is often a
characteristic of a subject, or something someone already possesses. Similarities and differences
noted between the both are that they both influence the outcome of the selected dependent
variable and both can be used to establish correlations within a study. Key differences are that
active independent variable can be manipulated by the researcher, while the other cannot. These
types of variables are also used in different research methodologies, active independent variable
often in experimental research, and attribute independent variable in observational or
correlational research. I did also research and review this topic further and there is additional
present discussion of manipulating the independent variable and also manipulating hypothesized
mediator to ensure both internal and external validity (Eden et. al., 2015). This would essentially
allow multiple analysis when synthesizing two sets of experiments.
D1.2. What kind of independent variable (active or attribute) is necessary to infer cause?
Can one always infer cause from this type of independent variable? If so, why? If not, when
can one infer cause and when might causal inferences be more questionable?
An active independent variable is necessary to infer cause. The why behind this is in
order to establish cause, there needs to be changes in the independent variable that impacts and
have an effect the dependent variable (Morgan et. al., 2019). Active independent variables are
able to be manipulated to observe impact. Changing one variable in a study to examine how it
affect another is called manipulating independent variables (Shi et. al., 2019). This does not
necessarily mean that active independent variables always infer cause because one, manipulating
the independent variable may not necessarily cause changes with the dependent variable, and
two, there can not be any confounding variables identified in the research. For example, if there
are other variables that could explain the observed correlation, then the ability to infer cause is
weakened.
D1.3. What is the difference between the independent variable and the dependent variable?
As identified above, the independent variable is the variable manipulated or controlled by
the researcher and is often changed depending on what the researcher thinks will impact the
dependent variable. The dependent variable is what is being observed and measured. The overall
outcome is what the researcher is interested in and it’s important to design the study in a way to
see the outcome of impact on whatever the dependent variable is, and ruling out any influence of
other variables as possible.
D1.4. Compare and contrast associational, difference, and descriptive types of research
questions.
Associational research questions are used when researchers want to review if there is a
relationship between more than two variables, and often use regression or correlation analysis to
analyze the strength or impact of a relationship. Difference research questions are used to
compare the means of two or more groups to identify differences. This research question type is
often utilized for statistical analysis. Descriptive research questions are used to describe
characteristics of a phenomenon or population, and often use measures like averages or
dispersions to describe the data (Morgan et. al., 2019).
D1.5. Write a research question and a corresponding hypothesis regarding variables of
interest to you but not in the HSB dataset. Is it an associational, difference, or descriptive
question?
My decision to pursue a doctorate degree all started after I was a leader, and I was
managing a multi-generational workforce. I noticed key differences between generations,
primarily with my Generation Z, or “Gen Z” employees. This was the key driver for me to want
to go and complete research surrounding this and what organizations can do to help. Any type of
question can be relevant and useful to support evidence, but the question has to be well-defined
and matched to the right design study (Kamper, 2020). My research question is, “How does the
level of job satisfaction impact the retention rates of Generation Z employees in the workplace.”
My null hypothesis could be, “There is no significant relationship between job satisfaction and
the retention rates of Generation Z employees int eh workplace.” An alternative Hypothesis
could be, “There is a significant positive relationship between job satisfaction and the retention
rates of Generation Z employees in the workplace.” I will be honest, I am having a hard time
identifying what question it is, as a part of me thinks it’s descriptive as measuring Gen Z as a
population group, but I am thinking it’s more associational research to review two variables and
impact of the relationship between the two.
D1.6. Using one or more of the following HSB variables, religion, mosaic pattern test, and
visualization score
(a.) Write an associational question.
Does the level of religious commitment correlate with moral judgement in young adults?
(b.) Write a difference question.
Is there a significant difference in the level of charitable giving between individuals who identify
as religious and those who do not?
(c.) Write a descriptive question.
What are the most common religious believes among college student in the United States?
References
Eden, D., Stone-Romero, E.F., & Rothstein, H.R. (2015). Synthesizing results of multiple
randomized experiments to establish causality in mediation testing. Human Resource
Management Review, 25 (4). Retrieved from https://dx.doi.org/10.1016/j.hrmr.2015.02.001
Kamper, S.J. (2020). Types of Research Questions: Descriptive, Predictive, or Causal. The
Journal of Orthopedic and Sports Physical Therapy, 50(8). Retrieved from
https://dx.doi.org/10.2519/jospt.2020.0703
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2019). IBM SPSS for
Introductory Statistics: Use and Interpretation (6th ed.). Routledge.
Shi, L., Westerhuis, J. A.,
Rosén, J., Landberg, R.,
Brunius, C., & Sveriges
lantbruksuniversitet.
(2019). Variable
selection and validation
in multivariate
modelling.
Bioinformatics
(Oxford, England), 35(6),
972-980.
https://doi.org/10.1093/bi
oinformatics/bty710
Shi, L., Westerhuis, J. A., Rosén, J., Landberg, R., & Brunius, C. (2019). Variable selection and
validation in multivariate modelling. Bioinformatics, 35(6). Retrieved from
https://doi.org/10.1093/bioinformatics/bty710
References
Lane, S. (2018). A good
study starts with a
clearly defined question:
Research question 1 of 2:
How to pose a good
research question. BJOG:
An International Journal
of Obstetrics and
