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Variables, Research Questions and Data Coding
Sara Marie Brenner
School of Business, Liberty University
BUSI 820
Fall 2025, Subterm D
October 26, 2025
Dr. David Danley
Author Note
Sara Marie Brenner is a doctoral student at Liberty University in the School of Business.
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Sara Marie Brenner.
Email: [email protected]
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Variables, Research Questions and Data Coding
Quantitative research provides a systematic approach for examining relationships,
differences, and patterns within data to inform evidence-based decision-making. In the field of
business and real estate, understanding how to identify and classify variables forms the
foundation of sound research design. By distinguishing between types of variables, formulating
clear research questions, and applying appropriate analytical methods, researchers can generate
valid, actionable insights. This discussion explores these concepts using the High School and
Beyond (HSB) dataset as a framework for application.
D1.1: Active and Attribute Independent Variables
An active independent variable is intentionally manipulated or assigned by the researcher
to observe its effect on a dependent variable (Morgan et al., 2020). Examples include applying a
marketing strategy or adjusting pricing levels in a real estate market experiment. An attribute
independent variable represents a characteristic inherent to participants or data that cannot be
manipulated, such as property location or investor experience (Morgan et al., 2020). Both serve
as independent variables because each predicts change in the dependent variable, yet they differ
in researcher control. Active variables enable experimental manipulation, while attribute
variables require observational or correlational methods due to the lack of control (Morgan et al.,
2020).
D1.2: Inferring Causation from Independent Variables
Causal inference most reliably arises when the independent variable is active and
participants are randomly assigned to conditions, ensuring that any observed effect results from
the manipulation rather than external influences (Stuart, 2010). However, even with
randomization, causation cannot always be assumed. Real estate markets, for instance, are
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influenced by macroeconomic factors that cannot be fully controlled. Causal inference is valid
only when temporal order, covariation, and elimination of alternative explanations are met
(Morgan et al., 2020). When these standards are unmet, as in natural experiments or
observational studies, researchers should interpret findings as correlational, not causal (Stuart,
2010).
D1.3: Independent and Dependent Variables
The independent variable (IV) is the predictor or presumed cause that influences another
variable, while the dependent variable (DV) is the outcome or observed effect (Morgan et al.,
2020). In a real estate context, the IV might be mortgage interest rate changes, and the DV could
be housing demand. The IV provides the input or condition tested, and the DV captures the
measurable response. Clearly identifying each variable ensures appropriate statistical testing and
strengthens internal validity.
D1.4: Associational, Difference, and Descriptive Research Questions
An associational research question explores relationships between variables without
implying causation, such as how property value relates to neighborhood amenities (Morgan et
al., 2020). A difference research question compares two or more groups on a particular outcome;
for example, whether commercial and residential properties differ in average return on
investment (Morgan et al., 2020). A descriptive research question focuses on summarizing
characteristics or trends, such as identifying the average square-foot cost of real estate in a
specific region (Kamper, 2020; Morgan et al., 2020). Associational and difference questions
often guide inferential statistics, whereas descriptive questions rely on summary measures.
D1.5: Personal Research Question
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Research Question: What is the relationship between the level of digital marketing
investment and property sales performance among real estate agencies?
Hypothesis: Agencies that allocate higher budgets to digital marketing will demonstrate
higher levels of property sales performance compared to agencies that invest less in digital
marketing.
This is an associational research question because it examines the relationship between
two continuous variables, digital marketing investment and sales performance, without implying
a direct causal link (Morgan et al., 2020).
D1.6: HSB-Based Research Questions
Associational Question
What is the relationship between mosaic pattern test scores and visualization scores
among students?
This question is associational because it examines whether two continuous variables,
mosaic pattern test scores and visualization scores, are related without suggesting that one
variable causes changes in the other. Associational questions are used to determine the strength
and direction of relationships between variables (Morgan et al., 2020; Kamper, 2020).
Difference Question
Do students’ visualization scores differ based on their reported religious affiliation?
This question is a difference question because it compares mean visualization scores
across groups defined by a categorical variable, religious affiliation. Difference questions test
whether statistically significant variations exist between groups on a specific dependent variable
(Morgan et al., 2020).
Descriptive Question
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What is the mean visualization score among all students in the HSB dataset?
This question is descriptive because it seeks to summarize a single variable, the
visualization score, by reporting measures such as the mean or standard deviation, without
assessing relationships or group differences. Descriptive questions focus on presenting or
characterizing data as they exist (Morgan et al., 2020).
Conclusion
A comprehensive understanding of variables and research question types is essential for
producing rigorous quantitative research. By distinguishing between associational, difference,
and descriptive questions, researchers can align their study designs and statistical methods
appropriately. Accurate variable identification also strengthens the validity and interpretability of
results. When these foundational principles are applied effectively, they lead to more credible,
data-driven insights that can inform practical decision-making in business and real estate
contexts.
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References
Kamper, S. J. (2020). Types of research questions: Descriptive, predictive, or causal. Journal of
Orthopaedic & Sports Physical Therapy, 50(8), 468–469.
https://pubmed.ncbi.nlm.nih.gov/32736498/
Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2020). IBM SPSS for
introductory statistics: Use and interpretation (5th ed.). Routledge.
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward.
Statistical Science, 25(1), 1–21. https://doi.org/10.1214/09-STS313
