MODULE ONE: PREPARING FOR QUANTITATIVE ANALYSIS
D1.1.Compare the terms active independent variable and attribute independent variable. What
are the similarities and differences?
+ + +An independent variable is a variable that is manipulated or varied in an experiment to
explore its effects. There are many independent variables, but the two most prevalent forms are
attribute-independent and active-independent variables. While active-independent variables are
used to forecast values for an item, attribute-independent variables are used to characterize an
object (Morgan et al., 2020). An active-independent variable is a variable manipulated by a
researcher or caused to vary systematically to study its effect on another variable. For example, a
researcher might change anxiety levels to see if pain medication is more effective (Andrade,
2021). In addition, active-independent variables can predict an object's values. For instance, a
person's weight could be an active independent variable to predict their BMI based on the
correlation between them.
+ + +An attribute-independent variable is a participant's characteristic that cannot be manipulated
and relates to their environment. Examples include age, ethnicity, gender, and socioeconomic
status. Attribute-independent variables can be used to describe an object but cannot be used to
predict values for that object. For example, a person's height could be an attribute-independent
variable. While it can describe a person, it cannot predict their weight (Kaliyadan et al., 2019).
Morgan et al. 2020, wrote that an active-independent variable may have fixed values and behave
like an attribute-independent variable; depending on the study, the two independent variables can
either rely on or act independently of each other.
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 a cause, and when might causal inferences be more questionable?
+ + +To infer cause in a study, an active-independent variable is required. According to Morgan et
al. 2020, when an active independent variable is used in a randomized experimental or, to a
lesser extent, a quasi-experimental research design, it provides data from which it is possible to
infer the cause of the change or difference in the dependent variable. An active-independent
variable can be manipulated or changed by the researcher. Using the active-independent variable,
researchers can observe its effects on other variables in the study.
+ + +Suppose a researcher wants to investigate how exercise affects weight loss. In this case,
exercise is the active-independent variable because the researcher can control and manipulate it.
The researcher can assign participants to various exercise regimens and track their weight loss.
Using an active-independent variable, the researchers can determine a causal relationship
between exercise and weight loss. Comparing the outcomes of different groups with varying
levels of exercise can reveal consistent patterns or changes.
+ + Morgan et al. 2020 established that relying on attribute-independent variables to make causal
inferences resulting in changes to the dependent variable is unreliable. Rather, attribute-
independent variables can reveal disparities or correlations between two groups.
D1.3.What is the difference between the independent variable and the dependent variable?
+ + +Variables are sample characteristics that are examined, measured, described, and interpreted.
Variables are so called because they vary in value from subject to subject in the study. For
example, if we wish to examine the relationship between age and height in a sample of children,
age and height are the variables of interest; their values vary from child to child (Andrade, 2021).
+ + +Independent variables are those that affect other variables. For instance, age, sex, smoking,
LDL cholesterol, and blood pressure are independent variables as they can affect the risk of
myocardial infarction. Other variables influence dependent variables, and it measures the effect
of the independent variable. For example, the risk of myocardial infarction is higher in older
persons, men, current smokers, etc. (Andrade, 2021).
D1.4.Compare and contrast associational, difference, and descriptive types of research
questions.
+ + +Associational research questions investigate the relationship between variables. They
determine how changes in one variable affect another. The focus is on the degree and direction
of the relationship. For instance, "Is there a relationship between healthy eating and weight
loss?"
+ + +Difference research questions aim to identify differences between groups or conditions. They
compare two or more groups or conditions to understand how they differ. The objective is to
examine the effects of a particular factor on different groups. A difference type of research
question is suitable for randomized or quasi-experimental studies. For example, "Is there a
significant difference in blood pressure levels between individuals who exercise regularly and
those who do not?"
+ + +Descriptive research questions provide a comprehensive understanding of a phenomenon or
population without establishing relationships or differences. Descriptive research questions often
involve collecting data through surveys, observations, or existing records. For example, "What
are the demographic characteristics of smartphone users in a particular region?"
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?
+ + +Research question: How does regular exercise affect the mental health of adults aged 45 and
above?
+ + +Hypothesis: Regular exercise in adults aged 45 and above is positively associated with better
mental health.
+ + +This study aims to examine the correlation between frequent exercise and mental health. The
hypothesis suggests that regular exercise is linked to better mental health in the specified age
group.
D1.6.Using one or more of the following HSB variables: religion, mosaic pattern test, and
visualization score
D1.6.a.Write an associational question.
+ + +An example of an associational research question is "Is there any association between religion
and visual score?"
(b.)Write a difference question.
+ + +An example of a difference research question is, "Is there any difference between mosaic
pattern test scores among the Protestants."
(c.)Write a descriptive question.
+ + +An example of a descriptive research question is, "What is the average visualization score
among individuals of different religions?"
References
Andrade C. (2021). A student's guide to classifying and operationalizing Variables in the
Conceptualization and Design of a Clinical Study: Part 1.+Indian journal of psychological
medicine,+43(2), 177–179.+https://doi.org/10.1177/0253717621994334 Links to an
external site.
Morgan, G., Leech, N., Gloeckner, G., Barrett, K. (2020). IBM SPSS for introductory statistics
(6th Ed.). Routledge.
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample
size.+Indian Dermatology Online Journal,+10(1), 82–
86. https://doi.org/10.4103/idoj.IDOJ_468_18
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