Mediation and Moderation Analyses and their AdvantagesIn both mo

Mediation and Moderation Analyses and their Advantages
In both moderation and mediation analyses, the researcher is
seeking to better understand the relationship between an
independent and dependent variable. Moderation can determine
whether a third variable influences the strength of the
relationship between an independent and dependent variable
(Warner, 2013). The moderator variable might change the
strength of the relationship from high to low. An example
within the criminal justice field is if you expected that the
number of crimes committed related to the number of convicted
individuals sent to prison. However, that relationship may not
always be true, and a variable such as type of crime may be a
moderator. As a result, this analysis is advantage ous in the
criminal justice field because a major issue within criminology
is studying how to control or prevent crime. If a researcher can
determine which crimes are causal or correlational, then that
will be a significant step for leaders who are trying to create
policies which address crime.
In addition, another advantage is seeking to understand which
crime problems persist in certain areas over long periods of
time. This is accomplished in mediation analysis by using
mediators as intervening variables that determine cause and
effect between more than two other variables; within criminal
justice, mediation can assist with the context of prevention and
intervention studies, which can include conducting an analysis
of the data to understand how or why an effect occurred
(Fairchild & McDaniel, 2017). This can assist in determining if
social issues may need to be addressed or if it is another matter.
Then, police organizations can decide whether crimes are
isolated or preventable. Therefore, mediation techniq ues
provide a cautious way to gauge legacy effects where problems
persist over generations (Pais, 2017).
Mediation analysis contains one mediator which facilitates the
relationship between the independent and dependent variables.

Mediation involves a set of causal hypotheses, wherein an
initial causal variable may influence an outcome variable
through a mediating variable (Warner, 2013). This mediator will
help explain why the relationship between two things exists. A
mediator variable should have some effect which causes an
independent variable to lead to a change in outcome of the
dependent variable. Therefore, by using a mediator variable, a
researcher can determine if the influence of the mediator is
stronger than the direct influence of the independent variable.
This is advantageous because it can be useful when trying to
consider if one issue is significantly related to a criminal justice
issue or if the mediator is more heavily responsible.
The biggest advantage to using this analysis, particularly within
criminal justice data, is that it can determine what factors are
causing or correlating with crime issues. Thus, if a police
organization can mitigate certain issues, they can lower the
impact of crime on a community. It is important for criminal
justice organizations to connect information with participative
strategies to assist in the successful implementation of change
(Pick & Teo, 2017). In addition, moderation and mediation
analyses can help improve the wellness of police officers.
Organizations can determine which initiatives cause changes to
job-related stressors that cause officer happiness (Pick & Teo,
2017)
How Poor Design and Assumptions Affect Analysis
The use of these two analyses is affected by how the researcher
approaches the question. There needs to be a causal or
correlational factor between two variables. Therefore, if
designed in any other way, it will be a flawed analysis strategy.
For example, if the first variable is to be hypothesized as a
cause of the second variable, the first should occur before the
second, and there should be a plausible mechanism through
which the first could influence the second (Warner, 2013). Also,
there are six assumptions which should be met to make
plausible causal or correlational inferences: directional ity,
reliability, unconfoundedness, distinctiveness, power, and

meditation (Pieters, 2017). If the researcher meets these
conditions, then it will strengthen the link between the mediator
and the second outcome variable.
Importance of Theoretical Framework
The theoretical framework represents the conceptual foundation
of the study, and a lack of this framework will affect how the
analysis is applied. Thus, the research design should include a
clear set of logically derived hypotheses and assumptions that
can be tested in relation to the research problem. If this does
not occur, then the entire analysis will be faulty since the
framework is illogical. Because a causal model includes the
hypothesis that one variable causes or influences a second and
the hypothesis that a separate variable causes or influences a
third variable, it does not make sense to consider these analyses
in situations where one or both hypotheses would not fit the
framework (Warner, 2013).
Biblical Worldview
In a Biblical perspective, causal and correlational analysis are
important because it helps mankind understand reasoning
behind certain issues. A researcher can use the gifts given by
God to solve some of the problems facing mankind on this
planet. “Be transformed by the renewing of your mind. Then
you will be able to test and prove what God’s will is” (New
International Version Holy Bible, 1973/2011, Romans 12:2).
Thus, it is important for Christians to seek the steps within
mediation and moderation analysis and perform them to
determine the factors influencing certain variables. Studies must
not believe anything just because it is there, since the prudent
researcher will give thought to their steps and be rewarded with
knowledge (New International Version Holy Bible, 1973/2011,
Proverbs 14:15-18).
References
Fairchild, A. J., & McDaniel, H. L. (2017). Best (but oft-
forgotten) practices: mediation analysis. The American Journal
of Clinical Nutrition, 105(6), 1259-1271.

New International Version Bible. (2011). Zondervan. (Original
work published 1973).
Pais, J. (2017). Intergenerational neighborhood attainment and
the legacy of racial residential segregation: A causal mediation
analysis. Demography, 54(4), 1221-1250.
Pieters, R. (2017). Meaningful mediation analysis: Plausible
causal inference and informative communication. Journal of
Consumer Research, 44(3), 692-716.
Pick, D. & Teo, S.T.T (2017). Job satisfaction of public sector
middle managers in the process of NPM change. Public
Management Review, 19(5), 705-724.
Warner, R.M. (2013). Applied Statistics from Bivariate Through
Multivariate Techniques (2nd Edition). Sage Publications.
Mediation and Moderation Analysis
“Be not deceived: evil communications corrupt good manners”
(King James Bible, 1769/1989, 1 Corinthians 15:33). The Bible
often speaks about the influences that people can have over
each other. In this particular verse, it is explained that an
outside influence can greatly inspire the effect that one person
has on another. It is this effect that is seen in medication and
moderation analysis, when an outside influence can greatly
affect how one variable affects another. It is up to people to
ensure that this influence is positive.
Mediation analysis, or process analysis, is used to study how an
antecedent variable effects a consequent variable both directly
and through a third variable (Hayes, n.d.). Similarly,
moderation analysis is used to study how an antecedent variable
effects a consequent variable as a consequence of a third
variable (Hayes, n.d.). These two methods are so similar that
they are often combined to form a single analysis technique
(Hayes, n.d.). This form of analysis is useful for many reasons,
however, one of the most important is that it allows for real
world effects on questions asked about problems that are easily
influenced by a third variable that is not normally accounted

for. In other words, since some researchers do not feel satisfied
with how and when an effect exists, they are now examining the
contingencies to this process (Hayes & Rockwood, 2020).
Jeremy Pais (2017) utilized moderation analysis to determine
the effects that first generation neighborhood status and second-
generation residential segregation has on both first-generation
residential segregation and second-generation neighborhood
status. He found that the effect of parental exposure to
segregation is largely mediated by the environment that the
child was raised in (Pais, 2017). The effectiveness of
moderation analysis for this particular problem was driven by
real world thinking. Likewise, David Pick and Stephen Teo
(2017) utilized moderation analysis to determine that is
psychological wellbeing had an effect on whether stressors that
result from change had a direct effect on job satisfaction of
public sector middle management. Since the purpose of the
study was to determine how organizational change effects
managers in a realistic setting with real situations, it seems that
a moderation analysis was the best choice for this type of
problem.
Inadequate design can affect the use of mediation and
moderation analysis. An example of this can be researchers
assuming that a large sample size for one variable is adequate
even if the same size is not represented across all variables
(Aguinis et al., 2017). This is not the case, however, as this can
lead to underestimating the effects of one or more of the
variables. A flawed analysis strategy can seriously affect the
use of mediation and moderation analysis as well. An example
of this can be a researcher categorizing variables into subgroups
(Aguinis et al., 2017). This can often lead to information loss
and reduced statistical power to detect the effects of moderation
(Aguinis et al., 2017). Another example can be found in a study
completed by Lejla Turulja and Nijaz Bajgoric (2020). Since
mediation analysis can be carried out either by the structural
equation modelling method or a regression analysis, they
examined the effect that each would have on the analysis to

determine if there would be a difference (Turulja & Bajgoric,
2020). Although they found no significant difference, there
were empirical limitations noted about multiple regression
(Turulja & Bajgoric, 2020). This could lead to an error in the
results of a mediation analysis. A lack of attention to
assumptions can affect the use of mediation and moderation
analysis as well. For example, a lack of attention provided to
measurement error can lead to decreased reliability and
dismissing moderating effects that could seem improbable in
flawed analyses (Aguinis et al., 2017). Another example can be
found in researchers assuming that only a single variable can
mediate the effect of an antecedent variable on a consequent
variable (Hayes & Rockwood, 20017). This is not the case since
there can be more than one mediating variable.
Researchers can often lack in areas that are important to their
conclusions, such as incorrectly assuming that the theoretical
framework that they have chosen to represent their variables in
a study is sound. Another example of this can be found in the
fact that moderation analysis assumes that the distributions that
are being tested represent the full range of possibilities,
however, this is not always so (Aguinis et al., 2017).
Restrictions in range can adversely affect the accuracy of the
conclusions reached in the analysis by an underrepresentation of
real-world effects (Aguinis et al., 2017). Another example can
be found in researchers incorrectly assuming that, in a
mediation analysis, if an indirect effect does not exist, then a
direct effect cannot exist as well (Hayes & Rockwood, 2017).
With the lack of an effect of a mediating variable, there can still
be an effect of an antecedent variable on a consequent variable
(Hayes & Rockwood, 2017).
References
Aguinis, H., Edwards, J. & Bradley, K. (October 2017).
Improving Our Understanding of Moderation and Mediation in

Strategic Management Research. Organizational Research
Methods, 20(4), 665-685. DOI: 10.1177/1094428115627498.
Hayes, A. (n.d.). On the Moderation of Mechanisms: A
Conceptual Overview of Conditional Process
Analysis. [PowerPoint
Slides]. https://casaa.unm.edu/download/MOBC2014/Hayes.pdf.
Hayes, A. & Rockwood, N. (January 2020). Conditional Process
Analysis: Concepts, Computation, and Advances in the
Modeling of the Contingencies of Mechanisms. American
Behavioral Scientists, 64(1), 19-54. DOI:
10.177/0002764219859633.
Hayes, A. & Rockwood, N. (November 2017). Regression-Based
Statistical Mediation and Moderation Analysis in Clinical
Research: Observations, Recommendations, and
Implementation. Behaviour Research and Therapy, 98, 39-57.
DOI: http://dx.doi.org/10.1016/j.brat.2016.11.001.
King James Bible. (1989). Thomas Nelson Publishers. (Original
Work Published 1769).
Pais, J. (July 2017). Intergenerational Neighborhood Attainment
and the Legacy of Racial Residential Segregation: A Causal
Mediation Analysis. Demography, 54, 1221-1250. DOI:
10.1007/s13524-017-0597-8.
Pick, D. & Teo, S. (July 2016). Job Satisfaction of Public
Sector Middle Managers in the Process of NPM Change. Public
Management Review, 19(5), 705-724. DOI:
10.1080/14719037.2016.1203012.
Turulja, L. & Bajgoric, N. (January 2020). Comparison of
Structural Equation Modelling and Multiple Regression
Techniques for Moderation and Mediation Effect
Analysis. Sarajevo Business and Economics Review, 38, 29-50.
Ryan Eagleson
Mediation and Moderation
COLLAPSE
Mediation and moderation analyses are statistically utilized

when the researcher wants to acquire a more satisfying
understanding of the relationship between an independent and
dependent variable. Mediation and moderation, although
similarly named and used, actually have different functions.
Mediation analysis involves a third variable, which is generally
called the mediator variable. The mediator variable is first
affected by the independent variable, which then affects the
dependent variable. Mediation analysis is used to determine if
the combined effects of the independent and mediator variables
are greater than the independent variable alone (Hayes, 2013).
Pais (2017) demonstrates the concept of mediation analysis in
his study of legacy effects of racial segregation in
neighborhoods across the United States. He suggests that
neighborhood effects on the first generation of black Americans
will impact second-generation black Americans (Pais, 2017).
On the other hand, moderation analysis involves a third
variable, referred to as the moderation variable. Unlike
mediation, the moderation variable acts as a relationship
influence over the independent and dependent variables. For
example, athletes who train to run more are more likely to be
faster. As it stands, this concept makes sense and could be
statistically analyzed. The dependent variable (speed) is
impacted by the independent variable (frequency). Moderation
comes into action when a researcher changes their sample
population (moderator variable) from professional athletes to
grade school athletes (Hayes, 2013). Both mediation and
moderation statistical analysis methods have similar strengths.
They can be used to help determine if indirect relationships
have a statistically significant impact on the result of the
dependent variable (Pick & Teo, 2016). Furthermore, these
analyses can be used to determine the overall strength of the
relationship between the independent and dependent variables
by dialing the moderator variable or altering the mediator
variable (Agler & De Boeck, 2017).

Moderation and mediation analysis, although highly
advanced, remains within the stringent parameters of
fundamental research design. In other words, these analyses are
not immune to a poor research design, flawed analysis strategy,
and lack of attention to statistical assumptions. Mothapo et al.
(2020) discuss the fundamentals of bad science, specifically in
regard to population categories that are not mutually exclusive.
Data need to be clearly defined and accurately measured for the
mediation and moderation analyses to work properly. This will
help determine if there are outside variables that have an
influence on the relationship between the independent and
dependent variables. For example, populations that are only
allowed to be defined in certain manners that are too controlled,
followed by a category of “other,” are more likely to provide
results that do not meet rigorous moderation and mediation
analysis standards. Clearly outlining the population,
independent variable, and dependent variables are absolutely
required for a successful mediation or moderation analysis.
Lastly, failing to follow a theoretical framework when using
moderation and mediation analyses is similar to building a
house without a scale blueprint. To determine what factors may
impact dependent variables, the literature must be consulted to
determine previously found data trends. Otherwise, the
researcher may not know where to begin appropriately (Heale &
Noble, 2019). It is also important for the researcher to develop a
keen understanding of information within their research topic to
know specifically what needs to be studied. Along with the
concept of understanding information about the research topic, a
researcher needs to look at how each applicable theory has
conceptualized and operationalized its variables and hypotheses
(Meier, 2015). Otherwise, others may scrutinize their research
more easily, and it may not fit within the accepted literature
base.
It is clear that for moderation and mediation statistical

analyses to be run correctly, the research design must fit well,
assumptions are appropriately accounted for, and the theoretical
framework is well researched and applied. As long as all
research components are properly included, the likelihood of
publishing flawed, inaccurate, or simply untruthful information
greatly diminishes. The Holy Bible supports the concept of
being thorough and truthful in the book of 2 Timothy, “Study to
shew thyself approved unto God, a workman that needeth not to
be ashamed, rightly dividing the word of truth” (King James
Bible, 1769/2021, 2 Timothy 2:15). In this verse, Paul is telling
Timothy to ensure he studies hard and to disregard the false
teachings of other teachers. Timothy was challenged to work
hard to please God through his work and not other people;
however, Timothy would need to work hard to accomplish this.
Hard work, in the eyes of God and Paul, is a cornerstone of
success. Moderation and mediation analyses are among the most
poignant and advanced statistical techniques used to date. Like
Timothy, researchers need to work hard to ensure the accuracy
of their work to publish the truth as God desires ultimately.
References
Agler, R., & De Boeck, P. (2017). On the interpretation and use
of mediation: Multiple perspectives on mediation analysis.
Frontiers in Psychology, 8.
https://doi.org/10.3389/fpsyg.2017.01984
Hayes, A. F. (2013). Introduction to mediation, moderation, and
conditional process analysis. New York: The Guilford Press.
Heale, R., & Noble, H. (2019). Integration of a theoretical
framework into your research study. Evidence-Based Nursing,
22(2), 36–37. https://doi.org/10.1136/ebnurs-2019-103077
King James Bible. (2021). King James Bible Online.

https://www.kingjamesbibleonline.org/ (Original Work
Published 1769)
Meier, K. J., Brudney, J. L., & Bohte, J. (2015). Applied
Statistics for public and nonprofit administration (9th ed.).
Cengage Learning.
Mothapo, P. N., Phiri, E. E., Maduna, T. L., Malgas, R.,
Richards, R., Sylvester, T. T., Nsikani, M., Boonzaaier-Davids,
M. K., & Moshobane, M. C. (2020). We object to bad science:
Poor research practices should be discouraged! South African
Journal of Science, 116.
https://doi.org/10.17159/sajs.2020/8592
Pais, J. (2017). Intergenerational neighborhood attainment and
the legacy of racial residential segregation: A causal mediation
analysis. Demography, 54(4), 1221–1250.
https://doi.org/10.1007/s13524-017-0597-8
Pick, D., & Teo, S. T. (2016). Job satisfaction of public sector
middle managers in the process of npm change. Public
Management Review, 19(5), 705–724.
https://doi.org/10.1080/14719037.2016.1203012
CJUS 745
Discussion Grading Rubric
Criteria
Levels of Achievement
Content
(70%)
Advanced
92-100%
Proficient

84-91%
Developing
1-83%
Not present
Total
Thread: Content
14 to 15 points:
· Each question/prompt is answered thoroughly and logically.
· Major points are stated clearly and effectively.
· Clear, logical flow to post; stayed on topic.
12.75 to 13.75 points:
· Each question/prompt is answered.
· Major points are stated clearly and effectively for the most
part.
· Clear, logical and focused for the most part.
1 to 12.5 points:
· Not all facets of the prompt/questions are answered.
· Lack of clarity, coherence, logic and focus in key areas.
0 points
Not present
Thread: Research Engagement
11.5 to 12.5 points:
· Ideas from all the required reading and presentations from the
Module/Week and 2 scholarly sources are integrated.
· Relates topic to Scripture/biblical principles where
appropriate.
10.5 to 11.25 points:
· Ideas from most the required reading and presentations from
the Module/Week and 2 scholarly sources are integrated.
· Scripture/biblical principles are included but unfocused at
times.
1 to 10.25 points:
· Ideas from few of the required reading and presentations from

the Module/Week and 2 scholarly sources are integrated.
· Missing Biblical integration.
0 points
Not present
Reply: Content
14 to 15 points:
· At least 3 unique interaction posts with classmates
· One reply posted to each of 3 classmates’ threads.
· Moves the conversation forward with new ideas, research, and
analysis.
· Student’s response delivered in a thorough, thoughtful, and
analytical manner with the student’s position clearly evident.
12.75 to 13.75 points:
· At least 3 unique interaction posts with classmates
· One reply posted to each of 3 classmates’ threads.
· New ideas, research, and analysis are not always included.
· Some commentary is repetitive from one reply to the next.
· At times, reply posts do not seem to actually build upon
classmate’s post.
1 to 12.5 points:
· Missing one or more reply posts.
· Reply posts are redundant.
· Little in the way of new ideas, research, and analysis are not
always included.
0 points
Not present
Reply: Research Engagement
9.25 to 10 points:
· Relates topic to Scripture/biblical principles where
appropriate.
· Contains abundant citations from required reading,
presentations, and 2 scholarly sources.

8.5 to 9 points:
· Scripture/biblical principles are included but unfocused at
times.
· Contains some citations from reading, presentations, and
scholarly sources.
1 to 8.25 points:
· Missing Biblical integration.
· Limited citations from the required reading and presentations.
0 points
Not present
Structure (30%)
Advanced
92-100%
Proficient
84-91%
Developing
1-83%
Not present
Total
Grammar, Spelling & APA
14 to 15 points:
Minimal to no errors in grammar, spelling, or APA.
12.75 to 13.75 points:
Some errors in grammar, spelling, or APA.
1 to 12.5 points:
Numerous errors in grammar, spelling, or APA.
0 points
Not present
Word Count
7 to 7.5 points:
Appropriate word count:
750-800 words for thread; 200–250 words per reply.
6.25 to 6.75 points:
100 words more or less than the required length.

1 to 6 points:
Over 100 words more or less than the required length.
0 points
Not present
Instructor’s Comments:
Total:
/75
Page 1 of 2
CJUS 745
Discussion Instructions
You will take part in 3 Discussions in which you will post a
thread presenting your scholarly response on the assigned topic,
writing 750–850 words (supported with at least four cites) by
Thursday at 11:59pm. Then, you will post replies of 250–300
words (supported with at least two cites) each to 3 or more
classmates’ threads by Sunday at 11:59pm. The discussion in
the final week of the class’ due dates are by Wednesday at
11:59pm and the replies are due by Friday at 11:59pm. For each
thread, students must support their assertions with at least four
(4) scholarly citations in APA format. Each reply must
incorporate at least two (2) scholarly citation(s) in APA format.
Any sources cited must have been published within the last five
years. The original thread must incorporate ideas and several
scholarly citations from all of the required readings and
presentations for that module/week. The reply posts can
integrate ideas and citations from the required readings and

presentations for other modules/weeks. Integrate Biblical
principles in your personal thread and in all replies to peers.
Question
· One of the most advanced quantitative methods that can be
applied to criminal justice data is mediation and moderation
analysis. After reading the two articles by Pick and Teo (2017)
and Pais (2017), as well as the articles by Hayes and others,
what are the advantages of applying this analysis?
· How do an inadequate design, a flawed analysis strategy, and
lack of attention to assumptions affect the use of mediation and
moderation analysis?
· How does the researcher’s lack of theoretical framework
concerning variables affect the application of mediation and
moderation analysis?
Required materials
two articles by Pick and Teo (2017)
Pais (2017), as well as the articles by Hayes and others, what
are the advantages of applying this analysis?

Mediation and Moderation Analyses and their AdvantagesIn both mo

Recommended

Recommended

More Related Content

Similar to Mediation and Moderation Analyses and their AdvantagesIn both mo

Similar to Mediation and Moderation Analyses and their AdvantagesIn both mo (19)

More from AbramMartino96

More from AbramMartino96 (20)

Recently uploaded

Recently uploaded (20)

Mediation and Moderation Analyses and their AdvantagesIn both mo