SM Nonprofit Ad Campaign Term Project InstructionsOverview.docx

SM Nonprofit Ad Campaign Term Project Instructions
Overview
Marketing can inspire change, generate donations, and inform
the public. However, nonprofits often lack the marketing
prowess needed to reach consumers. You are tasked to create a
marketing ad and social media campaign for a nonprofit you
choose. Visit the nonprofit website to understand their purpose
and align the ad campaign you create.
Choose one of the following charities on the website to build a
social media ad campaign.
https://charity.lovetoknow.com/Top_100_Charities
The below items are to be turned in on Blackboard in a Word
document. Each student is responsible for submitting their own
unique work. Check SafeAssign at submission for plagiarism;
unintentional plagiarism is still plagiarism.
Ad Content
1) Ad/image that can standalone (if posted on a social media
platform in smartphone or desktop view). Ad should be
unambiguously clear, easy to read within moments, and eye-
catching. Content needs to be accurate, informative, and
convincing to change behavior.
Include the name of the charity and method to connect with
existing platforms.
Written responses describing the ad campaign
Written work: 2-3 pages, 1-inch margins 12-point Times New

Roman/Arial font, double spaced. Be specific with details, use
examples, and thoroughly explain your reasoning. The written
work should articulate your knowledge about social media
strategy, marketing mix, research measures, and other materials
covered throughout this course. Demonstrate your
understanding of course materials by writing about these topics.
2) Describe what is the content in the ad (e.g. Call to Action
(CTA) hashtag, information guide, change behavior, draw clicks
to website)? Be specific. Restating these examples is
insufficient.
3) What is the name of the ad campaign? What is this social
media ad campaign trying to do? Thoroughly explain your
reasoning and be specific.
4) What is the method for releasing on social media (e.g.
platform, timing, target market, length of campaign)?
Elaborating and be specific with details.
5) What are the measures for success (e.g. how many
likes/comments/shares)? State specific goals and figures that
would indicate if the campaign was successful.
Sample Ads
Note: These are basic samples. The quality of work and
attention to details should be greater. Expectations in the
workplace are higher for employees creating marketing
materials because businesses depend on generating revenue
from these kinds of ads. In fact, these sample ads draw website
visits. Notice what works and doesn’t work from the samples.
https://www.studentdigz.co.za/sharing-is-caring/

Graphic CPR ad example with moving pictures:
https://carrington.edu/blog/medical/how-to-perform-cpr/
https://www.cprcertified.com/how-to-perform-hands-only-cpr-
infographic
https://www.dreamstime.com/stock-illustration-benefits-
drinking-water-infographic-vector-illustration-image67570223
http://ankhrahhq.blogspot.com/2015/06/vegetable-benefits-you-
should-know.html
MORE
EXPIREMENTAL
DESIGNS
Within Subjects and Factorial Research Designs
https://my.visme.co/render/1454648890/www.erau.edu
Slide 1 Transcript
Within-subjects design differs from between-subjects design in
that the same participants perform at all levels of the

independent variable. These sometimes are called repeated
measures designs. In a good within-subjects design, the various
interventions are introduced very close together in time, in
some cases simultaneously. For the within-subjects
design to work, the interventions must be introduced so the
effects are fairly localized and unlikely to spread beyond
specifically targeted behaviors. The interventions should be
introduced in a balanced but somewhat random order. Research
covered in the between-subjects and within subject’s designs
has focused on studies with just one independent
variable. When two or more independent variables need to be
examined, the approach is known as a factorial design. There
are several types, and they can be combined with
other research designs. For purposes of this course, only a few
forms will be covered, since there are many more complicated
versions possible.
Controls
Between-subjects designs
Have different participants in each
measure
Within-subjects design
used to
Manage sample size
(smaller N needed)
Observe over time
Within-subjects designs
Have the same participants in all
measures

Randomization
compensated for by
Time-related factors
(risk of intervening variables)
Order effects (sequence of
exposure to levels of IV)
Usually limited to one
independent variable
Slide 3 Transcript
In the between-subjects designs that we covered earlier, a
different set of participants was observed in each of the groups.
So that we might measure the same participants within the
groups,
the within-subjects design is used. So, the same participants
now can be measured for each of the levels created for the
independent variable. Yes, this design also is usually limited to
just
one experimental factor. The within-subjects design would be
chosen to manage the sample size and to observe changes in
something over time. As for the sample size, since we are
measuring the same participants in each of the levels, only one
group is needed, which reduces the number from what would be
required if we had to find different participants for each of the
levels. The second issue, measures of the same participant over
time, becomes possible in the within-subjects design. However,
it challenges the requirement for randomization needed to
qualify as an experiment. To compensate for this, researchers
control for time-related factors and for order effects to
strengthen internal validity.

Time-related factors are introduced into a study because the
same participants are involved with several levels of the
variable over a period of time. Consequently, confounding
variables like
maturation, testing effects, regression toward the mean,
attrition, and fatigue, to name a few, threaten the study’s
internal validity. These effects must be the same between the
groups, or,
controlled. So, to control these time-related factors, two
strategies are available and will be covered in the next slide.
Counterbalancing and Timing
Order effects are the
sequence in which the IV
is presented
Timing controlsCounterbalancing
Interval of time between IV
applications
Managing order effects
increases internal validity
Ideally, extend intervals and
minimize overall time
Alternating the order of IV
presentation for groups
Partial counterbalancing
Slide 5 Transcript
Time-related factors can be controlled by addressing order

effects which are what they sound like, the order in which
variations of the independent variable are presented to
participants. Typically, all the participants in one group will
receive the interventions at the same time in the same sequence.
Another group may receive them in a different, even
opposite, order.
This is called counterbalancing. For studies with a large number
of groups, more than three, for instance, it is not possible to
symmetrically counterbalance all the levels, so, the
number and general sequence of interventions is designed to be
as representative of other groups as is possible. This variation
is called partial counterbalancing. A second way to
control for threats to internal validity is to control timing of the
treatment applications. A couple of approaches work well.
First, the interval of time between administrations of the
independent variable can be adjusted to minimize undesired
effects by shortening the intervals, or to maximize the interval
to offset undesired effects like heightened adrenalin.
Second, the researcher can manage the overall elapsed time for
the experiment by minimizing the total duration. Ideally, to
control for threats to internal validity, the researcher
would extend the intervals and minimize the total time for the
experiment.
Sources for Variability
Review
Between-groups and
within-groups are SS
and MS deviations
Between-subjects
variation is from
individual differences

Within-subjects
variation is from
participant change over
time
Within-subjects
research
Multiple measures,
same subjects
Between groups
variability
reflected in
mean differences
Determine effects are
from IV, not
individual/group
differences
Error
Assumes removal of
individual differences
Any other difference is
attributed to influence of
IV
Within-group variability
remains
Slide 7 Transcript
Let’s take a deep breathe here and review something. It is
important to keep the distinctions clear regarding what between-

groups and between-subjects is about. This also applies to
within-groups and within-subjects designs. The terms between
or within-groups refers to the sum of squares and mean squares
calculations to define deviations, something we find in
an ANOVA, for example. The whole purpose of an ANOVA is
to compare the ratio of between group variance to within group
variance. However, in the last two modules we have been
covering between and within subjects designs. Between-subjects
variation is from individual differences among participants.
And within-subjects variation is a measure of how much
an individual in the sample tends to change over time. In other
words, it is the mean of the change for the average individual in
your sample. Within-subjects research makes multiple
measurements on the same subjects while they serve as their
own controls. When making these multiple measures, the
researcher wants to be sure any effects are from the intervention
and not attributable to individual differences among the
participants or differences between groups. How participants
differ within each group is within-groups variability. There is
another source of variability and that is error. Error is what is
left over when the individual differences attributed to between
group differences are removed, and the within-groups
variability for the within-subjects experiment is all that
remains.
Sampling for Within-Subjects Designs
Two sampling methodologies
typically used
Select two groups from same population,
observe separately
Match pairs between groups on common
measure

Multiple samples (3+) use only
repeated measures each group
One-way Within-Subjects ANOVA as statistic
Post-hoc tests of all possible pairings
Solomon Four-Group Design
12 separate analyses
GGrroouupp PPrreetteestst TTrreeaattmmeenntt PPooststtteestst
Group A
Group B
Group C
Group D
The experimental group is Group A, and the three control
groups are included to account
confounds or estraneous factors that could be causing
differences pre- to posttreatment.
Used with permission by G. Privitera and Sage Publications
(copyright 2017).
for possiblee
Slide 9 Transcript
There are two predominant ways to sample for participants in a
within-subjects design. With two groups being selected from the
same population the participants are observed in each

group separately. The other way is to match pairs based on a
measure common to the dependent variable. This limits the
comparison to something under the control of the researcher,
and not occurring naturally So, the researcher matches a
participant in Group A with a participant from Group B, then all
the matched participants within a group can be evaluated using
a paired-samples t-test. So far, we have been using two groups
from the population. An experiment studying three or more
groups from the same population obviously cannot use the
matching technique, but the repeated measures design is fine to
use. The same controls and provisions for an experiment are
applied. The one-way within-subjects analysis of variance
statistic is appropriate here and measures the variance of
differences between groups divided by the variance of
differences due to error or individual differences. With the
result, we can
tell if there is a difference somewhere among the sample
groups. This would be followed by post-hoc tests of all the
possible pairings to see which showed notable differences. It is
worth
mentioning one more design that uses both the between subjects
and within-subjects analysis in the same study. It is called the
Solomon Four-group design and meets the requirements
for an experiment, so cause and effect can be determined. The
chart below shows the different combinations to isolate effects
from the independent variable. As you can see, there are 12
possible combinations to analyze.
Types of Factorial Designs
Multiple independent factors (variables) can be measured
Investigate effects of one variable on another independently

Allows cross testing various combinations of factor levels
Studies effects of multiple factors upon a dependent variable
Types of designs
Between subjects – separate group for each condition
Within subjects – single group for all conditions
Mixed – combines between and within subjects factors
May combine experimental factors with quasi-experimental
Notation system identifies number of factors and levels
Slide 11 Transcript
The factorial analysis was introduced earlier as a methodology
used in quasi-experimental research designs where the single
independent variable was pre-existing and therefore not able to
be controlled by the researcher. Factorial designs are
elaborations of single-variable experimental designs typically
performed for complex issues. At times, multiple independent
variables,
also called factors, must be tested with the same participants.
This allows the researcher to investigate questions about
whether the effect of one independent variable depends on a
level of
another and is referred to as an interaction between or among
the independent variables. The factorial analysis allows for each
variable to be tested independent of other variables. It also
allows a combination of variables to be tested as all levels of
one variable are crossed with all levels of the other variables.
Now, an advantage of the multiple independent variables
approach
is that the researcher can intentionally examine influences of

several variables upon the target, or dependent variable. If just
one independent variable is being investigated, an ANOVA
would be appropriate. When multiple independent variables are
involved, the factorial analysis is best. The purpose of a
factorial design is to determine whether the effects of an
independent variable are generalizable across all levels or
whether the effects are specific to particular levels.
There are three types of factorial design. In the between
subjects designs there is a separate group of participants for
each of the treatment conditions, which requires a large number
of
participants in each condition. For the within-subjects designs a
single group of participants is involved in all of the separate
treatment conditions. A factorial study that combines two
different research designs is called a mixed design and may
include at least one factor between and one within. There are
also factorial designs that use an experimental strategy for one
factor and a quasi-experimental factor or non-experimental
strategy for another factor. Factorial designs use a notation
system that identifies both the number of factors and the
number of
values or levels that exist for each factor.
Sampling for Factorial Designs
Between subjects factor
Large number of participants
Within subjects factor
Controls for timing and order
One factor at a time (OFAT) approach with few factors

As variables increase design becomes unwieldy
Latin hypercube
Cluster sampling
Sample size
Issues of power and effect size
Slide 13 Transcript
In some respects, sampling is similar to what we have seen with
other experimental designs. For instance, if it’s a between-
subjects factor then random assignment to groups and
observation of different participants is best. This can require a
substantial number of participants. If it’s a within-subjects
factor then observation of the same participants using
controls for timing and order effects is best, and fewer
participants are needed. With so many potential combinations of
factor levels to deal with in a factorial design, using OFAT,
or the one factor at a time approach, is often applied with a
limited number of variables. This is also called the randomized
control trials methodology, but it has limitations when
used with a factorial design. We can see this in the 2 x 2 design
where each of the four combinations is a factor and a group
being assessed. But, as the number of variables and
levels increase, the matrix becomes somewhat unwieldy, plus it
becomes impractical to keep increasing the number of sample
sets. By combining various grouping, the issue can be
managed effectively, although some validity threats may
increase. There are very sophisticated approaches for handling
large numbers of factors being analyzed which might
include the Latin hypercube or cluster sampling of an
elementary effect. In determining sample size, we return to the
issue of power, so the smallest effect size is maintained at the
desired level.

Sometimes researchers add another factor after the initial
design, however, the sample size requirements change very
little as long as the expected effect size doesn’t change.
Main Effects and Interactions
Main effects are main
differences among levels of a
factor
Overall difference among levels of a factor
consistent across levels of another factor
Interactions are relationships
among levels of factors
Mean differences between cells differs from
prediction
Indicated by nonparallel lines
Both can be examined by
graphing cell means
Two-way ANOVA used for
analysis
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Slide 15 Transcript
Since factorial designs involve two or more independent
variables, researchers want to be able to assess the effects of
each one separately as well as how they interact with each
other.
When doing factorial designs there are two classes of effects to
understand – main effects and interactions. There is the

possibility of a main effect associated with each factor and
there
is the possibility of an interaction associated with each
relationship among the factors. The main differences among the
levels of one factor are called the main effect of that factor. A
simple way to examine for main effects and interactions is by
graphing the cell means. A main effect would be an overall
difference among levels of a particular factor that is consistent
across the levels of another factor. After all, that is what the
researchers are looking for – some indication that the
independent variable was making a difference somewhere. Once
a
main effect is identified, the next step is to determine if it is
significant. To do this, the statistic commonly used is the two-
way analysis of variance. Note that calculations for a factorial
design are different from just a mathematical approach where
the overall difference may exist but is not consistent.
An interaction between factors occurs whenever the mean
differences between individual treatment conditions, or cells,
are different from what is predicted from the overall main effect
of the factors. When the effects of one factor depend on the
different levels of a second factor, then there is an interaction
between the factors. When the results of a two-factor study
are graphed, the existence of nonparallel lines (lines that cross
or converge) is an indication of an interaction between the two
factors and where that interaction occurs. This is shown
here using a graph from the textbook. An interaction is
identified from cell means at levels of a factor that change
across levels of another factor. When the combined levels of the
two
factors vary significantly an interaction is noted.
Examining Multiple Factors

Initial research may indicate
interaction of variables
Advantages of multiple
factor designs (factorials)
1. Testing several hypotheses
simultaneously
2. One experiment to address several
complex questions
3. Reveals difference where variables
interact simultaneously
4. When variables cannot be controlled
5. Assign variables removes threat to
internal validity
Slide 17 Transcript
Seldom in natural settings would you find just one variable
influencing everything. More than likely there are several
factors or variables that are influencing each other in many
different
ways and degrees. Fortunately, there are several advantages of
factorial designs over classical experimental designs. As we
discovered earlier, initial research results may indicate the need
to examine multiple variables interacting. So, some advantages
of factorial designs are that they permit testing of several
hypotheses simultaneously, rather than having to conduct a
series
of single variable experiments. Second, they permit the conduct
of only one experiment to address several complex questions at
once. Third, where interaction between two or more
variables simultaneously makes a difference, it reveals this
difference. Fourth, it can be performed where control of all

variables, but one is impractical or impossible. And fifth, by
adding more variables many of the threats to internal validity
can be neutralized.
Higher Order Factorial Designs
Applies to designs with 3
or more factors (variables)
Fractional factorial design
Subsets of full factorial
Observe over time
Each added factor
increases number of
participants required
Reducing sample size decreases
representativeness
Interactions become difficult to
interpret
More complex factorials
e.g., cubic
As number of factors
increase, so do main
effects
Slide 19 Transcript
The basic concepts of a two- factor research design can be

extended to more complex designs involving three or more
factors; such designs are referred to as higher- order factorial
designs. Theoretically, a researcher could simultaneously
investigate 10 or more factors at the same time, but it is rare to
go beyond 3 or even 4 because each additional factor increases
the number of participants needed to complete the study.
Reducing the number of participants in each cell doesn’t help
because as sample size decreases, so does representativeness.
Also, interactions involving many factors are difficult to
interpret. As the number of factors increases in the design, so
too do the number of main effects. Particularly when the
factors
reach four or more, it becomes difficult to understand just what
is influencing results and the relationships among all the
factors. So far, what has been covered are full factorials that
measure all combinations of the factor levels. As the number of
factors increases with higher order designs, an approach known
as fractional factorial design can be applied. A fractional
design is one where experimenters conduct only a selected
subset or fraction of the runs in the full factorial design.
Basically, the researcher will identify which interactions to
isolate, and
construct a block design for studying the confounding main
effects of the selected factors. And then, there are the cubic
factorials. The designs for such higher order factorial studies
can
become exceptionally complex and we need not go into further
details about them now.
With this module we conclude the overview of experimental
designs. Next, we will figure out how to analyze the data from
our various research methodologies. Until then, have a
productive and healthy week.
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· 7.2 Lecture: More Experimental Designs: Within-subjects and
Factorials
View the presentation and listen to the explanations offered.
When completed, reflect on the presentation and write a brief
statement that describes what you found to be an important
aspect of the information and how that might help you with your
research process.
· Must demonstrate understanding of the task.
· Must be able to illustrate critical thinking and the ability to
express an opinion on the covered material constructively.
· Grading will reflect whether the assignment has been
completed satisfactorily.

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