Dr. Festus OlorunniwoMGMT 4600 Supply Chain Strategy -Take Ho.docx

Dr. Festus Olorunniwo
MGMT 4600: Supply Chain Strategy -Take Home EXAM 2
Hint:
i. Use One Excel File and the SOLVER. You should use the
same FORMAT as we did in class (see Content page of eLearn
and the work we did in class on Thursday)
ii. Answer questions 1 and 2 in a report format, using other
worksheets within the same ONE file
iii. Upload your ONE file onto the DropBox under Exam 2
Chapter 9)
(30 Points) Lavare, located in the Chicago suburbs, is a major
manufacturer of stainless steel sinks. Lavare is in the middle of
the demand and supply planning exercise for the coming year.
Anticipated monthly demand from distributors over the 12
months is shown in Table below (ODD and EVEN Groups
should use ONE designated different demand forecasts.)
Month
Month #
ODD GRPs Forecast Demand
EVEN GRPs Forecast Demand
January
1
10,000
12,000
February
2
12,000
14,000
March
3
15,000

14,000
April
4
16,000
18,000
May
5
25,000
19,000
June
6
30,000
20,000
July
7
28,000
22,000
August
8
24,000
27,000
September
9
20,000
30,000
October
10
17,000
28,000
November
11
13,000
14,000
December
12
11,000

11,000
Capacity at Lavare is governed by the number of machine
operators it hires. The firm works 20 days a month, with a
regular operating shift of eight hours per day. Any time beyond
that is considered overtime. Regular-time pay is $15 per hour
and overtime is $22 per hour. Overtime is limited to 20 hours
per month per employee. The plant currently has 250
employees. Each sink requires two hours of labor input. It costs
$3 to carry a sink in inventory for a month. Materials cost per
sink is $40. Sinks are sold to distributors at a price for $125
each. We assume that no stockouts are allowed and the starting
inventory entering January is 5,000 units and the desired ending
inventory in December is also 5,000 units.
1. (Use a separate Worksheet SAME File!!,) Market research
has indicated that a promotion dropping prices by: 5 percent
(Odd groups) and 10 percent (Even Groups) in a given month
will increase sales in that month by 15 percent and bring
forward 20, 15, and 10 percent demand from each of the
following three months.
a. What is the optimal production plan for the year if we assume
no promotions? What is the annual profit from this plan? What
is the cost of this plan?
b. Is it better to promote in: ODD Groups: April or June/ EVEN
Groups – July or September? How much increase in profit can
be achieved as a result?
c. If sinks are sold for $250 instead of $125, does the decision
about the timing of the promotion change? Why?
2. (Use another separate Worksheet, SAME File!!) Consider the
data for Lavare above. We now assume that Lavare can change
the size of the workforce by laying off or hiring employees.
Hiring a new employee incurs a cost of $1,000; laying off an

employee incurs a layoff cost of $2,000. Also, now assume that
a third party has offered to produce sinks at $74 per unit. How
does this change affect the optimal production plan without a
promotion? How does this change affect the optimal timing of a
promotion? Explain the changes.
a. What is the optimal production plan for the year if we assume
no promotions? What is the annual profit with this plan? What
is the cost of this plan?
b. Is it better to promote in ODD Groups: April or June/ EVEN
Groups – July or September? How much increase in profit can
be achieved as a result?
NOTE: Your Report: Use same Excel file but separate
worksheet(s) for your reports
2
1
Running head: ATTITUDES AND APPROACHES TOWARDS
RESEARCH
7
ATTITUDES AND APPROACHES TOWARDS RESEARCH

Attitudes and Approaches towards Research and Statistics
Methods
C. Williams & K. Darnell
Georgia State University
Attitudes and Approaches towards Research and Statistics
Methods
A common attitude between students and Research Methods and
Statistics classes have been shown to professors that students do
not typically enjoy and retain information from this required
course for all Psychology majors in college. The phrases that
students have used were “why is this course required”, “this
course is so boring” and every student’s favorite “we don’t need
this material in the workforce”. Professors who taught this

specific course noticed these types of comments were frequently
being made by students and that they were uninterested in the
material, but what professors decided to do was extra ordinary.
In the article, Ciarocco, Lewandowski, and Van Volkom
(2013) decided to take matters in their own hand, they did an
experiment with 70 students enrolled in a scaffolding and
multifaceted Research and Statistics Methods course, they
hypothesized that “students in an active learning version course
of Research Methods and Statistics will demonstrate
significantly better performance on measures of Design
Knowledge and Statistics Skills than students in the traditional
learning condition. They tested students who were in a
systematic lecture class and compared it to students who were
able to be mostly active in the lecture course. Even though there
wasn’t a big difference in the active learning course rather than
the lecture only course, results were still higher for being active
in class. The second article, Stansbury and Munro (2013) as
well did an experiment with students who were taking the same
course at different semester times but instead they used a
gaming approach. They tested the effectiveness of video games
for instruction to establish if this non-traditional teaching
manner would have students more involved and knowledgeable.
The students were to play Dance, Dance Revolution with the
score being their statistics that was being measured. The results
for this were very high. Students retained more information and
did not mind being involved in the class.
Like numerous experiments there are short comings, depending
on the type of trials being tested some glitches are not always
avoidable. In the, Ciarocco', Lewandowski Jr.', and Van Volkom
(2013) article, I established that the Internal validity was
extremely weak. Seeing that the students were able to decide on
the time, semester and professor they desired for their class,
there was no way to fully rule out confounds in these trials.
There were multiple professors instilling this course material
each semester, and every professor has a different teaching
style. Some professors educate directly from PowerPoints

whereas some professors use a PowerPoint as a teaching tool to
guide them through a lecture while also showing some
enthusiasm toward the material. Last but not least, the classes
were taught during the Spring and Fall seasons. I have found
that when students return from summer break they are more
inspired about classes and passing them whereas Spring
semester students are looking forward to Spring break,
graduation, and Summer break. Since the researchers were not
able to randomly assign students to conditions and choose when
they took the course this led to many open circumstances and
that could create a dilemma causing many confounds. Even
though data was collected the instructors may have had more
eagerness or given more effort in teaching the subject, an
instructor’s attitude and teaching style play a vast role on a
student’s performance and how well they attain the material.
Because there were confounds found I believe if this course was
offered only in the Fall, this could perhaps exclude some
confounds. With this course being offered only in the Fall I feel
that students would be more focused and maybe do better. Also,
every class should have some type of active learning available,
whether some courses have more activities is strictly up to the
researchers but testing if more hand-on approaches worked
better could also help remove problems.
Methods
Participants
The participants that were used for this study came from a large
university and two classes learning the exact same material
were compared. These two separate classes were taught by the
same professor, teacher assistant, and also had the same study
opportunities as each other. The participants were an active
face-to-face group (20 females, 5 males) with a mean age of
26.25 years (SD=8.76) and the lecture face-to-face group (21
females, 8 males) the average age was 21.2 years (SD=4.76).
The active face-to-face course was held in Spring of 2016 while
the lecture face-to-face was held in the Fall of 2015. These two
groups had mixed ethnicities in both semesters; there were

Blacks, Whites, Latinos, Asians and Others.
Materials/Apparatus
The materials used for the study were, a pre-test, post-test and
lastly an assessment on satisfaction with the class. The initial
data was collected on the first day of lab using multiple choice,
fill in the blank, and calculation problems seeking what we
recalled from the pre-requisite class taken before this Research
Method and Statistic 3530 course. The second assessment is the
final exam, which will be evaluating how much we retained
throughout the course. Lastly, a Likert scale was used to
retrieve feedback and data on the level of satisfaction of
learning in this course. The satisfaction assessment consisted of
questions, utilizing a 5-point scale ranging from 5 – “strongly
agree” to 1 = “strongly disagree. Tests were provided in the Fall
of 2015 and also in Spring of 2016.
Procedure
The control group in the study was the students who received a
standard method of learning, meaning they were not hands on
group. They did not make up experiments and have an
opportunity to play games and recall what worked best for
students.
The experimental group in the study was my class that took
PSYC 3530 an online, active learning formatted class. Being in
this particular class we were able to have lectured online,
having access to see our professor and ask questions like
students would in a regular face-to-face course. With taking this
online course, my class has an advantage; we are able to re-
watch lectures as many times as needed verses someone who is
only meeting face-to-face. We also made up experiments and
were able to tell what worked best for us and helped us learn all
this new and difficult material.

References
Stansbury, J. A., & Munro, G. D. (2013). Gaming in the
classroom: An innovative way to teach
factorial designs. Teaching of Psychology, 40(2), 148-152.
doi:10.1177/0098628312475037
Ciarocco, N. J., Lewandowski, G. W., & Volkom, M. V. (2012).
The Impact of a Multifaceted
Approach to Teaching Research Methods on Students' Attitudes.
Teaching of Psychology, 40(1), 20-25.
doi:10.1177/0098628312465859

PSYC 3530
Spring 2017
K. Darnell
Synthesis Paper Overview
For this assignment, you will design and describe a pedagogical
experiment inspired by the studies of Ciarocco, Lewandowski,
and Van Volkom (2013) and Stansbury and Munro (2013). Your
proposed experiment must improve upon these previous studies
by addressing problems with their design and/or implementation
we have identified, such as potential confounds, concerns about
generalizability, variations in instructional quality, and so on.
The target population for your study is undergraduate
psychology majors who are enrolled in an advanced research
design and analysis course like PSYC 3530, which focuses on
different experimental designs, the relevant statistics for
analyzing the data for those designs, and scientific literacy.
Your design must integrate elements from both of the sources,
including comparison of active and passive learning, use
innovative instructional tools and techniques, and measurement
of learning-related behaviors and attitudes. In addition, your
experiment must each have a factorial design with TWO
independent variables and TWO or more dependent variables,
and this design must be suited to test at least TWO specific
hypotheses. For example:
1) Students who learn via an active method online will
demonstrate the same improvement of knowledge regarding
research methods, statistics, and scientific writing as students
who learn via an active method face-to-face, and both will show
knowledge improvement relative to student who learn via a
passive, face-to-face method.
2) Students who learn via an active method online will

demonstrate the same positive attitudes regarding research
methods, statistics, and scientific writing as students who learn
via an active method face-to-face, and both will more positive
attitudes relative to student who learn via a passive, face-to-
face method.
Your sources for this paper must include Ciarocco,
Lewandowski, and Van Volkom (2013) and Stansbury and
Munro (2013). You may use other appropriate peer-reviewed
sources, such as peer-reviewed journal articles and websites for
government agencies (e.g., the U.S. Department of Education)
and relevant professional organizations (e.g., the American
Psychological Association) ONLY if you actually read and
study the sources as we have the two studies above and include
PDFs of these additional sources with the revised version of
your paper. You may not simply use a source that another
source has used. Of course, you must properly cite and
reference all sources that you use according to APA style.
What kinds of things go in the introduction of an APA style
research proposal?
The purpose of the introduction in a research manuscript is to
give the reader a clear understanding of the topic under
consideration and why it is interesting. This is where you
motivate the study: Describe the broader issues related to your
research question, then gradually narrow your focus to the
specific hypothesis (or hypotheses) being tested. You have to
convince the reader that your way of investigating your research
question makes sense. This process involves providing the
reader with a brief overview of what previous research has
revealed about the topic (i.e., a literature review) and showing
how the current study fills a gap in our understanding of the
issue. This is where you link your work to the "chain of
knowledge" that has come before. The more convincingly you
forge this link, the more likely the reader will be to believe
your study has intellectual merit.

In general, the introduction of an APA-style paper is organized
in the following way:
· A description of the topic of interest and why it is important
to study it further (e.g., what interesting questions still remain)
· A brief discussion of what is known – and not known – about
this topic, with reference to scholarly findings that the reader
must be familiar with to understand the argument that the author
is going to make (in the next part of the introduction) for
conducting the current study.
· The author’s argument for conducting the current study,
including references to scholarly literature and other
appropriately scientific sources (e.g., the Centers for Disease
Control) that support the claims made. Show how the current
study will address gaps in the existing literature and answer
questions that still exist about the topic
· A statement of the hypothesis being tested and a general
description of the expected results
Each part of the Introduction must lead clearly and directly to
the one that follows it. The reader should never have to
mentally fill in gaps in the author’s reasoning or make
inferences about why claim or referenced finding is relevant to
the author’s argument. All content from outside sources must
be cited in APA (6th ed.) style and paraphrased accurately and
appropriately (e.g., no quotations, no cutting and pasting of
sentences or phrases, and no copying of sentences structures
with word substitutions from a thesaurus).
What kinds of things go in the Method of an APA style research
proposal?
The Method section of an APA-style research proposal is where
you detail how you conducted your study and what the subjects
experienced. This section typically includes information about
the source(s) of the data, instruments, and measures, as well as
a step-by-step description of what the researchers and the
subjects will do during the data collection process. The general
rule is that the Method must be specific enough that another
researcher could use this section as "directions" to replicate the

study and check your findings.
The Method section typically has three major subsections, each
of which gets its own subsection heading in the text of your
paper: Participants, Materials/Apparatus, and Procedure. A
description of each subsection is provided below. Check your
APA resources for details regarding how to format the section
and subsection headings. Also, review the Method sections of
your primary sources to see what kinds of information they
include and how they present it. You may have more or
different subsections to your Method section, depending on the
design of your study.
· Participants– Describe the individuals who provided the data,
focusing on the characteristics of the subjects that made them
eligible to participate in your study or are relevant to the
potential generalizability of your findings. Provide available
descriptive statistics (e.g., N, n, M, s) for demographic
variables such as age, race/ethnicity, and sex/gender. Do the
same for any other subject variables that are relevant to the
hypothesis/es you are testing (e.g., research experience). Also,
be clear about where the subjects are coming from (e.g., Type
of class? Type of school?).
· Materials/Apparatus – List all the equipment, measurement
tools, and stimuli or other sources that you used to conduct the
study. Where applicable, include the names of the authors or
creators of standardized materials, as well as the type and model
(or version) of any hardware or software you used. If you are
basing any of your stimuli on those from a database or another
scholarly source, reference the source (in APA style, of course).
You will also have to explain any new stimuli you created and
why they were necessary.
If your stimuli/sources are too numerous to list in the body of
paper, it is acceptable to provide only a sample of
stimuli/sources here, being sure to include 1-2 stimuli/source
items from each instrument or subscale.
· Procedure -- List the various stages and activities of your data
collection in a clear, logical sequence. This section should be

detailed enough to allow another researcher to replicate the
study in every way relevant to the scientific outcome.
Other relevant details
This is a research proposal. That means that you should write
your paper as if the data have not yet been gathered, in the
future tense. Talk about what participants WILL DO, not what
they do or did. Base all of your predictions on established
evidence and reason; where there is no preexisting evidence in
favor of your claim, use reason to help the reader see why you
think the outcome will pattern as you expect. Make links to
sources to support your predictions whenever possible, rather
than simply assuming that the reader understands why you think
something will happen under particular circumstances. Explain
your thinking whenever necessary (i.e., don’t assume the reader
can read your mind).
Remember to cite your sources at the first mention in each
paragraph and do NOT hypercite. This means you should not
have more than ONE citation of any source in any paragraph
where you refer to it. Once you have introduced the sources in a
paragraph, you can simply use the authors’ names without the
year of publication and the reader will know what source you
are referring to. Use APA style for citation.
The body of your essay must be between 3.5-4 double-spaced
pages in length. The entire paper must conform to APA style
(6th edition) in every dimension, including an APA-style title
page and APA-style References page. If you are unsure what
APA style calls for with regard to some aspect of your paper
(e.g., what APA style headings are and how to properly format
them), refer to the current APA style manual (i.e., “the Blue
Book”) and/or other provided resources.
Your assignment will be evaluated in three areas:
APA Style – The extent to which you follow APA (6th ed.)

guidelines for structure and style
Content – The extent to which you include relevant and
necessary information about the studies for the reader to
understand what the researchers did, with particular emphasis
on the elements of the studies relevant to the different types of
the validity and the argument you are making
Organization – The extent to which you arrange ideas
thoughtfully and appropriately, given the purpose and audience
of the assignment, as well as the degree to which the logical
connections you make between ideas/sentences/paragraphs are
consistently clear to the reader
When your final document is done, save it with the appropriate
filename before uploading it to iCollege. Details about the
required filename structure are provided in the syllabus. The
abbreviation for the primary version of the assignment is
synthesis1; the abbreviation for the revised version is
synthesis2.
The primary version of this paper is due on iCollege at the start
of lab Eastern Time on Tuesday, April 11th. The peer review
for this paper will occur in lab on the same day, so bring a hard
copy of your paper to lab to exchange with a classmate. The lab
instructor will provide you and your peer review partner with a
copy of the peer review checklist for this assignment to
complete during lab.
Remember: You may not submit this assignment (or any other
writing assignment in the class) for credit if you have not
submitted your signed Plagiarism Contract. This requirement
cannot be fulfilled retroactively, so if you submit this
assignment and have not turned in your completed contract, you
will automatically get a 0, even if you subsequently submit the
necessary documentation.

Primary version: 5 points (APP)
Peer review: 5 points (APP)
Revised version: 100 points (SL: 30/40/30)
Faculty Forum
Gaming in the Classroom: An Innovative
Way to Teach Factorial Designs
Jessica A. Stansbury1 and Geoffrey D. Munro1
Abstract
This study tested the effectiveness of video game use for
instruction of factorial designs in a research methods course.
Students
designed and conducted a mini study, playing Dance, Dance,
Revolution, using video game scores as the dependent variable.
A mixed-design analysis of variance revealed a significantly
greater increase from pretest to posttest in content knowledge in
the
Wii activity condition compared to lecture-only and no-content
control conditions. Implementing this nontraditional teaching
method engaged students and increased knowledge of factorial
designs in a research method course.
Keywords
video games, teaching, research methods
One of the hallmarks of a successful psychology student is a
firm understanding of the underlying principles of research
design and methodology (American Psychological Association

, 2007). Psychology educators believe the research methods
course to be one of the most important courses for psychology
majors (Dunn et al., 2010). Students, however, often have
views about research methods courses that are very different
from psychology faculty. Students often do not appreciate the
‘‘real world’’ and career relevance (Johanson & Fried, 2002;
Keith-Spiegel, Tabachnick, & Spiegel, 1994; Sizemore &
Lewandowski, 2009) are less motivated to succeed in (Saville,
Zinn, Lawrence, Barron, & Andre, 2008) and report high levels
of anxiety about these courses (Bos & Schneider, 2009;
Papanastasiou & Zembylas, 2008). Psychology educators are
well aware of the difficulty of successfully engaging students
in research methods courses (Saville et al., 2008) and have
devised numerous pedagogical techniques to address the
obstacles (e.g., Beins, 1985; Burkley & Burkley, 2009; Cleary,
2008; Saville, Zinn, & Elliott, 2005). The current research
tested the possibility that supplementing research methods
lectures with a video game activity could be an effective

pedagogical technique.
Educators have successfully used video games in the
classroom to teach a multitude of subjects (i.e., history, math,
health) at the elementary, middle, and high school levels
(Charsky & Mims, 2008; Malala, 2009; Meterissian, Liberman,
& McLoed, 2007; Shaffer, Halverson, Squire, Gee, & Wiscon-
sin Center for Education Research, 2005; Shreve, 2005; Simp-
son & Clem, 2008). Video games improve lower and higher
order thinking skills (Rice, 2007a), information and knowledge
seeking skills (Gee, 2003), and core research skills (Squire &
Steinkuehler, 2005). However, there has been minimal research
on using video games in higher education to help effectively
teach principles of research design and statistics. Because a key
struggle of teaching research methods in psychology involves
students’ lack of interest, video gaming, due to its inherently
engaging nature, might be particularly well-suited to address
students’ perceptions that the course is uninteresting and lacks
real-world relevance (Denham, 1997; Gesler, 2007).

It is crucial to develop pedagogy that intrinsically motivates
and captures undergraduate students’ interest. However, it
is also essential that the pedagogy assist in knowledge
development, connecting with the course material in a way that
improves learning. One quality of video games is the highly
accessible numerical scores that can easily be used for data. For
example, scores from a 1-min racing game can serve as a depen-
dent variable for an experimental design or as one measured
variable in a correlational design. Students can identify
variables
(e.g., hours of sleep, experience level) that could be used along
with the video game scores in correlational, experimental,
quasi-
experimental, factorial, and even multivariate regression
designs
(Ring, 2009). Thus, a video game ‘‘mini-study’’ in class could
provide fun and a creative way for students to learn about
topics such as the difference between dependent and indepen-
dent variables, the importance of operational definitions, the

relevance of the different scales of measurement, the process
of formulating hypotheses, and the overall distinction between
different kinds of research designs. In addition to these design
issues, students can then proceed to perform statistical analyses
1 Department of Psychology, Towson University, Towson, MD,
USA
Corresponding Author:
Jessica A. Stansbury, Department of Psychology, Towson
University, 8000
York Road, Towson, MD 21252, USA.
Email: [email protected]
Teaching of Psychology
40(2) 148-152
ª The Author(s) 2013
Reprints and permission:
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DOI: 10.1177/0098628312475037
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and interpret the results using actual data they have collected
from a study they designed.
In the current study, we tested whether or not supplementing

traditional lecture, via video game playing, in an undergraduate
research methods course increases students’ engagement with
and their knowledge of factorial designs. We included two con-
trol groups to determine whether increased content knowledge
was due to the video gaming activity coupled with lecture, the
lecture alone, or simply taking the content knowledge test
twice. We hypothesized that the combination of the lecture and
the video gaming activity would increase content knowledge to
a greater degree than the lecture only (without the video game
activity) and simply taking the content knowledge assessment
twice before and after an unrelated research methods lecture.
Additionally, we hypothesized that students in the video
gaming condition would report a high degree of engagement
with the activity.
Method
Participants
In five different sections of research methods in psychology
courses at a midsize university, 65 undergraduate students par-

ticipated. We created the video gaming condition by combining
two sections taught by one instructor, the lecture-only control
condition by combining two sections taught by a second and
third instructor, and the no-content control condition using one
section taught by a fourth instructor. All four instructors were
proficient and experienced in teaching research methods in
psychology. The video game condition consisted of 21 women
and 5 men (age: M ¼ 21.7, SD ¼ 2.56). The lecture-only
condition consisted of 20 women and 3 men (age: M ¼ 22.0,
SD ¼ 2.53). The no-content condition consisted of eight
women and eight men (age: M ¼ 22.0, SD ¼ 2.64).
Procedure
Students in the video gaming and lecture-only conditions
completed a pretest measure assessing their content knowledge
regarding factorial designs before class lecture began but after
students were supposed to have read the factorial designs text-
book chapter. The instructor informed students that the pretest
would not be graded and that the data would be used to help
other educators evaluate innovative teaching methods. Due to

the complexity of factorial designs, the lecture (and the activity
for those in the video game condition) was conducted over the
course of two 130-min class periods. Thus, students completed
the pretest at the beginning of the one class period and the
posttest at the end of the next class period. Between the pretest
and posttest, the instructor presented a traditional lecture on the
topic of designing and conducting experiments with multiple
independent variables as well as analyzing and interpreting
data from those experiments. Lecture content was the same for
both the gaming and lecture-only conditions. However, instruc-
tion in the lecture-only condition contained more examples
including presentation of a hypothetical experiment with fake
data. Those in the video game condition discussed relevant
material throughout the video gaming activity. The pretest and
posttest measures each consisted of the same 14 multiple
choice questions taken directly from the online test bank
associated with the textbook chapter Complex Experimental
Designs (Cozby, 2007).

Video Gaming Activity. Near the end of the first class period,
students created a 2 � 2 mixed factorial design that could be
implemented with the Wii game Dance Dance Revolution
(DDR; Konami Digitial Entertainment, 2010), using dance
scores as the dependent variable. Each class section designed
a different 2 � 2 mixed factorial design. Students in one section
created a 2 (Song choice: Lady Gaga, Bad Romance vs. Sean
Paul, So Fine) � 2 (Difficulty level: easy vs. basic) mixed fac-
torial design. Students in the second section created a 2 (Song
choice) � 2 (Sound: no sound vs. sound) mixed factorial
design. However, the procedure was identical for both sections.
The instructor set up DDR (including two dance mats to
accompany the game) in the classroom before the second
class period. In DDR, each player dances on a mat that has
four directional arrows. The characters ‘‘dance’’ by attempt-
ing to press the arrows with their feet in time with the corre-
sponding arrows presented on the screen during the song.
They then receive a score for their performance. When using
the audience approval setting of the game, players can earn no
score if they are booed off the stage. Thus, to generate data
and avoid embarrassment, the audience approval setting was

turned off.
At the start of class Period 2, the instructor randomly
assigned students to one of the two between-groups conditions.
Then, students reviewed the design by devising a way to best
record the data. Students flipped a coin to determine which
between-groups condition would go first. They also counterba-
lanced the within-group condition. After all the students
danced, they entered and analyzed the data and interpreted their
results. Finally, students completed the posttest measures. We
added an additional seven questions using 1 (not at all) to 7
(extremely) Likert-type scales to the posttest in the video
gaming condition to assess students’ engagement with the
course content (see Table 1).
No-Content Condition. At the start of the class period on
another,
unrelated subject matter (i.e., t-tests), students in the no-content
condition completed the pretest assessing content knowledge of
factorial designs. Upon completion of the unrelated lecture,
students completed the identical posttest. Students had no

in-class exposure to factorial designs prior to completing the
content knowledge measure.
Results
Content Knowledge
We divided the number of correct responses by the total num-
ber of questions to create a proportion of correct responses for
Stansbury and Munro 149
each student for the pretest and posttest knowledge questions.
A 3 (Condition: video gaming vs. lecture only vs. no-content)
� 2 (Time: pre vs. post) mixed analysis of variance (ANOVA)
revealed a significant condition main effect, F(1, 62) ¼ 4.43,
p < .05, Z2 ¼ .13, and a significant time main effect,
F (1, 62) ¼ 24.61, p < .001, Z2 ¼ .28. Most importantly, the
ANOVA revealed the significant Condition � Time interac-
tion, F(2, 62) ¼ 13.83, p < .001, Z2 ¼ .31 (see Figure 1). Those
in the video game condition significantly improved from
pretest (M ¼ .54, standard error [SE] ¼ .03) to posttest
(M ¼ .70, SE ¼ .03), t(25) ¼ �6.11, p < .001. However, the
difference between pretest (M ¼ .52, SE ¼ .03) and posttest
(M ¼ .55, SE ¼ .03) for those in the lecture-only condition only
approached significance, t(22) ¼�1.94, p ¼ .07.1 As expected,
those in the no-content condition did not change from pretest

(M ¼ .52, SE ¼ .03) to posttest (M ¼ .52, SE ¼ .04), t(15) ¼
�.38, p ¼ .71.
Student Engagement
As shown in Table 1, students reported that playing Wii helped
them understand the class material, was a good supplement to
lecture, and made the class more enjoyable. Students reported
playing Wii and analyzing the collected data helped them
understand the principles of experimental methods better.
Furthermore, students recommended using this activity in other
research methods courses. Interestingly, students reported mild
anxiety due to performing in front of the class; yet, most
students were willing to participate in playing Wii again.
Discussion
The current study showed that integrating video games into a
research methods course in psychology has a positive impact
on students’ interest, motivation, and ability to learn factorial
designs. Not only is this pedagogical technique perceived
favorably by the students but also it improves comprehension
of material over and above a lecture-only control group. The

activity did not significantly increase the amount of time
devoted to the topic because the game is brief, it can be played
in pairs, and different subsets of students completed other tasks
(e.g., recording of scores, creation of the data file) while the
game score data were being obtained. Also, the effectiveness
of the video gaming activity extends to at least two class peri-
ods involving a 1-week time delay. Therefore, once a data set is
obtained, it may be able to be used successfully to illustrate
multiple concepts throughout the course (e.g., hypothesis test-
ing, statistical correlations, graphing). Ultimately, the gaming
activity can be incorporated into courses without a significant
loss of time or attention to the subject matter of the course. One
design weakness was that different instructors taught the video
gaming and lecture-only conditions. Thus, future research
should assess the effectiveness of the gaming activity across
a wider variety of instructors with varying teaching styles.
Some academics hold negative perceptions of video games
(Baek, 2008; Malala, 2009; Rice, 2007b) and we recommend

careful consideration before implementing gaming activities
into the classroom. For example, the effects of violent video
games on players are clear (Anderson & Bushman, 2001), so
instructors should select nonviolent and nonoffensive games
with assistance from the video game rating system. However,
Figure 1. Results reveal a significant Condition � Time
interaction,
F(2, 62) ¼ 13.83, p < .001, Z2 ¼ .31.
Table 1. Means and Standard Deviations (SDs) for Student
Engagement of Playing Wii.
Item M SD
1. To what extent did the in-class activity, playing Wii, help me
understand the class material better? 5.85 0.88
2. To what extent was the in-class activity, playing Wii, a good
supplement to lecture? 6.42 0.11
3. To what extent did the in-class activity, playing Wii, and
analyzing the data collected, help me understand the principles
of
experimental methods?
5.88 0.82
4. To what extent did the in-class activity, playing Wii, make
the class more enjoyable? 6.92 0.27
5. To what extent do you recommend the in-class activity,
playing Wii, in a research methods course? 6.88 0.33
6. How anxious did you feel playing Wii in front of other

classmates? 3.42 2.18
7. How willing are you to participate in the in-class activity,
playing Wii, again? 6.77 0.52
Note. Responses were made on a 7-point Likert-type scale with
higher numbers indicating greater agreement.
150 Teaching of Psychology 40(2)
our findings indicate that video gaming can address one of the
more difficult struggles of teaching students about research
methods in psychology, the lack of engagement with the
course material. Video gaming can encourage exploration,
socialization, and creativity. At the same time, it might help our
students become better scientific thinkers.
Acknowledgments
We thank Dr. Bryan Devan and Mr. Chris Magalis for their help
in
data collection.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with
respect to
the research, authorship, and/or publication of this article.

Funding
The authors received no financial support for the research,
authorship,
and/or publication of this article.
Note
1. When the two video game class sections (n ¼ 16, n ¼ 10)
were
analyzed separately, significant results supporting the
hypothesis
were found for both sections for the content knowledge items
(both
p values < .01). When the two lecture-only sections were
analyzed
separately, neither section showed a significant increase in
content
knowledge from pretest to posttest (p values ¼ .071 and .553).
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Topical Article
The Impact of a Multifaceted Approach to
Teaching Research Methods on Students’
Attitudes
Natalie J. Ciarocco
1
, Gary W. Lewandowski Jr.
1
, and
Michele Van Volkom
1
Abstract
A multifaceted approach to teaching five experimental designs
in a research methodology course was tested. Participants
included
70 students enrolled in an experimental research methods course
in the semester both before and after the implementation of
instructional change. When using a multifaceted approach to

teaching research methods that included both active learning
and a
form of scaffolding, students reported a greater efficacy in
American Psychological Association style writing, a higher
perceived
utility of research and statistics, better attitudes toward
statistics, and higher perceived skills/abilities in statistics. This
approach
benefitted students’ perception of an often disliked but required
course in psychology.
Keywords
research methods, statistics, teaching of psychology
Providing solid research training is one of the established
bench-
marks of a strong undergraduate program in psychology (Dunn,
McCarthy, Baker, Halonen, & Hill, 2007). To this end, both
research methodology and statistics courses are among the most
frequently taught classes in psychology curricula (Perlman &
McCann, 1999a, 1999b). Students acquire many of the market-
able skills from psychology within these courses. Unfortunately,
students have a variety of negative associations with such
courses (Manning, Zachar, Ray, & LoBello, 2006; Rajecki,
Appleby, Williams, Johnson, & Jeschke, 2005). Despite
increased knowledge about course content, students report a

decreased utility for both methodology and statistics as the
semester progresses (Sizemore & Lewandowski, 2009). So
while they are indeed learning the material, they perceive little
utility in that knowledge. Likewise, across the major, psychol-
ogy students display enhanced scientific thinking as they
advance in the discipline, but their view of psychology as a
science remains unchanged (Holmes & Beins, 2009). Students
lack a connection between what they are successfully learning
in their research courses and the utility of these skills in their
field.
The very aspects of psychology that makes it a science are the
same ones that students devalue. As a result, teaching a research
or statistics course presents a unique challenge for instructors.
As educators, our goal is to help students become learners,
not just transfer easily memorized information. Students can
learn all of the concepts and formulas needed to pass a test
or complete a class, but if they dislike it and find the knowledge
useless, there is less of a chance that they will want to learn

more outside of the classroom. However, if students’ attitudes
improve and they see the content’s utility, they will be more
likely to apply their knowledge and continue learning after the
class has ended. Additionally, if students believe these skills
are
useful and have positive attitudes toward them, they will feel
more confident and will market their skills more effectively in
their professional lives. Further, if students see the value in
learning research and statistics, engagement, course attitudes,
and perception of psychology as a science should all improve.
Educators are starting to recognize the importance of shifting
attitudes toward methodology and statistics. For example, Size-
more and Lewandowski (2011) found that placing methodologi-
cal concepts in a clinical context did not change the learning of
the material but did increase students’ interest in the lesson. It
appears there are approaches to presenting the material that
improves attitudes without compromising learning.
Perhaps the incongruence between the skills and knowledge
gained through a research methods course and students’

perceived benefit is due to the way instructors structure these
courses. For example, instructors often assign textbook read-
ings, but these texts often rely on terminology with limited
demonstration of how a researcher would use those concepts
1
Department of Psychology, Monmouth University, West Long
Branch, NJ,
USA
Corresponding Author:
Natalie J. Ciarocco, Department of Psychology, Monmouth
University, West
Long Branch, NJ 07764, USA
Email: [email protected]
Teaching of Psychology
40(1) 20-25
ª The Author(s) 2013
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0098628312465859
http://top.sagepub.com
to shape a research project. Additionally, at the undergraduate
level, students do not always get the chance to conduct their

own
research. Therefore, students may not see concepts ‘‘in action,’’
leaving little real-life application for research concepts. Perhaps
as a result, students complete research methods and statistics
courses with more knowledge but less appreciation for this
knowledge (Sizemore & Lewandowski, 2009). Incorporating
more active learning strategies into a research methods course
may improve students’ enthusiasm and perceived utility (Silber-
man, 1996). Finding novel and effective ways for students to
engage in the material is crucial to the understanding of
difficult
concepts and should create enthusiasm for topics (Wenglinsky,
2000). An active approach may also change students from
surface learners into deep learners who see the value and
applic-
ability of their research methodology education (Bain, 2004).
To
this end, in the present study, the experimental group learned
research method concepts in the context of active learning,
allowing them to simultaneously learn and engage in concepts,

while the control group followed the concept first approach to
learning methodology.
Additionally, research methodology courses may benefit
from adapting scaffolding. Instructional scaffolding involves
a variety of tools and guides that promote greater student
understanding while a more knowledgeable other provides
supports to facilitate the learner’s development (Jackson, Strat-
ford, Krajcik, & Soloway, 1996; McKenzie, 2000). The idea is
to implement a temporary framework to facilitate the learning
of new material or a skill (Cazden, 1983). This allows students
to exceed what they may be able to do independently. Expert
scaffolding, in particular, is often utilized in classroom settings,
as the instructor is the expert responsible for the scaffolding of
students (Holton & Clarke, 2006). As students learn the new
material or skill, the instructor removes the scaffolding,
allowing independence. Scaffolding can take many forms but
often starts with modeling, followed by explanation and then
invited student participation (Hogan & Pressley, 1997). For

example, an educator teaching students how to diagram a
sentence may start by modeling how to diagram a sentence and
then go back and explain each choice and decision made in the
process. Next, the whole class might work through several
problems with the teacher, eventually resulting in students
working independently on the skill.
There are clear benefits to using scaffolding. For example,
students using scaffolding are more organized and more
productive in their problem solving (Simons & Klein, 2007).
However, researchers have yet to study scaffolding in the
context
of research methods. Additionally, researchers have not
assessed
the role of scaffolding in attitude change. The scaffolding
approach by nature may increase students’ feelings of compe-
tence as well as increase their awareness of how to use the skill.
Research clearly indicates the benefits of hands-on research
(Lutsky, 1986). In an effort to improve students’ perceptions of
research and statistics, the present approach is unique in how

we combine several techniques and use scaffolding within each
unit. Specifically, instead of focusing on one engaging activity
at a time, we combined several active learning approaches and
used expert scaffolding for each of the five major designs. To
test the effectiveness of our approach, we compared multiple
sections of a research methods class that used traditional
lecture focused methods of instruction (control) to multiple sec-
tions that used the new multifaceted active learning approach
(experimental). The purpose of this endeavor was to test
whether
the multifaceted approach would improve students’ perceptions
of research design and statistics. In an effort to build upon prior
research involving perceptions of research and statistics and
following from previous research (Sizemore & Lewandowski,
2009, 2011), we assessed students’ perceptions on the utility
of research and statistics, their attitudes toward research and
statistics, and perceptions of their research and statistical abil-
ities. We predicted that our approach would provide more
oppor-

tunity to experience and understand the utility of both statistics
and methods. We also expected this approach to lead to better
attitudes toward statistics and higher perceived skills/abilities
in statistics. Additionally, we created a measure of efficacy in
American Psychological Association (APA) style writing as this
component of the scientific process was previously missing
from
studies. We hypothesized that efficacy in scientific writing
would increase as a result of our approach.
Method
Participants
Participants included 70 undergraduate psychology majors
enrolled in a required 300-level experimental methods course at
a medium-sized private liberal arts university in the Northeast
United States. The participants ranged in age from 19 to 53,
with
a mean age of 22.53 (standard deviation [SD]¼5.52). The
sample
included 1.4% sophomores, 54.3% juniors, and 44.3% seniors.
Participants were required to take a research methods course

before enrolling in the present course and had completed or
were
currently enrolled in a statistics course. The control group com-
prised of 33 participants (11 men, 21 women, and 1 unreported;
response rate of 86.8%) and the experimental group involved
37 participants (5 men and 32 women; response rate of 82.2%).
Participants did not receive compensation for participation.
Materials
The materials consisted of a survey comprised of three sec-
tions, including a demographics questionnaire, a survey of atti-
tudes toward research and statistics, and a course efficacy
survey created for this study.
Demographics. The demographic section inquired about the
age, sex, and academic standing of the participants.
Perceptions of research and statistics. This measure assessed
various attitudes toward both research and statistics by asking
participants to rate their level of agreement with 30 positively
and negatively keyed statements on a 7-point scale (1 ¼
strongly
disagree; 7 ¼ strongly agree; Sizemore & Lewandowski, 2009).
There were six subscales: attitudes about research (e.g.,
Ciarocco et al. 21

‘‘Writing about research is something I like to do’’; a ¼ .86),
attitudes about statistics (e.g., ‘‘I have a good feeling toward
statistics’’; a ¼ .89), perceived utility of research (e.g., ‘‘Most
research ideas and principles are not very useful’’; a ¼ .77),
perceived utility of statistics (e.g., ‘‘Most people should study
some statistics’’; a ¼ .77), perceived ability in research (e.g.,
‘‘I have a hard time understanding things I read related to
research’’; a ¼ .57), and perceived ability in statistics (e.g.,
‘‘Statistics is easy for me’’; a ¼ .72).
Scientific writing. The researchers created this measure to
assess efficacy in APA style writing (a¼ .89). Participants rated
eight statements on a 7-point scale (1 ¼ not at all; 7 ¼ defi-
nitely). Questions included, ‘‘I feel I could write an effective
method section of an APA style paper’’ and ‘‘I have learned a
lot
about APA style from this course.’’
Procedure
We surveyed three course sections in the spring of 2009 for our
control sample and three sections in the fall of 2009 for our
experimental sample. In each case, research assistants surveyed
students during their last week of classes after the instructor left
the room. Both the experimental sample and the control sample

did collectively conduct a class research study during the
semester. This project was the control sample’s one opportunity
to conduct a research study from beginning to end.
Control group. In the control group, the instructor assigned
standard textbook readings on the same five experimental
designs
as the experimental group and gave lectures that focused on
rein-
forcing the concepts from the chapters. Importantly, the control
group did not read a published research article that exemplified
the design, they did not participate in an in-class design
demonstration and did not conduct a study. Students did design
studies, but they never created study materials or standardized a
procedure for the study, nor did they collect, analyze, or report
any
research findings.
Experimental group. Our guiding premise is that engaging in
and conducting research is the best way to learn and appreciate
research. Therefore, with the experimental group, we tested our
instructional method for teaching research methodology using

a modified form of scaffolding and interactive learning activi-
ties to teach five specific experimental designs (two-group
between subjects, multigroup between subjects, 2 � 2 factorial
between subjects, repeated measures, and mixed designs).
First, to provide students with the opportunity to see how to use
an experimental design to answer a research question, prior to
attending class students read a published psychological research
article that exemplified a particular research design. These
articles focused on a variety of topics in psychology (e.g.,
physio-
logical, cognitive, social).
1
Students wrote an in-class summary
of the article, describing and critiquing the study’s method and
results. In the case of teaching the two-group design, students
read
an article detailing how the unconscious exposure to a
citrus-scented cleaner, compared to an empty control group,
influenced participants’ cleaning behavior (Holland, Hendriks,
& Aarts, 2005). The instructor provided expert scaffolding by

discussing the article with the class and specifically asking
ques-
tions about the variables, procedure, and statistical analyses of
the
research, and giving feedback on the students’ critiques of the
studies’ strengths and weakness.
Next, the instructor conducted an in-class demonstration of
the same research design in which students served as research
participants.
2
For example, when teaching the two-group
design, students participated in a demonstration based on the
misattribution of arousal research of Dutton and Aron (1974).
The instructor randomly assigned students to a group that had
to give a 1- to 3-min speech (high arousal) or a group that had
to evaluate a speech (low arousal), had students rate the attrac-
tiveness of several pictures, and complete a manipulation
check. After such demonstrations, the instructors helped
students dissect the demonstration to determine the relevant
design elements (e.g., variables, order effects, manipulation

checks), the potential hypothesis, and potential issues
(e.g., problems with the design and potential improvements).
The instructor provided expert support during students’ initial
learning of these designs during the research article critique
and the demonstration.
To mimic the research process, the instructor then created small
groups and assigned students a general research question
focused
on the same design (e.g., use a two-group design to determine
what
influences attraction). In this phase, the instructor began to
remove
the supportive scaffolding, as students independently designed
and implemented a study. First, groups undertook a 30- to 45-
minutes guided planning process, where students developed a
more specific research question, a working hypothesis,
operation-
ally defined all relevant variables, created all necessary
materials,
and developed a procedure for data collection. Next, upon
instruc-

tor approval of the method, students spent approximately 30
min-
utes independently collecting data from a small convenience
sample around campus. Next, students analyzed their findings
by collectively creating a data file, entering the data, and
running
the appropriate statistical analyses without instructor guidance.
Finally, students reported their results by writing an APA style
results and an abbreviated discussion section.
Results
We conducted a series of analyses comparing the student
characteristics across the two semesters. There were no signif-
icant differences between the two semesters in terms of age,
t(68) ¼ 0.11, p ¼ .91, year in school, w2 (1, N ¼ 70) ¼ 2.47,
p ¼ .29, or whether they worked in a professor’s research lab,
w2 (1, N ¼ 70) ¼ .61, p ¼ .44. There was a significant differ-
ence in terms of gender distribution such that there were more
males in the spring/control semester (n ¼ 11) than the fall/
experimental semester (n ¼ 5), w2 (1, N ¼ 69) ¼ 4.19, p ¼
.041.
We conducted a series of independent samples t tests to

examine the differences between the control sample and
experimental sample on each of the dependent variables. As
predicted, the results of the analysis in Table 1 show that gen-
erally students self-reported more benefits from the course with
the multifaceted approach. Specifically, students taught
research methods through scaffolding and active learning
reported greater APA style efficacy, perceived utility of
research, attitudes toward statistics, skills/abilities in statistics,
and perceived utility of statistics. The only exceptions were in
terms of research attitudes and research skills/abilities where
the differences were in the predicted direction but failed to
reach significance.
Students in the course that utilized the multifaceted
approach reported greater APA style efficacy overall. We
decided to explore this further to determine whether students
experienced perceived increases on all sections, or in select

areas. The results revealed that when the course used the new
approach, students’ efficacy for writing method sections,
t(68) ¼ �2.24, p ¼ .03, Mcontrol ¼ 6.09 (SD ¼ 0.88),
Mexperimental ¼ 6.54 (SD ¼ 0.80), res ¼ .26, and results
sections,
t(68) ¼ �3.03, p ¼ .003, Mcontrol ¼ 5.64 (SD ¼ 1.27),
Mexperimental ¼ 6.41 (SD ¼ 0.83), res ¼ .34, improved.
Discussion
As expected, students benefitted from the multifaceted
approach.
Consistent with research on the effectiveness of active learning
(Silberman, 1996; Wenglinsky, 2000) and on the benefits of
add-
ing labs to general psychology classes (Thieman, Clary, Olson,
Dauner, & Ring, 2009), the ‘‘hands-on’’ process of reading
research, being a participant, and then developing a research
study multiple times over the semester enhanced student atti-
tudes. This approach allowed students to learn concepts in
research methodology as they were simultaneously experiencing
those concepts in action. Moreover, students’ exposure to a
vari-
ety of psychological subdisciplines likely made the utility of

research principles more salient. This approach seems to help
attenuate the decreases in perceived utility of research and
statis-
tics reported in the past (Sizemore & Lewandowski, 2009).
Students also benefitted from our scaffolding approach,
which was provided for each experimental design presented
to students. In the initial phases of teaching each experimental
design, the instructor provided substantial support to students.
They read and summarized an article followed by in-class
instructor feedback and discussion. The instructor then led the
students through a demonstration with more feedback and
guidance, building them up along the way. In the last phase, the
instructor took that supportive scaffolding away by having
students work independently in small groups to design and
implement a specific experimental design. The scaffolding
process of providing structure at first, and later removing it,
influenced the higher levels of writing efficacy, better attitudes
toward statistics, and higher perceived skills/abilities in

statistics found in this study.
Although students reported a greater perceived utility of
research, students’ attitudes and self-reported skills/abilities did
not improve with the multifaceted approach. This is consistent
with the unchanged attitudes about research methods overall and
perceptions of research skills and abilities reported by Sizemore
and Lewandowski (2009). Participants in our control and
experi-
mental samples completed a 200-level research methods class
before the class in question, while fewer of the control (55%)
and
experimental participants (57%) completed a statistics course
prior to the class. Therefore, their attitudes about research
meth-
ods in general and their specific abilities in the discipline may
have been already established and therefore resistant to our
course changes (Eagly & Chaiken, 1995). Additionally,
assessment took place in the final week of the semester. Given
the impending final exam, students may have had a more nega-
tive outlook about research methods in general and their skills
and abilities in particular.

Our multifaceted approach also improved perceptions of
statistics such that students reported more positive attitudes,
higher skills/abilities, and higher perceived utility of statistics.
These findings contrast previous findings that learning
statistics negatively correlates with self-efficacy and reported
competence (Onwuegbuzie, 2000; Thompson & Smith,
1982). Our successful influence on perceptions of statistics
may be the result of students learning statistics in the context
of design. By immersing statistics in the framework of answer-
ing a self-generated research question, students may have a
fuller context in which to appreciate statistical concepts,
leading students to have a more positive impression of statis-
tics, have more confidence in their own statistical abilities, and
to better appreciate statistics’ utility.
After the multifaceted approach, students reported higher
levels of efficacy for formal research writing overall and
specifically in our targeted areas of method and results
sections. Students’ feelings of efficacy in formal research writ-

ing after engaging in multiple writing activities throughout the
semester show the importance of learning writing in the context
of design as well as spaced repetition. Providing students with
opportunities to write in 5- to 10-minutes blocks of time models
successful writing habits (e.g., Boice, 1990). This finding also
suggests that a cohort or semester effect was not prevalent. As
Table 1. Differences between Control and Experimental
Methods
Courses
Control Experimental
Variable M SD M SD df t p res
APA style efficacy 6.14 .67 6.50 .65 68 �2.32 .024 .27
Research attitudes 3.61 1.38 4.03 1.41 68 �1.26 .211 .15
Research skills/abilities 4.94 .76 5.02 .94 67 �0.37 .709 .05
Research perceived
utility
4.75 1.13 5.55 .95 68 �3.22 .002 .36
Statistics attitudes 3.89 1.59 5.12 2.56 68 �2.39 .020 .28
Statistics skills/abilities 4.85 1.01 5.44 1.13 67 �2.25 .028 .27
Statistics perceived
utility
4.66 1.21 5.39 1.00 67 �2.75 .008 .32

Note. N ¼ 33 for the control course (before implementing the
change); N ¼ 37
for the experimental course (after the change was implemented).
All analyses
are two-tailed.
Ciarocco et al. 23
writing efficacy improved in the areas specifically targeted, it
seems we did not have a postimplementation sample made
up of generally better or especially confident students.
Our goal was to develop a new multifaceted approach to
teaching research methods that would improve students’
attitudes
toward research and statistics by combining features of expert
scaffolding within a series of active learning activities. Because
we modified several aspects simultaneously, we are unable to
determine whether one facet of our approach was more effective
than others. However, we do believe that part of this approach’s
success is due to the combination of factors and that the whole
may be more influential than the sum of its parts. We also
recog-

nize that our inability to use random assignment in this research
prevents us from ruling out systematic differences between
groups at the start of the study. Likewise, we cannot rule out
confounds. Although there were multiple professors each seme-
ster of data collection, the instructors might have had more
enthusiasm or given more effort in teaching the new approach
with the experimental group as compared to the control group.
However, problems with random assignment and other design
issues are common and unavoidable given the nature of
pedagogical research (Dunn, 2008).
Psychologists obviously value the skills that research and
statistical training provides to our students. Hence, most
programs require it (Perlman & McCann, 1999a, 1999b) and
use it as a benchmark for their programs (Dunn et al., 2007).
While our students successfully learn content, their attitudes
about research and statistics are poor (Sizemore & Lewan-
dowski, 2009, 2011). Perhaps most importantly, students fail
to see the usefulness of this skill set. As these are marketable

skills for psychology majors after graduation, utility and
comfort with the subject is crucial for students in the long run.
The present study supports the notion that teaching approach
can shift students’ attitudes about research and statistics in a
positive way. Our multifaceted approach, involving both active
learning and scaffolding, increased students’ perceptions of
research, statistics, and scientific writing.
Acknowledgment
We thank Michelle Pettenato and Kaitlin Weldon for their help
on this
project.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with
respect to
the research, authorship, and/or publication of this article.
Funding
The authors received no financial support for the research,
authorship,
and/or publication of this article.
Notes

1. We utilized articles selected from a teaching resource created
by the
first two authors. The resource is available on the Society for
the
Teaching of Psychology’s Office of Teaching Resource in
Psychol-
ogy’s website (http://teachpsych.org/otrp/resources/index.php).
2. The first two authors created each in class demonstration. We
provide specifics for each in-class demonstration on
www.teachp-
sychscience.org, an online peer-reviewed resource for teaching
research and statistics in psychology.
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