Note from the Editor-in-Chief: We are pleased to share Volume 6, Issue 1, which offers our readers three research articles, two teaching briefs and two book reviews. The articles cover a variety of topics: public diplomacy training around the world, a comparison of expectations for PR graduates made by practitioners at different levels in their careers, and suggestions for helping students increase their knowledge and confidence in using statistics. We believe you will gain both inspiration and guidance from the teaching briefs, as they explore multicultural training through writing assignments and building recognition of the connections within and across personal networks. Finally, the book reviews offer helpful insights into how these two books might fit into your classes.
The editorial team expanded in November 2019 to include Dr. Kelly Vibber. We are grateful to have her join us as Dr. Lucinda Austin transitions deeper into leadership within the AEJMC PR Division. Dr. Austin has been a great help these past 2 years and will be missed. I am thankful for this entire team, which invests countless hours into proofreading, formatting and preparing each issue. Their service to the field is greatly appreciated. I also want to express my gratitude to our reviewers who offer useful advice through the blind- review process and help us maintain a solid reputation. Thank you!
A publication of the Public Relations Division of AEJMC Copyright 2020 AEJMC Public Relations Division
The Journal of Public Relations Education (JPRE) is devoted to the presentation of research and commentary that advance the field of public relations education. JPRE invites submissions in the following three categories:
Learn more by visiting the About JPRE page and the Authors/Contributors page for submission guidelines. All submissions should follow the guidelines of the most recent edition of the Publication Manual of the American Psychological Association (APA).
Editorial Record: Original draft submitted to JPRE August 26, 2019. Revision submitted October 25, 2019. Manuscript accepted for publication November 15, 2019. First published online January 21, 2020.
The author would like to thank the members of the 2018-2019 Scholarship of Teaching and Learning Faculty Learning Community at Georgia Southern University for their encouragement and advice during this project. Special thanks go to Dr. Claudia Cornejo Happel, Dr. Kathryn Haughney, and Dr. Taylor Norman.
To improve public relations students’ self-efficacy and knowledge of statistics, two hands-on activities were created. One activity used data simulation in the software program SPSS, and the other used printed statistical outputs. Both activities were introduced in a flipped-classroom format as part of a crossover experimental design. The results indicated that knowledge of statistics and self-efficacy for learning statistics increased following the two activities. The results also suggested that the activity using data simulation may have been especially effective for increasing self-efficacy for learning statistics.
Keywords: public relations research, statistics, constructivism, active learning
Demystifying Data: A Constructivist Approach to Teaching Statistical Concepts Using SPSS
Certain challenges are inherent in teaching research
methods to public relations students. In particular, public relations students
tend to have high levels of math anxiety, and this anxiety causes them to avoid
quantitative information (Laskin & Sisco, 2010). Although many public
relations students may dislike numbers, a basic understanding of statistics is
increasingly important for future public relations practitioners (Jain, 2016).
Not all public relations programs require separate statistics courses, but,
even so, public relations students increasingly need a basic understanding of
how statistics relate to strategic decision-making. According to the Commission
on Public Relations Education (CPRE, 2018),
students “must be able to go beyond the numbers and identify the implications
of those numbers to produce actionable insights for the client” (p. 83). The
ability to use data for strategic planning may be particularly important for
public relations practitioners operating in convergent communication models
(Burton, 2019). Therefore, even if they do not plan to run statistical analyses
in their future jobs, public relations students need a conceptual understanding
of statistics so they can make strategic decisions based on statistical data.
In particular, future public relations practitioners should understand
information such as the meaning of correlations and p-values (Public Relations
Society of America & The American Statistical Association, 2011). Although
some public relations undergraduate programs require introductory statistics
courses, the task of teaching students basic statistical concepts often falls
to instructors of public relations research courses.
By using active learning strategies, public relations
instructors can encourage math-averse students to use statistical data for
strategic decision-making. For example,
students can use the online platform Perusall, which requires students to
comment on readings and respond to each other’s comments for a grade, to promote
an active learning experience. Such readings could include articles from Harvard Business Review such as “A
Refresher on Statistical Significance” (Gallo, 2016), “When to Act on a
Correlation, and When Not To” (Ritter, 2014), and “Beware Spurious Correlations”
(2015). However, some students retain their fear of numbers even following
active engagement with such applied reading activities.
Therefore, the author developed an in-class activity that
used statistical software as an innovative way to allow for more in-depth,
hands-on learning. Statistical Package for the Social Sciences (SPSS) was used
to teach basic statistical concepts to public relations students who had never
taken a statistics course. The software program activity, which required students
to take a more proactive approach and simulate data, was used alongside a more
traditional, paper-based activity using SPSS outputs. This paper examines how a
flipped classroom approach incorporating both of these activities affected
students’ statistics knowledge and self-efficacy to learn statistics.
Furthermore, using an experimental crossover design, this paper compares the
relative effects of the two different activities.
In an analysis of the Great Ideas for Teaching (GIFT)
awards presented by the Association for Education in Journalism and Mass
Communication from 2000-2009, it was found that 45% of the awards were given
for teaching elements that reflected a constructivist approach for “higher
& Schwalbe, 2010). Constructivism suggests that, rather than simply
disseminating information, teachers should give students opportunities to
intentionally build knowledge (Anthony, 1996). This approach, which relies
heavily on experiences and activities, may be more useful for helping students
learn to evaluate ideas on their own (Cuillier & Schwalbe, 2010) and, therefore, may be appropriate
for students who need statistics primarily for strategic decision-making (CPRE,
Building Knowledge Through Active Learning
Constructivism is the theoretical background driving active
learning in flipped classrooms, where lecture may be delivered via online
videos and students take an active learning approach in the classroom through
group activities (Bishop & Verleger, 2013). Research has suggested that the
active learning itself, and not the flipped classroom, is the real reason that
students develop a better understanding of material delivered in this format
(Jensen et al., 2015). Virtual reality learning environments, for example, have
been described as helpful because, in line with the constructivist paradigm,
knowledge is based on active experience and dependent on the environment in
which learning takes place (Huang et al., 2010). Similarly, computer-based
modeling tools and simulations have long been considered useful for helping
students build a deep understanding of course concepts (e.g., Dalgarno, 2001).
Active learning interventions have been demonstrated to
improve learning outcomes significantly across a variety of different science
and math courses (Freeman et al., 2014). Furthermore, several studies have
demonstrated that active learning exercises consistent with a constructivist
approach lead to student success in introductory statistics courses (e.g. Gnanadesikan et al., 1997; Smith,
1998). Instructors for statistics courses have propounded the use of real-life
activities as a form of active learning, including simple field experiments,
observation of chance events, and observation of correlated events (Smith,
Improving Self-Efficacy to Learn Statistics Through Active Learning Strategies
In addition to improving knowledge, hands-on activities may
be particularly important for improving self-efficacy when teaching statistics
to public relations students. Self-efficacy to learn statistics represents a
student’s confidence in learning to use statistical data (Finney & Schraw,
2003). Past research has
found that active-learning class formats can improve
self-efficacy for statistical analysis in both biology (Ebert-May et al., 1997)
and psychology (Harlow et al., 2006). Social science
classes incorporating statistics into the coursework frequently use active
learning strategies, and several instructors have created activities around
SPSS. One study found that undergraduate criminal justice majors were less
anxious when learning about statistics using SPSS rather than with a
traditional class with calculations (Stickels & Dobbs, 2007). Journalism
classes have used computer software to teach statistical concepts for decades
(e.g. Burkhart, 1987) because learning to use SPSS allows students to gain the
skills to analyze and interpret their own survey data. Statistical software
programs may also improve communication students’ self-efficacy for learning
statistics. Burkhart (1987) argued that “intellectually, students who learn to use computer programs
to perform statistical analysis of data will gain confidence in critical use of
statistical data provided by news sources” (p. 4). Therefore, the use of
SPSS to improve self-efficacy for learning statistics is worth examining in the
context of public relations research.
Active Learning Through Simulations
SPSS and similar applications may be particularly useful as
platforms for simulation activities for teaching quantitative methods in public
relations research courses. For example, students can simulate their own
datasets in SPSS that demonstrate what participants’ responses would be if
certain survey items are or are not correlated; SPSS can then be used to test
whether their simulated responses do or do not correlate. Simulations, a form
of active learning used in other types of public relations courses like crisis communication, can help students gain key skills
(e.g. Wang, 2017). Simulations using statistical data may be similarly useful to help students gain knowledge of quantitative data
analysis that can be applied for strategic communication practices.
Student-simulated datasets have the particular advantage of requiring students
to actively create patterns in responses, whereas other activities require
students to more passively recognize those patterns.
Simulation-type activities have been used to teach
statistics in public relations in the past, though generally not with SPSS.
Gallicano (2017) demonstrated the usefulness of an active, problem-based
approach to teaching statistics in a public relations course using social media
analytics from Facebook. Using information about social media interactions from
a Facebook fan page, students learned about normal distributions using an
online standard deviation calculating tool. Students then created
visualizations of standard deviations for the purpose of understanding a social
media fan page’s distribution of interactions
for the purpose of objective-setting. In Gallicano’s (2017) activity, the
combination of visualizations and active calculations may have been
particularly beneficial. For concepts such as correlations and significance
levels, however, other tools may be required.
Public relations faculty members at major universities do
sometimes use SPSS in public relations research courses, but they may not use
it for data simulation activities or to teach basic statistics. Current uses of
SPSS in public relations research courses may include an overview of the
application’s uses and interpretation of an SPSS output. More involved uses of
SPSS may include statistical analysis as part of a class research project.
Because SPSS is already used in public relations research courses for other
purposes, however, this software could be repurposed for simulation activities.
In particular, by allowing students to simulate their own datasets and create
graphs, activities using SPSS simulations may prove helpful for improving
students’ understanding of correlations.
Classroom exercises were developed by the author to provide
students with active learning opportunities to gain a deep understanding of
Pearson’s correlation and significance. Two types of exercises were devised: a
lab-based exercise, which allowed students to simulate data consistent with a
constructivist approach, and a paper-based activity, which required students to
interpret statistical outputs without data simulation. Both the lab activity
and the output activity were created using SPSS version 25.0. Although past
research suggests that a simulated activity using SPSS would be more effective
for improving statistics self-efficacy and knowledge (e.g. Burkhart; 1987;
Gallicano, 2017), the seemingly advanced nature of the program and public
relations students’ level of math anxiety may make a simpler, paper-based
activity more helpful for students.
RQ1a: Does learning about statistics using the combination of a computer simulation and a paper-based activity improve students’ level of understanding of statistics?
RQ1b: Does the use of a computer-based simulation activity or a paper-based activity more greatly improve knowledge of statistics?
RQ2a: Does learning about statistics using the combination of a computer simulation and a paper-based activity improve students’ self-efficacy to learn statistics?
RQ2b: Does the use of a computer-based simulation activity or a paper-based activity more greatly improve self-efficacy to learn statistics?
Data regarding the use of the classroom activity were
gathered from two public relations research classes during the spring 2019
semester. To establish a baseline, all students were given a survey regarding
statistics knowledge and self-efficacy to learn statistics a week before any of
the activities. The survey was given again following each of the activities the
students completed. A total of 45 students took part in the study, but only 30
completed all measures and were included in the final analysis (n = 17 for Group A and n = 13 for Group B). Although small, a
sample of 30 is considered acceptable for within-subjects studies (Oberfeld
& Franke, 2013), and, therefore, the data could be used to test RQ1a and
RQ2a. However, the between-subjects tests for RQ1b and RQ2b were considered
exploratory due to the small number of participants in each group.
No statistics courses are required as a prerequisite for
this course, and basic quantitative methods, including survey question design,
probability sampling, and basic statistics, are taught over a four-week period
in the course. After one week discussing the normal curve, confidence
intervals, and confidence levels in class, students began a two-week period of flipped classroom-style lessons
consistent with active learning approaches. To support the flipped classroom
format, students watched videos created by the instructor explaining basic
statistical concepts. All students took an online quiz on these videos before
class on Day 1 of the study, when the first in-class activity took place.
Videos of SPSS demonstrations also prepared students ahead of time to interpret
SPSS outputs and complete the lab-based SPSS activities.
SPSS Lab Activity
The simulation activity took place in a computer lab reserved for the class. This activity used an SPSS file with several variables in place but no participant responses (i.e., no data appeared in “Data View”). Students were guided through the steps in the exercise with written instructions and screenshots (see Appendix A). Several variables were named. For each variable, the original survey question was included in “Variable View,” and the response options were entered under “Values.” The primary variables used in this case were two scale items adapted for a coffee company from the integrity dimension of trust in Hon and Grunig’s (1999) relationship scale for publics: “Starbucks treats people like me fairly and justly” and “Whenever Starbucks makes an important decision, I know it will be concerned about people like me.” Students taking part in the exercise had previously used this integrity scale in a classroom activity while discussing types of survey questions. For that activity, students responded to the items in class for a different chain restaurant and then averaged their responses. They also observed whether or not they answered similarly to the items to spark a conversation about the nature of reliability. As a result, students were expected to understand that people were likely to respond in a similar direction for the items in the integrity scale.
Students began with a warm-up activity in which they reviewed all of the variables in “Variable View,” labeled each variable as scale or nominal, and created new variables in SPSS. In addition to improving their comfort with using the application, this warm-up allowed students to become familiar with the two key scale items. Next, students engaged in the active learning activity for correlation. Students were given these instructions: “Create responses for participants 1-30 for the first two variables, Trust_1 and Trust_2, so that the responses should be strongly, but not perfectly, correlated.” Students then ran a Pearson’s correlation in SPSS and were asked to assess whether the correlation was strong and significant. The interpretation of Pearson’s correlation had previously been demonstrated and explained in the videos that students viewed before class as part of the flipped classroom format. If they were confused, students interacted with the instructor when they were unsure of how to interpret the information.
Students who were unable to generate a strong, significant
correlation repeated the exercise until they were successful. Students
frequently had to attempt the exercise several times, but all were able to
successfully complete the exercise. Once they were successful, students were
directed to generate a scatter-dot plot for the two variables and assess what a
strong correlation looked like. Finally, students were instructed to repeat all
steps for a weak correlation. If students finished the assignment early, they
were told to repeat it for a correlation in a different direction (e.g. a
negative correlation if the previous one were positive). Measures for knowledge
and self-efficacy were administered at the end of class after all students had
completed the exercise.
SPSS Output Activity
Outputs from statistical analyses in SPSS are already used
in some public relations research courses, and therefore, an output-focused
activity was used as the comparison activity. For this activity, students were
asked to get into groups of three to four
people. Students then worked through the steps outlined on an activity sheet
(see Appendix B). The activity sheet was attached to a list of survey questions
regarding students’ perceptions of a local nonprofit gym. Also attached was an
output of analyses for the dataset, including frequencies, descriptive
statistics, an independent-samples t-test, and two correlation matrices. One of
the correlation matrices was made up of items for the same integrity dimension
of trust used in the lab-based activity, but in this case the scales were used
for the nonprofit gym.
For the paper-based activity, students worked through
similar information as in the lab-based activity. To make the activity more interactive
and similar to the computer lab-based activity, the instructions required
students to write a hypothesis about the relationship between two scale
variables and drawing a scatter dot plot depicting one of the predicted
relationships. Furthermore, to meet the constraints of the course schedule,
this activity included information regarding descriptive statistics and a very
brief introduction to an independent-samples t-test. Students worked through
the problems in their groups, and then the groups shared their findings with
the rest of the class. The instructor discussed any problems with the
hypotheses they formed in their groups, gave a demonstration on a whiteboard
for what scatter dot plots might look like for different types of associations,
and corrected any erroneous interpretations from the outputs. Therefore, the
activity was interactive and covered similar material to the lab-based
activity. However, the activity did not allow students to simulate their own
datasets to produce outputs and graphs.
An experimental crossover design was used to evaluate the
overall usefulness of SPSS-based activities in the context of a flipped
classroom and to compare the relative value of each kind of activity. Before
covering information regarding probability and statistics, all students were
given the opportunity to complete a paper-based survey measuring both
self-efficacy to learn statistics and knowledge of Pearson’s correlation. This
measure took place one week before the first activity was scheduled. Subsequent
data collection then took place over the course of three consecutive class
Students were randomly assigned to two groups. Group A took
part in a lab activity first and a paper-based activity second, and Group B completed
the paper-based activity first and the lab activity second (see Table D1 for
full schedule). On Day 1, Group A took part in the in-class activity using the
statistical software SPSS in a computer lab and filled out the measures for
statistics self-efficacy and statistics knowledge at the end of that class
period. Group B used this time outside of class to complete a separate
assignment not related to statistics. Then, both groups met in class for Day 2;
all students took part in an activity using printed outputs from statistical
analyses to learn about basic statistics. Measures for knowledge and
self-efficacy (see Appendix C) were administered at the end of class. Finally,
for Day 3, Group B took part in the same in-class activity Group A completed on
Day 1 using SPSS in a computer lab setting while Group A completed an unrelated
class assignment outside of the classroom.
Students were offered five points extra credit for taking
part at three different points in the intervention (the baseline and two activities;
see Table D1). If they completed all three components, students then received
an additional five points extra credit. All measures were administered by
colleagues from a different field of study so that students did not feel
pressured by the presence of the instructor, and all students used anonymous
identifiers on their surveys. All elements of the study were approved by the
institutional review board before the study began.
Survey questions for the statistics self-efficacy scale were adapted from Finney and Schraw’s (2003) scale for self-efficacy to learn statistics. This scale asks participants to rate their confidence in their ability to learn about a number of statistical procedures (see Appendix C). Finney and Schraw’s (2003) scale includes advanced statistical concepts not covered in the course and does not include measures for applied use of statistics in a public relations context. As a result, the original measures were adjusted for this project. For example, one of the items in the self-efficacy scale asked students to rate their ability to learn to, “analyze the results from a survey using a Pearson’s correlation” on a scale running from 1 (no confidence) to 7 (complete confidence). Following the administration of the surveys, the measures were assessed for internal consistency, which was found to be high (α = .96).
The measure of statistics knowledge, which was reviewed by an education expert to ensure content
validity, took the form of five open-ended questions (see Appendix C).
Open-ended questions were used so as to best capture students’ constructed
knowledge created through active learning. These questions were coded by the
instructor with 1 point or half-point partial credit given per question. The
measures for statistics knowledge and self-efficacy to learn statistics were
administered simultaneously at all three points of measurement.
Demographic information was gathered from students on the last scheduled activity day for their group; one student declined to provide any demographic information but responded to all scale items and was retained for the final sample. As previously stated, 30 students (n = 17 for Group A and n = 13 for Group B) completed measures for the scale items all three times, and those who did not complete all three were removed from the sample. The demographic make-up of the students who completed all measures was 73% White, 17% African American, and 3% mixed race. The sample was 80% female and comprised of 18 juniors and 11 seniors. Only 30% of the students reported previously taking a statistics course.
The first research question addressed whether a) the use of
hands-on activities as part of a flipped classroom would improve public
relations students’ understanding of statistics and b) if one type of activity
was more helpful than another. A paired-samples t-test comparing knowledge
measures for the entire sample both before the flipped classroom format began
(baseline) (M = .28, SD = .67) and following the final
activity (activity 2) (M = 2.60, SD = 1.30) did show a significant
increase in knowledge (t(29) = -8.10,
p < .001). Therefore, when used as
part of a flipped classroom format, the activities did help students understand
To further explore the relative advantages of the two types
of activities, a mixed-design ANOVA with group (Group A, Group B) as a
between-subjects measure and day in class (baseline, activity 1, activity 2) as
a within-subjects factor was used to test for an interaction. No interaction
was found for the between group assignment and students’ knowledge across the
three measurement points (p >
.05). Although the study did not demonstrate that using SPSS was better for
constructing knowledge than using a paper-based activity, the findings support
that the use of both exercises together contribute to the construction of
Tests were also conducted to answer the second research
question regarding the effects of the activities on self-efficacy for learning
statistics. A paired-samples t-test comparing self-efficacy measures for the
entire sample from the baseline (M =
3.4, SD = 1.81) to the activity 2 (M = 5.47, SD = .96) also showed a significant increase in self-efficacy (t(29) = -6.16, p < .001). To explore the second part of RQ2 and compare the
effects of the two activities on self-efficacy for learning statistics, a
mixed-design ANOVA with group (Group A, Group B) as a between-subjects measure
and class day of measurement (baseline, activity 1, activity 2) as a
within-subjects factor was used to test for an interaction. Because Mauchly’s
test for the assumption of sphericity was significant (χ2(2) = 18.63, p <
.05), the degrees of freedom were corrected using Greenhouse-Geisser estimates
of sphericity (ε = .67). The interaction between group and measurement time was
significant (F(1.34, 37.37) = 6.59, p < .05 ), and inspection of the
marginal means indicated that this interaction was driven by Group A’s greater
self-efficacy for learning statistics following activity 1 compared to Group
B’s self-efficacy following its activity 1 (See Figure D1).
Follow-up tests were conducted to interpret the
interaction. Independent-samples t-tests
indicated that, although the difference in self-efficacy for the two groups was
not significant at baseline, when both groups were assessed before they took
part in any activities (p > .05),
there was a significant difference between the two groups in the measure of
self-efficacy for activity 1 (t(28) =
2.67, p < .05). Group A, which was
made up of students who took part in the lab-based activity as their first
active learning experience, had higher self-efficacy for learning statistics
following the activity 1 (M = 5.52, SD = .98) compared to students in Group
B (M = 4.47, SD = 1.20), who took part in the paper output-based activity as
their first active learning experience. Group A maintained this level of
self-efficacy when measured again following the paper-based activity for
activity 2 (see Figure D1 for a graph of the marginal means). Due to the low
number of students in each group, the analysis comparing the two different
teaching activities (lab activity for Group A vs. paper-based activity for
Group B) should be considered exploratory. However, the findings do suggest
that the lab-based activity may have had a particularly strong influence on
self-efficacy for learning about statistics.
This paper supports past findings that active learning
strategies can improve both learning self-efficacy (Ebert-May, et al.1997; Harlow,
et al., 2006) and knowledge (Gnanadesikan, et al. 1997; Smith, 1998). Students’ self-efficacy
improved primarily when they participated in activities that allowed them to
experience the data through virtual simulation in SPSS. This finding is
consistent with research that the learning environment is key for learning
construction to take place (Huang, et al.
2010). Students can gain confidence about statistics by using statistical
analysis software and creating their own dataset simulations. Most of the
students in this study had never previously taken a statistics course, but they
were able to engage with SPSS in a way that improved their self-efficacy for
learning statistics. Based on the findings from this study, both activities are
recommended for improving the self-efficacy of public relations students, but
students may receive the greatest benefit by taking part in the lab-based
Furthermore, although the study was not able to distinguish
between the effects of two types of activities on knowledge, over the course of
the entire study, students’ knowledge of statistics also improved. Students did
not demonstrate significantly different knowledge of statistics following the
first activity; this may be in part due to the challenging nature of the
open-ended questions, which may have weakened the discrimination power of the
knowledge measure. However, by the second hands-on activity, knowledge for all
basic statistical concepts increased significantly for both groups of students.
Therefore, although the study could not demonstrate that one activity was
better than another for increasing knowledge, there is evidence that the
combination of both activities improved knowledge of statistics. These
activities may therefore be used over a two-day period as a part of a flipped
classroom to improve student knowledge of basic statistical concepts.
Related Activity Learning Opportunities
The lab activity also offered multiple opportunities for discussion regarding other aspects of correlations and survey questions related to the course content. For example, students in the study who did not use the whole 5-point scale for their simulated responses had difficulty finding significant Pearson’s correlations. This experience led to discussions regarding the nature of scale variables and why 5-point and 7-point scales are often used to measure attitudes rather than scales with fewer items. These discussions were used to teach students to design surveys with specific uses of the data in mind.
Similarly, when they tried to assess if they had simulated
the activity properly, some students initially had difficulty distinguishing
between the meaning of correlation coefficients and the significance level. The
instructor used this opportunity to reinforce the importance of the two
separate pieces of information and, in particular, to note that significance is
important for all of the statistical tests discussed in class, but that the
correlation coefficient was information unique to correlations. To demonstrate
how the correlation coefficient indicates the strength and direction of a
relationship between two scale variables, the instructor used the scatter dot
plots students created as part of the lab activity (see Appendix A).
activity also gave students the opportunity to learn the process for analyzing
actual survey data, even though that was not the main purpose of the exercise.
The steps for running correlations in SPSS are the same for real data as for
simulated data. Students who want to conduct quantitative research in the
future have, therefore, not only improved knowledge and self-efficacy for
statistics but have also been given instructions that can be used to analyze
their own survey results. However, even if these students never analyze their
own data again, they will have experience with how survey data can be analyzed
and interpreted, which may improve their ability to discuss analysis provided by others.
Limitations and Future Research
This study used a crossover design with random assignment to understand which active learning strategies best improve statistics knowledge and self-efficacy to learn statistics. Due to class schedule constraints, the crossover design did require that students from the two groups take part in the printout activity during the same class (see Table 1). The presence of both groups could have influenced the results even though students were told to engage only members from their respective groups for the printout activity.
Furthermore, although the randomized groups are a strength
of the study, participant attrition was a problem. A total of 15 participants
did not complete all components of the study. Slightly more students in Group B
did not complete the study, which, with the small sample available, could have
influenced the results. However, based on the findings, students who completed
the lab-based activity first (Group A) should have had higher self-efficacy.
Their relatively higher self-efficacy may have made members of Group A more
disposed to come to class for subsequent lessons, which may explain the
differences in participant attrition for the two groups. Furthermore, although
the sample was sufficiently large to make reasonable conclusions about changes
in knowledge and self-efficacy based on the pretests and posttests for the
entire sample (N = 30), the interaction and comparisons between Group A (n = 17) and Group B (n = 13) for self-efficacy should be
considered exploratory. Therefore, the small sample size limits the conclusions
that can be drawn about the different effects of the lab-based and
In addition to limits in the study design, limits should
also be acknowledged in the classroom exercises described here. Additional
activities should be used to complement this approach and to make sure students
understand how correlational data may be used in the context of strategic
planning. Furthermore, the activity described here does not address the common
misconception that correlation can lead to causation. Therefore, students who
took part in this study also participated in other learning assignments during
the semester to supplement their understanding of correlation and significance.
Future studies of activities for teaching public relations
students statistical concepts might explore
how learning about Pearson’s correlation may lay a foundation for understanding
other statistics. Learning about correlation can help students to understand
the general principle of covariance. Therefore, this single activity can be
used as a springboard to discuss the conceptual meaning of Cronbach’s alpha and
simple linear regression by drawing conceptual comparisons. Cronbach’s alpha
can be compared to a correlation with more than two items, and linear
regression may be described as a more advanced form of correlation that can be
used to draw lines and make predictions. Although these definitions are not
precise, a vague and intuitive understanding of information, called gist, is
often more likely to be used in decision making (Reyna, 2008). “The gist
representation is the answer to the question ‘What does 22.2% mean?’ to that individual” (Reyna, 2008,
p. 851). Consequently, , students are more
likely to use an intuitive understanding of statistics for decision-making
rather than a technical definition. Therefore, this exercise for a single
statistical test prepares students to have constructed conceptual knowledge of
several statistical procedures without using any equations or mathematical
calculations. Future research may also examine other activities with
student-simulated datasets, which could be used to explore, for example,
concepts like standard deviation.
Using an experimental crossover design, this study examined how statistical software can be used to teach basic statistical concepts. SPSS and similar programs allow students to create simulated datasets. By attempting to create participant responses that would lead to certain statistical outcomes, students learn on a deeper level about what those outcomes mean. As part of an active learning approach in a flipped class format, this activity significantly increased students’ self-efficacy for learning statistics. Knowledge of basic statistical concepts also improved, although this change was not specifically tied to the simulated data activity. An active learning approach to teaching quantitative methods is recommended for encouraging public relations students who may be otherwise intimidated by statistics in the public relations research curriculum. Using SPSS in lab-based activities, either alone or in combination with activities based on SPSS outputs, may help students gain confidence and construct knowledge for how to use statistics in their professional practice.
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Note: Screenshots of
SPSS have been removed from the original activity.
Working with Nominal and Scale Variables
You have been emailed the dataset for this exercise in
a file called SPSS_Blank_Exercise_SP19.sav; please download this file to your
desktop. Please note that you will only be able to open files ending in .sav
(and .spv for the output files) on computers that run the application SPSS.
Review the variables currently in Variable View,
especially the Label and the Values for the first two variables, Trust_1 and
To see the values, click once and
then click again on the […] that appears
Label each of the variables as Scale, Ordinal, or
Nominal under the Measure Column
Create a new nominal
variable, including the information for Name, Label (the question as it would appear
in Qualtrics), Values (response options), and Measure category.
Create a new scale
variable, including the information for Name, Label (the question as it would
appear in Qualtrics), Values (response options), and Measure category.
Understanding Strong and Weak Correlations
Switch to Data View.
Create responses for participants 1-30 for the first
two variables, Trust_1 and Trust_2, so that the responses should be strongly,
but not perfectly, correlated.
Test the Pearson’s correlation between Trust_1 and
Trust_2 in SPSS: Analyze > Correlate > Bivariate
Select the variable (please note that the Label, not
the Name, appears in SPSS, but if you hover the cursor, the Name will appear).
Click the arrow button in the middle to move the
variables over that we want to test.
Review the Output window. Look for the correlation
coefficient – is it strong? How do you know? Is it significant? How do you
Visualize the results by creating a scatter dot plot.
Go to Graphs > Chart Builder.
Choose the first “Scatter/Dot” option (Select
Scatter/Dot and then double click on the first option that appears)
Place Trust_1 on the x-axis and Trust_2 on the y-axis
by dragging the variable over to the picture of the graph.
What does the plot look like for a strong correlation?
Repeat steps 1-6,
but this time create a weak
SPSS Paper-Based Output Activity
Review the survey questions on p. 1 (Variable View).
Review the responses for participants 1-10 on p. 2 (Data View).
Write a hypothesis for the relationship between 2 of the starred scale variables.
Draw one conclusion from the information on p. 3 (Frequencies Output) that is relevant to a campaign for a gym.
Draw one conclusion from the information on pg. 5 (Descriptive Statistics).
Which variable has the highest standard deviation? What does this mean?
Which variable has the lowest standard deviation? What does this mean?
Find the information that will give you the confidence interval on p. 7 (you will have to do some simple math).
What do the 1s mean in the correlation matrix on p. 9?
Circle all of the negative correlations on p. 9.
Put a square around all of the nonsignificant correlations on p. 9.
Draw a picture of what a graph for what one of the significant correlations would look like. Is it strong? Weak?
Find the significance level for the t-tests on pg. 10. Signal the instructor once you have finished this step.
Measures for Self-Efficacy to Learn Statistics and
Part 1: Self-Efficacy to Learn Statistics
Directions: Please indicate how confident you are that you could learn to complete the
following tasks (1=no confidence, 7=complete confidence).
Explain what a Pearson’s correlation is conceptually.
Interpret the information regarding a Pearson’s correlation (correlation coefficient) that appears in a journal article.
Use Pearson’s correlation coefficient to draw a basic graph indicating what the results might look like when graphed.
Explain what significance level (p value) means conceptually.
Interpret a significance level (p value) that appears in a journal article.
Design survey questions that will allow you to run a correlation.
Analyze the results from a survey using a Pearson’s correlation.
Part 2: Statistics Knowledge
Directions: Please give as much information as you can to answer
each of the following questions. We have intentionally given you more room than
we think you will need; please write as much information as you can. Make sure
to put something down for each question even if you are unsure of the answer.
What does a Pearson’s correlation tell us?
What does it mean if a Pearson’s correlation is strong?
What does it mean if a Pearson’s correlation is weak?
What is a significance level (p value)?
How does the significance level (p value) for a Pearson’s correlation influence how we interpret the correlation?
Tables and Figures
Figure 1. Graph showing
increase in self efficacy for Group A and Group B.