COUNTERFACTUAL THINKING 14COUNTERFACTUAL THIN

COUNTERFACTUAL THINKING
1
4
COUNTERFACTUAL THINKING: APPOINTING BLAME
2
Comment by Ryan Winter: Note the running head up here.
The correct APA format includes a shortened title in ALL
CAPS.
Your header title should be no more than 50 characters.
This title page also starts on page one, and you can see the page
number is flush to the right side of the page while the running
head is flush to the left Comment by Ryan Winter: Do you
know how to enter a header? Click on the “Insert” menu at the
top of word, click on “Header”, and then type in the header
whatever you want. Alternatively, click anywhere on the top of
the page and it will open the header

Counterfactual Thinking: Appointing Blame Comment by
Ryan Winter: The title page here is essentially the same one
from Paper I. It has the title (in APA format), author name, and
university affiliation.
Want my advice? If you did well on the Paper I title page, reuse
it!
Former Student
Florida International University
Comment by Ryan Winter: The good news is that this
example paper is on the same topic as the example paper from
Paper I. I’m going to show you the progress of the paper
throughout the semester, so you can see how you will eventually
combine Papers I, II, III, and IV into Paper V. Let’s continue

looking at counterfactual thinking!
But again, this is an EXAMPLE paper. The topic here
(counterfactual thinking) differs from your study. Do NOT
discuss counterfactuals or any of the variables in this example
in your paper unless they are relevant to your own topic.
Methods Comment by Ryan Winter: The word Method here is
centered and bolded, as is recommended by the APA
Participants Comment by Ryan Winter: Participant (also
bolded) is flush left
One hundred and twenty six students from Florida
International University were randomly selected to participate
in our study. Of these 126 participants, 37% (
n = 47) were male and 63% (
n = 79) were female. Ages ranged from a minimum of
17 to a maximum of 58 with an average of 22.32 years (
SD = 6.30). The sample population consisted of 68.3%
Hispanic Americans (
n = 86), 8.7% African Americans (
n = 11), 19% Caucasians (
n = 24), 1.6% Asians (
n = 2), and 2.4% who did not specify their ethnicity (
n = 3). See Table 1. Comment by Ryan Winter: When
a number starts a sentence, spell out the number Comment by
Ryan Winter: Note the mean and standard deviation here, which
is helpful for knowing about the makeup of the sample. The
mean, of course, is the average Comment by Ryan Winter:
Make sure to have a callout (“Table 1”) followed immedi ately
by the table. You can group all demographics into the same
table (Include the “Statistics”, “Gender”, and “Ethnicity” tables

all under the general “Table 1” phrase)
Table 1 Comment by Ryan Winter: You will have at least four
tables for Study One. Label them in terms of table number (and
make sure to provide a callout for the table in the results
section). Tables are numbered sequentially, with the word Table
flush left and in bold.
Demographics – Study One Comment by Ryan Winter: The
table title is right above the table itself. It is flush left and is in
italics. For Table 1, include all of your demographics (the
statistics table, the gender table, and the ethnicity table). Note:
We do not need to see the age table, which focuses on the age
frequencies. It is better to use the mean age in the statistics
table (rather than the age frequency in the age table).
Make sure each table is flush left
Comment by Ryan Winter: To add tables, simply go into
your SPSS output. You can right-click on the table and then
copy it. Then just paste it into your table page!
Alternatively, you can use the “Snipping tool” function
available on most computers. (Do a search for it!). This allows
you to draw a virtual box around text and then copy it like a
picture. Then just paste the picture into the table page
Finally, your last option is to do the work by hand. Insert a
table with rows and columns and transfer over the information.
This is the hard way, though. Both of the options above took me
less than a minute. Recreating a table manually will take a much
longer time!
Materials and Procedure Comment by Ryan Winter: Also

bolded and flush left. You will notice that this author combined
materials and procedures, which was good for this simple study.
She could have separated them, though, and talked about the
taxi scenario and questionnaires in a “materials” section and the
procedure separately in the “procedure” section. I like this
combined choice, though, for this design.
In accordance with the standardized guidelines for
informed consent, prospective participants were notified of the
potential risks and benefits of participating in the study before
being introduced to the research material. If the student verbally
agreed to participate, he or she was given one of three different
documents, each of which consisted of four parts or sections. In
part one of the study, the participant read a short scenario
concerning a paraplegic couple, Tina and Eugene, who
requested a taxi for a night out with friends. Each of the three
documents depicted the same initial situation with alternate
conditions (changeable, unchangeable, or neutral). Comment by
Ryan Winter: Noting the IV helps a lot. You can tell the author
knows what his IV is. There is only one, with three levels
In the changeable condition, the taxi driver arrived to pick
up the couple, only to promptly decline their fare upon seeing
that they were both paraplegic. Without enough time to call for
another taxi, Tina and Eugene decided to take Tina’s car, which
was handicap equipped. In order to reach their destination, they
had to cross a bridge that had been weakened the night before
due to a severe storm. The damaged bridge collapsed mere
minutes before the couple reached it. Unable to see the missing
portion of the bridge in the night, Tina and Eugene drove off the
road, into the river below, and drowned. The taxi driver, who
had left 15 minutes earlier, managed to make it safely across,
before the collapse. In the unchangeable condition, the situation
remained mostly the same with the exception that the taxi driver
arrived at the bridge after it had collapsed and plummeted into
the water as well. He managed to make it out of the car and
swim to safety, but Tina and Eugene drowned. In the neutral

condition, the taxi arrived to pick up the couple but promptly
refused their fare as soon as he realized that they were both
paraplegic. In this condition, the taxi driver did eventually
agree to take Tina and Eugene to their destination downtown,
albeit after much argument. Due to the recently collapsed
bridge, the taxi driver drove his passengers and himself off the
road and into the river below. He barely managed to make it out
of the car before drowning. Tina and Eugene’s outcome
remained the same. Comment by Ryan Winter: Notice how
thorough the description of the scenario is here. If you wanted
to replicate this study, you would know exactly what to do
because the author tells you exactly what she did. Make sure the
description of your IV is equally clear.
After reading one of the scenarios described above, the
participant continued on to the remainder of the study, which
was composed of a series of open, partially open, and close -
ended questions.
In part two, the student participating in the study was asked to
procure as many ‘If Only’ statements as possible, meaning that
they had to list all the factors they could think of that could
have possibly changed the outcome of the event.
In part three, the participant was presented with a series of
questions about their thoughts regarding the specific situation
they read about. After reading each question, the participant
was asked to record his or her response in a scale of one to nine.
These questions included how avoidable they thought the
accident was (1 = not at all avoidable, 9 = very avoidable), the
causal role of the taxi driver in the couple’s death (1 = not at all
causal, 9 = the most important cause), their thoughts on how
much control the taxi driver had (1 = no control, 9 = complete
control), the negligence of the taxi driver (1 = not at all
negligent, 9 = completely negligent), how much money for
damages the taxi driver was responsible for (1 = no money, 9 =
as much as possible), the foreseeability of the couple’s death (1
= not at all foreseeable, 9 = completely foreseeable), and how
much blame the taxi driver deserved for the event (1 = no blame

at all, 9 = total blame). Remaining questions focused on a series
of statements about the taxi drive, all rated on scales ranging
from 1 (Strongly Disagree) to 9 (Strongly Agree). These
statements included, “The taxi driver was reckless”, “the taxi
driver was patient”, “The taxi driver was careful”, and “The taxi
driver was hasty”. The last question of part three was a yes or
no question that asked the participant whether the taxi driver
agreed to drive the couple or not. This final question served as
an attention check, which informed us if the participant was
attentive to the study and allowed us to exclude potentially
misrepresentative responses from our data. Comment by
Ryan Winter: You know exactly what the DVs are here, and you
know the range for each scale. This is VERY important. If you
tell me the scale was 1 to 9 but that is it, I won’t know if 1 is a
good score or a bad score. Does 9 mean they could avoid it or
they could not avoid it? I need to see both the scale AND the
labels for the DV to make sense Comment by Ryan Winter:
Since these four questions all use the same 1 (Strongly
Disagree) to 9 (Strongly Agree) scale, the student only provide
the scale once. It gets repetitive if you add the same scale after
each question.
Part four asked for the participant’s demographic information,
including gender, age, ethnicity, their first language, and
whether they were a student at Florida International University.
Concluding the study, the participant was debriefed on his or
her contribution to the study as well as our insights on
counterfactual thinking and our main hypothesis. Comment by
Ryan Winter: You can see her procedure, right! Very clear, very
step-by-step
Although we had several dependent variables, our primary focus
involved the perceived blameworthiness of the taxi driver, the
number of ‘If Only’ statements the participants could create,
and the manipulation check regarding whether the driver agreed
to take the couple. As such, these are the only three dependent
variables that we analyzed.
Results Comment by Ryan Winter: Results is centered and

bold. The results section comes right after the methods – there
is no page break
Using survey condition (changeable vs. unchangeable vs.
neutral) as our independent variable and whether participants
recalled whether the taxi driver picked up the paraplegic couple
as the dependent variable, we ran a manipulation check in w hich
we saw a significant effect,
X2(2) = 93.95,
p < .001. Participants in the changeable and
unchangeable conditions correctly said the taxi did not pick up
the couple (95.2% and 90.5%, respectively) while few
participants in the neutral condition said the driver picked up
the couple (4.8%). Cramer’s V, which is most appropriate for a
3 X 2 chi square, showed a large effect. This indicates that
participants did pay attention to whether the taxi driver picked
up the couple. See Table 2. Comment by Ryan Winter: The
chi square here is useful for data that is nominal in nature (that
is, there is no numerical difference between factors). Here, they
either read about a taxi picking up the couple or they didn’t. We
cannot look at a mean or average value here (what is the
average between yes and no?), so the chi square looks at the
number of people who say yes and the number who say no.
Here, we want the participants in some conditions to say yes (if
the taxi picked up the couple) and no (if he didn’t pick them up)
Comment by Ryan Winter: Add in the callout “Table 2”
and then add the table immediately after the callout
Table 2
Crosstabs and Chi Square – Study One
For our main analysis, our first One-Way ANOVA test revealed
significant differences among our independent variable, the
scenario conditions (changeable, unchangeable, or neutral) and

our dependent variable, perceived blameworthiness of the taxi
driver,
F(2, 122) = 3.55,
p = .032. A subsequent Tukey post hoc test supported
our hypothesis by demonstrating that participants were more
likely to blame the taxi driver in the changeable condition (
M = 4.51,
SD = 2.06) than in the unchangeable condition (
M = 3.38,
SD = 2.14).. However, there were no significant
difference for perceived blame between the neutral condition (
M = 4.36,
SD = 2.11) and either the changeable or unchangeable
conditions. These results indicate that in situations where the
outcome is perceived as mutable (changeable), individuals are
more likely to assign blame to the actor who could have acted
differently (unchangeable). See Table 3. Comment by Ryan
Winter: A One Way ANOVA is appropriate here since there are
three levels to the single IV and the DV is on an interval scale
(it ranges from 1 to 9) Comment by Ryan Winter: The
student here provided an exact p value. This is acceptable,
though you can also use p < .05, p > .05, or p < .01 where
appropriate Comment by Ryan Winter: As you can see, this
student did find significance, so she ran post hoc tests on the
ANOVA using Tukey. But what if there was no significance,?
Well, look what happens in the next ANOVA!
Comment by Ryan Winter: Again, have the callout (Table
3) followed by the actual Table 3
Table 3
ANOVA Blame – Study One Comment by Ryan Winter: Make
sure to give a good description of YOUR dependent variable. In
this paper, she looked at blame as a DV, so she put that word
here. Use YOUR dependent variable in the description

We were also interested in the number of ‘If Only’ statements
generated for each condition. We ran a One-Way ANOVA test
using the different conditions (changeable, unchangeable, or
neutral) as our independent variable, and the number of
counterfactuals produced as our dependent variable. The results
revealed that the relationship between condition and number of
‘If Only’ statements produced was not significant,
F(2, 123) = 1.79,
p = .171. Our initial prediction that participants would
develop more counterfactuals in the changeable condition was
not supported since the number of counterfactuals generated in
the changeable condition (
M = 5.41,
SD = 2.21), the unchangeable condition (
M = 4.57,
SD = 2.04), and the neutral condition (
M = 4.88,
SD = 1.85) did not differ. Since the
p-value for the ANOVA test was not significant, there
was no need to run post hoc tests. See Table 4. Comment by
Ryan Winter: So this student ran a second ANOVA, which I
think is best. But since the dependent variable used here was
scaled (confidence, which is on a 1 to 9 scale), the student
could have just as easily run a t-Test focusing on only two
levels of the IV. Let me show you what that might look like.
“We ran a t-Test looking only at the changeable and
unchangeable conditions as our independent variable and
number of If Only statements generated as our dependent
variable. The t-Test was not significant, t(72) = 1.76, p > .05.
Participants did not generate any more counterfactuals in the
changeable condition (M = 5.56, SD = 2.76) than in the

unchangeable condition (M = 4.36, SD = 2.06).”
I could do something similar comparing the changeable and
neutral conditions with a t-Test or comparing the neutral and
unchangeable conditions, but running three t-Tests is a lot.
Much easier to do it with one ANOVA, which looks at all three
comparisons at the same time! Comment by Ryan Winter: Even
though the ANOVA was not significant, I’d still like you to
provide the means and standard deviations for the analysis
Table 4
ANOVA Number of Counterfactuals – Study One
Finally, we ran an independent samples
t-Test with the changeable and unchangeable conditions
only and “How avoidable was the accident” as the dependent
variable, which was significant,
t(82) = 2.71,
p < .01. Participants thought the accident was more
avoidable in the changeable condition (
M = 5.31,
M = 4.21,
SD = 1.85). See Table 5.
Table 5
t-Test “Was the accident avoidable?” – Study One Comment by
Ryan Winter: Note that you may not run a t-Test in your study.
If you do, make sure to include both the group statistics and the
independent samples t-Test tables! Comment by Ryan Winter:
If your t-Table goes onto multiple lines, that is okay. This
student just deleted a few columns from the t-Test to make it fit
the page, but if your t-Table goes over into other rows, that is

okay.
Discussion Comment by Ryan Winter: Your discussion does
not need to be extensive, but I do want you to note whether you
supported or did not support your hypothesis and provide some
possible reasons for your findings. You can make some
educated guesses about what might be going on, but make them
reasonable!
We predicted that participants would place more blame on an
actor whose behavior led to an undesirable outcome (death)
when that actor could have acted differently primarily because
these participants would generate more “If Only” counterfactual
statements that would lead them to see the outcome could have
been avoided. Conversely, we predicted that participants who
read about an undesirable outcome that could not have been
avoided would assign less blame to the actor and would think of
fewer counterfactual “If Only” statements. Results partially
supported these predictions, as we did find more blame for in
the changeable condition compared to the unchangeable (though
neither differed from the neutral condition), and they thought
the accident was more avoidable in the changeable condition
than in the unchangeable condition. However, the number of
counterfactual statements that participants generated did not
differ among our three conditions. It could be that participants
were unfamiliar with the counterfactual task, which requires
some deep thinking, though on a more unconscious level they
could have seen the changeable condition as evidencing more
elements of blame. This begs the question: what if participants
were forced to think deeper? This is the focus of our second
study. Comment by Ryan Winter: This question here is
actually a lead-in to the student’s next study. Your own
methods, results, and discussion paper can end here, but keep in
mind that your final paper is only halfway done right now! In

Paper III, IV, and V, you will help design a follow -up study to
your first study, so as you write this paper try to think about
what you would do differently and what you might add in a
follow-up study.
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1
4
COUNTERFACTUAL THINKING: APPOINTING BLAME
2
Comment by Ryan Winter: Note the running head up here.
The correct APA format includes a shortened title in ALL
CAPS.
Your header title should be no more than 50 characters.
This title page also starts on page one, and you can see the page
number is flush to the right side of the page while the running

head is flush to the left Comment by Ryan Winter: Do you
know how to enter a header? Click on the “Insert” menu at the
top of word, click on “Header”, and then type in the header
whatever you want. Alternatively, click anywhere on the top of
the page and it will open the header
Counterfactual Thinking: Appointing Blame Comment by
Ryan Winter: The title page here is essentially the same one
from Paper I. It has the title (in APA format), author name, and
university affiliation.
Want my advice? If you did well on the Paper I title page, reuse
it!
Former Student

Comment by Ryan Winter: The good news is that this
example paper is on the same topic as the example paper from
Paper I. I’m going to show you the progress of the paper
throughout the semester, so you can see how you will eventually
combine Papers I, II, III, and IV into Paper V. Let’s continue
looking at counterfactual thinking!
But again, this is an EXAMPLE paper. The topic here
(counterfactual thinking) differs from your study. Do NOT
discuss counterfactuals or any of the variables in this example
in your paper unless they are relevant to your own topic.
Methods Comment by Ryan Winter: The word Method here is
centered and bolded, as is recommended by the APA
Participants Comment by Ryan Winter: Participant (also
bolded) is flush left
One hundred and twenty six students from Florida

International University were randomly selected to participate
in our study. Of these 126 participants, 37% (
n = 47) were male and 63% (
n = 79) were female. Ages ranged from a minimum of
17 to a maximum of 58 with an average of 22.32 years (
SD = 6.30). The sample population consisted of 68.3%
Hispanic Americans (
n = 86), 8.7% African Americans (
n = 11), 19% Caucasians (
n = 24), 1.6% Asians (
n = 2), and 2.4% who did not specify their ethnicity (
n = 3). See Table 1. Comment by Ryan Winter: When
a number starts a sentence, spell out the number Comment by
Ryan Winter: Note the mean and standard deviation here, which
is helpful for knowing about the makeup of the sample. The
mean, of course, is the average Comment by Ryan Winter:
Make sure to have a callout (“Table 1”) followed immediately
by the table. You can group all demographics into the same
table (Include the “Statistics”, “Gender”, and “Ethnicity” tables
all under the general “Table 1” phrase)
Table 1 Comment by Ryan Winter: You will have at least four
tables for Study One. Label them in terms of table number (and
make sure to provide a callout for the table in the results
section). Tables are numbered sequentially, with the word Table
flush left and in bold.
Demographics – Study One Comment by Ryan Winter: The
table title is right above the table itself. It is flush left and is in
italics. For Table 1, include all of your demographics (the
statistics table, the gender table, and the ethnicity table). Note:
We do not need to see the age table, which focuses on the age
frequencies. It is better to use the mean age in the statistics
table (rather than the age frequency in the age table).
Make sure each table is flush left

Comment by Ryan Winter: To add tables, simply go into
your SPSS output. You can right-click on the table and then
copy it. Then just paste it into your table page!
Alternatively, you can use the “Snipping tool” function
available on most computers. (Do a search for it!). This allows
you to draw a virtual box around text and then copy it like a
picture. Then just paste the picture into the table page
Finally, your last option is to do the work by hand. Insert a
table with rows and columns and transfer over the information.
This is the hard way, though. Both of the options above took me
less than a minute. Recreating a table manually will take a much
longer time!
Materials and Procedure Comment by Ryan Winter: Also
bolded and flush left. You will notice that this author combined
materials and procedures, which was good for this simple study.
She could have separated them, though, and talked about the
taxi scenario and questionnaires in a “materials” section and the
procedure separately in the “procedure” section. I like this
combined choice, though, for this design.
In accordance with the standardized guidelines for
informed consent, prospective participants were notified of the
potential risks and benefits of participating in the study before
being introduced to the research material. If the student verbally
agreed to participate, he or she was given one of three different
documents, each of which consisted of four parts or sections. In
part one of the study, the participant read a short scenario
concerning a paraplegic couple, Tina and Eugene, who
requested a taxi for a night out with friends. Each of the three
documents depicted the same initial situation with alternate

conditions (changeable, unchangeable, or neutral). Comment by
Ryan Winter: Noting the IV helps a lot. You can tell the author
knows what his IV is. There is only one, with three levels
In the changeable condition, the taxi driver arrived to pick
up the couple, only to promptly decline their fare upon seeing
that they were both paraplegic. Without enough time to call for
another taxi, Tina and Eugene decided to take Tina’s car, which
was handicap equipped. In order to reach their destination, they
had to cross a bridge that had been weakened the night before
due to a severe storm. The damaged bridge collapsed mere
minutes before the couple reached it. Unable to see the missing
portion of the bridge in the night, Tina and Eugene drove off the
road, into the river below, and drowned. The taxi driver, who
had left 15 minutes earlier, managed to make it safely across,
before the collapse. In the unchangeable condition, the situation
remained mostly the same with the exception that the taxi driver
arrived at the bridge after it had collapsed and plummeted into
the water as well. He managed to make it out of the car and
swim to safety, but Tina and Eugene drowned. In the neutral
condition, the taxi arrived to pick up the couple but promptly
refused their fare as soon as he realized that they were both
paraplegic. In this condition, the taxi driver did eventually
agree to take Tina and Eugene to their destination downtown,
albeit after much argument. Due to the recently collapsed
bridge, the taxi driver drove his passengers and himself off the
road and into the river below. He barely managed to make it out
of the car before drowning. Tina and Eugene’s outcome
remained the same. Comment by Ryan Winter: Notice how
thorough the description of the scenario is here. If you wanted
to replicate this study, you would know exactly what to do
because the author tells you exactly what she did. Make sure the
description of your IV is equally clear.
After reading one of the scenarios described above, the
participant continued on to the remainder of the study, which
was composed of a series of open, partially open, and close -
ended questions.

In part two, the student participating in the study was asked to
procure as many ‘If Only’ statements as possible, meaning that
they had to list all the factors they could think of that could
have possibly changed the outcome of the event.
In part three, the participant was presented with a series of
questions about their thoughts regarding the specific situation
they read about. After reading each question, the participant
was asked to record his or her response in a scale of one to nine.
These questions included how avoidable they thought the
accident was (1 = not at all avoidable, 9 = very avoidable), the
causal role of the taxi driver in the couple’s death (1 = not at all
causal, 9 = the most important cause), their thoughts on how
much control the taxi driver had (1 = no control, 9 = complete
control), the negligence of the taxi driver (1 = not at all
negligent, 9 = completely negligent), how much money for
damages the taxi driver was responsible for (1 = no money, 9 =
as much as possible), the foreseeability of the couple’s death (1
= not at all foreseeable, 9 = completely foreseeable), and how
much blame the taxi driver deserved for the event (1 = no blame
at all, 9 = total blame). Remaining questions focused on a series
of statements about the taxi drive, all rated on scales ranging
from 1 (Strongly Disagree) to 9 (Strongly Agree). These
statements included, “The taxi driver was reckless”, “the taxi
driver was patient”, “The taxi driver was careful”, and “The taxi
driver was hasty”. The last question of part three was a yes or
no question that asked the participant whether the taxi driver
agreed to drive the couple or not. This final question served as
an attention check, which informed us if the participant was
attentive to the study and allowed us to exclude potentially
misrepresentative responses from our data. Comment by
Ryan Winter: You know exactly what the DVs are here, and you
know the range for each scale. This is VERY important. If you
tell me the scale was 1 to 9 but that is it, I won’t know if 1 is a
good score or a bad score. Does 9 mean they could avoid it or
they could not avoid it? I need to see both the scale AND the
labels for the DV to make sense Comment by Ryan Winter:

Since these four questions all use the same 1 (Strongly
Disagree) to 9 (Strongly Agree) scale, the student only provide
the scale once. It gets repetitive if you add the same scale after
each question.
Part four asked for the participant’s demographic information,
including gender, age, ethnicity, their first language, and
whether they were a student at Florida International University.
Concluding the study, the participant was debriefed on his or
her contribution to the study as well as our insights on
counterfactual thinking and our main hypothesis. Comment by
Ryan Winter: You can see her procedure, right! Very clear, very
step-by-step
Although we had several dependent variables, our primary focus
involved the perceived blameworthiness of the taxi driver, the
number of ‘If Only’ statements the participants could create,
and the manipulation check regarding whether the driver agreed
to take the couple. As such, these are the only three dependent
variables that we analyzed.
Results Comment by Ryan Winter: Results is centered and
bold. The results section comes right after the methods – there
is no page break
Using survey condition (changeable vs. unchangeable vs.
neutral) as our independent variable and whether participants
recalled whether the taxi driver picked up the paraplegic couple
as the dependent variable, we ran a manipulation check in which
we saw a significant effect,
X2(2) = 93.95,
p < .001. Participants in the changeable and
unchangeable conditions correctly said the taxi did not pick up
the couple (95.2% and 90.5%, respectively) while few
participants in the neutral condition said the driver picked up
the couple (4.8%). Cramer’s V, which is most appropriate for a
3 X 2 chi square, showed a large effect. This indicates that
participants did pay attention to whether the taxi driver picked
up the couple. See Table 2. Comment by Ryan Winter: The

chi square here is useful for data that is nominal in nature (that
is, there is no numerical difference between factors). Here, they
either read about a taxi picking up the couple or they didn’t. We
cannot look at a mean or average value here (what is the
average between yes and no?), so the chi square looks at the
number of people who say yes and the number who say no.
Here, we want the participants in some conditions to say yes (if
the taxi picked up the couple) and no (if he didn’t pick them up)
Comment by Ryan Winter: Add in the callout “Table 2”
and then add the table immediately after the callout
Table 2
Crosstabs and Chi Square – Study One
For our main analysis, our first One-Way ANOVA test revealed
significant differences among our independent variable, the
scenario conditions (changeable, unchangeable, or neutral) and
our dependent variable, perceived blameworthiness of the taxi
driver,
F(2, 122) = 3.55,
p = .032. A subsequent Tukey post hoc test supported
our hypothesis by demonstrating that participants were more
likely to blame the taxi driver in the changeable condition (
M = 4.51,
M = 3.38,
SD = 2.14).. However, there were no significant
difference for perceived blame between the neutral condition (
M = 4.36,
SD = 2.11) and either the changeable or unchangeable
conditions. These results indicate that in situations where the
outcome is perceived as mutable (changeable), individuals are
more likely to assign blame to the actor who could have acted
differently (unchangeable). See Table 3. Comment by Ryan

Winter: A One Way ANOVA is appropriate here since there are
three levels to the single IV and the DV is on an interval scale
(it ranges from 1 to 9) Comment by Ryan Winter: The
student here provided an exact p value. This is acceptable,
though you can also use p < .05, p > .05, or p < .01 where
appropriate Comment by Ryan Winter: As you can see, this
student did find significance, so she ran post hoc tests on the
ANOVA using Tukey. But what if there was no significance,?
Well, look what happens in the next ANOVA!
Comment by Ryan Winter: Again, have the callout (Table
3) followed by the actual Table 3
Table 3
ANOVA Blame – Study One Comment by Ryan Winter: Make
sure to give a good description of YOUR dependent variable. In
this paper, she looked at blame as a DV, so she put that word
here. Use YOUR dependent variable in the description
We were also interested in the number of ‘If Only’ statements
generated for each condition. We ran a One-Way ANOVA test
using the different conditions (changeable, unchangeable, or
neutral) as our independent variable, and the number of
counterfactuals produced as our dependent variable. The results
revealed that the relationship between condition and number of
‘If Only’ statements produced was not significant,
F(2, 123) = 1.79,
p = .171. Our initial prediction that participants would
develop more counterfactuals in the changeable condition was
not supported since the number of counterfactuals generated in
the changeable condition (
M = 5.41,

SD = 2.21), the unchangeable condition (
M = 4.57,
SD = 2.04), and the neutral condition (
M = 4.88,
SD = 1.85) did not differ. Since the
p-value for the ANOVA test was not significant, there
was no need to run post hoc tests. See Table 4. Comment by
Ryan Winter: So this student ran a second ANOVA, which I
think is best. But since the dependent variable used here was
scaled (confidence, which is on a 1 to 9 scale), the student
could have just as easily run a t-Test focusing on only two
levels of the IV. Let me show you what that might look like.
“We ran a t-Test looking only at the changeable and
unchangeable conditions as our independent variable and
number of If Only statements generated as our dependent
variable. The t-Test was not significant, t(72) = 1.76, p > .05.
Participants did not generate any more counterfactuals in the
changeable condition (M = 5.56, SD = 2.76) than in the
unchangeable condition (M = 4.36, SD = 2.06).”
I could do something similar comparing the changeable and
neutral conditions with a t-Test or comparing the neutral and
unchangeable conditions, but running three t-Tests is a lot.
Much easier to do it with one ANOVA, which looks at all three
comparisons at the same time! Comment by Ryan Winter: Even
though the ANOVA was not significant, I’d still like you to
provide the means and standard deviations for the analysis
Table 4
ANOVA Number of Counterfactuals – Study One
Finally, we ran an independent samples

t-Test with the changeable and unchangeable conditions
only and “How avoidable was the accident” as the dependent
variable, which was significant,
t(82) = 2.71,
p < .01. Participants thought the accident was more
avoidable in the changeable condition (
M = 5.31,
M = 4.21,
SD = 1.85). See Table 5.
Table 5
t-Test “Was the accident avoidable?” – Study One Comment by
Ryan Winter: Note that you may not run a t-Test in your study.
If you do, make sure to include both the group statistics and the
independent samples t-Test tables! Comment by Ryan Winter:
If your t-Table goes onto multiple lines, that is okay. This
student just deleted a few columns from the t-Test to make it fit
the page, but if your t-Table goes over into other rows, that is
okay.
Discussion Comment by Ryan Winter: Your discussion does
not need to be extensive, but I do want you to note whether you
supported or did not support your hypothesis and provide some
possible reasons for your findings. You can make some
educated guesses about what might be going on, but make them
reasonable!
We predicted that participants would place more blame on an
actor whose behavior led to an undesirable outcome (death)
when that actor could have acted differently primarily because
these participants would generate more “If Only” counterfactual
statements that would lead them to see the outcome could have
been avoided. Conversely, we predicted that participants who

read about an undesirable outcome that could not have been
avoided would assign less blame to the actor and would think of
fewer counterfactual “If Only” statements. Results partially
supported these predictions, as we did find more blame for in
the changeable condition compared to the unchangeable (though
neither differed from the neutral condition), and they thought
the accident was more avoidable in the changeable condition
than in the unchangeable condition. However, the number of
counterfactual statements that participants generated did not
differ among our three conditions. It could be that participants
were unfamiliar with the counterfactual task, which requires
some deep thinking, though on a more unconscious level they
could have seen the changeable condition as evidencing more
elements of blame. This begs the question: what if participants
were forced to think deeper? This is the focus of our second
study. Comment by Ryan Winter: This question here is
actually a lead-in to the student’s next study. Your own
methods, results, and discussion paper can end here, but keep in
mind that your final paper is only halfway done right now! In
Paper III, IV, and V, you will help design a follow-up study to
your first study, so as you write this paper try to think about
what you would do differently and what you might add in a
follow-up study.
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STUDY ONE METHODS, RESULTS DISCUSSION
INSTRUCTIONS 1
STUDY ONE METHODS, RESULTS, DISCUSSION 2
Instructions for Paper II: Study One Methods, Results, and
Discussion (Worth 35 Points)
Ryan J. Winter
Purpose of Paper II: Study One Methods, Results, and
Discussion
1). Psychological Purpose
The psychological purpose behind Paper II is to make sure you
can tell your reader what you did on your study, how you did it,
and what you found. By now you have read several empirical
studies in psychology, so you should be familiar with the
Methods, Results, and Discussion sections. Now is your chance
to write your own sections!
Similar to the studies you cited in Paper I, your Paper II will
provide information about your study participants, materials,
and procedure in your Methods section. Your participant section

goes first, and it includes descriptive statistics about your
sample (means and standard deviations for age as well as
percentages for gender and race/ethnicity). Your materials and
procedure section includes information about what you did and
how you did it.
You should write this section for an audience who is
unfamiliar with your specific study, but assume that they do
know research methods. Thus educate your reader about your
materials and procedure, giving enough detail so they could
replicate the study. This includes explicitly describing your
independent and dependent variables and discussing how you
presented that material to your participants. My suggestion is to
look at the articles you cited in Paper I and see how they wrote
their Methods sections. This will give you a good idea about the
level of depth and detail you need in your own Methods section.
Your Results section follows. The purpose of this section is to
show how you analyzed the data and describe what you found.
Finally, you will include a short description of your findings in
a Discussion section. Tell me if you supported or did not
support your hypotheses and explain why you got those results
(you can actually speculate here if you like, but make it an
“educated” speculation!)
2). APA Formatting Purpose
The second purpose of Paper II: Methods, Results and
Discussion is to once again teach you proper American
Psychological Association (APA) formatting for these sections.
In the pages below, I will tell you how to format your paper
using APA style. There are a lot of very specific requirements
in APA papers (as specific as what to italicize), so pay attention
to the instructions below as well as the APA formatting
powerpoint presentation!
3). Writing Purpose
Finally, this paper is intended to help you figure out how to
write a Methods, Results, and Discussion section. Many
students find statistics daunting, but my hope here is that

writing this paper will help you understand both the logic and
format of statistics in your results sections. We will once again
give you a lot of feedback and help in this paper, which you
help you when you write Papers IV and V later in the course.
Make sure that you write this for an audience familiar with APA
methods and results, but also for someone who needs you to tell
them what you found.
Note #1: The plagiarism limit is higher in this paper (up to
65%) since your classmates are doing the same design. Don’t go
higher than that, though! 65% is the maximum allowed!
Note #2: You do NOT need to include your literature review /
hypotheses in Paper II, as Paper II focuses just on your
methods, results, and discussion. However, you’ll include those
Paper I components later in Paper III, so do keep them handy!
Note #3: Unlike Paper I, there is no set minimum or maximum
page limit for Paper II. However, we are still looking for good
detail about your study design and your study results
Note #4: Sorry for the length of the instructions! They are long,
but take it one section at a time and you will get all of the
content you need for your paper. It also increases your chances
of getting a great grade!
Instructions for Paper II: Study One Methods, Results, and
Discussion (Worth 35 Points)
1. Title Page: I expect the following format
(1 point):
a. The title page for your Paper II is identical to the one you
used for Paper I: Literature Review Study One. For proper APA
formatting, either copy your title page from Paper I or review
the instructions I gave you in Paper I. You can change your title
if you like, but make sure it helps to describe your study (much
like a title in PsycInfo describes what the authors did in their
paper)
2. Abstract?
a. You DO NOT need an abstract for Paper I. In fact, because

your abstract needs to summarize the results for both study one
and study two, you cannot write it until you run both studies
and have results to summarize. So omit the abstract until you
get to Paper V.
3. Methods Section: I expect the following format
(15 points):
a. For this paper, the methods section starts on page 2.
b. Write
Method at the top of this page, make it
bold, and center it (see the top of this page as an
example!)
c. The participants section comes next. The word
Participants is bolded and left justified. In this section
…
i. Tell me who your participants were (college students, family
members, friends?) and how many there were.
1. Note: If a number starts a sentence, then spell out the
number. That is, “Two-hundred and five participants
participated in this study.” If a number is mid-sentence, you can
use numerals. “There were 205 participants in this study.”
a. But keep it consistent. If you spell out a number at the start
of the sentence, carry that through and spell out other numbers
in the rest of the sentence.
2. For statistics or scales, always use numbers (the mean,
SD, %, etc.)
ii. Provide frequencies and descriptive statistics for relevant
demographics.
1. Some variables—like ethnicity and gender—are
nominal/categorical, so you provide frequency information (the
number of participants who fit that category). “There were 100
men (49%) and 105 women (51%) in the study.” Or “The sample
was 49% male (

N = 100) and 51% female (
N = 105).”
2. Other variables—like age—are interval or ratio, so use
descriptive statistics (the range, mean, and the standard
deviation). “Participants ranged in age from 18 to 77 (
M = 24.03,
SD = 3.50).” or “The average age of participants was
24.03 (
SD = 3.50), and ranged from 18 to 77.”
3. Make sure to italicize the
N,
M, and
SD (the letters, not the numbers)
iii. Make sure to include a “callout” to the demographics table
at the end of the participant section. That is, write “See Table
1” to direct readers to your demographics table.
1. Then, supply the table right below the callout. APA allows
the tables to be in-text after the callout OR in an appendix at
the end of the paper. This methods course prefers the former, so
include your SPSS tables in-text after the callout. You should
include the descriptive statistics table, the table for gender, and
the table for ethnicity. See the example paper for a visual aide.
d.
Materials and Procedure
i. For this section, things are flexible. Some studies include
Materials and Procedure in the same section while others break
them up into two sections. This is a matter of choice.
1. In general, the more complex the design, the better it is to
split up the methods and results. In one section, the author may
describe the materials; in the next, they describe what
participants did with those materials (the procedure). This is
one option for you. However …
2. However, your study is simple enough that I strongly

recommend combining them into
one overall Materials and Procedure section.
ii. Again, the words
Materials andProcedure are flush left. In this section,
provide information about your materials and your procedure. I
suggest starting with your procedure. Tell your reader what
your participants did in the exact order that participants did
them.
Be very specific here. I have the following
recommendations:
1. First, talk about the oral informed consent procedure.
2. Second, talk about the Twitter Apology survey.
Provide enough detail so your reader could replicate
your design if they wanted to do so. YOU need to give them
enough detail so they can mimic what you did. (Hint: If you
want, copy and paste the various questions or refer the reader to
an appendix with the actual surveys at the end of the paper)
a. I want to stress this detail concept – Pretend that I have no
idea what you did or what your materials look like, but I want
to replicate your study. Thus teach me your design and your
procedures. Be VERY clear and detailed about what you did and
how you did it.
b. Go into painstaking detail about what EACH section of the
survey page looked like, including what the participant
instructions say and the look of the stimulus materials. If there
are advertisements on the page, describe them. If there are
pictures, describe them. If there is a profile, describe it. If these
items are identical across all conditions, note that fact.
c. Importantly, describe how the surveys differ. That is, you
have three versions of the survey, with the main difference in
the last few tweets. Describe those tweets (you can even copy
and paste them if you want!)
d. Note: At the end of the semester (for Paper V), someone

other than your instructor / TA may grade your paper. They may
know NOTHING about Apology research or research regarding
social media, but they do know methods. Write this section for
that methodology expert.
3. Third, talk about your dependent variables. That is, discuss
your survey questions. For these dependent variables, once
again provide enough detail so I know
exactly what questions you asked. For example,
“Participants provided their gender, age, and race”. For other
dependent variables, tell me how the responses were recorded
(yes/no, true/false, a scale of 1 to 6, etc.). If you used a scale,
note the endpoints (your reader needs to know whether a higher
number is better / worse than a lower number). For example,
“Participants were asked, ‘How frustrating was this task?’, and
they responded on a scale from 1 (very frustrating) to 9 (not at
all frustrating).’” Your study has a few really important DVs
(including several DVs about how sincere the apology seemed,
or whether the apology seemed to acknowledge the conduct was
wrong or whether it showed an expression of remorse). For
these DVs, you again need to tell me what they are
specifically!
4. Fourth, make sure to highlight which specific DVs you
analyzed. If there are DVs that participants completed but you
did not analyze, feel free to say that participants completed
them but since they were not analyzed, they are not discussed
further.
5. Fifth, make sure to be specific about your attention /
manipulation check question! What did you
specifically ask? How did you measure responses?
6. Finally, mention debriefing. You don’t need a lot of detail as,
most researchers understand what goes into a generic debriefing
statement
e. There is no set minimum or maximum on the length for the
methods section, but I would expect

at least a page or two, though probably more. After all,
your research script took up several pages – you should provide
a similar level of depth and detail in your methods section!
Missing important descriptions of your IVs and DVs or
presenting them in a confused manner will lower your score in
this section.
4. Results Section: I expect the following format
(10 points):
a. The results are the hardest part of this paper, and your lab
powerpoints will help you with this part of the paper (also refer
to the crash course statistics quizzes, which walk you through
similar analyses. They will help!).
b. Write
Results at the top of this section, center it, and use
boldface. This section comes at the end of the methods section,
so the results section DOES NOT start on its own page.
c. For the results section, include statistics about the most
important variables in your study, including your IV (Apology
condition – Sincere, Insincere, and No Apology) and the DVs
that you feel are most important to your hypotheses. There are
several important DVs in your survey, including all of those in
Part II (regarding apologies) and several DVs in Part III
(Charlie impressions). Note that some instructors may not do
this Twitter Apology study at all, but the results section should
follow the same guidelines regardless of your study topic.
d. Specifically,
you must run at least three different analyses on three
different dependent variables
. One analysis
must be a chi square for the question asking participants
to recall which hashtag they saw (our manipulation check,
which looks at three options for the Part V nominal variable),
one must be a One Way ANOVA, and the third can be

either an ANOVA or a
t-test. For the One Way ANOVA, I recommend looking
at Question #7 in Part II, which focuses on whether Charlie’s
apology seemed sincere. Questions #1 and #5 from Part II are
also good, as both look at important apology elements. Your
third analysis can be either an ANOVA or a
t-test, and the dependent variable you analyze is up to
you (it just needs to have an interval or ratio based scale).
Analyze a dependent variable that you think is important (and
one that helps you address an element you might have looked at
in your study one literature review).
Note: Although you can run a
t-Test for this third analysis, I do not recommend it. A
t-Test only looks at two conditions, but there are three
conditions in your study (sincere, insincere, and no apology), so
ignoring one of them doesn’t make empirical sense. Why collect
data for one condition and ignore it? If you do use a t-Test, just
note that you cannot look at the same DV with both your
t-Test and the ANOVA. We count the number of DVs
that you analyze – NOT the number of statistical tests you run!
e. Below are three of the tests that you can run in your results
section.
i.
Chi square: Your first analysis will be a chi square,
which you use if your DV is nominal (yes / no, or male /
female, or Caucasian / African American / Hispanic, etc.). In
our case, we have our “Hashtag recall” question in Part V,
which has three levels. So let’s discuss the chi square, which
doesn’t look at mean or average scores, but instead counts how
many responses there actually are compared to how many are
expected
1. Consider the DV in Part V of your questionnaire – “
Without looking back, what hashtag did Charlie end the
Twitter post with? (

Mark
one with an X
)” The options were #SorryNotSorry, #SorrySorrySorry,
or #WhatsDoneIsDoes. Here, you can run a chi square looking
at the frequencies of the three answer options
2. We are interested in the chi square (
χ2) and
p value. We also provide percentages for each of our
groups rather than means and
SDs, since we need interval or ration variables for
those. There are two ways to analyze a chi square:
a. 1). Easy Way: Look at how many in each category recall
seeing that hashtag. That is, “Using apology condition as our
independent variable (sincere, insincere, or no apology) and
recall of the hashtag Charlie used as the dependent variable, we
saw a significant effect,
χ2(4) = 68.49,
p < .001. Most “Sincere” condition participants recalled
#SorrySorrySorry (98%); most “Insincere” condition
participants recalled #SorryNotSorry (96%); and most “No
apology” condition participants recalled #WhatsDoneIsDone
(90%). Cramer’s V was strong. This indicates that participants
saw our manipulation as intended.”
i. Note: Cramer’s V is good for a 3 X 3 design. Here, we have
three conditions and three hashtags, so 3 X 3
b. 2). Hard Way: You can look at correct versus incorrect recall.
This is a bit trickier to run in SPSS, since you first need to
add ALL those who correctly remembered the hashtag
(Sincere participants who recalled #SorrySorrySorry +
Insincere participants who recalled #SorryNotSorry + No
apology participants who recalled #WhatsDoneIsDone) and
compare them to people who were incorrect in their recall.

i. In this instance, you wouldn’t want the chi square to be
significant. That is, “Using apology condition as our
independent variable (sincere, insincere, or no apology) and
recall of the hashtag Charlie used as the dependent variable, we
did not see a significant effect
χ2(4) = 1.49,
p > .05. Cramers V was weak. This indicates that there
was no difference between those who got the attention check
question correct across the three different conditions.”
c. My advice is to go with the chi square option in a. 1). Above,
though either is acceptable
d. Make sure to italicize the
χ and
p
ii.
ANOVA: Since you have a condition independent
variable with
three levels (e.g. Sincere, Insincere, or No Apology),
the most appropriate test is a One-Way ANOVA when your DV
is on an interval or ratio scale (like a 0 to 5 scale or a 1 to 6
scale). Your lab and lecture powerpoints show you how to
conduct an ANOVA, but there are some guidelines I want to
give you about how to write your results. Below, I am going to
walk you through an analysis specific to your Twitter Apology
paper.
1. First, note that there are several dependent variables to
choose from. For my example analysis below, I want to focus on
Part II in your survey (Apology variables). Since each of the
eight questions in that section are scaled variables that range
from 1 to 6, each uses an interval scale, which is perfect for an
ANOVA.
2. Second, given that this study has one IV with three levels and
we will look at one DV at a time, a
One-Way ANOVA is the best test to use to see if there

are significant differences among the three levels of the IV for
that one DV. We look first at the ANOVA table (or
F table) and focus on the between subject factor. We
note the degrees of freedom, the
F value itself, and the
p value. (We’ll get into two-way ANOVAs later in this
course, but here we only have one independent variable, so it is
a One-Way ANOVA. Yes, we have three levels to our IV, but it
is still only one IV).
3. Third, if the
p value is significant (less than .05), we have one more
step to take. Since this is a three-level IV, we need to compare
mean A to mean B, mean A to mean C, and mean B to mean C.
We do this using a post hoc test (try using Tukey!). That will
tell us which of the means differ significantly. You then write
up the results. For example, let’s say I ran an ANOVA on the
dependent variable “Charlie’s apology seemed sincere”. My
write up would look like the paragraph below (though note that
I completely made up the data below, so don’t copy the
numbers!) …
a. Significant Finding:
i. Using apology condition (sincere v. insincere v. no donation)
as our independent variable and ratings of “Charlie’s apology
seemed sincere” as the dependent variable, we found a
significant condition effect,
F(2, 203) = 4.32,
p < .05. Tukey post hoc tests showed that participants
agreed that the apology was more sincere in the sincere
condition (
M = 5.56,
SD = 1.21) than participants in both the insincere
condition (
M = 2.24,
SD = 0.89) and the no apology condition (

M = 3.23,
SD = 0.77). Participants also thought the no apology
was more sincere than the insincere apology, thus supporting
our prediction.
1. Note there are lots of possible outcomes. The one above
essentially says that the sincere condition was rated as more
sincere than the no apology and insincere conditions, and that
the no apology was rated as more sincere than the insincere
apology (In other words, Sincere is greater than no apology,
which is greater than insincere, or S > N > I). However, we
might also find that NONE of the three conditions differ from
each other, so they are all equal (S = N = I) or we might find
that two conditions differ from the third (S = N > I), so Sincere
and No apology don’t differ from each other, but both are rated
more sincere than the insincere apology.
b. Non-Significant Finding:
i. Using apology condition (sincere v. insincere v. no apology)
as our independent variable and ratings of “Charlie’s apology
seems sincere” as the dependent variable, we failed to find a
significant effect,
F(2, 203) = 2.32,
p > .05. Participant ratings of sincerity did not differ
between the sincere (
M = 4.45,
SD = 1.21), insincere (
M = 4.24,
SD = 0.89) and no apology (
M = 4.23,
SD = 0.77) conditions. This fails to confirm our
prediction that participants would find the apology more sincere
in some conditions versus others.
c. Make sure to italicize the
F,
p,

M, and
SD (as in the example)
d. Pretty simple, right! I require that you run an ANOVA on at
least one variable from Part II.
i. For your second ANOVA, you can run it on another Part II
dependent variable or one from Part III. The choice is yours.
My recommendation is to do another from Part II, since that
section focuses on apologies (the main element of your
hypotheses), but it might also be interesting to look at a Charlie
impression questions from Part III. The choice is up to you.
e. Note that you could also run a
t-Test on one of the Part II or Part III dependent
variables, looking only at two conditions (e.g. Sincere versus
Insincere, or Insincere versus No Apology). However, it makes
more sense to look at all three conditions this semester since
you collected data for all three conditions. Still, let me give you
some insight into the t-Test.
iii.
t-Test: If you have only two levels to your IV (e.g.
Sincere and Insincere only), things are even more simple.
However, I do NOT expect you to run a
t-Test since your study has three study levels.
1. Note once again that a
t-Test looks at differences between only two groups.
Your lab presentations tell you how to run a
t-Test, but you can do it on your own as well (you can
even run this if your study originally has three levels to the IV
– when you go into the
t-Test menu in SPSS, choose “define groups” and select
1 and 2 (Sincere = 1 and Insincere = 2). This will let you look at
two of the groups! You could also select “2 and 3” or “1 and 3”
where the No apology = 3).

2. Rather than an
F value, we will look at the
t value in the
t-Test data output. Here, we have one number for the
degree of freedom, we have the
t value, and we have the
p value.
3. The nice thing about a
t-Test is that since you only have two groups, you do
not need a post hoc test like Tukey (you only need that if you
have to compare three means. Here, we only have two means, so
we can just look at them and see which one is higher and which
is lower when our
t-Test is significant). Then just write it up …
a. “Using apology condition (sincere v. insincere) as our
independent variable and ratings of “Charlie’s apology seemed
sincere” as our dependent variable, we failed to find a
significant condition effect,
t(203) = 1.12,
p > .05. Participants in both the sincere condition (
M = 4.56,
SD = 1.21) and insincere condition (
M = 4.24,
SD = 0.89) rated the sincerity of Charlie’s apology
similarly.
b. “Using apology condition (sincere v. insincere) as our
independent variable and ratings of “Charlie’s apology seemed
sincere” as our dependent variable, we found a significant
condition effect,
t(203) = 7.12,
p < .05. Participants rated the apology as more sincere
in the sincere condition (
M = 5.23,

SD = 0.21) than in the insincere condition (
M = 3.34,
SD = 0.89).
c. Repeat for other dependent variables
iv. Make sure to italicize the
t,
p,
M , and
SD (as in the example)
v. Statistics order recommendation: For this paper, start your
results section with the chi square (your manipulation/attention
check). Then talk about your main analyses. Make sure the
analyses line up with your hypotheses.
f. There is no page minimum or maximum for the results
section, though I would expect it to be at least a paragraph or
two for
each dependent variable
5. Tables
(4 points)
a. I want to make sure you are including the correct numbers in
your results section, so I want you to include all relevant SPSS
tables for each of your analyses.
i. Table 1: Include your tables for age, gender, and ethnicity.
ii. Table 2: Include your tables for your chi square and the
crosstabs
iii. Table 3: Include your tables for your first dependent
variable (This must be an ANOVA table, the descriptive
statistics table for that ANOVA, and the post hoc test whether it
is significant or not)
iv. Table 4: Include your tables for you second dependent
variable (If it is a t-Test, include
t-Test tables here. This would involve both the
descriptives for the

t-Test and the
t-Test output itself. Again, though, I prefer that your
second analysis also be an ANOVA and NOT a
t-Test
v. Table 5: (If applicable)
b. Table Placement: Although the 7th Edition of the APA
Publication manual allows you to place your tables at either the
end of the manuscript (in a series of appendices) or embed it
within the text itself, we prefer the latter placement option. That
is, include your table(s) immediately after your table callout.
That means that you will include your participant tables (for
age, gender, and ethnicity) immediately after the participant
section (and before the methods / procedure section). You will
include your chi square tables (including the crosstabulation
table, chi square table, and symmetric measures table) right
after the callout. For the ANOVA, once again use a table
callout. Then copy the ANOVA tables (descriptive statistics,
ANOVA table, and post hoc tables) immediately after the
callout. See the example paper for a visual aide.
i. Hint: The best way to get these tables is to copy them directly
from SPSS. In the SPSS output, right click on the table, copy it,
and then paste it into your paper after the callout. (If you
double click the table in SPSS, you can adjust the width of cells
or even delete some of the columns). Another alternative is to
use a “snipping” tool (search “snipping tool” in Microsoft Word
to find it). You can highlight an area on any computer page and
save it as a picture. Copy the picture and paste it into your table
pages. Easy!
1. I’m not worried if your table spills over onto multiple lines.
If it spills over, that is fine. I just need to see the full table
c. Make sure to give a proper name to each table (e.g.
Table 1) followed by a good description of what is in
the table in italics (e.g.
Study One Demographics)

d. Each table is flush left, as is the title. See the example paper
for a visual aide
6. Discussion Study One
(2 points)
a. In this section, tell me about your findings and if they did or
did not support your results. It might help to refer back to your
hypotheses “We expected to find A, but instead we found B” or
“We predicted A, and results supported this hypothesis.”
Explain using plain English why you think your study turned
out the way it did.
b. IMPORTANT – Do NOT give me statistics again here. I can
find those in your results section. Here, all I want is a plain
English summary of your findings.
c. Also, don’t give me results for a DV if you did not run an
analysis on that DV. Only tell me about the results you actually
looked at in the results section.
d. There is no length requirement for this section, but I
recommend at least four or five sentences
7. Overall writing quality
(3 points)
a. Make sure you check your paper for proper spelling and
grammar. The FIU writing center is available if you want
someone to look over your paper (an extra eye is always good!)
and give you advice. I highly recommend them, as writing
quality will become even more important on future papers. I
also recommend visiting the FIU Research Methods Help Center
if you need additional guidance with writing or statistical
analyses. Also, remember to upload this paper through the
Pearson writer before uploading to Canvas!
b. Make sure to use the past tense throughout your paper. You
already did the study, so don’t tell me what participants are
going to do. Tell me what they already did!
Other Guidelines for Paper II – Methods and Results (Study
One)

1. 1). Page size is 8 1/2 X 11” with all 4 margins should be one
inch. You
must use a 12-point font in Times New Roman.
1. 2). PLEASE use a spell checker and the grammar checker to
prevent errors. Proofread everything you write. I actually
recommend reading some sentences aloud to see if they flow
well, or getting family or friends to read your work.
1. Use the Paper II Checklist before you turn in your paper to
make sure it is the best paper you can write!
1. Finally, go look at the supporting documents for this paper.
Like Paper I, there is a checklist, a grade rubric, and an example
paper for Paper II. All will give you more information about
what we are specifically looking for as well as a visual example
of how to put it all together in your paper. Good luck!
Twitter Apologies 2
The Impact Of Apology
Darielmys Diaz
Introduction
This paper presents the literature review for the report
titled 'Twitter Apologies.' It elaborates on several peer -reviewed
psychology papers that relate to the final 'twitter apologies'

essay.
In his attempt to explain the impact of apology on a media
figure's transgression (Hu et al., 2019) performed an experiment
where subjects were analyzed for their reaction on a
transgression. The study involved two conditions of
transgression, one with an apology and another without apology.
The results deduced that a media figure's apology holds a strong
role in improving the parasocial relationships (PSR) and
minimizes the audiences’ reaction. This paper presents how
people, particularly media figures, utilize apology to rebuild
and improve the PSRs and, ultimately, the image that the media
figure holds.
In this article, the effort is made to shed light on the
impact of apology on people’s reaction, forgiveness, and their
emotional response over a transgression. This particular article
reflects its research solely on media personnel like actors,
hosts, and media characters. The results deduced the positive
impacts of apology on a media person's image. This article
relates to the final document 'twitter apology' as it reflects the
impact of apology on the audience's reaction. It states the
outcomes when media personnel appears with an apologetical
speech over his transgression. It mainly focuses on the
psychological aspects of the audience and explores the reaction
of the audience.
The media personnel chosen for this study is George
Clooney, who is an eminent name in the US industry. The
subjects informed about a transgression by the character by
letting them read a character (a piece of fake news was used
solely for the experiment). Then a questionnaire is filled by
each personnel where the subjects responded for apologized
transgression and the opposite scenario. Conclusively, apology
assisted in having positive relationships and forgiveness.
The same hypothesis regarding the impact of apology is studied
by (Jehle et al., 2012). This article expressed a similar
conclusion that apology reflects a positive impact on

relationships. This article studies the impact of apology after
being insulted by a confederate. The Study involved four
situations to study the reactions of victims. The four scenarios
included reactions for
· Voluntary apology
· Implicit coerced apology
· Explicit coerced apology
· No apology at all
In the first scenario, the voluntary apology is when the offender
is self-motivated by his wrongdoing and voluntarily utters his
apology. This form of apology reflects the most effective results
of the relationship. The second scenario is an implicit apology
that means an apology without any consequences, while the
third one is an explicit coerced apology that means an apology
with negative consequences. These two types of apologies are
not voluntary and have a lower chance of a positive impact on
the victim's emotions. The last scenario is where the offender
doesn't conduct an apology, reflecting the severe results on the
victim's emotions.
Through this scenario, the authors aimed to make a point for the
impact of voluntary or coerced apology on the victim’s emotion.
This paper is relevant to Twitter Apologies paper as it explains
the impacts that apologies have on victims and how social
interactions and relationships are altered by the apologies.
Many other scholars and psychologists have also made
remarkable efforts in expressing the impacts of apology on
victims, a person's self-image, and the social relationships as
well as on apology. However, in his article (Leunissen et al.,
2013), the writers pen down regarding the mismatch of victim’s
and perpetrator’s willingness to apologize. The study reveals
that these two elements reflect a mismatch, and therefore, the
goal of apology for the victim and perpetrator are different.
The author in this article aimed to express the varying needs
regarding apology from the aspect of victim and perpetrator.
There is a difference between a victim wanting an apology once
a transgression has been witnessed and the perpetrator's

willingness to apologize for the transgression. The purpose of
the research is to highlight the forces that drive apology for
both perpetrators and victims. The paper mentioned that victims
need an apology for intentional transgression; however, the
opposite is the case for perpetrators where the willingness is
reflected upon unintentional transgression. Therefore the needs
are impacted by the intentionality of the transgression.
Referencing the intentionality of transgression, the author states
guilt and anger as the mediating forces for the perpetrator's
preference for an apology and the victim’s wanting to receive
an apology. Therefore, these forces are proportional to apology
and reflect that the more guilt a perpetrator feels, the more he is
willing to apologize. While the same is for the anger of victims,
where more anger reflects a more desire to receive an apology.
In the research of apology on social media and their impacts on
social interaction and social relationships. In his effort, the
author (Matley, 2018) explained the hashtag sorry, not sorry in
the non-apologetic posts on Instagram. In this article, the
authors have reported several posts on Instagram and their
function in the audience’s attraction. This article is a
description regarding the use of this hashtag by the users of
Instagram to balance the positive self-image and minimize the
impoliteness associated with the dissemination of posts that
may be nonapologetic in some way. The article is related to the
study of twitter apologies as the hashtag sorry not sorry is
observed in the social media as a nonapologetic marker. People
use this hashtag for expressing their comments or images in
social media that might seem inappropriate and thus maintain
their positive self-image by this hashtag as this may be a threat
to their positive self. This article talks mainly about
nonapologetic posts on the social media site than the impact of
apology.
The study of the impact of apology has also been witnessed in
(Manika et al., 2017), which attempted to study the influence of
apology for a service’s failure for its customers. The purpose of
the study was to note how a social media service's failure is

reacted to by the customer or potential customer of the service.
The results of this experiment revealed that the customer’s
loyalty and trust in service are built with an apology while
customers not receiving an apology face annoyance, distrust,
and weakened customer relationships.
Conclusively, there are several instances from literature where
the impact of apology has been studied, describing the positive
impact on social relations. The conduct of apologizing over
media and community and social interactions has been shown to
have a positive impact on the victim and offender and mutual
relationships and interactions.
References
Hu, M., Cotton, G., Zhang, B., & Jia, N. (2019). The influence
of apology on audiences’ reactions toward a media figure’s
transgression.
Psychology of Popular Media Culture,
8(4), 410.
Jehle, A., Miller, M. K., Kemmelmeier, M., & Maskaly, J.
(2012). How voluntariness of apologies affects actual and
hypothetical victims’ perceptions of the offender.
The Journal of Social Psychology,
152(6), 727–745.
Leunissen, J. M., De Cremer, D., Folmer, C. P. R., & Van
Dijke, M. (2013). The apology mismatch: Asymmetries between
victim’s need for apologies and perpetrator’s willingness to
apologize.
Journal of Experimental Social Psychology,
49(3), 315–324.
Manika, D., Papagiannidis, S., & Bourlakis, M. (2017).
Understanding the effects of a social media service failure
apology: A comparative study of customers vs. potential
customers.

International Journal of Information Management,
37(3), 214–228.
Matley, D. (2018). "Let's see how many of you mother fuckers
unfollow me for this": The pragmatic function of the hashtag#
sorry not sorry in non-apologetic Instagram posts.
Journal of Pragmatics,
133, 66–78.

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