Chapter 17 (Salkind)What To Do When You’re Not Normal.docx

Chapter 17 (Salkind)
What To Do When You’re Not Normal
Overview of this ChapterThe Good News and the Bad News
First up, the Bad News. Once again, we will look at statistics.
Here, that means the Chi Square, a type of statistics we rely on
when our scales are nominal or ordinal
The other Bad News is that this there are formulas and tables
associated with this chapter. I know, ugh
The Good News? Some of this might be a review! But you will
need some of the new information here as you work on one
statistical calculation for your research paper: The Chi Square
Overview of this ChapterIn this chapter, we will focus on …
Part One: Introduction To Non-Parametric Statistics
Part Two (A): Introduction To The One-Sample Chi-Square
Part Two (B): Chi Square Test Of Independence
Part Three: Computing The Chi-Square Statistic
Part Four: Using The Computer To Perform A Chi-Square Test
Part Five: Other Non-Parametric Tests You Should Know
Part Six: An Eye Toward The Future

Part One
Introduction To Non-Parametric Statistics
Introduction - Non-Parametric StatisticsIntroduction To Non-
Parametric Statistics
Last semester in Research Methods and Design One (and last
week in Chapter 9, Smith and Davis), we talked about normal
curves and why we need normality in order to run ANOVAs, t-
Tests, and other “parametric” tests.
“Parametric tests” infer that the results obtained from a sample
in the study easily applies to a population from which that
sample was drawn. But such “normal” tests are based on a series
of assumptions …
Four parametric test assumptions:
Assumption #1: Variances in each group are homogenous (that
is, the two or more groups are similar in variability)
Assumption #2: The sample is large enough to adequately
represent the population (e.g. it isn’t a biased sample)
Four parametric test assumptions:

Assumption #3: The statistical test uses interval or ratio scales
of measurement (the I and R in NOIR)
Assumption #4: The characteristic under consideration is
normally distributed (i.e. has a normal curve)
So what happens when/if a test violates these assumptions?
In some cases, t-Tests, ANOVAs, and other parametric tests are
robust (e.g. strong enough) that the assumptions can be violated
without too much hassle.
Non-parametric tests may be used when assumptions are
violated
“Non-parametric” statistics are essentially distribution-free,
meaning they don’t follow the same rules as the parametric tests
They don’t require homogeneity of variance and they can
examine more than just interval and ratio data
Researchers often use non-parametric statistics used when the
data set relies on frequencies or percentages (rather than
scales), and we can test whether the percentages we see in a

data set are what we would expect by chance alone
This takes us to one of the more common non-parametric tests,
the chi square (something you’ll use for your first study this
semester!)
Before we get too far into this chapter, I just want you to think
about the concept of “expectations”
Let’s say I go to a pet store to look at kittens, and there are
dozens of them. Just looking at them from afar, what percent
would you expect to be female?
About a 50 / 50 chance, right? Although we might “expect” this,
we might be wrong. Chi Squares can help us see if our
expectations match reality!
Pop Quiz – Quiz YourselfIf you have 30 respondents identifying
their political preference (i.e., Democrat, Republican,
Independent), how many of each political affiliation would you
expect?
A). 10
B). 20
C). 30
D). 40
Pop Quiz – Quiz YourselfIf you have 30 respondents identifying
their political preference (i.e., Democrat, Republican,
Independent), how many of each political affiliation would you

expect?
A). 10
B). 20
C). 30
D). 40
Maybe, right? We SHOULD get 10 of each, but in reality there
tend to be very few Independents (voters usually fall into either
Democrat or Republican camps, so “10” might be too high for
Independents!)
In the next part of this presentation, I want to tell you about two
different types of chi squares we can run. We will split them up
into two “flavors”:
Part Two (Section A): The One-Sample Chi Square
This one is more FYI (though you will be tested on it!
Part Two (Section B): Chi Square Test of Independence
This one is very important for your Paper II analysis!
We’ll figure out how to compute each when we start Part Three
Part Two (Section A)
Introduction To The One-Sample Chi-Square
Introduction: One-Sample Chi-SquareIntroduction To One-
Sample Chi-Square
What is the one-sample chi-square all about?
The one sample chi-square is a non-parametric test that allows

you to determine if what you observe in a frequency distribution
of scores is what you would expect by chance, though this is
limited to a single sample
Sample Chi-Square
The one sample chi-square is a non-parametric test that allows
you to determine if what you observe in a frequency distribution
of scores is what you would expect by chance, though this is
limited to a single sample
Consider ”year in college” as our “one sample” for students at
FIU. For expectations, we ask, “What percent represents
Freshmen, Sophomores, Juniors, and Seniors?” We can then
compare our “expectations” to our “observations.”
Sample Chi-Square
We would probably expect a few more Freshmen than other
groups, right? After all, not all Freshmen will return for their
senior year, and not all Sophomores return as Juniors, etc.
But generally, let’s say we expect around 25% of each class
each year. If we look at the actual observations, would they be
higher or lower than what we would “expect” by chance?
Sample Chi-Square

That’s a question we can answer using the chi square
At FIU, our total enrollment is around 50,000
So we might expect around 12,500 Freshmen (or one fourth of
the total enrollment)? What if we found 15,000 Freshmen?
Would that be outside the realm of expectation?
FIU has a retention rate of 84% of Freshmen (84% of Freshmen
return as Sophomores), which is high but still shows that some
students are not “retained”
Sample Chi-Square
That’s a question we can answer using the chi square
The chi square tests the actual occurrences against the expected
occurrences to see if they differ significantly
This means that if there is no difference between what we
observe and what we would expect by chance, our chi square
will be close to zero
Pop Quiz – Quiz YourselfIf you have 100 respondents identify
their region of residence (i.e., north, south, east, or west), what
would the expected frequency be for each category?
A). 33
B). 50
C). 25
D). 100
Pop Quiz – Quiz YourselfIf you have 100 respondents identify

their region of residence (i.e., north, south, east, or west), what
would the expected frequency be for each category?
A). 33
B). 50
C). 25
D). 100
But again, expectation and reality may differ a lot!
Sample Chi-Square
As you can see, the one-sample chi square focuses on just one
variable, or one sample
Here, we looked at the number of students who fall into each
year (Freshmen, Sophomore, Junior, or Senior)
But what if we want to look at more than one variable? Well,
that calls for a chi square test of independence …
Part Two (Section B)
The Chi-Square Test Of Independence
The Chi-Square Test Of IndependenceThe Chi Square Test Of
Independence
As you just saw, we can see if the observed counts of a single
variable match (or do not match) the counts we would expect by
chance
Often, though, you will also want to see if the observed counts

across two variables match (or mismatch) the counts we would
expect by chance. In this situation, you use a chi square test of
independence (two samples)
Independence
Go back to our Freshmen, Sophomore, Junior, and Seniors at
FIU. Do you think there is a difference in terms of percentages
of students in each year?
We could answer this using a one-sample chi square
But do you think there might also be a difference for each of
these classes between male and female students?
This question deals with two samples (year and gender), so we
must answer it using a chi square test of independence
Independence
Given four “years” (Freshmen, Sophomore, Junior, and Senior)
and two “genders” (Male and Female), we might expect 12.5%
of students to fall into each of our eight table cells:
Will our “observations” match our “expectations”? Let’s find
outGenderYear in
CollegeFreshmanSophomoreJuniorSeniorMale12.5%12.5%12.5
%12.5%Female12.5%12.5%12.5%12.5%

Pop Quiz – Quiz YourselfA two-sample chi-square is also
known as a ________.
A). Goodness of fit test
B). Test of independence
C). Wilcoxon rank
D). Mann-Whitney U
Pop Quiz – Quiz YourselfA two-sample chi-square is also
known as a ________.
A). Goodness of fit test
B). Test of independence
C). Wilcoxon rank
D). Mann-Whitney U
Part Three
Computing The Chi-Square Test Statistic

Computing The Chi-Square Test StatisticLet’s focus on each
test separately
1). Computing the one sample chi square test statistic
2). Computing the chi square of independence test statistic
Computing The Chi-Square Test Statistic1). Computing The
One Sample Chi-Square Test Statistic
The one sample chi square test compares what we observe with
what we expect by chance. It uses this formula
X2 is the chi-square value
Σ is the summation sign
O Is the observed frequency
E is the expected frequency
X2 = Σ
(O – E )2
E
One Sample Chi-Square Test Stastic
Let’s say we get the following data from our enrollment rosters
at FIU (including all online, live, MMC, and BBC students!)

Time to walk through out eight research steps! I trust you recall
all of these from Research Methods and Design
One!FreshmenSophomoresJuniorsSeniorsTotal15,00013,50011,0
0010,50050,000
Step One: State the null and alternative research hypotheses
Our null hypothesis is that the four groups do not differ
HO: PFresh = PSoph = PJunior = PSenior
Our research (alternative) hypothesis is there are differences in
the proportion of occurrences in each “year” category
H1: PFresh ≠ PSoph ≠ PJunior ≠ PSenior
Step Two: State the level of risk
Similar to last semester, we get to set our own risk. We’ll go
with the usual psychology suspects, either p < .05 or p < .01

Step Three: Select the appropriate test statistic
We are looking at categories for our one sample data set, or
Freshmen, Sophomores, Juniors, and Seniors
As such, we are dealing with a nominal variable, right!
We need to use the mean if we want to run parametric tests like
a t-Test or an ANOVA, but since we have a nominal variable,
the mean is … meaningless here
What would our mean even be? Something between a Freshman
and a Sophomore. What is that, some kind of Freshomore?
Makes no sense!
Step Three: Select the appropriate test statistic
We are looking at categories for our one sample data set, or
Freshmen, Sophomores, Juniors, and Seniors
Given our nominal “year” variable, we have to use a non-
parametric test here.
The chi-square is perfect, as it can examine categorical
(nominal) variables

Step Four: Compute the test statistic
Consider our “year” data again (Note: I did make these up!)
To set up our chi-square calculations, we need to look at the
observed frequency (tabled above), our expected frequency
(there are four groups, so divide 50,000 by 4 to get 12,500
each). We need the difference, too, and some squaring! …
FreshmenSophomoresJuniorsSeniorsTotal15,00013,50011,00010
,50050,000

Here are our observed and expected values
YearObserveExpectDifference(O – E)2(O – E)2 /
EFresh.1500012500Soph.1350012500Junior1100012500Senior1
050012500Total

Subtract observed from the expected (ignore negative signs)
EFresh.15000125002500Soph.13500125001000Junior110001250
01500Senior10500125002000Total
Square each difference number (e.g. 2500 X 2500 = 6250000)

EFresh.150001250025006250000Soph.135001250010001000000
Junior110001250015002250000Senior105001250020004000000
Total
Divide the square of each difference by its “expected” number

EFresh.150001250025006250000500Soph.135001250010001000
00080Junior110001250015002250000180Senior1050012500200
04000000320Total
Our total chi square value is 500 + 80 + 180 + 300 = 1080

EFresh.150001250025006250000500Soph.135001250010001000
00080Junior110001250015002250000180Senior1050012500200
04000000320Total1080
Step Five: Determine the value needed to reject the null
If you look in Appendix B (Salkind), you’ll see the chi-square
table starting on page 380
But we must first determine our degrees of freedom. For the one
sample chi square, this is r – 1, where r is the # of rows
In this case, we have four rows (four “years”), so r – 1 gives us
4 – 1, or 3 for our degrees of freedom

Step Five: Determine the value needed to reject the null
Using df = 3, look up the critical value
In this case, with a df of 3, we need to surpass a critical value
of 7.82 for the p < .05 level and 11.34 to surpass the p < .01
level
Step Six: Compare the obtained value and the critical value
We compare our obtained value of 1080 to our critical value of
7.82 (for p < .05) and 11.34 (for p < .01)
Is 1080 larger than either 7.82 or 11.34?
Well …
Step Seven / Eight: Make a decision
Since 1080 is clearly larger than our critical values, we can
conclude that the null hypothesis cannot be accepted. Our
observed values differ from our expected values

The “goodness of fit” (another name for the chi-square test) is
not very “good” here. That is, our observed data does not “fit”
the expected data
Computing The Chi-Square Test StatisticSo How Do I Interpret
X2(3) = 1080, p < .01
X2 represents the test statistic (Chi square)
3 is the number of degrees of freedom (r – 1, or 4 – 1 = 3)
1080 is the obtained value
p < .01 indicates that the probability is less than 1% that the
null hypothesis is correct across all categories by chance alone
Computing The Chi-Square Test StatisticHow Would I Write Up
This Result In A Results Section?
“A chi-square goodness-of-fit test was performed to determine
whether FIU students were equally distributed across the four
years in college. Results showed that the students were not
equally distributed, X2(3) = 1080, p < .01.”

Pop Quiz – Quiz YourselfIf our degrees of freedom is 20, what
critical value do we need to overcome to conclude that our
obtained value is significant at the p < .01 level?
A). 24.89
B). 31.41
C). 36.19
D). 37.57
E). 38.93
Pop Quiz – Quiz YourselfIf our degrees of freedom is 20, what
critical value do we need to overcome to conclude that our
obtained value is significant at the p < .01 level?
A). 24.89
B). 31.41
C). 36.19
D). 37.57
E). 38.93
Chi-Square Of Independence Test Statistic
We just looked at a one sample chi square, but sometimes we
have more than one variable that we may want to assess, all of
which are nominal in nature
For example, what if we want to see if there is a difference in
“year” based on “gender” of the student.
We might get a table like this for our “expectations” for a
population of 50,000 FIU students …

Two group design
This includes 50,000 students total, or 25,000 males and 25,000
females (if you do the 50/50 split for gender). Divide 25,000 by
four years, and you get 6250 per year (12.5% of 50,000 gets us
to this 6250 as well!). Nice and easy, right!
GenderFreshmenSophs.JuniorsSeniorsMales6250625062506250
Females6250625062506250

Two group design
Yeah, nothing is really easy in statistics. In fact, when you look
at more than one variable, the simple “expectation” route is not
really appropriate.
In fact …
Females6250625062506250
Two group design

FORGET the scores above! The chi-square of independence
uses a statistical calculation of the expectation, which is based
on the expected value for one variable working in concert with
the expected value for the second variable. Ugh. Calculations:
Females6250625062506250
Two group design – The “Real Expected” values
Do you want to know what the “Real Expected” values are?
Well, here they are …

GenderFreshmenSophs.JuniorsSeniorsMalesFemales
Two group design – The “Real Expected” values
You’re probably scratching your head right now, wondering
how I got these numbers. This is where some calculations come
into play. Believe it or not, we need to begin with our
“observed” values to calculate our “expected” values …
GenderFreshmenSophs.JuniorsSeniorsMales69756277.55115488
2.5Females80257222.558855617.5

Consider our “observed” values below, the values we actually
observe. (Note: I made up the data below, but it is possible!)
What we need now are totals for the columns and rows …
Females8000750057505500

Here’s a rearranged table that adds blank cells for each row (?)
and each column (?) as well as a Column Total + Row Total (?)
Let’s fill in the blank cells by doing some basic addition
GenderFreshmenSophs.JuniorsSeniorsRow
TotalMale7000600052505000?Female8000750057505500?Colu
mn Total?????

Pretty easy, right.
Our male total is 7000 + 6000 + 5250 + 5000 = 23250
Freshmen total is 7000 + 8000 = 15000, and so forth
TotalMale700060005250500023250Female80007500575055002
6750Column Total1500013500110001050050000

Now multiply each row by each column and divide by total N,
which will give us our expectation for each gender*year cell

6750Column Total1500013500110001050050000
For Freshman males, we have 15000*23250 / 50000 = 6975

6750Column Total1500013500110001050050000
That is, for Freshman males, our expected value is 6975! Thus

we expect 6975 Freshman males. Let’s table that quickly …
6750Column Total1500013500110001050050000
Here is our new “Expectation” (Mathematically Derived)

GenderFreshmenSophs.JuniorsSeniorsMales6975Females
For Soph. males, we have 13500*23250 / 50000 = 6277.5

6750Column Total1500013500110001050050000
Here is our new “Expectation” (Mathematically Derived)
And so on …

GenderFreshmenSophs.JuniorsSeniorsMales69756277.5Females
For Junior males, we have 11000*23250 / 50000 = 5115

6750Column Total1500013500110001050050000

For Senior males, we have 10500*23250 / 50000 = 4882.5
6750Column Total1500013500110001050050000

For Freshman females, we have 15000*26750 / 50000 = 8025
6750Column Total1500013500110001050050000

For Soph. females, we have 13500*26750 / 50000 = 7222.5
6750Column Total1500013500110001050050000

For junior females, we have 11100*26750 / 50000 = 5885
6750Column Total1500013500110001050050000

For senior females, we have 10500*26750 / 50000 = 5617.5
6750Column Total1500013500110001050050000

So, this is our final set of “Expectation” data (familiar, right!)
Here is our “Observation” data. Time to calculate chi square!
GenderFreshmenSophs.JuniorsSeniorsMales69756277.55115488
2.5Females80257222.558855617.5GenderFreshmenSophs.Junior
sSeniorsMales7000600052505000Females8000750057505500

G / Yr.ObserveExpectDifference(O – E)2(O – E)2 / EM.
Fr.70006975M. So.60006277.5M. Jr.52505115M.
Sr.50004882.5F. Fr.80008025F. So.75007222.5F. Jr.57505885F.
Sr.55005617.5Total

Fr.7000697525M. So.60006277.5277.5M. Jr.52505115135M.
Sr.50004882.5117.5F. Fr.8000802525F. So.75007222.5277.5F.
Jr.57505885135F. Sr.55005617.5117.5Total

Fr.7000697525625M. So.60006277.5277.577006.25M.
Jr.5250511513518225M. Sr.50004882.5117.513806F.
Fr.8000802525625F. So.75007222.5277.577006.25F.
Jr.5750588513518225F. Sr.55005617.5117.513806.25Total

Fr.7000697525625.089M. So.60006277.5277.577006.2512.27M.
Jr.52505115135182253.56M. Sr.50004882.5117.5138062.82F.
Fr.8000802525625.078F. So.75007222.5277.577006.2510.66F.
Jr.57505885135182253.10F.

Sr.55005617.5117.513806.252.45Total
Fr.7000697525625.089M. So.60006277.5277.577006.2512.27M.
Jr.5250511513518225…

Lab Presentation
Week 7 Lab
Generating an Idea for Study Two
Overview of The LabThis week during the lab, we are going to
focus on your Study Two (a follow-up to Study One that takes
the Facebook Consensus study one step further).
In this presentation, we will discuss the following:
Part One: The Papers to Come (Papers III, IV, and V)
Part Two: Generating a Study Two Idea
Part Three: Your Task This Week
Part Four: An Eye Toward The Future
*
Part One
The Papers to Come: Papers III, IV, and V

The Papers to Come: Papers III, IV, & VThis week during the
lab, we have a big project: Thinking about study two
Before we get to that idea, let me give you more information
about Papers III, IV, and V …
*
The Papers to Come: Papers III, IV, & VPaper III: Literature
Review (Study Two)
Paper III is your second chance to write a literature review.
Once again, you will use your Facebook Consensus Study as a
starting point, writing an APA formatted introduction to your
second study that sums up how prior research led to your
research hypotheses
Paper III should be easy, as it is simply an extension of Papers I
and II! That is, you know the basic process of starting broad and
narrowing your paper down to your hypothesis (using APA
formatting along the way, of course!)
*

Review (Study Two)
So how does Paper III differ from Paper I?
Essentially, Paper III combines the title page and literature
review from your Paper I with the methods, results, and brief
discussion from your Paper II into one longer paper.
Paper III then adds a second “literature” review (after the brief
Paper II discussion) based on an extension study (study two).
This new study two literature review highlights a second IV of
interest to you and your classmates. Paper III focuses on both
your new IV and your old IV to see how they might interact
*
Review (Study Two)
So how does Paper III differ from Paper I?
Consider study one. We used three levels of Facebook feedback.
In study two, we will drop one of those levels (we will retain
either the Support vs. Oppose conditions only, OR we will
retain the Support vs. Mixed only)
We will then add a second IV that has two levels.
I’ll talk more about that in a few slides.
For now, think about Paper III as a continuation of Papers I and
II
*

The Papers to Come: Papers III, IV, & VPaper IV: Methods and
Results (Study Two)
Paper IV is very similar to Paper II. You will write a Methods,
Results and brief discussion section, but this time for a factorial
research design (a 2 X 2 study)
IMPORTANT: Paper IV is not a simple repeat of Paper II. It has
a new methods and results section using a new study design. If
you simply copy and paste your Paper II results into this paper,
you will NOT receive credit for Paper IV.
*
The Papers to Come: Papers III, IV, & VPaper V: The Final
Paper (Study Two)
Paper V is your final paper. This will be fairly easy, as it will
combine Papers I, II, III and IV into one cohesive paper, with:
Your title page
An abstract (brand new for the final paper)
Study one literature review, methods, results, discussion
Study two literature review, methods, results, discussion
General discussion (brand new for the final paper)
References
SPSS tables (copied from SPSS output)
*

The Papers to Come: Papers III, IV, & VPaper V: The Final
Paper (Study Two)
The final paper thus incorporates everything you will have
learned in the course, focusing once again on the concept of
Facebook Consensus. You have a lot of time to work on these
papers, so we will go at a nice steady pace.
The only thing to figure out now is where to go with this topic
as we create Study Two …
*
Test Your UnderstandingHow many conditions will your study
two have?
A. One
B. Two
C. Three
D. Four
E. None of the above
*
Test Your UnderstandingHow many conditions will your study
two have?

A. One
B. Two
C. Three
D. Four
E. None of the above
Your study two uses a 2 X 2 design. That is, we will have two
independent variables, each with two levels. This will create
four different conditions (all independently / randomly
assigned)
*
Part Two
Generating a Study Two Idea
Generating a Study Two IdeaFor the rest of this lab, we are
going to discuss the following:
1). Study Two Topic
2). Study Two Guidelines
3). Your Task This Week
4). An Eye Toward the Future

*
Generating a Study Two Idea1). Study Two Topic
For Study Two, I want you to use a factorial study design. That
is, rather than just one independent variable with three levels,
this new study will have two independent variables, each of
which have two levels (a 2 X 2 study with four conditions
total).
Thus, for this final study, I want you to do a follow-up study on
the Consensus topic using a second independent variable
*
I want to repeat that again, because it is VERY important
For your final study, you will design a factorial study (more
than one IV) to expand on your Facebook Consensus study
For this second study, use your first study as a starting point.
That is, use S vs. M as one IV and then add a second IV. Or you
can look at O vs. M. It’s your lab’s choice (and all members
must agree), but I highly recommend S versus M
This week, I want each of YOU to propose some potential new
study ideas and come up with potential hypotheses for your
follow-up study

*
Keep in mind some constraints that we have for Study Two
You are going to collect data for study two (just as you did in
study one), but we are going to collect data online this time
using an internet survey program called “Qualtrics”
In a few weeks, you and your instructor will post materials on
Qualtrics and you will personally recruit at least 5 people to
participate on your behalf. First, though, we need to develop
your independent variables and your hypotheses
*
Study Two Guidelines2). Study Two Guidelines
Using Qualtrics, we will randomly assign our participants to
one of four different conditions, creating a true experimental
design
For Study Two, we are going to develop a 2 X 2 design
This means we have two independent variables, and each IV has
two levels (I know I’m being repetitive – it’s important)
Just as a comparison, a 2 X 3 design has two IV’s, one of which
has 2 levels and one that has 3 levels
A 2 X 2 X 3 design has three IV’s, one with 2 levels, another
with 2 levels, and the last with 3 levels
*

I’ve been thinking about the following set-up on the next few
slides myself, but this is just one possibility. Your class can go
in a completely different direction if you want (and I encourage
your creativity! The info below is just a suggestion)
We can present participants with a multipage internet survey
and then have them complete questions at the end.
Each page presents them with either IVs or DVs
You’ve probably done online studies yourself already. Well,
imagine this set-up for an online study that you control …
*
A. Page one: Informed consent page
B. Page two: IV page
C. Page three: DV page
D. Page four: Demographics page
E. Page five: Debriefing form
*
A. Page one is easy. We have to create an informed consent
page. We will get to this document in the lab next week.

For now, let’s look at page two …
*
B. Page two: First IV page (2 versions)
Recall your Facebook Consensus topic from study one. In study
two, we can manipulate the survey in a similar way
Page two is where you have input. In this “priming” page, we
expose some participants to one level of our IV and the rest of
the participants to the other level of the IV
*
First, we could keep it as is. Some look at Support; some look at
Mixed
Note: Why not look at the Oppose condition? Our study is about
consensus, so we need one condition that has consensus and one
that does not. It is thus important to drop a consensus condition.
Here, I think the Support consensus is more useful than Oppose

*
Second, we could manipulate consensus differently
Rather than listing eight supportive comments, we could have
ONE person say all of his friends thought cheating was ok (vs.
“most” friends said it was ok)
Or we could provide a percentage. That is, we could tell our
participants that 100% of prior participants said cheating was ok
(vs. 20% said it was ok).
*
My advice, though, is to keep the original comments. That way
you have a better connection between Study One and Study
Two, allowing you to draw much better comparisons between
the two studies in your final paper
*

Now let’s talk about your second IV. This second variable is
more flexible, and can be either manipulated or measured
First, recall that measured IVs get at characteristics the
participants bring with them to the laboratory.
This can involve demographics (e.g. do men respond differently
than women?) or attitudes (e.g. do people high in need for
consistency respond differently than those low in need for
consistency?).
Or what if we determined if participants also cheated
*
Remember that with measured variables, you cannot draw causal
conclusions (we cannot assign someone to an attitude or a
demographic characteristic).
For our second study, a measured variables might be based on
participant locus of control …
*

An internal locus of control focuses on a person believing they
are responsible for an outcome; an external locus of control
focuses on outside factors being responsible. Would internal
LOC p’s feel like cheating was okay versus not okay?
*
Second, I usually prefer manipulated IVs. Here, we alter
something else (in addition to our S and M groups)
For example, we could see if forewarning people about the
effects of consensus influences their ratings of cheating. That is
…
Idea #1
*

… we could tell some participants about the idea of consensus
before they see the Facebook posts to see if the warning impacts
their DV ratings. Others would not get this warning. Thus …
Idea #1
*
… some participants would get support + a warning, some get
mixed + a warning, some get support + no warning, and the rest
mixed + no warning.
Four conditions total in this 2 X 2 design!
Idea #1
*

Or, we could alter the gender of the Facebook user. What if we
have Abigail (female) vs. Albert (male)? Would the user’s
gender interact with the support versus mixed comments?
(support + male, support + female, mixed + male, mixed +
female)
Idea #2
*
Or, what if Abigail was young for some participants (new
college student, early twenties) versus an older student (forties)
for others?
Or, what if Abigail is Caucasian in some conditions but African
American or Hispanic in others?
Idea #3
*

Or what if Abigail’s posts received a lot of “likes” for the
support (vs. mixed) posts versus very few “likes”
Or what if we used different emojies (like response, love
response, angry face response, laughing face response, angry
face response, etc.)
Idea #4
*
Or we could alter the type or number of “comments”. Since this
is an online study, we could do more than eight comments back.
So what if one condition has 20 supportive comments; one has 8
supportive; one has 20 mixed comments; one has 8 mixed?
Idea #5
*

What if we look at different “moral” situations? We can keep
the test-cheating Abigail post for some participants (with both
support and mixed comments for conditions 1 and 2), but add in
a new morality situation for conditions 3 and 4 …
Idea #6
*
That is, in conditions 3 and 4, Abigail admits that she saw a
woman drop a gift card outside a store for $100. The woman
drove off before Abigail could say anything, so rather than
alerting the store, she kept the gift card and wants to know if it
was bad …
Idea #6
*

For this Idea #6, would participants see the behavior as more
immoral if it involved a test-cheating situation or if it involved
not telling anyone about found money?
Idea #6
*
My final idea is to see if participants alter their views
depending on whether they are asked to think about Abigail’s
cheating emotionally (versus rationally).
We could even have participants write about an emotional
(versus rational) experience to prime them
Idea #7
*

I gave you a bunch of possible studies, but PLEASE feel free to
come up with your own idea, as there are THOUSANDS of
other possibilities. Use your lit review experience as a jumping
off point. Just remember that our class will choose ONE of
them.
*
Test Your UnderstandingWhy is it important to reuse at least
some material (either IV or DV related) from study one when
we engage in study two?
A. We want to see if we can replicate some study one results
B. We want to extend study one to see how a new independent
variable interacts with the original independent variable
C. We want to be able to draw good comparisons between study
one and study two in our eventual final paper
D. All of the above
*

Test Your UnderstandingWhy is it important to reuse at least
some material (either IV or DV related) from study one when
we engage in study two?
A. We want to see if we can replicate some study one results
B. We want to extend study one to see how a new independent
variable interacts with the original independent variable
C. We want to be able to draw good comparisons between study
one and study two in our eventual final paper
D. All of the above
Easy one here – all of these elements are important
*
Please be creative with your IVs. Don’t feel restricted to the
design we used for study one (though feel free to replicate it if
you really want!). If you do go in a different direction, note that
your DVs are going to differ as well. At minimum, we need two
independent variables, so keep that in mind!
C. Okay, let’s move on and look at page three and some of those
new DVs…
*

C. Page three: DVs page
The third page of your new study should ask about your
dependent variables. Make sure to measure responses that are
pertinent to your study design
You can use as many of the variables from study one as you
want or you can have different variables. Just recall that you
want to have connections between study one and study two, so
the more they overlap the better you can compare and contrast
them in Paper V later this semester (e.g. “Study two findings
replicated study one”)
*
D. Page four: Demographics page
Page four will be easy. You can reuse the demographics set-up
from Study One (though add potential new factors if you find
them important. For example, if you do a religious based second
independent variable, you might want to ask for the
participant’s religion)
Demographics can come at either the start of the study or the
end: put them where you think they work best
*

E. Page five: Debriefing Statement
Page five is also easy. You’ll need to tell your participants what
you did, why you did it, and what you predict.
Of course, you’ll need to have your study idea and its
hypothesis in mind, which brings us to your next task for this
lab session …
*
Part Three
Your Task This Week
Your Task This Week3). Your task this week
Note: For a 2 X 2 study, we will have a more complex set of
hypotheses. For each dependent variable we will actually have
three types of hypotheses. This includes two main effects and
one interaction for each dependent variable
A main effect looks at the impact of one IV regardless of the
presence of the other IV
An interaction looks at the simultaneous impact of both IVs
working in concert on the DV

*
For now, imagine we create a 2 X 2 study where one IV is
consensus (support vs mixed) and a second is manipulated
warning (warn about consensus vs do not warn).
This gives us four study conditions: Support + Warning,
Support + No Warning, Oppose + Warning, Oppose + No
Warning
*
So, imagine we have consensus as IV #1 (support v. mixed) and
warning as IV #2 (warned v. not warned)
Let’s see look at main effect and interaction hypotheses …

*
2 X 2 study: Main Effects
Consider a 2 (Condition: Support vs. Mixed) X 2 (Warning:
Warned vs. Not Warned) study
Condition and Warning are our two IVs (2 X 2)
Consider “Abigail’s behavior was wrong” as our DV
Main effect #1: Looking only at condition, I expect a main
effect for condition, with support participants feeling the
cheating was less wrong than mixed participants
Note: this ONLY looks at condition, NOT self-esteem
#1
*
2 X 2 study: Main Effects
Main effect #2: Looking only at warning, I don’t expect there to
be a significant impact of warning all on its own (it needs the
other IV to impact participants).
Still, this looks ONLY at warning, NOT condition
#2

*
2 X 2 study: Interactions
Interaction: Here, participants should rate the behavior as less
wrong in the support + no warning condition and most wrong in
the mixed + warning condition. The other two conditions
(support + warning and mixed + no warning) will fall in
between these extremes
Int
*
2 X 2 study: Main effects and Interactions
So keep in mind that each DV we look at may have three
hypotheses (2 main effects and 1 interaction).
We could go back and look at other DVs as well.
“Abigail’s behavior was unacceptable”
“I would advise Abigail to keep silent”
“Abigail seems warm” etc.
Okay, time for your assignment. Come up with a 2 X 2 study
design idea and share it with your classmates!

*
As soon as your lab instructor sees all of your wonderful ideas,
he or she will pick out the ones that seem most interesting and
let the class choose which one to pursue.
Sorry, we will only do one idea for the whole class this
semester. Well … actually, we will do one study across all
online sections, so this will go beyond just your specific online
class as well.
Sorry again! You can concentrate on more individualized
studies in your future psychological career!
*
Test Your UnderstandingHow many main effects and
interactions will we look at in a 2 X 2 study design?
A. One main effect and one interaction
B. One main effect and two interactions
C. Two main effects and one interaction
D. Two main effects and two interactions
*

Test Your UnderstandingHow many main effects and
interactions will we look at in a 2 X 2 study design?
A. One main effect and one interaction
B. One main effect and two interactions
C. Two main effects and one interaction
D. Two main effects and two interactions
Consider independent variables A and B. We will have one main
effect for variable A, one main effect for variable B, and one
interaction of variables A and B
*
Part Two
An Eye to the Future
An Eye To The FutureStudy Two Materials
Don’t worry about your materials for your new study this week,
but start thinking about some of the materials you will need for
your study
Next week, once we have established your 2 X 2 design for the
study, you will start working on your materials (informed
consent, IV 1, IV 2, DVs, demographics, debriefing form)
We have a few weeks to work on this, as the materials won’t be
due for some time, but start thinking ahead while you build your
research hypothesis!

*
An Eye To The FutureOther assignments
Keep in mind that your Paper II: Study One Methods, Results,
and Discussion will be due soon. Instructions and guidelines for
that paper (as well as an example paper) are available on
Canvas
*

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