Speaker 1: All right, welcome back to everybody.
Speaker 2: It's continuing and doing statistics now, so it hasn't really
gone away yet and we're diving.
Speaker 1: Into advanced statistics. But don't panic.
Speaker 2: The E triple P is not expecting you to calculate
these analysis. Instead, the exam wants you to recognize when
each procedure is appropriate. So think of today's lesson as
learning the right tool for the right research question. First
one off the gate is MANOVA. It stands for multivariate
analysis of variants. It compares groups on multiple dependent variables simultaneously.
Think one independent variable with multiple dependent variables.
Speaker 1: For instance, if you want.
Speaker 2: To assign clients to three therapy groups CBT, psychodynamic, and medication,
instead of measuring only depression, they measure depression, anxiety, and
stress at the same time. Rather than running three adova's,
they perform one Manova.
Speaker 1: So why use it?
Speaker 2: Because running several Andova's increases the chance of a type
one or false positive simply by chance. Manova helps control
that inflation. So a Manova again is one independent variable,
multiple dependent variables that compares groups on multiple dependent variables simultaneously.
All right, heading over to do I have one of
the tips you can think of is do I have
one independent variable and multiple dependent variables? If yes, think
Manova and Kova analysis of covariance and Cova compares group
means while statistically controlling for another variable called a covariate.
It's a variable that might influence the dependent variable, but
isn't the main focus of the study. An example be
researchers compare CBT versus medication for depression. However, patients begin
treatment with different levels of depression baseline depression could affect results.
Researchers statistically remove that influence using Ancova. Think of Ancouva
as leveling the playing field before comparing treatments. Discriminate function
analysis This predicts which group someone belongs to using several
predictor variables. So let me give you an example. Can
depression scores, anxiety scores, sleep quality, and trauma history correctly
classify someone as having PTSD or generalized anxiety disorder. The
dependent variables categorical, The predictors are continuous, perfect situation for
discriminate functional or function analysis again predicts which group someone
belongs to using several predictor variables. Right, the predictor variables
being anxiety scores, depression scores, and then, of course, the
dependent variables categorical.
Speaker 1: So as, PTSD or generalized anxieties.
Speaker 2: NeXT's factor analysis, which discovers hidden psychological traits called latent variables.
A latent variables a psychological characteristic. It cannot be directly observed,
but can be measured indirectly. Examples include intelligence, depression, anxiety,
self esteem. You cannot see self esteem, you measure it
using questionnaires. So I imagine a personality test with one
hundred questions. Many of these questions actually measure only five
personality traits. Factor analysis uncovers those hidden dimensions EFA the
exploratory factor analysis. It explores data without having a prior theory.
Researchers ask what factors naturally appear.
Speaker 1: So if you do a study, a.
Speaker 2: Psychologist develops a new trauma questionnaire, nobody knows how many
dimensions exist. Yet maybe there's three, maybe five. Exploratory factor
analysis lets the data decide. Another important vocabulary word is eigenvalue.
Speaker 1: That's E I G E and V A l u E.
Speaker 2: It measures how much variance a factor explains. Higher eigenvalues
usually indicate more important factors. A screen plot s c
R E e is a graph showing where meaningful factors
begin to level off.
Speaker 1: Imagine a mountain. The point where it flatteness tells you
how many factors to keep.
Speaker 2: Factor loading the correlation between a question and a factor.
Large loadings mean that that question strongly measures that factor.
Commonality the pro proportion of variants in a variable explained
by all factors together.
Speaker 1: It might be easier to actually go back as I
was reading this to give you example.
Speaker 2: So the eigenvalue again, the higher the more important the
factor is. So imagine you create a forty questions personality questionnaire.
After running an exploratory factor analysis that we talked about earlier,
you discover four factors. Factor one has an eigenvalue of
eight point five, factor three one point four. Factor one
explains the largest amount of personality differences among participants. Most
researchers keep factors with an eigenvalue greater than one, So
factors one through three in the example which I didn't
give you all the numbers would likely be retained well.
Factor four at a zero point six would probably be discarded.
Think of of an eigenvalue as a factor's importance score.
The screen plot is a graph that helps researchers decide
how many factors should be kept. Researchers look for the
point where the graph suddenly levels off like.
Speaker 1: I was telling you about the mountains. Is called the elbow.
Speaker 2: Suppose your questionnaire initially identifies eight possible factors. When you
examine the screen plot, the first three factors drop sharply,
and after the third factor.
Speaker 1: The line becomes flat.
Speaker 2: That tells you that only three meaningful psychological factors exist,
while the remaining factors mostly represent noise. Factor loading, as
we told you earlier, as the correlation between an individual
test item and a particular factor. Suppose one factor's label depression.
One questionnaire item says I feel hopeless most days. It's
factor loading is zero point eighty seven. Another one says
I enjoy watching sports. It's factor loading a zero point nine.
The first question strongly measures depression, in the second almost
nothing to that factor. Factor loadings are like magnets. The
stronger the loading, the more stronger the questions pulled toward
that factor.
Speaker 1: Another one we did not discuss.
Speaker 2: Yes, communality represents the percentage of a question's variance explained
by all of the retained factors combined. Higher communalities we
mean that the factor model explains that question well. Suppose
the questionnaire item I feel nervous in crowds has a
communality of zero point eight two. That means that eighty
two percent of the differences in people's responses can be
explained by the psychological fing identified in your model, such
as anxiety and social fear. Imagine a different one, I
prefer chocolate over vanilla. Its communality is zero point one eight.
Only eighteen percent of the variation is explained by your factors,
suggesting this question probably doesn't.
Speaker 1: Belong to an anxiety questionnaire.
Speaker 2: Another one is orth orthogonal rotation. Orthogonal rotation it's O
R thho g O n A L or verimax assumes
that the factors are completely independent and unrelated to one another.
It's a researcher develops a vocational aptitude test measuring mathematical ability,
musical ability, mechanical ability. The researcher assumes these abilities are unrelated.
Verimax rotation keeps the factors independent and easier to interpret.
Orthogonical orthogonal equals orthodox and dependence.
Speaker 1: The factors stay separate.
Speaker 2: Oblique rotation or promax allows factors to be correlated. Because
many psychological traits naturally overlap, for instance, depression, anxiety, and stress,
these constructs often occur together.
Speaker 1: Some estimate over seventy percent of the time.
Speaker 2: Someone with severe anxiety frequently expresses experiences depression as well. Promax,
which a rotation allows those factors to correlate.
Speaker 1: Making the results much more realistic.
Speaker 2: Oblique means the factors are allowed to lean toward one
another instead of standing apart. Another quick rule to help
you is I can value is how important is the factor?
Screenplot is? How many factors should I keep? Factor loading?
Which questions belong to each factor?
Speaker 1: Communality?
Speaker 2: How well do the factors explain each question? Verimax factors
stay independent promax the factors.
Speaker 1: Are allowed to correlate.
Speaker 2: Yes, this is going to be something that's probably pretty
Foreign's covered quite a bit in classes that I remember.
All right, so we move along past that now and
we're heading into a different territory. So now we're going
into what they call the structured sorry, what they call
the structural equation modeling the SEM. This combines several statistical
techniques into one large model. It can test regression path analysis,
confirmatory factor analysis, direct effects, indirect effects latent variables all
at once. The structural equation modeling combines several statistical techniques
into one large model. So, for example, researchers propose parenting style,
self esteem, and academic performance.
Speaker 1: That's what they're going to look at.
Speaker 2: SEM tests the entire model simultaneously. One analysis, multiple relationship.
NeXT's mediation. A mediator explains how or why one variable
affects another. So if you look at mindfulness improves emotion
and regulation, which then reduces anxiety. Emotion regulation explains why
mindfulness works. It's the mediator, right, mindfulness anxiety the two players.
But why does mindfulness reduce anxiety because emotional regulation improves
Remember mediator answers how. Moderation as a moderator tells us when,
for whom or under what conditions the relationship exists. CBT
reduces oppression. Now you're looking for the questions when, for
whom and under what? But only among younger adults. That's
for whom age changes the strength of treatment under what conditions?
And age is the moderator. Remember moderator answers when or whom?
Non parametric tests these are used when assumptions like normality
or violated remember that from the.
Speaker 1: Distribution that we talked about.
Speaker 2: When they're evenly distributed, non parametric version of the independent
samples T tests you use Man Whitney you so non
parametric version of the independent samples T test you use
for two independent groups, so comparing depression scores between males
and females. When scores are highly skewed, they're not equally
distributed like the Bell curve, then you would go to
the Man Whitney You A non parametric equivalent of the
paired samples T test is will Coxin signed ranked test,
well Coxon signed ranked test. It's used for two related groups,
so depression before therapy versus after therapy.
Speaker 1: But again they're skewed. Equivalent of a one way on nova.
Speaker 2: It's something called the Cruse Call Wallace test k r
U SKA l WA LI s so Cruse Call Wallace test.
It looks at more than two independent groups, so it's
comparing three therapy programs using ordinal data right in order.
So again it's a non parametric freedoman test FRI E
D M A N is the equivalent of repeated measures
a NOVA more than two repeated measurements. Example, anxiety measured
before treatment, mid treatment, and after treatment using ordinal rankings again.
Now head over to hierarchical linear modeling also called multi
level modeling.
Speaker 1: Use whenever data are nested.
Speaker 2: Nested data means participants naturally belong inside larger groups. Examples
of students in side classrooms, patients inside hospitals, employees inside companies.
Speaker 1: You even think about it as clients in.
Speaker 2: Site therapists psychology example, fifty therapists each treat ten clients.
Clients treated by the same therapists resemble one another. Ordinary
regression assumes everyone is independent.
Speaker 1: That assumption is violated.
Speaker 2: HLM properly analyzes the nested data right, because these individuals
are seeing the therapists for particular reasons, similar reasons right,
psychiatric issues, depression, anxiety, whatnot. Statistical control that means you
remove the influence of confounding variables.
Speaker 1: Probably heard this word before.
Speaker 2: It's a variable related to both the predictor and outcome
that may falsely explain an unobserved relationship. So exercise appears
to reduce suppression. However, younger people exercise more age could
explain the finding.
Speaker 1: Researchers statistically control.
Speaker 2: For age, so they eliminate the young people or they
separate the groups to track to see if there's differences.
Effect size measures the magnitude of an effect, not merely
whether it's statistically significant.
Speaker 1: So we use Coen's D for this.
Speaker 2: Used with T tests, small as zero point two zero,
medium is zero point five, and then large as zero point.
Speaker 1: Eight to zero.
Speaker 2: Partial Eta squared us in n Nova and Manova is
small zero point point zero one, medium point zero six,
and largest point one four. For the partial Eta square,
these again I'm measuring the magnitude of an effect. The
last one is Cohen's F using regression analysis, small as
point zero two, medium point one five, and largest point
three five. The most common ones are the ones used
with T tests, which is Cohen's D, and every so
often you'll probably see partial Eta squared on a nova
in Manova. Our square represents the percentage of variants explained
by the predictors. Example, are squared equals point six to
zero means the model explains sixty percent of the variability
and depression scores. All right, rapid fire review, ask yourself
multiple dependent variables Boom Manova control for baseline differences, and
COVA if you want to find hidden personality traits EFA,
exploratory factor analysis, confirm an existing theory, confirmatory factor analysis
right in the word right, So confirm an existing theory.
Confirmatory factor analysis, predict group membership, discriminate function analysis, and
model complex relationships. Sem explain how something works. Mediation explain
when it works. Moderation or for whom Nested data, hierarchical
linear modeling, and assumptions are violent. You go to non
parametric tests, all right, So let's get some practice questions
and wrap it up today. Psychologist compares three different therapy programs,
measures both depression and anxiety after treatment, which statistical test
is most appropriate and Cova Manova or multiple regression. If
you said Manova, you're right. There is one independent variable,
which is therapy type. Even though they're three different therapy
programs and multiple dependent variables depression and anxiety manova and
analyzes them simultaneously. Are ConTroll controlling for inflated type one
false positives?
Speaker 1: All right.
Speaker 2: Researchers discover that mindfulness reduces anxiety because it approves emotion regulation.
Emotion regulation is best described as what moderator, mediator or
covariate right explains how and why an independent variable influences
and outcome. That's it for now. Hopefully enjoyed this podcast.
We'll see you all next time.