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<v Speaker 1>All right, welcome back to everybody.

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<v Speaker 2>It's continuing and doing statistics now, so it hasn't really

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<v Speaker 2>gone away yet and we're diving.

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<v Speaker 1>Into advanced statistics. But don't panic.

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<v Speaker 2>The E triple P is not expecting you to calculate

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<v Speaker 2>these analysis. Instead, the exam wants you to recognize when

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<v Speaker 2>each procedure is appropriate. So think of today's lesson as

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<v Speaker 2>learning the right tool for the right research question. First

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<v Speaker 2>one off the gate is MANOVA. It stands for multivariate

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<v Speaker 2>analysis of variants. It compares groups on multiple dependent variables simultaneously.

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<v Speaker 2>Think one independent variable with multiple dependent variables.

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<v Speaker 1>For instance, if you want.

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<v Speaker 2>To assign clients to three therapy groups CBT, psychodynamic, and medication,

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<v Speaker 2>instead of measuring only depression, they measure depression, anxiety, and

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<v Speaker 2>stress at the same time. Rather than running three adova's,

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<v Speaker 2>they perform one Manova.

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<v Speaker 1>So why use it?

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<v Speaker 2>Because running several Andova's increases the chance of a type

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<v Speaker 2>one or false positive simply by chance. Manova helps control

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<v Speaker 2>that inflation. So a Manova again is one independent variable,

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<v Speaker 2>multiple dependent variables that compares groups on multiple dependent variables simultaneously.

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<v Speaker 2>All right, heading over to do I have one of

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<v Speaker 2>the tips you can think of is do I have

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<v Speaker 2>one independent variable and multiple dependent variables? If yes, think

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<v Speaker 2>Manova and Kova analysis of covariance and Cova compares group

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<v Speaker 2>means while statistically controlling for another variable called a covariate.

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<v Speaker 2>It's a variable that might influence the dependent variable, but

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<v Speaker 2>isn't the main focus of the study. An example be

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<v Speaker 2>researchers compare CBT versus medication for depression. However, patients begin

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<v Speaker 2>treatment with different levels of depression baseline depression could affect results.

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<v Speaker 2>Researchers statistically remove that influence using Ancova. Think of Ancouva

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<v Speaker 2>as leveling the playing field before comparing treatments. Discriminate function

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<v Speaker 2>analysis This predicts which group someone belongs to using several

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<v Speaker 2>predictor variables. So let me give you an example. Can

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<v Speaker 2>depression scores, anxiety scores, sleep quality, and trauma history correctly

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<v Speaker 2>classify someone as having PTSD or generalized anxiety disorder. The

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<v Speaker 2>dependent variables categorical, The predictors are continuous, perfect situation for

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<v Speaker 2>discriminate functional or function analysis again predicts which group someone

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<v Speaker 2>belongs to using several predictor variables. Right, the predictor variables

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<v Speaker 2>being anxiety scores, depression scores, and then, of course, the

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<v Speaker 2>dependent variables categorical.

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<v Speaker 1>So as, PTSD or generalized anxieties.

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<v Speaker 2>NeXT's factor analysis, which discovers hidden psychological traits called latent variables.

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<v Speaker 2>A latent variables a psychological characteristic. It cannot be directly observed,

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<v Speaker 2>but can be measured indirectly. Examples include intelligence, depression, anxiety,

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<v Speaker 2>self esteem. You cannot see self esteem, you measure it

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<v Speaker 2>using questionnaires. So I imagine a personality test with one

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<v Speaker 2>hundred questions. Many of these questions actually measure only five

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<v Speaker 2>personality traits. Factor analysis uncovers those hidden dimensions EFA the

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<v Speaker 2>exploratory factor analysis. It explores data without having a prior theory.

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<v Speaker 2>Researchers ask what factors naturally appear.

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<v Speaker 1>So if you do a study, a.

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<v Speaker 2>Psychologist develops a new trauma questionnaire, nobody knows how many

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<v Speaker 2>dimensions exist. Yet maybe there's three, maybe five. Exploratory factor

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<v Speaker 2>analysis lets the data decide. Another important vocabulary word is eigenvalue.

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<v Speaker 1>That's E I G E and V A l u E.

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<v Speaker 2>It measures how much variance a factor explains. Higher eigenvalues

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<v Speaker 2>usually indicate more important factors. A screen plot s c

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<v Speaker 2>R E e is a graph showing where meaningful factors

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<v Speaker 2>begin to level off.

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<v Speaker 1>Imagine a mountain. The point where it flatteness tells you

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<v Speaker 1>how many factors to keep.

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<v Speaker 2>Factor loading the correlation between a question and a factor.

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<v Speaker 2>Large loadings mean that that question strongly measures that factor.

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<v Speaker 2>Commonality the pro proportion of variants in a variable explained

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<v Speaker 2>by all factors together.

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<v Speaker 1>It might be easier to actually go back as I

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<v Speaker 1>was reading this to give you example.

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<v Speaker 2>So the eigenvalue again, the higher the more important the

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<v Speaker 2>factor is. So imagine you create a forty questions personality questionnaire.

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<v Speaker 2>After running an exploratory factor analysis that we talked about earlier,

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<v Speaker 2>you discover four factors. Factor one has an eigenvalue of

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<v Speaker 2>eight point five, factor three one point four. Factor one

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<v Speaker 2>explains the largest amount of personality differences among participants. Most

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<v Speaker 2>researchers keep factors with an eigenvalue greater than one, So

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<v Speaker 2>factors one through three in the example which I didn't

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<v Speaker 2>give you all the numbers would likely be retained well.

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<v Speaker 2>Factor four at a zero point six would probably be discarded.

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<v Speaker 2>Think of of an eigenvalue as a factor's importance score.

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<v Speaker 2>The screen plot is a graph that helps researchers decide

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<v Speaker 2>how many factors should be kept. Researchers look for the

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<v Speaker 2>point where the graph suddenly levels off like.

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<v Speaker 1>I was telling you about the mountains. Is called the elbow.

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<v Speaker 2>Suppose your questionnaire initially identifies eight possible factors. When you

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<v Speaker 2>examine the screen plot, the first three factors drop sharply,

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<v Speaker 2>and after the third factor.

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<v Speaker 1>The line becomes flat.

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<v Speaker 2>That tells you that only three meaningful psychological factors exist,

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<v Speaker 2>while the remaining factors mostly represent noise. Factor loading, as

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<v Speaker 2>we told you earlier, as the correlation between an individual

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<v Speaker 2>test item and a particular factor. Suppose one factor's label depression.

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<v Speaker 2>One questionnaire item says I feel hopeless most days. It's

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<v Speaker 2>factor loading is zero point eighty seven. Another one says

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<v Speaker 2>I enjoy watching sports. It's factor loading a zero point nine.

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<v Speaker 2>The first question strongly measures depression, in the second almost

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<v Speaker 2>nothing to that factor. Factor loadings are like magnets. The

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<v Speaker 2>stronger the loading, the more stronger the questions pulled toward

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<v Speaker 2>that factor.

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<v Speaker 1>Another one we did not discuss.

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<v Speaker 2>Yes, communality represents the percentage of a question's variance explained

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<v Speaker 2>by all of the retained factors combined. Higher communalities we

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<v Speaker 2>mean that the factor model explains that question well. Suppose

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<v Speaker 2>the questionnaire item I feel nervous in crowds has a

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<v Speaker 2>communality of zero point eight two. That means that eighty

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<v Speaker 2>two percent of the differences in people's responses can be

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<v Speaker 2>explained by the psychological fing identified in your model, such

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<v Speaker 2>as anxiety and social fear. Imagine a different one, I

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<v Speaker 2>prefer chocolate over vanilla. Its communality is zero point one eight.

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<v Speaker 2>Only eighteen percent of the variation is explained by your factors,

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<v Speaker 2>suggesting this question probably doesn't.

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<v Speaker 1>Belong to an anxiety questionnaire.

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<v Speaker 2>Another one is orth orthogonal rotation. Orthogonal rotation it's O

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<v Speaker 2>R thho g O n A L or verimax assumes

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<v Speaker 2>that the factors are completely independent and unrelated to one another.

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<v Speaker 2>It's a researcher develops a vocational aptitude test measuring mathematical ability,

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<v Speaker 2>musical ability, mechanical ability. The researcher assumes these abilities are unrelated.

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<v Speaker 2>Verimax rotation keeps the factors independent and easier to interpret.

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<v Speaker 2>Orthogonical orthogonal equals orthodox and dependence.

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<v Speaker 1>The factors stay separate.

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<v Speaker 2>Oblique rotation or promax allows factors to be correlated. Because

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<v Speaker 2>many psychological traits naturally overlap, for instance, depression, anxiety, and stress,

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<v Speaker 2>these constructs often occur together.

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<v Speaker 1>Some estimate over seventy percent of the time.

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<v Speaker 2>Someone with severe anxiety frequently expresses experiences depression as well. Promax,

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<v Speaker 2>which a rotation allows those factors to correlate.

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<v Speaker 1>Making the results much more realistic.

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<v Speaker 2>Oblique means the factors are allowed to lean toward one

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<v Speaker 2>another instead of standing apart. Another quick rule to help

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<v Speaker 2>you is I can value is how important is the factor?

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<v Speaker 2>Screenplot is? How many factors should I keep? Factor loading?

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<v Speaker 2>Which questions belong to each factor?

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<v Speaker 1>Communality?

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<v Speaker 2>How well do the factors explain each question? Verimax factors

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<v Speaker 2>stay independent promax the factors.

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<v Speaker 1>Are allowed to correlate.

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<v Speaker 2>Yes, this is going to be something that's probably pretty

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<v Speaker 2>Foreign's covered quite a bit in classes that I remember.

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<v Speaker 2>All right, so we move along past that now and

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<v Speaker 2>we're heading into a different territory. So now we're going

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<v Speaker 2>into what they call the structured sorry, what they call

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<v Speaker 2>the structural equation modeling the SEM. This combines several statistical

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<v Speaker 2>techniques into one large model. It can test regression path analysis,

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<v Speaker 2>confirmatory factor analysis, direct effects, indirect effects latent variables all

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<v Speaker 2>at once. The structural equation modeling combines several statistical techniques

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<v Speaker 2>into one large model. So, for example, researchers propose parenting style,

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<v Speaker 2>self esteem, and academic performance.

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<v Speaker 1>That's what they're going to look at.

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<v Speaker 2>SEM tests the entire model simultaneously. One analysis, multiple relationship.

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<v Speaker 2>NeXT's mediation. A mediator explains how or why one variable

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<v Speaker 2>affects another. So if you look at mindfulness improves emotion

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<v Speaker 2>and regulation, which then reduces anxiety. Emotion regulation explains why

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<v Speaker 2>mindfulness works. It's the mediator, right, mindfulness anxiety the two players.

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<v Speaker 2>But why does mindfulness reduce anxiety because emotional regulation improves

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<v Speaker 2>Remember mediator answers how. Moderation as a moderator tells us when,

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<v Speaker 2>for whom or under what conditions the relationship exists. CBT

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<v Speaker 2>reduces oppression. Now you're looking for the questions when, for

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<v Speaker 2>whom and under what? But only among younger adults. That's

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<v Speaker 2>for whom age changes the strength of treatment under what conditions?

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<v Speaker 2>And age is the moderator. Remember moderator answers when or whom?

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<v Speaker 2>Non parametric tests these are used when assumptions like normality

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<v Speaker 2>or violated remember that from the.

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<v Speaker 1>Distribution that we talked about.

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<v Speaker 2>When they're evenly distributed, non parametric version of the independent

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<v Speaker 2>samples T tests you use Man Whitney you so non

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<v Speaker 2>parametric version of the independent samples T test you use

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<v Speaker 2>for two independent groups, so comparing depression scores between males

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<v Speaker 2>and females. When scores are highly skewed, they're not equally

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<v Speaker 2>distributed like the Bell curve, then you would go to

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<v Speaker 2>the Man Whitney You A non parametric equivalent of the

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<v Speaker 2>paired samples T test is will Coxin signed ranked test,

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<v Speaker 2>well Coxon signed ranked test. It's used for two related groups,

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<v Speaker 2>so depression before therapy versus after therapy.

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<v Speaker 1>But again they're skewed. Equivalent of a one way on nova.

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<v Speaker 2>It's something called the Cruse Call Wallace test k r

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<v Speaker 2>U SKA l WA LI s so Cruse Call Wallace test.

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<v Speaker 2>It looks at more than two independent groups, so it's

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<v Speaker 2>comparing three therapy programs using ordinal data right in order.

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<v Speaker 2>So again it's a non parametric freedoman test FRI E

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<v Speaker 2>D M A N is the equivalent of repeated measures

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<v Speaker 2>a NOVA more than two repeated measurements. Example, anxiety measured

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<v Speaker 2>before treatment, mid treatment, and after treatment using ordinal rankings again.

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<v Speaker 2>Now head over to hierarchical linear modeling also called multi

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<v Speaker 2>level modeling.

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<v Speaker 1>Use whenever data are nested.

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<v Speaker 2>Nested data means participants naturally belong inside larger groups. Examples

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<v Speaker 2>of students in side classrooms, patients inside hospitals, employees inside companies.

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<v Speaker 1>You even think about it as clients in.

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<v Speaker 2>Site therapists psychology example, fifty therapists each treat ten clients.

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<v Speaker 2>Clients treated by the same therapists resemble one another. Ordinary

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<v Speaker 2>regression assumes everyone is independent.

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<v Speaker 1>That assumption is violated.

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<v Speaker 2>HLM properly analyzes the nested data right, because these individuals

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<v Speaker 2>are seeing the therapists for particular reasons, similar reasons right,

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<v Speaker 2>psychiatric issues, depression, anxiety, whatnot. Statistical control that means you

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<v Speaker 2>remove the influence of confounding variables.

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<v Speaker 1>Probably heard this word before.

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<v Speaker 2>It's a variable related to both the predictor and outcome

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<v Speaker 2>that may falsely explain an unobserved relationship. So exercise appears

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<v Speaker 2>to reduce suppression. However, younger people exercise more age could

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<v Speaker 2>explain the finding.

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<v Speaker 1>Researchers statistically control.

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<v Speaker 2>For age, so they eliminate the young people or they

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<v Speaker 2>separate the groups to track to see if there's differences.

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<v Speaker 2>Effect size measures the magnitude of an effect, not merely

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<v Speaker 2>whether it's statistically significant.

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<v Speaker 1>So we use Coen's D for this.

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<v Speaker 2>Used with T tests, small as zero point two zero,

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<v Speaker 2>medium is zero point five, and then large as zero point.

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<v Speaker 1>Eight to zero.

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<v Speaker 2>Partial Eta squared us in n Nova and Manova is

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<v Speaker 2>small zero point point zero one, medium point zero six,

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<v Speaker 2>and largest point one four. For the partial Eta square,

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<v Speaker 2>these again I'm measuring the magnitude of an effect. The

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<v Speaker 2>last one is Cohen's F using regression analysis, small as

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<v Speaker 2>point zero two, medium point one five, and largest point

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<v Speaker 2>three five. The most common ones are the ones used

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<v Speaker 2>with T tests, which is Cohen's D, and every so

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<v Speaker 2>often you'll probably see partial Eta squared on a nova

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<v Speaker 2>in Manova. Our square represents the percentage of variants explained

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<v Speaker 2>by the predictors. Example, are squared equals point six to

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<v Speaker 2>zero means the model explains sixty percent of the variability

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<v Speaker 2>and depression scores. All right, rapid fire review, ask yourself

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<v Speaker 2>multiple dependent variables Boom Manova control for baseline differences, and

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<v Speaker 2>COVA if you want to find hidden personality traits EFA,

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<v Speaker 2>exploratory factor analysis, confirm an existing theory, confirmatory factor analysis

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<v Speaker 2>right in the word right, So confirm an existing theory.

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<v Speaker 2>Confirmatory factor analysis, predict group membership, discriminate function analysis, and

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<v Speaker 2>model complex relationships. Sem explain how something works. Mediation explain

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<v Speaker 2>when it works. Moderation or for whom Nested data, hierarchical

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<v Speaker 2>linear modeling, and assumptions are violent. You go to non

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<v Speaker 2>parametric tests, all right, So let's get some practice questions

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<v Speaker 2>and wrap it up today. Psychologist compares three different therapy programs,

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<v Speaker 2>measures both depression and anxiety after treatment, which statistical test

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<v Speaker 2>is most appropriate and Cova Manova or multiple regression. If

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<v Speaker 2>you said Manova, you're right. There is one independent variable,

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<v Speaker 2>which is therapy type. Even though they're three different therapy

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<v Speaker 2>programs and multiple dependent variables depression and anxiety manova and

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<v Speaker 2>analyzes them simultaneously. Are ConTroll controlling for inflated type one

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<v Speaker 2>false positives?

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<v Speaker 1>All right.

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<v Speaker 2>Researchers discover that mindfulness reduces anxiety because it approves emotion regulation.

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<v Speaker 2>Emotion regulation is best described as what moderator, mediator or

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<v Speaker 2>covariate right explains how and why an independent variable influences

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<v Speaker 2>and outcome. That's it for now. Hopefully enjoyed this podcast.

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<v Speaker 2>We'll see you all next time.