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