Speaker 1: All right, welcome back, folks.
Speaker 2: So this is our last podcast on statistics, so we're
Speaker 2: finally started to get past that point. We're heading into
Speaker 2: ethics next. So let's start the two variables. Everything else
Speaker 2: depends on independent variables. Is the manipulated factor with the
Speaker 2: experiment or changes. Say a researcher wants to know if
Speaker 2: EMDR reduces PTSD symptoms faster than prolonged exposure. The type
Speaker 2: of therapy is the independent variable. It has to be
Speaker 2: operationally defined, meaning it specified enough that another researcher could
Speaker 2: replicated exactly, not just therapy eight sessions of manualized EMDR protocol,
Speaker 2: standardized fidelity checklist, same therapist training level. The dependent variable,
Speaker 2: the DV, is the measured outcome what changes as a
Speaker 2: result of the independent variable and our PTSD study. That's
Speaker 2: the change in score on a standardized instrument for PTSD.
Speaker 2: The dependent variable needs to be reliable, valid, and sensitive
Speaker 2: enough to detect real change. To use the blunt instrument
Speaker 2: that can't move week a week, you'd miss a true
Speaker 2: effect and think that therapy failed when it didn't. Operational
Speaker 2: definitions apply to both sides. They define how constructs are
Speaker 2: measured or manipulated in observable times, Depression becomes score on
Speaker 2: the BDI two. Social support becomes number of perceived supportive
Speaker 2: individuals rated seven or higher on a ten point scale.
Speaker 2: This matter is because vague constructs can't be tested, replicated,
Speaker 2: or falsified. Manipulation checks verify the independent variable actually had
Speaker 2: its intended effect before you even look up the dependent variable.
Speaker 2: If you're running a mood induction study and the sad
Speaker 2: film clip didn't actually make people sadder, any downstream finding
Speaker 2: about your mood and meaning and memory is meaningless, so
Speaker 2: you insert a brief mood scale right after the induction
Speaker 2: to confirm participants felt what you needed them to feel
Speaker 2: before moving forward. Control conditions allow the comparison to baseline
Speaker 2: or no treatment. This is what you let rule out
Speaker 2: the possibility that people would have improved anyway. Some of
Speaker 2: the types are include placebo, weight list, or treatment as usual.
Speaker 2: A weight list control on a CBT for insomnia trial
Speaker 2: holds people in their normal life circumstances while the treatment
Speaker 2: group gets the intervention, so any difference between the groups
Speaker 2: is more plausibly attributed to the therapy itself. Now controlling
Speaker 2: extraaneous of variables, this is where four techniques do most
Speaker 2: of the heavy lifting. Randomization assigns participants to groups by chance,
Speaker 2: distributing uncontrolled variables evenly across conditions. It is the single
Speaker 2: most powerful method for ensuring group equivalents because it doesn't
Speaker 2: just balance the variables you thought of, it balances the
Speaker 2: ones you didn't. Matching insures groups are equivalent on specific
Speaker 2: variables like age or baseline severity. You'd use this when
Speaker 2: your sample size as small or true randomization is infeasible,
Speaker 2: say a specialty clinic with twelve treatment resistant patients. Matching
Speaker 2: can be individual match pairs are frequency match groups, where
Speaker 2: their overall distribution lines up even if individual pairs don't.
Speaker 2: Blocking groups participants into blocks based on a known extraneous
Speaker 2: variable like gender, and then randomizes within each block. If
Speaker 2: you suspect gender might interact with your treatment effect, blocking
Speaker 2: lets your control for that no known source of variation,
Speaker 2: while still preserving randomization's benefits inside each block. Counterbalancing is
Speaker 2: used within subject designs to control for order effects where
Speaker 2: participants receive conditions in different sequences. Complete counterbalancing runs all
Speaker 2: possible orders in Latin square is the efficient version, where
Speaker 2: each condition appears once in each position across groups, so
Speaker 2: you get most of the protection with far fewer participants.
Speaker 2: Counterbalancing protects against practice effects where people get better just
Speaker 2: from repetition, fatigue where people get worse from exhaustion, and
Speaker 2: carry over, where the effects of one condition bleed into
Speaker 2: the next. Blinding procedures address a different entirely expectancy, and
Speaker 2: a single blind design participants are unaware of their group assignment.
Speaker 1: This controls for.
Speaker 2: Placebo effects, expectancy and demand characteristics where participants pick up
Speaker 2: on what the study is looking for and unconsciously perform it,
Speaker 2: and a double blind design both participants and experimenters are
Speaker 2: blind to condition. This is critical in drug trial, psychotherapy,
Speaker 2: comparison studies, and placebo controlled designs. It reduces experimental bias,
Speaker 2: observer expectancy, and subtle queuing, where a researcher who knows
Speaker 2: who got the real drug might unconsciously.
Speaker 1: Rate their symptoms more favorably.
Speaker 2: Let's look at the specialized designs, because the EPE trip
Speaker 2: A P loves asking you to match the design to
Speaker 2: threat the Solomon four group design. The Solomon four group
Speaker 2: design combines pretest, post test and post test only designs
Speaker 2: to control for pretest sensitization, also called the testing effect,
Speaker 2: where simply taking a pretest changes how someone responds later.
Speaker 2: It includes four groups pretest treatment post test, pretest, no
Speaker 2: treatment post test, no pretest treatment post test, and no
Speaker 2: pretest no treatment post test. This lets the research or
Speaker 2: disentangle the effective treatment, the effect of testing itself, and
Speaker 2: the interaction between the two. Imagine a study on self
Speaker 2: compassion intervention with just filling out a self compassion scale
Speaker 2: and four Ham primes people to think differently, and Solomon
Speaker 2: design will catch that. The Latin square design controls for
Speaker 2: other order effects. For order effects, the Latin square design
Speaker 2: and repeated measure designs so where each treatment appears once
Speaker 2: in each row and column, meaning each position, it's efficient
Speaker 2: for controlling sequence effects with fewer participants than complete counterbalancing
Speaker 2: would require three treatments, three orders, three groups, and you've
Speaker 2: covered your sequence confound without needing six separate orderings. Fractional
Speaker 2: factorial design get used when full factorial designs, which test
Speaker 2: all independent variable combinations become too large to run. Only
Speaker 2: they test only a subset of combinations while retaining interpretability.
Speaker 2: This shows up in applied settings with many variables like
Speaker 2: public health, messaging campaign, testing a message, tone, channel, and
Speaker 2: framing all at once without needing. Every single combination represented
Speaker 2: now single case experimental designs used in clinical and applied
Speaker 2: psychology where a group designs simply aren't feasible. The ABA design,
Speaker 2: also called reversal design, has three phases. As baseline B
Speaker 2: is intervention and the second A is withdrawal. It measures
Speaker 2: behavior change and reversibility whether the behavior returns toward baseline
Speaker 2: once treatment is removed. The clear limitation is an ethical concern.
Speaker 2: You're deliberately withdrawing a treatment that might be working.
Speaker 1: Think of a token.
Speaker 2: Economy for a child with disruptive behavior in a classroom.
Speaker 2: You hesitate to pull an effective intervention just to prove
Speaker 2: a point. Scientifically, the multiple baseline design introduces treatment at
Speaker 2: different times across behaviors, settings, or individuals. It shows control
Speaker 2: without needing withdrawal, which solves the ABA ethical problem. It's
Speaker 2: appropriate for irreversible effects or when.
Speaker 1: Removal would be harmful.
Speaker 2: Think of a skill like reading fluency you can unlearn
Speaker 2: to prove a point.
Speaker 1: If a therapist introduces treatment for.
Speaker 2: Behavior A at week two, behavior B at week four,
Speaker 2: and behavior C at week six, and each behavior only
Speaker 2: improves after its own treatment starts.
Speaker 1: That staggered pattern lets you infer causality.
Speaker 2: Single case designs emphasize repeated measurements, stability, and visual analysis,
Speaker 2: meaning researchers look at graph data trends rather than relying
Speaker 2: solely on inferential statistics. All of those connects back to
Speaker 2: internal validity the extent to its changes in the dependent
Speaker 2: variable are caused by the independent variable and not extraneous factors.
Speaker 2: History refers to outside events that influence the outcome.
Speaker 1: If a natural disaster hits.
Speaker 2: The community in mid study, that history is contaminating the results.
Speaker 2: Maturation is natural change over time. Kids get better at
Speaker 2: emotional regulations simply by growing older. Testing is the effect
Speaker 2: where the pretest itself affects the protest. This same testing
Speaker 2: effect that Solomon design was built to catch. Instrumentation that
Speaker 2: covers changes in the measurement tool or radar over the
Speaker 2: course of the study. If a rater becomes more lenient
Speaker 2: by the end of data collection, that instrumentation not real change.
Speaker 2: Selection refers to group difference as a baseline before the
Speaker 2: study even begins. If your treatment group happened to be
Speaker 2: less severe going in, any improvement might just reflect who
Speaker 2: ended up where attrition has dropped out that Alders group composition.
Speaker 2: The most symptomatic participants drop out of the treatment are
Speaker 2: in the remaining group, but looks artificially healthier. Regression to
Speaker 2: the mean is when extreme scores move toward average over time,
Speaker 2: regardless of treatment. Someone who scored unusually high on anxiety
Speaker 2: measure at intake is statistically likely to score lower next time,
Speaker 2: even with no intervention at all. Good design eliminates or
Speaker 2: controls for these threats using random assignments, control groups, blinding
Speaker 2: manipulation checks, and consistent measurement procedures, meaning the same protocol,
Speaker 2: same instruments, same conditions, apply the same way across the
Speaker 2: entire study.
Speaker 1: Well that's the episode. Know of those
Speaker 2: Terms, but more importantly, know what each problem is solving.