Welcome back (+ midterm review) 18 March 2020 Modern Research - - PowerPoint PPT Presentation

Welcome back (+ midterm review)

18 March 2020 Modern Research Methods

Logistics

• As a reminder, this session will be

audio/video recorded for educational use by other students in this course.

• Getting to the classroom (links in

Canvas and on website)

• Office hours – same time, but over

Zoom

• Must si

sign-up up using spreadsheet

• If you’re unable to make those times, let us

know and we will do our best to accommodate you

Zoom etiquette

• Mute yourself when you’re not actively

speaking

• Unmute to speak
• Then, mute again after you speak
• Use headphones if possible
• Prevents feedback from your speakers
• When you want to speak, raise your hand
• When I see your hand raised, I’ll call on you

(or hold spacebar to unmute)

Midterm

• Grades are posted
• Spell check!
• Still some formatting issues
• 5pts on the midterm for appropriately formatted responses
• If you got penalized for this, there should be a note in canvas
• You may fix your midterm and turn it back into us to get this points

back (by next Wednesday)

• Formatting issues:
• Printing out whole dataset
• Not using any headers
• Writing responses as comments in R code

What does the rest of the semester look like?

• Today, go over midterm
• On Friday, more practice with effect sizes
• Then, we’re going to start working on the final project – a

meta-analysis

• We’ll talk more about this next week, but start thinking about

what psychology questions you might be interested in studying

• New scientific knowledge is built upon on the findings in

previous research and each other’s research

• Involves reproducibility; tools like R markdown and Github

facilitate this

• Involves replicability; p-hacking and other QRPs slow

cumulative science

What makes a good measurement?

Re Relia liabilit bility – Consistency of measurement (Do you say the

same thing today as yesterday?)

Va Validity – Are we measuring the construct we want to measure? Suppose the thing we’re trying to measure is the center of the bullseye…

• P-value: binary statement about whether there’s effect [significant/not significant]
• note: p-value doesn’t tell you about the size of the effect since the p-value depends
• n the confidence interval
• Confidence interval: Range of plausible values for the mean
• Effect size (e.g. Cohen’s d): magnitude of the difference between the two means

Co Confidence intervals:

• Set of plausible values for an

estimate

• If you run an experiment 100

times, the true population value will be included in 95

• f the confidence intervals

What are violin plots?

• Same as probability density (geom_density()), except

mirrored/doubled and turned on its side

• Often preferable to bar graphs because it allows you to see the

distribution of the underlying data

• Another alternative “rain cloud” plots

On Friday

• (will post solutions to midterm online)
• More practice with effect sizes
• Reading: