Psy 611: Data Analysis I

You are a scientist

Goals of this sequence

  • Develop the basic quantitative skills necessary to be a research scientist
    • Not all the skills you will need (not the only courses you need)
    • Foundations of statistics, methods, and data science
  • Contextualize those skills
    • Building a toolbox, not a cookbook
    • How can I use this? Under what circumstances? When should I not use this?

Goals of this sequence

  • PSY 611: Tools of statistics
  • PSY 612: Building models
  • PSY 613: Taking it to the next level

Goals of this course

  • Conceptualize statistics as a method for specifying and testing a model of how the world works

  • Execute and understand of the use and limitations of null hypothesis significance testing

  • Wrangle, summarize, test, and display data using R

  • Independence

    • If you only learn one thing this term, focus on this!

Challenges

  • Many basic statistical tests were developed a century ago.

    • Developed for small samples and hand calculations.
    • Example: minimum sample size = 30 per cell. Why?
  • This class has variance.

Road map

Where we’re going this term
Week(s) Topics
1-2 Collecting and summarizing data
3-4 Probability
5 Sampling and making inferences about the unknown
6-9 Making a decision
10 Evaluating that decision

Graduate school vs undergraduate

Undergraduate

  • Complete assignment
  • Receive grade and (maybe) feedback
  • Move on to next assignment

Graduate

  • Attempt assignment
  • Receive feedback
  • Edit/retry/improve work
  • Receive feedback
  • … rinse, repeat…

My goals (practical)

  • Teach you the basics of statistics and R.

  • Challenge you – this class is designed to push you to your personal limit and then just beyond.

    • If you feel like you’re sailing through this class, come talk to me.
    • If you feel like you’re drowning, come talk to me.
  • Create situations in which you can practice skills that will help throughout your career.

    • learning things on your own, asking for help, saying no, teaching others…
  • Give everyone an A.

Format

  • Learn
    • Lectures
    • Labs
    • Readings
  • Practice
    • Weekly Quizzes (9 total)
    • Homework (3 total)
  • Demonstrate mastery
    • Oral exam
  • Get support
    • Journals (10 total)

Materials

R and RMarkdown

This course will use R and RStudio for all analyses. Why?

  • Statistical tool
  • Reproducibility
  • Workflow
  • Marketability

If you want extra help, the library offers R workshops, as well as workshops on reproducibility (data management, database and spreadsheets, version control with Git, etc).

The library also has R consultants (GEs) available during most work hours to help troubleshoot.

https://library.uoregon.edu/research-data-management/training-workshops

Questions?

  • Please read the rest of the syllabus on your own.

For the rest of today:

  • some terminology
  • scales of measurement

Kinds of statistics

  • Descriptive
  • Inferential

Kinds of statistics

  • Exploratory
  • Confirmatory

Exploratory —————————————- Confirmatory

Kinds of research

  • Experimental
  • Observational

Typically we pair some kinds of statistical tests with experimental work and other kinds of tests with observational work. Examples?

Kinds of research

In reality, most statistical tests can be used with most kinds of research. It’s not so much the kind of research that matters, but which statistic helps to answer your question and what types of variables do you have?

  • We’ll discuss the first point throughout the course
  • Let’s discuss variables now

Scales of measurement

Four scales

  • Nominal
  • Ordinal
  • Interval
  • Ratio

Nominal

  • response options are groups
  • no specific order
  • example: What is your favorite fruit? Blueberries, apples, coconuts

Ordinal

  • response options are ordered
  • no consistent distance between possible scores
  • example: List the following fruits in order of preference: peaches, grapes, raspberries

Interval

  • response options are ordered
  • distance between response options is the same
  • no meaningful 0
  • example: On a scale from 0-5, how much do you like bananas?

Ratio

  • response options are ordered
  • distance between response options is the same
  • 0 indicates an absence of something
  • example: how many times have you eaten watermelon in the last 6 months?

Breakout groups

Pick one of the following constructs and come up with four ways of measuring it (one for each of the scales: nominal, ordinal, interval, ratio):

  • Self-esteem
  • Political preference
  • Memory
  • Dog-enthusiasm
  • Anxiety

Scales of measurement

  • Nominal

  • Ordinal

  • Interval

  • Ratio

Why does this matter?

  • Different amounts of information

  • Different mathematical properties

    • Example: If Bobby scores 3 and Megan scores 6, can we say Megan scored twice as high as Bobby?

Scales of measurement

  • Nominal

  • Ordinal

  • Interval

  • Ratio

What is this?

  • “On a scale from 1 to 6, how much does the word outgoing describe you?”

Scales of measurement

Two types

  • Categorical
  • Continuous

Scales of measurement

  • Nominal

  • Ordinal

  • Interval

  • Ratio

  • Categorical
  • Continuous

How do these two methods of describing scales match up?

Next time

Measurement validity

Before class, please download and install R and RStudio.