KRIGOLSON TEACHING
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops

EPHE 245 LABORATORY

For the laboratory portion of EPHE 245 you will be completing a research project in a small group. You will design your own experiment to test either the distribution, random, or variable practice hypothesis (see below). Your must experiment must be novel.

Research Project and Paper (60 points: 15% of course grade)

This semester you will be conducting a research study in a small group. In terms of reporting however, each person in the group is responsible for submitting their own research paper. Obviously, you will be able to work with your group on the paper but you must write your own paper for submission.
You will hand in parts of the paper as the semester progresses and at the end of the semester you will hand in a complete research paper. The research paper assignment is worth 15% of your course grade. You will note this is a group assignment and but an individual submission – there will be no exceptions. You are adults and are responsible for dividing up the duties for running your experiment. The research paper will include 5 components:
 
1. Introduction (10 points). You will write a 3 to 4 paragraph introduction for your research paper. Instructions will be provided as to how to write an introduction on the course website.
GRADING RUBRIC
 
2. Methods (10 points). Your will write a 3 to 4 paragraph methods section for your research paper. Instructions will be provided as to how to write a methods section on the course website.
GRADING RUBRIC
 
3. Results (10 points). You will write a 1 to 2 paragraph methods section for your research paper. Instructions will be provided as to how to write a results section on the course website.
GRADING RUBRIC

4. Figures and Tables (10 points). You will generate at least one results figure and one results table.
GRADING RUBRIC

 
5. Discussion (10 points). Your will write a 3 to 4 paragraph discussion section for your research paper. Instructions will be provided as to how to write a discussion section on the course website.
GRADING RUBRIC
 
6. Final Paper (10 points). At the end of the semester, you will submit a final research paper. Your paper must include sections 1 to 5 and also have a title page, abstract, and reference list. The whole paper will be formatted according to APA guidelines.
 
To write your research paper you may find THIS LINK very useful.

Laboratory Grading Guidelines
 
Total: 60 points

Introduction:                            10 points
Methods:                                  10 points
Results:                                     10 points
Figures and Tables:                 10 points
Discussion:                               10 points
Final Paper:                              10 points

Note: A grade of A+ ( > 90 ) will only be awarded with the course instructors review and approval. The course instructor reserves the right to regrade the assignment.
Note: ATTENDANCE OF LABORATORY SECTIONS IS MANDATORY. IF YOU FAIL TO ATTEND, YOU WILL AUTOMATICALLY FAIL THE LABORATORY PORTION OF THE COURSE WHICH IS AN AUTOMATIC FAIL OF THE COURSE.


Lab Schedule
Week

Week One
Sep 14, 15



​Week Two
​Sep 21, 22


​Week Three
​Sep 28, 29


Week Four
​Oct 5, 6




Week Five
​Oct 12, 13


Week Six
​Oct 19, 20



Week Seven
​Oct 26, 27

​
Week Eight
Nov 2, 3


Week Nine
Nov 9, 10

Week Ten
Nov 16, 17

Week Eleven
Nov 23, 24

Week Twelve
Nov 30, Dec 1

EXAM PERIOD
Dec 7, 8

EXAM PERIOD
Dec 14, 15

​EXAM PERIOD
Dec 21, 22
​To Do


​Form groups
Discuss research questions
​Plan experiment

​
Finalize research question and experimental design


​How to write an introduction
​Pilot data collection


​Data collection 1
Discuss experimental design - between vs within
​Discuss data analysis


​Data collection 2
​How to write a methods section


​Data collection  3
Discuss data analysis
​Discuss plotting


How to write a results section
​Start data analysis


​Data analysis
​How to write a discussion


READING BREAK


​
Title, abstract, references


​​Extra Help


Extra Help
Due




















​
​Introduction due



​Introduction marks returned




​Methods due



Methods returned



NB, no lab on 9th.


​Results Due


​Results returned


Discussion due

Discussion returned


Final paper due


Final lab grades posted


​
Research Topics

The students will plan and design an experiment that examines either:
           
            Distributed vs Massed Practice
                       Sample Paper
            Random vs Blocked Practice
                       Sample Paper
            Variable vs Constant Practice
                       Sample Paper
 
Another great resource is HERE.

After explaining these concepts to the students (laboratory session one), the students will form groups and design an experiment testing one of these pairs of constructs. The experimental “task” is up to you as students.
 
Students will be in groups of four.
 
Participants will be all the students in the lab section, plus anyone else they want to test.
 
The students are responsible for designing the experiment. Guidance will be provided but the expectation is the students will design the experiment.
 
There will be workshop days to teach you how to write up an experiment, etc.
 
Try to keep the design simple – t-tests or correlation for analysis.
 
Students will be expected to analyze their own data in EXCEL. We have resources available to you to assist with this (i.e., videos). These resources are HERE.
 
The course does have a redo policy. But in the case of the lab, it is that they can show you a draft of their submission the week before it is due. You provide feedback, they submit on time. Once the final product is submitted there are no more redos.

Instructor Contact: 

Veronica Planella: planella@uvic.ca

Robert Trska: 

Mathew Hammerstrom: mathewhammerstrom@uvic.ca
Office Hours: Thursdays at 2:30-3:30 via Zoom  
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops