Course

# Data Analysis in Psychology

Faculty
Humanities & Social Sciences
Department
Psychology
Course Code
PSYC 2300
Credits
3.00
Semester Length
15
Max Class Size
35
Method(s) Of Instruction
Lecture
Typically Offered
To be determined

## Overview

Course Description
This course introduces students to the concepts and applications of statistics and focuses on the analysis and interpretation of data from experiments and surveys using descriptive and inferential statistics. Computerized data analysis is also introduced.
Course Content
1. Abuses of statistics
2. Organizing and describing data
3. Measures of central tendency
4. Measures of variability
5. Standard scores
6. Description of frequency distributions
7. Properties of normal distributions
8. Central Limit Theorem
9. Introduction to probability concepts
10. Null hypothesis significance testing
11. Analysis of Variance and t-tests
12. Correlation methods
13. Regression and prediction
14. Nonparametric statistical methods
15. Statistical significance versus practical importance
16. Measures of effect size and confidence intervals
Learning Activities

This course will employ a number of instructional methods to accomplish its objectives and will include some of the following:

• lectures
• audio visual materials
• small group discussion
• research projects
• computer based tutorial exercises
Means of Assessment

Evaluation will be carried out in accordance with Douglas College policy.  Evaluation will be based on course objectives and will include some of the following: quizzes, multiple choice exams, essay type exams, term paper or research project, computer based assignments, etc.  The instructor will provide the students with a course outline listing the criteria for course evaluation.

An example of one evaluation scheme:

 12 quizzes 40% Computer based homework assignments 10% Homework exercises 10% Midterm exam 20% Final exam 20% Total 100%
Learning Outcomes

At the conclusion of the course the successful student will be able to:

1. Define, describe and distinguish between descriptive and inferential statistics, and identify in what contexts each is appropriate.
2. Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key descriptive statistical terms, such as: empirical distribution, frequency distribution, histogram, percentile, quartile, measures of central tendency (median, mode, mean), sum of squares, measures of variance (range, variance, standard deviation, within-groups variance, between-groups variance), standard score/z-scores, covariance, Pearson r, regression coefficient, model, and effect size (e.g., Cohen’s d, Eta squared).
3. Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key research designs and inferential statistical terms, such as: scales of variables (nominal, ordinal, interval, ratio),  independent variable (IV), dependent variable (DV), theoretical distribution, population, sample, statistic, random sampling, estimator, estimate, probability distribution, parameter, normal distribution, t distribution, F-distribution, Chi-square distribution, sampling distribution, null hypothesis significance testing (NHST), null hypothesis, alternative hypothesis, p-value, alpha, beta, power, type 1 error, type 2 error, critical value, statistical significance, and confidence interval.
4. Describe and explain the logic of inferential statistics. This includes being able to explain what a p-value is, what statistical significance means, and how various factors, such as sample size, effect size, alpha and violation of assumptions, influence the p-value and statistical significance.
5. Calculate, interpret, explain the rationale for, and analyze the assumptions for appropriate test statistics and p-values for situations such as the following: a) there is one IV that has two or more levels and the DV is a scale variable – the IV may be a between or within subjects IV, b) there may be a linear relationship between two variables and the null hypothesis is that the population relationship is 0, c) there are observed frequencies for one or two variables and the null hypothesis is that the distribution of observed frequencies is non-proportional.
6. Interpret basic research results as published in academic journals.
Textbook Materials

Textbook(s) such as the following, the list to be updated periodically.

• Aron, A., Coups, E.J., & Aron, E. N. (2013) Statistics for psychology (6th ed.) Upper Saddle River, NJ: Pearson Education.
• Howell, D. C. (2017) Fundamental statistics for the behavioral sciences (9th Ed.) Pacific Grove, CA: Brooks/Cole.
• Gravetter, F.J., Wallnau, L.B. & Forzano, L.B. (2018). Essentials of statistics for the behavioral sciences (9th ed.). Boston, MA: Nelson/Cengage.

## Requisites

### Prerequisites

and

• a C or better in Foundations of Math 11 or Pre-calculus 11 (or equivalent)

### Corequisites

Courses listed here must be completed either prior to or simultaneously with this course:

• No corequisite courses

### Equivalencies

Courses listed here are equivalent to this course and cannot be taken for further credit:

• No equivalency courses

## Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

## Course Transfers

These are for current course guidelines only. For a full list of archived courses please see https://www.bctransferguide.ca

Institution Transfer Details for PSYC 2300
Athabasca University (AU) AU SOCI 301 (3)
Capilano University (CAPU) CAPU PSYC 213 (3)
Coast Mountain College (CMTN) CMTN PSYC 2XX (3)
College of New Caledonia (CNC) CNC PSYC 201 (3)
Kwantlen Polytechnic University (KPU) KPU PSYC 2300 (3)
Langara College (LANG) LANG PSYC 2321 (3)
Okanagan College (OC) OC PSYC 2XX (3)
Simon Fraser University (SFU) SFU PSYC 210 (3)
Thompson Rivers University (TRU) TRU PSYC 2100 (3)
Trinity Western University (TWU) TWU PSYC 207 (3)
University of British Columbia - Okanagan (UBCO) UBCO PSYO 271 (3)
University of British Columbia - Vancouver (UBCV) UBCV PSYC 218 (3)
University of Northern BC (UNBC) UNBC PSYC 315 (4)
University of the Fraser Valley (UFV) UFV PSYC 110 (3) or UFV STAT 104 (3)
University of Victoria (UVIC) UVIC PSYC 300A (1.5)
Vancouver Community College (VCC) DOUG MATH 1160 (3) or DOUG PSYC 2300 (3) = VCC MATH 1111 (3)
Vancouver Island University (VIU) VIU PSYC 204 (3)

## Course Offerings

### Fall 2023

CRN
Days
Dates
Start Date
End Date
Instructor
Status
CRN
34242
Thu
Start Date
-
End Date
Start Date
End Date
Instructor Last Name
Di Pietro
Instructor First Name
Nina
Course Status
Waitlist
Max
Enrolled
Remaining
Waitlist
Max Seats Count
35
Actual Seats Count
35
0
Actual Wait Count
1
Days
Building
Room
Time
Thu
Building
Coquitlam - Bldg. D
Room
D2024
Start Time
9:30
-
End Time
12:20
CRN
Days
Dates
Start Date
End Date
Instructor
Status
CRN
34363
Wed
Start Date
-
End Date
Start Date
End Date
Instructor Last Name
Di Pietro
Instructor First Name
Nina
Course Status
Waitlist
Max
Enrolled
Remaining
Waitlist
Max Seats Count
35
Actual Seats Count
35
0
Actual Wait Count
1
Days
Building
Room
Time
Wed
Building
Coquitlam - Bldg. D
Room
D2012
Start Time
9:30
-
End Time
12:20
CRN
Days
Dates
Start Date
End Date
Instructor
Status
CRN
34396
Wed
Start Date
-
End Date
Start Date
End Date
Instructor Last Name
Pedersen
Instructor First Name
Cory
Course Status
Open
Max
Enrolled
Remaining
Waitlist
Max Seats Count
35
Actual Seats Count
34
1
Actual Wait Count
0
Days
Building
Room
Time
Wed
Building
Anvil Office Tower
Room
816
Start Time
9:30
-
End Time
12:20
CRN
Days
Dates
Start Date
End Date
Instructor
Status
CRN
34941
Start Date
-
End Date
Start Date
End Date
Instructor Last Name
Wagner
Instructor First Name
Kristin
Course Status
Waitlist
Max
Enrolled
Remaining
Waitlist
Max Seats Count
35
Actual Seats Count
36
-1
Actual Wait Count
4
Days
Building
Room
Time
Building
None
Room
None
-
CRN
Days
Dates
Start Date
End Date
Instructor
Status
CRN
34942
Start Date
-
End Date
Start Date
End Date
Instructor Last Name
Wagner
Instructor First Name
Kristin
Course Status
Waitlist
Max
Enrolled
Remaining
Waitlist
Max Seats Count
35
Actual Seats Count
36
-1
Actual Wait Count
4
Days
Building
Room
Time
Building
None
Room
None
-