COVID-19 information and resources
Douglas College wordmark
Facebook logo Twitter logo Instagram logo Snapchat logo YouTube logo Wordpress logo
back to search

Data Analysis in Psychology

Course Code: PSYC 2300
Faculty: Humanities & Social Sciences
Department: Psychology
Credits: 3.0
Semester: 15
Learning Format: Lecture
Typically Offered: TBD. Contact Department Chair for more info.
course overview

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

Methods of Instruction

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.

course prerequisites

  • PSYC 1100 AND PSYC 1200


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


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

  • No corequisite courses


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

  • No equivalency courses

curriculum 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 schedule and availability
course transferability

Below shows how this course and its credits transfer within the BC transfer system. 

A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.

For more information on transfer visit the BC Transfer Guide and BCCAT websites.


If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.