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Registration for the Fall 2019 semester begins June 25.  Watch your email for more details.

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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. Description of frequency distributions
  6. Properties of normal distributions
  7. Central Limit Theorem
  8. Introduction to probability concepts
  9. Hypothesis testing
  10. Analysis of Variance
  11. t-tests
  12. Correlation methods
  13. Regression and prediction
  14. Nonparametric statistical methods
  15. Statistical significance versus practical importance

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  50%
Computer based homework assignments      10%
Homework exercises  10%
Term project paper  20%
Final exam  10%
Total 100%

Learning Outcomes

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

  1.  Distinguish between descriptive and inferential statistics.
  2. Define various key statistical terms, such as population, sample, parameter, variable, random sample, sampling distribution, level of significance, critical value, Type I and Type II errors, and the null hypothesis.
  3. Define and describe various measures of central tendency.
  4. Explain the concept of variability.
  5. Calculate various statistics such as standard deviation, variance, z scores correlation coefficient (r), t-test, analysis of variance, chi square.
  6. Distinguish between correlation and causation.
  7. Explain the meaning and use of the regression equation.
  8. Compute regression coefficients and fit a regression line to a set of data.
  9. Distinguish between a theoretical and empirical distribution.
  10. List the characteristics of the normal distribution.
  11. Calculate confidence intervals about a sample mean and explain what they mean.
  12. Explain the logic of inferential statistics.
  13. Describe the factors that affect rejection of the null hypothesis.
  14. Distinguish an independent-samples design from a correlated samples design.
  15. List and explain the assumptions for the t-test and ANOVA.
  16. Identify the independent and dependent variables in a one-way ANOVA and a two-way ANOVA.
  17. Explain the rationale of ANOVA.
  18. Define F and explain its relationship to t.
  19. Compute sums of squares, mean squares, degrees of freedom, and F for an ANOVA.
  20. Interpret an F value obtained in an experiment.
  21. Construct a summary table of ANOVA results.
  22. Distinguish between a priori and a posteriori tests.
  23. Identify the sources of variance in a factorial design.
  24. Compute F values and test their significance in a factorial design.
  25. Interpret main effects and interactions.

course prerequisites

PSYC 1100 AND PSYC 1200 and a C grade or better in Foundations of Math 11 (or equivalent)

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.

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