This course expands on concepts learned in PSYC 2300 and 2301. Students will learn how to choose, apply and analyze appropriate research designs using both data analysis and inferential statistics. Applied projects will allow students to gain experience with computer programs such as SPSS or Microsoft Excel. Topics may include ANOVA (both one- and two-way), correlation, and the general linear model (bivariate and multivariate regression). Focus will be on the application of research designs along with interpretation and effective communication of research results.
- The Scientific Approach: Theory, Hypotheses and Formulating a Research Question
- Measurement: Operational definitions, Validity and Reliability, Types of variables
- Data collection procedures and sampling, including practical and ethical issues
- Evaluation of published research in scholarly journals and other research reports
- Review of Statistical Inference: sampling distributions, critical values, understanding p-values, Type I and Type II errors, power, effect size and hypothesis testing
- Experimental Designs: ANOVA and Factorial Designs
- Relationships: Correlation and Regression
- General Liner Model and Multiple Regression
- Writing a formal APA style research report
Methods of Instruction
This course will employ a number of instructional methods to accomplish its objectives and will include some of the following:
- audio visual materials
- small group discussion
- research projects
- computer-based tutorial exercises
There will be laboratory meetings throughout the semester in which students will develop and carry out their own research projects.
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:
10 Statistics assignments -- 30%
4 Critical Summaries -- 20%
Midterm exam -- 25%
Final exam -- 25%
Total -- 100%
At the conclusion of the course the successful student will be able to:
- Demonstrate and apply key statistical concepts, such as random sample, variability, sampling distribution, level of significance, critical value, p-value, effect size, power, Type I and Type II errors, and hypothesis testing
- Demonstrate knowledge of the strengths, weaknesses, and applications of specific research designs, including correlational, complex experimental and quasi-experimental designs.
- Explain the rationale and assumptions of ANOVA
- Compute and interpret the results of a One-Way ANOVA
- Construct a summary table of ANOVA results
- Compute and interpret the results from factorial designs, including main effects and interactions
- Explain the rationale of the General Linear Model (Multiple Regression)
- Interpret model fit and coefficients of a regression model
- Construct a summary table of Regression results
- Explain the difference between parametric and non-parametric statistics
- Choose and apply the appropriate statistical analysis in a real or hypothetical applied research setting
- Interpret and communicate the results of an applied research study
- Gain enhanced APA style writing knowledge and skills for full research reports
- Have a working and practical knowledge of computerized data analysis software, such as SPSS or Microsoft Excel
PSYC 1100 AND 1200, both with a C- or better
PSYC 2300 with a C or better AND one of PSYC 2301 OR CRIM 2254 with a C or better
Courses listed here must be completed either prior to or simultaneously with this course:
Courses listed here are equivalent to this course and cannot be taken for further credit:
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.
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.