Lecture: 3 hours per week
Lab: 1 hour per week
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.
- 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
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
Evaluation will be carried out in accordance with the Douglas College Evaluation 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%
Textbooks and Materials to be Purchased by Students:
Textbook(s) and materials such as the following, the list to be updated periodically:
- Freeman, W. H.; Keppel, G.; Saufley, W. H. Jr.; Tokunaga, H. (1992). Introduction to Design & Analysis: A Student’s Handbook (2nd Ed.). Worth.
- Gliner, J.A., Morgan, G.A., & Leech, N.L. (2009) Research methods in applied settings: An integrated approach to design and analysis (2nd ed.). New York, NY: Taylor-Francis.
- Howell, D. C. (2010). Statistical methods for psychology (7th ed.). Pacific Grove, CA: Thompson-Wadsworth.
- SPSS Student Software (also available in DC computer labs)
PSYC 1100 AND 1200, both with a C- or better
Admission to the Bachelor of Arts in Applied Psychology Program or the Bachelor of Arts in Applied Psychology Honours Program or Bachelor of Arts in Applied Criminology or Bachelor of Arts in Applied Criminology-Honours or permission from the instructor.