Applied Intermediate Research Methods & Data Analysis

Curriculum guideline

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Course
PSYC 3300
Discontinued
No
Course code
PSYC 3300
Descriptive
Applied Intermediate Research Methods & Data Analysis
Department
Psychology
Faculty
Humanities & Social Sciences
Credits
3.00
Start date
End term
202020
PLAR
No
Semester length
15
Max class size
35
Contact hours
Lecture 3 hours per week/ semester Lab 1 hour per week/ semester
Method(s) of instruction
Lecture
Lab
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

There will be laboratory meetings throughout the semester in which students will develop and carry out their own research projects.

Course description
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.
Course content
  1. The Scientific Approach: Theory, Hypotheses and Formulating a Research Question
  2. Measurement: Operational definitions, Validity and Reliability, Types of variables
  3. Data collection procedures and sampling, including practical and ethical issues
  4. Evaluation of published research in scholarly journals and other research reports
  5. Review of Statistical Inference: sampling distributions, critical values, understanding p-values, Type I and Type II errors, power, effect size and hypothesis testing
  6. Experimental Designs: ANOVA and Factorial Designs
  7. Relationships: Correlation and Regression
  8. General Liner Model and Multiple Regression
  9. Writing a formal APA style research report
Learning outcomes

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

  1. 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
  2. Demonstrate knowledge of the strengths, weaknesses, and applications of specific research designs, including correlational, complex experimental and quasi-experimental designs.
  3. Explain the rationale and assumptions of ANOVA
  4. Compute and interpret the results of a One-Way ANOVA
  5. Construct a summary table of ANOVA results
  6. Compute and interpret the results from factorial designs, including main effects and interactions
  7. Explain the rationale of the General Linear Model (Multiple Regression)
  8. Interpret model fit and coefficients of a regression model
  9. Construct a summary table of Regression results
  10. Explain the difference between parametric and non-parametric statistics
  11. Choose and apply the appropriate statistical analysis in a real or hypothetical applied research setting
  12. Interpret and communicate the results of an applied research study
  13. Gain enhanced APA style writing knowledge and skills for full research reports
  14. Have a working and practical knowledge of computerized data analysis software, such as SPSS or  Microsoft Excel
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:

Two Midterms                                               30%

Computer based homework assignments         20%

Term project paper                                        30%

Final Exam                                                    20%

                                                                  100%

Textbook materials

Textbooks and Materials to be Purchased by Students:

A textbook such as:

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.

OR

Howell, D. C. (2010). Statistical methods for psychology (7th ed.). Pacific Grove, CA: Thompson-Wadsworth.

SPSS Student Software (also available in DC computer labs)

Prerequisites

PSYC 1100,  PSYC 1200, PSYC 2300, PSYC 2301