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Dr Jackson Home     Readings      Learning Objectives      Assignments      Term Assignment  
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Welcome to Applied Research Methods in Psychology. From this page you can access all the information you will need to complete the course. Links are available to course lecture templates, selected lecture notes, readings, learning objectives, and instructor contact information. I hope you enjoy the course. Jeremy Jackson
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The Course Syllabus......
Jeremy Jackson
|     January 6, 2018
NW 3431
|     New Westminster
Thomas Edison: "Hell, there are no rules here - we're trying to accomplish something"

Instructor Contact Information

Email: metrixconsulting@shaw.ca or jacksonj@douglascollege.ca.

Email Availability: Please ask substantive questions in class or in my office hours. Email me for emergencies or issues that can not be addressed in class or in my office hours.

Office hours: Tuesday and Thursday 11:30 am - 12:20 pm or by appointment.

Office number: NW 3431.

Classroom: NW 6111, 4:30-6:20 Tuesday and Thursday

Email Requirements : All emails should include: 1) Your name, student number and the number of the course you are enrolled in, 2) A salutation such as "Hello Dr Jackson....", 3) An appropriate ending to the email thanking the person for their time in considering your request.


Course Description

The purpose of this course is to teach students with basic knowledge of statistics to analyze data using two popular statistical software packages. Students will gain sufficient familiarity with Excel and SPSS to conduct univariate, bivariate and multivariate data analyses, estimate population parameters, fit basic linear models and conduct hypotheses tests on experimental data. Many forms of data management including data entry, auditing, imputation, coding, recoding, and transformation will also be covered. By the end of the course, students should be able to conduct an analysis of data generated in a survey or experiment and give a presentation of the results of the analysis.

In addition to covering practical issues faced in the analysis of data, the course also covers basic issues in hypothesis testing and psychological measurement that are not usually addressed in undergraduate statistics courses.


How Does The Course Work

In the course there will be 1 midterm exam worth 20% of the course grade and 1 final exam worth 25% of the course grade. Students will have 2 hours IN CLASS to complete each exam. The exams will consist of essay and multiple choice questions.

There are 10 essay questions for the course. Please see the "learning objectives" document for the 10 questions. I shall randomly select 2 of the questions for each exam. I am expecting 2 page, well-written, grammatically correct, thoughtful, and factually correct answers to these questions. Students can bring in to the exam an 8.5 x 11 sheet of paper with whatever they like written on the paper.

All exams are to be completed in class. ONLY MEDICAL extensions are accepted for quizzes. Supporting medical documentation must be provided for a missed exam.

Students are also required to complete 1 term assignment. The term assignment is a report/presentation of an analysis. Depending on enrollment, students will either submit a written report of the analysis or present their analysis to the class. All Honors students will present an analysis of their Honors Project research to the class. For applied degree students, each student must design, develop, administer, analyze, and present the results of a survey. The survey will be administered to a minimum of 50 respondents. The survey can be of any type and administered to any set of subjects in which the student is interested (students, faculty, work colleagues, etc). If held, presentations will be 15 minutes long with an additional 10 minutes for questions. The assignment is worth 25% of the final course grade.

Honors students will present an analysis of their own data. The presentation is 20 minutes with 10 minutes for questions.

There are 3 analysis assignments in the course. Click on the "Assignments" tab above for details on each of the assignments. Assignments are worth 10% each. Due dates are given in the syllabus.

Week 1

Introduction to the course. Description of course project. Software/materials for the course.


Reading: None

Week 2

Research design review: APA Task Force paper pages 594-597.

Key concepts: value of information as it relates to type of design, what is a population, the relationship of a sample to a population, random assignment and causal inference, random assignment in the real world, operational definitions and their relationship to constructs, the relationship of power to good data, the importance of visualizing data, missing data and outliers.

Reading: APA task force on statistical inference

Week 3

Research design review: APA Task Force paper pages 598-602.

Key concepts: Statistical thinking vs statistical recipe following (read the top of page 598 until it sinks in), assumptions for statistical tests, residuals, hypothesis testing vs interval estimation, fishing expeditions and their relationship to "all possible pairwise" comparison methods, generalizability, replication vs hypothesis testing, gratuitous suggestions.

Reading: APA task force on statistical inference

Week 4

Hypothesis testing: Cohen and others

Key concepts: A short history...Fisher vs Neyman Pearson, the logic of hypothesis testing, is the globe warming....can hypothesis testing help, the objectives of science (description, identification of lawful relationships between variables, causality) vs the products of hypothesis testing, journal editors and your supervisor...how to talk to them about hypothesis testing.

Reading: Hypothesis testing, Cohen (1994)

Week 5 - Jan 30th & Feb 1st

Jan 30th, exam 1 review. Surveys and your survey project.

First Exam - 30 multiple choice, 2 SA.


Week 6

Using Excel and SPSS to conduct basic univariate data analysis. Data preparation - data file structure, data entry, coding, auditing, outlier identification, missing values, out of range values, imputation, recoding in SPSS.

Reading: Data processing

Videos: See the "Readings" page for all the videos for this week.


Week 7 - Feb 12th - Feb 16th
Spring Break - No Classes
Week 8 - Feb 20th & Feb 22nd

Basic data preparation and analysis continued. Subject and variable space. Correlation and distance in subject space. Principal Components Analysis (PCA). PCA for data reduction. PCA for nearest neighbour imputation. PCA to identify outliers.

Videos: PCA 1 , PCA 2, PCA 3, PCA 4, PCA 5, PCA 6

Reading: Principal Components Analysis

Assignment 1 due on Blackboard at midnight on Thursday.

Week 9- Feb 27th & Mar 1st

Basic issues in measurement. Types of validity. How to speak about and assess validity. Reliability and validity analyses in SPSS. Imputing missing values using multiple regression.

Assignment 2 review. Your survey project.


Reading: None


Week10 - Mar 6th & Mar 8th

Basic concepts in multiple regression - the regression model, residuals, assessment of model fit, multiple R and multiple R squared, outlier/model unfit diagnostics, the "best" model. Conducting multiple regression in SPSS.

Reading: Multiple regression

Videos: Multiple regression 1, Multiple regression 2, Multiple regression 3

Assignment 2 due on Blackboard at midnight on Thursday

Week 11 - Mar 13th & Mar 15th

Hypothesis testing in SPSS for within and between subjects cases with 1 IV and 1 DV (One-Way ANOVA). Planned comparisons (the only kind there should be). SPSS for within and between subjects two-way ANOVA.

Reading: ANOVA

Videos: ANOVA 1, ANOVA 2, ANOVA 3

Week 12 - Mar 20th & Mar 22nd

Anova continued. Assignment 3.

Surveys and your survey project.

Analysis of student data set (time permitting)

Assignment 3 due on Blackboard at midnight on Thursday

Week 13 - Mar 27th & Mar 29th

Final exam review

Final Exam - 30 multiple choice, 2 SA.

Week 14 - April 3rd & April 5th


Week 15 - April 10th


Academic Dishonesty - Plagiarism & Cheating

Cheating , which includes plagiarism, occurs where a student or group of students uses or attempts to use unauthorized aids, assistance, materials or methods. Cheating is a serious educational offense.

Plagiarism occurs where the student represents the work of another person as his or her own. Douglas College condemns all forms of cheating.

The college will discipline students found to be cheating. Discipline may include:

1. a grade of zero may be awarded for the affected assignment, test, paper, analysis, etc.;

2. a failing grade may be assigned in the affected course;

3. referral to the College President for the assignment of discipline, which may include suspension from the college.

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