Applied Data Analysis in Psychology

Curriculum guideline

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Course
PSYC 3301
Discontinued
No
Course code
PSYC 3301
Descriptive
Applied Data Analysis in Psychology
Department
Psychology
Faculty
Humanities & Social Sciences
Credits
3.00
Start date
End term
202210
PLAR
No
Semester length
15
Max class size
35
Contact hours
Lecture 3 hours per week Lab 1 hour per week
Method(s) of instruction
Lecture
Lab
Hybrid
Learning activities

The course will involve a number of instructional methods, such as the following:

1. Lecture

2. Online videos

3. Group discussion

4. Lab

Course description
The purpose of this course is to teach students how to analyze data using current data analysis computer software. The course covers major analytic methods, as well as methods appropriate to dealing with missing values, and analyzing the psychometric properties of scales. Students will analyze a number of datasets using XLSTAT and/or SPSS. Emphasis will be placed on generating results and interpreting results appropriately, not statistical theories. Upon completion of this course, students should be able to prepare datasets for analysis, and conduct a wide range of descriptive and inferential analyses of data.
Course content

The topics covered may include:

1. Data structure: How are data files structured, what types of data files are there, how should data providers be instructed to enter data so that it is in an analyzable form?

2. Data coding: How should data be coded to maximize the efficiency of analysis?

3. Data auditing: What are the issues with data accuracy? How should data be audited to ensure accuracy?

4. Data security: How should data files be securely managed? What information should and should not be included in shared data files? How is anonymity and confidentiality ensured?

5. Data preparation: How should missing and out of range values be identified? What should be done with missing and out of range values? What are the various analytic methods of dealing with missing values (multiple regression, nearest neighbour PCA).

6. Recoding: What is recoding? What are the issues with recoding data? What are the basic methods of recoding?

7. Data types: What are the basic data types (ordered vs. unordered, continuous vs. discrete, ranks, metric vs. non-metric)? How does data type influence the sorts of analyses that should be conducted on the data?

8. Univariate descriptive statistical analysis: What are the basic univariate descriptive statistics that should be calculated on data (distributions, central tendency, variability, kurtosis, graphical representation)?

9. Bivariate and multivariate descriptive statistics: What are the basic bivariate and multivariate statistics that should be calculated on data (conditional distributions, centroids, covariance, linear and non-correlation, correlation matrices, multi-dimensional scaling, PCA, multivariate graphical representation)?

10. Hypothesis tests of mean differences: t-test for dependent and independent groups, one-way ANOVA, factorial ANOVA.

11. Regression: Bivariate regression, multiple regression.

12. Tests of the psychometric properties of scales: Tests for homogeneity and unidimensionality of items (Cronbach's Alpha and linear factor analysis).

 

Learning outcomes

At the conclusion of the course students will be able to:  

  1. Understand and make effective use of descriptive statistics for different analyses.  

  1. Compare basic data types and identify the limitations they pose on statistical analyses.  

  1. Demonstrate understanding of suitable ways to identify and deal with missing values in a data set.  

  1. Describe appropriate methods of data security. 

  1. Identify proper data structure and data coding.  

  1. Use widely available software tools to analyze and present results of research. 

  1. Assess psychometric properties of scales.  

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.

Grading in the course will be a combination of at least 3 analysis assignments and/or tests. An example of one evaluation scheme:

1 exam 30%

5 computer-based assignments 70%

Textbook materials

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)
  • IBM SPSS Statistics User Manual (free online)
Prerequisites

PSYC 1100 AND 1200, both with a C- or better

AND

PSYC 2300 with a C or better AND one of PSYC 2301 OR CRIM 2254 with a C or better

Corequisites

Courses listed here must be completed either prior to or simultaneously with this course:

  • No corequisite courses
Equivalencies

Courses listed here are equivalent to this course and cannot be taken for further credit:

  • No equivalency courses