Data Analysis in Psychology

Curriculum guideline

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Course
PSYC 2300
Discontinued
No
Course code
PSYC 2300
Descriptive
Data Analysis in Psychology
Department
Psychology
Faculty
Humanities & Social Sciences
Credits
3.00
Start date
End term
201420
PLAR
No
Semester length
15
Max class size
35
Contact hours
Lecture: 4 hrs. per week / semester
Method(s) of instruction
Lecture
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
Course description
This course introduces students to the concepts and applications of statistics and focuses on the analysis and interpretation of data from experiments and surveys using descriptive and inferential statistics. Computerized data analysis is also introduced.
Course content
  1. Abuses of statistics
  2. Organizing and describing data
  3. Measures of central tendency
  4. Measures of variability
  5. Description of frequency distributions
  6. Properties of normal distributions
  7. Central Limit Theorem
  8. Introduction to probability concepts
  9. Hypothesis testing
  10. Analysis of Variance and t-tests
  11. Correlation methods
  12. Regression and prediction
  13. Nonparametric statistical methods
  14. Statistical significance versus practical importance
Learning outcomes

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

  1.  Distinguish between descriptive and inferential statistics.
  2. Define various key statistical terms, such as population, sample, parameter, variable, random sample, sampling distribution, level of significance, critical value, Type I and Type II errors, and the null hypothesis.
  3. Define and describe various measures of central tendency.
  4. Explain the concept of variability.
  5. Calculate various statistics such as standard deviation, variance, z scores correlation coefficient (r), t-test, analysis of variance, chi square.
  6. Distinguish between correlation and causation.
  7. Explain the meaning and use of the regression equation.
  8. Compute regression coefficients and fit a regression line to a set of data.
  9. Distinguish between a theoretical and empirical distribution.
  10. List the characteristics of the normal distribution.
  11. Calculate confidence intervals about a sample mean and explain what they mean.
  12. Explain the logic of inferential statistics.
  13. Describe the factors that affect rejection of the null hypothesis.
  14. Distinguish an independent-samples design form a correlated samples design.
  15. List and explain the assumptions for the t-test and ANOVA.
  16. Identify the independent and dependent variables in a one-way ANOVA and a two-way ANOVA.
  17. Explain the rationale of ANOVA.
  18. Define F and explain its relationship to t.
  19. Compute sums of squares, mean squares, degrees of freedom, and F for an ANOVA.
  20. Interpret an F value obtained in an experiment.
  21. Construct a summary table of ANOVA results.
  22. Distinguish between a priori and a posteriori tests.
  23. Identify the sources of variance in a factorial design.
  24. Compute F values and test their significance in a factorial design.
  25. Interpret main effects and interactions.
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:

 

 

12 quizzes  50%
Computer based homework assignments      10%
Homework exercises  10%
Term project paper  20%
Final exam  10%
Total 100%
Textbook materials

Textbooks and Materials to be Purchased by Students

 

Aron, A. & Aron, E. N., (1999) Statistics for Psychology (2nd Ed.)

     Upper Saddle River, NJ, Prentice-Hall.

 

Howell, D. C., (1999) Fundamental Statistics for the Behavioral Sciences (4th Ed.)

     Pacific Grove, CA, Brooks/Cole.

 

Or some comparable textbook.

 

Text will be updated periodically.

Which prerequisite

PSYC 1200 and a C grade or better in BC Principles of Math 11 (or equivalent)