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
- Abuses of statistics
- Organizing and describing data
- Measures of central tendency
- Measures of variability
- Description of frequency distributions
- Properties of normal distributions
- Central Limit Theorem
- Introduction to probability concepts
- Hypothesis testing
- Analysis of Variance
- t-tests
- Correlation methods
- Regression and prediction
- Nonparametric statistical methods
- Statistical significance versus practical importance
At the conclusion of the course the successful student will be able to:
- Distinguish between descriptive and inferential statistics.
- 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.
- Define and describe various measures of central tendency.
- Explain the concept of variability.
- Calculate various statistics such as standard deviation, variance, z scores correlation coefficient (r), t-test, analysis of variance, chi square.
- Distinguish between correlation and causation.
- Explain the meaning and use of the regression equation.
- Compute regression coefficients and fit a regression line to a set of data.
- Distinguish between a theoretical and empirical distribution.
- List the characteristics of the normal distribution.
- Calculate confidence intervals about a sample mean and explain what they mean.
- Explain the logic of inferential statistics.
- Describe the factors that affect rejection of the null hypothesis.
- Distinguish an independent-samples design from a correlated samples design.
- List and explain the assumptions for the t-test and ANOVA.
- Identify the independent and dependent variables in a one-way ANOVA and a two-way ANOVA.
- Explain the rationale of ANOVA.
- Define F and explain its relationship to t.
- Compute sums of squares, mean squares, degrees of freedom, and F for an ANOVA.
- Interpret an F value obtained in an experiment.
- Construct a summary table of ANOVA results.
- Distinguish between a priori and a posteriori tests.
- Identify the sources of variance in a factorial design.
- Compute F values and test their significance in a factorial design.
- Interpret main effects and interactions.
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(s) such as the following, the list to be updated periodically.
Aron, A., Coups, E.J., & Aron, E.N.(2012) Statistics for Psychology (2nd Ed.) Upper Saddle River, NJ: Pearson Education.
Howell, D. C., (2014) Statistical methods for psychology (8th Ed.). Belmont, CA: Wadsworth.