## Psychology 218

### Introduction to Data Analysis in Psychology

Credits: 3

Length of Course: 14 weeks

Classroom hours per week: 4 hours

Prerequisite: Psychology 120 and Precalculus 11 or Math 110 or Math 100 and 12 credits

Corequisite: English 100

Text: Understanding Statistics in the Behavioural Sciences, 10th edition. Cengage, Chapters 1-15

### Course Description

An introductory course in statistics, emphasizing the underlying theory and practical application of statistical analysis in the field of Psychology.

### Course Outline/Topics

• Introduction (Scientific Method, Population versus sample, Basic terms, Types of variables, Sources of data and variability, Summation notation)
• Organizing and Graphing Data (Organizing and graphing qualitative data, Organizing and graphing qualitative data, Cumulative frequency distribution, Steam and leaf displays, Dot plots, Box plots, Histograms)
• Numerical Descriptive Measures (Measures of central tendency, Dispersion, Mean, Variance, Standard deviation)
• Correlation and Regression analysis to describe the relationship between two variables
• Probability (Definitions, addition rule, multiplication rule, probabilities through simulation, counting)
• Probability Distributions (Random variables, Binomial experiments, mean, variance and standard deviation for the Binomial distribution, normal distribution, standard scores)
• Continuous Random Variables and the Normal Distribution (Definitions, Standardizing a Normal distribution, Applications)
• Hypothesis Tests About the Mean and Proportion, sign test
• Inferences from Single, Two, and Three Samples (single sample Student’s t test, Paired and Independent sample Student’s t test, Analysis of Variance)

At the end of the course, the successful student should be able to:

• Define the terms “population” and “sample” as they apply to Statistics
• Define and differentiate between the nominal, ordinal, interval and ratio levels of measurement
• Explain the proper use of Statistics within real world application and provide examples of its abuse
• Have an understanding of experimental design and the use of random number tables and generators in random sampling
• Create and interpret frequency tables, histograms, cumulative frequency tables, stem and leaf displays and scatter plots
• Calculate and interpret measures of central tendency and variability
• Describe the relationship between two variables using standard correlation or regression techniques
• Calculate and interpret standard scores (z scores)
• Understand the classical and relative frequency approaches to probability
• Know and apply the addition and multiplication rules for probability and the concept of conditional probability
• Be able to differentiate between discrete and continuous random variables
• Determine whether the conditions for a Binomial experiment apply and compute the Binomial probabilities
• Compute the mean, variance and standard deviation for the Binomial distribution
• Determine probabilities of standard and non-standard normal random variables
• Use the Normal distribution to approximate Binomial probabilities
• Understand and apply the Student t distribution
• Perform hypothesis tests on population parameters or the difference between population parameters using large and small samples

### Evaluation:

15% Assignments Lab Work Midterm Exams (2 at 20%) Final Examination

### Instructor:

Shahnaz Koji, B. Sc., M.A., Ph.D. (Waterloo)