Understanding Independent Variables in Research

Experience IVs

• Manipulation of the amount or type of training or learning
• Example: Testing a school program for teaching children abduction-prevention skills

Stimulus/Environmental IVs

• Manipulation of some aspect of the environment
• Example: Examining the effects of changing various conditions on worker performance
• Example: Looking at whether the presence of a weapon decreases ID accuracy

Participant/Subject IVs

• Aspects of the participant treated as if they are IVs
• Some examples: Age, gender, personality traits

Participant/Subject IV Example

• Overrepresentation by older pedestrians in serious injury and fatal crashes may be due to age-related diminished ability to select gaps in oncoming traffic for safe road crossing
• Participants tested in three groups: young (30-45 yrs), young-old (60-69 yrs), and old-old (>75 yrs).
• Despite an apparent ability to process the distance and speed of oncoming traffic when given enough time, significantly more old-old adults were willing to cross the road when there was an insufficiently safe gap.

Dependent Variables

• Variable that is measured to see the effects of the independent variable
• Should relate back to your hypothesis
• Must be operationally definable
• Must be both VALID and RELIABLE

Types of DVs

• Correctness: How many were right?
• Rate/Frequency: How often did it occur within a certain amount of time?
• Degree or Amount: How much of it was there?
• Latency or Duration: How fast did it happen? How long did it last?

Order Effects

• When position in a series affects how participants respond
• Doesn't depend on the EVENT but rather the POSITION

Carryover Effects

• When the effects of one event influence responses to the next event
• Depends on the EVENT not the POSITION

Counterbalancing

• Vary the order in which items are presented
• Can be either within-subject or within-group
• Can be either complete or incomplete

Complete Counterbalancing

• Each event must be presented to each participant an equal number of times
• Each event must occur an equal number of times at each session
• Each event must precede and follow all other events an equal number of times

Interpreting Standard Deviation: Empirical Rule (68 – 95 – 99.7 Rule)

• About 68% of the data lie within 1 SD of the mean
• About 95% of the data lie within 2 SDs of the mean
• About 99.7% of the data lie within 3 SDs of the mean

Example: Using the Empirical Rule

• In a survey conducted by the Stats Canada, the sample mean height of women in Canada (ages 20-29) was 64 inches, with a sample standard deviation of 2.71 inches
• Estimate the percent of the women whose heights are between 64 inches and 69.42 inches

Solution: Using the Empirical Rule

• Because the distribution is bell-shaped, you can use the Empirical Rule

Non-Experimental Methods I: Observational Research

• Using Census data to determine whether lower income families are more likely to have health problems
• Recording the behaviour of children on the playground to determine the prevalence of bullying
• Detailed observations of the abilities and behaviour of a man with synesthesia
• Jane Goodall's research of chimpanzees in the wild

Observational Techniques: Clinical Perspective

• Descriptive approach aimed at understanding and correcting a particular behavioural problem
• Example: Does an “after-death” phone call help family members of a person with mental illness?
• Noted increased feelings of validation, comfort, and thankfulness to mental health provider for the call.
• Based on these findings, the authors recommended that after-death calls should be made in the future.

Observational Techniques: Issues

• Reactivity: When knowledge one is being observed affects his/her behaviour (AKA “The Hawthorne Effect”)
• Objectivity
• Cannot make cause-and-effect statements

Surveys & Questionnaires: Descriptive Surveys

• Seek to determine what % of the population have particular characteristics, beliefs, or behaviours
• Examples:
  • Who are you? Do you live alone? How many children do you have?
  • Do you suffer from depression? Diabetes? Asthma?
  • Do you have a driver’s license? Are you employed?
  • What do you think? Do you believe in gay marriage? Which do you like better? Pepsi or Coke?
  • Who do you think will win the Stanley Cup? How would you rate the service at this restaurant?
  • Who do you plan to vote for?

Describing Distributions

• Unimodal Symmetrical
• Bimodal Symmetrical
• Negative Skew
• Positive Skew
• Kurtosis: Extent of deviation from normal curve in width of curve and thickness of tails
  • Normal Distribution
  • Tall and skinny versus flat and wide

Effective and Accurate Use of Graphs

• Goal of graphs: To provide an ACCURATE visual representation of your results
• Not all graphs are created equal! Some common ways that people use graphs to distort data...

Cheating Graphs

• Problem 1: Distortion of the Y axis scale
• Problem 2: Failure to use the same scale for all comparisons
• Problem 3: Failing to adjust for distorting confounds (e.g., population sizes)

Reminders and Announcements

• Midterm: October 25th
  • 25 multiple choice questions, 5 short answer questions
  • Do the practice questions posted on D2L.
  • More practice questions will be uploaded this week and the next as we approach the midterm date.
• Review sessions on October 23rd and October 24th
• Go over list of important topics to study.