PSYC100 slides 9

Welcome and Retrieval Practice

• Test Yourself! Before looking at your notes, try to:

  • Recall the timeline of psychology's foundations.

  • Define the three validities.

• Afterward, check your answers for accuracy.

• Remember to prepare PollEv at www.pollev.com/punchyocean for class.

Today's Focus: Research Designs

• We will cover the three main research designs used in psychology.

Types of Research Methods

1. Descriptive Methods

• Purpose: To simply describe what is happening.

• Forms:

  • Observational studies: Researchers watch and record behavior without interfering.

  • Self-reports: Participants share information about themselves, often through surveys or interviews.

  • Case studies: A detailed study of one individual, group, or unique situation.

2. Correlational Methods

• Purpose: To explore the relationship between two or more factors (variables).

• Key Concept: Operational Definition: This specifies exactly how a variable will be measured or manipulated. For example, if studying "academic performance," you might define it as the "number of points earned on an exam." If studying "distance to professor," you might define it as "feet from the front of the classroom."

• Correlation Coefficient ($r$):

  • A number between $-1.0$ and $+1.0$ that shows the strength and direction of a linear relationship between two variables.

  • $+1.0$: A perfect positive correlation. As one variable increases, the other increases in exact proportion (e.g., more study time, perfectly higher grades).

  • $+0.5$: A moderate positive correlation. As one variable increases, the other tends to increase (e.g., more metacognition generally leads to higher grades).

  • $0$: No linear correlation. There's no consistent relationship between the variables.

  • $-0.7$: A moderate negative correlation. As one variable increases, the other tends to decrease (e.g., more absences generally lead to lower grades).

  • $-1.0$: A perfect negative correlation. As one variable increases, the other decreases in exact proportion (e.g., more minutes exercising, perfectly fewer sedentary minutes).

• Examples:

  • Positive Correlation: If researchers find that students who engage more in metacognition (thinking about their thinking) tend to have higher grades, this is a positive correlation. Both variables increase together.

  • Another Positive Correlation: If fewer parents use diapers, and their babies have fewer urinary tract infections, this is also a positive correlation. Both variables decrease together (less diaper use correlating with less UTIs).

  • Existing Groups: Correlational studies often look at relationships between variables in already existing groups (e.g., comparing IQ levels with brain size) rather than assigning participants to different conditions.

• Why Correlation Does Not Equal Causation (Important!): Finding a correlation means two things are related, but it does NOT mean one causes the other. Here are two reasons why:

  • Directionality Problem: It's often unclear which variable causes which. Does sitting closer to the professor lead to higher exam points, or do students who are more motivated (and thus get higher points) choose to sit closer? We don't know the direction of influence.

  • Third Variable Problem: An unmeasured or hidden variable (a "confounding variable") might be causing the observed relationship between the two variables. For example, parental involvement (the third variable) might cause both closer seating to the professor AND higher exam points, rather than closer seating directly causing higher points. The correlation between seating distance and exam points is actually due to this unmeasured third variable.