Quiz 2 Correlational/Experimentation Review

Purpose of Correlational research

To measure the strength and direction of the relationship between two variables in order to make predictions, without showing cause-and-effect. It helps psychologists see whether variables are related (positively, negatively, or not at all), but it cannot determine which variable causes the other.

Difference between positive and negative correlations

Positive correlation means that as one variable increases, the other also increases (or as one decreases, the other also decreases).
Negative correlation means that as one variable increases, the other decreases (and vice versa). Positive = they move in the same direction. Negative = they move in opposite directions.

Correlational coeffiecennt and what does it mean

A statistical number (ranging from -1.00 to +1.00) that shows both the strength and direction of a relationship between two variables. A number closer to ±1.00 means a stronger relationship, while a number near 0 means little or no relationship. It's the number that tells you how strong and what kind of correlation exists.

Scatterplots

A type of graph that shows the relationship between two variables by displaying data points on a coordinate plane. The overall pattern of the dots helps reveal the direction (positive or negative) and strength (strong or weak) of a correlation. Is a dot graph that shows if two things are related and how strongly.

Independent and Dependent varibles, what are they, what do they show

The independent variable (IV) is the factor the researcher manipulates to see its effect.
The dependent variable (DV) is the outcome that is measured to see how it changes in response to the IV. IV = what's changed. DV = what's measured.

Confounding variables

Factors other than the independent variable that could influence the dependent variable, making it hard to tell if the IV actually caused the results. Researchers try to control these to keep the experiment valid. Extra variables that mess up results by getting in the way of the real cause-and-effect.

Population

The entire group of people a researcher is interested in studying and drawing conclusions about. It represents everyone who fits the study's criteria, not just the participants tested.

Difference between random assignment and random sampling

Random Sampling: A method of selecting participants from a population so that everyone has an equal chance of being chosen. It ensures the sample represents the population.
Random Assignment: A method used in experiments to place participants into groups (like experimental vs. control) by chance, so groups are similar and differences are due to the independent variable, not bias.

Difference between experimental and control groups

Experimental Group: The group in an experiment that receives the treatment or independent variable being tested.
Control Group: The group that does not receive the treatment, it's used for comparison to see if the independent variable actually caused a change.

Single blind vs double blind research

Single-Blind Research: An experimental design where the participants do not know whether they are in the experimental or control group, but the researchers do. This helps reduce participant bias (like placebo effects).
Double-Blind Research: An experimental design where both the participants and the researchers do not know who is in which group. This helps reduce both participant bias and researcher bias.

Placebo and Placebo effect

Placebo: A fake treatment or substance (like a sugar pill) that looks real but has no active ingredient. It's given to the control group to compare against the real treatment.
Placebo Effect: When participants improve or change simply because they believe they received the treatment, even though they only got the placebo.

Sample Bias vs Experimenter Bias

Sample Bias: When the participants chosen for a study don't fairly represent the larger population, which makes the results less generalizable.
Experimenter Bias: When the researcher unintentionally influences the results (like giving subtle clues or treating groups differently) to fit their expectations.

Operational definitions and replications

Operational Definitions: Specific, clear explanations of how variables are measured or defined in a study. They make sure other researchers know exactly what was tested.
Replications: Repeating a study with the same methods to see if the results are consistent and reliable.