Module 2 – Types Of Studies + Types Of Data
Statistics and Data
Statistics are procedures and rules to organize and interpret data.
Psychologists convert behaviors into numbers for statistical analysis.
Variables
A variable is a property that can take on different values.
Continuous variables: Take on any value (quantitative or numerical data).
Categorical variables: Take on a small set of possible values (frequency or count data).
Example: Food dish visits by mice can be a continuous variable (any number of visits).
Sex of a mouse is a categorical variable (male or female).
Continuous variables can be "chopped up" into discrete categories.
Variable type is important because statistical tests require specific variable types.
Dependent vs. Independent Variables
Dependent variables: Measured variables (also known as response variables).
In the hungry mice example, food dish visits are dependent variables.
Independent variables: Usually controlled by the researcher (also known as explanatory or predictor variables).
Researchers may observe or manipulate these variables.
Types of Studies
Observational Study
Researchers observe ongoing behavior without intervening.
Example: Facebook analyzed user posts to study laughter types.
Dependent variable: Frequency or count of laughter types (categorical).
Independent variable: Sex (categorical).
Independent variable: Age (continuous).
Experiment
Researchers manipulate a variable to see if that variable have any effects.
Example: Giron et al. (2015) studied whether "anger inoculation" decreases road rage.
40 college students used a driving simulator.
Half argued against aggressive driving before the simulator.
Dependent variable: Accident or no accident (categorical).
If the total number of accidents compared, then the dependent variable would be continuous
Independent variable: Anger inoculation or no anger inoculation (categorical).
50% of participants who argued against aggressive driving had an accident, versus 70% of those who didn’t.
Causality
Observational studies cannot infer causality.
There might be a third variable affecting both independent and dependent variables.
Example: Ice cream sales and shark attacks.
Association does not equal causation.
A third variable like heat could cause both.
Using emoji or LOL won’t change a person’s age.
There is an association between online laughter and age.
Experimental Research & Eliminating Third Variables
Control: Scientists manipulate the independent variable and try to keep everything else constant.
Random assignment: Each participant has an equal chance of being in a condition.
Population vs. Sample
A population is the entire collection of events of interest.
Facebook was able to analyze the entire population of Facebook users.
A sample is data collected from a subset of the population.
Inferential statistics are used to infer things about the population based on a sample.
Samples must be random.
Every member of the population has an equal chance of being included.
Psychological findings may only relate to Westernized, Educated, Industrialized, Rich, Democracies because most research subjects are American college students.
Observational Study Example
A graduate student determined the efficacy of an educational program that used the environment for teaching.
Fourth-grade teachers took students to a nature reserve.
The idea was that being outside would increase learner engagement and improve learning.
The student chose an observational study.
Compared students who went to the site to those who didn’t.
Independent variable: number of visits to the site (categorical - never, 1, 3, or 4 times).
Dependent variable: Commonwealth Accountability Test (CATs) scores (science and reading), which are two continuous dependent variables.
ANOVA test: A statistically significant relationship between program participation and reading and science scores.
Cannot infer causality without an experiment.
The study has the third variable problem.
Example: Only the best teachers take their students, and these teachers might try harder every day than teachers that didn’t go to the site.
Experiment: Program Increases Scores?
To show the program increases scores:
Select a random sample of the population.
Randomly assign half of classrooms to visit the site and half to do something else.
Then any differences in CATS performance could be attributed to the program.