Chapter 1 - Key Words Flashcards
Population
The overall group that you’re trying to apply your research to.
Sample
The group of people, from the population, that you actually collected data from.
Participants
The people who participated in a study.
If you think “participants” has pretty much the same definition as “sample” then don’t worry, they are so similar that I don’t think it’s worth debating their technical differences in the first chapter of this class.
Variable
Something that can change.
Quantitative
Something that changes in a numerical fashion.
Examples: body weight (133 lbs.), length of time waiting for the bus (11 min.), number of wins your favorite basketball team has (16), the temperature outside (54 degrees), depth of a lake (177 ft.), price of milk ($3.49), score on a history exam (66%), blood pressure (120/80 torrs), age (55 years).
Qualitative
Something that changes in a non-numerical fashion.
Examples: political party affiliation (Libertarian), favorite restaurant (Domingo’s), gender (Female), kinds of pets you’ve owned (dogs, fish)
Relationship
A pattern between variables (at least 2, but you could have more involved in a relationship) where one variable changes because of how the other variable changes.
Examples: The more tobacco you smoke, the more your risk of developing cancer increases. The fewer baseball games you attend, the less likely you’ll be to witness a no-hitter.
Descriptive Statistics
Statistics and procedures that “describe” your data.
Examples: The average number of children in a French family, the number of graduates from this school in 2015, the lowest gas price in California
Inferential Statistics
Statistics and procedures that “infer” things about the population based on what you saw in your sample.
Statistics
A number that describes a sample.
Parameter
A number that describes a population.
Design
The organization of a study.
(e.g. how the participants are recruited, what variables will be measured, how often the variables will be measured, what calculations will be made etc.)
Experiment
A type of study design that involves changing (or manipulating) one variable to see if it influences another.
Independent Variable
The variable you’re manipulating.
Dependent Variable
The variable that the independent variable may or may not influence.
Condition
A category, or level, of an independent variable in a study.
For example, consider an experiment where participants would wear different brands of athletic shoes and see how high they could jump with each pair of shoes. The independent variable is shoe brand, the dependent variable is height of jump, the conditions of the independent variable would be the names of each of the shoe brands used in the study.
Correlational Study
A study design where two (or more) variables are investigated together, with no manipulation, to see if they form a relationship.
You would often do this if you could not ethically or realistically “control” an independent variable.
In a correlational study, we do not call the variables “independent” and “dependent,” instead we call them “study variables” or just “variables.”
Quasi-experiment
A study design where an independent variable is NOT being actively manipulated, but it is being recorded and the researcher analyzes the data without correlation.
Nominal scale
A scale where the data for a variable are organized by name or category.
In a nominal scale variable, there is no numerical value whatsoever within the different categories of the variable.
Examples: favorite TV show, first name, gender
Ordinal scale
A scale where the data for a variable are organized by rank or order.
Examples: Developmental stages (prenatal, infant, toddler), competition rankings (1st, 2nd, 3rd)
Interval/Ratio scale
A scale where the data for a variable are numbers which have a consistent interval between each other.
This means that the difference between scores of 12 and 16 is the same as the difference between scores of 32 and 36, for example.
Examples: Money, time, weight
Discrete variable
A variable that can be counted to an EXACT number.
Examples: number of pens on my desk, number of apples on a tree, number of books on a shelf
Continuous variable
A variable that does not have a perfectly exact numerical value.
Examples: body weight, miles to Las Vegas, temperature in this room, current time of day, speed of a vehicle.
The bathroom scale might say you weigh 200 lbs. but it’s just rounding off to the nearest number, just like navigation software rounds off to the nearest 1/10th mile instead of saying “It’s 22.37130368703048380665 etc. miles to Anaheim.”
