Chapter 1

Front 1

Statistics

Back 1

A set of mathematical procedures for organizing, summarizing, and interpreting information.

Front 2

Population

Back 2

Set of all individuals of interest in a particular study.

Front 3

Sample

Back 3

A set of individuals selected from a population, usually intended to represent the population in a research study.

Front 4

Variable

Back 4

A characteristic or condition that changes or has different values for different individuals.

Front 5

Data (plural)

Back 5

Measurements or observations (of a variable)

Front 6

Data Set

Back 6

A collection of measurements or observations.

Front 7

Datum/Raw Score/Score

Back 7

A single measurement or observation.

Front 8

Parameter

Back 8

A value that describes a population.

Front 9

Statistic

Back 9

A value that describes a sample.

Front 10

Descriptive Statistics

Back 10

Statistical procedures used to summarize, organize, and simplify data.

Front 11

Inferential Statistics

Back 11

Techniques that allow us to study samples and then make generalizations about the population from which they were selected.

Front 12

Sampling Error

Back 12

The discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter.

Hint 12

The difference between a population and sample.

Front 13

Correlational Method

Back 13

Two different variables are observed to determine whether there is a relationship between them.

Hint 13

Tells us if there is a relationship between two variables. It does not explain anything! Correlation does not equal causation!

Front 14

Experimental Method

Back 14

The goal of an experimental study is to demonstrate a cause-and-effect relationship between two variables.

Hint 14

Specifically, an experiment attempts to show that changing the value of one variable causes changes to occur in the second variable.

Front 15

Manipulation

Back 15

The researcher manipulates one variable while observing a second variable to determine whether the manipulation caused a change to occur.

Front 16

Control

Back 16

The researcher controls the research situation to ensure that variables do not influence the relationship being examined.

Front 17

Participant Variables

Back 17

Anything a participant might bring with them to the experiment.

Hint 17

Example: Age, height, knowledge, any skills they may have, weight, speed, disabilities, etc.

Front 18

Environmental Variables

Back 18

Anything in the environment that may impact the results of the experiment.

Hint 18

Examples: Lighting, time of day, carbon monoxide, temperature, weather conditions, etc.

Front 19

Experimental Method (final definition)

Back 19

One variable is manipulated while another variable is observed and measured.

Hint 19

To establish a cause-and-effect relationship between the two variables, an experiment attempts to control all other variables to prevent them from influencing the results.

Front 20

Independent Variable

Back 20

The variable that is manipulated by the researcher.

Hint 20

Also called the "treatment". This is the variable that we think CAUSES something to happen. This is what the researcher manipulates.

Front 21

Dependant Variable

Back 21

The variable that is observed/measured to assess the effect of the treatment.

Hint 21

This is the variable we MEASURE after applying treatment. This is the EFFECT part of the "cause-and-effect " relationship in the experimental method.

Front 22

Control Group

Back 22

The group that does not receive the treatment.

Hint 22

They will receive a placebo or nothing at all.

Front 23

Experimental Condition

Back 23

The group that receives treatment.

Front 24

Quasi-Independent Variables

Back 24

Variables the researcher can NOT manipulate, such as gender or race.

Hint 24

Sometimes we don't separate groups based on "treatment".

Front 25

Constructs

Back 25

Internal attributes or characteristics that cannot be directly observed but are useful for describing and explaining behavior.

Hint 25

Examples: Anger, sadness, hunger, happiness, intelligence, anxiety, etc.

Front 26

Operational Definition

Back 26

Identifies a measurement procedure for measuring an external behavior and uses the resulting measurements as a definition and a measurement of a hypothetical construct.

Hint 26

Example: The book talks about how we can define hunger. What can we measure that might mean someone is hungry? What can we measure that might mean someone is angry? Happy? Sad? Anxious? Etc.

Front 27

Discrete Variable

Back 27

Consists of separate, individual categories. No values exist between categories.

Hint 27

Examples: Political Party, Major, Occupation, Room Number, how many people are here, etc.

Front 28

Continuous Variable

Back 28

There are an infinite number of possible values that fall between any two observed values.

Hint 28

Examples: Height, Weight, Speed, Time, Temperature, etc.

Front 29

Real Limits

Back 29

The boundaries of intervals for scores that are on a continuous number line.

Hint 29

Example: You might say an object weighs 70 lbs., but it is really 70.291821 lbs. Think of it as where you round numbers.

Front 30

The Upper Real Limit

Back 30

The top of the interval

Hint 30

The top of the interval of 70 would be 70.49. (Round down)

Front 31

The Lower Real Limit

Back 31

Bottom of the interval

Hint 31

The bottom of the interval of 70 would be 69.5 (round up).

Front 32

Nominal Scale

Back 32

Consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observation but do not make any quantitative distinctions between observations.

Hint 32

Think: Nominal=Names and categories. Example: Political party affiliation, occupation, major, year in school, brand of coffee, etc. Anything with names or categories.

Front 33

Ordinal Scale

Back 33

Consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude.

Hint 33

The ordinal scale puts things in order! (Does not tell you how far apart they are, just what order they are in). Example: Place in a race (1st, 2nd, 3rd, etc).

Front 34

Interval Scale

Back 34

Consists of ordered categories that are all intervals of exactly the same size. Equal differences between numbers on a scale reflect equal differences in magnitude. However, the zero point on an interval scale is arbitrary and does not indicate a zero amount of the variable being measured.

Hint 34

It means these values go in order and have equal distances between each other. But, zero does not mean zero. You can go below zero. Examples: Temperature, Networth (You can be in debt), golf scores, distance from an object, etc.

Front 35

Ratio Scale

Back 35

An interval scale with the additional feature of an absolute zero point. With a ratio scale, ratios of numbers do reflect ratios of magnitude.

Hint 35

This is exactly like an interval scale except that you can NOT have negative numbers. You can NOT go below zero. Zero means it is not present/does not exist. Examples: Height, Weight, money in your pocket, score on a statistics test, how many clouds are in the sky, etc.