Chapter 1: Introduction to Statistics and Research Design Flashcards
Descriptive Statistcs
Organize, summarize, and communicates a group of numerical observations; describe large amounts of data in a single number or in just a few numbers; Summarize numerical information about a sample
Inferential Statistics
Uses a sample data to make general estimates about the larger population; Infers or makes an intelligent guess about an entire population
Sample
Set of observations drawn from the population of interest; Draw conclusions about the broader population based on numerical information from a sample
Population
Includes all possible observations about which we’d like to know something
Variable
Any observation of a physical, attitudinal, or behavioral characteristic that can take on different values; can be abstract such as motivation, self-esteem, and attitudes
Discrete Observations
Can take on only specific values; no other values can exist between these numbers; number of times participants get up early in a particular week, the only possible values would be whole numbers
Continuous Observations
Can take on a full range of values (e.g. numbers out to several places); an infinite number of potential values exists; completion of tasks in 12.839 seconds; limited only by the number of decimal places we choose to use
Nominal Variables
Used for observations that have categories or names as their values; always discrete (whole numbers); Gender (1=Male, 2=Female)
Ordinal Variables
Observations that have rankings as their values; 1st, 2nd, or 3rd; Always discrete
Interval Variables
Used for observations that have numbers as their values; the distance (or interval) between pairs of consecutive numbers is assumed to be equal;
Number of times one has to get up early each week
Interval Variable because distance between numerical observations is assumed to be equal; Discreet Variable (Difference between 1 and 2 is the same as the difference between 5 or 6)
Social Science Measures
Treated as interval measures but are also discrete, personality and attitude measures
Studies that measure time and distance
Continuous, interval observations; ratio observations because zero has meaning for time and distance
Ratio Variables
Variables that meet the criteria for interval variables but also have meaningful zero points; cognitive studies use ratio variable for reaction time; Time always implies a meaningful zero
Stroop Test
Gives response times in whole numbers, although other versions are more specific and give response times to several decimal places
Scale Variable
Variable that meets the criteria for an interval or a ratio variable
Types of Variables used to quantify observations
Nominal - Always Discrete/Never Continuous
Ordinal - Always Discrete/Never Continuous
Interval - Sometimes Discrete/Sometimes Continuous
Ratio - Seldom Discrete/Almost always continuous
Level
Discrete value or condition that a variable can take on
Independent Variable
Has at least two levels that we either manipulate or observe to determine its effects on the dependent variable
Example of an Independent variable
If we are studying whether gender predicts one’s attitude about politics, the independent variable is gender with two levels, male and female
Dependent Variable
The outcome variable that we hypothesize to be related to, or caused by, changes in the independent variable; variable that depends on the other
Example of Dependent Variable and Independent Variable
Attitude about politics (Dependent variable)
Gender (Independent variable)
Confounding Variable
Any variable that systematically varies with the independent variable so that we cannot logically determine which variable is at work; also called a confound
Example of a confounding variable
Want to lose weight and start sing a diet drug and begin exercising at the same time; we cannot logically tell which one is responsible for any weight loss
