Gov. 100

Front 1

Nominal

Back 1

Categories, no numerical value. No rank order. ie. Republican, Democrats, Male, Female

Mode

Front 2

Ordinal

Back 2

Ranked category, but it doesn't show the distance between the order. ie: Poor (1), Middle income (2), rich (3) can't prove that 2 is twice as much as 1.
Mode, Median

Front 3

Interval

Back 3

Distance between numbers is meaningful. More specific, can do math on numbers, ie: State income, Population.
Mode, Median, Mean

Front 4

Dichotomous

Back 4

Either one of two things, (1) or (2), Male or Female, Dead or Alive.

Front 5

Mean

Back 5

The Average

Front 6

Median

Back 6

The middle of the ordered data

Front 7

Mode

Back 7

Most occurring number or variable

Front 8

Dispersion

Back 8

How the data is distributed.
Low dispersion is when most of the data is in one value, median and mean may be the same
High dispersion, the data is more evenly distributed. Cases spread out, no single peak

Front 9

Standard Deviation

Back 9

Average distance from the mean (how close to the mean each average is)

Front 10

Positive Skew

Back 10

The tail is longer on the right meaning there are more outliers to the right of the mean (use median as measure of central tendency)

Front 11

Negative Skew

Back 11

The tail is longer on the left meaning there are more outliers to the right of the mean (use median as a measure of central tendency)

Front 12

Theories

Back 12

Are explanations which generally make causal statements—If X happens, then Y will follow as a result

Front 13

Dependent Variable

Back 13

variable that represents the effect in a causal explanation
(Y axis)

Front 14

Independent Variable

Back 14

variable that represents a causal factor in an explanation
(X axis)

Front 15

Difference between Theories and Hypotheses

Back 15

Theory—argument explaining why X has some effect on Y
Hypothesis—empirical prediction from that argument
Example:
Theory—the costs of losing war are higher for leaders of democracies because they will be removed from office. Therefore, leaders of democracies are very selective about what wars they will fight
Hypothesis—Democracies are more likely to win wars than non-democracies

Front 16

Intervening Variable

Back 16

The middle man between two variables. ie: the more education you have the more money you'll make. The intervening variable is the job you get that effects the amount of money you make as well.

Front 17

Cross-Tab

Back 17

IV values are the columns, DV values are the rows.

Calculate percent of categories of the IV
Columns always sum to 100, rows do not
Compare across columns for the same value of the DV

Front 18

Experiment

Back 18

Two groups: Control and Treatment
Make the sample as random as possible

Front 19

Control Group

Back 19

Is given a placebo to see the effect; or is not altered

Front 20

Control Variable

Back 20

Is a variable added to the experiment to rule out rival explanations.

Front 21

Internal Validity

Back 21

the researchers have a negative or positive impact on the experiment.

Front 22

External Validity

Back 22

The way individuals act that effects the experiment

Front 23

Spurious relationship

Back 23

Two variables that aren't related (no causation), but may seem related. Usually third variable affects it.

When weather is hot, there is more crime. When it's hot people eat more ice cream. but they're not related.

Shoe size and IQ

Front 24

Additive relationship

Back 24

DV= IV + IV
Knowing about the IV tells me about the DV. If you know one value then you know a little about the DV.

parallel lines on the graph
If numbers are about the same its prob additive

Front 25

Interactive Relationship

Back 25

DV= IV x IV
Knowing about one IV tells you nothing, you need to know both

The lines on a graph intersect
Flat line is a weak relationship and a steep line is a strong relationship.
When numbers are way different

Front 26

Zero Order Relationship

Back 26

The association between two variables does not take into account the differences.

Front 27

Controlled Comparison Table

Back 27

Allows us to determine the controlled effect between the IV and DV

Front 28

Population

Back 28

The universe of subjects the researcher wants to look at.
The group the researcher wants to study
ie-students at UMD

Front 29

Sample

Back 29

Observations from the larger population
ie-the 20 students from maryland you research

Front 30

Population Parameter

Back 30

Actual Value
(Actual number of people who voted for Romney)

Front 31

Sample Statistic

Back 31

Number of people that say they voted for Romney during exit polls

(its socially desirable)

Front 32

Why sample?

Back 32

Its not possible to collect population data
-time consuming, expensive, difficult
Sample is easy, quick, not too much awareness

Front 33

Random Sample

Back 33

Every member has an equal chance of being chosen from the population

Front 34

Sampling Frame

Back 34

Method for defining the population the researcher wants to study

Front 35

Selection Bias

Back 35

Some members of the population are more likely to be included than others

Front 36

Response Bias

Back 36

Members of the population are more likely to respond than others

Front 37

Quazi random/ Cluster sampling

Back 37

Picking specific localities and then picking
Minimizes cost but less accurate

Front 38

Purposive sampling

Back 38

Overrepresentation some groups to make comparisons
You want a sample of Pg county, and only ask college park students

Front 39

Standard Deviation

Back 39

Distance from mean
Marked by Sigma

Front 40

Mu

Back 40

True Mean of population

Front 41

Central Limit Theorem

Back 41

If you take enough samples it will be Normal Distribution
68% 95% 99%

Front 42

Z score

Back 42

Deviation from mean/Standard deviation

Front 43

t-Distribution

Back 43

Probability distribution used when the sample size is small