Crim 320 Midterm

Front 1

Hypothesis

Back 1

Testable statement about the nature of social reality; reasons for relationships.

Front 2

Measurement

Back 2

Assigning a unit of analysis to an attribute on a variable.

Front 3

Unit of analysis

Back 3

Person or thing from which data is collected.

Front 4

Variable

Back 4

Set of logical attributes that are of interest to the researcher.

Front 5

Conceptualization

Back 5

Process of formulating and clarifying concepts.

Front 6

Operationalization

Back 6

Describes the research operations that will specify value/category of variable on each case.

Front 7

Indicator

Back 7

Observable measure. Imperfect representation of concepts.

Front 8

Ratio

Back 8

Implied relation to 1.

Front 9

Proportions and Percentages

Back 9

fa/N
fa/Nx100

Front 10

Rates

Back 10

Make values comparable to each other.
fa/D x 100,000

Front 11

Inferential statistics

Back 11

Moving from description to explanation.

Front 12

Population

Back 12

Entire amount of subjects: large group actually interested in.

Front 13

Sample

Back 13

Selection from population - don't have access to entire population.

Infer from samples to population. Should be representative.

Front 14

Hypothesis testing

Back 14

The extent to which samples reflect true numbers of population.

Front 15

Dependent variable

Back 15

What we are trying to explain. Variable that is measured. Depends on independent variable.

Front 16

Independent variable

Back 16

What is manipulated; what is causing dependent variable.

Front 17

Nominal

Back 17

Categories of this variable are mutually exclusive. No mathematical properties.

Front 18

Ordinal

Back 18

These variables can be logically ranked, but have no true mathematical properties.

Front 19

Interval

Back 19

These variable have true mathematical properties. No true zero point.

Front 20

Ratio

Back 20

These variables have mathematical properties and have a true zero point.

Front 21

Sampling distribution

Back 21

A hypothetical distribution of all possible sample outcomes for a statistic.

Bridge between sample & population.

Front 22

Central Limits Theorem

Back 22

Many statistics have sampling distribution that is approximately normal.

On average, the sample mean is the same as the population mean.

Front 23

Areas under the normal curve

Back 23

68% within 1 standard deviation.

95% within 2 standard deviations.

Front 24

Statistical significance

Back 24

Unlikely it happened just by chance. Difference big enough/rare enough.

Front 25

Parameter

Back 25

Variable related to population.

Front 26

Two tasks of classical inference

Back 26

1. Estimate magnitude of parameter.

2. Test specific claims about magnitude of parameter.

Front 27

Confidence Interval

Back 27

Estimate from standard deviation of statistic.

1.96

Front 28

Point & interval estimation

Back 28

Using statistic as estimate of the parameter is risky. Unknown variability. Instead, create interval estimate.

Front 29

Alternatives to chi-square

Back 29

Phi: 2x2 table only

Cramer's V

Lambda: PRE measure

Front 30

Proportional Reduction of Error

Back 30

PRE measures compute prediction errors in two different situation:
a) when only raw totals are used for prediction
b) when an independent variable is used for prediction

Front 31

Concordant and Discordant pairs

Back 31

most PRE measures for ordinal variables based on assessment of pairs of cases.

Con: same directionality
Dis: opposite directionality

Front 32

Goodman & Kruskal's Gamma

Back 32

only uses cases with concordant and discordant pairs

Front 33

T-tests

Back 33

All types of t-tests are designed to compare sample means.
Comparison of 2 "groups". Based on t distribution.

Front 34

Independent samples t-test

Back 34

What does "independent" mean? Score on test variable for members of 1st group are not dependent on scores of 2nd group.
Standard form.

Front 35

Independent samples t test: assumptions

Back 35

1. test variable is normally distributed in each of the 2 populations
2. the variances of the normally distributed test variable are equal
3. cases have been randomly samples from population
4. scores are independent

Front 36

Levene's test for equality of variances

Back 36

Tests assumption of equality of variances.
Null=equal variances assumed.

Front 37

Paired sample t test

Back 37

What we use when assumption of independent samples is violated.
Same person measured twice (per/post), or when pairs of subjects are matched in some way.

Front 38

Paired sample t test: assumptions

Back 38

1. Difference scores are normally distributed.
2. cases have been randomly samples from population.
3. the difference scores are independent from each other (among sample)

Front 39

One sample t test

Back 39

Used when comparison mean is:
a) unknown
b) arbitrarily chosen

Front 40

Test Value

Back 40

Key consideration of one sample t test.
a) midpoint of test variable
b) average based on past research
c) chance level of performance

Front 41

One sample t test: assumptions

Back 41

1. Test variable normally distributed
2. cases have been randomly sampled
3. scores are independent of each other

Front 42

Mann-Whitney U test

Back 42

Nonparametric substitute for equal variance t test.
Used when assumption of normality not valid.

Front 43

Significance testing

Back 43

Test specific claims about magnitude of parameter. Interested in parameters that indicate relationship. Idea of null hypothesis: reject or fail to reject.

Front 44

0.05 alpha level

Back 44

Willing to risk 5% chance of wrong answer. If probability of observed relationship happening by chance is <5%, reject null.

Front 45

Type I Error

Back 45

Rejecting a null hypothesis that is true (saying there is a relationship when there is actually none).

Front 46

Type II Error

Back 46

Failing to reject a null hypothesis that is not true (saying there is not a relationship when there actually is).

Front 47

One-tailed test

Back 47

If we have directionality.

Critical value is 1.64

Front 48

Basic ideas of chi-square

Back 48

Are two variables related to one another?
Null hypothesis: the two variables are independent.

Front 49

Logic of chi-square

Back 49

Observed and expected counts.

Reject null if observed counts are sufficiently different from expected counts.

Front 50

How to conduct chi-square

Back 50

1. Calculate marginals
2. Calculate expected counts
3. (observe-expected) ²/expected
4. then, sum across all cells
5. calculate degrees of freedom
6. compare observed w/ expected, determine if can reject null

Front 51

Calculation of expected counts

Back 51

row total x column total / grand total

what we would expect to see if the two variables are independent of each other

Front 52

Degrees of freedom (chi-square)

Back 52

(row-1)x(column-1)

How many pieces of information would I need in order to fill in the remainder of the cells?

Front 53

Limitations of chi-square

Back 53

Expected cell counts must be greater than 5.
Often can't tell us relative strength of relationship.