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53 Cards in this Set

  • Front
  • Back
Testable statement about the nature of social reality; reasons for relationships.
Assigning a unit of analysis to an attribute on a variable.
Unit of analysis
Person or thing from which data is collected.
Set of logical attributes that are of interest to the researcher.
Process of formulating and clarifying concepts.
Describes the research operations that will specify value/category of variable on each case.
Observable measure. Imperfect representation of concepts.
Implied relation to 1.
Proportions and Percentages
Make values comparable to each other.
fa/D x 100,000
Inferential statistics
Moving from description to explanation.
Entire amount of subjects: large group actually interested in.
Selection from population - don't have access to entire population.

Infer from samples to population. Should be representative.
Hypothesis testing
The extent to which samples reflect true numbers of population.
Dependent variable
What we are trying to explain. Variable that is measured. Depends on independent variable.
Independent variable
What is manipulated; what is causing dependent variable.
Categories of this variable are mutually exclusive. No mathematical properties.
These variables can be logically ranked, but have no true mathematical properties.
These variable have true mathematical properties. No true zero point.
These variables have mathematical properties and have a true zero point.
Sampling distribution
A hypothetical distribution of all possible sample outcomes for a statistic.

Bridge between sample & population.
Central Limits Theorem
Many statistics have sampling distribution that is approximately normal.

On average, the sample mean is the same as the population mean.
Areas under the normal curve
68% within 1 standard deviation.

95% within 2 standard deviations.
Statistical significance
Unlikely it happened just by chance. Difference big enough/rare enough.
Variable related to population.
Two tasks of classical inference
1. Estimate magnitude of parameter.

2. Test specific claims about magnitude of parameter.
Confidence Interval
Estimate from standard deviation of statistic.

Point & interval estimation
Using statistic as estimate of the parameter is risky. Unknown variability. Instead, create interval estimate.
Alternatives to chi-square
Phi: 2x2 table only

Cramer's V

Lambda: PRE measure
Proportional Reduction of Error
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
Concordant and Discordant pairs
most PRE measures for ordinal variables based on assessment of pairs of cases.

Con: same directionality
Dis: opposite directionality
Goodman & Kruskal's Gamma
only uses cases with concordant and discordant pairs
All types of t-tests are designed to compare sample means.
Comparison of 2 "groups". Based on t distribution.
Independent samples t-test
What does "independent" mean? Score on test variable for members of 1st group are not dependent on scores of 2nd group.
Standard form.
Independent samples t test: assumptions
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
Levene's test for equality of variances
Tests assumption of equality of variances.
Null=equal variances assumed.
Paired sample t test
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.
Paired sample t test: assumptions
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)
One sample t test
Used when comparison mean is:
a) unknown
b) arbitrarily chosen
Test Value
Key consideration of one sample t test.
a) midpoint of test variable
b) average based on past research
c) chance level of performance
One sample t test: assumptions
1. Test variable normally distributed
2. cases have been randomly sampled
3. scores are independent of each other
Mann-Whitney U test
Nonparametric substitute for equal variance t test.
Used when assumption of normality not valid.
Significance testing
Test specific claims about magnitude of parameter. Interested in parameters that indicate relationship. Idea of null hypothesis: reject or fail to reject.
0.05 alpha level
Willing to risk 5% chance of wrong answer. If probability of observed relationship happening by chance is <5%, reject null.
Type I Error
Rejecting a null hypothesis that is true (saying there is a relationship when there is actually none).
Type II Error
Failing to reject a null hypothesis that is not true (saying there is not a relationship when there actually is).
One-tailed test
If we have directionality.

Critical value is 1.64
Basic ideas of chi-square
Are two variables related to one another?
Null hypothesis: the two variables are independent.
Logic of chi-square
Observed and expected counts.

Reject null if observed counts are sufficiently different from expected counts.
How to conduct chi-square
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
Calculation of expected counts
row total x column total / grand total

what we would expect to see if the two variables are independent of each other
Degrees of freedom (chi-square)

How many pieces of information would I need in order to fill in the remainder of the cells?
Limitations of chi-square
Expected cell counts must be greater than 5.
Often can't tell us relative strength of relationship.