Term Test Flashcards
hierarchical scales
simplify process of developing a statistical study design
1. sampling unit
2. sample
3. observation unit
4. statistical population
5. population of interest
sampling unit
unit being selected at random (can be same as observation unit)
sample
collection of sampling units that you randomly selected
observation unit
scale for data collection
- subject of the study
statistical population
collection of all sampling units that could’ve been in your sample
- is defined by your study design
population of interest
collection of sampling units that you hope to draw a conclusion about
- defined by your research question
- same as statistical population, but often population of interest is larger
Ex. of hierarchy for design (street address)
pop. of interest: all people of voting age in kingston
statistical population: all addresses in kingston
sampling unit: street address
sample: 100 random street adresses
observation unit: a person
measurement variable: voting intent
measurement unit: none cause measurement is categorical
measurement variable
what we want to measure about the obervation unit (height, age)
measurement unit
scale of measurement variable (cm for height, years for age)
descriptive statistics
characterize data in your sample (quantitative)
- averages, tables & graphs
inferential statistics
uses information from sample to make a probabilistic statement about statistical population (qualitative)
- confidence intervals
***takes uncertainty into account
4 steps to statistical framework
- sampling
- measuring
- calculating descriptive statistics
- calculating inferential statistics
inferential vs descriptive statistics
inferential:use info from data to make statement about STATISTICAL POPULATION
descriptive: use info from data to make statement about OUR SAMPLE
subgroups
divide the population in groups
sampling design
describe how to sample a statistical population in a fair way
4 goals of an ideal sampling design
- all sampling units are selectable
- selection is unbiased
- selection is independent
- all samples are possible
- all sampling units are selectable
every sampling unit has probability of being included
- selection is unbiased
probability of selecting certain sampling units cannot depend on any attribute of that sampling unit
- selection is independent
selection of sampling unit must not decrease or increase the probability that any other sampling unit is selected
- all samples are possible
all samples that could be created from statistical population are possible
bias
over-or-under estimate of a value from an average sample compared to a statistical population
observational studies
based on observations of a statistical population where researchers do not have any control over the variables which impact our conclusions
- ex. cant control confounding variable so relationships aren’t causal
goal of observational studies
characterize something about an existing statistical population that allows us to investigate relationships among variables
limitations of observational studies
cannot make statements about whether a factor causes the response you’re interested in