Data Management pt. 1 Flashcards
2 types of statistics
Descriptive and inferential
Collection, organization, summary, and presentation of data. Beginning
i.e. measures of location, measures of variability, skewness and kurtosis
Descriptive
interpretation and analysis of data.
conclusion is drawn based on the subset of the population
i.e hypothesis testing and regression analysis
Inferential
the characteristic that is being studied
varies across individuals or objects
Variables
data that can assume values that manifest the concept of attributes
AKA categorical data
Cannot be measured
Qualitative Variables
finite number of possible values
CAN be counted but CANNOT be measured
Whole numbers
Discrete Variables
data from counting or measuring
numerical data representing numerical value
Quantitative Variables
infinite number of probable values, can be selected within a given rage
CAN be measured but CANNOT be counted
Continuous Variables
Levels of Measurement
Nominal, ordinal, ratio and interval (NORI)
used to label or classify variables using letters, words, and alpha numeric symbols. No particular order.
Nominal
represents discrete and ordered units, follows a natural order
Ordinal
tells the distances between measurements in addition to the classification and ordering
no true zero point
Interval
most informative as it combines the first three levels,
order units that have the same difference
Ratio
Examples of ratio
kelvin, height, weight, length, and time/duration
steps in statistical inquiry or investigation
Defining the problem
Collection/ gathering of info or data
Organization/presentation of data
Interpretation of data
2 types of sampling methods
Probability and Non-Probability
equal chance of getting selected, includes entire population
lottery, fishbowl method, and table of random numbers
Simple Random Sampling
everyone is assigned a number and individuals are chosen at regular intervals.
Systematic Sampling
populations -> subgroups (strata) based on a relevant characteristic not all members are included though
(e.g. age, income, job role, gender …)
Stratified Random Sampling
area sampling
population -> subgroups but all members are included
Cluster Sampling
most accessible individuals to the researcher
Convenience sampling
can be biased as some people are more likely to volunteer than others
Voluntary Response Sampling