Data Descriptive and Measures Flashcards
Data
Pieces of information and may be thought of as observations or measurements of a phenomenon of interest.
An object
A person or thing upon which we collect data is an experimental unit.
Variables
The properties being observed or measured
Quantitative data
Observations measured on a numerical scale and can be measured as how many, how long, how much, and so on…
Qualitative data
Non-nummerical data and can only be classified into one of a group of categories. Examples are: Marital status, racial/ethnic classification, place of residence. Qualitative also describes, hot, yellow, or longer…
Qualitative research
Based on an individual’s, typically subjective, analysis.
Case report
A description of a single individual
Case series
description of a small number of cases with a similar diagnosis
A population
The data set that represent the target of interest. A set or collection of items of interest in a study.
A sample
A subset of items that have been selected from the population
Random sample
The most common type of sampling procedure. Used two obtain a representative subgroup of the population
Nominal scale
Qualitative observations(describes a quality of a person or thing being studied) or categorical (The level of the variable fit into categories) observations. Examples: Sex, race, marital status, education, exposed (yes, no), disease (Yes, no).
Ordinal scale
Qualitative observations. Example: Preference rating (agree, neutral, disagree), rank order scale
Numerical scale
Quantitative observations. There are two types: Continuous (Interval) which has values on a continuum, and Discrete scales, which has values equal to integers
Tabular and graphic data formats
Known as empirical frequency distributions. Useful for describing data or extracting information from a set of data. It is often of interest for a set of data to identify the pattern or grouping into which the data fall
Frequency table or distribution
The number of observations (cases) falling into each of the several values of ranges of values (Time periods). They are portrayed as a frequency table or graph
Relative frequency
The proportion of cases that fall into each level of the variable. The frequency of the category is divided by the number of observations, where n is the total number of observations. Relative Risk= frequency/n
A proportion
The number of observations with the characteristic of interest divided by the total number of observations. It is used to summarize counts
A rate
A number of cases of a particular outcome divided by the size of the population in that time period, multiplied by a base e.g, 100, 1000, 100,00.
Ratio
A part divided by another part.The number of observations with the characteristic of interest, divided by the number without the c characteristic of interest
