STATS CH1 Data collection

Statistics

the science of collecting/organizing/summarizing/analyzing information to draw conclusions/answer questions

Data

a fact used to draw a conclusion/make a decision; words that describe the “stuff”

A statistic

numerical summary based on a sample

A parameter

numerical summary of a population

Descriptive statistics

consist of organizing/summarizing data; describe data through numerical summaries, tables, and graphs.

Inferential statistics

uses methods that take results from a sample, extends them to the population, and measures the reliability of the result

Variable

are the characteristics of the individuals within the population

Qualitative variable

allow for classification of individuals based on some attribute or characteristic.

Quantitative variable

provide numerical measures of individuals; the values of a quantitative variable can be added or subtracted and provide meaningful results.

Two types: discrete & continuous

Discrete variable

is a quantitative variable that has either a finite number of possible values or a countable number of possible values

Continuous variable

is a quantitative variable that has an infinite number of possible values that are not countable; may take on every possible value between 2 values.

Qualitative data

are observations corresponding to a qualitative variable

Quantitative data

are observations corresponding to a quantitative variable

Discrete data

are observations corresponding to a discrete variable

Continuous data

are observations corresponding to a continuous variable

4 levels of measurement for variables

norminal lvl of measurement

ordinal lvl of measurement

interval lvl of measurement

ratio lvl of measurement

Nominal level of measurement

when the values of the variable name/label/categorize; does not allow the values to be ranked in a specific order

Ordinal level of measurement

values are nominal & can be ranked in a specific order

Interval level of measurement

values are ordinal, value of zero does not mean complete absence, & arithmetic operations (±) can be applied with meaning

Ratio level of measurement

values are interval, zero means complete absence, & arithmetic operations (x/÷) can be performed with meaning

Observational study

measures response variable without influencing the variables; observe the behavior of an individual without influencing the outcome of the study

Three kind of observational study: cross-sectional, case-control, & cohort

Cross-sectional study

observational studies that collect info about individuals at a specific point in time/very short period.

Case-control study

retrospective (looking back in time); requires individuals to look at existing records; individuals with one characteristic are matched together (case) & compared to a control

ex - trying to find if smoking leads to lung cancer; looking back into their past smoking habits to see if they contributed to the development of lung cancer

Ppl who have lung cancer (case) & ppl who don’t (control)

Cohort study

prospective (over a long time) identifies a group of individuals to participate (cohort) & they are observed over a long period of time.

Experiment

researcher assigns the individuals to groups & intentionally manipulates the independent variable, then records the dependent variable.

Explanatory variable

independent variable

Response variable

dependent variable

Confounding

this occurs when the effects of 2 variables are not separated; the relation between the independent & dependent could be caused by something else. 

Confounding variable

explanatory variable that was considered in the study, but its effects can not be separated from a different variable.

Lurking variable

explanatory variable that is not considered in the study but could effect the response variable. 

Types of random sampling

1. simple random sampling 

2. systematic sampling

3. stratified sampling

4. cluster sampling

Simple random sampling

every possible sample of size n has an equal chance of occurring; requires a frame

Frame

a list of all the individuals within the population

Sample without replacement

an individual who is selected is removed from the population & CANNOT be chosen again

Sample with replacement

selected individual is placed back into the population & could be chosen again

Systematic sample

obtained by selecting every kth individual from the population

Stratified sample

obtained by separating the population into nonoverlapping groups (strata) & obtaining a simple random sample of each

Cluster sample

obtained by selecting all individuals within a randomly selected collection/group of individuals

Convenience/voluntary response samples

sample where the individuals in the sample are easily obtained (usually self-selected)

Multistage sampling

obtaining samples using multiple sampling techniques

Three sources of bias

1. Sampling bias

2. Nonresponse bias

3. Response bias

Sampling bias

the technique used to obtain individuals in the sample favors one part of the population over another; caused by undercoverage

Nonresponse bias

exists when individuals selected to be in the sample who do not respond to the survey have different opinions from those who do

Response bias

exists when the answers on a survey do not reflect the true feelings of the respondent

6 types of response bias:

1. Interviewer error

2. Misrepresented answers

3. Wording of questions

4. Ordering of questions or words

5. Type of question: open or closed question

6. Data-entry error

Sampling error

nonsampling error

result of undercoverage, nonresponse bias, response bias, or data-entry error (occur during the recording process and could lead to results that may not be representative of the population)