NSE 212 Weeks 4-7| Ques ons and answers | Updated
RATED A+ | 2026 | NEW EDITION
Popula on
- Well defined set; has certain specified characteris cs from which data are collected & analyzed
- Has certain proper es (people, animals, events, objects)
Popula on Descriptor Examples
- Gender
- Age
- Marital status
- Socioeconomic status
- Religion
- Ethnicity
- Educa on
- Health Status
- Diagnosis
- Comorbidi es
Homogeneity
- Similar
- Limited varia on in characteris cs
Heterogeneity
- Diverse
- Dissimilari es in sample group inhibits ability to interpret findings meaningfully & to make
generaliza ons
Target Popula on
En re set of cases about which researcher wants to make generaliza ons about
Accessible Popula on
One that meets study criteria & is available
Sampling
, Process of selec ng por on/subset of popula on to represent en re popula on
Sample
- Set of elements that makeup popula on
- Element is most basic unit about which info. is collected
Representa veness
- Sample must be similar in characteris cs to popula on from which sample was selected
-- Use probability sampling approach
-- Clear eligibility criteria
-- Large enough sample
2 Sampling Strategies
1. Nonprobability sampling
2. Probability sampling
Nonprobability Sampling
- Convenience: most readily accessible persons/objects
- Quota: Knowledge about popula on of interest is used to seek representa veness by using
strata
Probability Sampling
- Random selec on: all par cipants have equal chance of being selected
- Random assignment/Randomiza on: assignment to experimental or control group on random
basis; doesn't mean par cipants were first randomly selected
Types of Probability Sampling
- Simple random sampling
- Stra fied random sampling
- Mul stage sampling (cluster)
- Systema c sampling
Simple Random Sampling
- Most basic approach
- Uses table of random #'s or randomizer to select par cipants randomly for sample size needed
Stra fied Random Sampling
RATED A+ | 2026 | NEW EDITION
Popula on
- Well defined set; has certain specified characteris cs from which data are collected & analyzed
- Has certain proper es (people, animals, events, objects)
Popula on Descriptor Examples
- Gender
- Age
- Marital status
- Socioeconomic status
- Religion
- Ethnicity
- Educa on
- Health Status
- Diagnosis
- Comorbidi es
Homogeneity
- Similar
- Limited varia on in characteris cs
Heterogeneity
- Diverse
- Dissimilari es in sample group inhibits ability to interpret findings meaningfully & to make
generaliza ons
Target Popula on
En re set of cases about which researcher wants to make generaliza ons about
Accessible Popula on
One that meets study criteria & is available
Sampling
, Process of selec ng por on/subset of popula on to represent en re popula on
Sample
- Set of elements that makeup popula on
- Element is most basic unit about which info. is collected
Representa veness
- Sample must be similar in characteris cs to popula on from which sample was selected
-- Use probability sampling approach
-- Clear eligibility criteria
-- Large enough sample
2 Sampling Strategies
1. Nonprobability sampling
2. Probability sampling
Nonprobability Sampling
- Convenience: most readily accessible persons/objects
- Quota: Knowledge about popula on of interest is used to seek representa veness by using
strata
Probability Sampling
- Random selec on: all par cipants have equal chance of being selected
- Random assignment/Randomiza on: assignment to experimental or control group on random
basis; doesn't mean par cipants were first randomly selected
Types of Probability Sampling
- Simple random sampling
- Stra fied random sampling
- Mul stage sampling (cluster)
- Systema c sampling
Simple Random Sampling
- Most basic approach
- Uses table of random #'s or randomizer to select par cipants randomly for sample size needed
Stra fied Random Sampling
