DATA COLLECTION & SAMPLING Flashcards
2 process of collecting information
- Census
2. Sampling
It is the complete enumeration or the process of collecting information from the whole population.
Census
It is the process of getting a sample from a population.
Sampling
It is a list of used to define researcher’s population of interest.
Sampling frame
Why do sampling?
- Low cost of sampling
- Less time consuming in sampling
- Scope of sampling is high
- Accuracy of data is high
The error attributed to the variation present among the computed values of the statistic from the different possible samples consisting of n elemnts.
Sampling error
The error from other sources apart from the sampling fluctuations.
Nonsampling error
2 nonsampling errors
- measurement error
2. error in the implementation of the sampling design
The difference between the true value and the observed value of the variable and the observed value used in the study.
Measurement error
Occurs when we do not adhere to the procedures and requirements as specified in the sampling design.
Error in the implementation of the sampling design
3 of the Error in the implementation of the sampling design
- selection error
- frame error
- population specification error
Error in the design of the questionnaire.
Instrument Error
This is the incorrect input of data recorded in the questionnaire into the computer.
Processing Error
It is the use of the wrong variable to measure the concept under study.
Surrogate Information Error
This is the response bias that happens when the respondent does not give truthful answers.
Response Bias Error
Measurements Errors
- Instrument Error
- Processing Error
- Surrogate Information Error
- Response Error
- Interviewer Error
A response bias which happens when the respondent does not know the answer or simply refuses to answer certain questions.
Nonresponse Bias Error
This error happens when the enumerator asks the question in a manner that influences the answer of the respondent.
Interviewer Error
Method of selecting a sample wherein each element in the population has a known, nonzero chance of being included in the sample.
Random (Probability) Sampling
2 Sampling Techniques
Random sampling
Non-random sampling
5 Random Sampling
- Simple random sampling
- Systematic random sampling
- Stratified random sampling
- Cluster sampling
- Multistage sampling
2 Non-random Sampling
- Quota Sampling
- Snowball Sampling
- Convenience Sampling
- Judgmental Sampling
A random sampling wherein all units of the frame are given an equal chance of being selected.
Ex: random number generators, lottery, draw lots
Simple random sampling
Ordering of all units in the sampling frame then picking the number from 1 to m. With the number labelled as k. Then every kth number in the list is selected.
Systematic random sampling
m=N/n
The population is divided into 2 or more homogenous group. Then the samples are randomly selected from each stratum.
Stratified random sampling
Population is divided into natural groups. Randomly pick some clusters and completely enumerate all samples from chosen clusters.
Cluster sampling
True or False. In stratified random sampling, only simple random sampling can be used to select samples from each stratum.
False. Both simple and systematic random sampling can be used.
True or False. In stratified random sampling, only simple random sampling can be used to select samples from each stratum.
False. Both simple and systematic random sampling can be used.
natural groups
clusters
This sampling technique is carried out in stages using smaller and smaller sampling units at each stage. With simple random samplings utilized first and systematic sampling used last.
Multistage sampling
Choosing respondents at the convenience of the researcher.
Convenience Sampling
True or False. Convenience sampling restricts generalization.
True
Wherein the researchers employ his/her own expert judgment.
Judgmental Sampling
True or False. Judgmental sampling has the risk of bias selection.
True
Strata exists but nonrandom selection of an individual within the group.
Quota Sampling
It is when researchers has a key person and introduce the next one to become a chain.
Snowball Sampling
True or False. Snowball sampling is useful in specific circumstances and for locating rare populations.
True
symbol for population and sample
population=N
sample=n
Infinite population formula for sample size
n0= [Z^2a/2 * pq] divided by d2
Finite population formula for sample size
n= n0 divided by {1+ [(n0-1) divided by N]}
level of significance symbol
alpha (a)
a=0.05, z a/2=1.96
a=0.01, z a/2=2.575
This is what you expect the results to be.
Sample proportion (p)
q=1-p
The difference between the population parameter and the sample statistic.
Margin of Error (d)
Proportion formula
Pi= Ni/N
Sample needed formula
ni = n x Pi
True or False. When answer for sample is in decimal point always round up.
True
