Week_7_Sampling Technique & Sample Size Flashcards
Two types of sampling technique
Non-probability sampling: Relies on convenience or the personal judgment of the researcher rather than chance to select sample elements. Yield good estimates of the population characteristics. Do not allow for objective evaluation of the precision of the sample results.
Probability sampling: Sampling units are selected by chance. Can determine the precision of the sample estimates of the characteristics of interest.
What are four probability sampling methods
– Simple random sampling
– Systematic sampling
– Stratified sampling
– Cluster sampling
Simple random sampling
A probability sampling technique in which each element in the population has a known and equal probability of selection. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame.
When can use simple random sampling
- Use when population is small.
2. Use when population is large but with electronic database which can draw random sample.
How to calculate probability of selection
Sample size/population size
Procedure for drawing simple random samples
- Select a suitable sampling frame.
- Assign each element a number from 1 to N
(population size). - Generate n (sample size) different random numbers
between 1 and N. This can be done using a software
package or using a table of simple random numbers. 4. The numbers generated denote the elements that
should be included in the sample.
What is random digit dialing? (RDD)
a method of randomly generating numbers to represent telephone numbers.
Used in telephone surveys to overcome the problem of unlisted phone numbers.
What is plus-one dialing
Number drawn from the directory has the last digit in the number replaced by a random number.
Ensures that both listed and unlisted numbers are included in the sample
Systematic Sampling
Chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame. The sample interval is constant.
For instance: 23, 123,223,323
Stratified Sampling
Population contains subgroups. Subgroup response differently to questions. Stratified sampling is appropriate when we expect each subgroup to respond to research questions DIFFERENTLY.
The strata in stratified sampling
Must be mutual exclusive and collectively exhaustive.
The mean and variance are different for each stratum
More flatter the distribution, greater the variance.
Small group usually has flat distribution, need to select more sample from small group
The proportionate sample definition
Select sample size based on each stratum’s proportionate share of total population
The disproportionate sample definition
Select sample size based on each stratum’s variance per sample size formula. which means select bigger sample size on higher variance strata, smaller sample size from lower variance strata.
Which sample selection of stratified sampling is statistic al efficiency
Disproportionate sample.
How to calculate the overall mean for the entire population by using stratified sampling?
Weighted average formula:
AveragePopulation = (AverageA)(ProportionA) + (AverageB)(ProportionB)
Cluster sampling definition
Conduct survey by zip code.
One step area sample: if each cluster is representative, then sample from one or more group
Two step area sample: if cluster is different, sampling from different group
Stratified sampling and cluster sampling subpopulation
stratified sampling need to select from each group. cluster sampling can choose one group.
Comparison for stratified and cluster sampling
See slides 21
What is sampling frame?
Like student ID, company code list
Non-probability sampling plans are sometimes used
- No time.
2. Managers are perfectly happy to ask n number of persons a question to help them make a decision
Four non-probability sampling
– Convenience samples
– Judgment samples
– Referral samples
– Quota samples
Quota sample definition
a specific quota for various types of individuals to be interviewed.
Probability and non-probability sampling comparison
See slide 30
Weakness of SRS
hard to construct sampling frame, lower precision, no assurance of representativeness.
Advantage of systemic sampling
better than SRS because it has groups, representativeness increase.
Disadvantage of systemic sampling
If cyclical patterns exist, representativeness decrease.
Pros and cons for stratified sampling
Includes all important subpopulations, precision.
Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive.
Pros and cons for cluster sampling
Easy to implement, cost- effective.
Imprecise, difficult to compute and interpret results
Online Sampling Techniques
- Random online intercept sampling relies on a random selection of website visitors
- Invitation online sampling is when potential respondents are alerted that they may fill out a questionnaire that is hosted at a specific website
- Online panel sampling refers to consumer or other respondent panels that are set up by marketing research companies for the explicit purpose of conducting online surveys with representative samples
Which online sampling techniques can use marketing company?
Online panel sampling
Challenges for international sampling
Hard to get sampling frame
Hard to find decision maker
Accuracy
Challenges for social media sampling
Sample not representative
Careful to choose keywords
Ethics in sampling
- Appropriate definitions of the population, sampling frame, and sampling technique are essential if the research is to be conducted and the findings are to be used ethically.
- Probability sampling techniques should be used whenever the results are to be projected to the population.
- When conducting research with small populations, as in business-to-business marketing or employee research, researchers must be sensitive to preserving the respondents’ anonymity.
Census definition
An accounting of everyone in the population.
What are 3 factors affect sample size?
– Amount of Diversity or Variation
As diversity/variation increases, larger samples are
required
– Degree of Precision
As need for precision increases, larger samples are
required
– Degree of Confidence
Confidence increases as sample size increases
trade-off between confidence and precision.
Higher precision means lower confidence unless we can increase the sample size
What does accuracy of sample do?
Describe amount of sample error
Trade off between sample size and margin of error
Increase sample size, decrease the margin error
Z value
Standard error associated with chosen level of confidence (95% = 1.96 or 99% = 2.58)
Sample Size Formula
n = z^2 (p * q)/e^2 Where: n= sample size p=estimated % in population q=(100%-p) e= acceptable error (accuracy) normally keep it as 5%
Low variability definition for sample size
Most of people select one category (P=90%), other select the other (q=10%).
Hence, low variability gives low sample size (n)
Hight variability definition sample size
In contrast to low variability
Sample Size When Estimating a Mean
n = s^2*z^2/e^2
Where:
n= sample size
s=variability estimated by one standard deviation
z= Standard error associated with chosen level of confidence (1.96 or 2.58)
e= acceptable error (accuracy)
Estimating Variability (s) in Populations
There are three ways to estimate variability (indicated by one standard deviation s, in the population):
1. First, do you have a previous study on the same population from which we can calculate s?
2. Second, do you have a pilot study to calculate s? …and, third…
3. When the first two choices are not available we
estimate the range of values that may be derived from the question and divide this range by 6.
(+/-3 standard deviations account for 99% of the area under the normal curve, so 6 standard deviations are synonymous with the range). By dividing the range by 6 we can estimate 1 s!
