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SWOT Analysis
SWOT stands for 'Strengths, Weaknesses, Opportunities and Threats'. This is a method of analysis of the environment and the company's standing in it.
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Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally.
Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups.
The researcher can represent even the smallest sub-group in the population. There are two types of stratified sampling – one is proportionate stratified random sampling and another is disproportionate stratified random sampling. In the proportionate random sampling, each stratum would have the same sampling fraction. For example, you have three sub-groups with a population size of 150, 200, 250 subjects in each subgroup respectively. Now, to make it proportionate, the researcher uses one specific fraction or a percentage to be applied on its subgroups of population. The sample for first group would be 150*0.5= 75, 200*0.5=100 and 250*0.5= 125. Here the constant factor is the proportion ration for each population subset.
The only difference is the sampling fraction in the disproportionate stratified sampling technique. The researcher could use different fractions for various subgroups depending on the type of research or conclusion he wants to derive from the population. The only disadvantage to that is the fact that if the researcher lays too much emphasis on one subgroup, the result could be skewed.
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SWOT Analysis
SWOT stands for 'Strengths, Weaknesses, Opportunities and Threats'. This is a method of analysis of the environment and the company's standing in it.
Read More
In probability sampling, it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected. The following sampling methods are examples of probability sampling:
- Simple Random Sampling (SRS)
- Stratified Sampling
- Cluster Sampling
- Systematic Sampling
- Multistage Sampling (in which some of the methods above are combined in stages)
Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling.
Stratified Sampling is possible when it makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. These groups are then called strata. An individual group is called a stratum. With stratified sampling one should:
- partition the population into groups (strata)
- obtain a simple random sample from each group (stratum)
- collect data on each sampling unit that was randomly sampled from each group (stratum)
Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups. Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample. Table 2.2 shows some examples of ways to obtain a stratified sample.
Table 2.2. Examples of Stratified SamplesExample 1Example 2Example 3 | ||
All people in the U.S. | All PSU intercollegiate athletes | All elementary students in the local school district |
4 Time Zones in the U.S. (Eastern, Central, Mountain, Pacific) | 26 PSU intercollegiate teams | 11 different elementary schools in the local school district |
500 people from each of the 4 time zones | 5 athletes from each of the 26 PSU teams | 20 students from each of the 11 elementary schools |
4 × 500 = 2000 selected people | 26 × 5 = 130 selected athletes | 11 × 20 = 220 selected students |
Cluster Sampling is very different from Stratified Sampling. With cluster sampling, one should
- divide the population into groups (clusters).
- obtain a simple random sample of so many clusters from all possible clusters.
- obtain data on every sampling unit in each of the randomly selected clusters.
It is important to note that, unlike with the strata in stratified sampling, the clusters should be microcosms, rather than subsections, of the population. Each cluster should be heterogeneous. Additionally, the statistical analysis used with cluster sampling is not only different but also more complicated than that used with stratified sampling.
Table 2.3. Examples of Cluster SamplesExample 1Example 2Example 3 | ||
All people in the U.S. | All PSU intercollegiate athletes | All elementary students in a local school district |
4 Time Zones in the U.S. (Eastern, Central, Mountain, Pacific.) | 26 PSU intercollegiate teams | 11 different elementary schools in the local school district |
2 time zones from the 4 possible time zones | 8 teams from the 26 possible teams | 4 elementary schools from the l1 possible elementary schools |
every person in the 2 selected time zones | every athlete on the 8 selected teams | every student in the 4 selected elementary schools |
Each of the three examples that are found in Tables 2.2 and 2.3 was used to illustrate how both stratified and cluster sampling could be accomplished. However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method.
- With Example 1: Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone. For example, the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in. Cluster sampling really works best when there are a reasonable number of clusters relative to the entire population. In this case, selecting 2 clusters from 4 possible clusters really does not provide many advantages over simple random sampling.
- With Example 2: Either stratified sampling or cluster sampling could be used. It would depend on what questions are being asked. For instance, consider the question "Do you agree or disagree that you receive adequate attention from the team of doctors at the Sports Medicine Clinic when injured?" The answer to this question would probably not be team dependent, so cluster sampling would be fine. In contrast, if the question of interest is "Do you agree or disagree that weather affects your performance during an athletic event?" The answer to this question would probably be influenced by whether or not the sport is played outside or inside. Consequently, stratified sampling would be preferred.
- With Example 3: Cluster sampling would probably be better than stratified sampling if each individual elementary school appropriately represents the entire population as in a school district where students from throughout the district can attend any school. Stratified sampling could be used if the elementary schools had very different locations and served only their local neighborhood (i.e., one elementary school is located in a rural setting while another elementary school is located in an urban setting.) Again, the questions of interest would affect which sampling method should be used.
The most common method of carrying out a poll today is using Random Digit Dialing in which a machine random dials phone numbers. Some polls go even farther and have a machine conduct the interview itself rather than just dialing the number! Such "robocall polls" can be very biased because they have extremely low response rates (most people don't like speaking to a machine) and because federal law prevents such calls to cell phones. Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced. National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey.