2. 20 of true/false questions (Part B=40%)
3. Two of structure questions (Part C=20%)
-Types of research (basic and applied research)
-Induction and deduction
-Literitur review
-Research design-tpes of research design, time horizon
-Research Proposal
-The meaning of qualitative and quantitative research
-Research Process
-Research topic
-Research question
-Symptoms
-Theoritical framework
-Conceptualization
-Types of variables
-Hipotesis
-Types of Sampling- process and also the advantages and disadvantages
-Sampel and population
-Scientific research
Sampling Method
Stratified sampling
In statistics, stratified sampling is a method of sampling from a population.
When sub-populations vary considerably, it is advantageous to sample each subpopulation (stratum) independently. Stratification is the process of grouping members of the population into relatively homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then random or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population.
Stratified sampling strategies
1. Proportionate (berkadar)allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. If the population consists of 60% in the male stratum and 40% in the female stratum, then the relative size of the two samples (three males, two females) should reflect this proportion.
2. Optimum allocation (or Disproportionate allocation/(tidak berkadar)) - Each stratum is proportionate to the standard deviation of the distribution of the variable. Larger samples are taken in the strata with the greatest variability to generate the least possible sampling variance.
Practical example
In general the size of the sample in each stratum is taken in proportion to the size of the stratum. This is called proportional allocation. Suppose that in a company there are the following staff:
Systematic sampling
Systematic sampling is a statistical method involving the selection of every kth element from a sampling frame, where k, the sampling interval, is calculated as:
k = population size (N) / sample size (n)
Using this procedure each element in the population has a known and equal probability of selection. This makes systematic sampling functionally similar to simple random sampling. It is however, much more efficient (if variance within systematic sample is more than variance of population) and much less expensive to carry out.
The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. A random starting point must also be selected.
Systematic sampling is to be applied only if the given population is logically homogeneous, because systematic sample units are uniformly distributed over the population.
Example: Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample.
This is random sampling with a system. From the sampling frame, a starting point is chosen at random, and choices thereafter are at regular intervals. For example, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.
If, as more frequently, the population is not evenly divisible (suppose you want to sample 8 houses out of 125, where 125/8=15.625), should you take every 15th house or every 16th house? If you take every 16th house, 8*16=128, so there is a risk that the last house chosen does not exist. On the other hand, if you take every 15th house, 8*15=120, so the last five houses will never be selected. The random starting point should instead be selected as a noninteger between 0 and 15.625 (inclusive on one endpoint only) to ensure that every house has equal chance of being selected; the interval should now be nonintegral (15.625); and each noninteger selected should be rounded up to the next integer. If the random starting point is 3.3, then the houses selected are 4, 19, 35, 51, 66, 82, 98, and 113, where there are 3 cyclic intervals of 15 and 5 intervals of 16.
systematic sampling - every nth member of the population is sampled. The list being sampled may be ordered (alphabetical, seniority, street number, etc).
Question : Is it equivalent to simple random sampling? Strictly speaking the answer is No!, unless the list itself is in random order, which it never is (alphabetical, seniority, street number, etc).
Advantages
1. easier to draw, without mistakes (cards in file)
2. more precise than simple random sampling as more evenly spread over population
Disadvantages
1. if list has periodic arrangement then sample collected may not be an accurate representation of the entire population.
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