Sampling Techniques

Sampling Techniques

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Activity 1:

1. What is a sample?

Answer: Sample is a group of people, goods, or objects, from which the research data are obtained.

2.  Why is sampling necessary?

Answer: Sampling is necessary to represent the whole of population. Sampling is done because we usually cannot gather data from the entire population. Even in relatively small populations, the data may be needed urgently, and including everyone in the population in our data collection may take too long.

3. Mention three important characteristics of a sample, and discuss each!

Answer: Characteristics of a sample that must be met/related with the three conditions:

a. The Source : the must be selected out of the population to which it belongs

b.  The Size : a sample must be large enough to be representative of the population.

c. The Techniques: in order to represent the population a sample should be properly     selected

4. What is meant by random sampling, and what is its advantage?

Answer: random sampling is a method used to cull a smaller sample size from a larger population and use it to research and make generalizations about the larger group. Researchers generate a simple random sample by obtaining an exhaustive list of a larger population and then selecting, at random, a certain number of individuals to comprise the sample. With a simple random sample, every member of the larger population has an equal chance of being selected.

The advantage: The advantages of a simple random sample include its ease of use and its accurate representation of the larger population. It is free of classification error, and it requires minimum advance knowledge of the population other than the frame. Its simplicity also makes it relatively easy to interpret data collected in this manner.

Activity 2:

1. What is meant by “population”?

Answer: The definition of the target population is a study most reasonably based on the independent, moderator, and control variables in the study design along with the practical considerations such as availability of subjects or respondents.  

2. Why is it necessary to define the population before the sample is drawn?

Answer: The term defining the population refers to the establishment boundary conditions that specify who shall be included in or excluded from the population.

3. Specify the steps in stratified random sampling!

Answer: To create a stratified random sample, there are seven steps:

a. The first step is to identify the stratification parameters or variables.

b. Defining the population; 

c. Choosing the relevant stratification; 

d. Listing the population according to the chosen stratification; 

e. Choosing your sample size; 

f. Calculating a proportionate stratification; and 

g. Using a simple random or systematic sample to select your sample.

4. Describe the way to determine the sample size!

Answer: The primary issue in choosing a sample size is that it be sufficient to assure researcher that the sample will be representative of the population from which it is drawn. Before you can calculate a sample size, you need to determine a few things about the target population and the sample you need:

a. Population Size — How many total people fit your demographic? For instance, if you want to know about mothers living in the US, your population size would be the total number of mothers living in the US. Don’t worry if you are unsure about this number. It is common for the population to be unknown or approximated.

b. Margin of Error (Confidence Interval) — No sample will be perfect, so you need to decide how much error to allow. The confidence interval determines how much higher or lower than the population mean you are willing to let your sample mean fall. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. It will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”

c. Confidence Level — How confident do you want to be that the actual mean falls within your confidence interval? The most common confidence intervals are 90% confident, 95% confident, and 99% confident.

d. Standard of Deviation — How much variance do you expect in your responses? Since we haven’t actually administered our survey yet, the safe decision is to use .5 – this is the most forgiving number and ensures that your sample will be large enough.

The formula may be used for determining a sample size suitable for obtaining sample representative on this parameter for a given confidence level and a given sampling error.

N=(z/e)² (p) (1-p)

N = the sample size

z = the standard score corresponding to given confidence level

e = the proportion of sampling error in a given situation

p = the estimated proportion or incidence of cases in the population

References:

1. Brown, J.D. 1988. Understanding Research in Language Learning. Cambridge: CUP.

2. Nunan, D. 1989. Research Methods in  Language Learning. Cambridge: CUP.

3. Saleh, M. 2001. Pengantar Praktik Penelitian Pengajaran Bahasa. Semarang: IKIP Semarang  Press.

4. Tuckman, B.W. 1978. Conducting Educational Research. London: Harcourt Brace  Jacobovitz.

Baca Juga:

Research Instruments   Data   Variables   Research problems & hypotheses   Introduction (What is & why do research?)